def run(self):
features = functions.features(self.seq[:30])
self.s_scores = functions.s_score(features)
self.c_scores, self.cs_probs_all, self.cleavage_sites = functions.c_score(
self.seq, max_scan=self.max_scan)
self.y_scores = functions.np.sqrt(self.s_scores * self.c_scores)
def test_output():
post_URL = request.args.get("postURL")
eg_subreddit = fu.subreddit(post_URL)
postAtt = fu.rcommentLink(post_URL)
eg_postTitle = postAtt.title
eg_comment = [comment.body for comment in postAtt.comments][0]
commentCreated = int([comment.created_utc for comment in postAtt.comments][0])
postCreated = postAtt.created_utc
timeDiffC = fu.timePresent(round(commentCreated - postCreated))
timeDiffCur = fu.timePresent(round(time.time() - commentCreated))
comLength = fu.cLength(fu.comLength(eg_comment))
sentiment = ""
sentiment = fu.sentiment(eg_comment)
features = fu.features(eg_subreddit, post_URL, eg_comment, commentCreated)
eg_prob = ""
eg_prob = fu.probability(features)
eg_score = [comment.score for comment in postAtt.comments][0]
eg_commentForm = fu.commentSent(eg_comment)
return render_template(
"testOutput.html",
eg_postTitle=eg_postTitle,
eg_commentForm=eg_commentForm,
eg_score=eg_score,
eg_prob=eg_prob,
sentiment=sentiment,
comLength=comLength,
timeDiffCur=timeDiffCur,
timeDiffC=timeDiffC,
eg_comment=eg_comment,
post_URL=post_URL,
eg_subreddit=eg_subreddit,
)
def output():
post = request.args.get("post")
comment = request.args.get("comment")
postAtt = fu.rcommentLink(post)
postTitle = postAtt.title
the_subreddit = ""
the_subreddit = fu.subreddit(post)
the_post = ""
the_post = post
your_comment = ""
your_comment = comment
features = fu.features(the_subreddit, post, comment, time.time())
timePost = ""
timePost = fu.timePresent(features[4])
comLength = ""
comLength = fu.cLength(features[5])
sentiment = ""
sentiment = fu.sentiment(your_comment)
prob = ""
prob = fu.probability(features)
commentForm = fu.commentSent(your_comment)
return render_template(
"output.html",
postTitle=postTitle,
commentForm=commentForm,
prob=prob,
timePost=timePost,
comLength=comLength,
sentiment=sentiment,
the_subreddit=the_subreddit,
the_post=the_post,
your_comment=your_comment,
)
print "\n******* Analysis started", datetime.now()\
.strftime("%A, %d. %B %Y %I:%M%p"), "*******\n"
print "Running GoShifter with following parameters:\n"
for arg in args:
print "\t", arg, args[arg]
print "\n"
args = validate_args(args)
### read pheno mappings
snpInfoChr = data.readSnpMapByChr(args['--snpmap'])
#### find median annotation size to expand LD boundries
if args['--ld-extend'] == "False":
expand = functions.features(args['--annotation']) * 2
else:
expand = args['--ld-extend']
### read peaks as interval tree
peaksTree = functions.intervalTree(args['--annotation'])
### test for enrichment with peak shifting (random shift)
functions.enrichPermRandBoundryPeakShift_tabixLd(
snpInfoChr, args['--ld'], args['--rsquared'], args['--window'], expand,
peaksTree, args['--min-shift'], args['--max-shift'], args['--permute'],
args['--out'], args['--no-ld'], args['--proxies'])
print "\n******* Analysis ended", datetime\
.now().strftime("%A, %d. %B %Y %I:%M%p"), "*******\n"
.strftime("%A, %d. %B %Y %I:%M%p"), "*******\n"
print "Running GoShifter with following parameters:\n"
for arg in args:
print "\t", arg, args[arg]
print "\n"
args = validate_args(args)
### read pheno mappings
snpInfoChr = data.readSnpMapByChr(args['--snpmap'])
#### find median annotation size to expand LD boundries
if args['--ld-extend'] == "False":
expand = functions.features(args['--annotation-a'])*2
else:
expand = args['--ld-extend']
## merge overlapping annotation sites from A
a_mergePeaks = functions.mergeTree(args['--annotation-a'])
## convert to intervalTree
a_tree = functions.merge2IntervalTree(a_mergePeaks)
## merge overlapping annotations from B
b_mergePeaks = functions.mergeTree(args['--annotation-b'])
## convert to intervalTree
b_tree = functions.merge2IntervalTree(b_mergePeaks)
### test for enrichment with peak shifting (random shift)
functions.enrich_shift_conditional_tabixLd(snpInfoChr,args['--ld'],\
#p_theme = {'color_1': '#ABABAB', 'color_2': '#ABABAB', 'color_3': '#ABABAB', 'font_color_1': '#ABABAB',
# 'font_size_1': 12, 'font_size_2': 16}
#p_dims = {'width': 1450, 'height': 800}
#p_vlines = [data['timestamp'].head(1), data['timestamp'].tail(1)]
#p_labels = {'title': 'Main title', 'x_title': 'x axis title', 'y_title': 'y axis title'}
# cargar funcion importada desde GIST de visualizaciones
#ohlc = vs['g_ohlc'](p_ohlc=data, p_theme=p_theme, p_dims=p_dims, p_vlines=p_vlines, p_labels=p_labels)
# mostrar plot
# ohlc.show()
# -- ------------------------------------------------------------------------------- Feature Engineering -- #
# Feature engineering (autoregressive and hadamard functions)
data_t, data_y, data_x, features_names = fn.features(p_data=all_data, p_nmax=7)
# Data scaling
data_x = fn.data_trans(p_data=data_x, p_trans='Rubust')
var = fn.variables(data_y, data_x, N=4)
busqueda = fn.busqueda_en_train(var, n=4)
# Rearange of data for regression model
# Rearange of data for classification model
data_cla = {'x_data': data_x, 'y_data': data_y['co_d']}
# -- ---------------------------------------------------------------------------------- Feature analysis -- #
# matriz de correlacion
cor_mat = data_x.corr()
