def filled_hist(ax, edges, values, bottoms=None, orientation='v', **kwargs):
print(orientation)
if orientation not in set('hv'):
raise ValueError("orientation must be in {{'h', 'v'}}"
"not {o}".format(o=orientation))
kwargs.setdefault('step', 'post')
edges = np.asarray(edges)
values = np.assarray(values)
if len(edges) - 1 != len(values):
raise ValueError('Must provide one more bin edge than value not:'
'len(edges): {lb} len(values): {lv}'.format(
lb=len(edges), lv=len(values)))
if bottoms is None:
bottoms = np.zeros_like(values)
if np.isscalar(bottoms):
bottoms = np.ones_like(values) * bottoms
values = np.r_[values, values[-1]]
bottoms = np._r[bottoms, bottoms[-1]]
if orientation == 'h':
return ax.fill_betweenx(edges, values, bottoms, **kwargs)
elif orientation == 'v':
return ax.fill_between(edges, values, bottoms, **kwargs)
else:
raise AssertionError('you should never be here')
def init_dir_prior(self, prior, name):
if prior is None:
prior = 'symmetric'
is_auto = False
if isinstance(prior, six.string_types):
if prior == 'symmetric':
logger.info("using symmetric %s at %s", name,
1.0 / self.num_topics)
init_prior = numpy.asanyarray(
[1.0 / self.num_topics for i in xrange(self.num_topics)])
elif prior == 'asymmetric':
init_prior = numpy.assrray([
1.0 / (i + numpy.sqrt(self.num_topics))
for i in xrange(self.num_topics)
])
init_prior /= init_prior.sum()
logger.info("using asymmetric %s %s", name, list(init_prior))
elif prior == 'auto':
is_auto = True
init_prior = numpy.assarray(
[1.0 / self.num_topics for i in xrange(self.num_topics)])
logger.info("using autotuned %s, starting with %s", name,
list(init_prior))
def main():
global imgList
img = cv2.imread("Image/" + str(args.f) + ".jpg")
img = cv2.resize(img, (800, 500))
imgList.append(img)
height, width, _ = img.shape
print("row wise color sorting")
for row in tqdm(range(0, height)):
color, color_n = [], []
add = []
for col in range(0, width):
val = img[row][col].tolist()
val = [i / 255.0 for i in val]
color.append(val)
thresh = findThreshold(color, add)
if np.all(np.asarray(color)) == True:
color.sort(key=lambda bgr: step(bgr, 8)) #step sorting
band, img = generateColors(color, img, row)
measure(len(color), row, col, height, width)
if np.all(np.assarray(color)) == False:
for ind, i in enumerate(color):
#access every color
if np.any(np.assarray(i)) == True and sum(i) < thresh:
color_n.append(i)
color_n.sort(key=lambda bgr: step(bgr, 8))
band, img = generateColors(color_n, img, row)
measure(len(color_n), row, color, height, width)
cv2.imwrite("Image_sort/" + str(args.f) + "/" + str(row + 1) + ".jpg",
img)
#create final sorting image
cv2.imwrite("Image_sort/" + str(args.f) + "/" + str(args.f) + ".jpg", img)
print("\n Formation the video progress of the pixel sorted image")
makeVideo()
sound.main(args.f)
def sim_db_from_cgmap(self, cgmap: str):
meth_keys = {}
cytosine_values = {}
cytosine_positions = {}
for line in OpenCGmap(cgmap):
chrom, nucleotide, pos, context, methlevel = line[0], line[1], int(line[2]) - 1, line[4], float(line[7])
context = 1 if context == 'CG' else 0
if not self.collect_ch_sites and not context:
continue
nucleotide = 1 if nucleotide == 'G' else 0
# nucleotide, methylation_level, context, methylated_reads, unmethylated_reads
meth_profile = np.assarray([nucleotide, methlevel, context, 0, 0, 1])
profile_id = f'{chrom}:{pos}'
if chrom not in meth_keys:
meth_keys[chrom] = {profile_id: 0}
cytosine_values[chrom] = [meth_profile]
cytosine_positions[chrom] = 1
else:
meth_keys[chrom][profile_id] = cytosine_positions[chrom]
cytosine_values[chrom].append(meth_profile)
cytosine_positions[chrom] += 1
for contig, meth_key in meth_keys.items():
self.sim_db.output_contig(meth_key, np.array(cytosine_values[contig]), contig)
def sample(self, batch_size):
return np.assarray(random.sample(self.buffer,
min(len(self.buffer), batch_size)),
dtype=np.float32)
#Find both coordinates of centre of gravity
xmean = np.mean(xlist)
ymean = np.mean(ylist)
#Calculate distance centre <-> other points
xcentral = [(x-xmean) for x in xlist]
ycentral = [(y-ymean) for y in ylist]
landmarks_vectorform =[]
for x,y,w,z in zip(xcentral,ycentral,xlist,ylist):
landmarks_vectorform.append(w)
landmarks_vectorform.append(z)
meannp = np.asarray((ymean,xmean))
coornp = np.assarray((z,w))
dist = np.linalg.norm(coornp-meannp)
landmarks_vectorised.append(dist)
landmarks_vectorised.append((math.atan2(y, x)*360)/(2*math.pi))
# show the output image with the face detections + facial landmarks
cv2.imshow("Output", image)
cv2.waitKey(0)