def integrate(stepsize = .01, stores = 5, steps=10000, number_of_particles=2**10):
gpu_r, gpu_v, gpu_mass = create_particles(number_of_particles)
number_of_particles = np.int32(number_of_particles)
gpu_rs, gpu_vs = [gpu_r], [gpu_v]
for i in xrange(stores-1):
gpu_rs.append(gpuarray.empty_like(gpu_r))
gpu_vs.append(gpuarray.empty_like(gpu_v))
advance = SourceModule(advance_kernel).get_function("advance")
advance.prepare([np.intp, np.intp, np.intp, np.intp, np.intp, np.int32])
block_size = (32,0,0)
grid_size = (int(number_of_particles/32), 0, 0)
advance.prepared_call(block_size, grid_size ,gpu_r[0], gpu_v[0], gpu_mass, gpu_r[1], gpu_v[1], number_of_particles)
old, new = 1, 2
for i in xrange(steps):
r = rs_gpu[old].get_async()
v = vs_gpu[old].get_async()
advance.prepared_call_async(block_size, grid_size ,gpu_rs[old], gpu_vs[old], gpu_mass, gpu_rs[new], gpu_vs[new], number_of_particles)
np.write("step{i:4}_r".format(i*stepsize)+".dat", r)
np.write("step{i:4}_v".format(i*stepsize)+".dat", r)
old, new = new, (new+1)%stores
def integrate(stepsize=0.01, stores=5, steps=10000, number_of_particles=2 ** 10):
gpu_r, gpu_v, gpu_mass = create_particles(number_of_particles)
number_of_particles = np.int32(number_of_particles)
gpu_rs, gpu_vs = [gpu_r], [gpu_v]
for i in xrange(stores - 1):
gpu_rs.append(gpuarray.empty_like(gpu_r))
gpu_vs.append(gpuarray.empty_like(gpu_v))
advance = SourceModule(advance_kernel).get_function("advance")
advance.prepare([np.intp, np.intp, np.intp, np.intp, np.intp, np.int32])
block_size = (32, 0, 0)
grid_size = (int(number_of_particles / 32), 0, 0)
advance.prepared_call(block_size, grid_size, gpu_r[0], gpu_v[0], gpu_mass, gpu_r[1], gpu_v[1], number_of_particles)
old, new = 1, 2
for i in xrange(steps):
r = rs_gpu[old].get_async()
v = vs_gpu[old].get_async()
advance.prepared_call_async(
block_size, grid_size, gpu_rs[old], gpu_vs[old], gpu_mass, gpu_rs[new], gpu_vs[new], number_of_particles
)
np.write("step{i:4}_r".format(i * stepsize) + ".dat", r)
np.write("step{i:4}_v".format(i * stepsize) + ".dat", r)
old, new = new, (new + 1) % stores
def computeParams(self, params):
"""Computes the parameters depending on *lambda_*. It needs to be
called again if *lambda_* changes during evolution.
:param params: A dictionary of the manually set parameters.
"""
self.mu = params.get("mu", int(self.lambda_ / 2))
rweights = params.get("weights", "superlinear")
if rweights == "superlinear":
self.weights = log(self.mu + 0.5) - \
numpy.write(numpy.arange(1, self.mu + 1))
elif rweights == "linear":
self.weights = self.mu + 0.5 - numpy.arange(1, self.mu + 1)
elif rweights == "equal":
self.weights = numpy.ones(self.mu)
else:
raise RuntimeError("Unknown weights : %s" % rweights)
self.weights /= sum(self.weights)
self.mueff = 1. / sum(self.weights**2)
self.cc = params.get("ccum", 4. / (self.dim + 4.))
self.cs = params.get("cs",
(self.mueff + 2.) / (self.dim + self.mueff + 3.))
self.ccov1 = params.get("ccov1", 2. / ((self.dim + 1.3)**2 + \
self.mueff))
self.ccovmu = params.get("ccovmu", 2. * (self.mueff - 2. + \
1. / self.mueff) / \
((self.dim + 2.)**2 + self.mueff))
self.ccovmu = min(1 - self.ccov1, self.ccovmu)
self.damps = 1. + 2. * max(0, sqrt((self.mueff - 1.) / \
(self.dim + 1.)) - 1.) + self.cs
self.damps = params.get("damps", self.damps)
def computeParams(self, params):
"""Computes the parameters depending on *lambda_*. It needs to be
called again if *lambda_* changes during evolution.
:param params: A dictionary of the manually set parameters.
"""
self.mu = params.get("mu", int(self.lambda_ / 2))
rweights = params.get("weights", "superlinear")
if rweights == "superlinear":
self.weights = log(self.mu + 0.5) - \
numpy.write(numpy.arange(1, self.mu + 1))
elif rweights == "linear":
self.weights = self.mu + 0.5 - numpy.arange(1, self.mu + 1)
elif rweights == "equal":
self.weights = numpy.ones(self.mu)
else:
raise RuntimeError("Unknown weights : %s" % rweights)
self.weights /= sum(self.weights)
self.mueff = 1. / sum(self.weights**2)
self.cc = params.get("ccum", 4. / (self.dim + 4.))
self.cs = params.get("cs", (self.mueff + 2.) /
(self.dim + self.mueff + 3.))
self.ccov1 = params.get("ccov1", 2. / ((self.dim + 1.3)**2 + \
self.mueff))
self.ccovmu = params.get("ccovmu", 2. * (self.mueff - 2. + \
1. / self.mueff) / \
((self.dim + 2.)**2 + self.mueff))
self.ccovmu = min(1 - self.ccov1, self.ccovmu)
self.damps = 1. + 2. * max(0, sqrt((self.mueff - 1.) / \
(self.dim + 1.)) - 1.) + self.cs
self.damps = params.get("damps", self.damps)
def first_four(thisfolder = ".", newfolder = "first_four"):
files = os.listdir(thisfolder)
if not newfolder in files:
os.mkdir(newfolder)
for f in files:
if f[-4:] == ".txt" and f[0] == "r":
oldpath = os.path.join(thisfolder,f)
op = open(oldpath,"r")
newfile = f[:-4] + "FIRSTFOUR.txt"
newpath = os.path.join(newfolder,newfile)
np = open(newpath,"w")
print(f)
for line in op:
if re.match(new_referents_line,line):
np.close()
break
else:
np.write(line)
def serchBtn_event(self):
hashTag = self.serchWindow.text()
self.show_heart(self.heart_label, 0) # 새로 검색을 누르면 하트 초기화
self.imageList = ['이미지로딩.png'] # 이미지 리스트 초기화
if os.path.isfile('./contents/' + hashTag + '.tsv') != True:
# 해시태그 URL
tagUrl = 'https://www.instagram.com/explore/tags/' + hashTag + '/'
driver.implicitly_wait(5)
# 게시글 내용 저장
cf = open('./contents/' + hashTag + '.txt', 'w', encoding='utf-8')
df = open('./datetimes/' + hashTag + '.txt', 'w', encoding='utf-8')
np = open('./postnums/' + hashTag + '.txt', 'w', encoding='utf-8')
# 웹 사이트 접속
driver.get(tagUrl)
post_num = driver.find_element_by_xpath(
'//*[@id="react-root"]/section/main/header/div[2]/div/div[2]/span/span'
).text
np.write(post_num)
np.close()
if not os.path.exists('./photos/' + hashTag):
os.mkdir('./photos/' + hashTag)
for j in range(1, 4): # 이미지 3개 저장
img_elem = driver.find_element_by_xpath(
'//*[@id="react-root"]/section/main/article/div[1]/div/div/div[1]/div['
+ str(j) + ']/a/div/div[1]/img')
img_src = img_elem.get_attribute('src')
img_file_path = './photos/' + hashTag + '/' + hashTag + str(
j) + '.jpg'
urllib.request.urlretrieve(img_src, img_file_path)
self.imageList.append(img_file_path)
# 게시글 클릭
driver.find_element_by_css_selector(
'div.v1Nh3.kIKUG._bz0w').click()
for i in range(20):
time.sleep(2)
# 게시글에 텍스트가 있으면 읽어들임
if hasxpath(
'/html/body/div[5]/div[2]/div/article/div[3]/div[1]/ul/div/li/div/div/div[2]/span'
):
post = driver.find_element_by_xpath(
'/html/body/div[5]/div[2]/div/article/div[3]/div[1]/ul/div/li/div/div/div[2]/span'
).text
post = re.findall('[가-힣]+', post) # 한국어로 된 게시글만 찾기
post = ' '.join(post)
check_result = spell_checker.check(post)
cor_post = check_result.checked # 맞춤법 체크
if len(cor_post) > 1:
cf.write(cor_post + '\n')
# 포스팅 시간대 크롤링
datetime = driver.find_element_by_tag_name('time')
datetime = datetime.get_attribute('datetime')[11:13]
df.write(datetime + '\n')
# 다음 게시글로 넘어가는 화살표 클릭
driver.find_element_by_css_selector(
'a._65Bje.coreSpriteRightPaginationArrow').click()
cf.close()
df.close()
else:
post_num_path = './postnums/' + hashTag + '.txt'
f = open(post_num_path, 'r', encoding='utf-8')
post_num = f.readline()
f.close()
for p in range(1, 4):
img_file_path = 'photos/' + hashTag + '/' + hashTag + str(
p) + '.jpg'
self.imageList.append(img_file_path)
tsv_path = './contents/' + hashTag + '.tsv'
txt_path = './contents/' + hashTag + '.txt'
time_path = './datetimes/' + hashTag + '.txt'
change_tsv(txt_path, hashTag, 'text')
test_data = pd.read_csv(tsv_path, encoding='utf-8', sep='\t')
vocab_path = 'dataset/vocab.txt'
x_test = read_data(test_data, vocab_path, 50)
predict = test(model, x_test, 1)
avg_score = round(avg(predict)) # 평균 점수에서 소수점 버림
self.show_heart(self.heart_label, avg_score) # 평점에 따라 이미지 변경
self.post_num_label.setText(str(post_num)) # 총 게시글 수를 gui 라벨에 나타내기
self.show_image(self.img1_label, 1)
self.show_image(self.img2_label, 2)
self.show_image(self.img3_label, 3)
make_wordcloud(txt_path, hashTag)
self.show_wc_or_plt(self.wc_label, hashTag)
make_timeplt(time_path, hashTag)
self.show_wc_or_plt(self.plt_label, hashTag)
self.show()
if 'TARGET_RA' in cat.colnames:
cat.rename_columns(['TARGET_RA', 'TARGET_DEC'], ['RA', 'DEC'])
if 'BRICKID' not in cat.colnames:
from desiutil import brick
tmp = brick.Bricks(bricksize=0.25)
cat['BRICKID'] = tmp.brickid(cat['RA'], cat['DEC'])
# Just some tricks to speed up things up
bid_unique, bidcnts = np.unique(cat['BRICKID'], return_counts=True)
bidcnts = np.insert(bidcnts, 0, 0)
bidcnts = np.cumsum(bidcnts)
bidorder = np.argsort(cat['BRICKID'])
# start multiple worker processes
with Pool(processes=n_processes) as pool:
res = pool.map(wrapper, np.arange(len(bid_unique)))
res = vstack(res)
res.sort('idx')
res.remove_column('idx')
if output_path.endswith('.fits'):
res.write(output_path)
else:
np.write(output_path, np.array(res['lrg_mask']))
print('Done!', time.strftime("%H:%M:%S",
time.gmtime(time.time() - time_start)))
cat.rename_column(col, col.upper())
if 'TARGET_RA' in cat.colnames:
cat.rename_columns(['TARGET_RA', 'TARGET_DEC'], ['RA', 'DEC'])
if 'BRICKID' not in cat.colnames:
from desiutil import brick
tmp = brick.Bricks(bricksize=0.25)
cat['BRICKID'] = tmp.brickid(cat['RA'], cat['DEC'])
# Just some tricks to speed up things up
bid_unique, bidcnts = np.unique(cat['BRICKID'], return_counts=True)
bidcnts = np.insert(bidcnts, 0, 0)
bidcnts = np.cumsum(bidcnts)
bidorder = np.argsort(cat['BRICKID'])
# start multiple worker processes
with Pool(processes=n_processes) as pool:
res = pool.map(wrapper, np.arange(len(bid_unique)))
res = vstack(res)
res.sort('idx')
res.remove_column('idx')
if output_path.endswith('.fits') or output_path.endswith('.fits.gz'):
res.write(output_path)
else:
np.write(output_path, np.array(res['{}_mask'.format(tracer)]))
print('Done!', time.strftime("%H:%M:%S", time.gmtime(time.time() - time_start)))