本文整理汇总了Python中tqdm.tqdm函数的典型用法代码示例。如果您正苦于以下问题：Python tqdm函数的具体用法？Python tqdm怎么用？Python tqdm使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了tqdm函数的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: augment_arrays
def augment_arrays(project):
array_path = os.path.join(project['path'], 'array')
augmented_path = os.path.join(project['path'], 'augmented')
shutil.rmtree(augmented_path,ignore_errors=True)
os.makedirs(augmented_path)
if project['augmentations'] is None:
print('No augmentations selected: copying train arrays as is.')
files = os.listdir(array_path)
for file in tqdm(files):
shutil.copy(os.path.join(array_path, file),augmented_path)
else:
print('Generating image augmentations:')
for img_idx, (array, label, label_name) in tqdm(enumerate(gen_arrays_from_dir(array_path))):
split_label_name = '-'.join(label_name.split('-')[2:-1])
for aug_idx, (array_aug, label_aug) in enumerate(gen_augment_arrays(array, label, project['augmentations'], project['category_rounds'][split_label_name])):
cat_idx = np.argmax(label_aug)
cat = project['categories'][cat_idx]
img_name = '{}-{:02d}-img-{}-{}'.format(img_idx, aug_idx,
cat, cat_idx)
label_name = '{}-{:02d}-label-{}-{}'.format(img_idx, aug_idx,
cat, cat_idx)
aug_path = os.path.join(augmented_path, img_name)
label_path = os.path.join(augmented_path, label_name)
np.save(aug_path, array_aug)
np.save(label_path, label_aug)
project['is_augmented'] = True
return project
开发者ID:codealphago，项目名称:transfer，代码行数:32，代码来源:augment_arrays.py
示例2: to_html
def to_html(self, outdir, template=None):
pages_set = self.pages_set
if template is None:
template = textwrap.dedent("""\
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title>Page {page}</title>
<link rel="stylesheet" type="text/css" href="teletext.css" title="Default Style"/>
<link rel="alternative stylesheet" type="text/css" href="teletext-noscanlines.css" title="No Scanlines"/>
<script type="text/javascript" src="cssswitch.js"></script>
</head>
<body onload="set_style_from_cookie()">
{body}
</body>
</html>
""")
for magazineno, magazine in tqdm(self.magazines.items(), desc='Magazines', unit='M'):
for pageno, page in tqdm(magazine.pages.items(), desc='Pages', unit='P'):
pagestr = f'{magazineno}{pageno:02x}'
outfile = open(os.path.join(outdir, f'{pagestr}.html'), 'w')
body = '\n'.join(
subpage.to_html(pages_set) for n, subpage in sorted(page.subpages.items())
)
outfile.write(template.format(page=pagestr, body=body))
开发者ID:ali1234，项目名称:vhs-teletext，代码行数:28，代码来源:service.py
示例3: find_duplicates
def find_duplicates(directories):
for d in directories:
if not os.path.exists(d):
raise ValueError("Directory %s does not exist" % d)
elif not os.path.isdir(d):
raise ValueError("Expected %s to be a directory" % d)
file_hashes = defaultdict(set)
print("Scanning for files…")
all_files = deque()
for filename in tqdm(find_files(directories)):
all_files.append(filename)
print("Hashing %d files" % len(all_files))
with ThreadPoolExecutor() as executor:
for filename, digest in tqdm(
executor.map(get_file_hash, all_files), total=len(all_files)
):
file_hashes[digest].add(filename)
for digest, filenames in file_hashes.items():
if len(filenames) < 2:
continue
else:
yield digest, filenames
开发者ID:acdha，项目名称:unix_tools，代码行数:29，代码来源:dupinator.py
示例4: generate_code
def generate_code(self, Modal, bit, generate):
batch_size = 128
if generate=="label":
num_data = Modal.shape[0]
index = np.linspace(0, num_data - 1, num_data).astype(int)
B = np.zeros([num_data, bit], dtype=np.float32)
for iter in tqdm(xrange(num_data / batch_size + 1)):
ind = index[iter * batch_size: min((iter + 1) * batch_size, num_data)]
label = Modal[ind, :].astype(np.float32)
label = label.reshape([label.shape[0], 1, label.shape[1], 1])
Hsh_L = self.Hsh_L.eval(feed_dict={self.ph['label_input']: label})
B[ind, :] = Hsh_L
elif generate=="image":
num_data = len(Modal)
index = np.linspace(0, num_data - 1, num_data).astype(int)
B = np.zeros([num_data, bit], dtype=np.float32)
for iter in tqdm(xrange(num_data / batch_size + 1)):
ind = index[iter * batch_size: min((iter + 1) * batch_size, num_data)]
mean_pixel = np.repeat(self.meanpix[:, :, :, np.newaxis], len(ind), axis=3)
image = Modal[ind,:,:,:].astype(np.float64)
image = image - mean_pixel.astype(np.float64).transpose(3, 0, 1, 2)
Hsh_I = self.Hsh_I.eval(feed_dict={self.ph['image_input']: image})
B[ind, :] = Hsh_I
else:
num_data = Modal.shape[0]
index = np.linspace(0, num_data - 1, num_data).astype(int)
B = np.zeros([num_data, bit], dtype=np.float32)
for iter in tqdm(xrange(num_data / batch_size + 1)):
ind = index[iter * batch_size: min((iter + 1) * batch_size, num_data)]
text = Modal[ind, :].astype(np.float32)
text = text.reshape([text.shape[0], 1, text.shape[1], 1])
Hsh_T = self.Hsh_T.eval(feed_dict={self.ph['text_input']: text})
B[ind, :] = Hsh_T
B = np.sign(B)
return B
开发者ID:StatML，项目名称:SSAH，代码行数:35，代码来源:SSAH.py
示例5: train_word2id
def train_word2id():
"""把训练集的所有词转成对应的id。"""
time0 = time.time()
print('Processing train data.')
df_train = pd.read_csv('../raw_data/question_train_set.txt', sep='\t', usecols=[0, 2, 4],
names=['question_id', 'word_title', 'word_content'], dtype={'question_id': object})
print('training question number %d ' % len(df_train))
# 没有 content 的问题用 title 来替换
na_content_indexs = list()
for i in tqdm(xrange(len(df_train))):
word_content = df_train.word_content.values[i]
if type(word_content) is float:
na_content_indexs.append(i)
print('There are %d train questions without content.' % len(na_content_indexs))
for na_index in tqdm(na_content_indexs):
df_train.at[na_index, 'word_content'] = df_train.at[na_index, 'word_title']
# 没有 title 的问题， 丢弃
na_title_indexs = list()
for i in xrange(len(df_train)):
word_title = df_train.word_title.values[i]
if type(word_title) is float:
na_title_indexs.append(i)
print('There are %d train questions without title.' % len(na_title_indexs))
df_train = df_train.drop(na_title_indexs)
print('After dropping, training question number(should be 2999952) = %d' % len(df_train))
# 转为 id 形式
p = Pool()
train_title = np.asarray(p.map(get_id4words, df_train.word_title.values))
np.save('../data/wd_train_title.npy', train_title)
train_content = np.asarray(p.map(get_id4words, df_train.word_content.values))
np.save('../data/wd_train_content.npy', train_content)
p.close()
p.join()
print('Finished changing the training words to ids. Costed time %g s' % (time.time() - time0))
开发者ID:brucexia6116，项目名称:zhihu-text-classification，代码行数:34，代码来源:word2id.py
示例6: createDataTxt
def createDataTxt(imagePath, annotationPath, imagesInDir, split=False):
JPG = '.jpg'
TRAINING = 'training/'
VALIDATION = 'validation/'
if split:
annotatedImages = os.listdir(annotationPath)
# np.random.shuffle(annotatedImages)
splitSize = ceil(len(annotatedImages) * 0.85)
annotatedImagesTrain = annotatedImages[:splitSize]
annotatedImagesValidation = annotatedImages[splitSize:]
else:
annotatedImagesTrain = os.listdir(join(annotationPath, TRAINING))
annotatedImagesValidation = os.listdir(join(annotationPath, VALIDATION))
with open(imagesInDir + 'train.txt', 'w') as file:
for ann in tqdm(annotatedImagesTrain, desc='Writing train.txt for input dataset'):
if isfile(join(imagePath, TRAINING, splitext(ann)[0]) + JPG):
file.write(' '.join(
[join(imagePath, TRAINING, splitext(ann)[0]) + JPG,
join(annotationPath, TRAINING, ann)]) + '\n')
with open(imagesInDir + 'val.txt', 'w') as file:
for annv in tqdm(annotatedImagesValidation, desc='Writing valid.txt for input dataset'):
if isfile(join(imagePath, VALIDATION, splitext(annv)[0]) + JPG):
file.write(' '.join(
[join(imagePath, VALIDATION, splitext(annv)[0]) + JPG,
join(annotationPath, VALIDATION, annv)]) + '\n')
return
开发者ID:ruyi345，项目名称:Fully-convolutional-networks-TF，代码行数:31，代码来源:dataGenerator.py
示例7: pro_progess
def pro_progess(filepath="../data"):
height = 299
train_files = os.listdir(filepath + '/train')
train = np.zeros((len(train_files), height, height, 3), dtype=np.uint8)
labels = list(filter(lambda x: x[:3] == 'dog', train_files))
test_files = os.listdir(filepath + '/test')
test = np.zeros((len(test_files), height, height, 3), dtype=np.uint8)
for i in tqdm(range(len(train_files))):
filename = filepath + train_files[i]
img = cv2.imread(filename)
img = cv2.resize(img, (height, height))
train[i] = img[:, :, ::-1]
for i in tqdm(range(len(test_files))):
filename = filepath + test_files[i]
img = cv2.imread(filename)
img = cv2.resize(img, (height, height))
test[i] = img[:, :, ::-1]
print ('Training Data Size = %.2 GB' % (sys.getsizeof(train)/1024**3))
print ('Testing Data Size = %.2 GB' % (sys.getsizeof(test)/1024**3))
X_train, X_val, y_train, y_val = train_test_split(
train, labels, shuffle=True, test_size=0.2, random_state=42)
return X_train, X_val, y_train, y_val
开发者ID:Suluo，项目名称:Kaggle，代码行数:26，代码来源:get_data.py
示例8: normalize_features
def normalize_features(X_train, X_test):
n_features = X_train.shape[1]
feature_sums = np.sum(X_test, axis=1)
nonblack_vectors = np.where(feature_sums > 0,1,0)
#print nonblack_vectors.shape
mask = []
for x in range(X_test.shape[0]):
mask.append([nonblack_vectors[x]]*n_features)
mask = np.array(mask)
X_test_nonblack = X_test[np.where(feature_sums > 0)]
X = np.concatenate((X_train, X_test_nonblack))
#print X, X.shape
mean = np.mean(X,axis=0)
std = np.std(X,axis=0)
for d in tqdm(range(len(X_train))):
X_train[d] = (X_train[d] - mean) / std
for d in tqdm(range(len(X_test))):
X_test[d] = (X_test[d] - mean) / std
#Make once fully black vectors fully black again
X_test = X_test*mask
return X_train, X_test
开发者ID:gzuidhof，项目名称:cad，代码行数:29，代码来源:rebalance.py
示例9: make_tqdm_iterator
def make_tqdm_iterator(**kwargs):
options = {
"file": sys.stdout,
"leave": True
}
options.update(kwargs)
if session_type() == 'kernel':
# from IPython import display
# capture_stderr = StringIO()
# with RedirectStdStreams(stderr=capture_stderr):
# try:
# iterator = tqdm_notebook(**options)
# except:
# failed = True
# else:
# failed = False
# err_out = capture_stderr.getvalue()
# capture_stderr.close()
# if failed or err_out.lower().find("widget javascript not detected") > -1:
# display.clear_output(wait=True)
# iterator = tqdm(**options)
iterator = tqdm(**options)
else:
iterator = tqdm(**options)
return iterator
开发者ID:rgolovnya，项目名称:featuretools，代码行数:27，代码来源:gen_utils.py
示例10: scan_dir
def scan_dir(path, dir_json):
# Preprocess the total files count
for root, dirs, files in tqdm(os.walk(path)):
for name in files:
path = os.path.join(root, name)
if os.path.getsize(path) > (25*1024*1024):
ext = os.path.splitext(name)[1]
if ext in EXT:
movie_name.append(name)
with tqdm(total=len(movie_name), leave=True, unit='B',
unit_scale=True) as pbar:
for name in movie_name:
data = get_movie_info(name)
pbar.update()
if data is not None and data['Response'] == 'True':
for key, val in data.items():
if val == "N/A":
data[key] = "-" # Should N/A be replaced with `-`?
movies.append(data)
else:
if data is not None:
movie_not_found.append(name)
with open(dir_json, "w") as out:
json.dump(movies, out, indent=2)
开发者ID:iCHAIT，项目名称:moviemon，代码行数:25，代码来源:moviemon.py
示例11: compare_assemblies
def compare_assemblies(assemblies, chunk_size = 2000, identity_threshold = 0.40):
"""
compares a set of assemblies:
assemblies is a dictionary with names of the assemblies as keys and fasta-files of the assemblies as values
"""
similarities = {}
print "make blast dbs"
for subject_name, subject in tqdm(assemblies.iteritems()):
blast_db_cmd = ["makeblastdb" ,"-in", subject, "-dbtype", "nucl", "-out", subject]
with open("/dev/null") as null:
blastdb_return = call(blast_db_cmd, stdout=null)
print "Run the hell out of it"
for scaff_name, scaff in tqdm(assemblies.iteritems()):
similarities[scaff_name] = {}
chopped_up_query = "tmp.fasta"
nb_chunks = len(cut_up_fasta(scaff, chopped_up_query, chunk_size))
for subject_name, subject in assemblies.iteritems():
nics = find_NICs(chopped_up_query, subject, identity_threshold, blast_db = False)
# print scaff_name, "vs", subject_name
similarities[scaff_name][subject_name] = len(nics.keys())/nb_chunks
os.remove(chopped_up_query)
print "clean up"
for subject_name, subject in tqdm(assemblies.iteritems()):
blast_db_files = [subject + ".nhr", subject + ".nin", subject + ".nsq"]
for f in blast_db_files:
os.remove(f)
similars = DataFrame.from_dict(similarities)
return similars
开发者ID:moritzbuck，项目名称:MiComPy，代码行数:34，代码来源:intrasimilarity.py
示例12: run
def run():
batch_size = 4000
print 'reading image hashes from image_hashes.csv...',
t0 = time()
global df_hashes
df_hashes = pd.read_csv('image_hashes.csv')
df_hashes.set_index('image_id', inplace=1)
print 'took %0.5fs' % (time() - t0)
pool = avito_utils.PoolWrapper(processes=4)
print 'processing train data...'
t0 = time()
df = pd.read_csv('../input/ItemPairs_train.csv')
delete_file_if_exists('features_imagehash_train.csv')
for batch_no, batch in tqdm(list(prepare_batches(df, batch_size))):
features = process_batch(batch, pool)
append_to_csv(features, 'features_imagehash_train.csv')
print 'processing train data took %0.5fs' % (time() - t0)
print 'processinig test data...'
t0 = time()
df = pd.read_csv('../input/ItemPairs_test.csv')
delete_file_if_exists('features_imagehash_test.csv')
for batch_no, batch in tqdm(list(prepare_batches(df, batch_size))):
features = process_batch(batch, pool)
append_to_csv(features, 'features_imagehash_test.csv')
print 'processing test data took %0.5fs' % (time() - t0)
pool.close()
开发者ID:alexeygrigorev，项目名称:avito-duplicates-kaggle，代码行数:35，代码来源:calculate_imagehash_features.py
示例13: run
def run(*args):
"""Reset the in_stock Card property. It was set to True by default, it
should be False. So each card that was bought once or added from
an inventory should be to True.
"""
yes_answers = ["y", "Y", "o", "O", ""]
go_all_cards = raw_input("Go with all cards ? [Y/n]")
go_inventories = raw_input("Go with cards applied from inventories ? [Y/n]")
if go_all_cards in yes_answers:
print("Setting all cards to not in stock...")
for card in tqdm(Card.objects.all()):
card.in_stock = False
card.save()
if go_inventories in yes_answers:
print("Registering cards applied from inventories...")
for inv in tqdm(Inventory.objects.filter(applied=True)):
print("Going with inv {}".format(inv.name))
for card_set in inv.inventorycopies_set.all():
card_set.card.in_stock = True
card_set.card.save()
print("All done.")
开发者ID:vindarel，项目名称:abelujo，代码行数:25，代码来源:reset_in_stock.py
示例14: download_url
def download_url(url, root, filename, md5):
from six.moves import urllib
root = os.path.expanduser(root)
fpath = os.path.join(root, filename)
try:
os.makedirs(root)
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
# downloads file
if os.path.isfile(fpath) and check_integrity(fpath, md5):
print('Using downloaded and verified file: ' + fpath)
else:
try:
print('Downloading ' + url + ' to ' + fpath)
urllib.request.urlretrieve(
url, fpath,
reporthook=gen_bar_updater(tqdm(unit='B', unit_scale=True))
)
except:
if url[:5] == 'https':
url = url.replace('https:', 'http:')
print('Failed download. Trying https -> http instead.'
' Downloading ' + url + ' to ' + fpath)
urllib.request.urlretrieve(
url, fpath,
reporthook=gen_bar_updater(tqdm(unit='B', unit_scale=True))
)
开发者ID:Lynkzhang，项目名称:vision，代码行数:33，代码来源:utils.py
示例15: preprocess_simple_predict
def preprocess_simple_predict():
df = pd.read_csv('data/data_full.csv')
df = df[df.is_fake==0]
res_df = df.ID.values
df_target = df[df.target > 0].drop('ID,is_train,is_fake'.split(','), axis=1)
target = df_target.target.values
data = df_target.drop(['target',], axis=1).values.astype(int)
val_sum = {}
for i, dat in tqdm(enumerate(data)):
for d in dat:
if d <= 0:
continue
if d not in val_sum:
val_sum[d] = [0, 0]
val_sum[d][0] += target[i]
val_sum[d][1] += 1
df['simple_predict'] = 0
for i, row in tqdm(df.drop('ID,is_train,is_fake,target'.split(','), axis=1).iterrows()):
summ = 0
cnt = 0.000001
for val in row:
if val not in val_sum or val_sum[val][1] < 10:
continue
summ += val_sum[val][0]
cnt += val_sum[val][1]
df.loc[i, 'simple_predict'] = summ / cnt
df[['ID', 'simple_predict']].to_csv('data/feat_simple_predict.csv', index=False)
开发者ID:vlarine，项目名称:kaggle，代码行数:27，代码来源:santander.py
示例16: predict_kfold
def predict_kfold(cls, X, y, n_folds=10, seed=0, textModel_params={},
kfolds=None, pool=None, use_tqdm=True):
try:
from tqdm import tqdm
except ImportError:
def tqdm(x, **kwargs):
return x
le = preprocessing.LabelEncoder().fit(y)
y = np.array(le.transform(y))
hy = np.zeros(len(y), dtype=np.int)
if kfolds is None:
kfolds = StratifiedKFold(n_splits=n_folds, shuffle=True,
random_state=seed).split(X, y)
args = [(X, y, tr, ts, textModel_params) for tr, ts in kfolds]
if pool is not None:
if use_tqdm:
res = [x for x in tqdm(pool.imap_unordered(cls.train_predict_pool, args),
desc='Params', total=len(args))]
else:
res = [x for x in pool.imap_unordered(cls.train_predict_pool, args)]
else:
if use_tqdm:
args = tqdm(args)
res = [cls.train_predict_pool(x) for x in args]
for ts, _hy in res:
hy[ts] = _hy
return le.inverse_transform(hy)
开发者ID:INGEOTEC，项目名称:b4msa，代码行数:28，代码来源:classifier.py
示例17: run
def run():
textfiles = glob.glob('anjuke_new_house/*txt')
if len(textfiles) != 0:
print ">> compress files under anjuke_new_house"
f = zipfile.ZipFile('anjuke_new_house/anjuke_new_house.zip', 'w', zipfile.ZIP_DEFLATED)
for textfile in tqdm(textfiles):
f.write(textfile)
os.remove(textfile)
f.close()
textfiles = glob.glob('anjuke_second_house/*txt')
if len(textfiles) != 0:
print ">> compress files under anjuke_second_house"
f = zipfile.ZipFile('anjuke_second_house/anjuke_second_house.zip', 'w', zipfile.ZIP_DEFLATED)
for textfile in tqdm(textfiles):
f.write(textfile)
os.remove(textfile)
f.close()
textfiles = glob.glob('anjuke_renting_house/*txt')
if len(textfiles) != 0:
print ">> compress files under anjuke_renting_house"
f = zipfile.ZipFile('anjuke_renting_house/anjuke_renting_house.zip', 'w', zipfile.ZIP_DEFLATED)
for textfile in tqdm(textfiles):
f.write(textfile)
os.remove(textfile)
f.close()
开发者ID:summychou，项目名称:HousePriceAcrossTheCountry，代码行数:28，代码来源:anjuke_data_compress.py
示例18: test_ascii
def test_ascii():
""" Test ascii/unicode bar """
# Test ascii autodetection
with closing(StringIO()) as our_file:
with tqdm(total=10, file=our_file, ascii=None) as t:
assert t.ascii # TODO: this may fail in the future
# Test ascii bar
with closing(StringIO()) as our_file:
for _ in tqdm(_range(3), total=15, file=our_file, miniters=1,
mininterval=0, ascii=True):
pass
our_file.seek(0)
res = our_file.read().strip("\r").split("\r")
assert '7%|6' in res[1]
assert '13%|#3' in res[2]
assert '20%|##' in res[3]
# Test unicode bar
with closing(UnicodeIO()) as our_file:
with tqdm(total=15, file=our_file, ascii=False, mininterval=0) as t:
for _ in _range(3):
t.update()
our_file.seek(0)
res = our_file.read().strip("\r").split("\r")
assert "7%|\u258b" in res[1]
assert "13%|\u2588\u258e" in res[2]
assert "20%|\u2588\u2588" in res[3]
开发者ID:CrazyPython，项目名称:tqdm，代码行数:28，代码来源:tests_tqdm.py
示例19: read_raw_docs
def read_raw_docs(lines: List[str], size: int, workers: int) -> np.ndarray:
if size == -1:
size = len(lines)
lines = lines[:size]
documents = np.empty(size, dtype=object)
memory_impact = sum([sys.getsizeof(s) for s in lines])
# jeopardy 32862372
# recipes 187414159
if memory_impact < 50000000:
offset = 0
linebins = np.array_split(lines, workers) # this is the offending large memory line
with concurrent.futures.ProcessPoolExecutor() as executor:
futures = {executor.submit(clean_text, linebins[i]): i
for i in range(workers)}
for future in tqdm(concurrent.futures.as_completed(futures),
desc='Tokenizing Documents', total=workers, leave=True):
index = futures[future]
for i, line in enumerate(future.result()):
documents[offset + i] = line
offset += len(future.result())
else:
print('Use Large Memory Algorithm')
offset = 0
with concurrent.futures.ProcessPoolExecutor() as executor:
futures = {executor.submit(clean_line, lines[i]): i
for i in range(size)}
for future in tqdm(concurrent.futures.as_completed(futures),
desc='Tokenizing Documents', total=size, leave=True):
documents[offset] = future.result()
offset += 1
return documents
开发者ID:willzfarmer，项目名称:Python-LSA，代码行数:31，代码来源:LSA.py
示例20: getFeatures
def getFeatures(self):
files = glob.glob(self.objectPath+self.preProcessedData+'*.npy')
split_length = None
if self.windowSize != "None":
split_length = self.windowSize * self.samplingFrequency
split_based = open(self.objectPath+self.dataFeatures+self.featureExtracted, 'w', newline='')
writer = csv.writer(split_based, delimiter=',')
header_writen = False
for file in tqdm(files):
file_split = file.split('_')
recording_class = file_split[2]
recording = np.load(file)
i = 0
for channel in tqdm(recording):
if self.windowSize == "None":
split_length = len(channel)
limit = int(len(channel)/split_length)*split_length
channel = channel[0:limit]
splits = np.split(channel,limit//split_length)
j = 1
for split in tqdm(splits):
self.channel_data = split
data_ = self.runPipeline()
temp = [file_split[0],recording_class,self.channels[i],j]
features = list(data_[0])
if not header_writen:
writer.writerow( ['filename','experiment_identifier','channel_name','split_number'] + list(data_[1]) )
header_writen = True
writer.writerow(temp+features)
#break
j += 1
#break
i += 1
开发者ID:utkarshshukla2912，项目名称:pyEEGpipeline，代码行数:35，代码来源:featureExtractor.py
注：本文中的tqdm.tqdm函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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