body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
1605276693eb9051c872eef4a87d03c4e37b722287653a36765a8ee550e79efa | def get_one_page(self, url):
'\n 请求url返回响应结果\n :param url:\n :return:\n '
try:
response = requests.get(url, headers=self.generate_random_ua)
if (response.status_code == 200):
return response.text
except Exception as e:
print('连接糗事百科失败,错误原因', e)... | 请求url返回响应结果
:param url:
:return: | qiushibaike/qiushibaike.py | get_one_page | jumploop/Python3_WebSpider | 1 | python | def get_one_page(self, url):
'\n 请求url返回响应结果\n :param url:\n :return:\n '
try:
response = requests.get(url, headers=self.generate_random_ua)
if (response.status_code == 200):
return response.text
except Exception as e:
print('连接糗事百科失败,错误原因', e)... | def get_one_page(self, url):
'\n 请求url返回响应结果\n :param url:\n :return:\n '
try:
response = requests.get(url, headers=self.generate_random_ua)
if (response.status_code == 200):
return response.text
except Exception as e:
print('连接糗事百科失败,错误原因', e)... |
468cd31f5ee4a6c35065727db9860d81356424e4143e3bd354a23a79ac70c5e0 | @staticmethod
def parse_one_page(contents):
'\n 解析页面数据，提取数据\n :param content:\n :return:\n '
html = etree.HTML(contents)
items = html.xpath('//div[contains(@id,"qiushi_tag")]')
pageStories = []
for item in items:
author = item.xpath('.//div[@class="author clearfix... | 解析页面数据，提取数据
:param content:
:return: | qiushibaike/qiushibaike.py | parse_one_page | jumploop/Python3_WebSpider | 1 | python | @staticmethod
def parse_one_page(contents):
'\n 解析页面数据，提取数据\n :param content:\n :return:\n '
html = etree.HTML(contents)
items = html.xpath('//div[contains(@id,"qiushi_tag")]')
pageStories = []
for item in items:
author = item.xpath('.//div[@class="author clearfix... | @staticmethod
def parse_one_page(contents):
'\n 解析页面数据，提取数据\n :param content:\n :return:\n '
html = etree.HTML(contents)
items = html.xpath('//div[contains(@id,"qiushi_tag")]')
pageStories = []
for item in items:
author = item.xpath('.//div[@class="author clearfix... |
6cb20982d733841adaf6b674acdecba8584ef057abf9023a35a7f2d404ba0af7 | def write_to_file_by_csv(self, content):
'\n 将数据写入文件\n :param content:\n :return:\n '
with open('result.csv', 'w', newline='', encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerow(self.fieldnames)
writer.writerows(content) | 将数据写入文件
:param content:
:return: | qiushibaike/qiushibaike.py | write_to_file_by_csv | jumploop/Python3_WebSpider | 1 | python | def write_to_file_by_csv(self, content):
'\n 将数据写入文件\n :param content:\n :return:\n '
with open('result.csv', 'w', newline=, encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerow(self.fieldnames)
writer.writerows(content) | def write_to_file_by_csv(self, content):
'\n 将数据写入文件\n :param content:\n :return:\n '
with open('result.csv', 'w', newline=, encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerow(self.fieldnames)
writer.writerows(content)<|docstring|>将数据写入文件
:param co... |
cf20d2b6a3cccc8ed009f1248e2365f6f3e9c4025bdf0e8ecb5e2b6592c261b9 | def write_to_file_by_pandas(self, content):
'\n 通过pandas模块将数据写入文件\n :param content:\n :return:\n '
content = [line for line in content]
df = pd.DataFrame(content, columns=self.fieldnames)
df.to_excel('results.xlsx', index=False) | 通过pandas模块将数据写入文件
:param content:
:return: | qiushibaike/qiushibaike.py | write_to_file_by_pandas | jumploop/Python3_WebSpider | 1 | python | def write_to_file_by_pandas(self, content):
'\n 通过pandas模块将数据写入文件\n :param content:\n :return:\n '
content = [line for line in content]
df = pd.DataFrame(content, columns=self.fieldnames)
df.to_excel('results.xlsx', index=False) | def write_to_file_by_pandas(self, content):
'\n 通过pandas模块将数据写入文件\n :param content:\n :return:\n '
content = [line for line in content]
df = pd.DataFrame(content, columns=self.fieldnames)
df.to_excel('results.xlsx', index=False)<|docstring|>通过pandas模块将数据写入文件
:param content:
:... |
28d90bb2a77dabc51a68a5e3dc43eca233bdd510da58f9b3165133aa47900909 | def run(self):
'\n 主方法\n :return:\n '
results = []
urls = [self.url.format(i) for i in range(1, 14)]
for url in urls:
time.sleep(random.randint(1, 3))
content = self.get_one_page(url)
item = self.parse_one_page(content)
print(item)
results.ext... | 主方法
:return: | qiushibaike/qiushibaike.py | run | jumploop/Python3_WebSpider | 1 | python | def run(self):
'\n 主方法\n :return:\n '
results = []
urls = [self.url.format(i) for i in range(1, 14)]
for url in urls:
time.sleep(random.randint(1, 3))
content = self.get_one_page(url)
item = self.parse_one_page(content)
print(item)
results.ext... | def run(self):
'\n 主方法\n :return:\n '
results = []
urls = [self.url.format(i) for i in range(1, 14)]
for url in urls:
time.sleep(random.randint(1, 3))
content = self.get_one_page(url)
item = self.parse_one_page(content)
print(item)
results.ext... |
91aa9b031d9f2d991b1349c39e66ba11ab59dbc73314dbef523223f38a16d1a1 | def load_moonshot_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
moonshot_labeled = pinot.data.moonshot()
moonshot_unlabeled = pinot.da... | Parameters
----------
unlabeled_size :
(Default value = 0.1)
seed :
(Default value = 2666)
Returns
------- | pinot/data/unlabeled_datasets.py | load_moonshot_semi_supervised | choderalab/pinot | 13 | python | def load_moonshot_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
moonshot_labeled = pinot.data.moonshot()
moonshot_unlabeled = pinot.da... | def load_moonshot_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
moonshot_labeled = pinot.data.moonshot()
moonshot_unlabeled = pinot.da... |
8558f5e8303d32e0afc20e3495ed675c450c4dc2fbd254bcad2dc1023e2f4e72 | def load_esol_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
esol_labeled = pinot.data.esol()
esol_unlabeled = utils.load_unlabeled_dat... | Parameters
----------
unlabeled_size :
(Default value = 0.1)
seed :
(Default value = 2666)
Returns
------- | pinot/data/unlabeled_datasets.py | load_esol_semi_supervised | choderalab/pinot | 13 | python | def load_esol_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
esol_labeled = pinot.data.esol()
esol_unlabeled = utils.load_unlabeled_dat... | def load_esol_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
esol_labeled = pinot.data.esol()
esol_unlabeled = utils.load_unlabeled_dat... |
47d2dc790cfec4096f40b408ab2e50cfb62bab2b95e8fd1a5e3b770bdda4765c | def version(filename):
'Extract the version number from the dictionary file name.'
match = dict_version_re.match(filename)
if (match is None):
message.warning('Found a dictionary with a malformed name: {}'.format(filename))
return None
return tuple((int(n) for n in match.group('version')... | Extract the version number from the dictionary file name. | luminos/browser/webengine/Spell.py | version | linuxaddict89/luminos | 0 | python | def version(filename):
match = dict_version_re.match(filename)
if (match is None):
message.warning('Found a dictionary with a malformed name: {}'.format(filename))
return None
return tuple((int(n) for n in match.group('version').split('-'))) | def version(filename):
match = dict_version_re.match(filename)
if (match is None):
message.warning('Found a dictionary with a malformed name: {}'.format(filename))
return None
return tuple((int(n) for n in match.group('version').split('-')))<|docstring|>Extract the version number from t... |
4e25247a13c08076166afe07a363400cf83a4b7380fed74433a0c991ff1d9027 | def dictionary_dir(old=False):
"Return the path (str) to the QtWebEngine's dictionaries directory."
if (qtutils.version_check('5.10', compiled=False) and (not old)):
datapath = standarddir.data()
else:
datapath = QLibraryInfo.location(QLibraryInfo.DataPath)
return os.path.join(datapath, ... | Return the path (str) to the QtWebEngine's dictionaries directory. | luminos/browser/webengine/Spell.py | dictionary_dir | linuxaddict89/luminos | 0 | python | def dictionary_dir(old=False):
if (qtutils.version_check('5.10', compiled=False) and (not old)):
datapath = standarddir.data()
else:
datapath = QLibraryInfo.location(QLibraryInfo.DataPath)
return os.path.join(datapath, 'qtwebengine_dictionaries') | def dictionary_dir(old=False):
if (qtutils.version_check('5.10', compiled=False) and (not old)):
datapath = standarddir.data()
else:
datapath = QLibraryInfo.location(QLibraryInfo.DataPath)
return os.path.join(datapath, 'qtwebengine_dictionaries')<|docstring|>Return the path (str) to the... |
06699badca172b0cebd13d6c159964e8a6a2d992c914c5a2201a034c8eba2da8 | def local_files(code):
'Return all installed dictionaries for the given code.\n\n The returned dictionaries are sorted by version, therefore the latest will\n be the first element. The list will be empty if no dictionaries are found.\n '
pathname = os.path.join(dictionary_dir(), '{}*.bdic'.format(code)... | Return all installed dictionaries for the given code.
The returned dictionaries are sorted by version, therefore the latest will
be the first element. The list will be empty if no dictionaries are found. | luminos/browser/webengine/Spell.py | local_files | linuxaddict89/luminos | 0 | python | def local_files(code):
'Return all installed dictionaries for the given code.\n\n The returned dictionaries are sorted by version, therefore the latest will\n be the first element. The list will be empty if no dictionaries are found.\n '
pathname = os.path.join(dictionary_dir(), '{}*.bdic'.format(code)... | def local_files(code):
'Return all installed dictionaries for the given code.\n\n The returned dictionaries are sorted by version, therefore the latest will\n be the first element. The list will be empty if no dictionaries are found.\n '
pathname = os.path.join(dictionary_dir(), '{}*.bdic'.format(code)... |
2d2792aeaf637ce674081958e01b045c74ce5269f6005e5c96b58ae0ad00d193 | def local_filename(code):
'Return the newest installed dictionary for the given code.\n\n Return the filename of the installed dictionary with the highest version\n number or None if the dictionary is not installed.\n '
all_installed = local_files(code)
return (os.path.splitext(all_installed[0])[0]... | Return the newest installed dictionary for the given code.
Return the filename of the installed dictionary with the highest version
number or None if the dictionary is not installed. | luminos/browser/webengine/Spell.py | local_filename | linuxaddict89/luminos | 0 | python | def local_filename(code):
'Return the newest installed dictionary for the given code.\n\n Return the filename of the installed dictionary with the highest version\n number or None if the dictionary is not installed.\n '
all_installed = local_files(code)
return (os.path.splitext(all_installed[0])[0]... | def local_filename(code):
'Return the newest installed dictionary for the given code.\n\n Return the filename of the installed dictionary with the highest version\n number or None if the dictionary is not installed.\n '
all_installed = local_files(code)
return (os.path.splitext(all_installed[0])[0]... |
4386382a6a7715683cc7cda7d185d020555d69add1b3d183570d30e4e77048d6 | def init():
'Initialize the dictionary path if supported.'
if qtutils.version_check('5.10', compiled=False):
new_dir = dictionary_dir()
old_dir = dictionary_dir(old=True)
os.environ['QTWEBENGINE_DICTIONARIES_PATH'] = new_dir
try:
if (os.path.exists(old_dir) and (not o... | Initialize the dictionary path if supported. | luminos/browser/webengine/Spell.py | init | linuxaddict89/luminos | 0 | python | def init():
if qtutils.version_check('5.10', compiled=False):
new_dir = dictionary_dir()
old_dir = dictionary_dir(old=True)
os.environ['QTWEBENGINE_DICTIONARIES_PATH'] = new_dir
try:
if (os.path.exists(old_dir) and (not os.path.exists(new_dir))):
shut... | def init():
if qtutils.version_check('5.10', compiled=False):
new_dir = dictionary_dir()
old_dir = dictionary_dir(old=True)
os.environ['QTWEBENGINE_DICTIONARIES_PATH'] = new_dir
try:
if (os.path.exists(old_dir) and (not os.path.exists(new_dir))):
shut... |
cc06da47a12d635c870c86ea64d9de9bac629bc7b649a4bea7e5c8b7be89802d | def customer_image_file_path(instance, file_name):
'Generate file path for new customer image'
ext = file_name.split('.')[(- 1)]
file_name = f'{uuid.uuid4()}.{ext}'
return os.path.join('images/', file_name) | Generate file path for new customer image | billing_shop/apps/clients/models/clients.py | customer_image_file_path | sandoval19/build_crew | 0 | python | def customer_image_file_path(instance, file_name):
ext = file_name.split('.')[(- 1)]
file_name = f'{uuid.uuid4()}.{ext}'
return os.path.join('images/', file_name) | def customer_image_file_path(instance, file_name):
ext = file_name.split('.')[(- 1)]
file_name = f'{uuid.uuid4()}.{ext}'
return os.path.join('images/', file_name)<|docstring|>Generate file path for new customer image<|endoftext|> |
0428aa6039ba9cb7173e6d7d9ffc88d41299ddbe8115f6774d5162b057a95259 | def generate_unique_anonymous_username():
'\n Generate an unique username for a player. Check in database if the username already exists.\n TODO: check in db if a user with the generated username already exists\n '
unique_id = get_random_string(length=10)
new_username = ('u_%s' % unique_id)
ret... | Generate an unique username for a player. Check in database if the username already exists.
TODO: check in db if a user with the generated username already exists | web/utils.py | generate_unique_anonymous_username | NejcZupec/tictactoe | 1 | python | def generate_unique_anonymous_username():
'\n Generate an unique username for a player. Check in database if the username already exists.\n TODO: check in db if a user with the generated username already exists\n '
unique_id = get_random_string(length=10)
new_username = ('u_%s' % unique_id)
ret... | def generate_unique_anonymous_username():
'\n Generate an unique username for a player. Check in database if the username already exists.\n TODO: check in db if a user with the generated username already exists\n '
unique_id = get_random_string(length=10)
new_username = ('u_%s' % unique_id)
ret... |
5212bc37b7af3f6478baf91708147f5f4e972b7868316ad29e720144910d0f48 | def create_new_game(p1_type, p2_type):
'\n Generate two random players and create a new Game instance.\n '
player1 = Player.objects.create(username=generate_unique_anonymous_username(), type=p1_type)
player2 = Player.objects.create(username=generate_unique_anonymous_username(), type=p2_type)
retur... | Generate two random players and create a new Game instance. | web/utils.py | create_new_game | NejcZupec/tictactoe | 1 | python | def create_new_game(p1_type, p2_type):
'\n \n '
player1 = Player.objects.create(username=generate_unique_anonymous_username(), type=p1_type)
player2 = Player.objects.create(username=generate_unique_anonymous_username(), type=p2_type)
return Game.objects.create(player1=player1, player2=player2) | def create_new_game(p1_type, p2_type):
'\n \n '
player1 = Player.objects.create(username=generate_unique_anonymous_username(), type=p1_type)
player2 = Player.objects.create(username=generate_unique_anonymous_username(), type=p2_type)
return Game.objects.create(player1=player1, player2=player2)<|do... |
f4f7acfacc3270e66c237768148fcad3edc883902e78fe9af47ac151817f3410 | def download_content(url, dst, proxy=None, verbose=True):
"\n\n Download web content.\n\n Parameters\n ----------\n url: str\n Content url.\n\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value. Defaults to None, ind... | Download web content.
Parameters
----------
url: str
Content url.
dst: str
Destination for file saving.
proxy: dict
Dictionary with 'https' as key and a string indicating
the https proxy as value. Defaults to None, indicating
that the env variable https_proxy will be searched. In
case of not found, the proxy will be... | lib/utils/utils.py | download_content | jonathanzjl/cam-vision | 0 | python | def download_content(url, dst, proxy=None, verbose=True):
"\n\n Download web content.\n\n Parameters\n ----------\n url: str\n Content url.\n\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value. Defaults to None, ind... | def download_content(url, dst, proxy=None, verbose=True):
"\n\n Download web content.\n\n Parameters\n ----------\n url: str\n Content url.\n\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value. Defaults to None, ind... |
11286e6b7b205b27dccb87e3e2d2031580bb0e893071a29a28e7b1ce4b757a67 | def download_yoolov3tiny_weights(dst, proxy=None, verbose=True):
"\n\n Download YOLOv3-Tiny weight file from official darknet\n website.\n\n Parameters\n ----------\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value.... | Download YOLOv3-Tiny weight file from official darknet
website.
Parameters
----------
dst: str
Destination for file saving.
proxy: dict
Dictionary with 'https' as key and a string indicating
the https proxy as value. Defaults to None, indicating
that the env variable https_proxy will be searched. In
case of not found... | lib/utils/utils.py | download_yoolov3tiny_weights | jonathanzjl/cam-vision | 0 | python | def download_yoolov3tiny_weights(dst, proxy=None, verbose=True):
"\n\n Download YOLOv3-Tiny weight file from official darknet\n website.\n\n Parameters\n ----------\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value.... | def download_yoolov3tiny_weights(dst, proxy=None, verbose=True):
"\n\n Download YOLOv3-Tiny weight file from official darknet\n website.\n\n Parameters\n ----------\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value.... |
b23020a4a5323bac9d34b65f69c299dd28e196bee8ffce1b50cf72f298e474fb | def print_mat(mat, width=10, prec=4):
'\n A nice printer for floating point\n matrices.\n\n Parameters\n ----------\n mat: 2D matrix\n An input 2D matrix to print.\n\n width: int\n Minimum width for each element to print.\n\n prec: int\n Floating point precision for each element\n to print.\n\n '
fo... | A nice printer for floating point
matrices.
Parameters
----------
mat: 2D matrix
An input 2D matrix to print.
width: int
Minimum width for each element to print.
prec: int
Floating point precision for each element
to print. | lib/utils/utils.py | print_mat | jonathanzjl/cam-vision | 0 | python | def print_mat(mat, width=10, prec=4):
'\n A nice printer for floating point\n matrices.\n\n Parameters\n ----------\n mat: 2D matrix\n An input 2D matrix to print.\n\n width: int\n Minimum width for each element to print.\n\n prec: int\n Floating point precision for each element\n to print.\n\n '
fo... | def print_mat(mat, width=10, prec=4):
'\n A nice printer for floating point\n matrices.\n\n Parameters\n ----------\n mat: 2D matrix\n An input 2D matrix to print.\n\n width: int\n Minimum width for each element to print.\n\n prec: int\n Floating point precision for each element\n to print.\n\n '
fo... |
f146be34658f0bb4f992c8fa2b82cb087fe395fbeeed361e7f95969654315f44 | def read_txt_as_strs(txt_path, strip=' ', cmnt=None):
'\n\n Read a txt file. Each line will be treated\n as a string.\n\n Empty lines will be skipped. Spaces will be\n automatically stripped.\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file.\n\n strip: bool\n Character(s) stripped from t... | Read a txt file. Each line will be treated
as a string.
Empty lines will be skipped. Spaces will be
automatically stripped.
Parameters
----------
txt_path: str
Path to the txt file.
strip: bool
Character(s) stripped from the beginning and
the end of each line. Defaults to whitespace.
Use `None` to indicate no-op.
c... | lib/utils/utils.py | read_txt_as_strs | jonathanzjl/cam-vision | 0 | python | def read_txt_as_strs(txt_path, strip=' ', cmnt=None):
'\n\n Read a txt file. Each line will be treated\n as a string.\n\n Empty lines will be skipped. Spaces will be\n automatically stripped.\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file.\n\n strip: bool\n Character(s) stripped from t... | def read_txt_as_strs(txt_path, strip=' ', cmnt=None):
'\n\n Read a txt file. Each line will be treated\n as a string.\n\n Empty lines will be skipped. Spaces will be\n automatically stripped.\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file.\n\n strip: bool\n Character(s) stripped from t... |
dd49f3fe44b580c35b0d9d171b7347d5b675d4644a388ae414f1136addfc4a04 | def load_img(img_path, target_size, normalize=True):
'\n\n Load image for TF prediction mode.\n\n Parameters\n ----------\n img_path: str\n Path to image file.\n\n target_size: int\n Target square size for image resizing.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n Returns\n -... | Load image for TF prediction mode.
Parameters
----------
img_path: str
Path to image file.
target_size: int
Target square size for image resizing.
normalize: bool
Whether input image should be divided by 255.
Returns
----------
np.ndarray
Tensor with rank 4, to be used for
TF model prediction. | lib/utils/utils.py | load_img | jonathanzjl/cam-vision | 0 | python | def load_img(img_path, target_size, normalize=True):
'\n\n Load image for TF prediction mode.\n\n Parameters\n ----------\n img_path: str\n Path to image file.\n\n target_size: int\n Target square size for image resizing.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n Returns\n -... | def load_img(img_path, target_size, normalize=True):
'\n\n Load image for TF prediction mode.\n\n Parameters\n ----------\n img_path: str\n Path to image file.\n\n target_size: int\n Target square size for image resizing.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n Returns\n -... |
18933248c5606bef95d8395c9a715b9f622c51cb0cddae7e84b3bad1b4750726 | def make_predict_inp(img, target_size=None, normalize=True, permute_br=True, letter_box=None, to_channel_first=False):
'\n\n Transform an image for prediction mode. Pixel\n values will be rescaled to between 0 and 1.\n\n Parameters\n ----------\n img: np.ndarray\n An input image array. Assumed to be RGB image... | Transform an image for prediction mode. Pixel
values will be rescaled to between 0 and 1.
Parameters
----------
img: np.ndarray
An input image array. Assumed to be RGB image.
target_size: int
Target square size for image resizing. Defaults
to None, i.e. no resizing.
normalize: bool
Whether input image should be divi... | lib/utils/utils.py | make_predict_inp | jonathanzjl/cam-vision | 0 | python | def make_predict_inp(img, target_size=None, normalize=True, permute_br=True, letter_box=None, to_channel_first=False):
'\n\n Transform an image for prediction mode. Pixel\n values will be rescaled to between 0 and 1.\n\n Parameters\n ----------\n img: np.ndarray\n An input image array. Assumed to be RGB image... | def make_predict_inp(img, target_size=None, normalize=True, permute_br=True, letter_box=None, to_channel_first=False):
'\n\n Transform an image for prediction mode. Pixel\n values will be rescaled to between 0 and 1.\n\n Parameters\n ----------\n img: np.ndarray\n An input image array. Assumed to be RGB image... |
7929c0099a2e5f39faee7a38162674eec8bdb8ae6214da16f802f8f6991b5ca1 | def predict_top(model, img, top_classes, label_dict):
'\n\n Run prediction on input image and get\n prediction scores and class indices for\n `top_classes` classes.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n img: np.ndarray\n Input image in form of 4D tensor.\n\n top_c... | Run prediction on input image and get
prediction scores and class indices for
`top_classes` classes.
Parameters
----------
model: tf.keras.models.Model
A keras model.
img: np.ndarray
Input image in form of 4D tensor.
top_classes: int
Number of top classes for prediction.
label_dict: dict
Dictionary with keys the pr... | lib/utils/utils.py | predict_top | jonathanzjl/cam-vision | 0 | python | def predict_top(model, img, top_classes, label_dict):
'\n\n Run prediction on input image and get\n prediction scores and class indices for\n `top_classes` classes.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n img: np.ndarray\n Input image in form of 4D tensor.\n\n top_c... | def predict_top(model, img, top_classes, label_dict):
'\n\n Run prediction on input image and get\n prediction scores and class indices for\n `top_classes` classes.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n img: np.ndarray\n Input image in form of 4D tensor.\n\n top_c... |
d9af481db73556e58e514f0602304e500207388520da0fe3dd04aa54d44cf799 | def get_imagenet_dict(txt_path):
'\n\n Make ImageNet ground truth dict.\n The ground truth dictionay maps\n a class index to its label.\n\n The .txt file can be found at:\n https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file with ImageNet\n... | Make ImageNet ground truth dict.
The ground truth dictionay maps
a class index to its label.
The .txt file can be found at:
https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
Parameters
----------
txt_path: str
Path to the txt file with ImageNet
class index-to-label mappings.
Returns
----------
dict
A dictionay wit... | lib/utils/utils.py | get_imagenet_dict | jonathanzjl/cam-vision | 0 | python | def get_imagenet_dict(txt_path):
'\n\n Make ImageNet ground truth dict.\n The ground truth dictionay maps\n a class index to its label.\n\n The .txt file can be found at:\n https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file with ImageNet\n... | def get_imagenet_dict(txt_path):
'\n\n Make ImageNet ground truth dict.\n The ground truth dictionay maps\n a class index to its label.\n\n The .txt file can be found at:\n https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file with ImageNet\n... |
d3df50c856658bed26a06cbf0d67e06c509e0d678ee063c5b30b26907792790c | def classify_frame(model, frame, target_size, top_classes, label_dict, normalize=True, permute_br=True, to_channel_first=False, verbose=True):
'\n\n Run classification on input frame.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n frame: np.ndarray\n An input image frame.\n\n... | Run classification on input frame.
Parameters
----------
model: tf.keras.models.Model
A keras model.
frame: np.ndarray
An input image frame.
target_size: int
Target square image size for resizing.
None indicates no resizing.
top_classes: int
Number of top classes for prediction.
label_dict: dict
Dictionary with ke... | lib/utils/utils.py | classify_frame | jonathanzjl/cam-vision | 0 | python | def classify_frame(model, frame, target_size, top_classes, label_dict, normalize=True, permute_br=True, to_channel_first=False, verbose=True):
'\n\n Run classification on input frame.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n frame: np.ndarray\n An input image frame.\n\n... | def classify_frame(model, frame, target_size, top_classes, label_dict, normalize=True, permute_br=True, to_channel_first=False, verbose=True):
'\n\n Run classification on input frame.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n frame: np.ndarray\n An input image frame.\n\n... |
23ec06474e3a5bd772c46a2928c76cd9c78a6bf91f329568bac3ba1be090b7e4 | def load_dkn_weights(w_path, dtype, skip_bytes=20):
'\n\n Load Darknet weight file.\n\n Parameters\n ----------\n w_path: str\n Path to the weight file.\n\n dtype: str or datatype\n Data type of stored weights.\n\n skip_bytes: int\n Number of bytes to skip. Darknet weight\n file starts with 5 x int32 (20 ... | Load Darknet weight file.
Parameters
----------
w_path: str
Path to the weight file.
dtype: str or datatype
Data type of stored weights.
skip_bytes: int
Number of bytes to skip. Darknet weight
file starts with 5 x int32 (20 bytes) header
elements.
Returns
----------
np.array
Weight array. | lib/utils/utils.py | load_dkn_weights | jonathanzjl/cam-vision | 0 | python | def load_dkn_weights(w_path, dtype, skip_bytes=20):
'\n\n Load Darknet weight file.\n\n Parameters\n ----------\n w_path: str\n Path to the weight file.\n\n dtype: str or datatype\n Data type of stored weights.\n\n skip_bytes: int\n Number of bytes to skip. Darknet weight\n file starts with 5 x int32 (20 ... | def load_dkn_weights(w_path, dtype, skip_bytes=20):
'\n\n Load Darknet weight file.\n\n Parameters\n ----------\n w_path: str\n Path to the weight file.\n\n dtype: str or datatype\n Data type of stored weights.\n\n skip_bytes: int\n Number of bytes to skip. Darknet weight\n file starts with 5 x int32 (20 ... |
64a02305751c0e0c51ff32c729fcf1b30505d35c7dd193f0df6fbf5d567ef6a1 | def load_img_folder(folder, ext, permute_br=True, normalize=True, loader=None):
'\n\n Load all images inside given folder.\n\n Parameters\n ----------\n folder: str\n Absolute folder to image folder.\n\n ext: str\n Image file extension. Must be recognizable by\n OpenCV.\n\n permute_br: bool\n Whether blue... | Load all images inside given folder.
Parameters
----------
folder: str
Absolute folder to image folder.
ext: str
Image file extension. Must be recognizable by
OpenCV.
permute_br: bool
Whether blue and red channel permutation should
be performed.
normalize: bool
Indicating whether the image pixel value should
be div... | lib/utils/utils.py | load_img_folder | jonathanzjl/cam-vision | 0 | python | def load_img_folder(folder, ext, permute_br=True, normalize=True, loader=None):
'\n\n Load all images inside given folder.\n\n Parameters\n ----------\n folder: str\n Absolute folder to image folder.\n\n ext: str\n Image file extension. Must be recognizable by\n OpenCV.\n\n permute_br: bool\n Whether blue... | def load_img_folder(folder, ext, permute_br=True, normalize=True, loader=None):
'\n\n Load all images inside given folder.\n\n Parameters\n ----------\n folder: str\n Absolute folder to image folder.\n\n ext: str\n Image file extension. Must be recognizable by\n OpenCV.\n\n permute_br: bool\n Whether blue... |
41f829b3f8895c13ac6b09a3a89e584075004983f9c3e52f6712f52d830d42e8 | def letterbox_image(img, frame_size, fill=0.5, normalize=True):
'\n\n Letter box an input image.\n\n Image will be centered into a squared frame,\n where the longer side of the image is resized\n to the frame size and the shorter side is resized\n by keepng the same aspect ratio.\n\n Parameters\n ----------\... | Letter box an input image.
Image will be centered into a squared frame,
where the longer side of the image is resized
to the frame size and the shorter side is resized
by keepng the same aspect ratio.
Parameters
----------
img: np.array
The input image. Assumed to be rank-3, channel-last.
frame_size: int
Size of the... | lib/utils/utils.py | letterbox_image | jonathanzjl/cam-vision | 0 | python | def letterbox_image(img, frame_size, fill=0.5, normalize=True):
'\n\n Letter box an input image.\n\n Image will be centered into a squared frame,\n where the longer side of the image is resized\n to the frame size and the shorter side is resized\n by keepng the same aspect ratio.\n\n Parameters\n ----------\... | def letterbox_image(img, frame_size, fill=0.5, normalize=True):
'\n\n Letter box an input image.\n\n Image will be centered into a squared frame,\n where the longer side of the image is resized\n to the frame size and the shorter side is resized\n by keepng the same aspect ratio.\n\n Parameters\n ----------\... |
f21f16227c86cfe4135f94a19a1444caa454cadcac7b0f9cc19ee8477098175a | def correct_bboxes(dets, shift, ratio):
'\n\n Correct bounding box centers and scales\n to match original input image before\n letter boxing.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each ... | Correct bounding box centers and scales
to match original input image before
letter boxing.
Parameters
----------
dets: torch.tensor
A rank-2 tensor, where each col is a size-6
vector representing a detection bounding box.
The meaning of each element in the vector is
as follows:
1. bbox begin point x coordinate.
2. bb... | lib/utils/utils.py | correct_bboxes | jonathanzjl/cam-vision | 0 | python | def correct_bboxes(dets, shift, ratio):
'\n\n Correct bounding box centers and scales\n to match original input image before\n letter boxing.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each ... | def correct_bboxes(dets, shift, ratio):
'\n\n Correct bounding box centers and scales\n to match original input image before\n letter boxing.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each ... |
a1edcf48f0951e192bbb833f8169f0b9d12c562e5c90b0992a09b5f42e5b188e | def nms(dets, nms_thresh):
'\n\n Do non-maximum suppression.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. ... | Do non-maximum suppression.
Parameters
----------
dets: torch.tensor
A rank-2 tensor, where each col is a size-6
vector representing a detection bounding box.
The meaning of each element in the vector is
as follows:
1. bbox begin point x coordinate.
2. bbox begin point y coordinate.
3. bbox width.
4. bbox height.
5. m... | lib/utils/utils.py | nms | jonathanzjl/cam-vision | 0 | python | def nms(dets, nms_thresh):
'\n\n Do non-maximum suppression.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. ... | def nms(dets, nms_thresh):
'\n\n Do non-maximum suppression.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. ... |
0209748ba636f48d0ddb946a50617106be0d99db3e375be6b91dd352b0ee0296 | def compute_iou(lhs, rhs):
'\n\n Compute the intersection over union of two\n bounding boxes.\n\n Parameters\n ----------\n lhs: torch.tensor\n Bounding box 1.\n\n rhs: torch.tensor\n Bounding box 2.\n\n Returns\n ----------\n float\n The intersection over union.\n\n '
__beg = np.array([max(lhs[0],... | Compute the intersection over union of two
bounding boxes.
Parameters
----------
lhs: torch.tensor
Bounding box 1.
rhs: torch.tensor
Bounding box 2.
Returns
----------
float
The intersection over union. | lib/utils/utils.py | compute_iou | jonathanzjl/cam-vision | 0 | python | def compute_iou(lhs, rhs):
'\n\n Compute the intersection over union of two\n bounding boxes.\n\n Parameters\n ----------\n lhs: torch.tensor\n Bounding box 1.\n\n rhs: torch.tensor\n Bounding box 2.\n\n Returns\n ----------\n float\n The intersection over union.\n\n '
__beg = np.array([max(lhs[0],... | def compute_iou(lhs, rhs):
'\n\n Compute the intersection over union of two\n bounding boxes.\n\n Parameters\n ----------\n lhs: torch.tensor\n Bounding box 1.\n\n rhs: torch.tensor\n Bounding box 2.\n\n Returns\n ----------\n float\n The intersection over union.\n\n '
__beg = np.array([max(lhs[0],... |
b1f48b117ee01bbe0a48569ab83d99e7c203e000292cd8edb35299612acf7caa | def detect_frame(model, frame, obj_thresh=0.5, nms_thresh=None, box_correction=None):
'\n\n Detect objects in a frame.\n\n Parameters\n ----------\n model: YOLO\n The YOLO detector model.\n\n frame: torch.tensor\n The input frame as a torch rank-4 tensor.\n\n obj_thresh: float\n Threshold on objectiveness ... | Detect objects in a frame.
Parameters
----------
model: YOLO
The YOLO detector model.
frame: torch.tensor
The input frame as a torch rank-4 tensor.
obj_thresh: float
Threshold on objectiveness and class
probabilities.
nms_thresh: float
Threshold on IOU used during nms.
box_correction: tuple or None
A tuple of (shi... | lib/utils/utils.py | detect_frame | jonathanzjl/cam-vision | 0 | python | def detect_frame(model, frame, obj_thresh=0.5, nms_thresh=None, box_correction=None):
'\n\n Detect objects in a frame.\n\n Parameters\n ----------\n model: YOLO\n The YOLO detector model.\n\n frame: torch.tensor\n The input frame as a torch rank-4 tensor.\n\n obj_thresh: float\n Threshold on objectiveness ... | def detect_frame(model, frame, obj_thresh=0.5, nms_thresh=None, box_correction=None):
'\n\n Detect objects in a frame.\n\n Parameters\n ----------\n model: YOLO\n The YOLO detector model.\n\n frame: torch.tensor\n The input frame as a torch rank-4 tensor.\n\n obj_thresh: float\n Threshold on objectiveness ... |
a3e59ea570463414ef9d26de334c41b1393057cbc4ae9cb2d5c363677e776e26 | @nb.njit('uint64(uint8, uint8, uint8)')
def separate_n_nb(packed, n, chunk_bits):
'\n A relatively inefficient generalization of the "separate bits"\n step of Morton encoding. Assuming that each of the `n` coordinates\n has `chunk_bits` bits, we can "space out" each bit of each coordinate\n `n` spaces a... | A relatively inefficient generalization of the "separate bits"
step of Morton encoding. Assuming that each of the `n` coordinates
has `chunk_bits` bits, we can "space out" each bit of each coordinate
`n` spaces at a time.
>>> for i in range(8):
... print(i,
... format(separate_n_nb(i, 3, 3), '#012b'),
..... | morton.py | separate_n_nb | AnimatedRNG/lsc | 0 | python | @nb.njit('uint64(uint8, uint8, uint8)')
def separate_n_nb(packed, n, chunk_bits):
'\n A relatively inefficient generalization of the "separate bits"\n step of Morton encoding. Assuming that each of the `n` coordinates\n has `chunk_bits` bits, we can "space out" each bit of each coordinate\n `n` spaces a... | @nb.njit('uint64(uint8, uint8, uint8)')
def separate_n_nb(packed, n, chunk_bits):
'\n A relatively inefficient generalization of the "separate bits"\n step of Morton encoding. Assuming that each of the `n` coordinates\n has `chunk_bits` bits, we can "space out" each bit of each coordinate\n `n` spaces a... |
3434840cac354ebbc3ee887e2e90976fb692dea23839f49078cd42ad38207d9a | @nb.njit('uint64(uint8[:], uint8)')
def encode_single_coord(coord, chunk_bits):
'\n Encodes a coordinate in ℝⁿ in ℝ¹ using Morton ordering, assuming that\n the size of each dimension is 0..2^{chunk_bits}\n\n >>> morton_offsets = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... ... | Encodes a coordinate in ℝⁿ in ℝ¹ using Morton ordering, assuming that
the size of each dimension is 0..2^{chunk_bits}
>>> morton_offsets = set()
>>> for i in range(16):
... for j in range(16):
... morton_offsets.add(encode_single_coord(
... np.array([i, j], dtype=np.uint8),
... ... | morton.py | encode_single_coord | AnimatedRNG/lsc | 0 | python | @nb.njit('uint64(uint8[:], uint8)')
def encode_single_coord(coord, chunk_bits):
'\n Encodes a coordinate in ℝⁿ in ℝ¹ using Morton ordering, assuming that\n the size of each dimension is 0..2^{chunk_bits}\n\n >>> morton_offsets = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... ... | @nb.njit('uint64(uint8[:], uint8)')
def encode_single_coord(coord, chunk_bits):
'\n Encodes a coordinate in ℝⁿ in ℝ¹ using Morton ordering, assuming that\n the size of each dimension is 0..2^{chunk_bits}\n\n >>> morton_offsets = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... ... |
67cdc69d9e657bf849bbbeee41f035524ee55804420ba176448cb7ea326cddf9 | @nb.njit('uint8[:](uint64, uint8, uint8)')
def decode_single_coord(offset, n, chunk_bits):
'\n The reverse of the Morton encode function above\n\n >>> verify_decode = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... coord = np.array([i, j], dtype=np.uint8)\n ... ... | The reverse of the Morton encode function above
>>> verify_decode = set()
>>> for i in range(16):
... for j in range(16):
... coord = np.array([i, j], dtype=np.uint8)
... encoded = encode_single_coord(coord, 4)
... decoded = decode_single_coord(encoded, 2, 4)
... verify_decode.add(n... | morton.py | decode_single_coord | AnimatedRNG/lsc | 0 | python | @nb.njit('uint8[:](uint64, uint8, uint8)')
def decode_single_coord(offset, n, chunk_bits):
'\n The reverse of the Morton encode function above\n\n >>> verify_decode = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... coord = np.array([i, j], dtype=np.uint8)\n ... ... | @nb.njit('uint8[:](uint64, uint8, uint8)')
def decode_single_coord(offset, n, chunk_bits):
'\n The reverse of the Morton encode function above\n\n >>> verify_decode = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... coord = np.array([i, j], dtype=np.uint8)\n ... ... |
29960a3fc043eca1d7f7e320721d2385051c7618be9ef089c7d2e2f4d66c1cca | @nb.njit
def morton_encode_nb(coords):
'\n >>> x, y = np.arange(8), np.arange(8)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing=\'ij\')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 8, 8)\n\n For the sake of clarity, let\'s inspect these values\n\n >>> with np.printoptions(formatter={\'f... | >>> x, y = np.arange(8), np.arange(8)
>>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')
>>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 8, 8)
For the sake of clarity, let's inspect these values
>>> with np.printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)}):
... print(inp)
[[[0.000 1.000 2... | morton.py | morton_encode_nb | AnimatedRNG/lsc | 0 | python | @nb.njit
def morton_encode_nb(coords):
'\n >>> x, y = np.arange(8), np.arange(8)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing=\'ij\')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 8, 8)\n\n For the sake of clarity, let\'s inspect these values\n\n >>> with np.printoptions(formatter={\'f... | @nb.njit
def morton_encode_nb(coords):
'\n >>> x, y = np.arange(8), np.arange(8)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing=\'ij\')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 8, 8)\n\n For the sake of clarity, let\'s inspect these values\n\n >>> with np.printoptions(formatter={\'f... |
d0391f801e0deb333572ae383dc20fb46156af9baae3779f00a720700f840945 | @nb.njit
def morton_decode_nb(offsets, output):
"\n >>> x, y = np.arange(64), np.arange(64)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 64, 64)\n >>> recon = np.zeros_like(inp)\n\n >>> morton_decode_nb(morton_encode_nb(inp), recon)\n... | >>> x, y = np.arange(64), np.arange(64)
>>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')
>>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 64, 64)
>>> recon = np.zeros_like(inp)
>>> morton_decode_nb(morton_encode_nb(inp), recon)
>>> (inp - recon).max() < 1e-5
True
This function is basically the inverse of `m... | morton.py | morton_decode_nb | AnimatedRNG/lsc | 0 | python | @nb.njit
def morton_decode_nb(offsets, output):
"\n >>> x, y = np.arange(64), np.arange(64)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 64, 64)\n >>> recon = np.zeros_like(inp)\n\n >>> morton_decode_nb(morton_encode_nb(inp), recon)\n... | @nb.njit
def morton_decode_nb(offsets, output):
"\n >>> x, y = np.arange(64), np.arange(64)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 64, 64)\n >>> recon = np.zeros_like(inp)\n\n >>> morton_decode_nb(morton_encode_nb(inp), recon)\n... |
ce75354e5c403f7637dc56d58cfd7cf9cae0aa8f57268ea402b781a5d3308b62 | def detect_windows(self, images_windows):
'\n Do windowed detection over given images and windows. Windows are\n extracted then warped to the input dimensions of the net.\n\n Parameters\n ----------\n images_windows: (image filename, window list) iterable.\n context_crop: s... | Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Parameters
----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
Returns
-------
detections: list of {filename: image filename... | python/caffe/detector.py | detect_windows | raytroop/caffe | 36,275 | python | def detect_windows(self, images_windows):
'\n Do windowed detection over given images and windows. Windows are\n extracted then warped to the input dimensions of the net.\n\n Parameters\n ----------\n images_windows: (image filename, window list) iterable.\n context_crop: s... | def detect_windows(self, images_windows):
'\n Do windowed detection over given images and windows. Windows are\n extracted then warped to the input dimensions of the net.\n\n Parameters\n ----------\n images_windows: (image filename, window list) iterable.\n context_crop: s... |
041edc0924a6af4db8ba04e76bc5a04d463f69d850a31241f9222ef1a6d5886f | def detect_selective_search(self, image_fnames):
'\n Do windowed detection over Selective Search proposals by extracting\n the crop and warping to the input dimensions of the net.\n\n Parameters\n ----------\n image_fnames: list\n\n Returns\n -------\n detecti... | Do windowed detection over Selective Search proposals by extracting
the crop and warping to the input dimensions of the net.
Parameters
----------
image_fnames: list
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts. | python/caffe/detector.py | detect_selective_search | raytroop/caffe | 36,275 | python | def detect_selective_search(self, image_fnames):
'\n Do windowed detection over Selective Search proposals by extracting\n the crop and warping to the input dimensions of the net.\n\n Parameters\n ----------\n image_fnames: list\n\n Returns\n -------\n detecti... | def detect_selective_search(self, image_fnames):
'\n Do windowed detection over Selective Search proposals by extracting\n the crop and warping to the input dimensions of the net.\n\n Parameters\n ----------\n image_fnames: list\n\n Returns\n -------\n detecti... |
5ac097739c4c85253b500f00ca85b36c4f9cd4e9d58eb6368d2803c0f3376405 | def crop(self, im, window):
'\n Crop a window from the image for detection. Include surrounding context\n according to the `context_pad` configuration.\n\n Parameters\n ----------\n im: H x W x K image ndarray to crop.\n window: bounding box coordinates as ymin, xmin, ymax,... | Crop a window from the image for detection. Include surrounding context
according to the `context_pad` configuration.
Parameters
----------
im: H x W x K image ndarray to crop.
window: bounding box coordinates as ymin, xmin, ymax, xmax.
Returns
-------
crop: cropped window. | python/caffe/detector.py | crop | raytroop/caffe | 36,275 | python | def crop(self, im, window):
'\n Crop a window from the image for detection. Include surrounding context\n according to the `context_pad` configuration.\n\n Parameters\n ----------\n im: H x W x K image ndarray to crop.\n window: bounding box coordinates as ymin, xmin, ymax,... | def crop(self, im, window):
'\n Crop a window from the image for detection. Include surrounding context\n according to the `context_pad` configuration.\n\n Parameters\n ----------\n im: H x W x K image ndarray to crop.\n window: bounding box coordinates as ymin, xmin, ymax,... |
437017d115d6e4a198f32630da47d0a536bcd713011102ef050a297aee9ebb18 | def configure_crop(self, context_pad):
'\n Configure crop dimensions and amount of context for cropping.\n If context is included, make the special input mean for context padding.\n\n Parameters\n ----------\n context_pad : amount of context for cropping.\n '
in_ = self... | Configure crop dimensions and amount of context for cropping.
If context is included, make the special input mean for context padding.
Parameters
----------
context_pad : amount of context for cropping. | python/caffe/detector.py | configure_crop | raytroop/caffe | 36,275 | python | def configure_crop(self, context_pad):
'\n Configure crop dimensions and amount of context for cropping.\n If context is included, make the special input mean for context padding.\n\n Parameters\n ----------\n context_pad : amount of context for cropping.\n '
in_ = self... | def configure_crop(self, context_pad):
'\n Configure crop dimensions and amount of context for cropping.\n If context is included, make the special input mean for context padding.\n\n Parameters\n ----------\n context_pad : amount of context for cropping.\n '
in_ = self... |
46ec2f9a685dae1253decf4df5e26d050322aa85bd16cbf66ddb99c27c7b6bae | def lon360to180(lon):
'\n\tConverts longitude values in the range [0,360]\n\tto longitude values in the range [-180,+180].\n\t'
lon = np.asanyarray(lon)
return (((lon + 180.0) % 360.0) - 180.0) | Converts longitude values in the range [0,360]
to longitude values in the range [-180,+180]. | calc_deriv/201e-calc_vortbdgt_daily.py | lon360to180 | apaloczy/AntarcticaVorticityBudget | 1 | python | def lon360to180(lon):
'\n\tConverts longitude values in the range [0,360]\n\tto longitude values in the range [-180,+180].\n\t'
lon = np.asanyarray(lon)
return (((lon + 180.0) % 360.0) - 180.0) | def lon360to180(lon):
'\n\tConverts longitude values in the range [0,360]\n\tto longitude values in the range [-180,+180].\n\t'
lon = np.asanyarray(lon)
return (((lon + 180.0) % 360.0) - 180.0)<|docstring|>Converts longitude values in the range [0,360]
to longitude values in the range [-180,+180].<|endoftex... |
b817905d83e8a82d5d83302fac332f78b077bd5d64f357914ceb2ede15c79a0f | def test_post_now_application_nda_happy_path(self, test_client, db_session, auth_headers):
'Should return a new NoW NDA'
mine = MineFactory()
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_header... | Should return a new NoW NDA | services/core-api/tests/now_submissions/resources/test_application_nda_list_resource.py | test_post_now_application_nda_happy_path | bcgov/mds | 25 | python | def test_post_now_application_nda_happy_path(self, test_client, db_session, auth_headers):
mine = MineFactory()
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
... | def test_post_now_application_nda_happy_path(self, test_client, db_session, auth_headers):
mine = MineFactory()
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
... |
965c4647735b377c138271416df52f8586dbbe80b5a2df9ac302e80ea6a2e1b4 | def test_post_now_application_messageid_in_use(self, test_client, db_session, auth_headers):
'Should return a 400 messageid in use for NDA'
mine = MineFactory()
application = NOWApplicationNDAFactory(mine=mine)
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
APPLICATION_NDA_DATA['messageid'] = app... | Should return a 400 messageid in use for NDA | services/core-api/tests/now_submissions/resources/test_application_nda_list_resource.py | test_post_now_application_messageid_in_use | bcgov/mds | 25 | python | def test_post_now_application_messageid_in_use(self, test_client, db_session, auth_headers):
mine = MineFactory()
application = NOWApplicationNDAFactory(mine=mine)
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
APPLICATION_NDA_DATA['messageid'] = application.messageid
post_resp = test_client... | def test_post_now_application_messageid_in_use(self, test_client, db_session, auth_headers):
mine = MineFactory()
application = NOWApplicationNDAFactory(mine=mine)
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
APPLICATION_NDA_DATA['messageid'] = application.messageid
post_resp = test_client... |
73f8caae406c9505f1728f318bb0c74924ce1e00fbe73e1350d38e9a94626086 | def test_post_now_application_no_mine_found(self, test_client, db_session, auth_headers):
'Should return a 400 mine not found for NDA'
APPLICATION_NDA_DATA['minenumber'] = '1234567'
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_... | Should return a 400 mine not found for NDA | services/core-api/tests/now_submissions/resources/test_application_nda_list_resource.py | test_post_now_application_no_mine_found | bcgov/mds | 25 | python | def test_post_now_application_no_mine_found(self, test_client, db_session, auth_headers):
APPLICATION_NDA_DATA['minenumber'] = '1234567'
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
assert (post_resp.status_... | def test_post_now_application_no_mine_found(self, test_client, db_session, auth_headers):
APPLICATION_NDA_DATA['minenumber'] = '1234567'
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
assert (post_resp.status_... |
17d509abfd8a4c010cb47859d3c55d7e544f55ccdac5a7a1b03841fb3c5602c5 | def minMutation(self, start, end, bank):
'\n :type start: str\n :type end: str\n :type bank: List[str]\n :rtype: int\n '
if ((len(start) < 1) or (len(end) < 1) or (len(bank) < 1) or (not (end in bank))):
return (- 1)
queue = []
queue.append((start, 0))
bank... | :type start: str
:type end: str
:type bank: List[str]
:rtype: int | solutions/0433_MinimumGeneticMutation.py | minMutation | alexwawl/leetcode-solutions-javascript-python | 11 | python | def minMutation(self, start, end, bank):
'\n :type start: str\n :type end: str\n :type bank: List[str]\n :rtype: int\n '
if ((len(start) < 1) or (len(end) < 1) or (len(bank) < 1) or (not (end in bank))):
return (- 1)
queue = []
queue.append((start, 0))
bank... | def minMutation(self, start, end, bank):
'\n :type start: str\n :type end: str\n :type bank: List[str]\n :rtype: int\n '
if ((len(start) < 1) or (len(end) < 1) or (len(bank) < 1) or (not (end in bank))):
return (- 1)
queue = []
queue.append((start, 0))
bank... |
66fbb5470b02a119e48d610656138fa9cc8b8874ab07b56e304947b65471f09c | def __init__(__self__, *, endpoint: str, name: str):
'\n :param str endpoint: Specifies the endpoint of the action.\n :param str name: Specifies the name of the action.\n '
pulumi.set(__self__, 'endpoint', endpoint)
pulumi.set(__self__, 'name', name) | :param str endpoint: Specifies the endpoint of the action.
:param str name: Specifies the name of the action. | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, endpoint: str, name: str):
'\n :param str endpoint: Specifies the endpoint of the action.\n :param str name: Specifies the name of the action.\n '
pulumi.set(__self__, 'endpoint', endpoint)
pulumi.set(__self__, 'name', name) | def __init__(__self__, *, endpoint: str, name: str):
'\n :param str endpoint: Specifies the endpoint of the action.\n :param str name: Specifies the name of the action.\n '
pulumi.set(__self__, 'endpoint', endpoint)
pulumi.set(__self__, 'name', name)<|docstring|>:param str endpoint: Spe... |
5a5a08451321bd197fa93441b4f1c9585a3cb16c8d43c14ad57d79c1cc637afa | @property
@pulumi.getter
def endpoint(self) -> str:
'\n Specifies the endpoint of the action.\n '
return pulumi.get(self, 'endpoint') | Specifies the endpoint of the action. | sdk/python/pulumi_azure/core/outputs.py | endpoint | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def endpoint(self) -> str:
'\n \n '
return pulumi.get(self, 'endpoint') | @property
@pulumi.getter
def endpoint(self) -> str:
'\n \n '
return pulumi.get(self, 'endpoint')<|docstring|>Specifies the endpoint of the action.<|endoftext|> |
b82bd907534ea5da88886ddb936d3d4816d562083a26e0ea6ef048fbcab3588e | @property
@pulumi.getter
def name(self) -> str:
'\n Specifies the name of the action.\n '
return pulumi.get(self, 'name') | Specifies the name of the action. | sdk/python/pulumi_azure/core/outputs.py | name | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>Specifies the name of the action.<|endoftext|> |
b466b6028bb8715d03acb93b8f0c7947eb5d997b6217517035af4c4399af3ac8 | def __init__(__self__, *, endpoint: str, name: str, routing_type: Optional[str]=None):
'\n :param str endpoint: Specifies the endpoint of the route definition.\n :param str name: Specifies the name of the route definition.\n :param str routing_type: The routing type that is supported for the re... | :param str endpoint: Specifies the endpoint of the route definition.
:param str name: Specifies the name of the route definition.
:param str routing_type: The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `... | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, endpoint: str, name: str, routing_type: Optional[str]=None):
'\n :param str endpoint: Specifies the endpoint of the route definition.\n :param str name: Specifies the name of the route definition.\n :param str routing_type: The routing type that is supported for the re... | def __init__(__self__, *, endpoint: str, name: str, routing_type: Optional[str]=None):
'\n :param str endpoint: Specifies the endpoint of the route definition.\n :param str name: Specifies the name of the route definition.\n :param str routing_type: The routing type that is supported for the re... |
d1c203303027ff73f781fb0b8325df8cae3d8995a3ba4a43ab36de60cee18c8e | @property
@pulumi.getter
def endpoint(self) -> str:
'\n Specifies the endpoint of the route definition.\n '
return pulumi.get(self, 'endpoint') | Specifies the endpoint of the route definition. | sdk/python/pulumi_azure/core/outputs.py | endpoint | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def endpoint(self) -> str:
'\n \n '
return pulumi.get(self, 'endpoint') | @property
@pulumi.getter
def endpoint(self) -> str:
'\n \n '
return pulumi.get(self, 'endpoint')<|docstring|>Specifies the endpoint of the route definition.<|endoftext|> |
6ec4fb712825d1316db353cbb5b59ad2e4ff9b712a4ca0eaacac8a150c8b666f | @property
@pulumi.getter
def name(self) -> str:
'\n Specifies the name of the route definition.\n '
return pulumi.get(self, 'name') | Specifies the name of the route definition. | sdk/python/pulumi_azure/core/outputs.py | name | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>Specifies the name of the route definition.<|endoftext|> |
94d7185fe5f40f1cdfe53a1c51f9c5539c0b50bf1778bab5b3e0de4e0fa78625 | @property
@pulumi.getter(name='routingType')
def routing_type(self) -> Optional[str]:
'\n The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`.\n '
return pulum... | The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`. | sdk/python/pulumi_azure/core/outputs.py | routing_type | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='routingType')
def routing_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'routing_type') | @property
@pulumi.getter(name='routingType')
def routing_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'routing_type')<|docstring|>The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value... |
18cfd879e84778ba2c986f4f093f7b49bec1e296747a1819a30d981d2ed97bc3 | def __init__(__self__, *, specification: str):
'\n :param str specification: The endpoint where the validation specification is located.\n '
pulumi.set(__self__, 'specification', specification) | :param str specification: The endpoint where the validation specification is located. | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, specification: str):
'\n \n '
pulumi.set(__self__, 'specification', specification) | def __init__(__self__, *, specification: str):
'\n \n '
pulumi.set(__self__, 'specification', specification)<|docstring|>:param str specification: The endpoint where the validation specification is located.<|endoftext|> |
db11e8718f7a1d1d2839cbaf59153ad2a04895e3a2ae79e13ccebebf95700bc7 | @property
@pulumi.getter
def specification(self) -> str:
'\n The endpoint where the validation specification is located.\n '
return pulumi.get(self, 'specification') | The endpoint where the validation specification is located. | sdk/python/pulumi_azure/core/outputs.py | specification | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def specification(self) -> str:
'\n \n '
return pulumi.get(self, 'specification') | @property
@pulumi.getter
def specification(self) -> str:
'\n \n '
return pulumi.get(self, 'specification')<|docstring|>The endpoint where the validation specification is located.<|endoftext|> |
82c041f5be5d9a830a5e3eae2a9095bbc23958f72d90d1117d2908b067f74d24 | def __init__(__self__, *, id: str, location: str, name: str, tags: Mapping[(str, str)], type: str):
'\n :param str id: The ID of this Resource.\n :param str location: The Azure Region in which this Resource exists.\n :param str name: The name of the Resource.\n :param Mapping[str, str] t... | :param str id: The ID of this Resource.
:param str location: The Azure Region in which this Resource exists.
:param str name: The name of the Resource.
:param Mapping[str, str] tags: A map of tags assigned to this Resource.
:param str type: The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/vi... | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, id: str, location: str, name: str, tags: Mapping[(str, str)], type: str):
'\n :param str id: The ID of this Resource.\n :param str location: The Azure Region in which this Resource exists.\n :param str name: The name of the Resource.\n :param Mapping[str, str] t... | def __init__(__self__, *, id: str, location: str, name: str, tags: Mapping[(str, str)], type: str):
'\n :param str id: The ID of this Resource.\n :param str location: The Azure Region in which this Resource exists.\n :param str name: The name of the Resource.\n :param Mapping[str, str] t... |
6dfb29f8cbddc3b05598b0e1f884b1e8469c08070a4e2b6ec31c0e5f5e0b7372 | @property
@pulumi.getter
def id(self) -> str:
'\n The ID of this Resource.\n '
return pulumi.get(self, 'id') | The ID of this Resource. | sdk/python/pulumi_azure/core/outputs.py | id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def id(self) -> str:
'\n \n '
return pulumi.get(self, 'id') | @property
@pulumi.getter
def id(self) -> str:
'\n \n '
return pulumi.get(self, 'id')<|docstring|>The ID of this Resource.<|endoftext|> |
1834d17ce1d3a6f83ffcb95ecbcc063494db1f45806c980d1dfdeca998790183 | @property
@pulumi.getter
def location(self) -> str:
'\n The Azure Region in which this Resource exists.\n '
return pulumi.get(self, 'location') | The Azure Region in which this Resource exists. | sdk/python/pulumi_azure/core/outputs.py | location | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def location(self) -> str:
'\n \n '
return pulumi.get(self, 'location') | @property
@pulumi.getter
def location(self) -> str:
'\n \n '
return pulumi.get(self, 'location')<|docstring|>The Azure Region in which this Resource exists.<|endoftext|> |
ae4134ad03102542e3e8617953e789d012fc31a9a10f8f8273c04f8a3bec9985 | @property
@pulumi.getter
def name(self) -> str:
'\n The name of the Resource.\n '
return pulumi.get(self, 'name') | The name of the Resource. | sdk/python/pulumi_azure/core/outputs.py | name | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>The name of the Resource.<|endoftext|> |
8c00abb590634528ba691e4e25844180fd0b3a84cbb82283654ba001b9ef1c0e | @property
@pulumi.getter
def tags(self) -> Mapping[(str, str)]:
'\n A map of tags assigned to this Resource.\n '
return pulumi.get(self, 'tags') | A map of tags assigned to this Resource. | sdk/python/pulumi_azure/core/outputs.py | tags | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def tags(self) -> Mapping[(str, str)]:
'\n \n '
return pulumi.get(self, 'tags') | @property
@pulumi.getter
def tags(self) -> Mapping[(str, str)]:
'\n \n '
return pulumi.get(self, 'tags')<|docstring|>A map of tags assigned to this Resource.<|endoftext|> |
0e7e73d12011bc9cb6972cb017ea8c537e7141d912019a508a688f09fc160f9b | @property
@pulumi.getter
def type(self) -> str:
'\n The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers).\n ... | The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers). | sdk/python/pulumi_azure/core/outputs.py | type | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def type(self) -> str:
'\n \n '
return pulumi.get(self, 'type') | @property
@pulumi.getter
def type(self) -> str:
'\n \n '
return pulumi.get(self, 'type')<|docstring|>The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/az... |
0ad40ffb6c17ddd502c137f7ec7d505c0b5f5f7953870b8c9abd70d30552bdf6 | def __init__(__self__, *, display_name: str, location_placement_id: str, quota_id: str, spending_limit: str, state: str, subscription_id: str, tenant_id: str):
'\n :param str display_name: The subscription display name.\n :param str location_placement_id: The subscription location placement ID.\n ... | :param str display_name: The subscription display name.
:param str location_placement_id: The subscription location placement ID.
:param str quota_id: The subscription quota ID.
:param str spending_limit: The subscription spending limit.
:param str state: The subscription state. Possible values are Enabled, Warned, Pas... | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, display_name: str, location_placement_id: str, quota_id: str, spending_limit: str, state: str, subscription_id: str, tenant_id: str):
'\n :param str display_name: The subscription display name.\n :param str location_placement_id: The subscription location placement ID.\n ... | def __init__(__self__, *, display_name: str, location_placement_id: str, quota_id: str, spending_limit: str, state: str, subscription_id: str, tenant_id: str):
'\n :param str display_name: The subscription display name.\n :param str location_placement_id: The subscription location placement ID.\n ... |
6167b089fc26e32a33c4e49b58d082d31fe2ebc0b687d6e9855cd842adf4ad03 | @property
@pulumi.getter(name='displayName')
def display_name(self) -> str:
'\n The subscription display name.\n '
return pulumi.get(self, 'display_name') | The subscription display name. | sdk/python/pulumi_azure/core/outputs.py | display_name | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='displayName')
def display_name(self) -> str:
'\n \n '
return pulumi.get(self, 'display_name') | @property
@pulumi.getter(name='displayName')
def display_name(self) -> str:
'\n \n '
return pulumi.get(self, 'display_name')<|docstring|>The subscription display name.<|endoftext|> |
890a666c2af921e269e7df1917521c2ea09bafd983df0d88a20cfe9b6aebdbfb | @property
@pulumi.getter(name='locationPlacementId')
def location_placement_id(self) -> str:
'\n The subscription location placement ID.\n '
return pulumi.get(self, 'location_placement_id') | The subscription location placement ID. | sdk/python/pulumi_azure/core/outputs.py | location_placement_id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='locationPlacementId')
def location_placement_id(self) -> str:
'\n \n '
return pulumi.get(self, 'location_placement_id') | @property
@pulumi.getter(name='locationPlacementId')
def location_placement_id(self) -> str:
'\n \n '
return pulumi.get(self, 'location_placement_id')<|docstring|>The subscription location placement ID.<|endoftext|> |
cb7a9fbfe34078649b021cd2a5c2f68265529b204603ffd9f6890b9dc942e129 | @property
@pulumi.getter(name='quotaId')
def quota_id(self) -> str:
'\n The subscription quota ID.\n '
return pulumi.get(self, 'quota_id') | The subscription quota ID. | sdk/python/pulumi_azure/core/outputs.py | quota_id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='quotaId')
def quota_id(self) -> str:
'\n \n '
return pulumi.get(self, 'quota_id') | @property
@pulumi.getter(name='quotaId')
def quota_id(self) -> str:
'\n \n '
return pulumi.get(self, 'quota_id')<|docstring|>The subscription quota ID.<|endoftext|> |
0ee8b1fa3641bff0ff52034e073b4fee89ebffabbfaf1feefcedae50973bdfbc | @property
@pulumi.getter(name='spendingLimit')
def spending_limit(self) -> str:
'\n The subscription spending limit.\n '
return pulumi.get(self, 'spending_limit') | The subscription spending limit. | sdk/python/pulumi_azure/core/outputs.py | spending_limit | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='spendingLimit')
def spending_limit(self) -> str:
'\n \n '
return pulumi.get(self, 'spending_limit') | @property
@pulumi.getter(name='spendingLimit')
def spending_limit(self) -> str:
'\n \n '
return pulumi.get(self, 'spending_limit')<|docstring|>The subscription spending limit.<|endoftext|> |
4479ab8edb8d8cbfdea39e3206640c5ef0d794cbd9d05ac5f6c340966a3fcd4e | @property
@pulumi.getter
def state(self) -> str:
'\n The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted.\n '
return pulumi.get(self, 'state') | The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted. | sdk/python/pulumi_azure/core/outputs.py | state | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def state(self) -> str:
'\n \n '
return pulumi.get(self, 'state') | @property
@pulumi.getter
def state(self) -> str:
'\n \n '
return pulumi.get(self, 'state')<|docstring|>The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted.<|endoftext|> |
458075aefcdf493f661e2d1848e2d1483f9ec9ca6f8316dddd656bead9588fc8 | @property
@pulumi.getter(name='subscriptionId')
def subscription_id(self) -> str:
'\n The subscription GUID.\n '
return pulumi.get(self, 'subscription_id') | The subscription GUID. | sdk/python/pulumi_azure/core/outputs.py | subscription_id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='subscriptionId')
def subscription_id(self) -> str:
'\n \n '
return pulumi.get(self, 'subscription_id') | @property
@pulumi.getter(name='subscriptionId')
def subscription_id(self) -> str:
'\n \n '
return pulumi.get(self, 'subscription_id')<|docstring|>The subscription GUID.<|endoftext|> |
368362b34c317b35c366779ea4126012de0ac469bf6ee47bb4bc69bd73fe3c8e | @property
@pulumi.getter(name='tenantId')
def tenant_id(self) -> str:
'\n The subscription tenant ID.\n '
return pulumi.get(self, 'tenant_id') | The subscription tenant ID. | sdk/python/pulumi_azure/core/outputs.py | tenant_id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='tenantId')
def tenant_id(self) -> str:
'\n \n '
return pulumi.get(self, 'tenant_id') | @property
@pulumi.getter(name='tenantId')
def tenant_id(self) -> str:
'\n \n '
return pulumi.get(self, 'tenant_id')<|docstring|>The subscription tenant ID.<|endoftext|> |
824f4f35619c6b82972bdb044f69deb9f22862f47dfc841ef116f22437883823 | def RoadnetPa(directed: bool=False, verbose: int=2, cache_path: str='graphs/networkrepository', **additional_graph_kwargs: Dict) -> EnsmallenGraph:
'Return new instance of the roadNet-PA graph.\n\n The graph is automatically retrieved from the NetworkRepository repository. \n\n\t\n\n Parameters\n ---------... | Return new instance of the roadNet-PA graph.
The graph is automatically retrieved from the NetworkRepository repository.
Parameters
-------------------
directed: bool = False,
Wether to load the graph as directed or undirected.
By default false.
verbose: int = 2,
Wether to show loading bars during ... | bindings/python/ensmallen_graph/datasets/networkrepository/roadnetpa.py | RoadnetPa | caufieldjh/ensmallen_graph | 0 | python | def RoadnetPa(directed: bool=False, verbose: int=2, cache_path: str='graphs/networkrepository', **additional_graph_kwargs: Dict) -> EnsmallenGraph:
'Return new instance of the roadNet-PA graph.\n\n The graph is automatically retrieved from the NetworkRepository repository. \n\n\t\n\n Parameters\n ---------... | def RoadnetPa(directed: bool=False, verbose: int=2, cache_path: str='graphs/networkrepository', **additional_graph_kwargs: Dict) -> EnsmallenGraph:
'Return new instance of the roadNet-PA graph.\n\n The graph is automatically retrieved from the NetworkRepository repository. \n\n\t\n\n Parameters\n ---------... |
ed1f588a9117a99ce4783e5a70e015a7f89ea47c3d1a09b164428d394024c7d6 | def model_resnet50_keras(input_shape: tuple, classes: int, include_top=True, weights='imagenet') -> keras.Model:
'\n Keras Applicationsに用意されているResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Arg... | Keras Applicationsに用意されているResNet50を読み込む。
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
https://arxiv.org/abs/1512.03385
Args:
input_shape tuple:
入力の形状を指定する。
num_classes int:
分類するクラス数を指定する。
Returns:
keras.Model:
ResNet50を返す。 | models/resnet50.py | model_resnet50_keras | sugaok/my-deep-learning-base | 1 | python | def model_resnet50_keras(input_shape: tuple, classes: int, include_top=True, weights='imagenet') -> keras.Model:
'\n Keras Applicationsに用意されているResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Arg... | def model_resnet50_keras(input_shape: tuple, classes: int, include_top=True, weights='imagenet') -> keras.Model:
'\n Keras Applicationsに用意されているResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Arg... |
d558c7449849fec455c88a665440847b31e17a43f87d60b2e5c5bb7453752001 | def model_resnet50(input_shape: tuple, classes: int) -> keras.Model:
'\n ResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Args:\n input_shape tuple:\n 入力の形状を指定する。\n num_c... | ResNet50を読み込む。
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
https://arxiv.org/abs/1512.03385
Args:
input_shape tuple:
入力の形状を指定する。
num_classes int:
分類するクラス数を指定する。
Returns:
keras.Model:
ResNet50を返す。 | models/resnet50.py | model_resnet50 | sugaok/my-deep-learning-base | 1 | python | def model_resnet50(input_shape: tuple, classes: int) -> keras.Model:
'\n ResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Args:\n input_shape tuple:\n 入力の形状を指定する。\n num_c... | def model_resnet50(input_shape: tuple, classes: int) -> keras.Model:
'\n ResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Args:\n input_shape tuple:\n 入力の形状を指定する。\n num_c... |
cc09e176516213f63e22aa96c51173f4d0c4d0f2f7ba026ec27ba3e5832cab66 | def get_single_run_config(out_dir):
'Load the config file from a specified experiment.\n\n Args:\n out_dir (str): The path to the experiment.\n\n Returns:\n The Namespace object containing argument names and values.\n '
print(('Loading the configuration of run: %s' % out_dir))
if (not... | Load the config file from a specified experiment.
Args:
out_dir (str): The path to the experiment.
Returns:
The Namespace object containing argument names and values. | hypnettorch/hpsearch/gather_random_seeds.py | get_single_run_config | pennfranc/hypnettorch | 31 | python | def get_single_run_config(out_dir):
'Load the config file from a specified experiment.\n\n Args:\n out_dir (str): The path to the experiment.\n\n Returns:\n The Namespace object containing argument names and values.\n '
print(('Loading the configuration of run: %s' % out_dir))
if (not... | def get_single_run_config(out_dir):
'Load the config file from a specified experiment.\n\n Args:\n out_dir (str): The path to the experiment.\n\n Returns:\n The Namespace object containing argument names and values.\n '
print(('Loading the configuration of run: %s' % out_dir))
if (not... |
3647241f3893a7fc6ba9b5c6e9bf0e4bf70b17d7a65f8f9e2a9b4b9973f623ae | def get_best_hpsearch_config(out_dir):
'Load the config file from the best run of a hyperparameter search.\n\n This file loads the results of the hyperparameter search, and select the\n configuration that lead to the best performance score.\n\n Args:\n out_dir (str): The path to the hpsearch result ... | Load the config file from the best run of a hyperparameter search.
This file loads the results of the hyperparameter search, and select the
configuration that lead to the best performance score.
Args:
out_dir (str): The path to the hpsearch result folder.
Returns:
(tuple): Tuple containing:
- **config**... | hypnettorch/hpsearch/gather_random_seeds.py | get_best_hpsearch_config | pennfranc/hypnettorch | 31 | python | def get_best_hpsearch_config(out_dir):
'Load the config file from the best run of a hyperparameter search.\n\n This file loads the results of the hyperparameter search, and select the\n configuration that lead to the best performance score.\n\n Args:\n out_dir (str): The path to the hpsearch result ... | def get_best_hpsearch_config(out_dir):
'Load the config file from the best run of a hyperparameter search.\n\n This file loads the results of the hyperparameter search, and select the\n configuration that lead to the best performance score.\n\n Args:\n out_dir (str): The path to the hpsearch result ... |
bed1c12c4f6db9806efcb8fd5f621a3693e11b23ce04e289b488c9b3234d5dbc | def build_grid_and_conditions(cmd_args, config, seeds_list):
'Build the hpconfig for the random seed gathering.\n\n Args:\n cmd_args: CLI arguments of this script.\n config: The config to be translated into a search grid.\n seeds_list (list): The random seeds to be gathered.\n\n (tuple): ... | Build the hpconfig for the random seed gathering.
Args:
cmd_args: CLI arguments of this script.
config: The config to be translated into a search grid.
seeds_list (list): The random seeds to be gathered.
(tuple): Tuple containing:
- **grid** (dict): The search grid.
- **conditions** (list): Const... | hypnettorch/hpsearch/gather_random_seeds.py | build_grid_and_conditions | pennfranc/hypnettorch | 31 | python | def build_grid_and_conditions(cmd_args, config, seeds_list):
'Build the hpconfig for the random seed gathering.\n\n Args:\n cmd_args: CLI arguments of this script.\n config: The config to be translated into a search grid.\n seeds_list (list): The random seeds to be gathered.\n\n (tuple): ... | def build_grid_and_conditions(cmd_args, config, seeds_list):
'Build the hpconfig for the random seed gathering.\n\n Args:\n cmd_args: CLI arguments of this script.\n config: The config to be translated into a search grid.\n seeds_list (list): The random seeds to be gathered.\n\n (tuple): ... |
5d5bdd5e2d9be29522c2fb6601bf7436ce2134ca97750a5af7306eb479b388ee | def get_hpsearch_call(cmd_args, num_seeds, grid_config, hpsearch_dir=None):
'Generate the command line for the hpsearch.\n\n Args:\n cmd_args: The command line arguments.\n num_seeds (int): Number of searches.\n grid_config (str): Location of search grid.\n hpsearch_dir (str, optional... | Generate the command line for the hpsearch.
Args:
cmd_args: The command line arguments.
num_seeds (int): Number of searches.
grid_config (str): Location of search grid.
hpsearch_dir (str, optional): Where the hpsearch should write its
results to.
Returns:
(str): The command line to be exec... | hypnettorch/hpsearch/gather_random_seeds.py | get_hpsearch_call | pennfranc/hypnettorch | 31 | python | def get_hpsearch_call(cmd_args, num_seeds, grid_config, hpsearch_dir=None):
'Generate the command line for the hpsearch.\n\n Args:\n cmd_args: The command line arguments.\n num_seeds (int): Number of searches.\n grid_config (str): Location of search grid.\n hpsearch_dir (str, optional... | def get_hpsearch_call(cmd_args, num_seeds, grid_config, hpsearch_dir=None):
'Generate the command line for the hpsearch.\n\n Args:\n cmd_args: The command line arguments.\n num_seeds (int): Number of searches.\n grid_config (str): Location of search grid.\n hpsearch_dir (str, optional... |
4ee53bf97b210b5ab754d4d724247207d0f3efd40582bd38a560eef54f306aa1 | def write_seeds_summary(results_dir, summary_keys, summary_sem, summary_precs, ret_seeds=False, summary_fn=None, seeds_summary_fn='seeds_summary_text.txt'):
'Write the MEAN and STD (resp. SEM) while aggregating all seeds to text\n file.\n\n Args:\n results_dir (str): The results directory.\n sum... | Write the MEAN and STD (resp. SEM) while aggregating all seeds to text
file.
Args:
results_dir (str): The results directory.
summary_keys (list): See argument ``summary_keys`` of function
:func:`run`.
summary_sem (bool): See argument ``summary_sem`` of function
:func:`run`.
summary_prec... | hypnettorch/hpsearch/gather_random_seeds.py | write_seeds_summary | pennfranc/hypnettorch | 31 | python | def write_seeds_summary(results_dir, summary_keys, summary_sem, summary_precs, ret_seeds=False, summary_fn=None, seeds_summary_fn='seeds_summary_text.txt'):
'Write the MEAN and STD (resp. SEM) while aggregating all seeds to text\n file.\n\n Args:\n results_dir (str): The results directory.\n sum... | def write_seeds_summary(results_dir, summary_keys, summary_sem, summary_precs, ret_seeds=False, summary_fn=None, seeds_summary_fn='seeds_summary_text.txt'):
'Write the MEAN and STD (resp. SEM) while aggregating all seeds to text\n file.\n\n Args:\n results_dir (str): The results directory.\n sum... |
f37708db3eaa56455a9d22f49dd274716b720fda2db47b735fe10f9dd363ddde | def run(grid_module=None, results_dir='./out/random_seeds', config=None, ignore_kwds=None, forced_params=None, summary_keys=None, summary_sem=False, summary_precs=None, hpmod_path=None):
"Run the script.\n\n Args:\n grid_module (str, optional): Name of the reference module which contains\n the ... | Run the script.
Args:
grid_module (str, optional): Name of the reference module which contains
the hyperparameter search config that can be modified to gather
random seeds.
results_dir (str, optional): The path where the hpsearch should store
its results.
config: The Namespace objec... | hypnettorch/hpsearch/gather_random_seeds.py | run | pennfranc/hypnettorch | 31 | python | def run(grid_module=None, results_dir='./out/random_seeds', config=None, ignore_kwds=None, forced_params=None, summary_keys=None, summary_sem=False, summary_precs=None, hpmod_path=None):
"Run the script.\n\n Args:\n grid_module (str, optional): Name of the reference module which contains\n the ... | def run(grid_module=None, results_dir='./out/random_seeds', config=None, ignore_kwds=None, forced_params=None, summary_keys=None, summary_sem=False, summary_precs=None, hpmod_path=None):
"Run the script.\n\n Args:\n grid_module (str, optional): Name of the reference module which contains\n the ... |
f4246c08450ca6fe2daed8261fd4a9d9915e6b94503dd42663449788dbc5970d | def get_domains_to_update_es_filter():
"\n Returns ES filter to filter domains that are never updated or\n domains that haven't been updated since a week or domains that\n have been updated within last week but have new form submissions\n in the last day.\n "
last_week = (datetime.utc... | Returns ES filter to filter domains that are never updated or
domains that haven't been updated since a week or domains that
have been updated within last week but have new form submissions
in the last day. | corehq/apps/reports/tasks.py | get_domains_to_update_es_filter | kkrampa/commcare-hq | 1 | python | def get_domains_to_update_es_filter():
"\n Returns ES filter to filter domains that are never updated or\n domains that haven't been updated since a week or domains that\n have been updated within last week but have new form submissions\n in the last day.\n "
last_week = (datetime.utc... | def get_domains_to_update_es_filter():
"\n Returns ES filter to filter domains that are never updated or\n domains that haven't been updated since a week or domains that\n have been updated within last week but have new form submissions\n in the last day.\n "
last_week = (datetime.utc... |
be391f733777f9ae0dd4e4955c5abde3f76bf1feb4babc675fc1c8082ca41bd3 | def _get_export_properties(export_id):
'\n Return a list of strings corresponding to form questions that are\n included in the export.\n '
properties = set()
if export_id:
from corehq.apps.export.models import FormExportInstance
export = FormExportInstance.get(export_id)
for... | Return a list of strings corresponding to form questions that are
included in the export. | corehq/apps/reports/tasks.py | _get_export_properties | kkrampa/commcare-hq | 1 | python | def _get_export_properties(export_id):
'\n Return a list of strings corresponding to form questions that are\n included in the export.\n '
properties = set()
if export_id:
from corehq.apps.export.models import FormExportInstance
export = FormExportInstance.get(export_id)
for... | def _get_export_properties(export_id):
'\n Return a list of strings corresponding to form questions that are\n included in the export.\n '
properties = set()
if export_id:
from corehq.apps.export.models import FormExportInstance
export = FormExportInstance.get(export_id)
for... |
ec2fe7c6668e3ffa633f4686b0a9a71cd5b08ab4dda34a3e887416ff15f92d73 | def _extract_form_attachment_info(form, properties):
'\n This is a helper function for build_form_multimedia_zip.\n Return a dict containing information about the given form and its relevant\n attachments\n '
def find_question_id(form, value):
for (k, v) in six.iteritems(form):
... | This is a helper function for build_form_multimedia_zip.
Return a dict containing information about the given form and its relevant
attachments | corehq/apps/reports/tasks.py | _extract_form_attachment_info | kkrampa/commcare-hq | 1 | python | def _extract_form_attachment_info(form, properties):
'\n This is a helper function for build_form_multimedia_zip.\n Return a dict containing information about the given form and its relevant\n attachments\n '
def find_question_id(form, value):
for (k, v) in six.iteritems(form):
... | def _extract_form_attachment_info(form, properties):
'\n This is a helper function for build_form_multimedia_zip.\n Return a dict containing information about the given form and its relevant\n attachments\n '
def find_question_id(form, value):
for (k, v) in six.iteritems(form):
... |
781d7c27232059dae25f9d95c09eb7fc35787735be1f2f950635659a167d65ba | def read_csv(filename):
'\n\n Parameters\n ----------\n filename : str\n Path to the CSV file.\n\n Returns\n -------\n df_new : dataframe\n Normalised coordinates of 3D pose.\n\n '
dataframe = pd.read_csv(filename, index_col='Body Part')
xmax = (- 10000)
ymax = (- 1000... | Parameters
----------
filename : str
Path to the CSV file.
Returns
-------
df_new : dataframe
Normalised coordinates of 3D pose. | utils/create_blank_3d.py | read_csv | alisonrclarke/raga-pose-estimation-1 | 1 | python | def read_csv(filename):
'\n\n Parameters\n ----------\n filename : str\n Path to the CSV file.\n\n Returns\n -------\n df_new : dataframe\n Normalised coordinates of 3D pose.\n\n '
dataframe = pd.read_csv(filename, index_col='Body Part')
xmax = (- 10000)
ymax = (- 1000... | def read_csv(filename):
'\n\n Parameters\n ----------\n filename : str\n Path to the CSV file.\n\n Returns\n -------\n df_new : dataframe\n Normalised coordinates of 3D pose.\n\n '
dataframe = pd.read_csv(filename, index_col='Body Part')
xmax = (- 10000)
ymax = (- 1000... |
cf21f847fd6c089dc03aa470f49e7bc154155d85750c428f31319c061692bb64 | def create_3d_video(output_path, df, parts=PARTS, skeleton=SKELETON_EDGES, output=True):
'\n\n Parameters\n ----------\n output_path : str\n Path for the created video.\n df : dataframe\n 3D pose\n parts : list, optional\n The name of body parts. The default is PARTS.\n skelet... | Parameters
----------
output_path : str
Path for the created video.
df : dataframe
3D pose
parts : list, optional
The name of body parts. The default is PARTS.
skeleton : narray, optional
Indicating which two keypoints are connected. The default is SKELETON_EDGES.
output : bool, optional
True for st... | utils/create_blank_3d.py | create_3d_video | alisonrclarke/raga-pose-estimation-1 | 1 | python | def create_3d_video(output_path, df, parts=PARTS, skeleton=SKELETON_EDGES, output=True):
'\n\n Parameters\n ----------\n output_path : str\n Path for the created video.\n df : dataframe\n 3D pose\n parts : list, optional\n The name of body parts. The default is PARTS.\n skelet... | def create_3d_video(output_path, df, parts=PARTS, skeleton=SKELETON_EDGES, output=True):
'\n\n Parameters\n ----------\n output_path : str\n Path for the created video.\n df : dataframe\n 3D pose\n parts : list, optional\n The name of body parts. The default is PARTS.\n skelet... |
be5344ab918a97f12d47f11c8a705e418a5dc1a6aca20cf2eace2bc331b41bb1 | def _read_in_raw_data(data_dir: str) -> Tuple[(DataFrame, DataFrame)]:
'Read in the raw water pump features and labels.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `water_pump_labels.csv` can be found.\n\n Returns\n -------\... | Read in the raw water pump features and labels.
Parameters
----------
data_dir : str
Path of the directory where `water_pump_features.csv` and
`water_pump_labels.csv` can be found.
Returns
-------
Tuple[DataFrame, DataFrame]
DataFrames of the features and labels respectively. | src/data/dataset.py | _read_in_raw_data | amritpurshotam/mlops-example | 0 | python | def _read_in_raw_data(data_dir: str) -> Tuple[(DataFrame, DataFrame)]:
'Read in the raw water pump features and labels.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `water_pump_labels.csv` can be found.\n\n Returns\n -------\... | def _read_in_raw_data(data_dir: str) -> Tuple[(DataFrame, DataFrame)]:
'Read in the raw water pump features and labels.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `water_pump_labels.csv` can be found.\n\n Returns\n -------\... |
817da33f6dc3a7652e253e2ec0501f838394d2bba1f64cca3509023b3b6dda83 | def _align_features_and_labels(features: DataFrame, labels: DataFrame) -> Tuple[(DataFrame, DataFrame)]:
"Align the `feature`s and `labels` DataFrames so they're both in the same order\n removing the need to check the `id` columns in each.\n\n Parameters\n ----------\n features : DataFrame\n Data... | Align the `feature`s and `labels` DataFrames so they're both in the same order
removing the need to check the `id` columns in each.
Parameters
----------
features : DataFrame
DataFrame containing the `id` attribute.
labels : DataFrame
DataFrame containing the `id` attribute that corresponds to the `id` in
... | src/data/dataset.py | _align_features_and_labels | amritpurshotam/mlops-example | 0 | python | def _align_features_and_labels(features: DataFrame, labels: DataFrame) -> Tuple[(DataFrame, DataFrame)]:
"Align the `feature`s and `labels` DataFrames so they're both in the same order\n removing the need to check the `id` columns in each.\n\n Parameters\n ----------\n features : DataFrame\n Data... | def _align_features_and_labels(features: DataFrame, labels: DataFrame) -> Tuple[(DataFrame, DataFrame)]:
"Align the `feature`s and `labels` DataFrames so they're both in the same order\n removing the need to check the `id` columns in each.\n\n Parameters\n ----------\n features : DataFrame\n Data... |
e9e0a5b65a0eec28e67d45df31da0b9bedb6f2e0f075b87e61d35d301d806e82 | def _split(features: DataFrame, labels: DataFrame, random_state: int=42) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Deterministic random 80/10/10 train/val/test split of the dataset stratified by\n the labels.\n\n Parameters\n ----------\n features : DataFrame\n\n ... | Deterministic random 80/10/10 train/val/test split of the dataset stratified by
the labels.
Parameters
----------
features : DataFrame
labels : DataFrame
Returns
-------
Tuple[DataFrame, DataFrame, DataFrame, DataFrame]
A tuple of four DataFrames corresponding to the training features,
testing features, trai... | src/data/dataset.py | _split | amritpurshotam/mlops-example | 0 | python | def _split(features: DataFrame, labels: DataFrame, random_state: int=42) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Deterministic random 80/10/10 train/val/test split of the dataset stratified by\n the labels.\n\n Parameters\n ----------\n features : DataFrame\n\n ... | def _split(features: DataFrame, labels: DataFrame, random_state: int=42) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Deterministic random 80/10/10 train/val/test split of the dataset stratified by\n the labels.\n\n Parameters\n ----------\n features : DataFrame\n\n ... |
546ee13fed348901d420beb9db19ca2e0ab84f411543c1c43de567a4606c493c | def load_dataset(data_dir: str) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Read in the water pump dataset and split into the training and test sets.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `... | Read in the water pump dataset and split into the training and test sets.
Parameters
----------
data_dir : str
Path of the directory where `water_pump_features.csv` and
`water_pump_labels.csv` can be found.
Returns
-------
Tuple[DataFrame, DataFrame, DataFrame, DataFrame]
A tuple of four DataFrames corres... | src/data/dataset.py | load_dataset | amritpurshotam/mlops-example | 0 | python | def load_dataset(data_dir: str) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Read in the water pump dataset and split into the training and test sets.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `... | def load_dataset(data_dir: str) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Read in the water pump dataset and split into the training and test sets.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `... |
4da268fdbe9c63b87734d4510b4787fee8435875c315ea31520ad4b9cfd966de | def __init__(self, options):
'Constructor\n\n Args -\n options - The result of OptionParser which contains, as attributes, all the options for the running program.\n '
self.options = options | Constructor
Args -
options - The result of OptionParser which contains, as attributes, all the options for the running program. | testify/test_reporter.py | __init__ | osarood/Testify | 1 | python | def __init__(self, options):
'Constructor\n\n Args -\n options - The result of OptionParser which contains, as attributes, all the options for the running program.\n '
self.options = options | def __init__(self, options):
'Constructor\n\n Args -\n options - The result of OptionParser which contains, as attributes, all the options for the running program.\n '
self.options = options<|docstring|>Constructor
Args -
options - The result of OptionParser which contains, as at... |
27faa50a741dca4491aaa5fa883d9d9565c5a79135dfa8df1ee00976c6db1385 | def test_counts(self, test_case_count, test_method_count):
'Called after discovery finishes. May not be called by all test runners, e.g. TestRunnerClient.'
pass | Called after discovery finishes. May not be called by all test runners, e.g. TestRunnerClient. | testify/test_reporter.py | test_counts | osarood/Testify | 1 | python | def test_counts(self, test_case_count, test_method_count):
pass | def test_counts(self, test_case_count, test_method_count):
pass<|docstring|>Called after discovery finishes. May not be called by all test runners, e.g. TestRunnerClient.<|endoftext|> |
60e39a6b259a988906f68b1de0aff06ef8860a253286e7df625fb31ae9390159 | def test_start(self, result):
'Called when a test method is being run. Gets passed a TestResult dict which should not be complete.'
pass | Called when a test method is being run. Gets passed a TestResult dict which should not be complete. | testify/test_reporter.py | test_start | osarood/Testify | 1 | python | def test_start(self, result):
pass | def test_start(self, result):
pass<|docstring|>Called when a test method is being run. Gets passed a TestResult dict which should not be complete.<|endoftext|> |
1e86bd749a2113cb2f9f129271295bd92158abebc81a814f6124a04b1e6842fa | def test_complete(self, result):
'Called when a test method is complete. result is a TestResult dict which should be complete.'
pass | Called when a test method is complete. result is a TestResult dict which should be complete. | testify/test_reporter.py | test_complete | osarood/Testify | 1 | python | def test_complete(self, result):
pass | def test_complete(self, result):
pass<|docstring|>Called when a test method is complete. result is a TestResult dict which should be complete.<|endoftext|> |
598d94cede61f31b4983d1ac402a3dfee04530e3a72f6edeca10169433940ada | def test_discovery_failure(self, exc):
'Called when there was a failure during test discovery. exc is the exception object generated during the error.' | Called when there was a failure during test discovery. exc is the exception object generated during the error. | testify/test_reporter.py | test_discovery_failure | osarood/Testify | 1 | python | def test_discovery_failure(self, exc):
| def test_discovery_failure(self, exc):
<|docstring|>Called when there was a failure during test discovery. exc is the exception object generated during the error.<|endoftext|> |
ef580bcb794087e4ef1d68c1ead6f4730a47d083e2796d829155d4dbc075f375 | def class_setup_start(self, result):
'Called when a class_setup or the first half of a class_setup_teardown starts'
pass | Called when a class_setup or the first half of a class_setup_teardown starts | testify/test_reporter.py | class_setup_start | osarood/Testify | 1 | python | def class_setup_start(self, result):
pass | def class_setup_start(self, result):
pass<|docstring|>Called when a class_setup or the first half of a class_setup_teardown starts<|endoftext|> |
be9757e522dad5bc0d97ab659b097f294a945c307631e4cc60f7d7cec2dfd3a1 | def class_setup_complete(self, result):
'Called when a class_setup or the first half of a class_setup_teardown finishes'
pass | Called when a class_setup or the first half of a class_setup_teardown finishes | testify/test_reporter.py | class_setup_complete | osarood/Testify | 1 | python | def class_setup_complete(self, result):
pass | def class_setup_complete(self, result):
pass<|docstring|>Called when a class_setup or the first half of a class_setup_teardown finishes<|endoftext|> |
b13993cbc2dac87e4a77f8f46bb4904b05ad5c96affe7891df7b71276f1b23fb | def class_teardown_start(self, result):
'Called when a class_teardown or the second half of a class_setup_teardown starts'
pass | Called when a class_teardown or the second half of a class_setup_teardown starts | testify/test_reporter.py | class_teardown_start | osarood/Testify | 1 | python | def class_teardown_start(self, result):
pass | def class_teardown_start(self, result):
pass<|docstring|>Called when a class_teardown or the second half of a class_setup_teardown starts<|endoftext|> |
b8769243139eb21f367fcf56641ffa963348c6ee59739328c945126a93dbbcdd | def class_teardown_complete(self, result):
'Called when a class_teardown or the second half of a class_setup_teardown finishes'
pass | Called when a class_teardown or the second half of a class_setup_teardown finishes | testify/test_reporter.py | class_teardown_complete | osarood/Testify | 1 | python | def class_teardown_complete(self, result):
pass | def class_teardown_complete(self, result):
pass<|docstring|>Called when a class_teardown or the second half of a class_setup_teardown finishes<|endoftext|> |
405ef5616bc14f45c089fafcb2b260cd0476646f85dfdfb1c91da92a4180c8cb | def test_case_start(self, result):
'Called when a test case is being run. Gets passed the special "run" method as a TestResult.'
pass | Called when a test case is being run. Gets passed the special "run" method as a TestResult. | testify/test_reporter.py | test_case_start | osarood/Testify | 1 | python | def test_case_start(self, result):
pass | def test_case_start(self, result):
pass<|docstring|>Called when a test case is being run. Gets passed the special "run" method as a TestResult.<|endoftext|> |
f1f622bc51f7ca932fff4ae5cf67c5ca3c4c4421d489740345cea21ea2072c9d | def test_case_complete(self, result):
'Called when a test case and all of its fixtures have been run.'
pass | Called when a test case and all of its fixtures have been run. | testify/test_reporter.py | test_case_complete | osarood/Testify | 1 | python | def test_case_complete(self, result):
pass | def test_case_complete(self, result):
pass<|docstring|>Called when a test case and all of its fixtures have been run.<|endoftext|> |
45fe5e328eb28c269bd948f1123006c9409c3bcb722d0a6e188d28dff32a575a | def report(self):
'Called at the end of the test run to report results\n\n Should return a bool to indicate if the reporter thinks the test run was successful\n '
return True | Called at the end of the test run to report results
Should return a bool to indicate if the reporter thinks the test run was successful | testify/test_reporter.py | report | osarood/Testify | 1 | python | def report(self):
'Called at the end of the test run to report results\n\n Should return a bool to indicate if the reporter thinks the test run was successful\n '
return True | def report(self):
'Called at the end of the test run to report results\n\n Should return a bool to indicate if the reporter thinks the test run was successful\n '
return True<|docstring|>Called at the end of the test run to report results
Should return a bool to indicate if the reporter thinks th... |
6986547924c4b504463a555bf5f4f919765ba60da31b8ed95b5f52da9401448a | def html_fragment(source):
'\n Parse an HTML string representing a single element, and return that element\n '
return BeautifulSoup(source, 'html.parser').contents[0] | Parse an HTML string representing a single element, and return that element | chirun/filter.py | html_fragment | sthagen/chirun-ncl-chirun | 5 | python | def html_fragment(source):
'\n \n '
return BeautifulSoup(source, 'html.parser').contents[0] | def html_fragment(source):
'\n \n '
return BeautifulSoup(source, 'html.parser').contents[0]<|docstring|>Parse an HTML string representing a single element, and return that element<|endoftext|> |
32691050f1c4a0af72bfb7b250b57755c343758a2dae61d86eb039e362bb2fcf | def fix_local_links(soup, item):
"\n Rewrite URLs relative to the top level, i.e. those starting with a /,\n to use the course's root URL or into paths relative to the item.\n "
tags = {'a': ['href'], 'img': ['src'], 'source': ['src'], 'section': ['data-background', 'data-background-video']}
... | Rewrite URLs relative to the top level, i.e. those starting with a /,
to use the course's root URL or into paths relative to the item. | chirun/filter.py | fix_local_links | sthagen/chirun-ncl-chirun | 5 | python | def fix_local_links(soup, item):
"\n Rewrite URLs relative to the top level, i.e. those starting with a /,\n to use the course's root URL or into paths relative to the item.\n "
tags = {'a': ['href'], 'img': ['src'], 'source': ['src'], 'section': ['data-background', 'data-background-video']}
... | def fix_local_links(soup, item):
"\n Rewrite URLs relative to the top level, i.e. those starting with a /,\n to use the course's root URL or into paths relative to the item.\n "
tags = {'a': ['href'], 'img': ['src'], 'source': ['src'], 'section': ['data-background', 'data-background-video']}
... |
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