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def dict_lookup(dict_, keys, default=None):
'Looks up :attr:`keys` in the dict, returns the corresponding values.\n\n The :attr:`default` is used for keys not present in the dict.\n\n Args:\n dict_ (dict): A dictionary for lookup.\n keys: A numpy array or a (possibly nested) list of keys.\n default (optional): Value to be returned when a key is not in\n :attr:`dict_`. Error is raised if :attr:`default` is not given and\n key is not in the dict.\n\n Returns:\n A numpy array of values with the same structure as :attr:`keys`.\n\n Raises:\n TypeError: If key is not in :attr:`dict_` and :attr:`default` is `None`.\n '
return np.vectorize((lambda x: dict_.get(x, default)))(keys) | 2,886,590,621,723,122,700 | Looks up :attr:`keys` in the dict, returns the corresponding values.
The :attr:`default` is used for keys not present in the dict.
Args:
dict_ (dict): A dictionary for lookup.
keys: A numpy array or a (possibly nested) list of keys.
default (optional): Value to be returned when a key is not in
:attr:`dict_`. Error is raised if :attr:`default` is not given and
key is not in the dict.
Returns:
A numpy array of values with the same structure as :attr:`keys`.
Raises:
TypeError: If key is not in :attr:`dict_` and :attr:`default` is `None`. | texar/tf/utils/utils.py | dict_lookup | awesomemachinelearning/texar | python | def dict_lookup(dict_, keys, default=None):
'Looks up :attr:`keys` in the dict, returns the corresponding values.\n\n The :attr:`default` is used for keys not present in the dict.\n\n Args:\n dict_ (dict): A dictionary for lookup.\n keys: A numpy array or a (possibly nested) list of keys.\n default (optional): Value to be returned when a key is not in\n :attr:`dict_`. Error is raised if :attr:`default` is not given and\n key is not in the dict.\n\n Returns:\n A numpy array of values with the same structure as :attr:`keys`.\n\n Raises:\n TypeError: If key is not in :attr:`dict_` and :attr:`default` is `None`.\n '
return np.vectorize((lambda x: dict_.get(x, default)))(keys) |
def dict_fetch(src_dict, tgt_dict_or_keys):
'Fetches a sub dict of :attr:`src_dict` with the keys in\n :attr:`tgt_dict_or_keys`.\n\n Args:\n src_dict: A dict or instance of :class:`~texar.tf.HParams`.\n The source dict to fetch values from.\n tgt_dict_or_keys: A dict, instance of :class:`~texar.tf.HParams`,\n or a list (or a dict_keys) of keys to be included in the output\n dict.\n\n Returns:\n A new dict that is a subdict of :attr:`src_dict`.\n '
if (src_dict is None):
return src_dict
if isinstance(tgt_dict_or_keys, HParams):
tgt_dict_or_keys = tgt_dict_or_keys.todict()
if isinstance(tgt_dict_or_keys, dict):
tgt_dict_or_keys = tgt_dict_or_keys.keys()
keys = list(tgt_dict_or_keys)
if isinstance(src_dict, HParams):
src_dict = src_dict.todict()
return {k: src_dict[k] for k in keys if (k in src_dict)} | -8,687,013,080,932,570,000 | Fetches a sub dict of :attr:`src_dict` with the keys in
:attr:`tgt_dict_or_keys`.
Args:
src_dict: A dict or instance of :class:`~texar.tf.HParams`.
The source dict to fetch values from.
tgt_dict_or_keys: A dict, instance of :class:`~texar.tf.HParams`,
or a list (or a dict_keys) of keys to be included in the output
dict.
Returns:
A new dict that is a subdict of :attr:`src_dict`. | texar/tf/utils/utils.py | dict_fetch | awesomemachinelearning/texar | python | def dict_fetch(src_dict, tgt_dict_or_keys):
'Fetches a sub dict of :attr:`src_dict` with the keys in\n :attr:`tgt_dict_or_keys`.\n\n Args:\n src_dict: A dict or instance of :class:`~texar.tf.HParams`.\n The source dict to fetch values from.\n tgt_dict_or_keys: A dict, instance of :class:`~texar.tf.HParams`,\n or a list (or a dict_keys) of keys to be included in the output\n dict.\n\n Returns:\n A new dict that is a subdict of :attr:`src_dict`.\n '
if (src_dict is None):
return src_dict
if isinstance(tgt_dict_or_keys, HParams):
tgt_dict_or_keys = tgt_dict_or_keys.todict()
if isinstance(tgt_dict_or_keys, dict):
tgt_dict_or_keys = tgt_dict_or_keys.keys()
keys = list(tgt_dict_or_keys)
if isinstance(src_dict, HParams):
src_dict = src_dict.todict()
return {k: src_dict[k] for k in keys if (k in src_dict)} |
def dict_pop(dict_, pop_keys, default=None):
'Removes keys from a dict and returns their values.\n\n Args:\n dict_ (dict): A dictionary from which items are removed.\n pop_keys: A key or a list of keys to remove and return respective\n values or :attr:`default`.\n default (optional): Value to be returned when a key is not in\n :attr:`dict_`. The default value is `None`.\n\n Returns:\n A `dict` of the items removed from :attr:`dict_`.\n '
if (not isinstance(pop_keys, (list, tuple))):
pop_keys = [pop_keys]
ret_dict = {key: dict_.pop(key, default) for key in pop_keys}
return ret_dict | -5,061,628,030,914,188,000 | Removes keys from a dict and returns their values.
Args:
dict_ (dict): A dictionary from which items are removed.
pop_keys: A key or a list of keys to remove and return respective
values or :attr:`default`.
default (optional): Value to be returned when a key is not in
:attr:`dict_`. The default value is `None`.
Returns:
A `dict` of the items removed from :attr:`dict_`. | texar/tf/utils/utils.py | dict_pop | awesomemachinelearning/texar | python | def dict_pop(dict_, pop_keys, default=None):
'Removes keys from a dict and returns their values.\n\n Args:\n dict_ (dict): A dictionary from which items are removed.\n pop_keys: A key or a list of keys to remove and return respective\n values or :attr:`default`.\n default (optional): Value to be returned when a key is not in\n :attr:`dict_`. The default value is `None`.\n\n Returns:\n A `dict` of the items removed from :attr:`dict_`.\n '
if (not isinstance(pop_keys, (list, tuple))):
pop_keys = [pop_keys]
ret_dict = {key: dict_.pop(key, default) for key in pop_keys}
return ret_dict |
def flatten_dict(dict_, parent_key='', sep='.'):
'Flattens a nested dictionary. Namedtuples within the dictionary are\n converted to dicts.\n\n Adapted from:\n https://github.com/google/seq2seq/blob/master/seq2seq/models/model_base.py\n\n Args:\n dict_ (dict): The dictionary to flatten.\n parent_key (str): A prefix to prepend to each key.\n sep (str): Separator that intervenes between parent and child keys.\n E.g., if `sep` == \'.\', then `{ "a": { "b": 3 } }` is converted\n into `{ "a.b": 3 }`.\n\n Returns:\n A new flattened `dict`.\n '
items = []
for (key, value) in dict_.items():
key_ = (((parent_key + sep) + key) if parent_key else key)
if isinstance(value, collections.MutableMapping):
items.extend(flatten_dict(value, key_, sep=sep).items())
elif (isinstance(value, tuple) and hasattr(value, '_asdict')):
dict_items = collections.OrderedDict(zip(value._fields, value))
items.extend(flatten_dict(dict_items, key_, sep=sep).items())
else:
items.append((key_, value))
return dict(items) | 2,483,426,072,466,088,400 | Flattens a nested dictionary. Namedtuples within the dictionary are
converted to dicts.
Adapted from:
https://github.com/google/seq2seq/blob/master/seq2seq/models/model_base.py
Args:
dict_ (dict): The dictionary to flatten.
parent_key (str): A prefix to prepend to each key.
sep (str): Separator that intervenes between parent and child keys.
E.g., if `sep` == '.', then `{ "a": { "b": 3 } }` is converted
into `{ "a.b": 3 }`.
Returns:
A new flattened `dict`. | texar/tf/utils/utils.py | flatten_dict | awesomemachinelearning/texar | python | def flatten_dict(dict_, parent_key=, sep='.'):
'Flattens a nested dictionary. Namedtuples within the dictionary are\n converted to dicts.\n\n Adapted from:\n https://github.com/google/seq2seq/blob/master/seq2seq/models/model_base.py\n\n Args:\n dict_ (dict): The dictionary to flatten.\n parent_key (str): A prefix to prepend to each key.\n sep (str): Separator that intervenes between parent and child keys.\n E.g., if `sep` == \'.\', then `{ "a": { "b": 3 } }` is converted\n into `{ "a.b": 3 }`.\n\n Returns:\n A new flattened `dict`.\n '
items = []
for (key, value) in dict_.items():
key_ = (((parent_key + sep) + key) if parent_key else key)
if isinstance(value, collections.MutableMapping):
items.extend(flatten_dict(value, key_, sep=sep).items())
elif (isinstance(value, tuple) and hasattr(value, '_asdict')):
dict_items = collections.OrderedDict(zip(value._fields, value))
items.extend(flatten_dict(dict_items, key_, sep=sep).items())
else:
items.append((key_, value))
return dict(items) |
def default_str(str_, default_str):
'Returns :attr:`str_` if it is not `None` or empty, otherwise returns\n :attr:`default_str`.\n\n Args:\n str_: A string.\n default_str: A string.\n\n Returns:\n Either :attr:`str_` or :attr:`default_str`.\n '
if ((str_ is not None) and (str_ != '')):
return str_
else:
return default_str | 506,481,686,451,076,000 | Returns :attr:`str_` if it is not `None` or empty, otherwise returns
:attr:`default_str`.
Args:
str_: A string.
default_str: A string.
Returns:
Either :attr:`str_` or :attr:`default_str`. | texar/tf/utils/utils.py | default_str | awesomemachinelearning/texar | python | def default_str(str_, default_str):
'Returns :attr:`str_` if it is not `None` or empty, otherwise returns\n :attr:`default_str`.\n\n Args:\n str_: A string.\n default_str: A string.\n\n Returns:\n Either :attr:`str_` or :attr:`default_str`.\n '
if ((str_ is not None) and (str_ != )):
return str_
else:
return default_str |
def uniquify_str(str_, str_set):
"Uniquifies :attr:`str_` if :attr:`str_` is included in :attr:`str_set`.\n\n This is done by appending a number to :attr:`str_`. Returns\n :attr:`str_` directly if it is not included in :attr:`str_set`.\n\n Args:\n str_ (string): A string to uniquify.\n str_set (set, dict, or list): A collection of strings. The returned\n string is guaranteed to be different from the elements in the\n collection.\n\n Returns:\n The uniquified string. Returns :attr:`str_` directly if it is\n already unique.\n\n Example:\n\n .. code-block:: python\n\n print(uniquify_str('name', ['name', 'name_1']))\n # 'name_2'\n\n "
if (str_ not in str_set):
return str_
else:
for i in range(1, (len(str_set) + 1)):
unique_str = (str_ + ('_%d' % i))
if (unique_str not in str_set):
return unique_str
raise ValueError(('Fails to uniquify string: ' + str_)) | -2,783,406,203,036,694,000 | Uniquifies :attr:`str_` if :attr:`str_` is included in :attr:`str_set`.
This is done by appending a number to :attr:`str_`. Returns
:attr:`str_` directly if it is not included in :attr:`str_set`.
Args:
str_ (string): A string to uniquify.
str_set (set, dict, or list): A collection of strings. The returned
string is guaranteed to be different from the elements in the
collection.
Returns:
The uniquified string. Returns :attr:`str_` directly if it is
already unique.
Example:
.. code-block:: python
print(uniquify_str('name', ['name', 'name_1']))
# 'name_2' | texar/tf/utils/utils.py | uniquify_str | awesomemachinelearning/texar | python | def uniquify_str(str_, str_set):
"Uniquifies :attr:`str_` if :attr:`str_` is included in :attr:`str_set`.\n\n This is done by appending a number to :attr:`str_`. Returns\n :attr:`str_` directly if it is not included in :attr:`str_set`.\n\n Args:\n str_ (string): A string to uniquify.\n str_set (set, dict, or list): A collection of strings. The returned\n string is guaranteed to be different from the elements in the\n collection.\n\n Returns:\n The uniquified string. Returns :attr:`str_` directly if it is\n already unique.\n\n Example:\n\n .. code-block:: python\n\n print(uniquify_str('name', ['name', 'name_1']))\n # 'name_2'\n\n "
if (str_ not in str_set):
return str_
else:
for i in range(1, (len(str_set) + 1)):
unique_str = (str_ + ('_%d' % i))
if (unique_str not in str_set):
return unique_str
raise ValueError(('Fails to uniquify string: ' + str_)) |
def _recur_split(s, dtype_as):
'Splits (possibly nested list of) strings recursively.\n '
if is_str(s):
return _maybe_list_to_array(s.split(), dtype_as)
else:
s_ = [_recur_split(si, dtype_as) for si in s]
return _maybe_list_to_array(s_, s) | 2,575,277,361,996,269,000 | Splits (possibly nested list of) strings recursively. | texar/tf/utils/utils.py | _recur_split | awesomemachinelearning/texar | python | def _recur_split(s, dtype_as):
'\n '
if is_str(s):
return _maybe_list_to_array(s.split(), dtype_as)
else:
s_ = [_recur_split(si, dtype_as) for si in s]
return _maybe_list_to_array(s_, s) |
def strip_token(str_, token, is_token_list=False, compat=True):
"Returns a copy of strings with leading and trailing tokens removed.\n\n Note that besides :attr:`token`, all leading and trailing whitespace\n characters are also removed.\n\n If :attr:`is_token_list` is False, then the function assumes tokens in\n :attr:`str_` are separated with whitespace character.\n\n Args:\n str_: A `str`, or an `n`-D numpy array or (possibly nested)\n list of `str`.\n token (str): The token to strip, e.g., the '<PAD>' token defined in\n :class:`~texar.tf.data.SpecialTokens`.PAD\n is_token_list (bool): Whether each sentence in :attr:`str_` is a list\n of tokens. If False, each sentence in :attr:`str_` is assumed to\n contain tokens separated with space character.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n The stripped strings of the same structure/shape as :attr:`str_`.\n\n Example:\n\n .. code-block:: python\n\n str_ = '<PAD> a sentence <PAD> <PAD> '\n str_stripped = strip_token(str_, '<PAD>')\n # str_stripped == 'a sentence'\n\n str_ = ['<PAD>', 'a', 'sentence', '<PAD>', '<PAD>', '', '']\n str_stripped = strip_token(str_, '<PAD>', is_token_list=True)\n # str_stripped == 'a sentence'\n "
def _recur_strip(s):
if is_str(s):
if (token == ''):
return ' '.join(s.strip().split())
else:
return ' '.join(s.strip().split()).replace((' ' + token), '').replace((token + ' '), '')
else:
s_ = [_recur_strip(si) for si in s]
return _maybe_list_to_array(s_, s)
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
strp_str = _recur_strip(s)
if is_token_list:
strp_str = _recur_split(strp_str, str_)
return strp_str | -3,285,159,593,065,620,000 | Returns a copy of strings with leading and trailing tokens removed.
Note that besides :attr:`token`, all leading and trailing whitespace
characters are also removed.
If :attr:`is_token_list` is False, then the function assumes tokens in
:attr:`str_` are separated with whitespace character.
Args:
str_: A `str`, or an `n`-D numpy array or (possibly nested)
list of `str`.
token (str): The token to strip, e.g., the '<PAD>' token defined in
:class:`~texar.tf.data.SpecialTokens`.PAD
is_token_list (bool): Whether each sentence in :attr:`str_` is a list
of tokens. If False, each sentence in :attr:`str_` is assumed to
contain tokens separated with space character.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
The stripped strings of the same structure/shape as :attr:`str_`.
Example:
.. code-block:: python
str_ = '<PAD> a sentence <PAD> <PAD> '
str_stripped = strip_token(str_, '<PAD>')
# str_stripped == 'a sentence'
str_ = ['<PAD>', 'a', 'sentence', '<PAD>', '<PAD>', '', '']
str_stripped = strip_token(str_, '<PAD>', is_token_list=True)
# str_stripped == 'a sentence' | texar/tf/utils/utils.py | strip_token | awesomemachinelearning/texar | python | def strip_token(str_, token, is_token_list=False, compat=True):
"Returns a copy of strings with leading and trailing tokens removed.\n\n Note that besides :attr:`token`, all leading and trailing whitespace\n characters are also removed.\n\n If :attr:`is_token_list` is False, then the function assumes tokens in\n :attr:`str_` are separated with whitespace character.\n\n Args:\n str_: A `str`, or an `n`-D numpy array or (possibly nested)\n list of `str`.\n token (str): The token to strip, e.g., the '<PAD>' token defined in\n :class:`~texar.tf.data.SpecialTokens`.PAD\n is_token_list (bool): Whether each sentence in :attr:`str_` is a list\n of tokens. If False, each sentence in :attr:`str_` is assumed to\n contain tokens separated with space character.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n The stripped strings of the same structure/shape as :attr:`str_`.\n\n Example:\n\n .. code-block:: python\n\n str_ = '<PAD> a sentence <PAD> <PAD> '\n str_stripped = strip_token(str_, '<PAD>')\n # str_stripped == 'a sentence'\n\n str_ = ['<PAD>', 'a', 'sentence', '<PAD>', '<PAD>', , ]\n str_stripped = strip_token(str_, '<PAD>', is_token_list=True)\n # str_stripped == 'a sentence'\n "
def _recur_strip(s):
if is_str(s):
if (token == ):
return ' '.join(s.strip().split())
else:
return ' '.join(s.strip().split()).replace((' ' + token), ).replace((token + ' '), )
else:
s_ = [_recur_strip(si) for si in s]
return _maybe_list_to_array(s_, s)
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
strp_str = _recur_strip(s)
if is_token_list:
strp_str = _recur_split(strp_str, str_)
return strp_str |
def strip_eos(str_, eos_token='<EOS>', is_token_list=False, compat=True):
"Remove the EOS token and all subsequent tokens.\n\n If :attr:`is_token_list` is False, then the function assumes tokens in\n :attr:`str_` are separated with whitespace character.\n\n Args:\n str_: A `str`, or an `n`-D numpy array or (possibly nested)\n list of `str`.\n eos_token (str): The EOS token. Default is '<EOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.EOS\n is_token_list (bool): Whether each sentence in :attr:`str_` is a list\n of tokens. If False, each sentence in :attr:`str_` is assumed to\n contain tokens separated with space character.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n Strings of the same structure/shape as :attr:`str_`.\n "
def _recur_strip(s):
if is_str(s):
s_tokens = s.split()
if (eos_token in s_tokens):
return ' '.join(s_tokens[:s_tokens.index(eos_token)])
else:
return s
else:
s_ = [_recur_strip(si) for si in s]
return _maybe_list_to_array(s_, s)
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
strp_str = _recur_strip(s)
if is_token_list:
strp_str = _recur_split(strp_str, str_)
return strp_str | -6,987,736,455,651,290,000 | Remove the EOS token and all subsequent tokens.
If :attr:`is_token_list` is False, then the function assumes tokens in
:attr:`str_` are separated with whitespace character.
Args:
str_: A `str`, or an `n`-D numpy array or (possibly nested)
list of `str`.
eos_token (str): The EOS token. Default is '<EOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.EOS
is_token_list (bool): Whether each sentence in :attr:`str_` is a list
of tokens. If False, each sentence in :attr:`str_` is assumed to
contain tokens separated with space character.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
Strings of the same structure/shape as :attr:`str_`. | texar/tf/utils/utils.py | strip_eos | awesomemachinelearning/texar | python | def strip_eos(str_, eos_token='<EOS>', is_token_list=False, compat=True):
"Remove the EOS token and all subsequent tokens.\n\n If :attr:`is_token_list` is False, then the function assumes tokens in\n :attr:`str_` are separated with whitespace character.\n\n Args:\n str_: A `str`, or an `n`-D numpy array or (possibly nested)\n list of `str`.\n eos_token (str): The EOS token. Default is '<EOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.EOS\n is_token_list (bool): Whether each sentence in :attr:`str_` is a list\n of tokens. If False, each sentence in :attr:`str_` is assumed to\n contain tokens separated with space character.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n Strings of the same structure/shape as :attr:`str_`.\n "
def _recur_strip(s):
if is_str(s):
s_tokens = s.split()
if (eos_token in s_tokens):
return ' '.join(s_tokens[:s_tokens.index(eos_token)])
else:
return s
else:
s_ = [_recur_strip(si) for si in s]
return _maybe_list_to_array(s_, s)
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
strp_str = _recur_strip(s)
if is_token_list:
strp_str = _recur_split(strp_str, str_)
return strp_str |
def strip_bos(str_, bos_token='<BOS>', is_token_list=False, compat=True):
"Remove all leading BOS tokens.\n\n Note that besides :attr:`bos_token`, all leading and trailing whitespace\n characters are also removed.\n\n If :attr:`is_token_list` is False, then the function assumes tokens in\n :attr:`str_` are separated with whitespace character.\n\n Args:\n str_: A `str`, or an `n`-D numpy array or (possibly nested)\n list of `str`.\n bos_token (str): The BOS token. Default is '<BOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.BOS\n is_token_list (bool): Whether each sentence in :attr:`str_` is a list\n of tokens. If False, each sentence in :attr:`str_` is assumed to\n contain tokens separated with space character.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n Strings of the same structure/shape as :attr:`str_`.\n "
def _recur_strip(s):
if is_str(s):
if (bos_token == ''):
return ' '.join(s.strip().split())
else:
return ' '.join(s.strip().split()).replace((bos_token + ' '), '')
else:
s_ = [_recur_strip(si) for si in s]
return _maybe_list_to_array(s_, s)
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
strp_str = _recur_strip(s)
if is_token_list:
strp_str = _recur_split(strp_str, str_)
return strp_str | 2,917,346,976,797,500,000 | Remove all leading BOS tokens.
Note that besides :attr:`bos_token`, all leading and trailing whitespace
characters are also removed.
If :attr:`is_token_list` is False, then the function assumes tokens in
:attr:`str_` are separated with whitespace character.
Args:
str_: A `str`, or an `n`-D numpy array or (possibly nested)
list of `str`.
bos_token (str): The BOS token. Default is '<BOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.BOS
is_token_list (bool): Whether each sentence in :attr:`str_` is a list
of tokens. If False, each sentence in :attr:`str_` is assumed to
contain tokens separated with space character.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
Strings of the same structure/shape as :attr:`str_`. | texar/tf/utils/utils.py | strip_bos | awesomemachinelearning/texar | python | def strip_bos(str_, bos_token='<BOS>', is_token_list=False, compat=True):
"Remove all leading BOS tokens.\n\n Note that besides :attr:`bos_token`, all leading and trailing whitespace\n characters are also removed.\n\n If :attr:`is_token_list` is False, then the function assumes tokens in\n :attr:`str_` are separated with whitespace character.\n\n Args:\n str_: A `str`, or an `n`-D numpy array or (possibly nested)\n list of `str`.\n bos_token (str): The BOS token. Default is '<BOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.BOS\n is_token_list (bool): Whether each sentence in :attr:`str_` is a list\n of tokens. If False, each sentence in :attr:`str_` is assumed to\n contain tokens separated with space character.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n Strings of the same structure/shape as :attr:`str_`.\n "
def _recur_strip(s):
if is_str(s):
if (bos_token == ):
return ' '.join(s.strip().split())
else:
return ' '.join(s.strip().split()).replace((bos_token + ' '), )
else:
s_ = [_recur_strip(si) for si in s]
return _maybe_list_to_array(s_, s)
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
strp_str = _recur_strip(s)
if is_token_list:
strp_str = _recur_split(strp_str, str_)
return strp_str |
def strip_special_tokens(str_, strip_pad='<PAD>', strip_bos='<BOS>', strip_eos='<EOS>', is_token_list=False, compat=True):
"Removes special tokens in strings, including:\n\n - Removes EOS and all subsequent tokens\n - Removes leading and and trailing PAD tokens\n - Removes leading BOS tokens\n\n Note that besides the special tokens, all leading and trailing whitespace\n characters are also removed.\n\n This is a joint function of :func:`strip_eos`, :func:`strip_pad`, and\n :func:`strip_bos`\n\n Args:\n str_: A `str`, or an `n`-D numpy array or (possibly nested)\n list of `str`.\n strip_pad (str): The PAD token to strip from the strings (i.e., remove\n the leading and trailing PAD tokens of the strings). Default\n is '<PAD>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.PAD.\n Set to `None` or `False` to disable the stripping.\n strip_bos (str): The BOS token to strip from the strings (i.e., remove\n the leading BOS tokens of the strings).\n Default is '<BOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.BOS.\n Set to `None` or `False` to disable the stripping.\n strip_eos (str): The EOS token to strip from the strings (i.e., remove\n the EOS tokens and all subsequent tokens of the strings).\n Default is '<EOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.EOS.\n Set to `None` or `False` to disable the stripping.\n is_token_list (bool): Whether each sentence in :attr:`str_` is a list\n of tokens. If False, each sentence in :attr:`str_` is assumed to\n contain tokens separated with space character.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n Strings of the same shape of :attr:`str_` with special tokens stripped.\n "
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
if ((strip_eos is not None) and (strip_eos is not False)):
s = _strip_eos_(s, strip_eos, is_token_list=False, compat=False)
if ((strip_pad is not None) and (strip_pad is not False)):
s = strip_token(s, strip_pad, is_token_list=False, compat=False)
if ((strip_bos is not None) and (strip_bos is not False)):
s = _strip_bos_(s, strip_bos, is_token_list=False, compat=False)
if is_token_list:
s = _recur_split(s, str_)
return s | 8,869,456,144,817,604,000 | Removes special tokens in strings, including:
- Removes EOS and all subsequent tokens
- Removes leading and and trailing PAD tokens
- Removes leading BOS tokens
Note that besides the special tokens, all leading and trailing whitespace
characters are also removed.
This is a joint function of :func:`strip_eos`, :func:`strip_pad`, and
:func:`strip_bos`
Args:
str_: A `str`, or an `n`-D numpy array or (possibly nested)
list of `str`.
strip_pad (str): The PAD token to strip from the strings (i.e., remove
the leading and trailing PAD tokens of the strings). Default
is '<PAD>' as defined in
:class:`~texar.tf.data.SpecialTokens`.PAD.
Set to `None` or `False` to disable the stripping.
strip_bos (str): The BOS token to strip from the strings (i.e., remove
the leading BOS tokens of the strings).
Default is '<BOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.BOS.
Set to `None` or `False` to disable the stripping.
strip_eos (str): The EOS token to strip from the strings (i.e., remove
the EOS tokens and all subsequent tokens of the strings).
Default is '<EOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.EOS.
Set to `None` or `False` to disable the stripping.
is_token_list (bool): Whether each sentence in :attr:`str_` is a list
of tokens. If False, each sentence in :attr:`str_` is assumed to
contain tokens separated with space character.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
Strings of the same shape of :attr:`str_` with special tokens stripped. | texar/tf/utils/utils.py | strip_special_tokens | awesomemachinelearning/texar | python | def strip_special_tokens(str_, strip_pad='<PAD>', strip_bos='<BOS>', strip_eos='<EOS>', is_token_list=False, compat=True):
"Removes special tokens in strings, including:\n\n - Removes EOS and all subsequent tokens\n - Removes leading and and trailing PAD tokens\n - Removes leading BOS tokens\n\n Note that besides the special tokens, all leading and trailing whitespace\n characters are also removed.\n\n This is a joint function of :func:`strip_eos`, :func:`strip_pad`, and\n :func:`strip_bos`\n\n Args:\n str_: A `str`, or an `n`-D numpy array or (possibly nested)\n list of `str`.\n strip_pad (str): The PAD token to strip from the strings (i.e., remove\n the leading and trailing PAD tokens of the strings). Default\n is '<PAD>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.PAD.\n Set to `None` or `False` to disable the stripping.\n strip_bos (str): The BOS token to strip from the strings (i.e., remove\n the leading BOS tokens of the strings).\n Default is '<BOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.BOS.\n Set to `None` or `False` to disable the stripping.\n strip_eos (str): The EOS token to strip from the strings (i.e., remove\n the EOS tokens and all subsequent tokens of the strings).\n Default is '<EOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.EOS.\n Set to `None` or `False` to disable the stripping.\n is_token_list (bool): Whether each sentence in :attr:`str_` is a list\n of tokens. If False, each sentence in :attr:`str_` is assumed to\n contain tokens separated with space character.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n Strings of the same shape of :attr:`str_` with special tokens stripped.\n "
s = str_
if compat:
s = compat_as_text(s)
if is_token_list:
s = str_join(s, compat=False)
if ((strip_eos is not None) and (strip_eos is not False)):
s = _strip_eos_(s, strip_eos, is_token_list=False, compat=False)
if ((strip_pad is not None) and (strip_pad is not False)):
s = strip_token(s, strip_pad, is_token_list=False, compat=False)
if ((strip_bos is not None) and (strip_bos is not False)):
s = _strip_bos_(s, strip_bos, is_token_list=False, compat=False)
if is_token_list:
s = _recur_split(s, str_)
return s |
def str_join(tokens, sep=' ', compat=True):
'Concats :attr:`tokens` along the last dimension with intervening\n occurrences of :attr:`sep`.\n\n Args:\n tokens: An `n`-D numpy array or (possibly nested) list of `str`.\n sep (str): The string intervening between the tokens.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n An `(n-1)`-D numpy array (or list) of `str`.\n '
def _recur_join(s):
if (len(s) == 0):
return ''
elif is_str(s[0]):
return sep.join(s)
else:
s_ = [_recur_join(si) for si in s]
return _maybe_list_to_array(s_, s)
if compat:
tokens = compat_as_text(tokens)
str_ = _recur_join(tokens)
return str_ | -414,424,562,989,632,100 | Concats :attr:`tokens` along the last dimension with intervening
occurrences of :attr:`sep`.
Args:
tokens: An `n`-D numpy array or (possibly nested) list of `str`.
sep (str): The string intervening between the tokens.
compat (bool): Whether to convert tokens into `unicode` (Python 2)
or `str` (Python 3).
Returns:
An `(n-1)`-D numpy array (or list) of `str`. | texar/tf/utils/utils.py | str_join | awesomemachinelearning/texar | python | def str_join(tokens, sep=' ', compat=True):
'Concats :attr:`tokens` along the last dimension with intervening\n occurrences of :attr:`sep`.\n\n Args:\n tokens: An `n`-D numpy array or (possibly nested) list of `str`.\n sep (str): The string intervening between the tokens.\n compat (bool): Whether to convert tokens into `unicode` (Python 2)\n or `str` (Python 3).\n\n Returns:\n An `(n-1)`-D numpy array (or list) of `str`.\n '
def _recur_join(s):
if (len(s) == 0):
return
elif is_str(s[0]):
return sep.join(s)
else:
s_ = [_recur_join(si) for si in s]
return _maybe_list_to_array(s_, s)
if compat:
tokens = compat_as_text(tokens)
str_ = _recur_join(tokens)
return str_ |
def map_ids_to_strs(ids, vocab, join=True, strip_pad='<PAD>', strip_bos='<BOS>', strip_eos='<EOS>', compat=True):
"Transforms `int` indexes to strings by mapping ids to tokens,\n concatenating tokens into sentences, and stripping special tokens, etc.\n\n Args:\n ids: An n-D numpy array or (possibly nested) list of `int` indexes.\n vocab: An instance of :class:`~texar.tf.data.Vocab`.\n join (bool): Whether to concat along the last dimension of the\n the tokens into a string separated with a space character.\n strip_pad (str): The PAD token to strip from the strings (i.e., remove\n the leading and trailing PAD tokens of the strings). Default\n is '<PAD>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.PAD.\n Set to `None` or `False` to disable the stripping.\n strip_bos (str): The BOS token to strip from the strings (i.e., remove\n the leading BOS tokens of the strings).\n Default is '<BOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.BOS.\n Set to `None` or `False` to disable the stripping.\n strip_eos (str): The EOS token to strip from the strings (i.e., remove\n the EOS tokens and all subsequent tokens of the strings).\n Default is '<EOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.EOS.\n Set to `None` or `False` to disable the stripping.\n\n Returns:\n If :attr:`join` is True, returns a `(n-1)`-D numpy array (or list) of\n concatenated strings. If :attr:`join` is False, returns an `n`-D numpy\n array (or list) of str tokens.\n\n Example:\n\n .. code-block:: python\n\n text_ids = [[1, 9, 6, 2, 0, 0], [1, 28, 7, 8, 2, 0]]\n\n text = map_ids_to_strs(text_ids, data.vocab)\n # text == ['a sentence', 'parsed from ids']\n\n text = map_ids_to_strs(\n text_ids, data.vocab, join=False,\n strip_pad=None, strip_bos=None, strip_eos=None)\n # text == [['<BOS>', 'a', 'sentence', '<EOS>', '<PAD>', '<PAD>'],\n # ['<BOS>', 'parsed', 'from', 'ids', '<EOS>', '<PAD>']]\n "
tokens = vocab.map_ids_to_tokens_py(ids)
if isinstance(ids, (list, tuple)):
tokens = tokens.tolist()
if compat:
tokens = compat_as_text(tokens)
str_ = str_join(tokens, compat=False)
str_ = strip_special_tokens(str_, strip_pad=strip_pad, strip_bos=strip_bos, strip_eos=strip_eos, compat=False)
if join:
return str_
else:
return _recur_split(str_, ids) | 2,363,225,252,825,321,000 | Transforms `int` indexes to strings by mapping ids to tokens,
concatenating tokens into sentences, and stripping special tokens, etc.
Args:
ids: An n-D numpy array or (possibly nested) list of `int` indexes.
vocab: An instance of :class:`~texar.tf.data.Vocab`.
join (bool): Whether to concat along the last dimension of the
the tokens into a string separated with a space character.
strip_pad (str): The PAD token to strip from the strings (i.e., remove
the leading and trailing PAD tokens of the strings). Default
is '<PAD>' as defined in
:class:`~texar.tf.data.SpecialTokens`.PAD.
Set to `None` or `False` to disable the stripping.
strip_bos (str): The BOS token to strip from the strings (i.e., remove
the leading BOS tokens of the strings).
Default is '<BOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.BOS.
Set to `None` or `False` to disable the stripping.
strip_eos (str): The EOS token to strip from the strings (i.e., remove
the EOS tokens and all subsequent tokens of the strings).
Default is '<EOS>' as defined in
:class:`~texar.tf.data.SpecialTokens`.EOS.
Set to `None` or `False` to disable the stripping.
Returns:
If :attr:`join` is True, returns a `(n-1)`-D numpy array (or list) of
concatenated strings. If :attr:`join` is False, returns an `n`-D numpy
array (or list) of str tokens.
Example:
.. code-block:: python
text_ids = [[1, 9, 6, 2, 0, 0], [1, 28, 7, 8, 2, 0]]
text = map_ids_to_strs(text_ids, data.vocab)
# text == ['a sentence', 'parsed from ids']
text = map_ids_to_strs(
text_ids, data.vocab, join=False,
strip_pad=None, strip_bos=None, strip_eos=None)
# text == [['<BOS>', 'a', 'sentence', '<EOS>', '<PAD>', '<PAD>'],
# ['<BOS>', 'parsed', 'from', 'ids', '<EOS>', '<PAD>']] | texar/tf/utils/utils.py | map_ids_to_strs | awesomemachinelearning/texar | python | def map_ids_to_strs(ids, vocab, join=True, strip_pad='<PAD>', strip_bos='<BOS>', strip_eos='<EOS>', compat=True):
"Transforms `int` indexes to strings by mapping ids to tokens,\n concatenating tokens into sentences, and stripping special tokens, etc.\n\n Args:\n ids: An n-D numpy array or (possibly nested) list of `int` indexes.\n vocab: An instance of :class:`~texar.tf.data.Vocab`.\n join (bool): Whether to concat along the last dimension of the\n the tokens into a string separated with a space character.\n strip_pad (str): The PAD token to strip from the strings (i.e., remove\n the leading and trailing PAD tokens of the strings). Default\n is '<PAD>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.PAD.\n Set to `None` or `False` to disable the stripping.\n strip_bos (str): The BOS token to strip from the strings (i.e., remove\n the leading BOS tokens of the strings).\n Default is '<BOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.BOS.\n Set to `None` or `False` to disable the stripping.\n strip_eos (str): The EOS token to strip from the strings (i.e., remove\n the EOS tokens and all subsequent tokens of the strings).\n Default is '<EOS>' as defined in\n :class:`~texar.tf.data.SpecialTokens`.EOS.\n Set to `None` or `False` to disable the stripping.\n\n Returns:\n If :attr:`join` is True, returns a `(n-1)`-D numpy array (or list) of\n concatenated strings. If :attr:`join` is False, returns an `n`-D numpy\n array (or list) of str tokens.\n\n Example:\n\n .. code-block:: python\n\n text_ids = [[1, 9, 6, 2, 0, 0], [1, 28, 7, 8, 2, 0]]\n\n text = map_ids_to_strs(text_ids, data.vocab)\n # text == ['a sentence', 'parsed from ids']\n\n text = map_ids_to_strs(\n text_ids, data.vocab, join=False,\n strip_pad=None, strip_bos=None, strip_eos=None)\n # text == [['<BOS>', 'a', 'sentence', '<EOS>', '<PAD>', '<PAD>'],\n # ['<BOS>', 'parsed', 'from', 'ids', '<EOS>', '<PAD>']]\n "
tokens = vocab.map_ids_to_tokens_py(ids)
if isinstance(ids, (list, tuple)):
tokens = tokens.tolist()
if compat:
tokens = compat_as_text(tokens)
str_ = str_join(tokens, compat=False)
str_ = strip_special_tokens(str_, strip_pad=strip_pad, strip_bos=strip_bos, strip_eos=strip_eos, compat=False)
if join:
return str_
else:
return _recur_split(str_, ids) |
def ceildiv(a, b):
'Divides with ceil.\n\n E.g., `5 / 2 = 2.5`, `ceildiv(5, 2) = 3`.\n\n Args:\n a (int): Dividend integer.\n b (int): Divisor integer.\n\n Returns:\n int: Ceil quotient.\n '
return (- ((- a) // b)) | -7,981,933,774,480,831,000 | Divides with ceil.
E.g., `5 / 2 = 2.5`, `ceildiv(5, 2) = 3`.
Args:
a (int): Dividend integer.
b (int): Divisor integer.
Returns:
int: Ceil quotient. | texar/tf/utils/utils.py | ceildiv | awesomemachinelearning/texar | python | def ceildiv(a, b):
'Divides with ceil.\n\n E.g., `5 / 2 = 2.5`, `ceildiv(5, 2) = 3`.\n\n Args:\n a (int): Dividend integer.\n b (int): Divisor integer.\n\n Returns:\n int: Ceil quotient.\n '
return (- ((- a) // b)) |
def straight_through(fw_tensor, bw_tensor):
'Use a tensor in forward pass while backpropagating gradient to another.\n\n Args:\n fw_tensor: A tensor to be used in the forward pass.\n bw_tensor: A tensor to which gradient is backpropagated. Must have the\n same shape and type with :attr:`fw_tensor`.\n\n Returns:\n A tensor of the same shape and value with :attr:`fw_tensor` but will\n direct gradient to bw_tensor.\n '
return ((tf.stop_gradient(fw_tensor) + bw_tensor) - tf.stop_gradient(bw_tensor)) | 3,524,259,948,960,443,000 | Use a tensor in forward pass while backpropagating gradient to another.
Args:
fw_tensor: A tensor to be used in the forward pass.
bw_tensor: A tensor to which gradient is backpropagated. Must have the
same shape and type with :attr:`fw_tensor`.
Returns:
A tensor of the same shape and value with :attr:`fw_tensor` but will
direct gradient to bw_tensor. | texar/tf/utils/utils.py | straight_through | awesomemachinelearning/texar | python | def straight_through(fw_tensor, bw_tensor):
'Use a tensor in forward pass while backpropagating gradient to another.\n\n Args:\n fw_tensor: A tensor to be used in the forward pass.\n bw_tensor: A tensor to which gradient is backpropagated. Must have the\n same shape and type with :attr:`fw_tensor`.\n\n Returns:\n A tensor of the same shape and value with :attr:`fw_tensor` but will\n direct gradient to bw_tensor.\n '
return ((tf.stop_gradient(fw_tensor) + bw_tensor) - tf.stop_gradient(bw_tensor)) |
def truncate_seq_pair(tokens_a: Union[(List[int], List[str])], tokens_b: Union[(List[int], List[str])], max_length: int):
"Truncates a sequence pair in place to the maximum length.\n\n This is a simple heuristic which will always truncate the longer sequence\n one token at a time. This makes more sense than truncating an equal\n percent of tokens from each, since if one sequence is very short then\n each token that's truncated likely contains more information than a\n longer sequence.\n\n Example:\n\n .. code-block:: python\n\n tokens_a = [1, 2, 3, 4, 5]\n tokens_b = [6, 7]\n truncate_seq_pair(tokens_a, tokens_b, 5)\n tokens_a # [1, 2, 3]\n tokens_b # [6, 7]\n\n Args:\n tokens_a: A list of tokens or token ids.\n tokens_b: A list of tokens or token ids.\n max_length: maximum sequence length.\n "
while True:
total_length = (len(tokens_a) + len(tokens_b))
if (total_length <= max_length):
break
if (len(tokens_a) > len(tokens_b)):
tokens_a.pop()
else:
tokens_b.pop() | 2,531,554,402,548,770,000 | Truncates a sequence pair in place to the maximum length.
This is a simple heuristic which will always truncate the longer sequence
one token at a time. This makes more sense than truncating an equal
percent of tokens from each, since if one sequence is very short then
each token that's truncated likely contains more information than a
longer sequence.
Example:
.. code-block:: python
tokens_a = [1, 2, 3, 4, 5]
tokens_b = [6, 7]
truncate_seq_pair(tokens_a, tokens_b, 5)
tokens_a # [1, 2, 3]
tokens_b # [6, 7]
Args:
tokens_a: A list of tokens or token ids.
tokens_b: A list of tokens or token ids.
max_length: maximum sequence length. | texar/tf/utils/utils.py | truncate_seq_pair | awesomemachinelearning/texar | python | def truncate_seq_pair(tokens_a: Union[(List[int], List[str])], tokens_b: Union[(List[int], List[str])], max_length: int):
"Truncates a sequence pair in place to the maximum length.\n\n This is a simple heuristic which will always truncate the longer sequence\n one token at a time. This makes more sense than truncating an equal\n percent of tokens from each, since if one sequence is very short then\n each token that's truncated likely contains more information than a\n longer sequence.\n\n Example:\n\n .. code-block:: python\n\n tokens_a = [1, 2, 3, 4, 5]\n tokens_b = [6, 7]\n truncate_seq_pair(tokens_a, tokens_b, 5)\n tokens_a # [1, 2, 3]\n tokens_b # [6, 7]\n\n Args:\n tokens_a: A list of tokens or token ids.\n tokens_b: A list of tokens or token ids.\n max_length: maximum sequence length.\n "
while True:
total_length = (len(tokens_a) + len(tokens_b))
if (total_length <= max_length):
break
if (len(tokens_a) > len(tokens_b)):
tokens_a.pop()
else:
tokens_b.pop() |
def run_on_app_servers(command):
'\n A helper to copy a single file across app servers\n '
log.info(('Running %s on app servers' % command))
ret_val = 0
if getattr(settings, 'MULTIPLE_APP_SERVERS', None):
for server in settings.MULTIPLE_APP_SERVERS:
ret = os.system(('ssh %s@%s %s' % (SYNC_USER, server, command)))
if (ret != 0):
ret_val = ret
return ret_val
else:
ret = os.system(command)
return ret | -740,737,386,652,347,800 | A helper to copy a single file across app servers | readthedocs/core/utils/__init__.py | run_on_app_servers | ank-forked/readthedocs.org | python | def run_on_app_servers(command):
'\n \n '
log.info(('Running %s on app servers' % command))
ret_val = 0
if getattr(settings, 'MULTIPLE_APP_SERVERS', None):
for server in settings.MULTIPLE_APP_SERVERS:
ret = os.system(('ssh %s@%s %s' % (SYNC_USER, server, command)))
if (ret != 0):
ret_val = ret
return ret_val
else:
ret = os.system(command)
return ret |
def trigger_build(project, version=None, record=True, force=False, basic=False):
'\n An API to wrap the triggering of a build.\n '
from readthedocs.projects.tasks import update_docs
if project.skip:
return None
if (not version):
version = project.versions.get(slug=LATEST)
if record:
build = Build.objects.create(project=project, version=version, type='html', state='triggered', success=True)
update_docs.delay(pk=project.pk, version_pk=version.pk, record=record, force=force, basic=basic, build_pk=build.pk)
else:
build = None
update_docs.delay(pk=project.pk, version_pk=version.pk, record=record, force=force, basic=basic)
return build | 6,877,365,090,643,703,000 | An API to wrap the triggering of a build. | readthedocs/core/utils/__init__.py | trigger_build | ank-forked/readthedocs.org | python | def trigger_build(project, version=None, record=True, force=False, basic=False):
'\n \n '
from readthedocs.projects.tasks import update_docs
if project.skip:
return None
if (not version):
version = project.versions.get(slug=LATEST)
if record:
build = Build.objects.create(project=project, version=version, type='html', state='triggered', success=True)
update_docs.delay(pk=project.pk, version_pk=version.pk, record=record, force=force, basic=basic, build_pk=build.pk)
else:
build = None
update_docs.delay(pk=project.pk, version_pk=version.pk, record=record, force=force, basic=basic)
return build |
def send_email(recipient, subject, template, template_html, context=None, request=None):
'\n Send multipart email\n\n recipient\n Email recipient address\n\n subject\n Email subject header\n\n template\n Plain text template to send\n\n template_html\n HTML template to send as new message part\n\n context\n A dictionary to pass into the template calls\n\n request\n Request object for determining absolute URL\n '
if request:
scheme = ('https' if request.is_secure() else 'http')
context['uri'] = '{scheme}://{host}'.format(scheme=scheme, host=request.get_host())
ctx = {}
ctx.update(context)
msg = EmailMultiAlternatives(subject, get_template(template).render(ctx), settings.DEFAULT_FROM_EMAIL, [recipient])
msg.attach_alternative(get_template(template_html).render(ctx), 'text/html')
msg.send() | -415,740,951,742,102,200 | Send multipart email
recipient
Email recipient address
subject
Email subject header
template
Plain text template to send
template_html
HTML template to send as new message part
context
A dictionary to pass into the template calls
request
Request object for determining absolute URL | readthedocs/core/utils/__init__.py | send_email | ank-forked/readthedocs.org | python | def send_email(recipient, subject, template, template_html, context=None, request=None):
'\n Send multipart email\n\n recipient\n Email recipient address\n\n subject\n Email subject header\n\n template\n Plain text template to send\n\n template_html\n HTML template to send as new message part\n\n context\n A dictionary to pass into the template calls\n\n request\n Request object for determining absolute URL\n '
if request:
scheme = ('https' if request.is_secure() else 'http')
context['uri'] = '{scheme}://{host}'.format(scheme=scheme, host=request.get_host())
ctx = {}
ctx.update(context)
msg = EmailMultiAlternatives(subject, get_template(template).render(ctx), settings.DEFAULT_FROM_EMAIL, [recipient])
msg.attach_alternative(get_template(template_html).render(ctx), 'text/html')
msg.send() |
@staticmethod
def get_schema(max_nesting_depth: Optional[int]=6, nesting_depth: int=0, nesting_list: List[str]=[], max_recursion_limit: Optional[int]=2, include_extension: Optional[bool]=False, extension_fields: Optional[List[str]]=None, extension_depth: int=0, max_extension_depth: Optional[int]=2, include_modifierExtension: Optional[bool]=False, use_date_for: Optional[List[str]]=None, parent_path: Optional[str]='') -> Union[(StructType, DataType)]:
"\n Information about a medication that is used to support knowledge.\n\n\n id: Unique id for the element within a resource (for internal references). This\n may be any string value that does not contain spaces.\n\n extension: May be used to represent additional information that is not part of the basic\n definition of the element. To make the use of extensions safe and manageable,\n there is a strict set of governance applied to the definition and use of\n extensions. Though any implementer can define an extension, there is a set of\n requirements that SHALL be met as part of the definition of the extension.\n\n modifierExtension: May be used to represent additional information that is not part of the basic\n definition of the element and that modifies the understanding of the element\n in which it is contained and/or the understanding of the containing element's\n descendants. Usually modifier elements provide negation or qualification. To\n make the use of extensions safe and manageable, there is a strict set of\n governance applied to the definition and use of extensions. Though any\n implementer can define an extension, there is a set of requirements that SHALL\n be met as part of the definition of the extension. Applications processing a\n resource are required to check for modifier extensions.\n\n Modifier extensions SHALL NOT change the meaning of any elements on Resource\n or DomainResource (including cannot change the meaning of modifierExtension\n itself).\n\n dosage: Dosage for the medication for the specific guidelines.\n\n indicationCodeableConcept: Indication for use that apply to the specific administration guidelines.\n\n indicationReference: Indication for use that apply to the specific administration guidelines.\n\n patientCharacteristics: Characteristics of the patient that are relevant to the administration\n guidelines (for example, height, weight, gender, etc.).\n\n "
if (extension_fields is None):
extension_fields = ['valueBoolean', 'valueCode', 'valueDate', 'valueDateTime', 'valueDecimal', 'valueId', 'valueInteger', 'valuePositiveInt', 'valueString', 'valueTime', 'valueUnsignedInt', 'valueUri', 'valueUrl', 'valueReference', 'valueCodeableConcept', 'valueAddress']
from spark_fhir_schemas.r4.complex_types.extension import ExtensionSchema
from spark_fhir_schemas.r4.complex_types.medicationknowledge_dosage import MedicationKnowledge_DosageSchema
from spark_fhir_schemas.r4.complex_types.codeableconcept import CodeableConceptSchema
from spark_fhir_schemas.r4.complex_types.reference import ReferenceSchema
from spark_fhir_schemas.r4.complex_types.medicationknowledge_patientcharacteristics import MedicationKnowledge_PatientCharacteristicsSchema
if ((max_recursion_limit and (nesting_list.count('MedicationKnowledge_AdministrationGuidelines') >= max_recursion_limit)) or (max_nesting_depth and (nesting_depth >= max_nesting_depth))):
return StructType([StructField('id', StringType(), True)])
my_nesting_list: List[str] = (nesting_list + ['MedicationKnowledge_AdministrationGuidelines'])
my_parent_path = ((parent_path + '.medicationknowledge_administrationguidelines') if parent_path else 'medicationknowledge_administrationguidelines')
schema = StructType([StructField('id', StringType(), True), StructField('extension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path)), True), StructField('modifierExtension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path)), True), StructField('dosage', ArrayType(MedicationKnowledge_DosageSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path)), True), StructField('indicationCodeableConcept', CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path), True), StructField('indicationReference', ReferenceSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path), True), StructField('patientCharacteristics', ArrayType(MedicationKnowledge_PatientCharacteristicsSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path)), True)])
if (not include_extension):
schema.fields = [(c if (c.name != 'extension') else StructField('extension', StringType(), True)) for c in schema.fields]
if (not include_modifierExtension):
schema.fields = [(c if (c.name != 'modifierExtension') else StructField('modifierExtension', StringType(), True)) for c in schema.fields]
return schema | -3,726,750,171,931,559,000 | Information about a medication that is used to support knowledge.
id: Unique id for the element within a resource (for internal references). This
may be any string value that does not contain spaces.
extension: May be used to represent additional information that is not part of the basic
definition of the element. To make the use of extensions safe and manageable,
there is a strict set of governance applied to the definition and use of
extensions. Though any implementer can define an extension, there is a set of
requirements that SHALL be met as part of the definition of the extension.
modifierExtension: May be used to represent additional information that is not part of the basic
definition of the element and that modifies the understanding of the element
in which it is contained and/or the understanding of the containing element's
descendants. Usually modifier elements provide negation or qualification. To
make the use of extensions safe and manageable, there is a strict set of
governance applied to the definition and use of extensions. Though any
implementer can define an extension, there is a set of requirements that SHALL
be met as part of the definition of the extension. Applications processing a
resource are required to check for modifier extensions.
Modifier extensions SHALL NOT change the meaning of any elements on Resource
or DomainResource (including cannot change the meaning of modifierExtension
itself).
dosage: Dosage for the medication for the specific guidelines.
indicationCodeableConcept: Indication for use that apply to the specific administration guidelines.
indicationReference: Indication for use that apply to the specific administration guidelines.
patientCharacteristics: Characteristics of the patient that are relevant to the administration
guidelines (for example, height, weight, gender, etc.). | spark_fhir_schemas/r4/complex_types/medicationknowledge_administrationguidelines.py | get_schema | icanbwell/SparkFhirSchemas | python | @staticmethod
def get_schema(max_nesting_depth: Optional[int]=6, nesting_depth: int=0, nesting_list: List[str]=[], max_recursion_limit: Optional[int]=2, include_extension: Optional[bool]=False, extension_fields: Optional[List[str]]=None, extension_depth: int=0, max_extension_depth: Optional[int]=2, include_modifierExtension: Optional[bool]=False, use_date_for: Optional[List[str]]=None, parent_path: Optional[str]=) -> Union[(StructType, DataType)]:
"\n Information about a medication that is used to support knowledge.\n\n\n id: Unique id for the element within a resource (for internal references). This\n may be any string value that does not contain spaces.\n\n extension: May be used to represent additional information that is not part of the basic\n definition of the element. To make the use of extensions safe and manageable,\n there is a strict set of governance applied to the definition and use of\n extensions. Though any implementer can define an extension, there is a set of\n requirements that SHALL be met as part of the definition of the extension.\n\n modifierExtension: May be used to represent additional information that is not part of the basic\n definition of the element and that modifies the understanding of the element\n in which it is contained and/or the understanding of the containing element's\n descendants. Usually modifier elements provide negation or qualification. To\n make the use of extensions safe and manageable, there is a strict set of\n governance applied to the definition and use of extensions. Though any\n implementer can define an extension, there is a set of requirements that SHALL\n be met as part of the definition of the extension. Applications processing a\n resource are required to check for modifier extensions.\n\n Modifier extensions SHALL NOT change the meaning of any elements on Resource\n or DomainResource (including cannot change the meaning of modifierExtension\n itself).\n\n dosage: Dosage for the medication for the specific guidelines.\n\n indicationCodeableConcept: Indication for use that apply to the specific administration guidelines.\n\n indicationReference: Indication for use that apply to the specific administration guidelines.\n\n patientCharacteristics: Characteristics of the patient that are relevant to the administration\n guidelines (for example, height, weight, gender, etc.).\n\n "
if (extension_fields is None):
extension_fields = ['valueBoolean', 'valueCode', 'valueDate', 'valueDateTime', 'valueDecimal', 'valueId', 'valueInteger', 'valuePositiveInt', 'valueString', 'valueTime', 'valueUnsignedInt', 'valueUri', 'valueUrl', 'valueReference', 'valueCodeableConcept', 'valueAddress']
from spark_fhir_schemas.r4.complex_types.extension import ExtensionSchema
from spark_fhir_schemas.r4.complex_types.medicationknowledge_dosage import MedicationKnowledge_DosageSchema
from spark_fhir_schemas.r4.complex_types.codeableconcept import CodeableConceptSchema
from spark_fhir_schemas.r4.complex_types.reference import ReferenceSchema
from spark_fhir_schemas.r4.complex_types.medicationknowledge_patientcharacteristics import MedicationKnowledge_PatientCharacteristicsSchema
if ((max_recursion_limit and (nesting_list.count('MedicationKnowledge_AdministrationGuidelines') >= max_recursion_limit)) or (max_nesting_depth and (nesting_depth >= max_nesting_depth))):
return StructType([StructField('id', StringType(), True)])
my_nesting_list: List[str] = (nesting_list + ['MedicationKnowledge_AdministrationGuidelines'])
my_parent_path = ((parent_path + '.medicationknowledge_administrationguidelines') if parent_path else 'medicationknowledge_administrationguidelines')
schema = StructType([StructField('id', StringType(), True), StructField('extension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path)), True), StructField('modifierExtension', ArrayType(ExtensionSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path)), True), StructField('dosage', ArrayType(MedicationKnowledge_DosageSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path)), True), StructField('indicationCodeableConcept', CodeableConceptSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path), True), StructField('indicationReference', ReferenceSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=(extension_depth + 1), max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path), True), StructField('patientCharacteristics', ArrayType(MedicationKnowledge_PatientCharacteristicsSchema.get_schema(max_nesting_depth=max_nesting_depth, nesting_depth=(nesting_depth + 1), nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path)), True)])
if (not include_extension):
schema.fields = [(c if (c.name != 'extension') else StructField('extension', StringType(), True)) for c in schema.fields]
if (not include_modifierExtension):
schema.fields = [(c if (c.name != 'modifierExtension') else StructField('modifierExtension', StringType(), True)) for c in schema.fields]
return schema |
def handler(event, context):
'\n Boto3 Proxy API Handler\n '
headers = event.get('Headers', event.get('headers'))
if ('request-context-id' in headers):
logger.set_uuid(headers['request-context-id'])
logger.info({'Incoming event': event})
logger.info('Incoming context: %s', context)
request_body = json.loads(event.get('body', {}))
try:
secrets_client = boto3.client('secretsmanager')
lambda_auth.authorize_lambda_request(event, MSFT_IDP_TENANT_ID, MSFT_IDP_APP_ID, MSFT_IDP_CLIENT_ROLES, LDAP_SERVER, LDAP_USERNAME, secrets.retrieve_ldap_password(secrets_client, logger, LDAP_PASSWORD_SECRET_NAME), LDAP_SEARCH_BASE, LDAP_OBJECT_CLASS, LDAP_GROUP_NAME, LDAP_LOOKUP_ATTRIBUTE)
ssm_client = boto3.client('ssm')
except Exception as e:
traceback.print_exc()
return {'statusCode': UNAUTHORIZED_STATUS, 'body': json.dumps({'error': f'Unauthorized. {str(e)}'})}
resp_headers = {'Content-Type': 'application/json', 'request-context-id': logger.get_uuid()}
if hasattr(context, 'local_test'):
logger.info('Running at local')
path_params = event.get('pathParameters', {})
request_headers = event.get('headers', {})
vpcxiam_endpoint = os.environ.get('vpcxiam_endpoint')
vpcxiam_scope = os.environ.get('vpcxiam_scope')
vpcxiam_host = os.environ.get('vpcxiam_host')
resp = {'message': 'API action has been successfully completed'}
status_code = SUCCESS_STATUS
try:
account = path_params.get('account-id')
region = path_params.get('region-name')
service = path_params.get('boto3-service')
action = path_params.get('boto3-action')
logger.info(f'Account: {account}')
logger.info(f'Region: {region}')
logger.info(f'Boto3 Service: {service}')
logger.info(f'Boto3 Action: {action}')
logger.info(f'is_authorized({request_headers}, {MSFT_IDP_APP_ID}, {MSFT_IDP_TENANT_ID}, {MSFT_IDP_CLIENT_ROLES}')
url = (vpcxiam_endpoint + f'/v1/accounts/{account}/roles/admin/credentials')
scope = vpcxiam_scope
additional_headers = {'Host': vpcxiam_host}
api_requests = api_request.ApiRequests()
credentials = json.loads(api_requests.request(url=url, method='get', scope=scope, additional_headers=additional_headers).text)
error = credentials.get('error', {})
if error:
logger.error(error)
raise ValueError(error)
credentials = credentials.get('credentials', {})
try:
ssm_parameter_name = ('/vpcx/aws/boto3-proxy/allowed-actions/' + service)
logger.info(('Looking up parameter ' + ssm_parameter_name))
allowed_actions = ssm_client.get_parameter(Name=ssm_parameter_name)
except botocore.exceptions.ClientError as err:
logger.error(err)
if (err.response['Error']['Code'] == 'ParameterNotFound'):
raise InvalidInputException((('Service ' + service) + ' is not an allowed service for the API'))
else:
raise error
if (action not in allowed_actions['Parameter']['Value']):
raise InvalidInputException((('Action ' + action) + ' is not an allowed action for the API'))
ec2_client = boto3.client(service_name='ec2', aws_access_key_id=credentials.get('AccessKeyId', ''), aws_secret_access_key=credentials.get('SecretAccessKey', ''), aws_session_token=credentials.get('SessionToken', ''))
helpers.is_region_valid(ec2_client, region)
logger.info(f'{region} is a valid region')
boto3_client = boto3.client(service_name=service, region_name=region, aws_access_key_id=credentials.get('AccessKeyId', ''), aws_secret_access_key=credentials.get('SecretAccessKey', ''), aws_session_token=credentials.get('SessionToken', ''))
kwargs = request_body
getattr(boto3_client, action)(**kwargs)
except botocore.exceptions.ClientError as err:
status_code = INTERNAL_SERVICE_ERROR_STATUS
resp = {'error': f'{type(err).__name__}: {err}'}
except InvalidRegionException:
status_code = NOT_FOUND_STATUS
resp = {'error': 'Please enter a valid region in the url path'}
except InvalidInputException as err:
status_code = BAD_REQUEST_STATUS
resp = {'error': str(err)}
except ValueError as err:
status_code = NOT_FOUND_STATUS
resp = {'error': str(err)}
except Exception as err:
status_code = INTERNAL_SERVICE_ERROR_STATUS
resp = {'error': f'{type(err).__name__}: {err}'}
resp = helpers.lambda_returns(status_code, resp_headers, json.dumps(resp))
logger.info(f'response: {resp}')
return resp | -4,410,066,952,002,910,000 | Boto3 Proxy API Handler | boto3_proxy/index.py | handler | aws-samples/boto3-proxy-api-sls | python | def handler(event, context):
'\n \n '
headers = event.get('Headers', event.get('headers'))
if ('request-context-id' in headers):
logger.set_uuid(headers['request-context-id'])
logger.info({'Incoming event': event})
logger.info('Incoming context: %s', context)
request_body = json.loads(event.get('body', {}))
try:
secrets_client = boto3.client('secretsmanager')
lambda_auth.authorize_lambda_request(event, MSFT_IDP_TENANT_ID, MSFT_IDP_APP_ID, MSFT_IDP_CLIENT_ROLES, LDAP_SERVER, LDAP_USERNAME, secrets.retrieve_ldap_password(secrets_client, logger, LDAP_PASSWORD_SECRET_NAME), LDAP_SEARCH_BASE, LDAP_OBJECT_CLASS, LDAP_GROUP_NAME, LDAP_LOOKUP_ATTRIBUTE)
ssm_client = boto3.client('ssm')
except Exception as e:
traceback.print_exc()
return {'statusCode': UNAUTHORIZED_STATUS, 'body': json.dumps({'error': f'Unauthorized. {str(e)}'})}
resp_headers = {'Content-Type': 'application/json', 'request-context-id': logger.get_uuid()}
if hasattr(context, 'local_test'):
logger.info('Running at local')
path_params = event.get('pathParameters', {})
request_headers = event.get('headers', {})
vpcxiam_endpoint = os.environ.get('vpcxiam_endpoint')
vpcxiam_scope = os.environ.get('vpcxiam_scope')
vpcxiam_host = os.environ.get('vpcxiam_host')
resp = {'message': 'API action has been successfully completed'}
status_code = SUCCESS_STATUS
try:
account = path_params.get('account-id')
region = path_params.get('region-name')
service = path_params.get('boto3-service')
action = path_params.get('boto3-action')
logger.info(f'Account: {account}')
logger.info(f'Region: {region}')
logger.info(f'Boto3 Service: {service}')
logger.info(f'Boto3 Action: {action}')
logger.info(f'is_authorized({request_headers}, {MSFT_IDP_APP_ID}, {MSFT_IDP_TENANT_ID}, {MSFT_IDP_CLIENT_ROLES}')
url = (vpcxiam_endpoint + f'/v1/accounts/{account}/roles/admin/credentials')
scope = vpcxiam_scope
additional_headers = {'Host': vpcxiam_host}
api_requests = api_request.ApiRequests()
credentials = json.loads(api_requests.request(url=url, method='get', scope=scope, additional_headers=additional_headers).text)
error = credentials.get('error', {})
if error:
logger.error(error)
raise ValueError(error)
credentials = credentials.get('credentials', {})
try:
ssm_parameter_name = ('/vpcx/aws/boto3-proxy/allowed-actions/' + service)
logger.info(('Looking up parameter ' + ssm_parameter_name))
allowed_actions = ssm_client.get_parameter(Name=ssm_parameter_name)
except botocore.exceptions.ClientError as err:
logger.error(err)
if (err.response['Error']['Code'] == 'ParameterNotFound'):
raise InvalidInputException((('Service ' + service) + ' is not an allowed service for the API'))
else:
raise error
if (action not in allowed_actions['Parameter']['Value']):
raise InvalidInputException((('Action ' + action) + ' is not an allowed action for the API'))
ec2_client = boto3.client(service_name='ec2', aws_access_key_id=credentials.get('AccessKeyId', ), aws_secret_access_key=credentials.get('SecretAccessKey', ), aws_session_token=credentials.get('SessionToken', ))
helpers.is_region_valid(ec2_client, region)
logger.info(f'{region} is a valid region')
boto3_client = boto3.client(service_name=service, region_name=region, aws_access_key_id=credentials.get('AccessKeyId', ), aws_secret_access_key=credentials.get('SecretAccessKey', ), aws_session_token=credentials.get('SessionToken', ))
kwargs = request_body
getattr(boto3_client, action)(**kwargs)
except botocore.exceptions.ClientError as err:
status_code = INTERNAL_SERVICE_ERROR_STATUS
resp = {'error': f'{type(err).__name__}: {err}'}
except InvalidRegionException:
status_code = NOT_FOUND_STATUS
resp = {'error': 'Please enter a valid region in the url path'}
except InvalidInputException as err:
status_code = BAD_REQUEST_STATUS
resp = {'error': str(err)}
except ValueError as err:
status_code = NOT_FOUND_STATUS
resp = {'error': str(err)}
except Exception as err:
status_code = INTERNAL_SERVICE_ERROR_STATUS
resp = {'error': f'{type(err).__name__}: {err}'}
resp = helpers.lambda_returns(status_code, resp_headers, json.dumps(resp))
logger.info(f'response: {resp}')
return resp |
def test_wait_for_db_ready(self):
'Test waiting for db when db is available'
with patch('django.db.utils.ConnectionHandler.__getitem__') as gi:
gi.return_value = True
call_command('wait_for_db')
self.assertEqual(gi.call_count, 1) | -6,795,844,142,386,390,000 | Test waiting for db when db is available | app/core/tests/test_commands.py | test_wait_for_db_ready | anirudhs1998/recipe-app-api | python | def test_wait_for_db_ready(self):
with patch('django.db.utils.ConnectionHandler.__getitem__') as gi:
gi.return_value = True
call_command('wait_for_db')
self.assertEqual(gi.call_count, 1) |
@patch('time.sleep', return_value=True)
def test_wait_for_db(self, ts):
'Test waiting for db'
with patch('django.db.utils.ConnectionHandler.__getitem__') as gi:
gi.side_effect = (([OperationalError] * 5) + [True])
call_command('wait_for_db')
self.assertEqual(gi.call_count, 6) | -1,949,014,688,296,956,200 | Test waiting for db | app/core/tests/test_commands.py | test_wait_for_db | anirudhs1998/recipe-app-api | python | @patch('time.sleep', return_value=True)
def test_wait_for_db(self, ts):
with patch('django.db.utils.ConnectionHandler.__getitem__') as gi:
gi.side_effect = (([OperationalError] * 5) + [True])
call_command('wait_for_db')
self.assertEqual(gi.call_count, 6) |
def csrf_failure(request, reason=''):
'\n customize the response for csrf_token invalid\n '
get_token(request)
return JResponse(codes.get('csrf_invalid'), status=403) | -6,097,828,015,095,883,000 | customize the response for csrf_token invalid | apps/core/views.py | csrf_failure | dlooto/driver-vision | python | def csrf_failure(request, reason=):
'\n \n '
get_token(request)
return JResponse(codes.get('csrf_invalid'), status=403) |
def handle_exception(self, exc):
'\n Handle any exception that occurs, by returning an appropriate response,\n or re-raising the error.\n '
if isinstance(exc, exceptions.Throttled):
self.headers['X-Throttle-Wait-Seconds'] = ('%d' % exc.wait)
if isinstance(exc, (exceptions.NotAuthenticated, exceptions.AuthenticationFailed)):
auth_header = self.get_authenticate_header(self.request)
if auth_header:
self.headers['WWW-Authenticate'] = auth_header
else:
exc.status_code = status.HTTP_403_FORBIDDEN
if isinstance(exc, exceptions.MethodNotAllowed):
return Response(codes.get('invalid_request_method'), status=exc.status_code, exception=True)
elif isinstance(exc, CsrfError):
return Response(codes.get('csrf_invalid'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.ParseError):
return Response(codes.get('parse_error'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.AuthenticationFailed):
return Response(codes.get('authentication_failed'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.NotAuthenticated):
return Response(codes.get('not_authenticated'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.PermissionDenied):
return Response(codes.get('permission_denied'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.NotAcceptable):
return Response(codes.get('not_acceptable'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.UnsupportedMediaType):
return Response(codes.get('unsupported_media_type'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.Throttled):
return Response(codes.get('throttled'), status=exc.status_code, exception=True)
elif isinstance(exc, Http404):
return Response(codes.get('not_found'), status=status.HTTP_404_NOT_FOUND, exception=True)
elif isinstance(exc, PermissionDenied):
return Response(codes.get('permission_denied'), status=status.HTTP_403_FORBIDDEN, exception=True)
raise | 632,493,756,669,750,400 | Handle any exception that occurs, by returning an appropriate response,
or re-raising the error. | apps/core/views.py | handle_exception | dlooto/driver-vision | python | def handle_exception(self, exc):
'\n Handle any exception that occurs, by returning an appropriate response,\n or re-raising the error.\n '
if isinstance(exc, exceptions.Throttled):
self.headers['X-Throttle-Wait-Seconds'] = ('%d' % exc.wait)
if isinstance(exc, (exceptions.NotAuthenticated, exceptions.AuthenticationFailed)):
auth_header = self.get_authenticate_header(self.request)
if auth_header:
self.headers['WWW-Authenticate'] = auth_header
else:
exc.status_code = status.HTTP_403_FORBIDDEN
if isinstance(exc, exceptions.MethodNotAllowed):
return Response(codes.get('invalid_request_method'), status=exc.status_code, exception=True)
elif isinstance(exc, CsrfError):
return Response(codes.get('csrf_invalid'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.ParseError):
return Response(codes.get('parse_error'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.AuthenticationFailed):
return Response(codes.get('authentication_failed'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.NotAuthenticated):
return Response(codes.get('not_authenticated'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.PermissionDenied):
return Response(codes.get('permission_denied'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.NotAcceptable):
return Response(codes.get('not_acceptable'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.UnsupportedMediaType):
return Response(codes.get('unsupported_media_type'), status=exc.status_code, exception=True)
elif isinstance(exc, exceptions.Throttled):
return Response(codes.get('throttled'), status=exc.status_code, exception=True)
elif isinstance(exc, Http404):
return Response(codes.get('not_found'), status=status.HTTP_404_NOT_FOUND, exception=True)
elif isinstance(exc, PermissionDenied):
return Response(codes.get('permission_denied'), status=status.HTTP_403_FORBIDDEN, exception=True)
raise |
def import_migration_script(filepath: Path) -> ModuleType:
'\n 像导入模块一样导入迁移脚本。\n\n :param filepath: 文件路径对象。\n :return:\n '
spec = importlib.util.spec_from_file_location(filepath.stem, filepath)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module | -2,049,019,262,425,342,000 | 像导入模块一样导入迁移脚本。
:param filepath: 文件路径对象。
:return: | scripts/benchmark_migration.py | import_migration_script | psbsgic/rabbitai | python | def import_migration_script(filepath: Path) -> ModuleType:
'\n 像导入模块一样导入迁移脚本。\n\n :param filepath: 文件路径对象。\n :return:\n '
spec = importlib.util.spec_from_file_location(filepath.stem, filepath)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module |
def extract_modified_tables(module: ModuleType) -> Set[str]:
'\n 提取由迁移脚本修改的表。\n\n 此函数使用一种简单的方法来查看迁移脚本的源代码以查找模式。它可以通过实际遍历AST来改进。\n '
tables: Set[str] = set()
for function in {'upgrade', 'downgrade'}:
source = getsource(getattr(module, function))
tables.update(re.findall('alter_table\\(\\s*"(\\w+?)"\\s*\\)', source, re.DOTALL))
tables.update(re.findall('add_column\\(\\s*"(\\w+?)"\\s*,', source, re.DOTALL))
tables.update(re.findall('drop_column\\(\\s*"(\\w+?)"\\s*,', source, re.DOTALL))
return tables | -3,717,283,260,242,543,600 | 提取由迁移脚本修改的表。
此函数使用一种简单的方法来查看迁移脚本的源代码以查找模式。它可以通过实际遍历AST来改进。 | scripts/benchmark_migration.py | extract_modified_tables | psbsgic/rabbitai | python | def extract_modified_tables(module: ModuleType) -> Set[str]:
'\n 提取由迁移脚本修改的表。\n\n 此函数使用一种简单的方法来查看迁移脚本的源代码以查找模式。它可以通过实际遍历AST来改进。\n '
tables: Set[str] = set()
for function in {'upgrade', 'downgrade'}:
source = getsource(getattr(module, function))
tables.update(re.findall('alter_table\\(\\s*"(\\w+?)"\\s*\\)', source, re.DOTALL))
tables.update(re.findall('add_column\\(\\s*"(\\w+?)"\\s*,', source, re.DOTALL))
tables.update(re.findall('drop_column\\(\\s*"(\\w+?)"\\s*,', source, re.DOTALL))
return tables |
def find_models(module: ModuleType) -> List[Type[Model]]:
'\n 在迁移脚本中查找所有模型。\n\n :param module:\n :return:\n '
models: List[Type[Model]] = []
tables = extract_modified_tables(module)
queue = list(module.__dict__.values())
while queue:
obj = queue.pop()
if hasattr(obj, '__tablename__'):
tables.add(obj.__tablename__)
elif isinstance(obj, list):
queue.extend(obj)
elif isinstance(obj, dict):
queue.extend(obj.values())
for obj in Model._decl_class_registry.values():
if (hasattr(obj, '__table__') and (obj.__table__.fullname in tables)):
models.append(obj)
sorter = TopologicalSorter()
for model in models:
inspector = inspect(model)
dependent_tables: List[str] = []
for column in inspector.columns.values():
for foreign_key in column.foreign_keys:
dependent_tables.append(foreign_key.target_fullname.split('.')[0])
sorter.add(model.__tablename__, *dependent_tables)
order = list(sorter.static_order())
models.sort(key=(lambda model: order.index(model.__tablename__)))
return models | 84,442,892,764,014,050 | 在迁移脚本中查找所有模型。
:param module:
:return: | scripts/benchmark_migration.py | find_models | psbsgic/rabbitai | python | def find_models(module: ModuleType) -> List[Type[Model]]:
'\n 在迁移脚本中查找所有模型。\n\n :param module:\n :return:\n '
models: List[Type[Model]] = []
tables = extract_modified_tables(module)
queue = list(module.__dict__.values())
while queue:
obj = queue.pop()
if hasattr(obj, '__tablename__'):
tables.add(obj.__tablename__)
elif isinstance(obj, list):
queue.extend(obj)
elif isinstance(obj, dict):
queue.extend(obj.values())
for obj in Model._decl_class_registry.values():
if (hasattr(obj, '__table__') and (obj.__table__.fullname in tables)):
models.append(obj)
sorter = TopologicalSorter()
for model in models:
inspector = inspect(model)
dependent_tables: List[str] = []
for column in inspector.columns.values():
for foreign_key in column.foreign_keys:
dependent_tables.append(foreign_key.target_fullname.split('.')[0])
sorter.add(model.__tablename__, *dependent_tables)
order = list(sorter.static_order())
models.sort(key=(lambda model: order.index(model.__tablename__)))
return models |
def testCollection(self):
'Test Collection'
pass | -6,335,657,216,248,031,000 | Test Collection | test/test_collection.py | testCollection | FlatIO/api-client-python | python | def testCollection(self):
pass |
def main():
'Run administrative tasks.'
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'projeto_curso_2.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError("Couldn't import Django. Are you sure it's installed and available on your PYTHONPATH environment variable? Did you forget to activate a virtual environment?") from exc
execute_from_command_line(sys.argv) | 6,405,390,842,609,496,000 | Run administrative tasks. | Python/Django/projeto_curso_2/manage.py | main | Jhonattan-rocha/Meus-Projetos | python | def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'projeto_curso_2.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError("Couldn't import Django. Are you sure it's installed and available on your PYTHONPATH environment variable? Did you forget to activate a virtual environment?") from exc
execute_from_command_line(sys.argv) |
def __init__(__self__, *, event_time_feature_name: pulumi.Input[str], feature_definitions: pulumi.Input[Sequence[pulumi.Input['FeatureGroupFeatureDefinitionArgs']]], record_identifier_feature_name: pulumi.Input[str], description: Optional[pulumi.Input[str]]=None, feature_group_name: Optional[pulumi.Input[str]]=None, offline_store_config: Optional[pulumi.Input['OfflineStoreConfigPropertiesArgs']]=None, online_store_config: Optional[pulumi.Input['OnlineStoreConfigPropertiesArgs']]=None, role_arn: Optional[pulumi.Input[str]]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['FeatureGroupTagArgs']]]]=None):
"\n The set of arguments for constructing a FeatureGroup resource.\n :param pulumi.Input[str] event_time_feature_name: The Event Time Feature Name.\n :param pulumi.Input[Sequence[pulumi.Input['FeatureGroupFeatureDefinitionArgs']]] feature_definitions: An Array of Feature Definition\n :param pulumi.Input[str] record_identifier_feature_name: The Record Identifier Feature Name.\n :param pulumi.Input[str] description: Description about the FeatureGroup.\n :param pulumi.Input[str] feature_group_name: The Name of the FeatureGroup.\n :param pulumi.Input[str] role_arn: Role Arn\n :param pulumi.Input[Sequence[pulumi.Input['FeatureGroupTagArgs']]] tags: An array of key-value pair to apply to this resource.\n "
pulumi.set(__self__, 'event_time_feature_name', event_time_feature_name)
pulumi.set(__self__, 'feature_definitions', feature_definitions)
pulumi.set(__self__, 'record_identifier_feature_name', record_identifier_feature_name)
if (description is not None):
pulumi.set(__self__, 'description', description)
if (feature_group_name is not None):
pulumi.set(__self__, 'feature_group_name', feature_group_name)
if (offline_store_config is not None):
pulumi.set(__self__, 'offline_store_config', offline_store_config)
if (online_store_config is not None):
pulumi.set(__self__, 'online_store_config', online_store_config)
if (role_arn is not None):
pulumi.set(__self__, 'role_arn', role_arn)
if (tags is not None):
pulumi.set(__self__, 'tags', tags) | 4,726,648,118,209,927,000 | The set of arguments for constructing a FeatureGroup resource.
:param pulumi.Input[str] event_time_feature_name: The Event Time Feature Name.
:param pulumi.Input[Sequence[pulumi.Input['FeatureGroupFeatureDefinitionArgs']]] feature_definitions: An Array of Feature Definition
:param pulumi.Input[str] record_identifier_feature_name: The Record Identifier Feature Name.
:param pulumi.Input[str] description: Description about the FeatureGroup.
:param pulumi.Input[str] feature_group_name: The Name of the FeatureGroup.
:param pulumi.Input[str] role_arn: Role Arn
:param pulumi.Input[Sequence[pulumi.Input['FeatureGroupTagArgs']]] tags: An array of key-value pair to apply to this resource. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | __init__ | AaronFriel/pulumi-aws-native | python | def __init__(__self__, *, event_time_feature_name: pulumi.Input[str], feature_definitions: pulumi.Input[Sequence[pulumi.Input['FeatureGroupFeatureDefinitionArgs']]], record_identifier_feature_name: pulumi.Input[str], description: Optional[pulumi.Input[str]]=None, feature_group_name: Optional[pulumi.Input[str]]=None, offline_store_config: Optional[pulumi.Input['OfflineStoreConfigPropertiesArgs']]=None, online_store_config: Optional[pulumi.Input['OnlineStoreConfigPropertiesArgs']]=None, role_arn: Optional[pulumi.Input[str]]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['FeatureGroupTagArgs']]]]=None):
"\n The set of arguments for constructing a FeatureGroup resource.\n :param pulumi.Input[str] event_time_feature_name: The Event Time Feature Name.\n :param pulumi.Input[Sequence[pulumi.Input['FeatureGroupFeatureDefinitionArgs']]] feature_definitions: An Array of Feature Definition\n :param pulumi.Input[str] record_identifier_feature_name: The Record Identifier Feature Name.\n :param pulumi.Input[str] description: Description about the FeatureGroup.\n :param pulumi.Input[str] feature_group_name: The Name of the FeatureGroup.\n :param pulumi.Input[str] role_arn: Role Arn\n :param pulumi.Input[Sequence[pulumi.Input['FeatureGroupTagArgs']]] tags: An array of key-value pair to apply to this resource.\n "
pulumi.set(__self__, 'event_time_feature_name', event_time_feature_name)
pulumi.set(__self__, 'feature_definitions', feature_definitions)
pulumi.set(__self__, 'record_identifier_feature_name', record_identifier_feature_name)
if (description is not None):
pulumi.set(__self__, 'description', description)
if (feature_group_name is not None):
pulumi.set(__self__, 'feature_group_name', feature_group_name)
if (offline_store_config is not None):
pulumi.set(__self__, 'offline_store_config', offline_store_config)
if (online_store_config is not None):
pulumi.set(__self__, 'online_store_config', online_store_config)
if (role_arn is not None):
pulumi.set(__self__, 'role_arn', role_arn)
if (tags is not None):
pulumi.set(__self__, 'tags', tags) |
@property
@pulumi.getter(name='eventTimeFeatureName')
def event_time_feature_name(self) -> pulumi.Input[str]:
'\n The Event Time Feature Name.\n '
return pulumi.get(self, 'event_time_feature_name') | -6,416,241,475,527,700,000 | The Event Time Feature Name. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | event_time_feature_name | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='eventTimeFeatureName')
def event_time_feature_name(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'event_time_feature_name') |
@property
@pulumi.getter(name='featureDefinitions')
def feature_definitions(self) -> pulumi.Input[Sequence[pulumi.Input['FeatureGroupFeatureDefinitionArgs']]]:
'\n An Array of Feature Definition\n '
return pulumi.get(self, 'feature_definitions') | -5,340,760,594,595,287,000 | An Array of Feature Definition | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | feature_definitions | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='featureDefinitions')
def feature_definitions(self) -> pulumi.Input[Sequence[pulumi.Input['FeatureGroupFeatureDefinitionArgs']]]:
'\n \n '
return pulumi.get(self, 'feature_definitions') |
@property
@pulumi.getter(name='recordIdentifierFeatureName')
def record_identifier_feature_name(self) -> pulumi.Input[str]:
'\n The Record Identifier Feature Name.\n '
return pulumi.get(self, 'record_identifier_feature_name') | 6,774,048,955,246,120,000 | The Record Identifier Feature Name. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | record_identifier_feature_name | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='recordIdentifierFeatureName')
def record_identifier_feature_name(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'record_identifier_feature_name') |
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
'\n Description about the FeatureGroup.\n '
return pulumi.get(self, 'description') | -3,992,035,966,188,649,500 | Description about the FeatureGroup. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | description | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'description') |
@property
@pulumi.getter(name='featureGroupName')
def feature_group_name(self) -> Optional[pulumi.Input[str]]:
'\n The Name of the FeatureGroup.\n '
return pulumi.get(self, 'feature_group_name') | 2,869,057,682,488,510,500 | The Name of the FeatureGroup. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | feature_group_name | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='featureGroupName')
def feature_group_name(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'feature_group_name') |
@property
@pulumi.getter(name='roleArn')
def role_arn(self) -> Optional[pulumi.Input[str]]:
'\n Role Arn\n '
return pulumi.get(self, 'role_arn') | 1,805,397,546,919,502,800 | Role Arn | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | role_arn | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='roleArn')
def role_arn(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'role_arn') |
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['FeatureGroupTagArgs']]]]:
'\n An array of key-value pair to apply to this resource.\n '
return pulumi.get(self, 'tags') | -8,411,379,860,920,348,000 | An array of key-value pair to apply to this resource. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | tags | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['FeatureGroupTagArgs']]]]:
'\n \n '
return pulumi.get(self, 'tags') |
@overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, description: Optional[pulumi.Input[str]]=None, event_time_feature_name: Optional[pulumi.Input[str]]=None, feature_definitions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupFeatureDefinitionArgs']]]]]=None, feature_group_name: Optional[pulumi.Input[str]]=None, offline_store_config: Optional[pulumi.Input[pulumi.InputType['OfflineStoreConfigPropertiesArgs']]]=None, online_store_config: Optional[pulumi.Input[pulumi.InputType['OnlineStoreConfigPropertiesArgs']]]=None, record_identifier_feature_name: Optional[pulumi.Input[str]]=None, role_arn: Optional[pulumi.Input[str]]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupTagArgs']]]]]=None, __props__=None):
"\n Resource Type definition for AWS::SageMaker::FeatureGroup\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] description: Description about the FeatureGroup.\n :param pulumi.Input[str] event_time_feature_name: The Event Time Feature Name.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupFeatureDefinitionArgs']]]] feature_definitions: An Array of Feature Definition\n :param pulumi.Input[str] feature_group_name: The Name of the FeatureGroup.\n :param pulumi.Input[str] record_identifier_feature_name: The Record Identifier Feature Name.\n :param pulumi.Input[str] role_arn: Role Arn\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupTagArgs']]]] tags: An array of key-value pair to apply to this resource.\n "
... | 7,006,342,061,316,322,000 | Resource Type definition for AWS::SageMaker::FeatureGroup
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] description: Description about the FeatureGroup.
:param pulumi.Input[str] event_time_feature_name: The Event Time Feature Name.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupFeatureDefinitionArgs']]]] feature_definitions: An Array of Feature Definition
:param pulumi.Input[str] feature_group_name: The Name of the FeatureGroup.
:param pulumi.Input[str] record_identifier_feature_name: The Record Identifier Feature Name.
:param pulumi.Input[str] role_arn: Role Arn
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupTagArgs']]]] tags: An array of key-value pair to apply to this resource. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | __init__ | AaronFriel/pulumi-aws-native | python | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, description: Optional[pulumi.Input[str]]=None, event_time_feature_name: Optional[pulumi.Input[str]]=None, feature_definitions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupFeatureDefinitionArgs']]]]]=None, feature_group_name: Optional[pulumi.Input[str]]=None, offline_store_config: Optional[pulumi.Input[pulumi.InputType['OfflineStoreConfigPropertiesArgs']]]=None, online_store_config: Optional[pulumi.Input[pulumi.InputType['OnlineStoreConfigPropertiesArgs']]]=None, record_identifier_feature_name: Optional[pulumi.Input[str]]=None, role_arn: Optional[pulumi.Input[str]]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupTagArgs']]]]]=None, __props__=None):
"\n Resource Type definition for AWS::SageMaker::FeatureGroup\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] description: Description about the FeatureGroup.\n :param pulumi.Input[str] event_time_feature_name: The Event Time Feature Name.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupFeatureDefinitionArgs']]]] feature_definitions: An Array of Feature Definition\n :param pulumi.Input[str] feature_group_name: The Name of the FeatureGroup.\n :param pulumi.Input[str] record_identifier_feature_name: The Record Identifier Feature Name.\n :param pulumi.Input[str] role_arn: Role Arn\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FeatureGroupTagArgs']]]] tags: An array of key-value pair to apply to this resource.\n "
... |
@overload
def __init__(__self__, resource_name: str, args: FeatureGroupArgs, opts: Optional[pulumi.ResourceOptions]=None):
"\n Resource Type definition for AWS::SageMaker::FeatureGroup\n\n :param str resource_name: The name of the resource.\n :param FeatureGroupArgs args: The arguments to use to populate this resource's properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
... | 4,174,137,226,898,446,000 | Resource Type definition for AWS::SageMaker::FeatureGroup
:param str resource_name: The name of the resource.
:param FeatureGroupArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | __init__ | AaronFriel/pulumi-aws-native | python | @overload
def __init__(__self__, resource_name: str, args: FeatureGroupArgs, opts: Optional[pulumi.ResourceOptions]=None):
"\n Resource Type definition for AWS::SageMaker::FeatureGroup\n\n :param str resource_name: The name of the resource.\n :param FeatureGroupArgs args: The arguments to use to populate this resource's properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
... |
@staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'FeatureGroup':
"\n Get an existing FeatureGroup resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = FeatureGroupArgs.__new__(FeatureGroupArgs)
__props__.__dict__['description'] = None
__props__.__dict__['event_time_feature_name'] = None
__props__.__dict__['feature_definitions'] = None
__props__.__dict__['feature_group_name'] = None
__props__.__dict__['offline_store_config'] = None
__props__.__dict__['online_store_config'] = None
__props__.__dict__['record_identifier_feature_name'] = None
__props__.__dict__['role_arn'] = None
__props__.__dict__['tags'] = None
return FeatureGroup(resource_name, opts=opts, __props__=__props__) | 1,944,888,711,795,488,300 | Get an existing FeatureGroup resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | get | AaronFriel/pulumi-aws-native | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'FeatureGroup':
"\n Get an existing FeatureGroup resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = FeatureGroupArgs.__new__(FeatureGroupArgs)
__props__.__dict__['description'] = None
__props__.__dict__['event_time_feature_name'] = None
__props__.__dict__['feature_definitions'] = None
__props__.__dict__['feature_group_name'] = None
__props__.__dict__['offline_store_config'] = None
__props__.__dict__['online_store_config'] = None
__props__.__dict__['record_identifier_feature_name'] = None
__props__.__dict__['role_arn'] = None
__props__.__dict__['tags'] = None
return FeatureGroup(resource_name, opts=opts, __props__=__props__) |
@property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
'\n Description about the FeatureGroup.\n '
return pulumi.get(self, 'description') | 7,839,220,219,234,934,000 | Description about the FeatureGroup. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | description | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'description') |
@property
@pulumi.getter(name='eventTimeFeatureName')
def event_time_feature_name(self) -> pulumi.Output[str]:
'\n The Event Time Feature Name.\n '
return pulumi.get(self, 'event_time_feature_name') | 2,729,640,645,715,223,000 | The Event Time Feature Name. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | event_time_feature_name | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='eventTimeFeatureName')
def event_time_feature_name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'event_time_feature_name') |
@property
@pulumi.getter(name='featureDefinitions')
def feature_definitions(self) -> pulumi.Output[Sequence['outputs.FeatureGroupFeatureDefinition']]:
'\n An Array of Feature Definition\n '
return pulumi.get(self, 'feature_definitions') | -5,263,120,084,766,599,000 | An Array of Feature Definition | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | feature_definitions | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='featureDefinitions')
def feature_definitions(self) -> pulumi.Output[Sequence['outputs.FeatureGroupFeatureDefinition']]:
'\n \n '
return pulumi.get(self, 'feature_definitions') |
@property
@pulumi.getter(name='featureGroupName')
def feature_group_name(self) -> pulumi.Output[str]:
'\n The Name of the FeatureGroup.\n '
return pulumi.get(self, 'feature_group_name') | 76,128,626,260,792,700 | The Name of the FeatureGroup. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | feature_group_name | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='featureGroupName')
def feature_group_name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'feature_group_name') |
@property
@pulumi.getter(name='recordIdentifierFeatureName')
def record_identifier_feature_name(self) -> pulumi.Output[str]:
'\n The Record Identifier Feature Name.\n '
return pulumi.get(self, 'record_identifier_feature_name') | -413,171,169,813,399,740 | The Record Identifier Feature Name. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | record_identifier_feature_name | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='recordIdentifierFeatureName')
def record_identifier_feature_name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'record_identifier_feature_name') |
@property
@pulumi.getter(name='roleArn')
def role_arn(self) -> pulumi.Output[Optional[str]]:
'\n Role Arn\n '
return pulumi.get(self, 'role_arn') | -1,009,756,846,288,155,300 | Role Arn | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | role_arn | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter(name='roleArn')
def role_arn(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'role_arn') |
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Sequence['outputs.FeatureGroupTag']]]:
'\n An array of key-value pair to apply to this resource.\n '
return pulumi.get(self, 'tags') | -6,255,290,692,497,689,000 | An array of key-value pair to apply to this resource. | sdk/python/pulumi_aws_native/sagemaker/feature_group.py | tags | AaronFriel/pulumi-aws-native | python | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Sequence['outputs.FeatureGroupTag']]]:
'\n \n '
return pulumi.get(self, 'tags') |
def deleteByUrl(self, url):
'\n called by the user handler when a user file is deleted\n @param url:\n @return:\n '
if (url is None):
return
for addon in self.addonList:
if ((addon.get('canDelete') == True) and (addon.get('url') == url)):
self.handleDelete(addon.get('name')) | 7,618,676,562,966,029,000 | called by the user handler when a user file is deleted
@param url:
@return: | server/handler/avnuserapps.py | deleteByUrl | Littlechay/avnav | python | def deleteByUrl(self, url):
'\n called by the user handler when a user file is deleted\n @param url:\n @return:\n '
if (url is None):
return
for addon in self.addonList:
if ((addon.get('canDelete') == True) and (addon.get('url') == url)):
self.handleDelete(addon.get('name')) |
@home.route('/edit/<page>')
def edit(page: str=None):
'\n The edit page allows for direct editing of a top-level element such as\n title, abstract, creators, etc. This function simply redirects to the\n specified page, passing the packageid as the only parameter.\n '
if (current_user.is_authenticated and page):
current_filename = user_data.get_active_document()
if current_filename:
eml_node = load_eml(filename=current_filename)
new_page = (page if eml_node else PAGE_FILE_ERROR)
return redirect(url_for(new_page, filename=current_filename))
return render_template('index.html') | -8,779,591,209,362,613,000 | The edit page allows for direct editing of a top-level element such as
title, abstract, creators, etc. This function simply redirects to the
specified page, passing the packageid as the only parameter. | webapp/home/views.py | edit | mother-db/ezEMLmotherDB | python | @home.route('/edit/<page>')
def edit(page: str=None):
'\n The edit page allows for direct editing of a top-level element such as\n title, abstract, creators, etc. This function simply redirects to the\n specified page, passing the packageid as the only parameter.\n '
if (current_user.is_authenticated and page):
current_filename = user_data.get_active_document()
if current_filename:
eml_node = load_eml(filename=current_filename)
new_page = (page if eml_node else PAGE_FILE_ERROR)
return redirect(url_for(new_page, filename=current_filename))
return render_template('index.html') |
def _parse_dsn(self, dsn):
'Method parses dsn\n\n Args: \n dsn (str): dsn\n\n Returns:\n bool: True\n\n Raises:\n exception: Exception\n\n '
dsn_opt = dsn.split(':')[1]
dsn_opt_tokens = dsn_opt.split(';')
for dsn_opt_token in dsn_opt_tokens:
opt = dsn_opt_token.split('=')
if (opt[0] == 'host'):
self._host = opt[1]
if (opt[0] == 'port'):
self._port = int(opt[1])
if (opt[0] == 'database'):
self._dbname = opt[1]
if (opt[0] == 'user'):
self._username = opt[1]
if (opt[0] == 'password'):
self._password = opt[1]
return True | 5,464,980,362,926,090,000 | Method parses dsn
Args:
dsn (str): dsn
Returns:
bool: True
Raises:
exception: Exception | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | _parse_dsn | hydratk/hydratk-lib-network | python | def _parse_dsn(self, dsn):
'Method parses dsn\n\n Args: \n dsn (str): dsn\n\n Returns:\n bool: True\n\n Raises:\n exception: Exception\n\n '
dsn_opt = dsn.split(':')[1]
dsn_opt_tokens = dsn_opt.split(';')
for dsn_opt_token in dsn_opt_tokens:
opt = dsn_opt_token.split('=')
if (opt[0] == 'host'):
self._host = opt[1]
if (opt[0] == 'port'):
self._port = int(opt[1])
if (opt[0] == 'database'):
self._dbname = opt[1]
if (opt[0] == 'user'):
self._username = opt[1]
if (opt[0] == 'password'):
self._password = opt[1]
return True |
def _apply_driver_options(self, driver_options):
'Method sets driver options\n\n Args: \n driver_option (dict): driver options\n\n Returns:\n void\n\n '
for (optname, optval) in driver_options.items():
if (optname in self._driver_options):
self._driver_options[optname] = optval | 5,377,836,542,734,329,000 | Method sets driver options
Args:
driver_option (dict): driver options
Returns:
void | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | _apply_driver_options | hydratk/hydratk-lib-network | python | def _apply_driver_options(self, driver_options):
'Method sets driver options\n\n Args: \n driver_option (dict): driver options\n\n Returns:\n void\n\n '
for (optname, optval) in driver_options.items():
if (optname in self._driver_options):
self._driver_options[optname] = optval |
def connect(self):
'Method connects to database\n\n Args: \n none\n\n Returns:\n void\n\n '
self._dbcon = pymssql.connect(server=self._host, port=self._port, database=self._dbname, user=self._username, password=self._password)
self.result_as_dict(self._result_as_dict) | 4,906,923,915,497,192,000 | Method connects to database
Args:
none
Returns:
void | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | connect | hydratk/hydratk-lib-network | python | def connect(self):
'Method connects to database\n\n Args: \n none\n\n Returns:\n void\n\n '
self._dbcon = pymssql.connect(server=self._host, port=self._port, database=self._dbname, user=self._username, password=self._password)
self.result_as_dict(self._result_as_dict) |
def close(self):
'Method disconnects from database\n\n Args: \n none \n\n Returns:\n void\n\n Raises:\n exception: DBODriverException\n\n '
if (type(self._dbcon).__name__.lower() == 'connection'):
self._dbcon.close()
else:
raise dbodriver.DBODriverException('Not connected') | 3,477,112,732,506,575,000 | Method disconnects from database
Args:
none
Returns:
void
Raises:
exception: DBODriverException | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | close | hydratk/hydratk-lib-network | python | def close(self):
'Method disconnects from database\n\n Args: \n none \n\n Returns:\n void\n\n Raises:\n exception: DBODriverException\n\n '
if (type(self._dbcon).__name__.lower() == 'connection'):
self._dbcon.close()
else:
raise dbodriver.DBODriverException('Not connected') |
def commit(self):
'Method commits transaction\n\n Args:\n none \n\n Returns:\n void\n\n Raises:\n exception: DBODriverException\n\n '
if (type(self._dbcon).__name__.lower() == 'connection'):
self._dbcon.commit()
else:
raise dbodriver.DBODriverException('Not connected') | -362,832,216,166,127,700 | Method commits transaction
Args:
none
Returns:
void
Raises:
exception: DBODriverException | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | commit | hydratk/hydratk-lib-network | python | def commit(self):
'Method commits transaction\n\n Args:\n none \n\n Returns:\n void\n\n Raises:\n exception: DBODriverException\n\n '
if (type(self._dbcon).__name__.lower() == 'connection'):
self._dbcon.commit()
else:
raise dbodriver.DBODriverException('Not connected') |
def execute(self, sql, *parameters):
'Method executes query\n\n Args: \n sql (str): SQL query\n parameters (args): query parameters\n\n Returns:\n obj: cursor\n\n '
self._cursor.execute(sql, tuple(parameters))
return self._cursor | -4,254,376,136,096,039,000 | Method executes query
Args:
sql (str): SQL query
parameters (args): query parameters
Returns:
obj: cursor | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | execute | hydratk/hydratk-lib-network | python | def execute(self, sql, *parameters):
'Method executes query\n\n Args: \n sql (str): SQL query\n parameters (args): query parameters\n\n Returns:\n obj: cursor\n\n '
self._cursor.execute(sql, tuple(parameters))
return self._cursor |
def rollback(self):
'Method rollbacks transaction\n\n Args: \n none \n\n Returns:\n void\n\n Raises:\n exception: DBODriverException\n\n '
if (type(self._dbcon).__name__.lower() == 'connection'):
self._dbcon.rollback()
else:
raise dbodriver.DBODriverException('Not connected') | -8,367,256,448,439,113,000 | Method rollbacks transaction
Args:
none
Returns:
void
Raises:
exception: DBODriverException | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | rollback | hydratk/hydratk-lib-network | python | def rollback(self):
'Method rollbacks transaction\n\n Args: \n none \n\n Returns:\n void\n\n Raises:\n exception: DBODriverException\n\n '
if (type(self._dbcon).__name__.lower() == 'connection'):
self._dbcon.rollback()
else:
raise dbodriver.DBODriverException('Not connected') |
def __getitem__(self, name):
'Method gets item\n\n Args: \n name (str): item name\n\n Returns:\n obj: item value\n\n '
if hasattr(pymssql, name):
return getattr(pymssql, name) | -8,807,495,268,656,493,000 | Method gets item
Args:
name (str): item name
Returns:
obj: item value | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | __getitem__ | hydratk/hydratk-lib-network | python | def __getitem__(self, name):
'Method gets item\n\n Args: \n name (str): item name\n\n Returns:\n obj: item value\n\n '
if hasattr(pymssql, name):
return getattr(pymssql, name) |
def __getattr__(self, name):
'Method gets attribute\n\n Args: \n name (str): attribute name\n\n Returns:\n obj: attribute value\n\n '
if (type(self._dbcon).__name__.lower() == 'connection'):
if hasattr(self._dbcon, name):
return getattr(self._dbcon, name)
if hasattr(pymssql, name):
return getattr(pymssql, name) | 6,261,033,816,165,780,000 | Method gets attribute
Args:
name (str): attribute name
Returns:
obj: attribute value | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | __getattr__ | hydratk/hydratk-lib-network | python | def __getattr__(self, name):
'Method gets attribute\n\n Args: \n name (str): attribute name\n\n Returns:\n obj: attribute value\n\n '
if (type(self._dbcon).__name__.lower() == 'connection'):
if hasattr(self._dbcon, name):
return getattr(self._dbcon, name)
if hasattr(pymssql, name):
return getattr(pymssql, name) |
def table_exists(self, table_name):
'Method checks if table exists\n\n Args: \n table_name (str): table\n\n Returns:\n bool: result\n\n '
if ((table_name is not None) and (table_name != '')):
query = "SELECT count(*) found FROM information_schema.tables WHERE table_catalog=%s AND table_type='BASE TABLE' and table_name=%s"
self._cursor.execute(query, (self._dbname, table_name))
recs = self._cursor.fetchall()
result = (True if (recs[0]['found'] == 1) else False)
return result | 5,841,529,789,103,357,000 | Method checks if table exists
Args:
table_name (str): table
Returns:
bool: result | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | table_exists | hydratk/hydratk-lib-network | python | def table_exists(self, table_name):
'Method checks if table exists\n\n Args: \n table_name (str): table\n\n Returns:\n bool: result\n\n '
if ((table_name is not None) and (table_name != )):
query = "SELECT count(*) found FROM information_schema.tables WHERE table_catalog=%s AND table_type='BASE TABLE' and table_name=%s"
self._cursor.execute(query, (self._dbname, table_name))
recs = self._cursor.fetchall()
result = (True if (recs[0]['found'] == 1) else False)
return result |
def result_as_dict(self, state):
'Method enables query result in dictionary form\n\n Args: \n state (bool): enable dictionary\n\n Returns:\n void\n\n Raises:\n error: TypeError\n\n '
if (state in (True, False)):
self._result_as_dict = state
if (state == True):
self._cursor = self._dbcon.cursor(as_dict=True)
else:
self._cursor = self._dbcon.cursor()
else:
raise TypeError('Boolean value expected') | -2,179,482,725,482,715,100 | Method enables query result in dictionary form
Args:
state (bool): enable dictionary
Returns:
void
Raises:
error: TypeError | src/hydratk/lib/database/dbo/drivers/mssql/driver.py | result_as_dict | hydratk/hydratk-lib-network | python | def result_as_dict(self, state):
'Method enables query result in dictionary form\n\n Args: \n state (bool): enable dictionary\n\n Returns:\n void\n\n Raises:\n error: TypeError\n\n '
if (state in (True, False)):
self._result_as_dict = state
if (state == True):
self._cursor = self._dbcon.cursor(as_dict=True)
else:
self._cursor = self._dbcon.cursor()
else:
raise TypeError('Boolean value expected') |
def decode_annotations(annotaitons_str):
'decode annotations in string to list of dict'
return literal_eval(annotaitons_str) | 1,117,040,142,727,590,400 | decode annotations in string to list of dict | src/util.py | decode_annotations | VincentWang25/Kaggle_TGBR | python | def decode_annotations(annotaitons_str):
return literal_eval(annotaitons_str) |
def calc_is_correct(gt_bboxes, pred_bboxes, iou_th=0.5):
'\n gt_bboxes: (N, 4) np.array in xywh format\n pred_bboxes: (N, 5) np.array in conf+xywh format\n '
if ((len(gt_bboxes) == 0) and (len(pred_bboxes) == 0)):
(tps, fps, fns) = (0, 0, 0)
return (tps, fps, fns)
elif (len(gt_bboxes) == 0):
(tps, fps, fns) = (0, len(pred_bboxes), 0)
return (tps, fps, fns)
elif (len(pred_bboxes) == 0):
(tps, fps, fns) = (0, 0, len(gt_bboxes))
return (tps, fps, fns)
pred_bboxes = pred_bboxes[pred_bboxes[:, 0].argsort()[::(- 1)]]
(tps, fps, fns) = (0, 0, 0)
(tp, fp, fn) = calc_is_correct_at_iou_th(gt_bboxes, pred_bboxes, iou_th)
tps += tp
fps += fp
fns += fn
return (tps, fps, fns) | -4,513,493,385,936,297,000 | gt_bboxes: (N, 4) np.array in xywh format
pred_bboxes: (N, 5) np.array in conf+xywh format | src/util.py | calc_is_correct | VincentWang25/Kaggle_TGBR | python | def calc_is_correct(gt_bboxes, pred_bboxes, iou_th=0.5):
'\n gt_bboxes: (N, 4) np.array in xywh format\n pred_bboxes: (N, 5) np.array in conf+xywh format\n '
if ((len(gt_bboxes) == 0) and (len(pred_bboxes) == 0)):
(tps, fps, fns) = (0, 0, 0)
return (tps, fps, fns)
elif (len(gt_bboxes) == 0):
(tps, fps, fns) = (0, len(pred_bboxes), 0)
return (tps, fps, fns)
elif (len(pred_bboxes) == 0):
(tps, fps, fns) = (0, 0, len(gt_bboxes))
return (tps, fps, fns)
pred_bboxes = pred_bboxes[pred_bboxes[:, 0].argsort()[::(- 1)]]
(tps, fps, fns) = (0, 0, 0)
(tp, fp, fn) = calc_is_correct_at_iou_th(gt_bboxes, pred_bboxes, iou_th)
tps += tp
fps += fp
fns += fn
return (tps, fps, fns) |
def calc_f2_score(gt_bboxes_list, pred_bboxes_list, verbose=False):
'\n gt_bboxes_list: list of (N, 4) np.array in xywh format\n pred_bboxes_list: list of (N, 5) np.array in conf+xywh format\n '
f2_dict = {'f2': 0, 'P': 0, 'R': 0}
all_tps = [list(([0] * 11)) for _ in range(len(gt_bboxes_list))]
all_fps = [list(([0] * 11)) for _ in range(len(gt_bboxes_list))]
all_fns = [list(([0] * 11)) for _ in range(len(gt_bboxes_list))]
for (k, iou_th) in enumerate(np.arange(0.3, 0.85, 0.05)):
(tps, fps, fns) = (0, 0, 0)
for (i, (gt_bboxes, pred_bboxes)) in enumerate(zip(gt_bboxes_list, pred_bboxes_list)):
(tp, fp, fn) = calc_is_correct(gt_bboxes, pred_bboxes, iou_th)
tps += tp
fps += fp
fns += fn
all_tps[i][k] = tp
all_fps[i][k] = fp
all_fns[i][k] = fn
if verbose:
num_gt = len(gt_bboxes)
num_pred = len(pred_bboxes)
print(f'num_gt:{num_gt:<3} num_pred:{num_pred:<3} tp:{tp:<3} fp:{fp:<3} fn:{fn:<3}')
f2 = f_beta(tps, fps, fns, beta=2)
precision = f_beta(tps, fps, fns, beta=0)
recall = f_beta(tps, fps, fns, beta=100)
f2_dict[('f2_' + str(round(iou_th, 3)))] = f2
f2_dict[('P_' + str(round(iou_th, 3)))] = precision
f2_dict[('R_' + str(round(iou_th, 3)))] = recall
f2_dict['f2'] += (f2 / 11)
f2_dict['P'] += (precision / 11)
f2_dict['R'] += (recall / 11)
f2_dict['tps'] = all_tps
f2_dict['fps'] = all_fps
f2_dict['fns'] = all_fns
return f2_dict | 2,122,643,224,007,596,000 | gt_bboxes_list: list of (N, 4) np.array in xywh format
pred_bboxes_list: list of (N, 5) np.array in conf+xywh format | src/util.py | calc_f2_score | VincentWang25/Kaggle_TGBR | python | def calc_f2_score(gt_bboxes_list, pred_bboxes_list, verbose=False):
'\n gt_bboxes_list: list of (N, 4) np.array in xywh format\n pred_bboxes_list: list of (N, 5) np.array in conf+xywh format\n '
f2_dict = {'f2': 0, 'P': 0, 'R': 0}
all_tps = [list(([0] * 11)) for _ in range(len(gt_bboxes_list))]
all_fps = [list(([0] * 11)) for _ in range(len(gt_bboxes_list))]
all_fns = [list(([0] * 11)) for _ in range(len(gt_bboxes_list))]
for (k, iou_th) in enumerate(np.arange(0.3, 0.85, 0.05)):
(tps, fps, fns) = (0, 0, 0)
for (i, (gt_bboxes, pred_bboxes)) in enumerate(zip(gt_bboxes_list, pred_bboxes_list)):
(tp, fp, fn) = calc_is_correct(gt_bboxes, pred_bboxes, iou_th)
tps += tp
fps += fp
fns += fn
all_tps[i][k] = tp
all_fps[i][k] = fp
all_fns[i][k] = fn
if verbose:
num_gt = len(gt_bboxes)
num_pred = len(pred_bboxes)
print(f'num_gt:{num_gt:<3} num_pred:{num_pred:<3} tp:{tp:<3} fp:{fp:<3} fn:{fn:<3}')
f2 = f_beta(tps, fps, fns, beta=2)
precision = f_beta(tps, fps, fns, beta=0)
recall = f_beta(tps, fps, fns, beta=100)
f2_dict[('f2_' + str(round(iou_th, 3)))] = f2
f2_dict[('P_' + str(round(iou_th, 3)))] = precision
f2_dict[('R_' + str(round(iou_th, 3)))] = recall
f2_dict['f2'] += (f2 / 11)
f2_dict['P'] += (precision / 11)
f2_dict['R'] += (recall / 11)
f2_dict['tps'] = all_tps
f2_dict['fps'] = all_fps
f2_dict['fns'] = all_fns
return f2_dict |
def voc2yolo(image_height, image_width, bboxes):
'\n voc => [x1, y1, x2, y1]\n yolo => [xmid, ymid, w, h] (normalized)\n '
bboxes = bboxes.copy().astype(float)
bboxes[(..., [0, 2])] = (bboxes[(..., [0, 2])] / image_width)
bboxes[(..., [1, 3])] = (bboxes[(..., [1, 3])] / image_height)
w = (bboxes[(..., 2)] - bboxes[(..., 0)])
h = (bboxes[(..., 3)] - bboxes[(..., 1)])
bboxes[(..., 0)] = (bboxes[(..., 0)] + (w / 2))
bboxes[(..., 1)] = (bboxes[(..., 1)] + (h / 2))
bboxes[(..., 2)] = w
bboxes[(..., 3)] = h
return bboxes | 2,176,480,684,608,862,700 | voc => [x1, y1, x2, y1]
yolo => [xmid, ymid, w, h] (normalized) | src/util.py | voc2yolo | VincentWang25/Kaggle_TGBR | python | def voc2yolo(image_height, image_width, bboxes):
'\n voc => [x1, y1, x2, y1]\n yolo => [xmid, ymid, w, h] (normalized)\n '
bboxes = bboxes.copy().astype(float)
bboxes[(..., [0, 2])] = (bboxes[(..., [0, 2])] / image_width)
bboxes[(..., [1, 3])] = (bboxes[(..., [1, 3])] / image_height)
w = (bboxes[(..., 2)] - bboxes[(..., 0)])
h = (bboxes[(..., 3)] - bboxes[(..., 1)])
bboxes[(..., 0)] = (bboxes[(..., 0)] + (w / 2))
bboxes[(..., 1)] = (bboxes[(..., 1)] + (h / 2))
bboxes[(..., 2)] = w
bboxes[(..., 3)] = h
return bboxes |
def yolo2voc(image_height, image_width, bboxes):
'\n yolo => [xmid, ymid, w, h] (normalized)\n voc => [x1, y1, x2, y1]\n \n '
bboxes = bboxes.copy().astype(float)
bboxes[(..., [0, 2])] = (bboxes[(..., [0, 2])] * image_width)
bboxes[(..., [1, 3])] = (bboxes[(..., [1, 3])] * image_height)
bboxes[(..., [0, 1])] = (bboxes[(..., [0, 1])] - (bboxes[(..., [2, 3])] / 2))
bboxes[(..., [2, 3])] = (bboxes[(..., [0, 1])] + bboxes[(..., [2, 3])])
return bboxes | -8,946,346,664,116,537,000 | yolo => [xmid, ymid, w, h] (normalized)
voc => [x1, y1, x2, y1] | src/util.py | yolo2voc | VincentWang25/Kaggle_TGBR | python | def yolo2voc(image_height, image_width, bboxes):
'\n yolo => [xmid, ymid, w, h] (normalized)\n voc => [x1, y1, x2, y1]\n \n '
bboxes = bboxes.copy().astype(float)
bboxes[(..., [0, 2])] = (bboxes[(..., [0, 2])] * image_width)
bboxes[(..., [1, 3])] = (bboxes[(..., [1, 3])] * image_height)
bboxes[(..., [0, 1])] = (bboxes[(..., [0, 1])] - (bboxes[(..., [2, 3])] / 2))
bboxes[(..., [2, 3])] = (bboxes[(..., [0, 1])] + bboxes[(..., [2, 3])])
return bboxes |
def coco2yolo(image_height, image_width, bboxes):
'\n coco => [xmin, ymin, w, h]\n yolo => [xmid, ymid, w, h] (normalized)\n '
bboxes = bboxes.copy().astype(float)
bboxes[(..., [0, 2])] = (bboxes[(..., [0, 2])] / image_width)
bboxes[(..., [1, 3])] = (bboxes[(..., [1, 3])] / image_height)
bboxes[(..., [0, 1])] = (bboxes[(..., [0, 1])] + (bboxes[(..., [2, 3])] / 2))
return bboxes | -7,745,974,295,583,745,000 | coco => [xmin, ymin, w, h]
yolo => [xmid, ymid, w, h] (normalized) | src/util.py | coco2yolo | VincentWang25/Kaggle_TGBR | python | def coco2yolo(image_height, image_width, bboxes):
'\n coco => [xmin, ymin, w, h]\n yolo => [xmid, ymid, w, h] (normalized)\n '
bboxes = bboxes.copy().astype(float)
bboxes[(..., [0, 2])] = (bboxes[(..., [0, 2])] / image_width)
bboxes[(..., [1, 3])] = (bboxes[(..., [1, 3])] / image_height)
bboxes[(..., [0, 1])] = (bboxes[(..., [0, 1])] + (bboxes[(..., [2, 3])] / 2))
return bboxes |
def yolo2coco(image_height, image_width, bboxes):
'\n yolo => [xmid, ymid, w, h] (normalized)\n coco => [xmin, ymin, w, h]\n \n '
bboxes = bboxes.copy().astype(float)
bboxes[(..., [0, 2])] = (bboxes[(..., [0, 2])] * image_width)
bboxes[(..., [1, 3])] = (bboxes[(..., [1, 3])] * image_height)
bboxes[(..., [0, 1])] = (bboxes[(..., [0, 1])] - (bboxes[(..., [2, 3])] / 2))
return bboxes | -199,636,694,656,450,980 | yolo => [xmid, ymid, w, h] (normalized)
coco => [xmin, ymin, w, h] | src/util.py | yolo2coco | VincentWang25/Kaggle_TGBR | python | def yolo2coco(image_height, image_width, bboxes):
'\n yolo => [xmid, ymid, w, h] (normalized)\n coco => [xmin, ymin, w, h]\n \n '
bboxes = bboxes.copy().astype(float)
bboxes[(..., [0, 2])] = (bboxes[(..., [0, 2])] * image_width)
bboxes[(..., [1, 3])] = (bboxes[(..., [1, 3])] * image_height)
bboxes[(..., [0, 1])] = (bboxes[(..., [0, 1])] - (bboxes[(..., [2, 3])] / 2))
return bboxes |
def py_cpu_softnms(dets, sc, Nt=0.3, sigma=0.5, thresh=0.001, method=2):
'\n py_cpu_softnms\n :param dets: boexs 坐标矩阵 format [y1, x1, y2, x2]\n :param sc: 每个 boxes 对应的分数\n :param Nt: iou 交叠门限\n :param sigma: 使用 gaussian 函数的方差\n :param thresh: 最后的分数门限\n :param method: 使用的方法\n :return: 留下的 boxes 的 index\n '
N = dets.shape[0]
indexes = np.array([np.arange(N)])
dets = np.concatenate((dets, indexes.T), axis=1)
y1 = dets[:, 0]
x1 = dets[:, 1]
y2 = dets[:, 2]
x2 = dets[:, 3]
scores = sc
areas = (((x2 - x1) + 1) * ((y2 - y1) + 1))
for i in range(N):
tBD = dets[i, :].copy()
tscore = scores[i].copy()
tarea = areas[i].copy()
pos = (i + 1)
if (i != (N - 1)):
maxscore = np.max(scores[pos:], axis=0)
maxpos = np.argmax(scores[pos:], axis=0)
else:
maxscore = scores[(- 1)]
maxpos = 0
if (tscore < maxscore):
dets[i, :] = dets[((maxpos + i) + 1), :]
dets[((maxpos + i) + 1), :] = tBD
tBD = dets[i, :]
scores[i] = scores[((maxpos + i) + 1)]
scores[((maxpos + i) + 1)] = tscore
tscore = scores[i]
areas[i] = areas[((maxpos + i) + 1)]
areas[((maxpos + i) + 1)] = tarea
tarea = areas[i]
xx1 = np.maximum(dets[(i, 1)], dets[pos:, 1])
yy1 = np.maximum(dets[(i, 0)], dets[pos:, 0])
xx2 = np.minimum(dets[(i, 3)], dets[pos:, 3])
yy2 = np.minimum(dets[(i, 2)], dets[pos:, 2])
w = np.maximum(0.0, ((xx2 - xx1) + 1))
h = np.maximum(0.0, ((yy2 - yy1) + 1))
inter = (w * h)
ovr = (inter / ((areas[i] + areas[pos:]) - inter))
if (method == 1):
weight = np.ones(ovr.shape)
weight[(ovr > Nt)] = (weight[(ovr > Nt)] - ovr[(ovr > Nt)])
elif (method == 2):
weight = np.exp(((- (ovr * ovr)) / sigma))
else:
weight = np.ones(ovr.shape)
weight[(ovr > Nt)] = 0
scores[pos:] = (weight * scores[pos:])
inds = dets[:, 4][(scores > thresh)]
keep = inds.astype(int)
return keep | 2,295,457,227,040,895,200 | py_cpu_softnms
:param dets: boexs 坐标矩阵 format [y1, x1, y2, x2]
:param sc: 每个 boxes 对应的分数
:param Nt: iou 交叠门限
:param sigma: 使用 gaussian 函数的方差
:param thresh: 最后的分数门限
:param method: 使用的方法
:return: 留下的 boxes 的 index | src/util.py | py_cpu_softnms | VincentWang25/Kaggle_TGBR | python | def py_cpu_softnms(dets, sc, Nt=0.3, sigma=0.5, thresh=0.001, method=2):
'\n py_cpu_softnms\n :param dets: boexs 坐标矩阵 format [y1, x1, y2, x2]\n :param sc: 每个 boxes 对应的分数\n :param Nt: iou 交叠门限\n :param sigma: 使用 gaussian 函数的方差\n :param thresh: 最后的分数门限\n :param method: 使用的方法\n :return: 留下的 boxes 的 index\n '
N = dets.shape[0]
indexes = np.array([np.arange(N)])
dets = np.concatenate((dets, indexes.T), axis=1)
y1 = dets[:, 0]
x1 = dets[:, 1]
y2 = dets[:, 2]
x2 = dets[:, 3]
scores = sc
areas = (((x2 - x1) + 1) * ((y2 - y1) + 1))
for i in range(N):
tBD = dets[i, :].copy()
tscore = scores[i].copy()
tarea = areas[i].copy()
pos = (i + 1)
if (i != (N - 1)):
maxscore = np.max(scores[pos:], axis=0)
maxpos = np.argmax(scores[pos:], axis=0)
else:
maxscore = scores[(- 1)]
maxpos = 0
if (tscore < maxscore):
dets[i, :] = dets[((maxpos + i) + 1), :]
dets[((maxpos + i) + 1), :] = tBD
tBD = dets[i, :]
scores[i] = scores[((maxpos + i) + 1)]
scores[((maxpos + i) + 1)] = tscore
tscore = scores[i]
areas[i] = areas[((maxpos + i) + 1)]
areas[((maxpos + i) + 1)] = tarea
tarea = areas[i]
xx1 = np.maximum(dets[(i, 1)], dets[pos:, 1])
yy1 = np.maximum(dets[(i, 0)], dets[pos:, 0])
xx2 = np.minimum(dets[(i, 3)], dets[pos:, 3])
yy2 = np.minimum(dets[(i, 2)], dets[pos:, 2])
w = np.maximum(0.0, ((xx2 - xx1) + 1))
h = np.maximum(0.0, ((yy2 - yy1) + 1))
inter = (w * h)
ovr = (inter / ((areas[i] + areas[pos:]) - inter))
if (method == 1):
weight = np.ones(ovr.shape)
weight[(ovr > Nt)] = (weight[(ovr > Nt)] - ovr[(ovr > Nt)])
elif (method == 2):
weight = np.exp(((- (ovr * ovr)) / sigma))
else:
weight = np.ones(ovr.shape)
weight[(ovr > Nt)] = 0
scores[pos:] = (weight * scores[pos:])
inds = dets[:, 4][(scores > thresh)]
keep = inds.astype(int)
return keep |
@pytest.fixture(autouse=True)
def mock_upnp_device():
'Mock homeassistant.components.upnp.Device.'
async def mock_async_create_upnp_device(hass: HomeAssistant, location: str) -> UpnpDevice:
'Create UPnP device.'
return MockUpnpDevice(location)
with patch('homeassistant.components.upnp.device.async_create_upnp_device', side_effect=mock_async_create_upnp_device) as mock_async_create_upnp_device, patch('homeassistant.components.upnp.device.IgdDevice', new=MockIgdDevice) as mock_igd_device:
(yield (mock_async_create_upnp_device, mock_igd_device)) | 1,470,672,891,934,273,500 | Mock homeassistant.components.upnp.Device. | tests/components/upnp/conftest.py | mock_upnp_device | Aeroid/home-assistant-core | python | @pytest.fixture(autouse=True)
def mock_upnp_device():
async def mock_async_create_upnp_device(hass: HomeAssistant, location: str) -> UpnpDevice:
'Create UPnP device.'
return MockUpnpDevice(location)
with patch('homeassistant.components.upnp.device.async_create_upnp_device', side_effect=mock_async_create_upnp_device) as mock_async_create_upnp_device, patch('homeassistant.components.upnp.device.IgdDevice', new=MockIgdDevice) as mock_igd_device:
(yield (mock_async_create_upnp_device, mock_igd_device)) |
@pytest.fixture
def mock_setup_entry():
'Mock async_setup_entry.'
with patch('homeassistant.components.upnp.async_setup_entry', return_value=AsyncMock(True)) as mock_setup:
(yield mock_setup) | 7,327,902,129,160,842,000 | Mock async_setup_entry. | tests/components/upnp/conftest.py | mock_setup_entry | Aeroid/home-assistant-core | python | @pytest.fixture
def mock_setup_entry():
with patch('homeassistant.components.upnp.async_setup_entry', return_value=AsyncMock(True)) as mock_setup:
(yield mock_setup) |
@pytest.fixture(autouse=True)
async def silent_ssdp_scanner(hass):
'Start SSDP component and get Scanner, prevent actual SSDP traffic.'
with patch('homeassistant.components.ssdp.Scanner._async_start_ssdp_listeners'), patch('homeassistant.components.ssdp.Scanner._async_stop_ssdp_listeners'), patch('homeassistant.components.ssdp.Scanner.async_scan'):
(yield) | -6,081,952,182,349,832,000 | Start SSDP component and get Scanner, prevent actual SSDP traffic. | tests/components/upnp/conftest.py | silent_ssdp_scanner | Aeroid/home-assistant-core | python | @pytest.fixture(autouse=True)
async def silent_ssdp_scanner(hass):
with patch('homeassistant.components.ssdp.Scanner._async_start_ssdp_listeners'), patch('homeassistant.components.ssdp.Scanner._async_stop_ssdp_listeners'), patch('homeassistant.components.ssdp.Scanner.async_scan'):
(yield) |
@pytest.fixture
async def ssdp_instant_discovery():
'Instance discovery.'
async def register_callback(hass, callback, match_dict):
'Immediately do callback.'
(await callback(TEST_DISCOVERY, ssdp.SsdpChange.ALIVE))
return MagicMock()
with patch('homeassistant.components.ssdp.async_register_callback', side_effect=register_callback) as mock_register, patch('homeassistant.components.ssdp.async_get_discovery_info_by_st', return_value=[TEST_DISCOVERY]) as mock_get_info:
(yield (mock_register, mock_get_info)) | 8,603,913,981,920,339,000 | Instance discovery. | tests/components/upnp/conftest.py | ssdp_instant_discovery | Aeroid/home-assistant-core | python | @pytest.fixture
async def ssdp_instant_discovery():
async def register_callback(hass, callback, match_dict):
'Immediately do callback.'
(await callback(TEST_DISCOVERY, ssdp.SsdpChange.ALIVE))
return MagicMock()
with patch('homeassistant.components.ssdp.async_register_callback', side_effect=register_callback) as mock_register, patch('homeassistant.components.ssdp.async_get_discovery_info_by_st', return_value=[TEST_DISCOVERY]) as mock_get_info:
(yield (mock_register, mock_get_info)) |
@pytest.fixture
async def ssdp_no_discovery():
'No discovery.'
async def register_callback(hass, callback, match_dict):
"Don't do callback."
return MagicMock()
with patch('homeassistant.components.ssdp.async_register_callback', side_effect=register_callback) as mock_register, patch('homeassistant.components.ssdp.async_get_discovery_info_by_st', return_value=[]) as mock_get_info, patch('homeassistant.components.upnp.config_flow.SSDP_SEARCH_TIMEOUT', 0.1):
(yield (mock_register, mock_get_info)) | -4,688,020,496,969,370,000 | No discovery. | tests/components/upnp/conftest.py | ssdp_no_discovery | Aeroid/home-assistant-core | python | @pytest.fixture
async def ssdp_no_discovery():
async def register_callback(hass, callback, match_dict):
"Don't do callback."
return MagicMock()
with patch('homeassistant.components.ssdp.async_register_callback', side_effect=register_callback) as mock_register, patch('homeassistant.components.ssdp.async_get_discovery_info_by_st', return_value=[]) as mock_get_info, patch('homeassistant.components.upnp.config_flow.SSDP_SEARCH_TIMEOUT', 0.1):
(yield (mock_register, mock_get_info)) |
@pytest.fixture
async def setup_integration(hass: HomeAssistant, mock_get_source_ip, ssdp_instant_discovery, mock_upnp_device):
'Create an initialized integration.'
entry = MockConfigEntry(domain=DOMAIN, data={CONFIG_ENTRY_UDN: TEST_UDN, CONFIG_ENTRY_ST: TEST_ST})
entry.add_to_hass(hass)
(await hass.config_entries.async_setup(entry.entry_id))
(await hass.async_block_till_done())
(yield entry) | 8,657,213,048,427,507,000 | Create an initialized integration. | tests/components/upnp/conftest.py | setup_integration | Aeroid/home-assistant-core | python | @pytest.fixture
async def setup_integration(hass: HomeAssistant, mock_get_source_ip, ssdp_instant_discovery, mock_upnp_device):
entry = MockConfigEntry(domain=DOMAIN, data={CONFIG_ENTRY_UDN: TEST_UDN, CONFIG_ENTRY_ST: TEST_ST})
entry.add_to_hass(hass)
(await hass.config_entries.async_setup(entry.entry_id))
(await hass.async_block_till_done())
(yield entry) |
def __init__(self, location: str) -> None:
'Initialize.'
self.device_url = location | -5,251,167,248,682,339,000 | Initialize. | tests/components/upnp/conftest.py | __init__ | Aeroid/home-assistant-core | python | def __init__(self, location: str) -> None:
self.device_url = location |
@property
def manufacturer(self) -> str:
'Get manufacturer.'
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_MANUFACTURER] | -1,840,653,333,174,673,000 | Get manufacturer. | tests/components/upnp/conftest.py | manufacturer | Aeroid/home-assistant-core | python | @property
def manufacturer(self) -> str:
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_MANUFACTURER] |
@property
def name(self) -> str:
'Get name.'
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_FRIENDLY_NAME] | -168,337,571,497,190,340 | Get name. | tests/components/upnp/conftest.py | name | Aeroid/home-assistant-core | python | @property
def name(self) -> str:
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_FRIENDLY_NAME] |
@property
def model_name(self) -> str:
'Get the model name.'
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_MODEL_NAME] | 7,037,925,087,428,130,000 | Get the model name. | tests/components/upnp/conftest.py | model_name | Aeroid/home-assistant-core | python | @property
def model_name(self) -> str:
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_MODEL_NAME] |
@property
def device_type(self) -> str:
'Get the device type.'
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_DEVICE_TYPE] | 6,534,857,306,003,408,000 | Get the device type. | tests/components/upnp/conftest.py | device_type | Aeroid/home-assistant-core | python | @property
def device_type(self) -> str:
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_DEVICE_TYPE] |
@property
def udn(self) -> str:
'Get the UDN.'
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_UDN] | -4,505,216,607,875,272,700 | Get the UDN. | tests/components/upnp/conftest.py | udn | Aeroid/home-assistant-core | python | @property
def udn(self) -> str:
return TEST_DISCOVERY.upnp[ssdp.ATTR_UPNP_UDN] |
@property
def usn(self) -> str:
'Get the USN.'
return f'{self.udn}::{self.device_type}' | -9,055,645,516,802,463,000 | Get the USN. | tests/components/upnp/conftest.py | usn | Aeroid/home-assistant-core | python | @property
def usn(self) -> str:
return f'{self.udn}::{self.device_type}' |
@property
def unique_id(self) -> str:
'Get the unique id.'
return self.usn | -1,300,460,451,944,561,700 | Get the unique id. | tests/components/upnp/conftest.py | unique_id | Aeroid/home-assistant-core | python | @property
def unique_id(self) -> str:
return self.usn |
def reinit(self, new_upnp_device: UpnpDevice) -> None:
'Reinitialize.'
self.device_url = new_upnp_device.device_url | -3,690,047,482,408,017,000 | Reinitialize. | tests/components/upnp/conftest.py | reinit | Aeroid/home-assistant-core | python | def reinit(self, new_upnp_device: UpnpDevice) -> None:
self.device_url = new_upnp_device.device_url |
def __init__(self, device: MockUpnpDevice, event_handler: UpnpEventHandler) -> None:
'Initialize mock device.'
self.device = device
self.profile_device = device
self._timestamp = dt.utcnow()
self.traffic_times_polled = 0
self.status_times_polled = 0
self.traffic_data = {BYTES_RECEIVED: 0, BYTES_SENT: 0, PACKETS_RECEIVED: 0, PACKETS_SENT: 0}
self.status_data = {WAN_STATUS: 'Connected', ROUTER_UPTIME: 10, ROUTER_IP: '0.0.0.0'} | 1,895,974,175,736,167,700 | Initialize mock device. | tests/components/upnp/conftest.py | __init__ | Aeroid/home-assistant-core | python | def __init__(self, device: MockUpnpDevice, event_handler: UpnpEventHandler) -> None:
self.device = device
self.profile_device = device
self._timestamp = dt.utcnow()
self.traffic_times_polled = 0
self.status_times_polled = 0
self.traffic_data = {BYTES_RECEIVED: 0, BYTES_SENT: 0, PACKETS_RECEIVED: 0, PACKETS_SENT: 0}
self.status_data = {WAN_STATUS: 'Connected', ROUTER_UPTIME: 10, ROUTER_IP: '0.0.0.0'} |
@property
def name(self) -> str:
'Get the name of the device.'
return self.profile_device.name | 8,179,922,228,888,954,000 | Get the name of the device. | tests/components/upnp/conftest.py | name | Aeroid/home-assistant-core | python | @property
def name(self) -> str:
return self.profile_device.name |
@property
def manufacturer(self) -> str:
'Get the manufacturer of this device.'
return self.profile_device.manufacturer | 2,394,092,014,073,006,600 | Get the manufacturer of this device. | tests/components/upnp/conftest.py | manufacturer | Aeroid/home-assistant-core | python | @property
def manufacturer(self) -> str:
return self.profile_device.manufacturer |
@property
def model_name(self) -> str:
'Get the model name of this device.'
return self.profile_device.model_name | 7,306,830,325,269,109,000 | Get the model name of this device. | tests/components/upnp/conftest.py | model_name | Aeroid/home-assistant-core | python | @property
def model_name(self) -> str:
return self.profile_device.model_name |
@property
def udn(self) -> str:
'Get the UDN of the device.'
return self.profile_device.udn | -2,713,029,806,131,443,700 | Get the UDN of the device. | tests/components/upnp/conftest.py | udn | Aeroid/home-assistant-core | python | @property
def udn(self) -> str:
return self.profile_device.udn |
@property
def device_type(self) -> str:
'Get the device type of this device.'
return self.profile_device.device_type | -5,713,704,791,920,005,000 | Get the device type of this device. | tests/components/upnp/conftest.py | device_type | Aeroid/home-assistant-core | python | @property
def device_type(self) -> str:
return self.profile_device.device_type |
async def async_get_total_bytes_received(self) -> Optional[int]:
'Get total bytes received.'
self.traffic_times_polled += 1
return self.traffic_data[BYTES_RECEIVED] | 3,561,064,990,076,080,600 | Get total bytes received. | tests/components/upnp/conftest.py | async_get_total_bytes_received | Aeroid/home-assistant-core | python | async def async_get_total_bytes_received(self) -> Optional[int]:
self.traffic_times_polled += 1
return self.traffic_data[BYTES_RECEIVED] |
async def async_get_total_bytes_sent(self) -> Optional[int]:
'Get total bytes sent.'
return self.traffic_data[BYTES_SENT] | -8,137,145,791,173,879,000 | Get total bytes sent. | tests/components/upnp/conftest.py | async_get_total_bytes_sent | Aeroid/home-assistant-core | python | async def async_get_total_bytes_sent(self) -> Optional[int]:
return self.traffic_data[BYTES_SENT] |
async def async_get_total_packets_received(self) -> Optional[int]:
'Get total packets received.'
return self.traffic_data[PACKETS_RECEIVED] | -42,403,311,728,915,400 | Get total packets received. | tests/components/upnp/conftest.py | async_get_total_packets_received | Aeroid/home-assistant-core | python | async def async_get_total_packets_received(self) -> Optional[int]:
return self.traffic_data[PACKETS_RECEIVED] |
async def async_get_total_packets_sent(self) -> Optional[int]:
'Get total packets sent.'
return self.traffic_data[PACKETS_SENT] | -4,864,117,032,536,250,000 | Get total packets sent. | tests/components/upnp/conftest.py | async_get_total_packets_sent | Aeroid/home-assistant-core | python | async def async_get_total_packets_sent(self) -> Optional[int]:
return self.traffic_data[PACKETS_SENT] |
async def async_get_external_ip_address(self, services: Optional[Sequence[str]]=None) -> Optional[str]:
'\n Get the external IP address.\n\n :param services List of service names to try to get action from, defaults to [WANIPC,WANPPP]\n '
return self.status_data[ROUTER_IP] | -1,945,486,992,808,566,300 | Get the external IP address.
:param services List of service names to try to get action from, defaults to [WANIPC,WANPPP] | tests/components/upnp/conftest.py | async_get_external_ip_address | Aeroid/home-assistant-core | python | async def async_get_external_ip_address(self, services: Optional[Sequence[str]]=None) -> Optional[str]:
'\n Get the external IP address.\n\n :param services List of service names to try to get action from, defaults to [WANIPC,WANPPP]\n '
return self.status_data[ROUTER_IP] |
async def async_get_status_info(self, services: Optional[Sequence[str]]=None) -> Optional[StatusInfo]:
'\n Get status info.\n\n :param services List of service names to try to get action from, defaults to [WANIPC,WANPPP]\n '
self.status_times_polled += 1
return StatusInfo(self.status_data[WAN_STATUS], '', self.status_data[ROUTER_UPTIME]) | 4,109,035,423,599,629,300 | Get status info.
:param services List of service names to try to get action from, defaults to [WANIPC,WANPPP] | tests/components/upnp/conftest.py | async_get_status_info | Aeroid/home-assistant-core | python | async def async_get_status_info(self, services: Optional[Sequence[str]]=None) -> Optional[StatusInfo]:
'\n Get status info.\n\n :param services List of service names to try to get action from, defaults to [WANIPC,WANPPP]\n '
self.status_times_polled += 1
return StatusInfo(self.status_data[WAN_STATUS], , self.status_data[ROUTER_UPTIME]) |
async def mock_async_create_upnp_device(hass: HomeAssistant, location: str) -> UpnpDevice:
'Create UPnP device.'
return MockUpnpDevice(location) | 7,088,582,572,463,551,000 | Create UPnP device. | tests/components/upnp/conftest.py | mock_async_create_upnp_device | Aeroid/home-assistant-core | python | async def mock_async_create_upnp_device(hass: HomeAssistant, location: str) -> UpnpDevice:
return MockUpnpDevice(location) |
async def register_callback(hass, callback, match_dict):
'Immediately do callback.'
(await callback(TEST_DISCOVERY, ssdp.SsdpChange.ALIVE))
return MagicMock() | 4,879,946,810,967,589,000 | Immediately do callback. | tests/components/upnp/conftest.py | register_callback | Aeroid/home-assistant-core | python | async def register_callback(hass, callback, match_dict):
(await callback(TEST_DISCOVERY, ssdp.SsdpChange.ALIVE))
return MagicMock() |
async def register_callback(hass, callback, match_dict):
"Don't do callback."
return MagicMock() | 4,131,950,536,775,038,000 | Don't do callback. | tests/components/upnp/conftest.py | register_callback | Aeroid/home-assistant-core | python | async def register_callback(hass, callback, match_dict):
return MagicMock() |
def test():
'Initiate poliastro testing\n\n '
pytest.main([os.path.dirname(os.path.abspath(__file__))]) | -6,125,574,878,467,370,000 | Initiate poliastro testing | src/poliastro/testing.py | test | AunSiro/poliastro | python | def test():
'\n\n '
pytest.main([os.path.dirname(os.path.abspath(__file__))]) |