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def _get_latest_log_files(name, testing=False) -> List[str]:
'Returns a list with the paths of all available logfiles for `name` sorted by latest first.'
log_dir = dirs.get_log_dir(name)
files = filter((lambda filename: (name in filename)), os.listdir(log_dir))
files = filter((lambda filename: (('testing' in filename) if testing else ('testing' not in filename))), files)
return [os.path.join(log_dir, filename) for filename in sorted(files, reverse=True)] | 2,716,176,745,821,204,000 | Returns a list with the paths of all available logfiles for `name` sorted by latest first. | aw_core/log.py | _get_latest_log_files | minhlt9196/activeseconds-aw-core | python | def _get_latest_log_files(name, testing=False) -> List[str]:
log_dir = dirs.get_log_dir(name)
files = filter((lambda filename: (name in filename)), os.listdir(log_dir))
files = filter((lambda filename: (('testing' in filename) if testing else ('testing' not in filename))), files)
return [os.path.join(log_dir, filename) for filename in sorted(files, reverse=True)] |
def get_latest_log_file(name, testing=False) -> Optional[str]:
'\n Returns the filename of the last logfile with `name`.\n Useful when you want to read the logfile of another TimeBench service.\n '
last_logs = _get_latest_log_files(name, testing=testing)
return (last_logs[0] if last_logs else None) | -1,499,907,841,220,113,000 | Returns the filename of the last logfile with `name`.
Useful when you want to read the logfile of another TimeBench service. | aw_core/log.py | get_latest_log_file | minhlt9196/activeseconds-aw-core | python | def get_latest_log_file(name, testing=False) -> Optional[str]:
'\n Returns the filename of the last logfile with `name`.\n Useful when you want to read the logfile of another TimeBench service.\n '
last_logs = _get_latest_log_files(name, testing=testing)
return (last_logs[0] if last_logs else None) |
def log_format(x):
'Used to give JsonFormatter proper parameter format'
return ['%({0:s})'.format(i) for i in x] | 4,862,817,577,818,626,000 | Used to give JsonFormatter proper parameter format | aw_core/log.py | log_format | minhlt9196/activeseconds-aw-core | python | def log_format(x):
return ['%({0:s})'.format(i) for i in x] |
def fib_sum(limit):
'\n Считает сумму положительных чисел Фибаначи, которые меньше указанного значения\n '
u1 = 1
u2 = 2
summ = 2
_u = (u1 + u2)
while (_u < limit):
if ((_u % 2) == 0):
summ += _u
u1 = u2
u2 = _u
_u = (u1 + u2)
return summ | -7,042,709,406,166,169,000 | Считает сумму положительных чисел Фибаначи, которые меньше указанного значения | t002-fibanatchi.py | fib_sum | z1365/euler | python | def fib_sum(limit):
'\n \n '
u1 = 1
u2 = 2
summ = 2
_u = (u1 + u2)
while (_u < limit):
if ((_u % 2) == 0):
summ += _u
u1 = u2
u2 = _u
_u = (u1 + u2)
return summ |
def test_angular_momentum_and_linear_momentum():
"A rod with length 2l, centroidal inertia I, and mass M along with a\n particle of mass m fixed to the end of the rod rotate with an angular rate\n of omega about point O which is fixed to the non-particle end of the rod.\n The rod's reference frame is A and the inertial frame is N."
(m, M, l, I) = symbols('m, M, l, I')
omega = dynamicsymbols('omega')
N = ReferenceFrame('N')
a = ReferenceFrame('a')
O = Point('O')
Ac = O.locatenew('Ac', (l * N.x))
P = Ac.locatenew('P', (l * N.x))
O.set_vel(N, (0 * N.x))
a.set_ang_vel(N, (omega * N.z))
Ac.v2pt_theory(O, N, a)
P.v2pt_theory(O, N, a)
Pa = Particle('Pa', P, m)
A = RigidBody('A', Ac, a, M, ((I * outer(N.z, N.z)), Ac))
expected = (((((2 * m) * omega) * l) * N.y) + (((M * l) * omega) * N.y))
assert (linear_momentum(N, A, Pa) == expected)
raises(TypeError, (lambda : angular_momentum(N, N, A, Pa)))
raises(TypeError, (lambda : angular_momentum(O, O, A, Pa)))
raises(TypeError, (lambda : angular_momentum(O, N, O, Pa)))
expected = ((((I + (M * (l ** 2))) + ((4 * m) * (l ** 2))) * omega) * N.z)
assert (angular_momentum(O, N, A, Pa) == expected) | -4,550,189,536,131,389,400 | A rod with length 2l, centroidal inertia I, and mass M along with a
particle of mass m fixed to the end of the rod rotate with an angular rate
of omega about point O which is fixed to the non-particle end of the rod.
The rod's reference frame is A and the inertial frame is N. | sympy/physics/mechanics/tests/test_functions.py | test_angular_momentum_and_linear_momentum | Abhi58/sympy | python | def test_angular_momentum_and_linear_momentum():
"A rod with length 2l, centroidal inertia I, and mass M along with a\n particle of mass m fixed to the end of the rod rotate with an angular rate\n of omega about point O which is fixed to the non-particle end of the rod.\n The rod's reference frame is A and the inertial frame is N."
(m, M, l, I) = symbols('m, M, l, I')
omega = dynamicsymbols('omega')
N = ReferenceFrame('N')
a = ReferenceFrame('a')
O = Point('O')
Ac = O.locatenew('Ac', (l * N.x))
P = Ac.locatenew('P', (l * N.x))
O.set_vel(N, (0 * N.x))
a.set_ang_vel(N, (omega * N.z))
Ac.v2pt_theory(O, N, a)
P.v2pt_theory(O, N, a)
Pa = Particle('Pa', P, m)
A = RigidBody('A', Ac, a, M, ((I * outer(N.z, N.z)), Ac))
expected = (((((2 * m) * omega) * l) * N.y) + (((M * l) * omega) * N.y))
assert (linear_momentum(N, A, Pa) == expected)
raises(TypeError, (lambda : angular_momentum(N, N, A, Pa)))
raises(TypeError, (lambda : angular_momentum(O, O, A, Pa)))
raises(TypeError, (lambda : angular_momentum(O, N, O, Pa)))
expected = ((((I + (M * (l ** 2))) + ((4 * m) * (l ** 2))) * omega) * N.z)
assert (angular_momentum(O, N, A, Pa) == expected) |
def to_timedelta(arg, unit=None, errors='raise'):
'\n Convert argument to timedelta.\n\n Timedeltas are absolute differences in times, expressed in difference\n units (e.g. days, hours, minutes, seconds). This method converts\n an argument from a recognized timedelta format / value into\n a Timedelta type.\n\n Parameters\n ----------\n arg : str, timedelta, list-like or Series\n The data to be converted to timedelta. The character M by itself,\n e.g. \'1M\', is treated as minute, not month. The characters Y and y\n are treated as the mean length of the Gregorian calendar year -\n 365.2425 days or 365 days 5 hours 49 minutes 12 seconds.\n unit : str, optional\n Denotes the unit of the arg for numeric `arg`. Defaults to ``"ns"``.\n\n Possible values:\n\n * \'W\'\n * \'D\' / \'days\' / \'day\'\n * \'hours\' / \'hour\' / \'hr\' / \'h\'\n * \'m\' / \'minute\' / \'min\' / \'minutes\' / \'T\'\n * \'S\' / \'seconds\' / \'sec\' / \'second\'\n * \'ms\' / \'milliseconds\' / \'millisecond\' / \'milli\' / \'millis\' / \'L\'\n * \'us\' / \'microseconds\' / \'microsecond\' / \'micro\' / \'micros\' / \'U\'\n * \'ns\' / \'nanoseconds\' / \'nano\' / \'nanos\' / \'nanosecond\' / \'N\'\n\n .. versionchanged:: 1.1.0\n\n Must not be specified when `arg` context strings and\n ``errors="raise"``.\n\n errors : {\'ignore\', \'raise\', \'coerce\'}, default \'raise\'\n - If \'raise\', then invalid parsing will raise an exception.\n - If \'coerce\', then invalid parsing will be set as NaT.\n - If \'ignore\', then invalid parsing will return the input.\n\n Returns\n -------\n timedelta64 or numpy.array of timedelta64\n Output type returned if parsing succeeded.\n\n See Also\n --------\n DataFrame.astype : Cast argument to a specified dtype.\n to_datetime : Convert argument to datetime.\n convert_dtypes : Convert dtypes.\n\n Examples\n --------\n Parsing a single string to a Timedelta:\n\n >>> pd.to_timedelta(\'1 days 06:05:01.00003\')\n Timedelta(\'1 days 06:05:01.000030\')\n >>> pd.to_timedelta(\'15.5us\')\n Timedelta(\'0 days 00:00:00.000015500\')\n\n Parsing a list or array of strings:\n\n >>> pd.to_timedelta([\'1 days 06:05:01.00003\', \'15.5us\', \'nan\'])\n TimedeltaIndex([\'1 days 06:05:01.000030\', \'0 days 00:00:00.000015500\', NaT],\n dtype=\'timedelta64[ns]\', freq=None)\n\n Converting numbers by specifying the `unit` keyword argument:\n\n >>> pd.to_timedelta(np.arange(5), unit=\'s\')\n TimedeltaIndex([\'0 days 00:00:00\', \'0 days 00:00:01\', \'0 days 00:00:02\',\n \'0 days 00:00:03\', \'0 days 00:00:04\'],\n dtype=\'timedelta64[ns]\', freq=None)\n >>> pd.to_timedelta(np.arange(5), unit=\'d\')\n TimedeltaIndex([\'0 days\', \'1 days\', \'2 days\', \'3 days\', \'4 days\'],\n dtype=\'timedelta64[ns]\', freq=None)\n '
if (unit is not None):
unit = parse_timedelta_unit(unit)
if (errors not in ('ignore', 'raise', 'coerce')):
raise ValueError("errors must be one of 'ignore', 'raise', or 'coerce'}")
if (unit in {'Y', 'y', 'M'}):
raise ValueError("Units 'M', 'Y', and 'y' are no longer supported, as they do not represent unambiguous timedelta values durations.")
if (arg is None):
return arg
elif isinstance(arg, ABCSeries):
values = _convert_listlike(arg._values, unit=unit, errors=errors)
return arg._constructor(values, index=arg.index, name=arg.name)
elif isinstance(arg, ABCIndexClass):
return _convert_listlike(arg, unit=unit, errors=errors, name=arg.name)
elif (isinstance(arg, np.ndarray) and (arg.ndim == 0)):
arg = arg.item()
elif (is_list_like(arg) and (getattr(arg, 'ndim', 1) == 1)):
return _convert_listlike(arg, unit=unit, errors=errors)
elif (getattr(arg, 'ndim', 1) > 1):
raise TypeError('arg must be a string, timedelta, list, tuple, 1-d array, or Series')
if (isinstance(arg, str) and (unit is not None)):
raise ValueError('unit must not be specified if the input is/contains a str')
return _coerce_scalar_to_timedelta_type(arg, unit=unit, errors=errors) | -4,619,909,879,631,674,000 | Convert argument to timedelta.
Timedeltas are absolute differences in times, expressed in difference
units (e.g. days, hours, minutes, seconds). This method converts
an argument from a recognized timedelta format / value into
a Timedelta type.
Parameters
----------
arg : str, timedelta, list-like or Series
The data to be converted to timedelta. The character M by itself,
e.g. '1M', is treated as minute, not month. The characters Y and y
are treated as the mean length of the Gregorian calendar year -
365.2425 days or 365 days 5 hours 49 minutes 12 seconds.
unit : str, optional
Denotes the unit of the arg for numeric `arg`. Defaults to ``"ns"``.
Possible values:
* 'W'
* 'D' / 'days' / 'day'
* 'hours' / 'hour' / 'hr' / 'h'
* 'm' / 'minute' / 'min' / 'minutes' / 'T'
* 'S' / 'seconds' / 'sec' / 'second'
* 'ms' / 'milliseconds' / 'millisecond' / 'milli' / 'millis' / 'L'
* 'us' / 'microseconds' / 'microsecond' / 'micro' / 'micros' / 'U'
* 'ns' / 'nanoseconds' / 'nano' / 'nanos' / 'nanosecond' / 'N'
.. versionchanged:: 1.1.0
Must not be specified when `arg` context strings and
``errors="raise"``.
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception.
- If 'coerce', then invalid parsing will be set as NaT.
- If 'ignore', then invalid parsing will return the input.
Returns
-------
timedelta64 or numpy.array of timedelta64
Output type returned if parsing succeeded.
See Also
--------
DataFrame.astype : Cast argument to a specified dtype.
to_datetime : Convert argument to datetime.
convert_dtypes : Convert dtypes.
Examples
--------
Parsing a single string to a Timedelta:
>>> pd.to_timedelta('1 days 06:05:01.00003')
Timedelta('1 days 06:05:01.000030')
>>> pd.to_timedelta('15.5us')
Timedelta('0 days 00:00:00.000015500')
Parsing a list or array of strings:
>>> pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])
TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT],
dtype='timedelta64[ns]', freq=None)
Converting numbers by specifying the `unit` keyword argument:
>>> pd.to_timedelta(np.arange(5), unit='s')
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02',
'0 days 00:00:03', '0 days 00:00:04'],
dtype='timedelta64[ns]', freq=None)
>>> pd.to_timedelta(np.arange(5), unit='d')
TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
dtype='timedelta64[ns]', freq=None) | pandas/core/tools/timedeltas.py | to_timedelta | cdeil/pandas | python | def to_timedelta(arg, unit=None, errors='raise'):
'\n Convert argument to timedelta.\n\n Timedeltas are absolute differences in times, expressed in difference\n units (e.g. days, hours, minutes, seconds). This method converts\n an argument from a recognized timedelta format / value into\n a Timedelta type.\n\n Parameters\n ----------\n arg : str, timedelta, list-like or Series\n The data to be converted to timedelta. The character M by itself,\n e.g. \'1M\', is treated as minute, not month. The characters Y and y\n are treated as the mean length of the Gregorian calendar year -\n 365.2425 days or 365 days 5 hours 49 minutes 12 seconds.\n unit : str, optional\n Denotes the unit of the arg for numeric `arg`. Defaults to ``"ns"``.\n\n Possible values:\n\n * \'W\'\n * \'D\' / \'days\' / \'day\'\n * \'hours\' / \'hour\' / \'hr\' / \'h\'\n * \'m\' / \'minute\' / \'min\' / \'minutes\' / \'T\'\n * \'S\' / \'seconds\' / \'sec\' / \'second\'\n * \'ms\' / \'milliseconds\' / \'millisecond\' / \'milli\' / \'millis\' / \'L\'\n * \'us\' / \'microseconds\' / \'microsecond\' / \'micro\' / \'micros\' / \'U\'\n * \'ns\' / \'nanoseconds\' / \'nano\' / \'nanos\' / \'nanosecond\' / \'N\'\n\n .. versionchanged:: 1.1.0\n\n Must not be specified when `arg` context strings and\n ``errors="raise"``.\n\n errors : {\'ignore\', \'raise\', \'coerce\'}, default \'raise\'\n - If \'raise\', then invalid parsing will raise an exception.\n - If \'coerce\', then invalid parsing will be set as NaT.\n - If \'ignore\', then invalid parsing will return the input.\n\n Returns\n -------\n timedelta64 or numpy.array of timedelta64\n Output type returned if parsing succeeded.\n\n See Also\n --------\n DataFrame.astype : Cast argument to a specified dtype.\n to_datetime : Convert argument to datetime.\n convert_dtypes : Convert dtypes.\n\n Examples\n --------\n Parsing a single string to a Timedelta:\n\n >>> pd.to_timedelta(\'1 days 06:05:01.00003\')\n Timedelta(\'1 days 06:05:01.000030\')\n >>> pd.to_timedelta(\'15.5us\')\n Timedelta(\'0 days 00:00:00.000015500\')\n\n Parsing a list or array of strings:\n\n >>> pd.to_timedelta([\'1 days 06:05:01.00003\', \'15.5us\', \'nan\'])\n TimedeltaIndex([\'1 days 06:05:01.000030\', \'0 days 00:00:00.000015500\', NaT],\n dtype=\'timedelta64[ns]\', freq=None)\n\n Converting numbers by specifying the `unit` keyword argument:\n\n >>> pd.to_timedelta(np.arange(5), unit=\'s\')\n TimedeltaIndex([\'0 days 00:00:00\', \'0 days 00:00:01\', \'0 days 00:00:02\',\n \'0 days 00:00:03\', \'0 days 00:00:04\'],\n dtype=\'timedelta64[ns]\', freq=None)\n >>> pd.to_timedelta(np.arange(5), unit=\'d\')\n TimedeltaIndex([\'0 days\', \'1 days\', \'2 days\', \'3 days\', \'4 days\'],\n dtype=\'timedelta64[ns]\', freq=None)\n '
if (unit is not None):
unit = parse_timedelta_unit(unit)
if (errors not in ('ignore', 'raise', 'coerce')):
raise ValueError("errors must be one of 'ignore', 'raise', or 'coerce'}")
if (unit in {'Y', 'y', 'M'}):
raise ValueError("Units 'M', 'Y', and 'y' are no longer supported, as they do not represent unambiguous timedelta values durations.")
if (arg is None):
return arg
elif isinstance(arg, ABCSeries):
values = _convert_listlike(arg._values, unit=unit, errors=errors)
return arg._constructor(values, index=arg.index, name=arg.name)
elif isinstance(arg, ABCIndexClass):
return _convert_listlike(arg, unit=unit, errors=errors, name=arg.name)
elif (isinstance(arg, np.ndarray) and (arg.ndim == 0)):
arg = arg.item()
elif (is_list_like(arg) and (getattr(arg, 'ndim', 1) == 1)):
return _convert_listlike(arg, unit=unit, errors=errors)
elif (getattr(arg, 'ndim', 1) > 1):
raise TypeError('arg must be a string, timedelta, list, tuple, 1-d array, or Series')
if (isinstance(arg, str) and (unit is not None)):
raise ValueError('unit must not be specified if the input is/contains a str')
return _coerce_scalar_to_timedelta_type(arg, unit=unit, errors=errors) |
def _coerce_scalar_to_timedelta_type(r, unit='ns', errors='raise'):
"Convert string 'r' to a timedelta object."
try:
result = Timedelta(r, unit)
except ValueError:
if (errors == 'raise'):
raise
elif (errors == 'ignore'):
return r
result = NaT
return result | -6,240,162,069,825,605,000 | Convert string 'r' to a timedelta object. | pandas/core/tools/timedeltas.py | _coerce_scalar_to_timedelta_type | cdeil/pandas | python | def _coerce_scalar_to_timedelta_type(r, unit='ns', errors='raise'):
try:
result = Timedelta(r, unit)
except ValueError:
if (errors == 'raise'):
raise
elif (errors == 'ignore'):
return r
result = NaT
return result |
def _convert_listlike(arg, unit=None, errors='raise', name=None):
'Convert a list of objects to a timedelta index object.'
if (isinstance(arg, (list, tuple)) or (not hasattr(arg, 'dtype'))):
arg = np.array(list(arg), dtype=object)
try:
value = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0]
except ValueError:
if (errors == 'ignore'):
return arg
else:
raise
from pandas import TimedeltaIndex
value = TimedeltaIndex(value, unit='ns', name=name)
return value | 6,420,085,564,580,702,000 | Convert a list of objects to a timedelta index object. | pandas/core/tools/timedeltas.py | _convert_listlike | cdeil/pandas | python | def _convert_listlike(arg, unit=None, errors='raise', name=None):
if (isinstance(arg, (list, tuple)) or (not hasattr(arg, 'dtype'))):
arg = np.array(list(arg), dtype=object)
try:
value = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0]
except ValueError:
if (errors == 'ignore'):
return arg
else:
raise
from pandas import TimedeltaIndex
value = TimedeltaIndex(value, unit='ns', name=name)
return value |
def __init__(self, mrns: Optional[Set[str]]=None, dimensions: Optional[Set[str]]=None, clause=None, data_columns=None, **kwargs):
'\n Args:\n mrns: Set of MRN-Strings for which the condition is true.\n children: List of child conditions which were combined with an\n operator.\n operator: String representing the combination of the child\n condition (e.g. ``_BaseCondition.AND``)\n dimensions: A set of tables that need to be joined on the\n ``base_table``\n clause: The SQLAlchemy clause of the current\n condition to select patients.\n data_columns: The SQLAlchemy data_columns that should\n be returned when ``.get_data()`` is called.\n '
super().__init__(**kwargs)
self.dimensions = (dimensions or set())
self._clause = (sql.true() if (clause is None) else clause)
self.data_columns = (data_columns or []) | 6,810,422,015,448,396,000 | Args:
mrns: Set of MRN-Strings for which the condition is true.
children: List of child conditions which were combined with an
operator.
operator: String representing the combination of the child
condition (e.g. ``_BaseCondition.AND``)
dimensions: A set of tables that need to be joined on the
``base_table``
clause: The SQLAlchemy clause of the current
condition to select patients.
data_columns: The SQLAlchemy data_columns that should
be returned when ``.get_data()`` is called. | fiber/condition/database.py | __init__ | hpi-dhc/fiber | python | def __init__(self, mrns: Optional[Set[str]]=None, dimensions: Optional[Set[str]]=None, clause=None, data_columns=None, **kwargs):
'\n Args:\n mrns: Set of MRN-Strings for which the condition is true.\n children: List of child conditions which were combined with an\n operator.\n operator: String representing the combination of the child\n condition (e.g. ``_BaseCondition.AND``)\n dimensions: A set of tables that need to be joined on the\n ``base_table``\n clause: The SQLAlchemy clause of the current\n condition to select patients.\n data_columns: The SQLAlchemy data_columns that should\n be returned when ``.get_data()`` is called.\n '
super().__init__(**kwargs)
self.dimensions = (dimensions or set())
self._clause = (sql.true() if (clause is None) else clause)
self.data_columns = (data_columns or []) |
@property
def base_table(self) -> Table:
'\n Must be set by subclasses to the database table which the class uses to\n select patients and data. This is also used to optimize queries on the\n same table.\n '
raise NotImplementedError | -9,211,096,098,496,574,000 | Must be set by subclasses to the database table which the class uses to
select patients and data. This is also used to optimize queries on the
same table. | fiber/condition/database.py | base_table | hpi-dhc/fiber | python | @property
def base_table(self) -> Table:
'\n Must be set by subclasses to the database table which the class uses to\n select patients and data. This is also used to optimize queries on the\n same table.\n '
raise NotImplementedError |
@property
def _default_columns(self):
'\n Must be set by subclasses.\n\n This should return an array of columns which are in the result table of\n ``._create_query()``. These columns will be returned by default when\n ``.get_data()`` is called.\n '
raise NotImplementedError | 8,196,578,915,108,377,000 | Must be set by subclasses.
This should return an array of columns which are in the result table of
``._create_query()``. These columns will be returned by default when
``.get_data()`` is called. | fiber/condition/database.py | _default_columns | hpi-dhc/fiber | python | @property
def _default_columns(self):
'\n Must be set by subclasses.\n\n This should return an array of columns which are in the result table of\n ``._create_query()``. These columns will be returned by default when\n ``.get_data()`` is called.\n '
raise NotImplementedError |
@property
def mrn_column(self):
'\n Must be set by subclasses.\n\n This is used to specify the column in the result table of\n ``._create_query()`` which is holding the MRNs.\n '
raise NotImplementedError | -4,138,846,600,661,252,600 | Must be set by subclasses.
This is used to specify the column in the result table of
``._create_query()`` which is holding the MRNs. | fiber/condition/database.py | mrn_column | hpi-dhc/fiber | python | @property
def mrn_column(self):
'\n Must be set by subclasses.\n\n This is used to specify the column in the result table of\n ``._create_query()`` which is holding the MRNs.\n '
raise NotImplementedError |
@property
def age_column(self):
'\n Must be set by subclasses.\n\n This is used to specify the column in the result table of\n ``._create_query()`` which is holding the age in days.\n '
raise NotImplementedError | -211,457,758,802,844,300 | Must be set by subclasses.
This is used to specify the column in the result table of
``._create_query()`` which is holding the age in days. | fiber/condition/database.py | age_column | hpi-dhc/fiber | python | @property
def age_column(self):
'\n Must be set by subclasses.\n\n This is used to specify the column in the result table of\n ``._create_query()`` which is holding the age in days.\n '
raise NotImplementedError |
@property
def data_columns(self):
'\n Returns columns which are in the result table of\n ``._create_query()``. These columns will be returned when\n ``.get_data()`` is called.\n '
return [str(col) for col in (self._specified_columns or self._default_columns)] | -1,208,874,599,390,524,400 | Returns columns which are in the result table of
``._create_query()``. These columns will be returned when
``.get_data()`` is called. | fiber/condition/database.py | data_columns | hpi-dhc/fiber | python | @property
def data_columns(self):
'\n Returns columns which are in the result table of\n ``._create_query()``. These columns will be returned when\n ``.get_data()`` is called.\n '
return [str(col) for col in (self._specified_columns or self._default_columns)] |
@property
def clause(self):
'\n Returns the clause of the current condition or runs\n ``._create_clause()`` to create it.\n '
if (not isinstance(self._clause, sql.elements.True_)):
return self._clause
else:
return self._create_clause() | -8,420,029,666,076,889,000 | Returns the clause of the current condition or runs
``._create_clause()`` to create it. | fiber/condition/database.py | clause | hpi-dhc/fiber | python | @property
def clause(self):
'\n Returns the clause of the current condition or runs\n ``._create_clause()`` to create it.\n '
if (not isinstance(self._clause, sql.elements.True_)):
return self._clause
else:
return self._create_clause() |
def _create_clause(self):
'\n Should be overwritten by subclasses to create a SQLAlchemy clause based\n on the defined condition. It is used to select the correct patients.\n '
return sql.true() | -2,366,013,195,273,496,600 | Should be overwritten by subclasses to create a SQLAlchemy clause based
on the defined condition. It is used to select the correct patients. | fiber/condition/database.py | _create_clause | hpi-dhc/fiber | python | def _create_clause(self):
'\n Should be overwritten by subclasses to create a SQLAlchemy clause based\n on the defined condition. It is used to select the correct patients.\n '
return sql.true() |
def _create_query(self) -> orm.Query:
'\n Must be implemented by subclasses to return an instance of a SQLAlchemy\n query which only returns MRNs.\n\n This query should yield all medical record numbers in the\n ``base_table`` of the condition. It uses the ``.clause`` to select\n the relevant patients.\n\n This query is also used by other function which change the selected\n columns to get data about the patients.\n '
raise NotImplementedError | 4,027,134,133,308,272,600 | Must be implemented by subclasses to return an instance of a SQLAlchemy
query which only returns MRNs.
This query should yield all medical record numbers in the
``base_table`` of the condition. It uses the ``.clause`` to select
the relevant patients.
This query is also used by other function which change the selected
columns to get data about the patients. | fiber/condition/database.py | _create_query | hpi-dhc/fiber | python | def _create_query(self) -> orm.Query:
'\n Must be implemented by subclasses to return an instance of a SQLAlchemy\n query which only returns MRNs.\n\n This query should yield all medical record numbers in the\n ``base_table`` of the condition. It uses the ``.clause`` to select\n the relevant patients.\n\n This query is also used by other function which change the selected\n columns to get data about the patients.\n '
raise NotImplementedError |
def _fetch_mrns(self, limit: Optional[int]=None):
'Fetches MRNs from the results of ``._create_query()``.'
q = self._create_query()
if limit:
q = q.limit(limit)
mrn_df = read_with_progress(q.statement, self.engine)
if mrn_df.empty:
mrn_df = pd.DataFrame(columns=['medical_record_number'])
assert (len(mrn_df.columns) == 1), '_create_query must return only MRNs'
result = set((mrn for mrn in mrn_df.iloc[:, 0]))
return result | -8,381,494,308,814,900,000 | Fetches MRNs from the results of ``._create_query()``. | fiber/condition/database.py | _fetch_mrns | hpi-dhc/fiber | python | def _fetch_mrns(self, limit: Optional[int]=None):
q = self._create_query()
if limit:
q = q.limit(limit)
mrn_df = read_with_progress(q.statement, self.engine)
if mrn_df.empty:
mrn_df = pd.DataFrame(columns=['medical_record_number'])
assert (len(mrn_df.columns) == 1), '_create_query must return only MRNs'
result = set((mrn for mrn in mrn_df.iloc[:, 0]))
return result |
def _fetch_data(self, included_mrns: Optional[Set]=None, limit: Optional[int]=None):
'\n Fetches the data defined with ``.data_columns`` for each patient\n defined by this condition and via ``included_mrns`` from the results of\n ``._create_query()``.\n '
q = self._create_query()
if included_mrns:
q = q.filter(self.mrn_column.in_(included_mrns))
if limit:
q = q.limit(limit)
q = q.with_entities(*self.data_columns).distinct()
result = read_with_progress(q.statement, self.engine, silent=bool(included_mrns))
return result | 5,996,257,617,156,554,000 | Fetches the data defined with ``.data_columns`` for each patient
defined by this condition and via ``included_mrns`` from the results of
``._create_query()``. | fiber/condition/database.py | _fetch_data | hpi-dhc/fiber | python | def _fetch_data(self, included_mrns: Optional[Set]=None, limit: Optional[int]=None):
'\n Fetches the data defined with ``.data_columns`` for each patient\n defined by this condition and via ``included_mrns`` from the results of\n ``._create_query()``.\n '
q = self._create_query()
if included_mrns:
q = q.filter(self.mrn_column.in_(included_mrns))
if limit:
q = q.limit(limit)
q = q.with_entities(*self.data_columns).distinct()
result = read_with_progress(q.statement, self.engine, silent=bool(included_mrns))
return result |
def example_values(self):
"\n Returns ten values of the current condition.\n\n Example:\n >>> Patient(gender='Female', religion='Hindu').example_values()\n "
return self.get_data(limit=10) | 1,292,491,153,136,693,200 | Returns ten values of the current condition.
Example:
>>> Patient(gender='Female', religion='Hindu').example_values() | fiber/condition/database.py | example_values | hpi-dhc/fiber | python | def example_values(self):
"\n Returns ten values of the current condition.\n\n Example:\n >>> Patient(gender='Female', religion='Hindu').example_values()\n "
return self.get_data(limit=10) |
def values_per(self, *columns: Set[str]):
'\n Counts occurence of unique values in the specified columns.\n '
return self._grouped_count('*', *columns, label='values') | -8,414,278,900,176,086,000 | Counts occurence of unique values in the specified columns. | fiber/condition/database.py | values_per | hpi-dhc/fiber | python | def values_per(self, *columns: Set[str]):
'\n \n '
return self._grouped_count('*', *columns, label='values') |
def patients_per(self, *columns: Set[str]):
'\n Counts distinct patients for unique values in the specified columns.\n '
return self._grouped_count(self.mrn_column.distinct(), *columns, label='patients') | 1,264,087,047,943,287,300 | Counts distinct patients for unique values in the specified columns. | fiber/condition/database.py | patients_per | hpi-dhc/fiber | python | def patients_per(self, *columns: Set[str]):
'\n \n '
return self._grouped_count(self.mrn_column.distinct(), *columns, label='patients') |
def distinct(self, *columns: Set[str]):
'Returns distinct values based on the specified ``columns``'
if (not columns):
raise ValueError('Supply one or multiple columns as arguments.')
q = self._create_query()
q = q.with_entities(*columns).distinct()
return read_with_progress(q.statement, self.engine) | 3,780,552,309,333,347,300 | Returns distinct values based on the specified ``columns`` | fiber/condition/database.py | distinct | hpi-dhc/fiber | python | def distinct(self, *columns: Set[str]):
if (not columns):
raise ValueError('Supply one or multiple columns as arguments.')
q = self._create_query()
q = q.with_entities(*columns).distinct()
return read_with_progress(q.statement, self.engine) |
def __or__(self, other: _BaseCondition):
'\n The _DatabaseCondition optimizes the SQL statements for ``|`` by\n combining the clauses of condition which run on the same database\n table. This is done via the ``.base_table`` attribute.\n '
if ((self.base_table == other.base_table) and (not (self._mrns or other._mrns))):
unique_columns = list(dict.fromkeys(chain(self.data_columns, other.data_columns)))
return self.__class__(dimensions=(self.dimensions | other.dimensions), clause=(self.clause | other.clause), data_columns=unique_columns, children=[self, other], operator=_BaseCondition.OR)
else:
return _BaseCondition(mrns=(self.get_mrns() | other.get_mrns()), children=[self, other], operator=_BaseCondition.OR) | -943,715,415,913,724,000 | The _DatabaseCondition optimizes the SQL statements for ``|`` by
combining the clauses of condition which run on the same database
table. This is done via the ``.base_table`` attribute. | fiber/condition/database.py | __or__ | hpi-dhc/fiber | python | def __or__(self, other: _BaseCondition):
'\n The _DatabaseCondition optimizes the SQL statements for ``|`` by\n combining the clauses of condition which run on the same database\n table. This is done via the ``.base_table`` attribute.\n '
if ((self.base_table == other.base_table) and (not (self._mrns or other._mrns))):
unique_columns = list(dict.fromkeys(chain(self.data_columns, other.data_columns)))
return self.__class__(dimensions=(self.dimensions | other.dimensions), clause=(self.clause | other.clause), data_columns=unique_columns, children=[self, other], operator=_BaseCondition.OR)
else:
return _BaseCondition(mrns=(self.get_mrns() | other.get_mrns()), children=[self, other], operator=_BaseCondition.OR) |
def __repr__(self):
'Shows the running query or the resulting MRNs'
if self._mrns:
return f'{self.__class__.__name__}: {len(self.get_mrns())} mrns'
else:
clause = (compile_sqla(self.clause, self.engine) if fiber.config.VERBOSE else '...')
return f'{self.__class__.__name__} ({clause})' | 8,681,375,925,406,334,000 | Shows the running query or the resulting MRNs | fiber/condition/database.py | __repr__ | hpi-dhc/fiber | python | def __repr__(self):
if self._mrns:
return f'{self.__class__.__name__}: {len(self.get_mrns())} mrns'
else:
clause = (compile_sqla(self.clause, self.engine) if fiber.config.VERBOSE else '...')
return f'{self.__class__.__name__} ({clause})' |
def get_netmask(ip, subnet):
'Returns the netmask appropriate for injection into a guest.'
if (ip['version'] == 4):
return str(subnet.as_netaddr().netmask)
return subnet.as_netaddr()._prefixlen | 3,273,456,509,661,132,000 | Returns the netmask appropriate for injection into a guest. | nova/network/model.py | get_netmask | Alex-Sizov/nova | python | def get_netmask(ip, subnet):
if (ip['version'] == 4):
return str(subnet.as_netaddr().netmask)
return subnet.as_netaddr()._prefixlen |
def get_meta(self, key, default=None):
"calls get(key, default) on self['meta']."
return self['meta'].get(key, default) | 950,897,229,093,025,500 | calls get(key, default) on self['meta']. | nova/network/model.py | get_meta | Alex-Sizov/nova | python | def get_meta(self, key, default=None):
return self['meta'].get(key, default) |
def as_netaddr(self):
'Convenient function to get cidr as a netaddr object.'
return netaddr.IPNetwork(self['cidr']) | -1,883,720,367,734,164,000 | Convenient function to get cidr as a netaddr object. | nova/network/model.py | as_netaddr | Alex-Sizov/nova | python | def as_netaddr(self):
return netaddr.IPNetwork(self['cidr']) |
def labeled_ips(self):
"Returns the list of all IPs\n\n The return value looks like this flat structure::\n\n {'network_label': 'my_network',\n 'network_id': 'n8v29837fn234782f08fjxk3ofhb84',\n 'ips': [{'address': '0.0.0.0',\n 'version': 4,\n 'type: 'fixed',\n 'meta': {...}},\n {'address': '0.0.0.0',\n 'version': 4,\n 'type': 'floating',\n 'meta': {...}},\n {'address': 'fe80::4',\n 'version': 6,\n 'type': 'fixed',\n 'meta': {...}}]\n "
if self['network']:
ips = [IP(**ip) for ip in self.fixed_ips()]
for ip in ips:
del ip['meta']['floating_ips']
ips.extend(self.floating_ips())
return {'network_label': self['network']['label'], 'network_id': self['network']['id'], 'ips': ips}
return [] | 3,862,719,527,362,788,000 | Returns the list of all IPs
The return value looks like this flat structure::
{'network_label': 'my_network',
'network_id': 'n8v29837fn234782f08fjxk3ofhb84',
'ips': [{'address': '0.0.0.0',
'version': 4,
'type: 'fixed',
'meta': {...}},
{'address': '0.0.0.0',
'version': 4,
'type': 'floating',
'meta': {...}},
{'address': 'fe80::4',
'version': 6,
'type': 'fixed',
'meta': {...}}] | nova/network/model.py | labeled_ips | Alex-Sizov/nova | python | def labeled_ips(self):
"Returns the list of all IPs\n\n The return value looks like this flat structure::\n\n {'network_label': 'my_network',\n 'network_id': 'n8v29837fn234782f08fjxk3ofhb84',\n 'ips': [{'address': '0.0.0.0',\n 'version': 4,\n 'type: 'fixed',\n 'meta': {...}},\n {'address': '0.0.0.0',\n 'version': 4,\n 'type': 'floating',\n 'meta': {...}},\n {'address': 'fe80::4',\n 'version': 6,\n 'type': 'fixed',\n 'meta': {...}}]\n "
if self['network']:
ips = [IP(**ip) for ip in self.fixed_ips()]
for ip in ips:
del ip['meta']['floating_ips']
ips.extend(self.floating_ips())
return {'network_label': self['network']['label'], 'network_id': self['network']['id'], 'ips': ips}
return [] |
def fixed_ips(self):
'Returns all fixed_ips without floating_ips attached.'
return [ip for vif in self for ip in vif.fixed_ips()] | -4,682,522,704,627,597,000 | Returns all fixed_ips without floating_ips attached. | nova/network/model.py | fixed_ips | Alex-Sizov/nova | python | def fixed_ips(self):
return [ip for vif in self for ip in vif.fixed_ips()] |
def floating_ips(self):
'Returns all floating_ips.'
return [ip for vif in self for ip in vif.floating_ips()] | -8,499,458,769,679,128,000 | Returns all floating_ips. | nova/network/model.py | floating_ips | Alex-Sizov/nova | python | def floating_ips(self):
return [ip for vif in self for ip in vif.floating_ips()] |
def wait(self, do_raise=True):
'Wait for asynchronous call to finish.'
pass | 1,854,355,436,926,017,300 | Wait for asynchronous call to finish. | nova/network/model.py | wait | Alex-Sizov/nova | python | def wait(self, do_raise=True):
pass |
def _sync_wrapper(self, wrapped, *args, **kwargs):
'Synchronize the model before running a method.'
self.wait()
return wrapped(*args, **kwargs) | -5,115,433,604,785,415,000 | Synchronize the model before running a method. | nova/network/model.py | _sync_wrapper | Alex-Sizov/nova | python | def _sync_wrapper(self, wrapped, *args, **kwargs):
self.wait()
return wrapped(*args, **kwargs) |
def wait(self, do_raise=True):
'Wait for asynchronous call to finish.'
if (self._gt is not None):
try:
self[:] = self._gt.wait()
except Exception:
if do_raise:
raise
finally:
self._gt = None | 1,603,477,702,000,076,500 | Wait for asynchronous call to finish. | nova/network/model.py | wait | Alex-Sizov/nova | python | def wait(self, do_raise=True):
if (self._gt is not None):
try:
self[:] = self._gt.wait()
except Exception:
if do_raise:
raise
finally:
self._gt = None |
def phrase_extractor(doc_sents):
'\n doc_sents is a list where each element is a list with elements corresponding to individual sentences of a document\n '
Phraser = models.phrases.Phraser
Phrases = models.phrases.Phrases
sentence_stream = sum(doc_sents, [])
common_terms = ['of', 'with', 'without', 'and', 'or', 'the', 'a', 'as']
phrases = Phrases(sentence_stream, common_terms=common_terms)
bigram = Phraser(phrases)
trigram = Phrases(bigram[sentence_stream])
output_strs = []
for idx in range(0, len(doc_sents)):
doc = doc_sents[idx]
output_doc = list(trigram[doc])
output_str = sum(output_doc, [])
output_strs.append(' '.join(output_str))
return output_strs | 7,986,132,740,859,710,000 | doc_sents is a list where each element is a list with elements corresponding to individual sentences of a document | src/scraping/read_and_parse.py | phrase_extractor | avbatchelor/insight-articles-project | python | def phrase_extractor(doc_sents):
'\n \n '
Phraser = models.phrases.Phraser
Phrases = models.phrases.Phrases
sentence_stream = sum(doc_sents, [])
common_terms = ['of', 'with', 'without', 'and', 'or', 'the', 'a', 'as']
phrases = Phrases(sentence_stream, common_terms=common_terms)
bigram = Phraser(phrases)
trigram = Phrases(bigram[sentence_stream])
output_strs = []
for idx in range(0, len(doc_sents)):
doc = doc_sents[idx]
output_doc = list(trigram[doc])
output_str = sum(output_doc, [])
output_strs.append(' '.join(output_str))
return output_strs |
@property
def height(self):
'\n The effective height of this placeholder shape; its directly-applied\n height if it has one, otherwise the height of its parent layout\n placeholder.\n '
return self._effective_value('height') | 7,468,113,242,768,823,000 | The effective height of this placeholder shape; its directly-applied
height if it has one, otherwise the height of its parent layout
placeholder. | pptx/shapes/placeholder.py | height | Adriyst/python-pptx | python | @property
def height(self):
'\n The effective height of this placeholder shape; its directly-applied\n height if it has one, otherwise the height of its parent layout\n placeholder.\n '
return self._effective_value('height') |
@property
def left(self):
'\n The effective left of this placeholder shape; its directly-applied\n left if it has one, otherwise the left of its parent layout\n placeholder.\n '
return self._effective_value('left') | -6,564,549,180,737,365,000 | The effective left of this placeholder shape; its directly-applied
left if it has one, otherwise the left of its parent layout
placeholder. | pptx/shapes/placeholder.py | left | Adriyst/python-pptx | python | @property
def left(self):
'\n The effective left of this placeholder shape; its directly-applied\n left if it has one, otherwise the left of its parent layout\n placeholder.\n '
return self._effective_value('left') |
@property
def shape_type(self):
'\n Member of :ref:`MsoShapeType` specifying the type of this shape.\n Unconditionally ``MSO_SHAPE_TYPE.PLACEHOLDER`` in this case.\n Read-only.\n '
return MSO_SHAPE_TYPE.PLACEHOLDER | -2,477,442,499,677,329,000 | Member of :ref:`MsoShapeType` specifying the type of this shape.
Unconditionally ``MSO_SHAPE_TYPE.PLACEHOLDER`` in this case.
Read-only. | pptx/shapes/placeholder.py | shape_type | Adriyst/python-pptx | python | @property
def shape_type(self):
'\n Member of :ref:`MsoShapeType` specifying the type of this shape.\n Unconditionally ``MSO_SHAPE_TYPE.PLACEHOLDER`` in this case.\n Read-only.\n '
return MSO_SHAPE_TYPE.PLACEHOLDER |
@property
def top(self):
'\n The effective top of this placeholder shape; its directly-applied\n top if it has one, otherwise the top of its parent layout\n placeholder.\n '
return self._effective_value('top') | -2,393,262,479,391,269,400 | The effective top of this placeholder shape; its directly-applied
top if it has one, otherwise the top of its parent layout
placeholder. | pptx/shapes/placeholder.py | top | Adriyst/python-pptx | python | @property
def top(self):
'\n The effective top of this placeholder shape; its directly-applied\n top if it has one, otherwise the top of its parent layout\n placeholder.\n '
return self._effective_value('top') |
@property
def width(self):
'\n The effective width of this placeholder shape; its directly-applied\n width if it has one, otherwise the width of its parent layout\n placeholder.\n '
return self._effective_value('width') | -1,524,664,289,232,801,500 | The effective width of this placeholder shape; its directly-applied
width if it has one, otherwise the width of its parent layout
placeholder. | pptx/shapes/placeholder.py | width | Adriyst/python-pptx | python | @property
def width(self):
'\n The effective width of this placeholder shape; its directly-applied\n width if it has one, otherwise the width of its parent layout\n placeholder.\n '
return self._effective_value('width') |
@property
def _base_placeholder(self):
'\n Return the layout or master placeholder shape this placeholder\n inherits from. Not to be confused with an instance of\n |BasePlaceholder| (necessarily).\n '
raise NotImplementedError('Must be implemented by all subclasses.') | 3,763,772,925,094,577,000 | Return the layout or master placeholder shape this placeholder
inherits from. Not to be confused with an instance of
|BasePlaceholder| (necessarily). | pptx/shapes/placeholder.py | _base_placeholder | Adriyst/python-pptx | python | @property
def _base_placeholder(self):
'\n Return the layout or master placeholder shape this placeholder\n inherits from. Not to be confused with an instance of\n |BasePlaceholder| (necessarily).\n '
raise NotImplementedError('Must be implemented by all subclasses.') |
def _effective_value(self, attr_name):
'\n The effective value of *attr_name* on this placeholder shape; its\n directly-applied value if it has one, otherwise the value on the\n layout placeholder it inherits from.\n '
directly_applied_value = getattr(super(_InheritsDimensions, self), attr_name)
if (directly_applied_value is not None):
return directly_applied_value
return self._inherited_value(attr_name) | 3,257,120,458,678,706,700 | The effective value of *attr_name* on this placeholder shape; its
directly-applied value if it has one, otherwise the value on the
layout placeholder it inherits from. | pptx/shapes/placeholder.py | _effective_value | Adriyst/python-pptx | python | def _effective_value(self, attr_name):
'\n The effective value of *attr_name* on this placeholder shape; its\n directly-applied value if it has one, otherwise the value on the\n layout placeholder it inherits from.\n '
directly_applied_value = getattr(super(_InheritsDimensions, self), attr_name)
if (directly_applied_value is not None):
return directly_applied_value
return self._inherited_value(attr_name) |
def _inherited_value(self, attr_name):
"\n Return the attribute value, e.g. 'width' of the base placeholder this\n placeholder inherits from.\n "
base_placeholder = self._base_placeholder
if (base_placeholder is None):
return None
inherited_value = getattr(base_placeholder, attr_name)
return inherited_value | -3,245,702,123,604,787,700 | Return the attribute value, e.g. 'width' of the base placeholder this
placeholder inherits from. | pptx/shapes/placeholder.py | _inherited_value | Adriyst/python-pptx | python | def _inherited_value(self, attr_name):
"\n Return the attribute value, e.g. 'width' of the base placeholder this\n placeholder inherits from.\n "
base_placeholder = self._base_placeholder
if (base_placeholder is None):
return None
inherited_value = getattr(base_placeholder, attr_name)
return inherited_value |
@property
def is_placeholder(self):
'\n Boolean indicating whether this shape is a placeholder.\n Unconditionally |True| in this case.\n '
return True | -5,988,402,684,897,886,000 | Boolean indicating whether this shape is a placeholder.
Unconditionally |True| in this case. | pptx/shapes/placeholder.py | is_placeholder | Adriyst/python-pptx | python | @property
def is_placeholder(self):
'\n Boolean indicating whether this shape is a placeholder.\n Unconditionally |True| in this case.\n '
return True |
@property
def shape_type(self):
'\n Member of :ref:`MsoShapeType` specifying the type of this shape.\n Unconditionally ``MSO_SHAPE_TYPE.PLACEHOLDER`` in this case.\n Read-only.\n '
return MSO_SHAPE_TYPE.PLACEHOLDER | -2,477,442,499,677,329,000 | Member of :ref:`MsoShapeType` specifying the type of this shape.
Unconditionally ``MSO_SHAPE_TYPE.PLACEHOLDER`` in this case.
Read-only. | pptx/shapes/placeholder.py | shape_type | Adriyst/python-pptx | python | @property
def shape_type(self):
'\n Member of :ref:`MsoShapeType` specifying the type of this shape.\n Unconditionally ``MSO_SHAPE_TYPE.PLACEHOLDER`` in this case.\n Read-only.\n '
return MSO_SHAPE_TYPE.PLACEHOLDER |
@property
def _base_placeholder(self):
'\n Return the layout placeholder this slide placeholder inherits from.\n Not to be confused with an instance of |BasePlaceholder|\n (necessarily).\n '
(layout, idx) = (self.part.slide_layout, self._element.ph_idx)
return layout.placeholders.get(idx=idx) | -6,442,858,045,183,785,000 | Return the layout placeholder this slide placeholder inherits from.
Not to be confused with an instance of |BasePlaceholder|
(necessarily). | pptx/shapes/placeholder.py | _base_placeholder | Adriyst/python-pptx | python | @property
def _base_placeholder(self):
'\n Return the layout placeholder this slide placeholder inherits from.\n Not to be confused with an instance of |BasePlaceholder|\n (necessarily).\n '
(layout, idx) = (self.part.slide_layout, self._element.ph_idx)
return layout.placeholders.get(idx=idx) |
def _replace_placeholder_with(self, element):
"\n Substitute *element* for this placeholder element in the shapetree.\n This placeholder's `._element` attribute is set to |None| and its\n original element is free for garbage collection. Any attribute access\n (including a method call) on this placeholder after this call raises\n |AttributeError|.\n "
element._nvXxPr.nvPr._insert_ph(self._element.ph)
self._element.addprevious(element)
self._element.getparent().remove(self._element)
self._element = None | 119,120,443,463,842,640 | Substitute *element* for this placeholder element in the shapetree.
This placeholder's `._element` attribute is set to |None| and its
original element is free for garbage collection. Any attribute access
(including a method call) on this placeholder after this call raises
|AttributeError|. | pptx/shapes/placeholder.py | _replace_placeholder_with | Adriyst/python-pptx | python | def _replace_placeholder_with(self, element):
"\n Substitute *element* for this placeholder element in the shapetree.\n This placeholder's `._element` attribute is set to |None| and its\n original element is free for garbage collection. Any attribute access\n (including a method call) on this placeholder after this call raises\n |AttributeError|.\n "
element._nvXxPr.nvPr._insert_ph(self._element.ph)
self._element.addprevious(element)
self._element.getparent().remove(self._element)
self._element = None |
@property
def idx(self):
"\n Integer placeholder 'idx' attribute, e.g. 0\n "
return self._sp.ph_idx | -5,393,356,322,313,205,000 | Integer placeholder 'idx' attribute, e.g. 0 | pptx/shapes/placeholder.py | idx | Adriyst/python-pptx | python | @property
def idx(self):
"\n \n "
return self._sp.ph_idx |
@property
def orient(self):
'\n Placeholder orientation, e.g. ST_Direction.HORZ\n '
return self._sp.ph_orient | -7,293,633,352,992,293,000 | Placeholder orientation, e.g. ST_Direction.HORZ | pptx/shapes/placeholder.py | orient | Adriyst/python-pptx | python | @property
def orient(self):
'\n \n '
return self._sp.ph_orient |
@property
def ph_type(self):
'\n Placeholder type, e.g. PP_PLACEHOLDER.CENTER_TITLE\n '
return self._sp.ph_type | 6,022,595,088,119,431,000 | Placeholder type, e.g. PP_PLACEHOLDER.CENTER_TITLE | pptx/shapes/placeholder.py | ph_type | Adriyst/python-pptx | python | @property
def ph_type(self):
'\n \n '
return self._sp.ph_type |
@property
def sz(self):
"\n Placeholder 'sz' attribute, e.g. ST_PlaceholderSize.FULL\n "
return self._sp.ph_sz | 574,434,602,822,876,800 | Placeholder 'sz' attribute, e.g. ST_PlaceholderSize.FULL | pptx/shapes/placeholder.py | sz | Adriyst/python-pptx | python | @property
def sz(self):
"\n \n "
return self._sp.ph_sz |
@property
def _base_placeholder(self):
'\n Return the master placeholder this layout placeholder inherits from.\n '
base_ph_type = {PP_PLACEHOLDER.BODY: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.CHART: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.BITMAP: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.CENTER_TITLE: PP_PLACEHOLDER.TITLE, PP_PLACEHOLDER.ORG_CHART: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.DATE: PP_PLACEHOLDER.DATE, PP_PLACEHOLDER.FOOTER: PP_PLACEHOLDER.FOOTER, PP_PLACEHOLDER.MEDIA_CLIP: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.OBJECT: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.PICTURE: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.SLIDE_NUMBER: PP_PLACEHOLDER.SLIDE_NUMBER, PP_PLACEHOLDER.SUBTITLE: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.TABLE: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.TITLE: PP_PLACEHOLDER.TITLE}[self._element.ph_type]
slide_master = self.part.slide_master
return slide_master.placeholders.get(base_ph_type, None) | -8,909,599,504,009,577,000 | Return the master placeholder this layout placeholder inherits from. | pptx/shapes/placeholder.py | _base_placeholder | Adriyst/python-pptx | python | @property
def _base_placeholder(self):
'\n \n '
base_ph_type = {PP_PLACEHOLDER.BODY: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.CHART: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.BITMAP: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.CENTER_TITLE: PP_PLACEHOLDER.TITLE, PP_PLACEHOLDER.ORG_CHART: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.DATE: PP_PLACEHOLDER.DATE, PP_PLACEHOLDER.FOOTER: PP_PLACEHOLDER.FOOTER, PP_PLACEHOLDER.MEDIA_CLIP: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.OBJECT: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.PICTURE: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.SLIDE_NUMBER: PP_PLACEHOLDER.SLIDE_NUMBER, PP_PLACEHOLDER.SUBTITLE: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.TABLE: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.TITLE: PP_PLACEHOLDER.TITLE}[self._element.ph_type]
slide_master = self.part.slide_master
return slide_master.placeholders.get(base_ph_type, None) |
@property
def _base_placeholder(self):
'\n Return the notes master placeholder this notes slide placeholder\n inherits from, or |None| if no placeholder of the matching type is\n present.\n '
notes_master = self.part.notes_master
ph_type = self.element.ph_type
return notes_master.placeholders.get(ph_type=ph_type) | -3,631,829,249,761,758,700 | Return the notes master placeholder this notes slide placeholder
inherits from, or |None| if no placeholder of the matching type is
present. | pptx/shapes/placeholder.py | _base_placeholder | Adriyst/python-pptx | python | @property
def _base_placeholder(self):
'\n Return the notes master placeholder this notes slide placeholder\n inherits from, or |None| if no placeholder of the matching type is\n present.\n '
notes_master = self.part.notes_master
ph_type = self.element.ph_type
return notes_master.placeholders.get(ph_type=ph_type) |
def insert_chart(self, chart_type, chart_data):
'\n Return a |PlaceholderGraphicFrame| object containing a new chart of\n *chart_type* depicting *chart_data* and having the same position and\n size as this placeholder. *chart_type* is one of the\n :ref:`XlChartType` enumeration values. *chart_data* is a |ChartData|\n object populated with the categories and series values for the chart.\n Note that the new |Chart| object is not returned directly. The chart\n object may be accessed using the\n :attr:`~.PlaceholderGraphicFrame.chart` property of the returned\n |PlaceholderGraphicFrame| object.\n '
rId = self.part.add_chart_part(chart_type, chart_data)
graphicFrame = self._new_chart_graphicFrame(rId, self.left, self.top, self.width, self.height)
self._replace_placeholder_with(graphicFrame)
return PlaceholderGraphicFrame(graphicFrame, self._parent) | -5,297,237,133,140,146,000 | Return a |PlaceholderGraphicFrame| object containing a new chart of
*chart_type* depicting *chart_data* and having the same position and
size as this placeholder. *chart_type* is one of the
:ref:`XlChartType` enumeration values. *chart_data* is a |ChartData|
object populated with the categories and series values for the chart.
Note that the new |Chart| object is not returned directly. The chart
object may be accessed using the
:attr:`~.PlaceholderGraphicFrame.chart` property of the returned
|PlaceholderGraphicFrame| object. | pptx/shapes/placeholder.py | insert_chart | Adriyst/python-pptx | python | def insert_chart(self, chart_type, chart_data):
'\n Return a |PlaceholderGraphicFrame| object containing a new chart of\n *chart_type* depicting *chart_data* and having the same position and\n size as this placeholder. *chart_type* is one of the\n :ref:`XlChartType` enumeration values. *chart_data* is a |ChartData|\n object populated with the categories and series values for the chart.\n Note that the new |Chart| object is not returned directly. The chart\n object may be accessed using the\n :attr:`~.PlaceholderGraphicFrame.chart` property of the returned\n |PlaceholderGraphicFrame| object.\n '
rId = self.part.add_chart_part(chart_type, chart_data)
graphicFrame = self._new_chart_graphicFrame(rId, self.left, self.top, self.width, self.height)
self._replace_placeholder_with(graphicFrame)
return PlaceholderGraphicFrame(graphicFrame, self._parent) |
def _new_chart_graphicFrame(self, rId, x, y, cx, cy):
'\n Return a newly created `p:graphicFrame` element having the specified\n position and size and containing the chart identified by *rId*.\n '
(id_, name) = (self.shape_id, self.name)
return CT_GraphicalObjectFrame.new_chart_graphicFrame(id_, name, rId, x, y, cx, cy) | 3,742,909,272,327,434,000 | Return a newly created `p:graphicFrame` element having the specified
position and size and containing the chart identified by *rId*. | pptx/shapes/placeholder.py | _new_chart_graphicFrame | Adriyst/python-pptx | python | def _new_chart_graphicFrame(self, rId, x, y, cx, cy):
'\n Return a newly created `p:graphicFrame` element having the specified\n position and size and containing the chart identified by *rId*.\n '
(id_, name) = (self.shape_id, self.name)
return CT_GraphicalObjectFrame.new_chart_graphicFrame(id_, name, rId, x, y, cx, cy) |
def insert_picture(self, image_file):
'Return a |PlaceholderPicture| object depicting the image in `image_file`.\n\n `image_file` may be either a path (string) or a file-like object. The image is\n cropped to fill the entire space of the placeholder. A |PlaceholderPicture|\n object has all the properties and methods of a |Picture| shape except that the\n value of its :attr:`~._BaseSlidePlaceholder.shape_type` property is\n `MSO_SHAPE_TYPE.PLACEHOLDER` instead of `MSO_SHAPE_TYPE.PICTURE`.\n '
pic = self._new_placeholder_pic(image_file)
self._replace_placeholder_with(pic)
return PlaceholderPicture(pic, self._parent) | -7,307,511,244,919,935,000 | Return a |PlaceholderPicture| object depicting the image in `image_file`.
`image_file` may be either a path (string) or a file-like object. The image is
cropped to fill the entire space of the placeholder. A |PlaceholderPicture|
object has all the properties and methods of a |Picture| shape except that the
value of its :attr:`~._BaseSlidePlaceholder.shape_type` property is
`MSO_SHAPE_TYPE.PLACEHOLDER` instead of `MSO_SHAPE_TYPE.PICTURE`. | pptx/shapes/placeholder.py | insert_picture | Adriyst/python-pptx | python | def insert_picture(self, image_file):
'Return a |PlaceholderPicture| object depicting the image in `image_file`.\n\n `image_file` may be either a path (string) or a file-like object. The image is\n cropped to fill the entire space of the placeholder. A |PlaceholderPicture|\n object has all the properties and methods of a |Picture| shape except that the\n value of its :attr:`~._BaseSlidePlaceholder.shape_type` property is\n `MSO_SHAPE_TYPE.PLACEHOLDER` instead of `MSO_SHAPE_TYPE.PICTURE`.\n '
pic = self._new_placeholder_pic(image_file)
self._replace_placeholder_with(pic)
return PlaceholderPicture(pic, self._parent) |
def _new_placeholder_pic(self, image_file):
'\n Return a new `p:pic` element depicting the image in *image_file*,\n suitable for use as a placeholder. In particular this means not\n having an `a:xfrm` element, allowing its extents to be inherited from\n its layout placeholder.\n '
(rId, desc, image_size) = self._get_or_add_image(image_file)
(shape_id, name) = (self.shape_id, self.name)
pic = CT_Picture.new_ph_pic(shape_id, name, desc, rId)
pic.crop_to_fit(image_size, (self.width, self.height))
return pic | -1,111,839,340,163,950,600 | Return a new `p:pic` element depicting the image in *image_file*,
suitable for use as a placeholder. In particular this means not
having an `a:xfrm` element, allowing its extents to be inherited from
its layout placeholder. | pptx/shapes/placeholder.py | _new_placeholder_pic | Adriyst/python-pptx | python | def _new_placeholder_pic(self, image_file):
'\n Return a new `p:pic` element depicting the image in *image_file*,\n suitable for use as a placeholder. In particular this means not\n having an `a:xfrm` element, allowing its extents to be inherited from\n its layout placeholder.\n '
(rId, desc, image_size) = self._get_or_add_image(image_file)
(shape_id, name) = (self.shape_id, self.name)
pic = CT_Picture.new_ph_pic(shape_id, name, desc, rId)
pic.crop_to_fit(image_size, (self.width, self.height))
return pic |
def _get_or_add_image(self, image_file):
'\n Return an (rId, description, image_size) 3-tuple identifying the\n related image part containing *image_file* and describing the image.\n '
(image_part, rId) = self.part.get_or_add_image_part(image_file)
(desc, image_size) = (image_part.desc, image_part._px_size)
return (rId, desc, image_size) | 1,590,408,181,353,432,800 | Return an (rId, description, image_size) 3-tuple identifying the
related image part containing *image_file* and describing the image. | pptx/shapes/placeholder.py | _get_or_add_image | Adriyst/python-pptx | python | def _get_or_add_image(self, image_file):
'\n Return an (rId, description, image_size) 3-tuple identifying the\n related image part containing *image_file* and describing the image.\n '
(image_part, rId) = self.part.get_or_add_image_part(image_file)
(desc, image_size) = (image_part.desc, image_part._px_size)
return (rId, desc, image_size) |
@property
def is_placeholder(self):
'\n Boolean indicating whether this shape is a placeholder.\n Unconditionally |True| in this case.\n '
return True | -5,988,402,684,897,886,000 | Boolean indicating whether this shape is a placeholder.
Unconditionally |True| in this case. | pptx/shapes/placeholder.py | is_placeholder | Adriyst/python-pptx | python | @property
def is_placeholder(self):
'\n Boolean indicating whether this shape is a placeholder.\n Unconditionally |True| in this case.\n '
return True |
@property
def _base_placeholder(self):
'\n Return the layout placeholder this picture placeholder inherits from.\n '
(layout, idx) = (self.part.slide_layout, self._element.ph_idx)
return layout.placeholders.get(idx=idx) | -4,162,995,238,305,199,000 | Return the layout placeholder this picture placeholder inherits from. | pptx/shapes/placeholder.py | _base_placeholder | Adriyst/python-pptx | python | @property
def _base_placeholder(self):
'\n \n '
(layout, idx) = (self.part.slide_layout, self._element.ph_idx)
return layout.placeholders.get(idx=idx) |
def insert_table(self, rows, cols):
'Return |PlaceholderGraphicFrame| object containing a `rows` by `cols` table.\n\n The position and width of the table are those of the placeholder and its height\n is proportional to the number of rows. A |PlaceholderGraphicFrame| object has\n all the properties and methods of a |GraphicFrame| shape except that the value\n of its :attr:`~._BaseSlidePlaceholder.shape_type` property is unconditionally\n `MSO_SHAPE_TYPE.PLACEHOLDER`. Note that the return value is not the new table\n but rather *contains* the new table. The table can be accessed using the\n :attr:`~.PlaceholderGraphicFrame.table` property of the returned\n |PlaceholderGraphicFrame| object.\n '
graphicFrame = self._new_placeholder_table(rows, cols)
self._replace_placeholder_with(graphicFrame)
return PlaceholderGraphicFrame(graphicFrame, self._parent) | -2,900,114,170,172,023,000 | Return |PlaceholderGraphicFrame| object containing a `rows` by `cols` table.
The position and width of the table are those of the placeholder and its height
is proportional to the number of rows. A |PlaceholderGraphicFrame| object has
all the properties and methods of a |GraphicFrame| shape except that the value
of its :attr:`~._BaseSlidePlaceholder.shape_type` property is unconditionally
`MSO_SHAPE_TYPE.PLACEHOLDER`. Note that the return value is not the new table
but rather *contains* the new table. The table can be accessed using the
:attr:`~.PlaceholderGraphicFrame.table` property of the returned
|PlaceholderGraphicFrame| object. | pptx/shapes/placeholder.py | insert_table | Adriyst/python-pptx | python | def insert_table(self, rows, cols):
'Return |PlaceholderGraphicFrame| object containing a `rows` by `cols` table.\n\n The position and width of the table are those of the placeholder and its height\n is proportional to the number of rows. A |PlaceholderGraphicFrame| object has\n all the properties and methods of a |GraphicFrame| shape except that the value\n of its :attr:`~._BaseSlidePlaceholder.shape_type` property is unconditionally\n `MSO_SHAPE_TYPE.PLACEHOLDER`. Note that the return value is not the new table\n but rather *contains* the new table. The table can be accessed using the\n :attr:`~.PlaceholderGraphicFrame.table` property of the returned\n |PlaceholderGraphicFrame| object.\n '
graphicFrame = self._new_placeholder_table(rows, cols)
self._replace_placeholder_with(graphicFrame)
return PlaceholderGraphicFrame(graphicFrame, self._parent) |
def _new_placeholder_table(self, rows, cols):
"\n Return a newly added `p:graphicFrame` element containing an empty\n table with *rows* rows and *cols* columns, positioned at the location\n of this placeholder and having its same width. The table's height is\n determined by the number of rows.\n "
(shape_id, name, height) = (self.shape_id, self.name, Emu((rows * 370840)))
return CT_GraphicalObjectFrame.new_table_graphicFrame(shape_id, name, rows, cols, self.left, self.top, self.width, height) | -2,623,100,528,436,390,000 | Return a newly added `p:graphicFrame` element containing an empty
table with *rows* rows and *cols* columns, positioned at the location
of this placeholder and having its same width. The table's height is
determined by the number of rows. | pptx/shapes/placeholder.py | _new_placeholder_table | Adriyst/python-pptx | python | def _new_placeholder_table(self, rows, cols):
"\n Return a newly added `p:graphicFrame` element containing an empty\n table with *rows* rows and *cols* columns, positioned at the location\n of this placeholder and having its same width. The table's height is\n determined by the number of rows.\n "
(shape_id, name, height) = (self.shape_id, self.name, Emu((rows * 370840)))
return CT_GraphicalObjectFrame.new_table_graphicFrame(shape_id, name, rows, cols, self.left, self.top, self.width, height) |
def get_asymmetry(arr):
'\n Returns asymmetry of an array.\n \n Parameters\n ----------\n arr : ndarray\n an array to find asymmetry\n\n Returns\n -------\n ndarray\n an array capturing asymmetry of original array\n '
arr_rev = arr[::(- 1), ::(- 1), ::(- 1)]
denom = ((arr + arr_rev) / 2.0)
denom_nz = np.where((denom == 0), 1.0, denom)
asym = np.where((denom > 0.0), (abs((arr - arr_rev)) / denom_nz), 0.0)
return np.where((arr > 0), asym, 0) | 4,152,776,174,522,038,300 | Returns asymmetry of an array.
Parameters
----------
arr : ndarray
an array to find asymmetry
Returns
-------
ndarray
an array capturing asymmetry of original array | scripts/alien_tools.py | get_asymmetry | AdvancedPhotonSource/cdisupp | python | def get_asymmetry(arr):
'\n Returns asymmetry of an array.\n \n Parameters\n ----------\n arr : ndarray\n an array to find asymmetry\n\n Returns\n -------\n ndarray\n an array capturing asymmetry of original array\n '
arr_rev = arr[::(- 1), ::(- 1), ::(- 1)]
denom = ((arr + arr_rev) / 2.0)
denom_nz = np.where((denom == 0), 1.0, denom)
asym = np.where((denom > 0.0), (abs((arr - arr_rev)) / denom_nz), 0.0)
return np.where((arr > 0), asym, 0) |
def analyze_clusters(arr, labels, nz):
'\n Analyzes clusters and returns characteristics in arrays.\n \n Parameters\n ----------\n arr : ndarray\n the analyzed array\n labels: arr\n cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1.\n nz : tuple\n tuple of arrays, each array containing indices of elements in arr that are non-zero along one axis.\n \n Returns\n -------\n tuple\n tuple containing the following arrays:\n nlabels # number of labels, i.e clusters\n labels_arr # array with label for each non zero point\n rel_cluster_size # array with cluster size divided by max cluster size for each\n # non zero point\n cluster_avg # array with cluster average for each non zero point\n noise_arr # array with points that are non zero but not in cluster\n no_noise # array with noise poits set to 0\n label_counts # tuple of two arrays: First is label number, second is number of\n # occurances of that label (size of cluster)\n cluster_avg_asym # array with average asymmetry of a points in cluster\n asymmetry # array of asymmetry with regard to entire array\n cluster_size # array with cluster size for each non zero point\n '
labels_arr = np.zeros_like(arr)
noise_arr = np.zeros_like(arr)
cluster_size = np.zeros_like(arr)
cluster_avg = np.zeros_like(arr).astype(np.float32)
cluster_avg_asym = np.zeros_like(arr).astype(np.float32)
asymmetry = get_asymmetry(arr)
label_counts = np.unique(labels, return_counts=True)
labels_arr[nz] = labels
noise_pts = tuple([nz[n][(labels == (- 1))] for n in range(3)])
no_noise = arr
noise_arr[noise_pts] = arr[noise_pts]
no_noise[noise_pts] = 0
nlabels = len(label_counts[0])
for n in range(1, nlabels):
n_lab = label_counts[0][n]
cluspts = tuple([nz[d][(labels == n_lab)] for d in range(3)])
cluster_size[cluspts] = label_counts[1][n]
cluster_avg[cluspts] = (np.sum(arr[cluspts]) / cluspts[0].size)
cluster_avg_asym[cluspts] = (np.sum(asymmetry[cluspts]) / cluspts[0].size)
rel_cluster_size = (cluster_size / cluster_size.max())
return (nlabels, labels_arr, rel_cluster_size, cluster_avg, noise_arr, no_noise, label_counts, cluster_avg_asym, asymmetry, cluster_size) | -8,017,559,099,873,137,000 | Analyzes clusters and returns characteristics in arrays.
Parameters
----------
arr : ndarray
the analyzed array
labels: arr
cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1.
nz : tuple
tuple of arrays, each array containing indices of elements in arr that are non-zero along one axis.
Returns
-------
tuple
tuple containing the following arrays:
nlabels # number of labels, i.e clusters
labels_arr # array with label for each non zero point
rel_cluster_size # array with cluster size divided by max cluster size for each
# non zero point
cluster_avg # array with cluster average for each non zero point
noise_arr # array with points that are non zero but not in cluster
no_noise # array with noise poits set to 0
label_counts # tuple of two arrays: First is label number, second is number of
# occurances of that label (size of cluster)
cluster_avg_asym # array with average asymmetry of a points in cluster
asymmetry # array of asymmetry with regard to entire array
cluster_size # array with cluster size for each non zero point | scripts/alien_tools.py | analyze_clusters | AdvancedPhotonSource/cdisupp | python | def analyze_clusters(arr, labels, nz):
'\n Analyzes clusters and returns characteristics in arrays.\n \n Parameters\n ----------\n arr : ndarray\n the analyzed array\n labels: arr\n cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1.\n nz : tuple\n tuple of arrays, each array containing indices of elements in arr that are non-zero along one axis.\n \n Returns\n -------\n tuple\n tuple containing the following arrays:\n nlabels # number of labels, i.e clusters\n labels_arr # array with label for each non zero point\n rel_cluster_size # array with cluster size divided by max cluster size for each\n # non zero point\n cluster_avg # array with cluster average for each non zero point\n noise_arr # array with points that are non zero but not in cluster\n no_noise # array with noise poits set to 0\n label_counts # tuple of two arrays: First is label number, second is number of\n # occurances of that label (size of cluster)\n cluster_avg_asym # array with average asymmetry of a points in cluster\n asymmetry # array of asymmetry with regard to entire array\n cluster_size # array with cluster size for each non zero point\n '
labels_arr = np.zeros_like(arr)
noise_arr = np.zeros_like(arr)
cluster_size = np.zeros_like(arr)
cluster_avg = np.zeros_like(arr).astype(np.float32)
cluster_avg_asym = np.zeros_like(arr).astype(np.float32)
asymmetry = get_asymmetry(arr)
label_counts = np.unique(labels, return_counts=True)
labels_arr[nz] = labels
noise_pts = tuple([nz[n][(labels == (- 1))] for n in range(3)])
no_noise = arr
noise_arr[noise_pts] = arr[noise_pts]
no_noise[noise_pts] = 0
nlabels = len(label_counts[0])
for n in range(1, nlabels):
n_lab = label_counts[0][n]
cluspts = tuple([nz[d][(labels == n_lab)] for d in range(3)])
cluster_size[cluspts] = label_counts[1][n]
cluster_avg[cluspts] = (np.sum(arr[cluspts]) / cluspts[0].size)
cluster_avg_asym[cluspts] = (np.sum(asymmetry[cluspts]) / cluspts[0].size)
rel_cluster_size = (cluster_size / cluster_size.max())
return (nlabels, labels_arr, rel_cluster_size, cluster_avg, noise_arr, no_noise, label_counts, cluster_avg_asym, asymmetry, cluster_size) |
def crop_center(arr):
'\n Finds max element in array and crops the array to be symetrical with regard to this point in each direction.\n \n Parameters\n ----------\n arr : ndarray\n an array\n\n Returns\n -------\n centered : ndarray\n an array symetrical in all dimensions around the max element of input array\n '
shape = arr.shape
center = np.unravel_index(np.argmax(arr, axis=None), shape)
principium = []
finis = []
for i in range(len(shape)):
half_shape = min(center[i], ((shape[i] - center[i]) - 1))
principium.append((center[i] - half_shape))
finis.append(((center[i] + half_shape) + 1))
centered = arr[principium[0]:finis[0], principium[1]:finis[1], principium[2]:finis[2]]
return centered | -1,185,450,012,814,468,600 | Finds max element in array and crops the array to be symetrical with regard to this point in each direction.
Parameters
----------
arr : ndarray
an array
Returns
-------
centered : ndarray
an array symetrical in all dimensions around the max element of input array | scripts/alien_tools.py | crop_center | AdvancedPhotonSource/cdisupp | python | def crop_center(arr):
'\n Finds max element in array and crops the array to be symetrical with regard to this point in each direction.\n \n Parameters\n ----------\n arr : ndarray\n an array\n\n Returns\n -------\n centered : ndarray\n an array symetrical in all dimensions around the max element of input array\n '
shape = arr.shape
center = np.unravel_index(np.argmax(arr, axis=None), shape)
principium = []
finis = []
for i in range(len(shape)):
half_shape = min(center[i], ((shape[i] - center[i]) - 1))
principium.append((center[i] - half_shape))
finis.append(((center[i] + half_shape) + 1))
centered = arr[principium[0]:finis[0], principium[1]:finis[1], principium[2]:finis[2]]
return centered |
def save_arr(arr, dir, fname):
"\n Saves an array in 'tif' format file.\n \n Parameters\n ----------\n arr : ndarray\n an array to save\n dir : str\n directory to save the file to\n fname : str\n file name\n\n Returns\n -------\n nothing\n "
if (dir is not None):
full_name = os.path.join(dir, fname)
else:
full_name = fname
tif.imsave(full_name, arr.transpose().astype(np.float32)) | 6,513,454,682,440,310,000 | Saves an array in 'tif' format file.
Parameters
----------
arr : ndarray
an array to save
dir : str
directory to save the file to
fname : str
file name
Returns
-------
nothing | scripts/alien_tools.py | save_arr | AdvancedPhotonSource/cdisupp | python | def save_arr(arr, dir, fname):
"\n Saves an array in 'tif' format file.\n \n Parameters\n ----------\n arr : ndarray\n an array to save\n dir : str\n directory to save the file to\n fname : str\n file name\n\n Returns\n -------\n nothing\n "
if (dir is not None):
full_name = os.path.join(dir, fname)
else:
full_name = fname
tif.imsave(full_name, arr.transpose().astype(np.float32)) |
def save_arrays(arrs, iter, thresh, eps, dir):
"\n Saves multiple arrays in 'tif' format files. Determines file name from given parameters: iteration, threshold, and eps.\n \n Parameters\n ----------\n arr : tuple\n a tuple of arrays to save\n iter, thresh, eps : str, str, str\n parameters: iteration, threshold, and eps, to deliver file name from\n dir : str\n directory to save the file to\n\n Returns\n -------\n nothing\n "
save_arr(arrs[1], dir, ('db%d_%3.2f_labels_arr%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[2], dir, ('db%d_%3.2f_rel_clustersizes%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[3], dir, ('db%d_%3.2f_clusteravg%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[4], dir, ('db%d_%3.2f_noise%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[5], dir, ('db%d_%3.2f_no_noise%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[7], dir, ('db%d_%3.2f_clusteravgasym%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[8], dir, ('db%d_%3.2f_asym%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[9], dir, ('db%d_%3.2f_abs_clustersizes%3.2f.tif' % (iter, thresh, eps))) | 7,043,521,248,600,779,000 | Saves multiple arrays in 'tif' format files. Determines file name from given parameters: iteration, threshold, and eps.
Parameters
----------
arr : tuple
a tuple of arrays to save
iter, thresh, eps : str, str, str
parameters: iteration, threshold, and eps, to deliver file name from
dir : str
directory to save the file to
Returns
-------
nothing | scripts/alien_tools.py | save_arrays | AdvancedPhotonSource/cdisupp | python | def save_arrays(arrs, iter, thresh, eps, dir):
"\n Saves multiple arrays in 'tif' format files. Determines file name from given parameters: iteration, threshold, and eps.\n \n Parameters\n ----------\n arr : tuple\n a tuple of arrays to save\n iter, thresh, eps : str, str, str\n parameters: iteration, threshold, and eps, to deliver file name from\n dir : str\n directory to save the file to\n\n Returns\n -------\n nothing\n "
save_arr(arrs[1], dir, ('db%d_%3.2f_labels_arr%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[2], dir, ('db%d_%3.2f_rel_clustersizes%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[3], dir, ('db%d_%3.2f_clusteravg%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[4], dir, ('db%d_%3.2f_noise%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[5], dir, ('db%d_%3.2f_no_noise%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[7], dir, ('db%d_%3.2f_clusteravgasym%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[8], dir, ('db%d_%3.2f_asym%3.2f.tif' % (iter, thresh, eps)))
save_arr(arrs[9], dir, ('db%d_%3.2f_abs_clustersizes%3.2f.tif' % (iter, thresh, eps))) |
def auto_alien1(data, config, data_dir=None):
"\n Removes aliens from experimental CDI data using iterative algorithm and returns the result.\n \n The algorithm follows the steps:\n 1. Initialization:\n - initialize variables with the configuration parameters\n - crop the data array around maximum element to it's biggest size\n - sets points below threshold value to 0\n - finds non-zero elements of the data array and keeps them as tuples of indices\n 2. Iteration loop, runs until number of clasters remains unchanged\n - runs DBSCAN algorithm on the non-zero and returns clasters labels\n - analyzes the results to find relative clusters sizes, and clusters average asymmetry, and other characteristics\n - removes alien clusters, i.e. the ones with relative cluster size below configured size threshold and with average asymmetry over configured asymmetry threshold\n - go back to the loop using the non-zero elements of alien removed array to the DBSCAN\n 3. If configured, add final step to apply gaussian convolusion to the result and use it as a filter with configured sigma as threshold\n \n Parameters\n ----------\n data : ndarray\n an array with experiment data\n config : Object\n configuration object providing access to configuration parameters\n data_dir : str\n a directory where 'alien_analysis' subdirectory will be created to save results of analysis if configured\n\n Returns\n -------\n cuboid : ndarray\n data array with removed aliens\n "
try:
size_threshold = config.AA1_size_threshold
except AttributeError:
size_threshold = 0.01
except Exception as e:
print('error parsing AA1_size_threshold ', str(e))
try:
asym_threshold = config.AA1_asym_threshold
except AttributeError:
asym_threshold = 1.75
except Exception as e:
print('error parsing AA1_asym_threshold ', str(e))
try:
min_pts = config.AA1_min_pts
except AttributeError:
min_pts = 5
except Exception as e:
print('error parsing AA1_min_pts ', str(e))
try:
eps = config.AA1_eps
except AttributeError:
eps = 1.1
except Exception as e:
print('error parsing AA1_eps ', str(e))
try:
threshold = config.AA1_amp_threshold
except AttributeError:
print('AA1_amp_threshold parameter not configured, not removing aliens')
return data
except Exception as e:
print('error parsing AA1_amp_threshold ', str(e))
try:
save_arrs = config.AA1_save_arrs
if save_arrs:
save_dir = os.path.join(data_dir, 'alien_analysis')
if (not os.path.exists(save_dir)):
os.makedirs(save_dir)
except AttributeError:
save_arrs = False
except Exception as e:
print('error parsing save_arrs ', str(e))
try:
expandcleanedsig = config.AA1_expandcleanedsigma
except AttributeError:
expandcleanedsig = 0.0
except Exception as e:
print('error parsing expandcleanedsig ', str(e))
cuboid = crop_center(data)
cuboid = np.where((cuboid >= threshold), cuboid, 0)
if save_arrs:
save_arr(cuboid, save_dir, ('db%3.2f_cuboid%3.2f.tif' % (threshold, eps)))
save_arr(cuboid[::(- 1), ::(- 1), ::(- 1)], save_dir, ('db%3.2f_cuboidrev%3.2f.tif' % (threshold, eps)))
non_zero = cuboid.nonzero()
iter = 0
nclusters = 0
finished = False
while (not finished):
non_zero = cuboid.nonzero()
labels = DBSCAN(eps=eps, metric='euclidean', min_samples=min_pts, n_jobs=(- 1)).fit_predict(np.array(non_zero).transpose().astype(np.float32))
arrs = analyze_clusters(cuboid, labels, non_zero)
if save_arrs:
save_arrays(arrs, iter, threshold, eps, save_dir)
if (nclusters == arrs[0]):
finished = True
nclusters = arrs[0]
if (iter == 0):
rel_cluster_size = arrs[2]
cluster_avg_asym = arrs[7]
cuboid = np.where(np.logical_and((rel_cluster_size < size_threshold), (cluster_avg_asym > asym_threshold)), 0.0, cuboid)
iter += 1
if (expandcleanedsig > 0):
cuboid = np.where((cuboid > 0), 1.0, 0.0)
sig = [expandcleanedsig, expandcleanedsig, 1.0]
cuboid = ut.gauss_conv_fft(cuboid, sig)
no_thresh_cuboid = crop_center(data)
cuboid = np.where((cuboid > 0.1), no_thresh_cuboid, 0.0)
return cuboid | 1,125,620,878,779,161,500 | Removes aliens from experimental CDI data using iterative algorithm and returns the result.
The algorithm follows the steps:
1. Initialization:
- initialize variables with the configuration parameters
- crop the data array around maximum element to it's biggest size
- sets points below threshold value to 0
- finds non-zero elements of the data array and keeps them as tuples of indices
2. Iteration loop, runs until number of clasters remains unchanged
- runs DBSCAN algorithm on the non-zero and returns clasters labels
- analyzes the results to find relative clusters sizes, and clusters average asymmetry, and other characteristics
- removes alien clusters, i.e. the ones with relative cluster size below configured size threshold and with average asymmetry over configured asymmetry threshold
- go back to the loop using the non-zero elements of alien removed array to the DBSCAN
3. If configured, add final step to apply gaussian convolusion to the result and use it as a filter with configured sigma as threshold
Parameters
----------
data : ndarray
an array with experiment data
config : Object
configuration object providing access to configuration parameters
data_dir : str
a directory where 'alien_analysis' subdirectory will be created to save results of analysis if configured
Returns
-------
cuboid : ndarray
data array with removed aliens | scripts/alien_tools.py | auto_alien1 | AdvancedPhotonSource/cdisupp | python | def auto_alien1(data, config, data_dir=None):
"\n Removes aliens from experimental CDI data using iterative algorithm and returns the result.\n \n The algorithm follows the steps:\n 1. Initialization:\n - initialize variables with the configuration parameters\n - crop the data array around maximum element to it's biggest size\n - sets points below threshold value to 0\n - finds non-zero elements of the data array and keeps them as tuples of indices\n 2. Iteration loop, runs until number of clasters remains unchanged\n - runs DBSCAN algorithm on the non-zero and returns clasters labels\n - analyzes the results to find relative clusters sizes, and clusters average asymmetry, and other characteristics\n - removes alien clusters, i.e. the ones with relative cluster size below configured size threshold and with average asymmetry over configured asymmetry threshold\n - go back to the loop using the non-zero elements of alien removed array to the DBSCAN\n 3. If configured, add final step to apply gaussian convolusion to the result and use it as a filter with configured sigma as threshold\n \n Parameters\n ----------\n data : ndarray\n an array with experiment data\n config : Object\n configuration object providing access to configuration parameters\n data_dir : str\n a directory where 'alien_analysis' subdirectory will be created to save results of analysis if configured\n\n Returns\n -------\n cuboid : ndarray\n data array with removed aliens\n "
try:
size_threshold = config.AA1_size_threshold
except AttributeError:
size_threshold = 0.01
except Exception as e:
print('error parsing AA1_size_threshold ', str(e))
try:
asym_threshold = config.AA1_asym_threshold
except AttributeError:
asym_threshold = 1.75
except Exception as e:
print('error parsing AA1_asym_threshold ', str(e))
try:
min_pts = config.AA1_min_pts
except AttributeError:
min_pts = 5
except Exception as e:
print('error parsing AA1_min_pts ', str(e))
try:
eps = config.AA1_eps
except AttributeError:
eps = 1.1
except Exception as e:
print('error parsing AA1_eps ', str(e))
try:
threshold = config.AA1_amp_threshold
except AttributeError:
print('AA1_amp_threshold parameter not configured, not removing aliens')
return data
except Exception as e:
print('error parsing AA1_amp_threshold ', str(e))
try:
save_arrs = config.AA1_save_arrs
if save_arrs:
save_dir = os.path.join(data_dir, 'alien_analysis')
if (not os.path.exists(save_dir)):
os.makedirs(save_dir)
except AttributeError:
save_arrs = False
except Exception as e:
print('error parsing save_arrs ', str(e))
try:
expandcleanedsig = config.AA1_expandcleanedsigma
except AttributeError:
expandcleanedsig = 0.0
except Exception as e:
print('error parsing expandcleanedsig ', str(e))
cuboid = crop_center(data)
cuboid = np.where((cuboid >= threshold), cuboid, 0)
if save_arrs:
save_arr(cuboid, save_dir, ('db%3.2f_cuboid%3.2f.tif' % (threshold, eps)))
save_arr(cuboid[::(- 1), ::(- 1), ::(- 1)], save_dir, ('db%3.2f_cuboidrev%3.2f.tif' % (threshold, eps)))
non_zero = cuboid.nonzero()
iter = 0
nclusters = 0
finished = False
while (not finished):
non_zero = cuboid.nonzero()
labels = DBSCAN(eps=eps, metric='euclidean', min_samples=min_pts, n_jobs=(- 1)).fit_predict(np.array(non_zero).transpose().astype(np.float32))
arrs = analyze_clusters(cuboid, labels, non_zero)
if save_arrs:
save_arrays(arrs, iter, threshold, eps, save_dir)
if (nclusters == arrs[0]):
finished = True
nclusters = arrs[0]
if (iter == 0):
rel_cluster_size = arrs[2]
cluster_avg_asym = arrs[7]
cuboid = np.where(np.logical_and((rel_cluster_size < size_threshold), (cluster_avg_asym > asym_threshold)), 0.0, cuboid)
iter += 1
if (expandcleanedsig > 0):
cuboid = np.where((cuboid > 0), 1.0, 0.0)
sig = [expandcleanedsig, expandcleanedsig, 1.0]
cuboid = ut.gauss_conv_fft(cuboid, sig)
no_thresh_cuboid = crop_center(data)
cuboid = np.where((cuboid > 0.1), no_thresh_cuboid, 0.0)
return cuboid |
def remove_blocks(data, config_map):
'\n Sets to zero given alien blocks in the data array.\n \n Parameters\n ----------\n data : ndarray\n an array with experiment data\n config : Object\n configuration object providing access to configuration parameters\n\n Returns\n -------\n data : ndarray\n data array with zeroed out aliens\n '
try:
aliens = config_map.aliens
for alien in aliens:
data[alien[0]:alien[3], alien[1]:alien[4], alien[2]:alien[5]] = 0
except AttributeError:
print('aliens parameter not configured')
except Exception as e:
print('did not remove aliens, error in aliens removal ', str(e))
return data | -1,441,866,952,189,308,200 | Sets to zero given alien blocks in the data array.
Parameters
----------
data : ndarray
an array with experiment data
config : Object
configuration object providing access to configuration parameters
Returns
-------
data : ndarray
data array with zeroed out aliens | scripts/alien_tools.py | remove_blocks | AdvancedPhotonSource/cdisupp | python | def remove_blocks(data, config_map):
'\n Sets to zero given alien blocks in the data array.\n \n Parameters\n ----------\n data : ndarray\n an array with experiment data\n config : Object\n configuration object providing access to configuration parameters\n\n Returns\n -------\n data : ndarray\n data array with zeroed out aliens\n '
try:
aliens = config_map.aliens
for alien in aliens:
data[alien[0]:alien[3], alien[1]:alien[4], alien[2]:alien[5]] = 0
except AttributeError:
print('aliens parameter not configured')
except Exception as e:
print('did not remove aliens, error in aliens removal ', str(e))
return data |
def filter_aliens(data, config_map):
'\n Sets to zero points in the data array defined by a file.\n \n Parameters\n ----------\n data : ndarray\n an array with experiment data\n config : Object\n configuration object providing access to configuration parameters\n\n Returns\n -------\n data : ndarray\n data array with zeroed out aliens\n '
try:
alien_file = config_map.alien_file
if os.path.isfile(alien_file):
mask = np.load(alien_file)
for i in range(len(mask.shape)):
if (mask.shape[i] != data.shape[i]):
print('exiting, mask must be of the same shape as data:', data.shape)
return
data = np.where((mask == 1), data, 0.0)
except AttributeError:
print('alien_file parameter not configured')
except Exception as e:
print('did not remove aliens, error in aliens removal ', str(e))
return data | 8,194,168,656,737,036,000 | Sets to zero points in the data array defined by a file.
Parameters
----------
data : ndarray
an array with experiment data
config : Object
configuration object providing access to configuration parameters
Returns
-------
data : ndarray
data array with zeroed out aliens | scripts/alien_tools.py | filter_aliens | AdvancedPhotonSource/cdisupp | python | def filter_aliens(data, config_map):
'\n Sets to zero points in the data array defined by a file.\n \n Parameters\n ----------\n data : ndarray\n an array with experiment data\n config : Object\n configuration object providing access to configuration parameters\n\n Returns\n -------\n data : ndarray\n data array with zeroed out aliens\n '
try:
alien_file = config_map.alien_file
if os.path.isfile(alien_file):
mask = np.load(alien_file)
for i in range(len(mask.shape)):
if (mask.shape[i] != data.shape[i]):
print('exiting, mask must be of the same shape as data:', data.shape)
return
data = np.where((mask == 1), data, 0.0)
except AttributeError:
print('alien_file parameter not configured')
except Exception as e:
print('did not remove aliens, error in aliens removal ', str(e))
return data |
def remove_aliens(data, config_map, data_dir=None):
"\n Finds which algorithm is cofigured to remove the aliens and applies it to clean the data.\n \n Parameters\n ----------\n data : ndarray\n an array with experiment data\n config : Object\n configuration object providing access to configuration parameters\n data_dir : str\n a directory where 'alien_analysis' subdirectory will be created to save results of analysis if configured\n Returns\n -------\n data : ndarray\n data array without aliens\n "
try:
algorithm = config_map.alien_alg
if (algorithm == 'block_aliens'):
data = remove_blocks(data, config_map)
elif (algorithm == 'alien_file'):
data = filter_aliens(data, config_map)
elif (algorithm == 'AutoAlien1'):
data = auto_alien1(data, config_map, data_dir)
elif (algorithm != 'none'):
print('not supported alien removal algorithm', algorithm)
except AttributeError:
pass
except Exception as e:
print('did not remove aliens, error in aliens removal, error: ', str(e))
return data | 6,373,080,184,290,245,000 | Finds which algorithm is cofigured to remove the aliens and applies it to clean the data.
Parameters
----------
data : ndarray
an array with experiment data
config : Object
configuration object providing access to configuration parameters
data_dir : str
a directory where 'alien_analysis' subdirectory will be created to save results of analysis if configured
Returns
-------
data : ndarray
data array without aliens | scripts/alien_tools.py | remove_aliens | AdvancedPhotonSource/cdisupp | python | def remove_aliens(data, config_map, data_dir=None):
"\n Finds which algorithm is cofigured to remove the aliens and applies it to clean the data.\n \n Parameters\n ----------\n data : ndarray\n an array with experiment data\n config : Object\n configuration object providing access to configuration parameters\n data_dir : str\n a directory where 'alien_analysis' subdirectory will be created to save results of analysis if configured\n Returns\n -------\n data : ndarray\n data array without aliens\n "
try:
algorithm = config_map.alien_alg
if (algorithm == 'block_aliens'):
data = remove_blocks(data, config_map)
elif (algorithm == 'alien_file'):
data = filter_aliens(data, config_map)
elif (algorithm == 'AutoAlien1'):
data = auto_alien1(data, config_map, data_dir)
elif (algorithm != 'none'):
print('not supported alien removal algorithm', algorithm)
except AttributeError:
pass
except Exception as e:
print('did not remove aliens, error in aliens removal, error: ', str(e))
return data |
def print_current_status(status):
'\n Prints the current status\n :param status:\n :return:\n '
print(status)
for i in range((str(status).count('\n') + 1)):
sys.stdout.write('\x1b[F') | 4,987,133,255,887,730,000 | Prints the current status
:param status:
:return: | nodes/gnss_status_viewer_node.py | print_current_status | naoki-mizuno/gnss_status_viewer | python | def print_current_status(status):
'\n Prints the current status\n :param status:\n :return:\n '
print(status)
for i in range((str(status).count('\n') + 1)):
sys.stdout.write('\x1b[F') |
def ensemble_negative_log_likelihood(labels, logits):
"Negative log-likelihood for ensemble.\n\n For each datapoint (x,y), the ensemble's negative log-likelihood is:\n\n ```\n -log p(y|x) = -log sum_{m=1}^{ensemble_size} exp(log p(y|x,theta_m)) +\n log ensemble_size.\n ```\n\n Args:\n labels: tf.Tensor of shape [...].\n logits: tf.Tensor of shape [ensemble_size, ..., num_classes].\n\n Returns:\n tf.Tensor of shape [...].\n "
labels = tf.cast(labels, tf.int32)
logits = tf.convert_to_tensor(logits)
ensemble_size = float(logits.shape[0])
nll = tf.nn.sparse_softmax_cross_entropy_with_logits(tf.broadcast_to(labels[(tf.newaxis, ...)], tf.shape(logits)[:(- 1)]), logits)
return ((- tf.reduce_logsumexp((- nll), axis=0)) + tf.math.log(ensemble_size)) | 4,068,252,308,176,723,500 | Negative log-likelihood for ensemble.
For each datapoint (x,y), the ensemble's negative log-likelihood is:
```
-log p(y|x) = -log sum_{m=1}^{ensemble_size} exp(log p(y|x,theta_m)) +
log ensemble_size.
```
Args:
labels: tf.Tensor of shape [...].
logits: tf.Tensor of shape [ensemble_size, ..., num_classes].
Returns:
tf.Tensor of shape [...]. | baselines/imagenet/ensemble.py | ensemble_negative_log_likelihood | mhavasi/edward2 | python | def ensemble_negative_log_likelihood(labels, logits):
"Negative log-likelihood for ensemble.\n\n For each datapoint (x,y), the ensemble's negative log-likelihood is:\n\n ```\n -log p(y|x) = -log sum_{m=1}^{ensemble_size} exp(log p(y|x,theta_m)) +\n log ensemble_size.\n ```\n\n Args:\n labels: tf.Tensor of shape [...].\n logits: tf.Tensor of shape [ensemble_size, ..., num_classes].\n\n Returns:\n tf.Tensor of shape [...].\n "
labels = tf.cast(labels, tf.int32)
logits = tf.convert_to_tensor(logits)
ensemble_size = float(logits.shape[0])
nll = tf.nn.sparse_softmax_cross_entropy_with_logits(tf.broadcast_to(labels[(tf.newaxis, ...)], tf.shape(logits)[:(- 1)]), logits)
return ((- tf.reduce_logsumexp((- nll), axis=0)) + tf.math.log(ensemble_size)) |
def gibbs_cross_entropy(labels, logits):
"Average cross entropy for ensemble members (Gibbs cross entropy).\n\n For each datapoint (x,y), the ensemble's Gibbs cross entropy is:\n\n ```\n GCE = - (1/ensemble_size) sum_{m=1}^ensemble_size log p(y|x,theta_m).\n ```\n\n The Gibbs cross entropy approximates the average cross entropy of a single\n model drawn from the (Gibbs) ensemble.\n\n Args:\n labels: tf.Tensor of shape [...].\n logits: tf.Tensor of shape [ensemble_size, ..., num_classes].\n\n Returns:\n tf.Tensor of shape [...].\n "
labels = tf.cast(labels, tf.int32)
logits = tf.convert_to_tensor(logits)
nll = tf.nn.sparse_softmax_cross_entropy_with_logits(tf.broadcast_to(labels[(tf.newaxis, ...)], tf.shape(logits)[:(- 1)]), logits)
return tf.reduce_mean(nll, axis=0) | 5,129,305,994,803,191,000 | Average cross entropy for ensemble members (Gibbs cross entropy).
For each datapoint (x,y), the ensemble's Gibbs cross entropy is:
```
GCE = - (1/ensemble_size) sum_{m=1}^ensemble_size log p(y|x,theta_m).
```
The Gibbs cross entropy approximates the average cross entropy of a single
model drawn from the (Gibbs) ensemble.
Args:
labels: tf.Tensor of shape [...].
logits: tf.Tensor of shape [ensemble_size, ..., num_classes].
Returns:
tf.Tensor of shape [...]. | baselines/imagenet/ensemble.py | gibbs_cross_entropy | mhavasi/edward2 | python | def gibbs_cross_entropy(labels, logits):
"Average cross entropy for ensemble members (Gibbs cross entropy).\n\n For each datapoint (x,y), the ensemble's Gibbs cross entropy is:\n\n ```\n GCE = - (1/ensemble_size) sum_{m=1}^ensemble_size log p(y|x,theta_m).\n ```\n\n The Gibbs cross entropy approximates the average cross entropy of a single\n model drawn from the (Gibbs) ensemble.\n\n Args:\n labels: tf.Tensor of shape [...].\n logits: tf.Tensor of shape [ensemble_size, ..., num_classes].\n\n Returns:\n tf.Tensor of shape [...].\n "
labels = tf.cast(labels, tf.int32)
logits = tf.convert_to_tensor(logits)
nll = tf.nn.sparse_softmax_cross_entropy_with_logits(tf.broadcast_to(labels[(tf.newaxis, ...)], tf.shape(logits)[:(- 1)]), logits)
return tf.reduce_mean(nll, axis=0) |
@pytest.fixture(scope='session')
def app(app_settings):
' WSGI application level functional testing. '
return main({}, **app_settings) | -9,000,578,610,465,128,000 | WSGI application level functional testing. | src/encoded/tests/conftest.py | app | dbmi-bgm/cgap-portal | python | @pytest.fixture(scope='session')
def app(app_settings):
' '
return main({}, **app_settings) |
@pytest.fixture(scope='session')
def es_app(es_app_settings, **kwargs):
'\n App that uses both Postgres and ES - pass this as "app" argument to TestApp.\n Pass all kwargs onto create_mapping\n '
app = main({}, **es_app_settings)
create_mapping.run(app, **kwargs)
return app | -7,586,295,019,175,009,000 | App that uses both Postgres and ES - pass this as "app" argument to TestApp.
Pass all kwargs onto create_mapping | src/encoded/tests/conftest.py | es_app | dbmi-bgm/cgap-portal | python | @pytest.fixture(scope='session')
def es_app(es_app_settings, **kwargs):
'\n App that uses both Postgres and ES - pass this as "app" argument to TestApp.\n Pass all kwargs onto create_mapping\n '
app = main({}, **es_app_settings)
create_mapping.run(app, **kwargs)
return app |
@pytest.fixture
def anontestapp(app):
'TestApp for anonymous user (i.e., no user specified), accepting JSON data.'
environ = {'HTTP_ACCEPT': 'application/json'}
return webtest.TestApp(app, environ) | 7,349,053,589,448,217,000 | TestApp for anonymous user (i.e., no user specified), accepting JSON data. | src/encoded/tests/conftest.py | anontestapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def anontestapp(app):
environ = {'HTTP_ACCEPT': 'application/json'}
return webtest.TestApp(app, environ) |
@pytest.fixture
def anonhtmltestapp(app):
'TestApp for anonymous (not logged in) user, accepting text/html content.'
environ = {'HTTP_ACCEPT': 'text/html'}
test_app = webtest.TestApp(app, environ)
return test_app | 8,208,020,015,496,335,000 | TestApp for anonymous (not logged in) user, accepting text/html content. | src/encoded/tests/conftest.py | anonhtmltestapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def anonhtmltestapp(app):
environ = {'HTTP_ACCEPT': 'text/html'}
test_app = webtest.TestApp(app, environ)
return test_app |
@pytest.fixture
def anon_es_testapp(es_app):
' TestApp simulating a bare Request entering the application (with ES enabled) '
environ = {'HTTP_ACCEPT': 'application/json'}
return webtest.TestApp(es_app, environ) | -720,368,871,870,829,800 | TestApp simulating a bare Request entering the application (with ES enabled) | src/encoded/tests/conftest.py | anon_es_testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def anon_es_testapp(es_app):
' '
environ = {'HTTP_ACCEPT': 'application/json'}
return webtest.TestApp(es_app, environ) |
@pytest.fixture
def anon_html_es_testapp(es_app):
'TestApp with ES + Postgres for anonymous (not logged in) user, accepting text/html content.'
environ = {'HTTP_ACCEPT': 'text/html'}
return webtest.TestApp(es_app, environ) | -607,309,052,424,212,100 | TestApp with ES + Postgres for anonymous (not logged in) user, accepting text/html content. | src/encoded/tests/conftest.py | anon_html_es_testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def anon_html_es_testapp(es_app):
environ = {'HTTP_ACCEPT': 'text/html'}
return webtest.TestApp(es_app, environ) |
@pytest.fixture(scope='session')
def testapp(app):
'TestApp for username TEST, accepting JSON data.'
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST'}
return webtest.TestApp(app, environ) | -9,211,947,515,465,539,000 | TestApp for username TEST, accepting JSON data. | src/encoded/tests/conftest.py | testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture(scope='session')
def testapp(app):
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST'}
return webtest.TestApp(app, environ) |
@pytest.fixture
def htmltestapp(app):
'TestApp for TEST user, accepting text/html content.'
environ = {'HTTP_ACCEPT': 'text/html', 'REMOTE_USER': 'TEST'}
test_app = webtest.TestApp(app, environ)
return test_app | 6,502,149,716,404,919,000 | TestApp for TEST user, accepting text/html content. | src/encoded/tests/conftest.py | htmltestapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def htmltestapp(app):
environ = {'HTTP_ACCEPT': 'text/html', 'REMOTE_USER': 'TEST'}
test_app = webtest.TestApp(app, environ)
return test_app |
@pytest.fixture(scope='session')
def es_testapp(es_app):
' TestApp with ES + Postgres. Must be imported where it is needed. '
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST'}
return webtest.TestApp(es_app, environ) | -4,503,890,784,941,239,000 | TestApp with ES + Postgres. Must be imported where it is needed. | src/encoded/tests/conftest.py | es_testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture(scope='session')
def es_testapp(es_app):
' '
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST'}
return webtest.TestApp(es_app, environ) |
@pytest.fixture
def html_es_testapp(es_app):
'TestApp with ES + Postgres for TEST user, accepting text/html content.'
environ = {'HTTP_ACCEPT': 'text/html', 'REMOTE_USER': 'TEST'}
return webtest.TestApp(es_app, environ) | -6,523,108,449,352,916,000 | TestApp with ES + Postgres for TEST user, accepting text/html content. | src/encoded/tests/conftest.py | html_es_testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def html_es_testapp(es_app):
environ = {'HTTP_ACCEPT': 'text/html', 'REMOTE_USER': 'TEST'}
return webtest.TestApp(es_app, environ) |
@pytest.fixture
def authenticated_testapp(app):
'TestApp for an authenticated, non-admin user (TEST_AUTHENTICATED), accepting JSON data.'
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST_AUTHENTICATED'}
return webtest.TestApp(app, environ) | 3,389,445,688,238,822,000 | TestApp for an authenticated, non-admin user (TEST_AUTHENTICATED), accepting JSON data. | src/encoded/tests/conftest.py | authenticated_testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def authenticated_testapp(app):
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST_AUTHENTICATED'}
return webtest.TestApp(app, environ) |
@pytest.fixture
def authenticated_es_testapp(es_app):
' TestApp for authenticated non-admin user with ES '
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST_AUTHENTICATED'}
return webtest.TestApp(es_app, environ) | 2,806,828,281,893,692,000 | TestApp for authenticated non-admin user with ES | src/encoded/tests/conftest.py | authenticated_es_testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def authenticated_es_testapp(es_app):
' '
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST_AUTHENTICATED'}
return webtest.TestApp(es_app, environ) |
@pytest.fixture
def submitter_testapp(app):
'TestApp for a non-admin user (TEST_SUBMITTER), accepting JSON data.'
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST_SUBMITTER'}
return webtest.TestApp(app, environ) | 3,713,334,603,033,996,300 | TestApp for a non-admin user (TEST_SUBMITTER), accepting JSON data. | src/encoded/tests/conftest.py | submitter_testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def submitter_testapp(app):
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST_SUBMITTER'}
return webtest.TestApp(app, environ) |
@pytest.fixture
def indexer_testapp(es_app):
' Indexer testapp, meant for manually triggering indexing runs by posting to /index.\n Always uses the ES app (obviously, but not so obvious previously) '
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'INDEXER'}
return webtest.TestApp(es_app, environ) | -5,551,315,579,333,300,000 | Indexer testapp, meant for manually triggering indexing runs by posting to /index.
Always uses the ES app (obviously, but not so obvious previously) | src/encoded/tests/conftest.py | indexer_testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def indexer_testapp(es_app):
' Indexer testapp, meant for manually triggering indexing runs by posting to /index.\n Always uses the ES app (obviously, but not so obvious previously) '
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'INDEXER'}
return webtest.TestApp(es_app, environ) |
@pytest.fixture
def embed_testapp(app):
'TestApp for user EMBED, accepting JSON data.'
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'EMBED'}
return webtest.TestApp(app, environ) | 4,015,416,991,047,454,000 | TestApp for user EMBED, accepting JSON data. | src/encoded/tests/conftest.py | embed_testapp | dbmi-bgm/cgap-portal | python | @pytest.fixture
def embed_testapp(app):
environ = {'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'EMBED'}
return webtest.TestApp(app, environ) |
@pytest.fixture
def wsgi_app(wsgi_server):
'TestApp for WSGI server.'
return webtest.TestApp(wsgi_server) | -7,219,069,910,056,173,000 | TestApp for WSGI server. | src/encoded/tests/conftest.py | wsgi_app | dbmi-bgm/cgap-portal | python | @pytest.fixture
def wsgi_app(wsgi_server):
return webtest.TestApp(wsgi_server) |
@pytest.fixture(scope='session')
def workbook(es_app):
" Loads a bunch of data (tests/data/workbook-inserts) into the system on first run\n (session scope doesn't work). "
WorkbookCache.initialize_if_needed(es_app) | -6,721,380,994,656,458,000 | Loads a bunch of data (tests/data/workbook-inserts) into the system on first run
(session scope doesn't work). | src/encoded/tests/conftest.py | workbook | dbmi-bgm/cgap-portal | python | @pytest.fixture(scope='session')
def workbook(es_app):
" Loads a bunch of data (tests/data/workbook-inserts) into the system on first run\n (session scope doesn't work). "
WorkbookCache.initialize_if_needed(es_app) |
@distributed_trace
def get_method_local_valid(self, **kwargs):
"Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n "
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_method_local_valid_request(template_url=self.get_method_local_valid.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) | -2,154,966,697,742,777,000 | Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError | test/azure/legacy/Expected/AcceptanceTests/AzureSpecials/azurespecialproperties/operations/_api_version_local_operations.py | get_method_local_valid | Azure/autorest.python | python | @distributed_trace
def get_method_local_valid(self, **kwargs):
"Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n "
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_method_local_valid_request(template_url=self.get_method_local_valid.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) |
@distributed_trace
def get_method_local_null(self, api_version=None, **kwargs):
'Get method with api-version modeled in the method. pass in api-version = null to succeed.\n\n :param api_version: This should appear as a method parameter, use value null, this should\n result in no serialized parameter.\n :type api_version: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_method_local_null_request(api_version=api_version, template_url=self.get_method_local_null.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) | -1,328,737,055,622,945,000 | Get method with api-version modeled in the method. pass in api-version = null to succeed.
:param api_version: This should appear as a method parameter, use value null, this should
result in no serialized parameter.
:type api_version: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError | test/azure/legacy/Expected/AcceptanceTests/AzureSpecials/azurespecialproperties/operations/_api_version_local_operations.py | get_method_local_null | Azure/autorest.python | python | @distributed_trace
def get_method_local_null(self, api_version=None, **kwargs):
'Get method with api-version modeled in the method. pass in api-version = null to succeed.\n\n :param api_version: This should appear as a method parameter, use value null, this should\n result in no serialized parameter.\n :type api_version: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_method_local_null_request(api_version=api_version, template_url=self.get_method_local_null.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) |
@distributed_trace
def get_path_local_valid(self, **kwargs):
"Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n "
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_path_local_valid_request(template_url=self.get_path_local_valid.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) | -1,937,686,304,681,717,200 | Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError | test/azure/legacy/Expected/AcceptanceTests/AzureSpecials/azurespecialproperties/operations/_api_version_local_operations.py | get_path_local_valid | Azure/autorest.python | python | @distributed_trace
def get_path_local_valid(self, **kwargs):
"Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n "
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_path_local_valid_request(template_url=self.get_path_local_valid.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) |
@distributed_trace
def get_swagger_local_valid(self, **kwargs):
"Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n "
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_swagger_local_valid_request(template_url=self.get_swagger_local_valid.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) | 889,666,015,888,593,800 | Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError | test/azure/legacy/Expected/AcceptanceTests/AzureSpecials/azurespecialproperties/operations/_api_version_local_operations.py | get_swagger_local_valid | Azure/autorest.python | python | @distributed_trace
def get_swagger_local_valid(self, **kwargs):
"Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\n :raises: ~azure.core.exceptions.HttpResponseError\n "
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_swagger_local_valid_request(template_url=self.get_swagger_local_valid.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {}) |
@app.template_filter()
def friendly_time(dt, past_='ago', future_='from now', default='just now'):
'\n Returns string representing "time since"\n or "time until" e.g.\n 3 days ago, 5 hours from now etc.\n '
if (dt is None):
return ''
if isinstance(dt, basestring):
dt = iso8601.parse_date(dt)
if dt.tzinfo:
dt = dt.astimezone(pytz.utc).replace(tzinfo=None)
now = datetime.datetime.utcnow()
if (now > dt):
diff = (now - dt)
dt_is_past = True
else:
diff = (dt - now)
dt_is_past = False
periods = (((diff.days / 365), 'year', 'years'), ((diff.days / 30), 'month', 'months'), ((diff.days / 7), 'week', 'weeks'), (diff.days, 'day', 'days'), ((diff.seconds / 3600), 'hour', 'hours'), ((diff.seconds / 60), 'minute', 'minutes'), (diff.seconds, 'second', 'seconds'))
for (period, singular, plural) in periods:
if period:
return ('%d %s %s' % (period, (singular if (period == 1) else plural), (past_ if dt_is_past else future_)))
return default | 5,157,354,521,017,599,000 | Returns string representing "time since"
or "time until" e.g.
3 days ago, 5 hours from now etc. | app.py | friendly_time | codeforamerica/srtracker | python | @app.template_filter()
def friendly_time(dt, past_='ago', future_='from now', default='just now'):
'\n Returns string representing "time since"\n or "time until" e.g.\n 3 days ago, 5 hours from now etc.\n '
if (dt is None):
return
if isinstance(dt, basestring):
dt = iso8601.parse_date(dt)
if dt.tzinfo:
dt = dt.astimezone(pytz.utc).replace(tzinfo=None)
now = datetime.datetime.utcnow()
if (now > dt):
diff = (now - dt)
dt_is_past = True
else:
diff = (dt - now)
dt_is_past = False
periods = (((diff.days / 365), 'year', 'years'), ((diff.days / 30), 'month', 'months'), ((diff.days / 7), 'week', 'weeks'), (diff.days, 'day', 'days'), ((diff.seconds / 3600), 'hour', 'hours'), ((diff.seconds / 60), 'minute', 'minutes'), (diff.seconds, 'second', 'seconds'))
for (period, singular, plural) in periods:
if period:
return ('%d %s %s' % (period, (singular if (period == 1) else plural), (past_ if dt_is_past else future_)))
return default |
@app.template_filter()
def title_address(address):
'Slightly improved title() method for address strings\n Makes sure state abbreviations are upper-case.'
titled = address.title()
titled = state_pattern.sub((lambda match: (match.group(1).upper() + (match.group(2) or ''))), titled)
return titled | 5,237,181,232,790,722,000 | Slightly improved title() method for address strings
Makes sure state abbreviations are upper-case. | app.py | title_address | codeforamerica/srtracker | python | @app.template_filter()
def title_address(address):
'Slightly improved title() method for address strings\n Makes sure state abbreviations are upper-case.'
titled = address.title()
titled = state_pattern.sub((lambda match: (match.group(1).upper() + (match.group(2) or ))), titled)
return titled |
def render_app_template(template, **kwargs):
'Add some goodies to all templates.'
if ('config' not in kwargs):
kwargs['config'] = app.config
if ('__version__' not in kwargs):
kwargs['__version__'] = __version__
return render_template(template, **kwargs) | 7,080,468,876,659,081,000 | Add some goodies to all templates. | app.py | render_app_template | codeforamerica/srtracker | python | def render_app_template(template, **kwargs):
if ('config' not in kwargs):
kwargs['config'] = app.config
if ('__version__' not in kwargs):
kwargs['__version__'] = __version__
return render_template(template, **kwargs) |
def fixup_sr(sr, request_id=None):
"\n Fix up an SR to try and ensure some basic info.\n (In Chicago's API, any field can be missing, even if it's required.)\n "
remove_blacklisted_fields(sr)
if ('service_request_id' not in sr):
sr['service_request_id'] = (request_id or sr.get('token', 'UNKNOWN'))
if ('status' not in sr):
sr['status'] = 'open'
if ('service_name' not in sr):
sr['service_name'] = 'Miscellaneous Services'
return sr | -3,728,430,768,534,553,000 | Fix up an SR to try and ensure some basic info.
(In Chicago's API, any field can be missing, even if it's required.) | app.py | fixup_sr | codeforamerica/srtracker | python | def fixup_sr(sr, request_id=None):
"\n Fix up an SR to try and ensure some basic info.\n (In Chicago's API, any field can be missing, even if it's required.)\n "
remove_blacklisted_fields(sr)
if ('service_request_id' not in sr):
sr['service_request_id'] = (request_id or sr.get('token', 'UNKNOWN'))
if ('status' not in sr):
sr['status'] = 'open'
if ('service_name' not in sr):
sr['service_name'] = 'Miscellaneous Services'
return sr |
def _css_select(soup, css_selector):
' Returns the content of the element pointed by the CSS selector,\n or an empty string if not found '
selection = soup.select(css_selector)
if (len(selection) > 0):
if hasattr(selection[0], 'text'):
retour = selection[0].text.strip()
else:
retour = ''
else:
retour = ''
return retour | 6,685,540,123,375,371,000 | Returns the content of the element pointed by the CSS selector,
or an empty string if not found | precisionmapper/__init__.py | _css_select | tducret/precisionmapper-python | python | def _css_select(soup, css_selector):
' Returns the content of the element pointed by the CSS selector,\n or an empty string if not found '
selection = soup.select(css_selector)
if (len(selection) > 0):
if hasattr(selection[0], 'text'):
retour = selection[0].text.strip()
else:
retour =
else:
retour =
return retour |