body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
b902d17abe789d41104c59352edd738cb5bfd758e9b422c738da54594c712c45 | def size(self, name=None):
'Compute the number of elements in this queue.\n\n Args:\n name: A name for the operation (optional).\n\n Returns:\n A scalar tensor containing the number of elements in this queue.\n '
if (name is None):
name = ('%s_Size' % self._name)
return gen_data_f... | Compute the number of elements in this queue.
Args:
name: A name for the operation (optional).
Returns:
A scalar tensor containing the number of elements in this queue. | tensorflow/python/ops/data_flow_ops.py | size | habangar/tensorflow | 73 | python | def size(self, name=None):
'Compute the number of elements in this queue.\n\n Args:\n name: A name for the operation (optional).\n\n Returns:\n A scalar tensor containing the number of elements in this queue.\n '
if (name is None):
name = ('%s_Size' % self._name)
return gen_data_f... | def size(self, name=None):
'Compute the number of elements in this queue.\n\n Args:\n name: A name for the operation (optional).\n\n Returns:\n A scalar tensor containing the number of elements in this queue.\n '
if (name is None):
name = ('%s_Size' % self._name)
return gen_data_f... |
9a606c23df9d3bf8dced02f0098c09ac9d78d6716726c8d4dfe81e9e0b6aa069 | def __init__(self, capacity, min_after_dequeue, dtypes, shapes=None, names=None, seed=None, shared_name=None, name='random_shuffle_queue'):
'Create a queue that dequeues elements in a random order.\n\n A `RandomShuffleQueue` has bounded capacity; supports multiple\n concurrent producers and consumers; and pro... | Create a queue that dequeues elements in a random order.
A `RandomShuffleQueue` has bounded capacity; supports multiple
concurrent producers and consumers; and provides exactly-once
delivery.
A `RandomShuffleQueue` holds a list of up to `capacity`
elements. Each element is a fixed-length tuple of tensors whose
dtypes... | tensorflow/python/ops/data_flow_ops.py | __init__ | habangar/tensorflow | 73 | python | def __init__(self, capacity, min_after_dequeue, dtypes, shapes=None, names=None, seed=None, shared_name=None, name='random_shuffle_queue'):
'Create a queue that dequeues elements in a random order.\n\n A `RandomShuffleQueue` has bounded capacity; supports multiple\n concurrent producers and consumers; and pro... | def __init__(self, capacity, min_after_dequeue, dtypes, shapes=None, names=None, seed=None, shared_name=None, name='random_shuffle_queue'):
'Create a queue that dequeues elements in a random order.\n\n A `RandomShuffleQueue` has bounded capacity; supports multiple\n concurrent producers and consumers; and pro... |
9910fa826977a547d74fc7ae5cca5464c315082a411d48ec9138fe4cf221cdae | def __init__(self, capacity, dtypes, shapes=None, names=None, shared_name=None, name='fifo_queue'):
'Creates a queue that dequeues elements in a first-in first-out order.\n\n A `FIFOQueue` has bounded capacity; supports multiple concurrent\n producers and consumers; and provides exactly-once delivery.\n\n ... | Creates a queue that dequeues elements in a first-in first-out order.
A `FIFOQueue` has bounded capacity; supports multiple concurrent
producers and consumers; and provides exactly-once delivery.
A `FIFOQueue` holds a list of up to `capacity` elements. Each
element is a fixed-length tuple of tensors whose dtypes are
... | tensorflow/python/ops/data_flow_ops.py | __init__ | habangar/tensorflow | 73 | python | def __init__(self, capacity, dtypes, shapes=None, names=None, shared_name=None, name='fifo_queue'):
'Creates a queue that dequeues elements in a first-in first-out order.\n\n A `FIFOQueue` has bounded capacity; supports multiple concurrent\n producers and consumers; and provides exactly-once delivery.\n\n ... | def __init__(self, capacity, dtypes, shapes=None, names=None, shared_name=None, name='fifo_queue'):
'Creates a queue that dequeues elements in a first-in first-out order.\n\n A `FIFOQueue` has bounded capacity; supports multiple concurrent\n producers and consumers; and provides exactly-once delivery.\n\n ... |
35c11710d0d177207522f9e1996a3c9d580c3653d45ece0c346b366bf5e44a3c | def __init__(self, capacity, dtypes, shapes, names=None, shared_name=None, name='padding_fifo_queue'):
"Creates a queue that dequeues elements in a first-in first-out order.\n\n A `PaddingFIFOQueue` has bounded capacity; supports multiple concurrent\n producers and consumers; and provides exactly-once deliver... | Creates a queue that dequeues elements in a first-in first-out order.
A `PaddingFIFOQueue` has bounded capacity; supports multiple concurrent
producers and consumers; and provides exactly-once delivery.
A `PaddingFIFOQueue` holds a list of up to `capacity` elements. Each
element is a fixed-length tuple of tensors who... | tensorflow/python/ops/data_flow_ops.py | __init__ | habangar/tensorflow | 73 | python | def __init__(self, capacity, dtypes, shapes, names=None, shared_name=None, name='padding_fifo_queue'):
"Creates a queue that dequeues elements in a first-in first-out order.\n\n A `PaddingFIFOQueue` has bounded capacity; supports multiple concurrent\n producers and consumers; and provides exactly-once deliver... | def __init__(self, capacity, dtypes, shapes, names=None, shared_name=None, name='padding_fifo_queue'):
"Creates a queue that dequeues elements in a first-in first-out order.\n\n A `PaddingFIFOQueue` has bounded capacity; supports multiple concurrent\n producers and consumers; and provides exactly-once deliver... |
8dec1916ad6557ae2799eb622ef5db859ed9818cbb4e9979653a750f51f909cd | def parse(map_data_def, input) -> dict:
' Traverses a map-data: definition and calls value.parse on each value '
logger.debug(f'Entering: Key count = {len(map_data_def)}, data count = {(len(input) if isinstance(input, list) else 1)}')
output = {}
for key_value in map_data_def:
if isinstance(key_... | Traverses a map-data: definition and calls value.parse on each value | data/map.py | parse | samadhicsec/threatware | 0 | python | def parse(map_data_def, input) -> dict:
' '
logger.debug(f'Entering: Key count = {len(map_data_def)}, data count = {(len(input) if isinstance(input, list) else 1)}')
output = {}
for key_value in map_data_def:
if isinstance(key_value['key'], str):
key_def = key.key(key_value['key'])
... | def parse(map_data_def, input) -> dict:
' '
logger.debug(f'Entering: Key count = {len(map_data_def)}, data count = {(len(input) if isinstance(input, list) else 1)}')
output = {}
for key_value in map_data_def:
if isinstance(key_value['key'], str):
key_def = key.key(key_value['key'])
... |
216eb4fde65cbf2d3b89547b1f835cc31610133a16ffb2bd201b68d0536d5cce | def list_reserved_resources():
'Displays the currently reserved resources on all agents via state.json;\n Currently for INFINITY-1881 where we believe uninstall may not be\n always doing its job correctly.'
state_json_slaveinfo = dcos.mesos.DCOSClient().get_state_summary()['slaves']
for slave in... | Displays the currently reserved resources on all agents via state.json;
Currently for INFINITY-1881 where we believe uninstall may not be
always doing its job correctly. | testing/sdk_utils.py | list_reserved_resources | greggomann/dcos-commons | 7 | python | def list_reserved_resources():
'Displays the currently reserved resources on all agents via state.json;\n Currently for INFINITY-1881 where we believe uninstall may not be\n always doing its job correctly.'
state_json_slaveinfo = dcos.mesos.DCOSClient().get_state_summary()['slaves']
for slave in... | def list_reserved_resources():
'Displays the currently reserved resources on all agents via state.json;\n Currently for INFINITY-1881 where we believe uninstall may not be\n always doing its job correctly.'
state_json_slaveinfo = dcos.mesos.DCOSClient().get_state_summary()['slaves']
for slave in... |
0657807e8261b6b6a4861a8856eff62c2fe9091d2c9721e662603d039f0013f0 | def check_dcos_min_version_mark(item: pytest.Item):
"Enforces the dcos_min_version pytest annotation, which should be used like this:\n\n @pytest.mark.dcos_min_version('1.10')\n def your_test_here(): ...\n\n In order for this annotation to take effect, this function must be called by a pytest_runtest_setup... | Enforces the dcos_min_version pytest annotation, which should be used like this:
@pytest.mark.dcos_min_version('1.10')
def your_test_here(): ...
In order for this annotation to take effect, this function must be called by a pytest_runtest_setup() hook. | testing/sdk_utils.py | check_dcos_min_version_mark | greggomann/dcos-commons | 7 | python | def check_dcos_min_version_mark(item: pytest.Item):
"Enforces the dcos_min_version pytest annotation, which should be used like this:\n\n @pytest.mark.dcos_min_version('1.10')\n def your_test_here(): ...\n\n In order for this annotation to take effect, this function must be called by a pytest_runtest_setup... | def check_dcos_min_version_mark(item: pytest.Item):
"Enforces the dcos_min_version pytest annotation, which should be used like this:\n\n @pytest.mark.dcos_min_version('1.10')\n def your_test_here(): ...\n\n In order for this annotation to take effect, this function must be called by a pytest_runtest_setup... |
1ca22585da97136f9398df3bbe5cb20c691d83b0ddc7aea9071a18dff84c2bd7 | def is_open_dcos():
'Determine if the tests are being run against open DC/OS. This is presently done by\n checking the envvar DCOS_ENTERPRISE.'
return (not (os.environ.get('DCOS_ENTERPRISE', 'true').lower() == 'true')) | Determine if the tests are being run against open DC/OS. This is presently done by
checking the envvar DCOS_ENTERPRISE. | testing/sdk_utils.py | is_open_dcos | greggomann/dcos-commons | 7 | python | def is_open_dcos():
'Determine if the tests are being run against open DC/OS. This is presently done by\n checking the envvar DCOS_ENTERPRISE.'
return (not (os.environ.get('DCOS_ENTERPRISE', 'true').lower() == 'true')) | def is_open_dcos():
'Determine if the tests are being run against open DC/OS. This is presently done by\n checking the envvar DCOS_ENTERPRISE.'
return (not (os.environ.get('DCOS_ENTERPRISE', 'true').lower() == 'true'))<|docstring|>Determine if the tests are being run against open DC/OS. This is presently don... |
bb74f336e2cce520c6aec1b9398d47470139de814c36970067c5a51ffce23c6b | def is_strict_mode():
'Determine if the tests are being run on a strict mode cluster.'
return (os.environ.get('SECURITY', '') == 'strict') | Determine if the tests are being run on a strict mode cluster. | testing/sdk_utils.py | is_strict_mode | greggomann/dcos-commons | 7 | python | def is_strict_mode():
return (os.environ.get('SECURITY', ) == 'strict') | def is_strict_mode():
return (os.environ.get('SECURITY', ) == 'strict')<|docstring|>Determine if the tests are being run on a strict mode cluster.<|endoftext|> |
50a53140288649f04327eacede9d5205cef4343e80fe5068a134769f9ca1f6f4 | def get_in(keys, coll, default=None):
" Reaches into nested associative data structures. Returns the value for path ``keys``.\n\n If the path doesn't exist returns ``default``.\n\n >>> transaction = {'name': 'Alice',\n ... 'purchase': {'items': ['Apple', 'Orange'],\n ... ... | Reaches into nested associative data structures. Returns the value for path ``keys``.
If the path doesn't exist returns ``default``.
>>> transaction = {'name': 'Alice',
... 'purchase': {'items': ['Apple', 'Orange'],
... 'costs': [0.50, 1.25]},
... 'credit card... | testing/sdk_utils.py | get_in | greggomann/dcos-commons | 7 | python | def get_in(keys, coll, default=None):
" Reaches into nested associative data structures. Returns the value for path ``keys``.\n\n If the path doesn't exist returns ``default``.\n\n >>> transaction = {'name': 'Alice',\n ... 'purchase': {'items': ['Apple', 'Orange'],\n ... ... | def get_in(keys, coll, default=None):
" Reaches into nested associative data structures. Returns the value for path ``keys``.\n\n If the path doesn't exist returns ``default``.\n\n >>> transaction = {'name': 'Alice',\n ... 'purchase': {'items': ['Apple', 'Orange'],\n ... ... |
f45e9c81930b2904fa35be67ff3ef3375e932a7e73d88f3262da1eb50e83e9b1 | def saveJSON(fileName, data):
'\n Function for/to <short description of `netpyne.sim.save.saveJSON`>\n\n Parameters\n ----------\n fileName : <type>\n <Short description of fileName>\n **Default:** *required*\n\n data : <type>\n <Short description of data>\n **Default:** *... | Function for/to <short description of `netpyne.sim.save.saveJSON`>
Parameters
----------
fileName : <type>
<Short description of fileName>
**Default:** *required*
data : <type>
<Short description of data>
**Default:** *required* | netpyne/sim/save.py | saveJSON | ghafari2019/netpyne | 1 | python | def saveJSON(fileName, data):
'\n Function for/to <short description of `netpyne.sim.save.saveJSON`>\n\n Parameters\n ----------\n fileName : <type>\n <Short description of fileName>\n **Default:** *required*\n\n data : <type>\n <Short description of data>\n **Default:** *... | def saveJSON(fileName, data):
'\n Function for/to <short description of `netpyne.sim.save.saveJSON`>\n\n Parameters\n ----------\n fileName : <type>\n <Short description of fileName>\n **Default:** *required*\n\n data : <type>\n <Short description of data>\n **Default:** *... |
fdc283c3c5bb1c2c85c12fdec429c161c5108fc60866204d8e544a56bdd11755 | def saveData(include=None, filename=None):
'\n Function for/to <short description of `netpyne.sim.save.saveData`>\n\n Parameters\n ----------\n include : <``None``?>\n <Short description of include>\n **Default:** ``None``\n **Options:** ``<option>`` <description of option>\n\n f... | Function for/to <short description of `netpyne.sim.save.saveData`>
Parameters
----------
include : <``None``?>
<Short description of include>
**Default:** ``None``
**Options:** ``<option>`` <description of option>
filename : <``None``?>
<Short description of filename>
**Default:** ``None``
**O... | netpyne/sim/save.py | saveData | ghafari2019/netpyne | 1 | python | def saveData(include=None, filename=None):
'\n Function for/to <short description of `netpyne.sim.save.saveData`>\n\n Parameters\n ----------\n include : <``None``?>\n <Short description of include>\n **Default:** ``None``\n **Options:** ``<option>`` <description of option>\n\n f... | def saveData(include=None, filename=None):
'\n Function for/to <short description of `netpyne.sim.save.saveData`>\n\n Parameters\n ----------\n include : <``None``?>\n <Short description of include>\n **Default:** ``None``\n **Options:** ``<option>`` <description of option>\n\n f... |
93a53853b250b1c8ab0880fe1d1a96d0e1aa53ec5b50c24f349f34984efbe245 | def distributedSaveHDF5():
'\n Function for/to <short description of `netpyne.sim.save.distributedSaveHDF5`>\n\n\n '
from .. import sim
import h5py
if (sim.rank == 0):
sim.timing('start', 'saveTimeHDF5')
sim.compactConnFormat()
conns = [([cell.gid] + conn) for cell in sim.net.cells... | Function for/to <short description of `netpyne.sim.save.distributedSaveHDF5`> | netpyne/sim/save.py | distributedSaveHDF5 | ghafari2019/netpyne | 1 | python | def distributedSaveHDF5():
'\n \n\n\n '
from .. import sim
import h5py
if (sim.rank == 0):
sim.timing('start', 'saveTimeHDF5')
sim.compactConnFormat()
conns = [([cell.gid] + conn) for cell in sim.net.cells for conn in cell.conns]
conns = sim.copyRemoveItemObj(conns, keystart='h... | def distributedSaveHDF5():
'\n \n\n\n '
from .. import sim
import h5py
if (sim.rank == 0):
sim.timing('start', 'saveTimeHDF5')
sim.compactConnFormat()
conns = [([cell.gid] + conn) for cell in sim.net.cells for conn in cell.conns]
conns = sim.copyRemoveItemObj(conns, keystart='h... |
51d6669f16669f6fb92ca10876c9bcef4cfac2d755f84d492b0a238a001daf1f | def compactConnFormat():
'\n Function for/to <short description of `netpyne.sim.save.compactConnFormat`>\n\n\n '
from .. import sim
if (type(sim.cfg.compactConnFormat) is not list):
if (len(sim.net.params.stimTargetParams) > 0):
sim.cfg.compactConnFormat = ['preGid', 'preLabel', 's... | Function for/to <short description of `netpyne.sim.save.compactConnFormat`> | netpyne/sim/save.py | compactConnFormat | ghafari2019/netpyne | 1 | python | def compactConnFormat():
'\n \n\n\n '
from .. import sim
if (type(sim.cfg.compactConnFormat) is not list):
if (len(sim.net.params.stimTargetParams) > 0):
sim.cfg.compactConnFormat = ['preGid', 'preLabel', 'sec', 'loc', 'synMech', 'weight', 'delay']
else:
sim.cfg... | def compactConnFormat():
'\n \n\n\n '
from .. import sim
if (type(sim.cfg.compactConnFormat) is not list):
if (len(sim.net.params.stimTargetParams) > 0):
sim.cfg.compactConnFormat = ['preGid', 'preLabel', 'sec', 'loc', 'synMech', 'weight', 'delay']
else:
sim.cfg... |
52737175f4c74b2339887f17813e08acd86903bb7835865a97ba017e1bd4442b | def intervalSave(t):
'\n Function for/to <short description of `netpyne.sim.save.intervalSave`>\n\n Parameters\n ----------\n t : <type>\n <Short description of t>\n **Default:** *required*\n\n\n '
from .. import sim
from ..specs import Dict
import pickle, os
import nump... | Function for/to <short description of `netpyne.sim.save.intervalSave`>
Parameters
----------
t : <type>
<Short description of t>
**Default:** *required* | netpyne/sim/save.py | intervalSave | ghafari2019/netpyne | 1 | python | def intervalSave(t):
'\n Function for/to <short description of `netpyne.sim.save.intervalSave`>\n\n Parameters\n ----------\n t : <type>\n <Short description of t>\n **Default:** *required*\n\n\n '
from .. import sim
from ..specs import Dict
import pickle, os
import nump... | def intervalSave(t):
'\n Function for/to <short description of `netpyne.sim.save.intervalSave`>\n\n Parameters\n ----------\n t : <type>\n <Short description of t>\n **Default:** *required*\n\n\n '
from .. import sim
from ..specs import Dict
import pickle, os
import nump... |
7dec5c6bba34e172ebed71397f681865763f0509d79286712d5f105bb6a5ba78 | def saveDataInNodes(filename=None, saveLFP=True, removeTraces=False, dataDir=None):
'\n Function to save simulation data by node rather than as a whole\n\n Parameters\n ----------\n filename : str\n The name to use for the saved files.\n **Default:** ``None``\n\n saveLFP : bool\n ... | Function to save simulation data by node rather than as a whole
Parameters
----------
filename : str
The name to use for the saved files.
**Default:** ``None``
saveLFP : bool
Whether to save any LFP data.
**Default:** ``True`` saves LFP data.
**Options:** ``False`` does not save LFP data.
removeT... | netpyne/sim/save.py | saveDataInNodes | ghafari2019/netpyne | 1 | python | def saveDataInNodes(filename=None, saveLFP=True, removeTraces=False, dataDir=None):
'\n Function to save simulation data by node rather than as a whole\n\n Parameters\n ----------\n filename : str\n The name to use for the saved files.\n **Default:** ``None``\n\n saveLFP : bool\n ... | def saveDataInNodes(filename=None, saveLFP=True, removeTraces=False, dataDir=None):
'\n Function to save simulation data by node rather than as a whole\n\n Parameters\n ----------\n filename : str\n The name to use for the saved files.\n **Default:** ``None``\n\n saveLFP : bool\n ... |
edff29e09386ea64f35aec9745c0faabe1d6f5655e44f06c0ceac7cf16ed62d8 | def __init__(self, data: list, merger):
'\n 初始化线段树\n __data：\n 区间内的数据\n __tree:\n 构建的线段树\n __merger:\n 自定义的融合规则，通常是 lambda 表达式传来的 function 对象\n '
self.__merger = merger
self.__data = data
self.__tree = (([None] * len(data)) * 4)
... | 初始化线段树
__data：
区间内的数据
__tree:
构建的线段树
__merger:
自定义的融合规则，通常是 lambda 表达式传来的 function 对象 | datastruct/segment_tree/SegmentTree.py | __init__ | LibertyDream/algorithm_data_structure | 0 | python | def __init__(self, data: list, merger):
'\n 初始化线段树\n __data：\n 区间内的数据\n __tree:\n 构建的线段树\n __merger:\n 自定义的融合规则，通常是 lambda 表达式传来的 function 对象\n '
self.__merger = merger
self.__data = data
self.__tree = (([None] * len(data)) * 4)
... | def __init__(self, data: list, merger):
'\n 初始化线段树\n __data：\n 区间内的数据\n __tree:\n 构建的线段树\n __merger:\n 自定义的融合规则，通常是 lambda 表达式传来的 function 对象\n '
self.__merger = merger
self.__data = data
self.__tree = (([None] * len(data)) * 4)
... |
f820ea559427a87d88fb39dda564ad56b66ce27b89fa0a0325a3f83a0bd1d3c7 | def query(self, queryL: int, queryR: int):
'\n 查询 [queryL...queryR] 范围内的内容\n '
if ((queryL < 0) or (queryL >= len(self.__data)) or (queryR < 0) or (queryR >= len(self.__data))):
raise IndexError('Index is illegal')
return self.__query(0, 0, (len(self.__data) - 1), queryL, queryR) | 查询 [queryL...queryR] 范围内的内容 | datastruct/segment_tree/SegmentTree.py | query | LibertyDream/algorithm_data_structure | 0 | python | def query(self, queryL: int, queryR: int):
'\n \n '
if ((queryL < 0) or (queryL >= len(self.__data)) or (queryR < 0) or (queryR >= len(self.__data))):
raise IndexError('Index is illegal')
return self.__query(0, 0, (len(self.__data) - 1), queryL, queryR) | def query(self, queryL: int, queryR: int):
'\n \n '
if ((queryL < 0) or (queryL >= len(self.__data)) or (queryR < 0) or (queryR >= len(self.__data))):
raise IndexError('Index is illegal')
return self.__query(0, 0, (len(self.__data) - 1), queryL, queryR)<|docstring|>查询 [queryL...que... |
6361f8cc61fe2501ea265ab0208c356e2b1bd4784c3b28aa565e10fb10a81d3d | def __query(self, tree_index: int, left, right, queryL: int, queryR: int):
'\n 查询以 treeIndex 为根，[left,right] 为界，目标范围为 [queryL,queryR] 中的内容\n '
if ((left == queryL) and (right == queryR)):
return self.__tree[tree_index]
mid = (left + ((right - left) // 2))
left_index = self.left... | 查询以 treeIndex 为根，[left,right] 为界，目标范围为 [queryL,queryR] 中的内容 | datastruct/segment_tree/SegmentTree.py | __query | LibertyDream/algorithm_data_structure | 0 | python | def __query(self, tree_index: int, left, right, queryL: int, queryR: int):
'\n \n '
if ((left == queryL) and (right == queryR)):
return self.__tree[tree_index]
mid = (left + ((right - left) // 2))
left_index = self.left_child(tree_index)
right_index = self.right_child(tree_... | def __query(self, tree_index: int, left, right, queryL: int, queryR: int):
'\n \n '
if ((left == queryL) and (right == queryR)):
return self.__tree[tree_index]
mid = (left + ((right - left) // 2))
left_index = self.left_child(tree_index)
right_index = self.right_child(tree_... |
aa8445fe14d785f14bf9bb76d358fe4fa0223e9a0b75b907b149121322779d5b | def update(self, index: int, ele):
'\n 更新 index 处的值为 ele\n '
if ((index < 0) or (index >= len(self.__data))):
raise IndexError('Invalid index')
self.__data[index] = ele
self.__update(0, 0, (len(self.__data) - 1), index, ele) | 更新 index 处的值为 ele | datastruct/segment_tree/SegmentTree.py | update | LibertyDream/algorithm_data_structure | 0 | python | def update(self, index: int, ele):
'\n \n '
if ((index < 0) or (index >= len(self.__data))):
raise IndexError('Invalid index')
self.__data[index] = ele
self.__update(0, 0, (len(self.__data) - 1), index, ele) | def update(self, index: int, ele):
'\n \n '
if ((index < 0) or (index >= len(self.__data))):
raise IndexError('Invalid index')
self.__data[index] = ele
self.__update(0, 0, (len(self.__data) - 1), index, ele)<|docstring|>更新 index 处的值为 ele<|endoftext|> |
ab3a44092065a6f5bebe46144ff9c794c1aa0936b8df57340dd598de66db051d | def __update(self, tree_index: int, left: int, right: int, index: int, ele):
'\n 在以 treeIndex 为根的 [left,right] 的区间内更新 index 处的值为 e\n '
if (left == right):
self.__tree[tree_index] = ele
return
mid = (left + ((right - left) // 2))
left_index = self.left_child(tree_index)
... | 在以 treeIndex 为根的 [left,right] 的区间内更新 index 处的值为 e | datastruct/segment_tree/SegmentTree.py | __update | LibertyDream/algorithm_data_structure | 0 | python | def __update(self, tree_index: int, left: int, right: int, index: int, ele):
'\n \n '
if (left == right):
self.__tree[tree_index] = ele
return
mid = (left + ((right - left) // 2))
left_index = self.left_child(tree_index)
right_index = self.right_child(tree_index)
... | def __update(self, tree_index: int, left: int, right: int, index: int, ele):
'\n \n '
if (left == right):
self.__tree[tree_index] = ele
return
mid = (left + ((right - left) // 2))
left_index = self.left_child(tree_index)
right_index = self.right_child(tree_index)
... |
8841aa2f5f7cfe54440513b9b897ea05edf2ca51c30ed25be30f32bf26c39bb9 | def rnacentral_id(context: Context, entry: HgncEntry) -> ty.Optional[str]:
'\n Map HGNC ncRNAs to RNAcentral using RefSeq, Vega, gtRNAdb accessions\n and sequence matches.\n '
if entry.refseq_id:
refseq_based = helpers.refseq_id_to_urs(context, entry.refseq_id)
if refseq_based:
... | Map HGNC ncRNAs to RNAcentral using RefSeq, Vega, gtRNAdb accessions
and sequence matches. | rnacentral_pipeline/databases/hgnc/parser.py | rnacentral_id | RNAcentral/rnacentral-import-pipeline | 1 | python | def rnacentral_id(context: Context, entry: HgncEntry) -> ty.Optional[str]:
'\n Map HGNC ncRNAs to RNAcentral using RefSeq, Vega, gtRNAdb accessions\n and sequence matches.\n '
if entry.refseq_id:
refseq_based = helpers.refseq_id_to_urs(context, entry.refseq_id)
if refseq_based:
... | def rnacentral_id(context: Context, entry: HgncEntry) -> ty.Optional[str]:
'\n Map HGNC ncRNAs to RNAcentral using RefSeq, Vega, gtRNAdb accessions\n and sequence matches.\n '
if entry.refseq_id:
refseq_based = helpers.refseq_id_to_urs(context, entry.refseq_id)
if refseq_based:
... |
f0e31a426fb07b3e9817fd4fc01bdf56e4cc18fff7518a7a087ea1ac5599fce2 | def test_basic(self) -> None:
'Test to create projectConfig class.'
cfg = ProjectConfig(database='mydb', type='SQLite3 (SQLITE3)')
self.assertTrue(cfg)
self.assertEqual(cfg.SAVE_VERSION, VERSION_1_2) | Test to create projectConfig class. | pineboolib/loader/tests/test_projectconfig.py | test_basic | Aulla/pineboo | 2 | python | def test_basic(self) -> None:
cfg = ProjectConfig(database='mydb', type='SQLite3 (SQLITE3)')
self.assertTrue(cfg)
self.assertEqual(cfg.SAVE_VERSION, VERSION_1_2) | def test_basic(self) -> None:
cfg = ProjectConfig(database='mydb', type='SQLite3 (SQLITE3)')
self.assertTrue(cfg)
self.assertEqual(cfg.SAVE_VERSION, VERSION_1_2)<|docstring|>Test to create projectConfig class.<|endoftext|> |
e4610f5cc717edfcabc423cacc07c27481c551876b198e794f081b4cf531d0e2 | def test_read_write(self) -> None:
'Test that we can read a file, save it back, read it again and stays the same.'
project_test1 = fixture_read('project_test1.xml')
with tempfile.TemporaryDirectory() as tmpdirname:
cfg = ProjectConfig(database='mydb', type='SQLite3 (SQLITE3)', filename=os.path.join(... | Test that we can read a file, save it back, read it again and stays the same. | pineboolib/loader/tests/test_projectconfig.py | test_read_write | Aulla/pineboo | 2 | python | def test_read_write(self) -> None:
project_test1 = fixture_read('project_test1.xml')
with tempfile.TemporaryDirectory() as tmpdirname:
cfg = ProjectConfig(database='mydb', type='SQLite3 (SQLITE3)', filename=os.path.join(tmpdirname, 'test.xml'))
cfg.SAVE_VERSION = VERSION_1_1
cfg.sav... | def test_read_write(self) -> None:
project_test1 = fixture_read('project_test1.xml')
with tempfile.TemporaryDirectory() as tmpdirname:
cfg = ProjectConfig(database='mydb', type='SQLite3 (SQLITE3)', filename=os.path.join(tmpdirname, 'test.xml'))
cfg.SAVE_VERSION = VERSION_1_1
cfg.sav... |
61881d36da1311389b4105b9bdad06d5bdb541056b960e13babc80fc3e0fb23a | @patch('time.time')
@patch('os.urandom')
def test_read_write2(self, mock_urandom: Mock, mock_time: Mock) -> None:
'Test we can read and write and stays equal (slightly more complicated).'
mock_urandom.side_effect = (lambda n: b'1234567890123456789012345678901234567890'[:n])
mock_time.side_effect = (lambda :... | Test we can read and write and stays equal (slightly more complicated). | pineboolib/loader/tests/test_projectconfig.py | test_read_write2 | Aulla/pineboo | 2 | python | @patch('time.time')
@patch('os.urandom')
def test_read_write2(self, mock_urandom: Mock, mock_time: Mock) -> None:
mock_urandom.side_effect = (lambda n: b'1234567890123456789012345678901234567890'[:n])
mock_time.side_effect = (lambda : 10000)
project_test2 = fixture_read('project_test2.xml')
project... | @patch('time.time')
@patch('os.urandom')
def test_read_write2(self, mock_urandom: Mock, mock_time: Mock) -> None:
mock_urandom.side_effect = (lambda n: b'1234567890123456789012345678901234567890'[:n])
mock_time.side_effect = (lambda : 10000)
project_test2 = fixture_read('project_test2.xml')
project... |
b9c337bef1270bd4274fe98fd77ee41df6f2f92a27fa902edeab811b629c984e | def check_experimenter_input(js):
'\n Valida el input de entrada.\n\n Args:\n js (dict): Diccionario con el json parseado.\n '
experimenter_schema = get_full_schema()
try:
jsonschema.validate(js, experimenter_schema)
except jsonschema.ValidationError as err:
return models... | Valida el input de entrada.
Args:
js (dict): Diccionario con el json parseado. | experimenter/utils.py | check_experimenter_input | imfd/TextPerimenter | 1 | python | def check_experimenter_input(js):
'\n Valida el input de entrada.\n\n Args:\n js (dict): Diccionario con el json parseado.\n '
experimenter_schema = get_full_schema()
try:
jsonschema.validate(js, experimenter_schema)
except jsonschema.ValidationError as err:
return models... | def check_experimenter_input(js):
'\n Valida el input de entrada.\n\n Args:\n js (dict): Diccionario con el json parseado.\n '
experimenter_schema = get_full_schema()
try:
jsonschema.validate(js, experimenter_schema)
except jsonschema.ValidationError as err:
return models... |
dc4b0a22ead3501ce673e543b8d925347e43c8fea96c3ef7b29a7eddc4367967 | def creator(models_dic, metrics_dic):
'\n Llama a las funciones creadoras de modelos y métricas.\n\n Args:\n models_dic (dict): Diccionario de modelos ingresados.\n metrics_dic (dict): Diccionario de métricas ingresadas.\n\n Returns:\n tuple: Tupla cuyo primer elemento corresponde a la... | Llama a las funciones creadoras de modelos y métricas.
Args:
models_dic (dict): Diccionario de modelos ingresados.
metrics_dic (dict): Diccionario de métricas ingresadas.
Returns:
tuple: Tupla cuyo primer elemento corresponde a la lista de modelos
instanciados, y el segundo elemento es una lis... | experimenter/utils.py | creator | imfd/TextPerimenter | 1 | python | def creator(models_dic, metrics_dic):
'\n Llama a las funciones creadoras de modelos y métricas.\n\n Args:\n models_dic (dict): Diccionario de modelos ingresados.\n metrics_dic (dict): Diccionario de métricas ingresadas.\n\n Returns:\n tuple: Tupla cuyo primer elemento corresponde a la... | def creator(models_dic, metrics_dic):
'\n Llama a las funciones creadoras de modelos y métricas.\n\n Args:\n models_dic (dict): Diccionario de modelos ingresados.\n metrics_dic (dict): Diccionario de métricas ingresadas.\n\n Returns:\n tuple: Tupla cuyo primer elemento corresponde a la... |
6973ca05b195ede8ba60cc34d67d14203fbcaa9e7c9c0c4f751f3fc8f029ec1b | def filter_models(ranked_models, n):
'\n Retorna los n primeros reportes de modelos.\n\n Args:\n ranked_models (list): Reportes de modelos ordenados.\n n (int): Cantidad de modelos a escoger.\n\n Returns:\n list: Lista de n reportes de modelos ordenados.\n '
if (n == 0):
... | Retorna los n primeros reportes de modelos.
Args:
ranked_models (list): Reportes de modelos ordenados.
n (int): Cantidad de modelos a escoger.
Returns:
list: Lista de n reportes de modelos ordenados. | experimenter/utils.py | filter_models | imfd/TextPerimenter | 1 | python | def filter_models(ranked_models, n):
'\n Retorna los n primeros reportes de modelos.\n\n Args:\n ranked_models (list): Reportes de modelos ordenados.\n n (int): Cantidad de modelos a escoger.\n\n Returns:\n list: Lista de n reportes de modelos ordenados.\n '
if (n == 0):
... | def filter_models(ranked_models, n):
'\n Retorna los n primeros reportes de modelos.\n\n Args:\n ranked_models (list): Reportes de modelos ordenados.\n n (int): Cantidad de modelos a escoger.\n\n Returns:\n list: Lista de n reportes de modelos ordenados.\n '
if (n == 0):
... |
7053215e44b30726254e35ee2a905960000c8d4e1690e73950d4edb0698e97e1 | def get_optimizer_params(metrics_dic):
'\n Función auxiliar, para obtener directamente los parámetros\n del optimizador.\n\n Args:\n metrics_dic (dict): Diccionario de métricas entregado en el input.\n\n Returns:\n tuple: Tupla de tres elementos. El primero corresponde a la label\n ... | Función auxiliar, para obtener directamente los parámetros
del optimizador.
Args:
metrics_dic (dict): Diccionario de métricas entregado en el input.
Returns:
tuple: Tupla de tres elementos. El primero corresponde a la label
según la cual se optimizará, mientras que el segundo es la
mét... | experimenter/utils.py | get_optimizer_params | imfd/TextPerimenter | 1 | python | def get_optimizer_params(metrics_dic):
'\n Función auxiliar, para obtener directamente los parámetros\n del optimizador.\n\n Args:\n metrics_dic (dict): Diccionario de métricas entregado en el input.\n\n Returns:\n tuple: Tupla de tres elementos. El primero corresponde a la label\n ... | def get_optimizer_params(metrics_dic):
'\n Función auxiliar, para obtener directamente los parámetros\n del optimizador.\n\n Args:\n metrics_dic (dict): Diccionario de métricas entregado en el input.\n\n Returns:\n tuple: Tupla de tres elementos. El primero corresponde a la label\n ... |
9e394327049d76181fdb69d1764d1b17cf0dcec0b2e96af036d7ac0089a0d27d | def metrics_creator(list_metrics):
'\n Entrega las clases de las métricas introducidas.\n\n Args:\n list_metrics (list): Lista de strings con los nombres de las métricas.\n\n Returns:\n list: Lista de las clases de las métricas.\n '
metrics_mapping = get_metrics_mapping()
return [m... | Entrega las clases de las métricas introducidas.
Args:
list_metrics (list): Lista de strings con los nombres de las métricas.
Returns:
list: Lista de las clases de las métricas. | experimenter/utils.py | metrics_creator | imfd/TextPerimenter | 1 | python | def metrics_creator(list_metrics):
'\n Entrega las clases de las métricas introducidas.\n\n Args:\n list_metrics (list): Lista de strings con los nombres de las métricas.\n\n Returns:\n list: Lista de las clases de las métricas.\n '
metrics_mapping = get_metrics_mapping()
return [m... | def metrics_creator(list_metrics):
'\n Entrega las clases de las métricas introducidas.\n\n Args:\n list_metrics (list): Lista de strings con los nombres de las métricas.\n\n Returns:\n list: Lista de las clases de las métricas.\n '
metrics_mapping = get_metrics_mapping()
return [m... |
d6549024f8373c2f57ba790132afbdd4998d2f7962a7e24dafd6c10685e93f87 | def models_creator(dic_models):
'\n Inicializa los modelos.\n\n Args:\n dic_models (dict): Diccionario de modelos entregado en el input.\n\n Returns:\n list: Lista de modelos instanciados.\n '
models_mapping = get_models_mapping()
models = []
for model_info in dic_models:
... | Inicializa los modelos.
Args:
dic_models (dict): Diccionario de modelos entregado en el input.
Returns:
list: Lista de modelos instanciados. | experimenter/utils.py | models_creator | imfd/TextPerimenter | 1 | python | def models_creator(dic_models):
'\n Inicializa los modelos.\n\n Args:\n dic_models (dict): Diccionario de modelos entregado en el input.\n\n Returns:\n list: Lista de modelos instanciados.\n '
models_mapping = get_models_mapping()
models = []
for model_info in dic_models:
... | def models_creator(dic_models):
'\n Inicializa los modelos.\n\n Args:\n dic_models (dict): Diccionario de modelos entregado en el input.\n\n Returns:\n list: Lista de modelos instanciados.\n '
models_mapping = get_models_mapping()
models = []
for model_info in dic_models:
... |
6f01fd59625637bbce39567ccf15457a737a287b0c72403b3893782712809201 | def models_preprocess(model, datasets_list):
'\n Aplica el preprocesamiento de model en cada uno de los datasets contenidos\n en la lista datasets_list.\n\n Args:\n model (Model): Modelo instanciado.\n datasets_list (list): Lista de datasets a preprocesar.\n\n Returns:\n list: Lista... | Aplica el preprocesamiento de model en cada uno de los datasets contenidos
en la lista datasets_list.
Args:
model (Model): Modelo instanciado.
datasets_list (list): Lista de datasets a preprocesar.
Returns:
list: Lista de datasets preprocesados. | experimenter/utils.py | models_preprocess | imfd/TextPerimenter | 1 | python | def models_preprocess(model, datasets_list):
'\n Aplica el preprocesamiento de model en cada uno de los datasets contenidos\n en la lista datasets_list.\n\n Args:\n model (Model): Modelo instanciado.\n datasets_list (list): Lista de datasets a preprocesar.\n\n Returns:\n list: Lista... | def models_preprocess(model, datasets_list):
'\n Aplica el preprocesamiento de model en cada uno de los datasets contenidos\n en la lista datasets_list.\n\n Args:\n model (Model): Modelo instanciado.\n datasets_list (list): Lista de datasets a preprocesar.\n\n Returns:\n list: Lista... |
0cf88e2d6e8cfd13a5307c8a2146646c07367db3d9bf95cf708ad4a713b6c34b | def get_set_result(models, metrics, X, y, exp_id):
'\n Genera los resultados por metrica para una lista de modelos, dado un\n conjunto de entrenamiento (X e y), ademas guarda los resultados con un\n nombre unico.\n\n Args:\n models (list): Lista de listas que contienen los modelos.\n metri... | Genera los resultados por metrica para una lista de modelos, dado un
conjunto de entrenamiento (X e y), ademas guarda los resultados con un
nombre unico.
Args:
models (list): Lista de listas que contienen los modelos.
metrics (list): Lista que contiene las metricas que se usaran
para generar el reporte... | experimenter/utils.py | get_set_result | imfd/TextPerimenter | 1 | python | def get_set_result(models, metrics, X, y, exp_id):
'\n Genera los resultados por metrica para una lista de modelos, dado un\n conjunto de entrenamiento (X e y), ademas guarda los resultados con un\n nombre unico.\n\n Args:\n models (list): Lista de listas que contienen los modelos.\n metri... | def get_set_result(models, metrics, X, y, exp_id):
'\n Genera los resultados por metrica para una lista de modelos, dado un\n conjunto de entrenamiento (X e y), ademas guarda los resultados con un\n nombre unico.\n\n Args:\n models (list): Lista de listas que contienen los modelos.\n metri... |
f8de4c7050c436dab54d4d39e04e15e87eeb020c5a1f3a1f10e5316d55fc67b7 | def models_tester(models, metrics, X_test, y_test, exp_id):
'\n Realiza predicciones con respecto al set de testeo de los mejores modelos\n obtenidos en el trainer. Se genera el output del experimenter, al cual se\n le añaden los reportes de desempeño.\n\n Args:\n models (list): Lista de listas, ... | Realiza predicciones con respecto al set de testeo de los mejores modelos
obtenidos en el trainer. Se genera el output del experimenter, al cual se
le añaden los reportes de desempeño.
Args:
models (list): Lista de listas, en donde cada lista interna corresponde
a las n_best instancias de esa c... | experimenter/utils.py | models_tester | imfd/TextPerimenter | 1 | python | def models_tester(models, metrics, X_test, y_test, exp_id):
'\n Realiza predicciones con respecto al set de testeo de los mejores modelos\n obtenidos en el trainer. Se genera el output del experimenter, al cual se\n le añaden los reportes de desempeño.\n\n Args:\n models (list): Lista de listas, ... | def models_tester(models, metrics, X_test, y_test, exp_id):
'\n Realiza predicciones con respecto al set de testeo de los mejores modelos\n obtenidos en el trainer. Se genera el output del experimenter, al cual se\n le añaden los reportes de desempeño.\n\n Args:\n models (list): Lista de listas, ... |
481b61c1f8b067604b5696164524e8e5c1608013d71b47ec8448774f2cfab682 | def metrics_result(metrics, y_gold, y_pred):
'\n Genera un reporte de desempeño de prediccción con respecto\n a las metricas especificadas. El reporte queda particionado\n por metrica.\n\n Args:\n metrics (list): Lista con las clases de las métricas especificadas.\n y_gold (array): Matriz ... | Genera un reporte de desempeño de prediccción con respecto
a las metricas especificadas. El reporte queda particionado
por metrica.
Args:
metrics (list): Lista con las clases de las métricas especificadas.
y_gold (array): Matriz con las labels reales.
y_pred (array): Matriz con las labels predichas.
Retur... | experimenter/utils.py | metrics_result | imfd/TextPerimenter | 1 | python | def metrics_result(metrics, y_gold, y_pred):
'\n Genera un reporte de desempeño de prediccción con respecto\n a las metricas especificadas. El reporte queda particionado\n por metrica.\n\n Args:\n metrics (list): Lista con las clases de las métricas especificadas.\n y_gold (array): Matriz ... | def metrics_result(metrics, y_gold, y_pred):
'\n Genera un reporte de desempeño de prediccción con respecto\n a las metricas especificadas. El reporte queda particionado\n por metrica.\n\n Args:\n metrics (list): Lista con las clases de las métricas especificadas.\n y_gold (array): Matriz ... |
1e779ece48bccc0477e5e61d6d46a60db140acbb52239cc2a968210220e5f150 | def models_report(results):
'\n Genera reporte de resultados por label o nivel.\n\n Args:\n results (dict): Diccionartio con los resultados por metrica\n\n Returns:\n dict: Reporte de desempeño, particionado por label o nivel.\n '
def union(metric_name, metric_value, report):
... | Genera reporte de resultados por label o nivel.
Args:
results (dict): Diccionartio con los resultados por metrica
Returns:
dict: Reporte de desempeño, particionado por label o nivel. | experimenter/utils.py | models_report | imfd/TextPerimenter | 1 | python | def models_report(results):
'\n Genera reporte de resultados por label o nivel.\n\n Args:\n results (dict): Diccionartio con los resultados por metrica\n\n Returns:\n dict: Reporte de desempeño, particionado por label o nivel.\n '
def union(metric_name, metric_value, report):
... | def models_report(results):
'\n Genera reporte de resultados por label o nivel.\n\n Args:\n results (dict): Diccionartio con los resultados por metrica\n\n Returns:\n dict: Reporte de desempeño, particionado por label o nivel.\n '
def union(metric_name, metric_value, report):
... |
fa2d915e836b365004ab1d1145d99d2e84a4444d02efd98f02717d87f6773fd7 | def models_trainer(models, X_train, X_val, y_train, y_val):
'\n Entrena los modelos y genera reportes en base a todas\n las métricas.\n\n Args:\n models (list): Lista de listas de modelos, en donde cada lista interna\n corresponde a un modelo especificado en el input.\n ... | Entrena los modelos y genera reportes en base a todas
las métricas.
Args:
models (list): Lista de listas de modelos, en donde cada lista interna
corresponde a un modelo especificado en el input.
X_train (array): Arreglo con textos de entrenamiento preprocesados.
X_val (array): Arreglo c... | experimenter/utils.py | models_trainer | imfd/TextPerimenter | 1 | python | def models_trainer(models, X_train, X_val, y_train, y_val):
'\n Entrena los modelos y genera reportes en base a todas\n las métricas.\n\n Args:\n models (list): Lista de listas de modelos, en donde cada lista interna\n corresponde a un modelo especificado en el input.\n ... | def models_trainer(models, X_train, X_val, y_train, y_val):
'\n Entrena los modelos y genera reportes en base a todas\n las métricas.\n\n Args:\n models (list): Lista de listas de modelos, en donde cada lista interna\n corresponde a un modelo especificado en el input.\n ... |
e5e6d4db130b0f908f44945d31726cedfab100890d883922ebdf5210b94d2358 | def optimizer(reports, metrics_dic):
'\n Genera un ranking de los mejores reportes con respecto a la etiqueta y\n métrica especificadas en el input. Luego, filtra los reportes de manera\n tal de dejar los n mejores (especificados por el usuario).\n\n Args:\n reports (list): Lista con los reportes... | Genera un ranking de los mejores reportes con respecto a la etiqueta y
métrica especificadas en el input. Luego, filtra los reportes de manera
tal de dejar los n mejores (especificados por el usuario).
Args:
reports (list): Lista con los reportes obtenidos en el trainer.
metrics_dic (dict): Diccionario con las... | experimenter/utils.py | optimizer | imfd/TextPerimenter | 1 | python | def optimizer(reports, metrics_dic):
'\n Genera un ranking de los mejores reportes con respecto a la etiqueta y\n métrica especificadas en el input. Luego, filtra los reportes de manera\n tal de dejar los n mejores (especificados por el usuario).\n\n Args:\n reports (list): Lista con los reportes... | def optimizer(reports, metrics_dic):
'\n Genera un ranking de los mejores reportes con respecto a la etiqueta y\n métrica especificadas en el input. Luego, filtra los reportes de manera\n tal de dejar los n mejores (especificados por el usuario).\n\n Args:\n reports (list): Lista con los reportes... |
2c20ad9d20e6142829f9ba6b79759a9ff52e35d374bdd6a2c93ba358c3c0299f | def rank_reports(unranked, label, metric):
'\n Ordena una lista de listas de reportes con respecto a la label y métrica\n especificada.\n\n Args:\n unranked (list): Lista de listas de reportes.\n label (str): Nombre de la label con la cual se quiere optimizar.\n metric (str): Nombre de... | Ordena una lista de listas de reportes con respecto a la label y métrica
especificada.
Args:
unranked (list): Lista de listas de reportes.
label (str): Nombre de la label con la cual se quiere optimizar.
metric (str): Nombre de la métrica con la cual se quiere optimizar. | experimenter/utils.py | rank_reports | imfd/TextPerimenter | 1 | python | def rank_reports(unranked, label, metric):
'\n Ordena una lista de listas de reportes con respecto a la label y métrica\n especificada.\n\n Args:\n unranked (list): Lista de listas de reportes.\n label (str): Nombre de la label con la cual se quiere optimizar.\n metric (str): Nombre de... | def rank_reports(unranked, label, metric):
'\n Ordena una lista de listas de reportes con respecto a la label y métrica\n especificada.\n\n Args:\n unranked (list): Lista de listas de reportes.\n label (str): Nombre de la label con la cual se quiere optimizar.\n metric (str): Nombre de... |
51f58f37bdca21900fdc1fc666bd16d882d2b1fa8e2f0c19c3758a20a822dc79 | def search_value(dic, first_key, second_key):
'\n Retorna el valor obtenido de la métrica.\n\n Args:\n dic (dict): Diccionario que tiene un modelo y su reporte.\n first_key (str): String con la primera llave del reporte.\n second_key (str): String con la segunda llave del reporte.\n\n ... | Retorna el valor obtenido de la métrica.
Args:
dic (dict): Diccionario que tiene un modelo y su reporte.
first_key (str): String con la primera llave del reporte.
second_key (str): String con la segunda llave del reporte.
Returns:
float: Valor obtenido. | experimenter/utils.py | search_value | imfd/TextPerimenter | 1 | python | def search_value(dic, first_key, second_key):
'\n Retorna el valor obtenido de la métrica.\n\n Args:\n dic (dict): Diccionario que tiene un modelo y su reporte.\n first_key (str): String con la primera llave del reporte.\n second_key (str): String con la segunda llave del reporte.\n\n ... | def search_value(dic, first_key, second_key):
'\n Retorna el valor obtenido de la métrica.\n\n Args:\n dic (dict): Diccionario que tiene un modelo y su reporte.\n first_key (str): String con la primera llave del reporte.\n second_key (str): String con la segunda llave del reporte.\n\n ... |
005b37c24a1da9aebb1fe1d605e5b0cc450da18e53f45de4e5812c4afafdd5c2 | def set_params_to_list(js):
'\n Inserta en listas los parámetros ingresados individualmente.\n\n Args:\n js (dict): Diccionario con los parámetros modificados.\n '
models = js['models']
if isinstance(models, dict):
js['models'] = [models]
for model in models:
model_params... | Inserta en listas los parámetros ingresados individualmente.
Args:
js (dict): Diccionario con los parámetros modificados. | experimenter/utils.py | set_params_to_list | imfd/TextPerimenter | 1 | python | def set_params_to_list(js):
'\n Inserta en listas los parámetros ingresados individualmente.\n\n Args:\n js (dict): Diccionario con los parámetros modificados.\n '
models = js['models']
if isinstance(models, dict):
js['models'] = [models]
for model in models:
model_params... | def set_params_to_list(js):
'\n Inserta en listas los parámetros ingresados individualmente.\n\n Args:\n js (dict): Diccionario con los parámetros modificados.\n '
models = js['models']
if isinstance(models, dict):
js['models'] = [models]
for model in models:
model_params... |
ee3e73767a128deacbdeacbc1c513afacbe847829c826332c22ba206a6b15bcb | @pytest.mark.parametrize('measured_dist,distance_measure_params,expected_mmd', [(BitstringDistribution({'000': 0.1, '111': 0.9}), {'sigma': 0.5}, 0.32000000000000006), (BitstringDistribution({'000': 0.5, '111': 0.5}), {'sigma': 1}, 0.0), (BitstringDistribution({'000': 0.5, '111': 0.5}), {'sigma': [1, 0.5, 2]}, 0.0)])
d... | Maximum mean discrepancy (MMD) with gaussian kernel between distributions is
computed correctly. | tests/zquantum/core/bitstring_distribution/distance_measures/distance_measures_test.py | test_gaussian_mmd_is_computed_correctly | yukiizm/z-quantum-core | 24 | python | @pytest.mark.parametrize('measured_dist,distance_measure_params,expected_mmd', [(BitstringDistribution({'000': 0.1, '111': 0.9}), {'sigma': 0.5}, 0.32000000000000006), (BitstringDistribution({'000': 0.5, '111': 0.5}), {'sigma': 1}, 0.0), (BitstringDistribution({'000': 0.5, '111': 0.5}), {'sigma': [1, 0.5, 2]}, 0.0)])
d... | @pytest.mark.parametrize('measured_dist,distance_measure_params,expected_mmd', [(BitstringDistribution({'000': 0.1, '111': 0.9}), {'sigma': 0.5}, 0.32000000000000006), (BitstringDistribution({'000': 0.5, '111': 0.5}), {'sigma': 1}, 0.0), (BitstringDistribution({'000': 0.5, '111': 0.5}), {'sigma': [1, 0.5, 2]}, 0.0)])
d... |
f4ba6bac1f5e0dc9c38f8067e14971de05c197e55676c420dcf0d29d5dad346c | def test_jensen_shannon_divergence_is_computed_correctly():
'jensen shannon divergence between distributions is computed correctly.'
target_distr = BitstringDistribution({'000': 0.5, '111': 0.5})
measured_dist = BitstringDistribution({'000': 0.1, '111': 0.9})
distance_measure_params = {'epsilon': 0.1}
... | jensen shannon divergence between distributions is computed correctly. | tests/zquantum/core/bitstring_distribution/distance_measures/distance_measures_test.py | test_jensen_shannon_divergence_is_computed_correctly | yukiizm/z-quantum-core | 24 | python | def test_jensen_shannon_divergence_is_computed_correctly():
target_distr = BitstringDistribution({'000': 0.5, '111': 0.5})
measured_dist = BitstringDistribution({'000': 0.1, '111': 0.9})
distance_measure_params = {'epsilon': 0.1}
jensen_shannon_divergence = compute_jensen_shannon_divergence(target_... | def test_jensen_shannon_divergence_is_computed_correctly():
target_distr = BitstringDistribution({'000': 0.5, '111': 0.5})
measured_dist = BitstringDistribution({'000': 0.1, '111': 0.9})
distance_measure_params = {'epsilon': 0.1}
jensen_shannon_divergence = compute_jensen_shannon_divergence(target_... |
53206814c1af2002c93639fa917c9d8daedfc357a216206014d7b4af4f4cb42a | def run(self):
'Run (solve) the Genetic Algorithm.'
for i in range(self.generations):
log.info(f'Training population in generation {(i + 1)}...')
if (i == 0):
self.create_first_generation()
else:
self.create_next_generation()
log.info(f'best individual: {s... | Run (solve) the Genetic Algorithm. | easynas/genetic_algorithm.py | run | erap129/EasyNAS | 0 | python | def run(self):
for i in range(self.generations):
log.info(f'Training population in generation {(i + 1)}...')
if (i == 0):
self.create_first_generation()
else:
self.create_next_generation()
log.info(f'best individual: {self.best_individual()[1]}')
... | def run(self):
for i in range(self.generations):
log.info(f'Training population in generation {(i + 1)}...')
if (i == 0):
self.create_first_generation()
else:
self.create_next_generation()
log.info(f'best individual: {self.best_individual()[1]}')
... |
88d0b2eb98c3224b53536816a3094e7c4e7620c852cb09018684b9dc4e9b564d | def calculate_population_fitness(self):
'Calculate the fitness of every member of the given population using\n the supplied fitness_function.\n '
for individual in tqdm(self.current_generation):
individual.fitness = self.fitness_function(individual.genes, self.seed_data)
log.info(f'Cur... | Calculate the fitness of every member of the given population using
the supplied fitness_function. | easynas/genetic_algorithm.py | calculate_population_fitness | erap129/EasyNAS | 0 | python | def calculate_population_fitness(self):
'Calculate the fitness of every member of the given population using\n the supplied fitness_function.\n '
for individual in tqdm(self.current_generation):
individual.fitness = self.fitness_function(individual.genes, self.seed_data)
log.info(f'Cur... | def calculate_population_fitness(self):
'Calculate the fitness of every member of the given population using\n the supplied fitness_function.\n '
for individual in tqdm(self.current_generation):
individual.fitness = self.fitness_function(individual.genes, self.seed_data)
log.info(f'Cur... |
e42150652486303b224141080e5071b6fd0aabcdd00208d96c2d89141322883f | def test_return_type(self):
'\n Invalid requests should still return a complete response dict\n '
self.assertIsInstance(self.response_dict, dict)
self.assertEqual(len(self.response_dict), 6) | Invalid requests should still return a complete response dict | tests/test_response_handling.py | test_return_type | 5150brien/retsdk | 1 | python | def test_return_type(self):
'\n \n '
self.assertIsInstance(self.response_dict, dict)
self.assertEqual(len(self.response_dict), 6) | def test_return_type(self):
'\n \n '
self.assertIsInstance(self.response_dict, dict)
self.assertEqual(len(self.response_dict), 6)<|docstring|>Invalid requests should still return a complete response dict<|endoftext|> |
7447e23bbf40586bc27e2b27570897fcad8758bf61b8e2080f89a2c54d6754f2 | def test_response_data_payload(self):
"\n The 'rows' value should be an empty list (no data payload returned)\n "
self.assertIsInstance(self.response_dict['rows'], list)
self.assertEqual(len(self.response_dict['rows']), 0) | The 'rows' value should be an empty list (no data payload returned) | tests/test_response_handling.py | test_response_data_payload | 5150brien/retsdk | 1 | python | def test_response_data_payload(self):
"\n \n "
self.assertIsInstance(self.response_dict['rows'], list)
self.assertEqual(len(self.response_dict['rows']), 0) | def test_response_data_payload(self):
"\n \n "
self.assertIsInstance(self.response_dict['rows'], list)
self.assertEqual(len(self.response_dict['rows']), 0)<|docstring|>The 'rows' value should be an empty list (no data payload returned)<|endoftext|> |
835eae6137599f87ecebb12bb8a4df8311410a1156907e1974c4811f66510de5 | def test_error_reply_code(self):
'\n Reply code for bad requests should be non-null and non-zero\n '
self.assertIsNotNone(self.response_dict['reply_code'])
self.assertNotEqual(self.response_dict['reply_code'], '')
self.assertNotEqual(self.response_dict['reply_code'], '0') | Reply code for bad requests should be non-null and non-zero | tests/test_response_handling.py | test_error_reply_code | 5150brien/retsdk | 1 | python | def test_error_reply_code(self):
'\n \n '
self.assertIsNotNone(self.response_dict['reply_code'])
self.assertNotEqual(self.response_dict['reply_code'], )
self.assertNotEqual(self.response_dict['reply_code'], '0') | def test_error_reply_code(self):
'\n \n '
self.assertIsNotNone(self.response_dict['reply_code'])
self.assertNotEqual(self.response_dict['reply_code'], )
self.assertNotEqual(self.response_dict['reply_code'], '0')<|docstring|>Reply code for bad requests should be non-null and non-zero<|endof... |
229e1b6b04340446cbe53202e35393119e9346fcfece83130fc480727256fe78 | def test_reply_text(self):
'\n Reply text for bad requests should be non-null\n '
self.assertIsNotNone(self.response_dict['reply_text'])
self.assertNotEqual(self.response_dict['reply_text'], '') | Reply text for bad requests should be non-null | tests/test_response_handling.py | test_reply_text | 5150brien/retsdk | 1 | python | def test_reply_text(self):
'\n \n '
self.assertIsNotNone(self.response_dict['reply_text'])
self.assertNotEqual(self.response_dict['reply_text'], ) | def test_reply_text(self):
'\n \n '
self.assertIsNotNone(self.response_dict['reply_text'])
self.assertNotEqual(self.response_dict['reply_text'], )<|docstring|>Reply text for bad requests should be non-null<|endoftext|> |
e8fd10b09856bd1d27137f4c4c19a7326ed90644277aaa320db1655e1d7d40dd | def test_ok_value(self):
"\n The response dict's 'ok' val should be False for bad requests\n "
self.assertFalse(self.response_dict['ok']) | The response dict's 'ok' val should be False for bad requests | tests/test_response_handling.py | test_ok_value | 5150brien/retsdk | 1 | python | def test_ok_value(self):
"\n \n "
self.assertFalse(self.response_dict['ok']) | def test_ok_value(self):
"\n \n "
self.assertFalse(self.response_dict['ok'])<|docstring|>The response dict's 'ok' val should be False for bad requests<|endoftext|> |
bf056419043ff2133db3e8354916cccb5233b5961f1e851303c27a10f725aeaf | def test_more_rows_value(self):
"\n The response dict's 'more_rows' val should be False for bad requests\n "
self.assertFalse(self.response_dict['more_rows']) | The response dict's 'more_rows' val should be False for bad requests | tests/test_response_handling.py | test_more_rows_value | 5150brien/retsdk | 1 | python | def test_more_rows_value(self):
"\n \n "
self.assertFalse(self.response_dict['more_rows']) | def test_more_rows_value(self):
"\n \n "
self.assertFalse(self.response_dict['more_rows'])<|docstring|>The response dict's 'more_rows' val should be False for bad requests<|endoftext|> |
a73bd741b8e5b5517245e00ebc0231d4626ef8c46038ae6fb012f45bf47e04af | def test_response_rows(self):
'\n The response dict should contain a list of values (can be empty)\n '
self.assertIsInstance(self.response_dict['rows'], list)
self.assertGreaterEqual(len(self.response_dict['rows']), 0) | The response dict should contain a list of values (can be empty) | tests/test_response_handling.py | test_response_rows | 5150brien/retsdk | 1 | python | def test_response_rows(self):
'\n \n '
self.assertIsInstance(self.response_dict['rows'], list)
self.assertGreaterEqual(len(self.response_dict['rows']), 0) | def test_response_rows(self):
'\n \n '
self.assertIsInstance(self.response_dict['rows'], list)
self.assertGreaterEqual(len(self.response_dict['rows']), 0)<|docstring|>The response dict should contain a list of values (can be empty)<|endoftext|> |
dcae7e9cc1567d4ae81f81fe83fb68fdd978dd5cb9829466b57fc65c1863e3c6 | def test_ok_value(self):
"\n The response dict's 'ok' val should be True\n "
self.assertTrue(self.response_dict['ok']) | The response dict's 'ok' val should be True | tests/test_response_handling.py | test_ok_value | 5150brien/retsdk | 1 | python | def test_ok_value(self):
"\n \n "
self.assertTrue(self.response_dict['ok']) | def test_ok_value(self):
"\n \n "
self.assertTrue(self.response_dict['ok'])<|docstring|>The response dict's 'ok' val should be True<|endoftext|> |
b49c1259e8e2e7dd5ea1228236cadcfd3a26805a7e3905800ae5d90e5201278b | def test_more_rows_value(self):
"\n The response dict's 'more_rows' val should be False\n "
self.assertFalse(self.response_dict['more_rows']) | The response dict's 'more_rows' val should be False | tests/test_response_handling.py | test_more_rows_value | 5150brien/retsdk | 1 | python | def test_more_rows_value(self):
"\n \n "
self.assertFalse(self.response_dict['more_rows']) | def test_more_rows_value(self):
"\n \n "
self.assertFalse(self.response_dict['more_rows'])<|docstring|>The response dict's 'more_rows' val should be False<|endoftext|> |
a73bd741b8e5b5517245e00ebc0231d4626ef8c46038ae6fb012f45bf47e04af | def test_response_rows(self):
'\n The response dict should contain a list of values (can be empty)\n '
self.assertIsInstance(self.response_dict['rows'], list)
self.assertGreaterEqual(len(self.response_dict['rows']), 0) | The response dict should contain a list of values (can be empty) | tests/test_response_handling.py | test_response_rows | 5150brien/retsdk | 1 | python | def test_response_rows(self):
'\n \n '
self.assertIsInstance(self.response_dict['rows'], list)
self.assertGreaterEqual(len(self.response_dict['rows']), 0) | def test_response_rows(self):
'\n \n '
self.assertIsInstance(self.response_dict['rows'], list)
self.assertGreaterEqual(len(self.response_dict['rows']), 0)<|docstring|>The response dict should contain a list of values (can be empty)<|endoftext|> |
dcae7e9cc1567d4ae81f81fe83fb68fdd978dd5cb9829466b57fc65c1863e3c6 | def test_ok_value(self):
"\n The response dict's 'ok' val should be True\n "
self.assertTrue(self.response_dict['ok']) | The response dict's 'ok' val should be True | tests/test_response_handling.py | test_ok_value | 5150brien/retsdk | 1 | python | def test_ok_value(self):
"\n \n "
self.assertTrue(self.response_dict['ok']) | def test_ok_value(self):
"\n \n "
self.assertTrue(self.response_dict['ok'])<|docstring|>The response dict's 'ok' val should be True<|endoftext|> |
e7999c02f61086c39a5fc0a98e52c3fcee2371667d92c19b2fa8e0af2a6889e5 | def test_more_rows_value(self):
"\n The response dict's 'more_rows' val should be True\n "
self.assertTrue(self.response_dict['more_rows']) | The response dict's 'more_rows' val should be True | tests/test_response_handling.py | test_more_rows_value | 5150brien/retsdk | 1 | python | def test_more_rows_value(self):
"\n \n "
self.assertTrue(self.response_dict['more_rows']) | def test_more_rows_value(self):
"\n \n "
self.assertTrue(self.response_dict['more_rows'])<|docstring|>The response dict's 'more_rows' val should be True<|endoftext|> |
e2655bac50398a24f2aefc00be442e4dc4965892ba7fad2638dba732ac69a81f | def test_get_api_url():
'\n Make sure we get a functioning API URL\n '
api_url = get_api_url()
resp = requests.get(api_url)
check_response(resp)
content = resp.json()
assert ('cases' in content.keys()) | Make sure we get a functioning API URL | tests/test_utils.py | test_get_api_url | bensteinberg/cap-examples | 49 | python | def test_get_api_url():
'\n \n '
api_url = get_api_url()
resp = requests.get(api_url)
check_response(resp)
content = resp.json()
assert ('cases' in content.keys()) | def test_get_api_url():
'\n \n '
api_url = get_api_url()
resp = requests.get(api_url)
check_response(resp)
content = resp.json()
assert ('cases' in content.keys())<|docstring|>Make sure we get a functioning API URL<|endoftext|> |
2fe960a9a64446d8bf69f7931123ca58cad5491a39e7fb42d3b064a319170515 | def _transform(s_data: str, replacements: list, keep_license_text: bool=False) -> str:
'\n Internal function called to transform source data into templated data\n :param s_data: the source data to be transformed\n :param replacements: list of transformation pairs A->B\n :param keep_license_text: whether... | Internal function called to transform source data into templated data
:param s_data: the source data to be transformed
:param replacements: list of transformation pairs A->B
:param keep_license_text: whether or not you want to keep license text
:return: the potentially transformed data | scripts/o3de/o3de/engine_template.py | _transform | SparkyStudios/o3de | 11 | python | def _transform(s_data: str, replacements: list, keep_license_text: bool=False) -> str:
'\n Internal function called to transform source data into templated data\n :param s_data: the source data to be transformed\n :param replacements: list of transformation pairs A->B\n :param keep_license_text: whether... | def _transform(s_data: str, replacements: list, keep_license_text: bool=False) -> str:
'\n Internal function called to transform source data into templated data\n :param s_data: the source data to be transformed\n :param replacements: list of transformation pairs A->B\n :param keep_license_text: whether... |
f602e1ea028d4229ef70c8279c7777c95c9b6264fe8859b1239e24097028fe00 | def _transform_copy(source_file: pathlib.Path, destination_file: pathlib.Path, replacements: list, keep_license_text: bool=False) -> None:
'\n Internal function called to transform and copy a source file into templated destination file\n :param source_file: the source file to be transformed\n :param destin... | Internal function called to transform and copy a source file into templated destination file
:param source_file: the source file to be transformed
:param destination_file: the destination file, this is the transformed file
:param replacements: list of transformation pairs A->B
:param keep_license_text: whether or not y... | scripts/o3de/o3de/engine_template.py | _transform_copy | SparkyStudios/o3de | 11 | python | def _transform_copy(source_file: pathlib.Path, destination_file: pathlib.Path, replacements: list, keep_license_text: bool=False) -> None:
'\n Internal function called to transform and copy a source file into templated destination file\n :param source_file: the source file to be transformed\n :param destin... | def _transform_copy(source_file: pathlib.Path, destination_file: pathlib.Path, replacements: list, keep_license_text: bool=False) -> None:
'\n Internal function called to transform and copy a source file into templated destination file\n :param source_file: the source file to be transformed\n :param destin... |
bae588e00f74d14ad756c771453c2d0a3a92d6917da99da0b00e12d73c721497 | def _instantiate_template(template_json_data: dict, destination_name: str, template_name: str, destination_path: pathlib.Path, template_path: pathlib.Path, destination_restricted_path: pathlib.Path, template_restricted_path: pathlib.Path, destination_restricted_platform_relative_path: pathlib.Path, template_restricted_... | Internal function to create a concrete instance from a template
:param template_json_data: the template json data
:param destination_name: the name of folder you want to instantiate the template in
:param template_name: the name of the template
:param destination_path: the path you want to instantiate the template in
... | scripts/o3de/o3de/engine_template.py | _instantiate_template | SparkyStudios/o3de | 11 | python | def _instantiate_template(template_json_data: dict, destination_name: str, template_name: str, destination_path: pathlib.Path, template_path: pathlib.Path, destination_restricted_path: pathlib.Path, template_restricted_path: pathlib.Path, destination_restricted_platform_relative_path: pathlib.Path, template_restricted_... | def _instantiate_template(template_json_data: dict, destination_name: str, template_name: str, destination_path: pathlib.Path, template_path: pathlib.Path, destination_restricted_path: pathlib.Path, template_restricted_path: pathlib.Path, destination_restricted_platform_relative_path: pathlib.Path, template_restricted_... |
daced38d6fc416926d312ae10c7ce90a7f1f9eabb4fbcd6efe691dd84be38cce | def create_template(source_path: pathlib.Path, template_path: pathlib.Path, source_name: str=None, source_restricted_path: pathlib.Path=None, source_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, source_restricted_platform_relative_path: pathlib.Path=None, te... | Create a template from a source directory using replacement
:param source_path: The path to the source that you want to make into a template
:param template_path: the path of the template to create, can be absolute or relative to default templates path
:param source_name: Name to replace within template folder with ${... | scripts/o3de/o3de/engine_template.py | create_template | SparkyStudios/o3de | 11 | python | def create_template(source_path: pathlib.Path, template_path: pathlib.Path, source_name: str=None, source_restricted_path: pathlib.Path=None, source_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, source_restricted_platform_relative_path: pathlib.Path=None, te... | def create_template(source_path: pathlib.Path, template_path: pathlib.Path, source_name: str=None, source_restricted_path: pathlib.Path=None, source_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, source_restricted_platform_relative_path: pathlib.Path=None, te... |
455a51dc701b9f4241e6ae3ecdebbcc18a3a9865aa16031303a4cb9eecf01b7b | def create_from_template(destination_path: pathlib.Path, template_path: pathlib.Path=None, template_name: str=None, destination_name: str=None, destination_restricted_path: pathlib.Path=None, destination_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, destinat... | Generic template instantiation for non o3de object templates. This function makes NO assumptions!
Assumptions are made only for specializations like create_project or create_gem etc... So this function
will NOT try to divine intent.
:param destination_path: the folder you want to instantiate the template into
:param ... | scripts/o3de/o3de/engine_template.py | create_from_template | SparkyStudios/o3de | 11 | python | def create_from_template(destination_path: pathlib.Path, template_path: pathlib.Path=None, template_name: str=None, destination_name: str=None, destination_restricted_path: pathlib.Path=None, destination_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, destinat... | def create_from_template(destination_path: pathlib.Path, template_path: pathlib.Path=None, template_name: str=None, destination_name: str=None, destination_restricted_path: pathlib.Path=None, destination_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, destinat... |
3b614afa580d188c08130a66b10cc8ee608048d999c5a57a7e2fa1731b8f86d7 | def create_project(project_path: pathlib.Path, project_name: str=None, template_path: pathlib.Path=None, template_name: str=None, project_restricted_path: pathlib.Path=None, project_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, project_restricted_platform_re... | Template instantiation specialization that makes all default assumptions for a Project template instantiation,
reducing the effort needed in instancing a project
:param project_path: the project path, can be absolute or relative to default projects path
:param project_name: the project name, defaults to project_path b... | scripts/o3de/o3de/engine_template.py | create_project | SparkyStudios/o3de | 11 | python | def create_project(project_path: pathlib.Path, project_name: str=None, template_path: pathlib.Path=None, template_name: str=None, project_restricted_path: pathlib.Path=None, project_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, project_restricted_platform_re... | def create_project(project_path: pathlib.Path, project_name: str=None, template_path: pathlib.Path=None, template_name: str=None, project_restricted_path: pathlib.Path=None, project_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, project_restricted_platform_re... |
a77708bfbd97a7e52c6e4eb35f90184c0c4ccb9037cfb95e0410cbaa3039008c | def create_gem(gem_path: pathlib.Path, template_path: pathlib.Path=None, template_name: str=None, gem_name: str=None, gem_restricted_path: pathlib.Path=None, gem_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, gem_restricted_platform_relative_path: pathlib.Pat... | Template instantiation specialization that makes all default assumptions for a Gem template instantiation,
reducing the effort needed in instancing a gem
:param gem_path: the gem path, can be absolute or relative to default gems path
:param template_path: the template path you want to instance, can be absolute or rela... | scripts/o3de/o3de/engine_template.py | create_gem | SparkyStudios/o3de | 11 | python | def create_gem(gem_path: pathlib.Path, template_path: pathlib.Path=None, template_name: str=None, gem_name: str=None, gem_restricted_path: pathlib.Path=None, gem_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, gem_restricted_platform_relative_path: pathlib.Pat... | def create_gem(gem_path: pathlib.Path, template_path: pathlib.Path=None, template_name: str=None, gem_name: str=None, gem_restricted_path: pathlib.Path=None, gem_restricted_name: str=None, template_restricted_path: pathlib.Path=None, template_restricted_name: str=None, gem_restricted_platform_relative_path: pathlib.Pat... |
8cbb395c53200b008fd1637ec4c76d90a31ad9dbc3775df83b1425a8253a6d1f | def add_args(subparsers) -> None:
'\n add_args is called to add expected parser arguments and subparsers arguments to each command such that it can be\n invoked locally or aggregated by a central python file.\n Ex. Directly run from this file alone with: python engine_template.py create-gem --gem-path Test... | add_args is called to add expected parser arguments and subparsers arguments to each command such that it can be
invoked locally or aggregated by a central python file.
Ex. Directly run from this file alone with: python engine_template.py create-gem --gem-path TestGem
OR
o3de.py can aggregate commands by importing engi... | scripts/o3de/o3de/engine_template.py | add_args | SparkyStudios/o3de | 11 | python | def add_args(subparsers) -> None:
'\n add_args is called to add expected parser arguments and subparsers arguments to each command such that it can be\n invoked locally or aggregated by a central python file.\n Ex. Directly run from this file alone with: python engine_template.py create-gem --gem-path Test... | def add_args(subparsers) -> None:
'\n add_args is called to add expected parser arguments and subparsers arguments to each command such that it can be\n invoked locally or aggregated by a central python file.\n Ex. Directly run from this file alone with: python engine_template.py create-gem --gem-path Test... |
93774899140b07086fd0bba5071ec54fa34e62310ba799a4b8f189e193b837c1 | def _is_cpp_file(file_path: pathlib.Path) -> bool:
'\n Internal helper method to check if a file is a C++ file based\n on its extension, so we can determine if we need to prefer\n the ${SanitizedCppName}\n :param file_path: The input file path\n :return: bool: Whether or not the i... | Internal helper method to check if a file is a C++ file based
on its extension, so we can determine if we need to prefer
the ${SanitizedCppName}
:param file_path: The input file path
:return: bool: Whether or not the input file path has a C++ extension | scripts/o3de/o3de/engine_template.py | _is_cpp_file | SparkyStudios/o3de | 11 | python | def _is_cpp_file(file_path: pathlib.Path) -> bool:
'\n Internal helper method to check if a file is a C++ file based\n on its extension, so we can determine if we need to prefer\n the ${SanitizedCppName}\n :param file_path: The input file path\n :return: bool: Whether or not the i... | def _is_cpp_file(file_path: pathlib.Path) -> bool:
'\n Internal helper method to check if a file is a C++ file based\n on its extension, so we can determine if we need to prefer\n the ${SanitizedCppName}\n :param file_path: The input file path\n :return: bool: Whether or not the i... |
f3d4e5f8bb43b54ee179c37cf2686fe7b6005598ba0d806c6e619551f8b78cb7 | def _transform_into_template(s_data: object, prefer_sanitized_name: bool=False) -> (bool, str):
"\n Internal function to transform any data into templated data\n :param s_data: the input data, this could be file data or file name data\n :param prefer_sanitized_name: Optionally swap the sanitize... | Internal function to transform any data into templated data
:param s_data: the input data, this could be file data or file name data
:param prefer_sanitized_name: Optionally swap the sanitized name with the normal name
This can be necessary when creating the template, the source
... | scripts/o3de/o3de/engine_template.py | _transform_into_template | SparkyStudios/o3de | 11 | python | def _transform_into_template(s_data: object, prefer_sanitized_name: bool=False) -> (bool, str):
"\n Internal function to transform any data into templated data\n :param s_data: the input data, this could be file data or file name data\n :param prefer_sanitized_name: Optionally swap the sanitize... | def _transform_into_template(s_data: object, prefer_sanitized_name: bool=False) -> (bool, str):
"\n Internal function to transform any data into templated data\n :param s_data: the input data, this could be file data or file name data\n :param prefer_sanitized_name: Optionally swap the sanitize... |
3e8bbe1a675aa9685bb555c586c05d258e0a9eeece728bff5bd4e29c59f31058 | def _transform_restricted_into_copyfiles_and_createdirs(root_abs: pathlib.Path, path_abs: pathlib.Path=None) -> None:
'\n Internal function recursively called to transform any paths files into copyfiles and create dirs relative to\n the root. This will transform and copy the files, and save the copyfi... | Internal function recursively called to transform any paths files into copyfiles and create dirs relative to
the root. This will transform and copy the files, and save the copyfiles and createdirs data, no not save it
:param root_abs: This is the path everything will end up relative to
:path_abs: This is the path being... | scripts/o3de/o3de/engine_template.py | _transform_restricted_into_copyfiles_and_createdirs | SparkyStudios/o3de | 11 | python | def _transform_restricted_into_copyfiles_and_createdirs(root_abs: pathlib.Path, path_abs: pathlib.Path=None) -> None:
'\n Internal function recursively called to transform any paths files into copyfiles and create dirs relative to\n the root. This will transform and copy the files, and save the copyfi... | def _transform_restricted_into_copyfiles_and_createdirs(root_abs: pathlib.Path, path_abs: pathlib.Path=None) -> None:
'\n Internal function recursively called to transform any paths files into copyfiles and create dirs relative to\n the root. This will transform and copy the files, and save the copyfi... |
a982a37af872c1332cac60743c82a22b74979b196b7936656651895121898b14 | def _transform_dir_into_copyfiles_and_createdirs(root_abs: pathlib.Path, path_abs: pathlib.Path=None) -> None:
'\n Internal function recursively called to transform any paths files into copyfiles and create dirs relative to\n the root. This will transform and copy the files, and save the copyfiles and... | Internal function recursively called to transform any paths files into copyfiles and create dirs relative to
the root. This will transform and copy the files, and save the copyfiles and createdirs data, no not save it
:param root_abs: This is the path everything will end up relative to
:path_abs: This is the path being... | scripts/o3de/o3de/engine_template.py | _transform_dir_into_copyfiles_and_createdirs | SparkyStudios/o3de | 11 | python | def _transform_dir_into_copyfiles_and_createdirs(root_abs: pathlib.Path, path_abs: pathlib.Path=None) -> None:
'\n Internal function recursively called to transform any paths files into copyfiles and create dirs relative to\n the root. This will transform and copy the files, and save the copyfiles and... | def _transform_dir_into_copyfiles_and_createdirs(root_abs: pathlib.Path, path_abs: pathlib.Path=None) -> None:
'\n Internal function recursively called to transform any paths files into copyfiles and create dirs relative to\n the root. This will transform and copy the files, and save the copyfiles and... |
b37f1b6c47345d0d7093ae3fc828d5c02e0388b0e054fb811394af94afdf125d | def open(self):
' New connection has been established '
clients.append(self)
self.logger.info('New connection') | New connection has been established | lib/WebSocketServer.py | open | dorneanu/netgrafio | 71 | python | def open(self):
' '
clients.append(self)
self.logger.info('New connection') | def open(self):
' '
clients.append(self)
self.logger.info('New connection')<|docstring|>New connection has been established<|endoftext|> |
578f53199ca06f81e5b4a0522189112b5d51aeeb98c414a11a455fddab985ae3 | def on_message(self, message):
' Data income event callback '
self.write_message((u'%s' % message)) | Data income event callback | lib/WebSocketServer.py | on_message | dorneanu/netgrafio | 71 | python | def on_message(self, message):
' '
self.write_message((u'%s' % message)) | def on_message(self, message):
' '
self.write_message((u'%s' % message))<|docstring|>Data income event callback<|endoftext|> |
56e7a6a5f49e387c8eff7760af5636efdbc5ee2e90213ce497357ab498b75a92 | def on_close(self):
' Connection was closed '
clients.remove(self)
self.logger.info('Connection removed') | Connection was closed | lib/WebSocketServer.py | on_close | dorneanu/netgrafio | 71 | python | def on_close(self):
' '
clients.remove(self)
self.logger.info('Connection removed') | def on_close(self):
' '
clients.remove(self)
self.logger.info('Connection removed')<|docstring|>Connection was closed<|endoftext|> |
bbe3fbc457b80ed27b158dfe83de627c59f9507d8dbc7e265d457479688528bd | def __init__(self, host, port, in_queue=Queue()):
' Constructor for the WebSocketServer class\n\n Args:\n host(str): Hostname\n port(int): Port number to listen on\n in_queue(Queue): Thread-safe working queue\n\n '
self.application = Application()
self.server =... | Constructor for the WebSocketServer class
Args:
host(str): Hostname
port(int): Port number to listen on
in_queue(Queue): Thread-safe working queue | lib/WebSocketServer.py | __init__ | dorneanu/netgrafio | 71 | python | def __init__(self, host, port, in_queue=Queue()):
' Constructor for the WebSocketServer class\n\n Args:\n host(str): Hostname\n port(int): Port number to listen on\n in_queue(Queue): Thread-safe working queue\n\n '
self.application = Application()
self.server =... | def __init__(self, host, port, in_queue=Queue()):
' Constructor for the WebSocketServer class\n\n Args:\n host(str): Hostname\n port(int): Port number to listen on\n in_queue(Queue): Thread-safe working queue\n\n '
self.application = Application()
self.server =... |
1ae035e74ab9844b68374059cbd0b43d4ff9217817df40d6298221f30cf4ca6f | def start_server(self):
' Starts the HTTP server\n '
self.logger.info(('Starting WebSocket server on port %d' % self.port))
http_server = Thread(target=tornado.ioloop.IOLoop.instance().start)
http_server.start() | Starts the HTTP server | lib/WebSocketServer.py | start_server | dorneanu/netgrafio | 71 | python | def start_server(self):
' \n '
self.logger.info(('Starting WebSocket server on port %d' % self.port))
http_server = Thread(target=tornado.ioloop.IOLoop.instance().start)
http_server.start() | def start_server(self):
' \n '
self.logger.info(('Starting WebSocket server on port %d' % self.port))
http_server = Thread(target=tornado.ioloop.IOLoop.instance().start)
http_server.start()<|docstring|>Starts the HTTP server<|endoftext|> |
6d22b55c005d826aeb369b71a6f01ad98d0a9f618d8da560a5308fb81ef2844c | def start_collector(self):
' Starts collecting packages\n '
self.logger.info('Start collector server')
collector_server = Thread(target=self.collect_data)
collector_server.start() | Starts collecting packages | lib/WebSocketServer.py | start_collector | dorneanu/netgrafio | 71 | python | def start_collector(self):
' \n '
self.logger.info('Start collector server')
collector_server = Thread(target=self.collect_data)
collector_server.start() | def start_collector(self):
' \n '
self.logger.info('Start collector server')
collector_server = Thread(target=self.collect_data)
collector_server.start()<|docstring|>Starts collecting packages<|endoftext|> |
4a27201f6145d9b6deeb5a418af972d083ed42a73905898c5133eae83b030e10 | def collector_process_data(self, data):
' Process incoming data and send it to all available clients\n\n Args:\n data: Received data\n\n '
for c in clients:
c.on_message(json.dumps(data)) | Process incoming data and send it to all available clients
Args:
data: Received data | lib/WebSocketServer.py | collector_process_data | dorneanu/netgrafio | 71 | python | def collector_process_data(self, data):
' Process incoming data and send it to all available clients\n\n Args:\n data: Received data\n\n '
for c in clients:
c.on_message(json.dumps(data)) | def collector_process_data(self, data):
' Process incoming data and send it to all available clients\n\n Args:\n data: Received data\n\n '
for c in clients:
c.on_message(json.dumps(data))<|docstring|>Process incoming data and send it to all available clients
Args:
data: Rec... |
b66b6f396626a9b6bc1fa974ee56c105a05b52b6484dc4aaca06e07c60ec4b51 | def collect_data(self):
' Wait for data in individual thread\n '
self.logger.info('Waiting for incoming data ...')
while True:
item = self.in_queue.get()
self.logger.info('Received data!')
self.collector_process_data(item) | Wait for data in individual thread | lib/WebSocketServer.py | collect_data | dorneanu/netgrafio | 71 | python | def collect_data(self):
' \n '
self.logger.info('Waiting for incoming data ...')
while True:
item = self.in_queue.get()
self.logger.info('Received data!')
self.collector_process_data(item) | def collect_data(self):
' \n '
self.logger.info('Waiting for incoming data ...')
while True:
item = self.in_queue.get()
self.logger.info('Received data!')
self.collector_process_data(item)<|docstring|>Wait for data in individual thread<|endoftext|> |
1ab8658321b54e1787405bdd243f1ba5d8a40b8196501fb866be8787986d7bd9 | def start(self):
' Starts the server\n\n .. note::\n The server will listen for incoming JSON packets and pass them\n to all clients connected to the WebSocket.\n '
self.start_server()
self.start_collector() | Starts the server
.. note::
The server will listen for incoming JSON packets and pass them
to all clients connected to the WebSocket. | lib/WebSocketServer.py | start | dorneanu/netgrafio | 71 | python | def start(self):
' Starts the server\n\n .. note::\n The server will listen for incoming JSON packets and pass them\n to all clients connected to the WebSocket.\n '
self.start_server()
self.start_collector() | def start(self):
' Starts the server\n\n .. note::\n The server will listen for incoming JSON packets and pass them\n to all clients connected to the WebSocket.\n '
self.start_server()
self.start_collector()<|docstring|>Starts the server
.. note::
The server will lis... |
61bb960435bb430467b7fe65996ef6d4adab430e06e04a9982b2e9405717ce1e | async def async_change(self, change):
'Merge changes with queue and send when possible, returning True when done'
self.changes = update(self.changes, change)
if self.lock.locked():
return False
async with self.lock:
while self.changes:
(await asyncio.sleep(0))
pay... | Merge changes with queue and send when possible, returning True when done | advantage_air/__init__.py | async_change | Bre77/advantage_air | 3 | python | async def async_change(self, change):
self.changes = update(self.changes, change)
if self.lock.locked():
return False
async with self.lock:
while self.changes:
(await asyncio.sleep(0))
payload = self.changes
self.changes = {}
try:
... | async def async_change(self, change):
self.changes = update(self.changes, change)
if self.lock.locked():
return False
async with self.lock:
while self.changes:
(await asyncio.sleep(0))
payload = self.changes
self.changes = {}
try:
... |
479f85a78220653f22158fb6f07d0a5bd7c1256bdea48b3b8fe1556a8e83494f | async def validate_input(hass: HomeAssistant, data: dict[(str, Any)]) -> dict[(str, Any)]:
'Validate the user input allows us to connect.\n\n Data has the keys from STEP_USER_DATA_SCHEMA with values provided by the user.\n '
api = NYC311API(async_get_clientsession(hass), data['api_key'])
try:
... | Validate the user input allows us to connect.
Data has the keys from STEP_USER_DATA_SCHEMA with values provided by the user. | custom_components/nyc311/config_flow.py | validate_input | elahd/ha-nyc311 | 1 | python | async def validate_input(hass: HomeAssistant, data: dict[(str, Any)]) -> dict[(str, Any)]:
'Validate the user input allows us to connect.\n\n Data has the keys from STEP_USER_DATA_SCHEMA with values provided by the user.\n '
api = NYC311API(async_get_clientsession(hass), data['api_key'])
try:
... | async def validate_input(hass: HomeAssistant, data: dict[(str, Any)]) -> dict[(str, Any)]:
'Validate the user input allows us to connect.\n\n Data has the keys from STEP_USER_DATA_SCHEMA with values provided by the user.\n '
api = NYC311API(async_get_clientsession(hass), data['api_key'])
try:
... |
9a5357f08f964aa0e64a5c9732aee09ae71738c68f2500c7ef98571faec12758 | async def async_step_user(self, user_input: (dict[(str, Any)] | None)=None) -> FlowResult:
'Handle the initial step.'
if (user_input is None):
return self.async_show_form(step_id='user', data_schema=STEP_USER_DATA_SCHEMA)
errors = {}
if (user_input is not None):
try:
info = (... | Handle the initial step. | custom_components/nyc311/config_flow.py | async_step_user | elahd/ha-nyc311 | 1 | python | async def async_step_user(self, user_input: (dict[(str, Any)] | None)=None) -> FlowResult:
if (user_input is None):
return self.async_show_form(step_id='user', data_schema=STEP_USER_DATA_SCHEMA)
errors = {}
if (user_input is not None):
try:
info = (await validate_input(self.... | async def async_step_user(self, user_input: (dict[(str, Any)] | None)=None) -> FlowResult:
if (user_input is None):
return self.async_show_form(step_id='user', data_schema=STEP_USER_DATA_SCHEMA)
errors = {}
if (user_input is not None):
try:
info = (await validate_input(self.... |
5fd27ecf6981eb0e13f94749813ed55d44a532e41f4b838b931782c1d3140e87 | def resample_pcd(pcd, n):
'drop or duplicate points so that input of each object has exactly n points'
idx = np.random.permutation(pcd.shape[0])
if (idx.shape[0] < n):
idx = np.concatenate([idx, np.random.randint(pcd.shape[0], size=(n - pcd.shape[0]))])
return pcd[idx[:n]] | drop or duplicate points so that input of each object has exactly n points | OcCo_TF/utils/data_util.py | resample_pcd | sun-pyo/OcCo | 158 | python | def resample_pcd(pcd, n):
idx = np.random.permutation(pcd.shape[0])
if (idx.shape[0] < n):
idx = np.concatenate([idx, np.random.randint(pcd.shape[0], size=(n - pcd.shape[0]))])
return pcd[idx[:n]] | def resample_pcd(pcd, n):
idx = np.random.permutation(pcd.shape[0])
if (idx.shape[0] < n):
idx = np.concatenate([idx, np.random.randint(pcd.shape[0], size=(n - pcd.shape[0]))])
return pcd[idx[:n]]<|docstring|>drop or duplicate points so that input of each object has exactly n points<|endoftext|... |
4803135b83a70a68add73088bf4a8c769be466e8753c116e57f82cc170e4a728 | def lmdb_dataflow(lmdb_path, batch_size, input_size, output_size, is_training, test_speed=False):
'load LMDB files, then generate batches??'
df = dataflow.LMDBSerializer.load(lmdb_path, shuffle=False)
size = df.size()
if is_training:
df = dataflow.LocallyShuffleData(df, buffer_size=2000)
... | load LMDB files, then generate batches?? | OcCo_TF/utils/data_util.py | lmdb_dataflow | sun-pyo/OcCo | 158 | python | def lmdb_dataflow(lmdb_path, batch_size, input_size, output_size, is_training, test_speed=False):
df = dataflow.LMDBSerializer.load(lmdb_path, shuffle=False)
size = df.size()
if is_training:
df = dataflow.LocallyShuffleData(df, buffer_size=2000)
df = dataflow.PrefetchData(df, nr_prefetc... | def lmdb_dataflow(lmdb_path, batch_size, input_size, output_size, is_training, test_speed=False):
df = dataflow.LMDBSerializer.load(lmdb_path, shuffle=False)
size = df.size()
if is_training:
df = dataflow.LocallyShuffleData(df, buffer_size=2000)
df = dataflow.PrefetchData(df, nr_prefetc... |
09a2b7ccff93adcc16ceabcbf703beb202ea765664e3890909abed062e4503b7 | def __len__(self):
'get the number of batches'
ds_size = len(self.ds)
div = (ds_size // self.batch_size)
rem = (ds_size % self.batch_size)
if (rem == 0):
return div
return (div + int(self.remainder)) | get the number of batches | OcCo_TF/utils/data_util.py | __len__ | sun-pyo/OcCo | 158 | python | def __len__(self):
ds_size = len(self.ds)
div = (ds_size // self.batch_size)
rem = (ds_size % self.batch_size)
if (rem == 0):
return div
return (div + int(self.remainder)) | def __len__(self):
ds_size = len(self.ds)
div = (ds_size // self.batch_size)
rem = (ds_size % self.batch_size)
if (rem == 0):
return div
return (div + int(self.remainder))<|docstring|>get the number of batches<|endoftext|> |
f7a473a66147c832000e3f660fbc9ac67b86871a8702d83067e12904067434c3 | def __iter__(self):
'generating data in batches'
holder = []
for data in self.ds:
holder.append(data)
if (len(holder) == self.batch_size):
(yield self._aggregate_batch(holder, self.use_list))
del holder[:]
if (self.remainder and (len(holder) > 0)):
(yield ... | generating data in batches | OcCo_TF/utils/data_util.py | __iter__ | sun-pyo/OcCo | 158 | python | def __iter__(self):
holder = []
for data in self.ds:
holder.append(data)
if (len(holder) == self.batch_size):
(yield self._aggregate_batch(holder, self.use_list))
del holder[:]
if (self.remainder and (len(holder) > 0)):
(yield self._aggregate_batch(holder... | def __iter__(self):
holder = []
for data in self.ds:
holder.append(data)
if (len(holder) == self.batch_size):
(yield self._aggregate_batch(holder, self.use_list))
del holder[:]
if (self.remainder and (len(holder) > 0)):
(yield self._aggregate_batch(holder... |
f55f8257dad181540c0277bf1b47a9c390a6a56f266e2ed680418102a22ae826 | def _aggregate_batch(self, data_holder, use_list=False):
'\n Concatenate input points along the 0-th dimension\n Stack all other data along the 0-th dimension\n '
ids = np.stack([x[0] for x in data_holder])
inputs = [(resample_pcd(x[1], self.input_size) if (x[1].shape[0] > self.inpu... | Concatenate input points along the 0-th dimension
Stack all other data along the 0-th dimension | OcCo_TF/utils/data_util.py | _aggregate_batch | sun-pyo/OcCo | 158 | python | def _aggregate_batch(self, data_holder, use_list=False):
'\n Concatenate input points along the 0-th dimension\n Stack all other data along the 0-th dimension\n '
ids = np.stack([x[0] for x in data_holder])
inputs = [(resample_pcd(x[1], self.input_size) if (x[1].shape[0] > self.inpu... | def _aggregate_batch(self, data_holder, use_list=False):
'\n Concatenate input points along the 0-th dimension\n Stack all other data along the 0-th dimension\n '
ids = np.stack([x[0] for x in data_holder])
inputs = [(resample_pcd(x[1], self.input_size) if (x[1].shape[0] > self.inpu... |
2eaa97460f397399fba41fce31e92a2b2e7cd1c5254b02f0c8960aa9884c3069 | def get_gdf(self):
'\n Obtain OSM data and save as GeoDataFrame.\n\n Returns\n -------\n GeoDataFrame\n '
return json_to_gdf(osm_json=self.query(), data_type=self.data_type) | Obtain OSM data and save as GeoDataFrame.
Returns
-------
GeoDataFrame | osmsc/geogroup.py | get_gdf | ruirzma/osmsc | 9 | python | def get_gdf(self):
'\n Obtain OSM data and save as GeoDataFrame.\n\n Returns\n -------\n GeoDataFrame\n '
return json_to_gdf(osm_json=self.query(), data_type=self.data_type) | def get_gdf(self):
'\n Obtain OSM data and save as GeoDataFrame.\n\n Returns\n -------\n GeoDataFrame\n '
return json_to_gdf(osm_json=self.query(), data_type=self.data_type)<|docstring|>Obtain OSM data and save as GeoDataFrame.
Returns
-------
GeoDataFrame<|endoftext|> |
1d2031d6964801c6a6defd890835251777c53669c9a619f85816d5447f110930 | def get_gdf(self, tags=False):
'\n Obtain OSM data and save as GeoDataFrame.\n\n Parameters\n ----------\n tags : bool\n if False, the GeoDataFrame won\'t add OSM "tags" column.\n if True, need to extract tag info into current GeoDataFrame\n\n\n Returns\n ... | Obtain OSM data and save as GeoDataFrame.
Parameters
----------
tags : bool
if False, the GeoDataFrame won't add OSM "tags" column.
if True, need to extract tag info into current GeoDataFrame
Returns
-------
GeoDataFrame | osmsc/geogroup.py | get_gdf | ruirzma/osmsc | 9 | python | def get_gdf(self, tags=False):
'\n Obtain OSM data and save as GeoDataFrame.\n\n Parameters\n ----------\n tags : bool\n if False, the GeoDataFrame won\'t add OSM "tags" column.\n if True, need to extract tag info into current GeoDataFrame\n\n\n Returns\n ... | def get_gdf(self, tags=False):
'\n Obtain OSM data and save as GeoDataFrame.\n\n Parameters\n ----------\n tags : bool\n if False, the GeoDataFrame won\'t add OSM "tags" column.\n if True, need to extract tag info into current GeoDataFrame\n\n\n Returns\n ... |
c241a00c0b32c55f40bb1a46770c3b5184a1ef864bef9e5fb7d75ea20c28b3dc | @app.route('/registration', methods=['GET', 'POST'])
def reg():
'\n Отвечает за вывод страницы регистрации и регистрацию\n :return: Страница регистрации\n '
form = RegForm()
if form.validate_on_submit():
user = User(form.username_reg.data, form.email_reg.data)
user.set_password(form... | Отвечает за вывод страницы регистрации и регистрацию
:return: Страница регистрации | backend/app/controllers/auth.py | reg | DankanTsar/memesmerkatuan | 0 | python | @app.route('/registration', methods=['GET', 'POST'])
def reg():
'\n Отвечает за вывод страницы регистрации и регистрацию\n :return: Страница регистрации\n '
form = RegForm()
if form.validate_on_submit():
user = User(form.username_reg.data, form.email_reg.data)
user.set_password(form... | @app.route('/registration', methods=['GET', 'POST'])
def reg():
'\n Отвечает за вывод страницы регистрации и регистрацию\n :return: Страница регистрации\n '
form = RegForm()
if form.validate_on_submit():
user = User(form.username_reg.data, form.email_reg.data)
user.set_password(form... |
d13732d5ab2b584cc10b004ed53c1472e56ddb82afa9e8e4a32ba13d5df08847 | @app.route('/login', methods=['GET', 'POST'])
def log():
'\n Отвечает за вывод страницы входа и вход\n :return: Страница входа\n '
form = LogForm()
if form.validate_on_submit():
session['Username'] = form.username_log.data
return redirect(url_for('index'))
return render_template... | Отвечает за вывод страницы входа и вход
:return: Страница входа | backend/app/controllers/auth.py | log | DankanTsar/memesmerkatuan | 0 | python | @app.route('/login', methods=['GET', 'POST'])
def log():
'\n Отвечает за вывод страницы входа и вход\n :return: Страница входа\n '
form = LogForm()
if form.validate_on_submit():
session['Username'] = form.username_log.data
return redirect(url_for('index'))
return render_template... | @app.route('/login', methods=['GET', 'POST'])
def log():
'\n Отвечает за вывод страницы входа и вход\n :return: Страница входа\n '
form = LogForm()
if form.validate_on_submit():
session['Username'] = form.username_log.data
return redirect(url_for('index'))
return render_template... |
832e37a55fa87cacde34fe47020d875270c6d1df6bc06de824453f2922e821dc | def __init__(self, config: dict=None):
'Initialise a Milestones object.\n\n Args:\n config (dict): Arbitrary configuration.\n '
self.config = config | Initialise a Milestones object.
Args:
config (dict): Arbitrary configuration. | lexos/cutter/milestones.py | __init__ | scottkleinman/lexos | 0 | python | def __init__(self, config: dict=None):
'Initialise a Milestones object.\n\n Args:\n config (dict): Arbitrary configuration.\n '
self.config = config | def __init__(self, config: dict=None):
'Initialise a Milestones object.\n\n Args:\n config (dict): Arbitrary configuration.\n '
self.config = config<|docstring|>Initialise a Milestones object.
Args:
config (dict): Arbitrary configuration.<|endoftext|> |
6057a08a8d59094b74d702565d6843b8b7f863593353dd6a2bdb5ff320319d3b | def set(self, docs: Union[(object, list)], milestone: Union[(dict, str)]) -> Union[(List[object], object)]:
'Set the milestones for a doc or a list of docs.\n\n Args:\n docs (object): A spaCy doc or a list of spaCy docs.\n milestone (Union[dict, str]): The milestone token(s) to match.\n... | Set the milestones for a doc or a list of docs.
Args:
docs (object): A spaCy doc or a list of spaCy docs.
milestone (Union[dict, str]): The milestone token(s) to match.
Returns:
Union[List[object], object]: A spaCy doc or list of spacy docs with
`doc._.is_milestone` set. | lexos/cutter/milestones.py | set | scottkleinman/lexos | 0 | python | def set(self, docs: Union[(object, list)], milestone: Union[(dict, str)]) -> Union[(List[object], object)]:
'Set the milestones for a doc or a list of docs.\n\n Args:\n docs (object): A spaCy doc or a list of spaCy docs.\n milestone (Union[dict, str]): The milestone token(s) to match.\n... | def set(self, docs: Union[(object, list)], milestone: Union[(dict, str)]) -> Union[(List[object], object)]:
'Set the milestones for a doc or a list of docs.\n\n Args:\n docs (object): A spaCy doc or a list of spaCy docs.\n milestone (Union[dict, str]): The milestone token(s) to match.\n... |
056a9d8d951cf20a284f0b2283b13242fb60465a540107c4cb31d1ecad701989 | def _set_milestones(self, doc: object, milestone: str) -> object:
'Set the milestones for a doc.\n\n Args:\n doc (object): A spaCy doc.\n milestone (str): The milestone token(s) to match.\n\n Returns:\n object: A spaCy doc with `doc._.is_milestone` set.\n '
... | Set the milestones for a doc.
Args:
doc (object): A spaCy doc.
milestone (str): The milestone token(s) to match.
Returns:
object: A spaCy doc with `doc._.is_milestone` set. | lexos/cutter/milestones.py | _set_milestones | scottkleinman/lexos | 0 | python | def _set_milestones(self, doc: object, milestone: str) -> object:
'Set the milestones for a doc.\n\n Args:\n doc (object): A spaCy doc.\n milestone (str): The milestone token(s) to match.\n\n Returns:\n object: A spaCy doc with `doc._.is_milestone` set.\n '
... | def _set_milestones(self, doc: object, milestone: str) -> object:
'Set the milestones for a doc.\n\n Args:\n doc (object): A spaCy doc.\n milestone (str): The milestone token(s) to match.\n\n Returns:\n object: A spaCy doc with `doc._.is_milestone` set.\n '
... |
72188a2301b29f2898f5a26a5fef9393a5af7d50487c8dbbbb5c01b8f1900e17 | def _matches_milestone(self, token: object, milestone: Union[(dict, list, str)]) -> bool:
'Check if a token matches a milestone.\n\n Args:\n token (object): The token to test.\n milestone (Union[dict, list, str]): The milestone token(s) to match.\n\n Returns:\n bool: W... | Check if a token matches a milestone.
Args:
token (object): The token to test.
milestone (Union[dict, list, str]): The milestone token(s) to match.
Returns:
bool: Whether the token matches the milestone. | lexos/cutter/milestones.py | _matches_milestone | scottkleinman/lexos | 0 | python | def _matches_milestone(self, token: object, milestone: Union[(dict, list, str)]) -> bool:
'Check if a token matches a milestone.\n\n Args:\n token (object): The token to test.\n milestone (Union[dict, list, str]): The milestone token(s) to match.\n\n Returns:\n bool: W... | def _matches_milestone(self, token: object, milestone: Union[(dict, list, str)]) -> bool:
'Check if a token matches a milestone.\n\n Args:\n token (object): The token to test.\n milestone (Union[dict, list, str]): The milestone token(s) to match.\n\n Returns:\n bool: W... |
76af7c8fcdab80c53a44ad60b3ad31c960dd0789b115a64e993c8e52e36afddf | def _parse_milestone_dict(self, token, milestone_dict):
'Parse a milestone dictionary and get results for each criterion.\n\n Key-value pairs in `milestone_dict` will be interpreted as token\n attributes and their values. If the value is given as a tuple, it\n must have the form `(pattern, oper... | Parse a milestone dictionary and get results for each criterion.
Key-value pairs in `milestone_dict` will be interpreted as token
attributes and their values. If the value is given as a tuple, it
must have the form `(pattern, operator)`, where the pattern is the
string or regex pattern to match, and the operator is th... | lexos/cutter/milestones.py | _parse_milestone_dict | scottkleinman/lexos | 0 | python | def _parse_milestone_dict(self, token, milestone_dict):
'Parse a milestone dictionary and get results for each criterion.\n\n Key-value pairs in `milestone_dict` will be interpreted as token\n attributes and their values. If the value is given as a tuple, it\n must have the form `(pattern, oper... | def _parse_milestone_dict(self, token, milestone_dict):
'Parse a milestone dictionary and get results for each criterion.\n\n Key-value pairs in `milestone_dict` will be interpreted as token\n attributes and their values. If the value is given as a tuple, it\n must have the form `(pattern, oper... |
610ecab364b9dbc023d4bcf496aaa80c020d282dce83ab92bd7640419725eef4 | def _get_milestone_result(self, attr: str, token: object, value: Union[(str, tuple)]) -> bool:
'Test a token for a match.\n\n If value is a tuple, it must have the form `(pattern, operator)`,\n where pattern is the string or regex pattern to match, and\n operator is the method to use. Valid ope... | Test a token for a match.
If value is a tuple, it must have the form `(pattern, operator)`,
where pattern is the string or regex pattern to match, and
operator is the method to use. Valid operators are "in", "not_in",
"starts_with", "ends_with", "re_match", and "re_search".
The prefix "re_" implies that the pattern is... | lexos/cutter/milestones.py | _get_milestone_result | scottkleinman/lexos | 0 | python | def _get_milestone_result(self, attr: str, token: object, value: Union[(str, tuple)]) -> bool:
'Test a token for a match.\n\n If value is a tuple, it must have the form `(pattern, operator)`,\n where pattern is the string or regex pattern to match, and\n operator is the method to use. Valid ope... | def _get_milestone_result(self, attr: str, token: object, value: Union[(str, tuple)]) -> bool:
'Test a token for a match.\n\n If value is a tuple, it must have the form `(pattern, operator)`,\n where pattern is the string or regex pattern to match, and\n operator is the method to use. Valid ope... |
96f46542d299b6d7587cbd60554801f016a6142b9c46ae7153ba5fd06ab8f502 | def register(router, model, database=db, view=AdminView):
"Register an administration view for each model\n\n :param app: Flaks application\n :param list models: A list of AutomapModels\n :param Admin router: An instance of Flask's Admin\n :param ModelView view:\n :return:\n "
if (hasattr(mode... | Register an administration view for each model
:param app: Flaks application
:param list models: A list of AutomapModels
:param Admin router: An instance of Flask's Admin
:param ModelView view:
:return: | flask_sandman/admin.py | register | manaikan/sandman2 | 0 | python | def register(router, model, database=db, view=AdminView):
"Register an administration view for each model\n\n :param app: Flaks application\n :param list models: A list of AutomapModels\n :param Admin router: An instance of Flask's Admin\n :param ModelView view:\n :return:\n "
if (hasattr(mode... | def register(router, model, database=db, view=AdminView):
"Register an administration view for each model\n\n :param app: Flaks application\n :param list models: A list of AutomapModels\n :param Admin router: An instance of Flask's Admin\n :param ModelView view:\n :return:\n "
if (hasattr(mode... |
ff314682ea439080d7d3e36d75c45dd273f4b1eafde17be8b88ac9faf0efa30c | def hinge_loss(correct_answer, incorrect_answer, margin):
'\n Loss by calculating the correct/incorrect score difference in relation to given margin\n :param margin:\n :param correct_answer:\n :param incorrect_answer:\n :return:\n '
loss_sum = torch.sum(((margin + incorrect_answer) - correct_a... | Loss by calculating the correct/incorrect score difference in relation to given margin
:param margin:
:param correct_answer:
:param incorrect_answer:
:return: | utility/training.py | hinge_loss | RobinRojowiec/intent-recognition-in-doctor-patient-interviews | 0 | python | def hinge_loss(correct_answer, incorrect_answer, margin):
'\n Loss by calculating the correct/incorrect score difference in relation to given margin\n :param margin:\n :param correct_answer:\n :param incorrect_answer:\n :return:\n '
loss_sum = torch.sum(((margin + incorrect_answer) - correct_a... | def hinge_loss(correct_answer, incorrect_answer, margin):
'\n Loss by calculating the correct/incorrect score difference in relation to given margin\n :param margin:\n :param correct_answer:\n :param incorrect_answer:\n :return:\n '
loss_sum = torch.sum(((margin + incorrect_answer) - correct_a... |
8aaeb03e3332fbc9dc2657bd2e7141fee0bf6c4580de68886b77adacee7dae8c | def get_optimizer(model: nn.Module, name, **kwargs):
'\n initializes the optimizer\n :param model:\n :param name:\n :param kwargs:\n :return:\n '
if (name == 'SGD'):
return torch.optim.SGD(model.parameters(), **kwargs)
elif (name == 'Adam'):
return torch.optim.Adam(model.pa... | initializes the optimizer
:param model:
:param name:
:param kwargs:
:return: | utility/training.py | get_optimizer | RobinRojowiec/intent-recognition-in-doctor-patient-interviews | 0 | python | def get_optimizer(model: nn.Module, name, **kwargs):
'\n initializes the optimizer\n :param model:\n :param name:\n :param kwargs:\n :return:\n '
if (name == 'SGD'):
return torch.optim.SGD(model.parameters(), **kwargs)
elif (name == 'Adam'):
return torch.optim.Adam(model.pa... | def get_optimizer(model: nn.Module, name, **kwargs):
'\n initializes the optimizer\n :param model:\n :param name:\n :param kwargs:\n :return:\n '
if (name == 'SGD'):
return torch.optim.SGD(model.parameters(), **kwargs)
elif (name == 'Adam'):
return torch.optim.Adam(model.pa... |
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