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
|
|---|---|---|---|---|---|---|---|---|---|
b96987d71658c2a088aa2c88d7cf60979f0c9934b3cd065eb6832c46010b1215
|
def close(self):
'Closes the current peer connection.'
return self._native_obj.close()
|
Closes the current peer connection.
|
python-webrtc/python/webrtc/interfaces/rtc_peer_connection.py
|
close
|
MarshalX/python-webrtc
| 81
|
python
|
def close(self):
return self._native_obj.close()
|
def close(self):
return self._native_obj.close()<|docstring|>Closes the current peer connection.<|endoftext|>
|
dc275937eb30a5ae21a1b71529be95045855a73f38271b77d17b74eebc3bfddc
|
@property
def sctp(self) -> Optional['webrtc.RTCSctpTransport']:
":obj:`webrtc.RTCSctpTransport`, optional: An object describing the SCTP transport layer over which SCTP\n data is being sent and received. If SCTP hasn't been negotiated, this value is :obj:`None`"
from webrtc import RTCSctpTransport
sctp = self._native_obj.sctp
if sctp:
return RTCSctpTransport._wrap(sctp)
return None
|
:obj:`webrtc.RTCSctpTransport`, optional: An object describing the SCTP transport layer over which SCTP
data is being sent and received. If SCTP hasn't been negotiated, this value is :obj:`None`
|
python-webrtc/python/webrtc/interfaces/rtc_peer_connection.py
|
sctp
|
MarshalX/python-webrtc
| 81
|
python
|
@property
def sctp(self) -> Optional['webrtc.RTCSctpTransport']:
":obj:`webrtc.RTCSctpTransport`, optional: An object describing the SCTP transport layer over which SCTP\n data is being sent and received. If SCTP hasn't been negotiated, this value is :obj:`None`"
from webrtc import RTCSctpTransport
sctp = self._native_obj.sctp
if sctp:
return RTCSctpTransport._wrap(sctp)
return None
|
@property
def sctp(self) -> Optional['webrtc.RTCSctpTransport']:
":obj:`webrtc.RTCSctpTransport`, optional: An object describing the SCTP transport layer over which SCTP\n data is being sent and received. If SCTP hasn't been negotiated, this value is :obj:`None`"
from webrtc import RTCSctpTransport
sctp = self._native_obj.sctp
if sctp:
return RTCSctpTransport._wrap(sctp)
return None<|docstring|>:obj:`webrtc.RTCSctpTransport`, optional: An object describing the SCTP transport layer over which SCTP
data is being sent and received. If SCTP hasn't been negotiated, this value is :obj:`None`<|endoftext|>
|
ac5383c8656fb044e9f957515b1ff2c008679589219a3a65dd703a857ceb51bf
|
def parse_args():
'\n Parse arguments for this function\n '
parser = argparse.ArgumentParser()
parser.add_argument('--output_location', '-o', help='location to store output', default='sample_images')
parser.add_argument('--catalog', '-c', help='SEVIR catalog', default='CATALOG.csv')
parser.add_argument('--data_path', '-d', help='Path to SEVIR data', default='data/')
parser.add_argument('--id', '-i', help='SEVIR id (if not provided, a random case is selected', default=None)
parser.add_argument('--resolution', '-r', help='res for statelines', default='c')
args = parser.parse_args()
return args
|
Parse arguments for this function
|
notebooks/eie-sevir/view_sample.py
|
parse_args
|
BigDataArchitecture/Assignment1
| 2
|
python
|
def parse_args():
'\n \n '
parser = argparse.ArgumentParser()
parser.add_argument('--output_location', '-o', help='location to store output', default='sample_images')
parser.add_argument('--catalog', '-c', help='SEVIR catalog', default='CATALOG.csv')
parser.add_argument('--data_path', '-d', help='Path to SEVIR data', default='data/')
parser.add_argument('--id', '-i', help='SEVIR id (if not provided, a random case is selected', default=None)
parser.add_argument('--resolution', '-r', help='res for statelines', default='c')
args = parser.parse_args()
return args
|
def parse_args():
'\n \n '
parser = argparse.ArgumentParser()
parser.add_argument('--output_location', '-o', help='location to store output', default='sample_images')
parser.add_argument('--catalog', '-c', help='SEVIR catalog', default='CATALOG.csv')
parser.add_argument('--data_path', '-d', help='Path to SEVIR data', default='data/')
parser.add_argument('--id', '-i', help='SEVIR id (if not provided, a random case is selected', default=None)
parser.add_argument('--resolution', '-r', help='res for statelines', default='c')
args = parser.parse_args()
return args<|docstring|>Parse arguments for this function<|endoftext|>
|
f3b2616faa8e91c2066083f164d0dc1344704070b376c3e9712d6e7253ca8827
|
def main():
'\n Function to create sample images of a single event in SEVIR\n\n USAGE:\n \n python view_sample.py # only works in SEVIR directory\n\n or, \n\n python view_sample.py --output_location OUTLOC --catalog CATALOG.csv --data_path SEVIR_DATA_PATH \n\n where,\n OUTLOC is where your sample images will be saved (a new dir is made to hold them)\n SEVIR_DATA_PATH is path to the SEVIR data files ()\n\n '
args = parse_args()
catalog = pd.read_csv(args.catalog, low_memory=False)
cat_groups = catalog.groupby('id')
output_location = args.output_location
if args.id:
sevir_id = args.id
cat_groups.get_group(sevir_id)
else:
allsensors = (cat_groups.size() == 5)
ids = np.array(list(cat_groups.groups.keys()))
sevir_id = np.random.choice(ids[allsensors], 1)[0]
print('Using SEVIR ID', sevir_id)
output_location = f'{output_location}/{sevir_id}'
try:
os.mkdir(output_location)
except:
pass
data = get_data(sevir_id, cat_groups, args.data_path)
make_images2(data, output_location, sevir_id, res=args.resolution)
update_progress(1.0)
|
Function to create sample images of a single event in SEVIR
USAGE:
python view_sample.py # only works in SEVIR directory
or,
python view_sample.py --output_location OUTLOC --catalog CATALOG.csv --data_path SEVIR_DATA_PATH
where,
OUTLOC is where your sample images will be saved (a new dir is made to hold them)
SEVIR_DATA_PATH is path to the SEVIR data files ()
|
notebooks/eie-sevir/view_sample.py
|
main
|
BigDataArchitecture/Assignment1
| 2
|
python
|
def main():
'\n Function to create sample images of a single event in SEVIR\n\n USAGE:\n \n python view_sample.py # only works in SEVIR directory\n\n or, \n\n python view_sample.py --output_location OUTLOC --catalog CATALOG.csv --data_path SEVIR_DATA_PATH \n\n where,\n OUTLOC is where your sample images will be saved (a new dir is made to hold them)\n SEVIR_DATA_PATH is path to the SEVIR data files ()\n\n '
args = parse_args()
catalog = pd.read_csv(args.catalog, low_memory=False)
cat_groups = catalog.groupby('id')
output_location = args.output_location
if args.id:
sevir_id = args.id
cat_groups.get_group(sevir_id)
else:
allsensors = (cat_groups.size() == 5)
ids = np.array(list(cat_groups.groups.keys()))
sevir_id = np.random.choice(ids[allsensors], 1)[0]
print('Using SEVIR ID', sevir_id)
output_location = f'{output_location}/{sevir_id}'
try:
os.mkdir(output_location)
except:
pass
data = get_data(sevir_id, cat_groups, args.data_path)
make_images2(data, output_location, sevir_id, res=args.resolution)
update_progress(1.0)
|
def main():
'\n Function to create sample images of a single event in SEVIR\n\n USAGE:\n \n python view_sample.py # only works in SEVIR directory\n\n or, \n\n python view_sample.py --output_location OUTLOC --catalog CATALOG.csv --data_path SEVIR_DATA_PATH \n\n where,\n OUTLOC is where your sample images will be saved (a new dir is made to hold them)\n SEVIR_DATA_PATH is path to the SEVIR data files ()\n\n '
args = parse_args()
catalog = pd.read_csv(args.catalog, low_memory=False)
cat_groups = catalog.groupby('id')
output_location = args.output_location
if args.id:
sevir_id = args.id
cat_groups.get_group(sevir_id)
else:
allsensors = (cat_groups.size() == 5)
ids = np.array(list(cat_groups.groups.keys()))
sevir_id = np.random.choice(ids[allsensors], 1)[0]
print('Using SEVIR ID', sevir_id)
output_location = f'{output_location}/{sevir_id}'
try:
os.mkdir(output_location)
except:
pass
data = get_data(sevir_id, cat_groups, args.data_path)
make_images2(data, output_location, sevir_id, res=args.resolution)
update_progress(1.0)<|docstring|>Function to create sample images of a single event in SEVIR
USAGE:
python view_sample.py # only works in SEVIR directory
or,
python view_sample.py --output_location OUTLOC --catalog CATALOG.csv --data_path SEVIR_DATA_PATH
where,
OUTLOC is where your sample images will be saved (a new dir is made to hold them)
SEVIR_DATA_PATH is path to the SEVIR data files ()<|endoftext|>
|
659e10986f87a06c2d6d208f47f0b647eb6edc737536a9ed6047f439c8f6b21c
|
def get_data(sevir_id, grouped_catalog, path):
' \n returns dict { img_type : {"meta" : META, "data": DATA} }\n '
cases = grouped_catalog.get_group(sevir_id)
data = {}
for typ in TYPES:
data[typ] = {}
if (typ in cases.img_type.values):
meta = cases[(cases.img_type == typ)].squeeze()
data[typ]['meta'] = meta
file_name = f'{path}/{meta.file_name}'
with h5py.File(file_name, 'r') as hf:
if (typ == 'lght'):
data[typ]['data'] = hf[meta.id][:]
else:
data[typ]['data'] = hf[meta.img_type][meta.file_index]
return data
|
returns dict { img_type : {"meta" : META, "data": DATA} }
|
notebooks/eie-sevir/view_sample.py
|
get_data
|
BigDataArchitecture/Assignment1
| 2
|
python
|
def get_data(sevir_id, grouped_catalog, path):
' \n \n '
cases = grouped_catalog.get_group(sevir_id)
data = {}
for typ in TYPES:
data[typ] = {}
if (typ in cases.img_type.values):
meta = cases[(cases.img_type == typ)].squeeze()
data[typ]['meta'] = meta
file_name = f'{path}/{meta.file_name}'
with h5py.File(file_name, 'r') as hf:
if (typ == 'lght'):
data[typ]['data'] = hf[meta.id][:]
else:
data[typ]['data'] = hf[meta.img_type][meta.file_index]
return data
|
def get_data(sevir_id, grouped_catalog, path):
' \n \n '
cases = grouped_catalog.get_group(sevir_id)
data = {}
for typ in TYPES:
data[typ] = {}
if (typ in cases.img_type.values):
meta = cases[(cases.img_type == typ)].squeeze()
data[typ]['meta'] = meta
file_name = f'{path}/{meta.file_name}'
with h5py.File(file_name, 'r') as hf:
if (typ == 'lght'):
data[typ]['data'] = hf[meta.id][:]
else:
data[typ]['data'] = hf[meta.img_type][meta.file_index]
return data<|docstring|>returns dict { img_type : {"meta" : META, "data": DATA} }<|endoftext|>
|
86125d2c511a9e830dda9dc65947d68c497cb09145face1f119c48bc10999f50
|
def mac_from_iface(iface_name):
' Returns the mac address of the specified interface '
ifaddresses = netifaces.ifaddresses(iface_name)
return ifaddresses[netifaces.AF_LINK][0]['addr']
|
Returns the mac address of the specified interface
|
lte/gateway/python/scripts/fake_user.py
|
mac_from_iface
|
electrocucaracha/magma
| 849
|
python
|
def mac_from_iface(iface_name):
' '
ifaddresses = netifaces.ifaddresses(iface_name)
return ifaddresses[netifaces.AF_LINK][0]['addr']
|
def mac_from_iface(iface_name):
' '
ifaddresses = netifaces.ifaddresses(iface_name)
return ifaddresses[netifaces.AF_LINK][0]['addr']<|docstring|>Returns the mac address of the specified interface<|endoftext|>
|
3fe00ef1d297fe4792818719af1d95dab79b22c04111e0dce18b028cd23e3d74
|
def run(command, ignore_errors=False):
' Runs the shell command '
output(command, 'blue')
ret = os.system(command)
if ((ret != 0) and (not ignore_errors)):
output(('Error!! Command returned: %d' % ret), 'red')
sys.exit(1)
|
Runs the shell command
|
lte/gateway/python/scripts/fake_user.py
|
run
|
electrocucaracha/magma
| 849
|
python
|
def run(command, ignore_errors=False):
' '
output(command, 'blue')
ret = os.system(command)
if ((ret != 0) and (not ignore_errors)):
output(('Error!! Command returned: %d' % ret), 'red')
sys.exit(1)
|
def run(command, ignore_errors=False):
' '
output(command, 'blue')
ret = os.system(command)
if ((ret != 0) and (not ignore_errors)):
output(('Error!! Command returned: %d' % ret), 'red')
sys.exit(1)<|docstring|>Runs the shell command<|endoftext|>
|
2f25275fbfc8f089c091db1fe3a5357101203d927a3273e3a9afcacca3cdd311
|
def add_flow(table, filter, actions, priority=300):
" Adds/modifies an OVS flow.\n We use '0xface0ff' as the cookie for all flows created by this tool "
run(('sudo ovs-ofctl add-flow gtp_br0 "cookie=0xface0ff, table=%d, priority=%d,%s actions=%s"' % (table, priority, filter, actions)))
|
Adds/modifies an OVS flow.
We use '0xface0ff' as the cookie for all flows created by this tool
|
lte/gateway/python/scripts/fake_user.py
|
add_flow
|
electrocucaracha/magma
| 849
|
python
|
def add_flow(table, filter, actions, priority=300):
" Adds/modifies an OVS flow.\n We use '0xface0ff' as the cookie for all flows created by this tool "
run(('sudo ovs-ofctl add-flow gtp_br0 "cookie=0xface0ff, table=%d, priority=%d,%s actions=%s"' % (table, priority, filter, actions)))
|
def add_flow(table, filter, actions, priority=300):
" Adds/modifies an OVS flow.\n We use '0xface0ff' as the cookie for all flows created by this tool "
run(('sudo ovs-ofctl add-flow gtp_br0 "cookie=0xface0ff, table=%d, priority=%d,%s actions=%s"' % (table, priority, filter, actions)))<|docstring|>Adds/modifies an OVS flow.
We use '0xface0ff' as the cookie for all flows created by this tool<|endoftext|>
|
16a4edcc77d20ef529fd597537b3709656417ead1686b5ca90abd9a18c1c1f11
|
def pair_time(pos_k, vel_k, pos_l, vel_l, radius):
' pos_k, pos_l, vel_k, vel_l all have two elements as a list '
t_0 = 0.0
pos_x = (pos_l[0] - pos_k[0])
pos_y = (pos_l[1] - pos_k[1])
Delta_pos = np.array([pos_x, pos_y])
vel_x = (vel_l[0] - vel_k[0])
vel_y = (vel_l[1] - vel_k[1])
Delta_vel = np.array([vel_x, vel_y])
Upsilon = ((Delta_pos.dot(Delta_vel) ** 2) - (Delta_vel.dot(Delta_vel) * (Delta_pos.dot(Delta_pos) - (4.0 * (radius ** 2)))))
if ((Upsilon > 0.0) and (Delta_pos.dot(Delta_vel) < 0.0)):
return (t_0 - ((Delta_pos.dot(Delta_vel) + m.sqrt(Upsilon)) / Delta_vel.dot(Delta_vel)))
else:
return float(oo)
|
pos_k, pos_l, vel_k, vel_l all have two elements as a list
|
Chapter 2/event_disks_box_simulation.py
|
pair_time
|
indrag49/Computational-Stat-Mech
| 19
|
python
|
def pair_time(pos_k, vel_k, pos_l, vel_l, radius):
' '
t_0 = 0.0
pos_x = (pos_l[0] - pos_k[0])
pos_y = (pos_l[1] - pos_k[1])
Delta_pos = np.array([pos_x, pos_y])
vel_x = (vel_l[0] - vel_k[0])
vel_y = (vel_l[1] - vel_k[1])
Delta_vel = np.array([vel_x, vel_y])
Upsilon = ((Delta_pos.dot(Delta_vel) ** 2) - (Delta_vel.dot(Delta_vel) * (Delta_pos.dot(Delta_pos) - (4.0 * (radius ** 2)))))
if ((Upsilon > 0.0) and (Delta_pos.dot(Delta_vel) < 0.0)):
return (t_0 - ((Delta_pos.dot(Delta_vel) + m.sqrt(Upsilon)) / Delta_vel.dot(Delta_vel)))
else:
return float(oo)
|
def pair_time(pos_k, vel_k, pos_l, vel_l, radius):
' '
t_0 = 0.0
pos_x = (pos_l[0] - pos_k[0])
pos_y = (pos_l[1] - pos_k[1])
Delta_pos = np.array([pos_x, pos_y])
vel_x = (vel_l[0] - vel_k[0])
vel_y = (vel_l[1] - vel_k[1])
Delta_vel = np.array([vel_x, vel_y])
Upsilon = ((Delta_pos.dot(Delta_vel) ** 2) - (Delta_vel.dot(Delta_vel) * (Delta_pos.dot(Delta_pos) - (4.0 * (radius ** 2)))))
if ((Upsilon > 0.0) and (Delta_pos.dot(Delta_vel) < 0.0)):
return (t_0 - ((Delta_pos.dot(Delta_vel) + m.sqrt(Upsilon)) / Delta_vel.dot(Delta_vel)))
else:
return float(oo)<|docstring|>pos_k, pos_l, vel_k, vel_l all have two elements as a list<|endoftext|>
|
a57f8dba68776fabbe052347872546ace4324059789587f55494e7cb7dfa8be1
|
@staticmethod
def output_path(test_uid, suite_uid, base=None):
'\n Return the path which results for a specific test case should be\n stored.\n '
if (base is None):
base = config.result_path
return os.path.join(base, suite_uid.replace(os.path.sep, '-'), test_uid.replace(os.path.sep, '-'))
|
Return the path which results for a specific test case should be
stored.
|
flimsy/result.py
|
output_path
|
spwilson2/flimsy
| 0
|
python
|
@staticmethod
def output_path(test_uid, suite_uid, base=None):
'\n Return the path which results for a specific test case should be\n stored.\n '
if (base is None):
base = config.result_path
return os.path.join(base, suite_uid.replace(os.path.sep, '-'), test_uid.replace(os.path.sep, '-'))
|
@staticmethod
def output_path(test_uid, suite_uid, base=None):
'\n Return the path which results for a specific test case should be\n stored.\n '
if (base is None):
base = config.result_path
return os.path.join(base, suite_uid.replace(os.path.sep, '-'), test_uid.replace(os.path.sep, '-'))<|docstring|>Return the path which results for a specific test case should be
stored.<|endoftext|>
|
12c34202ca7c86a582f4c0c047c359c02e735f47f3345e39aa3d712df4a5a2b3
|
@staticmethod
def save(results, path):
'\n Compile the internal results into JUnit format writting it to the given file.\n '
results = JUnitTestSuites(results)
with open(path, 'w') as f:
results.write(f)
|
Compile the internal results into JUnit format writting it to the given file.
|
flimsy/result.py
|
save
|
spwilson2/flimsy
| 0
|
python
|
@staticmethod
def save(results, path):
'\n \n '
results = JUnitTestSuites(results)
with open(path, 'w') as f:
results.write(f)
|
@staticmethod
def save(results, path):
'\n \n '
results = JUnitTestSuites(results)
with open(path, 'w') as f:
results.write(f)<|docstring|>Compile the internal results into JUnit format writting it to the given file.<|endoftext|>
|
5f4aa51b55e15bd082bcd87ec0dde58a5f276acbc231fef0cd1a9216402c9deb
|
def _start_trace_dialog(self):
'Open Save As dialog to start recording a trace file.'
viewer = self._win._qt_viewer
dlg = QFileDialog()
hist = get_save_history()
dlg.setHistory(hist)
(filename, _) = dlg.getSaveFileName(parent=viewer, caption=trans._('Record performance trace file'), directory=hist[0], filter=trans._('Trace Files (*.json)'))
if filename:
if (not filename.endswith('.json')):
filename += '.json'
QTimer.singleShot(0, (lambda : self._start_trace(filename)))
update_save_history(filename)
|
Open Save As dialog to start recording a trace file.
|
napari/_qt/menus/debug_menu.py
|
_start_trace_dialog
|
perlman/napari
| 1,345
|
python
|
def _start_trace_dialog(self):
viewer = self._win._qt_viewer
dlg = QFileDialog()
hist = get_save_history()
dlg.setHistory(hist)
(filename, _) = dlg.getSaveFileName(parent=viewer, caption=trans._('Record performance trace file'), directory=hist[0], filter=trans._('Trace Files (*.json)'))
if filename:
if (not filename.endswith('.json')):
filename += '.json'
QTimer.singleShot(0, (lambda : self._start_trace(filename)))
update_save_history(filename)
|
def _start_trace_dialog(self):
viewer = self._win._qt_viewer
dlg = QFileDialog()
hist = get_save_history()
dlg.setHistory(hist)
(filename, _) = dlg.getSaveFileName(parent=viewer, caption=trans._('Record performance trace file'), directory=hist[0], filter=trans._('Trace Files (*.json)'))
if filename:
if (not filename.endswith('.json')):
filename += '.json'
QTimer.singleShot(0, (lambda : self._start_trace(filename)))
update_save_history(filename)<|docstring|>Open Save As dialog to start recording a trace file.<|endoftext|>
|
7818118598a96cde7de87b2d122493302bc290970c79f6b35321c0c1b17ba82b
|
def _stop_trace(self):
'Stop recording a trace file.'
perf.timers.stop_trace_file()
self._set_recording(False)
|
Stop recording a trace file.
|
napari/_qt/menus/debug_menu.py
|
_stop_trace
|
perlman/napari
| 1,345
|
python
|
def _stop_trace(self):
perf.timers.stop_trace_file()
self._set_recording(False)
|
def _stop_trace(self):
perf.timers.stop_trace_file()
self._set_recording(False)<|docstring|>Stop recording a trace file.<|endoftext|>
|
dda9048750a97e60cfa5ff17add3f34cded91c6d2b2ba893f9f5c87b1ecbbfa7
|
def _set_recording(self, recording: bool):
'Toggle which are enabled/disabled.\n\n Parameters\n ----------\n recording : bool\n Are we currently recording a trace file.\n '
for action in self._perf_menu.actions():
if (trans._('Start Recording') in action.text()):
action.setEnabled((not recording))
elif (trans._('Stop Recording') in action.text()):
action.setEnabled(recording)
|
Toggle which are enabled/disabled.
Parameters
----------
recording : bool
Are we currently recording a trace file.
|
napari/_qt/menus/debug_menu.py
|
_set_recording
|
perlman/napari
| 1,345
|
python
|
def _set_recording(self, recording: bool):
'Toggle which are enabled/disabled.\n\n Parameters\n ----------\n recording : bool\n Are we currently recording a trace file.\n '
for action in self._perf_menu.actions():
if (trans._('Start Recording') in action.text()):
action.setEnabled((not recording))
elif (trans._('Stop Recording') in action.text()):
action.setEnabled(recording)
|
def _set_recording(self, recording: bool):
'Toggle which are enabled/disabled.\n\n Parameters\n ----------\n recording : bool\n Are we currently recording a trace file.\n '
for action in self._perf_menu.actions():
if (trans._('Start Recording') in action.text()):
action.setEnabled((not recording))
elif (trans._('Stop Recording') in action.text()):
action.setEnabled(recording)<|docstring|>Toggle which are enabled/disabled.
Parameters
----------
recording : bool
Are we currently recording a trace file.<|endoftext|>
|
2a940da033744feb1daae7f27b718cc956b061d13942729883f96efc845a78fa
|
def label_smoothing(pred, target, eta=0.1):
'\n Refer from https://arxiv.org/pdf/1512.00567.pdf\n :param target: N,\n :param n_classes: int\n :param eta: float\n :return:\n N x C onehot smoothed vector\n '
n_classes = pred.size(1)
target = torch.unsqueeze(target, 1)
onehot_target = torch.zeros_like(pred)
onehot_target.scatter_(1, target, 1)
return ((onehot_target * (1 - eta)) + ((eta / n_classes) * 1))
|
Refer from https://arxiv.org/pdf/1512.00567.pdf
:param target: N,
:param n_classes: int
:param eta: float
:return:
N x C onehot smoothed vector
|
training/utils/bags_of_tricks.py
|
label_smoothing
|
KyuhwanYeom/proxylessnas
| 558
|
python
|
def label_smoothing(pred, target, eta=0.1):
'\n Refer from https://arxiv.org/pdf/1512.00567.pdf\n :param target: N,\n :param n_classes: int\n :param eta: float\n :return:\n N x C onehot smoothed vector\n '
n_classes = pred.size(1)
target = torch.unsqueeze(target, 1)
onehot_target = torch.zeros_like(pred)
onehot_target.scatter_(1, target, 1)
return ((onehot_target * (1 - eta)) + ((eta / n_classes) * 1))
|
def label_smoothing(pred, target, eta=0.1):
'\n Refer from https://arxiv.org/pdf/1512.00567.pdf\n :param target: N,\n :param n_classes: int\n :param eta: float\n :return:\n N x C onehot smoothed vector\n '
n_classes = pred.size(1)
target = torch.unsqueeze(target, 1)
onehot_target = torch.zeros_like(pred)
onehot_target.scatter_(1, target, 1)
return ((onehot_target * (1 - eta)) + ((eta / n_classes) * 1))<|docstring|>Refer from https://arxiv.org/pdf/1512.00567.pdf
:param target: N,
:param n_classes: int
:param eta: float
:return:
N x C onehot smoothed vector<|endoftext|>
|
fa266ed3619eec167c4b94f5d9e3d3b8cdcc43f884d5f99fb2559cb29ff4aaef
|
@pytest.mark.fast_test
def test_dict_list_space_representation():
'\n Tests whether the conversion of the dictionary and list representation\n of a point from a search space works properly.\n '
chef_space = {'Cooking time': (0, 1200), 'Main ingredient': ['cheese', 'cherimoya', 'chicken', 'chard', 'chocolate', 'chicory'], 'Secondary ingredient': ['love', 'passion', 'dedication'], 'Cooking temperature': ((- 273.16), 10000.0)}
opt = Optimizer(dimensions=dimensions_aslist(chef_space))
point = opt.ask()
assert_equal(point, point_aslist(chef_space, point_asdict(chef_space, point)))
|
Tests whether the conversion of the dictionary and list representation
of a point from a search space works properly.
|
ProcessOptimizer/tests/test_utils.py
|
test_dict_list_space_representation
|
dk-teknologisk-rtfh/ProcessOptimizer
| 0
|
python
|
@pytest.mark.fast_test
def test_dict_list_space_representation():
'\n Tests whether the conversion of the dictionary and list representation\n of a point from a search space works properly.\n '
chef_space = {'Cooking time': (0, 1200), 'Main ingredient': ['cheese', 'cherimoya', 'chicken', 'chard', 'chocolate', 'chicory'], 'Secondary ingredient': ['love', 'passion', 'dedication'], 'Cooking temperature': ((- 273.16), 10000.0)}
opt = Optimizer(dimensions=dimensions_aslist(chef_space))
point = opt.ask()
assert_equal(point, point_aslist(chef_space, point_asdict(chef_space, point)))
|
@pytest.mark.fast_test
def test_dict_list_space_representation():
'\n Tests whether the conversion of the dictionary and list representation\n of a point from a search space works properly.\n '
chef_space = {'Cooking time': (0, 1200), 'Main ingredient': ['cheese', 'cherimoya', 'chicken', 'chard', 'chocolate', 'chicory'], 'Secondary ingredient': ['love', 'passion', 'dedication'], 'Cooking temperature': ((- 273.16), 10000.0)}
opt = Optimizer(dimensions=dimensions_aslist(chef_space))
point = opt.ask()
assert_equal(point, point_aslist(chef_space, point_asdict(chef_space, point)))<|docstring|>Tests whether the conversion of the dictionary and list representation
of a point from a search space works properly.<|endoftext|>
|
bda1dfe696b81938f0d4de11e9145a962a593961c234598db5b03cb2c8ffb4ca
|
@pytest.mark.fast_test
def test_use_named_args():
'\n Test the function wrapper @use_named_args which is used\n for wrapping an objective function with named args so it\n can be called by the optimizers which only pass a single\n list as the arg.\n\n This test does not actually use the optimizers but merely\n simulates how they would call the function.\n '
dim1 = Real(name='foo', low=0.0, high=1.0)
dim2 = Real(name='bar', low=0.0, high=1.0)
dim3 = Real(name='baz', low=0.0, high=1.0)
dimensions = [dim1, dim2, dim3]
default_parameters = [0.5, 0.6, 0.8]
@use_named_args(dimensions=dimensions)
def func(foo, bar, baz):
assert (foo == default_parameters[0])
assert (bar == default_parameters[1])
assert (baz == default_parameters[2])
return (((foo ** 2) + (bar ** 4)) + (baz ** 8))
res = func(x=default_parameters)
assert isinstance(res, float)
res = func(default_parameters)
assert isinstance(res, float)
res = func(x=np.array(default_parameters))
assert isinstance(res, float)
res = func(np.array(default_parameters))
assert isinstance(res, float)
|
Test the function wrapper @use_named_args which is used
for wrapping an objective function with named args so it
can be called by the optimizers which only pass a single
list as the arg.
This test does not actually use the optimizers but merely
simulates how they would call the function.
|
ProcessOptimizer/tests/test_utils.py
|
test_use_named_args
|
dk-teknologisk-rtfh/ProcessOptimizer
| 0
|
python
|
@pytest.mark.fast_test
def test_use_named_args():
'\n Test the function wrapper @use_named_args which is used\n for wrapping an objective function with named args so it\n can be called by the optimizers which only pass a single\n list as the arg.\n\n This test does not actually use the optimizers but merely\n simulates how they would call the function.\n '
dim1 = Real(name='foo', low=0.0, high=1.0)
dim2 = Real(name='bar', low=0.0, high=1.0)
dim3 = Real(name='baz', low=0.0, high=1.0)
dimensions = [dim1, dim2, dim3]
default_parameters = [0.5, 0.6, 0.8]
@use_named_args(dimensions=dimensions)
def func(foo, bar, baz):
assert (foo == default_parameters[0])
assert (bar == default_parameters[1])
assert (baz == default_parameters[2])
return (((foo ** 2) + (bar ** 4)) + (baz ** 8))
res = func(x=default_parameters)
assert isinstance(res, float)
res = func(default_parameters)
assert isinstance(res, float)
res = func(x=np.array(default_parameters))
assert isinstance(res, float)
res = func(np.array(default_parameters))
assert isinstance(res, float)
|
@pytest.mark.fast_test
def test_use_named_args():
'\n Test the function wrapper @use_named_args which is used\n for wrapping an objective function with named args so it\n can be called by the optimizers which only pass a single\n list as the arg.\n\n This test does not actually use the optimizers but merely\n simulates how they would call the function.\n '
dim1 = Real(name='foo', low=0.0, high=1.0)
dim2 = Real(name='bar', low=0.0, high=1.0)
dim3 = Real(name='baz', low=0.0, high=1.0)
dimensions = [dim1, dim2, dim3]
default_parameters = [0.5, 0.6, 0.8]
@use_named_args(dimensions=dimensions)
def func(foo, bar, baz):
assert (foo == default_parameters[0])
assert (bar == default_parameters[1])
assert (baz == default_parameters[2])
return (((foo ** 2) + (bar ** 4)) + (baz ** 8))
res = func(x=default_parameters)
assert isinstance(res, float)
res = func(default_parameters)
assert isinstance(res, float)
res = func(x=np.array(default_parameters))
assert isinstance(res, float)
res = func(np.array(default_parameters))
assert isinstance(res, float)<|docstring|>Test the function wrapper @use_named_args which is used
for wrapping an objective function with named args so it
can be called by the optimizers which only pass a single
list as the arg.
This test does not actually use the optimizers but merely
simulates how they would call the function.<|endoftext|>
|
0ae3c3328fe106c8afb006db35b064e7b602caed3eac961afc99eb0a88e21285
|
def ave_last_hidden(self, all_layer_embedding):
'\n Average the output from last layer\n '
unmask_num = np.array([sum(mask) for mask in self.masks])
embedding = []
for i in range(len(unmask_num)):
sent_len = unmask_num[i]
hidden_state_sen = all_layer_embedding[i][((- 1), :, :)]
embedding.append(np.mean(hidden_state_sen[(:sent_len, :)], axis=0))
embedding = np.array(embedding)
return embedding
|
Average the output from last layer
|
malaya/model/sbert_wk.py
|
ave_last_hidden
|
AetherPrior/malaya
| 88
|
python
|
def ave_last_hidden(self, all_layer_embedding):
'\n \n '
unmask_num = np.array([sum(mask) for mask in self.masks])
embedding = []
for i in range(len(unmask_num)):
sent_len = unmask_num[i]
hidden_state_sen = all_layer_embedding[i][((- 1), :, :)]
embedding.append(np.mean(hidden_state_sen[(:sent_len, :)], axis=0))
embedding = np.array(embedding)
return embedding
|
def ave_last_hidden(self, all_layer_embedding):
'\n \n '
unmask_num = np.array([sum(mask) for mask in self.masks])
embedding = []
for i in range(len(unmask_num)):
sent_len = unmask_num[i]
hidden_state_sen = all_layer_embedding[i][((- 1), :, :)]
embedding.append(np.mean(hidden_state_sen[(:sent_len, :)], axis=0))
embedding = np.array(embedding)
return embedding<|docstring|>Average the output from last layer<|endoftext|>
|
821be0979f18ffaea88bac71e17df7af829b7891c1bb15548f09a7368a379ff9
|
def ave_one_layer(self, all_layer_embedding):
'\n Average the output from last layer\n '
unmask_num = np.array([sum(mask) for mask in self.masks])
embedding = []
for i in range(len(unmask_num)):
sent_len = unmask_num[i]
hidden_state_sen = all_layer_embedding[i][(4, :, :)]
embedding.append(np.mean(hidden_state_sen[(:sent_len, :)], axis=0))
embedding = np.array(embedding)
return embedding
|
Average the output from last layer
|
malaya/model/sbert_wk.py
|
ave_one_layer
|
AetherPrior/malaya
| 88
|
python
|
def ave_one_layer(self, all_layer_embedding):
'\n \n '
unmask_num = np.array([sum(mask) for mask in self.masks])
embedding = []
for i in range(len(unmask_num)):
sent_len = unmask_num[i]
hidden_state_sen = all_layer_embedding[i][(4, :, :)]
embedding.append(np.mean(hidden_state_sen[(:sent_len, :)], axis=0))
embedding = np.array(embedding)
return embedding
|
def ave_one_layer(self, all_layer_embedding):
'\n \n '
unmask_num = np.array([sum(mask) for mask in self.masks])
embedding = []
for i in range(len(unmask_num)):
sent_len = unmask_num[i]
hidden_state_sen = all_layer_embedding[i][(4, :, :)]
embedding.append(np.mean(hidden_state_sen[(:sent_len, :)], axis=0))
embedding = np.array(embedding)
return embedding<|docstring|>Average the output from last layer<|endoftext|>
|
964a90d2a10f157c367f2f2b92afa1f85dfc3e29f73368ebc6a91d83cc610298
|
def CLS(self, all_layer_embedding):
'\n CLS vector as embedding\n '
unmask_num = np.array([sum(mask) for mask in self.masks])
embedding = []
for i in range(len(unmask_num)):
sent_len = unmask_num[i]
hidden_state_sen = all_layer_embedding[i][((- 1), :, :)]
embedding.append(hidden_state_sen[0])
embedding = np.array(embedding)
return embedding
|
CLS vector as embedding
|
malaya/model/sbert_wk.py
|
CLS
|
AetherPrior/malaya
| 88
|
python
|
def CLS(self, all_layer_embedding):
'\n \n '
unmask_num = np.array([sum(mask) for mask in self.masks])
embedding = []
for i in range(len(unmask_num)):
sent_len = unmask_num[i]
hidden_state_sen = all_layer_embedding[i][((- 1), :, :)]
embedding.append(hidden_state_sen[0])
embedding = np.array(embedding)
return embedding
|
def CLS(self, all_layer_embedding):
'\n \n '
unmask_num = np.array([sum(mask) for mask in self.masks])
embedding = []
for i in range(len(unmask_num)):
sent_len = unmask_num[i]
hidden_state_sen = all_layer_embedding[i][((- 1), :, :)]
embedding.append(hidden_state_sen[0])
embedding = np.array(embedding)
return embedding<|docstring|>CLS vector as embedding<|endoftext|>
|
a791afed1e07f535fab83579cf4ab140da56d2b5f59d27dbf0a9b83eab1f5c8d
|
def dissecting(self, all_layer_embedding):
'\n dissecting deep contextualized model\n '
unmask_num = (np.array([sum(mask) for mask in self.masks]) - 1)
all_layer_embedding = np.array(all_layer_embedding)[(:, 4:, :, :)]
embedding = []
for sent_index in range(len(unmask_num)):
sentence_feature = all_layer_embedding[(sent_index, :, :unmask_num[sent_index], :)]
one_sentence_embedding = []
for token_index in range(sentence_feature.shape[1]):
token_feature = sentence_feature[(:, token_index, :)]
token_embedding = self.unify_token(token_feature)
one_sentence_embedding.append(token_embedding)
one_sentence_embedding = np.array(one_sentence_embedding)
sentence_embedding = self.unify_sentence(sentence_feature, one_sentence_embedding)
embedding.append(sentence_embedding)
embedding = np.array(embedding)
return embedding
|
dissecting deep contextualized model
|
malaya/model/sbert_wk.py
|
dissecting
|
AetherPrior/malaya
| 88
|
python
|
def dissecting(self, all_layer_embedding):
'\n \n '
unmask_num = (np.array([sum(mask) for mask in self.masks]) - 1)
all_layer_embedding = np.array(all_layer_embedding)[(:, 4:, :, :)]
embedding = []
for sent_index in range(len(unmask_num)):
sentence_feature = all_layer_embedding[(sent_index, :, :unmask_num[sent_index], :)]
one_sentence_embedding = []
for token_index in range(sentence_feature.shape[1]):
token_feature = sentence_feature[(:, token_index, :)]
token_embedding = self.unify_token(token_feature)
one_sentence_embedding.append(token_embedding)
one_sentence_embedding = np.array(one_sentence_embedding)
sentence_embedding = self.unify_sentence(sentence_feature, one_sentence_embedding)
embedding.append(sentence_embedding)
embedding = np.array(embedding)
return embedding
|
def dissecting(self, all_layer_embedding):
'\n \n '
unmask_num = (np.array([sum(mask) for mask in self.masks]) - 1)
all_layer_embedding = np.array(all_layer_embedding)[(:, 4:, :, :)]
embedding = []
for sent_index in range(len(unmask_num)):
sentence_feature = all_layer_embedding[(sent_index, :, :unmask_num[sent_index], :)]
one_sentence_embedding = []
for token_index in range(sentence_feature.shape[1]):
token_feature = sentence_feature[(:, token_index, :)]
token_embedding = self.unify_token(token_feature)
one_sentence_embedding.append(token_embedding)
one_sentence_embedding = np.array(one_sentence_embedding)
sentence_embedding = self.unify_sentence(sentence_feature, one_sentence_embedding)
embedding.append(sentence_embedding)
embedding = np.array(embedding)
return embedding<|docstring|>dissecting deep contextualized model<|endoftext|>
|
a3d19c2132c947c884aefedcd2fbbcc70ccc237c7f6706cda357b383610c30b6
|
def unify_token(self, token_feature):
'\n Unify Token Representation\n '
window_size = 2
alpha_alignment = np.zeros(token_feature.shape[0])
alpha_novelty = np.zeros(token_feature.shape[0])
for k in range(token_feature.shape[0]):
left_window = token_feature[((k - window_size):k, :)]
right_window = token_feature[((k + 1):((k + window_size) + 1), :)]
window_matrix = np.vstack([left_window, right_window, token_feature[(k, :)][(None, :)]])
(Q, R) = np.linalg.qr(window_matrix.T)
q = Q[(:, (- 1))]
r = R[(:, (- 1))]
alpha_alignment[k] = (np.mean(normalize(R[(:(- 1), :(- 1))], axis=0), axis=1).dot(R[(:(- 1), (- 1))]) / np.linalg.norm(r[:(- 1)]))
alpha_alignment[k] = (1 / ((alpha_alignment[k] * window_matrix.shape[0]) * 2))
alpha_novelty[k] = (abs(r[(- 1)]) / np.linalg.norm(r))
alpha_alignment = (alpha_alignment / np.sum(alpha_alignment))
alpha_novelty = (alpha_novelty / np.sum(alpha_novelty))
alpha = (alpha_novelty + alpha_alignment)
alpha = (alpha / np.sum(alpha))
out_embedding = token_feature.T.dot(alpha)
return out_embedding
|
Unify Token Representation
|
malaya/model/sbert_wk.py
|
unify_token
|
AetherPrior/malaya
| 88
|
python
|
def unify_token(self, token_feature):
'\n \n '
window_size = 2
alpha_alignment = np.zeros(token_feature.shape[0])
alpha_novelty = np.zeros(token_feature.shape[0])
for k in range(token_feature.shape[0]):
left_window = token_feature[((k - window_size):k, :)]
right_window = token_feature[((k + 1):((k + window_size) + 1), :)]
window_matrix = np.vstack([left_window, right_window, token_feature[(k, :)][(None, :)]])
(Q, R) = np.linalg.qr(window_matrix.T)
q = Q[(:, (- 1))]
r = R[(:, (- 1))]
alpha_alignment[k] = (np.mean(normalize(R[(:(- 1), :(- 1))], axis=0), axis=1).dot(R[(:(- 1), (- 1))]) / np.linalg.norm(r[:(- 1)]))
alpha_alignment[k] = (1 / ((alpha_alignment[k] * window_matrix.shape[0]) * 2))
alpha_novelty[k] = (abs(r[(- 1)]) / np.linalg.norm(r))
alpha_alignment = (alpha_alignment / np.sum(alpha_alignment))
alpha_novelty = (alpha_novelty / np.sum(alpha_novelty))
alpha = (alpha_novelty + alpha_alignment)
alpha = (alpha / np.sum(alpha))
out_embedding = token_feature.T.dot(alpha)
return out_embedding
|
def unify_token(self, token_feature):
'\n \n '
window_size = 2
alpha_alignment = np.zeros(token_feature.shape[0])
alpha_novelty = np.zeros(token_feature.shape[0])
for k in range(token_feature.shape[0]):
left_window = token_feature[((k - window_size):k, :)]
right_window = token_feature[((k + 1):((k + window_size) + 1), :)]
window_matrix = np.vstack([left_window, right_window, token_feature[(k, :)][(None, :)]])
(Q, R) = np.linalg.qr(window_matrix.T)
q = Q[(:, (- 1))]
r = R[(:, (- 1))]
alpha_alignment[k] = (np.mean(normalize(R[(:(- 1), :(- 1))], axis=0), axis=1).dot(R[(:(- 1), (- 1))]) / np.linalg.norm(r[:(- 1)]))
alpha_alignment[k] = (1 / ((alpha_alignment[k] * window_matrix.shape[0]) * 2))
alpha_novelty[k] = (abs(r[(- 1)]) / np.linalg.norm(r))
alpha_alignment = (alpha_alignment / np.sum(alpha_alignment))
alpha_novelty = (alpha_novelty / np.sum(alpha_novelty))
alpha = (alpha_novelty + alpha_alignment)
alpha = (alpha / np.sum(alpha))
out_embedding = token_feature.T.dot(alpha)
return out_embedding<|docstring|>Unify Token Representation<|endoftext|>
|
1669dd1d78706010894c6c400839499a767ec00eede62fca3e06e35080bc9cf7
|
def unify_sentence(self, sentence_feature, one_sentence_embedding):
'\n Unify Sentence By Token Importance\n '
sent_len = one_sentence_embedding.shape[0]
var_token = np.zeros(sent_len)
for token_index in range(sent_len):
token_feature = sentence_feature[(:, token_index, :)]
sim_map = cosine_similarity(token_feature)
var_token[token_index] = np.var(sim_map.diagonal((- 1)))
var_token = (var_token / np.sum(var_token))
sentence_embedding = one_sentence_embedding.T.dot(var_token)
return sentence_embedding
|
Unify Sentence By Token Importance
|
malaya/model/sbert_wk.py
|
unify_sentence
|
AetherPrior/malaya
| 88
|
python
|
def unify_sentence(self, sentence_feature, one_sentence_embedding):
'\n \n '
sent_len = one_sentence_embedding.shape[0]
var_token = np.zeros(sent_len)
for token_index in range(sent_len):
token_feature = sentence_feature[(:, token_index, :)]
sim_map = cosine_similarity(token_feature)
var_token[token_index] = np.var(sim_map.diagonal((- 1)))
var_token = (var_token / np.sum(var_token))
sentence_embedding = one_sentence_embedding.T.dot(var_token)
return sentence_embedding
|
def unify_sentence(self, sentence_feature, one_sentence_embedding):
'\n \n '
sent_len = one_sentence_embedding.shape[0]
var_token = np.zeros(sent_len)
for token_index in range(sent_len):
token_feature = sentence_feature[(:, token_index, :)]
sim_map = cosine_similarity(token_feature)
var_token[token_index] = np.var(sim_map.diagonal((- 1)))
var_token = (var_token / np.sum(var_token))
sentence_embedding = one_sentence_embedding.T.dot(var_token)
return sentence_embedding<|docstring|>Unify Sentence By Token Importance<|endoftext|>
|
2f5be53f72484f8fd6b95c8fe22a6fcf645097dee1a7de08f914dd8bd2ebefbc
|
def template_jacks(self: 'PtnCombo', minimum_length: int, keys: int) -> np.ndarray:
' A template to quickly create jack lines\n\n E.g. If the ``minimumLength==2``, all jacks that last at least 2 notes are highlighted.\n\n :param minimum_length: The minimum length of the jack\n :param keys: The keys of the map, used to detect pattern limits.\n :return:\n '
assert (minimum_length >= 2), f'Minimum Length must be at least 2, {minimum_length} < 2'
return self.combinations(size=minimum_length, flatten=True, make_size2=True, combo_filter=PtnFilterCombo.create([([0] * minimum_length)], keys=keys, method=PtnFilterCombo.Method.REPEAT, invert_filter=False).filter, type_filter=PtnFilterType.create([([HoldTail] + ([object] * (minimum_length - 1)))], keys=keys, method=PtnFilterType.Method.ANY_ORDER, invert_filter=True).filter)
|
A template to quickly create jack lines
E.g. If the ``minimumLength==2``, all jacks that last at least 2 notes are highlighted.
:param minimum_length: The minimum length of the jack
:param keys: The keys of the map, used to detect pattern limits.
:return:
|
reamber/algorithms/pattern/combos/_PtnCJack.py
|
template_jacks
|
Eve-ning/reamber_base_py
| 10
|
python
|
def template_jacks(self: 'PtnCombo', minimum_length: int, keys: int) -> np.ndarray:
' A template to quickly create jack lines\n\n E.g. If the ``minimumLength==2``, all jacks that last at least 2 notes are highlighted.\n\n :param minimum_length: The minimum length of the jack\n :param keys: The keys of the map, used to detect pattern limits.\n :return:\n '
assert (minimum_length >= 2), f'Minimum Length must be at least 2, {minimum_length} < 2'
return self.combinations(size=minimum_length, flatten=True, make_size2=True, combo_filter=PtnFilterCombo.create([([0] * minimum_length)], keys=keys, method=PtnFilterCombo.Method.REPEAT, invert_filter=False).filter, type_filter=PtnFilterType.create([([HoldTail] + ([object] * (minimum_length - 1)))], keys=keys, method=PtnFilterType.Method.ANY_ORDER, invert_filter=True).filter)
|
def template_jacks(self: 'PtnCombo', minimum_length: int, keys: int) -> np.ndarray:
' A template to quickly create jack lines\n\n E.g. If the ``minimumLength==2``, all jacks that last at least 2 notes are highlighted.\n\n :param minimum_length: The minimum length of the jack\n :param keys: The keys of the map, used to detect pattern limits.\n :return:\n '
assert (minimum_length >= 2), f'Minimum Length must be at least 2, {minimum_length} < 2'
return self.combinations(size=minimum_length, flatten=True, make_size2=True, combo_filter=PtnFilterCombo.create([([0] * minimum_length)], keys=keys, method=PtnFilterCombo.Method.REPEAT, invert_filter=False).filter, type_filter=PtnFilterType.create([([HoldTail] + ([object] * (minimum_length - 1)))], keys=keys, method=PtnFilterType.Method.ANY_ORDER, invert_filter=True).filter)<|docstring|>A template to quickly create jack lines
E.g. If the ``minimumLength==2``, all jacks that last at least 2 notes are highlighted.
:param minimum_length: The minimum length of the jack
:param keys: The keys of the map, used to detect pattern limits.
:return:<|endoftext|>
|
89f144a049cfdd3b93836b5f39990092c9aec22cad355d1bb606275763ab660b
|
def authenticate(username, password, session, alreadyHashed=False, retries=0):
'\n Authenticates with the router by sending the login information. cookies\n are stored in the given session.\n\n username - The plaintext username to use\n password - The plaintext password to use OR the MD5 hash of the password\n if the alreadyHashed flag is set\n session - requests.Session to use for making connections\n alreadyHashed - Set to True if the password given is the MD5 hash\n '
maxRetries = 2
error = False
quit = False
m = hashlib.md5()
username = username.encode('utf8-')
m.update(username)
usernameHash = m.hexdigest()
if (alreadyHashed == False):
m = hashlib.md5()
password = password.encode('utf-8')
m.update(password)
passwordHash = m.hexdigest()
else:
passwordHash = password
url = 'http://192.168.1.1/Forms/login_security_1'
session.headers = {'Host': '192.168.1.1', 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:46.0) Gecko/20100101 Firefox/46.0', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 'Accept-Language': 'en-US,en;q=0.5', 'Accept-Encoding': 'gzip, deflate', 'Referer': 'http://192.168.1.1/login_security.html', 'Connection': 'keep-alive'}
data = {'tipsFlag': '0', 'timevalue': '0', 'Login_Name': username, 'Login_Pwd': 'Ha2S+eOKqmzA6nrlmTeh7w==', 'uiWebLoginhiddenUsername': usernameHash, 'uiWebLoginhiddenPassword': passwordHash}
print('[INFO] Sending login details to router...')
try:
r = session.post(url, data=data, allow_redirects=False, timeout=5)
except requests.ConnectionError as e:
print('[ERROR] Connection error while authenticating with router!')
error = True
except requests.Timeout as e:
print('[ERROR] Request timeout while authenticating with router!')
error = True
except exception as e:
print(e.message)
if (error == False):
if (r.cookies['C1'] == '%00'):
print('[ERROR] Incorrect password!')
error = True
quit = True
else:
print('[INFO] Login successful!')
if ((error == True) and (quit == False)):
session.close()
if (retries < maxRetries):
retries += 1
print('[INFO] Retrying authentication {0}/{1}...'.format(retries, maxRetries))
time.sleep(10)
authenticate(username, password, session, alreadyHashed, retries)
else:
quit = True
if (quit == True):
print('[ERROR] Unable to autenticate with router!')
sys.stdout.flush()
exit(1)
return
|
Authenticates with the router by sending the login information. cookies
are stored in the given session.
username - The plaintext username to use
password - The plaintext password to use OR the MD5 hash of the password
if the alreadyHashed flag is set
session - requests.Session to use for making connections
alreadyHashed - Set to True if the password given is the MD5 hash
|
bandwidth-monitor.py
|
authenticate
|
egeldenhuys/bandwidth-monitor
| 0
|
python
|
def authenticate(username, password, session, alreadyHashed=False, retries=0):
'\n Authenticates with the router by sending the login information. cookies\n are stored in the given session.\n\n username - The plaintext username to use\n password - The plaintext password to use OR the MD5 hash of the password\n if the alreadyHashed flag is set\n session - requests.Session to use for making connections\n alreadyHashed - Set to True if the password given is the MD5 hash\n '
maxRetries = 2
error = False
quit = False
m = hashlib.md5()
username = username.encode('utf8-')
m.update(username)
usernameHash = m.hexdigest()
if (alreadyHashed == False):
m = hashlib.md5()
password = password.encode('utf-8')
m.update(password)
passwordHash = m.hexdigest()
else:
passwordHash = password
url = 'http://192.168.1.1/Forms/login_security_1'
session.headers = {'Host': '192.168.1.1', 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:46.0) Gecko/20100101 Firefox/46.0', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 'Accept-Language': 'en-US,en;q=0.5', 'Accept-Encoding': 'gzip, deflate', 'Referer': 'http://192.168.1.1/login_security.html', 'Connection': 'keep-alive'}
data = {'tipsFlag': '0', 'timevalue': '0', 'Login_Name': username, 'Login_Pwd': 'Ha2S+eOKqmzA6nrlmTeh7w==', 'uiWebLoginhiddenUsername': usernameHash, 'uiWebLoginhiddenPassword': passwordHash}
print('[INFO] Sending login details to router...')
try:
r = session.post(url, data=data, allow_redirects=False, timeout=5)
except requests.ConnectionError as e:
print('[ERROR] Connection error while authenticating with router!')
error = True
except requests.Timeout as e:
print('[ERROR] Request timeout while authenticating with router!')
error = True
except exception as e:
print(e.message)
if (error == False):
if (r.cookies['C1'] == '%00'):
print('[ERROR] Incorrect password!')
error = True
quit = True
else:
print('[INFO] Login successful!')
if ((error == True) and (quit == False)):
session.close()
if (retries < maxRetries):
retries += 1
print('[INFO] Retrying authentication {0}/{1}...'.format(retries, maxRetries))
time.sleep(10)
authenticate(username, password, session, alreadyHashed, retries)
else:
quit = True
if (quit == True):
print('[ERROR] Unable to autenticate with router!')
sys.stdout.flush()
exit(1)
return
|
def authenticate(username, password, session, alreadyHashed=False, retries=0):
'\n Authenticates with the router by sending the login information. cookies\n are stored in the given session.\n\n username - The plaintext username to use\n password - The plaintext password to use OR the MD5 hash of the password\n if the alreadyHashed flag is set\n session - requests.Session to use for making connections\n alreadyHashed - Set to True if the password given is the MD5 hash\n '
maxRetries = 2
error = False
quit = False
m = hashlib.md5()
username = username.encode('utf8-')
m.update(username)
usernameHash = m.hexdigest()
if (alreadyHashed == False):
m = hashlib.md5()
password = password.encode('utf-8')
m.update(password)
passwordHash = m.hexdigest()
else:
passwordHash = password
url = 'http://192.168.1.1/Forms/login_security_1'
session.headers = {'Host': '192.168.1.1', 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:46.0) Gecko/20100101 Firefox/46.0', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 'Accept-Language': 'en-US,en;q=0.5', 'Accept-Encoding': 'gzip, deflate', 'Referer': 'http://192.168.1.1/login_security.html', 'Connection': 'keep-alive'}
data = {'tipsFlag': '0', 'timevalue': '0', 'Login_Name': username, 'Login_Pwd': 'Ha2S+eOKqmzA6nrlmTeh7w==', 'uiWebLoginhiddenUsername': usernameHash, 'uiWebLoginhiddenPassword': passwordHash}
print('[INFO] Sending login details to router...')
try:
r = session.post(url, data=data, allow_redirects=False, timeout=5)
except requests.ConnectionError as e:
print('[ERROR] Connection error while authenticating with router!')
error = True
except requests.Timeout as e:
print('[ERROR] Request timeout while authenticating with router!')
error = True
except exception as e:
print(e.message)
if (error == False):
if (r.cookies['C1'] == '%00'):
print('[ERROR] Incorrect password!')
error = True
quit = True
else:
print('[INFO] Login successful!')
if ((error == True) and (quit == False)):
session.close()
if (retries < maxRetries):
retries += 1
print('[INFO] Retrying authentication {0}/{1}...'.format(retries, maxRetries))
time.sleep(10)
authenticate(username, password, session, alreadyHashed, retries)
else:
quit = True
if (quit == True):
print('[ERROR] Unable to autenticate with router!')
sys.stdout.flush()
exit(1)
return<|docstring|>Authenticates with the router by sending the login information. cookies
are stored in the given session.
username - The plaintext username to use
password - The plaintext password to use OR the MD5 hash of the password
if the alreadyHashed flag is set
session - requests.Session to use for making connections
alreadyHashed - Set to True if the password given is the MD5 hash<|endoftext|>
|
1a0d92389f5cd0f69ddca404230a8ace8197466b9835733c107f5f54fd2a4848
|
def extractValue(indexString, content):
"\n Extracts an integer value after the indexString from the given content.\n\n Searched for the given string, then moves over that string + 1 pos (new line),\n then reads the numbers until a '<' is found\n\n indexString - The string to search for.\n content - The content to search in.\n\n Returns:\n Integer if found, else -1\n "
index = content.find(indexString)
if (index == (- 1)):
raise ValueError('String not found!', indexString)
index += (len(indexString) + 1)
numberStr = ''
number = 0
while (content[index] != '<'):
if (content[index] != ','):
numberStr += content[index]
index = (index + 1)
number = int(numberStr)
return number
|
Extracts an integer value after the indexString from the given content.
Searched for the given string, then moves over that string + 1 pos (new line),
then reads the numbers until a '<' is found
indexString - The string to search for.
content - The content to search in.
Returns:
Integer if found, else -1
|
bandwidth-monitor.py
|
extractValue
|
egeldenhuys/bandwidth-monitor
| 0
|
python
|
def extractValue(indexString, content):
"\n Extracts an integer value after the indexString from the given content.\n\n Searched for the given string, then moves over that string + 1 pos (new line),\n then reads the numbers until a '<' is found\n\n indexString - The string to search for.\n content - The content to search in.\n\n Returns:\n Integer if found, else -1\n "
index = content.find(indexString)
if (index == (- 1)):
raise ValueError('String not found!', indexString)
index += (len(indexString) + 1)
numberStr =
number = 0
while (content[index] != '<'):
if (content[index] != ','):
numberStr += content[index]
index = (index + 1)
number = int(numberStr)
return number
|
def extractValue(indexString, content):
"\n Extracts an integer value after the indexString from the given content.\n\n Searched for the given string, then moves over that string + 1 pos (new line),\n then reads the numbers until a '<' is found\n\n indexString - The string to search for.\n content - The content to search in.\n\n Returns:\n Integer if found, else -1\n "
index = content.find(indexString)
if (index == (- 1)):
raise ValueError('String not found!', indexString)
index += (len(indexString) + 1)
numberStr =
number = 0
while (content[index] != '<'):
if (content[index] != ','):
numberStr += content[index]
index = (index + 1)
number = int(numberStr)
return number<|docstring|>Extracts an integer value after the indexString from the given content.
Searched for the given string, then moves over that string + 1 pos (new line),
then reads the numbers until a '<' is found
indexString - The string to search for.
content - The content to search in.
Returns:
Integer if found, else -1<|endoftext|>
|
7d7c96ec2e7a8d26b6301caddc8acf16dabec58cca6fb8d3ae7d7699bc96a786
|
def getStatistics(session):
'\n Scrape statistics from the web interface. Session needs to be authenticated\n beforehand.\n\n Returns an array containing the total bytes transfered\n [0] - Total Bytes Downloaded\n [1] - Total Bytes Sent\n\n Returns [-1, -1] if there is an error\n '
downUp = [(- 1), (- 1)]
url = 'http://192.168.1.1/Forms/status_statistics_1'
session.headers['Referer'] = 'http://192.168.1.1/status/status_statistics.htm'
data = {'Stat_Radio': 'Zero', 'StatRefresh': 'REFRESH'}
try:
r = session.post(url, data=data, allow_redirects=True, timeout=5)
if (r.url == 'http://192.168.1.1/login_security.html'):
downUp = [(- 2), (- 2)]
return downUp
searchString = '<font color="#000000">Transmit total Bytes</font></td><td class="tabdata"><div align=center>'
downUp[0] = extractValue(searchString, r.text)
searchString = '<font color="#000000">Receive total Bytes</font></td><td class="tabdata"><div align=center>'
downUp[1] = extractValue(searchString, r.text)
except requests.Timeout as e:
print('[ERROR] Request timed out while fetching statistics!')
time.sleep(5)
except requests.ConnectionError as e:
print('[ERROR] Connection error while fetching statistics!')
time.sleep(5)
except socket.error as e:
print('[ERROR] Socket error while fetching statistics!')
time.sleep(5)
except exception as e:
print(e.message)
finally:
return downUp
|
Scrape statistics from the web interface. Session needs to be authenticated
beforehand.
Returns an array containing the total bytes transfered
[0] - Total Bytes Downloaded
[1] - Total Bytes Sent
Returns [-1, -1] if there is an error
|
bandwidth-monitor.py
|
getStatistics
|
egeldenhuys/bandwidth-monitor
| 0
|
python
|
def getStatistics(session):
'\n Scrape statistics from the web interface. Session needs to be authenticated\n beforehand.\n\n Returns an array containing the total bytes transfered\n [0] - Total Bytes Downloaded\n [1] - Total Bytes Sent\n\n Returns [-1, -1] if there is an error\n '
downUp = [(- 1), (- 1)]
url = 'http://192.168.1.1/Forms/status_statistics_1'
session.headers['Referer'] = 'http://192.168.1.1/status/status_statistics.htm'
data = {'Stat_Radio': 'Zero', 'StatRefresh': 'REFRESH'}
try:
r = session.post(url, data=data, allow_redirects=True, timeout=5)
if (r.url == 'http://192.168.1.1/login_security.html'):
downUp = [(- 2), (- 2)]
return downUp
searchString = '<font color="#000000">Transmit total Bytes</font></td><td class="tabdata"><div align=center>'
downUp[0] = extractValue(searchString, r.text)
searchString = '<font color="#000000">Receive total Bytes</font></td><td class="tabdata"><div align=center>'
downUp[1] = extractValue(searchString, r.text)
except requests.Timeout as e:
print('[ERROR] Request timed out while fetching statistics!')
time.sleep(5)
except requests.ConnectionError as e:
print('[ERROR] Connection error while fetching statistics!')
time.sleep(5)
except socket.error as e:
print('[ERROR] Socket error while fetching statistics!')
time.sleep(5)
except exception as e:
print(e.message)
finally:
return downUp
|
def getStatistics(session):
'\n Scrape statistics from the web interface. Session needs to be authenticated\n beforehand.\n\n Returns an array containing the total bytes transfered\n [0] - Total Bytes Downloaded\n [1] - Total Bytes Sent\n\n Returns [-1, -1] if there is an error\n '
downUp = [(- 1), (- 1)]
url = 'http://192.168.1.1/Forms/status_statistics_1'
session.headers['Referer'] = 'http://192.168.1.1/status/status_statistics.htm'
data = {'Stat_Radio': 'Zero', 'StatRefresh': 'REFRESH'}
try:
r = session.post(url, data=data, allow_redirects=True, timeout=5)
if (r.url == 'http://192.168.1.1/login_security.html'):
downUp = [(- 2), (- 2)]
return downUp
searchString = '<font color="#000000">Transmit total Bytes</font></td><td class="tabdata"><div align=center>'
downUp[0] = extractValue(searchString, r.text)
searchString = '<font color="#000000">Receive total Bytes</font></td><td class="tabdata"><div align=center>'
downUp[1] = extractValue(searchString, r.text)
except requests.Timeout as e:
print('[ERROR] Request timed out while fetching statistics!')
time.sleep(5)
except requests.ConnectionError as e:
print('[ERROR] Connection error while fetching statistics!')
time.sleep(5)
except socket.error as e:
print('[ERROR] Socket error while fetching statistics!')
time.sleep(5)
except exception as e:
print(e.message)
finally:
return downUp<|docstring|>Scrape statistics from the web interface. Session needs to be authenticated
beforehand.
Returns an array containing the total bytes transfered
[0] - Total Bytes Downloaded
[1] - Total Bytes Sent
Returns [-1, -1] if there is an error<|endoftext|>
|
75b95f6072ab753793fdb72bcccbab82bcd9fbfb81177eb89011125246be0254
|
def get_target_times(ti, tf, fmax=0.1):
'\n Calculate desired target times given initial time, final time and\n maximum frequency.\n \n Time step is calculated using fmax, then the number of points is rounded up\n to get a power of two.\n \n Parameters\n ----------\n ti, tf: float\n initial and final time (days)\n fmax: float\n maximum frequency used to determine time step\n default = 0.1 (days^-1)\n '
T = (tf - ti)
Dt_try = (2 * (1 / fmax))
n = round_to_p2(((T / Dt_try) + 1))
target_times = np.linspace(ti, tf, num=n, endpoint=True)
return target_times
|
Calculate desired target times given initial time, final time and
maximum frequency.
Time step is calculated using fmax, then the number of points is rounded up
to get a power of two.
Parameters
----------
ti, tf: float
initial and final time (days)
fmax: float
maximum frequency used to determine time step
default = 0.1 (days^-1)
|
inspace/interpolation.py
|
get_target_times
|
Janna112358/nullstreams-enterprise
| 0
|
python
|
def get_target_times(ti, tf, fmax=0.1):
'\n Calculate desired target times given initial time, final time and\n maximum frequency.\n \n Time step is calculated using fmax, then the number of points is rounded up\n to get a power of two.\n \n Parameters\n ----------\n ti, tf: float\n initial and final time (days)\n fmax: float\n maximum frequency used to determine time step\n default = 0.1 (days^-1)\n '
T = (tf - ti)
Dt_try = (2 * (1 / fmax))
n = round_to_p2(((T / Dt_try) + 1))
target_times = np.linspace(ti, tf, num=n, endpoint=True)
return target_times
|
def get_target_times(ti, tf, fmax=0.1):
'\n Calculate desired target times given initial time, final time and\n maximum frequency.\n \n Time step is calculated using fmax, then the number of points is rounded up\n to get a power of two.\n \n Parameters\n ----------\n ti, tf: float\n initial and final time (days)\n fmax: float\n maximum frequency used to determine time step\n default = 0.1 (days^-1)\n '
T = (tf - ti)
Dt_try = (2 * (1 / fmax))
n = round_to_p2(((T / Dt_try) + 1))
target_times = np.linspace(ti, tf, num=n, endpoint=True)
return target_times<|docstring|>Calculate desired target times given initial time, final time and
maximum frequency.
Time step is calculated using fmax, then the number of points is rounded up
to get a power of two.
Parameters
----------
ti, tf: float
initial and final time (days)
fmax: float
maximum frequency used to determine time step
default = 0.1 (days^-1)<|endoftext|>
|
fd11cc4e314a922656e55e2db04e23c5303254aec44547d2d304440c1204da4a
|
def sinc_interpolation(x, x_data, y_data, TNy=1.0):
'\n http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf\n \n Parameters\n ----------\n x: NumPy Array\n target x values\n x_data: NumPy Array\n input x values\n y_data: NumPy Array\n input y values\n TNy: float\n default = 1.0\n time scale used in the interpolation,\n frequencies above f=1/2TNy are filtered out\n \n Returns\n -------\n NumPy Array:\n interpolated y values at target x values\n '
n = len(x_data)
T = ((max(x_data) - min(x_data)) / (n - 1))
shifts = (np.expand_dims(x, axis=1) - x_data)
sincs = np.sinc((shifts / TNy))
weighted_points = (y_data * sincs)
y_interp = ((T / TNy) * np.sum(weighted_points, axis=1))
return y_interp
|
http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf
Parameters
----------
x: NumPy Array
target x values
x_data: NumPy Array
input x values
y_data: NumPy Array
input y values
TNy: float
default = 1.0
time scale used in the interpolation,
frequencies above f=1/2TNy are filtered out
Returns
-------
NumPy Array:
interpolated y values at target x values
|
inspace/interpolation.py
|
sinc_interpolation
|
Janna112358/nullstreams-enterprise
| 0
|
python
|
def sinc_interpolation(x, x_data, y_data, TNy=1.0):
'\n http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf\n \n Parameters\n ----------\n x: NumPy Array\n target x values\n x_data: NumPy Array\n input x values\n y_data: NumPy Array\n input y values\n TNy: float\n default = 1.0\n time scale used in the interpolation,\n frequencies above f=1/2TNy are filtered out\n \n Returns\n -------\n NumPy Array:\n interpolated y values at target x values\n '
n = len(x_data)
T = ((max(x_data) - min(x_data)) / (n - 1))
shifts = (np.expand_dims(x, axis=1) - x_data)
sincs = np.sinc((shifts / TNy))
weighted_points = (y_data * sincs)
y_interp = ((T / TNy) * np.sum(weighted_points, axis=1))
return y_interp
|
def sinc_interpolation(x, x_data, y_data, TNy=1.0):
'\n http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf\n \n Parameters\n ----------\n x: NumPy Array\n target x values\n x_data: NumPy Array\n input x values\n y_data: NumPy Array\n input y values\n TNy: float\n default = 1.0\n time scale used in the interpolation,\n frequencies above f=1/2TNy are filtered out\n \n Returns\n -------\n NumPy Array:\n interpolated y values at target x values\n '
n = len(x_data)
T = ((max(x_data) - min(x_data)) / (n - 1))
shifts = (np.expand_dims(x, axis=1) - x_data)
sincs = np.sinc((shifts / TNy))
weighted_points = (y_data * sincs)
y_interp = ((T / TNy) * np.sum(weighted_points, axis=1))
return y_interp<|docstring|>http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf
Parameters
----------
x: NumPy Array
target x values
x_data: NumPy Array
input x values
y_data: NumPy Array
input y values
TNy: float
default = 1.0
time scale used in the interpolation,
frequencies above f=1/2TNy are filtered out
Returns
-------
NumPy Array:
interpolated y values at target x values<|endoftext|>
|
056467314e22c6a56277f1d9130a5905e4a9fa8ee2f4b036a189164cebd68131
|
def non_uniform_ToninaEldar(x, x_data, y_data):
'\n eq 14 + 15(a) in Tonina & Eldar\n http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf\n '
T = (max(x_data) - min(x_data))
sample_shifts = (np.expand_dims(x_data, axis=(- 1)) - np.expand_dims(x_data, axis=0))
product_bottom = np.sin(((np.pi * sample_shifts) / T))
target_shifts = (np.expand_dims(x, axis=(- 1)) - np.expand_dims(x_data, axis=0))
product_top = np.sin(((np.pi * target_shifts) / T))
product = np.zeros(shape=(len(x), len(x_data)))
for (i, j) in np.ndindex(product.shape):
fraction = (product_top[i] / product_bottom[j])
fraction[j] = 1
product[(i, j)] = np.product(fraction)
cosine_term = np.cos(((np.pi * target_shifts) / T))
weights = (cosine_term * product)
weighted_points = (y_data * weights)
interpolated = np.sum(weighted_points, axis=(- 1))
return interpolated
|
eq 14 + 15(a) in Tonina & Eldar
http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf
|
inspace/interpolation.py
|
non_uniform_ToninaEldar
|
Janna112358/nullstreams-enterprise
| 0
|
python
|
def non_uniform_ToninaEldar(x, x_data, y_data):
'\n eq 14 + 15(a) in Tonina & Eldar\n http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf\n '
T = (max(x_data) - min(x_data))
sample_shifts = (np.expand_dims(x_data, axis=(- 1)) - np.expand_dims(x_data, axis=0))
product_bottom = np.sin(((np.pi * sample_shifts) / T))
target_shifts = (np.expand_dims(x, axis=(- 1)) - np.expand_dims(x_data, axis=0))
product_top = np.sin(((np.pi * target_shifts) / T))
product = np.zeros(shape=(len(x), len(x_data)))
for (i, j) in np.ndindex(product.shape):
fraction = (product_top[i] / product_bottom[j])
fraction[j] = 1
product[(i, j)] = np.product(fraction)
cosine_term = np.cos(((np.pi * target_shifts) / T))
weights = (cosine_term * product)
weighted_points = (y_data * weights)
interpolated = np.sum(weighted_points, axis=(- 1))
return interpolated
|
def non_uniform_ToninaEldar(x, x_data, y_data):
'\n eq 14 + 15(a) in Tonina & Eldar\n http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf\n '
T = (max(x_data) - min(x_data))
sample_shifts = (np.expand_dims(x_data, axis=(- 1)) - np.expand_dims(x_data, axis=0))
product_bottom = np.sin(((np.pi * sample_shifts) / T))
target_shifts = (np.expand_dims(x, axis=(- 1)) - np.expand_dims(x_data, axis=0))
product_top = np.sin(((np.pi * target_shifts) / T))
product = np.zeros(shape=(len(x), len(x_data)))
for (i, j) in np.ndindex(product.shape):
fraction = (product_top[i] / product_bottom[j])
fraction[j] = 1
product[(i, j)] = np.product(fraction)
cosine_term = np.cos(((np.pi * target_shifts) / T))
weights = (cosine_term * product)
weighted_points = (y_data * weights)
interpolated = np.sum(weighted_points, axis=(- 1))
return interpolated<|docstring|>eq 14 + 15(a) in Tonina & Eldar
http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/70.pdf<|endoftext|>
|
57bdac190a271071bcf9ba8106437e3a278da96900bf718426e19c8eef54e24b
|
def hang(n):
'\n Hang body parts\n '
if (n == 6):
scaffold()
print('| ( * * ) |')
print('| ( V ) |')
for i in range(6):
print('| |')
elif (n == 5):
scaffold()
print('| ( * * ) |')
print('| ( V ) |')
print('| # |')
print('| # |')
print('| # |')
print('| |')
print('| |')
print('| |')
elif (n == 4):
scaffold()
print('| ( * * ) |')
print('| ( o ) |')
print('| # |')
print('| ~ ~ # |')
print('| # |')
print('| |')
print('| |')
print('| |')
elif (n == 3):
scaffold()
print('| ( * * ) |')
print('| ( o ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| |')
print('| |')
print('| |')
elif (n == 2):
scaffold()
print('| ( Q Q ) |')
print('| ( W ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| / |')
print('| \\ |')
print('| |')
elif (n == 1):
scaffold()
print('| ( Q Q ) |')
print('| ( W ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| / \\ |')
print('| \\ / |')
print('| |')
else:
scaffold()
print('| ( X X ) |')
print('| ( ^ ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| / \\ |')
print('| \\ / |')
print('| |')
|
Hang body parts
|
Github/Hangman/hangman.py
|
hang
|
ZachhHsu/Stancode_SC101_project
| 0
|
python
|
def hang(n):
'\n \n '
if (n == 6):
scaffold()
print('| ( * * ) |')
print('| ( V ) |')
for i in range(6):
print('| |')
elif (n == 5):
scaffold()
print('| ( * * ) |')
print('| ( V ) |')
print('| # |')
print('| # |')
print('| # |')
print('| |')
print('| |')
print('| |')
elif (n == 4):
scaffold()
print('| ( * * ) |')
print('| ( o ) |')
print('| # |')
print('| ~ ~ # |')
print('| # |')
print('| |')
print('| |')
print('| |')
elif (n == 3):
scaffold()
print('| ( * * ) |')
print('| ( o ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| |')
print('| |')
print('| |')
elif (n == 2):
scaffold()
print('| ( Q Q ) |')
print('| ( W ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| / |')
print('| \\ |')
print('| |')
elif (n == 1):
scaffold()
print('| ( Q Q ) |')
print('| ( W ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| / \\ |')
print('| \\ / |')
print('| |')
else:
scaffold()
print('| ( X X ) |')
print('| ( ^ ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| / \\ |')
print('| \\ / |')
print('| |')
|
def hang(n):
'\n \n '
if (n == 6):
scaffold()
print('| ( * * ) |')
print('| ( V ) |')
for i in range(6):
print('| |')
elif (n == 5):
scaffold()
print('| ( * * ) |')
print('| ( V ) |')
print('| # |')
print('| # |')
print('| # |')
print('| |')
print('| |')
print('| |')
elif (n == 4):
scaffold()
print('| ( * * ) |')
print('| ( o ) |')
print('| # |')
print('| ~ ~ # |')
print('| # |')
print('| |')
print('| |')
print('| |')
elif (n == 3):
scaffold()
print('| ( * * ) |')
print('| ( o ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| |')
print('| |')
print('| |')
elif (n == 2):
scaffold()
print('| ( Q Q ) |')
print('| ( W ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| / |')
print('| \\ |')
print('| |')
elif (n == 1):
scaffold()
print('| ( Q Q ) |')
print('| ( W ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| / \\ |')
print('| \\ / |')
print('| |')
else:
scaffold()
print('| ( X X ) |')
print('| ( ^ ) |')
print('| # |')
print('| ~ ~ # ~ ~ |')
print('| # |')
print('| / \\ |')
print('| \\ / |')
print('| |')<|docstring|>Hang body parts<|endoftext|>
|
47efa4c4e200b77da70b2bec7ab9ac6aba695543b893b7d0145be448b736135c
|
def scaffold():
'\n Create a scaffold\n '
print('<Your Status>')
print('-----------------')
print('| | |')
print('| | |')
|
Create a scaffold
|
Github/Hangman/hangman.py
|
scaffold
|
ZachhHsu/Stancode_SC101_project
| 0
|
python
|
def scaffold():
'\n \n '
print('<Your Status>')
print('-----------------')
print('| | |')
print('| | |')
|
def scaffold():
'\n \n '
print('<Your Status>')
print('-----------------')
print('| | |')
print('| | |')<|docstring|>Create a scaffold<|endoftext|>
|
a1abeacfbb68fc5c0773506a5cf8ba2e46bdd8112823a0333d99ec08b9bd61a1
|
def random_word():
'\n Here are some random vocabularies to be guessed\n '
num = random.choice(range(9))
if (num == 0):
return 'NOTORIOUS'
elif (num == 1):
return 'GLAMOROUS'
elif (num == 2):
return 'CAUTIOUS'
elif (num == 3):
return 'DEMOCRACY'
elif (num == 4):
return 'BOYCOTT'
elif (num == 5):
return 'ENTHUSIASTIC'
elif (num == 6):
return 'HOSPITALITY'
elif (num == 7):
return 'BUNDLE'
elif (num == 8):
return 'REFUND'
|
Here are some random vocabularies to be guessed
|
Github/Hangman/hangman.py
|
random_word
|
ZachhHsu/Stancode_SC101_project
| 0
|
python
|
def random_word():
'\n \n '
num = random.choice(range(9))
if (num == 0):
return 'NOTORIOUS'
elif (num == 1):
return 'GLAMOROUS'
elif (num == 2):
return 'CAUTIOUS'
elif (num == 3):
return 'DEMOCRACY'
elif (num == 4):
return 'BOYCOTT'
elif (num == 5):
return 'ENTHUSIASTIC'
elif (num == 6):
return 'HOSPITALITY'
elif (num == 7):
return 'BUNDLE'
elif (num == 8):
return 'REFUND'
|
def random_word():
'\n \n '
num = random.choice(range(9))
if (num == 0):
return 'NOTORIOUS'
elif (num == 1):
return 'GLAMOROUS'
elif (num == 2):
return 'CAUTIOUS'
elif (num == 3):
return 'DEMOCRACY'
elif (num == 4):
return 'BOYCOTT'
elif (num == 5):
return 'ENTHUSIASTIC'
elif (num == 6):
return 'HOSPITALITY'
elif (num == 7):
return 'BUNDLE'
elif (num == 8):
return 'REFUND'<|docstring|>Here are some random vocabularies to be guessed<|endoftext|>
|
4b3b1fabef201dbc67e35ca2086876f9cbee499deee749fa1d5d1e596639e1c8
|
def attach_spm_pet_grouptemplate(main_wf, wf_name='spm_pet_template'):
" Attach a PET pre-processing workflow that uses SPM12 to `main_wf`.\n This workflow picks all spm_pet_preproc outputs 'pet_output.warped_files' in `main_wf`\n to create a group template.\n\n Parameters\n ----------\n main_wf: nipype Workflow\n\n wf_name: str\n Name of the preprocessing workflow\n\n Nipype Inputs for `main_wf`\n ---------------------------\n Note: The `main_wf` workflow is expected to have an `input_files` and a `datasink` nodes.\n\n pet_output.warped_files: input node\n\n datasink: nipype Node\n\n spm_pet_preproc: nipype Workflow\n\n Nipype Outputs\n --------------\n group_template.pet_template: file\n The path to the PET group template.\n\n Nipype Workflow Dependencies\n ----------------------------\n This workflow depends on:\n - spm_pet_preproc\n - spm_anat_preproc if `spm_pet_template.do_petpvc` is True.\n\n Returns\n -------\n main_wf: nipype Workflow\n "
pet_wf = get_subworkflow(main_wf, 'spm_pet_preproc')
in_files = get_input_node(main_wf)
datasink = get_datasink(main_wf, name='datasink')
pet_fbasename = remove_ext(os.path.basename(get_input_file_name(in_files, 'pet')))
base_outdir = datasink.inputs.base_directory
grp_datasink = pe.Node(DataSink(parameterization=False, base_directory=base_outdir), name='{}_grouptemplate_datasink'.format(pet_fbasename))
grp_datasink.inputs.container = '{}_grouptemplate'.format(pet_fbasename)
warped_pets = pe.JoinNode(interface=IdentityInterface(fields=['warped_pets']), joinsource='infosrc', joinfield='warped_pets', name='warped_pets')
template_wf = spm_create_group_template_wf(wf_name)
output = setup_node(IdentityInterface(fields=['pet_template']), name='group_template')
regexp_subst = [('/wgrptemplate{pet}_merged_mean_smooth.nii$', '/{pet}_grouptemplate_mni.nii'), ('/w{pet}_merged_mean_smooth.nii$', '/{pet}_grouptemplate_mni.nii')]
regexp_subst = format_pair_list(regexp_subst, pet=pet_fbasename)
regexp_subst += extension_duplicates(regexp_subst)
grp_datasink.inputs.regexp_substitutions = extend_trait_list(grp_datasink.inputs.regexp_substitutions, regexp_subst)
main_wf.connect([(pet_wf, warped_pets, [('warp_output.warped_files', 'warped_pets')]), (warped_pets, template_wf, [(('warped_pets', flatten_list), 'grptemplate_input.in_files')]), (template_wf, output, [('grptemplate_output.template', 'pet_template')]), (output, grp_datasink, [('pet_template', '@pet_grouptemplate')])])
do_petpvc = get_config_setting('spm_pet_template.do_petpvc')
if do_petpvc:
get_subworkflow(main_wf, 'spm_anat_preproc')
preproc_wf_name = 'spm_mrpet_grouptemplate_preproc'
main_wf = attach_spm_mrpet_preprocessing(main_wf, wf_name=preproc_wf_name, do_group_template=True)
preproc_wf = get_subworkflow(main_wf, preproc_wf_name)
main_wf.connect([(output, preproc_wf, [('pet_template', 'pet_input.pet_template')])])
else:
reg_wf = spm_register_to_template_wf(wf_name='spm_pet_register_to_grouptemplate')
main_wf.connect([(output, reg_wf, [('pet_template', 'reg_input.template')]), (in_files, reg_wf, [('pet', 'reg_input.in_file')]), (reg_wf, datasink, [('reg_output.warped', 'pet.group_template.@warped'), ('reg_output.warp_field', 'pet.group_template.@warp_field')])])
regexp_subst = [('group_template/{pet}_sn.mat$', 'group_template/{pet}_grptemplate_params.mat'), ('group_template/wgrptemplate_{pet}.nii$', 'group_template/{pet}_grptemplate.nii'), ('group_template/w{pet}.nii', 'group_template/{pet}_grptemplate.nii')]
regexp_subst = format_pair_list(regexp_subst, pet=pet_fbasename)
regexp_subst += extension_duplicates(regexp_subst)
datasink.inputs.regexp_substitutions = extend_trait_list(datasink.inputs.regexp_substitutions, regexp_subst)
return main_wf
|
Attach a PET pre-processing workflow that uses SPM12 to `main_wf`.
This workflow picks all spm_pet_preproc outputs 'pet_output.warped_files' in `main_wf`
to create a group template.
Parameters
----------
main_wf: nipype Workflow
wf_name: str
Name of the preprocessing workflow
Nipype Inputs for `main_wf`
---------------------------
Note: The `main_wf` workflow is expected to have an `input_files` and a `datasink` nodes.
pet_output.warped_files: input node
datasink: nipype Node
spm_pet_preproc: nipype Workflow
Nipype Outputs
--------------
group_template.pet_template: file
The path to the PET group template.
Nipype Workflow Dependencies
----------------------------
This workflow depends on:
- spm_pet_preproc
- spm_anat_preproc if `spm_pet_template.do_petpvc` is True.
Returns
-------
main_wf: nipype Workflow
|
neuro_pypes/pet/grouptemplate.py
|
attach_spm_pet_grouptemplate
|
Neurita/pypes
| 14
|
python
|
def attach_spm_pet_grouptemplate(main_wf, wf_name='spm_pet_template'):
" Attach a PET pre-processing workflow that uses SPM12 to `main_wf`.\n This workflow picks all spm_pet_preproc outputs 'pet_output.warped_files' in `main_wf`\n to create a group template.\n\n Parameters\n ----------\n main_wf: nipype Workflow\n\n wf_name: str\n Name of the preprocessing workflow\n\n Nipype Inputs for `main_wf`\n ---------------------------\n Note: The `main_wf` workflow is expected to have an `input_files` and a `datasink` nodes.\n\n pet_output.warped_files: input node\n\n datasink: nipype Node\n\n spm_pet_preproc: nipype Workflow\n\n Nipype Outputs\n --------------\n group_template.pet_template: file\n The path to the PET group template.\n\n Nipype Workflow Dependencies\n ----------------------------\n This workflow depends on:\n - spm_pet_preproc\n - spm_anat_preproc if `spm_pet_template.do_petpvc` is True.\n\n Returns\n -------\n main_wf: nipype Workflow\n "
pet_wf = get_subworkflow(main_wf, 'spm_pet_preproc')
in_files = get_input_node(main_wf)
datasink = get_datasink(main_wf, name='datasink')
pet_fbasename = remove_ext(os.path.basename(get_input_file_name(in_files, 'pet')))
base_outdir = datasink.inputs.base_directory
grp_datasink = pe.Node(DataSink(parameterization=False, base_directory=base_outdir), name='{}_grouptemplate_datasink'.format(pet_fbasename))
grp_datasink.inputs.container = '{}_grouptemplate'.format(pet_fbasename)
warped_pets = pe.JoinNode(interface=IdentityInterface(fields=['warped_pets']), joinsource='infosrc', joinfield='warped_pets', name='warped_pets')
template_wf = spm_create_group_template_wf(wf_name)
output = setup_node(IdentityInterface(fields=['pet_template']), name='group_template')
regexp_subst = [('/wgrptemplate{pet}_merged_mean_smooth.nii$', '/{pet}_grouptemplate_mni.nii'), ('/w{pet}_merged_mean_smooth.nii$', '/{pet}_grouptemplate_mni.nii')]
regexp_subst = format_pair_list(regexp_subst, pet=pet_fbasename)
regexp_subst += extension_duplicates(regexp_subst)
grp_datasink.inputs.regexp_substitutions = extend_trait_list(grp_datasink.inputs.regexp_substitutions, regexp_subst)
main_wf.connect([(pet_wf, warped_pets, [('warp_output.warped_files', 'warped_pets')]), (warped_pets, template_wf, [(('warped_pets', flatten_list), 'grptemplate_input.in_files')]), (template_wf, output, [('grptemplate_output.template', 'pet_template')]), (output, grp_datasink, [('pet_template', '@pet_grouptemplate')])])
do_petpvc = get_config_setting('spm_pet_template.do_petpvc')
if do_petpvc:
get_subworkflow(main_wf, 'spm_anat_preproc')
preproc_wf_name = 'spm_mrpet_grouptemplate_preproc'
main_wf = attach_spm_mrpet_preprocessing(main_wf, wf_name=preproc_wf_name, do_group_template=True)
preproc_wf = get_subworkflow(main_wf, preproc_wf_name)
main_wf.connect([(output, preproc_wf, [('pet_template', 'pet_input.pet_template')])])
else:
reg_wf = spm_register_to_template_wf(wf_name='spm_pet_register_to_grouptemplate')
main_wf.connect([(output, reg_wf, [('pet_template', 'reg_input.template')]), (in_files, reg_wf, [('pet', 'reg_input.in_file')]), (reg_wf, datasink, [('reg_output.warped', 'pet.group_template.@warped'), ('reg_output.warp_field', 'pet.group_template.@warp_field')])])
regexp_subst = [('group_template/{pet}_sn.mat$', 'group_template/{pet}_grptemplate_params.mat'), ('group_template/wgrptemplate_{pet}.nii$', 'group_template/{pet}_grptemplate.nii'), ('group_template/w{pet}.nii', 'group_template/{pet}_grptemplate.nii')]
regexp_subst = format_pair_list(regexp_subst, pet=pet_fbasename)
regexp_subst += extension_duplicates(regexp_subst)
datasink.inputs.regexp_substitutions = extend_trait_list(datasink.inputs.regexp_substitutions, regexp_subst)
return main_wf
|
def attach_spm_pet_grouptemplate(main_wf, wf_name='spm_pet_template'):
" Attach a PET pre-processing workflow that uses SPM12 to `main_wf`.\n This workflow picks all spm_pet_preproc outputs 'pet_output.warped_files' in `main_wf`\n to create a group template.\n\n Parameters\n ----------\n main_wf: nipype Workflow\n\n wf_name: str\n Name of the preprocessing workflow\n\n Nipype Inputs for `main_wf`\n ---------------------------\n Note: The `main_wf` workflow is expected to have an `input_files` and a `datasink` nodes.\n\n pet_output.warped_files: input node\n\n datasink: nipype Node\n\n spm_pet_preproc: nipype Workflow\n\n Nipype Outputs\n --------------\n group_template.pet_template: file\n The path to the PET group template.\n\n Nipype Workflow Dependencies\n ----------------------------\n This workflow depends on:\n - spm_pet_preproc\n - spm_anat_preproc if `spm_pet_template.do_petpvc` is True.\n\n Returns\n -------\n main_wf: nipype Workflow\n "
pet_wf = get_subworkflow(main_wf, 'spm_pet_preproc')
in_files = get_input_node(main_wf)
datasink = get_datasink(main_wf, name='datasink')
pet_fbasename = remove_ext(os.path.basename(get_input_file_name(in_files, 'pet')))
base_outdir = datasink.inputs.base_directory
grp_datasink = pe.Node(DataSink(parameterization=False, base_directory=base_outdir), name='{}_grouptemplate_datasink'.format(pet_fbasename))
grp_datasink.inputs.container = '{}_grouptemplate'.format(pet_fbasename)
warped_pets = pe.JoinNode(interface=IdentityInterface(fields=['warped_pets']), joinsource='infosrc', joinfield='warped_pets', name='warped_pets')
template_wf = spm_create_group_template_wf(wf_name)
output = setup_node(IdentityInterface(fields=['pet_template']), name='group_template')
regexp_subst = [('/wgrptemplate{pet}_merged_mean_smooth.nii$', '/{pet}_grouptemplate_mni.nii'), ('/w{pet}_merged_mean_smooth.nii$', '/{pet}_grouptemplate_mni.nii')]
regexp_subst = format_pair_list(regexp_subst, pet=pet_fbasename)
regexp_subst += extension_duplicates(regexp_subst)
grp_datasink.inputs.regexp_substitutions = extend_trait_list(grp_datasink.inputs.regexp_substitutions, regexp_subst)
main_wf.connect([(pet_wf, warped_pets, [('warp_output.warped_files', 'warped_pets')]), (warped_pets, template_wf, [(('warped_pets', flatten_list), 'grptemplate_input.in_files')]), (template_wf, output, [('grptemplate_output.template', 'pet_template')]), (output, grp_datasink, [('pet_template', '@pet_grouptemplate')])])
do_petpvc = get_config_setting('spm_pet_template.do_petpvc')
if do_petpvc:
get_subworkflow(main_wf, 'spm_anat_preproc')
preproc_wf_name = 'spm_mrpet_grouptemplate_preproc'
main_wf = attach_spm_mrpet_preprocessing(main_wf, wf_name=preproc_wf_name, do_group_template=True)
preproc_wf = get_subworkflow(main_wf, preproc_wf_name)
main_wf.connect([(output, preproc_wf, [('pet_template', 'pet_input.pet_template')])])
else:
reg_wf = spm_register_to_template_wf(wf_name='spm_pet_register_to_grouptemplate')
main_wf.connect([(output, reg_wf, [('pet_template', 'reg_input.template')]), (in_files, reg_wf, [('pet', 'reg_input.in_file')]), (reg_wf, datasink, [('reg_output.warped', 'pet.group_template.@warped'), ('reg_output.warp_field', 'pet.group_template.@warp_field')])])
regexp_subst = [('group_template/{pet}_sn.mat$', 'group_template/{pet}_grptemplate_params.mat'), ('group_template/wgrptemplate_{pet}.nii$', 'group_template/{pet}_grptemplate.nii'), ('group_template/w{pet}.nii', 'group_template/{pet}_grptemplate.nii')]
regexp_subst = format_pair_list(regexp_subst, pet=pet_fbasename)
regexp_subst += extension_duplicates(regexp_subst)
datasink.inputs.regexp_substitutions = extend_trait_list(datasink.inputs.regexp_substitutions, regexp_subst)
return main_wf<|docstring|>Attach a PET pre-processing workflow that uses SPM12 to `main_wf`.
This workflow picks all spm_pet_preproc outputs 'pet_output.warped_files' in `main_wf`
to create a group template.
Parameters
----------
main_wf: nipype Workflow
wf_name: str
Name of the preprocessing workflow
Nipype Inputs for `main_wf`
---------------------------
Note: The `main_wf` workflow is expected to have an `input_files` and a `datasink` nodes.
pet_output.warped_files: input node
datasink: nipype Node
spm_pet_preproc: nipype Workflow
Nipype Outputs
--------------
group_template.pet_template: file
The path to the PET group template.
Nipype Workflow Dependencies
----------------------------
This workflow depends on:
- spm_pet_preproc
- spm_anat_preproc if `spm_pet_template.do_petpvc` is True.
Returns
-------
main_wf: nipype Workflow<|endoftext|>
|
7991f914942a2c1d12d9c25459aa1fa11b16cab4248aa68128c3f36273830995
|
def read_with_nulls(filepath: str, skiprows: Union[(None, int)]=None) -> pd.DataFrame:
'Read in CSV as a pandas DataFrame and fill in NaNs as empty strings'
df = pd.read_csv(filepath, sep=',', skiprows=skiprows).fillna('')
return df
|
Read in CSV as a pandas DataFrame and fill in NaNs as empty strings
|
clean_data.py
|
read_with_nulls
|
prrao87/application-graph
| 0
|
python
|
def read_with_nulls(filepath: str, skiprows: Union[(None, int)]=None) -> pd.DataFrame:
df = pd.read_csv(filepath, sep=',', skiprows=skiprows).fillna()
return df
|
def read_with_nulls(filepath: str, skiprows: Union[(None, int)]=None) -> pd.DataFrame:
df = pd.read_csv(filepath, sep=',', skiprows=skiprows).fillna()
return df<|docstring|>Read in CSV as a pandas DataFrame and fill in NaNs as empty strings<|endoftext|>
|
82ca194a853f248290d8b15c013417d563e53cfe7bdcf3e0ff22234b31df2d1c
|
def lookup_id(id_map: Dict[(str, int)], key: str) -> int:
'Return integer ID for a given string PERSID key'
return id_map[key]
|
Return integer ID for a given string PERSID key
|
clean_data.py
|
lookup_id
|
prrao87/application-graph
| 0
|
python
|
def lookup_id(id_map: Dict[(str, int)], key: str) -> int:
return id_map[key]
|
def lookup_id(id_map: Dict[(str, int)], key: str) -> int:
return id_map[key]<|docstring|>Return integer ID for a given string PERSID key<|endoftext|>
|
649368bc8564353b9a32c228861519de255e993b93674c8df7fb7706d8dd5eac
|
def clean_app_file(filename: str, rawfile_path: str, outpath: str) -> Dict[(str, int)]:
'Convert string IDs to int for apps/services persistent IDs and output to CSV'
apps_df = read_with_nulls(os.path.join(rawfile_path, filename))
persids = [item.strip() for item in list(apps_df['PERSID'])]
apps_df = apps_df.drop('PERSID', axis=1)
apps_df.insert(0, 'persid_int', (apps_df.index + 1))
apps_df.rename({'persid_int': 'PERSID'}, axis=1, inplace=True)
apps_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
app_id_map = dict(zip(persids, list(apps_df['PERSID'])))
return app_id_map
|
Convert string IDs to int for apps/services persistent IDs and output to CSV
|
clean_data.py
|
clean_app_file
|
prrao87/application-graph
| 0
|
python
|
def clean_app_file(filename: str, rawfile_path: str, outpath: str) -> Dict[(str, int)]:
apps_df = read_with_nulls(os.path.join(rawfile_path, filename))
persids = [item.strip() for item in list(apps_df['PERSID'])]
apps_df = apps_df.drop('PERSID', axis=1)
apps_df.insert(0, 'persid_int', (apps_df.index + 1))
apps_df.rename({'persid_int': 'PERSID'}, axis=1, inplace=True)
apps_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
app_id_map = dict(zip(persids, list(apps_df['PERSID'])))
return app_id_map
|
def clean_app_file(filename: str, rawfile_path: str, outpath: str) -> Dict[(str, int)]:
apps_df = read_with_nulls(os.path.join(rawfile_path, filename))
persids = [item.strip() for item in list(apps_df['PERSID'])]
apps_df = apps_df.drop('PERSID', axis=1)
apps_df.insert(0, 'persid_int', (apps_df.index + 1))
apps_df.rename({'persid_int': 'PERSID'}, axis=1, inplace=True)
apps_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
app_id_map = dict(zip(persids, list(apps_df['PERSID'])))
return app_id_map<|docstring|>Convert string IDs to int for apps/services persistent IDs and output to CSV<|endoftext|>
|
05a34362c08a95000b16ab7a48285ed8aa641e99ccd598204da12a325e48e765
|
def clean_org_file(filename: str, rawfile_path: str, outpath: str, app_id_map: Dict[(str, int)]) -> None:
"Convert string IDs to int for organization's app persistent IDs and output to CSV"
orgs_df = read_with_nulls(os.path.join(rawfile_path, filename))
orgs_df['PERSID'] = orgs_df['PERSID'].str.replace('nr:', '')
orgs_df.insert(0, 'persid_int', orgs_df['PERSID'].apply((lambda x: lookup_id(app_id_map, x))))
orgs_df = orgs_df.drop('PERSID', axis=1)
orgs_df.rename({'persid_int': 'APP_PERSID'}, axis=1, inplace=True)
orgs_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
|
Convert string IDs to int for organization's app persistent IDs and output to CSV
|
clean_data.py
|
clean_org_file
|
prrao87/application-graph
| 0
|
python
|
def clean_org_file(filename: str, rawfile_path: str, outpath: str, app_id_map: Dict[(str, int)]) -> None:
orgs_df = read_with_nulls(os.path.join(rawfile_path, filename))
orgs_df['PERSID'] = orgs_df['PERSID'].str.replace('nr:', )
orgs_df.insert(0, 'persid_int', orgs_df['PERSID'].apply((lambda x: lookup_id(app_id_map, x))))
orgs_df = orgs_df.drop('PERSID', axis=1)
orgs_df.rename({'persid_int': 'APP_PERSID'}, axis=1, inplace=True)
orgs_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
|
def clean_org_file(filename: str, rawfile_path: str, outpath: str, app_id_map: Dict[(str, int)]) -> None:
orgs_df = read_with_nulls(os.path.join(rawfile_path, filename))
orgs_df['PERSID'] = orgs_df['PERSID'].str.replace('nr:', )
orgs_df.insert(0, 'persid_int', orgs_df['PERSID'].apply((lambda x: lookup_id(app_id_map, x))))
orgs_df = orgs_df.drop('PERSID', axis=1)
orgs_df.rename({'persid_int': 'APP_PERSID'}, axis=1, inplace=True)
orgs_df.to_csv(os.path.join(outpath, filename), index=False, header=True)<|docstring|>Convert string IDs to int for organization's app persistent IDs and output to CSV<|endoftext|>
|
a5f1688e488676b12c207bbbc95b0b201e954688ae4e05dc4694816ec19e6f52
|
def clean_ahd_file(filename: str, rawfile_path: str, outpath: str, app_id_map: Dict[(str, int)]) -> None:
'Convert string IDs to int for AHD hitrate file and output to CSV'
ahd_df = read_with_nulls(os.path.join(rawfile_path, filename))
ahd_df['PERSID'] = ahd_df['PERSID'].str.replace('nr:', '')
ahd_df.insert(0, 'persid_int', ahd_df['PERSID'].apply((lambda x: lookup_id(app_id_map, x))))
ahd_df = ahd_df.drop('PERSID', axis=1)
ahd_df.rename({'persid_int': 'APP_PERSID'}, axis=1, inplace=True)
ahd_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
|
Convert string IDs to int for AHD hitrate file and output to CSV
|
clean_data.py
|
clean_ahd_file
|
prrao87/application-graph
| 0
|
python
|
def clean_ahd_file(filename: str, rawfile_path: str, outpath: str, app_id_map: Dict[(str, int)]) -> None:
ahd_df = read_with_nulls(os.path.join(rawfile_path, filename))
ahd_df['PERSID'] = ahd_df['PERSID'].str.replace('nr:', )
ahd_df.insert(0, 'persid_int', ahd_df['PERSID'].apply((lambda x: lookup_id(app_id_map, x))))
ahd_df = ahd_df.drop('PERSID', axis=1)
ahd_df.rename({'persid_int': 'APP_PERSID'}, axis=1, inplace=True)
ahd_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
|
def clean_ahd_file(filename: str, rawfile_path: str, outpath: str, app_id_map: Dict[(str, int)]) -> None:
ahd_df = read_with_nulls(os.path.join(rawfile_path, filename))
ahd_df['PERSID'] = ahd_df['PERSID'].str.replace('nr:', )
ahd_df.insert(0, 'persid_int', ahd_df['PERSID'].apply((lambda x: lookup_id(app_id_map, x))))
ahd_df = ahd_df.drop('PERSID', axis=1)
ahd_df.rename({'persid_int': 'APP_PERSID'}, axis=1, inplace=True)
ahd_df.to_csv(os.path.join(outpath, filename), index=False, header=True)<|docstring|>Convert string IDs to int for AHD hitrate file and output to CSV<|endoftext|>
|
60e7172e47b6150975a969bba34bf433b6daca367ca8661afc7e5b879e5a9fc9
|
def clean_os_instances_file(filename: str, rawfile_path: str, outpath: str) -> None:
'\n Convert string IDs to int for OS instances monthly usage file and output to CSV.\n Note that the PERSIDs in this file are NOT the same as the PERSIDs for the app file,\n hence we restart the numbering from 1.\n '
os_instances_df = read_with_nulls(os.path.join(rawfile_path, filename))
os_instances_df.rename({u'os_\ufeffPersID': 'PERSID'}, axis=1, inplace=True)
os_instances_df.insert(0, 'persid_int', (os_instances_df.index + 1))
persids = [item.strip() for item in list(os_instances_df['PERSID'])]
os_instances_df = os_instances_df.drop('PERSID', axis=1)
os_instances_df.rename({'persid_int': 'OS_PERSID'}, axis=1, inplace=True)
os_instances_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
os_id_map = dict(zip(persids, list(os_instances_df['OS_PERSID'])))
return os_id_map
|
Convert string IDs to int for OS instances monthly usage file and output to CSV.
Note that the PERSIDs in this file are NOT the same as the PERSIDs for the app file,
hence we restart the numbering from 1.
|
clean_data.py
|
clean_os_instances_file
|
prrao87/application-graph
| 0
|
python
|
def clean_os_instances_file(filename: str, rawfile_path: str, outpath: str) -> None:
'\n Convert string IDs to int for OS instances monthly usage file and output to CSV.\n Note that the PERSIDs in this file are NOT the same as the PERSIDs for the app file,\n hence we restart the numbering from 1.\n '
os_instances_df = read_with_nulls(os.path.join(rawfile_path, filename))
os_instances_df.rename({u'os_\ufeffPersID': 'PERSID'}, axis=1, inplace=True)
os_instances_df.insert(0, 'persid_int', (os_instances_df.index + 1))
persids = [item.strip() for item in list(os_instances_df['PERSID'])]
os_instances_df = os_instances_df.drop('PERSID', axis=1)
os_instances_df.rename({'persid_int': 'OS_PERSID'}, axis=1, inplace=True)
os_instances_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
os_id_map = dict(zip(persids, list(os_instances_df['OS_PERSID'])))
return os_id_map
|
def clean_os_instances_file(filename: str, rawfile_path: str, outpath: str) -> None:
'\n Convert string IDs to int for OS instances monthly usage file and output to CSV.\n Note that the PERSIDs in this file are NOT the same as the PERSIDs for the app file,\n hence we restart the numbering from 1.\n '
os_instances_df = read_with_nulls(os.path.join(rawfile_path, filename))
os_instances_df.rename({u'os_\ufeffPersID': 'PERSID'}, axis=1, inplace=True)
os_instances_df.insert(0, 'persid_int', (os_instances_df.index + 1))
persids = [item.strip() for item in list(os_instances_df['PERSID'])]
os_instances_df = os_instances_df.drop('PERSID', axis=1)
os_instances_df.rename({'persid_int': 'OS_PERSID'}, axis=1, inplace=True)
os_instances_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
os_id_map = dict(zip(persids, list(os_instances_df['OS_PERSID'])))
return os_id_map<|docstring|>Convert string IDs to int for OS instances monthly usage file and output to CSV.
Note that the PERSIDs in this file are NOT the same as the PERSIDs for the app file,
hence we restart the numbering from 1.<|endoftext|>
|
e9c2c9a36e3ae411a83ee99717298aa16e7ea01ba447fa6dc9161c7d694719d8
|
def clean_similarity_connectedcomps_file(filename: str, rawfile_path: str, outpath: str, app_id_map: Dict[(str, int)]) -> None:
'\n Clean up and format string IDs in the connected components similarity table and\n output to CSV.\n '
similarities_df = read_with_nulls(os.path.join(rawfile_path, filename))
similarities_df['PersID-1'] = similarities_df['PersID-1'].str.replace('nr:', '')
similarities_df['PersID-2'] = similarities_df['PersID-2'].str.replace('nr:', '')
similarities_df.insert(0, 'PERSID_1', similarities_df['PersID-1'].apply((lambda x: lookup_id(app_id_map, x))))
similarities_df.insert(1, 'PERSID_2', similarities_df['PersID-2'].apply((lambda x: lookup_id(app_id_map, x))))
similarities_df = similarities_df.drop(['PersID-1', 'PersID-2'], axis=1)
similarities_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
|
Clean up and format string IDs in the connected components similarity table and
output to CSV.
|
clean_data.py
|
clean_similarity_connectedcomps_file
|
prrao87/application-graph
| 0
|
python
|
def clean_similarity_connectedcomps_file(filename: str, rawfile_path: str, outpath: str, app_id_map: Dict[(str, int)]) -> None:
'\n Clean up and format string IDs in the connected components similarity table and\n output to CSV.\n '
similarities_df = read_with_nulls(os.path.join(rawfile_path, filename))
similarities_df['PersID-1'] = similarities_df['PersID-1'].str.replace('nr:', )
similarities_df['PersID-2'] = similarities_df['PersID-2'].str.replace('nr:', )
similarities_df.insert(0, 'PERSID_1', similarities_df['PersID-1'].apply((lambda x: lookup_id(app_id_map, x))))
similarities_df.insert(1, 'PERSID_2', similarities_df['PersID-2'].apply((lambda x: lookup_id(app_id_map, x))))
similarities_df = similarities_df.drop(['PersID-1', 'PersID-2'], axis=1)
similarities_df.to_csv(os.path.join(outpath, filename), index=False, header=True)
|
def clean_similarity_connectedcomps_file(filename: str, rawfile_path: str, outpath: str, app_id_map: Dict[(str, int)]) -> None:
'\n Clean up and format string IDs in the connected components similarity table and\n output to CSV.\n '
similarities_df = read_with_nulls(os.path.join(rawfile_path, filename))
similarities_df['PersID-1'] = similarities_df['PersID-1'].str.replace('nr:', )
similarities_df['PersID-2'] = similarities_df['PersID-2'].str.replace('nr:', )
similarities_df.insert(0, 'PERSID_1', similarities_df['PersID-1'].apply((lambda x: lookup_id(app_id_map, x))))
similarities_df.insert(1, 'PERSID_2', similarities_df['PersID-2'].apply((lambda x: lookup_id(app_id_map, x))))
similarities_df = similarities_df.drop(['PersID-1', 'PersID-2'], axis=1)
similarities_df.to_csv(os.path.join(outpath, filename), index=False, header=True)<|docstring|>Clean up and format string IDs in the connected components similarity table and
output to CSV.<|endoftext|>
|
3c95e9d4fe5b3892ee620ed3c5683b6129364d62e781e9c6c32e7526a4c08aef
|
def _log_record_context_injector(*args: Any, **kwargs: Any) -> logging.LogRecord:
'\n A custom logger LogRecord Factory that injects selected context parameters into newly\n created logs.\n\n Args:\n - *args: arguments to pass to the original LogRecord Factory\n - **kwargs: keyword arguments to pass to the original LogRecord Factory\n\n Returns:\n - logging.LogRecord: the newly created LogRecord\n '
record = _original_log_record_factory(*args, **kwargs)
additional_attrs = context.config.logging.get('log_attributes', [])
for attr in (PREFECT_LOG_RECORD_ATTRIBUTES + tuple(additional_attrs)):
value = context.get(attr, None)
if (value or (attr in additional_attrs)):
setattr(record, attr, value)
return record
|
A custom logger LogRecord Factory that injects selected context parameters into newly
created logs.
Args:
- *args: arguments to pass to the original LogRecord Factory
- **kwargs: keyword arguments to pass to the original LogRecord Factory
Returns:
- logging.LogRecord: the newly created LogRecord
|
src/prefect/utilities/logging.py
|
_log_record_context_injector
|
zschumacher/prefect
| 1
|
python
|
def _log_record_context_injector(*args: Any, **kwargs: Any) -> logging.LogRecord:
'\n A custom logger LogRecord Factory that injects selected context parameters into newly\n created logs.\n\n Args:\n - *args: arguments to pass to the original LogRecord Factory\n - **kwargs: keyword arguments to pass to the original LogRecord Factory\n\n Returns:\n - logging.LogRecord: the newly created LogRecord\n '
record = _original_log_record_factory(*args, **kwargs)
additional_attrs = context.config.logging.get('log_attributes', [])
for attr in (PREFECT_LOG_RECORD_ATTRIBUTES + tuple(additional_attrs)):
value = context.get(attr, None)
if (value or (attr in additional_attrs)):
setattr(record, attr, value)
return record
|
def _log_record_context_injector(*args: Any, **kwargs: Any) -> logging.LogRecord:
'\n A custom logger LogRecord Factory that injects selected context parameters into newly\n created logs.\n\n Args:\n - *args: arguments to pass to the original LogRecord Factory\n - **kwargs: keyword arguments to pass to the original LogRecord Factory\n\n Returns:\n - logging.LogRecord: the newly created LogRecord\n '
record = _original_log_record_factory(*args, **kwargs)
additional_attrs = context.config.logging.get('log_attributes', [])
for attr in (PREFECT_LOG_RECORD_ATTRIBUTES + tuple(additional_attrs)):
value = context.get(attr, None)
if (value or (attr in additional_attrs)):
setattr(record, attr, value)
return record<|docstring|>A custom logger LogRecord Factory that injects selected context parameters into newly
created logs.
Args:
- *args: arguments to pass to the original LogRecord Factory
- **kwargs: keyword arguments to pass to the original LogRecord Factory
Returns:
- logging.LogRecord: the newly created LogRecord<|endoftext|>
|
7fb587d81cbbc0caaac2f414460eacc38dac95e96521528c0261ebc30ee7f4b8
|
def _create_logger(name: str) -> logging.Logger:
'\n Creates a logger with a `StreamHandler` that has level and formatting\n set from `prefect.config`.\n\n Args:\n - name (str): Name to use for logger.\n\n Returns:\n - logging.Logger: a configured logging object\n '
logging.setLogRecordFactory(_log_record_context_injector)
logger = logging.getLogger(name)
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(context.config.logging.format, context.config.logging.datefmt)
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(context.config.logging.level)
logger.addHandler(CloudHandler())
return logger
|
Creates a logger with a `StreamHandler` that has level and formatting
set from `prefect.config`.
Args:
- name (str): Name to use for logger.
Returns:
- logging.Logger: a configured logging object
|
src/prefect/utilities/logging.py
|
_create_logger
|
zschumacher/prefect
| 1
|
python
|
def _create_logger(name: str) -> logging.Logger:
'\n Creates a logger with a `StreamHandler` that has level and formatting\n set from `prefect.config`.\n\n Args:\n - name (str): Name to use for logger.\n\n Returns:\n - logging.Logger: a configured logging object\n '
logging.setLogRecordFactory(_log_record_context_injector)
logger = logging.getLogger(name)
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(context.config.logging.format, context.config.logging.datefmt)
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(context.config.logging.level)
logger.addHandler(CloudHandler())
return logger
|
def _create_logger(name: str) -> logging.Logger:
'\n Creates a logger with a `StreamHandler` that has level and formatting\n set from `prefect.config`.\n\n Args:\n - name (str): Name to use for logger.\n\n Returns:\n - logging.Logger: a configured logging object\n '
logging.setLogRecordFactory(_log_record_context_injector)
logger = logging.getLogger(name)
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(context.config.logging.format, context.config.logging.datefmt)
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(context.config.logging.level)
logger.addHandler(CloudHandler())
return logger<|docstring|>Creates a logger with a `StreamHandler` that has level and formatting
set from `prefect.config`.
Args:
- name (str): Name to use for logger.
Returns:
- logging.Logger: a configured logging object<|endoftext|>
|
3487908d17355f7a8b991c421fcb83d9b3f4f763e7615aff9e2bccb9e79e2c84
|
def configure_logging(testing: bool=False) -> logging.Logger:
'\n Creates a "prefect" root logger with a `StreamHandler` that has level and formatting\n set from `prefect.config`.\n\n Args:\n - testing (bool, optional): a boolean specifying whether this configuration\n is for testing purposes only; this helps us isolate any global state during testing\n by configuring a "prefect-test-logger" instead of the standard "prefect" logger\n\n Returns:\n - logging.Logger: a configured logging object\n '
name = ('prefect-test-logger' if testing else 'prefect')
return _create_logger(name)
|
Creates a "prefect" root logger with a `StreamHandler` that has level and formatting
set from `prefect.config`.
Args:
- testing (bool, optional): a boolean specifying whether this configuration
is for testing purposes only; this helps us isolate any global state during testing
by configuring a "prefect-test-logger" instead of the standard "prefect" logger
Returns:
- logging.Logger: a configured logging object
|
src/prefect/utilities/logging.py
|
configure_logging
|
zschumacher/prefect
| 1
|
python
|
def configure_logging(testing: bool=False) -> logging.Logger:
'\n Creates a "prefect" root logger with a `StreamHandler` that has level and formatting\n set from `prefect.config`.\n\n Args:\n - testing (bool, optional): a boolean specifying whether this configuration\n is for testing purposes only; this helps us isolate any global state during testing\n by configuring a "prefect-test-logger" instead of the standard "prefect" logger\n\n Returns:\n - logging.Logger: a configured logging object\n '
name = ('prefect-test-logger' if testing else 'prefect')
return _create_logger(name)
|
def configure_logging(testing: bool=False) -> logging.Logger:
'\n Creates a "prefect" root logger with a `StreamHandler` that has level and formatting\n set from `prefect.config`.\n\n Args:\n - testing (bool, optional): a boolean specifying whether this configuration\n is for testing purposes only; this helps us isolate any global state during testing\n by configuring a "prefect-test-logger" instead of the standard "prefect" logger\n\n Returns:\n - logging.Logger: a configured logging object\n '
name = ('prefect-test-logger' if testing else 'prefect')
return _create_logger(name)<|docstring|>Creates a "prefect" root logger with a `StreamHandler` that has level and formatting
set from `prefect.config`.
Args:
- testing (bool, optional): a boolean specifying whether this configuration
is for testing purposes only; this helps us isolate any global state during testing
by configuring a "prefect-test-logger" instead of the standard "prefect" logger
Returns:
- logging.Logger: a configured logging object<|endoftext|>
|
ac2abaee8ac1a60264e2e3b300f7047b9f3f198b80ef502b2c483ba2da2d6b06
|
def configure_extra_loggers() -> None:
'\n Creates a "Prefect" configured logger for all strings in extra_loggers config list.\n The logging.extra_loggers config defaults to an empty list.\n '
loggers = context.config.logging.get('extra_loggers', [])
for l in loggers:
_create_logger(l)
|
Creates a "Prefect" configured logger for all strings in extra_loggers config list.
The logging.extra_loggers config defaults to an empty list.
|
src/prefect/utilities/logging.py
|
configure_extra_loggers
|
zschumacher/prefect
| 1
|
python
|
def configure_extra_loggers() -> None:
'\n Creates a "Prefect" configured logger for all strings in extra_loggers config list.\n The logging.extra_loggers config defaults to an empty list.\n '
loggers = context.config.logging.get('extra_loggers', [])
for l in loggers:
_create_logger(l)
|
def configure_extra_loggers() -> None:
'\n Creates a "Prefect" configured logger for all strings in extra_loggers config list.\n The logging.extra_loggers config defaults to an empty list.\n '
loggers = context.config.logging.get('extra_loggers', [])
for l in loggers:
_create_logger(l)<|docstring|>Creates a "Prefect" configured logger for all strings in extra_loggers config list.
The logging.extra_loggers config defaults to an empty list.<|endoftext|>
|
61503c269da0b95c6ee3d03c0e4167b8a49a14e6a5f4f8a53cfa333aadcb3168
|
def create_diagnostic_logger(name: str) -> logging.Logger:
'\n Create a logger that does not use the `CloudHandler` but preserves all other\n Prefect logging configuration. For diagnostic / debugging / internal use only.\n '
logger = _create_logger(name)
logger.handlers = [h for h in logger.handlers if (not isinstance(h, CloudHandler))]
return logger
|
Create a logger that does not use the `CloudHandler` but preserves all other
Prefect logging configuration. For diagnostic / debugging / internal use only.
|
src/prefect/utilities/logging.py
|
create_diagnostic_logger
|
zschumacher/prefect
| 1
|
python
|
def create_diagnostic_logger(name: str) -> logging.Logger:
'\n Create a logger that does not use the `CloudHandler` but preserves all other\n Prefect logging configuration. For diagnostic / debugging / internal use only.\n '
logger = _create_logger(name)
logger.handlers = [h for h in logger.handlers if (not isinstance(h, CloudHandler))]
return logger
|
def create_diagnostic_logger(name: str) -> logging.Logger:
'\n Create a logger that does not use the `CloudHandler` but preserves all other\n Prefect logging configuration. For diagnostic / debugging / internal use only.\n '
logger = _create_logger(name)
logger.handlers = [h for h in logger.handlers if (not isinstance(h, CloudHandler))]
return logger<|docstring|>Create a logger that does not use the `CloudHandler` but preserves all other
Prefect logging configuration. For diagnostic / debugging / internal use only.<|endoftext|>
|
795138f5b2488ee3c555852b620a509c1905eb928bedc7d3669360adcc95f03d
|
def get_logger(name: str=None) -> logging.Logger:
'\n Returns a "prefect" logger.\n\n Args:\n - name (str): if `None`, the root Prefect logger is returned. If provided, a child\n logger of the name `"prefect.{name}"` is returned. The child logger inherits\n the root logger\'s settings.\n\n Returns:\n - logging.Logger: a configured logging object with the appropriate name\n '
if (name is None):
return prefect_logger
else:
return prefect_logger.getChild(name)
|
Returns a "prefect" logger.
Args:
- name (str): if `None`, the root Prefect logger is returned. If provided, a child
logger of the name `"prefect.{name}"` is returned. The child logger inherits
the root logger's settings.
Returns:
- logging.Logger: a configured logging object with the appropriate name
|
src/prefect/utilities/logging.py
|
get_logger
|
zschumacher/prefect
| 1
|
python
|
def get_logger(name: str=None) -> logging.Logger:
'\n Returns a "prefect" logger.\n\n Args:\n - name (str): if `None`, the root Prefect logger is returned. If provided, a child\n logger of the name `"prefect.{name}"` is returned. The child logger inherits\n the root logger\'s settings.\n\n Returns:\n - logging.Logger: a configured logging object with the appropriate name\n '
if (name is None):
return prefect_logger
else:
return prefect_logger.getChild(name)
|
def get_logger(name: str=None) -> logging.Logger:
'\n Returns a "prefect" logger.\n\n Args:\n - name (str): if `None`, the root Prefect logger is returned. If provided, a child\n logger of the name `"prefect.{name}"` is returned. The child logger inherits\n the root logger\'s settings.\n\n Returns:\n - logging.Logger: a configured logging object with the appropriate name\n '
if (name is None):
return prefect_logger
else:
return prefect_logger.getChild(name)<|docstring|>Returns a "prefect" logger.
Args:
- name (str): if `None`, the root Prefect logger is returned. If provided, a child
logger of the name `"prefect.{name}"` is returned. The child logger inherits
the root logger's settings.
Returns:
- logging.Logger: a configured logging object with the appropriate name<|endoftext|>
|
601a68b385fccb46c39b7906ea216a33f63f94273f549598d5d4543de1e6eae5
|
def ensure_started(self) -> None:
'Ensure the log manager is started'
if (self.thread is None):
self.client = prefect.Client()
self.logging_period = context.config.cloud.logging_heartbeat
self.thread = threading.Thread(target=self._write_logs_loop, name='prefect-log-manager', daemon=True)
self.thread.start()
atexit.register(self._on_shutdown)
|
Ensure the log manager is started
|
src/prefect/utilities/logging.py
|
ensure_started
|
zschumacher/prefect
| 1
|
python
|
def ensure_started(self) -> None:
if (self.thread is None):
self.client = prefect.Client()
self.logging_period = context.config.cloud.logging_heartbeat
self.thread = threading.Thread(target=self._write_logs_loop, name='prefect-log-manager', daemon=True)
self.thread.start()
atexit.register(self._on_shutdown)
|
def ensure_started(self) -> None:
if (self.thread is None):
self.client = prefect.Client()
self.logging_period = context.config.cloud.logging_heartbeat
self.thread = threading.Thread(target=self._write_logs_loop, name='prefect-log-manager', daemon=True)
self.thread.start()
atexit.register(self._on_shutdown)<|docstring|>Ensure the log manager is started<|endoftext|>
|
71dd10a3115f34887fdf4cdbd617d0123778506eb31a4c1bd7bd45fd9c1ffefe
|
def _on_shutdown(self) -> None:
'Called via atexit, flushes all logs and stops the background thread'
for _ in range(3):
try:
self.stop()
return
except SystemExit:
pass
|
Called via atexit, flushes all logs and stops the background thread
|
src/prefect/utilities/logging.py
|
_on_shutdown
|
zschumacher/prefect
| 1
|
python
|
def _on_shutdown(self) -> None:
for _ in range(3):
try:
self.stop()
return
except SystemExit:
pass
|
def _on_shutdown(self) -> None:
for _ in range(3):
try:
self.stop()
return
except SystemExit:
pass<|docstring|>Called via atexit, flushes all logs and stops the background thread<|endoftext|>
|
b810ca2b3b3ea682f9767b0ab7a9dd8aa8e9099bc0d18819b3fceb0d0b2917e9
|
def stop(self) -> None:
'Flush all logs and stop the background thread'
if (self.thread is not None):
self._stopped.set()
self.thread.join()
self._write_logs()
self.thread = None
self.client = None
|
Flush all logs and stop the background thread
|
src/prefect/utilities/logging.py
|
stop
|
zschumacher/prefect
| 1
|
python
|
def stop(self) -> None:
if (self.thread is not None):
self._stopped.set()
self.thread.join()
self._write_logs()
self.thread = None
self.client = None
|
def stop(self) -> None:
if (self.thread is not None):
self._stopped.set()
self.thread.join()
self._write_logs()
self.thread = None
self.client = None<|docstring|>Flush all logs and stop the background thread<|endoftext|>
|
05d89cc162b1ea84cf772db55638ac2521a39f956557dd4dcb498c593e01ce2f
|
def _write_logs_loop(self) -> None:
'Runs in a background thread, uploads logs periodically in a loop'
while (not self._stopped.wait(self.logging_period)):
self._write_logs()
|
Runs in a background thread, uploads logs periodically in a loop
|
src/prefect/utilities/logging.py
|
_write_logs_loop
|
zschumacher/prefect
| 1
|
python
|
def _write_logs_loop(self) -> None:
while (not self._stopped.wait(self.logging_period)):
self._write_logs()
|
def _write_logs_loop(self) -> None:
while (not self._stopped.wait(self.logging_period)):
self._write_logs()<|docstring|>Runs in a background thread, uploads logs periodically in a loop<|endoftext|>
|
2cdbd1888ea1bcf79b6f6d847368f443c9633ad525d48e7118766775c804447c
|
def _write_logs(self) -> None:
'Upload logs in batches until the queue is empty'
assert (self.client is not None)
cont = True
while cont:
try:
while (self.pending_length < MAX_BATCH_LOG_LENGTH):
log = self.queue.get_nowait()
self.pending_length += len(log.get('message', ''))
self.pending_logs.append(log)
except Empty:
cont = False
if self.pending_logs:
try:
self.client.write_run_logs(self.pending_logs)
self.pending_logs = []
self.pending_length = 0
except Exception as exc:
warnings.warn(f'Failed to write logs with error: {exc!r}')
cont = False
|
Upload logs in batches until the queue is empty
|
src/prefect/utilities/logging.py
|
_write_logs
|
zschumacher/prefect
| 1
|
python
|
def _write_logs(self) -> None:
assert (self.client is not None)
cont = True
while cont:
try:
while (self.pending_length < MAX_BATCH_LOG_LENGTH):
log = self.queue.get_nowait()
self.pending_length += len(log.get('message', ))
self.pending_logs.append(log)
except Empty:
cont = False
if self.pending_logs:
try:
self.client.write_run_logs(self.pending_logs)
self.pending_logs = []
self.pending_length = 0
except Exception as exc:
warnings.warn(f'Failed to write logs with error: {exc!r}')
cont = False
|
def _write_logs(self) -> None:
assert (self.client is not None)
cont = True
while cont:
try:
while (self.pending_length < MAX_BATCH_LOG_LENGTH):
log = self.queue.get_nowait()
self.pending_length += len(log.get('message', ))
self.pending_logs.append(log)
except Empty:
cont = False
if self.pending_logs:
try:
self.client.write_run_logs(self.pending_logs)
self.pending_logs = []
self.pending_length = 0
except Exception as exc:
warnings.warn(f'Failed to write logs with error: {exc!r}')
cont = False<|docstring|>Upload logs in batches until the queue is empty<|endoftext|>
|
512b5bd8081b8f8ca1ad2b381f2c5071bd743bbf5270b9a3c159900ce9fd8006
|
def enqueue(self, message: dict) -> None:
'Enqueue a new log message to be uploaded.\n\n Args:\n - message (dict): a log message to upload.\n '
self.ensure_started()
self.queue.put(message)
|
Enqueue a new log message to be uploaded.
Args:
- message (dict): a log message to upload.
|
src/prefect/utilities/logging.py
|
enqueue
|
zschumacher/prefect
| 1
|
python
|
def enqueue(self, message: dict) -> None:
'Enqueue a new log message to be uploaded.\n\n Args:\n - message (dict): a log message to upload.\n '
self.ensure_started()
self.queue.put(message)
|
def enqueue(self, message: dict) -> None:
'Enqueue a new log message to be uploaded.\n\n Args:\n - message (dict): a log message to upload.\n '
self.ensure_started()
self.queue.put(message)<|docstring|>Enqueue a new log message to be uploaded.
Args:
- message (dict): a log message to upload.<|endoftext|>
|
98c49106477c11ba48e2601ce980ba290f707bd4ec221ba3038411d9b94ff5ef
|
def emit(self, record: logging.LogRecord) -> None:
'Emit a new log'
if (not context.config.logging.log_to_cloud):
return
config_level = getattr(logging, context.config.logging.level, logging.INFO)
if (record.levelno < config_level):
return
msg = self.format(record)
if (len(msg) > MAX_LOG_LENGTH):
get_logger('prefect.logging').warning('Received a log message of %d bytes, exceeding the limit of %d. The output will be truncated', len(msg), MAX_LOG_LENGTH)
msg = msg[:MAX_LOG_LENGTH]
log = {'flow_run_id': context.get('flow_run_id'), 'task_run_id': context.get('task_run_id'), 'timestamp': pendulum.from_timestamp((getattr(record, 'created', None) or time.time())).isoformat(), 'name': getattr(record, 'name', None), 'level': getattr(record, 'levelname', None), 'message': msg}
LOG_MANAGER.enqueue(log)
|
Emit a new log
|
src/prefect/utilities/logging.py
|
emit
|
zschumacher/prefect
| 1
|
python
|
def emit(self, record: logging.LogRecord) -> None:
if (not context.config.logging.log_to_cloud):
return
config_level = getattr(logging, context.config.logging.level, logging.INFO)
if (record.levelno < config_level):
return
msg = self.format(record)
if (len(msg) > MAX_LOG_LENGTH):
get_logger('prefect.logging').warning('Received a log message of %d bytes, exceeding the limit of %d. The output will be truncated', len(msg), MAX_LOG_LENGTH)
msg = msg[:MAX_LOG_LENGTH]
log = {'flow_run_id': context.get('flow_run_id'), 'task_run_id': context.get('task_run_id'), 'timestamp': pendulum.from_timestamp((getattr(record, 'created', None) or time.time())).isoformat(), 'name': getattr(record, 'name', None), 'level': getattr(record, 'levelname', None), 'message': msg}
LOG_MANAGER.enqueue(log)
|
def emit(self, record: logging.LogRecord) -> None:
if (not context.config.logging.log_to_cloud):
return
config_level = getattr(logging, context.config.logging.level, logging.INFO)
if (record.levelno < config_level):
return
msg = self.format(record)
if (len(msg) > MAX_LOG_LENGTH):
get_logger('prefect.logging').warning('Received a log message of %d bytes, exceeding the limit of %d. The output will be truncated', len(msg), MAX_LOG_LENGTH)
msg = msg[:MAX_LOG_LENGTH]
log = {'flow_run_id': context.get('flow_run_id'), 'task_run_id': context.get('task_run_id'), 'timestamp': pendulum.from_timestamp((getattr(record, 'created', None) or time.time())).isoformat(), 'name': getattr(record, 'name', None), 'level': getattr(record, 'levelname', None), 'message': msg}
LOG_MANAGER.enqueue(log)<|docstring|>Emit a new log<|endoftext|>
|
7c4b87ed02124f92e55e569a5c1566667682764ff76adc5c337e8198c3e53246
|
def write(self, s: str) -> None:
'\n Write message from stdout to a prefect logger.\n Note: blank newlines will not be logged.\n\n Args:\n s (str): the message from stdout to be logged\n '
if (not isinstance(s, str)):
raise TypeError(f'string argument expected, got {type(s)}')
if s.strip():
self.stdout_logger.info(s)
|
Write message from stdout to a prefect logger.
Note: blank newlines will not be logged.
Args:
s (str): the message from stdout to be logged
|
src/prefect/utilities/logging.py
|
write
|
zschumacher/prefect
| 1
|
python
|
def write(self, s: str) -> None:
'\n Write message from stdout to a prefect logger.\n Note: blank newlines will not be logged.\n\n Args:\n s (str): the message from stdout to be logged\n '
if (not isinstance(s, str)):
raise TypeError(f'string argument expected, got {type(s)}')
if s.strip():
self.stdout_logger.info(s)
|
def write(self, s: str) -> None:
'\n Write message from stdout to a prefect logger.\n Note: blank newlines will not be logged.\n\n Args:\n s (str): the message from stdout to be logged\n '
if (not isinstance(s, str)):
raise TypeError(f'string argument expected, got {type(s)}')
if s.strip():
self.stdout_logger.info(s)<|docstring|>Write message from stdout to a prefect logger.
Note: blank newlines will not be logged.
Args:
s (str): the message from stdout to be logged<|endoftext|>
|
392e3744bfac735c55bc254448c0237609642af25c5047ed76d9a2744b37993b
|
def flush(self) -> None:
'\n Implemented flush operation for logger handler\n '
for handler in self.stdout_logger.handlers:
handler.flush()
|
Implemented flush operation for logger handler
|
src/prefect/utilities/logging.py
|
flush
|
zschumacher/prefect
| 1
|
python
|
def flush(self) -> None:
'\n \n '
for handler in self.stdout_logger.handlers:
handler.flush()
|
def flush(self) -> None:
'\n \n '
for handler in self.stdout_logger.handlers:
handler.flush()<|docstring|>Implemented flush operation for logger handler<|endoftext|>
|
3e404d71f0c0fcca67dc3e5e5b062d39caaab52d65cc4733927f5af0bf5df01c
|
def __init__(self, feature_name=None, feature_version=None, local_vars_configuration=None):
'VendorSpecificFeature - a model defined in OpenAPI'
if (local_vars_configuration is None):
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._feature_name = None
self._feature_version = None
self.discriminator = None
self.feature_name = feature_name
self.feature_version = feature_version
|
VendorSpecificFeature - a model defined in OpenAPI
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
__init__
|
H21lab/5GC_build
| 12
|
python
|
def __init__(self, feature_name=None, feature_version=None, local_vars_configuration=None):
if (local_vars_configuration is None):
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._feature_name = None
self._feature_version = None
self.discriminator = None
self.feature_name = feature_name
self.feature_version = feature_version
|
def __init__(self, feature_name=None, feature_version=None, local_vars_configuration=None):
if (local_vars_configuration is None):
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._feature_name = None
self._feature_version = None
self.discriminator = None
self.feature_name = feature_name
self.feature_version = feature_version<|docstring|>VendorSpecificFeature - a model defined in OpenAPI<|endoftext|>
|
51d6668dd977e011001daa9b689c179eefec93390547cdc75140f76c982ca66f
|
@property
def feature_name(self):
'Gets the feature_name of this VendorSpecificFeature. # noqa: E501\n\n\n :return: The feature_name of this VendorSpecificFeature. # noqa: E501\n :rtype: str\n '
return self._feature_name
|
Gets the feature_name of this VendorSpecificFeature. # noqa: E501
:return: The feature_name of this VendorSpecificFeature. # noqa: E501
:rtype: str
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
feature_name
|
H21lab/5GC_build
| 12
|
python
|
@property
def feature_name(self):
'Gets the feature_name of this VendorSpecificFeature. # noqa: E501\n\n\n :return: The feature_name of this VendorSpecificFeature. # noqa: E501\n :rtype: str\n '
return self._feature_name
|
@property
def feature_name(self):
'Gets the feature_name of this VendorSpecificFeature. # noqa: E501\n\n\n :return: The feature_name of this VendorSpecificFeature. # noqa: E501\n :rtype: str\n '
return self._feature_name<|docstring|>Gets the feature_name of this VendorSpecificFeature. # noqa: E501
:return: The feature_name of this VendorSpecificFeature. # noqa: E501
:rtype: str<|endoftext|>
|
c146620143c81b6f50b576f130957e60d016d33c87915eff2c9511c8771c82ff
|
@feature_name.setter
def feature_name(self, feature_name):
'Sets the feature_name of this VendorSpecificFeature.\n\n\n :param feature_name: The feature_name of this VendorSpecificFeature. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (feature_name is None)):
raise ValueError('Invalid value for `feature_name`, must not be `None`')
self._feature_name = feature_name
|
Sets the feature_name of this VendorSpecificFeature.
:param feature_name: The feature_name of this VendorSpecificFeature. # noqa: E501
:type: str
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
feature_name
|
H21lab/5GC_build
| 12
|
python
|
@feature_name.setter
def feature_name(self, feature_name):
'Sets the feature_name of this VendorSpecificFeature.\n\n\n :param feature_name: The feature_name of this VendorSpecificFeature. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (feature_name is None)):
raise ValueError('Invalid value for `feature_name`, must not be `None`')
self._feature_name = feature_name
|
@feature_name.setter
def feature_name(self, feature_name):
'Sets the feature_name of this VendorSpecificFeature.\n\n\n :param feature_name: The feature_name of this VendorSpecificFeature. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (feature_name is None)):
raise ValueError('Invalid value for `feature_name`, must not be `None`')
self._feature_name = feature_name<|docstring|>Sets the feature_name of this VendorSpecificFeature.
:param feature_name: The feature_name of this VendorSpecificFeature. # noqa: E501
:type: str<|endoftext|>
|
44b3ce5b8a4d78b4e3bd5f331b5dbe62f3d7b71d8486b406073a3ad7b0b552a1
|
@property
def feature_version(self):
'Gets the feature_version of this VendorSpecificFeature. # noqa: E501\n\n\n :return: The feature_version of this VendorSpecificFeature. # noqa: E501\n :rtype: str\n '
return self._feature_version
|
Gets the feature_version of this VendorSpecificFeature. # noqa: E501
:return: The feature_version of this VendorSpecificFeature. # noqa: E501
:rtype: str
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
feature_version
|
H21lab/5GC_build
| 12
|
python
|
@property
def feature_version(self):
'Gets the feature_version of this VendorSpecificFeature. # noqa: E501\n\n\n :return: The feature_version of this VendorSpecificFeature. # noqa: E501\n :rtype: str\n '
return self._feature_version
|
@property
def feature_version(self):
'Gets the feature_version of this VendorSpecificFeature. # noqa: E501\n\n\n :return: The feature_version of this VendorSpecificFeature. # noqa: E501\n :rtype: str\n '
return self._feature_version<|docstring|>Gets the feature_version of this VendorSpecificFeature. # noqa: E501
:return: The feature_version of this VendorSpecificFeature. # noqa: E501
:rtype: str<|endoftext|>
|
6cd0f4836f1c74dfb2fd48b2ce0df925e4bd6d73ee6d6ef6bfad970dadd07b4e
|
@feature_version.setter
def feature_version(self, feature_version):
'Sets the feature_version of this VendorSpecificFeature.\n\n\n :param feature_version: The feature_version of this VendorSpecificFeature. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (feature_version is None)):
raise ValueError('Invalid value for `feature_version`, must not be `None`')
self._feature_version = feature_version
|
Sets the feature_version of this VendorSpecificFeature.
:param feature_version: The feature_version of this VendorSpecificFeature. # noqa: E501
:type: str
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
feature_version
|
H21lab/5GC_build
| 12
|
python
|
@feature_version.setter
def feature_version(self, feature_version):
'Sets the feature_version of this VendorSpecificFeature.\n\n\n :param feature_version: The feature_version of this VendorSpecificFeature. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (feature_version is None)):
raise ValueError('Invalid value for `feature_version`, must not be `None`')
self._feature_version = feature_version
|
@feature_version.setter
def feature_version(self, feature_version):
'Sets the feature_version of this VendorSpecificFeature.\n\n\n :param feature_version: The feature_version of this VendorSpecificFeature. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (feature_version is None)):
raise ValueError('Invalid value for `feature_version`, must not be `None`')
self._feature_version = feature_version<|docstring|>Sets the feature_version of this VendorSpecificFeature.
:param feature_version: The feature_version of this VendorSpecificFeature. # noqa: E501
:type: str<|endoftext|>
|
5a4e41bb6a0def746593298cb605df98f1366e957c4ca89b12010ea7db707963
|
def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result
|
Returns the model properties as a dict
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
to_dict
|
H21lab/5GC_build
| 12
|
python
|
def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result
|
def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result<|docstring|>Returns the model properties as a dict<|endoftext|>
|
cbb19eaa2fc8a113d9e32f924ef280a7e97563f8915f94f65dab438997af2e99
|
def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict())
|
Returns the string representation of the model
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
to_str
|
H21lab/5GC_build
| 12
|
python
|
def to_str(self):
return pprint.pformat(self.to_dict())
|
def to_str(self):
return pprint.pformat(self.to_dict())<|docstring|>Returns the string representation of the model<|endoftext|>
|
772243a2c2b3261a9b954d07aaf295e3c1242a579a495e2d6a5679c677861703
|
def __repr__(self):
'For `print` and `pprint`'
return self.to_str()
|
For `print` and `pprint`
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
__repr__
|
H21lab/5GC_build
| 12
|
python
|
def __repr__(self):
return self.to_str()
|
def __repr__(self):
return self.to_str()<|docstring|>For `print` and `pprint`<|endoftext|>
|
7ac6cad7de44177c6d8bc0e8cb82d921c044a10badb9ece5bf3b359e0d09f7dd
|
def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, VendorSpecificFeature)):
return False
return (self.to_dict() == other.to_dict())
|
Returns true if both objects are equal
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
__eq__
|
H21lab/5GC_build
| 12
|
python
|
def __eq__(self, other):
if (not isinstance(other, VendorSpecificFeature)):
return False
return (self.to_dict() == other.to_dict())
|
def __eq__(self, other):
if (not isinstance(other, VendorSpecificFeature)):
return False
return (self.to_dict() == other.to_dict())<|docstring|>Returns true if both objects are equal<|endoftext|>
|
f01f2f88141bac8926a59d63c0261034d4f0948db922ce4847cf23ef7cdb774b
|
def __ne__(self, other):
'Returns true if both objects are not equal'
if (not isinstance(other, VendorSpecificFeature)):
return True
return (self.to_dict() != other.to_dict())
|
Returns true if both objects are not equal
|
Examples/python_client/openapi_client/com/h21lab/TS29510_Nnrf_NFDiscovery/handler/vendor_specific_feature.py
|
__ne__
|
H21lab/5GC_build
| 12
|
python
|
def __ne__(self, other):
if (not isinstance(other, VendorSpecificFeature)):
return True
return (self.to_dict() != other.to_dict())
|
def __ne__(self, other):
if (not isinstance(other, VendorSpecificFeature)):
return True
return (self.to_dict() != other.to_dict())<|docstring|>Returns true if both objects are not equal<|endoftext|>
|
36f5e09b5fc8f915ca3cd2e151cb6b57bc5360b799add9e70ea4ad721217bc76
|
def predict(self, X, Z, clusters):
'\n Predict using trained MERF. For known clusters the trained random effect correction is applied. For unknown\n clusters the pure fixed effect (RF) estimate is used.\n :param X: fixed effect covariates\n :param Z: random effect covariates\n :param clusters: cluster assignments for samples\n :return: y_hat, i.e. predictions\n '
if (self.trained_rf is None):
raise NotFittedError("This MERF instance is not fitted yet. Call 'fit' with appropriate arguments before using this method")
Z = np.array(Z)
y_hat = self.trained_rf.predict(X)
for cluster_id in self.cluster_counts.index:
indices_i = (clusters == cluster_id)
if (len(indices_i) == 0):
continue
b_i = self.trained_b.loc[cluster_id]
Z_i = Z[indices_i]
y_hat[indices_i] += Z_i.dot(b_i)
return y_hat
|
Predict using trained MERF. For known clusters the trained random effect correction is applied. For unknown
clusters the pure fixed effect (RF) estimate is used.
:param X: fixed effect covariates
:param Z: random effect covariates
:param clusters: cluster assignments for samples
:return: y_hat, i.e. predictions
|
merf/merf.py
|
predict
|
ittegrat/merf
| 0
|
python
|
def predict(self, X, Z, clusters):
'\n Predict using trained MERF. For known clusters the trained random effect correction is applied. For unknown\n clusters the pure fixed effect (RF) estimate is used.\n :param X: fixed effect covariates\n :param Z: random effect covariates\n :param clusters: cluster assignments for samples\n :return: y_hat, i.e. predictions\n '
if (self.trained_rf is None):
raise NotFittedError("This MERF instance is not fitted yet. Call 'fit' with appropriate arguments before using this method")
Z = np.array(Z)
y_hat = self.trained_rf.predict(X)
for cluster_id in self.cluster_counts.index:
indices_i = (clusters == cluster_id)
if (len(indices_i) == 0):
continue
b_i = self.trained_b.loc[cluster_id]
Z_i = Z[indices_i]
y_hat[indices_i] += Z_i.dot(b_i)
return y_hat
|
def predict(self, X, Z, clusters):
'\n Predict using trained MERF. For known clusters the trained random effect correction is applied. For unknown\n clusters the pure fixed effect (RF) estimate is used.\n :param X: fixed effect covariates\n :param Z: random effect covariates\n :param clusters: cluster assignments for samples\n :return: y_hat, i.e. predictions\n '
if (self.trained_rf is None):
raise NotFittedError("This MERF instance is not fitted yet. Call 'fit' with appropriate arguments before using this method")
Z = np.array(Z)
y_hat = self.trained_rf.predict(X)
for cluster_id in self.cluster_counts.index:
indices_i = (clusters == cluster_id)
if (len(indices_i) == 0):
continue
b_i = self.trained_b.loc[cluster_id]
Z_i = Z[indices_i]
y_hat[indices_i] += Z_i.dot(b_i)
return y_hat<|docstring|>Predict using trained MERF. For known clusters the trained random effect correction is applied. For unknown
clusters the pure fixed effect (RF) estimate is used.
:param X: fixed effect covariates
:param Z: random effect covariates
:param clusters: cluster assignments for samples
:return: y_hat, i.e. predictions<|endoftext|>
|
eca3d416f4a26deda153098bd79f0da71a17b179e527ade27593ccf9aa388b9c
|
def fit(self, X, Z, clusters, y):
'\n Fit MERF using EM algorithm.\n :param X: fixed effect covariates\n :param Z: random effect covariates\n :param clusters: cluster assignments for samples\n :param y: response/target variable\n :return: fitted model\n '
assert (len(Z) == len(X))
assert (len(y) == len(X))
assert (len(clusters) == len(X))
n_clusters = clusters.nunique()
n_obs = len(y)
q = Z.shape[1]
Z = np.array(Z)
cluster_counts = clusters.value_counts()
Z_by_cluster = {}
y_by_cluster = {}
n_by_cluster = {}
I_by_cluster = {}
indices_by_cluster = {}
for cluster_id in cluster_counts.index:
indices_i = (clusters == cluster_id)
indices_by_cluster[cluster_id] = indices_i
Z_by_cluster[cluster_id] = Z[indices_i]
y_by_cluster[cluster_id] = y[indices_i]
n_by_cluster[cluster_id] = cluster_counts[cluster_id]
I_by_cluster[cluster_id] = np.eye(cluster_counts[cluster_id])
iteration = 0
b_hat_df = pd.DataFrame(np.zeros((n_clusters, q)), index=cluster_counts.index)
sigma2_hat = 1
D_hat = np.eye(q)
self.b_hat_history.append(b_hat_df)
self.sigma2_hat_history.append(sigma2_hat)
self.D_hat_history.append(D_hat)
early_stop_flag = False
while ((iteration < self.max_iterations) and (not early_stop_flag)):
iteration += 1
logger.debug('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
logger.debug('Iteration: {}'.format(iteration))
logger.debug('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
y_star = np.zeros(len(y))
for cluster_id in cluster_counts.index:
y_i = y_by_cluster[cluster_id]
Z_i = Z_by_cluster[cluster_id]
b_hat_i = b_hat_df.loc[cluster_id]
logger.debug('E-step, cluster {}, b_hat = {}'.format(cluster_id, b_hat_i))
indices_i = indices_by_cluster[cluster_id]
y_star_i = (y_i - Z_i.dot(b_hat_i))
y_star[indices_i] = y_star_i
assert (len(y_star.shape) == 1)
rf = RandomForestRegressor(**self.rf_params)
rf.fit(X, y_star)
f_hat = rf.oob_prediction_
sigma2_hat_sum = 0
D_hat_sum = 0
for cluster_id in cluster_counts.index:
indices_i = indices_by_cluster[cluster_id]
y_i = y_by_cluster[cluster_id]
Z_i = Z_by_cluster[cluster_id]
n_i = n_by_cluster[cluster_id]
I_i = I_by_cluster[cluster_id]
f_hat_i = f_hat[indices_i]
V_hat_i = (Z_i.dot(D_hat).dot(Z_i.T) + (sigma2_hat * I_i))
V_hat_inv_i = np.linalg.pinv(V_hat_i)
logger.debug('M-step, pre-update, cluster {}, b_hat = {}'.format(cluster_id, b_hat_df.loc[cluster_id]))
b_hat_i = D_hat.dot(Z_i.T).dot(V_hat_inv_i).dot((y_i - f_hat_i))
logger.debug('M-step, post-update, cluster {}, b_hat = {}'.format(cluster_id, b_hat_i))
eps_hat_i = ((y_i - f_hat_i) - Z_i.dot(b_hat_i))
logger.debug('------------------------------------------')
logger.debug('M-step, cluster {}'.format(cluster_id))
logger.debug('error squared for cluster = {}'.format(eps_hat_i.T.dot(eps_hat_i)))
b_hat_df.loc[(cluster_id, :)] = b_hat_i
logger.debug('M-step, post-update, recalled from db, cluster {}, b_hat = {}'.format(cluster_id, b_hat_df.loc[cluster_id]))
sigma2_hat_sum += (eps_hat_i.T.dot(eps_hat_i) + (sigma2_hat * (n_i - (sigma2_hat * np.trace(V_hat_inv_i)))))
D_hat_sum += (np.outer(b_hat_i, b_hat_i) + (D_hat - D_hat.dot(Z_i.T).dot(V_hat_inv_i).dot(Z_i).dot(D_hat)))
sigma2_hat = ((1.0 / n_obs) * sigma2_hat_sum)
D_hat = ((1.0 / n_clusters) * D_hat_sum)
logger.debug('b_hat = {}'.format(b_hat_df))
logger.debug('sigma2_hat = {}'.format(sigma2_hat))
logger.debug('D_hat = {}'.format(D_hat))
self.b_hat_history.append(b_hat_df.copy())
self.sigma2_hat_history.append(sigma2_hat)
self.D_hat_history.append(D_hat)
gll = 0
for cluster_id in cluster_counts.index:
indices_i = indices_by_cluster[cluster_id]
y_i = y_by_cluster[cluster_id]
Z_i = Z_by_cluster[cluster_id]
I_i = I_by_cluster[cluster_id]
f_hat_i = f_hat[indices_i]
R_hat_i = (sigma2_hat * I_i)
b_hat_i = b_hat_df.loc[cluster_id]
(_, logdet_D_hat) = np.linalg.slogdet(D_hat)
(_, logdet_R_hat_i) = np.linalg.slogdet(R_hat_i)
gll += (((((y_i - f_hat_i) - Z_i.dot(b_hat_i)).T.dot(np.linalg.pinv(R_hat_i)).dot(((y_i - f_hat_i) - Z_i.dot(b_hat_i))) + b_hat_i.T.dot(np.linalg.pinv(D_hat)).dot(b_hat_i)) + logdet_D_hat) + logdet_R_hat_i)
logger.info('GLL is {} at iteration {}.'.format(gll, iteration))
self.gll_history.append(gll)
if ((self.gll_early_stop_threshold is not None) and (len(self.gll_history) > 1)):
curr_threshold = np.abs(((gll - self.gll_history[(- 2)]) / self.gll_history[(- 2)]))
logger.debug('stop threshold = {}'.format(curr_threshold))
if (curr_threshold < self.gll_early_stop_threshold):
logger.info('Gll {} less than threshold {}, stopping early ...'.format(gll, curr_threshold))
early_stop_flag = True
self.cluster_counts = cluster_counts
self.trained_rf = rf
self.trained_b = b_hat_df
return self
|
Fit MERF using EM algorithm.
:param X: fixed effect covariates
:param Z: random effect covariates
:param clusters: cluster assignments for samples
:param y: response/target variable
:return: fitted model
|
merf/merf.py
|
fit
|
ittegrat/merf
| 0
|
python
|
def fit(self, X, Z, clusters, y):
'\n Fit MERF using EM algorithm.\n :param X: fixed effect covariates\n :param Z: random effect covariates\n :param clusters: cluster assignments for samples\n :param y: response/target variable\n :return: fitted model\n '
assert (len(Z) == len(X))
assert (len(y) == len(X))
assert (len(clusters) == len(X))
n_clusters = clusters.nunique()
n_obs = len(y)
q = Z.shape[1]
Z = np.array(Z)
cluster_counts = clusters.value_counts()
Z_by_cluster = {}
y_by_cluster = {}
n_by_cluster = {}
I_by_cluster = {}
indices_by_cluster = {}
for cluster_id in cluster_counts.index:
indices_i = (clusters == cluster_id)
indices_by_cluster[cluster_id] = indices_i
Z_by_cluster[cluster_id] = Z[indices_i]
y_by_cluster[cluster_id] = y[indices_i]
n_by_cluster[cluster_id] = cluster_counts[cluster_id]
I_by_cluster[cluster_id] = np.eye(cluster_counts[cluster_id])
iteration = 0
b_hat_df = pd.DataFrame(np.zeros((n_clusters, q)), index=cluster_counts.index)
sigma2_hat = 1
D_hat = np.eye(q)
self.b_hat_history.append(b_hat_df)
self.sigma2_hat_history.append(sigma2_hat)
self.D_hat_history.append(D_hat)
early_stop_flag = False
while ((iteration < self.max_iterations) and (not early_stop_flag)):
iteration += 1
logger.debug('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
logger.debug('Iteration: {}'.format(iteration))
logger.debug('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
y_star = np.zeros(len(y))
for cluster_id in cluster_counts.index:
y_i = y_by_cluster[cluster_id]
Z_i = Z_by_cluster[cluster_id]
b_hat_i = b_hat_df.loc[cluster_id]
logger.debug('E-step, cluster {}, b_hat = {}'.format(cluster_id, b_hat_i))
indices_i = indices_by_cluster[cluster_id]
y_star_i = (y_i - Z_i.dot(b_hat_i))
y_star[indices_i] = y_star_i
assert (len(y_star.shape) == 1)
rf = RandomForestRegressor(**self.rf_params)
rf.fit(X, y_star)
f_hat = rf.oob_prediction_
sigma2_hat_sum = 0
D_hat_sum = 0
for cluster_id in cluster_counts.index:
indices_i = indices_by_cluster[cluster_id]
y_i = y_by_cluster[cluster_id]
Z_i = Z_by_cluster[cluster_id]
n_i = n_by_cluster[cluster_id]
I_i = I_by_cluster[cluster_id]
f_hat_i = f_hat[indices_i]
V_hat_i = (Z_i.dot(D_hat).dot(Z_i.T) + (sigma2_hat * I_i))
V_hat_inv_i = np.linalg.pinv(V_hat_i)
logger.debug('M-step, pre-update, cluster {}, b_hat = {}'.format(cluster_id, b_hat_df.loc[cluster_id]))
b_hat_i = D_hat.dot(Z_i.T).dot(V_hat_inv_i).dot((y_i - f_hat_i))
logger.debug('M-step, post-update, cluster {}, b_hat = {}'.format(cluster_id, b_hat_i))
eps_hat_i = ((y_i - f_hat_i) - Z_i.dot(b_hat_i))
logger.debug('------------------------------------------')
logger.debug('M-step, cluster {}'.format(cluster_id))
logger.debug('error squared for cluster = {}'.format(eps_hat_i.T.dot(eps_hat_i)))
b_hat_df.loc[(cluster_id, :)] = b_hat_i
logger.debug('M-step, post-update, recalled from db, cluster {}, b_hat = {}'.format(cluster_id, b_hat_df.loc[cluster_id]))
sigma2_hat_sum += (eps_hat_i.T.dot(eps_hat_i) + (sigma2_hat * (n_i - (sigma2_hat * np.trace(V_hat_inv_i)))))
D_hat_sum += (np.outer(b_hat_i, b_hat_i) + (D_hat - D_hat.dot(Z_i.T).dot(V_hat_inv_i).dot(Z_i).dot(D_hat)))
sigma2_hat = ((1.0 / n_obs) * sigma2_hat_sum)
D_hat = ((1.0 / n_clusters) * D_hat_sum)
logger.debug('b_hat = {}'.format(b_hat_df))
logger.debug('sigma2_hat = {}'.format(sigma2_hat))
logger.debug('D_hat = {}'.format(D_hat))
self.b_hat_history.append(b_hat_df.copy())
self.sigma2_hat_history.append(sigma2_hat)
self.D_hat_history.append(D_hat)
gll = 0
for cluster_id in cluster_counts.index:
indices_i = indices_by_cluster[cluster_id]
y_i = y_by_cluster[cluster_id]
Z_i = Z_by_cluster[cluster_id]
I_i = I_by_cluster[cluster_id]
f_hat_i = f_hat[indices_i]
R_hat_i = (sigma2_hat * I_i)
b_hat_i = b_hat_df.loc[cluster_id]
(_, logdet_D_hat) = np.linalg.slogdet(D_hat)
(_, logdet_R_hat_i) = np.linalg.slogdet(R_hat_i)
gll += (((((y_i - f_hat_i) - Z_i.dot(b_hat_i)).T.dot(np.linalg.pinv(R_hat_i)).dot(((y_i - f_hat_i) - Z_i.dot(b_hat_i))) + b_hat_i.T.dot(np.linalg.pinv(D_hat)).dot(b_hat_i)) + logdet_D_hat) + logdet_R_hat_i)
logger.info('GLL is {} at iteration {}.'.format(gll, iteration))
self.gll_history.append(gll)
if ((self.gll_early_stop_threshold is not None) and (len(self.gll_history) > 1)):
curr_threshold = np.abs(((gll - self.gll_history[(- 2)]) / self.gll_history[(- 2)]))
logger.debug('stop threshold = {}'.format(curr_threshold))
if (curr_threshold < self.gll_early_stop_threshold):
logger.info('Gll {} less than threshold {}, stopping early ...'.format(gll, curr_threshold))
early_stop_flag = True
self.cluster_counts = cluster_counts
self.trained_rf = rf
self.trained_b = b_hat_df
return self
|
def fit(self, X, Z, clusters, y):
'\n Fit MERF using EM algorithm.\n :param X: fixed effect covariates\n :param Z: random effect covariates\n :param clusters: cluster assignments for samples\n :param y: response/target variable\n :return: fitted model\n '
assert (len(Z) == len(X))
assert (len(y) == len(X))
assert (len(clusters) == len(X))
n_clusters = clusters.nunique()
n_obs = len(y)
q = Z.shape[1]
Z = np.array(Z)
cluster_counts = clusters.value_counts()
Z_by_cluster = {}
y_by_cluster = {}
n_by_cluster = {}
I_by_cluster = {}
indices_by_cluster = {}
for cluster_id in cluster_counts.index:
indices_i = (clusters == cluster_id)
indices_by_cluster[cluster_id] = indices_i
Z_by_cluster[cluster_id] = Z[indices_i]
y_by_cluster[cluster_id] = y[indices_i]
n_by_cluster[cluster_id] = cluster_counts[cluster_id]
I_by_cluster[cluster_id] = np.eye(cluster_counts[cluster_id])
iteration = 0
b_hat_df = pd.DataFrame(np.zeros((n_clusters, q)), index=cluster_counts.index)
sigma2_hat = 1
D_hat = np.eye(q)
self.b_hat_history.append(b_hat_df)
self.sigma2_hat_history.append(sigma2_hat)
self.D_hat_history.append(D_hat)
early_stop_flag = False
while ((iteration < self.max_iterations) and (not early_stop_flag)):
iteration += 1
logger.debug('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
logger.debug('Iteration: {}'.format(iteration))
logger.debug('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
y_star = np.zeros(len(y))
for cluster_id in cluster_counts.index:
y_i = y_by_cluster[cluster_id]
Z_i = Z_by_cluster[cluster_id]
b_hat_i = b_hat_df.loc[cluster_id]
logger.debug('E-step, cluster {}, b_hat = {}'.format(cluster_id, b_hat_i))
indices_i = indices_by_cluster[cluster_id]
y_star_i = (y_i - Z_i.dot(b_hat_i))
y_star[indices_i] = y_star_i
assert (len(y_star.shape) == 1)
rf = RandomForestRegressor(**self.rf_params)
rf.fit(X, y_star)
f_hat = rf.oob_prediction_
sigma2_hat_sum = 0
D_hat_sum = 0
for cluster_id in cluster_counts.index:
indices_i = indices_by_cluster[cluster_id]
y_i = y_by_cluster[cluster_id]
Z_i = Z_by_cluster[cluster_id]
n_i = n_by_cluster[cluster_id]
I_i = I_by_cluster[cluster_id]
f_hat_i = f_hat[indices_i]
V_hat_i = (Z_i.dot(D_hat).dot(Z_i.T) + (sigma2_hat * I_i))
V_hat_inv_i = np.linalg.pinv(V_hat_i)
logger.debug('M-step, pre-update, cluster {}, b_hat = {}'.format(cluster_id, b_hat_df.loc[cluster_id]))
b_hat_i = D_hat.dot(Z_i.T).dot(V_hat_inv_i).dot((y_i - f_hat_i))
logger.debug('M-step, post-update, cluster {}, b_hat = {}'.format(cluster_id, b_hat_i))
eps_hat_i = ((y_i - f_hat_i) - Z_i.dot(b_hat_i))
logger.debug('------------------------------------------')
logger.debug('M-step, cluster {}'.format(cluster_id))
logger.debug('error squared for cluster = {}'.format(eps_hat_i.T.dot(eps_hat_i)))
b_hat_df.loc[(cluster_id, :)] = b_hat_i
logger.debug('M-step, post-update, recalled from db, cluster {}, b_hat = {}'.format(cluster_id, b_hat_df.loc[cluster_id]))
sigma2_hat_sum += (eps_hat_i.T.dot(eps_hat_i) + (sigma2_hat * (n_i - (sigma2_hat * np.trace(V_hat_inv_i)))))
D_hat_sum += (np.outer(b_hat_i, b_hat_i) + (D_hat - D_hat.dot(Z_i.T).dot(V_hat_inv_i).dot(Z_i).dot(D_hat)))
sigma2_hat = ((1.0 / n_obs) * sigma2_hat_sum)
D_hat = ((1.0 / n_clusters) * D_hat_sum)
logger.debug('b_hat = {}'.format(b_hat_df))
logger.debug('sigma2_hat = {}'.format(sigma2_hat))
logger.debug('D_hat = {}'.format(D_hat))
self.b_hat_history.append(b_hat_df.copy())
self.sigma2_hat_history.append(sigma2_hat)
self.D_hat_history.append(D_hat)
gll = 0
for cluster_id in cluster_counts.index:
indices_i = indices_by_cluster[cluster_id]
y_i = y_by_cluster[cluster_id]
Z_i = Z_by_cluster[cluster_id]
I_i = I_by_cluster[cluster_id]
f_hat_i = f_hat[indices_i]
R_hat_i = (sigma2_hat * I_i)
b_hat_i = b_hat_df.loc[cluster_id]
(_, logdet_D_hat) = np.linalg.slogdet(D_hat)
(_, logdet_R_hat_i) = np.linalg.slogdet(R_hat_i)
gll += (((((y_i - f_hat_i) - Z_i.dot(b_hat_i)).T.dot(np.linalg.pinv(R_hat_i)).dot(((y_i - f_hat_i) - Z_i.dot(b_hat_i))) + b_hat_i.T.dot(np.linalg.pinv(D_hat)).dot(b_hat_i)) + logdet_D_hat) + logdet_R_hat_i)
logger.info('GLL is {} at iteration {}.'.format(gll, iteration))
self.gll_history.append(gll)
if ((self.gll_early_stop_threshold is not None) and (len(self.gll_history) > 1)):
curr_threshold = np.abs(((gll - self.gll_history[(- 2)]) / self.gll_history[(- 2)]))
logger.debug('stop threshold = {}'.format(curr_threshold))
if (curr_threshold < self.gll_early_stop_threshold):
logger.info('Gll {} less than threshold {}, stopping early ...'.format(gll, curr_threshold))
early_stop_flag = True
self.cluster_counts = cluster_counts
self.trained_rf = rf
self.trained_b = b_hat_df
return self<|docstring|>Fit MERF using EM algorithm.
:param X: fixed effect covariates
:param Z: random effect covariates
:param clusters: cluster assignments for samples
:param y: response/target variable
:return: fitted model<|endoftext|>
|
d3a7515af18abb1a765fa21305538694dc12dc79a0fd4c98724d5b04a27a794f
|
def __init__(__self__, resource_name, opts=None, description=None, rest_api=None, stage_description=None, stage_name=None, triggers=None, variables=None, __props__=None, __name__=None, __opts__=None):
'\n Provides an API Gateway REST Deployment.\n\n > **Note:** This resource depends on having at least one `apigateway.Integration` created in the REST API, which\n itself has other dependencies. To avoid race conditions when all resources are being created together, you need to add\n implicit resource references via the `triggers` argument or explicit resource references using the\n [resource `dependsOn` meta-argument](https://www.pulumi.com/docs/intro/concepts/programming-model/#dependson).\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_aws as aws\n\n my_demo_api = aws.apigateway.RestApi("myDemoAPI", description="This is my API for demonstration purposes")\n my_demo_resource = aws.apigateway.Resource("myDemoResource",\n rest_api=my_demo_api.id,\n parent_id=my_demo_api.root_resource_id,\n path_part="test")\n my_demo_method = aws.apigateway.Method("myDemoMethod",\n rest_api=my_demo_api.id,\n resource_id=my_demo_resource.id,\n http_method="GET",\n authorization="NONE")\n my_demo_integration = aws.apigateway.Integration("myDemoIntegration",\n rest_api=my_demo_api.id,\n resource_id=my_demo_resource.id,\n http_method=my_demo_method.http_method,\n type="MOCK")\n my_demo_deployment = aws.apigateway.Deployment("myDemoDeployment",\n rest_api=my_demo_api.id,\n stage_name="test",\n variables={\n "answer": "42",\n },\n opts=ResourceOptions(depends_on=[my_demo_integration]))\n ```\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] description: The description of the deployment\n :param pulumi.Input[dict] rest_api: The ID of the associated REST API\n :param pulumi.Input[str] stage_description: The description of the stage\n :param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.\n :param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.\n :param pulumi.Input[dict] variables: A map that defines variables for the stage\n '
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['description'] = description
if (rest_api is None):
raise TypeError("Missing required property 'rest_api'")
__props__['rest_api'] = rest_api
__props__['stage_description'] = stage_description
__props__['stage_name'] = stage_name
__props__['triggers'] = triggers
__props__['variables'] = variables
__props__['created_date'] = None
__props__['execution_arn'] = None
__props__['invoke_url'] = None
super(Deployment, __self__).__init__('aws:apigateway/deployment:Deployment', resource_name, __props__, opts)
|
Provides an API Gateway REST Deployment.
> **Note:** This resource depends on having at least one `apigateway.Integration` created in the REST API, which
itself has other dependencies. To avoid race conditions when all resources are being created together, you need to add
implicit resource references via the `triggers` argument or explicit resource references using the
[resource `dependsOn` meta-argument](https://www.pulumi.com/docs/intro/concepts/programming-model/#dependson).
## Example Usage
```python
import pulumi
import pulumi_aws as aws
my_demo_api = aws.apigateway.RestApi("myDemoAPI", description="This is my API for demonstration purposes")
my_demo_resource = aws.apigateway.Resource("myDemoResource",
rest_api=my_demo_api.id,
parent_id=my_demo_api.root_resource_id,
path_part="test")
my_demo_method = aws.apigateway.Method("myDemoMethod",
rest_api=my_demo_api.id,
resource_id=my_demo_resource.id,
http_method="GET",
authorization="NONE")
my_demo_integration = aws.apigateway.Integration("myDemoIntegration",
rest_api=my_demo_api.id,
resource_id=my_demo_resource.id,
http_method=my_demo_method.http_method,
type="MOCK")
my_demo_deployment = aws.apigateway.Deployment("myDemoDeployment",
rest_api=my_demo_api.id,
stage_name="test",
variables={
"answer": "42",
},
opts=ResourceOptions(depends_on=[my_demo_integration]))
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] description: The description of the deployment
:param pulumi.Input[dict] rest_api: The ID of the associated REST API
:param pulumi.Input[str] stage_description: The description of the stage
:param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.
:param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.
:param pulumi.Input[dict] variables: A map that defines variables for the stage
|
sdk/python/pulumi_aws/apigateway/deployment.py
|
__init__
|
michael-golden/pulumi-aws
| 0
|
python
|
def __init__(__self__, resource_name, opts=None, description=None, rest_api=None, stage_description=None, stage_name=None, triggers=None, variables=None, __props__=None, __name__=None, __opts__=None):
'\n Provides an API Gateway REST Deployment.\n\n > **Note:** This resource depends on having at least one `apigateway.Integration` created in the REST API, which\n itself has other dependencies. To avoid race conditions when all resources are being created together, you need to add\n implicit resource references via the `triggers` argument or explicit resource references using the\n [resource `dependsOn` meta-argument](https://www.pulumi.com/docs/intro/concepts/programming-model/#dependson).\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_aws as aws\n\n my_demo_api = aws.apigateway.RestApi("myDemoAPI", description="This is my API for demonstration purposes")\n my_demo_resource = aws.apigateway.Resource("myDemoResource",\n rest_api=my_demo_api.id,\n parent_id=my_demo_api.root_resource_id,\n path_part="test")\n my_demo_method = aws.apigateway.Method("myDemoMethod",\n rest_api=my_demo_api.id,\n resource_id=my_demo_resource.id,\n http_method="GET",\n authorization="NONE")\n my_demo_integration = aws.apigateway.Integration("myDemoIntegration",\n rest_api=my_demo_api.id,\n resource_id=my_demo_resource.id,\n http_method=my_demo_method.http_method,\n type="MOCK")\n my_demo_deployment = aws.apigateway.Deployment("myDemoDeployment",\n rest_api=my_demo_api.id,\n stage_name="test",\n variables={\n "answer": "42",\n },\n opts=ResourceOptions(depends_on=[my_demo_integration]))\n ```\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] description: The description of the deployment\n :param pulumi.Input[dict] rest_api: The ID of the associated REST API\n :param pulumi.Input[str] stage_description: The description of the stage\n :param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.\n :param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.\n :param pulumi.Input[dict] variables: A map that defines variables for the stage\n '
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['description'] = description
if (rest_api is None):
raise TypeError("Missing required property 'rest_api'")
__props__['rest_api'] = rest_api
__props__['stage_description'] = stage_description
__props__['stage_name'] = stage_name
__props__['triggers'] = triggers
__props__['variables'] = variables
__props__['created_date'] = None
__props__['execution_arn'] = None
__props__['invoke_url'] = None
super(Deployment, __self__).__init__('aws:apigateway/deployment:Deployment', resource_name, __props__, opts)
|
def __init__(__self__, resource_name, opts=None, description=None, rest_api=None, stage_description=None, stage_name=None, triggers=None, variables=None, __props__=None, __name__=None, __opts__=None):
'\n Provides an API Gateway REST Deployment.\n\n > **Note:** This resource depends on having at least one `apigateway.Integration` created in the REST API, which\n itself has other dependencies. To avoid race conditions when all resources are being created together, you need to add\n implicit resource references via the `triggers` argument or explicit resource references using the\n [resource `dependsOn` meta-argument](https://www.pulumi.com/docs/intro/concepts/programming-model/#dependson).\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_aws as aws\n\n my_demo_api = aws.apigateway.RestApi("myDemoAPI", description="This is my API for demonstration purposes")\n my_demo_resource = aws.apigateway.Resource("myDemoResource",\n rest_api=my_demo_api.id,\n parent_id=my_demo_api.root_resource_id,\n path_part="test")\n my_demo_method = aws.apigateway.Method("myDemoMethod",\n rest_api=my_demo_api.id,\n resource_id=my_demo_resource.id,\n http_method="GET",\n authorization="NONE")\n my_demo_integration = aws.apigateway.Integration("myDemoIntegration",\n rest_api=my_demo_api.id,\n resource_id=my_demo_resource.id,\n http_method=my_demo_method.http_method,\n type="MOCK")\n my_demo_deployment = aws.apigateway.Deployment("myDemoDeployment",\n rest_api=my_demo_api.id,\n stage_name="test",\n variables={\n "answer": "42",\n },\n opts=ResourceOptions(depends_on=[my_demo_integration]))\n ```\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] description: The description of the deployment\n :param pulumi.Input[dict] rest_api: The ID of the associated REST API\n :param pulumi.Input[str] stage_description: The description of the stage\n :param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.\n :param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.\n :param pulumi.Input[dict] variables: A map that defines variables for the stage\n '
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['description'] = description
if (rest_api is None):
raise TypeError("Missing required property 'rest_api'")
__props__['rest_api'] = rest_api
__props__['stage_description'] = stage_description
__props__['stage_name'] = stage_name
__props__['triggers'] = triggers
__props__['variables'] = variables
__props__['created_date'] = None
__props__['execution_arn'] = None
__props__['invoke_url'] = None
super(Deployment, __self__).__init__('aws:apigateway/deployment:Deployment', resource_name, __props__, opts)<|docstring|>Provides an API Gateway REST Deployment.
> **Note:** This resource depends on having at least one `apigateway.Integration` created in the REST API, which
itself has other dependencies. To avoid race conditions when all resources are being created together, you need to add
implicit resource references via the `triggers` argument or explicit resource references using the
[resource `dependsOn` meta-argument](https://www.pulumi.com/docs/intro/concepts/programming-model/#dependson).
## Example Usage
```python
import pulumi
import pulumi_aws as aws
my_demo_api = aws.apigateway.RestApi("myDemoAPI", description="This is my API for demonstration purposes")
my_demo_resource = aws.apigateway.Resource("myDemoResource",
rest_api=my_demo_api.id,
parent_id=my_demo_api.root_resource_id,
path_part="test")
my_demo_method = aws.apigateway.Method("myDemoMethod",
rest_api=my_demo_api.id,
resource_id=my_demo_resource.id,
http_method="GET",
authorization="NONE")
my_demo_integration = aws.apigateway.Integration("myDemoIntegration",
rest_api=my_demo_api.id,
resource_id=my_demo_resource.id,
http_method=my_demo_method.http_method,
type="MOCK")
my_demo_deployment = aws.apigateway.Deployment("myDemoDeployment",
rest_api=my_demo_api.id,
stage_name="test",
variables={
"answer": "42",
},
opts=ResourceOptions(depends_on=[my_demo_integration]))
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] description: The description of the deployment
:param pulumi.Input[dict] rest_api: The ID of the associated REST API
:param pulumi.Input[str] stage_description: The description of the stage
:param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.
:param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.
:param pulumi.Input[dict] variables: A map that defines variables for the stage<|endoftext|>
|
104808536667a00e8471005f5407d723e883d283b4d88cb2a6b6cd9b3089e9bd
|
@staticmethod
def get(resource_name, id, opts=None, created_date=None, description=None, execution_arn=None, invoke_url=None, rest_api=None, stage_description=None, stage_name=None, triggers=None, variables=None):
"\n Get an existing Deployment resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param str id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] created_date: The creation date of the deployment\n :param pulumi.Input[str] description: The description of the deployment\n :param pulumi.Input[str] execution_arn: The execution ARN to be used in `lambda_permission` resource's `source_arn`\n when allowing API Gateway to invoke a Lambda function,\n e.g. `arn:aws:execute-api:eu-west-2:123456789012:z4675bid1j/prod`\n :param pulumi.Input[str] invoke_url: The URL to invoke the API pointing to the stage,\n e.g. `https://z4675bid1j.execute-api.eu-west-2.amazonaws.com/prod`\n :param pulumi.Input[dict] rest_api: The ID of the associated REST API\n :param pulumi.Input[str] stage_description: The description of the stage\n :param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.\n :param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.\n :param pulumi.Input[dict] variables: A map that defines variables for the stage\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['created_date'] = created_date
__props__['description'] = description
__props__['execution_arn'] = execution_arn
__props__['invoke_url'] = invoke_url
__props__['rest_api'] = rest_api
__props__['stage_description'] = stage_description
__props__['stage_name'] = stage_name
__props__['triggers'] = triggers
__props__['variables'] = variables
return Deployment(resource_name, opts=opts, __props__=__props__)
|
Get an existing Deployment resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param str id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] created_date: The creation date of the deployment
:param pulumi.Input[str] description: The description of the deployment
:param pulumi.Input[str] execution_arn: The execution ARN to be used in `lambda_permission` resource's `source_arn`
when allowing API Gateway to invoke a Lambda function,
e.g. `arn:aws:execute-api:eu-west-2:123456789012:z4675bid1j/prod`
:param pulumi.Input[str] invoke_url: The URL to invoke the API pointing to the stage,
e.g. `https://z4675bid1j.execute-api.eu-west-2.amazonaws.com/prod`
:param pulumi.Input[dict] rest_api: The ID of the associated REST API
:param pulumi.Input[str] stage_description: The description of the stage
:param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.
:param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.
:param pulumi.Input[dict] variables: A map that defines variables for the stage
|
sdk/python/pulumi_aws/apigateway/deployment.py
|
get
|
michael-golden/pulumi-aws
| 0
|
python
|
@staticmethod
def get(resource_name, id, opts=None, created_date=None, description=None, execution_arn=None, invoke_url=None, rest_api=None, stage_description=None, stage_name=None, triggers=None, variables=None):
"\n Get an existing Deployment resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param str id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] created_date: The creation date of the deployment\n :param pulumi.Input[str] description: The description of the deployment\n :param pulumi.Input[str] execution_arn: The execution ARN to be used in `lambda_permission` resource's `source_arn`\n when allowing API Gateway to invoke a Lambda function,\n e.g. `arn:aws:execute-api:eu-west-2:123456789012:z4675bid1j/prod`\n :param pulumi.Input[str] invoke_url: The URL to invoke the API pointing to the stage,\n e.g. `https://z4675bid1j.execute-api.eu-west-2.amazonaws.com/prod`\n :param pulumi.Input[dict] rest_api: The ID of the associated REST API\n :param pulumi.Input[str] stage_description: The description of the stage\n :param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.\n :param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.\n :param pulumi.Input[dict] variables: A map that defines variables for the stage\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['created_date'] = created_date
__props__['description'] = description
__props__['execution_arn'] = execution_arn
__props__['invoke_url'] = invoke_url
__props__['rest_api'] = rest_api
__props__['stage_description'] = stage_description
__props__['stage_name'] = stage_name
__props__['triggers'] = triggers
__props__['variables'] = variables
return Deployment(resource_name, opts=opts, __props__=__props__)
|
@staticmethod
def get(resource_name, id, opts=None, created_date=None, description=None, execution_arn=None, invoke_url=None, rest_api=None, stage_description=None, stage_name=None, triggers=None, variables=None):
"\n Get an existing Deployment resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param str id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] created_date: The creation date of the deployment\n :param pulumi.Input[str] description: The description of the deployment\n :param pulumi.Input[str] execution_arn: The execution ARN to be used in `lambda_permission` resource's `source_arn`\n when allowing API Gateway to invoke a Lambda function,\n e.g. `arn:aws:execute-api:eu-west-2:123456789012:z4675bid1j/prod`\n :param pulumi.Input[str] invoke_url: The URL to invoke the API pointing to the stage,\n e.g. `https://z4675bid1j.execute-api.eu-west-2.amazonaws.com/prod`\n :param pulumi.Input[dict] rest_api: The ID of the associated REST API\n :param pulumi.Input[str] stage_description: The description of the stage\n :param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.\n :param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.\n :param pulumi.Input[dict] variables: A map that defines variables for the stage\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['created_date'] = created_date
__props__['description'] = description
__props__['execution_arn'] = execution_arn
__props__['invoke_url'] = invoke_url
__props__['rest_api'] = rest_api
__props__['stage_description'] = stage_description
__props__['stage_name'] = stage_name
__props__['triggers'] = triggers
__props__['variables'] = variables
return Deployment(resource_name, opts=opts, __props__=__props__)<|docstring|>Get an existing Deployment resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param str id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] created_date: The creation date of the deployment
:param pulumi.Input[str] description: The description of the deployment
:param pulumi.Input[str] execution_arn: The execution ARN to be used in `lambda_permission` resource's `source_arn`
when allowing API Gateway to invoke a Lambda function,
e.g. `arn:aws:execute-api:eu-west-2:123456789012:z4675bid1j/prod`
:param pulumi.Input[str] invoke_url: The URL to invoke the API pointing to the stage,
e.g. `https://z4675bid1j.execute-api.eu-west-2.amazonaws.com/prod`
:param pulumi.Input[dict] rest_api: The ID of the associated REST API
:param pulumi.Input[str] stage_description: The description of the stage
:param pulumi.Input[str] stage_name: The name of the stage. If the specified stage already exists, it will be updated to point to the new deployment. If the stage does not exist, a new one will be created and point to this deployment.
:param pulumi.Input[dict] triggers: A map of arbitrary keys and values that, when changed, will trigger a redeployment.
:param pulumi.Input[dict] variables: A map that defines variables for the stage<|endoftext|>
|
5b106c1142390c6de90f111ac28498aaf497136c7ef9c23fbfc8d86634a40d05
|
def main(config):
'Read shapes, plot map\n '
data_path = config['paths']['data']
output_file = os.path.join(config['paths']['figures'], 'network-road-map.png')
road_edge_file_national = os.path.join(data_path, 'network', 'road_edges_national.shp')
road_edge_file_provincial = os.path.join(data_path, 'network', 'road_edges_provincial.shp')
proj_lat_lon = ccrs.PlateCarree()
ax = get_axes()
plot_basemap(ax, data_path)
scale_bar(ax, location=(0.8, 0.05))
plot_basemap_labels(ax, data_path, include_regions=False)
colors = {'National': '#ba0f03', 'Provincial': '#e0881f'}
edges_provincial = geopandas.read_file(road_edge_file_provincial)
ax.add_geometries(list(edges_provincial.geometry), crs=proj_lat_lon, linewidth=1.25, edgecolor=colors['Provincial'], facecolor='none', zorder=4)
edges_national = geopandas.read_file(road_edge_file_national)
ax.add_geometries(list(edges_national.geometry), crs=proj_lat_lon, linewidth=1.25, edgecolor=colors['National'], facecolor='none', zorder=5)
legend_handles = [mpatches.Patch(color=color, label=label) for (label, color) in colors.items()]
plt.legend(handles=legend_handles, loc='lower left')
save_fig(output_file)
|
Read shapes, plot map
|
src/atra/plot/network_road.py
|
main
|
oi-analytics/argentina-transport
| 1
|
python
|
def main(config):
'\n '
data_path = config['paths']['data']
output_file = os.path.join(config['paths']['figures'], 'network-road-map.png')
road_edge_file_national = os.path.join(data_path, 'network', 'road_edges_national.shp')
road_edge_file_provincial = os.path.join(data_path, 'network', 'road_edges_provincial.shp')
proj_lat_lon = ccrs.PlateCarree()
ax = get_axes()
plot_basemap(ax, data_path)
scale_bar(ax, location=(0.8, 0.05))
plot_basemap_labels(ax, data_path, include_regions=False)
colors = {'National': '#ba0f03', 'Provincial': '#e0881f'}
edges_provincial = geopandas.read_file(road_edge_file_provincial)
ax.add_geometries(list(edges_provincial.geometry), crs=proj_lat_lon, linewidth=1.25, edgecolor=colors['Provincial'], facecolor='none', zorder=4)
edges_national = geopandas.read_file(road_edge_file_national)
ax.add_geometries(list(edges_national.geometry), crs=proj_lat_lon, linewidth=1.25, edgecolor=colors['National'], facecolor='none', zorder=5)
legend_handles = [mpatches.Patch(color=color, label=label) for (label, color) in colors.items()]
plt.legend(handles=legend_handles, loc='lower left')
save_fig(output_file)
|
def main(config):
'\n '
data_path = config['paths']['data']
output_file = os.path.join(config['paths']['figures'], 'network-road-map.png')
road_edge_file_national = os.path.join(data_path, 'network', 'road_edges_national.shp')
road_edge_file_provincial = os.path.join(data_path, 'network', 'road_edges_provincial.shp')
proj_lat_lon = ccrs.PlateCarree()
ax = get_axes()
plot_basemap(ax, data_path)
scale_bar(ax, location=(0.8, 0.05))
plot_basemap_labels(ax, data_path, include_regions=False)
colors = {'National': '#ba0f03', 'Provincial': '#e0881f'}
edges_provincial = geopandas.read_file(road_edge_file_provincial)
ax.add_geometries(list(edges_provincial.geometry), crs=proj_lat_lon, linewidth=1.25, edgecolor=colors['Provincial'], facecolor='none', zorder=4)
edges_national = geopandas.read_file(road_edge_file_national)
ax.add_geometries(list(edges_national.geometry), crs=proj_lat_lon, linewidth=1.25, edgecolor=colors['National'], facecolor='none', zorder=5)
legend_handles = [mpatches.Patch(color=color, label=label) for (label, color) in colors.items()]
plt.legend(handles=legend_handles, loc='lower left')
save_fig(output_file)<|docstring|>Read shapes, plot map<|endoftext|>
|
441f043dd5fd2217b56864f855a04f442a14c78d3abe34e4124ce2404e6efc6d
|
@property
def should_save(self):
'True if the session should be saved. By default this is only\n true for :attr:`modified` cookies, not :attr:`new`.\n '
return self.modified
|
True if the session should be saved. By default this is only
true for :attr:`modified` cookies, not :attr:`new`.
|
jam/third_party/werkzeug/secure_cookie/securecookie.py
|
should_save
|
platipusica/AssetInventoryMSAccess
| 384
|
python
|
@property
def should_save(self):
'True if the session should be saved. By default this is only\n true for :attr:`modified` cookies, not :attr:`new`.\n '
return self.modified
|
@property
def should_save(self):
'True if the session should be saved. By default this is only\n true for :attr:`modified` cookies, not :attr:`new`.\n '
return self.modified<|docstring|>True if the session should be saved. By default this is only
true for :attr:`modified` cookies, not :attr:`new`.<|endoftext|>
|
a8791fa674c7f8ae62ea8dd45628f70dbb130509ce13d0b523075f7966cdfcc7
|
@classmethod
def quote(cls, value):
'Quote the value for the cookie. This can be any object\n supported by :attr:`serialization_method`.\n\n :param value: The value to quote.\n '
if (cls.serialization_method is not None):
value = cls.serialization_method.dumps(value)
if cls.quote_base64:
value = b''.join(base64.b64encode(to_bytes(value, 'utf8')).splitlines()).strip()
return value
|
Quote the value for the cookie. This can be any object
supported by :attr:`serialization_method`.
:param value: The value to quote.
|
jam/third_party/werkzeug/secure_cookie/securecookie.py
|
quote
|
platipusica/AssetInventoryMSAccess
| 384
|
python
|
@classmethod
def quote(cls, value):
'Quote the value for the cookie. This can be any object\n supported by :attr:`serialization_method`.\n\n :param value: The value to quote.\n '
if (cls.serialization_method is not None):
value = cls.serialization_method.dumps(value)
if cls.quote_base64:
value = b.join(base64.b64encode(to_bytes(value, 'utf8')).splitlines()).strip()
return value
|
@classmethod
def quote(cls, value):
'Quote the value for the cookie. This can be any object\n supported by :attr:`serialization_method`.\n\n :param value: The value to quote.\n '
if (cls.serialization_method is not None):
value = cls.serialization_method.dumps(value)
if cls.quote_base64:
value = b.join(base64.b64encode(to_bytes(value, 'utf8')).splitlines()).strip()
return value<|docstring|>Quote the value for the cookie. This can be any object
supported by :attr:`serialization_method`.
:param value: The value to quote.<|endoftext|>
|
96ba152f8942ced5b912220251f0f41e06d4c5fcfac5cc574c7bb7cce7a3f7dd
|
@classmethod
def unquote(cls, value):
'Unquote the value for the cookie. If unquoting does not work\n a :exc:`UnquoteError` is raised.\n\n :param value: The value to unquote.\n '
try:
if cls.quote_base64:
value = base64.b64decode(value)
if (cls.serialization_method is not None):
value = cls.serialization_method.loads(value)
return value
except Exception:
raise UnquoteError()
|
Unquote the value for the cookie. If unquoting does not work
a :exc:`UnquoteError` is raised.
:param value: The value to unquote.
|
jam/third_party/werkzeug/secure_cookie/securecookie.py
|
unquote
|
platipusica/AssetInventoryMSAccess
| 384
|
python
|
@classmethod
def unquote(cls, value):
'Unquote the value for the cookie. If unquoting does not work\n a :exc:`UnquoteError` is raised.\n\n :param value: The value to unquote.\n '
try:
if cls.quote_base64:
value = base64.b64decode(value)
if (cls.serialization_method is not None):
value = cls.serialization_method.loads(value)
return value
except Exception:
raise UnquoteError()
|
@classmethod
def unquote(cls, value):
'Unquote the value for the cookie. If unquoting does not work\n a :exc:`UnquoteError` is raised.\n\n :param value: The value to unquote.\n '
try:
if cls.quote_base64:
value = base64.b64decode(value)
if (cls.serialization_method is not None):
value = cls.serialization_method.loads(value)
return value
except Exception:
raise UnquoteError()<|docstring|>Unquote the value for the cookie. If unquoting does not work
a :exc:`UnquoteError` is raised.
:param value: The value to unquote.<|endoftext|>
|
d520b0ee173add3d8b9f2d77412dce55a95803781545d9e07a07530e16d72f6f
|
def serialize(self, expires=None):
'Serialize the secure cookie into a string.\n\n If expires is provided, the session will be automatically\n invalidated after expiration when you unseralize it. This\n provides better protection against session cookie theft.\n\n :param expires: An optional expiration date for the cookie (a\n :class:`datetime.datetime` object).\n '
if (self.secret_key is None):
raise RuntimeError('no secret key defined')
if expires:
self['_expires'] = _date_to_unix(expires)
result = []
mac = hmac(self.secret_key, None, self.hash_method)
for (key, value) in sorted(self.items()):
result.append(('%s=%s' % (url_quote_plus(key), self.quote(value).decode('ascii'))).encode('ascii'))
mac.update((b'|' + result[(- 1)]))
return b'?'.join([base64.b64encode(mac.digest()).strip(), b'&'.join(result)])
|
Serialize the secure cookie into a string.
If expires is provided, the session will be automatically
invalidated after expiration when you unseralize it. This
provides better protection against session cookie theft.
:param expires: An optional expiration date for the cookie (a
:class:`datetime.datetime` object).
|
jam/third_party/werkzeug/secure_cookie/securecookie.py
|
serialize
|
platipusica/AssetInventoryMSAccess
| 384
|
python
|
def serialize(self, expires=None):
'Serialize the secure cookie into a string.\n\n If expires is provided, the session will be automatically\n invalidated after expiration when you unseralize it. This\n provides better protection against session cookie theft.\n\n :param expires: An optional expiration date for the cookie (a\n :class:`datetime.datetime` object).\n '
if (self.secret_key is None):
raise RuntimeError('no secret key defined')
if expires:
self['_expires'] = _date_to_unix(expires)
result = []
mac = hmac(self.secret_key, None, self.hash_method)
for (key, value) in sorted(self.items()):
result.append(('%s=%s' % (url_quote_plus(key), self.quote(value).decode('ascii'))).encode('ascii'))
mac.update((b'|' + result[(- 1)]))
return b'?'.join([base64.b64encode(mac.digest()).strip(), b'&'.join(result)])
|
def serialize(self, expires=None):
'Serialize the secure cookie into a string.\n\n If expires is provided, the session will be automatically\n invalidated after expiration when you unseralize it. This\n provides better protection against session cookie theft.\n\n :param expires: An optional expiration date for the cookie (a\n :class:`datetime.datetime` object).\n '
if (self.secret_key is None):
raise RuntimeError('no secret key defined')
if expires:
self['_expires'] = _date_to_unix(expires)
result = []
mac = hmac(self.secret_key, None, self.hash_method)
for (key, value) in sorted(self.items()):
result.append(('%s=%s' % (url_quote_plus(key), self.quote(value).decode('ascii'))).encode('ascii'))
mac.update((b'|' + result[(- 1)]))
return b'?'.join([base64.b64encode(mac.digest()).strip(), b'&'.join(result)])<|docstring|>Serialize the secure cookie into a string.
If expires is provided, the session will be automatically
invalidated after expiration when you unseralize it. This
provides better protection against session cookie theft.
:param expires: An optional expiration date for the cookie (a
:class:`datetime.datetime` object).<|endoftext|>
|
8b0ba986c27d0204519cd92aa232aaaa520e2acb15d752459367bb219abc704c
|
@classmethod
def unserialize(cls, string, secret_key):
'Load the secure cookie from a serialized string.\n\n :param string: The cookie value to unserialize.\n :param secret_key: The secret key used to serialize the cookie.\n :return: A new :class:`SecureCookie`.\n '
if isinstance(string, text_type):
string = string.encode('utf-8', 'replace')
if isinstance(secret_key, text_type):
secret_key = secret_key.encode('utf-8', 'replace')
try:
(base64_hash, data) = string.split(b'?', 1)
except (ValueError, IndexError):
items = ()
else:
items = {}
mac = hmac(secret_key, None, cls.hash_method)
for item in data.split(b'&'):
mac.update((b'|' + item))
if (b'=' not in item):
items = None
break
(key, value) = item.split(b'=', 1)
key = url_unquote_plus(key.decode('ascii'))
try:
key = to_native(key)
except UnicodeError:
pass
items[key] = value
try:
client_hash = base64.b64decode(base64_hash)
except TypeError:
items = client_hash = None
if ((items is not None) and safe_str_cmp(client_hash, mac.digest())):
try:
for (key, value) in iteritems(items):
items[key] = cls.unquote(value)
except UnquoteError:
items = ()
else:
if ('_expires' in items):
if (time() > items['_expires']):
items = ()
else:
del items['_expires']
else:
items = ()
return cls(items, secret_key, False)
|
Load the secure cookie from a serialized string.
:param string: The cookie value to unserialize.
:param secret_key: The secret key used to serialize the cookie.
:return: A new :class:`SecureCookie`.
|
jam/third_party/werkzeug/secure_cookie/securecookie.py
|
unserialize
|
platipusica/AssetInventoryMSAccess
| 384
|
python
|
@classmethod
def unserialize(cls, string, secret_key):
'Load the secure cookie from a serialized string.\n\n :param string: The cookie value to unserialize.\n :param secret_key: The secret key used to serialize the cookie.\n :return: A new :class:`SecureCookie`.\n '
if isinstance(string, text_type):
string = string.encode('utf-8', 'replace')
if isinstance(secret_key, text_type):
secret_key = secret_key.encode('utf-8', 'replace')
try:
(base64_hash, data) = string.split(b'?', 1)
except (ValueError, IndexError):
items = ()
else:
items = {}
mac = hmac(secret_key, None, cls.hash_method)
for item in data.split(b'&'):
mac.update((b'|' + item))
if (b'=' not in item):
items = None
break
(key, value) = item.split(b'=', 1)
key = url_unquote_plus(key.decode('ascii'))
try:
key = to_native(key)
except UnicodeError:
pass
items[key] = value
try:
client_hash = base64.b64decode(base64_hash)
except TypeError:
items = client_hash = None
if ((items is not None) and safe_str_cmp(client_hash, mac.digest())):
try:
for (key, value) in iteritems(items):
items[key] = cls.unquote(value)
except UnquoteError:
items = ()
else:
if ('_expires' in items):
if (time() > items['_expires']):
items = ()
else:
del items['_expires']
else:
items = ()
return cls(items, secret_key, False)
|
@classmethod
def unserialize(cls, string, secret_key):
'Load the secure cookie from a serialized string.\n\n :param string: The cookie value to unserialize.\n :param secret_key: The secret key used to serialize the cookie.\n :return: A new :class:`SecureCookie`.\n '
if isinstance(string, text_type):
string = string.encode('utf-8', 'replace')
if isinstance(secret_key, text_type):
secret_key = secret_key.encode('utf-8', 'replace')
try:
(base64_hash, data) = string.split(b'?', 1)
except (ValueError, IndexError):
items = ()
else:
items = {}
mac = hmac(secret_key, None, cls.hash_method)
for item in data.split(b'&'):
mac.update((b'|' + item))
if (b'=' not in item):
items = None
break
(key, value) = item.split(b'=', 1)
key = url_unquote_plus(key.decode('ascii'))
try:
key = to_native(key)
except UnicodeError:
pass
items[key] = value
try:
client_hash = base64.b64decode(base64_hash)
except TypeError:
items = client_hash = None
if ((items is not None) and safe_str_cmp(client_hash, mac.digest())):
try:
for (key, value) in iteritems(items):
items[key] = cls.unquote(value)
except UnquoteError:
items = ()
else:
if ('_expires' in items):
if (time() > items['_expires']):
items = ()
else:
del items['_expires']
else:
items = ()
return cls(items, secret_key, False)<|docstring|>Load the secure cookie from a serialized string.
:param string: The cookie value to unserialize.
:param secret_key: The secret key used to serialize the cookie.
:return: A new :class:`SecureCookie`.<|endoftext|>
|
769110d184fadf6472e73c08e035039a9396ed17241e93e27455728b2cf34557
|
@classmethod
def load_cookie(cls, request, key='session', secret_key=None):
'Load a :class:`SecureCookie` from a cookie in the request. If\n the cookie is not set, a new :class:`SecureCookie` instance is\n returned.\n\n :param request: A request object that has a `cookies` attribute\n which is a dict of all cookie values.\n :param key: The name of the cookie.\n :param secret_key: The secret key used to unquote the cookie.\n Always provide the value even though it has no default!\n '
data = request.cookies.get(key)
if (not data):
return cls(secret_key=secret_key)
return cls.unserialize(data, secret_key)
|
Load a :class:`SecureCookie` from a cookie in the request. If
the cookie is not set, a new :class:`SecureCookie` instance is
returned.
:param request: A request object that has a `cookies` attribute
which is a dict of all cookie values.
:param key: The name of the cookie.
:param secret_key: The secret key used to unquote the cookie.
Always provide the value even though it has no default!
|
jam/third_party/werkzeug/secure_cookie/securecookie.py
|
load_cookie
|
platipusica/AssetInventoryMSAccess
| 384
|
python
|
@classmethod
def load_cookie(cls, request, key='session', secret_key=None):
'Load a :class:`SecureCookie` from a cookie in the request. If\n the cookie is not set, a new :class:`SecureCookie` instance is\n returned.\n\n :param request: A request object that has a `cookies` attribute\n which is a dict of all cookie values.\n :param key: The name of the cookie.\n :param secret_key: The secret key used to unquote the cookie.\n Always provide the value even though it has no default!\n '
data = request.cookies.get(key)
if (not data):
return cls(secret_key=secret_key)
return cls.unserialize(data, secret_key)
|
@classmethod
def load_cookie(cls, request, key='session', secret_key=None):
'Load a :class:`SecureCookie` from a cookie in the request. If\n the cookie is not set, a new :class:`SecureCookie` instance is\n returned.\n\n :param request: A request object that has a `cookies` attribute\n which is a dict of all cookie values.\n :param key: The name of the cookie.\n :param secret_key: The secret key used to unquote the cookie.\n Always provide the value even though it has no default!\n '
data = request.cookies.get(key)
if (not data):
return cls(secret_key=secret_key)
return cls.unserialize(data, secret_key)<|docstring|>Load a :class:`SecureCookie` from a cookie in the request. If
the cookie is not set, a new :class:`SecureCookie` instance is
returned.
:param request: A request object that has a `cookies` attribute
which is a dict of all cookie values.
:param key: The name of the cookie.
:param secret_key: The secret key used to unquote the cookie.
Always provide the value even though it has no default!<|endoftext|>
|
c223d1eafb62c5b4f0fb9a7524b3547f7ddba583d073f30bcc311ee3d73cec07
|
def save_cookie(self, response, key='session', expires=None, session_expires=None, max_age=None, path='/', domain=None, secure=None, httponly=False, force=False):
'Save the data securely in a cookie on response object. All\n parameters that are not described here are forwarded directly\n to :meth:`~BaseResponse.set_cookie`.\n\n :param response: A response object that has a\n :meth:`~BaseResponse.set_cookie` method.\n :param key: The name of the cookie.\n :param session_expires: The expiration date of the secure cookie\n stored information. If this is not provided the cookie\n ``expires`` date is used instead.\n '
if (force or self.should_save):
data = self.serialize((session_expires or expires))
response.set_cookie(key, data, expires=expires, max_age=max_age, path=path, domain=domain, secure=secure, httponly=httponly)
|
Save the data securely in a cookie on response object. All
parameters that are not described here are forwarded directly
to :meth:`~BaseResponse.set_cookie`.
:param response: A response object that has a
:meth:`~BaseResponse.set_cookie` method.
:param key: The name of the cookie.
:param session_expires: The expiration date of the secure cookie
stored information. If this is not provided the cookie
``expires`` date is used instead.
|
jam/third_party/werkzeug/secure_cookie/securecookie.py
|
save_cookie
|
platipusica/AssetInventoryMSAccess
| 384
|
python
|
def save_cookie(self, response, key='session', expires=None, session_expires=None, max_age=None, path='/', domain=None, secure=None, httponly=False, force=False):
'Save the data securely in a cookie on response object. All\n parameters that are not described here are forwarded directly\n to :meth:`~BaseResponse.set_cookie`.\n\n :param response: A response object that has a\n :meth:`~BaseResponse.set_cookie` method.\n :param key: The name of the cookie.\n :param session_expires: The expiration date of the secure cookie\n stored information. If this is not provided the cookie\n ``expires`` date is used instead.\n '
if (force or self.should_save):
data = self.serialize((session_expires or expires))
response.set_cookie(key, data, expires=expires, max_age=max_age, path=path, domain=domain, secure=secure, httponly=httponly)
|
def save_cookie(self, response, key='session', expires=None, session_expires=None, max_age=None, path='/', domain=None, secure=None, httponly=False, force=False):
'Save the data securely in a cookie on response object. All\n parameters that are not described here are forwarded directly\n to :meth:`~BaseResponse.set_cookie`.\n\n :param response: A response object that has a\n :meth:`~BaseResponse.set_cookie` method.\n :param key: The name of the cookie.\n :param session_expires: The expiration date of the secure cookie\n stored information. If this is not provided the cookie\n ``expires`` date is used instead.\n '
if (force or self.should_save):
data = self.serialize((session_expires or expires))
response.set_cookie(key, data, expires=expires, max_age=max_age, path=path, domain=domain, secure=secure, httponly=httponly)<|docstring|>Save the data securely in a cookie on response object. All
parameters that are not described here are forwarded directly
to :meth:`~BaseResponse.set_cookie`.
:param response: A response object that has a
:meth:`~BaseResponse.set_cookie` method.
:param key: The name of the cookie.
:param session_expires: The expiration date of the secure cookie
stored information. If this is not provided the cookie
``expires`` date is used instead.<|endoftext|>
|
4ccbf1b1b0ab6298b07e28b7546dc7273b9d7c5cf794912cd14a414f51b70ad6
|
def merge_dicts(dict1: dict, dict2: dict) -> dict:
'Merge 2 dictionaries\n\n Arguments:\n dict1 {dict} -- 1st Dictionary\n dict2 {dict} -- 2nd Dictionary\n\n Returns:\n dict -- Concatenated Dictionary\n '
return {**dict1, **dict2}
|
Merge 2 dictionaries
Arguments:
dict1 {dict} -- 1st Dictionary
dict2 {dict} -- 2nd Dictionary
Returns:
dict -- Concatenated Dictionary
|
stavroslib/dict.py
|
merge_dicts
|
spitoglou/stavros-lib
| 0
|
python
|
def merge_dicts(dict1: dict, dict2: dict) -> dict:
'Merge 2 dictionaries\n\n Arguments:\n dict1 {dict} -- 1st Dictionary\n dict2 {dict} -- 2nd Dictionary\n\n Returns:\n dict -- Concatenated Dictionary\n '
return {**dict1, **dict2}
|
def merge_dicts(dict1: dict, dict2: dict) -> dict:
'Merge 2 dictionaries\n\n Arguments:\n dict1 {dict} -- 1st Dictionary\n dict2 {dict} -- 2nd Dictionary\n\n Returns:\n dict -- Concatenated Dictionary\n '
return {**dict1, **dict2}<|docstring|>Merge 2 dictionaries
Arguments:
dict1 {dict} -- 1st Dictionary
dict2 {dict} -- 2nd Dictionary
Returns:
dict -- Concatenated Dictionary<|endoftext|>
|
2475af231c3b67ed51dcafe99aec1be8aa5e7f8c5f370625012e89ab9c14b317
|
async def send_message(endpoint: EndpointConfig, sender_id: Text, message: Text, parse_data: Optional[Dict[(Text, Any)]]=None) -> Dict[(Text, Any)]:
'Send a user message to a conversation.'
payload = {'sender': UserUttered.type_name, 'message': message, 'parse_data': parse_data}
return (await endpoint.request(json=payload, method='post', subpath='/conversations/{}/messages'.format(sender_id)))
|
Send a user message to a conversation.
|
rasa/core/training/interactive.py
|
send_message
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def send_message(endpoint: EndpointConfig, sender_id: Text, message: Text, parse_data: Optional[Dict[(Text, Any)]]=None) -> Dict[(Text, Any)]:
payload = {'sender': UserUttered.type_name, 'message': message, 'parse_data': parse_data}
return (await endpoint.request(json=payload, method='post', subpath='/conversations/{}/messages'.format(sender_id)))
|
async def send_message(endpoint: EndpointConfig, sender_id: Text, message: Text, parse_data: Optional[Dict[(Text, Any)]]=None) -> Dict[(Text, Any)]:
payload = {'sender': UserUttered.type_name, 'message': message, 'parse_data': parse_data}
return (await endpoint.request(json=payload, method='post', subpath='/conversations/{}/messages'.format(sender_id)))<|docstring|>Send a user message to a conversation.<|endoftext|>
|
4c3fc3040640bda7c2d7c27ed3f1959adcc0d2c6e36f27329361b3116bf289b4
|
async def request_prediction(endpoint: EndpointConfig, sender_id: Text) -> Dict[(Text, Any)]:
'Request the next action prediction from core.'
return (await endpoint.request(method='post', subpath='/conversations/{}/predict'.format(sender_id)))
|
Request the next action prediction from core.
|
rasa/core/training/interactive.py
|
request_prediction
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def request_prediction(endpoint: EndpointConfig, sender_id: Text) -> Dict[(Text, Any)]:
return (await endpoint.request(method='post', subpath='/conversations/{}/predict'.format(sender_id)))
|
async def request_prediction(endpoint: EndpointConfig, sender_id: Text) -> Dict[(Text, Any)]:
return (await endpoint.request(method='post', subpath='/conversations/{}/predict'.format(sender_id)))<|docstring|>Request the next action prediction from core.<|endoftext|>
|
91363d6822fdc2a5d774f2bac67a59ce1307e7d15d2dec40e38297581dc5a5d2
|
async def retrieve_domain(endpoint: EndpointConfig) -> Dict[(Text, Any)]:
'Retrieve the domain from core.'
return (await endpoint.request(method='get', subpath='/domain', headers={'Accept': 'application/json'}))
|
Retrieve the domain from core.
|
rasa/core/training/interactive.py
|
retrieve_domain
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def retrieve_domain(endpoint: EndpointConfig) -> Dict[(Text, Any)]:
return (await endpoint.request(method='get', subpath='/domain', headers={'Accept': 'application/json'}))
|
async def retrieve_domain(endpoint: EndpointConfig) -> Dict[(Text, Any)]:
return (await endpoint.request(method='get', subpath='/domain', headers={'Accept': 'application/json'}))<|docstring|>Retrieve the domain from core.<|endoftext|>
|
59efc24abdb5ebe0ec48fe9b06ffb4345eb2d9abe723a6a7f2e90f4690af428c
|
async def retrieve_status(endpoint: EndpointConfig) -> Dict[(Text, Any)]:
'Retrieve the status from core.'
return (await endpoint.request(method='get', subpath='/status'))
|
Retrieve the status from core.
|
rasa/core/training/interactive.py
|
retrieve_status
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def retrieve_status(endpoint: EndpointConfig) -> Dict[(Text, Any)]:
return (await endpoint.request(method='get', subpath='/status'))
|
async def retrieve_status(endpoint: EndpointConfig) -> Dict[(Text, Any)]:
return (await endpoint.request(method='get', subpath='/status'))<|docstring|>Retrieve the status from core.<|endoftext|>
|
9d6955093cbf4c76278010989eb2f9ab0db030edbd6eb641ddfbe26186799fe5
|
async def retrieve_tracker(endpoint: EndpointConfig, sender_id: Text, verbosity: EventVerbosity=EventVerbosity.ALL) -> Dict[(Text, Any)]:
'Retrieve a tracker from core.'
path = '/conversations/{}/tracker?include_events={}'.format(sender_id, verbosity.name)
return (await endpoint.request(method='get', subpath=path, headers={'Accept': 'application/json'}))
|
Retrieve a tracker from core.
|
rasa/core/training/interactive.py
|
retrieve_tracker
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def retrieve_tracker(endpoint: EndpointConfig, sender_id: Text, verbosity: EventVerbosity=EventVerbosity.ALL) -> Dict[(Text, Any)]:
path = '/conversations/{}/tracker?include_events={}'.format(sender_id, verbosity.name)
return (await endpoint.request(method='get', subpath=path, headers={'Accept': 'application/json'}))
|
async def retrieve_tracker(endpoint: EndpointConfig, sender_id: Text, verbosity: EventVerbosity=EventVerbosity.ALL) -> Dict[(Text, Any)]:
path = '/conversations/{}/tracker?include_events={}'.format(sender_id, verbosity.name)
return (await endpoint.request(method='get', subpath=path, headers={'Accept': 'application/json'}))<|docstring|>Retrieve a tracker from core.<|endoftext|>
|
3b9765c659f2ec4670469addda6d85b8ccd164207273d8d7b9b3debd3b11b44e
|
async def send_action(endpoint: EndpointConfig, sender_id: Text, action_name: Text, policy: Optional[Text]=None, confidence: Optional[float]=None, is_new_action: bool=False) -> Dict[(Text, Any)]:
'Log an action to a conversation.'
payload = ActionExecuted(action_name, policy, confidence).as_dict()
subpath = '/conversations/{}/execute'.format(sender_id)
try:
return (await endpoint.request(json=payload, method='post', subpath=subpath))
except ClientError:
if is_new_action:
warning_questions = questionary.confirm("WARNING: You have created a new action: '{}', which was not successfully executed. If this action does not return any events, you do not need to do anything. If this is a custom action which returns events, you are recommended to implement this action in your action server and try again.".format(action_name))
(await _ask_questions(warning_questions, sender_id, endpoint))
payload = ActionExecuted(action_name).as_dict()
return (await send_event(endpoint, sender_id, payload))
else:
logger.error('failed to execute action!')
raise
|
Log an action to a conversation.
|
rasa/core/training/interactive.py
|
send_action
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def send_action(endpoint: EndpointConfig, sender_id: Text, action_name: Text, policy: Optional[Text]=None, confidence: Optional[float]=None, is_new_action: bool=False) -> Dict[(Text, Any)]:
payload = ActionExecuted(action_name, policy, confidence).as_dict()
subpath = '/conversations/{}/execute'.format(sender_id)
try:
return (await endpoint.request(json=payload, method='post', subpath=subpath))
except ClientError:
if is_new_action:
warning_questions = questionary.confirm("WARNING: You have created a new action: '{}', which was not successfully executed. If this action does not return any events, you do not need to do anything. If this is a custom action which returns events, you are recommended to implement this action in your action server and try again.".format(action_name))
(await _ask_questions(warning_questions, sender_id, endpoint))
payload = ActionExecuted(action_name).as_dict()
return (await send_event(endpoint, sender_id, payload))
else:
logger.error('failed to execute action!')
raise
|
async def send_action(endpoint: EndpointConfig, sender_id: Text, action_name: Text, policy: Optional[Text]=None, confidence: Optional[float]=None, is_new_action: bool=False) -> Dict[(Text, Any)]:
payload = ActionExecuted(action_name, policy, confidence).as_dict()
subpath = '/conversations/{}/execute'.format(sender_id)
try:
return (await endpoint.request(json=payload, method='post', subpath=subpath))
except ClientError:
if is_new_action:
warning_questions = questionary.confirm("WARNING: You have created a new action: '{}', which was not successfully executed. If this action does not return any events, you do not need to do anything. If this is a custom action which returns events, you are recommended to implement this action in your action server and try again.".format(action_name))
(await _ask_questions(warning_questions, sender_id, endpoint))
payload = ActionExecuted(action_name).as_dict()
return (await send_event(endpoint, sender_id, payload))
else:
logger.error('failed to execute action!')
raise<|docstring|>Log an action to a conversation.<|endoftext|>
|
86ce40fd0d87b71396f7459fa3fb16501660a99df36f6187ec4a203be68037d2
|
async def send_event(endpoint: EndpointConfig, sender_id: Text, evt: Dict[(Text, Any)]) -> Dict[(Text, Any)]:
'Log an event to a conversation.'
subpath = '/conversations/{}/tracker/events'.format(sender_id)
return (await endpoint.request(json=evt, method='post', subpath=subpath))
|
Log an event to a conversation.
|
rasa/core/training/interactive.py
|
send_event
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def send_event(endpoint: EndpointConfig, sender_id: Text, evt: Dict[(Text, Any)]) -> Dict[(Text, Any)]:
subpath = '/conversations/{}/tracker/events'.format(sender_id)
return (await endpoint.request(json=evt, method='post', subpath=subpath))
|
async def send_event(endpoint: EndpointConfig, sender_id: Text, evt: Dict[(Text, Any)]) -> Dict[(Text, Any)]:
subpath = '/conversations/{}/tracker/events'.format(sender_id)
return (await endpoint.request(json=evt, method='post', subpath=subpath))<|docstring|>Log an event to a conversation.<|endoftext|>
|
6dc6944985a741e5f59b7b0786b8b806154ca49be43592ad00bd004893c05eea
|
async def replace_events(endpoint: EndpointConfig, sender_id: Text, evts: List[Dict[(Text, Any)]]) -> Dict[(Text, Any)]:
'Replace all the events of a conversation with the provided ones.'
subpath = '/conversations/{}/tracker/events'.format(sender_id)
return (await endpoint.request(json=evts, method='put', subpath=subpath))
|
Replace all the events of a conversation with the provided ones.
|
rasa/core/training/interactive.py
|
replace_events
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def replace_events(endpoint: EndpointConfig, sender_id: Text, evts: List[Dict[(Text, Any)]]) -> Dict[(Text, Any)]:
subpath = '/conversations/{}/tracker/events'.format(sender_id)
return (await endpoint.request(json=evts, method='put', subpath=subpath))
|
async def replace_events(endpoint: EndpointConfig, sender_id: Text, evts: List[Dict[(Text, Any)]]) -> Dict[(Text, Any)]:
subpath = '/conversations/{}/tracker/events'.format(sender_id)
return (await endpoint.request(json=evts, method='put', subpath=subpath))<|docstring|>Replace all the events of a conversation with the provided ones.<|endoftext|>
|
c77ff0ee0ff8fdb7494b4ff1c89ad3d308d3bbf13577615b5f8dfb9a572fad63
|
async def send_finetune(endpoint: EndpointConfig, evts: List[Dict[(Text, Any)]]) -> Dict[(Text, Any)]:
'Finetune a core model on the provided additional training samples.'
return (await endpoint.request(json=evts, method='post', subpath='/finetune'))
|
Finetune a core model on the provided additional training samples.
|
rasa/core/training/interactive.py
|
send_finetune
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def send_finetune(endpoint: EndpointConfig, evts: List[Dict[(Text, Any)]]) -> Dict[(Text, Any)]:
return (await endpoint.request(json=evts, method='post', subpath='/finetune'))
|
async def send_finetune(endpoint: EndpointConfig, evts: List[Dict[(Text, Any)]]) -> Dict[(Text, Any)]:
return (await endpoint.request(json=evts, method='post', subpath='/finetune'))<|docstring|>Finetune a core model on the provided additional training samples.<|endoftext|>
|
3e90a7e4e9fe39d40a8d9ecdef8414d0af8da1db8aba70d86fb814239570ade4
|
def format_bot_output(message: Dict[(Text, Any)]) -> Text:
'Format a bot response to be displayed in the history table.'
output = (message.get('text') or '')
data = message.get('data', {})
if (not data):
return output
if data.get('image'):
output += ('\nImage: ' + data.get('image'))
if data.get('attachment'):
output += ('\nAttachment: ' + data.get('attachment'))
if data.get('buttons'):
output += '\nButtons:'
for (idx, button) in enumerate(data.get('buttons')):
button_str = button_to_string(button, idx)
output += ('\n' + button_str)
if data.get('elements'):
output += '\nElements:'
for (idx, element) in enumerate(data.get('elements')):
element_str = element_to_string(element, idx)
output += ('\n' + element_str)
if data.get('quick_replies'):
output += '\nQuick replies:'
for (idx, element) in enumerate(data.get('quick_replies')):
element_str = element_to_string(element, idx)
output += ('\n' + element_str)
return output
|
Format a bot response to be displayed in the history table.
|
rasa/core/training/interactive.py
|
format_bot_output
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
def format_bot_output(message: Dict[(Text, Any)]) -> Text:
output = (message.get('text') or )
data = message.get('data', {})
if (not data):
return output
if data.get('image'):
output += ('\nImage: ' + data.get('image'))
if data.get('attachment'):
output += ('\nAttachment: ' + data.get('attachment'))
if data.get('buttons'):
output += '\nButtons:'
for (idx, button) in enumerate(data.get('buttons')):
button_str = button_to_string(button, idx)
output += ('\n' + button_str)
if data.get('elements'):
output += '\nElements:'
for (idx, element) in enumerate(data.get('elements')):
element_str = element_to_string(element, idx)
output += ('\n' + element_str)
if data.get('quick_replies'):
output += '\nQuick replies:'
for (idx, element) in enumerate(data.get('quick_replies')):
element_str = element_to_string(element, idx)
output += ('\n' + element_str)
return output
|
def format_bot_output(message: Dict[(Text, Any)]) -> Text:
output = (message.get('text') or )
data = message.get('data', {})
if (not data):
return output
if data.get('image'):
output += ('\nImage: ' + data.get('image'))
if data.get('attachment'):
output += ('\nAttachment: ' + data.get('attachment'))
if data.get('buttons'):
output += '\nButtons:'
for (idx, button) in enumerate(data.get('buttons')):
button_str = button_to_string(button, idx)
output += ('\n' + button_str)
if data.get('elements'):
output += '\nElements:'
for (idx, element) in enumerate(data.get('elements')):
element_str = element_to_string(element, idx)
output += ('\n' + element_str)
if data.get('quick_replies'):
output += '\nQuick replies:'
for (idx, element) in enumerate(data.get('quick_replies')):
element_str = element_to_string(element, idx)
output += ('\n' + element_str)
return output<|docstring|>Format a bot response to be displayed in the history table.<|endoftext|>
|
e2fd96c6dee1d5be9ac420e58922bb3fa6ded467c753b4cac730f074982f85bd
|
def latest_user_message(evts: List[Dict[(Text, Any)]]) -> Optional[Dict[(Text, Any)]]:
'Return most recent user message.'
for (i, e) in enumerate(reversed(evts)):
if (e.get('event') == UserUttered.type_name):
return e
return None
|
Return most recent user message.
|
rasa/core/training/interactive.py
|
latest_user_message
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
def latest_user_message(evts: List[Dict[(Text, Any)]]) -> Optional[Dict[(Text, Any)]]:
for (i, e) in enumerate(reversed(evts)):
if (e.get('event') == UserUttered.type_name):
return e
return None
|
def latest_user_message(evts: List[Dict[(Text, Any)]]) -> Optional[Dict[(Text, Any)]]:
for (i, e) in enumerate(reversed(evts)):
if (e.get('event') == UserUttered.type_name):
return e
return None<|docstring|>Return most recent user message.<|endoftext|>
|
5c2a79fb4042ef852c00c0c9634ee574122fecfed7081913fe210478a39ddbbc
|
def all_events_before_latest_user_msg(evts: List[Dict[(Text, Any)]]) -> List[Dict[(Text, Any)]]:
'Return all events that happened before the most recent user message.'
for (i, e) in enumerate(reversed(evts)):
if (e.get('event') == UserUttered.type_name):
return evts[:(- (i + 1))]
return evts
|
Return all events that happened before the most recent user message.
|
rasa/core/training/interactive.py
|
all_events_before_latest_user_msg
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
def all_events_before_latest_user_msg(evts: List[Dict[(Text, Any)]]) -> List[Dict[(Text, Any)]]:
for (i, e) in enumerate(reversed(evts)):
if (e.get('event') == UserUttered.type_name):
return evts[:(- (i + 1))]
return evts
|
def all_events_before_latest_user_msg(evts: List[Dict[(Text, Any)]]) -> List[Dict[(Text, Any)]]:
for (i, e) in enumerate(reversed(evts)):
if (e.get('event') == UserUttered.type_name):
return evts[:(- (i + 1))]
return evts<|docstring|>Return all events that happened before the most recent user message.<|endoftext|>
|
d9ab6becbbc945d51751ab94421716be9acb7c992cfab6b14a1e94136dbed080
|
async def _ask_questions(questions: Union[(Form, Question)], sender_id: Text, endpoint: EndpointConfig, is_abort: Callable[([Dict[(Text, Any)]], bool)]=(lambda x: False)) -> Any:
'Ask the user a question, if Ctrl-C is pressed provide user with menu.'
should_retry = True
answers = {}
while should_retry:
answers = questions.ask()
if ((answers is None) or is_abort(answers)):
should_retry = (await _ask_if_quit(sender_id, endpoint))
else:
should_retry = False
return answers
|
Ask the user a question, if Ctrl-C is pressed provide user with menu.
|
rasa/core/training/interactive.py
|
_ask_questions
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def _ask_questions(questions: Union[(Form, Question)], sender_id: Text, endpoint: EndpointConfig, is_abort: Callable[([Dict[(Text, Any)]], bool)]=(lambda x: False)) -> Any:
should_retry = True
answers = {}
while should_retry:
answers = questions.ask()
if ((answers is None) or is_abort(answers)):
should_retry = (await _ask_if_quit(sender_id, endpoint))
else:
should_retry = False
return answers
|
async def _ask_questions(questions: Union[(Form, Question)], sender_id: Text, endpoint: EndpointConfig, is_abort: Callable[([Dict[(Text, Any)]], bool)]=(lambda x: False)) -> Any:
should_retry = True
answers = {}
while should_retry:
answers = questions.ask()
if ((answers is None) or is_abort(answers)):
should_retry = (await _ask_if_quit(sender_id, endpoint))
else:
should_retry = False
return answers<|docstring|>Ask the user a question, if Ctrl-C is pressed provide user with menu.<|endoftext|>
|
e916f1fe00d9ac84fa7c12ee37e946bc2bc4727946dc1bee25374d1d3ae2a808
|
def _selection_choices_from_intent_prediction(predictions: List[Dict[(Text, Any)]]) -> List[Dict[(Text, Text)]]:
'"Given a list of ML predictions create a UI choice list.'
sorted_intents = sorted(predictions, key=(lambda k: ((- k['confidence']), k['name'])))
choices = []
for p in sorted_intents:
name_with_confidence = '{:03.2f} {:40}'.format(p.get('confidence'), p.get('name'))
choice = {'name': name_with_confidence, 'value': p.get('name')}
choices.append(choice)
return choices
|
"Given a list of ML predictions create a UI choice list.
|
rasa/core/training/interactive.py
|
_selection_choices_from_intent_prediction
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
def _selection_choices_from_intent_prediction(predictions: List[Dict[(Text, Any)]]) -> List[Dict[(Text, Text)]]:
sorted_intents = sorted(predictions, key=(lambda k: ((- k['confidence']), k['name'])))
choices = []
for p in sorted_intents:
name_with_confidence = '{:03.2f} {:40}'.format(p.get('confidence'), p.get('name'))
choice = {'name': name_with_confidence, 'value': p.get('name')}
choices.append(choice)
return choices
|
def _selection_choices_from_intent_prediction(predictions: List[Dict[(Text, Any)]]) -> List[Dict[(Text, Text)]]:
sorted_intents = sorted(predictions, key=(lambda k: ((- k['confidence']), k['name'])))
choices = []
for p in sorted_intents:
name_with_confidence = '{:03.2f} {:40}'.format(p.get('confidence'), p.get('name'))
choice = {'name': name_with_confidence, 'value': p.get('name')}
choices.append(choice)
return choices<|docstring|>"Given a list of ML predictions create a UI choice list.<|endoftext|>
|
b18359b2aa2fcd7a8e316644b4df99ce214936bd6f43df2cd3640a6f29f34a9b
|
async def _request_fork_from_user(sender_id, endpoint) -> Optional[List[Dict[(Text, Any)]]]:
'Take in a conversation and ask at which point to fork the conversation.\n\n Returns the list of events that should be kept. Forking means, the\n conversation will be reset and continued from this previous point.'
tracker = (await retrieve_tracker(endpoint, sender_id, EventVerbosity.AFTER_RESTART))
choices = []
for (i, e) in enumerate(tracker.get('events', [])):
if (e.get('event') == UserUttered.type_name):
choices.append({'name': e.get('text'), 'value': i})
fork_idx = (await _request_fork_point_from_list(list(reversed(choices)), sender_id, endpoint))
if (fork_idx is not None):
return tracker.get('events', [])[:int(fork_idx)]
else:
return None
|
Take in a conversation and ask at which point to fork the conversation.
Returns the list of events that should be kept. Forking means, the
conversation will be reset and continued from this previous point.
|
rasa/core/training/interactive.py
|
_request_fork_from_user
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def _request_fork_from_user(sender_id, endpoint) -> Optional[List[Dict[(Text, Any)]]]:
'Take in a conversation and ask at which point to fork the conversation.\n\n Returns the list of events that should be kept. Forking means, the\n conversation will be reset and continued from this previous point.'
tracker = (await retrieve_tracker(endpoint, sender_id, EventVerbosity.AFTER_RESTART))
choices = []
for (i, e) in enumerate(tracker.get('events', [])):
if (e.get('event') == UserUttered.type_name):
choices.append({'name': e.get('text'), 'value': i})
fork_idx = (await _request_fork_point_from_list(list(reversed(choices)), sender_id, endpoint))
if (fork_idx is not None):
return tracker.get('events', [])[:int(fork_idx)]
else:
return None
|
async def _request_fork_from_user(sender_id, endpoint) -> Optional[List[Dict[(Text, Any)]]]:
'Take in a conversation and ask at which point to fork the conversation.\n\n Returns the list of events that should be kept. Forking means, the\n conversation will be reset and continued from this previous point.'
tracker = (await retrieve_tracker(endpoint, sender_id, EventVerbosity.AFTER_RESTART))
choices = []
for (i, e) in enumerate(tracker.get('events', [])):
if (e.get('event') == UserUttered.type_name):
choices.append({'name': e.get('text'), 'value': i})
fork_idx = (await _request_fork_point_from_list(list(reversed(choices)), sender_id, endpoint))
if (fork_idx is not None):
return tracker.get('events', [])[:int(fork_idx)]
else:
return None<|docstring|>Take in a conversation and ask at which point to fork the conversation.
Returns the list of events that should be kept. Forking means, the
conversation will be reset and continued from this previous point.<|endoftext|>
|
515dc4dd877a067df4e2cac90e6f76e7576d561b5ab916ca2da179fa91d1661e
|
async def _request_intent_from_user(latest_message, intents, sender_id, endpoint) -> Dict[(Text, Any)]:
'Take in latest message and ask which intent it should have been.\n\n Returns the intent dict that has been selected by the user.'
predictions = latest_message.get('parse_data', {}).get('intent_ranking', [])
predicted_intents = {p['name'] for p in predictions}
for i in intents:
if (i not in predicted_intents):
predictions.append({'name': i, 'confidence': 0.0})
choices = ([{'name': '<create_new_intent>', 'value': OTHER_INTENT}] + _selection_choices_from_intent_prediction(predictions))
intent_name = (await _request_selection_from_intent_list(choices, sender_id, endpoint))
if (intent_name == OTHER_INTENT):
intent_name = (await _request_free_text_intent(sender_id, endpoint))
return {'name': intent_name, 'confidence': 1.0}
return next((x for x in predictions if (x['name'] == intent_name)), None)
|
Take in latest message and ask which intent it should have been.
Returns the intent dict that has been selected by the user.
|
rasa/core/training/interactive.py
|
_request_intent_from_user
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def _request_intent_from_user(latest_message, intents, sender_id, endpoint) -> Dict[(Text, Any)]:
'Take in latest message and ask which intent it should have been.\n\n Returns the intent dict that has been selected by the user.'
predictions = latest_message.get('parse_data', {}).get('intent_ranking', [])
predicted_intents = {p['name'] for p in predictions}
for i in intents:
if (i not in predicted_intents):
predictions.append({'name': i, 'confidence': 0.0})
choices = ([{'name': '<create_new_intent>', 'value': OTHER_INTENT}] + _selection_choices_from_intent_prediction(predictions))
intent_name = (await _request_selection_from_intent_list(choices, sender_id, endpoint))
if (intent_name == OTHER_INTENT):
intent_name = (await _request_free_text_intent(sender_id, endpoint))
return {'name': intent_name, 'confidence': 1.0}
return next((x for x in predictions if (x['name'] == intent_name)), None)
|
async def _request_intent_from_user(latest_message, intents, sender_id, endpoint) -> Dict[(Text, Any)]:
'Take in latest message and ask which intent it should have been.\n\n Returns the intent dict that has been selected by the user.'
predictions = latest_message.get('parse_data', {}).get('intent_ranking', [])
predicted_intents = {p['name'] for p in predictions}
for i in intents:
if (i not in predicted_intents):
predictions.append({'name': i, 'confidence': 0.0})
choices = ([{'name': '<create_new_intent>', 'value': OTHER_INTENT}] + _selection_choices_from_intent_prediction(predictions))
intent_name = (await _request_selection_from_intent_list(choices, sender_id, endpoint))
if (intent_name == OTHER_INTENT):
intent_name = (await _request_free_text_intent(sender_id, endpoint))
return {'name': intent_name, 'confidence': 1.0}
return next((x for x in predictions if (x['name'] == intent_name)), None)<|docstring|>Take in latest message and ask which intent it should have been.
Returns the intent dict that has been selected by the user.<|endoftext|>
|
b1813e510ecfd23eb9d84f965998f8edac4a5d732ac87a318a3091f95957d90d
|
async def _print_history(sender_id: Text, endpoint: EndpointConfig) -> None:
'Print information about the conversation for the user.'
tracker_dump = (await retrieve_tracker(endpoint, sender_id, EventVerbosity.AFTER_RESTART))
evts = tracker_dump.get('events', [])
table = _chat_history_table(evts)
slot_strs = _slot_history(tracker_dump)
print('------')
print('Chat History\n')
print(table)
if slot_strs:
print('\n')
print('Current slots: \n\t{}\n'.format(', '.join(slot_strs)))
print('------')
|
Print information about the conversation for the user.
|
rasa/core/training/interactive.py
|
_print_history
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def _print_history(sender_id: Text, endpoint: EndpointConfig) -> None:
tracker_dump = (await retrieve_tracker(endpoint, sender_id, EventVerbosity.AFTER_RESTART))
evts = tracker_dump.get('events', [])
table = _chat_history_table(evts)
slot_strs = _slot_history(tracker_dump)
print('------')
print('Chat History\n')
print(table)
if slot_strs:
print('\n')
print('Current slots: \n\t{}\n'.format(', '.join(slot_strs)))
print('------')
|
async def _print_history(sender_id: Text, endpoint: EndpointConfig) -> None:
tracker_dump = (await retrieve_tracker(endpoint, sender_id, EventVerbosity.AFTER_RESTART))
evts = tracker_dump.get('events', [])
table = _chat_history_table(evts)
slot_strs = _slot_history(tracker_dump)
print('------')
print('Chat History\n')
print(table)
if slot_strs:
print('\n')
print('Current slots: \n\t{}\n'.format(', '.join(slot_strs)))
print('------')<|docstring|>Print information about the conversation for the user.<|endoftext|>
|
8edb6a1e8f738ec277c03c55e8abfd488ce5c0d16beda463894933de94e587d9
|
def _chat_history_table(evts: List[Dict[(Text, Any)]]) -> Text:
'Create a table containing bot and user messages.\n\n Also includes additional information, like any events and\n prediction probabilities.'
def wrap(txt, max_width):
return '\n'.join(textwrap.wrap(txt, max_width, replace_whitespace=False))
def colored(txt, color):
return (((((('{' + color) + '}') + txt) + '{/') + color) + '}')
def format_user_msg(user_evt, max_width):
_parsed = user_evt.get('parse_data', {})
_intent = _parsed.get('intent', {}).get('name')
_confidence = _parsed.get('intent', {}).get('confidence', 1.0)
_md = _as_md_message(_parsed)
_lines = [colored(wrap(_md, max_width), 'hired'), 'intent: {} {:03.2f}'.format(_intent, _confidence)]
return '\n'.join(_lines)
def bot_width(_table: AsciiTable) -> int:
return _table.column_max_width(1)
def user_width(_table: AsciiTable) -> int:
return _table.column_max_width(3)
def add_bot_cell(data, cell):
data.append([len(data), Color(cell), '', ''])
def add_user_cell(data, cell):
data.append([len(data), '', '', Color(cell)])
table_data = [['# ', Color(colored('Bot ', 'autoblue')), ' ', Color(colored('You ', 'hired'))]]
table = SingleTable(table_data, 'Chat History')
bot_column = []
for (idx, evt) in enumerate(evts):
if (evt.get('event') == ActionExecuted.type_name):
bot_column.append(colored(evt['name'], 'autocyan'))
if (evt['confidence'] is not None):
bot_column[(- 1)] += colored(' {:03.2f}'.format(evt['confidence']), 'autowhite')
elif (evt.get('event') == UserUttered.type_name):
if bot_column:
text = '\n'.join(bot_column)
add_bot_cell(table_data, text)
bot_column = []
msg = format_user_msg(evt, user_width(table))
add_user_cell(table_data, msg)
elif (evt.get('event') == BotUttered.type_name):
wrapped = wrap(format_bot_output(evt), bot_width(table))
bot_column.append(colored(wrapped, 'autoblue'))
else:
e = Event.from_parameters(evt)
if e.as_story_string():
bot_column.append(wrap(e.as_story_string(), bot_width(table)))
if bot_column:
text = '\n'.join(bot_column)
add_bot_cell(table_data, text)
table.inner_heading_row_border = False
table.inner_row_border = True
table.inner_column_border = False
table.outer_border = False
table.justify_columns = {0: 'left', 1: 'left', 2: 'center', 3: 'right'}
return table.table
|
Create a table containing bot and user messages.
Also includes additional information, like any events and
prediction probabilities.
|
rasa/core/training/interactive.py
|
_chat_history_table
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
def _chat_history_table(evts: List[Dict[(Text, Any)]]) -> Text:
'Create a table containing bot and user messages.\n\n Also includes additional information, like any events and\n prediction probabilities.'
def wrap(txt, max_width):
return '\n'.join(textwrap.wrap(txt, max_width, replace_whitespace=False))
def colored(txt, color):
return (((((('{' + color) + '}') + txt) + '{/') + color) + '}')
def format_user_msg(user_evt, max_width):
_parsed = user_evt.get('parse_data', {})
_intent = _parsed.get('intent', {}).get('name')
_confidence = _parsed.get('intent', {}).get('confidence', 1.0)
_md = _as_md_message(_parsed)
_lines = [colored(wrap(_md, max_width), 'hired'), 'intent: {} {:03.2f}'.format(_intent, _confidence)]
return '\n'.join(_lines)
def bot_width(_table: AsciiTable) -> int:
return _table.column_max_width(1)
def user_width(_table: AsciiTable) -> int:
return _table.column_max_width(3)
def add_bot_cell(data, cell):
data.append([len(data), Color(cell), , ])
def add_user_cell(data, cell):
data.append([len(data), , , Color(cell)])
table_data = [['# ', Color(colored('Bot ', 'autoblue')), ' ', Color(colored('You ', 'hired'))]]
table = SingleTable(table_data, 'Chat History')
bot_column = []
for (idx, evt) in enumerate(evts):
if (evt.get('event') == ActionExecuted.type_name):
bot_column.append(colored(evt['name'], 'autocyan'))
if (evt['confidence'] is not None):
bot_column[(- 1)] += colored(' {:03.2f}'.format(evt['confidence']), 'autowhite')
elif (evt.get('event') == UserUttered.type_name):
if bot_column:
text = '\n'.join(bot_column)
add_bot_cell(table_data, text)
bot_column = []
msg = format_user_msg(evt, user_width(table))
add_user_cell(table_data, msg)
elif (evt.get('event') == BotUttered.type_name):
wrapped = wrap(format_bot_output(evt), bot_width(table))
bot_column.append(colored(wrapped, 'autoblue'))
else:
e = Event.from_parameters(evt)
if e.as_story_string():
bot_column.append(wrap(e.as_story_string(), bot_width(table)))
if bot_column:
text = '\n'.join(bot_column)
add_bot_cell(table_data, text)
table.inner_heading_row_border = False
table.inner_row_border = True
table.inner_column_border = False
table.outer_border = False
table.justify_columns = {0: 'left', 1: 'left', 2: 'center', 3: 'right'}
return table.table
|
def _chat_history_table(evts: List[Dict[(Text, Any)]]) -> Text:
'Create a table containing bot and user messages.\n\n Also includes additional information, like any events and\n prediction probabilities.'
def wrap(txt, max_width):
return '\n'.join(textwrap.wrap(txt, max_width, replace_whitespace=False))
def colored(txt, color):
return (((((('{' + color) + '}') + txt) + '{/') + color) + '}')
def format_user_msg(user_evt, max_width):
_parsed = user_evt.get('parse_data', {})
_intent = _parsed.get('intent', {}).get('name')
_confidence = _parsed.get('intent', {}).get('confidence', 1.0)
_md = _as_md_message(_parsed)
_lines = [colored(wrap(_md, max_width), 'hired'), 'intent: {} {:03.2f}'.format(_intent, _confidence)]
return '\n'.join(_lines)
def bot_width(_table: AsciiTable) -> int:
return _table.column_max_width(1)
def user_width(_table: AsciiTable) -> int:
return _table.column_max_width(3)
def add_bot_cell(data, cell):
data.append([len(data), Color(cell), , ])
def add_user_cell(data, cell):
data.append([len(data), , , Color(cell)])
table_data = [['# ', Color(colored('Bot ', 'autoblue')), ' ', Color(colored('You ', 'hired'))]]
table = SingleTable(table_data, 'Chat History')
bot_column = []
for (idx, evt) in enumerate(evts):
if (evt.get('event') == ActionExecuted.type_name):
bot_column.append(colored(evt['name'], 'autocyan'))
if (evt['confidence'] is not None):
bot_column[(- 1)] += colored(' {:03.2f}'.format(evt['confidence']), 'autowhite')
elif (evt.get('event') == UserUttered.type_name):
if bot_column:
text = '\n'.join(bot_column)
add_bot_cell(table_data, text)
bot_column = []
msg = format_user_msg(evt, user_width(table))
add_user_cell(table_data, msg)
elif (evt.get('event') == BotUttered.type_name):
wrapped = wrap(format_bot_output(evt), bot_width(table))
bot_column.append(colored(wrapped, 'autoblue'))
else:
e = Event.from_parameters(evt)
if e.as_story_string():
bot_column.append(wrap(e.as_story_string(), bot_width(table)))
if bot_column:
text = '\n'.join(bot_column)
add_bot_cell(table_data, text)
table.inner_heading_row_border = False
table.inner_row_border = True
table.inner_column_border = False
table.outer_border = False
table.justify_columns = {0: 'left', 1: 'left', 2: 'center', 3: 'right'}
return table.table<|docstring|>Create a table containing bot and user messages.
Also includes additional information, like any events and
prediction probabilities.<|endoftext|>
|
1271e0ba57b51835e508c24eb2f356533bf56ad497324fbf24e20c35cd64a8c6
|
def _slot_history(tracker_dump: Dict[(Text, Any)]) -> List[Text]:
'Create an array of slot representations to be displayed.'
slot_strs = []
for (k, s) in tracker_dump.get('slots').items():
colored_value = cliutils.wrap_with_color(str(s), rasa.cli.utils.bcolors.WARNING)
slot_strs.append('{}: {}'.format(k, colored_value))
return slot_strs
|
Create an array of slot representations to be displayed.
|
rasa/core/training/interactive.py
|
_slot_history
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
def _slot_history(tracker_dump: Dict[(Text, Any)]) -> List[Text]:
slot_strs = []
for (k, s) in tracker_dump.get('slots').items():
colored_value = cliutils.wrap_with_color(str(s), rasa.cli.utils.bcolors.WARNING)
slot_strs.append('{}: {}'.format(k, colored_value))
return slot_strs
|
def _slot_history(tracker_dump: Dict[(Text, Any)]) -> List[Text]:
slot_strs = []
for (k, s) in tracker_dump.get('slots').items():
colored_value = cliutils.wrap_with_color(str(s), rasa.cli.utils.bcolors.WARNING)
slot_strs.append('{}: {}'.format(k, colored_value))
return slot_strs<|docstring|>Create an array of slot representations to be displayed.<|endoftext|>
|
dfc1d21aeff736093686e3c20a415d1eebbfec498a44d0cc240743ab2ee28933
|
async def _write_data_to_file(sender_id: Text, endpoint: EndpointConfig):
'Write stories and nlu data to file.'
(story_path, nlu_path, domain_path) = _request_export_info()
tracker = (await retrieve_tracker(endpoint, sender_id))
evts = tracker.get('events', [])
(await _write_stories_to_file(story_path, evts))
(await _write_nlu_to_file(nlu_path, evts))
(await _write_domain_to_file(domain_path, evts, endpoint))
logger.info('Successfully wrote stories and NLU data')
|
Write stories and nlu data to file.
|
rasa/core/training/interactive.py
|
_write_data_to_file
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def _write_data_to_file(sender_id: Text, endpoint: EndpointConfig):
(story_path, nlu_path, domain_path) = _request_export_info()
tracker = (await retrieve_tracker(endpoint, sender_id))
evts = tracker.get('events', [])
(await _write_stories_to_file(story_path, evts))
(await _write_nlu_to_file(nlu_path, evts))
(await _write_domain_to_file(domain_path, evts, endpoint))
logger.info('Successfully wrote stories and NLU data')
|
async def _write_data_to_file(sender_id: Text, endpoint: EndpointConfig):
(story_path, nlu_path, domain_path) = _request_export_info()
tracker = (await retrieve_tracker(endpoint, sender_id))
evts = tracker.get('events', [])
(await _write_stories_to_file(story_path, evts))
(await _write_nlu_to_file(nlu_path, evts))
(await _write_domain_to_file(domain_path, evts, endpoint))
logger.info('Successfully wrote stories and NLU data')<|docstring|>Write stories and nlu data to file.<|endoftext|>
|
d98aa1081f3099be20f9480c2981951dae1e8d8e2226c1bfccf7513bf990d6f4
|
async def _ask_if_quit(sender_id: Text, endpoint: EndpointConfig) -> bool:
'Display the exit menu.\n\n Return `True` if the previous question should be retried.'
answer = questionary.select(message='Do you want to stop?', choices=[Choice('Continue', 'continue'), Choice('Undo Last', 'undo'), Choice('Fork', 'fork'), Choice('Start Fresh', 'restart'), Choice('Export & Quit', 'quit')]).ask()
if ((not answer) or (answer == 'quit')):
(await _write_data_to_file(sender_id, endpoint))
raise Abort()
elif (answer == 'continue'):
return True
elif (answer == 'undo'):
raise UndoLastStep()
elif (answer == 'fork'):
raise ForkTracker()
elif (answer == 'restart'):
raise RestartConversation()
|
Display the exit menu.
Return `True` if the previous question should be retried.
|
rasa/core/training/interactive.py
|
_ask_if_quit
|
LaudateCorpus1/rasa_core
| 2,433
|
python
|
async def _ask_if_quit(sender_id: Text, endpoint: EndpointConfig) -> bool:
'Display the exit menu.\n\n Return `True` if the previous question should be retried.'
answer = questionary.select(message='Do you want to stop?', choices=[Choice('Continue', 'continue'), Choice('Undo Last', 'undo'), Choice('Fork', 'fork'), Choice('Start Fresh', 'restart'), Choice('Export & Quit', 'quit')]).ask()
if ((not answer) or (answer == 'quit')):
(await _write_data_to_file(sender_id, endpoint))
raise Abort()
elif (answer == 'continue'):
return True
elif (answer == 'undo'):
raise UndoLastStep()
elif (answer == 'fork'):
raise ForkTracker()
elif (answer == 'restart'):
raise RestartConversation()
|
async def _ask_if_quit(sender_id: Text, endpoint: EndpointConfig) -> bool:
'Display the exit menu.\n\n Return `True` if the previous question should be retried.'
answer = questionary.select(message='Do you want to stop?', choices=[Choice('Continue', 'continue'), Choice('Undo Last', 'undo'), Choice('Fork', 'fork'), Choice('Start Fresh', 'restart'), Choice('Export & Quit', 'quit')]).ask()
if ((not answer) or (answer == 'quit')):
(await _write_data_to_file(sender_id, endpoint))
raise Abort()
elif (answer == 'continue'):
return True
elif (answer == 'undo'):
raise UndoLastStep()
elif (answer == 'fork'):
raise ForkTracker()
elif (answer == 'restart'):
raise RestartConversation()<|docstring|>Display the exit menu.
Return `True` if the previous question should be retried.<|endoftext|>
|
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