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stringlengths 64
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stringlengths 23
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| docstring
stringlengths 1
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| path
stringlengths 4
198
| name
stringlengths 1
115
| repository_name
stringlengths 7
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191k
| lang
stringclasses 1
value | body_without_docstring
stringlengths 14
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stringlengths 45
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|---|---|---|---|---|---|---|---|---|---|
ebbe7ec65fbaf1e02f85e057488ca4fa4ceb5ee0791f039443eb921b27b470a4
|
@pytest.fixture
def common_fixtures(loop, storage_v0_service_mock, mock_download_file, mock_upload_file, this_node_file: Path, another_node_file: Path, download_file_folder: Path):
'this module main fixture'
node_config.STORAGE_ENDPOINT = 'storage:8080'
|
this module main fixture
|
packages/simcore-sdk/tests/unit/test_node_ports_v2_port.py
|
common_fixtures
|
colinRawlings/osparc-simcore
| 25
|
python
|
@pytest.fixture
def common_fixtures(loop, storage_v0_service_mock, mock_download_file, mock_upload_file, this_node_file: Path, another_node_file: Path, download_file_folder: Path):
node_config.STORAGE_ENDPOINT = 'storage:8080'
|
@pytest.fixture
def common_fixtures(loop, storage_v0_service_mock, mock_download_file, mock_upload_file, this_node_file: Path, another_node_file: Path, download_file_folder: Path):
node_config.STORAGE_ENDPOINT = 'storage:8080'<|docstring|>this module main fixture<|endoftext|>
|
0375e20f9d1dd1cff34bfe08e19c5546b6073de43855617b12234454217907a9
|
def rotate(self, nums: List[int], k: int) -> None:
'\n Do not return anything, modify nums in-place instead.\n '
def numReverse(start, end):
while (start < end):
(nums[start], nums[end]) = (nums[end], nums[start])
start += 1
end -= 1
n = len(nums)
k %= n
numReverse(0, (n - 1))
numReverse(0, (k - 1))
numReverse(k, (n - 1))
|
Do not return anything, modify nums in-place instead.
|
leetcode-algorithms/189. Rotate Array/solution.py
|
rotate
|
joyfeel/leetcode
| 3
|
python
|
def rotate(self, nums: List[int], k: int) -> None:
'\n \n '
def numReverse(start, end):
while (start < end):
(nums[start], nums[end]) = (nums[end], nums[start])
start += 1
end -= 1
n = len(nums)
k %= n
numReverse(0, (n - 1))
numReverse(0, (k - 1))
numReverse(k, (n - 1))
|
def rotate(self, nums: List[int], k: int) -> None:
'\n \n '
def numReverse(start, end):
while (start < end):
(nums[start], nums[end]) = (nums[end], nums[start])
start += 1
end -= 1
n = len(nums)
k %= n
numReverse(0, (n - 1))
numReverse(0, (k - 1))
numReverse(k, (n - 1))<|docstring|>Do not return anything, modify nums in-place instead.<|endoftext|>
|
8aaf8f4659505f63c53fe1b9f63f6496f55ada2cd381431326589deb01cb9d92
|
def _add_simple_procparser(subparsers, name, helpstr, func, defname='proc'):
'Add a sub parser that can do a simple thing with no arguments.'
parser = _add_procparser(subparsers, name, helpstr, func, defname=defname)
_add_def_args(parser)
return parser
|
Add a sub parser that can do a simple thing with no arguments.
|
Src/PulseEKKO/impdar/bin/apdar.py
|
_add_simple_procparser
|
rdrews-dev/ReMeltRadar
| 0
|
python
|
def _add_simple_procparser(subparsers, name, helpstr, func, defname='proc'):
parser = _add_procparser(subparsers, name, helpstr, func, defname=defname)
_add_def_args(parser)
return parser
|
def _add_simple_procparser(subparsers, name, helpstr, func, defname='proc'):
parser = _add_procparser(subparsers, name, helpstr, func, defname=defname)
_add_def_args(parser)
return parser<|docstring|>Add a sub parser that can do a simple thing with no arguments.<|endoftext|>
|
3a6d3402f0af0327dbe419b597574669bcad27c0d69a9eae9dba5946c18f9951
|
def _add_procparser(subparsers, name, helpstr, func, defname='proc'):
'Wrap adding subparser because we mostly want the same args.'
parser = subparsers.add_parser(name, help=helpstr)
parser.set_defaults(func=func, name=defname)
return parser
|
Wrap adding subparser because we mostly want the same args.
|
Src/PulseEKKO/impdar/bin/apdar.py
|
_add_procparser
|
rdrews-dev/ReMeltRadar
| 0
|
python
|
def _add_procparser(subparsers, name, helpstr, func, defname='proc'):
parser = subparsers.add_parser(name, help=helpstr)
parser.set_defaults(func=func, name=defname)
return parser
|
def _add_procparser(subparsers, name, helpstr, func, defname='proc'):
parser = subparsers.add_parser(name, help=helpstr)
parser.set_defaults(func=func, name=defname)
return parser<|docstring|>Wrap adding subparser because we mostly want the same args.<|endoftext|>
|
b8b9158f65207e6f924e2f6b1945c74ae5b31f9cd2baa6be0b8a86f0bb3cf99f
|
def _add_def_args(parser):
'Set some default arguments common to the different processing types.'
parser.add_argument('fns', type=str, nargs='+', help='The files to process')
parser.add_argument('-o', type=str, help='Output to this file (folder if multiple inputs)')
|
Set some default arguments common to the different processing types.
|
Src/PulseEKKO/impdar/bin/apdar.py
|
_add_def_args
|
rdrews-dev/ReMeltRadar
| 0
|
python
|
def _add_def_args(parser):
parser.add_argument('fns', type=str, nargs='+', help='The files to process')
parser.add_argument('-o', type=str, help='Output to this file (folder if multiple inputs)')
|
def _add_def_args(parser):
parser.add_argument('fns', type=str, nargs='+', help='The files to process')
parser.add_argument('-o', type=str, help='Output to this file (folder if multiple inputs)')<|docstring|>Set some default arguments common to the different processing types.<|endoftext|>
|
ea042164d2c68a627b10cfbf70c0cc53e2aa2164893ccb40c5f63ee00d5d032d
|
def main():
'Get arguments, process data, handle saving.'
parser = _get_args()
args = parser.parse_args(sys.argv[1:])
if (not hasattr(args, 'func')):
parser.parse_args(['-h'])
apres_data = [load_apres.load_apres_single_file(fn) for fn in args.fns]
if (args.name == 'load'):
pass
else:
for dat in apres_data:
args.func(dat, **vars(args))
if (args.name == 'load'):
name = 'raw'
else:
name = args.name
if (args.o is not None):
if ((len(apres_data) > 1) or (args.o[(- 1)] == '/')):
for (d, f) in zip(apres_data, args.fns):
bn = os.path.split(os.path.splitext(f)[0])[1]
if (bn[(- 4):] == '_raw'):
bn = bn[:(- 4)]
out_fn = os.path.join(args.o, (bn + '_{:s}.h5'.format(name)))
d.save(out_fn)
else:
out_fn = args.o
apres_data[0].save(out_fn)
else:
for (d, f) in zip(apres_data, args.fns):
bn = os.path.splitext(f)[0]
if (bn[(- 4):] == '_raw'):
bn = bn[:(- 4)]
out_fn = (bn + '_{:s}.h5'.format(name))
d.save(out_fn)
|
Get arguments, process data, handle saving.
|
Src/PulseEKKO/impdar/bin/apdar.py
|
main
|
rdrews-dev/ReMeltRadar
| 0
|
python
|
def main():
parser = _get_args()
args = parser.parse_args(sys.argv[1:])
if (not hasattr(args, 'func')):
parser.parse_args(['-h'])
apres_data = [load_apres.load_apres_single_file(fn) for fn in args.fns]
if (args.name == 'load'):
pass
else:
for dat in apres_data:
args.func(dat, **vars(args))
if (args.name == 'load'):
name = 'raw'
else:
name = args.name
if (args.o is not None):
if ((len(apres_data) > 1) or (args.o[(- 1)] == '/')):
for (d, f) in zip(apres_data, args.fns):
bn = os.path.split(os.path.splitext(f)[0])[1]
if (bn[(- 4):] == '_raw'):
bn = bn[:(- 4)]
out_fn = os.path.join(args.o, (bn + '_{:s}.h5'.format(name)))
d.save(out_fn)
else:
out_fn = args.o
apres_data[0].save(out_fn)
else:
for (d, f) in zip(apres_data, args.fns):
bn = os.path.splitext(f)[0]
if (bn[(- 4):] == '_raw'):
bn = bn[:(- 4)]
out_fn = (bn + '_{:s}.h5'.format(name))
d.save(out_fn)
|
def main():
parser = _get_args()
args = parser.parse_args(sys.argv[1:])
if (not hasattr(args, 'func')):
parser.parse_args(['-h'])
apres_data = [load_apres.load_apres_single_file(fn) for fn in args.fns]
if (args.name == 'load'):
pass
else:
for dat in apres_data:
args.func(dat, **vars(args))
if (args.name == 'load'):
name = 'raw'
else:
name = args.name
if (args.o is not None):
if ((len(apres_data) > 1) or (args.o[(- 1)] == '/')):
for (d, f) in zip(apres_data, args.fns):
bn = os.path.split(os.path.splitext(f)[0])[1]
if (bn[(- 4):] == '_raw'):
bn = bn[:(- 4)]
out_fn = os.path.join(args.o, (bn + '_{:s}.h5'.format(name)))
d.save(out_fn)
else:
out_fn = args.o
apres_data[0].save(out_fn)
else:
for (d, f) in zip(apres_data, args.fns):
bn = os.path.splitext(f)[0]
if (bn[(- 4):] == '_raw'):
bn = bn[:(- 4)]
out_fn = (bn + '_{:s}.h5'.format(name))
d.save(out_fn)<|docstring|>Get arguments, process data, handle saving.<|endoftext|>
|
0229732920be02c7b6e4146f259cf577c7c0c6fe84c2130273ab1b6c4622934d
|
def crop(dat, lim=0, top_or_bottom='top', dimension='snum', **kwargs):
'Crop in the vertical.'
dat.crop(lim, top_or_bottom=top_or_bottom, dimension=dimension)
|
Crop in the vertical.
|
Src/PulseEKKO/impdar/bin/apdar.py
|
crop
|
rdrews-dev/ReMeltRadar
| 0
|
python
|
def crop(dat, lim=0, top_or_bottom='top', dimension='snum', **kwargs):
dat.crop(lim, top_or_bottom=top_or_bottom, dimension=dimension)
|
def crop(dat, lim=0, top_or_bottom='top', dimension='snum', **kwargs):
dat.crop(lim, top_or_bottom=top_or_bottom, dimension=dimension)<|docstring|>Crop in the vertical.<|endoftext|>
|
182a2f0545807bd28b5ccf989c97f45001e13f19803de93a0a229c20629e1c35
|
def readout(module, action, variable=None, show=False, numeric=True):
"\n Generic readout function, that wraps values in a json-compliant way.\n :module: TR-064 sub-modules, such as 'WANIPConn1'\n :action: Calls an action, e.g. 'GetStatusInfo', as defined by TR-04 (cf. https://avm.de/service/schnittstellen/)\n :variable: (optional) a specific variable out of this set to extract\n :show: print variable name\n :numeric: cast value to numeric\n "
try:
answer_dict = fc.call_action(module, action)
except BaseException:
print(f'Could not query {module} with action {action}')
raise
if (action == 'GetAddonInfos'):
answer_dict['NewX_AVM_DE_TotalBytesSent64'] = int(answer_dict['NewX_AVM_DE_TotalBytesSent64'])
answer_dict['NewX_AVM_DE_TotalBytesReceived64'] = int(answer_dict['NewX_AVM_DE_TotalBytesReceived64'])
if variable:
answer_dict = str(answer_dict[variable])
if (not numeric):
answer_dict = (('"' + answer_dict) + '"')
if show:
answer_dict = ((('"' + variable) + '": ') + answer_dict)
else:
entitiesToRemove = ('NewAllowedCharsSSID', 'NewDNSServer1', 'NewDNSServer2', 'NewVoipDNSServer1', 'NewVoipDNSServer2', 'NewATURVendor', 'NewATURCountry', 'NewDeviceLog')
entitiesToRemove = [answer_dict.pop(k, None) for k in entitiesToRemove]
answer_dict = str(answer_dict)[1:(- 1)]
answer_dict = answer_dict.replace('NewBytes', 'NewTotalBytes')
answer_dict = answer_dict.replace('NewPackets', 'NewTotalPackets')
flattened_string = answer_dict.replace("'", '"').replace('True', 'true').replace('False', 'false')
return flattened_string
|
Generic readout function, that wraps values in a json-compliant way.
:module: TR-064 sub-modules, such as 'WANIPConn1'
:action: Calls an action, e.g. 'GetStatusInfo', as defined by TR-04 (cf. https://avm.de/service/schnittstellen/)
:variable: (optional) a specific variable out of this set to extract
:show: print variable name
:numeric: cast value to numeric
|
checkfritz.py
|
readout
|
blackw1ng/FritzBox-monitor
| 42
|
python
|
def readout(module, action, variable=None, show=False, numeric=True):
"\n Generic readout function, that wraps values in a json-compliant way.\n :module: TR-064 sub-modules, such as 'WANIPConn1'\n :action: Calls an action, e.g. 'GetStatusInfo', as defined by TR-04 (cf. https://avm.de/service/schnittstellen/)\n :variable: (optional) a specific variable out of this set to extract\n :show: print variable name\n :numeric: cast value to numeric\n "
try:
answer_dict = fc.call_action(module, action)
except BaseException:
print(f'Could not query {module} with action {action}')
raise
if (action == 'GetAddonInfos'):
answer_dict['NewX_AVM_DE_TotalBytesSent64'] = int(answer_dict['NewX_AVM_DE_TotalBytesSent64'])
answer_dict['NewX_AVM_DE_TotalBytesReceived64'] = int(answer_dict['NewX_AVM_DE_TotalBytesReceived64'])
if variable:
answer_dict = str(answer_dict[variable])
if (not numeric):
answer_dict = (('"' + answer_dict) + '"')
if show:
answer_dict = ((('"' + variable) + '": ') + answer_dict)
else:
entitiesToRemove = ('NewAllowedCharsSSID', 'NewDNSServer1', 'NewDNSServer2', 'NewVoipDNSServer1', 'NewVoipDNSServer2', 'NewATURVendor', 'NewATURCountry', 'NewDeviceLog')
entitiesToRemove = [answer_dict.pop(k, None) for k in entitiesToRemove]
answer_dict = str(answer_dict)[1:(- 1)]
answer_dict = answer_dict.replace('NewBytes', 'NewTotalBytes')
answer_dict = answer_dict.replace('NewPackets', 'NewTotalPackets')
flattened_string = answer_dict.replace("'", '"').replace('True', 'true').replace('False', 'false')
return flattened_string
|
def readout(module, action, variable=None, show=False, numeric=True):
"\n Generic readout function, that wraps values in a json-compliant way.\n :module: TR-064 sub-modules, such as 'WANIPConn1'\n :action: Calls an action, e.g. 'GetStatusInfo', as defined by TR-04 (cf. https://avm.de/service/schnittstellen/)\n :variable: (optional) a specific variable out of this set to extract\n :show: print variable name\n :numeric: cast value to numeric\n "
try:
answer_dict = fc.call_action(module, action)
except BaseException:
print(f'Could not query {module} with action {action}')
raise
if (action == 'GetAddonInfos'):
answer_dict['NewX_AVM_DE_TotalBytesSent64'] = int(answer_dict['NewX_AVM_DE_TotalBytesSent64'])
answer_dict['NewX_AVM_DE_TotalBytesReceived64'] = int(answer_dict['NewX_AVM_DE_TotalBytesReceived64'])
if variable:
answer_dict = str(answer_dict[variable])
if (not numeric):
answer_dict = (('"' + answer_dict) + '"')
if show:
answer_dict = ((('"' + variable) + '": ') + answer_dict)
else:
entitiesToRemove = ('NewAllowedCharsSSID', 'NewDNSServer1', 'NewDNSServer2', 'NewVoipDNSServer1', 'NewVoipDNSServer2', 'NewATURVendor', 'NewATURCountry', 'NewDeviceLog')
entitiesToRemove = [answer_dict.pop(k, None) for k in entitiesToRemove]
answer_dict = str(answer_dict)[1:(- 1)]
answer_dict = answer_dict.replace('NewBytes', 'NewTotalBytes')
answer_dict = answer_dict.replace('NewPackets', 'NewTotalPackets')
flattened_string = answer_dict.replace("'", '"').replace('True', 'true').replace('False', 'false')
return flattened_string<|docstring|>Generic readout function, that wraps values in a json-compliant way.
:module: TR-064 sub-modules, such as 'WANIPConn1'
:action: Calls an action, e.g. 'GetStatusInfo', as defined by TR-04 (cf. https://avm.de/service/schnittstellen/)
:variable: (optional) a specific variable out of this set to extract
:show: print variable name
:numeric: cast value to numeric<|endoftext|>
|
2dee0b2efbd37d7b2ea850b4a98624eaba8cb876e6ebda81e7c806da39bec285
|
@staticmethod
def execute(list_download_order: Iterable[DownloadOrder], *, limit: int=5, media_filter: Optional[MediaFilter]=None, allow_http_status: List[int]=None) -> List[MediaDownloadResult]:
'Executes parallel media downloading.'
return asyncio.get_event_loop().run_until_complete(ParallelMediaDownloadCoroutine.execute(list_download_order, limit=limit, media_filter=media_filter, allow_http_status=allow_http_status))
|
Executes parallel media downloading.
|
parallelmediadownloader/parallel_media_downloader.py
|
execute
|
yukihiko-shinoda/parallel-media-downloader
| 1
|
python
|
@staticmethod
def execute(list_download_order: Iterable[DownloadOrder], *, limit: int=5, media_filter: Optional[MediaFilter]=None, allow_http_status: List[int]=None) -> List[MediaDownloadResult]:
return asyncio.get_event_loop().run_until_complete(ParallelMediaDownloadCoroutine.execute(list_download_order, limit=limit, media_filter=media_filter, allow_http_status=allow_http_status))
|
@staticmethod
def execute(list_download_order: Iterable[DownloadOrder], *, limit: int=5, media_filter: Optional[MediaFilter]=None, allow_http_status: List[int]=None) -> List[MediaDownloadResult]:
return asyncio.get_event_loop().run_until_complete(ParallelMediaDownloadCoroutine.execute(list_download_order, limit=limit, media_filter=media_filter, allow_http_status=allow_http_status))<|docstring|>Executes parallel media downloading.<|endoftext|>
|
99cee1b8b49227d5cdfd2069fa22c4a0e049eecd8c861f70d8e0fc2533125098
|
def smooth(x, window_len=5, window='hanning'):
'smooth the data using a window with requested size.\n \n This method is based on the convolution of a scaled window with the signal.\n The signal is prepared by introducing reflected copies of the signal \n (with the window size) in both ends so that transient parts are minimized\n in the begining and end part of the output signal.\n \n input:\n x: input signal \n window_len: dimension of the smoothing window; should be an odd integer\n window: type of window from "flat", "hanning", "hamming", "bartlett",\n "blackman"\n flat window will produce a moving average smoothing.\n\n output:\n smoothed signal\n \n example:\n t=linspace(-2,2,0.1)\n x=sin(t)+randn(len(t))*0.1\n y=smooth(x)\n \n see also: \n np.hanning, np.hamming, np.bartlett, np.blackman, np.convolve\n scipy.signal.lfilter\n \n TODO: the window parameter could be the window itself if an array instead\n of a string\n NOTE: length(output) != length(input)\n to correct: return y[(window_len/2-1):-(window_len/2)] instead of y.\n '
if (x.ndim != 1):
raise (ValueError, 'smooth only accepts 1 dimension arrays.')
elif (x.size < window_len):
raise (ValueError, 'Input vector needs to be bigger than window size.')
elif (window not in ('flat', 'hanning', 'hamming', 'bartlett', 'blackman')):
raise (ValueError, "Window is 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
if (window_len < 3):
return x
s = np.r_[(x[(window_len - 1):0:(- 1)], x, x[(- 2):((- 1) - window_len):(- 1)])]
if (window == 'flat'):
w = np.ones(window_len, 'd')
else:
w = eval('np.{}(window_len)'.format(window))
y = np.convolve((w / w.sum()), s, mode='valid')[2:(- 2)]
return y
|
smooth the data using a window with requested size.
This method is based on the convolution of a scaled window with the signal.
The signal is prepared by introducing reflected copies of the signal
(with the window size) in both ends so that transient parts are minimized
in the begining and end part of the output signal.
input:
x: input signal
window_len: dimension of the smoothing window; should be an odd integer
window: type of window from "flat", "hanning", "hamming", "bartlett",
"blackman"
flat window will produce a moving average smoothing.
output:
smoothed signal
example:
t=linspace(-2,2,0.1)
x=sin(t)+randn(len(t))*0.1
y=smooth(x)
see also:
np.hanning, np.hamming, np.bartlett, np.blackman, np.convolve
scipy.signal.lfilter
TODO: the window parameter could be the window itself if an array instead
of a string
NOTE: length(output) != length(input)
to correct: return y[(window_len/2-1):-(window_len/2)] instead of y.
|
flypy/responsetools/sampling.py
|
smooth
|
ikestar99/flypy
| 0
|
python
|
def smooth(x, window_len=5, window='hanning'):
'smooth the data using a window with requested size.\n \n This method is based on the convolution of a scaled window with the signal.\n The signal is prepared by introducing reflected copies of the signal \n (with the window size) in both ends so that transient parts are minimized\n in the begining and end part of the output signal.\n \n input:\n x: input signal \n window_len: dimension of the smoothing window; should be an odd integer\n window: type of window from "flat", "hanning", "hamming", "bartlett",\n "blackman"\n flat window will produce a moving average smoothing.\n\n output:\n smoothed signal\n \n example:\n t=linspace(-2,2,0.1)\n x=sin(t)+randn(len(t))*0.1\n y=smooth(x)\n \n see also: \n np.hanning, np.hamming, np.bartlett, np.blackman, np.convolve\n scipy.signal.lfilter\n \n TODO: the window parameter could be the window itself if an array instead\n of a string\n NOTE: length(output) != length(input)\n to correct: return y[(window_len/2-1):-(window_len/2)] instead of y.\n '
if (x.ndim != 1):
raise (ValueError, 'smooth only accepts 1 dimension arrays.')
elif (x.size < window_len):
raise (ValueError, 'Input vector needs to be bigger than window size.')
elif (window not in ('flat', 'hanning', 'hamming', 'bartlett', 'blackman')):
raise (ValueError, "Window is 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
if (window_len < 3):
return x
s = np.r_[(x[(window_len - 1):0:(- 1)], x, x[(- 2):((- 1) - window_len):(- 1)])]
if (window == 'flat'):
w = np.ones(window_len, 'd')
else:
w = eval('np.{}(window_len)'.format(window))
y = np.convolve((w / w.sum()), s, mode='valid')[2:(- 2)]
return y
|
def smooth(x, window_len=5, window='hanning'):
'smooth the data using a window with requested size.\n \n This method is based on the convolution of a scaled window with the signal.\n The signal is prepared by introducing reflected copies of the signal \n (with the window size) in both ends so that transient parts are minimized\n in the begining and end part of the output signal.\n \n input:\n x: input signal \n window_len: dimension of the smoothing window; should be an odd integer\n window: type of window from "flat", "hanning", "hamming", "bartlett",\n "blackman"\n flat window will produce a moving average smoothing.\n\n output:\n smoothed signal\n \n example:\n t=linspace(-2,2,0.1)\n x=sin(t)+randn(len(t))*0.1\n y=smooth(x)\n \n see also: \n np.hanning, np.hamming, np.bartlett, np.blackman, np.convolve\n scipy.signal.lfilter\n \n TODO: the window parameter could be the window itself if an array instead\n of a string\n NOTE: length(output) != length(input)\n to correct: return y[(window_len/2-1):-(window_len/2)] instead of y.\n '
if (x.ndim != 1):
raise (ValueError, 'smooth only accepts 1 dimension arrays.')
elif (x.size < window_len):
raise (ValueError, 'Input vector needs to be bigger than window size.')
elif (window not in ('flat', 'hanning', 'hamming', 'bartlett', 'blackman')):
raise (ValueError, "Window is 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
if (window_len < 3):
return x
s = np.r_[(x[(window_len - 1):0:(- 1)], x, x[(- 2):((- 1) - window_len):(- 1)])]
if (window == 'flat'):
w = np.ones(window_len, 'd')
else:
w = eval('np.{}(window_len)'.format(window))
y = np.convolve((w / w.sum()), s, mode='valid')[2:(- 2)]
return y<|docstring|>smooth the data using a window with requested size.
This method is based on the convolution of a scaled window with the signal.
The signal is prepared by introducing reflected copies of the signal
(with the window size) in both ends so that transient parts are minimized
in the begining and end part of the output signal.
input:
x: input signal
window_len: dimension of the smoothing window; should be an odd integer
window: type of window from "flat", "hanning", "hamming", "bartlett",
"blackman"
flat window will produce a moving average smoothing.
output:
smoothed signal
example:
t=linspace(-2,2,0.1)
x=sin(t)+randn(len(t))*0.1
y=smooth(x)
see also:
np.hanning, np.hamming, np.bartlett, np.blackman, np.convolve
scipy.signal.lfilter
TODO: the window parameter could be the window itself if an array instead
of a string
NOTE: length(output) != length(input)
to correct: return y[(window_len/2-1):-(window_len/2)] instead of y.<|endoftext|>
|
4d998de67fe79cae55ab45819681f46220d0cf25c5448996e5ef9c698b27afcc
|
def __init__(self, pp, cellarea, cmask, flowacc, slope, S_initial=None, outputs=False):
"\n sets up Topmodel for the catchment assuming homogenous\n effective soil depth 'm' and sat. hydr. conductivity 'ko'.\n This is the 'classic' version of Topmodel where hydrologic similarity index is TWI = log(a / tan(b)).\n \n Args:\n pp - parameter dict with keys:\n dt - timestep [s]\n ko - soil transmissivity at saturation [m/s]\n m - effective soil depth (m), i.e. decay factor of Ksat with depth\n twi_cutoff - max allowed twi -index\n so - initial catchment average saturation deficit (m)\n cmask - catchment mask, 1 = catchment_cell\n cellarea - gridcell area [m2]\n flowacc - flow accumulation per unit contour length (m)\n slope - local slope (deg)\n S_initial - initial storage deficit, overrides that in 'pp'\n outputs - True stores outputs after each timestep into dictionary\n "
if (not S_initial):
S_initial = pp['so']
self.dt = float(pp['dt'])
self.cmask = cmask
self.CellArea = cellarea
dx = (cellarea ** 0.5)
self.CatchmentArea = (np.size(cmask[(cmask == 1)]) * self.CellArea)
self.a = (flowacc * cmask)
self.slope = (slope * cmask)
self.M = pp['m']
self.To = (pp['ko'] * self.dt)
" \n local and catchment average hydrologic similarity indices (xi, X).\n Set xi > twi_cutoff equal to cutoff value to remove tail of twi-distribution.\n This concerns mainly the stream network cells. 'Outliers' in twi-distribution are\n problem for streamflow prediction\n "
slope_rad = np.radians(self.slope)
xi = np.log(((self.a / dx) / (np.tan(slope_rad) + eps)))
clim = np.percentile(xi[(xi > 0)], pp['twi_cutoff'])
xi[(xi > clim)] = clim
self.xi = xi
self.X = ((1.0 / self.CatchmentArea) * np.nansum((self.xi * self.CellArea)))
self.Qo = (self.To * np.exp((- self.X)))
s = self.local_s(S_initial)
s[(s < 0)] = 0.0
self.S = np.nanmean(s)
if outputs:
self.results = {'S': [], 'Qb': [], 'Qr': [], 'Qt': [], 'qr': [], 'fsat': [], 'Mbe': [], 'R': []}
|
sets up Topmodel for the catchment assuming homogenous
effective soil depth 'm' and sat. hydr. conductivity 'ko'.
This is the 'classic' version of Topmodel where hydrologic similarity index is TWI = log(a / tan(b)).
Args:
pp - parameter dict with keys:
dt - timestep [s]
ko - soil transmissivity at saturation [m/s]
m - effective soil depth (m), i.e. decay factor of Ksat with depth
twi_cutoff - max allowed twi -index
so - initial catchment average saturation deficit (m)
cmask - catchment mask, 1 = catchment_cell
cellarea - gridcell area [m2]
flowacc - flow accumulation per unit contour length (m)
slope - local slope (deg)
S_initial - initial storage deficit, overrides that in 'pp'
outputs - True stores outputs after each timestep into dictionary
|
topmodel.py
|
__init__
|
slauniainen/SpaFHy_v1
| 3
|
python
|
def __init__(self, pp, cellarea, cmask, flowacc, slope, S_initial=None, outputs=False):
"\n sets up Topmodel for the catchment assuming homogenous\n effective soil depth 'm' and sat. hydr. conductivity 'ko'.\n This is the 'classic' version of Topmodel where hydrologic similarity index is TWI = log(a / tan(b)).\n \n Args:\n pp - parameter dict with keys:\n dt - timestep [s]\n ko - soil transmissivity at saturation [m/s]\n m - effective soil depth (m), i.e. decay factor of Ksat with depth\n twi_cutoff - max allowed twi -index\n so - initial catchment average saturation deficit (m)\n cmask - catchment mask, 1 = catchment_cell\n cellarea - gridcell area [m2]\n flowacc - flow accumulation per unit contour length (m)\n slope - local slope (deg)\n S_initial - initial storage deficit, overrides that in 'pp'\n outputs - True stores outputs after each timestep into dictionary\n "
if (not S_initial):
S_initial = pp['so']
self.dt = float(pp['dt'])
self.cmask = cmask
self.CellArea = cellarea
dx = (cellarea ** 0.5)
self.CatchmentArea = (np.size(cmask[(cmask == 1)]) * self.CellArea)
self.a = (flowacc * cmask)
self.slope = (slope * cmask)
self.M = pp['m']
self.To = (pp['ko'] * self.dt)
" \n local and catchment average hydrologic similarity indices (xi, X).\n Set xi > twi_cutoff equal to cutoff value to remove tail of twi-distribution.\n This concerns mainly the stream network cells. 'Outliers' in twi-distribution are\n problem for streamflow prediction\n "
slope_rad = np.radians(self.slope)
xi = np.log(((self.a / dx) / (np.tan(slope_rad) + eps)))
clim = np.percentile(xi[(xi > 0)], pp['twi_cutoff'])
xi[(xi > clim)] = clim
self.xi = xi
self.X = ((1.0 / self.CatchmentArea) * np.nansum((self.xi * self.CellArea)))
self.Qo = (self.To * np.exp((- self.X)))
s = self.local_s(S_initial)
s[(s < 0)] = 0.0
self.S = np.nanmean(s)
if outputs:
self.results = {'S': [], 'Qb': [], 'Qr': [], 'Qt': [], 'qr': [], 'fsat': [], 'Mbe': [], 'R': []}
|
def __init__(self, pp, cellarea, cmask, flowacc, slope, S_initial=None, outputs=False):
"\n sets up Topmodel for the catchment assuming homogenous\n effective soil depth 'm' and sat. hydr. conductivity 'ko'.\n This is the 'classic' version of Topmodel where hydrologic similarity index is TWI = log(a / tan(b)).\n \n Args:\n pp - parameter dict with keys:\n dt - timestep [s]\n ko - soil transmissivity at saturation [m/s]\n m - effective soil depth (m), i.e. decay factor of Ksat with depth\n twi_cutoff - max allowed twi -index\n so - initial catchment average saturation deficit (m)\n cmask - catchment mask, 1 = catchment_cell\n cellarea - gridcell area [m2]\n flowacc - flow accumulation per unit contour length (m)\n slope - local slope (deg)\n S_initial - initial storage deficit, overrides that in 'pp'\n outputs - True stores outputs after each timestep into dictionary\n "
if (not S_initial):
S_initial = pp['so']
self.dt = float(pp['dt'])
self.cmask = cmask
self.CellArea = cellarea
dx = (cellarea ** 0.5)
self.CatchmentArea = (np.size(cmask[(cmask == 1)]) * self.CellArea)
self.a = (flowacc * cmask)
self.slope = (slope * cmask)
self.M = pp['m']
self.To = (pp['ko'] * self.dt)
" \n local and catchment average hydrologic similarity indices (xi, X).\n Set xi > twi_cutoff equal to cutoff value to remove tail of twi-distribution.\n This concerns mainly the stream network cells. 'Outliers' in twi-distribution are\n problem for streamflow prediction\n "
slope_rad = np.radians(self.slope)
xi = np.log(((self.a / dx) / (np.tan(slope_rad) + eps)))
clim = np.percentile(xi[(xi > 0)], pp['twi_cutoff'])
xi[(xi > clim)] = clim
self.xi = xi
self.X = ((1.0 / self.CatchmentArea) * np.nansum((self.xi * self.CellArea)))
self.Qo = (self.To * np.exp((- self.X)))
s = self.local_s(S_initial)
s[(s < 0)] = 0.0
self.S = np.nanmean(s)
if outputs:
self.results = {'S': [], 'Qb': [], 'Qr': [], 'Qt': [], 'qr': [], 'fsat': [], 'Mbe': [], 'R': []}<|docstring|>sets up Topmodel for the catchment assuming homogenous
effective soil depth 'm' and sat. hydr. conductivity 'ko'.
This is the 'classic' version of Topmodel where hydrologic similarity index is TWI = log(a / tan(b)).
Args:
pp - parameter dict with keys:
dt - timestep [s]
ko - soil transmissivity at saturation [m/s]
m - effective soil depth (m), i.e. decay factor of Ksat with depth
twi_cutoff - max allowed twi -index
so - initial catchment average saturation deficit (m)
cmask - catchment mask, 1 = catchment_cell
cellarea - gridcell area [m2]
flowacc - flow accumulation per unit contour length (m)
slope - local slope (deg)
S_initial - initial storage deficit, overrides that in 'pp'
outputs - True stores outputs after each timestep into dictionary<|endoftext|>
|
5a71ec90489f6743adaab9ffeba0fd80c74ce331a60e5e91e29a6e2e21f43e83
|
def local_s(self, Smean):
'\n computes local storage deficit s [m] from catchment average\n '
s = (Smean + (self.M * (self.X - self.xi)))
return s
|
computes local storage deficit s [m] from catchment average
|
topmodel.py
|
local_s
|
slauniainen/SpaFHy_v1
| 3
|
python
|
def local_s(self, Smean):
'\n \n '
s = (Smean + (self.M * (self.X - self.xi)))
return s
|
def local_s(self, Smean):
'\n \n '
s = (Smean + (self.M * (self.X - self.xi)))
return s<|docstring|>computes local storage deficit s [m] from catchment average<|endoftext|>
|
80da63775dfa58475005f20f2b3f65b582733bf95033e2779d5bdcecffcae2d4
|
def subsurfaceflow(self):
'subsurface flow to stream network (per unit catchment area)'
Qb = (self.Qo * np.exp(((- self.S) / (self.M + eps))))
return Qb
|
subsurface flow to stream network (per unit catchment area)
|
topmodel.py
|
subsurfaceflow
|
slauniainen/SpaFHy_v1
| 3
|
python
|
def subsurfaceflow(self):
Qb = (self.Qo * np.exp(((- self.S) / (self.M + eps))))
return Qb
|
def subsurfaceflow(self):
Qb = (self.Qo * np.exp(((- self.S) / (self.M + eps))))
return Qb<|docstring|>subsurface flow to stream network (per unit catchment area)<|endoftext|>
|
e19eab9081151fde34acfd762c793e05f0d24f96a4cada68cf395692dbb4bb21
|
def run_timestep(self, R):
'\n runs a timestep, updates saturation deficit and returns fluxes\n Args:\n R - recharge [m per unit catchment area] during timestep\n OUT:\n Qb - baseflow [m per unit area]\n Qr - returnflow [m per unit area]\n qr - distributed returnflow [m]\n fsat - saturated area fraction [-]\n Note: \n R is the mean drainage [m] from bucketgrid.\n '
So = self.S
s = self.local_s(So)
Qb = self.subsurfaceflow()
S = ((So + Qb) - R)
s = self.local_s(S)
qr = (- s)
qr[(qr < 0)] = 0.0
Qr = ((np.nansum(qr) * self.CellArea) / self.CatchmentArea)
S = (S + Qr)
self.S = S
ix = np.where((s <= 0))
fsat = ((len(ix[0]) * self.CellArea) / self.CatchmentArea)
del ix
dS = (So - self.S)
dF = ((R - Qb) - Qr)
mbe = (dS - dF)
if hasattr(self, 'results'):
self.results['R'].append(R)
self.results['S'].append(self.S)
self.results['Qb'].append(Qb)
self.results['Qr'].append(Qr)
self.results['qr'].append(qr)
self.results['fsat'].append(fsat)
self.results['Mbe'].append(mbe)
return (Qb, Qr, qr, fsat)
|
runs a timestep, updates saturation deficit and returns fluxes
Args:
R - recharge [m per unit catchment area] during timestep
OUT:
Qb - baseflow [m per unit area]
Qr - returnflow [m per unit area]
qr - distributed returnflow [m]
fsat - saturated area fraction [-]
Note:
R is the mean drainage [m] from bucketgrid.
|
topmodel.py
|
run_timestep
|
slauniainen/SpaFHy_v1
| 3
|
python
|
def run_timestep(self, R):
'\n runs a timestep, updates saturation deficit and returns fluxes\n Args:\n R - recharge [m per unit catchment area] during timestep\n OUT:\n Qb - baseflow [m per unit area]\n Qr - returnflow [m per unit area]\n qr - distributed returnflow [m]\n fsat - saturated area fraction [-]\n Note: \n R is the mean drainage [m] from bucketgrid.\n '
So = self.S
s = self.local_s(So)
Qb = self.subsurfaceflow()
S = ((So + Qb) - R)
s = self.local_s(S)
qr = (- s)
qr[(qr < 0)] = 0.0
Qr = ((np.nansum(qr) * self.CellArea) / self.CatchmentArea)
S = (S + Qr)
self.S = S
ix = np.where((s <= 0))
fsat = ((len(ix[0]) * self.CellArea) / self.CatchmentArea)
del ix
dS = (So - self.S)
dF = ((R - Qb) - Qr)
mbe = (dS - dF)
if hasattr(self, 'results'):
self.results['R'].append(R)
self.results['S'].append(self.S)
self.results['Qb'].append(Qb)
self.results['Qr'].append(Qr)
self.results['qr'].append(qr)
self.results['fsat'].append(fsat)
self.results['Mbe'].append(mbe)
return (Qb, Qr, qr, fsat)
|
def run_timestep(self, R):
'\n runs a timestep, updates saturation deficit and returns fluxes\n Args:\n R - recharge [m per unit catchment area] during timestep\n OUT:\n Qb - baseflow [m per unit area]\n Qr - returnflow [m per unit area]\n qr - distributed returnflow [m]\n fsat - saturated area fraction [-]\n Note: \n R is the mean drainage [m] from bucketgrid.\n '
So = self.S
s = self.local_s(So)
Qb = self.subsurfaceflow()
S = ((So + Qb) - R)
s = self.local_s(S)
qr = (- s)
qr[(qr < 0)] = 0.0
Qr = ((np.nansum(qr) * self.CellArea) / self.CatchmentArea)
S = (S + Qr)
self.S = S
ix = np.where((s <= 0))
fsat = ((len(ix[0]) * self.CellArea) / self.CatchmentArea)
del ix
dS = (So - self.S)
dF = ((R - Qb) - Qr)
mbe = (dS - dF)
if hasattr(self, 'results'):
self.results['R'].append(R)
self.results['S'].append(self.S)
self.results['Qb'].append(Qb)
self.results['Qr'].append(Qr)
self.results['qr'].append(qr)
self.results['fsat'].append(fsat)
self.results['Mbe'].append(mbe)
return (Qb, Qr, qr, fsat)<|docstring|>runs a timestep, updates saturation deficit and returns fluxes
Args:
R - recharge [m per unit catchment area] during timestep
OUT:
Qb - baseflow [m per unit area]
Qr - returnflow [m per unit area]
qr - distributed returnflow [m]
fsat - saturated area fraction [-]
Note:
R is the mean drainage [m] from bucketgrid.<|endoftext|>
|
83a9c24c54842ed4325e0ddfdf6a418d51d00131293abb4b9a5430770dc9d7a8
|
def train_classifiers(data_file, cache_dir=os.path.curdir):
'\n This function...\n\n :param data_file:\n :param cache_dir:\n '
data = read_data(data_file)
for matter in ['WM', 'GM']:
classifier_file = os.path.join(cache_dir, 'Classifier', '{0}_matter_classifier.pkl'.format(matter))
if (not os.path.exists(os.path.dirname(classifier_file))):
os.makedirs(os.path.dirname(classifier_file))
train_classifier(data['Features'].values, data['Truth'][matter].values, classifier_file)
|
This function...
:param data_file:
:param cache_dir:
|
AutoWorkup/logismosb/maclearn/train_classifiers.py
|
train_classifiers
|
pnlbwh/BRAINSTools
| 89
|
python
|
def train_classifiers(data_file, cache_dir=os.path.curdir):
'\n This function...\n\n :param data_file:\n :param cache_dir:\n '
data = read_data(data_file)
for matter in ['WM', 'GM']:
classifier_file = os.path.join(cache_dir, 'Classifier', '{0}_matter_classifier.pkl'.format(matter))
if (not os.path.exists(os.path.dirname(classifier_file))):
os.makedirs(os.path.dirname(classifier_file))
train_classifier(data['Features'].values, data['Truth'][matter].values, classifier_file)
|
def train_classifiers(data_file, cache_dir=os.path.curdir):
'\n This function...\n\n :param data_file:\n :param cache_dir:\n '
data = read_data(data_file)
for matter in ['WM', 'GM']:
classifier_file = os.path.join(cache_dir, 'Classifier', '{0}_matter_classifier.pkl'.format(matter))
if (not os.path.exists(os.path.dirname(classifier_file))):
os.makedirs(os.path.dirname(classifier_file))
train_classifier(data['Features'].values, data['Truth'][matter].values, classifier_file)<|docstring|>This function...
:param data_file:
:param cache_dir:<|endoftext|>
|
416c8464972a1a18c722fc5d148602bc6f81919c6f72f6cb9af1bf1deaac87bc
|
def initialize(self, **kwargs):
'Initialize.'
super().initialize(impl=PyExt.EvPoll(), **kwargs)
|
Initialize.
|
tests/TestCompile.py
|
initialize
|
pzread/judge
| 25
|
python
|
def initialize(self, **kwargs):
super().initialize(impl=PyExt.EvPoll(), **kwargs)
|
def initialize(self, **kwargs):
super().initialize(impl=PyExt.EvPoll(), **kwargs)<|docstring|>Initialize.<|endoftext|>
|
b004a3ba23d10323c27ea8d1e14486b2b7c6187ee09640ed5eb92cf936d2cff5
|
@testing.gen_test(timeout=60)
def test_stdchal(self):
'Test g++, A + B problems.'
chal = StdChal(1, 'tests/testdata/testce.cpp', 'g++', 'diff', 'tests/testdata/res', ([{'in': 'tests/testdata/res/testdata/0.in', 'ans': 'tests/testdata/res/testdata/0.out', 'timelimit': 10000, 'memlimit': ((256 * 1024) * 1024)}] * 4), {})
result_list = (yield chal.start())
self.assertEqual(len(result_list), 4)
for result in result_list:
(_, _, status, verdict) = result
self.assertNotEqual(verdict, '')
self.assertEqual(status, STATUS_CE)
|
Test g++, A + B problems.
|
tests/TestCompile.py
|
test_stdchal
|
pzread/judge
| 25
|
python
|
@testing.gen_test(timeout=60)
def test_stdchal(self):
chal = StdChal(1, 'tests/testdata/testce.cpp', 'g++', 'diff', 'tests/testdata/res', ([{'in': 'tests/testdata/res/testdata/0.in', 'ans': 'tests/testdata/res/testdata/0.out', 'timelimit': 10000, 'memlimit': ((256 * 1024) * 1024)}] * 4), {})
result_list = (yield chal.start())
self.assertEqual(len(result_list), 4)
for result in result_list:
(_, _, status, verdict) = result
self.assertNotEqual(verdict, )
self.assertEqual(status, STATUS_CE)
|
@testing.gen_test(timeout=60)
def test_stdchal(self):
chal = StdChal(1, 'tests/testdata/testce.cpp', 'g++', 'diff', 'tests/testdata/res', ([{'in': 'tests/testdata/res/testdata/0.in', 'ans': 'tests/testdata/res/testdata/0.out', 'timelimit': 10000, 'memlimit': ((256 * 1024) * 1024)}] * 4), {})
result_list = (yield chal.start())
self.assertEqual(len(result_list), 4)
for result in result_list:
(_, _, status, verdict) = result
self.assertNotEqual(verdict, )
self.assertEqual(status, STATUS_CE)<|docstring|>Test g++, A + B problems.<|endoftext|>
|
0b3b990d4df27f2082700c87760ed3bdc6805923a817afd7eea9137ba1bf516e
|
def parse_events(csvfile):
'\n Read list of events from the given CSV file.\n\n CSV columns: date,date_comment,end_time,category,comment\n\n The `date` is expected to appear only when the time crosses midnight. The\n `date_comment` is ignored. The `comment` column is optional. The logical\n `begin_time` of an event is the `end_time` of the event above it.\n\n Returns a list of Event objects. The events are in chronological order,\n cover a contiguous stretch of time, and do not overlap.\n '
reader = csv.reader(csvfile)
header = reader.next()
ref_header = ['date', 'date_comment', 'end_time', 'category', 'comment']
if (header != ref_header):
raise ValueError('Unexpected header line: expected {} but found {}'.format(ref_header, header))
prev_row = parse_csv_line(2, reader.next())
if (not prev_row.date):
raise ValueError('first row must specify date')
if (prev_row.end_time is None):
raise ValueError('first row must specify end_time')
prev_event_end = datetime.combine(prev_row.date, prev_row.end_time)
events = []
one_day = timedelta(days=1)
for (lineno, fields) in enumerate(reader, 3):
row = parse_csv_line(lineno, fields)
if row.date:
if (row.date != (prev_row.date + one_day)):
raise ValueError('line {}: expected the day after, but got a different date'.format(lineno))
if (row.end_time >= prev_row.end_time):
raise ValueError('line {}: got date but did not expect one'.format(lineno))
else:
if (row.end_time <= prev_row.end_time):
raise ValueError('line {}: expected date, but did not get one'.format(lineno))
row.date = prev_row.date
if (row.end_time is None):
raise ValueError('line {}: expected end_time'.format(lineno))
if (not row.category):
raise ValueError('line {}: expected category'.format(lineno))
event = Event()
event.begin = prev_event_end
event.end = datetime.combine(row.date, row.end_time)
event.category = row.category
event.comment = row.comment
events.append(event)
prev_row = row
prev_event_end = event.end
return events
|
Read list of events from the given CSV file.
CSV columns: date,date_comment,end_time,category,comment
The `date` is expected to appear only when the time crosses midnight. The
`date_comment` is ignored. The `comment` column is optional. The logical
`begin_time` of an event is the `end_time` of the event above it.
Returns a list of Event objects. The events are in chronological order,
cover a contiguous stretch of time, and do not overlap.
|
tools/events.py
|
parse_events
|
cberzan/ticktockman
| 0
|
python
|
def parse_events(csvfile):
'\n Read list of events from the given CSV file.\n\n CSV columns: date,date_comment,end_time,category,comment\n\n The `date` is expected to appear only when the time crosses midnight. The\n `date_comment` is ignored. The `comment` column is optional. The logical\n `begin_time` of an event is the `end_time` of the event above it.\n\n Returns a list of Event objects. The events are in chronological order,\n cover a contiguous stretch of time, and do not overlap.\n '
reader = csv.reader(csvfile)
header = reader.next()
ref_header = ['date', 'date_comment', 'end_time', 'category', 'comment']
if (header != ref_header):
raise ValueError('Unexpected header line: expected {} but found {}'.format(ref_header, header))
prev_row = parse_csv_line(2, reader.next())
if (not prev_row.date):
raise ValueError('first row must specify date')
if (prev_row.end_time is None):
raise ValueError('first row must specify end_time')
prev_event_end = datetime.combine(prev_row.date, prev_row.end_time)
events = []
one_day = timedelta(days=1)
for (lineno, fields) in enumerate(reader, 3):
row = parse_csv_line(lineno, fields)
if row.date:
if (row.date != (prev_row.date + one_day)):
raise ValueError('line {}: expected the day after, but got a different date'.format(lineno))
if (row.end_time >= prev_row.end_time):
raise ValueError('line {}: got date but did not expect one'.format(lineno))
else:
if (row.end_time <= prev_row.end_time):
raise ValueError('line {}: expected date, but did not get one'.format(lineno))
row.date = prev_row.date
if (row.end_time is None):
raise ValueError('line {}: expected end_time'.format(lineno))
if (not row.category):
raise ValueError('line {}: expected category'.format(lineno))
event = Event()
event.begin = prev_event_end
event.end = datetime.combine(row.date, row.end_time)
event.category = row.category
event.comment = row.comment
events.append(event)
prev_row = row
prev_event_end = event.end
return events
|
def parse_events(csvfile):
'\n Read list of events from the given CSV file.\n\n CSV columns: date,date_comment,end_time,category,comment\n\n The `date` is expected to appear only when the time crosses midnight. The\n `date_comment` is ignored. The `comment` column is optional. The logical\n `begin_time` of an event is the `end_time` of the event above it.\n\n Returns a list of Event objects. The events are in chronological order,\n cover a contiguous stretch of time, and do not overlap.\n '
reader = csv.reader(csvfile)
header = reader.next()
ref_header = ['date', 'date_comment', 'end_time', 'category', 'comment']
if (header != ref_header):
raise ValueError('Unexpected header line: expected {} but found {}'.format(ref_header, header))
prev_row = parse_csv_line(2, reader.next())
if (not prev_row.date):
raise ValueError('first row must specify date')
if (prev_row.end_time is None):
raise ValueError('first row must specify end_time')
prev_event_end = datetime.combine(prev_row.date, prev_row.end_time)
events = []
one_day = timedelta(days=1)
for (lineno, fields) in enumerate(reader, 3):
row = parse_csv_line(lineno, fields)
if row.date:
if (row.date != (prev_row.date + one_day)):
raise ValueError('line {}: expected the day after, but got a different date'.format(lineno))
if (row.end_time >= prev_row.end_time):
raise ValueError('line {}: got date but did not expect one'.format(lineno))
else:
if (row.end_time <= prev_row.end_time):
raise ValueError('line {}: expected date, but did not get one'.format(lineno))
row.date = prev_row.date
if (row.end_time is None):
raise ValueError('line {}: expected end_time'.format(lineno))
if (not row.category):
raise ValueError('line {}: expected category'.format(lineno))
event = Event()
event.begin = prev_event_end
event.end = datetime.combine(row.date, row.end_time)
event.category = row.category
event.comment = row.comment
events.append(event)
prev_row = row
prev_event_end = event.end
return events<|docstring|>Read list of events from the given CSV file.
CSV columns: date,date_comment,end_time,category,comment
The `date` is expected to appear only when the time crosses midnight. The
`date_comment` is ignored. The `comment` column is optional. The logical
`begin_time` of an event is the `end_time` of the event above it.
Returns a list of Event objects. The events are in chronological order,
cover a contiguous stretch of time, and do not overlap.<|endoftext|>
|
993c221493672cbb6023a166835449126cf2a1f8f0243deff90954f17b8d6649
|
def parse_csv_line(lineno, fields):
'\n Parse a CSV line (`fields` is a list of string) into a RawLine object.\n '
line = RawLine()
if (len(fields) != 5):
raise ValueError('line {}: expected {} fields, but found {}'.format(lineno, 5, len(fields)))
if fields[0]:
try:
line.date = dateutil.parser.parse(fields[0]).date()
except ValueError:
raise ValueError('line {}: could not parse date'.format(lineno))
if fields[1]:
line.date_comment = fields[1]
if fields[2]:
try:
line.end_time = dateutil.parser.parse(fields[2]).time()
except ValueError:
raise ValueError('line {}: could not parse time'.format(lineno))
if fields[3]:
line.category = fields[3]
if fields[4]:
line.comment = fields[4]
return line
|
Parse a CSV line (`fields` is a list of string) into a RawLine object.
|
tools/events.py
|
parse_csv_line
|
cberzan/ticktockman
| 0
|
python
|
def parse_csv_line(lineno, fields):
'\n \n '
line = RawLine()
if (len(fields) != 5):
raise ValueError('line {}: expected {} fields, but found {}'.format(lineno, 5, len(fields)))
if fields[0]:
try:
line.date = dateutil.parser.parse(fields[0]).date()
except ValueError:
raise ValueError('line {}: could not parse date'.format(lineno))
if fields[1]:
line.date_comment = fields[1]
if fields[2]:
try:
line.end_time = dateutil.parser.parse(fields[2]).time()
except ValueError:
raise ValueError('line {}: could not parse time'.format(lineno))
if fields[3]:
line.category = fields[3]
if fields[4]:
line.comment = fields[4]
return line
|
def parse_csv_line(lineno, fields):
'\n \n '
line = RawLine()
if (len(fields) != 5):
raise ValueError('line {}: expected {} fields, but found {}'.format(lineno, 5, len(fields)))
if fields[0]:
try:
line.date = dateutil.parser.parse(fields[0]).date()
except ValueError:
raise ValueError('line {}: could not parse date'.format(lineno))
if fields[1]:
line.date_comment = fields[1]
if fields[2]:
try:
line.end_time = dateutil.parser.parse(fields[2]).time()
except ValueError:
raise ValueError('line {}: could not parse time'.format(lineno))
if fields[3]:
line.category = fields[3]
if fields[4]:
line.comment = fields[4]
return line<|docstring|>Parse a CSV line (`fields` is a list of string) into a RawLine object.<|endoftext|>
|
070b8a63c71e4b3ef19e5eeada26c8ec2b4871f71a1d3aa2c8ac697de1283e53
|
def grep_process_exist(instance_ids: List[str]=None, process_name: str=None, configuration: Configuration=None, secrets: Secrets=None) -> List[AWSResponse]:
'\n Grep pid of process name\n\n Parameters\n ----------\n instance_ids : List[str]\n Filter the virtual machines. If the filter is omitted all machines in\n the subscription will be selected as potential chaos candidates.\n process_name : str\n Name of the process to be killed\n configuration : Configuration\n Chaostoolkit Configuration\n secrets : Secrets\n Chaostoolkit Secrets\n '
logger.debug("Start network_latency: configuration='{}', instance_ids='{}'".format(configuration, instance_ids))
response = []
try:
for instance in instance_ids:
param = dict()
param['duration'] = '1'
param['instance_id'] = instance
param['process_name'] = process_name
response.append(__simple_ssm_helper(instance_id=instance, configuration=configuration, secrets=secrets, action=GREP_PROCESS, parameters=param))
return response
except Exception as x:
raise FailedActivity('failed issuing a execute of shell script via AWS SSM {}'.format(str(x)))
|
Grep pid of process name
Parameters
----------
instance_ids : List[str]
Filter the virtual machines. If the filter is omitted all machines in
the subscription will be selected as potential chaos candidates.
process_name : str
Name of the process to be killed
configuration : Configuration
Chaostoolkit Configuration
secrets : Secrets
Chaostoolkit Secrets
|
chaosaws/ec2_os/probes.py
|
grep_process_exist
|
xpdable/chaostoolkit-aws
| 0
|
python
|
def grep_process_exist(instance_ids: List[str]=None, process_name: str=None, configuration: Configuration=None, secrets: Secrets=None) -> List[AWSResponse]:
'\n Grep pid of process name\n\n Parameters\n ----------\n instance_ids : List[str]\n Filter the virtual machines. If the filter is omitted all machines in\n the subscription will be selected as potential chaos candidates.\n process_name : str\n Name of the process to be killed\n configuration : Configuration\n Chaostoolkit Configuration\n secrets : Secrets\n Chaostoolkit Secrets\n '
logger.debug("Start network_latency: configuration='{}', instance_ids='{}'".format(configuration, instance_ids))
response = []
try:
for instance in instance_ids:
param = dict()
param['duration'] = '1'
param['instance_id'] = instance
param['process_name'] = process_name
response.append(__simple_ssm_helper(instance_id=instance, configuration=configuration, secrets=secrets, action=GREP_PROCESS, parameters=param))
return response
except Exception as x:
raise FailedActivity('failed issuing a execute of shell script via AWS SSM {}'.format(str(x)))
|
def grep_process_exist(instance_ids: List[str]=None, process_name: str=None, configuration: Configuration=None, secrets: Secrets=None) -> List[AWSResponse]:
'\n Grep pid of process name\n\n Parameters\n ----------\n instance_ids : List[str]\n Filter the virtual machines. If the filter is omitted all machines in\n the subscription will be selected as potential chaos candidates.\n process_name : str\n Name of the process to be killed\n configuration : Configuration\n Chaostoolkit Configuration\n secrets : Secrets\n Chaostoolkit Secrets\n '
logger.debug("Start network_latency: configuration='{}', instance_ids='{}'".format(configuration, instance_ids))
response = []
try:
for instance in instance_ids:
param = dict()
param['duration'] = '1'
param['instance_id'] = instance
param['process_name'] = process_name
response.append(__simple_ssm_helper(instance_id=instance, configuration=configuration, secrets=secrets, action=GREP_PROCESS, parameters=param))
return response
except Exception as x:
raise FailedActivity('failed issuing a execute of shell script via AWS SSM {}'.format(str(x)))<|docstring|>Grep pid of process name
Parameters
----------
instance_ids : List[str]
Filter the virtual machines. If the filter is omitted all machines in
the subscription will be selected as potential chaos candidates.
process_name : str
Name of the process to be killed
configuration : Configuration
Chaostoolkit Configuration
secrets : Secrets
Chaostoolkit Secrets<|endoftext|>
|
235d4974fe2975ba8f2f04c0592a88026e92e4ed3b7dd3d0a4ad19488c608d57
|
def _get_storage_subdict(param, json_storage_params):
'Get the JSON configuration subdictionary where the current parameter must be stored.'
parent_section = param.owner_section
sections_path = []
while parent_section:
sections_path.insert(0, parent_section)
parent_section = parent_section.parent_section
current_dict = json_storage_params
for section in sections_path:
dict_key = ((section.key + '_settings') if (section.max_resources > 1) else section.key)
section_key_dict = current_dict.get(dict_key, None)
if (not section_key_dict):
section_key_dict = OrderedDict({})
if (section.max_resources == 1):
section_key_dict['label'] = section.label
current_dict[dict_key] = section_key_dict
current_dict = section_key_dict
if (section.max_resources > 1):
section_label_dict = current_dict.get(section.label, None)
if (not section_label_dict):
section_label_dict = OrderedDict({})
current_dict[section.label] = section_label_dict
current_dict = section_label_dict
return current_dict
|
Get the JSON configuration subdictionary where the current parameter must be stored.
|
cli/pcluster/config/json_param_types.py
|
_get_storage_subdict
|
gkao123/aws-parallelcluster
| 0
|
python
|
def _get_storage_subdict(param, json_storage_params):
parent_section = param.owner_section
sections_path = []
while parent_section:
sections_path.insert(0, parent_section)
parent_section = parent_section.parent_section
current_dict = json_storage_params
for section in sections_path:
dict_key = ((section.key + '_settings') if (section.max_resources > 1) else section.key)
section_key_dict = current_dict.get(dict_key, None)
if (not section_key_dict):
section_key_dict = OrderedDict({})
if (section.max_resources == 1):
section_key_dict['label'] = section.label
current_dict[dict_key] = section_key_dict
current_dict = section_key_dict
if (section.max_resources > 1):
section_label_dict = current_dict.get(section.label, None)
if (not section_label_dict):
section_label_dict = OrderedDict({})
current_dict[section.label] = section_label_dict
current_dict = section_label_dict
return current_dict
|
def _get_storage_subdict(param, json_storage_params):
parent_section = param.owner_section
sections_path = []
while parent_section:
sections_path.insert(0, parent_section)
parent_section = parent_section.parent_section
current_dict = json_storage_params
for section in sections_path:
dict_key = ((section.key + '_settings') if (section.max_resources > 1) else section.key)
section_key_dict = current_dict.get(dict_key, None)
if (not section_key_dict):
section_key_dict = OrderedDict({})
if (section.max_resources == 1):
section_key_dict['label'] = section.label
current_dict[dict_key] = section_key_dict
current_dict = section_key_dict
if (section.max_resources > 1):
section_label_dict = current_dict.get(section.label, None)
if (not section_label_dict):
section_label_dict = OrderedDict({})
current_dict[section.label] = section_label_dict
current_dict = section_label_dict
return current_dict<|docstring|>Get the JSON configuration subdictionary where the current parameter must be stored.<|endoftext|>
|
5e325356e1174293d07c193fbf616a7c544f7cb75d9866ca57da32106f4edbe6
|
def get_value_type(self):
'Return the type of the value managed by the Param.'
return str
|
Return the type of the value managed by the Param.
|
cli/pcluster/config/json_param_types.py
|
get_value_type
|
gkao123/aws-parallelcluster
| 0
|
python
|
def get_value_type(self):
return str
|
def get_value_type(self):
return str<|docstring|>Return the type of the value managed by the Param.<|endoftext|>
|
7f29f67623417029c483e26ab1c951d3dc8a162d46a71b9d8a0313575109e000
|
def from_file(self, config_parser):
'Load the param value from configuration file.'
section_name = utils.get_file_section_name(self.section_key, self.section_label)
if config_parser.has_option(section_name, self.key):
try:
self.value = self._parse_value(config_parser, section_name)
self._check_allowed_values()
except ValueError:
self.pcluster_config.error("Configuration parameter '{0}' must be of '{1}' type".format(self.key, self.get_value_type().__name__))
return self
|
Load the param value from configuration file.
|
cli/pcluster/config/json_param_types.py
|
from_file
|
gkao123/aws-parallelcluster
| 0
|
python
|
def from_file(self, config_parser):
section_name = utils.get_file_section_name(self.section_key, self.section_label)
if config_parser.has_option(section_name, self.key):
try:
self.value = self._parse_value(config_parser, section_name)
self._check_allowed_values()
except ValueError:
self.pcluster_config.error("Configuration parameter '{0}' must be of '{1}' type".format(self.key, self.get_value_type().__name__))
return self
|
def from_file(self, config_parser):
section_name = utils.get_file_section_name(self.section_key, self.section_label)
if config_parser.has_option(section_name, self.key):
try:
self.value = self._parse_value(config_parser, section_name)
self._check_allowed_values()
except ValueError:
self.pcluster_config.error("Configuration parameter '{0}' must be of '{1}' type".format(self.key, self.get_value_type().__name__))
return self<|docstring|>Load the param value from configuration file.<|endoftext|>
|
8c0dbfcf5396342964b2e8e030b9d2addbb6226a3306b7676ed19490844ab4ce
|
def from_storage(self, storage_params):
'Load the param from the provided Json storage params dict.'
storage_value = _get_storage_subdict(self, storage_params.json_params).get(self.get_storage_key(), self.get_default_value())
self.value = storage_value
return self
|
Load the param from the provided Json storage params dict.
|
cli/pcluster/config/json_param_types.py
|
from_storage
|
gkao123/aws-parallelcluster
| 0
|
python
|
def from_storage(self, storage_params):
storage_value = _get_storage_subdict(self, storage_params.json_params).get(self.get_storage_key(), self.get_default_value())
self.value = storage_value
return self
|
def from_storage(self, storage_params):
storage_value = _get_storage_subdict(self, storage_params.json_params).get(self.get_storage_key(), self.get_default_value())
self.value = storage_value
return self<|docstring|>Load the param from the provided Json storage params dict.<|endoftext|>
|
6589702076aeab0e082dbaf20f8761ebadda22e3dbc3137789c7a44d217a3ee3
|
def to_storage(self, storage_params):
'Store the param into the provided Json storage params dict.'
_get_storage_subdict(self, storage_params.json_params)[self.get_storage_key()] = self.value
|
Store the param into the provided Json storage params dict.
|
cli/pcluster/config/json_param_types.py
|
to_storage
|
gkao123/aws-parallelcluster
| 0
|
python
|
def to_storage(self, storage_params):
_get_storage_subdict(self, storage_params.json_params)[self.get_storage_key()] = self.value
|
def to_storage(self, storage_params):
_get_storage_subdict(self, storage_params.json_params)[self.get_storage_key()] = self.value<|docstring|>Store the param into the provided Json storage params dict.<|endoftext|>
|
45ee91841520b31ce2b8e9dd0a3d15a16b22a19b6f0731d07cde1dd7c728e51e
|
def _parse_value(self, config_parser, section_name):
'Parse the value from config file, converting to the needed type for the specific param.'
return config_parser.get(section_name, self.key)
|
Parse the value from config file, converting to the needed type for the specific param.
|
cli/pcluster/config/json_param_types.py
|
_parse_value
|
gkao123/aws-parallelcluster
| 0
|
python
|
def _parse_value(self, config_parser, section_name):
return config_parser.get(section_name, self.key)
|
def _parse_value(self, config_parser, section_name):
return config_parser.get(section_name, self.key)<|docstring|>Parse the value from config file, converting to the needed type for the specific param.<|endoftext|>
|
e373a9c87284b4166cc0702e05a5c8ff03f1ce83c8be1a168edcfc75bfa74166
|
def get_value_type(self):
'Return the type of the value managed by the Param.'
return int
|
Return the type of the value managed by the Param.
|
cli/pcluster/config/json_param_types.py
|
get_value_type
|
gkao123/aws-parallelcluster
| 0
|
python
|
def get_value_type(self):
return int
|
def get_value_type(self):
return int<|docstring|>Return the type of the value managed by the Param.<|endoftext|>
|
b7a5fa7bdb66d323f794ef3333a3d3e8177dd8470efca659aa56d0794056a49b
|
def _parse_value(self, config_parser, section_name):
'Parse the value from config file, converting to the needed type for the specific param.'
return config_parser.getint(section_name, self.key)
|
Parse the value from config file, converting to the needed type for the specific param.
|
cli/pcluster/config/json_param_types.py
|
_parse_value
|
gkao123/aws-parallelcluster
| 0
|
python
|
def _parse_value(self, config_parser, section_name):
return config_parser.getint(section_name, self.key)
|
def _parse_value(self, config_parser, section_name):
return config_parser.getint(section_name, self.key)<|docstring|>Parse the value from config file, converting to the needed type for the specific param.<|endoftext|>
|
6b8bb72536e80575ec7ad83742aedff1159b90e157a54dde698bbfef47a024fd
|
def get_value_type(self):
'Return the type of the value managed by the Param.'
return bool
|
Return the type of the value managed by the Param.
|
cli/pcluster/config/json_param_types.py
|
get_value_type
|
gkao123/aws-parallelcluster
| 0
|
python
|
def get_value_type(self):
return bool
|
def get_value_type(self):
return bool<|docstring|>Return the type of the value managed by the Param.<|endoftext|>
|
116210e838859c118facdf38413b8fbb5f10a0f0eed1a17e702e3f99fbeb84cd
|
def _parse_value(self, config_parser, section_name):
'Parse the value from config file, converting to the needed type for the specific param.'
return config_parser.getboolean(section_name, self.key)
|
Parse the value from config file, converting to the needed type for the specific param.
|
cli/pcluster/config/json_param_types.py
|
_parse_value
|
gkao123/aws-parallelcluster
| 0
|
python
|
def _parse_value(self, config_parser, section_name):
return config_parser.getboolean(section_name, self.key)
|
def _parse_value(self, config_parser, section_name):
return config_parser.getboolean(section_name, self.key)<|docstring|>Parse the value from config file, converting to the needed type for the specific param.<|endoftext|>
|
43cd5101d1e6918cb4ae413689406b62b80ae88e99a0ee34a46abeeab75da07e
|
def get_string_value(self):
'Convert internal representation into string.'
return (self.get_default_value().lower() if (self.value is None) else str(bool(self.value)).lower())
|
Convert internal representation into string.
|
cli/pcluster/config/json_param_types.py
|
get_string_value
|
gkao123/aws-parallelcluster
| 0
|
python
|
def get_string_value(self):
return (self.get_default_value().lower() if (self.value is None) else str(bool(self.value)).lower())
|
def get_string_value(self):
return (self.get_default_value().lower() if (self.value is None) else str(bool(self.value)).lower())<|docstring|>Convert internal representation into string.<|endoftext|>
|
53a1fd5d01b00a7e2b76c2f5b6913956345c084a4c725f57af0b92721116bc5a
|
def get_value_type(self):
'Return the type of the value managed by the Param.'
return float
|
Return the type of the value managed by the Param.
|
cli/pcluster/config/json_param_types.py
|
get_value_type
|
gkao123/aws-parallelcluster
| 0
|
python
|
def get_value_type(self):
return float
|
def get_value_type(self):
return float<|docstring|>Return the type of the value managed by the Param.<|endoftext|>
|
43b20d87cd1bfa947bda9be5063a149a6eff28bc248a7df7874a5fb3734760f4
|
def _parse_value(self, config_parser, section_name):
'Parse the value from config file, converting to the needed type for the specific param.'
return config_parser.getfloat(section_name, self.key)
|
Parse the value from config file, converting to the needed type for the specific param.
|
cli/pcluster/config/json_param_types.py
|
_parse_value
|
gkao123/aws-parallelcluster
| 0
|
python
|
def _parse_value(self, config_parser, section_name):
return config_parser.getfloat(section_name, self.key)
|
def _parse_value(self, config_parser, section_name):
return config_parser.getfloat(section_name, self.key)<|docstring|>Parse the value from config file, converting to the needed type for the specific param.<|endoftext|>
|
4843b395a957d07f83a420d1b4b5962eb29651ea1334eb0ac6d497bef869c8fe
|
def refresh(self):
'Take the value from the scaledown_idletime cfn parameter.'
self.value = self.owner_section.get_param('scaledown_idletime').value
|
Take the value from the scaledown_idletime cfn parameter.
|
cli/pcluster/config/json_param_types.py
|
refresh
|
gkao123/aws-parallelcluster
| 0
|
python
|
def refresh(self):
self.value = self.owner_section.get_param('scaledown_idletime').value
|
def refresh(self):
self.value = self.owner_section.get_param('scaledown_idletime').value<|docstring|>Take the value from the scaledown_idletime cfn parameter.<|endoftext|>
|
c9648bb0fcfda28c941f2060aaf54d8f6e9c8bae86d7e21556efab930e157178
|
def get_storage_key(self):
'Return the key by which the current param must be stored in the JSON.'
return 'scaledown_idletime'
|
Return the key by which the current param must be stored in the JSON.
|
cli/pcluster/config/json_param_types.py
|
get_storage_key
|
gkao123/aws-parallelcluster
| 0
|
python
|
def get_storage_key(self):
return 'scaledown_idletime'
|
def get_storage_key(self):
return 'scaledown_idletime'<|docstring|>Return the key by which the current param must be stored in the JSON.<|endoftext|>
|
8cffd0d7c2e291800bec1eee7e51cec2970e0dfee69f00c7156649b83108d468
|
def refresh(self):
'Take the label of the first queue as value.'
queue_settings_param = self.pcluster_config.get_section('cluster').get_param('queue_settings')
if queue_settings_param:
queue_settings_param_value = queue_settings_param.value
if queue_settings_param_value:
self.value = queue_settings_param_value.split(',')[0].strip()
|
Take the label of the first queue as value.
|
cli/pcluster/config/json_param_types.py
|
refresh
|
gkao123/aws-parallelcluster
| 0
|
python
|
def refresh(self):
queue_settings_param = self.pcluster_config.get_section('cluster').get_param('queue_settings')
if queue_settings_param:
queue_settings_param_value = queue_settings_param.value
if queue_settings_param_value:
self.value = queue_settings_param_value.split(',')[0].strip()
|
def refresh(self):
queue_settings_param = self.pcluster_config.get_section('cluster').get_param('queue_settings')
if queue_settings_param:
queue_settings_param_value = queue_settings_param.value
if queue_settings_param_value:
self.value = queue_settings_param_value.split(',')[0].strip()<|docstring|>Take the label of the first queue as value.<|endoftext|>
|
80bdbc376702f2dcccd275c23a0eaa80c3597b59d195ff5136f9ea6285170186
|
def to_storage(self, storage_params):
'\n Convert the referred sections into the json storage representation.\n\n For each label, a subdictionary is created is generated under the param key, with the section label as key and\n the related section as value.\n Example of storage conversion:\n config file:\n queue_settings = queue1, queue2\n\n json config:\n "cluster": {\n ...\n "queue_settings": {\n "queue1": {...},\n "queue2": {...}\n }\n }\n '
if self.value:
labels = self.referred_section_labels
for label in labels:
section = self.pcluster_config.get_section(self.referred_section_key, label.strip())
section.to_storage(storage_params)
|
Convert the referred sections into the json storage representation.
For each label, a subdictionary is created is generated under the param key, with the section label as key and
the related section as value.
Example of storage conversion:
config file:
queue_settings = queue1, queue2
json config:
"cluster": {
...
"queue_settings": {
"queue1": {...},
"queue2": {...}
}
}
|
cli/pcluster/config/json_param_types.py
|
to_storage
|
gkao123/aws-parallelcluster
| 0
|
python
|
def to_storage(self, storage_params):
'\n Convert the referred sections into the json storage representation.\n\n For each label, a subdictionary is created is generated under the param key, with the section label as key and\n the related section as value.\n Example of storage conversion:\n config file:\n queue_settings = queue1, queue2\n\n json config:\n "cluster": {\n ...\n "queue_settings": {\n "queue1": {...},\n "queue2": {...}\n }\n }\n '
if self.value:
labels = self.referred_section_labels
for label in labels:
section = self.pcluster_config.get_section(self.referred_section_key, label.strip())
section.to_storage(storage_params)
|
def to_storage(self, storage_params):
'\n Convert the referred sections into the json storage representation.\n\n For each label, a subdictionary is created is generated under the param key, with the section label as key and\n the related section as value.\n Example of storage conversion:\n config file:\n queue_settings = queue1, queue2\n\n json config:\n "cluster": {\n ...\n "queue_settings": {\n "queue1": {...},\n "queue2": {...}\n }\n }\n '
if self.value:
labels = self.referred_section_labels
for label in labels:
section = self.pcluster_config.get_section(self.referred_section_key, label.strip())
section.to_storage(storage_params)<|docstring|>Convert the referred sections into the json storage representation.
For each label, a subdictionary is created is generated under the param key, with the section label as key and
the related section as value.
Example of storage conversion:
config file:
queue_settings = queue1, queue2
json config:
"cluster": {
...
"queue_settings": {
"queue1": {...},
"queue2": {...}
}
}<|endoftext|>
|
b6ec49a23260896f2f1d58ef942b521a107039c8a442d04f72f6b21634464929
|
def from_storage(self, storage_params):
'\n Load the referred sections from storage representation.\n\n This method rebuilds the settings labels by iterating through all subsections of the related section;\n then each subsection is loaded from storage as well.\n '
json_params = storage_params.json_params
json_subdict = _get_storage_subdict(self, json_params).get(self.key)
if json_subdict:
labels = [label for label in json_subdict.keys()]
self.value = ','.join(labels)
for label in labels:
section = self.referred_section_type(self.referred_section_definition, self.pcluster_config, section_label=label, parent_section=self.owner_section).from_storage(storage_params)
self.pcluster_config.add_section(section)
return self
|
Load the referred sections from storage representation.
This method rebuilds the settings labels by iterating through all subsections of the related section;
then each subsection is loaded from storage as well.
|
cli/pcluster/config/json_param_types.py
|
from_storage
|
gkao123/aws-parallelcluster
| 0
|
python
|
def from_storage(self, storage_params):
'\n Load the referred sections from storage representation.\n\n This method rebuilds the settings labels by iterating through all subsections of the related section;\n then each subsection is loaded from storage as well.\n '
json_params = storage_params.json_params
json_subdict = _get_storage_subdict(self, json_params).get(self.key)
if json_subdict:
labels = [label for label in json_subdict.keys()]
self.value = ','.join(labels)
for label in labels:
section = self.referred_section_type(self.referred_section_definition, self.pcluster_config, section_label=label, parent_section=self.owner_section).from_storage(storage_params)
self.pcluster_config.add_section(section)
return self
|
def from_storage(self, storage_params):
'\n Load the referred sections from storage representation.\n\n This method rebuilds the settings labels by iterating through all subsections of the related section;\n then each subsection is loaded from storage as well.\n '
json_params = storage_params.json_params
json_subdict = _get_storage_subdict(self, json_params).get(self.key)
if json_subdict:
labels = [label for label in json_subdict.keys()]
self.value = ','.join(labels)
for label in labels:
section = self.referred_section_type(self.referred_section_definition, self.pcluster_config, section_label=label, parent_section=self.owner_section).from_storage(storage_params)
self.pcluster_config.add_section(section)
return self<|docstring|>Load the referred sections from storage representation.
This method rebuilds the settings labels by iterating through all subsections of the related section;
then each subsection is loaded from storage as well.<|endoftext|>
|
89419186fe78827c451743135d6dd31ec77630f574c16d94c69dd0d8dc49a80f
|
def from_storage(self, storage_params):
'Load the section from storage params.'
for (param_key, param_definition) in self.definition.get('params').items():
param_type = param_definition.get('type', Param)
param = param_type(self.key, self.label, param_key, param_definition, self.pcluster_config, owner_section=self).from_storage(storage_params)
self.add_param(param)
return self
|
Load the section from storage params.
|
cli/pcluster/config/json_param_types.py
|
from_storage
|
gkao123/aws-parallelcluster
| 0
|
python
|
def from_storage(self, storage_params):
for (param_key, param_definition) in self.definition.get('params').items():
param_type = param_definition.get('type', Param)
param = param_type(self.key, self.label, param_key, param_definition, self.pcluster_config, owner_section=self).from_storage(storage_params)
self.add_param(param)
return self
|
def from_storage(self, storage_params):
for (param_key, param_definition) in self.definition.get('params').items():
param_type = param_definition.get('type', Param)
param = param_type(self.key, self.label, param_key, param_definition, self.pcluster_config, owner_section=self).from_storage(storage_params)
self.add_param(param)
return self<|docstring|>Load the section from storage params.<|endoftext|>
|
5b858eec0ad8efce95ca07e39c3e34cdbba0b9f55fc988b3ceaf5e08823054c1
|
def to_storage(self, storage_params):
'Write the section into storage params.'
for (param_key, _) in self.definition.get('params').items():
param = self.get_param(param_key)
if param:
param.to_storage(storage_params)
|
Write the section into storage params.
|
cli/pcluster/config/json_param_types.py
|
to_storage
|
gkao123/aws-parallelcluster
| 0
|
python
|
def to_storage(self, storage_params):
for (param_key, _) in self.definition.get('params').items():
param = self.get_param(param_key)
if param:
param.to_storage(storage_params)
|
def to_storage(self, storage_params):
for (param_key, _) in self.definition.get('params').items():
param = self.get_param(param_key)
if param:
param.to_storage(storage_params)<|docstring|>Write the section into storage params.<|endoftext|>
|
c502505fefcdf63e328b7e2c7a8cc3b0d58c07c9486be5649c33166da830708d
|
def has_metadata(self):
'No metadata must be stored in CloudFormation for Json Sections.'
return False
|
No metadata must be stored in CloudFormation for Json Sections.
|
cli/pcluster/config/json_param_types.py
|
has_metadata
|
gkao123/aws-parallelcluster
| 0
|
python
|
def has_metadata(self):
return False
|
def has_metadata(self):
return False<|docstring|>No metadata must be stored in CloudFormation for Json Sections.<|endoftext|>
|
1bebd8cbd2d522ecc28e9872c7f871a13e4cf51738b96f72a421cf2ba73f4698
|
def get_default_param_type(self):
'Get the default Param type managed by the Section type.'
return JsonParam
|
Get the default Param type managed by the Section type.
|
cli/pcluster/config/json_param_types.py
|
get_default_param_type
|
gkao123/aws-parallelcluster
| 0
|
python
|
def get_default_param_type(self):
return JsonParam
|
def get_default_param_type(self):
return JsonParam<|docstring|>Get the default Param type managed by the Section type.<|endoftext|>
|
e54a82699cf2847f7014339133a08c5ba90e39778c8b20f6aff225573d182036
|
def refresh(self):
'Refresh the Json section.'
self.refresh_section()
super(JsonSection, self).refresh()
|
Refresh the Json section.
|
cli/pcluster/config/json_param_types.py
|
refresh
|
gkao123/aws-parallelcluster
| 0
|
python
|
def refresh(self):
self.refresh_section()
super(JsonSection, self).refresh()
|
def refresh(self):
self.refresh_section()
super(JsonSection, self).refresh()<|docstring|>Refresh the Json section.<|endoftext|>
|
ed1dedc5377a6292337d5b74674ea765d0fe33d3738d4cbfa7c95f21177dfc10
|
def refresh_section(self):
'Perform custom refresh operations.'
pass
|
Perform custom refresh operations.
|
cli/pcluster/config/json_param_types.py
|
refresh_section
|
gkao123/aws-parallelcluster
| 0
|
python
|
def refresh_section(self):
pass
|
def refresh_section(self):
pass<|docstring|>Perform custom refresh operations.<|endoftext|>
|
c68472e7cf32e8a2912bf183686161919e938a4a7aca74741021673a63cd3d0f
|
def refresh_section(self):
'Take values of disable_hyperthreading and enable_efa from cluster section if not specified.'
if (self.get_param_value('disable_hyperthreading') is None):
cluster_disable_hyperthreading = self.pcluster_config.get_section('cluster').get_param_value('disable_hyperthreading')
self.get_param('disable_hyperthreading').value = (cluster_disable_hyperthreading is True)
if (self.get_param_value('enable_efa') is None):
cluster_enable_efa = self.pcluster_config.get_section('cluster').get_param_value('enable_efa')
self.get_param('enable_efa').value = (cluster_enable_efa == 'compute')
compute_resource_labels = self.get_param('compute_resource_settings').referred_section_labels
if compute_resource_labels:
for compute_resource_label in compute_resource_labels:
compute_resource_section = self.pcluster_config.get_section('compute_resource', compute_resource_label)
self.refresh_compute_resource(compute_resource_section)
|
Take values of disable_hyperthreading and enable_efa from cluster section if not specified.
|
cli/pcluster/config/json_param_types.py
|
refresh_section
|
gkao123/aws-parallelcluster
| 0
|
python
|
def refresh_section(self):
if (self.get_param_value('disable_hyperthreading') is None):
cluster_disable_hyperthreading = self.pcluster_config.get_section('cluster').get_param_value('disable_hyperthreading')
self.get_param('disable_hyperthreading').value = (cluster_disable_hyperthreading is True)
if (self.get_param_value('enable_efa') is None):
cluster_enable_efa = self.pcluster_config.get_section('cluster').get_param_value('enable_efa')
self.get_param('enable_efa').value = (cluster_enable_efa == 'compute')
compute_resource_labels = self.get_param('compute_resource_settings').referred_section_labels
if compute_resource_labels:
for compute_resource_label in compute_resource_labels:
compute_resource_section = self.pcluster_config.get_section('compute_resource', compute_resource_label)
self.refresh_compute_resource(compute_resource_section)
|
def refresh_section(self):
if (self.get_param_value('disable_hyperthreading') is None):
cluster_disable_hyperthreading = self.pcluster_config.get_section('cluster').get_param_value('disable_hyperthreading')
self.get_param('disable_hyperthreading').value = (cluster_disable_hyperthreading is True)
if (self.get_param_value('enable_efa') is None):
cluster_enable_efa = self.pcluster_config.get_section('cluster').get_param_value('enable_efa')
self.get_param('enable_efa').value = (cluster_enable_efa == 'compute')
compute_resource_labels = self.get_param('compute_resource_settings').referred_section_labels
if compute_resource_labels:
for compute_resource_label in compute_resource_labels:
compute_resource_section = self.pcluster_config.get_section('compute_resource', compute_resource_label)
self.refresh_compute_resource(compute_resource_section)<|docstring|>Take values of disable_hyperthreading and enable_efa from cluster section if not specified.<|endoftext|>
|
9a76f177010ec4388a236506b81063b0b03470925748265d74bb9594792d2300
|
def refresh_compute_resource(self, compute_resource_section):
'\n Populate additional settings needed for the linked compute resource like vcpus, gpus etc.\n\n These parameters are set according to queue settings and instance type capabilities.\n '
instance_type_param = compute_resource_section.get_param('instance_type')
if instance_type_param.value:
instance_type = utils.get_instance_type(instance_type_param.value)
ht_disabled = self.get_param_value('disable_hyperthreading')
vcpus_info = instance_type.get('VCpuInfo')
default_threads_per_core = utils.get_default_threads_per_core(instance_type_param.value, instance_type)
vcpus = ((vcpus_info.get('DefaultVCpus') // default_threads_per_core) if ht_disabled else vcpus_info.get('DefaultVCpus'))
compute_resource_section.get_param('vcpus').value = vcpus
gpu_info = instance_type.get('GpuInfo', None)
if gpu_info:
compute_resource_section.get_param('gpus').value = sum([gpus.get('Count') for gpus in gpu_info.get('Gpus')])
enable_efa = self.get_param_value('enable_efa')
compute_resource_section.get_param('enable_efa').value = (enable_efa and instance_type.get('NetworkInfo').get('EfaSupported'))
compute_resource_section.get_param('disable_hyperthreading').value = (ht_disabled and (default_threads_per_core != 1))
compute_resource_section.get_param('disable_hyperthreading_via_cpu_options').value = (compute_resource_section.get_param('disable_hyperthreading').value and utils.disable_ht_via_cpu_options(instance_type_param.value, utils.get_default_threads_per_core(instance_type_param.value, instance_type)))
initial_count_param = compute_resource_section.get_param('initial_count')
if (initial_count_param.value is None):
initial_count_param.value = compute_resource_section.get_param_value('min_count')
if (enable_efa and (not compute_resource_section.get_param_value('enable_efa'))):
self.pcluster_config.warn("EFA was enabled on queue '{0}', but instance type '{1}' does not support EFA.".format(self.label, instance_type_param.value))
|
Populate additional settings needed for the linked compute resource like vcpus, gpus etc.
These parameters are set according to queue settings and instance type capabilities.
|
cli/pcluster/config/json_param_types.py
|
refresh_compute_resource
|
gkao123/aws-parallelcluster
| 0
|
python
|
def refresh_compute_resource(self, compute_resource_section):
'\n Populate additional settings needed for the linked compute resource like vcpus, gpus etc.\n\n These parameters are set according to queue settings and instance type capabilities.\n '
instance_type_param = compute_resource_section.get_param('instance_type')
if instance_type_param.value:
instance_type = utils.get_instance_type(instance_type_param.value)
ht_disabled = self.get_param_value('disable_hyperthreading')
vcpus_info = instance_type.get('VCpuInfo')
default_threads_per_core = utils.get_default_threads_per_core(instance_type_param.value, instance_type)
vcpus = ((vcpus_info.get('DefaultVCpus') // default_threads_per_core) if ht_disabled else vcpus_info.get('DefaultVCpus'))
compute_resource_section.get_param('vcpus').value = vcpus
gpu_info = instance_type.get('GpuInfo', None)
if gpu_info:
compute_resource_section.get_param('gpus').value = sum([gpus.get('Count') for gpus in gpu_info.get('Gpus')])
enable_efa = self.get_param_value('enable_efa')
compute_resource_section.get_param('enable_efa').value = (enable_efa and instance_type.get('NetworkInfo').get('EfaSupported'))
compute_resource_section.get_param('disable_hyperthreading').value = (ht_disabled and (default_threads_per_core != 1))
compute_resource_section.get_param('disable_hyperthreading_via_cpu_options').value = (compute_resource_section.get_param('disable_hyperthreading').value and utils.disable_ht_via_cpu_options(instance_type_param.value, utils.get_default_threads_per_core(instance_type_param.value, instance_type)))
initial_count_param = compute_resource_section.get_param('initial_count')
if (initial_count_param.value is None):
initial_count_param.value = compute_resource_section.get_param_value('min_count')
if (enable_efa and (not compute_resource_section.get_param_value('enable_efa'))):
self.pcluster_config.warn("EFA was enabled on queue '{0}', but instance type '{1}' does not support EFA.".format(self.label, instance_type_param.value))
|
def refresh_compute_resource(self, compute_resource_section):
'\n Populate additional settings needed for the linked compute resource like vcpus, gpus etc.\n\n These parameters are set according to queue settings and instance type capabilities.\n '
instance_type_param = compute_resource_section.get_param('instance_type')
if instance_type_param.value:
instance_type = utils.get_instance_type(instance_type_param.value)
ht_disabled = self.get_param_value('disable_hyperthreading')
vcpus_info = instance_type.get('VCpuInfo')
default_threads_per_core = utils.get_default_threads_per_core(instance_type_param.value, instance_type)
vcpus = ((vcpus_info.get('DefaultVCpus') // default_threads_per_core) if ht_disabled else vcpus_info.get('DefaultVCpus'))
compute_resource_section.get_param('vcpus').value = vcpus
gpu_info = instance_type.get('GpuInfo', None)
if gpu_info:
compute_resource_section.get_param('gpus').value = sum([gpus.get('Count') for gpus in gpu_info.get('Gpus')])
enable_efa = self.get_param_value('enable_efa')
compute_resource_section.get_param('enable_efa').value = (enable_efa and instance_type.get('NetworkInfo').get('EfaSupported'))
compute_resource_section.get_param('disable_hyperthreading').value = (ht_disabled and (default_threads_per_core != 1))
compute_resource_section.get_param('disable_hyperthreading_via_cpu_options').value = (compute_resource_section.get_param('disable_hyperthreading').value and utils.disable_ht_via_cpu_options(instance_type_param.value, utils.get_default_threads_per_core(instance_type_param.value, instance_type)))
initial_count_param = compute_resource_section.get_param('initial_count')
if (initial_count_param.value is None):
initial_count_param.value = compute_resource_section.get_param_value('min_count')
if (enable_efa and (not compute_resource_section.get_param_value('enable_efa'))):
self.pcluster_config.warn("EFA was enabled on queue '{0}', but instance type '{1}' does not support EFA.".format(self.label, instance_type_param.value))<|docstring|>Populate additional settings needed for the linked compute resource like vcpus, gpus etc.
These parameters are set according to queue settings and instance type capabilities.<|endoftext|>
|
9f44491025f58fbe8ac32262ecae4eeb91a1b9e15dc17c464eee6bcbab6a800f
|
def plot_static_mapper_graph(pipeline, data, layout='kamada_kawai', layout_dim=2, color_variable=None, node_color_statistic=None, color_by_columns_dropdown=False, clone_pipeline=True, n_sig_figs=3, node_scale=12, plotly_params=None, labels=None):
'Plot Mapper graphs without interactivity on pipeline parameters.\n\n The output graph is a rendition of the :class:`igraph.Graph` object\n computed by calling the :meth:`fit_transform` method of the\n :class:`~gtda.mapper.pipeline.MapperPipeline` instance `pipeline` on the\n input `data`. The graph\'s nodes correspond to subsets of elements (rows) in\n `data`; these subsets are clusters in larger portions of `data` called\n "pullback (cover) sets", which are computed by means of the `pipeline`\'s\n "filter function" and "cover" and correspond to the differently-colored\n portions in `this diagram <../../../../_images/mapper_pipeline.svg>`_.\n Two clusters from different pullback cover sets can overlap; if they do, an\n edge between the corresponding nodes in the graph may be drawn.\n\n Nodes are colored according to `color_variable` and `node_color_statistic`\n and are sized according to the number of elements they represent. The\n hovertext on each node displays, in this order:\n\n - a globally unique ID for the node, which can be used to retrieve\n node information from the :class:`igraph.Graph` object, see\n :class:`~gtda.mapper.nerve.Nerve`;\n - the label of the pullback (cover) set which the node\'s elements\n form a cluster in;\n - a label identifying the node as a cluster within that pullback set;\n - the number of elements of `data` associated with the node;\n - the value of the summary statistic which determines the node\'s color.\n\n Parameters\n ----------\n pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object\n Mapper pipeline to act onto data.\n\n data : array-like of shape (n_samples, n_features)\n Data used to generate the Mapper graph. Can be a pandas dataframe.\n\n layout : None, str or callable, optional, default: ``"kamada-kawai"``\n Layout algorithm for the graph. Can be any accepted value for the\n ``layout`` parameter in the :meth:`layout` method of\n :class:`igraph.Graph` [1]_.\n\n layout_dim : int, default: ``2``\n The number of dimensions for the layout. Can be 2 or 3.\n\n color_variable : object or None, optional, default: ``None``\n Specifies a feature of interest to be used, together with\n `node_color_statistic`, to determine node colors.\n\n 1. If a numpy array or pandas dataframe, it must have the same\n length as `data`.\n 2. ``None`` is equivalent to passing `data`.\n 3. If an object implementing :meth:`transform` or\n :meth:`fit_transform`, it is applied to `data` to generate the\n feature of interest.\n 4. If an index or string, or list of indices/strings, it is\n equivalent to selecting a column or subset of columns from\n `data`.\n\n node_color_statistic : None, callable, or ndarray of shape (n_nodes,) or (n_nodes, 1), optional, default: ``None``\n If a callable, node colors will be computed as summary statistics from\n the feature array ``Y`` determined by `color_variable` – specifically,\n the color of a node representing the entries of `data` whose row\n indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``\n is equivalent to passing :func:`numpy.mean`. If a numpy array, it must\n have the same length as the number of nodes in the Mapper graph and its\n values are used directly as node colors (`color_variable` is ignored).\n\n color_by_columns_dropdown : bool, optional, default: ``False``\n If ``True``, a dropdown widget is generated which allows the user to\n color Mapper nodes according to any column in `data` (still using\n `node_color_statistic`) in addition to `color_variable`.\n\n clone_pipeline : bool, optional, default: ``True``\n If ``True``, the input `pipeline` is cloned before computing the\n Mapper graph to prevent unexpected side effects from in-place\n parameter updates.\n\n n_sig_figs : int or None, optional, default: ``3``\n If not ``None``, number of significant figures to which to round node\n summary statistics. If ``None``, no rounding is performed.\n\n node_scale : int or float, optional, default: ``12``\n Sets the scale factor used to determine the rendered size of the\n nodes. Increase for larger nodes. Implements a formula in the\n `Plotly documentation <https://plotly.com/python/bubble-charts/#scaling-the-size-of-bubble -charts>`_.\n\n plotly_params : dict or None, optional, default: ``None``\n Custom parameters to configure the plotly figure. Allowed keys are\n ``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the\n corresponding values should be dictionaries containing keyword\n arguments as would be fed to the :meth:`update_traces` and\n :meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.\n\n Returns\n -------\n fig : :class:`plotly.graph_objects.Figure` object\n Figure representing the Mapper graph with appropriate node colouring\n and size.\n\n Examples\n --------\n Setting a colorscale different from the default one:\n\n >>> import numpy as np\n >>> np.random.seed(1)\n >>> from gtda.mapper import make_mapper_pipeline, plot_static_mapper_graph\n >>> pipeline = make_mapper_pipeline()\n >>> data = np.random.random((100, 3))\n >>> plotly_params = {"node_trace": {"marker_colorscale": "Blues"}}\n >>> fig = plot_static_mapper_graph(pipeline, data,\n ... plotly_params=plotly_params)\n\n Inspect the composition of a node with "Node ID" displayed as 0 in the\n hovertext:\n\n >>> graph = pipeline.fit_transform(data)\n >>> graph.vs[0]["node_elements"]\n array([70])\n\n See also\n --------\n plot_interactive_mapper_graph, gtda.mapper.make_mapper_pipeline\n\n References\n ----------\n .. [1] `igraph.Graph.layout\n <https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_\n documentation.\n\n '
_pipeline = (clone(pipeline) if clone_pipeline else pipeline)
is_node_color_statistic_ndarray = hasattr(node_color_statistic, 'dtype')
if (not (is_node_color_statistic_ndarray or callable(node_color_statistic))):
raise ValueError('`node_color_statistic` must be a callable or ndarray.')
if is_node_color_statistic_ndarray:
_node_color_statistic = node_color_statistic
else:
_node_color_statistic = (node_color_statistic or np.mean)
is_data_dataframe = hasattr(data, 'columns')
(edge_trace, node_trace, node_elements, node_colors_color_variable) = _calculate_graph_data(_pipeline, data, is_data_dataframe, layout, layout_dim, color_variable, _node_color_statistic, n_sig_figs, node_scale, labels)
layout_options = go.Layout(**PLOT_OPTIONS_LAYOUT_DEFAULTS['common'], **PLOT_OPTIONS_LAYOUT_DEFAULTS[layout_dim])
fig = go.FigureWidget(data=[edge_trace, node_trace], layout=layout_options)
_plotly_params = deepcopy(plotly_params)
colorscale_for_hoverlabel = None
if (layout_dim == 3):
compute_hoverlabel_bgcolor = True
if _plotly_params:
if ('node_trace' in _plotly_params):
if ('hoverlabel_bgcolor' in _plotly_params['node_trace']):
fig.update_traces(hoverlabel_bgcolor=_plotly_params['node_trace'].pop('hoverlabel_bgcolor'), selector={'name': 'node_trace'})
compute_hoverlabel_bgcolor = False
if ('marker_colorscale' in _plotly_params['node_trace']):
fig.update_traces(marker_colorscale=_plotly_params['node_trace'].pop('marker_colorscale'), selector={'name': 'node_trace'})
if compute_hoverlabel_bgcolor:
colorscale_for_hoverlabel = fig.data[1].marker.colorscale
node_colors_color_variable = np.asarray(node_colors_color_variable)
min_col = np.min(node_colors_color_variable)
max_col = np.max(node_colors_color_variable)
try:
hoverlabel_bgcolor = _get_colors_for_vals(node_colors_color_variable, min_col, max_col, colorscale_for_hoverlabel)
except Exception as e:
if (e.args[0] == 'This colorscale is not supported.'):
warn('Data-dependent background hoverlabel colors cannot be generated with this choice of colorscale. Please use a standard hex- or RGB-formatted colorscale.', RuntimeWarning)
else:
warn('Something went wrong in generating data-dependent background hoverlabel colors. All background hoverlabel colors will be set to white.', RuntimeWarning)
hoverlabel_bgcolor = 'white'
colorscale_for_hoverlabel = None
fig.update_traces(hoverlabel_bgcolor=hoverlabel_bgcolor, selector={'name': 'node_trace'})
if color_by_columns_dropdown:
hovertext_color_variable = node_trace.hovertext
column_color_buttons = _get_column_color_buttons(data, is_data_dataframe, node_elements, node_colors_color_variable, _node_color_statistic, hovertext_color_variable, colorscale_for_hoverlabel, n_sig_figs)
column_color_buttons[0]['args'][0]['hoverlabel.bgcolor'] = [None, fig.data[1].hoverlabel.bgcolor]
else:
column_color_buttons = None
button_height = 1.1
fig.update_layout(updatemenus=[go.layout.Updatemenu(buttons=column_color_buttons, direction='down', pad={'r': 10, 't': 10}, showactive=True, x=0.11, xanchor='left', y=button_height, yanchor='top')])
if color_by_columns_dropdown:
fig.add_annotation(go.layout.Annotation(text='Color by:', x=0, xref='paper', y=(button_height - 0.045), yref='paper', align='left', showarrow=False))
if _plotly_params:
for key in ['node_trace', 'edge_trace']:
fig.update_traces(_plotly_params.pop(key, None), selector={'name': key})
fig.update_layout(_plotly_params.pop('layout', None))
return fig
|
Plot Mapper graphs without interactivity on pipeline parameters.
The output graph is a rendition of the :class:`igraph.Graph` object
computed by calling the :meth:`fit_transform` method of the
:class:`~gtda.mapper.pipeline.MapperPipeline` instance `pipeline` on the
input `data`. The graph's nodes correspond to subsets of elements (rows) in
`data`; these subsets are clusters in larger portions of `data` called
"pullback (cover) sets", which are computed by means of the `pipeline`'s
"filter function" and "cover" and correspond to the differently-colored
portions in `this diagram <../../../../_images/mapper_pipeline.svg>`_.
Two clusters from different pullback cover sets can overlap; if they do, an
edge between the corresponding nodes in the graph may be drawn.
Nodes are colored according to `color_variable` and `node_color_statistic`
and are sized according to the number of elements they represent. The
hovertext on each node displays, in this order:
- a globally unique ID for the node, which can be used to retrieve
node information from the :class:`igraph.Graph` object, see
:class:`~gtda.mapper.nerve.Nerve`;
- the label of the pullback (cover) set which the node's elements
form a cluster in;
- a label identifying the node as a cluster within that pullback set;
- the number of elements of `data` associated with the node;
- the value of the summary statistic which determines the node's color.
Parameters
----------
pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object
Mapper pipeline to act onto data.
data : array-like of shape (n_samples, n_features)
Data used to generate the Mapper graph. Can be a pandas dataframe.
layout : None, str or callable, optional, default: ``"kamada-kawai"``
Layout algorithm for the graph. Can be any accepted value for the
``layout`` parameter in the :meth:`layout` method of
:class:`igraph.Graph` [1]_.
layout_dim : int, default: ``2``
The number of dimensions for the layout. Can be 2 or 3.
color_variable : object or None, optional, default: ``None``
Specifies a feature of interest to be used, together with
`node_color_statistic`, to determine node colors.
1. If a numpy array or pandas dataframe, it must have the same
length as `data`.
2. ``None`` is equivalent to passing `data`.
3. If an object implementing :meth:`transform` or
:meth:`fit_transform`, it is applied to `data` to generate the
feature of interest.
4. If an index or string, or list of indices/strings, it is
equivalent to selecting a column or subset of columns from
`data`.
node_color_statistic : None, callable, or ndarray of shape (n_nodes,) or (n_nodes, 1), optional, default: ``None``
If a callable, node colors will be computed as summary statistics from
the feature array ``Y`` determined by `color_variable` – specifically,
the color of a node representing the entries of `data` whose row
indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``
is equivalent to passing :func:`numpy.mean`. If a numpy array, it must
have the same length as the number of nodes in the Mapper graph and its
values are used directly as node colors (`color_variable` is ignored).
color_by_columns_dropdown : bool, optional, default: ``False``
If ``True``, a dropdown widget is generated which allows the user to
color Mapper nodes according to any column in `data` (still using
`node_color_statistic`) in addition to `color_variable`.
clone_pipeline : bool, optional, default: ``True``
If ``True``, the input `pipeline` is cloned before computing the
Mapper graph to prevent unexpected side effects from in-place
parameter updates.
n_sig_figs : int or None, optional, default: ``3``
If not ``None``, number of significant figures to which to round node
summary statistics. If ``None``, no rounding is performed.
node_scale : int or float, optional, default: ``12``
Sets the scale factor used to determine the rendered size of the
nodes. Increase for larger nodes. Implements a formula in the
`Plotly documentation <https://plotly.com/python/bubble-charts/#scaling-the-size-of-bubble -charts>`_.
plotly_params : dict or None, optional, default: ``None``
Custom parameters to configure the plotly figure. Allowed keys are
``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the
corresponding values should be dictionaries containing keyword
arguments as would be fed to the :meth:`update_traces` and
:meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.
Returns
-------
fig : :class:`plotly.graph_objects.Figure` object
Figure representing the Mapper graph with appropriate node colouring
and size.
Examples
--------
Setting a colorscale different from the default one:
>>> import numpy as np
>>> np.random.seed(1)
>>> from gtda.mapper import make_mapper_pipeline, plot_static_mapper_graph
>>> pipeline = make_mapper_pipeline()
>>> data = np.random.random((100, 3))
>>> plotly_params = {"node_trace": {"marker_colorscale": "Blues"}}
>>> fig = plot_static_mapper_graph(pipeline, data,
... plotly_params=plotly_params)
Inspect the composition of a node with "Node ID" displayed as 0 in the
hovertext:
>>> graph = pipeline.fit_transform(data)
>>> graph.vs[0]["node_elements"]
array([70])
See also
--------
plot_interactive_mapper_graph, gtda.mapper.make_mapper_pipeline
References
----------
.. [1] `igraph.Graph.layout
<https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_
documentation.
|
files/gtda/mapper/visualization.py
|
plot_static_mapper_graph
|
Snopoff/Mapper-experiments
| 0
|
python
|
def plot_static_mapper_graph(pipeline, data, layout='kamada_kawai', layout_dim=2, color_variable=None, node_color_statistic=None, color_by_columns_dropdown=False, clone_pipeline=True, n_sig_figs=3, node_scale=12, plotly_params=None, labels=None):
'Plot Mapper graphs without interactivity on pipeline parameters.\n\n The output graph is a rendition of the :class:`igraph.Graph` object\n computed by calling the :meth:`fit_transform` method of the\n :class:`~gtda.mapper.pipeline.MapperPipeline` instance `pipeline` on the\n input `data`. The graph\'s nodes correspond to subsets of elements (rows) in\n `data`; these subsets are clusters in larger portions of `data` called\n "pullback (cover) sets", which are computed by means of the `pipeline`\'s\n "filter function" and "cover" and correspond to the differently-colored\n portions in `this diagram <../../../../_images/mapper_pipeline.svg>`_.\n Two clusters from different pullback cover sets can overlap; if they do, an\n edge between the corresponding nodes in the graph may be drawn.\n\n Nodes are colored according to `color_variable` and `node_color_statistic`\n and are sized according to the number of elements they represent. The\n hovertext on each node displays, in this order:\n\n - a globally unique ID for the node, which can be used to retrieve\n node information from the :class:`igraph.Graph` object, see\n :class:`~gtda.mapper.nerve.Nerve`;\n - the label of the pullback (cover) set which the node\'s elements\n form a cluster in;\n - a label identifying the node as a cluster within that pullback set;\n - the number of elements of `data` associated with the node;\n - the value of the summary statistic which determines the node\'s color.\n\n Parameters\n ----------\n pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object\n Mapper pipeline to act onto data.\n\n data : array-like of shape (n_samples, n_features)\n Data used to generate the Mapper graph. Can be a pandas dataframe.\n\n layout : None, str or callable, optional, default: ``"kamada-kawai"``\n Layout algorithm for the graph. Can be any accepted value for the\n ``layout`` parameter in the :meth:`layout` method of\n :class:`igraph.Graph` [1]_.\n\n layout_dim : int, default: ``2``\n The number of dimensions for the layout. Can be 2 or 3.\n\n color_variable : object or None, optional, default: ``None``\n Specifies a feature of interest to be used, together with\n `node_color_statistic`, to determine node colors.\n\n 1. If a numpy array or pandas dataframe, it must have the same\n length as `data`.\n 2. ``None`` is equivalent to passing `data`.\n 3. If an object implementing :meth:`transform` or\n :meth:`fit_transform`, it is applied to `data` to generate the\n feature of interest.\n 4. If an index or string, or list of indices/strings, it is\n equivalent to selecting a column or subset of columns from\n `data`.\n\n node_color_statistic : None, callable, or ndarray of shape (n_nodes,) or (n_nodes, 1), optional, default: ``None``\n If a callable, node colors will be computed as summary statistics from\n the feature array ``Y`` determined by `color_variable` – specifically,\n the color of a node representing the entries of `data` whose row\n indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``\n is equivalent to passing :func:`numpy.mean`. If a numpy array, it must\n have the same length as the number of nodes in the Mapper graph and its\n values are used directly as node colors (`color_variable` is ignored).\n\n color_by_columns_dropdown : bool, optional, default: ``False``\n If ``True``, a dropdown widget is generated which allows the user to\n color Mapper nodes according to any column in `data` (still using\n `node_color_statistic`) in addition to `color_variable`.\n\n clone_pipeline : bool, optional, default: ``True``\n If ``True``, the input `pipeline` is cloned before computing the\n Mapper graph to prevent unexpected side effects from in-place\n parameter updates.\n\n n_sig_figs : int or None, optional, default: ``3``\n If not ``None``, number of significant figures to which to round node\n summary statistics. If ``None``, no rounding is performed.\n\n node_scale : int or float, optional, default: ``12``\n Sets the scale factor used to determine the rendered size of the\n nodes. Increase for larger nodes. Implements a formula in the\n `Plotly documentation <https://plotly.com/python/bubble-charts/#scaling-the-size-of-bubble -charts>`_.\n\n plotly_params : dict or None, optional, default: ``None``\n Custom parameters to configure the plotly figure. Allowed keys are\n ``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the\n corresponding values should be dictionaries containing keyword\n arguments as would be fed to the :meth:`update_traces` and\n :meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.\n\n Returns\n -------\n fig : :class:`plotly.graph_objects.Figure` object\n Figure representing the Mapper graph with appropriate node colouring\n and size.\n\n Examples\n --------\n Setting a colorscale different from the default one:\n\n >>> import numpy as np\n >>> np.random.seed(1)\n >>> from gtda.mapper import make_mapper_pipeline, plot_static_mapper_graph\n >>> pipeline = make_mapper_pipeline()\n >>> data = np.random.random((100, 3))\n >>> plotly_params = {"node_trace": {"marker_colorscale": "Blues"}}\n >>> fig = plot_static_mapper_graph(pipeline, data,\n ... plotly_params=plotly_params)\n\n Inspect the composition of a node with "Node ID" displayed as 0 in the\n hovertext:\n\n >>> graph = pipeline.fit_transform(data)\n >>> graph.vs[0]["node_elements"]\n array([70])\n\n See also\n --------\n plot_interactive_mapper_graph, gtda.mapper.make_mapper_pipeline\n\n References\n ----------\n .. [1] `igraph.Graph.layout\n <https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_\n documentation.\n\n '
_pipeline = (clone(pipeline) if clone_pipeline else pipeline)
is_node_color_statistic_ndarray = hasattr(node_color_statistic, 'dtype')
if (not (is_node_color_statistic_ndarray or callable(node_color_statistic))):
raise ValueError('`node_color_statistic` must be a callable or ndarray.')
if is_node_color_statistic_ndarray:
_node_color_statistic = node_color_statistic
else:
_node_color_statistic = (node_color_statistic or np.mean)
is_data_dataframe = hasattr(data, 'columns')
(edge_trace, node_trace, node_elements, node_colors_color_variable) = _calculate_graph_data(_pipeline, data, is_data_dataframe, layout, layout_dim, color_variable, _node_color_statistic, n_sig_figs, node_scale, labels)
layout_options = go.Layout(**PLOT_OPTIONS_LAYOUT_DEFAULTS['common'], **PLOT_OPTIONS_LAYOUT_DEFAULTS[layout_dim])
fig = go.FigureWidget(data=[edge_trace, node_trace], layout=layout_options)
_plotly_params = deepcopy(plotly_params)
colorscale_for_hoverlabel = None
if (layout_dim == 3):
compute_hoverlabel_bgcolor = True
if _plotly_params:
if ('node_trace' in _plotly_params):
if ('hoverlabel_bgcolor' in _plotly_params['node_trace']):
fig.update_traces(hoverlabel_bgcolor=_plotly_params['node_trace'].pop('hoverlabel_bgcolor'), selector={'name': 'node_trace'})
compute_hoverlabel_bgcolor = False
if ('marker_colorscale' in _plotly_params['node_trace']):
fig.update_traces(marker_colorscale=_plotly_params['node_trace'].pop('marker_colorscale'), selector={'name': 'node_trace'})
if compute_hoverlabel_bgcolor:
colorscale_for_hoverlabel = fig.data[1].marker.colorscale
node_colors_color_variable = np.asarray(node_colors_color_variable)
min_col = np.min(node_colors_color_variable)
max_col = np.max(node_colors_color_variable)
try:
hoverlabel_bgcolor = _get_colors_for_vals(node_colors_color_variable, min_col, max_col, colorscale_for_hoverlabel)
except Exception as e:
if (e.args[0] == 'This colorscale is not supported.'):
warn('Data-dependent background hoverlabel colors cannot be generated with this choice of colorscale. Please use a standard hex- or RGB-formatted colorscale.', RuntimeWarning)
else:
warn('Something went wrong in generating data-dependent background hoverlabel colors. All background hoverlabel colors will be set to white.', RuntimeWarning)
hoverlabel_bgcolor = 'white'
colorscale_for_hoverlabel = None
fig.update_traces(hoverlabel_bgcolor=hoverlabel_bgcolor, selector={'name': 'node_trace'})
if color_by_columns_dropdown:
hovertext_color_variable = node_trace.hovertext
column_color_buttons = _get_column_color_buttons(data, is_data_dataframe, node_elements, node_colors_color_variable, _node_color_statistic, hovertext_color_variable, colorscale_for_hoverlabel, n_sig_figs)
column_color_buttons[0]['args'][0]['hoverlabel.bgcolor'] = [None, fig.data[1].hoverlabel.bgcolor]
else:
column_color_buttons = None
button_height = 1.1
fig.update_layout(updatemenus=[go.layout.Updatemenu(buttons=column_color_buttons, direction='down', pad={'r': 10, 't': 10}, showactive=True, x=0.11, xanchor='left', y=button_height, yanchor='top')])
if color_by_columns_dropdown:
fig.add_annotation(go.layout.Annotation(text='Color by:', x=0, xref='paper', y=(button_height - 0.045), yref='paper', align='left', showarrow=False))
if _plotly_params:
for key in ['node_trace', 'edge_trace']:
fig.update_traces(_plotly_params.pop(key, None), selector={'name': key})
fig.update_layout(_plotly_params.pop('layout', None))
return fig
|
def plot_static_mapper_graph(pipeline, data, layout='kamada_kawai', layout_dim=2, color_variable=None, node_color_statistic=None, color_by_columns_dropdown=False, clone_pipeline=True, n_sig_figs=3, node_scale=12, plotly_params=None, labels=None):
'Plot Mapper graphs without interactivity on pipeline parameters.\n\n The output graph is a rendition of the :class:`igraph.Graph` object\n computed by calling the :meth:`fit_transform` method of the\n :class:`~gtda.mapper.pipeline.MapperPipeline` instance `pipeline` on the\n input `data`. The graph\'s nodes correspond to subsets of elements (rows) in\n `data`; these subsets are clusters in larger portions of `data` called\n "pullback (cover) sets", which are computed by means of the `pipeline`\'s\n "filter function" and "cover" and correspond to the differently-colored\n portions in `this diagram <../../../../_images/mapper_pipeline.svg>`_.\n Two clusters from different pullback cover sets can overlap; if they do, an\n edge between the corresponding nodes in the graph may be drawn.\n\n Nodes are colored according to `color_variable` and `node_color_statistic`\n and are sized according to the number of elements they represent. The\n hovertext on each node displays, in this order:\n\n - a globally unique ID for the node, which can be used to retrieve\n node information from the :class:`igraph.Graph` object, see\n :class:`~gtda.mapper.nerve.Nerve`;\n - the label of the pullback (cover) set which the node\'s elements\n form a cluster in;\n - a label identifying the node as a cluster within that pullback set;\n - the number of elements of `data` associated with the node;\n - the value of the summary statistic which determines the node\'s color.\n\n Parameters\n ----------\n pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object\n Mapper pipeline to act onto data.\n\n data : array-like of shape (n_samples, n_features)\n Data used to generate the Mapper graph. Can be a pandas dataframe.\n\n layout : None, str or callable, optional, default: ``"kamada-kawai"``\n Layout algorithm for the graph. Can be any accepted value for the\n ``layout`` parameter in the :meth:`layout` method of\n :class:`igraph.Graph` [1]_.\n\n layout_dim : int, default: ``2``\n The number of dimensions for the layout. Can be 2 or 3.\n\n color_variable : object or None, optional, default: ``None``\n Specifies a feature of interest to be used, together with\n `node_color_statistic`, to determine node colors.\n\n 1. If a numpy array or pandas dataframe, it must have the same\n length as `data`.\n 2. ``None`` is equivalent to passing `data`.\n 3. If an object implementing :meth:`transform` or\n :meth:`fit_transform`, it is applied to `data` to generate the\n feature of interest.\n 4. If an index or string, or list of indices/strings, it is\n equivalent to selecting a column or subset of columns from\n `data`.\n\n node_color_statistic : None, callable, or ndarray of shape (n_nodes,) or (n_nodes, 1), optional, default: ``None``\n If a callable, node colors will be computed as summary statistics from\n the feature array ``Y`` determined by `color_variable` – specifically,\n the color of a node representing the entries of `data` whose row\n indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``\n is equivalent to passing :func:`numpy.mean`. If a numpy array, it must\n have the same length as the number of nodes in the Mapper graph and its\n values are used directly as node colors (`color_variable` is ignored).\n\n color_by_columns_dropdown : bool, optional, default: ``False``\n If ``True``, a dropdown widget is generated which allows the user to\n color Mapper nodes according to any column in `data` (still using\n `node_color_statistic`) in addition to `color_variable`.\n\n clone_pipeline : bool, optional, default: ``True``\n If ``True``, the input `pipeline` is cloned before computing the\n Mapper graph to prevent unexpected side effects from in-place\n parameter updates.\n\n n_sig_figs : int or None, optional, default: ``3``\n If not ``None``, number of significant figures to which to round node\n summary statistics. If ``None``, no rounding is performed.\n\n node_scale : int or float, optional, default: ``12``\n Sets the scale factor used to determine the rendered size of the\n nodes. Increase for larger nodes. Implements a formula in the\n `Plotly documentation <https://plotly.com/python/bubble-charts/#scaling-the-size-of-bubble -charts>`_.\n\n plotly_params : dict or None, optional, default: ``None``\n Custom parameters to configure the plotly figure. Allowed keys are\n ``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the\n corresponding values should be dictionaries containing keyword\n arguments as would be fed to the :meth:`update_traces` and\n :meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.\n\n Returns\n -------\n fig : :class:`plotly.graph_objects.Figure` object\n Figure representing the Mapper graph with appropriate node colouring\n and size.\n\n Examples\n --------\n Setting a colorscale different from the default one:\n\n >>> import numpy as np\n >>> np.random.seed(1)\n >>> from gtda.mapper import make_mapper_pipeline, plot_static_mapper_graph\n >>> pipeline = make_mapper_pipeline()\n >>> data = np.random.random((100, 3))\n >>> plotly_params = {"node_trace": {"marker_colorscale": "Blues"}}\n >>> fig = plot_static_mapper_graph(pipeline, data,\n ... plotly_params=plotly_params)\n\n Inspect the composition of a node with "Node ID" displayed as 0 in the\n hovertext:\n\n >>> graph = pipeline.fit_transform(data)\n >>> graph.vs[0]["node_elements"]\n array([70])\n\n See also\n --------\n plot_interactive_mapper_graph, gtda.mapper.make_mapper_pipeline\n\n References\n ----------\n .. [1] `igraph.Graph.layout\n <https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_\n documentation.\n\n '
_pipeline = (clone(pipeline) if clone_pipeline else pipeline)
is_node_color_statistic_ndarray = hasattr(node_color_statistic, 'dtype')
if (not (is_node_color_statistic_ndarray or callable(node_color_statistic))):
raise ValueError('`node_color_statistic` must be a callable or ndarray.')
if is_node_color_statistic_ndarray:
_node_color_statistic = node_color_statistic
else:
_node_color_statistic = (node_color_statistic or np.mean)
is_data_dataframe = hasattr(data, 'columns')
(edge_trace, node_trace, node_elements, node_colors_color_variable) = _calculate_graph_data(_pipeline, data, is_data_dataframe, layout, layout_dim, color_variable, _node_color_statistic, n_sig_figs, node_scale, labels)
layout_options = go.Layout(**PLOT_OPTIONS_LAYOUT_DEFAULTS['common'], **PLOT_OPTIONS_LAYOUT_DEFAULTS[layout_dim])
fig = go.FigureWidget(data=[edge_trace, node_trace], layout=layout_options)
_plotly_params = deepcopy(plotly_params)
colorscale_for_hoverlabel = None
if (layout_dim == 3):
compute_hoverlabel_bgcolor = True
if _plotly_params:
if ('node_trace' in _plotly_params):
if ('hoverlabel_bgcolor' in _plotly_params['node_trace']):
fig.update_traces(hoverlabel_bgcolor=_plotly_params['node_trace'].pop('hoverlabel_bgcolor'), selector={'name': 'node_trace'})
compute_hoverlabel_bgcolor = False
if ('marker_colorscale' in _plotly_params['node_trace']):
fig.update_traces(marker_colorscale=_plotly_params['node_trace'].pop('marker_colorscale'), selector={'name': 'node_trace'})
if compute_hoverlabel_bgcolor:
colorscale_for_hoverlabel = fig.data[1].marker.colorscale
node_colors_color_variable = np.asarray(node_colors_color_variable)
min_col = np.min(node_colors_color_variable)
max_col = np.max(node_colors_color_variable)
try:
hoverlabel_bgcolor = _get_colors_for_vals(node_colors_color_variable, min_col, max_col, colorscale_for_hoverlabel)
except Exception as e:
if (e.args[0] == 'This colorscale is not supported.'):
warn('Data-dependent background hoverlabel colors cannot be generated with this choice of colorscale. Please use a standard hex- or RGB-formatted colorscale.', RuntimeWarning)
else:
warn('Something went wrong in generating data-dependent background hoverlabel colors. All background hoverlabel colors will be set to white.', RuntimeWarning)
hoverlabel_bgcolor = 'white'
colorscale_for_hoverlabel = None
fig.update_traces(hoverlabel_bgcolor=hoverlabel_bgcolor, selector={'name': 'node_trace'})
if color_by_columns_dropdown:
hovertext_color_variable = node_trace.hovertext
column_color_buttons = _get_column_color_buttons(data, is_data_dataframe, node_elements, node_colors_color_variable, _node_color_statistic, hovertext_color_variable, colorscale_for_hoverlabel, n_sig_figs)
column_color_buttons[0]['args'][0]['hoverlabel.bgcolor'] = [None, fig.data[1].hoverlabel.bgcolor]
else:
column_color_buttons = None
button_height = 1.1
fig.update_layout(updatemenus=[go.layout.Updatemenu(buttons=column_color_buttons, direction='down', pad={'r': 10, 't': 10}, showactive=True, x=0.11, xanchor='left', y=button_height, yanchor='top')])
if color_by_columns_dropdown:
fig.add_annotation(go.layout.Annotation(text='Color by:', x=0, xref='paper', y=(button_height - 0.045), yref='paper', align='left', showarrow=False))
if _plotly_params:
for key in ['node_trace', 'edge_trace']:
fig.update_traces(_plotly_params.pop(key, None), selector={'name': key})
fig.update_layout(_plotly_params.pop('layout', None))
return fig<|docstring|>Plot Mapper graphs without interactivity on pipeline parameters.
The output graph is a rendition of the :class:`igraph.Graph` object
computed by calling the :meth:`fit_transform` method of the
:class:`~gtda.mapper.pipeline.MapperPipeline` instance `pipeline` on the
input `data`. The graph's nodes correspond to subsets of elements (rows) in
`data`; these subsets are clusters in larger portions of `data` called
"pullback (cover) sets", which are computed by means of the `pipeline`'s
"filter function" and "cover" and correspond to the differently-colored
portions in `this diagram <../../../../_images/mapper_pipeline.svg>`_.
Two clusters from different pullback cover sets can overlap; if they do, an
edge between the corresponding nodes in the graph may be drawn.
Nodes are colored according to `color_variable` and `node_color_statistic`
and are sized according to the number of elements they represent. The
hovertext on each node displays, in this order:
- a globally unique ID for the node, which can be used to retrieve
node information from the :class:`igraph.Graph` object, see
:class:`~gtda.mapper.nerve.Nerve`;
- the label of the pullback (cover) set which the node's elements
form a cluster in;
- a label identifying the node as a cluster within that pullback set;
- the number of elements of `data` associated with the node;
- the value of the summary statistic which determines the node's color.
Parameters
----------
pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object
Mapper pipeline to act onto data.
data : array-like of shape (n_samples, n_features)
Data used to generate the Mapper graph. Can be a pandas dataframe.
layout : None, str or callable, optional, default: ``"kamada-kawai"``
Layout algorithm for the graph. Can be any accepted value for the
``layout`` parameter in the :meth:`layout` method of
:class:`igraph.Graph` [1]_.
layout_dim : int, default: ``2``
The number of dimensions for the layout. Can be 2 or 3.
color_variable : object or None, optional, default: ``None``
Specifies a feature of interest to be used, together with
`node_color_statistic`, to determine node colors.
1. If a numpy array or pandas dataframe, it must have the same
length as `data`.
2. ``None`` is equivalent to passing `data`.
3. If an object implementing :meth:`transform` or
:meth:`fit_transform`, it is applied to `data` to generate the
feature of interest.
4. If an index or string, or list of indices/strings, it is
equivalent to selecting a column or subset of columns from
`data`.
node_color_statistic : None, callable, or ndarray of shape (n_nodes,) or (n_nodes, 1), optional, default: ``None``
If a callable, node colors will be computed as summary statistics from
the feature array ``Y`` determined by `color_variable` – specifically,
the color of a node representing the entries of `data` whose row
indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``
is equivalent to passing :func:`numpy.mean`. If a numpy array, it must
have the same length as the number of nodes in the Mapper graph and its
values are used directly as node colors (`color_variable` is ignored).
color_by_columns_dropdown : bool, optional, default: ``False``
If ``True``, a dropdown widget is generated which allows the user to
color Mapper nodes according to any column in `data` (still using
`node_color_statistic`) in addition to `color_variable`.
clone_pipeline : bool, optional, default: ``True``
If ``True``, the input `pipeline` is cloned before computing the
Mapper graph to prevent unexpected side effects from in-place
parameter updates.
n_sig_figs : int or None, optional, default: ``3``
If not ``None``, number of significant figures to which to round node
summary statistics. If ``None``, no rounding is performed.
node_scale : int or float, optional, default: ``12``
Sets the scale factor used to determine the rendered size of the
nodes. Increase for larger nodes. Implements a formula in the
`Plotly documentation <https://plotly.com/python/bubble-charts/#scaling-the-size-of-bubble -charts>`_.
plotly_params : dict or None, optional, default: ``None``
Custom parameters to configure the plotly figure. Allowed keys are
``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the
corresponding values should be dictionaries containing keyword
arguments as would be fed to the :meth:`update_traces` and
:meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.
Returns
-------
fig : :class:`plotly.graph_objects.Figure` object
Figure representing the Mapper graph with appropriate node colouring
and size.
Examples
--------
Setting a colorscale different from the default one:
>>> import numpy as np
>>> np.random.seed(1)
>>> from gtda.mapper import make_mapper_pipeline, plot_static_mapper_graph
>>> pipeline = make_mapper_pipeline()
>>> data = np.random.random((100, 3))
>>> plotly_params = {"node_trace": {"marker_colorscale": "Blues"}}
>>> fig = plot_static_mapper_graph(pipeline, data,
... plotly_params=plotly_params)
Inspect the composition of a node with "Node ID" displayed as 0 in the
hovertext:
>>> graph = pipeline.fit_transform(data)
>>> graph.vs[0]["node_elements"]
array([70])
See also
--------
plot_interactive_mapper_graph, gtda.mapper.make_mapper_pipeline
References
----------
.. [1] `igraph.Graph.layout
<https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_
documentation.<|endoftext|>
|
7aaee96966f8d04542554ee0b282b9a6e00b43175c4224a259fbacb3584aa123
|
def plot_interactive_mapper_graph(pipeline, data, layout='kamada_kawai', layout_dim=2, color_variable=None, node_color_statistic=None, clone_pipeline=True, color_by_columns_dropdown=False, n_sig_figs=3, node_scale=12, plotly_params=None):
'Plot Mapper graphs with interactivity on pipeline parameters.\n\n Extends :func:`~gtda.mapper.visualization.plot_static_mapper_graph` by\n providing functionality to interactively update parameters from the cover,\n clustering and graph construction steps defined in `pipeline`.\n\n Parameters\n ----------\n pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object\n Mapper pipeline to act on to data.\n\n data : array-like of shape (n_samples, n_features)\n Data used to generate the Mapper graph. Can be a pandas dataframe.\n\n layout : None, str or callable, optional, default: ``"kamada-kawai"``\n Layout algorithm for the graph. Can be any accepted value for the\n ``layout`` parameter in the :meth:`layout` method of\n :class:`igraph.Graph` [1]_.\n\n layout_dim : int, default: ``2``\n The number of dimensions for the layout. Can be 2 or 3.\n\n color_variable : object or None, optional, default: ``None``\n Specifies a feature of interest to be used, together with\n `node_color_statistic`, to determine node colors.\n\n 1. If a numpy array or pandas dataframe, it must have the same\n length as `data`.\n 2. ``None`` is equivalent to passing `data`.\n 3. If an object implementing :meth:`transform` or\n :meth:`fit_transform`, it is applied to `data` to generate the\n feature of interest.\n 4. If an index or string, or list of indices/strings, it is\n equivalent to selecting a column or subset of columns from\n `data`.\n\n node_color_statistic : callable or None, optional, default: ``None``\n If a callable, node colors will be computed as summary statistics from\n the feature array ``Y`` determined by `color_variable` – specifically,\n the color of a node representing the entries of `data` whose row\n indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``\n is equivalent to passing :func:`numpy.mean`.\n\n color_by_columns_dropdown : bool, optional, default: ``False``\n If ``True``, a dropdown widget is generated which allows the user to\n color Mapper nodes according to any column in `data` (still using\n `node_color_statistic`) in addition to `color_variable`.\n\n clone_pipeline : bool, optional, default: ``True``\n If ``True``, the input `pipeline` is cloned before computing the\n Mapper graph to prevent unexpected side effects from in-place\n parameter updates.\n\n n_sig_figs : int or None, optional, default: ``3``\n If not ``None``, number of significant figures to which to round node\n summary statistics. If ``None``, no rounding is performed.\n\n node_scale : int or float, optional, default: ``12``\n Sets the scale factor used to determine the rendered size of the\n nodes. Increase for larger nodes. Implements a formula in the\n `Plotly documentation <plotly.com/python/bubble-charts/#scaling-the-size-of-bubble-charts>`_.\n\n plotly_params : dict or None, optional, default: ``None``\n Custom parameters to configure the plotly figure. Allowed keys are\n ``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the\n corresponding values should be dictionaries containing keyword\n arguments as would be fed to the :meth:`update_traces` and\n :meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.\n\n Returns\n -------\n box : :class:`ipywidgets.VBox` object\n A box containing the following widgets: parameters of the clustering\n algorithm, parameters for the covering scheme, a Mapper graph arising\n from those parameters, a validation box, and logs.\n\n See also\n --------\n plot_static_mapper_graph, gtda.mapper.pipeline.make_mapper_pipeline\n\n References\n ----------\n .. [1] `igraph.Graph.layout\n <https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_\n documentation.\n\n '
_pipeline = (clone(pipeline) if clone_pipeline else pipeline)
_node_color_statistic = (node_color_statistic or np.mean)
def get_widgets_per_param(params):
for (key, value) in params.items():
style = {'description_width': 'initial'}
description = (key.split('__')[1] if ('__' in key) else key)
if isinstance(value, float):
(yield (key, widgets.FloatText(value=value, step=0.05, description=description, continuous_update=False, disabled=False, layout=Layout(width='90%'), style=style)))
elif isinstance(value, bool):
(yield (key, widgets.ToggleButton(value=value, description=description, disabled=False, layout=Layout(width='90%'), style=style)))
elif isinstance(value, int):
(yield (key, widgets.IntText(value=value, step=1, description=description, continuous_update=False, disabled=False, layout=Layout(width='90%'), style=style)))
elif isinstance(value, str):
(yield (key, widgets.Text(value=value, description=description, continuous_update=False, disabled=False, layout=Layout(width='90%'), style=style)))
def on_parameter_change(change):
handler.clear_logs()
try:
for (param, value) in cover_params.items():
if isinstance(value, (int, float, str)):
_pipeline.set_params(**{param: cover_params_widgets[param].value})
for (param, value) in cluster_params.items():
if isinstance(value, (int, float, str)):
_pipeline.set_params(**{param: cluster_params_widgets[param].value})
for (param, value) in nerve_params.items():
if isinstance(value, (int, bool)):
_pipeline.set_params(**{param: nerve_params_widgets[param].value})
logger.info('Updating figure...')
with fig.batch_update():
(edge_trace, node_trace, node_elements, node_colors_color_variable) = _calculate_graph_data(_pipeline, data, is_data_dataframe, layout, layout_dim, color_variable, _node_color_statistic, n_sig_figs, node_scale)
if (colorscale_for_hoverlabel is not None):
node_colors_color_variable = np.asarray(node_colors_color_variable)
min_col = np.min(node_colors_color_variable)
max_col = np.max(node_colors_color_variable)
hoverlabel_bgcolor = _get_colors_for_vals(node_colors_color_variable, min_col, max_col, colorscale_for_hoverlabel)
fig.update_traces(hoverlabel_bgcolor=hoverlabel_bgcolor, selector={'name': 'node_trace'})
fig.update_traces(x=node_trace.x, y=node_trace.y, marker_color=node_trace.marker.color, marker_size=node_trace.marker.size, marker_sizeref=node_trace.marker.sizeref, hovertext=node_trace.hovertext, **({'z': node_trace.z} if (layout_dim == 3) else dict()), selector={'name': 'node_trace'})
fig.update_traces(x=edge_trace.x, y=edge_trace.y, **({'z': edge_trace.z} if (layout_dim == 3) else dict()), selector={'name': 'edge_trace'})
if color_by_columns_dropdown:
hovertext_color_variable = node_trace.hovertext
column_color_buttons = _get_column_color_buttons(data, is_data_dataframe, node_elements, node_colors_color_variable, _node_color_statistic, hovertext_color_variable, colorscale_for_hoverlabel, n_sig_figs)
if (colorscale_for_hoverlabel is not None):
column_color_buttons[0]['args'][0]['hoverlabel.bgcolor'] = [None, hoverlabel_bgcolor]
else:
column_color_buttons = None
button_height = 1.1
fig.update_layout(updatemenus=[go.layout.Updatemenu(buttons=column_color_buttons, direction='down', pad={'r': 10, 't': 10}, showactive=True, x=0.11, xanchor='left', y=button_height, yanchor='top')])
valid.value = True
except Exception:
exception_data = traceback.format_exc().splitlines()
logger.exception(exception_data[(- 1)])
valid.value = False
def observe_widgets(params, widgets):
for (param, value) in params.items():
if isinstance(value, (int, float, str)):
widgets[param].observe(on_parameter_change, names='value')
out = widgets.Output()
@out.capture()
def click_box(change):
if logs_box.value:
out.clear_output()
handler.show_logs()
else:
out.clear_output()
logger = logging.getLogger(__name__)
handler = OutputWidgetHandler()
handler.setFormatter(logging.Formatter('%(asctime)s - [%(levelname)s] %(message)s'))
logger.addHandler(handler)
logger.setLevel(logging.INFO)
mapper_params_items = _pipeline.get_mapper_params().items()
cover_params = {key: value for (key, value) in mapper_params_items if key.startswith('cover__')}
cover_params_widgets = dict(get_widgets_per_param(cover_params))
cluster_params = {key: value for (key, value) in mapper_params_items if key.startswith('clusterer__')}
cluster_params_widgets = dict(get_widgets_per_param(cluster_params))
nerve_params = {key: value for (key, value) in mapper_params_items if (key in ['min_intersection', 'contract_nodes'])}
nerve_params_widgets = dict(get_widgets_per_param(nerve_params))
valid = widgets.Valid(value=True, description='Valid parameters', style={'description_width': '100px'})
logs_box = widgets.Checkbox(description='Show logs: ', value=False, indent=False)
fig = plot_static_mapper_graph(_pipeline, data, layout=layout, layout_dim=layout_dim, color_variable=color_variable, node_color_statistic=_node_color_statistic, color_by_columns_dropdown=color_by_columns_dropdown, clone_pipeline=False, n_sig_figs=n_sig_figs, node_scale=node_scale, plotly_params=plotly_params)
is_data_dataframe = hasattr(data, 'columns')
colorscale_for_hoverlabel = None
if (layout_dim == 3):
is_bgcolor_not_white = (fig.data[1].hoverlabel.bgcolor != 'white')
user_hoverlabel_bgcolor = False
if plotly_params:
if ('node_trace' in plotly_params):
if ('hoverlabel_bgcolor' in plotly_params['node_trace']):
user_hoverlabel_bgcolor = True
if (is_bgcolor_not_white and (not user_hoverlabel_bgcolor)):
colorscale_for_hoverlabel = fig.data[1].marker.colorscale
observe_widgets(cover_params, cover_params_widgets)
observe_widgets(cluster_params, cluster_params_widgets)
observe_widgets(nerve_params, nerve_params_widgets)
logs_box.observe(click_box, names='value')
cover_title = HTML(value='<b>Cover parameters</b>')
container_cover = widgets.VBox(children=([cover_title] + list(cover_params_widgets.values())))
container_cover.layout.align_items = 'center'
cluster_title = HTML(value='<b>Clusterer parameters</b>')
container_cluster = widgets.VBox(children=([cluster_title] + list(cluster_params_widgets.values())))
container_cluster.layout.align_items = 'center'
nerve_title = HTML(value='<b>Nerve parameters</b>')
container_nerve = widgets.VBox(children=([nerve_title] + list(nerve_params_widgets.values())))
container_nerve.layout.align_items = 'center'
container_parameters = widgets.HBox(children=[container_cover, container_cluster, container_nerve])
box = widgets.VBox([container_parameters, fig, valid, logs_box, out])
return box
|
Plot Mapper graphs with interactivity on pipeline parameters.
Extends :func:`~gtda.mapper.visualization.plot_static_mapper_graph` by
providing functionality to interactively update parameters from the cover,
clustering and graph construction steps defined in `pipeline`.
Parameters
----------
pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object
Mapper pipeline to act on to data.
data : array-like of shape (n_samples, n_features)
Data used to generate the Mapper graph. Can be a pandas dataframe.
layout : None, str or callable, optional, default: ``"kamada-kawai"``
Layout algorithm for the graph. Can be any accepted value for the
``layout`` parameter in the :meth:`layout` method of
:class:`igraph.Graph` [1]_.
layout_dim : int, default: ``2``
The number of dimensions for the layout. Can be 2 or 3.
color_variable : object or None, optional, default: ``None``
Specifies a feature of interest to be used, together with
`node_color_statistic`, to determine node colors.
1. If a numpy array or pandas dataframe, it must have the same
length as `data`.
2. ``None`` is equivalent to passing `data`.
3. If an object implementing :meth:`transform` or
:meth:`fit_transform`, it is applied to `data` to generate the
feature of interest.
4. If an index or string, or list of indices/strings, it is
equivalent to selecting a column or subset of columns from
`data`.
node_color_statistic : callable or None, optional, default: ``None``
If a callable, node colors will be computed as summary statistics from
the feature array ``Y`` determined by `color_variable` – specifically,
the color of a node representing the entries of `data` whose row
indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``
is equivalent to passing :func:`numpy.mean`.
color_by_columns_dropdown : bool, optional, default: ``False``
If ``True``, a dropdown widget is generated which allows the user to
color Mapper nodes according to any column in `data` (still using
`node_color_statistic`) in addition to `color_variable`.
clone_pipeline : bool, optional, default: ``True``
If ``True``, the input `pipeline` is cloned before computing the
Mapper graph to prevent unexpected side effects from in-place
parameter updates.
n_sig_figs : int or None, optional, default: ``3``
If not ``None``, number of significant figures to which to round node
summary statistics. If ``None``, no rounding is performed.
node_scale : int or float, optional, default: ``12``
Sets the scale factor used to determine the rendered size of the
nodes. Increase for larger nodes. Implements a formula in the
`Plotly documentation <plotly.com/python/bubble-charts/#scaling-the-size-of-bubble-charts>`_.
plotly_params : dict or None, optional, default: ``None``
Custom parameters to configure the plotly figure. Allowed keys are
``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the
corresponding values should be dictionaries containing keyword
arguments as would be fed to the :meth:`update_traces` and
:meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.
Returns
-------
box : :class:`ipywidgets.VBox` object
A box containing the following widgets: parameters of the clustering
algorithm, parameters for the covering scheme, a Mapper graph arising
from those parameters, a validation box, and logs.
See also
--------
plot_static_mapper_graph, gtda.mapper.pipeline.make_mapper_pipeline
References
----------
.. [1] `igraph.Graph.layout
<https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_
documentation.
|
files/gtda/mapper/visualization.py
|
plot_interactive_mapper_graph
|
Snopoff/Mapper-experiments
| 0
|
python
|
def plot_interactive_mapper_graph(pipeline, data, layout='kamada_kawai', layout_dim=2, color_variable=None, node_color_statistic=None, clone_pipeline=True, color_by_columns_dropdown=False, n_sig_figs=3, node_scale=12, plotly_params=None):
'Plot Mapper graphs with interactivity on pipeline parameters.\n\n Extends :func:`~gtda.mapper.visualization.plot_static_mapper_graph` by\n providing functionality to interactively update parameters from the cover,\n clustering and graph construction steps defined in `pipeline`.\n\n Parameters\n ----------\n pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object\n Mapper pipeline to act on to data.\n\n data : array-like of shape (n_samples, n_features)\n Data used to generate the Mapper graph. Can be a pandas dataframe.\n\n layout : None, str or callable, optional, default: ``"kamada-kawai"``\n Layout algorithm for the graph. Can be any accepted value for the\n ``layout`` parameter in the :meth:`layout` method of\n :class:`igraph.Graph` [1]_.\n\n layout_dim : int, default: ``2``\n The number of dimensions for the layout. Can be 2 or 3.\n\n color_variable : object or None, optional, default: ``None``\n Specifies a feature of interest to be used, together with\n `node_color_statistic`, to determine node colors.\n\n 1. If a numpy array or pandas dataframe, it must have the same\n length as `data`.\n 2. ``None`` is equivalent to passing `data`.\n 3. If an object implementing :meth:`transform` or\n :meth:`fit_transform`, it is applied to `data` to generate the\n feature of interest.\n 4. If an index or string, or list of indices/strings, it is\n equivalent to selecting a column or subset of columns from\n `data`.\n\n node_color_statistic : callable or None, optional, default: ``None``\n If a callable, node colors will be computed as summary statistics from\n the feature array ``Y`` determined by `color_variable` – specifically,\n the color of a node representing the entries of `data` whose row\n indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``\n is equivalent to passing :func:`numpy.mean`.\n\n color_by_columns_dropdown : bool, optional, default: ``False``\n If ``True``, a dropdown widget is generated which allows the user to\n color Mapper nodes according to any column in `data` (still using\n `node_color_statistic`) in addition to `color_variable`.\n\n clone_pipeline : bool, optional, default: ``True``\n If ``True``, the input `pipeline` is cloned before computing the\n Mapper graph to prevent unexpected side effects from in-place\n parameter updates.\n\n n_sig_figs : int or None, optional, default: ``3``\n If not ``None``, number of significant figures to which to round node\n summary statistics. If ``None``, no rounding is performed.\n\n node_scale : int or float, optional, default: ``12``\n Sets the scale factor used to determine the rendered size of the\n nodes. Increase for larger nodes. Implements a formula in the\n `Plotly documentation <plotly.com/python/bubble-charts/#scaling-the-size-of-bubble-charts>`_.\n\n plotly_params : dict or None, optional, default: ``None``\n Custom parameters to configure the plotly figure. Allowed keys are\n ``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the\n corresponding values should be dictionaries containing keyword\n arguments as would be fed to the :meth:`update_traces` and\n :meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.\n\n Returns\n -------\n box : :class:`ipywidgets.VBox` object\n A box containing the following widgets: parameters of the clustering\n algorithm, parameters for the covering scheme, a Mapper graph arising\n from those parameters, a validation box, and logs.\n\n See also\n --------\n plot_static_mapper_graph, gtda.mapper.pipeline.make_mapper_pipeline\n\n References\n ----------\n .. [1] `igraph.Graph.layout\n <https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_\n documentation.\n\n '
_pipeline = (clone(pipeline) if clone_pipeline else pipeline)
_node_color_statistic = (node_color_statistic or np.mean)
def get_widgets_per_param(params):
for (key, value) in params.items():
style = {'description_width': 'initial'}
description = (key.split('__')[1] if ('__' in key) else key)
if isinstance(value, float):
(yield (key, widgets.FloatText(value=value, step=0.05, description=description, continuous_update=False, disabled=False, layout=Layout(width='90%'), style=style)))
elif isinstance(value, bool):
(yield (key, widgets.ToggleButton(value=value, description=description, disabled=False, layout=Layout(width='90%'), style=style)))
elif isinstance(value, int):
(yield (key, widgets.IntText(value=value, step=1, description=description, continuous_update=False, disabled=False, layout=Layout(width='90%'), style=style)))
elif isinstance(value, str):
(yield (key, widgets.Text(value=value, description=description, continuous_update=False, disabled=False, layout=Layout(width='90%'), style=style)))
def on_parameter_change(change):
handler.clear_logs()
try:
for (param, value) in cover_params.items():
if isinstance(value, (int, float, str)):
_pipeline.set_params(**{param: cover_params_widgets[param].value})
for (param, value) in cluster_params.items():
if isinstance(value, (int, float, str)):
_pipeline.set_params(**{param: cluster_params_widgets[param].value})
for (param, value) in nerve_params.items():
if isinstance(value, (int, bool)):
_pipeline.set_params(**{param: nerve_params_widgets[param].value})
logger.info('Updating figure...')
with fig.batch_update():
(edge_trace, node_trace, node_elements, node_colors_color_variable) = _calculate_graph_data(_pipeline, data, is_data_dataframe, layout, layout_dim, color_variable, _node_color_statistic, n_sig_figs, node_scale)
if (colorscale_for_hoverlabel is not None):
node_colors_color_variable = np.asarray(node_colors_color_variable)
min_col = np.min(node_colors_color_variable)
max_col = np.max(node_colors_color_variable)
hoverlabel_bgcolor = _get_colors_for_vals(node_colors_color_variable, min_col, max_col, colorscale_for_hoverlabel)
fig.update_traces(hoverlabel_bgcolor=hoverlabel_bgcolor, selector={'name': 'node_trace'})
fig.update_traces(x=node_trace.x, y=node_trace.y, marker_color=node_trace.marker.color, marker_size=node_trace.marker.size, marker_sizeref=node_trace.marker.sizeref, hovertext=node_trace.hovertext, **({'z': node_trace.z} if (layout_dim == 3) else dict()), selector={'name': 'node_trace'})
fig.update_traces(x=edge_trace.x, y=edge_trace.y, **({'z': edge_trace.z} if (layout_dim == 3) else dict()), selector={'name': 'edge_trace'})
if color_by_columns_dropdown:
hovertext_color_variable = node_trace.hovertext
column_color_buttons = _get_column_color_buttons(data, is_data_dataframe, node_elements, node_colors_color_variable, _node_color_statistic, hovertext_color_variable, colorscale_for_hoverlabel, n_sig_figs)
if (colorscale_for_hoverlabel is not None):
column_color_buttons[0]['args'][0]['hoverlabel.bgcolor'] = [None, hoverlabel_bgcolor]
else:
column_color_buttons = None
button_height = 1.1
fig.update_layout(updatemenus=[go.layout.Updatemenu(buttons=column_color_buttons, direction='down', pad={'r': 10, 't': 10}, showactive=True, x=0.11, xanchor='left', y=button_height, yanchor='top')])
valid.value = True
except Exception:
exception_data = traceback.format_exc().splitlines()
logger.exception(exception_data[(- 1)])
valid.value = False
def observe_widgets(params, widgets):
for (param, value) in params.items():
if isinstance(value, (int, float, str)):
widgets[param].observe(on_parameter_change, names='value')
out = widgets.Output()
@out.capture()
def click_box(change):
if logs_box.value:
out.clear_output()
handler.show_logs()
else:
out.clear_output()
logger = logging.getLogger(__name__)
handler = OutputWidgetHandler()
handler.setFormatter(logging.Formatter('%(asctime)s - [%(levelname)s] %(message)s'))
logger.addHandler(handler)
logger.setLevel(logging.INFO)
mapper_params_items = _pipeline.get_mapper_params().items()
cover_params = {key: value for (key, value) in mapper_params_items if key.startswith('cover__')}
cover_params_widgets = dict(get_widgets_per_param(cover_params))
cluster_params = {key: value for (key, value) in mapper_params_items if key.startswith('clusterer__')}
cluster_params_widgets = dict(get_widgets_per_param(cluster_params))
nerve_params = {key: value for (key, value) in mapper_params_items if (key in ['min_intersection', 'contract_nodes'])}
nerve_params_widgets = dict(get_widgets_per_param(nerve_params))
valid = widgets.Valid(value=True, description='Valid parameters', style={'description_width': '100px'})
logs_box = widgets.Checkbox(description='Show logs: ', value=False, indent=False)
fig = plot_static_mapper_graph(_pipeline, data, layout=layout, layout_dim=layout_dim, color_variable=color_variable, node_color_statistic=_node_color_statistic, color_by_columns_dropdown=color_by_columns_dropdown, clone_pipeline=False, n_sig_figs=n_sig_figs, node_scale=node_scale, plotly_params=plotly_params)
is_data_dataframe = hasattr(data, 'columns')
colorscale_for_hoverlabel = None
if (layout_dim == 3):
is_bgcolor_not_white = (fig.data[1].hoverlabel.bgcolor != 'white')
user_hoverlabel_bgcolor = False
if plotly_params:
if ('node_trace' in plotly_params):
if ('hoverlabel_bgcolor' in plotly_params['node_trace']):
user_hoverlabel_bgcolor = True
if (is_bgcolor_not_white and (not user_hoverlabel_bgcolor)):
colorscale_for_hoverlabel = fig.data[1].marker.colorscale
observe_widgets(cover_params, cover_params_widgets)
observe_widgets(cluster_params, cluster_params_widgets)
observe_widgets(nerve_params, nerve_params_widgets)
logs_box.observe(click_box, names='value')
cover_title = HTML(value='<b>Cover parameters</b>')
container_cover = widgets.VBox(children=([cover_title] + list(cover_params_widgets.values())))
container_cover.layout.align_items = 'center'
cluster_title = HTML(value='<b>Clusterer parameters</b>')
container_cluster = widgets.VBox(children=([cluster_title] + list(cluster_params_widgets.values())))
container_cluster.layout.align_items = 'center'
nerve_title = HTML(value='<b>Nerve parameters</b>')
container_nerve = widgets.VBox(children=([nerve_title] + list(nerve_params_widgets.values())))
container_nerve.layout.align_items = 'center'
container_parameters = widgets.HBox(children=[container_cover, container_cluster, container_nerve])
box = widgets.VBox([container_parameters, fig, valid, logs_box, out])
return box
|
def plot_interactive_mapper_graph(pipeline, data, layout='kamada_kawai', layout_dim=2, color_variable=None, node_color_statistic=None, clone_pipeline=True, color_by_columns_dropdown=False, n_sig_figs=3, node_scale=12, plotly_params=None):
'Plot Mapper graphs with interactivity on pipeline parameters.\n\n Extends :func:`~gtda.mapper.visualization.plot_static_mapper_graph` by\n providing functionality to interactively update parameters from the cover,\n clustering and graph construction steps defined in `pipeline`.\n\n Parameters\n ----------\n pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object\n Mapper pipeline to act on to data.\n\n data : array-like of shape (n_samples, n_features)\n Data used to generate the Mapper graph. Can be a pandas dataframe.\n\n layout : None, str or callable, optional, default: ``"kamada-kawai"``\n Layout algorithm for the graph. Can be any accepted value for the\n ``layout`` parameter in the :meth:`layout` method of\n :class:`igraph.Graph` [1]_.\n\n layout_dim : int, default: ``2``\n The number of dimensions for the layout. Can be 2 or 3.\n\n color_variable : object or None, optional, default: ``None``\n Specifies a feature of interest to be used, together with\n `node_color_statistic`, to determine node colors.\n\n 1. If a numpy array or pandas dataframe, it must have the same\n length as `data`.\n 2. ``None`` is equivalent to passing `data`.\n 3. If an object implementing :meth:`transform` or\n :meth:`fit_transform`, it is applied to `data` to generate the\n feature of interest.\n 4. If an index or string, or list of indices/strings, it is\n equivalent to selecting a column or subset of columns from\n `data`.\n\n node_color_statistic : callable or None, optional, default: ``None``\n If a callable, node colors will be computed as summary statistics from\n the feature array ``Y`` determined by `color_variable` – specifically,\n the color of a node representing the entries of `data` whose row\n indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``\n is equivalent to passing :func:`numpy.mean`.\n\n color_by_columns_dropdown : bool, optional, default: ``False``\n If ``True``, a dropdown widget is generated which allows the user to\n color Mapper nodes according to any column in `data` (still using\n `node_color_statistic`) in addition to `color_variable`.\n\n clone_pipeline : bool, optional, default: ``True``\n If ``True``, the input `pipeline` is cloned before computing the\n Mapper graph to prevent unexpected side effects from in-place\n parameter updates.\n\n n_sig_figs : int or None, optional, default: ``3``\n If not ``None``, number of significant figures to which to round node\n summary statistics. If ``None``, no rounding is performed.\n\n node_scale : int or float, optional, default: ``12``\n Sets the scale factor used to determine the rendered size of the\n nodes. Increase for larger nodes. Implements a formula in the\n `Plotly documentation <plotly.com/python/bubble-charts/#scaling-the-size-of-bubble-charts>`_.\n\n plotly_params : dict or None, optional, default: ``None``\n Custom parameters to configure the plotly figure. Allowed keys are\n ``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the\n corresponding values should be dictionaries containing keyword\n arguments as would be fed to the :meth:`update_traces` and\n :meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.\n\n Returns\n -------\n box : :class:`ipywidgets.VBox` object\n A box containing the following widgets: parameters of the clustering\n algorithm, parameters for the covering scheme, a Mapper graph arising\n from those parameters, a validation box, and logs.\n\n See also\n --------\n plot_static_mapper_graph, gtda.mapper.pipeline.make_mapper_pipeline\n\n References\n ----------\n .. [1] `igraph.Graph.layout\n <https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_\n documentation.\n\n '
_pipeline = (clone(pipeline) if clone_pipeline else pipeline)
_node_color_statistic = (node_color_statistic or np.mean)
def get_widgets_per_param(params):
for (key, value) in params.items():
style = {'description_width': 'initial'}
description = (key.split('__')[1] if ('__' in key) else key)
if isinstance(value, float):
(yield (key, widgets.FloatText(value=value, step=0.05, description=description, continuous_update=False, disabled=False, layout=Layout(width='90%'), style=style)))
elif isinstance(value, bool):
(yield (key, widgets.ToggleButton(value=value, description=description, disabled=False, layout=Layout(width='90%'), style=style)))
elif isinstance(value, int):
(yield (key, widgets.IntText(value=value, step=1, description=description, continuous_update=False, disabled=False, layout=Layout(width='90%'), style=style)))
elif isinstance(value, str):
(yield (key, widgets.Text(value=value, description=description, continuous_update=False, disabled=False, layout=Layout(width='90%'), style=style)))
def on_parameter_change(change):
handler.clear_logs()
try:
for (param, value) in cover_params.items():
if isinstance(value, (int, float, str)):
_pipeline.set_params(**{param: cover_params_widgets[param].value})
for (param, value) in cluster_params.items():
if isinstance(value, (int, float, str)):
_pipeline.set_params(**{param: cluster_params_widgets[param].value})
for (param, value) in nerve_params.items():
if isinstance(value, (int, bool)):
_pipeline.set_params(**{param: nerve_params_widgets[param].value})
logger.info('Updating figure...')
with fig.batch_update():
(edge_trace, node_trace, node_elements, node_colors_color_variable) = _calculate_graph_data(_pipeline, data, is_data_dataframe, layout, layout_dim, color_variable, _node_color_statistic, n_sig_figs, node_scale)
if (colorscale_for_hoverlabel is not None):
node_colors_color_variable = np.asarray(node_colors_color_variable)
min_col = np.min(node_colors_color_variable)
max_col = np.max(node_colors_color_variable)
hoverlabel_bgcolor = _get_colors_for_vals(node_colors_color_variable, min_col, max_col, colorscale_for_hoverlabel)
fig.update_traces(hoverlabel_bgcolor=hoverlabel_bgcolor, selector={'name': 'node_trace'})
fig.update_traces(x=node_trace.x, y=node_trace.y, marker_color=node_trace.marker.color, marker_size=node_trace.marker.size, marker_sizeref=node_trace.marker.sizeref, hovertext=node_trace.hovertext, **({'z': node_trace.z} if (layout_dim == 3) else dict()), selector={'name': 'node_trace'})
fig.update_traces(x=edge_trace.x, y=edge_trace.y, **({'z': edge_trace.z} if (layout_dim == 3) else dict()), selector={'name': 'edge_trace'})
if color_by_columns_dropdown:
hovertext_color_variable = node_trace.hovertext
column_color_buttons = _get_column_color_buttons(data, is_data_dataframe, node_elements, node_colors_color_variable, _node_color_statistic, hovertext_color_variable, colorscale_for_hoverlabel, n_sig_figs)
if (colorscale_for_hoverlabel is not None):
column_color_buttons[0]['args'][0]['hoverlabel.bgcolor'] = [None, hoverlabel_bgcolor]
else:
column_color_buttons = None
button_height = 1.1
fig.update_layout(updatemenus=[go.layout.Updatemenu(buttons=column_color_buttons, direction='down', pad={'r': 10, 't': 10}, showactive=True, x=0.11, xanchor='left', y=button_height, yanchor='top')])
valid.value = True
except Exception:
exception_data = traceback.format_exc().splitlines()
logger.exception(exception_data[(- 1)])
valid.value = False
def observe_widgets(params, widgets):
for (param, value) in params.items():
if isinstance(value, (int, float, str)):
widgets[param].observe(on_parameter_change, names='value')
out = widgets.Output()
@out.capture()
def click_box(change):
if logs_box.value:
out.clear_output()
handler.show_logs()
else:
out.clear_output()
logger = logging.getLogger(__name__)
handler = OutputWidgetHandler()
handler.setFormatter(logging.Formatter('%(asctime)s - [%(levelname)s] %(message)s'))
logger.addHandler(handler)
logger.setLevel(logging.INFO)
mapper_params_items = _pipeline.get_mapper_params().items()
cover_params = {key: value for (key, value) in mapper_params_items if key.startswith('cover__')}
cover_params_widgets = dict(get_widgets_per_param(cover_params))
cluster_params = {key: value for (key, value) in mapper_params_items if key.startswith('clusterer__')}
cluster_params_widgets = dict(get_widgets_per_param(cluster_params))
nerve_params = {key: value for (key, value) in mapper_params_items if (key in ['min_intersection', 'contract_nodes'])}
nerve_params_widgets = dict(get_widgets_per_param(nerve_params))
valid = widgets.Valid(value=True, description='Valid parameters', style={'description_width': '100px'})
logs_box = widgets.Checkbox(description='Show logs: ', value=False, indent=False)
fig = plot_static_mapper_graph(_pipeline, data, layout=layout, layout_dim=layout_dim, color_variable=color_variable, node_color_statistic=_node_color_statistic, color_by_columns_dropdown=color_by_columns_dropdown, clone_pipeline=False, n_sig_figs=n_sig_figs, node_scale=node_scale, plotly_params=plotly_params)
is_data_dataframe = hasattr(data, 'columns')
colorscale_for_hoverlabel = None
if (layout_dim == 3):
is_bgcolor_not_white = (fig.data[1].hoverlabel.bgcolor != 'white')
user_hoverlabel_bgcolor = False
if plotly_params:
if ('node_trace' in plotly_params):
if ('hoverlabel_bgcolor' in plotly_params['node_trace']):
user_hoverlabel_bgcolor = True
if (is_bgcolor_not_white and (not user_hoverlabel_bgcolor)):
colorscale_for_hoverlabel = fig.data[1].marker.colorscale
observe_widgets(cover_params, cover_params_widgets)
observe_widgets(cluster_params, cluster_params_widgets)
observe_widgets(nerve_params, nerve_params_widgets)
logs_box.observe(click_box, names='value')
cover_title = HTML(value='<b>Cover parameters</b>')
container_cover = widgets.VBox(children=([cover_title] + list(cover_params_widgets.values())))
container_cover.layout.align_items = 'center'
cluster_title = HTML(value='<b>Clusterer parameters</b>')
container_cluster = widgets.VBox(children=([cluster_title] + list(cluster_params_widgets.values())))
container_cluster.layout.align_items = 'center'
nerve_title = HTML(value='<b>Nerve parameters</b>')
container_nerve = widgets.VBox(children=([nerve_title] + list(nerve_params_widgets.values())))
container_nerve.layout.align_items = 'center'
container_parameters = widgets.HBox(children=[container_cover, container_cluster, container_nerve])
box = widgets.VBox([container_parameters, fig, valid, logs_box, out])
return box<|docstring|>Plot Mapper graphs with interactivity on pipeline parameters.
Extends :func:`~gtda.mapper.visualization.plot_static_mapper_graph` by
providing functionality to interactively update parameters from the cover,
clustering and graph construction steps defined in `pipeline`.
Parameters
----------
pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object
Mapper pipeline to act on to data.
data : array-like of shape (n_samples, n_features)
Data used to generate the Mapper graph. Can be a pandas dataframe.
layout : None, str or callable, optional, default: ``"kamada-kawai"``
Layout algorithm for the graph. Can be any accepted value for the
``layout`` parameter in the :meth:`layout` method of
:class:`igraph.Graph` [1]_.
layout_dim : int, default: ``2``
The number of dimensions for the layout. Can be 2 or 3.
color_variable : object or None, optional, default: ``None``
Specifies a feature of interest to be used, together with
`node_color_statistic`, to determine node colors.
1. If a numpy array or pandas dataframe, it must have the same
length as `data`.
2. ``None`` is equivalent to passing `data`.
3. If an object implementing :meth:`transform` or
:meth:`fit_transform`, it is applied to `data` to generate the
feature of interest.
4. If an index or string, or list of indices/strings, it is
equivalent to selecting a column or subset of columns from
`data`.
node_color_statistic : callable or None, optional, default: ``None``
If a callable, node colors will be computed as summary statistics from
the feature array ``Y`` determined by `color_variable` – specifically,
the color of a node representing the entries of `data` whose row
indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``
is equivalent to passing :func:`numpy.mean`.
color_by_columns_dropdown : bool, optional, default: ``False``
If ``True``, a dropdown widget is generated which allows the user to
color Mapper nodes according to any column in `data` (still using
`node_color_statistic`) in addition to `color_variable`.
clone_pipeline : bool, optional, default: ``True``
If ``True``, the input `pipeline` is cloned before computing the
Mapper graph to prevent unexpected side effects from in-place
parameter updates.
n_sig_figs : int or None, optional, default: ``3``
If not ``None``, number of significant figures to which to round node
summary statistics. If ``None``, no rounding is performed.
node_scale : int or float, optional, default: ``12``
Sets the scale factor used to determine the rendered size of the
nodes. Increase for larger nodes. Implements a formula in the
`Plotly documentation <plotly.com/python/bubble-charts/#scaling-the-size-of-bubble-charts>`_.
plotly_params : dict or None, optional, default: ``None``
Custom parameters to configure the plotly figure. Allowed keys are
``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the
corresponding values should be dictionaries containing keyword
arguments as would be fed to the :meth:`update_traces` and
:meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.
Returns
-------
box : :class:`ipywidgets.VBox` object
A box containing the following widgets: parameters of the clustering
algorithm, parameters for the covering scheme, a Mapper graph arising
from those parameters, a validation box, and logs.
See also
--------
plot_static_mapper_graph, gtda.mapper.pipeline.make_mapper_pipeline
References
----------
.. [1] `igraph.Graph.layout
<https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_
documentation.<|endoftext|>
|
0fbaed30969b0b5f99acedd43d6b56c8add45b932873e7e2cb25f9cdbbf06004
|
def __init__(self, name=None, version=None, description=None, last_modified=None, alias_urn=None, additional_version_weights=None):
'UpdateVersionAliasResponse - a model defined in huaweicloud sdk'
super(UpdateVersionAliasResponse, self).__init__()
self._name = None
self._version = None
self._description = None
self._last_modified = None
self._alias_urn = None
self._additional_version_weights = None
self.discriminator = None
if (name is not None):
self.name = name
if (version is not None):
self.version = version
if (description is not None):
self.description = description
if (last_modified is not None):
self.last_modified = last_modified
if (alias_urn is not None):
self.alias_urn = alias_urn
if (additional_version_weights is not None):
self.additional_version_weights = additional_version_weights
|
UpdateVersionAliasResponse - a model defined in huaweicloud sdk
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
__init__
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
def __init__(self, name=None, version=None, description=None, last_modified=None, alias_urn=None, additional_version_weights=None):
super(UpdateVersionAliasResponse, self).__init__()
self._name = None
self._version = None
self._description = None
self._last_modified = None
self._alias_urn = None
self._additional_version_weights = None
self.discriminator = None
if (name is not None):
self.name = name
if (version is not None):
self.version = version
if (description is not None):
self.description = description
if (last_modified is not None):
self.last_modified = last_modified
if (alias_urn is not None):
self.alias_urn = alias_urn
if (additional_version_weights is not None):
self.additional_version_weights = additional_version_weights
|
def __init__(self, name=None, version=None, description=None, last_modified=None, alias_urn=None, additional_version_weights=None):
super(UpdateVersionAliasResponse, self).__init__()
self._name = None
self._version = None
self._description = None
self._last_modified = None
self._alias_urn = None
self._additional_version_weights = None
self.discriminator = None
if (name is not None):
self.name = name
if (version is not None):
self.version = version
if (description is not None):
self.description = description
if (last_modified is not None):
self.last_modified = last_modified
if (alias_urn is not None):
self.alias_urn = alias_urn
if (additional_version_weights is not None):
self.additional_version_weights = additional_version_weights<|docstring|>UpdateVersionAliasResponse - a model defined in huaweicloud sdk<|endoftext|>
|
cfdd11d73c9048b197e6cc3929a7ff693a864cae3d6bd8818a92bf32e98232a9
|
@property
def name(self):
'Gets the name of this UpdateVersionAliasResponse.\n\n 要获取的别名名称。\n\n :return: The name of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._name
|
Gets the name of this UpdateVersionAliasResponse.
要获取的别名名称。
:return: The name of this UpdateVersionAliasResponse.
:rtype: str
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
name
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@property
def name(self):
'Gets the name of this UpdateVersionAliasResponse.\n\n 要获取的别名名称。\n\n :return: The name of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._name
|
@property
def name(self):
'Gets the name of this UpdateVersionAliasResponse.\n\n 要获取的别名名称。\n\n :return: The name of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._name<|docstring|>Gets the name of this UpdateVersionAliasResponse.
要获取的别名名称。
:return: The name of this UpdateVersionAliasResponse.
:rtype: str<|endoftext|>
|
d06de1386930a687ae8c35ed76dc26829de6cb3cb4388116c28eab3382a888a5
|
@name.setter
def name(self, name):
'Sets the name of this UpdateVersionAliasResponse.\n\n 要获取的别名名称。\n\n :param name: The name of this UpdateVersionAliasResponse.\n :type: str\n '
self._name = name
|
Sets the name of this UpdateVersionAliasResponse.
要获取的别名名称。
:param name: The name of this UpdateVersionAliasResponse.
:type: str
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
name
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@name.setter
def name(self, name):
'Sets the name of this UpdateVersionAliasResponse.\n\n 要获取的别名名称。\n\n :param name: The name of this UpdateVersionAliasResponse.\n :type: str\n '
self._name = name
|
@name.setter
def name(self, name):
'Sets the name of this UpdateVersionAliasResponse.\n\n 要获取的别名名称。\n\n :param name: The name of this UpdateVersionAliasResponse.\n :type: str\n '
self._name = name<|docstring|>Sets the name of this UpdateVersionAliasResponse.
要获取的别名名称。
:param name: The name of this UpdateVersionAliasResponse.
:type: str<|endoftext|>
|
5438cc307a13a24c6528bbc4af5b7b5930a9af3994c6411b4ca3580a38a7a035
|
@property
def version(self):
'Gets the version of this UpdateVersionAliasResponse.\n\n 别名对应的版本名称。\n\n :return: The version of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._version
|
Gets the version of this UpdateVersionAliasResponse.
别名对应的版本名称。
:return: The version of this UpdateVersionAliasResponse.
:rtype: str
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
version
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@property
def version(self):
'Gets the version of this UpdateVersionAliasResponse.\n\n 别名对应的版本名称。\n\n :return: The version of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._version
|
@property
def version(self):
'Gets the version of this UpdateVersionAliasResponse.\n\n 别名对应的版本名称。\n\n :return: The version of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._version<|docstring|>Gets the version of this UpdateVersionAliasResponse.
别名对应的版本名称。
:return: The version of this UpdateVersionAliasResponse.
:rtype: str<|endoftext|>
|
ac61e395fda98f9ed6788a5d4f490c7b0e1d70c4bc4154b98fb7c1d1010e677a
|
@version.setter
def version(self, version):
'Sets the version of this UpdateVersionAliasResponse.\n\n 别名对应的版本名称。\n\n :param version: The version of this UpdateVersionAliasResponse.\n :type: str\n '
self._version = version
|
Sets the version of this UpdateVersionAliasResponse.
别名对应的版本名称。
:param version: The version of this UpdateVersionAliasResponse.
:type: str
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
version
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@version.setter
def version(self, version):
'Sets the version of this UpdateVersionAliasResponse.\n\n 别名对应的版本名称。\n\n :param version: The version of this UpdateVersionAliasResponse.\n :type: str\n '
self._version = version
|
@version.setter
def version(self, version):
'Sets the version of this UpdateVersionAliasResponse.\n\n 别名对应的版本名称。\n\n :param version: The version of this UpdateVersionAliasResponse.\n :type: str\n '
self._version = version<|docstring|>Sets the version of this UpdateVersionAliasResponse.
别名对应的版本名称。
:param version: The version of this UpdateVersionAliasResponse.
:type: str<|endoftext|>
|
2a74d1eece5594492de2342ade267f3e7539e623b164cf7f6bb2b82564dcad86
|
@property
def description(self):
'Gets the description of this UpdateVersionAliasResponse.\n\n 别名描述信息。\n\n :return: The description of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._description
|
Gets the description of this UpdateVersionAliasResponse.
别名描述信息。
:return: The description of this UpdateVersionAliasResponse.
:rtype: str
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
description
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@property
def description(self):
'Gets the description of this UpdateVersionAliasResponse.\n\n 别名描述信息。\n\n :return: The description of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._description
|
@property
def description(self):
'Gets the description of this UpdateVersionAliasResponse.\n\n 别名描述信息。\n\n :return: The description of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._description<|docstring|>Gets the description of this UpdateVersionAliasResponse.
别名描述信息。
:return: The description of this UpdateVersionAliasResponse.
:rtype: str<|endoftext|>
|
1529cc1bc90f47fa92f7e96e4b40a5e2655083e494ef6bc10299c28f5cbfca68
|
@description.setter
def description(self, description):
'Sets the description of this UpdateVersionAliasResponse.\n\n 别名描述信息。\n\n :param description: The description of this UpdateVersionAliasResponse.\n :type: str\n '
self._description = description
|
Sets the description of this UpdateVersionAliasResponse.
别名描述信息。
:param description: The description of this UpdateVersionAliasResponse.
:type: str
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
description
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@description.setter
def description(self, description):
'Sets the description of this UpdateVersionAliasResponse.\n\n 别名描述信息。\n\n :param description: The description of this UpdateVersionAliasResponse.\n :type: str\n '
self._description = description
|
@description.setter
def description(self, description):
'Sets the description of this UpdateVersionAliasResponse.\n\n 别名描述信息。\n\n :param description: The description of this UpdateVersionAliasResponse.\n :type: str\n '
self._description = description<|docstring|>Sets the description of this UpdateVersionAliasResponse.
别名描述信息。
:param description: The description of this UpdateVersionAliasResponse.
:type: str<|endoftext|>
|
7338c036cfa76ce19b655028cf70abb49054a6d1afdf40d440fee391d6c5ee46
|
@property
def last_modified(self):
'Gets the last_modified of this UpdateVersionAliasResponse.\n\n 别名最后修改时间。\n\n :return: The last_modified of this UpdateVersionAliasResponse.\n :rtype: datetime\n '
return self._last_modified
|
Gets the last_modified of this UpdateVersionAliasResponse.
别名最后修改时间。
:return: The last_modified of this UpdateVersionAliasResponse.
:rtype: datetime
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
last_modified
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@property
def last_modified(self):
'Gets the last_modified of this UpdateVersionAliasResponse.\n\n 别名最后修改时间。\n\n :return: The last_modified of this UpdateVersionAliasResponse.\n :rtype: datetime\n '
return self._last_modified
|
@property
def last_modified(self):
'Gets the last_modified of this UpdateVersionAliasResponse.\n\n 别名最后修改时间。\n\n :return: The last_modified of this UpdateVersionAliasResponse.\n :rtype: datetime\n '
return self._last_modified<|docstring|>Gets the last_modified of this UpdateVersionAliasResponse.
别名最后修改时间。
:return: The last_modified of this UpdateVersionAliasResponse.
:rtype: datetime<|endoftext|>
|
15d3b667685e5ccb4bd4807a8a9183c7d81d2b2844d2087c6c0f8db5f947c324
|
@last_modified.setter
def last_modified(self, last_modified):
'Sets the last_modified of this UpdateVersionAliasResponse.\n\n 别名最后修改时间。\n\n :param last_modified: The last_modified of this UpdateVersionAliasResponse.\n :type: datetime\n '
self._last_modified = last_modified
|
Sets the last_modified of this UpdateVersionAliasResponse.
别名最后修改时间。
:param last_modified: The last_modified of this UpdateVersionAliasResponse.
:type: datetime
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
last_modified
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@last_modified.setter
def last_modified(self, last_modified):
'Sets the last_modified of this UpdateVersionAliasResponse.\n\n 别名最后修改时间。\n\n :param last_modified: The last_modified of this UpdateVersionAliasResponse.\n :type: datetime\n '
self._last_modified = last_modified
|
@last_modified.setter
def last_modified(self, last_modified):
'Sets the last_modified of this UpdateVersionAliasResponse.\n\n 别名最后修改时间。\n\n :param last_modified: The last_modified of this UpdateVersionAliasResponse.\n :type: datetime\n '
self._last_modified = last_modified<|docstring|>Sets the last_modified of this UpdateVersionAliasResponse.
别名最后修改时间。
:param last_modified: The last_modified of this UpdateVersionAliasResponse.
:type: datetime<|endoftext|>
|
06009cab372b5e25bf2a95548736961582cf8c9f3131421d66820a837033dccc
|
@property
def alias_urn(self):
'Gets the alias_urn of this UpdateVersionAliasResponse.\n\n 版本别名唯一标识。\n\n :return: The alias_urn of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._alias_urn
|
Gets the alias_urn of this UpdateVersionAliasResponse.
版本别名唯一标识。
:return: The alias_urn of this UpdateVersionAliasResponse.
:rtype: str
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
alias_urn
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@property
def alias_urn(self):
'Gets the alias_urn of this UpdateVersionAliasResponse.\n\n 版本别名唯一标识。\n\n :return: The alias_urn of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._alias_urn
|
@property
def alias_urn(self):
'Gets the alias_urn of this UpdateVersionAliasResponse.\n\n 版本别名唯一标识。\n\n :return: The alias_urn of this UpdateVersionAliasResponse.\n :rtype: str\n '
return self._alias_urn<|docstring|>Gets the alias_urn of this UpdateVersionAliasResponse.
版本别名唯一标识。
:return: The alias_urn of this UpdateVersionAliasResponse.
:rtype: str<|endoftext|>
|
33d6b57d513d568ac56d3e234018513188ae5dc810ec97d772919133b2ada7b2
|
@alias_urn.setter
def alias_urn(self, alias_urn):
'Sets the alias_urn of this UpdateVersionAliasResponse.\n\n 版本别名唯一标识。\n\n :param alias_urn: The alias_urn of this UpdateVersionAliasResponse.\n :type: str\n '
self._alias_urn = alias_urn
|
Sets the alias_urn of this UpdateVersionAliasResponse.
版本别名唯一标识。
:param alias_urn: The alias_urn of this UpdateVersionAliasResponse.
:type: str
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
alias_urn
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@alias_urn.setter
def alias_urn(self, alias_urn):
'Sets the alias_urn of this UpdateVersionAliasResponse.\n\n 版本别名唯一标识。\n\n :param alias_urn: The alias_urn of this UpdateVersionAliasResponse.\n :type: str\n '
self._alias_urn = alias_urn
|
@alias_urn.setter
def alias_urn(self, alias_urn):
'Sets the alias_urn of this UpdateVersionAliasResponse.\n\n 版本别名唯一标识。\n\n :param alias_urn: The alias_urn of this UpdateVersionAliasResponse.\n :type: str\n '
self._alias_urn = alias_urn<|docstring|>Sets the alias_urn of this UpdateVersionAliasResponse.
版本别名唯一标识。
:param alias_urn: The alias_urn of this UpdateVersionAliasResponse.
:type: str<|endoftext|>
|
6bb581d94ce5a4488e75d0ec2273325f45f39886537eee749cf29bde23c9cecc
|
@property
def additional_version_weights(self):
'Gets the additional_version_weights of this UpdateVersionAliasResponse.\n\n 灰度版本信息\n\n :return: The additional_version_weights of this UpdateVersionAliasResponse.\n :rtype: dict(str, int)\n '
return self._additional_version_weights
|
Gets the additional_version_weights of this UpdateVersionAliasResponse.
灰度版本信息
:return: The additional_version_weights of this UpdateVersionAliasResponse.
:rtype: dict(str, int)
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
additional_version_weights
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@property
def additional_version_weights(self):
'Gets the additional_version_weights of this UpdateVersionAliasResponse.\n\n 灰度版本信息\n\n :return: The additional_version_weights of this UpdateVersionAliasResponse.\n :rtype: dict(str, int)\n '
return self._additional_version_weights
|
@property
def additional_version_weights(self):
'Gets the additional_version_weights of this UpdateVersionAliasResponse.\n\n 灰度版本信息\n\n :return: The additional_version_weights of this UpdateVersionAliasResponse.\n :rtype: dict(str, int)\n '
return self._additional_version_weights<|docstring|>Gets the additional_version_weights of this UpdateVersionAliasResponse.
灰度版本信息
:return: The additional_version_weights of this UpdateVersionAliasResponse.
:rtype: dict(str, int)<|endoftext|>
|
89781b89740018517af62b8703d20def5396d47d9a2824fbbf31319ccf7a334c
|
@additional_version_weights.setter
def additional_version_weights(self, additional_version_weights):
'Sets the additional_version_weights of this UpdateVersionAliasResponse.\n\n 灰度版本信息\n\n :param additional_version_weights: The additional_version_weights of this UpdateVersionAliasResponse.\n :type: dict(str, int)\n '
self._additional_version_weights = additional_version_weights
|
Sets the additional_version_weights of this UpdateVersionAliasResponse.
灰度版本信息
:param additional_version_weights: The additional_version_weights of this UpdateVersionAliasResponse.
:type: dict(str, int)
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
additional_version_weights
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
@additional_version_weights.setter
def additional_version_weights(self, additional_version_weights):
'Sets the additional_version_weights of this UpdateVersionAliasResponse.\n\n 灰度版本信息\n\n :param additional_version_weights: The additional_version_weights of this UpdateVersionAliasResponse.\n :type: dict(str, int)\n '
self._additional_version_weights = additional_version_weights
|
@additional_version_weights.setter
def additional_version_weights(self, additional_version_weights):
'Sets the additional_version_weights of this UpdateVersionAliasResponse.\n\n 灰度版本信息\n\n :param additional_version_weights: The additional_version_weights of this UpdateVersionAliasResponse.\n :type: dict(str, int)\n '
self._additional_version_weights = additional_version_weights<|docstring|>Sets the additional_version_weights of this UpdateVersionAliasResponse.
灰度版本信息
:param additional_version_weights: The additional_version_weights of this UpdateVersionAliasResponse.
:type: dict(str, int)<|endoftext|>
|
23795442a46e2cd10dec98fded44ed9172a29971e98983a30ad89baa6c9c0a03
|
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()))
elif (attr in self.sensitive_list):
result[attr] = '****'
else:
result[attr] = value
return result
|
Returns the model properties as a dict
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
to_dict
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
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()))
elif (attr in self.sensitive_list):
result[attr] = '****'
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()))
elif (attr in self.sensitive_list):
result[attr] = '****'
else:
result[attr] = value
return result<|docstring|>Returns the model properties as a dict<|endoftext|>
|
a85eb2dd57daf3998acb705f217af08ef0b14fd68fee87605500331b1a5f2987
|
def to_str(self):
'Returns the string representation of the model'
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)
|
Returns the string representation of the model
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
to_str
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
def to_str(self):
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)
|
def to_str(self):
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)<|docstring|>Returns the string representation of the model<|endoftext|>
|
122cefd5382ee9078015a8ccdeba1aa42a0625442bf0dcfc7748dc07a3e45d3f
|
def __repr__(self):
'For `print`'
return self.to_str()
|
For `print`
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
__repr__
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
def __repr__(self):
return self.to_str()
|
def __repr__(self):
return self.to_str()<|docstring|>For `print`<|endoftext|>
|
2fdc431bb6c99ebe3c5f3ce9b8ae54319d6dc7bb87884271ba454def1cda1afa
|
def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, UpdateVersionAliasResponse)):
return False
return (self.__dict__ == other.__dict__)
|
Returns true if both objects are equal
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
__eq__
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
def __eq__(self, other):
if (not isinstance(other, UpdateVersionAliasResponse)):
return False
return (self.__dict__ == other.__dict__)
|
def __eq__(self, other):
if (not isinstance(other, UpdateVersionAliasResponse)):
return False
return (self.__dict__ == other.__dict__)<|docstring|>Returns true if both objects are equal<|endoftext|>
|
43dc6740163eb9fc1161d09cb2208a64c7ad0cc8d9c8637ac3264522d3ec7e42
|
def __ne__(self, other):
'Returns true if both objects are not equal'
return (not (self == other))
|
Returns true if both objects are not equal
|
huaweicloud-sdk-functiongraph/huaweicloudsdkfunctiongraph/v2/model/update_version_alias_response.py
|
__ne__
|
huaweicloud/huaweicloud-sdk-python-v3
| 64
|
python
|
def __ne__(self, other):
return (not (self == other))
|
def __ne__(self, other):
return (not (self == other))<|docstring|>Returns true if both objects are not equal<|endoftext|>
|
d7dc5c674c41b3fa71647cf626daa4d8cd475560683ba128bc66deafd017a583
|
def find_all_get_param_in_yml(yml):
'\n Recursively find all referenced parameters in a parsed yaml body\n and return a list of parameters\n '
collector = ParameterCollector()
traverse(yml, 'get_param', collector)
return {p for p in collector.params if (not is_pseudo_param(p))}
|
Recursively find all referenced parameters in a parsed yaml body
and return a list of parameters
|
ice_validator/tests/utils/nested_iterables.py
|
find_all_get_param_in_yml
|
rohitagarwal0910/vvp-cnf-validation-scripts
| 1
|
python
|
def find_all_get_param_in_yml(yml):
'\n Recursively find all referenced parameters in a parsed yaml body\n and return a list of parameters\n '
collector = ParameterCollector()
traverse(yml, 'get_param', collector)
return {p for p in collector.params if (not is_pseudo_param(p))}
|
def find_all_get_param_in_yml(yml):
'\n Recursively find all referenced parameters in a parsed yaml body\n and return a list of parameters\n '
collector = ParameterCollector()
traverse(yml, 'get_param', collector)
return {p for p in collector.params if (not is_pseudo_param(p))}<|docstring|>Recursively find all referenced parameters in a parsed yaml body
and return a list of parameters<|endoftext|>
|
8e3d4a9e8d050bfb13a8ff07c9697f2cc343a949c23eb7b9a6053fad22ac6382
|
def find_all_get_resource_in_yml(yml):
'\n Recursively find all referenced resources\n in a parsed yaml body and return a list of resource ids\n '
collector = ParameterCollector()
traverse(yml, 'get_resource', collector)
return collector.params
|
Recursively find all referenced resources
in a parsed yaml body and return a list of resource ids
|
ice_validator/tests/utils/nested_iterables.py
|
find_all_get_resource_in_yml
|
rohitagarwal0910/vvp-cnf-validation-scripts
| 1
|
python
|
def find_all_get_resource_in_yml(yml):
'\n Recursively find all referenced resources\n in a parsed yaml body and return a list of resource ids\n '
collector = ParameterCollector()
traverse(yml, 'get_resource', collector)
return collector.params
|
def find_all_get_resource_in_yml(yml):
'\n Recursively find all referenced resources\n in a parsed yaml body and return a list of resource ids\n '
collector = ParameterCollector()
traverse(yml, 'get_resource', collector)
return collector.params<|docstring|>Recursively find all referenced resources
in a parsed yaml body and return a list of resource ids<|endoftext|>
|
9c86c77c5bbaf0accf3df781594aa45cd3c0088066d159a43eb2690964a318e5
|
def find_all_get_file_in_yml(yml):
'\n Recursively find all get_file in a parsed yaml body\n and return the list of referenced files/urls\n '
collector = ParameterCollector()
traverse(yml, 'get_file', collector)
return collector.params
|
Recursively find all get_file in a parsed yaml body
and return the list of referenced files/urls
|
ice_validator/tests/utils/nested_iterables.py
|
find_all_get_file_in_yml
|
rohitagarwal0910/vvp-cnf-validation-scripts
| 1
|
python
|
def find_all_get_file_in_yml(yml):
'\n Recursively find all get_file in a parsed yaml body\n and return the list of referenced files/urls\n '
collector = ParameterCollector()
traverse(yml, 'get_file', collector)
return collector.params
|
def find_all_get_file_in_yml(yml):
'\n Recursively find all get_file in a parsed yaml body\n and return the list of referenced files/urls\n '
collector = ParameterCollector()
traverse(yml, 'get_file', collector)
return collector.params<|docstring|>Recursively find all get_file in a parsed yaml body
and return the list of referenced files/urls<|endoftext|>
|
1ec3064d06846f0d40b4cd82fd124cc1127daa7b13200f335f7014e6bb0175cb
|
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_adafactor():
'\n Feature: AdaFactor\n Description: Test AdaFactor\n Expectation: Run success\n '
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
net = Net()
gradient = Tensor(np.ones(param_shape), mstype.float32)
net((1e-30, 0.001), 1.0, 0.9, 0.8, 0.01, 0.03, gradient)
diff = (net.param.asnumpy() - (np.ones(param_shape) * 0.97))
assert np.all((diff < 0.001))
|
Feature: AdaFactor
Description: Test AdaFactor
Expectation: Run success
|
tests/st/ops/cpu/test_fused_ada_factor_op.py
|
test_adafactor
|
Aaron911/mindspore
| 1
|
python
|
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_adafactor():
'\n Feature: AdaFactor\n Description: Test AdaFactor\n Expectation: Run success\n '
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
net = Net()
gradient = Tensor(np.ones(param_shape), mstype.float32)
net((1e-30, 0.001), 1.0, 0.9, 0.8, 0.01, 0.03, gradient)
diff = (net.param.asnumpy() - (np.ones(param_shape) * 0.97))
assert np.all((diff < 0.001))
|
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_adafactor():
'\n Feature: AdaFactor\n Description: Test AdaFactor\n Expectation: Run success\n '
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
net = Net()
gradient = Tensor(np.ones(param_shape), mstype.float32)
net((1e-30, 0.001), 1.0, 0.9, 0.8, 0.01, 0.03, gradient)
diff = (net.param.asnumpy() - (np.ones(param_shape) * 0.97))
assert np.all((diff < 0.001))<|docstring|>Feature: AdaFactor
Description: Test AdaFactor
Expectation: Run success<|endoftext|>
|
2a3dd7bb59e04bdd06757df1fb6d1d567bf0f13785db2a4200e3a9b84d586447
|
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_adafactor_with_global_norm():
'\n Feature: AdaFactor\n Description: Test AdaFactor\n Expectation: Run success\n '
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
net = NetWithGlobalNorm()
gradient = Tensor(np.ones(param_shape), mstype.float32)
net((1e-30, 0.001), 1.0, 0.9, 0.8, 0.01, 0.03, gradient, 10.0)
diff = (net.param.asnumpy() - (np.ones(param_shape) * 0.97))
assert np.all((diff < 0.001))
|
Feature: AdaFactor
Description: Test AdaFactor
Expectation: Run success
|
tests/st/ops/cpu/test_fused_ada_factor_op.py
|
test_adafactor_with_global_norm
|
Aaron911/mindspore
| 1
|
python
|
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_adafactor_with_global_norm():
'\n Feature: AdaFactor\n Description: Test AdaFactor\n Expectation: Run success\n '
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
net = NetWithGlobalNorm()
gradient = Tensor(np.ones(param_shape), mstype.float32)
net((1e-30, 0.001), 1.0, 0.9, 0.8, 0.01, 0.03, gradient, 10.0)
diff = (net.param.asnumpy() - (np.ones(param_shape) * 0.97))
assert np.all((diff < 0.001))
|
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_adafactor_with_global_norm():
'\n Feature: AdaFactor\n Description: Test AdaFactor\n Expectation: Run success\n '
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
net = NetWithGlobalNorm()
gradient = Tensor(np.ones(param_shape), mstype.float32)
net((1e-30, 0.001), 1.0, 0.9, 0.8, 0.01, 0.03, gradient, 10.0)
diff = (net.param.asnumpy() - (np.ones(param_shape) * 0.97))
assert np.all((diff < 0.001))<|docstring|>Feature: AdaFactor
Description: Test AdaFactor
Expectation: Run success<|endoftext|>
|
b244a33f7eaccd76bfb02653603238b2b1c41a9419512aba00ad014903a9374e
|
def setupWorld():
'\n Create the world, the scenes that can be visited, the objects in the\n scenes, and the player.\n '
world = worldFactory(name='Game World')
bedroom = world.addScene('The Bedroom')
bedroom.setSkin(Skin(imageLoader.load('bedroom.png')))
lounge = world.addScene('The Lounge')
lounge.setSkin(Skin(imageLoader.load('lounge.png')))
ian_curtis = bedroom.addPlayer(name='Ian Curtis', location=[90, 90, 100], size=[14, 14, 50], velocityModifier=2)
south_facing = imageLoader.load(['player/ian_curtis1.png', 'player/ian_curtis2.png', 'player/ian_curtis3.png'])
east_facing = imageLoader.load(['player/ian_curtis4.png', 'player/ian_curtis5.png', 'player/ian_curtis6.png'])
ian_curtis.setSkin(DirectedAnimatedSkin(south_facing, east_facing, frameSequence=[0, 2, 2, 1, 1, 2, 2, 0]))
ground = PhysicalThing('ground', [(- 1000), (- 1000), (- 100)], [2000, 2000, 100])
wall0 = PhysicalThing('wall', [180, 0, (- 20)], [20, 180, 120])
wall1 = PhysicalThing('wall', [0, 180, (- 20)], [180, 20, 120])
wall2 = PhysicalThing('wall', [0, (- 20), (- 20)], [180, 20, 120])
wall3 = PhysicalThing('wall', [(- 20), 0, (- 20)], [20, 180, 120])
door = Portal(name='door', location=[180, 105, 0], size=[10, 30, 56], toScene=lounge, toLocation=[10, 115, 0])
door.setSkin(Skin(imageLoader.load(['door.png'])))
bed = MovableThing(name='bed', location=[0, 100, 0], size=[70, 52, 28], fixed=False)
bed.setSkin(Skin(imageLoader.load(['bed.png'])))
guitar = PortableThing(name='guitar', location=[60, 0, 40], size=[20, 12, 20])
guitar.setSkin(Skin(imageLoader.load(['guitar.png'])))
guitar.text.setPickedUp('You feel your hands vibrate with anticiation as you pick up the guitar.')
guitar.text.setUsed('You strum the guitar and begin to rock out hard.')
guitar.text.setDropped('The guitar makes a startling, clanging sound when you drop it.')
bedroom.addObjects([ground, wall0, wall1, wall2, wall3, door, bed, guitar, ian_curtis])
door = Portal(name='door', location=[0, 105, 0], size=[10, 30, 56], toScene=bedroom, toLocation=[160, 115, 0])
door.setSkin(Skin(imageLoader.load(['door.png'])))
sofa = PhysicalThing(name='sofa', location=[0, 0, 0], size=[39, 66, 37], fixed=False)
sofa.setSkin(Skin(imageLoader.load(['sofa.png'])))
amp = PortableThing(name='amp', location=[60, 0, 25], size=[16, 10, 18])
amp.setSkin(Skin(imageLoader.load(['amp.png'])))
amp.text.setUsed('The amp crackles and pops and you turn it up to 11.')
lounge.addObjects([ground, wall0, wall1, wall2, wall3, door, sofa, amp])
return world
|
Create the world, the scenes that can be visited, the objects in the
scenes, and the player.
|
examples/TwoRooms/tworooms.py
|
setupWorld
|
dave-leblanc/isomyr
| 0
|
python
|
def setupWorld():
'\n Create the world, the scenes that can be visited, the objects in the\n scenes, and the player.\n '
world = worldFactory(name='Game World')
bedroom = world.addScene('The Bedroom')
bedroom.setSkin(Skin(imageLoader.load('bedroom.png')))
lounge = world.addScene('The Lounge')
lounge.setSkin(Skin(imageLoader.load('lounge.png')))
ian_curtis = bedroom.addPlayer(name='Ian Curtis', location=[90, 90, 100], size=[14, 14, 50], velocityModifier=2)
south_facing = imageLoader.load(['player/ian_curtis1.png', 'player/ian_curtis2.png', 'player/ian_curtis3.png'])
east_facing = imageLoader.load(['player/ian_curtis4.png', 'player/ian_curtis5.png', 'player/ian_curtis6.png'])
ian_curtis.setSkin(DirectedAnimatedSkin(south_facing, east_facing, frameSequence=[0, 2, 2, 1, 1, 2, 2, 0]))
ground = PhysicalThing('ground', [(- 1000), (- 1000), (- 100)], [2000, 2000, 100])
wall0 = PhysicalThing('wall', [180, 0, (- 20)], [20, 180, 120])
wall1 = PhysicalThing('wall', [0, 180, (- 20)], [180, 20, 120])
wall2 = PhysicalThing('wall', [0, (- 20), (- 20)], [180, 20, 120])
wall3 = PhysicalThing('wall', [(- 20), 0, (- 20)], [20, 180, 120])
door = Portal(name='door', location=[180, 105, 0], size=[10, 30, 56], toScene=lounge, toLocation=[10, 115, 0])
door.setSkin(Skin(imageLoader.load(['door.png'])))
bed = MovableThing(name='bed', location=[0, 100, 0], size=[70, 52, 28], fixed=False)
bed.setSkin(Skin(imageLoader.load(['bed.png'])))
guitar = PortableThing(name='guitar', location=[60, 0, 40], size=[20, 12, 20])
guitar.setSkin(Skin(imageLoader.load(['guitar.png'])))
guitar.text.setPickedUp('You feel your hands vibrate with anticiation as you pick up the guitar.')
guitar.text.setUsed('You strum the guitar and begin to rock out hard.')
guitar.text.setDropped('The guitar makes a startling, clanging sound when you drop it.')
bedroom.addObjects([ground, wall0, wall1, wall2, wall3, door, bed, guitar, ian_curtis])
door = Portal(name='door', location=[0, 105, 0], size=[10, 30, 56], toScene=bedroom, toLocation=[160, 115, 0])
door.setSkin(Skin(imageLoader.load(['door.png'])))
sofa = PhysicalThing(name='sofa', location=[0, 0, 0], size=[39, 66, 37], fixed=False)
sofa.setSkin(Skin(imageLoader.load(['sofa.png'])))
amp = PortableThing(name='amp', location=[60, 0, 25], size=[16, 10, 18])
amp.setSkin(Skin(imageLoader.load(['amp.png'])))
amp.text.setUsed('The amp crackles and pops and you turn it up to 11.')
lounge.addObjects([ground, wall0, wall1, wall2, wall3, door, sofa, amp])
return world
|
def setupWorld():
'\n Create the world, the scenes that can be visited, the objects in the\n scenes, and the player.\n '
world = worldFactory(name='Game World')
bedroom = world.addScene('The Bedroom')
bedroom.setSkin(Skin(imageLoader.load('bedroom.png')))
lounge = world.addScene('The Lounge')
lounge.setSkin(Skin(imageLoader.load('lounge.png')))
ian_curtis = bedroom.addPlayer(name='Ian Curtis', location=[90, 90, 100], size=[14, 14, 50], velocityModifier=2)
south_facing = imageLoader.load(['player/ian_curtis1.png', 'player/ian_curtis2.png', 'player/ian_curtis3.png'])
east_facing = imageLoader.load(['player/ian_curtis4.png', 'player/ian_curtis5.png', 'player/ian_curtis6.png'])
ian_curtis.setSkin(DirectedAnimatedSkin(south_facing, east_facing, frameSequence=[0, 2, 2, 1, 1, 2, 2, 0]))
ground = PhysicalThing('ground', [(- 1000), (- 1000), (- 100)], [2000, 2000, 100])
wall0 = PhysicalThing('wall', [180, 0, (- 20)], [20, 180, 120])
wall1 = PhysicalThing('wall', [0, 180, (- 20)], [180, 20, 120])
wall2 = PhysicalThing('wall', [0, (- 20), (- 20)], [180, 20, 120])
wall3 = PhysicalThing('wall', [(- 20), 0, (- 20)], [20, 180, 120])
door = Portal(name='door', location=[180, 105, 0], size=[10, 30, 56], toScene=lounge, toLocation=[10, 115, 0])
door.setSkin(Skin(imageLoader.load(['door.png'])))
bed = MovableThing(name='bed', location=[0, 100, 0], size=[70, 52, 28], fixed=False)
bed.setSkin(Skin(imageLoader.load(['bed.png'])))
guitar = PortableThing(name='guitar', location=[60, 0, 40], size=[20, 12, 20])
guitar.setSkin(Skin(imageLoader.load(['guitar.png'])))
guitar.text.setPickedUp('You feel your hands vibrate with anticiation as you pick up the guitar.')
guitar.text.setUsed('You strum the guitar and begin to rock out hard.')
guitar.text.setDropped('The guitar makes a startling, clanging sound when you drop it.')
bedroom.addObjects([ground, wall0, wall1, wall2, wall3, door, bed, guitar, ian_curtis])
door = Portal(name='door', location=[0, 105, 0], size=[10, 30, 56], toScene=bedroom, toLocation=[160, 115, 0])
door.setSkin(Skin(imageLoader.load(['door.png'])))
sofa = PhysicalThing(name='sofa', location=[0, 0, 0], size=[39, 66, 37], fixed=False)
sofa.setSkin(Skin(imageLoader.load(['sofa.png'])))
amp = PortableThing(name='amp', location=[60, 0, 25], size=[16, 10, 18])
amp.setSkin(Skin(imageLoader.load(['amp.png'])))
amp.text.setUsed('The amp crackles and pops and you turn it up to 11.')
lounge.addObjects([ground, wall0, wall1, wall2, wall3, door, sofa, amp])
return world<|docstring|>Create the world, the scenes that can be visited, the objects in the
scenes, and the player.<|endoftext|>
|
91dac5b7c63365e35923a97d6948898bfcaf6a4a5a512c1dae64e24a089bf10e
|
def euclidean_dist(x, y):
'\n Args:\n x: pytorch Variable, with shape [m, d]\n y: pytorch Variable, with shape [n, d]\n Returns:\n dist: pytorch Variable, with shape [m, n]\n '
(m, n) = (x.size(0), y.size(0))
xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t()
dist = (xx + yy)
dist.addmm_(1, (- 2), x, y.t())
dist = dist.clamp(min=1e-12).sqrt()
return dist
|
Args:
x: pytorch Variable, with shape [m, d]
y: pytorch Variable, with shape [n, d]
Returns:
dist: pytorch Variable, with shape [m, n]
|
reid/utils/reid_metric.py
|
euclidean_dist
|
raoyongming/CAL
| 58
|
python
|
def euclidean_dist(x, y):
'\n Args:\n x: pytorch Variable, with shape [m, d]\n y: pytorch Variable, with shape [n, d]\n Returns:\n dist: pytorch Variable, with shape [m, n]\n '
(m, n) = (x.size(0), y.size(0))
xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t()
dist = (xx + yy)
dist.addmm_(1, (- 2), x, y.t())
dist = dist.clamp(min=1e-12).sqrt()
return dist
|
def euclidean_dist(x, y):
'\n Args:\n x: pytorch Variable, with shape [m, d]\n y: pytorch Variable, with shape [n, d]\n Returns:\n dist: pytorch Variable, with shape [m, n]\n '
(m, n) = (x.size(0), y.size(0))
xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t()
dist = (xx + yy)
dist.addmm_(1, (- 2), x, y.t())
dist = dist.clamp(min=1e-12).sqrt()
return dist<|docstring|>Args:
x: pytorch Variable, with shape [m, d]
y: pytorch Variable, with shape [n, d]
Returns:
dist: pytorch Variable, with shape [m, n]<|endoftext|>
|
a9c8796b43bdc3370e701a07e8c5551bfdea87e5c73e3627dc9b3ebda559efcc
|
def cos_dist(x, y):
'\n Args:\n x: pytorch Variable, with shape [m, d]\n y: pytorch Variable, with shape [n, d]\n Returns:\n dist: pytorch Variable, with shape [m, n]\n '
xx = (x / x.norm(dim=1)[(:, None)])
yy = (y / y.norm(dim=1)[(:, None)])
dist = torch.mm(xx, yy.t())
return (1 - dist)
|
Args:
x: pytorch Variable, with shape [m, d]
y: pytorch Variable, with shape [n, d]
Returns:
dist: pytorch Variable, with shape [m, n]
|
reid/utils/reid_metric.py
|
cos_dist
|
raoyongming/CAL
| 58
|
python
|
def cos_dist(x, y):
'\n Args:\n x: pytorch Variable, with shape [m, d]\n y: pytorch Variable, with shape [n, d]\n Returns:\n dist: pytorch Variable, with shape [m, n]\n '
xx = (x / x.norm(dim=1)[(:, None)])
yy = (y / y.norm(dim=1)[(:, None)])
dist = torch.mm(xx, yy.t())
return (1 - dist)
|
def cos_dist(x, y):
'\n Args:\n x: pytorch Variable, with shape [m, d]\n y: pytorch Variable, with shape [n, d]\n Returns:\n dist: pytorch Variable, with shape [m, n]\n '
xx = (x / x.norm(dim=1)[(:, None)])
yy = (y / y.norm(dim=1)[(:, None)])
dist = torch.mm(xx, yy.t())
return (1 - dist)<|docstring|>Args:
x: pytorch Variable, with shape [m, d]
y: pytorch Variable, with shape [n, d]
Returns:
dist: pytorch Variable, with shape [m, n]<|endoftext|>
|
d9ce10a930983a4c4115fa7338b419207d341b6e31952b073f02cb51329e13c5
|
def is_ace(card):
'Boolean evaluation of whether `card` is an Ace'
return (card[VALUE] == ACE)
|
Boolean evaluation of whether `card` is an Ace
|
group_validation_sample.py
|
is_ace
|
HesterLim/Card_Game
| 0
|
python
|
def is_ace(card):
return (card[VALUE] == ACE)
|
def is_ace(card):
return (card[VALUE] == ACE)<|docstring|>Boolean evaluation of whether `card` is an Ace<|endoftext|>
|
72233f1bbeb17911675687ee85653abc9134c756d78ef3c73066665f0103d652
|
def get_score(card):
'return the score of `card`, based on its value'
return card_score[card[VALUE]]
|
return the score of `card`, based on its value
|
group_validation_sample.py
|
get_score
|
HesterLim/Card_Game
| 0
|
python
|
def get_score(card):
return card_score[card[VALUE]]
|
def get_score(card):
return card_score[card[VALUE]]<|docstring|>return the score of `card`, based on its value<|endoftext|>
|
f9ea02e12b08eb3fe087d4f72fc2474291096970fa328d5c76299cc469da7a09
|
def get_colour(card):
'Return the colour of `card` (`RED` or `BLACK`)'
if (card[SUIT] in RED_SUITS):
return RED
else:
return BLACK
|
Return the colour of `card` (`RED` or `BLACK`)
|
group_validation_sample.py
|
get_colour
|
HesterLim/Card_Game
| 0
|
python
|
def get_colour(card):
if (card[SUIT] in RED_SUITS):
return RED
else:
return BLACK
|
def get_colour(card):
if (card[SUIT] in RED_SUITS):
return RED
else:
return BLACK<|docstring|>Return the colour of `card` (`RED` or `BLACK`)<|endoftext|>
|
2e8fa1ca3dd30cf02b8e17e50a48c1a5be03e15adafb6a6accc6e0d177286628
|
def comp10001go_score_group(cards):
'Validate/score a group of cards (order unimportant), supplied as a \n list of cards (each a string); return the positive score of the group if \n valid, and negative score otherwise. Note, assumes that all cards are \n valid, and unique.'
values = sorted([get_score(card) for card in cards])
if ((len(set(values)) == 1) and (len(cards) >= MIN_CARDS_NKIND) and (not is_ace(cards[0]))):
return (factorial(len(cards)) * card_score[cards[0][VALUE]])
nonace_cards = sorted([card for card in cards if (not is_ace(card))], key=(lambda x: get_score(x)))
ace_cards = list((set(cards) - set(nonace_cards)))
if (len(nonace_cards) >= MIN_NONACE_RUN):
is_run = True
prev_val = prev_colour = None
score = 0
for card in nonace_cards:
if (prev_val is None):
score = prev_val = get_score(card)
prev_colour = get_colour(card)
elif ((get_score(card) - prev_val) == 1):
if (get_colour(card) != prev_colour):
prev_val = get_score(card)
prev_colour = get_colour(card)
score += prev_val
else:
is_run = False
break
elif (get_score(card) == prev_val):
is_run = False
break
else:
gap = ((get_score(card) - prev_val) - 1)
gap_filled = False
while (is_run and gap and (len(ace_cards) >= gap)):
gap_filled = False
for (i, ace) in enumerate(ace_cards):
if (get_colour(ace) != prev_colour):
ace_cards.pop(i)
prev_val += 1
prev_colour = get_colour(ace)
score += prev_val
gap -= 1
gap_filled = True
break
if (not gap_filled):
is_run = False
if (is_run and gap_filled and (get_colour(card) != prev_colour)):
prev_val = get_score(card)
prev_colour = get_colour(card)
score += prev_val
else:
is_run = False
if (is_run and (len(cards) >= MIN_RUN) and (not ace_cards)):
return score
return (- sum(values))
|
Validate/score a group of cards (order unimportant), supplied as a
list of cards (each a string); return the positive score of the group if
valid, and negative score otherwise. Note, assumes that all cards are
valid, and unique.
|
group_validation_sample.py
|
comp10001go_score_group
|
HesterLim/Card_Game
| 0
|
python
|
def comp10001go_score_group(cards):
'Validate/score a group of cards (order unimportant), supplied as a \n list of cards (each a string); return the positive score of the group if \n valid, and negative score otherwise. Note, assumes that all cards are \n valid, and unique.'
values = sorted([get_score(card) for card in cards])
if ((len(set(values)) == 1) and (len(cards) >= MIN_CARDS_NKIND) and (not is_ace(cards[0]))):
return (factorial(len(cards)) * card_score[cards[0][VALUE]])
nonace_cards = sorted([card for card in cards if (not is_ace(card))], key=(lambda x: get_score(x)))
ace_cards = list((set(cards) - set(nonace_cards)))
if (len(nonace_cards) >= MIN_NONACE_RUN):
is_run = True
prev_val = prev_colour = None
score = 0
for card in nonace_cards:
if (prev_val is None):
score = prev_val = get_score(card)
prev_colour = get_colour(card)
elif ((get_score(card) - prev_val) == 1):
if (get_colour(card) != prev_colour):
prev_val = get_score(card)
prev_colour = get_colour(card)
score += prev_val
else:
is_run = False
break
elif (get_score(card) == prev_val):
is_run = False
break
else:
gap = ((get_score(card) - prev_val) - 1)
gap_filled = False
while (is_run and gap and (len(ace_cards) >= gap)):
gap_filled = False
for (i, ace) in enumerate(ace_cards):
if (get_colour(ace) != prev_colour):
ace_cards.pop(i)
prev_val += 1
prev_colour = get_colour(ace)
score += prev_val
gap -= 1
gap_filled = True
break
if (not gap_filled):
is_run = False
if (is_run and gap_filled and (get_colour(card) != prev_colour)):
prev_val = get_score(card)
prev_colour = get_colour(card)
score += prev_val
else:
is_run = False
if (is_run and (len(cards) >= MIN_RUN) and (not ace_cards)):
return score
return (- sum(values))
|
def comp10001go_score_group(cards):
'Validate/score a group of cards (order unimportant), supplied as a \n list of cards (each a string); return the positive score of the group if \n valid, and negative score otherwise. Note, assumes that all cards are \n valid, and unique.'
values = sorted([get_score(card) for card in cards])
if ((len(set(values)) == 1) and (len(cards) >= MIN_CARDS_NKIND) and (not is_ace(cards[0]))):
return (factorial(len(cards)) * card_score[cards[0][VALUE]])
nonace_cards = sorted([card for card in cards if (not is_ace(card))], key=(lambda x: get_score(x)))
ace_cards = list((set(cards) - set(nonace_cards)))
if (len(nonace_cards) >= MIN_NONACE_RUN):
is_run = True
prev_val = prev_colour = None
score = 0
for card in nonace_cards:
if (prev_val is None):
score = prev_val = get_score(card)
prev_colour = get_colour(card)
elif ((get_score(card) - prev_val) == 1):
if (get_colour(card) != prev_colour):
prev_val = get_score(card)
prev_colour = get_colour(card)
score += prev_val
else:
is_run = False
break
elif (get_score(card) == prev_val):
is_run = False
break
else:
gap = ((get_score(card) - prev_val) - 1)
gap_filled = False
while (is_run and gap and (len(ace_cards) >= gap)):
gap_filled = False
for (i, ace) in enumerate(ace_cards):
if (get_colour(ace) != prev_colour):
ace_cards.pop(i)
prev_val += 1
prev_colour = get_colour(ace)
score += prev_val
gap -= 1
gap_filled = True
break
if (not gap_filled):
is_run = False
if (is_run and gap_filled and (get_colour(card) != prev_colour)):
prev_val = get_score(card)
prev_colour = get_colour(card)
score += prev_val
else:
is_run = False
if (is_run and (len(cards) >= MIN_RUN) and (not ace_cards)):
return score
return (- sum(values))<|docstring|>Validate/score a group of cards (order unimportant), supplied as a
list of cards (each a string); return the positive score of the group if
valid, and negative score otherwise. Note, assumes that all cards are
valid, and unique.<|endoftext|>
|
38cd885584b6d7f85f0aacc3bf9841462ecc37b74ef2f41948d5c74febd805b4
|
def _get_relationships_from_consul(consul_handle, service_name):
'Fetch the relationship information from Consul for a service by service\n name. Returns a list of service names.'
index = None
rel_key = '{0}:rel'.format(service_name)
while True:
(index, data) = consul_handle.kv.get(rel_key, index=index)
if data:
return json.loads(data['Value'].decode('utf-8'))
else:
_logger.warn('No relationships found for {0}. Try again in a bit.'.format(service_name))
time.sleep(5)
|
Fetch the relationship information from Consul for a service by service
name. Returns a list of service names.
|
python-discovery-client/discovery_client/discovery.py
|
_get_relationships_from_consul
|
onap/dcaegen2-utils
| 2
|
python
|
def _get_relationships_from_consul(consul_handle, service_name):
'Fetch the relationship information from Consul for a service by service\n name. Returns a list of service names.'
index = None
rel_key = '{0}:rel'.format(service_name)
while True:
(index, data) = consul_handle.kv.get(rel_key, index=index)
if data:
return json.loads(data['Value'].decode('utf-8'))
else:
_logger.warn('No relationships found for {0}. Try again in a bit.'.format(service_name))
time.sleep(5)
|
def _get_relationships_from_consul(consul_handle, service_name):
'Fetch the relationship information from Consul for a service by service\n name. Returns a list of service names.'
index = None
rel_key = '{0}:rel'.format(service_name)
while True:
(index, data) = consul_handle.kv.get(rel_key, index=index)
if data:
return json.loads(data['Value'].decode('utf-8'))
else:
_logger.warn('No relationships found for {0}. Try again in a bit.'.format(service_name))
time.sleep(5)<|docstring|>Fetch the relationship information from Consul for a service by service
name. Returns a list of service names.<|endoftext|>
|
770d718e73878942d5d37ccb2d408104bba1ff19ff93fb1839384f36b0b224bb
|
def _get_configuration_resolved_from_cbs(consul_handle, service_name):
'\n This is what a minimal python client library that wraps the CBS would look like.\n POSSIBLE TODO: break this out into pypi repo\n\n This call does not raise an exception if Consul or the CBS cannot complete the request.\n It logs an error and returns {} if the config is not bindable. \n It could be a temporary network outage. Call me again later. \n\n It will raise an exception if the necessary env parameters were not set because that is irrecoverable.\n This function is called in my /heatlhcheck, so this will be caught early.\n '
config = {}
results = _lookup_with_consul(consul_handle, 'config_binding_service', max_attempts=5)
if (results is None):
logger.error('Cannot bind config at this time, cbs is unreachable')
else:
cbs_hostname = results[0]['ServiceAddress']
cbs_port = results[0]['ServicePort']
cbs_url = 'http://{hostname}:{port}'.format(hostname=cbs_hostname, port=cbs_port)
my_config_endpoint = '{0}/service_component/{1}'.format(cbs_url, service_name)
res = requests.get(my_config_endpoint)
try:
res.raise_for_status()
config = res.json()
_logger.info('get_config returned the following configuration: {0}'.format(json.dumps(config)))
except:
_logger.error('in get_config, the config binding service endpoint {0} blew up on me. Error code: {1}, Error text: {2}'.format(my_config_endpoint, res.status_code, res.text))
return config
|
This is what a minimal python client library that wraps the CBS would look like.
POSSIBLE TODO: break this out into pypi repo
This call does not raise an exception if Consul or the CBS cannot complete the request.
It logs an error and returns {} if the config is not bindable.
It could be a temporary network outage. Call me again later.
It will raise an exception if the necessary env parameters were not set because that is irrecoverable.
This function is called in my /heatlhcheck, so this will be caught early.
|
python-discovery-client/discovery_client/discovery.py
|
_get_configuration_resolved_from_cbs
|
onap/dcaegen2-utils
| 2
|
python
|
def _get_configuration_resolved_from_cbs(consul_handle, service_name):
'\n This is what a minimal python client library that wraps the CBS would look like.\n POSSIBLE TODO: break this out into pypi repo\n\n This call does not raise an exception if Consul or the CBS cannot complete the request.\n It logs an error and returns {} if the config is not bindable. \n It could be a temporary network outage. Call me again later. \n\n It will raise an exception if the necessary env parameters were not set because that is irrecoverable.\n This function is called in my /heatlhcheck, so this will be caught early.\n '
config = {}
results = _lookup_with_consul(consul_handle, 'config_binding_service', max_attempts=5)
if (results is None):
logger.error('Cannot bind config at this time, cbs is unreachable')
else:
cbs_hostname = results[0]['ServiceAddress']
cbs_port = results[0]['ServicePort']
cbs_url = 'http://{hostname}:{port}'.format(hostname=cbs_hostname, port=cbs_port)
my_config_endpoint = '{0}/service_component/{1}'.format(cbs_url, service_name)
res = requests.get(my_config_endpoint)
try:
res.raise_for_status()
config = res.json()
_logger.info('get_config returned the following configuration: {0}'.format(json.dumps(config)))
except:
_logger.error('in get_config, the config binding service endpoint {0} blew up on me. Error code: {1}, Error text: {2}'.format(my_config_endpoint, res.status_code, res.text))
return config
|
def _get_configuration_resolved_from_cbs(consul_handle, service_name):
'\n This is what a minimal python client library that wraps the CBS would look like.\n POSSIBLE TODO: break this out into pypi repo\n\n This call does not raise an exception if Consul or the CBS cannot complete the request.\n It logs an error and returns {} if the config is not bindable. \n It could be a temporary network outage. Call me again later. \n\n It will raise an exception if the necessary env parameters were not set because that is irrecoverable.\n This function is called in my /heatlhcheck, so this will be caught early.\n '
config = {}
results = _lookup_with_consul(consul_handle, 'config_binding_service', max_attempts=5)
if (results is None):
logger.error('Cannot bind config at this time, cbs is unreachable')
else:
cbs_hostname = results[0]['ServiceAddress']
cbs_port = results[0]['ServicePort']
cbs_url = 'http://{hostname}:{port}'.format(hostname=cbs_hostname, port=cbs_port)
my_config_endpoint = '{0}/service_component/{1}'.format(cbs_url, service_name)
res = requests.get(my_config_endpoint)
try:
res.raise_for_status()
config = res.json()
_logger.info('get_config returned the following configuration: {0}'.format(json.dumps(config)))
except:
_logger.error('in get_config, the config binding service endpoint {0} blew up on me. Error code: {1}, Error text: {2}'.format(my_config_endpoint, res.status_code, res.text))
return config<|docstring|>This is what a minimal python client library that wraps the CBS would look like.
POSSIBLE TODO: break this out into pypi repo
This call does not raise an exception if Consul or the CBS cannot complete the request.
It logs an error and returns {} if the config is not bindable.
It could be a temporary network outage. Call me again later.
It will raise an exception if the necessary env parameters were not set because that is irrecoverable.
This function is called in my /heatlhcheck, so this will be caught early.<|endoftext|>
|
db5f9723aa7e7f770aa63e8cc16a963f371a23235076c86faf3a713f4d74c446
|
def _get_connection_types(config):
'Get all the connection types for a given configuration json\n\n Crawls through the entire config dict recursively and returns the entries\n that have been identified as service connections in the form of a list of tuples -\n\n [(config key, component type), ..]\n\n where "config key" is a compound key in the form of a tuple. Each entry in\n the compound key is a key to a level within the json data structure.'
def grab_component_type(v):
if isinstance(v, six.string_types):
result = re.match('^{{\\s*([-_.\\w]*)\\s*}}', v)
return (result.group(1) if result else None)
def crawl(config, parent_key=()):
if isinstance(config, dict):
rels = [crawl(value, (parent_key + (key,))) for (key, value) in config.items()]
rels = chain(*rels)
elif isinstance(config, list):
rels = [crawl(config[index], (parent_key + (index,))) for index in range(0, len(config))]
rels = chain(*rels)
else:
rels = [(parent_key, grab_component_type(config))]
rels = [(key, rel) for (key, rel) in rels if rel]
return rels
return crawl(config)
|
Get all the connection types for a given configuration json
Crawls through the entire config dict recursively and returns the entries
that have been identified as service connections in the form of a list of tuples -
[(config key, component type), ..]
where "config key" is a compound key in the form of a tuple. Each entry in
the compound key is a key to a level within the json data structure.
|
python-discovery-client/discovery_client/discovery.py
|
_get_connection_types
|
onap/dcaegen2-utils
| 2
|
python
|
def _get_connection_types(config):
'Get all the connection types for a given configuration json\n\n Crawls through the entire config dict recursively and returns the entries\n that have been identified as service connections in the form of a list of tuples -\n\n [(config key, component type), ..]\n\n where "config key" is a compound key in the form of a tuple. Each entry in\n the compound key is a key to a level within the json data structure.'
def grab_component_type(v):
if isinstance(v, six.string_types):
result = re.match('^{{\\s*([-_.\\w]*)\\s*}}', v)
return (result.group(1) if result else None)
def crawl(config, parent_key=()):
if isinstance(config, dict):
rels = [crawl(value, (parent_key + (key,))) for (key, value) in config.items()]
rels = chain(*rels)
elif isinstance(config, list):
rels = [crawl(config[index], (parent_key + (index,))) for index in range(0, len(config))]
rels = chain(*rels)
else:
rels = [(parent_key, grab_component_type(config))]
rels = [(key, rel) for (key, rel) in rels if rel]
return rels
return crawl(config)
|
def _get_connection_types(config):
'Get all the connection types for a given configuration json\n\n Crawls through the entire config dict recursively and returns the entries\n that have been identified as service connections in the form of a list of tuples -\n\n [(config key, component type), ..]\n\n where "config key" is a compound key in the form of a tuple. Each entry in\n the compound key is a key to a level within the json data structure.'
def grab_component_type(v):
if isinstance(v, six.string_types):
result = re.match('^{{\\s*([-_.\\w]*)\\s*}}', v)
return (result.group(1) if result else None)
def crawl(config, parent_key=()):
if isinstance(config, dict):
rels = [crawl(value, (parent_key + (key,))) for (key, value) in config.items()]
rels = chain(*rels)
elif isinstance(config, list):
rels = [crawl(config[index], (parent_key + (index,))) for index in range(0, len(config))]
rels = chain(*rels)
else:
rels = [(parent_key, grab_component_type(config))]
rels = [(key, rel) for (key, rel) in rels if rel]
return rels
return crawl(config)<|docstring|>Get all the connection types for a given configuration json
Crawls through the entire config dict recursively and returns the entries
that have been identified as service connections in the form of a list of tuples -
[(config key, component type), ..]
where "config key" is a compound key in the form of a tuple. Each entry in
the compound key is a key to a level within the json data structure.<|endoftext|>
|
8274e1eead6c2506407872073070100fc86245d9ecaf3ac0b579cb245ef81e44
|
def _resolve_name(lookup_func, service_name):
'Resolves the service component name to detailed connection information\n\n Currently this is grouped into two ways:\n 1. CDAP applications take a two step approach - call Consul then call the\n CDAP broker\n 2. All other applications just call Consul to get IP and port\n\n Args:\n ----\n lookup_func: fn(string) -> list of dicts\n The function should return a list of dicts that have "ServiceAddress" and\n "ServicePort" key value entries\n service_name: (string) service name to lookup\n\n Return depends upon the connection type:\n 1. CDAP applications return a dict\n 2. All other applications return a string\n '
def handle_result(result):
ip = result['ServiceAddress']
port = result['ServicePort']
if (not (ip and port)):
raise DiscoveryResolvingNameError('Failed to resolve name for {0}: ip, port not set'.format(service_name))
if ('cdap' in service_name):
redirectish_url = 'http://{0}:{1}/application/{2}'.format(ip, port, service_name)
r = requests.get(redirectish_url)
r.raise_for_status()
details = r.json()
return {key: details[key] for key in ['connectionurl', 'serviceendpoints']}
else:
return '{0}:{1}'.format(ip, port)
try:
results = lookup_func(service_name)
return [handle_result(result) for result in results]
except Exception as e:
raise DiscoveryResolvingNameError('Failed to resolve name for {0}: {1}'.format(service_name, e))
|
Resolves the service component name to detailed connection information
Currently this is grouped into two ways:
1. CDAP applications take a two step approach - call Consul then call the
CDAP broker
2. All other applications just call Consul to get IP and port
Args:
----
lookup_func: fn(string) -> list of dicts
The function should return a list of dicts that have "ServiceAddress" and
"ServicePort" key value entries
service_name: (string) service name to lookup
Return depends upon the connection type:
1. CDAP applications return a dict
2. All other applications return a string
|
python-discovery-client/discovery_client/discovery.py
|
_resolve_name
|
onap/dcaegen2-utils
| 2
|
python
|
def _resolve_name(lookup_func, service_name):
'Resolves the service component name to detailed connection information\n\n Currently this is grouped into two ways:\n 1. CDAP applications take a two step approach - call Consul then call the\n CDAP broker\n 2. All other applications just call Consul to get IP and port\n\n Args:\n ----\n lookup_func: fn(string) -> list of dicts\n The function should return a list of dicts that have "ServiceAddress" and\n "ServicePort" key value entries\n service_name: (string) service name to lookup\n\n Return depends upon the connection type:\n 1. CDAP applications return a dict\n 2. All other applications return a string\n '
def handle_result(result):
ip = result['ServiceAddress']
port = result['ServicePort']
if (not (ip and port)):
raise DiscoveryResolvingNameError('Failed to resolve name for {0}: ip, port not set'.format(service_name))
if ('cdap' in service_name):
redirectish_url = 'http://{0}:{1}/application/{2}'.format(ip, port, service_name)
r = requests.get(redirectish_url)
r.raise_for_status()
details = r.json()
return {key: details[key] for key in ['connectionurl', 'serviceendpoints']}
else:
return '{0}:{1}'.format(ip, port)
try:
results = lookup_func(service_name)
return [handle_result(result) for result in results]
except Exception as e:
raise DiscoveryResolvingNameError('Failed to resolve name for {0}: {1}'.format(service_name, e))
|
def _resolve_name(lookup_func, service_name):
'Resolves the service component name to detailed connection information\n\n Currently this is grouped into two ways:\n 1. CDAP applications take a two step approach - call Consul then call the\n CDAP broker\n 2. All other applications just call Consul to get IP and port\n\n Args:\n ----\n lookup_func: fn(string) -> list of dicts\n The function should return a list of dicts that have "ServiceAddress" and\n "ServicePort" key value entries\n service_name: (string) service name to lookup\n\n Return depends upon the connection type:\n 1. CDAP applications return a dict\n 2. All other applications return a string\n '
def handle_result(result):
ip = result['ServiceAddress']
port = result['ServicePort']
if (not (ip and port)):
raise DiscoveryResolvingNameError('Failed to resolve name for {0}: ip, port not set'.format(service_name))
if ('cdap' in service_name):
redirectish_url = 'http://{0}:{1}/application/{2}'.format(ip, port, service_name)
r = requests.get(redirectish_url)
r.raise_for_status()
details = r.json()
return {key: details[key] for key in ['connectionurl', 'serviceendpoints']}
else:
return '{0}:{1}'.format(ip, port)
try:
results = lookup_func(service_name)
return [handle_result(result) for result in results]
except Exception as e:
raise DiscoveryResolvingNameError('Failed to resolve name for {0}: {1}'.format(service_name, e))<|docstring|>Resolves the service component name to detailed connection information
Currently this is grouped into two ways:
1. CDAP applications take a two step approach - call Consul then call the
CDAP broker
2. All other applications just call Consul to get IP and port
Args:
----
lookup_func: fn(string) -> list of dicts
The function should return a list of dicts that have "ServiceAddress" and
"ServicePort" key value entries
service_name: (string) service name to lookup
Return depends upon the connection type:
1. CDAP applications return a dict
2. All other applications return a string<|endoftext|>
|
208b387e1b38812c6540fe7ab4357cb9dc2642509ec7540f2c67ade436e891ff
|
def _resolve_configuration_dict(ch, service_name, config):
'\n Helper used by both resolve_configuration_dict and get_configuration\n '
if _has_connections(config):
rels = _get_relationships_from_consul(ch, service_name)
connection_types = _get_connection_types(config)
connection_names = _resolve_connection_types(service_name, connection_types, rels)
for (key, conn) in [(key, [_resolve_name(partial(_lookup_with_consul, ch), name)[0] for name in names]) for (key, names) in connection_names]:
config = util.update_json(config, key, conn)
_logger.info('Generated config: {0}'.format(config))
return config
|
Helper used by both resolve_configuration_dict and get_configuration
|
python-discovery-client/discovery_client/discovery.py
|
_resolve_configuration_dict
|
onap/dcaegen2-utils
| 2
|
python
|
def _resolve_configuration_dict(ch, service_name, config):
'\n \n '
if _has_connections(config):
rels = _get_relationships_from_consul(ch, service_name)
connection_types = _get_connection_types(config)
connection_names = _resolve_connection_types(service_name, connection_types, rels)
for (key, conn) in [(key, [_resolve_name(partial(_lookup_with_consul, ch), name)[0] for name in names]) for (key, names) in connection_names]:
config = util.update_json(config, key, conn)
_logger.info('Generated config: {0}'.format(config))
return config
|
def _resolve_configuration_dict(ch, service_name, config):
'\n \n '
if _has_connections(config):
rels = _get_relationships_from_consul(ch, service_name)
connection_types = _get_connection_types(config)
connection_names = _resolve_connection_types(service_name, connection_types, rels)
for (key, conn) in [(key, [_resolve_name(partial(_lookup_with_consul, ch), name)[0] for name in names]) for (key, names) in connection_names]:
config = util.update_json(config, key, conn)
_logger.info('Generated config: {0}'.format(config))
return config<|docstring|>Helper used by both resolve_configuration_dict and get_configuration<|endoftext|>
|
8002f06f576da2045761737154c5e358735da3a52c5be263baadee140078323d
|
def get_consul_hostname(consul_hostname_override=None):
'Get the Consul hostname'
try:
return (consul_hostname_override if consul_hostname_override else os.environ['CONSUL_HOST'])
except:
raise DiscoveryInitError('CONSUL_HOST variable has not been set!')
|
Get the Consul hostname
|
python-discovery-client/discovery_client/discovery.py
|
get_consul_hostname
|
onap/dcaegen2-utils
| 2
|
python
|
def get_consul_hostname(consul_hostname_override=None):
try:
return (consul_hostname_override if consul_hostname_override else os.environ['CONSUL_HOST'])
except:
raise DiscoveryInitError('CONSUL_HOST variable has not been set!')
|
def get_consul_hostname(consul_hostname_override=None):
try:
return (consul_hostname_override if consul_hostname_override else os.environ['CONSUL_HOST'])
except:
raise DiscoveryInitError('CONSUL_HOST variable has not been set!')<|docstring|>Get the Consul hostname<|endoftext|>
|
d62628c82944300de18253deb93a34d3e5c085ab131e9f9ac3a29e6c7062dc18
|
def get_service_name():
'Get the full service name\n\n This is expected to be given from whatever entity is starting this service\n and given by an environment variable called "HOSTNAME".'
try:
return os.environ['HOSTNAME']
except:
raise DiscoveryInitError('HOSTNAME variable has not been set!')
|
Get the full service name
This is expected to be given from whatever entity is starting this service
and given by an environment variable called "HOSTNAME".
|
python-discovery-client/discovery_client/discovery.py
|
get_service_name
|
onap/dcaegen2-utils
| 2
|
python
|
def get_service_name():
'Get the full service name\n\n This is expected to be given from whatever entity is starting this service\n and given by an environment variable called "HOSTNAME".'
try:
return os.environ['HOSTNAME']
except:
raise DiscoveryInitError('HOSTNAME variable has not been set!')
|
def get_service_name():
'Get the full service name\n\n This is expected to be given from whatever entity is starting this service\n and given by an environment variable called "HOSTNAME".'
try:
return os.environ['HOSTNAME']
except:
raise DiscoveryInitError('HOSTNAME variable has not been set!')<|docstring|>Get the full service name
This is expected to be given from whatever entity is starting this service
and given by an environment variable called "HOSTNAME".<|endoftext|>
|
18a10528371b66bceedf311d72c1af5c5c929161b9b46c2a2088dd42232629d4
|
def resolve_name(consul_host, service_name, max_attempts=3):
'Resolve the service name\n\n Do a service discovery lookup from Consul and return back the detailed connection\n information.\n\n Returns:\n --------\n For CDAP apps, returns a dict. All others a string with the format "<ip>:<port>"\n '
ch = consul.Consul(host=consul_host)
lookup_func = partial(_lookup_with_consul, ch, max_attempts=max_attempts)
return _resolve_name(lookup_func, service_name)
|
Resolve the service name
Do a service discovery lookup from Consul and return back the detailed connection
information.
Returns:
--------
For CDAP apps, returns a dict. All others a string with the format "<ip>:<port>"
|
python-discovery-client/discovery_client/discovery.py
|
resolve_name
|
onap/dcaegen2-utils
| 2
|
python
|
def resolve_name(consul_host, service_name, max_attempts=3):
'Resolve the service name\n\n Do a service discovery lookup from Consul and return back the detailed connection\n information.\n\n Returns:\n --------\n For CDAP apps, returns a dict. All others a string with the format "<ip>:<port>"\n '
ch = consul.Consul(host=consul_host)
lookup_func = partial(_lookup_with_consul, ch, max_attempts=max_attempts)
return _resolve_name(lookup_func, service_name)
|
def resolve_name(consul_host, service_name, max_attempts=3):
'Resolve the service name\n\n Do a service discovery lookup from Consul and return back the detailed connection\n information.\n\n Returns:\n --------\n For CDAP apps, returns a dict. All others a string with the format "<ip>:<port>"\n '
ch = consul.Consul(host=consul_host)
lookup_func = partial(_lookup_with_consul, ch, max_attempts=max_attempts)
return _resolve_name(lookup_func, service_name)<|docstring|>Resolve the service name
Do a service discovery lookup from Consul and return back the detailed connection
information.
Returns:
--------
For CDAP apps, returns a dict. All others a string with the format "<ip>:<port>"<|endoftext|>
|
c26fb4f1a89dac837b4c581900440f90be531e336b989729cae7321d6e466d19
|
def resolve_configuration_dict(consul_host, service_name, config):
'\n Utility method for taking a given service_name, and config dict, and resolving it\n '
ch = consul.Consul(host=consul_host)
return _resolve_configuration_dict(ch, service_name, config)
|
Utility method for taking a given service_name, and config dict, and resolving it
|
python-discovery-client/discovery_client/discovery.py
|
resolve_configuration_dict
|
onap/dcaegen2-utils
| 2
|
python
|
def resolve_configuration_dict(consul_host, service_name, config):
'\n \n '
ch = consul.Consul(host=consul_host)
return _resolve_configuration_dict(ch, service_name, config)
|
def resolve_configuration_dict(consul_host, service_name, config):
'\n \n '
ch = consul.Consul(host=consul_host)
return _resolve_configuration_dict(ch, service_name, config)<|docstring|>Utility method for taking a given service_name, and config dict, and resolving it<|endoftext|>
|
1f904afafc96caabc3d61a4a0c1a1825c68748a95222df5fd95cda1b7a78e971
|
def get_configuration(override_consul_hostname=None, override_service_name=None, from_cbs=True):
"Provides this service component's configuration information fully resolved\n\n This method can either resolve the configuration locally here or make a\n remote call to the config binding service. The default is to use the config\n binding service.\n\n Args:\n -----\n override_consul_hostname (string): Consul hostname to use rather than the one\n set by the environment variable CONSUL_HOST\n override_service_name (string): Use this name over the name set on the\n HOSTNAME environment variable. Default is None.\n from_cbs (boolean): True (default) means use the config binding service otherwise\n set to False to have the config pulled and resolved by this library\n\n Returns the fully resolved service component configuration as a dict\n "
consul_hostname = get_consul_hostname(override_consul_hostname)
ch = consul.Consul(host=consul_hostname)
service_name = (override_service_name if override_service_name else get_service_name())
_logger.info('service name: {0}'.format(service_name))
if from_cbs:
return _get_configuration_resolved_from_cbs(ch, service_name)
else:
config = _get_configuration_from_consul(ch, service_name)
return _resolve_configuration_dict(ch, service_name, config)
|
Provides this service component's configuration information fully resolved
This method can either resolve the configuration locally here or make a
remote call to the config binding service. The default is to use the config
binding service.
Args:
-----
override_consul_hostname (string): Consul hostname to use rather than the one
set by the environment variable CONSUL_HOST
override_service_name (string): Use this name over the name set on the
HOSTNAME environment variable. Default is None.
from_cbs (boolean): True (default) means use the config binding service otherwise
set to False to have the config pulled and resolved by this library
Returns the fully resolved service component configuration as a dict
|
python-discovery-client/discovery_client/discovery.py
|
get_configuration
|
onap/dcaegen2-utils
| 2
|
python
|
def get_configuration(override_consul_hostname=None, override_service_name=None, from_cbs=True):
"Provides this service component's configuration information fully resolved\n\n This method can either resolve the configuration locally here or make a\n remote call to the config binding service. The default is to use the config\n binding service.\n\n Args:\n -----\n override_consul_hostname (string): Consul hostname to use rather than the one\n set by the environment variable CONSUL_HOST\n override_service_name (string): Use this name over the name set on the\n HOSTNAME environment variable. Default is None.\n from_cbs (boolean): True (default) means use the config binding service otherwise\n set to False to have the config pulled and resolved by this library\n\n Returns the fully resolved service component configuration as a dict\n "
consul_hostname = get_consul_hostname(override_consul_hostname)
ch = consul.Consul(host=consul_hostname)
service_name = (override_service_name if override_service_name else get_service_name())
_logger.info('service name: {0}'.format(service_name))
if from_cbs:
return _get_configuration_resolved_from_cbs(ch, service_name)
else:
config = _get_configuration_from_consul(ch, service_name)
return _resolve_configuration_dict(ch, service_name, config)
|
def get_configuration(override_consul_hostname=None, override_service_name=None, from_cbs=True):
"Provides this service component's configuration information fully resolved\n\n This method can either resolve the configuration locally here or make a\n remote call to the config binding service. The default is to use the config\n binding service.\n\n Args:\n -----\n override_consul_hostname (string): Consul hostname to use rather than the one\n set by the environment variable CONSUL_HOST\n override_service_name (string): Use this name over the name set on the\n HOSTNAME environment variable. Default is None.\n from_cbs (boolean): True (default) means use the config binding service otherwise\n set to False to have the config pulled and resolved by this library\n\n Returns the fully resolved service component configuration as a dict\n "
consul_hostname = get_consul_hostname(override_consul_hostname)
ch = consul.Consul(host=consul_hostname)
service_name = (override_service_name if override_service_name else get_service_name())
_logger.info('service name: {0}'.format(service_name))
if from_cbs:
return _get_configuration_resolved_from_cbs(ch, service_name)
else:
config = _get_configuration_from_consul(ch, service_name)
return _resolve_configuration_dict(ch, service_name, config)<|docstring|>Provides this service component's configuration information fully resolved
This method can either resolve the configuration locally here or make a
remote call to the config binding service. The default is to use the config
binding service.
Args:
-----
override_consul_hostname (string): Consul hostname to use rather than the one
set by the environment variable CONSUL_HOST
override_service_name (string): Use this name over the name set on the
HOSTNAME environment variable. Default is None.
from_cbs (boolean): True (default) means use the config binding service otherwise
set to False to have the config pulled and resolved by this library
Returns the fully resolved service component configuration as a dict<|endoftext|>
|
f9dc0e7e06ae2ea686d27d782ed6f806f0fe8f59ed1fae48159dbaf1f8fc137d
|
def register_for_discovery(consul_host, service_ip, service_port):
'Register the service component for service discovery\n\n This is required in order for other services to "discover" you so that you\n can service their requests.\n\n NOTE: Applications may not need to make this call depending upon if the\n environment is using Registrator.\n '
ch = consul.Consul(host=consul_host)
service_name = get_service_name()
if _register_with_consul(ch, service_name, service_ip, service_port, 'health'):
_logger.info('Registered to consul: {0}'.format(service_name))
else:
_logger.error('Failed to register to consul: {0}'.format(service_name))
raise DiscoveryRegistrationError()
|
Register the service component for service discovery
This is required in order for other services to "discover" you so that you
can service their requests.
NOTE: Applications may not need to make this call depending upon if the
environment is using Registrator.
|
python-discovery-client/discovery_client/discovery.py
|
register_for_discovery
|
onap/dcaegen2-utils
| 2
|
python
|
def register_for_discovery(consul_host, service_ip, service_port):
'Register the service component for service discovery\n\n This is required in order for other services to "discover" you so that you\n can service their requests.\n\n NOTE: Applications may not need to make this call depending upon if the\n environment is using Registrator.\n '
ch = consul.Consul(host=consul_host)
service_name = get_service_name()
if _register_with_consul(ch, service_name, service_ip, service_port, 'health'):
_logger.info('Registered to consul: {0}'.format(service_name))
else:
_logger.error('Failed to register to consul: {0}'.format(service_name))
raise DiscoveryRegistrationError()
|
def register_for_discovery(consul_host, service_ip, service_port):
'Register the service component for service discovery\n\n This is required in order for other services to "discover" you so that you\n can service their requests.\n\n NOTE: Applications may not need to make this call depending upon if the\n environment is using Registrator.\n '
ch = consul.Consul(host=consul_host)
service_name = get_service_name()
if _register_with_consul(ch, service_name, service_ip, service_port, 'health'):
_logger.info('Registered to consul: {0}'.format(service_name))
else:
_logger.error('Failed to register to consul: {0}'.format(service_name))
raise DiscoveryRegistrationError()<|docstring|>Register the service component for service discovery
This is required in order for other services to "discover" you so that you
can service their requests.
NOTE: Applications may not need to make this call depending upon if the
environment is using Registrator.<|endoftext|>
|
e4236f91b66995c44c57dfbdffd9f7ed8443213f18dafefd76f2ea433277177a
|
async def close(self) -> None:
'This method is to close the sockets opened by the client.\n It need not be used when using with a context manager.\n '
(await self._client.close())
|
This method is to close the sockets opened by the client.
It need not be used when using with a context manager.
|
sdk/tables/azure-data-tables/azure/data/tables/aio/_base_client_async.py
|
close
|
automagically/azure-sdk-for-python
| 1
|
python
|
async def close(self) -> None:
'This method is to close the sockets opened by the client.\n It need not be used when using with a context manager.\n '
(await self._client.close())
|
async def close(self) -> None:
'This method is to close the sockets opened by the client.\n It need not be used when using with a context manager.\n '
(await self._client.close())<|docstring|>This method is to close the sockets opened by the client.
It need not be used when using with a context manager.<|endoftext|>
|
9f5ff04c9be232c7a8ac87c5b80ae51116ede2b4440b9864cb2cd9f58c4c1ddb
|
async def _batch_send(self, *reqs: 'HttpRequest', **kwargs) -> List[Mapping[(str, Any)]]:
'Given a series of request, do a Storage batch call.'
policies = [StorageHeadersPolicy()]
changeset = HttpRequest('POST', None)
changeset.set_multipart_mixed(*reqs, policies=policies, boundary='changeset_{}'.format(uuid4()))
request = self._client._client.post(url='{}://{}/$batch'.format(self.scheme, self._primary_hostname), headers={'x-ms-version': self.api_version, 'DataServiceVersion': '3.0', 'MaxDataServiceVersion': '3.0;NetFx', 'Content-Type': 'application/json', 'Accept': 'application/json'})
request.set_multipart_mixed(changeset, policies=policies, enforce_https=False, boundary='batch_{}'.format(uuid4()))
pipeline_response = (await self._client._client._pipeline.run(request, **kwargs))
response = pipeline_response.http_response
if (response.status_code == 413):
raise _decode_error(response, error_message='The transaction request was too large', error_type=RequestTooLargeError)
if (response.status_code != 202):
raise _decode_error(response)
parts_iter = response.parts()
parts = []
async for p in parts_iter:
parts.append(p)
error_parts = [p for p in parts if (not (200 <= p.status_code < 300))]
if any(error_parts):
if (error_parts[0].status_code == 413):
raise _decode_error(response, error_message='The transaction request was too large', error_type=RequestTooLargeError)
raise _decode_error(response=error_parts[0], error_type=TableTransactionError)
return [extract_batch_part_metadata(p) for p in parts]
|
Given a series of request, do a Storage batch call.
|
sdk/tables/azure-data-tables/azure/data/tables/aio/_base_client_async.py
|
_batch_send
|
automagically/azure-sdk-for-python
| 1
|
python
|
async def _batch_send(self, *reqs: 'HttpRequest', **kwargs) -> List[Mapping[(str, Any)]]:
policies = [StorageHeadersPolicy()]
changeset = HttpRequest('POST', None)
changeset.set_multipart_mixed(*reqs, policies=policies, boundary='changeset_{}'.format(uuid4()))
request = self._client._client.post(url='{}://{}/$batch'.format(self.scheme, self._primary_hostname), headers={'x-ms-version': self.api_version, 'DataServiceVersion': '3.0', 'MaxDataServiceVersion': '3.0;NetFx', 'Content-Type': 'application/json', 'Accept': 'application/json'})
request.set_multipart_mixed(changeset, policies=policies, enforce_https=False, boundary='batch_{}'.format(uuid4()))
pipeline_response = (await self._client._client._pipeline.run(request, **kwargs))
response = pipeline_response.http_response
if (response.status_code == 413):
raise _decode_error(response, error_message='The transaction request was too large', error_type=RequestTooLargeError)
if (response.status_code != 202):
raise _decode_error(response)
parts_iter = response.parts()
parts = []
async for p in parts_iter:
parts.append(p)
error_parts = [p for p in parts if (not (200 <= p.status_code < 300))]
if any(error_parts):
if (error_parts[0].status_code == 413):
raise _decode_error(response, error_message='The transaction request was too large', error_type=RequestTooLargeError)
raise _decode_error(response=error_parts[0], error_type=TableTransactionError)
return [extract_batch_part_metadata(p) for p in parts]
|
async def _batch_send(self, *reqs: 'HttpRequest', **kwargs) -> List[Mapping[(str, Any)]]:
policies = [StorageHeadersPolicy()]
changeset = HttpRequest('POST', None)
changeset.set_multipart_mixed(*reqs, policies=policies, boundary='changeset_{}'.format(uuid4()))
request = self._client._client.post(url='{}://{}/$batch'.format(self.scheme, self._primary_hostname), headers={'x-ms-version': self.api_version, 'DataServiceVersion': '3.0', 'MaxDataServiceVersion': '3.0;NetFx', 'Content-Type': 'application/json', 'Accept': 'application/json'})
request.set_multipart_mixed(changeset, policies=policies, enforce_https=False, boundary='batch_{}'.format(uuid4()))
pipeline_response = (await self._client._client._pipeline.run(request, **kwargs))
response = pipeline_response.http_response
if (response.status_code == 413):
raise _decode_error(response, error_message='The transaction request was too large', error_type=RequestTooLargeError)
if (response.status_code != 202):
raise _decode_error(response)
parts_iter = response.parts()
parts = []
async for p in parts_iter:
parts.append(p)
error_parts = [p for p in parts if (not (200 <= p.status_code < 300))]
if any(error_parts):
if (error_parts[0].status_code == 413):
raise _decode_error(response, error_message='The transaction request was too large', error_type=RequestTooLargeError)
raise _decode_error(response=error_parts[0], error_type=TableTransactionError)
return [extract_batch_part_metadata(p) for p in parts]<|docstring|>Given a series of request, do a Storage batch call.<|endoftext|>
|
3ad42d8d4677fdd4f2a975ba4248fe98b06ee87fc7d9a03a6862780503be7caf
|
def ParetoCdf(x, alpha, xmin):
'Evaluates CDF of the Pareto distribution with parameters alpha, xmin.'
if (x < xmin):
return 0
return (1 - pow((x / xmin), (- alpha)))
|
Evaluates CDF of the Pareto distribution with parameters alpha, xmin.
|
DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/pareto_world.py
|
ParetoCdf
|
Hakuna-Patata/BU_MSDS_PTW
| 0
|
python
|
def ParetoCdf(x, alpha, xmin):
if (x < xmin):
return 0
return (1 - pow((x / xmin), (- alpha)))
|
def ParetoCdf(x, alpha, xmin):
if (x < xmin):
return 0
return (1 - pow((x / xmin), (- alpha)))<|docstring|>Evaluates CDF of the Pareto distribution with parameters alpha, xmin.<|endoftext|>
|
c81f091fdfc09bb56fc9a34e35745bb0d309c0197ac5b2ff80a8bc8bc25d7be7
|
def ParetoMedian(xmin, alpha):
'Computes the median of a Pareto distribution.'
return (xmin * pow(2, (1 / alpha)))
|
Computes the median of a Pareto distribution.
|
DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/pareto_world.py
|
ParetoMedian
|
Hakuna-Patata/BU_MSDS_PTW
| 0
|
python
|
def ParetoMedian(xmin, alpha):
return (xmin * pow(2, (1 / alpha)))
|
def ParetoMedian(xmin, alpha):
return (xmin * pow(2, (1 / alpha)))<|docstring|>Computes the median of a Pareto distribution.<|endoftext|>
|
8e0a155f2be90e07c5fd1ce2e01733fbc07bde3d0ac9f22e0572a173307b254e
|
def MakeParetoCdf():
'Generates a plot of the CDF of height in Pareto World.'
n = 50
max = 1000.0
xs = [((max * i) / n) for i in range(n)]
xmin = 100
alpha = 1.7
ps = [ParetoCdf(x, alpha, xmin) for x in xs]
print('Median', ParetoMedian(xmin, alpha))
pyplot.clf()
pyplot.plot(xs, ps, linewidth=2)
myplot.Save('pareto_world1', title='Pareto CDF', xlabel='height (cm)', ylabel='CDF', legend=False)
|
Generates a plot of the CDF of height in Pareto World.
|
DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/pareto_world.py
|
MakeParetoCdf
|
Hakuna-Patata/BU_MSDS_PTW
| 0
|
python
|
def MakeParetoCdf():
n = 50
max = 1000.0
xs = [((max * i) / n) for i in range(n)]
xmin = 100
alpha = 1.7
ps = [ParetoCdf(x, alpha, xmin) for x in xs]
print('Median', ParetoMedian(xmin, alpha))
pyplot.clf()
pyplot.plot(xs, ps, linewidth=2)
myplot.Save('pareto_world1', title='Pareto CDF', xlabel='height (cm)', ylabel='CDF', legend=False)
|
def MakeParetoCdf():
n = 50
max = 1000.0
xs = [((max * i) / n) for i in range(n)]
xmin = 100
alpha = 1.7
ps = [ParetoCdf(x, alpha, xmin) for x in xs]
print('Median', ParetoMedian(xmin, alpha))
pyplot.clf()
pyplot.plot(xs, ps, linewidth=2)
myplot.Save('pareto_world1', title='Pareto CDF', xlabel='height (cm)', ylabel='CDF', legend=False)<|docstring|>Generates a plot of the CDF of height in Pareto World.<|endoftext|>
|
f873227626a3fd92ad84dec7b6b8482fb3bf9f914d2dd7f921351d45dfbd5802
|
def MakeFigure(xmin=100, alpha=1.7, mu=150, sigma=25):
'Makes a figure showing the CDF of height in ParetoWorld.\n\n Compared to a normal distribution.\n\n xmin: parameter of the Pareto distribution\n alpha: parameter of the Pareto distribution\n mu: parameter of the Normal distribution\n sigma: parameter of the Normal distribution\n '
t1 = [(xmin * random.paretovariate(alpha)) for i in range(10000)]
cdf1 = Cdf.MakeCdfFromList(t1, name='pareto')
t2 = [random.normalvariate(mu, sigma) for i in range(10000)]
cdf2 = Cdf.MakeCdfFromList(t2, name='normal')
myplot.Clf()
myplot.Cdfs([cdf1, cdf2])
myplot.Save(root='pareto_world2', title='Pareto World', xlabel='height (cm)', ylabel='CDF')
|
Makes a figure showing the CDF of height in ParetoWorld.
Compared to a normal distribution.
xmin: parameter of the Pareto distribution
alpha: parameter of the Pareto distribution
mu: parameter of the Normal distribution
sigma: parameter of the Normal distribution
|
DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/pareto_world.py
|
MakeFigure
|
Hakuna-Patata/BU_MSDS_PTW
| 0
|
python
|
def MakeFigure(xmin=100, alpha=1.7, mu=150, sigma=25):
'Makes a figure showing the CDF of height in ParetoWorld.\n\n Compared to a normal distribution.\n\n xmin: parameter of the Pareto distribution\n alpha: parameter of the Pareto distribution\n mu: parameter of the Normal distribution\n sigma: parameter of the Normal distribution\n '
t1 = [(xmin * random.paretovariate(alpha)) for i in range(10000)]
cdf1 = Cdf.MakeCdfFromList(t1, name='pareto')
t2 = [random.normalvariate(mu, sigma) for i in range(10000)]
cdf2 = Cdf.MakeCdfFromList(t2, name='normal')
myplot.Clf()
myplot.Cdfs([cdf1, cdf2])
myplot.Save(root='pareto_world2', title='Pareto World', xlabel='height (cm)', ylabel='CDF')
|
def MakeFigure(xmin=100, alpha=1.7, mu=150, sigma=25):
'Makes a figure showing the CDF of height in ParetoWorld.\n\n Compared to a normal distribution.\n\n xmin: parameter of the Pareto distribution\n alpha: parameter of the Pareto distribution\n mu: parameter of the Normal distribution\n sigma: parameter of the Normal distribution\n '
t1 = [(xmin * random.paretovariate(alpha)) for i in range(10000)]
cdf1 = Cdf.MakeCdfFromList(t1, name='pareto')
t2 = [random.normalvariate(mu, sigma) for i in range(10000)]
cdf2 = Cdf.MakeCdfFromList(t2, name='normal')
myplot.Clf()
myplot.Cdfs([cdf1, cdf2])
myplot.Save(root='pareto_world2', title='Pareto World', xlabel='height (cm)', ylabel='CDF')<|docstring|>Makes a figure showing the CDF of height in ParetoWorld.
Compared to a normal distribution.
xmin: parameter of the Pareto distribution
alpha: parameter of the Pareto distribution
mu: parameter of the Normal distribution
sigma: parameter of the Normal distribution<|endoftext|>
|
3004eb372d3a1fd4b2262f4e6e03a42ef3f7152b1b9b0531f80b276d912c9131
|
def TallestPareto(iters=2, n=10000, xmin=100, alpha=1.7):
'Find the tallest person in Pareto World.\n\n iters: how many samples to generate\n n: how many in each sample\n xmin: parameter of the Pareto distribution\n alpha: parameter of the Pareto distribution\n '
tallest = 0
for i in range(iters):
t = [(xmin * random.paretovariate(alpha)) for i in range(n)]
tallest = max(max(t), tallest)
return tallest
|
Find the tallest person in Pareto World.
iters: how many samples to generate
n: how many in each sample
xmin: parameter of the Pareto distribution
alpha: parameter of the Pareto distribution
|
DSC 530 - Data Exploration and Analysis/ThinkStats2/solutions/pareto_world.py
|
TallestPareto
|
Hakuna-Patata/BU_MSDS_PTW
| 0
|
python
|
def TallestPareto(iters=2, n=10000, xmin=100, alpha=1.7):
'Find the tallest person in Pareto World.\n\n iters: how many samples to generate\n n: how many in each sample\n xmin: parameter of the Pareto distribution\n alpha: parameter of the Pareto distribution\n '
tallest = 0
for i in range(iters):
t = [(xmin * random.paretovariate(alpha)) for i in range(n)]
tallest = max(max(t), tallest)
return tallest
|
def TallestPareto(iters=2, n=10000, xmin=100, alpha=1.7):
'Find the tallest person in Pareto World.\n\n iters: how many samples to generate\n n: how many in each sample\n xmin: parameter of the Pareto distribution\n alpha: parameter of the Pareto distribution\n '
tallest = 0
for i in range(iters):
t = [(xmin * random.paretovariate(alpha)) for i in range(n)]
tallest = max(max(t), tallest)
return tallest<|docstring|>Find the tallest person in Pareto World.
iters: how many samples to generate
n: how many in each sample
xmin: parameter of the Pareto distribution
alpha: parameter of the Pareto distribution<|endoftext|>
|
5b4ec42c4b404a45c152b91f90732d309761ef984ab1df67ccaf2d86afae1120
|
def __init__(self, username=None, first_name=None, last_name=None, email=None, phone=None, company=None, timezone=None):
'UpdateCurrentUserInputObject - a model defined in Swagger'
self._username = None
self._first_name = None
self._last_name = None
self._email = None
self._phone = None
self._company = None
self._timezone = None
self.discriminator = None
if (username is not None):
self.username = username
if (first_name is not None):
self.first_name = first_name
if (last_name is not None):
self.last_name = last_name
if (email is not None):
self.email = email
if (phone is not None):
self.phone = phone
if (company is not None):
self.company = company
if (timezone is not None):
self.timezone = timezone
|
UpdateCurrentUserInputObject - a model defined in Swagger
|
TextMagic/models/update_current_user_input_object.py
|
__init__
|
textmagic/textmagic-rest-python-v2
| 2
|
python
|
def __init__(self, username=None, first_name=None, last_name=None, email=None, phone=None, company=None, timezone=None):
self._username = None
self._first_name = None
self._last_name = None
self._email = None
self._phone = None
self._company = None
self._timezone = None
self.discriminator = None
if (username is not None):
self.username = username
if (first_name is not None):
self.first_name = first_name
if (last_name is not None):
self.last_name = last_name
if (email is not None):
self.email = email
if (phone is not None):
self.phone = phone
if (company is not None):
self.company = company
if (timezone is not None):
self.timezone = timezone
|
def __init__(self, username=None, first_name=None, last_name=None, email=None, phone=None, company=None, timezone=None):
self._username = None
self._first_name = None
self._last_name = None
self._email = None
self._phone = None
self._company = None
self._timezone = None
self.discriminator = None
if (username is not None):
self.username = username
if (first_name is not None):
self.first_name = first_name
if (last_name is not None):
self.last_name = last_name
if (email is not None):
self.email = email
if (phone is not None):
self.phone = phone
if (company is not None):
self.company = company
if (timezone is not None):
self.timezone = timezone<|docstring|>UpdateCurrentUserInputObject - a model defined in Swagger<|endoftext|>
|
2c0786b72098da259115e0fb6b9f5c56ee9e6cde9cbb965c5283d8126d762d57
|
@property
def username(self):
'Gets the username of this UpdateCurrentUserInputObject. # noqa: E501\n\n\n :return: The username of this UpdateCurrentUserInputObject. # noqa: E501\n :rtype: str\n '
return self._username
|
Gets the username of this UpdateCurrentUserInputObject. # noqa: E501
:return: The username of this UpdateCurrentUserInputObject. # noqa: E501
:rtype: str
|
TextMagic/models/update_current_user_input_object.py
|
username
|
textmagic/textmagic-rest-python-v2
| 2
|
python
|
@property
def username(self):
'Gets the username of this UpdateCurrentUserInputObject. # noqa: E501\n\n\n :return: The username of this UpdateCurrentUserInputObject. # noqa: E501\n :rtype: str\n '
return self._username
|
@property
def username(self):
'Gets the username of this UpdateCurrentUserInputObject. # noqa: E501\n\n\n :return: The username of this UpdateCurrentUserInputObject. # noqa: E501\n :rtype: str\n '
return self._username<|docstring|>Gets the username of this UpdateCurrentUserInputObject. # noqa: E501
:return: The username of this UpdateCurrentUserInputObject. # noqa: E501
:rtype: str<|endoftext|>
|
2ea9f4c153f0a420234755b9eb82dc546adfd9ae79949577013943e991adee00
|
@username.setter
def username(self, username):
'Sets the username of this UpdateCurrentUserInputObject.\n\n\n :param username: The username of this UpdateCurrentUserInputObject. # noqa: E501\n :type: str\n '
self._username = username
|
Sets the username of this UpdateCurrentUserInputObject.
:param username: The username of this UpdateCurrentUserInputObject. # noqa: E501
:type: str
|
TextMagic/models/update_current_user_input_object.py
|
username
|
textmagic/textmagic-rest-python-v2
| 2
|
python
|
@username.setter
def username(self, username):
'Sets the username of this UpdateCurrentUserInputObject.\n\n\n :param username: The username of this UpdateCurrentUserInputObject. # noqa: E501\n :type: str\n '
self._username = username
|
@username.setter
def username(self, username):
'Sets the username of this UpdateCurrentUserInputObject.\n\n\n :param username: The username of this UpdateCurrentUserInputObject. # noqa: E501\n :type: str\n '
self._username = username<|docstring|>Sets the username of this UpdateCurrentUserInputObject.
:param username: The username of this UpdateCurrentUserInputObject. # noqa: E501
:type: str<|endoftext|>
|
fd9463d2e3777084dcc9a2f30e24b175c67a99739ec28247aafec57f5fa07e3b
|
@property
def first_name(self):
'Gets the first_name of this UpdateCurrentUserInputObject. # noqa: E501\n\n Account first name. # noqa: E501\n\n :return: The first_name of this UpdateCurrentUserInputObject. # noqa: E501\n :rtype: str\n '
return self._first_name
|
Gets the first_name of this UpdateCurrentUserInputObject. # noqa: E501
Account first name. # noqa: E501
:return: The first_name of this UpdateCurrentUserInputObject. # noqa: E501
:rtype: str
|
TextMagic/models/update_current_user_input_object.py
|
first_name
|
textmagic/textmagic-rest-python-v2
| 2
|
python
|
@property
def first_name(self):
'Gets the first_name of this UpdateCurrentUserInputObject. # noqa: E501\n\n Account first name. # noqa: E501\n\n :return: The first_name of this UpdateCurrentUserInputObject. # noqa: E501\n :rtype: str\n '
return self._first_name
|
@property
def first_name(self):
'Gets the first_name of this UpdateCurrentUserInputObject. # noqa: E501\n\n Account first name. # noqa: E501\n\n :return: The first_name of this UpdateCurrentUserInputObject. # noqa: E501\n :rtype: str\n '
return self._first_name<|docstring|>Gets the first_name of this UpdateCurrentUserInputObject. # noqa: E501
Account first name. # noqa: E501
:return: The first_name of this UpdateCurrentUserInputObject. # noqa: E501
:rtype: str<|endoftext|>
|
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