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4b5f6e94f37a4691367eb08ce125ede0439657ff7497c8145b3d840919424085 | def get_sequence_class(random_batches, balanced_sampling):
'\n Returns the appropriate BatchSequence sub-class given a set of parameters.\n\n Note: balanced_sampling cannot be True with random_batches=False\n\n Args:\n random_batches: (bool) The BatchSequence should sample random\n batches across the SleepStudyDataset\n balanced_sampling: (bool) The BatchSequence should sample randomly\n and uniformly across individual classes.\n\n Returns:\n A BatchSequence typed class (non-initialized)\n '
if random_batches:
if balanced_sampling:
return BalancedRandomBatchSequence
else:
return RandomBatchSequence
elif balanced_sampling:
raise ValueError("Cannot use 'balanced_sampling' with 'random_batches' set to False.")
else:
return BatchSequence | Returns the appropriate BatchSequence sub-class given a set of parameters.
Note: balanced_sampling cannot be True with random_batches=False
Args:
random_batches: (bool) The BatchSequence should sample random
batches across the SleepStudyDataset
balanced_sampling: (bool) The BatchSequence should sample randomly
and uniformly across individual classes.
Returns:
A BatchSequence typed class (non-initialized) | utime/sequences/utils.py | get_sequence_class | learning310/U-Time | 138 | python | def get_sequence_class(random_batches, balanced_sampling):
'\n Returns the appropriate BatchSequence sub-class given a set of parameters.\n\n Note: balanced_sampling cannot be True with random_batches=False\n\n Args:\n random_batches: (bool) The BatchSequence should sample random\n batches across the SleepStudyDataset\n balanced_sampling: (bool) The BatchSequence should sample randomly\n and uniformly across individual classes.\n\n Returns:\n A BatchSequence typed class (non-initialized)\n '
if random_batches:
if balanced_sampling:
return BalancedRandomBatchSequence
else:
return RandomBatchSequence
elif balanced_sampling:
raise ValueError("Cannot use 'balanced_sampling' with 'random_batches' set to False.")
else:
return BatchSequence | def get_sequence_class(random_batches, balanced_sampling):
'\n Returns the appropriate BatchSequence sub-class given a set of parameters.\n\n Note: balanced_sampling cannot be True with random_batches=False\n\n Args:\n random_batches: (bool) The BatchSequence should sample random\n batches across the SleepStudyDataset\n balanced_sampling: (bool) The BatchSequence should sample randomly\n and uniformly across individual classes.\n\n Returns:\n A BatchSequence typed class (non-initialized)\n '
if random_batches:
if balanced_sampling:
return BalancedRandomBatchSequence
else:
return RandomBatchSequence
elif balanced_sampling:
raise ValueError("Cannot use 'balanced_sampling' with 'random_batches' set to False.")
else:
return BatchSequence<|docstring|>Returns the appropriate BatchSequence sub-class given a set of parameters.
Note: balanced_sampling cannot be True with random_batches=False
Args:
random_batches: (bool) The BatchSequence should sample random
batches across the SleepStudyDataset
balanced_sampling: (bool) The BatchSequence should sample randomly
and uniformly across individual classes.
Returns:
A BatchSequence typed class (non-initialized)<|endoftext|> |
c1ffdebcd9684caeecbfbec8a1761c842b694b182c669f3902cf7b4874690278 | def get_batch_sequence(dataset_queue, batch_size=16, random_batches=True, balanced_sampling=True, n_classes=None, margin=0, augmenters=None, scaler=None, batch_wise_scaling=False, no_log=False, **kwargs):
'\n Return a utime.sequences BatchSequence object made from a dataset queue.\n A BatchSequence object is used to extract batches of data from all or\n individual SleepStudy objects represented by this SleepStudyDataset.\n\n All args pass to the BatchSequence object.\n Please refer to its documentation.\n\n Returns:\n A BatchSequence object\n '
(data_per_epoch, n_channels) = infer_dpe_and_chans(dataset_queue)
sequence_class = get_sequence_class(random_batches, balanced_sampling)
return sequence_class(dataset_queue=dataset_queue, batch_size=batch_size, data_per_period=data_per_epoch, n_classes=n_classes, n_channels=n_channels, margin=margin, augmenters=augmenters, batch_scaler=(scaler if batch_wise_scaling else None), logger=dataset_queue.logger, identifier=dataset_queue.dataset.identifier, no_log=no_log, **kwargs) | Return a utime.sequences BatchSequence object made from a dataset queue.
A BatchSequence object is used to extract batches of data from all or
individual SleepStudy objects represented by this SleepStudyDataset.
All args pass to the BatchSequence object.
Please refer to its documentation.
Returns:
A BatchSequence object | utime/sequences/utils.py | get_batch_sequence | learning310/U-Time | 138 | python | def get_batch_sequence(dataset_queue, batch_size=16, random_batches=True, balanced_sampling=True, n_classes=None, margin=0, augmenters=None, scaler=None, batch_wise_scaling=False, no_log=False, **kwargs):
'\n Return a utime.sequences BatchSequence object made from a dataset queue.\n A BatchSequence object is used to extract batches of data from all or\n individual SleepStudy objects represented by this SleepStudyDataset.\n\n All args pass to the BatchSequence object.\n Please refer to its documentation.\n\n Returns:\n A BatchSequence object\n '
(data_per_epoch, n_channels) = infer_dpe_and_chans(dataset_queue)
sequence_class = get_sequence_class(random_batches, balanced_sampling)
return sequence_class(dataset_queue=dataset_queue, batch_size=batch_size, data_per_period=data_per_epoch, n_classes=n_classes, n_channels=n_channels, margin=margin, augmenters=augmenters, batch_scaler=(scaler if batch_wise_scaling else None), logger=dataset_queue.logger, identifier=dataset_queue.dataset.identifier, no_log=no_log, **kwargs) | def get_batch_sequence(dataset_queue, batch_size=16, random_batches=True, balanced_sampling=True, n_classes=None, margin=0, augmenters=None, scaler=None, batch_wise_scaling=False, no_log=False, **kwargs):
'\n Return a utime.sequences BatchSequence object made from a dataset queue.\n A BatchSequence object is used to extract batches of data from all or\n individual SleepStudy objects represented by this SleepStudyDataset.\n\n All args pass to the BatchSequence object.\n Please refer to its documentation.\n\n Returns:\n A BatchSequence object\n '
(data_per_epoch, n_channels) = infer_dpe_and_chans(dataset_queue)
sequence_class = get_sequence_class(random_batches, balanced_sampling)
return sequence_class(dataset_queue=dataset_queue, batch_size=batch_size, data_per_period=data_per_epoch, n_classes=n_classes, n_channels=n_channels, margin=margin, augmenters=augmenters, batch_scaler=(scaler if batch_wise_scaling else None), logger=dataset_queue.logger, identifier=dataset_queue.dataset.identifier, no_log=no_log, **kwargs)<|docstring|>Return a utime.sequences BatchSequence object made from a dataset queue.
A BatchSequence object is used to extract batches of data from all or
individual SleepStudy objects represented by this SleepStudyDataset.
All args pass to the BatchSequence object.
Please refer to its documentation.
Returns:
A BatchSequence object<|endoftext|> |
0c353a9d87e0b447d8a869bba7209499e7456af8600f307b0756031e81d612b4 | def makedirs_touch(path):
'Creates the file and all parent directories in the supplied path'
basedir = os.path.dirname(path)
if (not os.path.exists(basedir)):
os.makedirs(basedir)
with open(path, 'a'):
os.utime(path, None) | Creates the file and all parent directories in the supplied path | pyjournal/utils.py | makedirs_touch | Lee-Sutton/pyjournal | 0 | python | def makedirs_touch(path):
basedir = os.path.dirname(path)
if (not os.path.exists(basedir)):
os.makedirs(basedir)
with open(path, 'a'):
os.utime(path, None) | def makedirs_touch(path):
basedir = os.path.dirname(path)
if (not os.path.exists(basedir)):
os.makedirs(basedir)
with open(path, 'a'):
os.utime(path, None)<|docstring|>Creates the file and all parent directories in the supplied path<|endoftext|> |
a1682e809a0ba0d4a74baa235b2d35f7f7ca56fb84443f5fa0bdaebfbf8f092b | def __init__(self, handler, thread_group=None, timeout=None):
'Initializes a new Watcher instance.\n\n :param handler: a `callable` object to be invoked for each observed\n K8s event with the event body as a single argument.\n Calling `handler` should never raise any exceptions\n other than `eventlet.greenlet.GreenletExit` caused by\n `eventlet.greenthread.GreenThread.kill` when the\n `Watcher` is operating in asynchronous mode.\n :param thread_group: an `oslo_service.threadgroup.ThreadGroup`\n object used to run the event processing loops\n asynchronously. If `thread_group` is not\n specified, the `Watcher` will operate in a\n synchronous mode.\n '
super(Watcher, self).__init__()
self._client = clients.get_kubernetes_client()
self._handler = handler
self._thread_group = thread_group
self._running = False
self._resources = set()
self._watching = {}
self._idle = {}
if (timeout is None):
timeout = CONF.kubernetes.watch_retry_timeout
self._timeout = timeout | Initializes a new Watcher instance.
:param handler: a `callable` object to be invoked for each observed
K8s event with the event body as a single argument.
Calling `handler` should never raise any exceptions
other than `eventlet.greenlet.GreenletExit` caused by
`eventlet.greenthread.GreenThread.kill` when the
`Watcher` is operating in asynchronous mode.
:param thread_group: an `oslo_service.threadgroup.ThreadGroup`
object used to run the event processing loops
asynchronously. If `thread_group` is not
specified, the `Watcher` will operate in a
synchronous mode. | kuryr_kubernetes/watcher.py | __init__ | BoringWenn/kuryr-kubernetes | 0 | python | def __init__(self, handler, thread_group=None, timeout=None):
'Initializes a new Watcher instance.\n\n :param handler: a `callable` object to be invoked for each observed\n K8s event with the event body as a single argument.\n Calling `handler` should never raise any exceptions\n other than `eventlet.greenlet.GreenletExit` caused by\n `eventlet.greenthread.GreenThread.kill` when the\n `Watcher` is operating in asynchronous mode.\n :param thread_group: an `oslo_service.threadgroup.ThreadGroup`\n object used to run the event processing loops\n asynchronously. If `thread_group` is not\n specified, the `Watcher` will operate in a\n synchronous mode.\n '
super(Watcher, self).__init__()
self._client = clients.get_kubernetes_client()
self._handler = handler
self._thread_group = thread_group
self._running = False
self._resources = set()
self._watching = {}
self._idle = {}
if (timeout is None):
timeout = CONF.kubernetes.watch_retry_timeout
self._timeout = timeout | def __init__(self, handler, thread_group=None, timeout=None):
'Initializes a new Watcher instance.\n\n :param handler: a `callable` object to be invoked for each observed\n K8s event with the event body as a single argument.\n Calling `handler` should never raise any exceptions\n other than `eventlet.greenlet.GreenletExit` caused by\n `eventlet.greenthread.GreenThread.kill` when the\n `Watcher` is operating in asynchronous mode.\n :param thread_group: an `oslo_service.threadgroup.ThreadGroup`\n object used to run the event processing loops\n asynchronously. If `thread_group` is not\n specified, the `Watcher` will operate in a\n synchronous mode.\n '
super(Watcher, self).__init__()
self._client = clients.get_kubernetes_client()
self._handler = handler
self._thread_group = thread_group
self._running = False
self._resources = set()
self._watching = {}
self._idle = {}
if (timeout is None):
timeout = CONF.kubernetes.watch_retry_timeout
self._timeout = timeout<|docstring|>Initializes a new Watcher instance.
:param handler: a `callable` object to be invoked for each observed
K8s event with the event body as a single argument.
Calling `handler` should never raise any exceptions
other than `eventlet.greenlet.GreenletExit` caused by
`eventlet.greenthread.GreenThread.kill` when the
`Watcher` is operating in asynchronous mode.
:param thread_group: an `oslo_service.threadgroup.ThreadGroup`
object used to run the event processing loops
asynchronously. If `thread_group` is not
specified, the `Watcher` will operate in a
synchronous mode.<|endoftext|> |
6b389fb3ec34e6c434a9e5212f2cfc551e4593d4f08fd4c25964adea602738ce | def add(self, path):
'Adds ths K8s resource to the Watcher.\n\n Adding a resource to a running `Watcher` also ensures that the event\n processing loop for that resource is running. This method could block\n for `Watcher`s operating in synchronous mode.\n\n :param path: K8s resource URL path\n '
self._resources.add(path)
if (self._running and (path not in self._watching)):
self._start_watch(path) | Adds ths K8s resource to the Watcher.
Adding a resource to a running `Watcher` also ensures that the event
processing loop for that resource is running. This method could block
for `Watcher`s operating in synchronous mode.
:param path: K8s resource URL path | kuryr_kubernetes/watcher.py | add | BoringWenn/kuryr-kubernetes | 0 | python | def add(self, path):
'Adds ths K8s resource to the Watcher.\n\n Adding a resource to a running `Watcher` also ensures that the event\n processing loop for that resource is running. This method could block\n for `Watcher`s operating in synchronous mode.\n\n :param path: K8s resource URL path\n '
self._resources.add(path)
if (self._running and (path not in self._watching)):
self._start_watch(path) | def add(self, path):
'Adds ths K8s resource to the Watcher.\n\n Adding a resource to a running `Watcher` also ensures that the event\n processing loop for that resource is running. This method could block\n for `Watcher`s operating in synchronous mode.\n\n :param path: K8s resource URL path\n '
self._resources.add(path)
if (self._running and (path not in self._watching)):
self._start_watch(path)<|docstring|>Adds ths K8s resource to the Watcher.
Adding a resource to a running `Watcher` also ensures that the event
processing loop for that resource is running. This method could block
for `Watcher`s operating in synchronous mode.
:param path: K8s resource URL path<|endoftext|> |
8b6af6574f3dd876c1488faddd6e7628d3ad8a4260ce7875d408f394e34a9402 | def remove(self, path):
'Removes the K8s resource from the Watcher.\n\n Also requests the corresponding event processing loop to stop if it\n is running.\n\n :param path: K8s resource URL path\n '
self._resources.discard(path)
if (path in self._watching):
self._stop_watch(path) | Removes the K8s resource from the Watcher.
Also requests the corresponding event processing loop to stop if it
is running.
:param path: K8s resource URL path | kuryr_kubernetes/watcher.py | remove | BoringWenn/kuryr-kubernetes | 0 | python | def remove(self, path):
'Removes the K8s resource from the Watcher.\n\n Also requests the corresponding event processing loop to stop if it\n is running.\n\n :param path: K8s resource URL path\n '
self._resources.discard(path)
if (path in self._watching):
self._stop_watch(path) | def remove(self, path):
'Removes the K8s resource from the Watcher.\n\n Also requests the corresponding event processing loop to stop if it\n is running.\n\n :param path: K8s resource URL path\n '
self._resources.discard(path)
if (path in self._watching):
self._stop_watch(path)<|docstring|>Removes the K8s resource from the Watcher.
Also requests the corresponding event processing loop to stop if it
is running.
:param path: K8s resource URL path<|endoftext|> |
d6b38122bab161bd2926cc067a9607e352107ad12a57e0e0f562de8038d9ad8b | def start(self):
'Starts the Watcher.\n\n Also ensures that the event processing loops are running. This method\n could block for `Watcher`s operating in synchronous mode.\n '
self._running = True
for path in (self._resources - set(self._watching)):
self._start_watch(path) | Starts the Watcher.
Also ensures that the event processing loops are running. This method
could block for `Watcher`s operating in synchronous mode. | kuryr_kubernetes/watcher.py | start | BoringWenn/kuryr-kubernetes | 0 | python | def start(self):
'Starts the Watcher.\n\n Also ensures that the event processing loops are running. This method\n could block for `Watcher`s operating in synchronous mode.\n '
self._running = True
for path in (self._resources - set(self._watching)):
self._start_watch(path) | def start(self):
'Starts the Watcher.\n\n Also ensures that the event processing loops are running. This method\n could block for `Watcher`s operating in synchronous mode.\n '
self._running = True
for path in (self._resources - set(self._watching)):
self._start_watch(path)<|docstring|>Starts the Watcher.
Also ensures that the event processing loops are running. This method
could block for `Watcher`s operating in synchronous mode.<|endoftext|> |
1d35b613eece6b6138378cce3e390b81095c6481a5cfe0702329f12ae5a153d6 | def stop(self):
'Stops the Watcher.\n\n Also requests all running event processing loops to stop.\n '
self._running = False
for path in list(self._watching):
self._stop_watch(path) | Stops the Watcher.
Also requests all running event processing loops to stop. | kuryr_kubernetes/watcher.py | stop | BoringWenn/kuryr-kubernetes | 0 | python | def stop(self):
'Stops the Watcher.\n\n Also requests all running event processing loops to stop.\n '
self._running = False
for path in list(self._watching):
self._stop_watch(path) | def stop(self):
'Stops the Watcher.\n\n Also requests all running event processing loops to stop.\n '
self._running = False
for path in list(self._watching):
self._stop_watch(path)<|docstring|>Stops the Watcher.
Also requests all running event processing loops to stop.<|endoftext|> |
18347931f6f758da5e586c02b09a81724a1611d86676fcdf1e38da069a717340 | def version() -> str:
'Returns the version number of this library.'
return VERSION | Returns the version number of this library. | redpandas/__init__.py | version | RedVoxInc/redpandas | 1 | python | def version() -> str:
return VERSION | def version() -> str:
return VERSION<|docstring|>Returns the version number of this library.<|endoftext|> |
dc55a383917dd4bd2fcc22b3c3d8c869db14863970454ea88f78344a12103ccf | def print_version() -> None:
'Prints the version number of this library'
print(version()) | Prints the version number of this library | redpandas/__init__.py | print_version | RedVoxInc/redpandas | 1 | python | def print_version() -> None:
print(version()) | def print_version() -> None:
print(version())<|docstring|>Prints the version number of this library<|endoftext|> |
7aa0edb6861393b0ffc4301d80126fbd4a222df6f8d6cb7b45d0ee5366bacd3e | def get_data_files():
' Get all data files for the package\n '
data_files = [('etc/jupyter/jupyter_server_config.d', ['etc/jupyter/jupyter_server_config.d/voila-gridstack.json']), ('etc/jupyter/jupyter_notebook_config.d', ['etc/jupyter/jupyter_notebook_config.d/voila-gridstack.json']), ('etc/jupyter/nbconfig/notebook.d', ['etc/jupyter/nbconfig/notebook.d/voila-gridstack.json']), ('share/jupyter/nbextensions/voila-gridstack', ['voila-gridstack/static/extension.js', 'voila-gridstack/static/voila-gridstack.js', 'voila-gridstack/static/voila-gridstack.css', 'voila-gridstack/static/gridstack.js', 'voila-gridstack/static/gridstack.jqueryUI_require.js'])]
for (root, dirs, files) in os.walk('share'):
root_files = [os.path.join(root, i) for i in files]
data_files.append((root, root_files))
return data_files | Get all data files for the package | setup.py | get_data_files | JohanMabille/voila-gridstack | 0 | python | def get_data_files():
' \n '
data_files = [('etc/jupyter/jupyter_server_config.d', ['etc/jupyter/jupyter_server_config.d/voila-gridstack.json']), ('etc/jupyter/jupyter_notebook_config.d', ['etc/jupyter/jupyter_notebook_config.d/voila-gridstack.json']), ('etc/jupyter/nbconfig/notebook.d', ['etc/jupyter/nbconfig/notebook.d/voila-gridstack.json']), ('share/jupyter/nbextensions/voila-gridstack', ['voila-gridstack/static/extension.js', 'voila-gridstack/static/voila-gridstack.js', 'voila-gridstack/static/voila-gridstack.css', 'voila-gridstack/static/gridstack.js', 'voila-gridstack/static/gridstack.jqueryUI_require.js'])]
for (root, dirs, files) in os.walk('share'):
root_files = [os.path.join(root, i) for i in files]
data_files.append((root, root_files))
return data_files | def get_data_files():
' \n '
data_files = [('etc/jupyter/jupyter_server_config.d', ['etc/jupyter/jupyter_server_config.d/voila-gridstack.json']), ('etc/jupyter/jupyter_notebook_config.d', ['etc/jupyter/jupyter_notebook_config.d/voila-gridstack.json']), ('etc/jupyter/nbconfig/notebook.d', ['etc/jupyter/nbconfig/notebook.d/voila-gridstack.json']), ('share/jupyter/nbextensions/voila-gridstack', ['voila-gridstack/static/extension.js', 'voila-gridstack/static/voila-gridstack.js', 'voila-gridstack/static/voila-gridstack.css', 'voila-gridstack/static/gridstack.js', 'voila-gridstack/static/gridstack.jqueryUI_require.js'])]
for (root, dirs, files) in os.walk('share'):
root_files = [os.path.join(root, i) for i in files]
data_files.append((root, root_files))
return data_files<|docstring|>Get all data files for the package<|endoftext|> |
e22b12ea4d06fc92775ab2863de86329138e1fb5237ab31fd48139b57418afa3 | def __call__(self, src):
'Augmenter body'
if (random.random() < self.p):
src = (255 - src)
return src | Augmenter body | cnocr/data_utils/aug.py | __call__ | breezedeus/cnocr | 1,562 | python | def __call__(self, src):
if (random.random() < self.p):
src = (255 - src)
return src | def __call__(self, src):
if (random.random() < self.p):
src = (255 - src)
return src<|docstring|>Augmenter body<|endoftext|> |
2f5617429f66a6d0657515f7369c5eaec22f1b62f4ab0860e36a8057abd50b3e | def __call__(self, img: torch.Tensor):
'\n\n :param img: [C, H, W]\n :return:\n '
if (random.random() >= self.p):
return img
pad_len = random.randint(1, self.max_pad_len)
pad_shape = list(img.shape)
pad_shape[(- 1)] = pad_len
padding = torch.zeros(pad_shape, dtype=img.dtype, device=img.device)
return torch.cat((img, padding), dim=(- 1)) | :param img: [C, H, W]
:return: | cnocr/data_utils/aug.py | __call__ | breezedeus/cnocr | 1,562 | python | def __call__(self, img: torch.Tensor):
'\n\n :param img: [C, H, W]\n :return:\n '
if (random.random() >= self.p):
return img
pad_len = random.randint(1, self.max_pad_len)
pad_shape = list(img.shape)
pad_shape[(- 1)] = pad_len
padding = torch.zeros(pad_shape, dtype=img.dtype, device=img.device)
return torch.cat((img, padding), dim=(- 1)) | def __call__(self, img: torch.Tensor):
'\n\n :param img: [C, H, W]\n :return:\n '
if (random.random() >= self.p):
return img
pad_len = random.randint(1, self.max_pad_len)
pad_shape = list(img.shape)
pad_shape[(- 1)] = pad_len
padding = torch.zeros(pad_shape, dtype=img.dtype, device=img.device)
return torch.cat((img, padding), dim=(- 1))<|docstring|>:param img: [C, H, W]
:return:<|endoftext|> |
3169287f5d289f9a248008c5e01a59194831ad14b9923a6a57b5e297177b4915 | def pickle_write(content, name, append=1):
'function to open file, pickle dump, then close'
f = (open(name, 'ab') if append else open(name, 'wb'))
pickle.dump(content, f)
f.close() | function to open file, pickle dump, then close | forge/blade/systems/visualizer/visualizer.py | pickle_write | LYX0429/neural-mmo | 4 | python | def pickle_write(content, name, append=1):
f = (open(name, 'ab') if append else open(name, 'wb'))
pickle.dump(content, f)
f.close() | def pickle_write(content, name, append=1):
f = (open(name, 'ab') if append else open(name, 'wb'))
pickle.dump(content, f)
f.close()<|docstring|>function to open file, pickle dump, then close<|endoftext|> |
b3ae758ef71ca8697b076523e0697ca09ae3d99c981329ffb79e709b91d3e116 | def pickle_read(name):
'function to open file, pickle load, then close'
f = open(name, 'rb')
ret = pickle.load(f)
f.close()
return ret | function to open file, pickle load, then close | forge/blade/systems/visualizer/visualizer.py | pickle_read | LYX0429/neural-mmo | 4 | python | def pickle_read(name):
f = open(name, 'rb')
ret = pickle.load(f)
f.close()
return ret | def pickle_read(name):
f = open(name, 'rb')
ret = pickle.load(f)
f.close()
return ret<|docstring|>function to open file, pickle load, then close<|endoftext|> |
5da824118f17a11ac7ee7c1380b874bbef059adfcd8f9cc477f1c7b546840486 | def __init__(self, config):
"Visualizes a stream of data with threaded refreshing. To\n add items, initialize using 'keys' kwarg or add to packet in\n stream()\n Args:\n keys : List of object names (str) to be displayed on market\n history_len : How far back to plot data\n scales : Scales for visualization\n title : Title of graph\n x : Name of x axis data\n ylabel : Name of y axis on plot\n "
self.colors = []
for color in [Neon.GREEN, Neon.CYAN, Neon.BLUE]:
color = bokeh.colors.RGB(*color.rgb)
self.colors.append(color)
self.history_len = config.HISTORY_LEN
self.title = config.TITLE
self.ylabel = config.YLABEL
self.x = config.XAXIS
self.XAXIS = config.XAXIS
self.scales = config.SCALES
self.scale = config.SCALES[0]
self.title = config.TITLE
self.log = config.LOG
self.load = config.LOAD_EXP
self.filename = config.NAME
self.data = defaultdict(list)
self.dataSource = {}
self.keys = 'lifetime'.split()
for key in self.keys:
self.dataSource[key] = [1, 2]
self.dataSource[(key + '_x')] = [1, 2]
self.dataSource[(key + '_lower')] = [1, 2]
self.dataSource[(key + '_upper')] = [1, 2]
self.dataSource[(key + '_smooth')] = [1, 2] | Visualizes a stream of data with threaded refreshing. To
add items, initialize using 'keys' kwarg or add to packet in
stream()
Args:
keys : List of object names (str) to be displayed on market
history_len : How far back to plot data
scales : Scales for visualization
title : Title of graph
x : Name of x axis data
ylabel : Name of y axis on plot | forge/blade/systems/visualizer/visualizer.py | __init__ | LYX0429/neural-mmo | 4 | python | def __init__(self, config):
"Visualizes a stream of data with threaded refreshing. To\n add items, initialize using 'keys' kwarg or add to packet in\n stream()\n Args:\n keys : List of object names (str) to be displayed on market\n history_len : How far back to plot data\n scales : Scales for visualization\n title : Title of graph\n x : Name of x axis data\n ylabel : Name of y axis on plot\n "
self.colors = []
for color in [Neon.GREEN, Neon.CYAN, Neon.BLUE]:
color = bokeh.colors.RGB(*color.rgb)
self.colors.append(color)
self.history_len = config.HISTORY_LEN
self.title = config.TITLE
self.ylabel = config.YLABEL
self.x = config.XAXIS
self.XAXIS = config.XAXIS
self.scales = config.SCALES
self.scale = config.SCALES[0]
self.title = config.TITLE
self.log = config.LOG
self.load = config.LOAD_EXP
self.filename = config.NAME
self.data = defaultdict(list)
self.dataSource = {}
self.keys = 'lifetime'.split()
for key in self.keys:
self.dataSource[key] = [1, 2]
self.dataSource[(key + '_x')] = [1, 2]
self.dataSource[(key + '_lower')] = [1, 2]
self.dataSource[(key + '_upper')] = [1, 2]
self.dataSource[(key + '_smooth')] = [1, 2] | def __init__(self, config):
"Visualizes a stream of data with threaded refreshing. To\n add items, initialize using 'keys' kwarg or add to packet in\n stream()\n Args:\n keys : List of object names (str) to be displayed on market\n history_len : How far back to plot data\n scales : Scales for visualization\n title : Title of graph\n x : Name of x axis data\n ylabel : Name of y axis on plot\n "
self.colors = []
for color in [Neon.GREEN, Neon.CYAN, Neon.BLUE]:
color = bokeh.colors.RGB(*color.rgb)
self.colors.append(color)
self.history_len = config.HISTORY_LEN
self.title = config.TITLE
self.ylabel = config.YLABEL
self.x = config.XAXIS
self.XAXIS = config.XAXIS
self.scales = config.SCALES
self.scale = config.SCALES[0]
self.title = config.TITLE
self.log = config.LOG
self.load = config.LOAD_EXP
self.filename = config.NAME
self.data = defaultdict(list)
self.dataSource = {}
self.keys = 'lifetime'.split()
for key in self.keys:
self.dataSource[key] = [1, 2]
self.dataSource[(key + '_x')] = [1, 2]
self.dataSource[(key + '_lower')] = [1, 2]
self.dataSource[(key + '_upper')] = [1, 2]
self.dataSource[(key + '_smooth')] = [1, 2]<|docstring|>Visualizes a stream of data with threaded refreshing. To
add items, initialize using 'keys' kwarg or add to packet in
stream()
Args:
keys : List of object names (str) to be displayed on market
history_len : How far back to plot data
scales : Scales for visualization
title : Title of graph
x : Name of x axis data
ylabel : Name of y axis on plot<|endoftext|> |
3fe6425e54903ad561c9bca1575fb36e60a3898ff325193a696d1e2e26071434 | def stream(self):
'Wrapper function for source.stream to enable\n adding new items mid-stream. Overwrite graph\n with new figure if packet has different keys.\n Args:\n packet: dictionary of singleton lists'
self.dataSource = dict(self.data.copy())
if self.log:
pickle_write(self.dataSource, self.filename)
self.source.stream(self.dataSource, self.history_len)
self.doc.remove_root(self.structure)
self.init(self.doc) | Wrapper function for source.stream to enable
adding new items mid-stream. Overwrite graph
with new figure if packet has different keys.
Args:
packet: dictionary of singleton lists | forge/blade/systems/visualizer/visualizer.py | stream | LYX0429/neural-mmo | 4 | python | def stream(self):
'Wrapper function for source.stream to enable\n adding new items mid-stream. Overwrite graph\n with new figure if packet has different keys.\n Args:\n packet: dictionary of singleton lists'
self.dataSource = dict(self.data.copy())
if self.log:
pickle_write(self.dataSource, self.filename)
self.source.stream(self.dataSource, self.history_len)
self.doc.remove_root(self.structure)
self.init(self.doc) | def stream(self):
'Wrapper function for source.stream to enable\n adding new items mid-stream. Overwrite graph\n with new figure if packet has different keys.\n Args:\n packet: dictionary of singleton lists'
self.dataSource = dict(self.data.copy())
if self.log:
pickle_write(self.dataSource, self.filename)
self.source.stream(self.dataSource, self.history_len)
self.doc.remove_root(self.structure)
self.init(self.doc)<|docstring|>Wrapper function for source.stream to enable
adding new items mid-stream. Overwrite graph
with new figure if packet has different keys.
Args:
packet: dictionary of singleton lists<|endoftext|> |
94b205e0d9afeb91e0dbd1f4e993b7749613f711877c2e0d10a54a5478c36436 | def __init__(self, middleman, config):
' Runs an asynchronous Bokeh data streaming server.\n \n Args:\n market : The market to visualize\n args : Additional arguments\n kwargs : Additional keyword arguments\n '
self.analytics = Analytics(config)
self.middleman = middleman
self.thread = None
server = Server({'/': self.init}, io_loop=IOLoop.current(), port=config.PORT, num_procs=1)
server.start()
self.server = server
server.io_loop.add_callback(server.show, '/')
server.io_loop.start() | Runs an asynchronous Bokeh data streaming server.
Args:
market : The market to visualize
args : Additional arguments
kwargs : Additional keyword arguments | forge/blade/systems/visualizer/visualizer.py | __init__ | LYX0429/neural-mmo | 4 | python | def __init__(self, middleman, config):
' Runs an asynchronous Bokeh data streaming server.\n \n Args:\n market : The market to visualize\n args : Additional arguments\n kwargs : Additional keyword arguments\n '
self.analytics = Analytics(config)
self.middleman = middleman
self.thread = None
server = Server({'/': self.init}, io_loop=IOLoop.current(), port=config.PORT, num_procs=1)
server.start()
self.server = server
server.io_loop.add_callback(server.show, '/')
server.io_loop.start() | def __init__(self, middleman, config):
' Runs an asynchronous Bokeh data streaming server.\n \n Args:\n market : The market to visualize\n args : Additional arguments\n kwargs : Additional keyword arguments\n '
self.analytics = Analytics(config)
self.middleman = middleman
self.thread = None
server = Server({'/': self.init}, io_loop=IOLoop.current(), port=config.PORT, num_procs=1)
server.start()
self.server = server
server.io_loop.add_callback(server.show, '/')
server.io_loop.start()<|docstring|>Runs an asynchronous Bokeh data streaming server.
Args:
market : The market to visualize
args : Additional arguments
kwargs : Additional keyword arguments<|endoftext|> |
df44bd0b694e189a156cbfbf6a11c3b376c12329bb44883ead94ea24e820c93d | def init(self, doc):
'Initialize document and threaded update loop\n Args:\n doc: A Bokeh document\n '
self.analytics.init(doc)
self.doc = doc
self.thread = Thread(target=self.update, args=[])
self.thread.start()
self.started = True | Initialize document and threaded update loop
Args:
doc: A Bokeh document | forge/blade/systems/visualizer/visualizer.py | init | LYX0429/neural-mmo | 4 | python | def init(self, doc):
'Initialize document and threaded update loop\n Args:\n doc: A Bokeh document\n '
self.analytics.init(doc)
self.doc = doc
self.thread = Thread(target=self.update, args=[])
self.thread.start()
self.started = True | def init(self, doc):
'Initialize document and threaded update loop\n Args:\n doc: A Bokeh document\n '
self.analytics.init(doc)
self.doc = doc
self.thread = Thread(target=self.update, args=[])
self.thread.start()
self.started = True<|docstring|>Initialize document and threaded update loop
Args:
doc: A Bokeh document<|endoftext|> |
fe5c4e2b8fae682bd4eff60b4f659134cf9e1f54c669f2da8e07a69646b30418 | def update(self):
'Blocking update call to be run in a separate thread\n Ingests packets from a remote market and streams to Bokeh client'
self.n = 0
while True:
time.sleep(0.05)
if (self.thread is None):
continue
if ray.get(self.middleman.getShutdown.remote()):
self.middleman.setData.remote(self.analytics.data)
sys.exit(0)
packet = ray.get(self.middleman.getData.remote())
if (packet is None):
continue
self.analytics.update(packet)
self.analytics.resample()
self.doc.add_next_tick_callback(partial(self.stream)) | Blocking update call to be run in a separate thread
Ingests packets from a remote market and streams to Bokeh client | forge/blade/systems/visualizer/visualizer.py | update | LYX0429/neural-mmo | 4 | python | def update(self):
'Blocking update call to be run in a separate thread\n Ingests packets from a remote market and streams to Bokeh client'
self.n = 0
while True:
time.sleep(0.05)
if (self.thread is None):
continue
if ray.get(self.middleman.getShutdown.remote()):
self.middleman.setData.remote(self.analytics.data)
sys.exit(0)
packet = ray.get(self.middleman.getData.remote())
if (packet is None):
continue
self.analytics.update(packet)
self.analytics.resample()
self.doc.add_next_tick_callback(partial(self.stream)) | def update(self):
'Blocking update call to be run in a separate thread\n Ingests packets from a remote market and streams to Bokeh client'
self.n = 0
while True:
time.sleep(0.05)
if (self.thread is None):
continue
if ray.get(self.middleman.getShutdown.remote()):
self.middleman.setData.remote(self.analytics.data)
sys.exit(0)
packet = ray.get(self.middleman.getData.remote())
if (packet is None):
continue
self.analytics.update(packet)
self.analytics.resample()
self.doc.add_next_tick_callback(partial(self.stream))<|docstring|>Blocking update call to be run in a separate thread
Ingests packets from a remote market and streams to Bokeh client<|endoftext|> |
de616b19a371cc4a7549999e79b5cfab563724a9d7facb38f706eb30505e3c02 | @gen.coroutine
def stream(self):
'Stream current data buffer to Bokeh client'
self.analytics.stream() | Stream current data buffer to Bokeh client | forge/blade/systems/visualizer/visualizer.py | stream | LYX0429/neural-mmo | 4 | python | @gen.coroutine
def stream(self):
self.analytics.stream() | @gen.coroutine
def stream(self):
self.analytics.stream()<|docstring|>Stream current data buffer to Bokeh client<|endoftext|> |
67e7680ef5d74a0c0647cb59f373b944ae6c611cdea402c9b02e79d01a18e042 | def __init__(self):
'Remote data buffer for two processes to dump and recv data.\n Interacts with Market and BokehServer.\n This is probably not safe'
self.data = None
self.shutdown = 0 | Remote data buffer for two processes to dump and recv data.
Interacts with Market and BokehServer.
This is probably not safe | forge/blade/systems/visualizer/visualizer.py | __init__ | LYX0429/neural-mmo | 4 | python | def __init__(self):
'Remote data buffer for two processes to dump and recv data.\n Interacts with Market and BokehServer.\n This is probably not safe'
self.data = None
self.shutdown = 0 | def __init__(self):
'Remote data buffer for two processes to dump and recv data.\n Interacts with Market and BokehServer.\n This is probably not safe'
self.data = None
self.shutdown = 0<|docstring|>Remote data buffer for two processes to dump and recv data.
Interacts with Market and BokehServer.
This is probably not safe<|endoftext|> |
95a5ed26cfb8d487d457e2f41808eb1c8bb1157d1cc2c4caf00f47096ee942f4 | def getData(self):
'Get data from buffer\n Returns:\n data: From buffer\n '
data = self.data
self.data = None
return data | Get data from buffer
Returns:
data: From buffer | forge/blade/systems/visualizer/visualizer.py | getData | LYX0429/neural-mmo | 4 | python | def getData(self):
'Get data from buffer\n Returns:\n data: From buffer\n '
data = self.data
self.data = None
return data | def getData(self):
'Get data from buffer\n Returns:\n data: From buffer\n '
data = self.data
self.data = None
return data<|docstring|>Get data from buffer
Returns:
data: From buffer<|endoftext|> |
039ec34e5615f601b42a38047baff9831439f7982584785eb5faf228208981e0 | def setData(self, data):
'Set buffer data\n Args:\n data: To set buffer\n '
self.data = data.copy() | Set buffer data
Args:
data: To set buffer | forge/blade/systems/visualizer/visualizer.py | setData | LYX0429/neural-mmo | 4 | python | def setData(self, data):
'Set buffer data\n Args:\n data: To set buffer\n '
self.data = data.copy() | def setData(self, data):
'Set buffer data\n Args:\n data: To set buffer\n '
self.data = data.copy()<|docstring|>Set buffer data
Args:
data: To set buffer<|endoftext|> |
da64a3ae64f2074c3a3f341385fce968d3832a05a4045c727c1ac6e68b0a9eda | def switch_scale(attr, old, new):
"Callback for RadioButtonGroup to switch tick scale\n and refresh document\n Args:\n attr: variable to be changed, in this case 'active'\n old: old index of active button\n new: new index of active button\n "
self.scale = self.scales[new]
self.source.data = self.data[self.scale] | Callback for RadioButtonGroup to switch tick scale
and refresh document
Args:
attr: variable to be changed, in this case 'active'
old: old index of active button
new: new index of active button | forge/blade/systems/visualizer/visualizer.py | switch_scale | LYX0429/neural-mmo | 4 | python | def switch_scale(attr, old, new):
"Callback for RadioButtonGroup to switch tick scale\n and refresh document\n Args:\n attr: variable to be changed, in this case 'active'\n old: old index of active button\n new: new index of active button\n "
self.scale = self.scales[new]
self.source.data = self.data[self.scale] | def switch_scale(attr, old, new):
"Callback for RadioButtonGroup to switch tick scale\n and refresh document\n Args:\n attr: variable to be changed, in this case 'active'\n old: old index of active button\n new: new index of active button\n "
self.scale = self.scales[new]
self.source.data = self.data[self.scale]<|docstring|>Callback for RadioButtonGroup to switch tick scale
and refresh document
Args:
attr: variable to be changed, in this case 'active'
old: old index of active button
new: new index of active button<|endoftext|> |
3206ea63ff836ad9d6f43a93f1b2c9db592c61050dde1bddbd269c06582f85ff | def learn(self):
'\n Performs numIters iterations with numEps episodes of self-play in each\n iteration. After every iteration, it retrains neural network with\n examples in trainExamples (which has a maximium length of maxlenofQueue).\n It then pits the new neural network against the old one and accepts it\n only if it wins >= updateThreshold fraction of games.\n '
import time
gamesNum = (self.args.numSelfPlayProcess * self.args.numPerProcessSelfPlay)
MyLogger.info('============== New Run ==============')
MyLogger.info('sims: {} cpuct: {} gamesNum: {} coeff: {} evalDepth: {} alpha: {} eps: {}'.format(self.args.numMCTSSims, self.args.cpuct, gamesNum, self.args.coeff, self.args.evaluationDepth, self.args.alpha, self.args.epsilon))
for i in range(1, (self.args.numIters + 1)):
start = time.time()
print((('------ITER ' + str(i)) + '------'))
iterationTrainExamples = deque([], maxlen=self.args.maxlenOfQueue)
temp = self.parallel_self_play()
iterationTrainExamples += temp
self.trainExamplesHistory.append(iterationTrainExamples)
self.parallel_train_network(i)
self.trainExamplesHistory.clear()
self.parallel_self_test_play(i)
if self.args.multiCPU:
resultRand = self.parallel_check_against(i, 'rp')
resultHeur = self.parallel_check_against(i, 'heuristic')
resultMCTS = self.parallel_check_against(i, 'n1p')
MyLogger.info('Iter:{} Heuristic: {} Random: {} MCTS: {}'.format(i, resultHeur, resultRand, resultMCTS))
else:
logCurrentCapabilities(self.game, i, self.args)
end = time.time()
diff = (end - start)
print(diff) | Performs numIters iterations with numEps episodes of self-play in each
iteration. After every iteration, it retrains neural network with
examples in trainExamples (which has a maximium length of maxlenofQueue).
It then pits the new neural network against the old one and accepts it
only if it wins >= updateThreshold fraction of games. | Coach.py | learn | danielvarga/alpha-zero-general | 0 | python | def learn(self):
'\n Performs numIters iterations with numEps episodes of self-play in each\n iteration. After every iteration, it retrains neural network with\n examples in trainExamples (which has a maximium length of maxlenofQueue).\n It then pits the new neural network against the old one and accepts it\n only if it wins >= updateThreshold fraction of games.\n '
import time
gamesNum = (self.args.numSelfPlayProcess * self.args.numPerProcessSelfPlay)
MyLogger.info('============== New Run ==============')
MyLogger.info('sims: {} cpuct: {} gamesNum: {} coeff: {} evalDepth: {} alpha: {} eps: {}'.format(self.args.numMCTSSims, self.args.cpuct, gamesNum, self.args.coeff, self.args.evaluationDepth, self.args.alpha, self.args.epsilon))
for i in range(1, (self.args.numIters + 1)):
start = time.time()
print((('------ITER ' + str(i)) + '------'))
iterationTrainExamples = deque([], maxlen=self.args.maxlenOfQueue)
temp = self.parallel_self_play()
iterationTrainExamples += temp
self.trainExamplesHistory.append(iterationTrainExamples)
self.parallel_train_network(i)
self.trainExamplesHistory.clear()
self.parallel_self_test_play(i)
if self.args.multiCPU:
resultRand = self.parallel_check_against(i, 'rp')
resultHeur = self.parallel_check_against(i, 'heuristic')
resultMCTS = self.parallel_check_against(i, 'n1p')
MyLogger.info('Iter:{} Heuristic: {} Random: {} MCTS: {}'.format(i, resultHeur, resultRand, resultMCTS))
else:
logCurrentCapabilities(self.game, i, self.args)
end = time.time()
diff = (end - start)
print(diff) | def learn(self):
'\n Performs numIters iterations with numEps episodes of self-play in each\n iteration. After every iteration, it retrains neural network with\n examples in trainExamples (which has a maximium length of maxlenofQueue).\n It then pits the new neural network against the old one and accepts it\n only if it wins >= updateThreshold fraction of games.\n '
import time
gamesNum = (self.args.numSelfPlayProcess * self.args.numPerProcessSelfPlay)
MyLogger.info('============== New Run ==============')
MyLogger.info('sims: {} cpuct: {} gamesNum: {} coeff: {} evalDepth: {} alpha: {} eps: {}'.format(self.args.numMCTSSims, self.args.cpuct, gamesNum, self.args.coeff, self.args.evaluationDepth, self.args.alpha, self.args.epsilon))
for i in range(1, (self.args.numIters + 1)):
start = time.time()
print((('------ITER ' + str(i)) + '------'))
iterationTrainExamples = deque([], maxlen=self.args.maxlenOfQueue)
temp = self.parallel_self_play()
iterationTrainExamples += temp
self.trainExamplesHistory.append(iterationTrainExamples)
self.parallel_train_network(i)
self.trainExamplesHistory.clear()
self.parallel_self_test_play(i)
if self.args.multiCPU:
resultRand = self.parallel_check_against(i, 'rp')
resultHeur = self.parallel_check_against(i, 'heuristic')
resultMCTS = self.parallel_check_against(i, 'n1p')
MyLogger.info('Iter:{} Heuristic: {} Random: {} MCTS: {}'.format(i, resultHeur, resultRand, resultMCTS))
else:
logCurrentCapabilities(self.game, i, self.args)
end = time.time()
diff = (end - start)
print(diff)<|docstring|>Performs numIters iterations with numEps episodes of self-play in each
iteration. After every iteration, it retrains neural network with
examples in trainExamples (which has a maximium length of maxlenofQueue).
It then pits the new neural network against the old one and accepts it
only if it wins >= updateThreshold fraction of games.<|endoftext|> |
f486fcc4a5e645644d38881afe05b66e1e0070987156154806b39456baa1b027 | def check_key():
'\n check TINY_KEY\n 代码中查找；文件中查找，没有的话，click装饰器会去环境变量中查找，再没有的话，提示用户手动输入\n :return:bool False：不需要用户输入；True:用户输入key\n '
_tiny_key = settings.TINY_KEY
if (not _tiny_key):
_tiny_key = os.environ.get('TINY_KEY')
if (_tiny_key is None):
if os.path.exists(settings.TINY_KEY_FILE):
with open(settings.TINY_KEY_FILE, 'r') as f:
_tiny_key = f.read()
ret = (True if (not _tiny_key) else False)
return (ret, _tiny_key) | check TINY_KEY
代码中查找；文件中查找，没有的话，click装饰器会去环境变量中查找，再没有的话，提示用户手动输入
:return:bool False：不需要用户输入；True:用户输入key | scripts/yst.py | check_key | imoyao/PyTinyImg | 0 | python | def check_key():
'\n check TINY_KEY\n 代码中查找；文件中查找，没有的话，click装饰器会去环境变量中查找，再没有的话，提示用户手动输入\n :return:bool False：不需要用户输入；True:用户输入key\n '
_tiny_key = settings.TINY_KEY
if (not _tiny_key):
_tiny_key = os.environ.get('TINY_KEY')
if (_tiny_key is None):
if os.path.exists(settings.TINY_KEY_FILE):
with open(settings.TINY_KEY_FILE, 'r') as f:
_tiny_key = f.read()
ret = (True if (not _tiny_key) else False)
return (ret, _tiny_key) | def check_key():
'\n check TINY_KEY\n 代码中查找；文件中查找，没有的话，click装饰器会去环境变量中查找，再没有的话，提示用户手动输入\n :return:bool False：不需要用户输入；True:用户输入key\n '
_tiny_key = settings.TINY_KEY
if (not _tiny_key):
_tiny_key = os.environ.get('TINY_KEY')
if (_tiny_key is None):
if os.path.exists(settings.TINY_KEY_FILE):
with open(settings.TINY_KEY_FILE, 'r') as f:
_tiny_key = f.read()
ret = (True if (not _tiny_key) else False)
return (ret, _tiny_key)<|docstring|>check TINY_KEY
代码中查找；文件中查找，没有的话，click装饰器会去环境变量中查找，再没有的话，提示用户手动输入
:return:bool False：不需要用户输入；True:用户输入key<|endoftext|> |
9dbc367204717250f5d56db0a83e32171244c2e6080530c3681086a27638ddd8 | def show_version(ctx, param, value):
'\n show the version\n :param ctx:\n :param param: del this will get: Warning: Invoked legacy parameter callback……\n :param value:\n :return:\n '
if ((not value) or ctx.resilient_parsing):
return
click.echo(settings.VERSION)
ctx.exit() | show the version
:param ctx:
:param param: del this will get: Warning: Invoked legacy parameter callback……
:param value:
:return: | scripts/yst.py | show_version | imoyao/PyTinyImg | 0 | python | def show_version(ctx, param, value):
'\n show the version\n :param ctx:\n :param param: del this will get: Warning: Invoked legacy parameter callback……\n :param value:\n :return:\n '
if ((not value) or ctx.resilient_parsing):
return
click.echo(settings.VERSION)
ctx.exit() | def show_version(ctx, param, value):
'\n show the version\n :param ctx:\n :param param: del this will get: Warning: Invoked legacy parameter callback……\n :param value:\n :return:\n '
if ((not value) or ctx.resilient_parsing):
return
click.echo(settings.VERSION)
ctx.exit()<|docstring|>show the version
:param ctx:
:param param: del this will get: Warning: Invoked legacy parameter callback……
:param value:
:return:<|endoftext|> |
1b37d03b4259bd0e0b1e16cf622f595d512f39ff13e32b1edeea8b918e70eb54 | def run(self):
"\n Build image inside current environment using imagebuilder;\n It's expected this may run within (privileged) docker container.\n\n Returns:\n BuildResult\n "
builder = self.workflow.builder
image = builder.image.to_str()
allow_repo_dir_in_dockerignore(builder.df_dir)
process_args = ['imagebuilder', '-t', image]
for (buildarg, buildargval) in builder.buildargs.items():
process_args.append('--build-arg')
process_args.append(('%s=%s' % (buildarg, buildargval)))
process_args.append(builder.df_dir)
ib_process = subprocess.Popen(process_args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True, encoding='utf-8', errors='replace')
self.log.debug('imagebuilder build has begun; waiting for it to finish')
self.log.debug(process_args)
output = []
while True:
poll = ib_process.poll()
out = ib_process.stdout.readline()
if out:
self.log.info('%s', out.rstrip())
output.append(out)
elif (poll is not None):
break
if (ib_process.returncode != 0):
err = (output[(- 1)] if output else '<imagebuilder had bad exit code but no output>')
return BuildResult(logs=output, fail_reason='image build failed (rc={}): {}'.format(ib_process.returncode, err))
image_id = builder.get_built_image_info()['Id']
if (':' not in image_id):
image_id = 'sha256:{}'.format(image_id)
self.log.info('fetching image %s from docker', image)
output_path = os.path.join(self.workflow.source.workdir, EXPORTED_SQUASHED_IMAGE_NAME)
try:
with open(output_path, 'w') as image_file:
image_file.write(self.tasker.get_image(image).data)
except AttributeError:
with open(output_path, 'wb') as image_file:
for chunk in self.tasker.get_image(image):
image_file.write(chunk)
img_metadata = get_exported_image_metadata(output_path, IMAGE_TYPE_DOCKER_ARCHIVE)
self.workflow.exported_image_sequence.append(img_metadata)
return BuildResult(logs=output, image_id=image_id, skip_layer_squash=True) | Build image inside current environment using imagebuilder;
It's expected this may run within (privileged) docker container.
Returns:
BuildResult | atomic_reactor/plugins/build_imagebuilder.py | run | mkosiarc/atomic-reactor | 0 | python | def run(self):
"\n Build image inside current environment using imagebuilder;\n It's expected this may run within (privileged) docker container.\n\n Returns:\n BuildResult\n "
builder = self.workflow.builder
image = builder.image.to_str()
allow_repo_dir_in_dockerignore(builder.df_dir)
process_args = ['imagebuilder', '-t', image]
for (buildarg, buildargval) in builder.buildargs.items():
process_args.append('--build-arg')
process_args.append(('%s=%s' % (buildarg, buildargval)))
process_args.append(builder.df_dir)
ib_process = subprocess.Popen(process_args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True, encoding='utf-8', errors='replace')
self.log.debug('imagebuilder build has begun; waiting for it to finish')
self.log.debug(process_args)
output = []
while True:
poll = ib_process.poll()
out = ib_process.stdout.readline()
if out:
self.log.info('%s', out.rstrip())
output.append(out)
elif (poll is not None):
break
if (ib_process.returncode != 0):
err = (output[(- 1)] if output else '<imagebuilder had bad exit code but no output>')
return BuildResult(logs=output, fail_reason='image build failed (rc={}): {}'.format(ib_process.returncode, err))
image_id = builder.get_built_image_info()['Id']
if (':' not in image_id):
image_id = 'sha256:{}'.format(image_id)
self.log.info('fetching image %s from docker', image)
output_path = os.path.join(self.workflow.source.workdir, EXPORTED_SQUASHED_IMAGE_NAME)
try:
with open(output_path, 'w') as image_file:
image_file.write(self.tasker.get_image(image).data)
except AttributeError:
with open(output_path, 'wb') as image_file:
for chunk in self.tasker.get_image(image):
image_file.write(chunk)
img_metadata = get_exported_image_metadata(output_path, IMAGE_TYPE_DOCKER_ARCHIVE)
self.workflow.exported_image_sequence.append(img_metadata)
return BuildResult(logs=output, image_id=image_id, skip_layer_squash=True) | def run(self):
"\n Build image inside current environment using imagebuilder;\n It's expected this may run within (privileged) docker container.\n\n Returns:\n BuildResult\n "
builder = self.workflow.builder
image = builder.image.to_str()
allow_repo_dir_in_dockerignore(builder.df_dir)
process_args = ['imagebuilder', '-t', image]
for (buildarg, buildargval) in builder.buildargs.items():
process_args.append('--build-arg')
process_args.append(('%s=%s' % (buildarg, buildargval)))
process_args.append(builder.df_dir)
ib_process = subprocess.Popen(process_args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True, encoding='utf-8', errors='replace')
self.log.debug('imagebuilder build has begun; waiting for it to finish')
self.log.debug(process_args)
output = []
while True:
poll = ib_process.poll()
out = ib_process.stdout.readline()
if out:
self.log.info('%s', out.rstrip())
output.append(out)
elif (poll is not None):
break
if (ib_process.returncode != 0):
err = (output[(- 1)] if output else '<imagebuilder had bad exit code but no output>')
return BuildResult(logs=output, fail_reason='image build failed (rc={}): {}'.format(ib_process.returncode, err))
image_id = builder.get_built_image_info()['Id']
if (':' not in image_id):
image_id = 'sha256:{}'.format(image_id)
self.log.info('fetching image %s from docker', image)
output_path = os.path.join(self.workflow.source.workdir, EXPORTED_SQUASHED_IMAGE_NAME)
try:
with open(output_path, 'w') as image_file:
image_file.write(self.tasker.get_image(image).data)
except AttributeError:
with open(output_path, 'wb') as image_file:
for chunk in self.tasker.get_image(image):
image_file.write(chunk)
img_metadata = get_exported_image_metadata(output_path, IMAGE_TYPE_DOCKER_ARCHIVE)
self.workflow.exported_image_sequence.append(img_metadata)
return BuildResult(logs=output, image_id=image_id, skip_layer_squash=True)<|docstring|>Build image inside current environment using imagebuilder;
It's expected this may run within (privileged) docker container.
Returns:
BuildResult<|endoftext|> |
209d0076ee22914df44c4066529312cbc8f40f42efea78f6024dff8016f629c2 | def evaluate_central(self, dataset, state):
'\n Evaluates a model in central server mode.\n \n Arguments:\n dataset: tf Dataset, contains all the test set examples as a single \n tf Dataset.\n state: tff state, the federated training state of the model. \n Contains model weights\n\n Returns:\n accuracy of model in state on dataset provided\n '
keras_model = self.keras_model_fn()
shape = tf.data.DatasetSpec.from_value(dataset)._element_spec[0].shape
keras_model.build(shape)
tff.learning.assign_weights_to_keras_model(keras_model, state.model)
metrics = keras_model.evaluate(dataset)
return (metrics[0].item(), metrics[1].item()) | Evaluates a model in central server mode.
Arguments:
dataset: tf Dataset, contains all the test set examples as a single
tf Dataset.
state: tff state, the federated training state of the model.
Contains model weights
Returns:
accuracy of model in state on dataset provided | experiments.py | evaluate_central | r-o-s-h-a-n/semisupervisedFL | 5 | python | def evaluate_central(self, dataset, state):
'\n Evaluates a model in central server mode.\n \n Arguments:\n dataset: tf Dataset, contains all the test set examples as a single \n tf Dataset.\n state: tff state, the federated training state of the model. \n Contains model weights\n\n Returns:\n accuracy of model in state on dataset provided\n '
keras_model = self.keras_model_fn()
shape = tf.data.DatasetSpec.from_value(dataset)._element_spec[0].shape
keras_model.build(shape)
tff.learning.assign_weights_to_keras_model(keras_model, state.model)
metrics = keras_model.evaluate(dataset)
return (metrics[0].item(), metrics[1].item()) | def evaluate_central(self, dataset, state):
'\n Evaluates a model in central server mode.\n \n Arguments:\n dataset: tf Dataset, contains all the test set examples as a single \n tf Dataset.\n state: tff state, the federated training state of the model. \n Contains model weights\n\n Returns:\n accuracy of model in state on dataset provided\n '
keras_model = self.keras_model_fn()
shape = tf.data.DatasetSpec.from_value(dataset)._element_spec[0].shape
keras_model.build(shape)
tff.learning.assign_weights_to_keras_model(keras_model, state.model)
metrics = keras_model.evaluate(dataset)
return (metrics[0].item(), metrics[1].item())<|docstring|>Evaluates a model in central server mode.
Arguments:
dataset: tf Dataset, contains all the test set examples as a single
tf Dataset.
state: tff state, the federated training state of the model.
Contains model weights
Returns:
accuracy of model in state on dataset provided<|endoftext|> |
2e64f03580629c31460288fd78b859c85d9009e2a40ca36f029d05a2e9ba30b8 | def evaluate_saved_model(self, dataset, model_fp=None):
'\n Evaluates trained model in central server mode.\n \n Arguments:\n dataset: tf Dataset, contains all the test set examples as a single \n tf Dataset.\n model_fp: str, if model filepath is provided, it will load \n the model from file and evaluate on that. Otherwise, will \n evaluate the model at the last federated state.\n\n Returns:\n Nothing, but writes accuracy to file.\n '
keras_model = self.keras_model_fn.load_model_weights(model_fp)
return keras_model.evaluate(dataset) | Evaluates trained model in central server mode.
Arguments:
dataset: tf Dataset, contains all the test set examples as a single
tf Dataset.
model_fp: str, if model filepath is provided, it will load
the model from file and evaluate on that. Otherwise, will
evaluate the model at the last federated state.
Returns:
Nothing, but writes accuracy to file. | experiments.py | evaluate_saved_model | r-o-s-h-a-n/semisupervisedFL | 5 | python | def evaluate_saved_model(self, dataset, model_fp=None):
'\n Evaluates trained model in central server mode.\n \n Arguments:\n dataset: tf Dataset, contains all the test set examples as a single \n tf Dataset.\n model_fp: str, if model filepath is provided, it will load \n the model from file and evaluate on that. Otherwise, will \n evaluate the model at the last federated state.\n\n Returns:\n Nothing, but writes accuracy to file.\n '
keras_model = self.keras_model_fn.load_model_weights(model_fp)
return keras_model.evaluate(dataset) | def evaluate_saved_model(self, dataset, model_fp=None):
'\n Evaluates trained model in central server mode.\n \n Arguments:\n dataset: tf Dataset, contains all the test set examples as a single \n tf Dataset.\n model_fp: str, if model filepath is provided, it will load \n the model from file and evaluate on that. Otherwise, will \n evaluate the model at the last federated state.\n\n Returns:\n Nothing, but writes accuracy to file.\n '
keras_model = self.keras_model_fn.load_model_weights(model_fp)
return keras_model.evaluate(dataset)<|docstring|>Evaluates trained model in central server mode.
Arguments:
dataset: tf Dataset, contains all the test set examples as a single
tf Dataset.
model_fp: str, if model filepath is provided, it will load
the model from file and evaluate on that. Otherwise, will
evaluate the model at the last federated state.
Returns:
Nothing, but writes accuracy to file.<|endoftext|> |
1adf3830ab39b2637c0808c950583f1eee3db8a8d0ffe8c6c9f085086e4b65f3 | def path(repo_ctx, additional_search_paths=[]):
'Return the value of the PATH environment variable that would be used by\n the which() command.'
search_paths = additional_search_paths
if (repo_ctx.os.name == 'mac os x'):
search_paths = (search_paths + ['/usr/local/bin'])
search_paths = (search_paths + ['/usr/bin', '/bin'])
return ':'.join(search_paths) | Return the value of the PATH environment variable that would be used by
the which() command. | third_party/drake_rules/execute.bzl | path | mingkaic/cortenn | 2 | python | def path(repo_ctx, additional_search_paths=[]):
'Return the value of the PATH environment variable that would be used by\n the which() command.'
search_paths = additional_search_paths
if (repo_ctx.os.name == 'mac os x'):
search_paths = (search_paths + ['/usr/local/bin'])
search_paths = (search_paths + ['/usr/bin', '/bin'])
return ':'.join(search_paths) | def path(repo_ctx, additional_search_paths=[]):
'Return the value of the PATH environment variable that would be used by\n the which() command.'
search_paths = additional_search_paths
if (repo_ctx.os.name == 'mac os x'):
search_paths = (search_paths + ['/usr/local/bin'])
search_paths = (search_paths + ['/usr/bin', '/bin'])
return ':'.join(search_paths)<|docstring|>Return the value of the PATH environment variable that would be used by
the which() command.<|endoftext|> |
6ea742387c29d746f0494e040bd419bb0c1aa187bda3594ddafc0baa3aeb9c72 | def which(repo_ctx, program, additional_search_paths=[]):
"Return the path of the given program or None if there is no such program\n in the PATH as defined by the path() function above. The value of the\n user's PATH environment variable is ignored.\n "
exec_result = repo_ctx.execute(['which', program], environment={'PATH': path(repo_ctx, additional_search_paths)})
if (exec_result.return_code != 0):
return None
return repo_ctx.path(exec_result.stdout.strip()) | Return the path of the given program or None if there is no such program
in the PATH as defined by the path() function above. The value of the
user's PATH environment variable is ignored. | third_party/drake_rules/execute.bzl | which | mingkaic/cortenn | 2 | python | def which(repo_ctx, program, additional_search_paths=[]):
"Return the path of the given program or None if there is no such program\n in the PATH as defined by the path() function above. The value of the\n user's PATH environment variable is ignored.\n "
exec_result = repo_ctx.execute(['which', program], environment={'PATH': path(repo_ctx, additional_search_paths)})
if (exec_result.return_code != 0):
return None
return repo_ctx.path(exec_result.stdout.strip()) | def which(repo_ctx, program, additional_search_paths=[]):
"Return the path of the given program or None if there is no such program\n in the PATH as defined by the path() function above. The value of the\n user's PATH environment variable is ignored.\n "
exec_result = repo_ctx.execute(['which', program], environment={'PATH': path(repo_ctx, additional_search_paths)})
if (exec_result.return_code != 0):
return None
return repo_ctx.path(exec_result.stdout.strip())<|docstring|>Return the path of the given program or None if there is no such program
in the PATH as defined by the path() function above. The value of the
user's PATH environment variable is ignored.<|endoftext|> |
d736d8f9cdb8f0ea856cf4537bf39dfb9a2caa5487eac9100fcbe23bbacca53f | def execute_and_return(repo_ctx, command, additional_search_paths=[]):
'Runs the `command` (list) and returns a status value. The return value\n is a struct with a field `error` that will be None on success or else a\n detailed message on command failure.\n '
if ('/' in command[0]):
program = command[0]
else:
program = which(repo_ctx, command[0], additional_search_paths)
if (not program):
error = "Could not find a program named '{}'".format(command[0])
return struct(error=error)
exec_result = repo_ctx.execute(([program] + command[1:]))
if (exec_result.return_code == 0):
error = None
else:
error = ('Failure running ' + ' '.join(["'{}'".format(x) for x in command]))
if exec_result.stdout:
error += ('\n' + exec_result.stdout)
if exec_result.stderr:
error += ('\n' + exec_result.stderr)
return struct(error=error, stdout=exec_result.stdout) | Runs the `command` (list) and returns a status value. The return value
is a struct with a field `error` that will be None on success or else a
detailed message on command failure. | third_party/drake_rules/execute.bzl | execute_and_return | mingkaic/cortenn | 2 | python | def execute_and_return(repo_ctx, command, additional_search_paths=[]):
'Runs the `command` (list) and returns a status value. The return value\n is a struct with a field `error` that will be None on success or else a\n detailed message on command failure.\n '
if ('/' in command[0]):
program = command[0]
else:
program = which(repo_ctx, command[0], additional_search_paths)
if (not program):
error = "Could not find a program named '{}'".format(command[0])
return struct(error=error)
exec_result = repo_ctx.execute(([program] + command[1:]))
if (exec_result.return_code == 0):
error = None
else:
error = ('Failure running ' + ' '.join(["'{}'".format(x) for x in command]))
if exec_result.stdout:
error += ('\n' + exec_result.stdout)
if exec_result.stderr:
error += ('\n' + exec_result.stderr)
return struct(error=error, stdout=exec_result.stdout) | def execute_and_return(repo_ctx, command, additional_search_paths=[]):
'Runs the `command` (list) and returns a status value. The return value\n is a struct with a field `error` that will be None on success or else a\n detailed message on command failure.\n '
if ('/' in command[0]):
program = command[0]
else:
program = which(repo_ctx, command[0], additional_search_paths)
if (not program):
error = "Could not find a program named '{}'".format(command[0])
return struct(error=error)
exec_result = repo_ctx.execute(([program] + command[1:]))
if (exec_result.return_code == 0):
error = None
else:
error = ('Failure running ' + ' '.join(["'{}'".format(x) for x in command]))
if exec_result.stdout:
error += ('\n' + exec_result.stdout)
if exec_result.stderr:
error += ('\n' + exec_result.stderr)
return struct(error=error, stdout=exec_result.stdout)<|docstring|>Runs the `command` (list) and returns a status value. The return value
is a struct with a field `error` that will be None on success or else a
detailed message on command failure.<|endoftext|> |
6d665fc09b5f16a4d214dd58529713686644f5c41bb581681cfd6312222319ef | def execute_or_fail(repo_ctx, command):
'Runs the `command` (list) and immediately fails on any error.\n Returns a struct with the stdout value.'
result = execute_and_return(repo_ctx, command)
if result.error:
fail('Unable to complete setup for @{} repository: {}'.format(repo_ctx.name, result.error))
return result | Runs the `command` (list) and immediately fails on any error.
Returns a struct with the stdout value. | third_party/drake_rules/execute.bzl | execute_or_fail | mingkaic/cortenn | 2 | python | def execute_or_fail(repo_ctx, command):
'Runs the `command` (list) and immediately fails on any error.\n Returns a struct with the stdout value.'
result = execute_and_return(repo_ctx, command)
if result.error:
fail('Unable to complete setup for @{} repository: {}'.format(repo_ctx.name, result.error))
return result | def execute_or_fail(repo_ctx, command):
'Runs the `command` (list) and immediately fails on any error.\n Returns a struct with the stdout value.'
result = execute_and_return(repo_ctx, command)
if result.error:
fail('Unable to complete setup for @{} repository: {}'.format(repo_ctx.name, result.error))
return result<|docstring|>Runs the `command` (list) and immediately fails on any error.
Returns a struct with the stdout value.<|endoftext|> |
a38bde9f8ff6ce410a1cab6536fd72935bbc359b80f5b3373b12640dfe9bccf8 | def update_create_computations_fn_kwargs(arg_names: Iterable[Text], kwargs: Dict[(Text, Any)], eval_config: Optional[config_pb2.EvalConfig]=None, schema: Optional[schema_pb2.Schema]=None, model_names: Optional[List[Text]]=None, output_names: Optional[List[Text]]=None, sub_keys: Optional[List[Optional[SubKey]]]=None, aggregation_type: Optional[AggregationType]=None, class_weights: Optional[Dict[(int, float)]]=None, query_key: Optional[Text]=None, is_diff: Optional[bool]=False):
"Updates create_computations_fn kwargs based on arg spec.\n\n Each metric's create_computations_fn is invoked with a variable set of\n parameters, depending on the argument names of the callable. If an argument\n name matches one of the reserved names, this function will update the kwargs\n with the appropriate value for that arg.\n\n Args:\n arg_names: The arg_names for the create_computations_fn.\n kwargs: The existing kwargs for create_computations_fn.\n eval_config: The value to use when `eval_config` is in arg_names.\n schema: The value to use when `schema` is in arg_names.\n model_names: The value to use when `model_names` is in arg_names.\n output_names: The value to use when `output_names` is in arg_names.\n sub_keys: The value to use when `sub_keys` is in arg_names.\n aggregation_type: The value to use when `aggregation_type` is in arg_names.\n class_weights: The value to use when `class_weights` is in arg_names.\n query_key: The value to use when `query_key` is in arg_names.\n is_diff: The value to use when `is_diff` is in arg_names.\n\n Returns:\n The kwargs passed as input, updated with the appropriate additional args.\n "
if ('eval_config' in arg_names):
kwargs['eval_config'] = eval_config
if ('schema' in arg_names):
kwargs['schema'] = schema
if ('model_names' in arg_names):
kwargs['model_names'] = model_names
if ('output_names' in arg_names):
kwargs['output_names'] = output_names
if ('sub_keys' in arg_names):
kwargs['sub_keys'] = sub_keys
if ('aggregation_type' in arg_names):
kwargs['aggregation_type'] = aggregation_type
if ('class_weights' in arg_names):
kwargs['class_weights'] = class_weights
if ('query_key' in arg_names):
kwargs['query_key'] = query_key
if ('is_diff' in arg_names):
kwargs['is_diff'] = is_diff
return kwargs | Updates create_computations_fn kwargs based on arg spec.
Each metric's create_computations_fn is invoked with a variable set of
parameters, depending on the argument names of the callable. If an argument
name matches one of the reserved names, this function will update the kwargs
with the appropriate value for that arg.
Args:
arg_names: The arg_names for the create_computations_fn.
kwargs: The existing kwargs for create_computations_fn.
eval_config: The value to use when `eval_config` is in arg_names.
schema: The value to use when `schema` is in arg_names.
model_names: The value to use when `model_names` is in arg_names.
output_names: The value to use when `output_names` is in arg_names.
sub_keys: The value to use when `sub_keys` is in arg_names.
aggregation_type: The value to use when `aggregation_type` is in arg_names.
class_weights: The value to use when `class_weights` is in arg_names.
query_key: The value to use when `query_key` is in arg_names.
is_diff: The value to use when `is_diff` is in arg_names.
Returns:
The kwargs passed as input, updated with the appropriate additional args. | tensorflow_model_analysis/metrics/metric_types.py | update_create_computations_fn_kwargs | jaymessina3/model-analysis | 1,118 | python | def update_create_computations_fn_kwargs(arg_names: Iterable[Text], kwargs: Dict[(Text, Any)], eval_config: Optional[config_pb2.EvalConfig]=None, schema: Optional[schema_pb2.Schema]=None, model_names: Optional[List[Text]]=None, output_names: Optional[List[Text]]=None, sub_keys: Optional[List[Optional[SubKey]]]=None, aggregation_type: Optional[AggregationType]=None, class_weights: Optional[Dict[(int, float)]]=None, query_key: Optional[Text]=None, is_diff: Optional[bool]=False):
"Updates create_computations_fn kwargs based on arg spec.\n\n Each metric's create_computations_fn is invoked with a variable set of\n parameters, depending on the argument names of the callable. If an argument\n name matches one of the reserved names, this function will update the kwargs\n with the appropriate value for that arg.\n\n Args:\n arg_names: The arg_names for the create_computations_fn.\n kwargs: The existing kwargs for create_computations_fn.\n eval_config: The value to use when `eval_config` is in arg_names.\n schema: The value to use when `schema` is in arg_names.\n model_names: The value to use when `model_names` is in arg_names.\n output_names: The value to use when `output_names` is in arg_names.\n sub_keys: The value to use when `sub_keys` is in arg_names.\n aggregation_type: The value to use when `aggregation_type` is in arg_names.\n class_weights: The value to use when `class_weights` is in arg_names.\n query_key: The value to use when `query_key` is in arg_names.\n is_diff: The value to use when `is_diff` is in arg_names.\n\n Returns:\n The kwargs passed as input, updated with the appropriate additional args.\n "
if ('eval_config' in arg_names):
kwargs['eval_config'] = eval_config
if ('schema' in arg_names):
kwargs['schema'] = schema
if ('model_names' in arg_names):
kwargs['model_names'] = model_names
if ('output_names' in arg_names):
kwargs['output_names'] = output_names
if ('sub_keys' in arg_names):
kwargs['sub_keys'] = sub_keys
if ('aggregation_type' in arg_names):
kwargs['aggregation_type'] = aggregation_type
if ('class_weights' in arg_names):
kwargs['class_weights'] = class_weights
if ('query_key' in arg_names):
kwargs['query_key'] = query_key
if ('is_diff' in arg_names):
kwargs['is_diff'] = is_diff
return kwargs | def update_create_computations_fn_kwargs(arg_names: Iterable[Text], kwargs: Dict[(Text, Any)], eval_config: Optional[config_pb2.EvalConfig]=None, schema: Optional[schema_pb2.Schema]=None, model_names: Optional[List[Text]]=None, output_names: Optional[List[Text]]=None, sub_keys: Optional[List[Optional[SubKey]]]=None, aggregation_type: Optional[AggregationType]=None, class_weights: Optional[Dict[(int, float)]]=None, query_key: Optional[Text]=None, is_diff: Optional[bool]=False):
"Updates create_computations_fn kwargs based on arg spec.\n\n Each metric's create_computations_fn is invoked with a variable set of\n parameters, depending on the argument names of the callable. If an argument\n name matches one of the reserved names, this function will update the kwargs\n with the appropriate value for that arg.\n\n Args:\n arg_names: The arg_names for the create_computations_fn.\n kwargs: The existing kwargs for create_computations_fn.\n eval_config: The value to use when `eval_config` is in arg_names.\n schema: The value to use when `schema` is in arg_names.\n model_names: The value to use when `model_names` is in arg_names.\n output_names: The value to use when `output_names` is in arg_names.\n sub_keys: The value to use when `sub_keys` is in arg_names.\n aggregation_type: The value to use when `aggregation_type` is in arg_names.\n class_weights: The value to use when `class_weights` is in arg_names.\n query_key: The value to use when `query_key` is in arg_names.\n is_diff: The value to use when `is_diff` is in arg_names.\n\n Returns:\n The kwargs passed as input, updated with the appropriate additional args.\n "
if ('eval_config' in arg_names):
kwargs['eval_config'] = eval_config
if ('schema' in arg_names):
kwargs['schema'] = schema
if ('model_names' in arg_names):
kwargs['model_names'] = model_names
if ('output_names' in arg_names):
kwargs['output_names'] = output_names
if ('sub_keys' in arg_names):
kwargs['sub_keys'] = sub_keys
if ('aggregation_type' in arg_names):
kwargs['aggregation_type'] = aggregation_type
if ('class_weights' in arg_names):
kwargs['class_weights'] = class_weights
if ('query_key' in arg_names):
kwargs['query_key'] = query_key
if ('is_diff' in arg_names):
kwargs['is_diff'] = is_diff
return kwargs<|docstring|>Updates create_computations_fn kwargs based on arg spec.
Each metric's create_computations_fn is invoked with a variable set of
parameters, depending on the argument names of the callable. If an argument
name matches one of the reserved names, this function will update the kwargs
with the appropriate value for that arg.
Args:
arg_names: The arg_names for the create_computations_fn.
kwargs: The existing kwargs for create_computations_fn.
eval_config: The value to use when `eval_config` is in arg_names.
schema: The value to use when `schema` is in arg_names.
model_names: The value to use when `model_names` is in arg_names.
output_names: The value to use when `output_names` is in arg_names.
sub_keys: The value to use when `sub_keys` is in arg_names.
aggregation_type: The value to use when `aggregation_type` is in arg_names.
class_weights: The value to use when `class_weights` is in arg_names.
query_key: The value to use when `query_key` is in arg_names.
is_diff: The value to use when `is_diff` is in arg_names.
Returns:
The kwargs passed as input, updated with the appropriate additional args.<|endoftext|> |
eaa3daacb2c2c7c815c84dcd89e37b39b8ec91969e6a16fd77cb0ae4559a8be5 | def register_metric(cls: Type[Metric]):
'Registers metric under the list of standard TFMA metrics.'
_METRIC_OBJECTS[cls.__name__] = cls | Registers metric under the list of standard TFMA metrics. | tensorflow_model_analysis/metrics/metric_types.py | register_metric | jaymessina3/model-analysis | 1,118 | python | def register_metric(cls: Type[Metric]):
_METRIC_OBJECTS[cls.__name__] = cls | def register_metric(cls: Type[Metric]):
_METRIC_OBJECTS[cls.__name__] = cls<|docstring|>Registers metric under the list of standard TFMA metrics.<|endoftext|> |
44e1fb5f2d205326e13c4b11a040c47b1bf93bac5a75cb1b740a33a9927e6a25 | def registered_metrics() -> Dict[(Text, Type[Metric])]:
'Returns standard TFMA metrics.'
return copy.copy(_METRIC_OBJECTS) | Returns standard TFMA metrics. | tensorflow_model_analysis/metrics/metric_types.py | registered_metrics | jaymessina3/model-analysis | 1,118 | python | def registered_metrics() -> Dict[(Text, Type[Metric])]:
return copy.copy(_METRIC_OBJECTS) | def registered_metrics() -> Dict[(Text, Type[Metric])]:
return copy.copy(_METRIC_OBJECTS)<|docstring|>Returns standard TFMA metrics.<|endoftext|> |
f3fc400545294cce03840e2a33ccc81935f03e7a0b315dd00a484ae1d93a3ea1 | def InputPreprocessor(include_default_inputs: bool=False) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for including raw inputs in StandardMetricInputs.\n\n Args:\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the inputs.\n '
return StandardMetricInputsPreprocessor(include_filter={constants.INPUT_KEY: {}}, include_default_inputs=include_default_inputs) | Returns preprocessor for including raw inputs in StandardMetricInputs.
Args:
include_default_inputs: True to include default inputs (labels, predictions,
example weights) in addition to the inputs. | tensorflow_model_analysis/metrics/metric_types.py | InputPreprocessor | jaymessina3/model-analysis | 1,118 | python | def InputPreprocessor(include_default_inputs: bool=False) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for including raw inputs in StandardMetricInputs.\n\n Args:\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the inputs.\n '
return StandardMetricInputsPreprocessor(include_filter={constants.INPUT_KEY: {}}, include_default_inputs=include_default_inputs) | def InputPreprocessor(include_default_inputs: bool=False) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for including raw inputs in StandardMetricInputs.\n\n Args:\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the inputs.\n '
return StandardMetricInputsPreprocessor(include_filter={constants.INPUT_KEY: {}}, include_default_inputs=include_default_inputs)<|docstring|>Returns preprocessor for including raw inputs in StandardMetricInputs.
Args:
include_default_inputs: True to include default inputs (labels, predictions,
example weights) in addition to the inputs.<|endoftext|> |
a6cb1d437f74dd32c25978156304c78f95467d4732938b54a4490936171682cb | def FeaturePreprocessor(feature_keys: Iterable[Text], include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for including features in StandardMetricInputs.\n\n Args:\n feature_keys: List of feature keys. An empty list means all.\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the features.\n model_names: Optional model names. Only used if include_default_inputs is\n True. If unset all models will be included with the default inputs.\n output_names: Optional output names. Only used if include_default_inputs is\n True. If unset all outputs will be included with the default inputs.\n '
if feature_keys:
include_features = {k: {} for k in feature_keys}
else:
include_features = {}
return StandardMetricInputsPreprocessor(include_filter={constants.FEATURES_KEY: include_features}, include_default_inputs=include_default_inputs, model_names=model_names, output_names=output_names) | Returns preprocessor for including features in StandardMetricInputs.
Args:
feature_keys: List of feature keys. An empty list means all.
include_default_inputs: True to include default inputs (labels, predictions,
example weights) in addition to the features.
model_names: Optional model names. Only used if include_default_inputs is
True. If unset all models will be included with the default inputs.
output_names: Optional output names. Only used if include_default_inputs is
True. If unset all outputs will be included with the default inputs. | tensorflow_model_analysis/metrics/metric_types.py | FeaturePreprocessor | jaymessina3/model-analysis | 1,118 | python | def FeaturePreprocessor(feature_keys: Iterable[Text], include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for including features in StandardMetricInputs.\n\n Args:\n feature_keys: List of feature keys. An empty list means all.\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the features.\n model_names: Optional model names. Only used if include_default_inputs is\n True. If unset all models will be included with the default inputs.\n output_names: Optional output names. Only used if include_default_inputs is\n True. If unset all outputs will be included with the default inputs.\n '
if feature_keys:
include_features = {k: {} for k in feature_keys}
else:
include_features = {}
return StandardMetricInputsPreprocessor(include_filter={constants.FEATURES_KEY: include_features}, include_default_inputs=include_default_inputs, model_names=model_names, output_names=output_names) | def FeaturePreprocessor(feature_keys: Iterable[Text], include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for including features in StandardMetricInputs.\n\n Args:\n feature_keys: List of feature keys. An empty list means all.\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the features.\n model_names: Optional model names. Only used if include_default_inputs is\n True. If unset all models will be included with the default inputs.\n output_names: Optional output names. Only used if include_default_inputs is\n True. If unset all outputs will be included with the default inputs.\n '
if feature_keys:
include_features = {k: {} for k in feature_keys}
else:
include_features = {}
return StandardMetricInputsPreprocessor(include_filter={constants.FEATURES_KEY: include_features}, include_default_inputs=include_default_inputs, model_names=model_names, output_names=output_names)<|docstring|>Returns preprocessor for including features in StandardMetricInputs.
Args:
feature_keys: List of feature keys. An empty list means all.
include_default_inputs: True to include default inputs (labels, predictions,
example weights) in addition to the features.
model_names: Optional model names. Only used if include_default_inputs is
True. If unset all models will be included with the default inputs.
output_names: Optional output names. Only used if include_default_inputs is
True. If unset all outputs will be included with the default inputs.<|endoftext|> |
8ea9172cdde1db243aa56eee208fad14dc347a105fa702e7fd99f349bf573e2b | def TransformedFeaturePreprocessor(feature_keys: Iterable[Text], include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for incl transformed features in StandardMetricInputs.\n\n Args:\n feature_keys: List of feature keys. An empty list means all.\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the transformed features.\n model_names: Optional model names (required if transformed_features used\n with multi-model evaluations).\n output_names: Optional output names. Only used if include_default_inputs is\n True. If unset all outputs will be included with the default inputs.\n '
if feature_keys:
include_features = {k: {} for k in feature_keys}
else:
include_features = {}
if model_names:
include_features = {name: include_features for name in model_names}
return StandardMetricInputsPreprocessor(include_filter={constants.TRANSFORMED_FEATURES_KEY: include_features}, include_default_inputs=include_default_inputs, model_names=model_names, output_names=output_names) | Returns preprocessor for incl transformed features in StandardMetricInputs.
Args:
feature_keys: List of feature keys. An empty list means all.
include_default_inputs: True to include default inputs (labels, predictions,
example weights) in addition to the transformed features.
model_names: Optional model names (required if transformed_features used
with multi-model evaluations).
output_names: Optional output names. Only used if include_default_inputs is
True. If unset all outputs will be included with the default inputs. | tensorflow_model_analysis/metrics/metric_types.py | TransformedFeaturePreprocessor | jaymessina3/model-analysis | 1,118 | python | def TransformedFeaturePreprocessor(feature_keys: Iterable[Text], include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for incl transformed features in StandardMetricInputs.\n\n Args:\n feature_keys: List of feature keys. An empty list means all.\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the transformed features.\n model_names: Optional model names (required if transformed_features used\n with multi-model evaluations).\n output_names: Optional output names. Only used if include_default_inputs is\n True. If unset all outputs will be included with the default inputs.\n '
if feature_keys:
include_features = {k: {} for k in feature_keys}
else:
include_features = {}
if model_names:
include_features = {name: include_features for name in model_names}
return StandardMetricInputsPreprocessor(include_filter={constants.TRANSFORMED_FEATURES_KEY: include_features}, include_default_inputs=include_default_inputs, model_names=model_names, output_names=output_names) | def TransformedFeaturePreprocessor(feature_keys: Iterable[Text], include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for incl transformed features in StandardMetricInputs.\n\n Args:\n feature_keys: List of feature keys. An empty list means all.\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the transformed features.\n model_names: Optional model names (required if transformed_features used\n with multi-model evaluations).\n output_names: Optional output names. Only used if include_default_inputs is\n True. If unset all outputs will be included with the default inputs.\n '
if feature_keys:
include_features = {k: {} for k in feature_keys}
else:
include_features = {}
if model_names:
include_features = {name: include_features for name in model_names}
return StandardMetricInputsPreprocessor(include_filter={constants.TRANSFORMED_FEATURES_KEY: include_features}, include_default_inputs=include_default_inputs, model_names=model_names, output_names=output_names)<|docstring|>Returns preprocessor for incl transformed features in StandardMetricInputs.
Args:
feature_keys: List of feature keys. An empty list means all.
include_default_inputs: True to include default inputs (labels, predictions,
example weights) in addition to the transformed features.
model_names: Optional model names (required if transformed_features used
with multi-model evaluations).
output_names: Optional output names. Only used if include_default_inputs is
True. If unset all outputs will be included with the default inputs.<|endoftext|> |
7331daddbe73a78576fb2e5b6a0241f0b60c500bae14b763cd26ad06834e18af | def AttributionPreprocessor(feature_keys: Iterable[Text], include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for including attributions in StandardMetricInputs.\n\n Args:\n feature_keys: List of feature keys under attributions to keep. An empty list\n means all.\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the transformed features.\n model_names: Optional model names (required for multi-model evaluations).\n output_names: Optional output names (required for multi-output evaluations).\n '
if feature_keys:
include_features = {k: {} for k in feature_keys}
else:
include_features = {}
if output_names:
include_features = {name: include_features for name in output_names}
if model_names:
include_features = {name: include_features for name in model_names}
return StandardMetricInputsPreprocessor(include_filter={constants.ATTRIBUTIONS_KEY: include_features}, include_default_inputs=include_default_inputs, model_names=model_names, output_names=output_names) | Returns preprocessor for including attributions in StandardMetricInputs.
Args:
feature_keys: List of feature keys under attributions to keep. An empty list
means all.
include_default_inputs: True to include default inputs (labels, predictions,
example weights) in addition to the transformed features.
model_names: Optional model names (required for multi-model evaluations).
output_names: Optional output names (required for multi-output evaluations). | tensorflow_model_analysis/metrics/metric_types.py | AttributionPreprocessor | jaymessina3/model-analysis | 1,118 | python | def AttributionPreprocessor(feature_keys: Iterable[Text], include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for including attributions in StandardMetricInputs.\n\n Args:\n feature_keys: List of feature keys under attributions to keep. An empty list\n means all.\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the transformed features.\n model_names: Optional model names (required for multi-model evaluations).\n output_names: Optional output names (required for multi-output evaluations).\n '
if feature_keys:
include_features = {k: {} for k in feature_keys}
else:
include_features = {}
if output_names:
include_features = {name: include_features for name in output_names}
if model_names:
include_features = {name: include_features for name in model_names}
return StandardMetricInputsPreprocessor(include_filter={constants.ATTRIBUTIONS_KEY: include_features}, include_default_inputs=include_default_inputs, model_names=model_names, output_names=output_names) | def AttributionPreprocessor(feature_keys: Iterable[Text], include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None) -> StandardMetricInputsPreprocessor:
'Returns preprocessor for including attributions in StandardMetricInputs.\n\n Args:\n feature_keys: List of feature keys under attributions to keep. An empty list\n means all.\n include_default_inputs: True to include default inputs (labels, predictions,\n example weights) in addition to the transformed features.\n model_names: Optional model names (required for multi-model evaluations).\n output_names: Optional output names (required for multi-output evaluations).\n '
if feature_keys:
include_features = {k: {} for k in feature_keys}
else:
include_features = {}
if output_names:
include_features = {name: include_features for name in output_names}
if model_names:
include_features = {name: include_features for name in model_names}
return StandardMetricInputsPreprocessor(include_filter={constants.ATTRIBUTIONS_KEY: include_features}, include_default_inputs=include_default_inputs, model_names=model_names, output_names=output_names)<|docstring|>Returns preprocessor for including attributions in StandardMetricInputs.
Args:
feature_keys: List of feature keys under attributions to keep. An empty list
means all.
include_default_inputs: True to include default inputs (labels, predictions,
example weights) in addition to the transformed features.
model_names: Optional model names (required for multi-model evaluations).
output_names: Optional output names (required for multi-output evaluations).<|endoftext|> |
d63f498c2abc21b7404a79406efb109cf2d70c972defe59255e577571e924c7a | def StandardMetricInputsPreprocessorList(preprocessors: List[StandardMetricInputsPreprocessor]) -> StandardMetricInputsPreprocessor:
'Returns preprocessor combining multiple standard preprocessors together.\n\n Args:\n preprocessors: List of StandardMetricInputsPreprocessors. Must be of type\n StandardMetricInputsPreprocessor (subclasses not supported).\n '
include_filter = {}
for p in preprocessors:
if (type(p) != StandardMetricInputsPreprocessor):
raise ValueError('Only direct instances of StandardMetricsInputPreprocessor (excluding sub-classes) are supported')
if (not include_filter):
include_filter = p.include_filter
else:
include_filter = util.merge_filters(include_filter, p.include_filter)
return StandardMetricInputsPreprocessor(include_filter=include_filter, include_default_inputs=False) | Returns preprocessor combining multiple standard preprocessors together.
Args:
preprocessors: List of StandardMetricInputsPreprocessors. Must be of type
StandardMetricInputsPreprocessor (subclasses not supported). | tensorflow_model_analysis/metrics/metric_types.py | StandardMetricInputsPreprocessorList | jaymessina3/model-analysis | 1,118 | python | def StandardMetricInputsPreprocessorList(preprocessors: List[StandardMetricInputsPreprocessor]) -> StandardMetricInputsPreprocessor:
'Returns preprocessor combining multiple standard preprocessors together.\n\n Args:\n preprocessors: List of StandardMetricInputsPreprocessors. Must be of type\n StandardMetricInputsPreprocessor (subclasses not supported).\n '
include_filter = {}
for p in preprocessors:
if (type(p) != StandardMetricInputsPreprocessor):
raise ValueError('Only direct instances of StandardMetricsInputPreprocessor (excluding sub-classes) are supported')
if (not include_filter):
include_filter = p.include_filter
else:
include_filter = util.merge_filters(include_filter, p.include_filter)
return StandardMetricInputsPreprocessor(include_filter=include_filter, include_default_inputs=False) | def StandardMetricInputsPreprocessorList(preprocessors: List[StandardMetricInputsPreprocessor]) -> StandardMetricInputsPreprocessor:
'Returns preprocessor combining multiple standard preprocessors together.\n\n Args:\n preprocessors: List of StandardMetricInputsPreprocessors. Must be of type\n StandardMetricInputsPreprocessor (subclasses not supported).\n '
include_filter = {}
for p in preprocessors:
if (type(p) != StandardMetricInputsPreprocessor):
raise ValueError('Only direct instances of StandardMetricsInputPreprocessor (excluding sub-classes) are supported')
if (not include_filter):
include_filter = p.include_filter
else:
include_filter = util.merge_filters(include_filter, p.include_filter)
return StandardMetricInputsPreprocessor(include_filter=include_filter, include_default_inputs=False)<|docstring|>Returns preprocessor combining multiple standard preprocessors together.
Args:
preprocessors: List of StandardMetricInputsPreprocessors. Must be of type
StandardMetricInputsPreprocessor (subclasses not supported).<|endoftext|> |
514a527692f2bf309951ff686199b64f93e7c2d63181e7ab572500a833919191 | def to_proto(self) -> metrics_for_slice_pb2.SubKey:
'Converts key to proto.'
sub_key = metrics_for_slice_pb2.SubKey()
if (self.class_id is not None):
sub_key.class_id.value = self.class_id
if (self.k is not None):
sub_key.k.value = self.k
if (self.top_k is not None):
sub_key.top_k.value = self.top_k
return sub_key | Converts key to proto. | tensorflow_model_analysis/metrics/metric_types.py | to_proto | jaymessina3/model-analysis | 1,118 | python | def to_proto(self) -> metrics_for_slice_pb2.SubKey:
sub_key = metrics_for_slice_pb2.SubKey()
if (self.class_id is not None):
sub_key.class_id.value = self.class_id
if (self.k is not None):
sub_key.k.value = self.k
if (self.top_k is not None):
sub_key.top_k.value = self.top_k
return sub_key | def to_proto(self) -> metrics_for_slice_pb2.SubKey:
sub_key = metrics_for_slice_pb2.SubKey()
if (self.class_id is not None):
sub_key.class_id.value = self.class_id
if (self.k is not None):
sub_key.k.value = self.k
if (self.top_k is not None):
sub_key.top_k.value = self.top_k
return sub_key<|docstring|>Converts key to proto.<|endoftext|> |
74d50e385d8297cb0d78addf3bc1f596fb56ec5b0fd7d5d9efae28c5971602eb | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.SubKey) -> Optional['SubKey']:
'Creates class from proto.'
class_id = None
if pb.HasField('class_id'):
class_id = pb.class_id.value
k = None
if pb.HasField('k'):
k = pb.k.value
top_k = None
if pb.HasField('top_k'):
top_k = pb.top_k.value
if ((class_id is None) and (k is None) and (top_k is None)):
return None
else:
return SubKey(class_id=class_id, k=k, top_k=top_k) | Creates class from proto. | tensorflow_model_analysis/metrics/metric_types.py | from_proto | jaymessina3/model-analysis | 1,118 | python | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.SubKey) -> Optional['SubKey']:
class_id = None
if pb.HasField('class_id'):
class_id = pb.class_id.value
k = None
if pb.HasField('k'):
k = pb.k.value
top_k = None
if pb.HasField('top_k'):
top_k = pb.top_k.value
if ((class_id is None) and (k is None) and (top_k is None)):
return None
else:
return SubKey(class_id=class_id, k=k, top_k=top_k) | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.SubKey) -> Optional['SubKey']:
class_id = None
if pb.HasField('class_id'):
class_id = pb.class_id.value
k = None
if pb.HasField('k'):
k = pb.k.value
top_k = None
if pb.HasField('top_k'):
top_k = pb.top_k.value
if ((class_id is None) and (k is None) and (top_k is None)):
return None
else:
return SubKey(class_id=class_id, k=k, top_k=top_k)<|docstring|>Creates class from proto.<|endoftext|> |
00b71c635498c322efa4038250ed794109da7ddced1f365426dffb8c65f5c1ee | def to_proto(self) -> metrics_for_slice_pb2.AggregationType:
'Converts key to proto.'
aggregration_type = metrics_for_slice_pb2.AggregationType()
if (self.micro_average is not None):
aggregration_type.micro_average = True
if (self.macro_average is not None):
aggregration_type.macro_average = True
if (self.weighted_macro_average is not None):
aggregration_type.weighted_macro_average = True
return aggregration_type | Converts key to proto. | tensorflow_model_analysis/metrics/metric_types.py | to_proto | jaymessina3/model-analysis | 1,118 | python | def to_proto(self) -> metrics_for_slice_pb2.AggregationType:
aggregration_type = metrics_for_slice_pb2.AggregationType()
if (self.micro_average is not None):
aggregration_type.micro_average = True
if (self.macro_average is not None):
aggregration_type.macro_average = True
if (self.weighted_macro_average is not None):
aggregration_type.weighted_macro_average = True
return aggregration_type | def to_proto(self) -> metrics_for_slice_pb2.AggregationType:
aggregration_type = metrics_for_slice_pb2.AggregationType()
if (self.micro_average is not None):
aggregration_type.micro_average = True
if (self.macro_average is not None):
aggregration_type.macro_average = True
if (self.weighted_macro_average is not None):
aggregration_type.weighted_macro_average = True
return aggregration_type<|docstring|>Converts key to proto.<|endoftext|> |
77a0779851d982b214c022cb2da60c2a1ce52a701f4d45d8c62cfc662b06ce15 | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.AggregationType) -> Optional['AggregationType']:
'Creates class from proto.'
if (pb.micro_average or pb.macro_average or pb.weighted_macro_average):
return AggregationType(micro_average=(pb.micro_average or None), macro_average=(pb.macro_average or None), weighted_macro_average=(pb.weighted_macro_average or None))
else:
return None | Creates class from proto. | tensorflow_model_analysis/metrics/metric_types.py | from_proto | jaymessina3/model-analysis | 1,118 | python | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.AggregationType) -> Optional['AggregationType']:
if (pb.micro_average or pb.macro_average or pb.weighted_macro_average):
return AggregationType(micro_average=(pb.micro_average or None), macro_average=(pb.macro_average or None), weighted_macro_average=(pb.weighted_macro_average or None))
else:
return None | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.AggregationType) -> Optional['AggregationType']:
if (pb.micro_average or pb.macro_average or pb.weighted_macro_average):
return AggregationType(micro_average=(pb.micro_average or None), macro_average=(pb.macro_average or None), weighted_macro_average=(pb.weighted_macro_average or None))
else:
return None<|docstring|>Creates class from proto.<|endoftext|> |
2dfead7cd95ebc69d270b37b7cb98a278f3e59b08789e2fa320fe859df678ed0 | def to_proto(self) -> metrics_for_slice_pb2.MetricKey:
'Converts key to proto.'
metric_key = metrics_for_slice_pb2.MetricKey()
if self.name:
metric_key.name = self.name
if self.model_name:
metric_key.model_name = self.model_name
if self.output_name:
metric_key.output_name = self.output_name
if self.sub_key:
metric_key.sub_key.CopyFrom(self.sub_key.to_proto())
if self.aggregation_type:
metric_key.aggregation_type.CopyFrom(self.aggregation_type.to_proto())
if self.is_diff:
metric_key.is_diff = self.is_diff
return metric_key | Converts key to proto. | tensorflow_model_analysis/metrics/metric_types.py | to_proto | jaymessina3/model-analysis | 1,118 | python | def to_proto(self) -> metrics_for_slice_pb2.MetricKey:
metric_key = metrics_for_slice_pb2.MetricKey()
if self.name:
metric_key.name = self.name
if self.model_name:
metric_key.model_name = self.model_name
if self.output_name:
metric_key.output_name = self.output_name
if self.sub_key:
metric_key.sub_key.CopyFrom(self.sub_key.to_proto())
if self.aggregation_type:
metric_key.aggregation_type.CopyFrom(self.aggregation_type.to_proto())
if self.is_diff:
metric_key.is_diff = self.is_diff
return metric_key | def to_proto(self) -> metrics_for_slice_pb2.MetricKey:
metric_key = metrics_for_slice_pb2.MetricKey()
if self.name:
metric_key.name = self.name
if self.model_name:
metric_key.model_name = self.model_name
if self.output_name:
metric_key.output_name = self.output_name
if self.sub_key:
metric_key.sub_key.CopyFrom(self.sub_key.to_proto())
if self.aggregation_type:
metric_key.aggregation_type.CopyFrom(self.aggregation_type.to_proto())
if self.is_diff:
metric_key.is_diff = self.is_diff
return metric_key<|docstring|>Converts key to proto.<|endoftext|> |
f92d649cab5127b2ff39af04132f8def5c16c2a3045bf69d19c8e41340708504 | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.MetricKey) -> 'MetricKey':
'Configures class from proto.'
return MetricKey(name=pb.name, model_name=pb.model_name, output_name=pb.output_name, sub_key=SubKey.from_proto(pb.sub_key), aggregation_type=AggregationType.from_proto(pb.aggregation_type), is_diff=pb.is_diff) | Configures class from proto. | tensorflow_model_analysis/metrics/metric_types.py | from_proto | jaymessina3/model-analysis | 1,118 | python | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.MetricKey) -> 'MetricKey':
return MetricKey(name=pb.name, model_name=pb.model_name, output_name=pb.output_name, sub_key=SubKey.from_proto(pb.sub_key), aggregation_type=AggregationType.from_proto(pb.aggregation_type), is_diff=pb.is_diff) | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.MetricKey) -> 'MetricKey':
return MetricKey(name=pb.name, model_name=pb.model_name, output_name=pb.output_name, sub_key=SubKey.from_proto(pb.sub_key), aggregation_type=AggregationType.from_proto(pb.aggregation_type), is_diff=pb.is_diff)<|docstring|>Configures class from proto.<|endoftext|> |
797bee3df2d00ab5d0d9533e9e3a3d9635b98af89b97b3b3fb0bc1e54fd1ff54 | def to_proto(self) -> metrics_for_slice_pb2.PlotKey:
'Converts key to proto.'
plot_key = metrics_for_slice_pb2.PlotKey()
if self.name:
raise ValueError('plot values must be combined into a single proto andstored under a plot key without a name')
if self.model_name:
plot_key.model_name = self.model_name
if self.output_name:
plot_key.output_name = self.output_name
if self.sub_key:
plot_key.sub_key.CopyFrom(self.sub_key.to_proto())
return plot_key | Converts key to proto. | tensorflow_model_analysis/metrics/metric_types.py | to_proto | jaymessina3/model-analysis | 1,118 | python | def to_proto(self) -> metrics_for_slice_pb2.PlotKey:
plot_key = metrics_for_slice_pb2.PlotKey()
if self.name:
raise ValueError('plot values must be combined into a single proto andstored under a plot key without a name')
if self.model_name:
plot_key.model_name = self.model_name
if self.output_name:
plot_key.output_name = self.output_name
if self.sub_key:
plot_key.sub_key.CopyFrom(self.sub_key.to_proto())
return plot_key | def to_proto(self) -> metrics_for_slice_pb2.PlotKey:
plot_key = metrics_for_slice_pb2.PlotKey()
if self.name:
raise ValueError('plot values must be combined into a single proto andstored under a plot key without a name')
if self.model_name:
plot_key.model_name = self.model_name
if self.output_name:
plot_key.output_name = self.output_name
if self.sub_key:
plot_key.sub_key.CopyFrom(self.sub_key.to_proto())
return plot_key<|docstring|>Converts key to proto.<|endoftext|> |
00930b216965987f280e99b98fb2fc1440fdd72591ac38046b8c4277aff31648 | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.PlotKey) -> 'PlotKey':
'Configures class from proto.'
return PlotKey(name='', model_name=pb.model_name, output_name=pb.output_name, sub_key=SubKey.from_proto(pb.sub_key)) | Configures class from proto. | tensorflow_model_analysis/metrics/metric_types.py | from_proto | jaymessina3/model-analysis | 1,118 | python | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.PlotKey) -> 'PlotKey':
return PlotKey(name=, model_name=pb.model_name, output_name=pb.output_name, sub_key=SubKey.from_proto(pb.sub_key)) | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.PlotKey) -> 'PlotKey':
return PlotKey(name=, model_name=pb.model_name, output_name=pb.output_name, sub_key=SubKey.from_proto(pb.sub_key))<|docstring|>Configures class from proto.<|endoftext|> |
587c450fee07fbd9047cea05974369513ff54139ebcb06c08fee2910137a77d6 | def to_proto(self) -> metrics_for_slice_pb2.AttributionsKey:
'Converts key to proto.'
attribution_key = metrics_for_slice_pb2.AttributionsKey()
if self.name:
attribution_key.name = self.name
if self.model_name:
attribution_key.model_name = self.model_name
if self.output_name:
attribution_key.output_name = self.output_name
if self.sub_key:
attribution_key.sub_key.CopyFrom(self.sub_key.to_proto())
return attribution_key | Converts key to proto. | tensorflow_model_analysis/metrics/metric_types.py | to_proto | jaymessina3/model-analysis | 1,118 | python | def to_proto(self) -> metrics_for_slice_pb2.AttributionsKey:
attribution_key = metrics_for_slice_pb2.AttributionsKey()
if self.name:
attribution_key.name = self.name
if self.model_name:
attribution_key.model_name = self.model_name
if self.output_name:
attribution_key.output_name = self.output_name
if self.sub_key:
attribution_key.sub_key.CopyFrom(self.sub_key.to_proto())
return attribution_key | def to_proto(self) -> metrics_for_slice_pb2.AttributionsKey:
attribution_key = metrics_for_slice_pb2.AttributionsKey()
if self.name:
attribution_key.name = self.name
if self.model_name:
attribution_key.model_name = self.model_name
if self.output_name:
attribution_key.output_name = self.output_name
if self.sub_key:
attribution_key.sub_key.CopyFrom(self.sub_key.to_proto())
return attribution_key<|docstring|>Converts key to proto.<|endoftext|> |
2be0303e5079ec8a2d9252e4b8e7508afa165ec4828ae1e07ba9a729a582b884 | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.AttributionsKey) -> 'AttributionsKey':
'Configures class from proto.'
return AttributionsKey(name=pb.name, model_name=pb.model_name, output_name=pb.output_name, sub_key=SubKey.from_proto(pb.sub_key)) | Configures class from proto. | tensorflow_model_analysis/metrics/metric_types.py | from_proto | jaymessina3/model-analysis | 1,118 | python | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.AttributionsKey) -> 'AttributionsKey':
return AttributionsKey(name=pb.name, model_name=pb.model_name, output_name=pb.output_name, sub_key=SubKey.from_proto(pb.sub_key)) | @staticmethod
def from_proto(pb: metrics_for_slice_pb2.AttributionsKey) -> 'AttributionsKey':
return AttributionsKey(name=pb.name, model_name=pb.model_name, output_name=pb.output_name, sub_key=SubKey.from_proto(pb.sub_key))<|docstring|>Configures class from proto.<|endoftext|> |
3be57738be6b4dab359f8895a292bdb3231ae1e63779a7ccb4eed0f51385fea3 | def __init__(self, create_computations_fn: Callable[(..., MetricComputations)], **kwargs):
'Initializes metric.\n\n Args:\n create_computations_fn: Function to create the metrics computations (e.g.\n mean_label, etc). This function should take the args passed to __init__\n as as input along with any of eval_config, schema, model_names,\n output_names, sub_keys, aggregation_type, or query_key (where needed).\n **kwargs: Any additional kwargs to pass to create_computations_fn. These\n should only contain primitive types or lists/dicts of primitive types.\n The kwargs passed to computations have precendence over these kwargs.\n '
self.create_computations_fn = create_computations_fn
self.kwargs = kwargs
if ('name' in kwargs):
self.name = kwargs['name']
else:
self.name = None
if hasattr(inspect, 'getfullargspec'):
self._args = inspect.getfullargspec(self.create_computations_fn).args
else:
self._args = inspect.getargspec(self.create_computations_fn).args | Initializes metric.
Args:
create_computations_fn: Function to create the metrics computations (e.g.
mean_label, etc). This function should take the args passed to __init__
as as input along with any of eval_config, schema, model_names,
output_names, sub_keys, aggregation_type, or query_key (where needed).
**kwargs: Any additional kwargs to pass to create_computations_fn. These
should only contain primitive types or lists/dicts of primitive types.
The kwargs passed to computations have precendence over these kwargs. | tensorflow_model_analysis/metrics/metric_types.py | __init__ | jaymessina3/model-analysis | 1,118 | python | def __init__(self, create_computations_fn: Callable[(..., MetricComputations)], **kwargs):
'Initializes metric.\n\n Args:\n create_computations_fn: Function to create the metrics computations (e.g.\n mean_label, etc). This function should take the args passed to __init__\n as as input along with any of eval_config, schema, model_names,\n output_names, sub_keys, aggregation_type, or query_key (where needed).\n **kwargs: Any additional kwargs to pass to create_computations_fn. These\n should only contain primitive types or lists/dicts of primitive types.\n The kwargs passed to computations have precendence over these kwargs.\n '
self.create_computations_fn = create_computations_fn
self.kwargs = kwargs
if ('name' in kwargs):
self.name = kwargs['name']
else:
self.name = None
if hasattr(inspect, 'getfullargspec'):
self._args = inspect.getfullargspec(self.create_computations_fn).args
else:
self._args = inspect.getargspec(self.create_computations_fn).args | def __init__(self, create_computations_fn: Callable[(..., MetricComputations)], **kwargs):
'Initializes metric.\n\n Args:\n create_computations_fn: Function to create the metrics computations (e.g.\n mean_label, etc). This function should take the args passed to __init__\n as as input along with any of eval_config, schema, model_names,\n output_names, sub_keys, aggregation_type, or query_key (where needed).\n **kwargs: Any additional kwargs to pass to create_computations_fn. These\n should only contain primitive types or lists/dicts of primitive types.\n The kwargs passed to computations have precendence over these kwargs.\n '
self.create_computations_fn = create_computations_fn
self.kwargs = kwargs
if ('name' in kwargs):
self.name = kwargs['name']
else:
self.name = None
if hasattr(inspect, 'getfullargspec'):
self._args = inspect.getfullargspec(self.create_computations_fn).args
else:
self._args = inspect.getargspec(self.create_computations_fn).args<|docstring|>Initializes metric.
Args:
create_computations_fn: Function to create the metrics computations (e.g.
mean_label, etc). This function should take the args passed to __init__
as as input along with any of eval_config, schema, model_names,
output_names, sub_keys, aggregation_type, or query_key (where needed).
**kwargs: Any additional kwargs to pass to create_computations_fn. These
should only contain primitive types or lists/dicts of primitive types.
The kwargs passed to computations have precendence over these kwargs.<|endoftext|> |
89477ac96d00ee4ef1253e18fc551418a7585d8378e85143a8d6a861904b85be | def get_config(self) -> Dict[(Text, Any)]:
'Returns serializable config.'
return self.kwargs | Returns serializable config. | tensorflow_model_analysis/metrics/metric_types.py | get_config | jaymessina3/model-analysis | 1,118 | python | def get_config(self) -> Dict[(Text, Any)]:
return self.kwargs | def get_config(self) -> Dict[(Text, Any)]:
return self.kwargs<|docstring|>Returns serializable config.<|endoftext|> |
02443b5486556b92195d41fd0fcbdb51cce11ec52548a6d69688efa3960dff7a | @property
def compute_confidence_interval(self) -> bool:
'Whether to compute confidence intervals for this metric.\n\n Note that this may not completely remove the computational overhead\n involved in computing a given metric. This is only respected by the\n jackknife confidence interval method.\n\n Returns:\n Whether to compute confidence intervals for this metric.\n '
return True | Whether to compute confidence intervals for this metric.
Note that this may not completely remove the computational overhead
involved in computing a given metric. This is only respected by the
jackknife confidence interval method.
Returns:
Whether to compute confidence intervals for this metric. | tensorflow_model_analysis/metrics/metric_types.py | compute_confidence_interval | jaymessina3/model-analysis | 1,118 | python | @property
def compute_confidence_interval(self) -> bool:
'Whether to compute confidence intervals for this metric.\n\n Note that this may not completely remove the computational overhead\n involved in computing a given metric. This is only respected by the\n jackknife confidence interval method.\n\n Returns:\n Whether to compute confidence intervals for this metric.\n '
return True | @property
def compute_confidence_interval(self) -> bool:
'Whether to compute confidence intervals for this metric.\n\n Note that this may not completely remove the computational overhead\n involved in computing a given metric. This is only respected by the\n jackknife confidence interval method.\n\n Returns:\n Whether to compute confidence intervals for this metric.\n '
return True<|docstring|>Whether to compute confidence intervals for this metric.
Note that this may not completely remove the computational overhead
involved in computing a given metric. This is only respected by the
jackknife confidence interval method.
Returns:
Whether to compute confidence intervals for this metric.<|endoftext|> |
f515fe8ea035a71f862c5626bd5f5f94ff84086e9230b37e441ef4fdb3b3b863 | def computations(self, eval_config: Optional[config_pb2.EvalConfig]=None, schema: Optional[schema_pb2.Schema]=None, model_names: Optional[List[Text]]=None, output_names: Optional[List[Text]]=None, sub_keys: Optional[List[Optional[SubKey]]]=None, aggregation_type: Optional[AggregationType]=None, class_weights: Optional[Dict[(int, float)]]=None, query_key: Optional[Text]=None, is_diff: Optional[bool]=False) -> MetricComputations:
'Creates computations associated with metric.'
updated_kwargs = update_create_computations_fn_kwargs(self._args, self.kwargs.copy(), eval_config, schema, model_names, output_names, sub_keys, aggregation_type, class_weights, query_key, is_diff)
return self.create_computations_fn(**updated_kwargs) | Creates computations associated with metric. | tensorflow_model_analysis/metrics/metric_types.py | computations | jaymessina3/model-analysis | 1,118 | python | def computations(self, eval_config: Optional[config_pb2.EvalConfig]=None, schema: Optional[schema_pb2.Schema]=None, model_names: Optional[List[Text]]=None, output_names: Optional[List[Text]]=None, sub_keys: Optional[List[Optional[SubKey]]]=None, aggregation_type: Optional[AggregationType]=None, class_weights: Optional[Dict[(int, float)]]=None, query_key: Optional[Text]=None, is_diff: Optional[bool]=False) -> MetricComputations:
updated_kwargs = update_create_computations_fn_kwargs(self._args, self.kwargs.copy(), eval_config, schema, model_names, output_names, sub_keys, aggregation_type, class_weights, query_key, is_diff)
return self.create_computations_fn(**updated_kwargs) | def computations(self, eval_config: Optional[config_pb2.EvalConfig]=None, schema: Optional[schema_pb2.Schema]=None, model_names: Optional[List[Text]]=None, output_names: Optional[List[Text]]=None, sub_keys: Optional[List[Optional[SubKey]]]=None, aggregation_type: Optional[AggregationType]=None, class_weights: Optional[Dict[(int, float)]]=None, query_key: Optional[Text]=None, is_diff: Optional[bool]=False) -> MetricComputations:
updated_kwargs = update_create_computations_fn_kwargs(self._args, self.kwargs.copy(), eval_config, schema, model_names, output_names, sub_keys, aggregation_type, class_weights, query_key, is_diff)
return self.create_computations_fn(**updated_kwargs)<|docstring|>Creates computations associated with metric.<|endoftext|> |
8221e00d24c4f375c6b820d368d07d1ad38b6a2455dc881490824ea337f3875f | @property
def label(self) -> Optional[types.TensorValueMaybeMultiLevelDict]:
'Same as labels (DEPRECATED - use labels).'
return self.get_labels() | Same as labels (DEPRECATED - use labels). | tensorflow_model_analysis/metrics/metric_types.py | label | jaymessina3/model-analysis | 1,118 | python | @property
def label(self) -> Optional[types.TensorValueMaybeMultiLevelDict]:
return self.get_labels() | @property
def label(self) -> Optional[types.TensorValueMaybeMultiLevelDict]:
return self.get_labels()<|docstring|>Same as labels (DEPRECATED - use labels).<|endoftext|> |
bb196a80ee1edffe967210bd3c8441460590700cfdc8b390e0749a394f5cfa81 | @property
def prediction(self) -> Optional[types.TensorValueMaybeMultiLevelDict]:
'Same as predictions (DEPRECATED - use predictions).'
return self.get_predictions() | Same as predictions (DEPRECATED - use predictions). | tensorflow_model_analysis/metrics/metric_types.py | prediction | jaymessina3/model-analysis | 1,118 | python | @property
def prediction(self) -> Optional[types.TensorValueMaybeMultiLevelDict]:
return self.get_predictions() | @property
def prediction(self) -> Optional[types.TensorValueMaybeMultiLevelDict]:
return self.get_predictions()<|docstring|>Same as predictions (DEPRECATED - use predictions).<|endoftext|> |
6516503272130855decee728e087e3ca7349db4763b131dfdfea65938f43c178 | @property
def example_weight(self) -> Optional[types.TensorValueMaybeMultiLevelDict]:
'Same as example_weights (DEPRECATED - use example_weights).'
return self.get_example_weights() | Same as example_weights (DEPRECATED - use example_weights). | tensorflow_model_analysis/metrics/metric_types.py | example_weight | jaymessina3/model-analysis | 1,118 | python | @property
def example_weight(self) -> Optional[types.TensorValueMaybeMultiLevelDict]:
return self.get_example_weights() | @property
def example_weight(self) -> Optional[types.TensorValueMaybeMultiLevelDict]:
return self.get_example_weights()<|docstring|>Same as example_weights (DEPRECATED - use example_weights).<|endoftext|> |
f494c4a30394e33d29eeb2f10bec8603f9c40cea37b3a57dd3b39506ea5afbd4 | def __init__(self, include_filter: Optional[Union[(Iterable[Text], Dict[(Text, Any)])]]=None, include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None):
"Initializes preprocessor.\n\n Args:\n include_filter: Optional list or map of extracts keys to include in\n output. If a map of keys is passed then the keys and sub-keys that exist\n in the map will be included in the output. An empty dict behaves as a\n wildcard matching all keys or the value itself. Since matching on values\n is not currently supported, an empty dict must be used to represent the\n leaf nodes. For example, {'key1': {'key1-subkey': {}}, 'key2': {}}.\n include_default_inputs: True to include default inputs (labels,\n predictions, example weights) in addition to any inputs that may be\n specified using include_filter.\n model_names: Optional model names. Only used if include_default_inputs is\n True. If unset all models will be included with the default inputs.\n output_names: Optional output names. Only used if include_default_inputs\n is True. If unset all outputs will be included with the default inputs.\n "
if (include_filter is None):
include_filter = {}
if (not isinstance(include_filter, MutableMapping)):
if isinstance(include_filter, Iterable):
include_filter = {k: {} for k in (include_filter or [])}
else:
raise ValueError('include_filter must be a list or dict')
if include_default_inputs:
default_filter = {}
if output_names:
default_filter = {name: default_filter for name in output_names}
if model_names:
default_filter = {name: default_filter for name in model_names}
include_filter = copy.copy(include_filter)
include_filter.update({constants.LABELS_KEY: default_filter, constants.PREDICTIONS_KEY: default_filter, constants.EXAMPLE_WEIGHTS_KEY: default_filter})
self.include_filter = include_filter | Initializes preprocessor.
Args:
include_filter: Optional list or map of extracts keys to include in
output. If a map of keys is passed then the keys and sub-keys that exist
in the map will be included in the output. An empty dict behaves as a
wildcard matching all keys or the value itself. Since matching on values
is not currently supported, an empty dict must be used to represent the
leaf nodes. For example, {'key1': {'key1-subkey': {}}, 'key2': {}}.
include_default_inputs: True to include default inputs (labels,
predictions, example weights) in addition to any inputs that may be
specified using include_filter.
model_names: Optional model names. Only used if include_default_inputs is
True. If unset all models will be included with the default inputs.
output_names: Optional output names. Only used if include_default_inputs
is True. If unset all outputs will be included with the default inputs. | tensorflow_model_analysis/metrics/metric_types.py | __init__ | jaymessina3/model-analysis | 1,118 | python | def __init__(self, include_filter: Optional[Union[(Iterable[Text], Dict[(Text, Any)])]]=None, include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None):
"Initializes preprocessor.\n\n Args:\n include_filter: Optional list or map of extracts keys to include in\n output. If a map of keys is passed then the keys and sub-keys that exist\n in the map will be included in the output. An empty dict behaves as a\n wildcard matching all keys or the value itself. Since matching on values\n is not currently supported, an empty dict must be used to represent the\n leaf nodes. For example, {'key1': {'key1-subkey': {}}, 'key2': {}}.\n include_default_inputs: True to include default inputs (labels,\n predictions, example weights) in addition to any inputs that may be\n specified using include_filter.\n model_names: Optional model names. Only used if include_default_inputs is\n True. If unset all models will be included with the default inputs.\n output_names: Optional output names. Only used if include_default_inputs\n is True. If unset all outputs will be included with the default inputs.\n "
if (include_filter is None):
include_filter = {}
if (not isinstance(include_filter, MutableMapping)):
if isinstance(include_filter, Iterable):
include_filter = {k: {} for k in (include_filter or [])}
else:
raise ValueError('include_filter must be a list or dict')
if include_default_inputs:
default_filter = {}
if output_names:
default_filter = {name: default_filter for name in output_names}
if model_names:
default_filter = {name: default_filter for name in model_names}
include_filter = copy.copy(include_filter)
include_filter.update({constants.LABELS_KEY: default_filter, constants.PREDICTIONS_KEY: default_filter, constants.EXAMPLE_WEIGHTS_KEY: default_filter})
self.include_filter = include_filter | def __init__(self, include_filter: Optional[Union[(Iterable[Text], Dict[(Text, Any)])]]=None, include_default_inputs: bool=True, model_names: Optional[Iterable[Text]]=None, output_names: Optional[Iterable[Text]]=None):
"Initializes preprocessor.\n\n Args:\n include_filter: Optional list or map of extracts keys to include in\n output. If a map of keys is passed then the keys and sub-keys that exist\n in the map will be included in the output. An empty dict behaves as a\n wildcard matching all keys or the value itself. Since matching on values\n is not currently supported, an empty dict must be used to represent the\n leaf nodes. For example, {'key1': {'key1-subkey': {}}, 'key2': {}}.\n include_default_inputs: True to include default inputs (labels,\n predictions, example weights) in addition to any inputs that may be\n specified using include_filter.\n model_names: Optional model names. Only used if include_default_inputs is\n True. If unset all models will be included with the default inputs.\n output_names: Optional output names. Only used if include_default_inputs\n is True. If unset all outputs will be included with the default inputs.\n "
if (include_filter is None):
include_filter = {}
if (not isinstance(include_filter, MutableMapping)):
if isinstance(include_filter, Iterable):
include_filter = {k: {} for k in (include_filter or [])}
else:
raise ValueError('include_filter must be a list or dict')
if include_default_inputs:
default_filter = {}
if output_names:
default_filter = {name: default_filter for name in output_names}
if model_names:
default_filter = {name: default_filter for name in model_names}
include_filter = copy.copy(include_filter)
include_filter.update({constants.LABELS_KEY: default_filter, constants.PREDICTIONS_KEY: default_filter, constants.EXAMPLE_WEIGHTS_KEY: default_filter})
self.include_filter = include_filter<|docstring|>Initializes preprocessor.
Args:
include_filter: Optional list or map of extracts keys to include in
output. If a map of keys is passed then the keys and sub-keys that exist
in the map will be included in the output. An empty dict behaves as a
wildcard matching all keys or the value itself. Since matching on values
is not currently supported, an empty dict must be used to represent the
leaf nodes. For example, {'key1': {'key1-subkey': {}}, 'key2': {}}.
include_default_inputs: True to include default inputs (labels,
predictions, example weights) in addition to any inputs that may be
specified using include_filter.
model_names: Optional model names. Only used if include_default_inputs is
True. If unset all models will be included with the default inputs.
output_names: Optional output names. Only used if include_default_inputs
is True. If unset all outputs will be included with the default inputs.<|endoftext|> |
de36835b3d7d43ebc133b90f0cc6c1ac4e8d743ea5552c8e7fdef2ebe531bdd9 | def includeme(config):
" Set up standard configurator registrations. Use via:\n\n .. code-block:: python\n\n config = Configurator()\n config.include('pyramid_keystone')\n\n "
def register():
registry = config.registry
settings = parse_settings(registry.settings)
registry.settings.update(settings)
def ensure():
if (config.registry.queryUtility(ISessionFactory) is None):
raise ConfigurationError('pyramid_keystone requires a registered session factory. (use the set_session_factory method)')
config.action('keystone-configure', register)
config.action(None, ensure, order=10)
config.add_directive('keystone_auth_policy', '.authentication.add_auth_policy')
config.add_request_method('.keystone.request_keystone', name='keystone', property=True, reify=True) | Set up standard configurator registrations. Use via:
.. code-block:: python
config = Configurator()
config.include('pyramid_keystone') | pyramid_keystone/__init__.py | includeme | bertjwregeer/pyramid_keystone | 0 | python | def includeme(config):
" Set up standard configurator registrations. Use via:\n\n .. code-block:: python\n\n config = Configurator()\n config.include('pyramid_keystone')\n\n "
def register():
registry = config.registry
settings = parse_settings(registry.settings)
registry.settings.update(settings)
def ensure():
if (config.registry.queryUtility(ISessionFactory) is None):
raise ConfigurationError('pyramid_keystone requires a registered session factory. (use the set_session_factory method)')
config.action('keystone-configure', register)
config.action(None, ensure, order=10)
config.add_directive('keystone_auth_policy', '.authentication.add_auth_policy')
config.add_request_method('.keystone.request_keystone', name='keystone', property=True, reify=True) | def includeme(config):
" Set up standard configurator registrations. Use via:\n\n .. code-block:: python\n\n config = Configurator()\n config.include('pyramid_keystone')\n\n "
def register():
registry = config.registry
settings = parse_settings(registry.settings)
registry.settings.update(settings)
def ensure():
if (config.registry.queryUtility(ISessionFactory) is None):
raise ConfigurationError('pyramid_keystone requires a registered session factory. (use the set_session_factory method)')
config.action('keystone-configure', register)
config.action(None, ensure, order=10)
config.add_directive('keystone_auth_policy', '.authentication.add_auth_policy')
config.add_request_method('.keystone.request_keystone', name='keystone', property=True, reify=True)<|docstring|>Set up standard configurator registrations. Use via:
.. code-block:: python
config = Configurator()
config.include('pyramid_keystone')<|endoftext|> |
6cc8678acce51ac876a05416a991db7c1ec5f6e301fcb5d3ced8d0dc16e4cf0d | def set_as_anonymous(self):
'Removes all IPs from the whitelist.'
self.testbed.setup_env(USER_EMAIL='', overwrite=True)
auth.ip_whitelist_key(auth.bots_ip_whitelist()).delete()
auth_testing.reset_local_state()
auth_testing.mock_get_current_identity(self, auth.Anonymous) | Removes all IPs from the whitelist. | appengine/swarming/test_env_handlers.py | set_as_anonymous | Swift1313/luci-py | 0 | python | def set_as_anonymous(self):
self.testbed.setup_env(USER_EMAIL=, overwrite=True)
auth.ip_whitelist_key(auth.bots_ip_whitelist()).delete()
auth_testing.reset_local_state()
auth_testing.mock_get_current_identity(self, auth.Anonymous) | def set_as_anonymous(self):
self.testbed.setup_env(USER_EMAIL=, overwrite=True)
auth.ip_whitelist_key(auth.bots_ip_whitelist()).delete()
auth_testing.reset_local_state()
auth_testing.mock_get_current_identity(self, auth.Anonymous)<|docstring|>Removes all IPs from the whitelist.<|endoftext|> |
d1ba39cf8aedab7bed91a8783755dfc5e437aab2892201a363a4133710fd8926 | def get_xsrf_token(self):
'Gets the generic XSRF token for web clients.'
resp = self.auth_app.post('/auth/api/v1/accounts/self/xsrf_token', headers={'X-XSRF-Token-Request': '1'}).json
return resp['xsrf_token'].encode('ascii') | Gets the generic XSRF token for web clients. | appengine/swarming/test_env_handlers.py | get_xsrf_token | Swift1313/luci-py | 0 | python | def get_xsrf_token(self):
resp = self.auth_app.post('/auth/api/v1/accounts/self/xsrf_token', headers={'X-XSRF-Token-Request': '1'}).json
return resp['xsrf_token'].encode('ascii') | def get_xsrf_token(self):
resp = self.auth_app.post('/auth/api/v1/accounts/self/xsrf_token', headers={'X-XSRF-Token-Request': '1'}).json
return resp['xsrf_token'].encode('ascii')<|docstring|>Gets the generic XSRF token for web clients.<|endoftext|> |
be73f0f1e427f226044fc5b76c7fddec0ab3aa710a28baa6baccc00d4b290b36 | def post_json(self, url, params, **kwargs):
'Does an HTTP POST with a JSON API and return JSON response.'
return self.app.post_json(url, params=params, **kwargs).json | Does an HTTP POST with a JSON API and return JSON response. | appengine/swarming/test_env_handlers.py | post_json | Swift1313/luci-py | 0 | python | def post_json(self, url, params, **kwargs):
return self.app.post_json(url, params=params, **kwargs).json | def post_json(self, url, params, **kwargs):
return self.app.post_json(url, params=params, **kwargs).json<|docstring|>Does an HTTP POST with a JSON API and return JSON response.<|endoftext|> |
feab837273acd36ceabe23b8ce66c7a69bc56c78f782fdcb1f1bd4b6c0ddd108 | def mock_task_service_accounts(self, exc=None):
'Mocks support for task-associated service accounts.'
self.mock(service_accounts, 'has_token_server', (lambda : True))
calls = []
def mocked(service_account, validity_duration):
calls.append((service_account, validity_duration))
if exc:
raise exc
return ('token-grant-%s-%d' % (str(service_account), validity_duration.total_seconds()))
self.mock(service_accounts, 'get_oauth_token_grant', mocked)
return calls | Mocks support for task-associated service accounts. | appengine/swarming/test_env_handlers.py | mock_task_service_accounts | Swift1313/luci-py | 0 | python | def mock_task_service_accounts(self, exc=None):
self.mock(service_accounts, 'has_token_server', (lambda : True))
calls = []
def mocked(service_account, validity_duration):
calls.append((service_account, validity_duration))
if exc:
raise exc
return ('token-grant-%s-%d' % (str(service_account), validity_duration.total_seconds()))
self.mock(service_accounts, 'get_oauth_token_grant', mocked)
return calls | def mock_task_service_accounts(self, exc=None):
self.mock(service_accounts, 'has_token_server', (lambda : True))
calls = []
def mocked(service_account, validity_duration):
calls.append((service_account, validity_duration))
if exc:
raise exc
return ('token-grant-%s-%d' % (str(service_account), validity_duration.total_seconds()))
self.mock(service_accounts, 'get_oauth_token_grant', mocked)
return calls<|docstring|>Mocks support for task-associated service accounts.<|endoftext|> |
9b389ea304a455d15f9194e0c2e97cbf37f5dadff55108bddba8970bcf87002c | def mock_default_pool_acl(self, service_accounts):
"Mocks ACLs of 'default' pool to allow usage of given service accounts."
assert isinstance(service_accounts, (list, tuple)), service_accounts
def mocked_fetch_pools_config():
default_isolate = pools_config.IsolateServer(server='https://pool.config.isolate.example.com', namespace='default-gzip')
default_cipd = pools_config.CipdServer(server='https://pool.config.cipd.example.com', client_version='from_pool_config')
return pools_config._PoolsCfg({'template': pools_config.PoolConfig(name='template', rev='pools_cfg_rev', scheduling_users=frozenset([auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]')]), scheduling_groups=frozenset(), trusted_delegatees={}, service_accounts=frozenset(service_accounts), service_accounts_groups=(), task_template_deployment=pools_config.TaskTemplateDeployment(prod=pools_config.TaskTemplate(cache=(), cipd_package=(), env=(pools_config.Env('VAR', 'prod', (), False),), inclusions=()), canary=pools_config.TaskTemplate(cache=(), cipd_package=(), env=(pools_config.Env('VAR', 'canary', (), False),), inclusions=()), canary_chance=0.5), default_isolate=default_isolate, default_cipd=default_cipd, bot_monitoring=None, external_schedulers=None), 'default': pools_config.PoolConfig(name='default', rev='pools_cfg_rev', scheduling_users=frozenset([auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]')]), scheduling_groups=frozenset(), trusted_delegatees={}, service_accounts=frozenset(service_accounts), service_accounts_groups=(), task_template_deployment=None, bot_monitoring=None, default_isolate=default_isolate, default_cipd=default_cipd, external_schedulers=None)}, (default_isolate, default_cipd))
self.mock(pools_config, '_fetch_pools_config', mocked_fetch_pools_config) | Mocks ACLs of 'default' pool to allow usage of given service accounts. | appengine/swarming/test_env_handlers.py | mock_default_pool_acl | Swift1313/luci-py | 0 | python | def mock_default_pool_acl(self, service_accounts):
assert isinstance(service_accounts, (list, tuple)), service_accounts
def mocked_fetch_pools_config():
default_isolate = pools_config.IsolateServer(server='https://pool.config.isolate.example.com', namespace='default-gzip')
default_cipd = pools_config.CipdServer(server='https://pool.config.cipd.example.com', client_version='from_pool_config')
return pools_config._PoolsCfg({'template': pools_config.PoolConfig(name='template', rev='pools_cfg_rev', scheduling_users=frozenset([auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]')]), scheduling_groups=frozenset(), trusted_delegatees={}, service_accounts=frozenset(service_accounts), service_accounts_groups=(), task_template_deployment=pools_config.TaskTemplateDeployment(prod=pools_config.TaskTemplate(cache=(), cipd_package=(), env=(pools_config.Env('VAR', 'prod', (), False),), inclusions=()), canary=pools_config.TaskTemplate(cache=(), cipd_package=(), env=(pools_config.Env('VAR', 'canary', (), False),), inclusions=()), canary_chance=0.5), default_isolate=default_isolate, default_cipd=default_cipd, bot_monitoring=None, external_schedulers=None), 'default': pools_config.PoolConfig(name='default', rev='pools_cfg_rev', scheduling_users=frozenset([auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]')]), scheduling_groups=frozenset(), trusted_delegatees={}, service_accounts=frozenset(service_accounts), service_accounts_groups=(), task_template_deployment=None, bot_monitoring=None, default_isolate=default_isolate, default_cipd=default_cipd, external_schedulers=None)}, (default_isolate, default_cipd))
self.mock(pools_config, '_fetch_pools_config', mocked_fetch_pools_config) | def mock_default_pool_acl(self, service_accounts):
assert isinstance(service_accounts, (list, tuple)), service_accounts
def mocked_fetch_pools_config():
default_isolate = pools_config.IsolateServer(server='https://pool.config.isolate.example.com', namespace='default-gzip')
default_cipd = pools_config.CipdServer(server='https://pool.config.cipd.example.com', client_version='from_pool_config')
return pools_config._PoolsCfg({'template': pools_config.PoolConfig(name='template', rev='pools_cfg_rev', scheduling_users=frozenset([auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]')]), scheduling_groups=frozenset(), trusted_delegatees={}, service_accounts=frozenset(service_accounts), service_accounts_groups=(), task_template_deployment=pools_config.TaskTemplateDeployment(prod=pools_config.TaskTemplate(cache=(), cipd_package=(), env=(pools_config.Env('VAR', 'prod', (), False),), inclusions=()), canary=pools_config.TaskTemplate(cache=(), cipd_package=(), env=(pools_config.Env('VAR', 'canary', (), False),), inclusions=()), canary_chance=0.5), default_isolate=default_isolate, default_cipd=default_cipd, bot_monitoring=None, external_schedulers=None), 'default': pools_config.PoolConfig(name='default', rev='pools_cfg_rev', scheduling_users=frozenset([auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]'), auth.Identity(auth.IDENTITY_USER, '[email protected]')]), scheduling_groups=frozenset(), trusted_delegatees={}, service_accounts=frozenset(service_accounts), service_accounts_groups=(), task_template_deployment=None, bot_monitoring=None, default_isolate=default_isolate, default_cipd=default_cipd, external_schedulers=None)}, (default_isolate, default_cipd))
self.mock(pools_config, '_fetch_pools_config', mocked_fetch_pools_config)<|docstring|>Mocks ACLs of 'default' pool to allow usage of given service accounts.<|endoftext|> |
a2364978af606f16ca4c2291a35558bee615a885dabaafe13ce650011060828e | def do_handshake(self, bot='bot1'):
'Performs bot handshake, returns data to be sent to bot handlers.\n\n Also populates self.bot_version.\n '
params = {'dimensions': {'id': [bot], 'os': ['Amiga'], 'pool': ['default']}, 'state': {'running_time': 1234.0, 'sleep_streak': 0, 'started_ts': 1410990411.111}, 'version': '123'}
response = self.app.post_json('/swarming/api/v1/bot/handshake', params=params).json
self.bot_version = response['bot_version']
params['version'] = self.bot_version
params['state']['bot_group_cfg_version'] = response['bot_group_cfg_version']
if response.get('bot_config'):
params['bot_config'] = response['bot_config']
return params | Performs bot handshake, returns data to be sent to bot handlers.
Also populates self.bot_version. | appengine/swarming/test_env_handlers.py | do_handshake | Swift1313/luci-py | 0 | python | def do_handshake(self, bot='bot1'):
'Performs bot handshake, returns data to be sent to bot handlers.\n\n Also populates self.bot_version.\n '
params = {'dimensions': {'id': [bot], 'os': ['Amiga'], 'pool': ['default']}, 'state': {'running_time': 1234.0, 'sleep_streak': 0, 'started_ts': 1410990411.111}, 'version': '123'}
response = self.app.post_json('/swarming/api/v1/bot/handshake', params=params).json
self.bot_version = response['bot_version']
params['version'] = self.bot_version
params['state']['bot_group_cfg_version'] = response['bot_group_cfg_version']
if response.get('bot_config'):
params['bot_config'] = response['bot_config']
return params | def do_handshake(self, bot='bot1'):
'Performs bot handshake, returns data to be sent to bot handlers.\n\n Also populates self.bot_version.\n '
params = {'dimensions': {'id': [bot], 'os': ['Amiga'], 'pool': ['default']}, 'state': {'running_time': 1234.0, 'sleep_streak': 0, 'started_ts': 1410990411.111}, 'version': '123'}
response = self.app.post_json('/swarming/api/v1/bot/handshake', params=params).json
self.bot_version = response['bot_version']
params['version'] = self.bot_version
params['state']['bot_group_cfg_version'] = response['bot_group_cfg_version']
if response.get('bot_config'):
params['bot_config'] = response['bot_config']
return params<|docstring|>Performs bot handshake, returns data to be sent to bot handlers.
Also populates self.bot_version.<|endoftext|> |
ac1143581c217770268a8c3dcc4b023abdd4384c24a8b46d5df911ff064172ff | def bot_poll(self, bot='bot1', params=None):
'Simulates a bot that polls for task.'
if (not params):
params = self.do_handshake(bot)
return self.post_json('/swarming/api/v1/bot/poll', params) | Simulates a bot that polls for task. | appengine/swarming/test_env_handlers.py | bot_poll | Swift1313/luci-py | 0 | python | def bot_poll(self, bot='bot1', params=None):
if (not params):
params = self.do_handshake(bot)
return self.post_json('/swarming/api/v1/bot/poll', params) | def bot_poll(self, bot='bot1', params=None):
if (not params):
params = self.do_handshake(bot)
return self.post_json('/swarming/api/v1/bot/poll', params)<|docstring|>Simulates a bot that polls for task.<|endoftext|> |
8a1ea0af904cb90d2c3212758e12d10799e27e8eb52a245a70849779184255cf | @staticmethod
def create_props(**kwargs):
'Returns a serialized swarming_rpcs.TaskProperties.'
out = {u'cipd_input': {u'client_package': {u'package_name': u'infra/tools/cipd/${platform}', u'version': u'git_revision:deadbeef'}, u'packages': [{u'package_name': u'rm', u'path': u'bin', u'version': u'git_revision:deadbeef'}], u'server': u'https://pool.config.cipd.example.com'}, u'dimensions': [{u'key': u'os', u'value': u'Amiga'}, {u'key': u'pool', u'value': u'default'}], u'env': [], u'execution_timeout_secs': 3600, u'io_timeout_secs': 1200, u'outputs': [u'foo', u'path/to/foobar']}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | Returns a serialized swarming_rpcs.TaskProperties. | appengine/swarming/test_env_handlers.py | create_props | Swift1313/luci-py | 0 | python | @staticmethod
def create_props(**kwargs):
out = {u'cipd_input': {u'client_package': {u'package_name': u'infra/tools/cipd/${platform}', u'version': u'git_revision:deadbeef'}, u'packages': [{u'package_name': u'rm', u'path': u'bin', u'version': u'git_revision:deadbeef'}], u'server': u'https://pool.config.cipd.example.com'}, u'dimensions': [{u'key': u'os', u'value': u'Amiga'}, {u'key': u'pool', u'value': u'default'}], u'env': [], u'execution_timeout_secs': 3600, u'io_timeout_secs': 1200, u'outputs': [u'foo', u'path/to/foobar']}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | @staticmethod
def create_props(**kwargs):
out = {u'cipd_input': {u'client_package': {u'package_name': u'infra/tools/cipd/${platform}', u'version': u'git_revision:deadbeef'}, u'packages': [{u'package_name': u'rm', u'path': u'bin', u'version': u'git_revision:deadbeef'}], u'server': u'https://pool.config.cipd.example.com'}, u'dimensions': [{u'key': u'os', u'value': u'Amiga'}, {u'key': u'pool', u'value': u'default'}], u'env': [], u'execution_timeout_secs': 3600, u'io_timeout_secs': 1200, u'outputs': [u'foo', u'path/to/foobar']}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out<|docstring|>Returns a serialized swarming_rpcs.TaskProperties.<|endoftext|> |
845bcfe0ea0a835a8636154b7d0964373bb2e7b59bc0cab91541feaa21954193 | def create_new_request(self, **kwargs):
'Returns an initialized swarming_rpcs.TaskNewRequest.\n\n Useful to use a swarming_rpcs.TaskSlice.\n '
out = {'expiration_secs': ((24 * 60) * 60), 'name': 'job1', 'priority': 20, 'tags': [u'a:tag'], 'user': 'joe@localhost'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return swarming_rpcs.NewTaskRequest(**out) | Returns an initialized swarming_rpcs.TaskNewRequest.
Useful to use a swarming_rpcs.TaskSlice. | appengine/swarming/test_env_handlers.py | create_new_request | Swift1313/luci-py | 0 | python | def create_new_request(self, **kwargs):
'Returns an initialized swarming_rpcs.TaskNewRequest.\n\n Useful to use a swarming_rpcs.TaskSlice.\n '
out = {'expiration_secs': ((24 * 60) * 60), 'name': 'job1', 'priority': 20, 'tags': [u'a:tag'], 'user': 'joe@localhost'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return swarming_rpcs.NewTaskRequest(**out) | def create_new_request(self, **kwargs):
'Returns an initialized swarming_rpcs.TaskNewRequest.\n\n Useful to use a swarming_rpcs.TaskSlice.\n '
out = {'expiration_secs': ((24 * 60) * 60), 'name': 'job1', 'priority': 20, 'tags': [u'a:tag'], 'user': 'joe@localhost'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return swarming_rpcs.NewTaskRequest(**out)<|docstring|>Returns an initialized swarming_rpcs.TaskNewRequest.
Useful to use a swarming_rpcs.TaskSlice.<|endoftext|> |
3e8971f4cce12994ea539c676f605e5b1884943c11626ddf2d49db7c4da8ef6d | def client_create_task(self, **kwargs):
'Creates a minimal task request via the Cloud Endpoints API.'
request = self.create_new_request(**kwargs)
response = self.endpoint_call(handlers_endpoints.SwarmingTasksService, 'new', request)
return (response, response['task_id']) | Creates a minimal task request via the Cloud Endpoints API. | appengine/swarming/test_env_handlers.py | client_create_task | Swift1313/luci-py | 0 | python | def client_create_task(self, **kwargs):
request = self.create_new_request(**kwargs)
response = self.endpoint_call(handlers_endpoints.SwarmingTasksService, 'new', request)
return (response, response['task_id']) | def client_create_task(self, **kwargs):
request = self.create_new_request(**kwargs)
response = self.endpoint_call(handlers_endpoints.SwarmingTasksService, 'new', request)
return (response, response['task_id'])<|docstring|>Creates a minimal task request via the Cloud Endpoints API.<|endoftext|> |
b01c8beaf703735a18731178e97cae2df91cd56838e5444442a7bcb0eafb2e4b | def client_create_task_isolated(self, properties=None, **kwargs):
'Creates a TaskRequest using an isolated tree via the Cloud Endpoints API.\n '
properties = (properties or {}).copy()
properties['inputs_ref'] = {'isolated': '0123456789012345678901234567890123456789', 'isolatedserver': 'http://localhost:1', 'namespace': 'default-gzip'}
return self.client_create_task(properties=self.create_props(**properties), **kwargs) | Creates a TaskRequest using an isolated tree via the Cloud Endpoints API. | appengine/swarming/test_env_handlers.py | client_create_task_isolated | Swift1313/luci-py | 0 | python | def client_create_task_isolated(self, properties=None, **kwargs):
'\n '
properties = (properties or {}).copy()
properties['inputs_ref'] = {'isolated': '0123456789012345678901234567890123456789', 'isolatedserver': 'http://localhost:1', 'namespace': 'default-gzip'}
return self.client_create_task(properties=self.create_props(**properties), **kwargs) | def client_create_task_isolated(self, properties=None, **kwargs):
'\n '
properties = (properties or {}).copy()
properties['inputs_ref'] = {'isolated': '0123456789012345678901234567890123456789', 'isolatedserver': 'http://localhost:1', 'namespace': 'default-gzip'}
return self.client_create_task(properties=self.create_props(**properties), **kwargs)<|docstring|>Creates a TaskRequest using an isolated tree via the Cloud Endpoints API.<|endoftext|> |
12baeee666a55e4dc5ddb3f7446aab6c9766d6f1b76912e2e31f098a13f2d8d1 | def client_create_task_raw(self, properties=None, **kwargs):
'Creates a raw command TaskRequest via the Cloud Endpoints API.'
properties = (properties or {}).copy()
properties['command'] = ['python', 'run_test.py']
return self.client_create_task(properties=self.create_props(**properties), **kwargs) | Creates a raw command TaskRequest via the Cloud Endpoints API. | appengine/swarming/test_env_handlers.py | client_create_task_raw | Swift1313/luci-py | 0 | python | def client_create_task_raw(self, properties=None, **kwargs):
properties = (properties or {}).copy()
properties['command'] = ['python', 'run_test.py']
return self.client_create_task(properties=self.create_props(**properties), **kwargs) | def client_create_task_raw(self, properties=None, **kwargs):
properties = (properties or {}).copy()
properties['command'] = ['python', 'run_test.py']
return self.client_create_task(properties=self.create_props(**properties), **kwargs)<|docstring|>Creates a raw command TaskRequest via the Cloud Endpoints API.<|endoftext|> |
bee7dc3a39f8c517591b3c2137f87f66a10d9cd3f891ab582db8f35792414103 | @staticmethod
def gen_props(**kwargs):
'Returns a serialized swarming_rpcs.TaskProperties.\n\n To be used for expectations.\n '
out = {u'cipd_input': {u'client_package': {u'package_name': u'infra/tools/cipd/${platform}', u'version': u'git_revision:deadbeef'}, u'packages': [{u'package_name': u'rm', u'path': u'bin', u'version': u'git_revision:deadbeef'}], u'server': u'https://pool.config.cipd.example.com'}, u'dimensions': [{u'key': u'os', u'value': u'Amiga'}, {u'key': u'pool', u'value': u'default'}], u'execution_timeout_secs': u'3600', u'grace_period_secs': u'30', u'idempotent': False, u'inputs_ref': {'isolatedserver': 'https://pool.config.isolate.example.com', 'namespace': 'default-gzip'}, u'io_timeout_secs': u'1200', u'outputs': [u'foo', u'path/to/foobar']}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | Returns a serialized swarming_rpcs.TaskProperties.
To be used for expectations. | appengine/swarming/test_env_handlers.py | gen_props | Swift1313/luci-py | 0 | python | @staticmethod
def gen_props(**kwargs):
'Returns a serialized swarming_rpcs.TaskProperties.\n\n To be used for expectations.\n '
out = {u'cipd_input': {u'client_package': {u'package_name': u'infra/tools/cipd/${platform}', u'version': u'git_revision:deadbeef'}, u'packages': [{u'package_name': u'rm', u'path': u'bin', u'version': u'git_revision:deadbeef'}], u'server': u'https://pool.config.cipd.example.com'}, u'dimensions': [{u'key': u'os', u'value': u'Amiga'}, {u'key': u'pool', u'value': u'default'}], u'execution_timeout_secs': u'3600', u'grace_period_secs': u'30', u'idempotent': False, u'inputs_ref': {'isolatedserver': 'https://pool.config.isolate.example.com', 'namespace': 'default-gzip'}, u'io_timeout_secs': u'1200', u'outputs': [u'foo', u'path/to/foobar']}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | @staticmethod
def gen_props(**kwargs):
'Returns a serialized swarming_rpcs.TaskProperties.\n\n To be used for expectations.\n '
out = {u'cipd_input': {u'client_package': {u'package_name': u'infra/tools/cipd/${platform}', u'version': u'git_revision:deadbeef'}, u'packages': [{u'package_name': u'rm', u'path': u'bin', u'version': u'git_revision:deadbeef'}], u'server': u'https://pool.config.cipd.example.com'}, u'dimensions': [{u'key': u'os', u'value': u'Amiga'}, {u'key': u'pool', u'value': u'default'}], u'execution_timeout_secs': u'3600', u'grace_period_secs': u'30', u'idempotent': False, u'inputs_ref': {'isolatedserver': 'https://pool.config.isolate.example.com', 'namespace': 'default-gzip'}, u'io_timeout_secs': u'1200', u'outputs': [u'foo', u'path/to/foobar']}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out<|docstring|>Returns a serialized swarming_rpcs.TaskProperties.
To be used for expectations.<|endoftext|> |
98ab125383422ecb75692173c2c52b9152fc0a3a2853966a25dc584fdb43dfc4 | @staticmethod
def gen_request(**kwargs):
'Returns a serialized swarming_rpcs.TaskRequest.\n\n To be used for expectations.\n '
out = {u'authenticated': u'user:[email protected]', u'expiration_secs': u'86400', u'name': u'job1', u'priority': u'20', u'service_account': u'none', u'tags': [u'a:tag', u'os:Amiga', u'pool:default', u'priority:20', u'service_account:none', u'swarming.pool.template:none', u'swarming.pool.version:pools_cfg_rev', u'user:joe@localhost'], u'user': u'joe@localhost'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | Returns a serialized swarming_rpcs.TaskRequest.
To be used for expectations. | appengine/swarming/test_env_handlers.py | gen_request | Swift1313/luci-py | 0 | python | @staticmethod
def gen_request(**kwargs):
'Returns a serialized swarming_rpcs.TaskRequest.\n\n To be used for expectations.\n '
out = {u'authenticated': u'user:[email protected]', u'expiration_secs': u'86400', u'name': u'job1', u'priority': u'20', u'service_account': u'none', u'tags': [u'a:tag', u'os:Amiga', u'pool:default', u'priority:20', u'service_account:none', u'swarming.pool.template:none', u'swarming.pool.version:pools_cfg_rev', u'user:joe@localhost'], u'user': u'joe@localhost'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | @staticmethod
def gen_request(**kwargs):
'Returns a serialized swarming_rpcs.TaskRequest.\n\n To be used for expectations.\n '
out = {u'authenticated': u'user:[email protected]', u'expiration_secs': u'86400', u'name': u'job1', u'priority': u'20', u'service_account': u'none', u'tags': [u'a:tag', u'os:Amiga', u'pool:default', u'priority:20', u'service_account:none', u'swarming.pool.template:none', u'swarming.pool.version:pools_cfg_rev', u'user:joe@localhost'], u'user': u'joe@localhost'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out<|docstring|>Returns a serialized swarming_rpcs.TaskRequest.
To be used for expectations.<|endoftext|> |
6284d9569b412f90f781559dee3b8bff1b137f8f9080936da63e9b6542754572 | @staticmethod
def gen_perf_stats(**kwargs):
'Returns a serialized swarming_rpcs.PerformanceStats.\n\n To be used for expectations.\n '
out = {u'bot_overhead': 0.1, u'isolated_download': {u'duration': 1.0, u'initial_number_items': u'10', u'initial_size': u'100000', u'items_cold': [20], u'items_hot': [30, 40], u'num_items_cold': u'1', u'total_bytes_items_cold': u'20', u'num_items_hot': u'2', u'total_bytes_items_hot': u'70'}, u'isolated_upload': {u'duration': 2.0, u'items_cold': [1, 2, 40], u'items_hot': [1, 2, 3, 50], u'num_items_cold': u'3', u'total_bytes_items_cold': u'43', u'num_items_hot': u'4', u'total_bytes_items_hot': u'56'}}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | Returns a serialized swarming_rpcs.PerformanceStats.
To be used for expectations. | appengine/swarming/test_env_handlers.py | gen_perf_stats | Swift1313/luci-py | 0 | python | @staticmethod
def gen_perf_stats(**kwargs):
'Returns a serialized swarming_rpcs.PerformanceStats.\n\n To be used for expectations.\n '
out = {u'bot_overhead': 0.1, u'isolated_download': {u'duration': 1.0, u'initial_number_items': u'10', u'initial_size': u'100000', u'items_cold': [20], u'items_hot': [30, 40], u'num_items_cold': u'1', u'total_bytes_items_cold': u'20', u'num_items_hot': u'2', u'total_bytes_items_hot': u'70'}, u'isolated_upload': {u'duration': 2.0, u'items_cold': [1, 2, 40], u'items_hot': [1, 2, 3, 50], u'num_items_cold': u'3', u'total_bytes_items_cold': u'43', u'num_items_hot': u'4', u'total_bytes_items_hot': u'56'}}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | @staticmethod
def gen_perf_stats(**kwargs):
'Returns a serialized swarming_rpcs.PerformanceStats.\n\n To be used for expectations.\n '
out = {u'bot_overhead': 0.1, u'isolated_download': {u'duration': 1.0, u'initial_number_items': u'10', u'initial_size': u'100000', u'items_cold': [20], u'items_hot': [30, 40], u'num_items_cold': u'1', u'total_bytes_items_cold': u'20', u'num_items_hot': u'2', u'total_bytes_items_hot': u'70'}, u'isolated_upload': {u'duration': 2.0, u'items_cold': [1, 2, 40], u'items_hot': [1, 2, 3, 50], u'num_items_cold': u'3', u'total_bytes_items_cold': u'43', u'num_items_hot': u'4', u'total_bytes_items_hot': u'56'}}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out<|docstring|>Returns a serialized swarming_rpcs.PerformanceStats.
To be used for expectations.<|endoftext|> |
3bcce5cd48a3ded3e2bd178ab166badf3c23ce676e6adec791763ffffbacd351 | def gen_result_summary(self, **kwargs):
'Returns a serialized swarming_rpcs.TaskResult initialized from a\n TaskResultSummary.\n\n To be used for expectations.\n '
out = {u'bot_dimensions': [{u'key': u'id', u'value': [u'bot1']}, {u'key': u'os', u'value': [u'Amiga']}, {u'key': u'pool', u'value': [u'default']}], u'bot_id': u'bot1', u'bot_version': self.bot_version, u'current_task_slice': u'0', u'failure': False, u'internal_failure': False, u'name': u'job1', u'run_id': u'5cee488008811', u'server_versions': [u'v1a'], u'state': u'COMPLETED', u'tags': [u'a:tag', u'os:Amiga', u'pool:default', u'priority:20', u'service_account:none', u'swarming.pool.template:no_config', u'user:joe@localhost'], u'task_id': u'5cee488008810', u'try_number': u'0', u'user': u'joe@localhost'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | Returns a serialized swarming_rpcs.TaskResult initialized from a
TaskResultSummary.
To be used for expectations. | appengine/swarming/test_env_handlers.py | gen_result_summary | Swift1313/luci-py | 0 | python | def gen_result_summary(self, **kwargs):
'Returns a serialized swarming_rpcs.TaskResult initialized from a\n TaskResultSummary.\n\n To be used for expectations.\n '
out = {u'bot_dimensions': [{u'key': u'id', u'value': [u'bot1']}, {u'key': u'os', u'value': [u'Amiga']}, {u'key': u'pool', u'value': [u'default']}], u'bot_id': u'bot1', u'bot_version': self.bot_version, u'current_task_slice': u'0', u'failure': False, u'internal_failure': False, u'name': u'job1', u'run_id': u'5cee488008811', u'server_versions': [u'v1a'], u'state': u'COMPLETED', u'tags': [u'a:tag', u'os:Amiga', u'pool:default', u'priority:20', u'service_account:none', u'swarming.pool.template:no_config', u'user:joe@localhost'], u'task_id': u'5cee488008810', u'try_number': u'0', u'user': u'joe@localhost'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | def gen_result_summary(self, **kwargs):
'Returns a serialized swarming_rpcs.TaskResult initialized from a\n TaskResultSummary.\n\n To be used for expectations.\n '
out = {u'bot_dimensions': [{u'key': u'id', u'value': [u'bot1']}, {u'key': u'os', u'value': [u'Amiga']}, {u'key': u'pool', u'value': [u'default']}], u'bot_id': u'bot1', u'bot_version': self.bot_version, u'current_task_slice': u'0', u'failure': False, u'internal_failure': False, u'name': u'job1', u'run_id': u'5cee488008811', u'server_versions': [u'v1a'], u'state': u'COMPLETED', u'tags': [u'a:tag', u'os:Amiga', u'pool:default', u'priority:20', u'service_account:none', u'swarming.pool.template:no_config', u'user:joe@localhost'], u'task_id': u'5cee488008810', u'try_number': u'0', u'user': u'joe@localhost'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out<|docstring|>Returns a serialized swarming_rpcs.TaskResult initialized from a
TaskResultSummary.
To be used for expectations.<|endoftext|> |
1b1a1658855a475756b10a1c0f8ff0798e6fb021237584c3f130230d4a36eb7c | def gen_run_result(self, **kwargs):
'Returns a serialized swarming_rpcs.TaskResult initialized from a\n TaskRunResult.\n\n To be used for expectations.\n '
out = {u'bot_dimensions': [{u'key': u'id', u'value': [u'bot1']}, {u'key': u'os', u'value': [u'Amiga']}, {u'key': u'pool', u'value': [u'default']}], u'bot_id': u'bot1', u'bot_version': self.bot_version, u'costs_usd': [0.0], u'current_task_slice': u'0', u'failure': False, u'internal_failure': False, u'name': u'job1', u'run_id': u'5cee488008811', u'server_versions': [u'v1a'], u'state': u'RUNNING', u'task_id': u'5cee488008811', u'try_number': u'1'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | Returns a serialized swarming_rpcs.TaskResult initialized from a
TaskRunResult.
To be used for expectations. | appengine/swarming/test_env_handlers.py | gen_run_result | Swift1313/luci-py | 0 | python | def gen_run_result(self, **kwargs):
'Returns a serialized swarming_rpcs.TaskResult initialized from a\n TaskRunResult.\n\n To be used for expectations.\n '
out = {u'bot_dimensions': [{u'key': u'id', u'value': [u'bot1']}, {u'key': u'os', u'value': [u'Amiga']}, {u'key': u'pool', u'value': [u'default']}], u'bot_id': u'bot1', u'bot_version': self.bot_version, u'costs_usd': [0.0], u'current_task_slice': u'0', u'failure': False, u'internal_failure': False, u'name': u'job1', u'run_id': u'5cee488008811', u'server_versions': [u'v1a'], u'state': u'RUNNING', u'task_id': u'5cee488008811', u'try_number': u'1'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out | def gen_run_result(self, **kwargs):
'Returns a serialized swarming_rpcs.TaskResult initialized from a\n TaskRunResult.\n\n To be used for expectations.\n '
out = {u'bot_dimensions': [{u'key': u'id', u'value': [u'bot1']}, {u'key': u'os', u'value': [u'Amiga']}, {u'key': u'pool', u'value': [u'default']}], u'bot_id': u'bot1', u'bot_version': self.bot_version, u'costs_usd': [0.0], u'current_task_slice': u'0', u'failure': False, u'internal_failure': False, u'name': u'job1', u'run_id': u'5cee488008811', u'server_versions': [u'v1a'], u'state': u'RUNNING', u'task_id': u'5cee488008811', u'try_number': u'1'}
out.update(((unicode(k), v) for (k, v) in kwargs.iteritems()))
return out<|docstring|>Returns a serialized swarming_rpcs.TaskResult initialized from a
TaskRunResult.
To be used for expectations.<|endoftext|> |
971e3637f3cdb7d57bc039d3d17555b3f14ef52935d98d73f9edd8911db4254d | def image(self, windowName, imgNumpyArray, **kwargs):
' Takes numpy array and plots it into visdom '
opts = {'caption': windowName, 'title': windowName}
for key in kwargs:
opts[key] = kwargs[key]
self.viz.image(imgNumpyArray, opts) | Takes numpy array and plots it into visdom | visualizer.py | image | smerzbach/pysmtb | 1 | python | def image(self, windowName, imgNumpyArray, **kwargs):
' '
opts = {'caption': windowName, 'title': windowName}
for key in kwargs:
opts[key] = kwargs[key]
self.viz.image(imgNumpyArray, opts) | def image(self, windowName, imgNumpyArray, **kwargs):
' '
opts = {'caption': windowName, 'title': windowName}
for key in kwargs:
opts[key] = kwargs[key]
self.viz.image(imgNumpyArray, opts)<|docstring|>Takes numpy array and plots it into visdom<|endoftext|> |
fa43f3dbe40c8d86831b5193cbc46322f85ee5e3cc4f29ca521890a7a68db4bc | def __init__(self):
'\n Initialize your data structure here.\n '
self.data = set() | Initialize your data structure here. | 0380_Insert_Delete_GetRandom_O(1).py | __init__ | coldmanck/leetcode-python | 4 | python | def __init__(self):
'\n \n '
self.data = set() | def __init__(self):
'\n \n '
self.data = set()<|docstring|>Initialize your data structure here.<|endoftext|> |
9c179052aa5e07d4314d1413d6f94d028ff95cf5ae20e6809b021f324d6c317d | def insert(self, val: int) -> bool:
'\n Inserts a value to the set. Returns true if the set did not already contain the specified element.\n '
if (val in self.data):
return False
self.data.add(val)
return True | Inserts a value to the set. Returns true if the set did not already contain the specified element. | 0380_Insert_Delete_GetRandom_O(1).py | insert | coldmanck/leetcode-python | 4 | python | def insert(self, val: int) -> bool:
'\n \n '
if (val in self.data):
return False
self.data.add(val)
return True | def insert(self, val: int) -> bool:
'\n \n '
if (val in self.data):
return False
self.data.add(val)
return True<|docstring|>Inserts a value to the set. Returns true if the set did not already contain the specified element.<|endoftext|> |
ed498a29655162578a4f2320b2b7ddf73dc256fcad1305f77bf7d7eacee1535b | def remove(self, val: int) -> bool:
'\n Removes a value from the set. Returns true if the set contained the specified element.\n '
if (not (val in self.data)):
return False
self.data.remove(val)
return True | Removes a value from the set. Returns true if the set contained the specified element. | 0380_Insert_Delete_GetRandom_O(1).py | remove | coldmanck/leetcode-python | 4 | python | def remove(self, val: int) -> bool:
'\n \n '
if (not (val in self.data)):
return False
self.data.remove(val)
return True | def remove(self, val: int) -> bool:
'\n \n '
if (not (val in self.data)):
return False
self.data.remove(val)
return True<|docstring|>Removes a value from the set. Returns true if the set contained the specified element.<|endoftext|> |
9367b360de65b68eb9f03eb05d297bea02b0c750850cfb1c5747c47343b07520 | def getRandom(self) -> int:
'\n Get a random element from the set.\n '
if (len(self.data) > 0):
return random.sample(self.data, 1)[0] | Get a random element from the set. | 0380_Insert_Delete_GetRandom_O(1).py | getRandom | coldmanck/leetcode-python | 4 | python | def getRandom(self) -> int:
'\n \n '
if (len(self.data) > 0):
return random.sample(self.data, 1)[0] | def getRandom(self) -> int:
'\n \n '
if (len(self.data) > 0):
return random.sample(self.data, 1)[0]<|docstring|>Get a random element from the set.<|endoftext|> |
7eaca8ce2f16d2f3cbd831a88281e754ec6fb11ed8171a9d4c7b3ec99b212eaf | def interact_with_user(device, user_source, source_type, username, my_username, interaction_strategy: InteractionStrategy, on_action) -> (bool, bool):
'\n :return: (whether some photos was liked, whether @username was followed during the interaction,\n whether stories were watched, whether was commented)\n '
global liked_count, is_followed, is_scrolled_down, is_commented
liked_count = 0
is_followed = False
is_watched = False
is_scrolled_down = False
is_commented = False
if (username == my_username):
print("It's you, skip.")
return ((liked_count == interaction_strategy.likes_count), is_followed, is_watched, is_commented)
if interaction_strategy.do_story_watch:
is_watched = _watch_stories(device, user_source, source_type, username, interaction_strategy.stories_count, on_action)
def do_like_actions():
global is_scrolled_down
if (interaction_strategy.do_like or interaction_strategy.do_comment):
suggestions_container = device.find(resourceId=f'{device.app_id}:id/similar_accounts_container', className='android.widget.LinearLayout')
if suggestions_container.exists(quick=True):
print('Close suggestions to avoid bugs while scrolling')
arrow_button = device.find(resourceId=f'{device.app_id}:id/row_profile_header_button_chaining', className='android.widget.Button')
arrow_button.click(ignore_if_missing=True)
sleeper.random_sleep()
coordinator_layout = device.find(resourceId=f'{device.app_id}:id/coordinator_root_layout')
if coordinator_layout.exists():
print('Scroll down to see more photos.')
coordinator_layout.scroll(DeviceFacade.Direction.BOTTOM)
is_scrolled_down = True
number_of_rows_to_use = min((((interaction_strategy.likes_count * 2) // 3) + 1), 4)
photos_indices = list(range(0, (number_of_rows_to_use * 3)))
shuffle(photos_indices)
photos_indices = photos_indices[:interaction_strategy.likes_count]
photos_indices = sorted(photos_indices)
def on_like():
global liked_count
liked_count += 1
print(((COLOR_OKGREEN + '@{} - photo been liked.'.format(username)) + COLOR_ENDC))
on_action(LikeAction(source_name=user_source, source_type=source_type, user=username))
def on_comment(comment):
global is_commented
is_commented = True
print(((COLOR_OKGREEN + '@{} - photo been commented.'.format(username)) + COLOR_ENDC))
on_action(CommentAction(source_name=user_source, source_type=source_type, user=username, comment=comment))
for i in range(0, interaction_strategy.likes_count):
photo_index = photos_indices[i]
row = (photo_index // 3)
column = (photo_index - (row * 3))
sleeper.random_sleep()
print((((((('Open and like photo #' + str((i + 1))) + ' (') + str((row + 1))) + ' row, ') + str((column + 1))) + ' column)'))
if (not _open_photo_and_like_and_comment(device, row, column, interaction_strategy.do_like, interaction_strategy.do_comment, interaction_strategy.like_percentage, on_like, interaction_strategy.comment_percentage, interaction_strategy.comments_list, my_username, on_comment)):
print(((((COLOR_OKGREEN + 'Less than ') + str((number_of_rows_to_use * 3))) + ' photos.') + COLOR_ENDC))
break
def do_follow_action():
global is_followed
if interaction_strategy.do_follow:
is_followed = _follow(device, username, interaction_strategy.follow_percentage, is_scrolled_down)
if is_followed:
on_action(FollowAction(source_name=user_source, source_type=source_type, user=username))
if (interaction_strategy.do_follow and (interaction_strategy.do_like or interaction_strategy.do_comment)):
like_first_chance = randint(1, 100)
if (like_first_chance > 50):
print('Going to like-images first and then follow')
do_like_actions()
do_follow_action()
else:
print('Going to follow first and then like-images')
do_follow_action()
do_like_actions()
else:
do_like_actions()
do_follow_action()
return ((liked_count > 0), is_followed, is_watched, is_commented) | :return: (whether some photos was liked, whether @username was followed during the interaction,
whether stories were watched, whether was commented) | insomniac/actions_impl.py | interact_with_user | davebaird/Insomniac | 0 | python | def interact_with_user(device, user_source, source_type, username, my_username, interaction_strategy: InteractionStrategy, on_action) -> (bool, bool):
'\n :return: (whether some photos was liked, whether @username was followed during the interaction,\n whether stories were watched, whether was commented)\n '
global liked_count, is_followed, is_scrolled_down, is_commented
liked_count = 0
is_followed = False
is_watched = False
is_scrolled_down = False
is_commented = False
if (username == my_username):
print("It's you, skip.")
return ((liked_count == interaction_strategy.likes_count), is_followed, is_watched, is_commented)
if interaction_strategy.do_story_watch:
is_watched = _watch_stories(device, user_source, source_type, username, interaction_strategy.stories_count, on_action)
def do_like_actions():
global is_scrolled_down
if (interaction_strategy.do_like or interaction_strategy.do_comment):
suggestions_container = device.find(resourceId=f'{device.app_id}:id/similar_accounts_container', className='android.widget.LinearLayout')
if suggestions_container.exists(quick=True):
print('Close suggestions to avoid bugs while scrolling')
arrow_button = device.find(resourceId=f'{device.app_id}:id/row_profile_header_button_chaining', className='android.widget.Button')
arrow_button.click(ignore_if_missing=True)
sleeper.random_sleep()
coordinator_layout = device.find(resourceId=f'{device.app_id}:id/coordinator_root_layout')
if coordinator_layout.exists():
print('Scroll down to see more photos.')
coordinator_layout.scroll(DeviceFacade.Direction.BOTTOM)
is_scrolled_down = True
number_of_rows_to_use = min((((interaction_strategy.likes_count * 2) // 3) + 1), 4)
photos_indices = list(range(0, (number_of_rows_to_use * 3)))
shuffle(photos_indices)
photos_indices = photos_indices[:interaction_strategy.likes_count]
photos_indices = sorted(photos_indices)
def on_like():
global liked_count
liked_count += 1
print(((COLOR_OKGREEN + '@{} - photo been liked.'.format(username)) + COLOR_ENDC))
on_action(LikeAction(source_name=user_source, source_type=source_type, user=username))
def on_comment(comment):
global is_commented
is_commented = True
print(((COLOR_OKGREEN + '@{} - photo been commented.'.format(username)) + COLOR_ENDC))
on_action(CommentAction(source_name=user_source, source_type=source_type, user=username, comment=comment))
for i in range(0, interaction_strategy.likes_count):
photo_index = photos_indices[i]
row = (photo_index // 3)
column = (photo_index - (row * 3))
sleeper.random_sleep()
print((((((('Open and like photo #' + str((i + 1))) + ' (') + str((row + 1))) + ' row, ') + str((column + 1))) + ' column)'))
if (not _open_photo_and_like_and_comment(device, row, column, interaction_strategy.do_like, interaction_strategy.do_comment, interaction_strategy.like_percentage, on_like, interaction_strategy.comment_percentage, interaction_strategy.comments_list, my_username, on_comment)):
print(((((COLOR_OKGREEN + 'Less than ') + str((number_of_rows_to_use * 3))) + ' photos.') + COLOR_ENDC))
break
def do_follow_action():
global is_followed
if interaction_strategy.do_follow:
is_followed = _follow(device, username, interaction_strategy.follow_percentage, is_scrolled_down)
if is_followed:
on_action(FollowAction(source_name=user_source, source_type=source_type, user=username))
if (interaction_strategy.do_follow and (interaction_strategy.do_like or interaction_strategy.do_comment)):
like_first_chance = randint(1, 100)
if (like_first_chance > 50):
print('Going to like-images first and then follow')
do_like_actions()
do_follow_action()
else:
print('Going to follow first and then like-images')
do_follow_action()
do_like_actions()
else:
do_like_actions()
do_follow_action()
return ((liked_count > 0), is_followed, is_watched, is_commented) | def interact_with_user(device, user_source, source_type, username, my_username, interaction_strategy: InteractionStrategy, on_action) -> (bool, bool):
'\n :return: (whether some photos was liked, whether @username was followed during the interaction,\n whether stories were watched, whether was commented)\n '
global liked_count, is_followed, is_scrolled_down, is_commented
liked_count = 0
is_followed = False
is_watched = False
is_scrolled_down = False
is_commented = False
if (username == my_username):
print("It's you, skip.")
return ((liked_count == interaction_strategy.likes_count), is_followed, is_watched, is_commented)
if interaction_strategy.do_story_watch:
is_watched = _watch_stories(device, user_source, source_type, username, interaction_strategy.stories_count, on_action)
def do_like_actions():
global is_scrolled_down
if (interaction_strategy.do_like or interaction_strategy.do_comment):
suggestions_container = device.find(resourceId=f'{device.app_id}:id/similar_accounts_container', className='android.widget.LinearLayout')
if suggestions_container.exists(quick=True):
print('Close suggestions to avoid bugs while scrolling')
arrow_button = device.find(resourceId=f'{device.app_id}:id/row_profile_header_button_chaining', className='android.widget.Button')
arrow_button.click(ignore_if_missing=True)
sleeper.random_sleep()
coordinator_layout = device.find(resourceId=f'{device.app_id}:id/coordinator_root_layout')
if coordinator_layout.exists():
print('Scroll down to see more photos.')
coordinator_layout.scroll(DeviceFacade.Direction.BOTTOM)
is_scrolled_down = True
number_of_rows_to_use = min((((interaction_strategy.likes_count * 2) // 3) + 1), 4)
photos_indices = list(range(0, (number_of_rows_to_use * 3)))
shuffle(photos_indices)
photos_indices = photos_indices[:interaction_strategy.likes_count]
photos_indices = sorted(photos_indices)
def on_like():
global liked_count
liked_count += 1
print(((COLOR_OKGREEN + '@{} - photo been liked.'.format(username)) + COLOR_ENDC))
on_action(LikeAction(source_name=user_source, source_type=source_type, user=username))
def on_comment(comment):
global is_commented
is_commented = True
print(((COLOR_OKGREEN + '@{} - photo been commented.'.format(username)) + COLOR_ENDC))
on_action(CommentAction(source_name=user_source, source_type=source_type, user=username, comment=comment))
for i in range(0, interaction_strategy.likes_count):
photo_index = photos_indices[i]
row = (photo_index // 3)
column = (photo_index - (row * 3))
sleeper.random_sleep()
print((((((('Open and like photo #' + str((i + 1))) + ' (') + str((row + 1))) + ' row, ') + str((column + 1))) + ' column)'))
if (not _open_photo_and_like_and_comment(device, row, column, interaction_strategy.do_like, interaction_strategy.do_comment, interaction_strategy.like_percentage, on_like, interaction_strategy.comment_percentage, interaction_strategy.comments_list, my_username, on_comment)):
print(((((COLOR_OKGREEN + 'Less than ') + str((number_of_rows_to_use * 3))) + ' photos.') + COLOR_ENDC))
break
def do_follow_action():
global is_followed
if interaction_strategy.do_follow:
is_followed = _follow(device, username, interaction_strategy.follow_percentage, is_scrolled_down)
if is_followed:
on_action(FollowAction(source_name=user_source, source_type=source_type, user=username))
if (interaction_strategy.do_follow and (interaction_strategy.do_like or interaction_strategy.do_comment)):
like_first_chance = randint(1, 100)
if (like_first_chance > 50):
print('Going to like-images first and then follow')
do_like_actions()
do_follow_action()
else:
print('Going to follow first and then like-images')
do_follow_action()
do_like_actions()
else:
do_like_actions()
do_follow_action()
return ((liked_count > 0), is_followed, is_watched, is_commented)<|docstring|>:return: (whether some photos was liked, whether @username was followed during the interaction,
whether stories were watched, whether was commented)<|endoftext|> |
b71bb699fa03229b0fd3094a73fcb30053e52f1767b70e5f0b4302472915a622 | def do_unfollow(device, my_username, username, storage, check_if_is_follower, username_view, follow_status_button_view, on_action):
'\n :return: whether unfollow was successful\n '
need_to_go_back_to_list = True
unfollow_from_list_chance = randint(1, 100)
if ((follow_status_button_view is not None) and (not check_if_is_follower) and (unfollow_from_list_chance > 50)):
need_to_go_back_to_list = False
print('Unfollowing a profile directly from the following list.')
follow_status_button_view.click()
else:
print('Unfollowing a profile from his profile page.')
username_view.click()
on_action(GetProfileAction(user=username))
sleeper.random_sleep()
if_profile_empty = softban_indicator.detect_empty_profile(device)
if if_profile_empty:
print('Back to the followings list.')
device.back()
return False
if (check_if_is_follower and _check_is_follower(device, username, my_username)):
print((('Skip @' + username) + '. This user is following you.'))
storage.update_follow_status(username, True, True)
print('Back to the followings list.')
device.back()
return False
unfollow_button = device.find(classNameMatches=TEXTVIEW_OR_BUTTON_REGEX, clickable=True, text='Following')
if (not unfollow_button.exists()):
print(((COLOR_FAIL + 'Cannot find Following button. Maybe not English language is set?') + COLOR_ENDC))
save_crash(device)
switch_to_english(device)
raise LanguageChangedException()
print(f'Unfollowing @{username}...')
unfollow_button.click()
sleeper.random_sleep()
confirm_unfollow_button = device.find(resourceId=f'{device.app_id}:id/follow_sheet_unfollow_row', className='android.widget.TextView')
if (not confirm_unfollow_button.exists()):
print(((COLOR_FAIL + 'Cannot confirm unfollow.') + COLOR_ENDC))
save_crash(device)
device.back()
return False
confirm_unfollow_button.click()
sleeper.random_sleep()
_close_confirm_dialog_if_shown(device)
softban_indicator.detect_action_blocked_dialog(device)
if need_to_go_back_to_list:
print('Back to the followings list.')
device.back()
return True | :return: whether unfollow was successful | insomniac/actions_impl.py | do_unfollow | davebaird/Insomniac | 0 | python | def do_unfollow(device, my_username, username, storage, check_if_is_follower, username_view, follow_status_button_view, on_action):
'\n \n '
need_to_go_back_to_list = True
unfollow_from_list_chance = randint(1, 100)
if ((follow_status_button_view is not None) and (not check_if_is_follower) and (unfollow_from_list_chance > 50)):
need_to_go_back_to_list = False
print('Unfollowing a profile directly from the following list.')
follow_status_button_view.click()
else:
print('Unfollowing a profile from his profile page.')
username_view.click()
on_action(GetProfileAction(user=username))
sleeper.random_sleep()
if_profile_empty = softban_indicator.detect_empty_profile(device)
if if_profile_empty:
print('Back to the followings list.')
device.back()
return False
if (check_if_is_follower and _check_is_follower(device, username, my_username)):
print((('Skip @' + username) + '. This user is following you.'))
storage.update_follow_status(username, True, True)
print('Back to the followings list.')
device.back()
return False
unfollow_button = device.find(classNameMatches=TEXTVIEW_OR_BUTTON_REGEX, clickable=True, text='Following')
if (not unfollow_button.exists()):
print(((COLOR_FAIL + 'Cannot find Following button. Maybe not English language is set?') + COLOR_ENDC))
save_crash(device)
switch_to_english(device)
raise LanguageChangedException()
print(f'Unfollowing @{username}...')
unfollow_button.click()
sleeper.random_sleep()
confirm_unfollow_button = device.find(resourceId=f'{device.app_id}:id/follow_sheet_unfollow_row', className='android.widget.TextView')
if (not confirm_unfollow_button.exists()):
print(((COLOR_FAIL + 'Cannot confirm unfollow.') + COLOR_ENDC))
save_crash(device)
device.back()
return False
confirm_unfollow_button.click()
sleeper.random_sleep()
_close_confirm_dialog_if_shown(device)
softban_indicator.detect_action_blocked_dialog(device)
if need_to_go_back_to_list:
print('Back to the followings list.')
device.back()
return True | def do_unfollow(device, my_username, username, storage, check_if_is_follower, username_view, follow_status_button_view, on_action):
'\n \n '
need_to_go_back_to_list = True
unfollow_from_list_chance = randint(1, 100)
if ((follow_status_button_view is not None) and (not check_if_is_follower) and (unfollow_from_list_chance > 50)):
need_to_go_back_to_list = False
print('Unfollowing a profile directly from the following list.')
follow_status_button_view.click()
else:
print('Unfollowing a profile from his profile page.')
username_view.click()
on_action(GetProfileAction(user=username))
sleeper.random_sleep()
if_profile_empty = softban_indicator.detect_empty_profile(device)
if if_profile_empty:
print('Back to the followings list.')
device.back()
return False
if (check_if_is_follower and _check_is_follower(device, username, my_username)):
print((('Skip @' + username) + '. This user is following you.'))
storage.update_follow_status(username, True, True)
print('Back to the followings list.')
device.back()
return False
unfollow_button = device.find(classNameMatches=TEXTVIEW_OR_BUTTON_REGEX, clickable=True, text='Following')
if (not unfollow_button.exists()):
print(((COLOR_FAIL + 'Cannot find Following button. Maybe not English language is set?') + COLOR_ENDC))
save_crash(device)
switch_to_english(device)
raise LanguageChangedException()
print(f'Unfollowing @{username}...')
unfollow_button.click()
sleeper.random_sleep()
confirm_unfollow_button = device.find(resourceId=f'{device.app_id}:id/follow_sheet_unfollow_row', className='android.widget.TextView')
if (not confirm_unfollow_button.exists()):
print(((COLOR_FAIL + 'Cannot confirm unfollow.') + COLOR_ENDC))
save_crash(device)
device.back()
return False
confirm_unfollow_button.click()
sleeper.random_sleep()
_close_confirm_dialog_if_shown(device)
softban_indicator.detect_action_blocked_dialog(device)
if need_to_go_back_to_list:
print('Back to the followings list.')
device.back()
return True<|docstring|>:return: whether unfollow was successful<|endoftext|> |
b4ae759cb3b3b202b96f100bbc290d9e86ecb14fc6a961dd87eb3106f70bdd5e | def ineichen(apparent_zenith, airmass_absolute, linke_turbidity, altitude=0, dni_extra=1364.0, perez_enhancement=False):
'\n Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model.\n\n Implements the Ineichen and Perez clear sky model for global\n horizontal irradiance (GHI), direct normal irradiance (DNI), and\n calculates the clear-sky diffuse horizontal (DHI) component as the\n difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A\n report on clear sky models found the Ineichen/Perez model to have\n excellent performance with a minimal input data set [3].\n\n Default values for monthly Linke turbidity provided by SoDa [4, 5].\n\n Parameters\n -----------\n apparent_zenith : numeric\n Refraction corrected solar zenith angle in degrees.\n\n airmass_absolute : numeric\n Pressure corrected airmass.\n\n linke_turbidity : numeric\n Linke Turbidity.\n\n altitude : numeric, default 0\n Altitude above sea level in meters.\n\n dni_extra : numeric, default 1364\n Extraterrestrial irradiance. The units of ``dni_extra``\n determine the units of the output.\n\n perez_enhancement : bool, default False\n Controls if the Perez enhancement factor should be applied.\n Setting to True may produce spurious results for times when\n the Sun is near the horizon and the airmass is high.\n See https://github.com/pvlib/pvlib-python/issues/435\n\n Returns\n -------\n clearsky : DataFrame (if Series input) or OrderedDict of arrays\n DataFrame/OrderedDict contains the columns/keys\n ``\'dhi\', \'dni\', \'ghi\'``.\n\n See also\n --------\n lookup_linke_turbidity\n pvlib.location.Location.get_clearsky\n\n References\n ----------\n .. [1] P. Ineichen and R. Perez, "A New airmass independent formulation for\n the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157,\n 2002.\n\n .. [2] R. Perez et. al., "A New Operational Model for Satellite-Derived\n Irradiances: Description and Validation", Solar Energy, vol 73, pp.\n 307-317, 2002.\n\n .. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance\n Clear Sky Models: Implementation and Analysis", Sandia National\n Laboratories, SAND2012-2389, 2012.\n\n .. [4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained\n July 17, 2012).\n\n .. [5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.\n ISES Solar World Congress, June 2003. Goteborg, Sweden.\n '
cos_zenith = np.maximum(tools.cosd(apparent_zenith), 0)
tl = linke_turbidity
fh1 = np.exp(((- altitude) / 8000.0))
fh2 = np.exp(((- altitude) / 1250.0))
cg1 = ((5.09e-05 * altitude) + 0.868)
cg2 = ((3.92e-05 * altitude) + 0.0387)
ghi = np.exp((((- cg2) * airmass_absolute) * (fh1 + (fh2 * (tl - 1)))))
if perez_enhancement:
ghi *= np.exp((0.01 * (airmass_absolute ** 1.8)))
ghi = (((((cg1 * dni_extra) * cos_zenith) * tl) / tl) * np.fmax(ghi, 0))
b = (0.664 + (0.163 / fh1))
bnci = (b * np.exp((((- 0.09) * airmass_absolute) * (tl - 1))))
bnci = (dni_extra * np.fmax(bnci, 0))
bnci_2 = ((1 - ((0.1 - (0.2 * np.exp((- tl)))) / (0.1 + (0.882 / fh1)))) / cos_zenith)
bnci_2 = (ghi * np.fmin(np.fmax(bnci_2, 0), 1e+20))
dni = np.minimum(bnci, bnci_2)
dhi = (ghi - (dni * cos_zenith))
irrads = OrderedDict()
irrads['ghi'] = ghi
irrads['dni'] = dni
irrads['dhi'] = dhi
if isinstance(dni, pd.Series):
irrads = pd.DataFrame.from_dict(irrads)
return irrads | Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model.
Implements the Ineichen and Perez clear sky model for global
horizontal irradiance (GHI), direct normal irradiance (DNI), and
calculates the clear-sky diffuse horizontal (DHI) component as the
difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A
report on clear sky models found the Ineichen/Perez model to have
excellent performance with a minimal input data set [3].
Default values for monthly Linke turbidity provided by SoDa [4, 5].
Parameters
-----------
apparent_zenith : numeric
Refraction corrected solar zenith angle in degrees.
airmass_absolute : numeric
Pressure corrected airmass.
linke_turbidity : numeric
Linke Turbidity.
altitude : numeric, default 0
Altitude above sea level in meters.
dni_extra : numeric, default 1364
Extraterrestrial irradiance. The units of ``dni_extra``
determine the units of the output.
perez_enhancement : bool, default False
Controls if the Perez enhancement factor should be applied.
Setting to True may produce spurious results for times when
the Sun is near the horizon and the airmass is high.
See https://github.com/pvlib/pvlib-python/issues/435
Returns
-------
clearsky : DataFrame (if Series input) or OrderedDict of arrays
DataFrame/OrderedDict contains the columns/keys
``'dhi', 'dni', 'ghi'``.
See also
--------
lookup_linke_turbidity
pvlib.location.Location.get_clearsky
References
----------
.. [1] P. Ineichen and R. Perez, "A New airmass independent formulation for
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157,
2002.
.. [2] R. Perez et. al., "A New Operational Model for Satellite-Derived
Irradiances: Description and Validation", Solar Energy, vol 73, pp.
307-317, 2002.
.. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance
Clear Sky Models: Implementation and Analysis", Sandia National
Laboratories, SAND2012-2389, 2012.
.. [4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained
July 17, 2012).
.. [5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.
ISES Solar World Congress, June 2003. Goteborg, Sweden. | pvlib/clearsky.py | ineichen | Antoine-0/pvlib-python | 695 | python | def ineichen(apparent_zenith, airmass_absolute, linke_turbidity, altitude=0, dni_extra=1364.0, perez_enhancement=False):
'\n Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model.\n\n Implements the Ineichen and Perez clear sky model for global\n horizontal irradiance (GHI), direct normal irradiance (DNI), and\n calculates the clear-sky diffuse horizontal (DHI) component as the\n difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A\n report on clear sky models found the Ineichen/Perez model to have\n excellent performance with a minimal input data set [3].\n\n Default values for monthly Linke turbidity provided by SoDa [4, 5].\n\n Parameters\n -----------\n apparent_zenith : numeric\n Refraction corrected solar zenith angle in degrees.\n\n airmass_absolute : numeric\n Pressure corrected airmass.\n\n linke_turbidity : numeric\n Linke Turbidity.\n\n altitude : numeric, default 0\n Altitude above sea level in meters.\n\n dni_extra : numeric, default 1364\n Extraterrestrial irradiance. The units of ``dni_extra``\n determine the units of the output.\n\n perez_enhancement : bool, default False\n Controls if the Perez enhancement factor should be applied.\n Setting to True may produce spurious results for times when\n the Sun is near the horizon and the airmass is high.\n See https://github.com/pvlib/pvlib-python/issues/435\n\n Returns\n -------\n clearsky : DataFrame (if Series input) or OrderedDict of arrays\n DataFrame/OrderedDict contains the columns/keys\n ``\'dhi\', \'dni\', \'ghi\'``.\n\n See also\n --------\n lookup_linke_turbidity\n pvlib.location.Location.get_clearsky\n\n References\n ----------\n .. [1] P. Ineichen and R. Perez, "A New airmass independent formulation for\n the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157,\n 2002.\n\n .. [2] R. Perez et. al., "A New Operational Model for Satellite-Derived\n Irradiances: Description and Validation", Solar Energy, vol 73, pp.\n 307-317, 2002.\n\n .. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance\n Clear Sky Models: Implementation and Analysis", Sandia National\n Laboratories, SAND2012-2389, 2012.\n\n .. [4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained\n July 17, 2012).\n\n .. [5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.\n ISES Solar World Congress, June 2003. Goteborg, Sweden.\n '
cos_zenith = np.maximum(tools.cosd(apparent_zenith), 0)
tl = linke_turbidity
fh1 = np.exp(((- altitude) / 8000.0))
fh2 = np.exp(((- altitude) / 1250.0))
cg1 = ((5.09e-05 * altitude) + 0.868)
cg2 = ((3.92e-05 * altitude) + 0.0387)
ghi = np.exp((((- cg2) * airmass_absolute) * (fh1 + (fh2 * (tl - 1)))))
if perez_enhancement:
ghi *= np.exp((0.01 * (airmass_absolute ** 1.8)))
ghi = (((((cg1 * dni_extra) * cos_zenith) * tl) / tl) * np.fmax(ghi, 0))
b = (0.664 + (0.163 / fh1))
bnci = (b * np.exp((((- 0.09) * airmass_absolute) * (tl - 1))))
bnci = (dni_extra * np.fmax(bnci, 0))
bnci_2 = ((1 - ((0.1 - (0.2 * np.exp((- tl)))) / (0.1 + (0.882 / fh1)))) / cos_zenith)
bnci_2 = (ghi * np.fmin(np.fmax(bnci_2, 0), 1e+20))
dni = np.minimum(bnci, bnci_2)
dhi = (ghi - (dni * cos_zenith))
irrads = OrderedDict()
irrads['ghi'] = ghi
irrads['dni'] = dni
irrads['dhi'] = dhi
if isinstance(dni, pd.Series):
irrads = pd.DataFrame.from_dict(irrads)
return irrads | def ineichen(apparent_zenith, airmass_absolute, linke_turbidity, altitude=0, dni_extra=1364.0, perez_enhancement=False):
'\n Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model.\n\n Implements the Ineichen and Perez clear sky model for global\n horizontal irradiance (GHI), direct normal irradiance (DNI), and\n calculates the clear-sky diffuse horizontal (DHI) component as the\n difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A\n report on clear sky models found the Ineichen/Perez model to have\n excellent performance with a minimal input data set [3].\n\n Default values for monthly Linke turbidity provided by SoDa [4, 5].\n\n Parameters\n -----------\n apparent_zenith : numeric\n Refraction corrected solar zenith angle in degrees.\n\n airmass_absolute : numeric\n Pressure corrected airmass.\n\n linke_turbidity : numeric\n Linke Turbidity.\n\n altitude : numeric, default 0\n Altitude above sea level in meters.\n\n dni_extra : numeric, default 1364\n Extraterrestrial irradiance. The units of ``dni_extra``\n determine the units of the output.\n\n perez_enhancement : bool, default False\n Controls if the Perez enhancement factor should be applied.\n Setting to True may produce spurious results for times when\n the Sun is near the horizon and the airmass is high.\n See https://github.com/pvlib/pvlib-python/issues/435\n\n Returns\n -------\n clearsky : DataFrame (if Series input) or OrderedDict of arrays\n DataFrame/OrderedDict contains the columns/keys\n ``\'dhi\', \'dni\', \'ghi\'``.\n\n See also\n --------\n lookup_linke_turbidity\n pvlib.location.Location.get_clearsky\n\n References\n ----------\n .. [1] P. Ineichen and R. Perez, "A New airmass independent formulation for\n the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157,\n 2002.\n\n .. [2] R. Perez et. al., "A New Operational Model for Satellite-Derived\n Irradiances: Description and Validation", Solar Energy, vol 73, pp.\n 307-317, 2002.\n\n .. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance\n Clear Sky Models: Implementation and Analysis", Sandia National\n Laboratories, SAND2012-2389, 2012.\n\n .. [4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained\n July 17, 2012).\n\n .. [5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.\n ISES Solar World Congress, June 2003. Goteborg, Sweden.\n '
cos_zenith = np.maximum(tools.cosd(apparent_zenith), 0)
tl = linke_turbidity
fh1 = np.exp(((- altitude) / 8000.0))
fh2 = np.exp(((- altitude) / 1250.0))
cg1 = ((5.09e-05 * altitude) + 0.868)
cg2 = ((3.92e-05 * altitude) + 0.0387)
ghi = np.exp((((- cg2) * airmass_absolute) * (fh1 + (fh2 * (tl - 1)))))
if perez_enhancement:
ghi *= np.exp((0.01 * (airmass_absolute ** 1.8)))
ghi = (((((cg1 * dni_extra) * cos_zenith) * tl) / tl) * np.fmax(ghi, 0))
b = (0.664 + (0.163 / fh1))
bnci = (b * np.exp((((- 0.09) * airmass_absolute) * (tl - 1))))
bnci = (dni_extra * np.fmax(bnci, 0))
bnci_2 = ((1 - ((0.1 - (0.2 * np.exp((- tl)))) / (0.1 + (0.882 / fh1)))) / cos_zenith)
bnci_2 = (ghi * np.fmin(np.fmax(bnci_2, 0), 1e+20))
dni = np.minimum(bnci, bnci_2)
dhi = (ghi - (dni * cos_zenith))
irrads = OrderedDict()
irrads['ghi'] = ghi
irrads['dni'] = dni
irrads['dhi'] = dhi
if isinstance(dni, pd.Series):
irrads = pd.DataFrame.from_dict(irrads)
return irrads<|docstring|>Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model.
Implements the Ineichen and Perez clear sky model for global
horizontal irradiance (GHI), direct normal irradiance (DNI), and
calculates the clear-sky diffuse horizontal (DHI) component as the
difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A
report on clear sky models found the Ineichen/Perez model to have
excellent performance with a minimal input data set [3].
Default values for monthly Linke turbidity provided by SoDa [4, 5].
Parameters
-----------
apparent_zenith : numeric
Refraction corrected solar zenith angle in degrees.
airmass_absolute : numeric
Pressure corrected airmass.
linke_turbidity : numeric
Linke Turbidity.
altitude : numeric, default 0
Altitude above sea level in meters.
dni_extra : numeric, default 1364
Extraterrestrial irradiance. The units of ``dni_extra``
determine the units of the output.
perez_enhancement : bool, default False
Controls if the Perez enhancement factor should be applied.
Setting to True may produce spurious results for times when
the Sun is near the horizon and the airmass is high.
See https://github.com/pvlib/pvlib-python/issues/435
Returns
-------
clearsky : DataFrame (if Series input) or OrderedDict of arrays
DataFrame/OrderedDict contains the columns/keys
``'dhi', 'dni', 'ghi'``.
See also
--------
lookup_linke_turbidity
pvlib.location.Location.get_clearsky
References
----------
.. [1] P. Ineichen and R. Perez, "A New airmass independent formulation for
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157,
2002.
.. [2] R. Perez et. al., "A New Operational Model for Satellite-Derived
Irradiances: Description and Validation", Solar Energy, vol 73, pp.
307-317, 2002.
.. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance
Clear Sky Models: Implementation and Analysis", Sandia National
Laboratories, SAND2012-2389, 2012.
.. [4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained
July 17, 2012).
.. [5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.
ISES Solar World Congress, June 2003. Goteborg, Sweden.<|endoftext|> |
f47894dcc8a3fcdf34fa5a46e698c401b1f1ad0bcd86e532f3e8360c6a07ff06 | def lookup_linke_turbidity(time, latitude, longitude, filepath=None, interp_turbidity=True):
'\n Look up the Linke Turibidity from the ``LinkeTurbidities.h5``\n data file supplied with pvlib.\n\n Parameters\n ----------\n time : pandas.DatetimeIndex\n\n latitude : float or int\n\n longitude : float or int\n\n filepath : None or string, default None\n The path to the ``.h5`` file.\n\n interp_turbidity : bool, default True\n If ``True``, interpolates the monthly Linke turbidity values\n found in ``LinkeTurbidities.h5`` to daily values.\n\n Returns\n -------\n turbidity : Series\n '
if (filepath is None):
pvlib_path = os.path.dirname(os.path.abspath(__file__))
filepath = os.path.join(pvlib_path, 'data', 'LinkeTurbidities.h5')
latitude_index = _degrees_to_index(latitude, coordinate='latitude')
longitude_index = _degrees_to_index(longitude, coordinate='longitude')
with h5py.File(filepath, 'r') as lt_h5_file:
lts = lt_h5_file['LinkeTurbidity'][(latitude_index, longitude_index)]
if interp_turbidity:
linke_turbidity = _interpolate_turbidity(lts, time)
else:
months = (time.month - 1)
linke_turbidity = pd.Series(lts[months], index=time)
linke_turbidity /= 20.0
return linke_turbidity | Look up the Linke Turibidity from the ``LinkeTurbidities.h5``
data file supplied with pvlib.
Parameters
----------
time : pandas.DatetimeIndex
latitude : float or int
longitude : float or int
filepath : None or string, default None
The path to the ``.h5`` file.
interp_turbidity : bool, default True
If ``True``, interpolates the monthly Linke turbidity values
found in ``LinkeTurbidities.h5`` to daily values.
Returns
-------
turbidity : Series | pvlib/clearsky.py | lookup_linke_turbidity | Antoine-0/pvlib-python | 695 | python | def lookup_linke_turbidity(time, latitude, longitude, filepath=None, interp_turbidity=True):
'\n Look up the Linke Turibidity from the ``LinkeTurbidities.h5``\n data file supplied with pvlib.\n\n Parameters\n ----------\n time : pandas.DatetimeIndex\n\n latitude : float or int\n\n longitude : float or int\n\n filepath : None or string, default None\n The path to the ``.h5`` file.\n\n interp_turbidity : bool, default True\n If ``True``, interpolates the monthly Linke turbidity values\n found in ``LinkeTurbidities.h5`` to daily values.\n\n Returns\n -------\n turbidity : Series\n '
if (filepath is None):
pvlib_path = os.path.dirname(os.path.abspath(__file__))
filepath = os.path.join(pvlib_path, 'data', 'LinkeTurbidities.h5')
latitude_index = _degrees_to_index(latitude, coordinate='latitude')
longitude_index = _degrees_to_index(longitude, coordinate='longitude')
with h5py.File(filepath, 'r') as lt_h5_file:
lts = lt_h5_file['LinkeTurbidity'][(latitude_index, longitude_index)]
if interp_turbidity:
linke_turbidity = _interpolate_turbidity(lts, time)
else:
months = (time.month - 1)
linke_turbidity = pd.Series(lts[months], index=time)
linke_turbidity /= 20.0
return linke_turbidity | def lookup_linke_turbidity(time, latitude, longitude, filepath=None, interp_turbidity=True):
'\n Look up the Linke Turibidity from the ``LinkeTurbidities.h5``\n data file supplied with pvlib.\n\n Parameters\n ----------\n time : pandas.DatetimeIndex\n\n latitude : float or int\n\n longitude : float or int\n\n filepath : None or string, default None\n The path to the ``.h5`` file.\n\n interp_turbidity : bool, default True\n If ``True``, interpolates the monthly Linke turbidity values\n found in ``LinkeTurbidities.h5`` to daily values.\n\n Returns\n -------\n turbidity : Series\n '
if (filepath is None):
pvlib_path = os.path.dirname(os.path.abspath(__file__))
filepath = os.path.join(pvlib_path, 'data', 'LinkeTurbidities.h5')
latitude_index = _degrees_to_index(latitude, coordinate='latitude')
longitude_index = _degrees_to_index(longitude, coordinate='longitude')
with h5py.File(filepath, 'r') as lt_h5_file:
lts = lt_h5_file['LinkeTurbidity'][(latitude_index, longitude_index)]
if interp_turbidity:
linke_turbidity = _interpolate_turbidity(lts, time)
else:
months = (time.month - 1)
linke_turbidity = pd.Series(lts[months], index=time)
linke_turbidity /= 20.0
return linke_turbidity<|docstring|>Look up the Linke Turibidity from the ``LinkeTurbidities.h5``
data file supplied with pvlib.
Parameters
----------
time : pandas.DatetimeIndex
latitude : float or int
longitude : float or int
filepath : None or string, default None
The path to the ``.h5`` file.
interp_turbidity : bool, default True
If ``True``, interpolates the monthly Linke turbidity values
found in ``LinkeTurbidities.h5`` to daily values.
Returns
-------
turbidity : Series<|endoftext|> |
ff346fb4c9c3a9691629e4c2ed0d78b763fc0582973b268c0c1fe1f46979a939 | def _is_leap_year(year):
'Determine if a year is leap year.\n\n Parameters\n ----------\n year : numeric\n\n Returns\n -------\n isleap : array of bools\n '
isleap = ((np.mod(year, 4) == 0) & ((np.mod(year, 100) != 0) | (np.mod(year, 400) == 0)))
return isleap | Determine if a year is leap year.
Parameters
----------
year : numeric
Returns
-------
isleap : array of bools | pvlib/clearsky.py | _is_leap_year | Antoine-0/pvlib-python | 695 | python | def _is_leap_year(year):
'Determine if a year is leap year.\n\n Parameters\n ----------\n year : numeric\n\n Returns\n -------\n isleap : array of bools\n '
isleap = ((np.mod(year, 4) == 0) & ((np.mod(year, 100) != 0) | (np.mod(year, 400) == 0)))
return isleap | def _is_leap_year(year):
'Determine if a year is leap year.\n\n Parameters\n ----------\n year : numeric\n\n Returns\n -------\n isleap : array of bools\n '
isleap = ((np.mod(year, 4) == 0) & ((np.mod(year, 100) != 0) | (np.mod(year, 400) == 0)))
return isleap<|docstring|>Determine if a year is leap year.
Parameters
----------
year : numeric
Returns
-------
isleap : array of bools<|endoftext|> |
705597f2634249965d1f729570e0e3b192efb36a0871f7a77aed41927136886d | def _interpolate_turbidity(lts, time):
'\n Interpolated monthly Linke turbidity onto daily values.\n\n Parameters\n ----------\n lts : np.array\n Monthly Linke turbidity values.\n time : pd.DatetimeIndex\n Times to be interpolated onto.\n\n Returns\n -------\n linke_turbidity : pd.Series\n The interpolated turbidity.\n '
lts_concat = np.concatenate([[lts[(- 1)]], lts, [lts[0]]])
try:
isleap = time.is_leap_year
except AttributeError:
year = time.year
isleap = _is_leap_year(year)
dayofyear = time.dayofyear
days_leap = _calendar_month_middles(2016)
days_no_leap = _calendar_month_middles(2015)
lt_leap = np.interp(dayofyear, days_leap, lts_concat)
lt_no_leap = np.interp(dayofyear, days_no_leap, lts_concat)
linke_turbidity = np.where(isleap, lt_leap, lt_no_leap)
linke_turbidity = pd.Series(linke_turbidity, index=time)
return linke_turbidity | Interpolated monthly Linke turbidity onto daily values.
Parameters
----------
lts : np.array
Monthly Linke turbidity values.
time : pd.DatetimeIndex
Times to be interpolated onto.
Returns
-------
linke_turbidity : pd.Series
The interpolated turbidity. | pvlib/clearsky.py | _interpolate_turbidity | Antoine-0/pvlib-python | 695 | python | def _interpolate_turbidity(lts, time):
'\n Interpolated monthly Linke turbidity onto daily values.\n\n Parameters\n ----------\n lts : np.array\n Monthly Linke turbidity values.\n time : pd.DatetimeIndex\n Times to be interpolated onto.\n\n Returns\n -------\n linke_turbidity : pd.Series\n The interpolated turbidity.\n '
lts_concat = np.concatenate([[lts[(- 1)]], lts, [lts[0]]])
try:
isleap = time.is_leap_year
except AttributeError:
year = time.year
isleap = _is_leap_year(year)
dayofyear = time.dayofyear
days_leap = _calendar_month_middles(2016)
days_no_leap = _calendar_month_middles(2015)
lt_leap = np.interp(dayofyear, days_leap, lts_concat)
lt_no_leap = np.interp(dayofyear, days_no_leap, lts_concat)
linke_turbidity = np.where(isleap, lt_leap, lt_no_leap)
linke_turbidity = pd.Series(linke_turbidity, index=time)
return linke_turbidity | def _interpolate_turbidity(lts, time):
'\n Interpolated monthly Linke turbidity onto daily values.\n\n Parameters\n ----------\n lts : np.array\n Monthly Linke turbidity values.\n time : pd.DatetimeIndex\n Times to be interpolated onto.\n\n Returns\n -------\n linke_turbidity : pd.Series\n The interpolated turbidity.\n '
lts_concat = np.concatenate([[lts[(- 1)]], lts, [lts[0]]])
try:
isleap = time.is_leap_year
except AttributeError:
year = time.year
isleap = _is_leap_year(year)
dayofyear = time.dayofyear
days_leap = _calendar_month_middles(2016)
days_no_leap = _calendar_month_middles(2015)
lt_leap = np.interp(dayofyear, days_leap, lts_concat)
lt_no_leap = np.interp(dayofyear, days_no_leap, lts_concat)
linke_turbidity = np.where(isleap, lt_leap, lt_no_leap)
linke_turbidity = pd.Series(linke_turbidity, index=time)
return linke_turbidity<|docstring|>Interpolated monthly Linke turbidity onto daily values.
Parameters
----------
lts : np.array
Monthly Linke turbidity values.
time : pd.DatetimeIndex
Times to be interpolated onto.
Returns
-------
linke_turbidity : pd.Series
The interpolated turbidity.<|endoftext|> |
4fcda9501b89799343d63565babdd7e91bec2778c1760221477f313a45838df3 | def _calendar_month_middles(year):
'List of middle day of each month, used by Linke turbidity lookup'
mdays = np.array(calendar.mdays[1:])
ydays = 365
if calendar.isleap(year):
mdays[1] = (mdays[1] + 1)
ydays = 366
middles = np.concatenate([[((- calendar.mdays[(- 1)]) / 2.0)], (np.cumsum(mdays) - (np.array(mdays) / 2.0)), [(ydays + (calendar.mdays[1] / 2.0))]])
return middles | List of middle day of each month, used by Linke turbidity lookup | pvlib/clearsky.py | _calendar_month_middles | Antoine-0/pvlib-python | 695 | python | def _calendar_month_middles(year):
mdays = np.array(calendar.mdays[1:])
ydays = 365
if calendar.isleap(year):
mdays[1] = (mdays[1] + 1)
ydays = 366
middles = np.concatenate([[((- calendar.mdays[(- 1)]) / 2.0)], (np.cumsum(mdays) - (np.array(mdays) / 2.0)), [(ydays + (calendar.mdays[1] / 2.0))]])
return middles | def _calendar_month_middles(year):
mdays = np.array(calendar.mdays[1:])
ydays = 365
if calendar.isleap(year):
mdays[1] = (mdays[1] + 1)
ydays = 366
middles = np.concatenate([[((- calendar.mdays[(- 1)]) / 2.0)], (np.cumsum(mdays) - (np.array(mdays) / 2.0)), [(ydays + (calendar.mdays[1] / 2.0))]])
return middles<|docstring|>List of middle day of each month, used by Linke turbidity lookup<|endoftext|> |
f9768d351408a1da6805b9a2f858880459c5e8d215b388c7f5f16f8f4df20b33 | def _degrees_to_index(degrees, coordinate):
"Transform input degrees to an output index integer. The Linke\n turbidity lookup tables have three dimensions, latitude, longitude, and\n month. Specify a degree value and either 'latitude' or 'longitude' to get\n the appropriate index number for the first two of these index numbers.\n\n Parameters\n ----------\n degrees : float or int\n Degrees of either latitude or longitude.\n coordinate : string\n Specify whether degrees arg is latitude or longitude. Must be set to\n either 'latitude' or 'longitude' or an error will be raised.\n\n Returns\n -------\n index : np.int16\n The latitude or longitude index number to use when looking up values\n in the Linke turbidity lookup table.\n "
if (coordinate == 'latitude'):
inputmin = 90
inputmax = (- 90)
outputmax = 2160
elif (coordinate == 'longitude'):
inputmin = (- 180)
inputmax = 180
outputmax = 4320
else:
raise IndexError("coordinate must be 'latitude' or 'longitude'.")
inputrange = (inputmax - inputmin)
scale = (outputmax / inputrange)
center = (inputmin + ((1 / scale) / 2))
outputmax -= 1
index = ((degrees - center) * scale)
err = IndexError(('Input, %g, is out of range (%g, %g).' % (degrees, inputmin, inputmax)))
if (index > outputmax):
if ((index - outputmax) <= 0.500001):
index = outputmax
else:
raise err
elif (index < 0):
if ((- index) <= 0.500001):
index = 0
else:
raise err
else:
index = int(np.around(index))
return index | Transform input degrees to an output index integer. The Linke
turbidity lookup tables have three dimensions, latitude, longitude, and
month. Specify a degree value and either 'latitude' or 'longitude' to get
the appropriate index number for the first two of these index numbers.
Parameters
----------
degrees : float or int
Degrees of either latitude or longitude.
coordinate : string
Specify whether degrees arg is latitude or longitude. Must be set to
either 'latitude' or 'longitude' or an error will be raised.
Returns
-------
index : np.int16
The latitude or longitude index number to use when looking up values
in the Linke turbidity lookup table. | pvlib/clearsky.py | _degrees_to_index | Antoine-0/pvlib-python | 695 | python | def _degrees_to_index(degrees, coordinate):
"Transform input degrees to an output index integer. The Linke\n turbidity lookup tables have three dimensions, latitude, longitude, and\n month. Specify a degree value and either 'latitude' or 'longitude' to get\n the appropriate index number for the first two of these index numbers.\n\n Parameters\n ----------\n degrees : float or int\n Degrees of either latitude or longitude.\n coordinate : string\n Specify whether degrees arg is latitude or longitude. Must be set to\n either 'latitude' or 'longitude' or an error will be raised.\n\n Returns\n -------\n index : np.int16\n The latitude or longitude index number to use when looking up values\n in the Linke turbidity lookup table.\n "
if (coordinate == 'latitude'):
inputmin = 90
inputmax = (- 90)
outputmax = 2160
elif (coordinate == 'longitude'):
inputmin = (- 180)
inputmax = 180
outputmax = 4320
else:
raise IndexError("coordinate must be 'latitude' or 'longitude'.")
inputrange = (inputmax - inputmin)
scale = (outputmax / inputrange)
center = (inputmin + ((1 / scale) / 2))
outputmax -= 1
index = ((degrees - center) * scale)
err = IndexError(('Input, %g, is out of range (%g, %g).' % (degrees, inputmin, inputmax)))
if (index > outputmax):
if ((index - outputmax) <= 0.500001):
index = outputmax
else:
raise err
elif (index < 0):
if ((- index) <= 0.500001):
index = 0
else:
raise err
else:
index = int(np.around(index))
return index | def _degrees_to_index(degrees, coordinate):
"Transform input degrees to an output index integer. The Linke\n turbidity lookup tables have three dimensions, latitude, longitude, and\n month. Specify a degree value and either 'latitude' or 'longitude' to get\n the appropriate index number for the first two of these index numbers.\n\n Parameters\n ----------\n degrees : float or int\n Degrees of either latitude or longitude.\n coordinate : string\n Specify whether degrees arg is latitude or longitude. Must be set to\n either 'latitude' or 'longitude' or an error will be raised.\n\n Returns\n -------\n index : np.int16\n The latitude or longitude index number to use when looking up values\n in the Linke turbidity lookup table.\n "
if (coordinate == 'latitude'):
inputmin = 90
inputmax = (- 90)
outputmax = 2160
elif (coordinate == 'longitude'):
inputmin = (- 180)
inputmax = 180
outputmax = 4320
else:
raise IndexError("coordinate must be 'latitude' or 'longitude'.")
inputrange = (inputmax - inputmin)
scale = (outputmax / inputrange)
center = (inputmin + ((1 / scale) / 2))
outputmax -= 1
index = ((degrees - center) * scale)
err = IndexError(('Input, %g, is out of range (%g, %g).' % (degrees, inputmin, inputmax)))
if (index > outputmax):
if ((index - outputmax) <= 0.500001):
index = outputmax
else:
raise err
elif (index < 0):
if ((- index) <= 0.500001):
index = 0
else:
raise err
else:
index = int(np.around(index))
return index<|docstring|>Transform input degrees to an output index integer. The Linke
turbidity lookup tables have three dimensions, latitude, longitude, and
month. Specify a degree value and either 'latitude' or 'longitude' to get
the appropriate index number for the first two of these index numbers.
Parameters
----------
degrees : float or int
Degrees of either latitude or longitude.
coordinate : string
Specify whether degrees arg is latitude or longitude. Must be set to
either 'latitude' or 'longitude' or an error will be raised.
Returns
-------
index : np.int16
The latitude or longitude index number to use when looking up values
in the Linke turbidity lookup table.<|endoftext|> |
5f22c1a8b094e14d55ba2d4bc872419bc0c8f9e937ce2ac9ed444bfe92c5acf1 | def haurwitz(apparent_zenith):
'\n Determine clear sky GHI using the Haurwitz model.\n\n Implements the Haurwitz clear sky model for global horizontal\n irradiance (GHI) as presented in [1, 2]. A report on clear\n sky models found the Haurwitz model to have the best performance\n in terms of average monthly error among models which require only\n zenith angle [3].\n\n Parameters\n ----------\n apparent_zenith : Series\n The apparent (refraction corrected) sun zenith angle\n in degrees.\n\n Returns\n -------\n ghi : DataFrame\n The modeled global horizonal irradiance in W/m^2 provided\n by the Haurwitz clear-sky model.\n\n References\n ----------\n\n .. [1] B. Haurwitz, "Insolation in Relation to Cloudiness and Cloud\n Density," Journal of Meteorology, vol. 2, pp. 154-166, 1945.\n\n .. [2] B. Haurwitz, "Insolation in Relation to Cloud Type," Journal of\n Meteorology, vol. 3, pp. 123-124, 1946.\n\n .. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance\n Clear Sky Models: Implementation and Analysis", Sandia National\n Laboratories, SAND2012-2389, 2012.\n '
cos_zenith = tools.cosd(apparent_zenith.values)
clearsky_ghi = np.zeros_like(apparent_zenith.values)
cos_zen_gte_0 = (cos_zenith > 0)
clearsky_ghi[cos_zen_gte_0] = ((1098.0 * cos_zenith[cos_zen_gte_0]) * np.exp(((- 0.059) / cos_zenith[cos_zen_gte_0])))
df_out = pd.DataFrame(index=apparent_zenith.index, data=clearsky_ghi, columns=['ghi'])
return df_out | Determine clear sky GHI using the Haurwitz model.
Implements the Haurwitz clear sky model for global horizontal
irradiance (GHI) as presented in [1, 2]. A report on clear
sky models found the Haurwitz model to have the best performance
in terms of average monthly error among models which require only
zenith angle [3].
Parameters
----------
apparent_zenith : Series
The apparent (refraction corrected) sun zenith angle
in degrees.
Returns
-------
ghi : DataFrame
The modeled global horizonal irradiance in W/m^2 provided
by the Haurwitz clear-sky model.
References
----------
.. [1] B. Haurwitz, "Insolation in Relation to Cloudiness and Cloud
Density," Journal of Meteorology, vol. 2, pp. 154-166, 1945.
.. [2] B. Haurwitz, "Insolation in Relation to Cloud Type," Journal of
Meteorology, vol. 3, pp. 123-124, 1946.
.. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance
Clear Sky Models: Implementation and Analysis", Sandia National
Laboratories, SAND2012-2389, 2012. | pvlib/clearsky.py | haurwitz | Antoine-0/pvlib-python | 695 | python | def haurwitz(apparent_zenith):
'\n Determine clear sky GHI using the Haurwitz model.\n\n Implements the Haurwitz clear sky model for global horizontal\n irradiance (GHI) as presented in [1, 2]. A report on clear\n sky models found the Haurwitz model to have the best performance\n in terms of average monthly error among models which require only\n zenith angle [3].\n\n Parameters\n ----------\n apparent_zenith : Series\n The apparent (refraction corrected) sun zenith angle\n in degrees.\n\n Returns\n -------\n ghi : DataFrame\n The modeled global horizonal irradiance in W/m^2 provided\n by the Haurwitz clear-sky model.\n\n References\n ----------\n\n .. [1] B. Haurwitz, "Insolation in Relation to Cloudiness and Cloud\n Density," Journal of Meteorology, vol. 2, pp. 154-166, 1945.\n\n .. [2] B. Haurwitz, "Insolation in Relation to Cloud Type," Journal of\n Meteorology, vol. 3, pp. 123-124, 1946.\n\n .. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance\n Clear Sky Models: Implementation and Analysis", Sandia National\n Laboratories, SAND2012-2389, 2012.\n '
cos_zenith = tools.cosd(apparent_zenith.values)
clearsky_ghi = np.zeros_like(apparent_zenith.values)
cos_zen_gte_0 = (cos_zenith > 0)
clearsky_ghi[cos_zen_gte_0] = ((1098.0 * cos_zenith[cos_zen_gte_0]) * np.exp(((- 0.059) / cos_zenith[cos_zen_gte_0])))
df_out = pd.DataFrame(index=apparent_zenith.index, data=clearsky_ghi, columns=['ghi'])
return df_out | def haurwitz(apparent_zenith):
'\n Determine clear sky GHI using the Haurwitz model.\n\n Implements the Haurwitz clear sky model for global horizontal\n irradiance (GHI) as presented in [1, 2]. A report on clear\n sky models found the Haurwitz model to have the best performance\n in terms of average monthly error among models which require only\n zenith angle [3].\n\n Parameters\n ----------\n apparent_zenith : Series\n The apparent (refraction corrected) sun zenith angle\n in degrees.\n\n Returns\n -------\n ghi : DataFrame\n The modeled global horizonal irradiance in W/m^2 provided\n by the Haurwitz clear-sky model.\n\n References\n ----------\n\n .. [1] B. Haurwitz, "Insolation in Relation to Cloudiness and Cloud\n Density," Journal of Meteorology, vol. 2, pp. 154-166, 1945.\n\n .. [2] B. Haurwitz, "Insolation in Relation to Cloud Type," Journal of\n Meteorology, vol. 3, pp. 123-124, 1946.\n\n .. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance\n Clear Sky Models: Implementation and Analysis", Sandia National\n Laboratories, SAND2012-2389, 2012.\n '
cos_zenith = tools.cosd(apparent_zenith.values)
clearsky_ghi = np.zeros_like(apparent_zenith.values)
cos_zen_gte_0 = (cos_zenith > 0)
clearsky_ghi[cos_zen_gte_0] = ((1098.0 * cos_zenith[cos_zen_gte_0]) * np.exp(((- 0.059) / cos_zenith[cos_zen_gte_0])))
df_out = pd.DataFrame(index=apparent_zenith.index, data=clearsky_ghi, columns=['ghi'])
return df_out<|docstring|>Determine clear sky GHI using the Haurwitz model.
Implements the Haurwitz clear sky model for global horizontal
irradiance (GHI) as presented in [1, 2]. A report on clear
sky models found the Haurwitz model to have the best performance
in terms of average monthly error among models which require only
zenith angle [3].
Parameters
----------
apparent_zenith : Series
The apparent (refraction corrected) sun zenith angle
in degrees.
Returns
-------
ghi : DataFrame
The modeled global horizonal irradiance in W/m^2 provided
by the Haurwitz clear-sky model.
References
----------
.. [1] B. Haurwitz, "Insolation in Relation to Cloudiness and Cloud
Density," Journal of Meteorology, vol. 2, pp. 154-166, 1945.
.. [2] B. Haurwitz, "Insolation in Relation to Cloud Type," Journal of
Meteorology, vol. 3, pp. 123-124, 1946.
.. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance
Clear Sky Models: Implementation and Analysis", Sandia National
Laboratories, SAND2012-2389, 2012.<|endoftext|> |
cfa06ec53980286c741bfd8950b55192bd97df60ace15612654bdbb0a5ed3cf5 | def simplified_solis(apparent_elevation, aod700=0.1, precipitable_water=1.0, pressure=101325.0, dni_extra=1364.0):
'\n Calculate the clear sky GHI, DNI, and DHI according to the\n simplified Solis model.\n\n Reference [1]_ describes the accuracy of the model as being 15, 20,\n and 18 W/m^2 for the beam, global, and diffuse components. Reference\n [2]_ provides comparisons with other clear sky models.\n\n Parameters\n ----------\n apparent_elevation : numeric\n The apparent elevation of the sun above the horizon (deg).\n\n aod700 : numeric, default 0.1\n The aerosol optical depth at 700 nm (unitless).\n Algorithm derived for values between 0 and 0.45.\n\n precipitable_water : numeric, default 1.0\n The precipitable water of the atmosphere (cm).\n Algorithm derived for values between 0.2 and 10 cm.\n Values less than 0.2 will be assumed to be equal to 0.2.\n\n pressure : numeric, default 101325.0\n The atmospheric pressure (Pascals).\n Algorithm derived for altitudes between sea level and 7000 m,\n or 101325 and 41000 Pascals.\n\n dni_extra : numeric, default 1364.0\n Extraterrestrial irradiance. The units of ``dni_extra``\n determine the units of the output.\n\n Returns\n -------\n clearsky : DataFrame (if Series input) or OrderedDict of arrays\n DataFrame/OrderedDict contains the columns/keys\n ``\'dhi\', \'dni\', \'ghi\'``.\n\n References\n ----------\n .. [1] P. Ineichen, "A broadband simplified version of the\n Solis clear sky model," Solar Energy, 82, 758-762 (2008).\n\n .. [2] P. Ineichen, "Validation of models that estimate the clear\n sky global and beam solar irradiance," Solar Energy, 132,\n 332-344 (2016).\n '
p = pressure
w = precipitable_water
w = np.maximum(w, 0.2)
i0p = _calc_i0p(dni_extra, w, aod700, p)
taub = _calc_taub(w, aod700, p)
b = _calc_b(w, aod700)
taug = _calc_taug(w, aod700, p)
g = _calc_g(w, aod700)
taud = _calc_taud(w, aod700, p)
d = _calc_d(aod700, p)
sin_elev = np.maximum(1e-30, np.sin(np.radians(apparent_elevation)))
dni = (i0p * np.exp(((- taub) / (sin_elev ** b))))
ghi = ((i0p * np.exp(((- taug) / (sin_elev ** g)))) * sin_elev)
dhi = (i0p * np.exp(((- taud) / (sin_elev ** d))))
irrads = OrderedDict()
irrads['ghi'] = ghi
irrads['dni'] = dni
irrads['dhi'] = dhi
if isinstance(dni, pd.Series):
irrads = pd.DataFrame.from_dict(irrads)
return irrads | Calculate the clear sky GHI, DNI, and DHI according to the
simplified Solis model.
Reference [1]_ describes the accuracy of the model as being 15, 20,
and 18 W/m^2 for the beam, global, and diffuse components. Reference
[2]_ provides comparisons with other clear sky models.
Parameters
----------
apparent_elevation : numeric
The apparent elevation of the sun above the horizon (deg).
aod700 : numeric, default 0.1
The aerosol optical depth at 700 nm (unitless).
Algorithm derived for values between 0 and 0.45.
precipitable_water : numeric, default 1.0
The precipitable water of the atmosphere (cm).
Algorithm derived for values between 0.2 and 10 cm.
Values less than 0.2 will be assumed to be equal to 0.2.
pressure : numeric, default 101325.0
The atmospheric pressure (Pascals).
Algorithm derived for altitudes between sea level and 7000 m,
or 101325 and 41000 Pascals.
dni_extra : numeric, default 1364.0
Extraterrestrial irradiance. The units of ``dni_extra``
determine the units of the output.
Returns
-------
clearsky : DataFrame (if Series input) or OrderedDict of arrays
DataFrame/OrderedDict contains the columns/keys
``'dhi', 'dni', 'ghi'``.
References
----------
.. [1] P. Ineichen, "A broadband simplified version of the
Solis clear sky model," Solar Energy, 82, 758-762 (2008).
.. [2] P. Ineichen, "Validation of models that estimate the clear
sky global and beam solar irradiance," Solar Energy, 132,
332-344 (2016). | pvlib/clearsky.py | simplified_solis | Antoine-0/pvlib-python | 695 | python | def simplified_solis(apparent_elevation, aod700=0.1, precipitable_water=1.0, pressure=101325.0, dni_extra=1364.0):
'\n Calculate the clear sky GHI, DNI, and DHI according to the\n simplified Solis model.\n\n Reference [1]_ describes the accuracy of the model as being 15, 20,\n and 18 W/m^2 for the beam, global, and diffuse components. Reference\n [2]_ provides comparisons with other clear sky models.\n\n Parameters\n ----------\n apparent_elevation : numeric\n The apparent elevation of the sun above the horizon (deg).\n\n aod700 : numeric, default 0.1\n The aerosol optical depth at 700 nm (unitless).\n Algorithm derived for values between 0 and 0.45.\n\n precipitable_water : numeric, default 1.0\n The precipitable water of the atmosphere (cm).\n Algorithm derived for values between 0.2 and 10 cm.\n Values less than 0.2 will be assumed to be equal to 0.2.\n\n pressure : numeric, default 101325.0\n The atmospheric pressure (Pascals).\n Algorithm derived for altitudes between sea level and 7000 m,\n or 101325 and 41000 Pascals.\n\n dni_extra : numeric, default 1364.0\n Extraterrestrial irradiance. The units of ``dni_extra``\n determine the units of the output.\n\n Returns\n -------\n clearsky : DataFrame (if Series input) or OrderedDict of arrays\n DataFrame/OrderedDict contains the columns/keys\n ``\'dhi\', \'dni\', \'ghi\'``.\n\n References\n ----------\n .. [1] P. Ineichen, "A broadband simplified version of the\n Solis clear sky model," Solar Energy, 82, 758-762 (2008).\n\n .. [2] P. Ineichen, "Validation of models that estimate the clear\n sky global and beam solar irradiance," Solar Energy, 132,\n 332-344 (2016).\n '
p = pressure
w = precipitable_water
w = np.maximum(w, 0.2)
i0p = _calc_i0p(dni_extra, w, aod700, p)
taub = _calc_taub(w, aod700, p)
b = _calc_b(w, aod700)
taug = _calc_taug(w, aod700, p)
g = _calc_g(w, aod700)
taud = _calc_taud(w, aod700, p)
d = _calc_d(aod700, p)
sin_elev = np.maximum(1e-30, np.sin(np.radians(apparent_elevation)))
dni = (i0p * np.exp(((- taub) / (sin_elev ** b))))
ghi = ((i0p * np.exp(((- taug) / (sin_elev ** g)))) * sin_elev)
dhi = (i0p * np.exp(((- taud) / (sin_elev ** d))))
irrads = OrderedDict()
irrads['ghi'] = ghi
irrads['dni'] = dni
irrads['dhi'] = dhi
if isinstance(dni, pd.Series):
irrads = pd.DataFrame.from_dict(irrads)
return irrads | def simplified_solis(apparent_elevation, aod700=0.1, precipitable_water=1.0, pressure=101325.0, dni_extra=1364.0):
'\n Calculate the clear sky GHI, DNI, and DHI according to the\n simplified Solis model.\n\n Reference [1]_ describes the accuracy of the model as being 15, 20,\n and 18 W/m^2 for the beam, global, and diffuse components. Reference\n [2]_ provides comparisons with other clear sky models.\n\n Parameters\n ----------\n apparent_elevation : numeric\n The apparent elevation of the sun above the horizon (deg).\n\n aod700 : numeric, default 0.1\n The aerosol optical depth at 700 nm (unitless).\n Algorithm derived for values between 0 and 0.45.\n\n precipitable_water : numeric, default 1.0\n The precipitable water of the atmosphere (cm).\n Algorithm derived for values between 0.2 and 10 cm.\n Values less than 0.2 will be assumed to be equal to 0.2.\n\n pressure : numeric, default 101325.0\n The atmospheric pressure (Pascals).\n Algorithm derived for altitudes between sea level and 7000 m,\n or 101325 and 41000 Pascals.\n\n dni_extra : numeric, default 1364.0\n Extraterrestrial irradiance. The units of ``dni_extra``\n determine the units of the output.\n\n Returns\n -------\n clearsky : DataFrame (if Series input) or OrderedDict of arrays\n DataFrame/OrderedDict contains the columns/keys\n ``\'dhi\', \'dni\', \'ghi\'``.\n\n References\n ----------\n .. [1] P. Ineichen, "A broadband simplified version of the\n Solis clear sky model," Solar Energy, 82, 758-762 (2008).\n\n .. [2] P. Ineichen, "Validation of models that estimate the clear\n sky global and beam solar irradiance," Solar Energy, 132,\n 332-344 (2016).\n '
p = pressure
w = precipitable_water
w = np.maximum(w, 0.2)
i0p = _calc_i0p(dni_extra, w, aod700, p)
taub = _calc_taub(w, aod700, p)
b = _calc_b(w, aod700)
taug = _calc_taug(w, aod700, p)
g = _calc_g(w, aod700)
taud = _calc_taud(w, aod700, p)
d = _calc_d(aod700, p)
sin_elev = np.maximum(1e-30, np.sin(np.radians(apparent_elevation)))
dni = (i0p * np.exp(((- taub) / (sin_elev ** b))))
ghi = ((i0p * np.exp(((- taug) / (sin_elev ** g)))) * sin_elev)
dhi = (i0p * np.exp(((- taud) / (sin_elev ** d))))
irrads = OrderedDict()
irrads['ghi'] = ghi
irrads['dni'] = dni
irrads['dhi'] = dhi
if isinstance(dni, pd.Series):
irrads = pd.DataFrame.from_dict(irrads)
return irrads<|docstring|>Calculate the clear sky GHI, DNI, and DHI according to the
simplified Solis model.
Reference [1]_ describes the accuracy of the model as being 15, 20,
and 18 W/m^2 for the beam, global, and diffuse components. Reference
[2]_ provides comparisons with other clear sky models.
Parameters
----------
apparent_elevation : numeric
The apparent elevation of the sun above the horizon (deg).
aod700 : numeric, default 0.1
The aerosol optical depth at 700 nm (unitless).
Algorithm derived for values between 0 and 0.45.
precipitable_water : numeric, default 1.0
The precipitable water of the atmosphere (cm).
Algorithm derived for values between 0.2 and 10 cm.
Values less than 0.2 will be assumed to be equal to 0.2.
pressure : numeric, default 101325.0
The atmospheric pressure (Pascals).
Algorithm derived for altitudes between sea level and 7000 m,
or 101325 and 41000 Pascals.
dni_extra : numeric, default 1364.0
Extraterrestrial irradiance. The units of ``dni_extra``
determine the units of the output.
Returns
-------
clearsky : DataFrame (if Series input) or OrderedDict of arrays
DataFrame/OrderedDict contains the columns/keys
``'dhi', 'dni', 'ghi'``.
References
----------
.. [1] P. Ineichen, "A broadband simplified version of the
Solis clear sky model," Solar Energy, 82, 758-762 (2008).
.. [2] P. Ineichen, "Validation of models that estimate the clear
sky global and beam solar irradiance," Solar Energy, 132,
332-344 (2016).<|endoftext|> |
fb97298b16fe0faa5fd31302097ee1d8ef9f74b4aa5b5a25c71fc00c865b2f48 | def _calc_i0p(i0, w, aod700, p):
'Calculate the "enhanced extraterrestrial irradiance".'
p0 = 101325.0
io0 = (1.08 * (w ** 0.0051))
i01 = (0.97 * (w ** 0.032))
i02 = (0.12 * (w ** 0.56))
i0p = (i0 * ((((i02 * (aod700 ** 2)) + (i01 * aod700)) + io0) + (0.071 * np.log((p / p0)))))
return i0p | Calculate the "enhanced extraterrestrial irradiance". | pvlib/clearsky.py | _calc_i0p | Antoine-0/pvlib-python | 695 | python | def _calc_i0p(i0, w, aod700, p):
p0 = 101325.0
io0 = (1.08 * (w ** 0.0051))
i01 = (0.97 * (w ** 0.032))
i02 = (0.12 * (w ** 0.56))
i0p = (i0 * ((((i02 * (aod700 ** 2)) + (i01 * aod700)) + io0) + (0.071 * np.log((p / p0)))))
return i0p | def _calc_i0p(i0, w, aod700, p):
p0 = 101325.0
io0 = (1.08 * (w ** 0.0051))
i01 = (0.97 * (w ** 0.032))
i02 = (0.12 * (w ** 0.56))
i0p = (i0 * ((((i02 * (aod700 ** 2)) + (i01 * aod700)) + io0) + (0.071 * np.log((p / p0)))))
return i0p<|docstring|>Calculate the "enhanced extraterrestrial irradiance".<|endoftext|> |
749607145f0742a24dcc2f8418f7d5e12d18b05eb87880d294c6dd42cf50dadc | def _calc_taub(w, aod700, p):
'Calculate the taub coefficient'
p0 = 101325.0
tb1 = ((1.82 + (0.056 * np.log(w))) + (0.0071 * (np.log(w) ** 2)))
tb0 = ((0.33 + (0.045 * np.log(w))) + (0.0096 * (np.log(w) ** 2)))
tbp = ((0.0089 * w) + 0.13)
taub = (((tb1 * aod700) + tb0) + (tbp * np.log((p / p0))))
return taub | Calculate the taub coefficient | pvlib/clearsky.py | _calc_taub | Antoine-0/pvlib-python | 695 | python | def _calc_taub(w, aod700, p):
p0 = 101325.0
tb1 = ((1.82 + (0.056 * np.log(w))) + (0.0071 * (np.log(w) ** 2)))
tb0 = ((0.33 + (0.045 * np.log(w))) + (0.0096 * (np.log(w) ** 2)))
tbp = ((0.0089 * w) + 0.13)
taub = (((tb1 * aod700) + tb0) + (tbp * np.log((p / p0))))
return taub | def _calc_taub(w, aod700, p):
p0 = 101325.0
tb1 = ((1.82 + (0.056 * np.log(w))) + (0.0071 * (np.log(w) ** 2)))
tb0 = ((0.33 + (0.045 * np.log(w))) + (0.0096 * (np.log(w) ** 2)))
tbp = ((0.0089 * w) + 0.13)
taub = (((tb1 * aod700) + tb0) + (tbp * np.log((p / p0))))
return taub<|docstring|>Calculate the taub coefficient<|endoftext|> |
de82d5f189d3579ee91be6efef88f510e9639504003180a65ed7873966297925 | def _calc_b(w, aod700):
'Calculate the b coefficient.'
b1 = (((0.00925 * (aod700 ** 2)) + (0.0148 * aod700)) - 0.0172)
b0 = ((((- 0.7565) * (aod700 ** 2)) + (0.5057 * aod700)) + 0.4557)
b = ((b1 * np.log(w)) + b0)
return b | Calculate the b coefficient. | pvlib/clearsky.py | _calc_b | Antoine-0/pvlib-python | 695 | python | def _calc_b(w, aod700):
b1 = (((0.00925 * (aod700 ** 2)) + (0.0148 * aod700)) - 0.0172)
b0 = ((((- 0.7565) * (aod700 ** 2)) + (0.5057 * aod700)) + 0.4557)
b = ((b1 * np.log(w)) + b0)
return b | def _calc_b(w, aod700):
b1 = (((0.00925 * (aod700 ** 2)) + (0.0148 * aod700)) - 0.0172)
b0 = ((((- 0.7565) * (aod700 ** 2)) + (0.5057 * aod700)) + 0.4557)
b = ((b1 * np.log(w)) + b0)
return b<|docstring|>Calculate the b coefficient.<|endoftext|> |
7325d6b7ba8de0236dc1c6a2439168160ee1aac94bab1ac9c4b1d0e5baa31dfc | def _calc_taug(w, aod700, p):
'Calculate the taug coefficient'
p0 = 101325.0
tg1 = ((1.24 + (0.047 * np.log(w))) + (0.0061 * (np.log(w) ** 2)))
tg0 = ((0.27 + (0.043 * np.log(w))) + (0.009 * (np.log(w) ** 2)))
tgp = ((0.0079 * w) + 0.1)
taug = (((tg1 * aod700) + tg0) + (tgp * np.log((p / p0))))
return taug | Calculate the taug coefficient | pvlib/clearsky.py | _calc_taug | Antoine-0/pvlib-python | 695 | python | def _calc_taug(w, aod700, p):
p0 = 101325.0
tg1 = ((1.24 + (0.047 * np.log(w))) + (0.0061 * (np.log(w) ** 2)))
tg0 = ((0.27 + (0.043 * np.log(w))) + (0.009 * (np.log(w) ** 2)))
tgp = ((0.0079 * w) + 0.1)
taug = (((tg1 * aod700) + tg0) + (tgp * np.log((p / p0))))
return taug | def _calc_taug(w, aod700, p):
p0 = 101325.0
tg1 = ((1.24 + (0.047 * np.log(w))) + (0.0061 * (np.log(w) ** 2)))
tg0 = ((0.27 + (0.043 * np.log(w))) + (0.009 * (np.log(w) ** 2)))
tgp = ((0.0079 * w) + 0.1)
taug = (((tg1 * aod700) + tg0) + (tgp * np.log((p / p0))))
return taug<|docstring|>Calculate the taug coefficient<|endoftext|> |
1aba7ae695f0658b0f47b8829a50d1e15301c9dbdb00277aff9d92ca9538468f | def _calc_g(w, aod700):
'Calculate the g coefficient.'
g = (((((- 0.0147) * np.log(w)) - (0.3079 * (aod700 ** 2))) + (0.2846 * aod700)) + 0.3798)
return g | Calculate the g coefficient. | pvlib/clearsky.py | _calc_g | Antoine-0/pvlib-python | 695 | python | def _calc_g(w, aod700):
g = (((((- 0.0147) * np.log(w)) - (0.3079 * (aod700 ** 2))) + (0.2846 * aod700)) + 0.3798)
return g | def _calc_g(w, aod700):
g = (((((- 0.0147) * np.log(w)) - (0.3079 * (aod700 ** 2))) + (0.2846 * aod700)) + 0.3798)
return g<|docstring|>Calculate the g coefficient.<|endoftext|> |
94a13d10d2a732c22f6ca5f42feab0fe273f81291d1523787f7805a02fe84e30 | def _calc_taud(w, aod700, p):
'Calculate the taud coefficient.'
if (np.isscalar(w) and np.isscalar(aod700)):
w = np.array([w])
aod700 = np.array([aod700])
elif np.isscalar(w):
w = np.full_like(aod700, w)
elif np.isscalar(aod700):
aod700 = np.full_like(w, aod700)
aod700_lt_0p05 = np.full_like(aod700, False, dtype='bool')
np.less(aod700, 0.05, where=(~ np.isnan(aod700)), out=aod700_lt_0p05)
aod700_mask = np.array([aod700_lt_0p05, (~ aod700_lt_0p05)], dtype=int)
td4 = (((86 * w) - 13800), (((- 0.21) * w) + 11.6))
td3 = ((((- 3.11) * w) + 79.4), ((0.27 * w) - 20.7))
td2 = ((((- 0.23) * w) + 74.8), (((- 0.134) * w) + 15.5))
td1 = (((0.092 * w) - 8.86), ((0.0554 * w) - 5.71))
td0 = (((0.0042 * w) + 3.12), ((0.0057 * w) + 2.94))
tdp = (((- 0.83) * ((1 + aod700) ** (- 17.2))), ((- 0.71) * ((1 + aod700) ** (- 15.0))))
tds = (np.array([td0, td1, td2, td3, td4, tdp]) * aod700_mask).sum(axis=1)
p0 = 101325.0
taud = ((((((tds[4] * (aod700 ** 4)) + (tds[3] * (aod700 ** 3))) + (tds[2] * (aod700 ** 2))) + (tds[1] * aod700)) + tds[0]) + (tds[5] * np.log((p / p0))))
if (len(taud) == 1):
taud = taud[0]
return taud | Calculate the taud coefficient. | pvlib/clearsky.py | _calc_taud | Antoine-0/pvlib-python | 695 | python | def _calc_taud(w, aod700, p):
if (np.isscalar(w) and np.isscalar(aod700)):
w = np.array([w])
aod700 = np.array([aod700])
elif np.isscalar(w):
w = np.full_like(aod700, w)
elif np.isscalar(aod700):
aod700 = np.full_like(w, aod700)
aod700_lt_0p05 = np.full_like(aod700, False, dtype='bool')
np.less(aod700, 0.05, where=(~ np.isnan(aod700)), out=aod700_lt_0p05)
aod700_mask = np.array([aod700_lt_0p05, (~ aod700_lt_0p05)], dtype=int)
td4 = (((86 * w) - 13800), (((- 0.21) * w) + 11.6))
td3 = ((((- 3.11) * w) + 79.4), ((0.27 * w) - 20.7))
td2 = ((((- 0.23) * w) + 74.8), (((- 0.134) * w) + 15.5))
td1 = (((0.092 * w) - 8.86), ((0.0554 * w) - 5.71))
td0 = (((0.0042 * w) + 3.12), ((0.0057 * w) + 2.94))
tdp = (((- 0.83) * ((1 + aod700) ** (- 17.2))), ((- 0.71) * ((1 + aod700) ** (- 15.0))))
tds = (np.array([td0, td1, td2, td3, td4, tdp]) * aod700_mask).sum(axis=1)
p0 = 101325.0
taud = ((((((tds[4] * (aod700 ** 4)) + (tds[3] * (aod700 ** 3))) + (tds[2] * (aod700 ** 2))) + (tds[1] * aod700)) + tds[0]) + (tds[5] * np.log((p / p0))))
if (len(taud) == 1):
taud = taud[0]
return taud | def _calc_taud(w, aod700, p):
if (np.isscalar(w) and np.isscalar(aod700)):
w = np.array([w])
aod700 = np.array([aod700])
elif np.isscalar(w):
w = np.full_like(aod700, w)
elif np.isscalar(aod700):
aod700 = np.full_like(w, aod700)
aod700_lt_0p05 = np.full_like(aod700, False, dtype='bool')
np.less(aod700, 0.05, where=(~ np.isnan(aod700)), out=aod700_lt_0p05)
aod700_mask = np.array([aod700_lt_0p05, (~ aod700_lt_0p05)], dtype=int)
td4 = (((86 * w) - 13800), (((- 0.21) * w) + 11.6))
td3 = ((((- 3.11) * w) + 79.4), ((0.27 * w) - 20.7))
td2 = ((((- 0.23) * w) + 74.8), (((- 0.134) * w) + 15.5))
td1 = (((0.092 * w) - 8.86), ((0.0554 * w) - 5.71))
td0 = (((0.0042 * w) + 3.12), ((0.0057 * w) + 2.94))
tdp = (((- 0.83) * ((1 + aod700) ** (- 17.2))), ((- 0.71) * ((1 + aod700) ** (- 15.0))))
tds = (np.array([td0, td1, td2, td3, td4, tdp]) * aod700_mask).sum(axis=1)
p0 = 101325.0
taud = ((((((tds[4] * (aod700 ** 4)) + (tds[3] * (aod700 ** 3))) + (tds[2] * (aod700 ** 2))) + (tds[1] * aod700)) + tds[0]) + (tds[5] * np.log((p / p0))))
if (len(taud) == 1):
taud = taud[0]
return taud<|docstring|>Calculate the taud coefficient.<|endoftext|> |
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