body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
@model_info.command('gtrnadb')
@click.argument('filename', type=click.File('r'))
@click.argument('output', default='-', type=click.File('w'))
def gtrnadb_model_info(filename, output):
'\n Parse the metadata.tsv file from R2DT for gtrnadb models to\n produce something we can put in our database.\n '
r2d... | -578,127,961,795,464,700 | Parse the metadata.tsv file from R2DT for gtrnadb models to
produce something we can put in our database. | rnacentral_pipeline/cli/r2dt.py | gtrnadb_model_info | RNAcentral/rnacentral-import-pipeline | python | @model_info.command('gtrnadb')
@click.argument('filename', type=click.File('r'))
@click.argument('output', default='-', type=click.File('w'))
def gtrnadb_model_info(filename, output):
'\n Parse the metadata.tsv file from R2DT for gtrnadb models to\n produce something we can put in our database.\n '
r2d... |
@model_info.command('rnase-p')
@click.argument('filename', type=click.File('r'))
@click.argument('output', default='-', type=click.File('w'))
def rnase_p_model_info(filename, output):
'\n Parse the metadata.tsv file from R2DT for Ribovision models to\n produce something we can put in our database.\n '
... | 2,337,609,762,601,865,000 | Parse the metadata.tsv file from R2DT for Ribovision models to
produce something we can put in our database. | rnacentral_pipeline/cli/r2dt.py | rnase_p_model_info | RNAcentral/rnacentral-import-pipeline | python | @model_info.command('rnase-p')
@click.argument('filename', type=click.File('r'))
@click.argument('output', default='-', type=click.File('w'))
def rnase_p_model_info(filename, output):
'\n Parse the metadata.tsv file from R2DT for Ribovision models to\n produce something we can put in our database.\n '
... |
def __init__(self, devpath):
'Return a disc object'
self.devpath = devpath
self.mountpoint = ('/mnt' + devpath)
self.hasnicetitle = False
self.video_type = 'unknown'
self.ejected = False
self.updated = False
if (cfg['VIDEOTYPE'] != 'auto'):
self.video_type = cfg['VIDEOTYPE']
... | 8,437,722,910,963,163,000 | Return a disc object | arm/models/models.py | __init__ | charmarkk/automatic-ripping-machine | python | def __init__(self, devpath):
self.devpath = devpath
self.mountpoint = ('/mnt' + devpath)
self.hasnicetitle = False
self.video_type = 'unknown'
self.ejected = False
self.updated = False
if (cfg['VIDEOTYPE'] != 'auto'):
self.video_type = cfg['VIDEOTYPE']
self.parse_udev()
... |
def parse_udev(self):
'Parse udev for properties of current disc'
context = pyudev.Context()
device = pyudev.Devices.from_device_file(context, self.devpath)
self.disctype = 'unknown'
for (key, value) in device.items():
if (key == 'ID_FS_LABEL'):
self.label = value
if ... | 5,184,117,535,612,883,000 | Parse udev for properties of current disc | arm/models/models.py | parse_udev | charmarkk/automatic-ripping-machine | python | def parse_udev(self):
context = pyudev.Context()
device = pyudev.Devices.from_device_file(context, self.devpath)
self.disctype = 'unknown'
for (key, value) in device.items():
if (key == 'ID_FS_LABEL'):
self.label = value
if (value == 'iso9660'):
self.... |
def identify_audio_cd(self):
'\n Get the title for audio cds to use for the logfile name.\n\n Needs the job class passed into it so it can be forwarded to mb\n\n return - only the logfile - setup_logging() adds the full path\n '
disc_id = music_brainz.get_disc_id(self)
mb_title =... | 6,480,684,719,705,463,000 | Get the title for audio cds to use for the logfile name.
Needs the job class passed into it so it can be forwarded to mb
return - only the logfile - setup_logging() adds the full path | arm/models/models.py | identify_audio_cd | charmarkk/automatic-ripping-machine | python | def identify_audio_cd(self):
'\n Get the title for audio cds to use for the logfile name.\n\n Needs the job class passed into it so it can be forwarded to mb\n\n return - only the logfile - setup_logging() adds the full path\n '
disc_id = music_brainz.get_disc_id(self)
mb_title =... |
def __str__(self):
'Returns a string of the object'
s = (self.__class__.__name__ + ': ')
for (attr, value) in self.__dict__.items():
s = (((((s + '(') + str(attr)) + '=') + str(value)) + ') ')
return s | 5,233,745,533,802,784,000 | Returns a string of the object | arm/models/models.py | __str__ | charmarkk/automatic-ripping-machine | python | def __str__(self):
s = (self.__class__.__name__ + ': ')
for (attr, value) in self.__dict__.items():
s = (((((s + '(') + str(attr)) + '=') + str(value)) + ') ')
return s |
def pretty_table(self):
'Returns a string of the prettytable'
x = PrettyTable()
x.field_names = ['Config', 'Value']
x._max_width = {'Config': 50, 'Value': 60}
for (attr, value) in self.__dict__.items():
if (attr == 'config'):
x.add_row([str(attr), str(value.pretty_table())])
... | -5,753,102,044,256,257,000 | Returns a string of the prettytable | arm/models/models.py | pretty_table | charmarkk/automatic-ripping-machine | python | def pretty_table(self):
x = PrettyTable()
x.field_names = ['Config', 'Value']
x._max_width = {'Config': 50, 'Value': 60}
for (attr, value) in self.__dict__.items():
if (attr == 'config'):
x.add_row([str(attr), str(value.pretty_table())])
else:
x.add_row([str(... |
def eject(self):
"Eject disc if it hasn't previously been ejected"
if (not self.ejected):
self.ejected = True
try:
if os.system(('umount ' + self.devpath)):
logging.debug(('we unmounted disc' + self.devpath))
if os.system(('eject ' + self.devpath)):
... | 3,129,454,796,627,657,000 | Eject disc if it hasn't previously been ejected | arm/models/models.py | eject | charmarkk/automatic-ripping-machine | python | def eject(self):
if (not self.ejected):
self.ejected = True
try:
if os.system(('umount ' + self.devpath)):
logging.debug(('we unmounted disc' + self.devpath))
if os.system(('eject ' + self.devpath)):
logging.debug(('we ejected disc' + self... |
def __init__(self, job_id, track_number, length, aspect_ratio, fps, main_feature, source, basename, filename):
'Return a track object'
self.job_id = job_id
self.track_number = track_number
self.length = length
self.aspect_ratio = aspect_ratio
self.fps = fps
self.main_feature = main_feature
... | 190,976,422,805,984,930 | Return a track object | arm/models/models.py | __init__ | charmarkk/automatic-ripping-machine | python | def __init__(self, job_id, track_number, length, aspect_ratio, fps, main_feature, source, basename, filename):
self.job_id = job_id
self.track_number = track_number
self.length = length
self.aspect_ratio = aspect_ratio
self.fps = fps
self.main_feature = main_feature
self.source = source... |
def list_params(self):
'Returns a string of the object'
s = (self.__class__.__name__ + ': ')
for (attr, value) in self.__dict__.items():
if s:
s = (s + '\n')
if ((str(attr) in hidden_attribs) and value):
value = HIDDEN_VALUE
s = (((s + str(attr)) + ':') + str(... | 6,946,453,928,283,418,000 | Returns a string of the object | arm/models/models.py | list_params | charmarkk/automatic-ripping-machine | python | def list_params(self):
s = (self.__class__.__name__ + ': ')
for (attr, value) in self.__dict__.items():
if s:
s = (s + '\n')
if ((str(attr) in hidden_attribs) and value):
value = HIDDEN_VALUE
s = (((s + str(attr)) + ':') + str(value))
return s |
def __str__(self):
'Returns a string of the object'
s = (self.__class__.__name__ + ': ')
for (attr, value) in self.__dict__.items():
if ((str(attr) in hidden_attribs) and value):
value = HIDDEN_VALUE
s = (((((s + '(') + str(attr)) + '=') + str(value)) + ') ')
return s | -2,756,119,392,359,758,000 | Returns a string of the object | arm/models/models.py | __str__ | charmarkk/automatic-ripping-machine | python | def __str__(self):
s = (self.__class__.__name__ + ': ')
for (attr, value) in self.__dict__.items():
if ((str(attr) in hidden_attribs) and value):
value = HIDDEN_VALUE
s = (((((s + '(') + str(attr)) + '=') + str(value)) + ') ')
return s |
def pretty_table(self):
'Returns a string of the prettytable'
x = PrettyTable()
x.field_names = ['Config', 'Value']
x._max_width = {'Config': 20, 'Value': 30}
for (attr, value) in self.__dict__.items():
if ((str(attr) in hidden_attribs) and value):
value = HIDDEN_VALUE
x.... | 2,637,011,702,280,520,700 | Returns a string of the prettytable | arm/models/models.py | pretty_table | charmarkk/automatic-ripping-machine | python | def pretty_table(self):
x = PrettyTable()
x.field_names = ['Config', 'Value']
x._max_width = {'Config': 20, 'Value': 30}
for (attr, value) in self.__dict__.items():
if ((str(attr) in hidden_attribs) and value):
value = HIDDEN_VALUE
x.add_row([str(attr), str(value)])
... |
def __str__(self):
'Returns a string of the object'
s = (self.__class__.__name__ + ': ')
for (attr, value) in self.__dict__.items():
s = (((((s + '(') + str(attr)) + '=') + str(value)) + ') ')
return s | 5,233,745,533,802,784,000 | Returns a string of the object | arm/models/models.py | __str__ | charmarkk/automatic-ripping-machine | python | def __str__(self):
s = (self.__class__.__name__ + ': ')
for (attr, value) in self.__dict__.items():
s = (((((s + '(') + str(attr)) + '=') + str(value)) + ') ')
return s |
def testDistributionGroupAppsDeleteRequest(self):
'Test DistributionGroupAppsDeleteRequest'
pass | -6,899,858,591,365,831,000 | Test DistributionGroupAppsDeleteRequest | sdks/python/test/test_DistributionGroupAppsDeleteRequest.py | testDistributionGroupAppsDeleteRequest | Brantone/appcenter-sdks | python | def testDistributionGroupAppsDeleteRequest(self):
pass |
def __fleiss_pi_linear__(dataset, **kwargs):
"\n Calculates Fleiss' :math:`\\pi` (or multi-:math:`\\pi`), originally proposed in\n [Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K`\n [SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\\pi`\n [Scott1955]_.\n ... | 5,795,092,333,693,014,000 | Calculates Fleiss' :math:`\pi` (or multi-:math:`\pi`), originally proposed in
[Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K`
[SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\pi`
[Scott1955]_. | segeval/agreement/pi.py | __fleiss_pi_linear__ | cfournie/segmentation.evaluation | python | def __fleiss_pi_linear__(dataset, **kwargs):
"\n Calculates Fleiss' :math:`\\pi` (or multi-:math:`\\pi`), originally proposed in\n [Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K`\n [SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\\pi`\n [Scott1955]_.\n ... |
def fleiss_pi_linear(dataset, **kwargs):
"\n Calculates Fleiss' :math:`\\pi` (or multi-:math:`\\pi`), originally proposed in\n [Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K`\n [SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\\pi`\n [Scott1955]_.\n "
... | 5,529,600,764,925,489,000 | Calculates Fleiss' :math:`\pi` (or multi-:math:`\pi`), originally proposed in
[Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K`
[SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\pi`
[Scott1955]_. | segeval/agreement/pi.py | fleiss_pi_linear | cfournie/segmentation.evaluation | python | def fleiss_pi_linear(dataset, **kwargs):
"\n Calculates Fleiss' :math:`\\pi` (or multi-:math:`\\pi`), originally proposed in\n [Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K`\n [SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\\pi`\n [Scott1955]_.\n "
... |
def _parse_general_counters(self, init_config):
'\n Return a dictionary for each job counter\n {\n counter_group_name: [\n counter_name\n ]\n }\n }\n '
job_counter = {}
if init_config.get('general_counters'):
for counter_group in ... | -5,951,628,006,159,175,000 | Return a dictionary for each job counter
{
counter_group_name: [
counter_name
]
}
} | checks.d/mapreduce.py | _parse_general_counters | WPMedia/dd-agent | python | def _parse_general_counters(self, init_config):
'\n Return a dictionary for each job counter\n {\n counter_group_name: [\n counter_name\n ]\n }\n }\n '
job_counter = {}
if init_config.get('general_counters'):
for counter_group in ... |
def _parse_job_specific_counters(self, init_config):
'\n Return a dictionary for each job counter\n {\n job_name: {\n counter_group_name: [\n counter_name\n ]\n }\n }\n }\n '
job_counter = {}
if init_config.get('... | -1,406,056,200,574,110,500 | Return a dictionary for each job counter
{
job_name: {
counter_group_name: [
counter_name
]
}
}
} | checks.d/mapreduce.py | _parse_job_specific_counters | WPMedia/dd-agent | python | def _parse_job_specific_counters(self, init_config):
'\n Return a dictionary for each job counter\n {\n job_name: {\n counter_group_name: [\n counter_name\n ]\n }\n }\n }\n '
job_counter = {}
if init_config.get('... |
def _get_running_app_ids(self, rm_address, **kwargs):
'\n Return a dictionary of {app_id: (app_name, tracking_url)} for the running MapReduce applications\n '
metrics_json = self._rest_request_to_json(rm_address, YARN_APPS_PATH, YARN_SERVICE_CHECK, states=YARN_APPLICATION_STATES, applicationTypes=... | -2,774,981,823,774,166,500 | Return a dictionary of {app_id: (app_name, tracking_url)} for the running MapReduce applications | checks.d/mapreduce.py | _get_running_app_ids | WPMedia/dd-agent | python | def _get_running_app_ids(self, rm_address, **kwargs):
'\n \n '
metrics_json = self._rest_request_to_json(rm_address, YARN_APPS_PATH, YARN_SERVICE_CHECK, states=YARN_APPLICATION_STATES, applicationTypes=YARN_APPLICATION_TYPES)
running_apps = {}
if metrics_json.get('apps'):
if (metri... |
def _mapreduce_job_metrics(self, running_apps, addl_tags):
"\n Get metrics for each MapReduce job.\n Return a dictionary for each MapReduce job\n {\n job_id: {\n 'job_name': job_name,\n 'app_name': app_name,\n 'user_name': user_name,\n 'track... | -1,703,499,876,109,679,600 | Get metrics for each MapReduce job.
Return a dictionary for each MapReduce job
{
job_id: {
'job_name': job_name,
'app_name': app_name,
'user_name': user_name,
'tracking_url': tracking_url
} | checks.d/mapreduce.py | _mapreduce_job_metrics | WPMedia/dd-agent | python | def _mapreduce_job_metrics(self, running_apps, addl_tags):
"\n Get metrics for each MapReduce job.\n Return a dictionary for each MapReduce job\n {\n job_id: {\n 'job_name': job_name,\n 'app_name': app_name,\n 'user_name': user_name,\n 'track... |
def _mapreduce_job_counters_metrics(self, running_jobs, addl_tags):
'\n Get custom metrics specified for each counter\n '
for (job_id, job_metrics) in running_jobs.iteritems():
job_name = job_metrics['job_name']
if (self.general_counters or (job_name in self.job_specific_counters))... | 1,464,761,827,869,469,700 | Get custom metrics specified for each counter | checks.d/mapreduce.py | _mapreduce_job_counters_metrics | WPMedia/dd-agent | python | def _mapreduce_job_counters_metrics(self, running_jobs, addl_tags):
'\n \n '
for (job_id, job_metrics) in running_jobs.iteritems():
job_name = job_metrics['job_name']
if (self.general_counters or (job_name in self.job_specific_counters)):
job_specific_metrics = self.job... |
def _mapreduce_task_metrics(self, running_jobs, addl_tags):
"\n Get metrics for each MapReduce task\n Return a dictionary of {task_id: 'tracking_url'} for each MapReduce task\n "
for (job_id, job_stats) in running_jobs.iteritems():
metrics_json = self._rest_request_to_json(job_stats... | -522,691,520,259,828,400 | Get metrics for each MapReduce task
Return a dictionary of {task_id: 'tracking_url'} for each MapReduce task | checks.d/mapreduce.py | _mapreduce_task_metrics | WPMedia/dd-agent | python | def _mapreduce_task_metrics(self, running_jobs, addl_tags):
"\n Get metrics for each MapReduce task\n Return a dictionary of {task_id: 'tracking_url'} for each MapReduce task\n "
for (job_id, job_stats) in running_jobs.iteritems():
metrics_json = self._rest_request_to_json(job_stats... |
def _set_metrics_from_json(self, tags, metrics_json, metrics):
'\n Parse the JSON response and set the metrics\n '
for (status, (metric_name, metric_type)) in metrics.iteritems():
metric_status = metrics_json.get(status)
if (metric_status is not None):
self._set_metric(... | -362,926,577,410,417,300 | Parse the JSON response and set the metrics | checks.d/mapreduce.py | _set_metrics_from_json | WPMedia/dd-agent | python | def _set_metrics_from_json(self, tags, metrics_json, metrics):
'\n \n '
for (status, (metric_name, metric_type)) in metrics.iteritems():
metric_status = metrics_json.get(status)
if (metric_status is not None):
self._set_metric(metric_name, metric_type, metric_status, ta... |
def _set_metric(self, metric_name, metric_type, value, tags=None, device_name=None):
'\n Set a metric\n '
if (metric_type == HISTOGRAM):
self.histogram(metric_name, value, tags=tags, device_name=device_name)
elif (metric_type == INCREMENT):
self.increment(metric_name, value, ta... | -4,149,084,100,854,976,500 | Set a metric | checks.d/mapreduce.py | _set_metric | WPMedia/dd-agent | python | def _set_metric(self, metric_name, metric_type, value, tags=None, device_name=None):
'\n \n '
if (metric_type == HISTOGRAM):
self.histogram(metric_name, value, tags=tags, device_name=device_name)
elif (metric_type == INCREMENT):
self.increment(metric_name, value, tags=tags, dev... |
def _rest_request_to_json(self, address, object_path, service_name, *args, **kwargs):
'\n Query the given URL and return the JSON response\n '
response_json = None
service_check_tags = [('url:%s' % self._get_url_base(address))]
url = address
if object_path:
url = self._join_url... | -6,006,647,949,090,792,000 | Query the given URL and return the JSON response | checks.d/mapreduce.py | _rest_request_to_json | WPMedia/dd-agent | python | def _rest_request_to_json(self, address, object_path, service_name, *args, **kwargs):
'\n \n '
response_json = None
service_check_tags = [('url:%s' % self._get_url_base(address))]
url = address
if object_path:
url = self._join_url_dir(url, object_path)
if args:
for ... |
def _join_url_dir(self, url, *args):
'\n Join a URL with multiple directories\n '
for path in args:
url = (url.rstrip('/') + '/')
url = urljoin(url, path.lstrip('/'))
return url | 8,838,647,529,342,381,000 | Join a URL with multiple directories | checks.d/mapreduce.py | _join_url_dir | WPMedia/dd-agent | python | def _join_url_dir(self, url, *args):
'\n \n '
for path in args:
url = (url.rstrip('/') + '/')
url = urljoin(url, path.lstrip('/'))
return url |
def _get_url_base(self, url):
'\n Return the base of a URL\n '
s = urlsplit(url)
return urlunsplit([s.scheme, s.netloc, '', '', '']) | 8,414,673,978,274,218,000 | Return the base of a URL | checks.d/mapreduce.py | _get_url_base | WPMedia/dd-agent | python | def _get_url_base(self, url):
'\n \n '
s = urlsplit(url)
return urlunsplit([s.scheme, s.netloc, , , ]) |
def build_gui_help_add_sine_attr():
' Creates GUI for Make Stretchy IK '
window_name = 'build_gui_help_add_sine_attr'
if cmds.window(window_name, exists=True):
cmds.deleteUI(window_name, window=True)
cmds.window(window_name, title=(script_name + ' Help'), mnb=False, mxb=False, s=True)
cmds.w... | 8,131,861,237,518,420,000 | Creates GUI for Make Stretchy IK | python-scripts/gt_add_sine_attributes.py | build_gui_help_add_sine_attr | freemanpro/gt-tools | python | def build_gui_help_add_sine_attr():
' '
window_name = 'build_gui_help_add_sine_attr'
if cmds.window(window_name, exists=True):
cmds.deleteUI(window_name, window=True)
cmds.window(window_name, title=(script_name + ' Help'), mnb=False, mxb=False, s=True)
cmds.window(window_name, e=True, s=Tru... |
def add_sine_attributes(obj, sine_prefix='sine', tick_source_attr='time1.outTime', hide_unkeyable=True, add_absolute_output=False, nice_name_prefix=True):
' \n Create Sine function without using third-party plugins or expressions\n \n Parameters:\n obj (string): Name of the object\n ... | -4,182,535,674,872,841,000 | Create Sine function without using third-party plugins or expressions
Parameters:
obj (string): Name of the object
sine (string): Prefix given to the name of the attributes (default is "sine")
tick_source_attr (string): Name of the attribute used as the source for time. It u... | python-scripts/gt_add_sine_attributes.py | add_sine_attributes | freemanpro/gt-tools | python | def add_sine_attributes(obj, sine_prefix='sine', tick_source_attr='time1.outTime', hide_unkeyable=True, add_absolute_output=False, nice_name_prefix=True):
' \n Create Sine function without using third-party plugins or expressions\n \n Parameters:\n obj (string): Name of the object\n ... |
def validate_operation():
' Checks elements one last time before running the script '
is_valid = False
stretchy_name = None
add_abs_output_value = cmds.checkBox(add_abs_output_chkbox, q=True, value=True)
add_prefix_nn_value = cmds.checkBox(add_prefix_nn_chkbox, q=True, value=True)
stretchy_prefi... | -4,784,751,434,494,775,000 | Checks elements one last time before running the script | python-scripts/gt_add_sine_attributes.py | validate_operation | freemanpro/gt-tools | python | def validate_operation():
' '
is_valid = False
stretchy_name = None
add_abs_output_value = cmds.checkBox(add_abs_output_chkbox, q=True, value=True)
add_prefix_nn_value = cmds.checkBox(add_prefix_nn_chkbox, q=True, value=True)
stretchy_prefix = cmds.textField(stretchy_system_prefix, q=True, text... |
def close_help_gui():
' Closes Help Window '
if cmds.window(window_name, exists=True):
cmds.deleteUI(window_name, window=True) | -6,909,947,570,174,779,000 | Closes Help Window | python-scripts/gt_add_sine_attributes.py | close_help_gui | freemanpro/gt-tools | python | def close_help_gui():
' '
if cmds.window(window_name, exists=True):
cmds.deleteUI(window_name, window=True) |
@event('manager.startup')
def init_parsers(manager):
'Prepare our list of parsing plugins and default parsers.'
for parser_type in PARSER_TYPES:
parsers[parser_type] = {}
for p in plugin.get_plugins(group=(parser_type + '_parser')):
parsers[parser_type][p.name.replace('parser_', '')]... | 8,651,478,703,859,171,000 | Prepare our list of parsing plugins and default parsers. | flexget/plugins/parsers/plugin_parsing.py | init_parsers | jbones89/Flexget | python | @event('manager.startup')
def init_parsers(manager):
for parser_type in PARSER_TYPES:
parsers[parser_type] = {}
for p in plugin.get_plugins(group=(parser_type + '_parser')):
parsers[parser_type][p.name.replace('parser_', )] = p.instance
func_name = ('parse_' + parser_type)
... |
def parse_series(self, data, name=None, **kwargs):
'\n Use the selected series parser to parse series information from `data`\n\n :param data: The raw string to parse information from.\n :param name: The series name to parse data for. If not supplied, parser will attempt to guess series name\n ... | 5,577,953,992,979,921,000 | Use the selected series parser to parse series information from `data`
:param data: The raw string to parse information from.
:param name: The series name to parse data for. If not supplied, parser will attempt to guess series name
automatically from `data`.
:returns: An object containing the parsed information. ... | flexget/plugins/parsers/plugin_parsing.py | parse_series | jbones89/Flexget | python | def parse_series(self, data, name=None, **kwargs):
'\n Use the selected series parser to parse series information from `data`\n\n :param data: The raw string to parse information from.\n :param name: The series name to parse data for. If not supplied, parser will attempt to guess series name\n ... |
def parse_movie(self, data, **kwargs):
'\n Use the selected movie parser to parse movie information from `data`\n\n :param data: The raw string to parse information from\n\n :returns: An object containing the parsed information. The `valid` attribute will be set depending on success.\n '... | 3,685,681,231,583,774,700 | Use the selected movie parser to parse movie information from `data`
:param data: The raw string to parse information from
:returns: An object containing the parsed information. The `valid` attribute will be set depending on success. | flexget/plugins/parsers/plugin_parsing.py | parse_movie | jbones89/Flexget | python | def parse_movie(self, data, **kwargs):
'\n Use the selected movie parser to parse movie information from `data`\n\n :param data: The raw string to parse information from\n\n :returns: An object containing the parsed information. The `valid` attribute will be set depending on success.\n '... |
def save_users(users, filename='output.csv'):
"Save users out to a .csv file\n\n Each row will represent a user UID, following by all the user's students\n (if the user has any)\n\n INPUT:\n > users: set of User objects\n > filename: filename to save .csv to."
with open(filename, 'w') as ... | -6,584,503,492,924,957,000 | Save users out to a .csv file
Each row will represent a user UID, following by all the user's students
(if the user has any)
INPUT:
> users: set of User objects
> filename: filename to save .csv to. | save_load.py | save_users | Garrett-R/infections | python | def save_users(users, filename='output.csv'):
"Save users out to a .csv file\n\n Each row will represent a user UID, following by all the user's students\n (if the user has any)\n\n INPUT:\n > users: set of User objects\n > filename: filename to save .csv to."
with open(filename, 'w') as ... |
def load_users(filename):
"Load users from a .csv file\n\n Each row will represent a user uid, following by all the user's student\n (if the user has any). Note: the uid is not assumed to be an integer,\n so it read in as a string, which shouldn't matter anyway.\n\n TODO: we could probably speed this u... | 6,156,269,794,225,563,000 | Load users from a .csv file
Each row will represent a user uid, following by all the user's student
(if the user has any). Note: the uid is not assumed to be an integer,
so it read in as a string, which shouldn't matter anyway.
TODO: we could probably speed this up by loading multiple lines at a time.
INPUT:
> ... | save_load.py | load_users | Garrett-R/infections | python | def load_users(filename):
"Load users from a .csv file\n\n Each row will represent a user uid, following by all the user's student\n (if the user has any). Note: the uid is not assumed to be an integer,\n so it read in as a string, which shouldn't matter anyway.\n\n TODO: we could probably speed this u... |
def check_best(self, metric_dict):
'\n Hook function, called after metrics are calculated\n '
if (metric_dict['bl_acc'] > self.best_value):
if (self.iters > 0):
LOGGER.text(f"Evaluation improved from {self.best_value} to {metric_dict['bl_acc']}", level=LoggerObserver.INFO)
... | 969,269,354,907,937,000 | Hook function, called after metrics are calculated | theseus/classification/trainer/trainer.py | check_best | lannguyen0910/theseus | python | def check_best(self, metric_dict):
'\n \n '
if (metric_dict['bl_acc'] > self.best_value):
if (self.iters > 0):
LOGGER.text(f"Evaluation improved from {self.best_value} to {metric_dict['bl_acc']}", level=LoggerObserver.INFO)
self.best_value = metric_dict['bl_acc']
... |
def save_checkpoint(self, outname='last'):
'\n Save all information of the current iteration\n '
weights = {'model': self.model.model.state_dict(), 'optimizer': self.optimizer.state_dict(), 'iters': self.iters, 'best_value': self.best_value}
if (self.scaler is not None):
weights[self.s... | -3,763,531,432,588,769,300 | Save all information of the current iteration | theseus/classification/trainer/trainer.py | save_checkpoint | lannguyen0910/theseus | python | def save_checkpoint(self, outname='last'):
'\n \n '
weights = {'model': self.model.model.state_dict(), 'optimizer': self.optimizer.state_dict(), 'iters': self.iters, 'best_value': self.best_value}
if (self.scaler is not None):
weights[self.scaler.state_dict_key] = self.scaler.state_dic... |
def load_checkpoint(self, path: str):
'\n Load all information the current iteration from checkpoint \n '
LOGGER.text('Loading checkpoints...', level=LoggerObserver.INFO)
state_dict = torch.load(path, map_location='cpu')
self.iters = load_state_dict(self.iters, state_dict, 'iters')
sel... | -1,633,654,067,348,363,500 | Load all information the current iteration from checkpoint | theseus/classification/trainer/trainer.py | load_checkpoint | lannguyen0910/theseus | python | def load_checkpoint(self, path: str):
'\n \n '
LOGGER.text('Loading checkpoints...', level=LoggerObserver.INFO)
state_dict = torch.load(path, map_location='cpu')
self.iters = load_state_dict(self.iters, state_dict, 'iters')
self.best_value = load_state_dict(self.best_value, state_dict... |
def visualize_gt(self):
'\n Visualize dataloader for sanity check \n '
LOGGER.text('Visualizing dataset...', level=LoggerObserver.DEBUG)
visualizer = Visualizer()
batch = next(iter(self.trainloader))
images = batch['inputs']
batch = []
for (idx, inputs) in enumerate(images):
... | 7,366,678,842,902,099,000 | Visualize dataloader for sanity check | theseus/classification/trainer/trainer.py | visualize_gt | lannguyen0910/theseus | python | def visualize_gt(self):
'\n \n '
LOGGER.text('Visualizing dataset...', level=LoggerObserver.DEBUG)
visualizer = Visualizer()
batch = next(iter(self.trainloader))
images = batch['inputs']
batch = []
for (idx, inputs) in enumerate(images):
img_show = visualizer.denormali... |
@torch.enable_grad()
def visualize_pred(self):
'Visualize model prediction and CAM\n \n '
LOGGER.text('Visualizing model predictions...', level=LoggerObserver.DEBUG)
visualizer = Visualizer()
batch = next(iter(self.valloader))
images = batch['inputs']
targets = batch['targets']
... | -2,684,438,551,393,966,000 | Visualize model prediction and CAM | theseus/classification/trainer/trainer.py | visualize_pred | lannguyen0910/theseus | python | @torch.enable_grad()
def visualize_pred(self):
'\n \n '
LOGGER.text('Visualizing model predictions...', level=LoggerObserver.DEBUG)
visualizer = Visualizer()
batch = next(iter(self.valloader))
images = batch['inputs']
targets = batch['targets']
self.model.eval()
model_name ... |
def analyze_gt(self):
'\n Perform simple data analysis\n '
LOGGER.text('Analyzing datasets...', level=LoggerObserver.DEBUG)
analyzer = ClassificationAnalyzer()
analyzer.add_dataset(self.trainloader.dataset)
fig = analyzer.analyze(figsize=(10, 5))
LOGGER.log([{'tag': 'Sanitycheck/an... | 2,394,997,057,899,787,000 | Perform simple data analysis | theseus/classification/trainer/trainer.py | analyze_gt | lannguyen0910/theseus | python | def analyze_gt(self):
'\n \n '
LOGGER.text('Analyzing datasets...', level=LoggerObserver.DEBUG)
analyzer = ClassificationAnalyzer()
analyzer.add_dataset(self.trainloader.dataset)
fig = analyzer.analyze(figsize=(10, 5))
LOGGER.log([{'tag': 'Sanitycheck/analysis/train', 'value': fig,... |
def sanitycheck(self):
'Sanity check before training\n '
self.visualize_gt()
self.analyze_gt()
self.visualize_model()
self.evaluate_epoch() | 2,781,777,548,193,682,000 | Sanity check before training | theseus/classification/trainer/trainer.py | sanitycheck | lannguyen0910/theseus | python | def sanitycheck(self):
'\n '
self.visualize_gt()
self.analyze_gt()
self.visualize_model()
self.evaluate_epoch() |
def test_message_causes_disconnect(self, message):
'Add a p2p connection that sends a message and check that it disconnects.'
peer = self.nodes[0].add_p2p_connection(P2PInterface())
peer.send_message(message)
peer.wait_for_disconnect()
assert_equal(self.nodes[0].getconnectioncount(), 0) | 1,046,188,331,780,544,800 | Add a p2p connection that sends a message and check that it disconnects. | test/functional/p2p_nobloomfilter_messages.py | test_message_causes_disconnect | BakedInside/Beans-Core | python | def test_message_causes_disconnect(self, message):
peer = self.nodes[0].add_p2p_connection(P2PInterface())
peer.send_message(message)
peer.wait_for_disconnect()
assert_equal(self.nodes[0].getconnectioncount(), 0) |
def evaluate(datasource, select, result_table, model, label_name=None, model_params=None, result_column_names=[], pai_table=None):
'TBD\n '
if (model_params is None):
model_params = {}
validation_metrics = model_params.get('validation.metrics', 'accuracy_score')
validation_metrics = [m.strip(... | -4,562,751,783,326,163,500 | TBD | python/runtime/step/xgboost/evaluate.py | evaluate | awsl-dbq/sqlflow | python | def evaluate(datasource, select, result_table, model, label_name=None, model_params=None, result_column_names=[], pai_table=None):
'\n '
if (model_params is None):
model_params = {}
validation_metrics = model_params.get('validation.metrics', 'accuracy_score')
validation_metrics = [m.strip() f... |
def _store_evaluate_result(preds, feature_file_name, label_desc, result_table, result_column_names, validation_metrics, conn):
'\n Save the evaluation result in the table.\n\n Args:\n preds: the prediction result.\n feature_file_name (str): the file path where the feature dumps.\n label_d... | -7,471,850,992,633,782,000 | Save the evaluation result in the table.
Args:
preds: the prediction result.
feature_file_name (str): the file path where the feature dumps.
label_desc (FieldDesc): the label FieldDesc object.
result_table (str): the result table name.
result_column_names (list[str]): the result column names.
v... | python/runtime/step/xgboost/evaluate.py | _store_evaluate_result | awsl-dbq/sqlflow | python | def _store_evaluate_result(preds, feature_file_name, label_desc, result_table, result_column_names, validation_metrics, conn):
'\n Save the evaluation result in the table.\n\n Args:\n preds: the prediction result.\n feature_file_name (str): the file path where the feature dumps.\n label_d... |
def chunk_fg_comp_dict_by_nbls(fg_model_comps_dict, use_redundancy=False, grp_size_threshold=5):
"\n Order dict keys in order of number of baselines in each group\n\n\n chunk fit_groups in fg_model_comps_dict into chunks where all groups in the\n same chunk have the same number of baselines in each group.\... | -964,472,370,095,410,200 | Order dict keys in order of number of baselines in each group
chunk fit_groups in fg_model_comps_dict into chunks where all groups in the
same chunk have the same number of baselines in each group.
Parameters
----------
fg_model_comps_dict: dict
dictionary with keys that are tuples of tuples of 2-tuples (thats r... | calamity/calibration.py | chunk_fg_comp_dict_by_nbls | aewallwi/calamity | python | def chunk_fg_comp_dict_by_nbls(fg_model_comps_dict, use_redundancy=False, grp_size_threshold=5):
"\n Order dict keys in order of number of baselines in each group\n\n\n chunk fit_groups in fg_model_comps_dict into chunks where all groups in the\n same chunk have the same number of baselines in each group.\... |
def tensorize_fg_model_comps_dict(fg_model_comps_dict, ants_map, nfreqs, use_redundancy=False, dtype=np.float32, notebook_progressbar=False, verbose=False, grp_size_threshold=5):
'Convert per-baseline model components into a Ndata x Ncomponent tensor\n\n Parameters\n ----------\n fg_model_comps_dict: dict\... | -8,335,094,441,089,768,000 | Convert per-baseline model components into a Ndata x Ncomponent tensor
Parameters
----------
fg_model_comps_dict: dict
dictionary where each key is a 2-tuple (nbl, nvecs) referring to the number
of baselines in each vector and the number of vectors. Each 2-tuple points to
a dictionary where each key is the... | calamity/calibration.py | tensorize_fg_model_comps_dict | aewallwi/calamity | python | def tensorize_fg_model_comps_dict(fg_model_comps_dict, ants_map, nfreqs, use_redundancy=False, dtype=np.float32, notebook_progressbar=False, verbose=False, grp_size_threshold=5):
'Convert per-baseline model components into a Ndata x Ncomponent tensor\n\n Parameters\n ----------\n fg_model_comps_dict: dict\... |
def tensorize_data(uvdata, corr_inds, ants_map, polarization, time, data_scale_factor=1.0, weights=None, nsamples_in_weights=False, dtype=np.float32):
'Convert data in uvdata object to a tensor\n\n Parameters\n ----------\n uvdata: UVData object\n UVData object containing data, flags, and nsamples t... | -2,030,708,951,956,363,000 | Convert data in uvdata object to a tensor
Parameters
----------
uvdata: UVData object
UVData object containing data, flags, and nsamples to tensorize.
corr_inds: list
list of list of lists of 2-tuples. Hierarchy of lists is
chunk
group
baseline - (int 2-tuple)
ants_map: dict mapping int... | calamity/calibration.py | tensorize_data | aewallwi/calamity | python | def tensorize_data(uvdata, corr_inds, ants_map, polarization, time, data_scale_factor=1.0, weights=None, nsamples_in_weights=False, dtype=np.float32):
'Convert data in uvdata object to a tensor\n\n Parameters\n ----------\n uvdata: UVData object\n UVData object containing data, flags, and nsamples t... |
def renormalize(uvdata_reference_model, uvdata_deconv, gains, polarization, time, additional_flags=None):
'Remove arbitrary phase and amplitude from deconvolved model and gains.\n\n Parameters\n ----------\n uvdata_reference_model: UVData object\n Reference model for "true" visibilities.\n uvdata... | 2,437,212,031,765,043,700 | Remove arbitrary phase and amplitude from deconvolved model and gains.
Parameters
----------
uvdata_reference_model: UVData object
Reference model for "true" visibilities.
uvdata_deconv: UVData object
"Deconvolved" data solved for in self-cal loop.
gains: UVCal object
Gains solved for in self-cal loop.
pol... | calamity/calibration.py | renormalize | aewallwi/calamity | python | def renormalize(uvdata_reference_model, uvdata_deconv, gains, polarization, time, additional_flags=None):
'Remove arbitrary phase and amplitude from deconvolved model and gains.\n\n Parameters\n ----------\n uvdata_reference_model: UVData object\n Reference model for "true" visibilities.\n uvdata... |
def tensorize_gains(uvcal, polarization, time, dtype=np.float32):
'Helper function to extract gains into fitting tensors.\n\n Parameters\n ----------\n uvcal: UVCal object\n UVCal object holding gain data to tensorize.\n polarization: str\n pol-str of gain to extract.\n time: float\n ... | 933,491,289,463,469,600 | Helper function to extract gains into fitting tensors.
Parameters
----------
uvcal: UVCal object
UVCal object holding gain data to tensorize.
polarization: str
pol-str of gain to extract.
time: float
JD of time to convert to tensor.
dtype: numpy.dtype
dtype of tensors to output.
Returns
-------
gains_... | calamity/calibration.py | tensorize_gains | aewallwi/calamity | python | def tensorize_gains(uvcal, polarization, time, dtype=np.float32):
'Helper function to extract gains into fitting tensors.\n\n Parameters\n ----------\n uvcal: UVCal object\n UVCal object holding gain data to tensorize.\n polarization: str\n pol-str of gain to extract.\n time: float\n ... |
def yield_fg_model_array(nants, nfreqs, fg_model_comps, fg_coeffs, corr_inds):
'Compute tensor foreground model.\n\n Parameters\n ----------\n nants: int\n number of antennas in data to model.\n freqs: int\n number of frequencies in data to model.\n fg_model_comps: list\n list of... | -4,389,784,291,805,237,000 | Compute tensor foreground model.
Parameters
----------
nants: int
number of antennas in data to model.
freqs: int
number of frequencies in data to model.
fg_model_comps: list
list of fg modeling tf.Tensor objects
representing foreground modeling vectors.
Each tensor is (nvecs, ngrps, nbls, nfreqs)
... | calamity/calibration.py | yield_fg_model_array | aewallwi/calamity | python | def yield_fg_model_array(nants, nfreqs, fg_model_comps, fg_coeffs, corr_inds):
'Compute tensor foreground model.\n\n Parameters\n ----------\n nants: int\n number of antennas in data to model.\n freqs: int\n number of frequencies in data to model.\n fg_model_comps: list\n list of... |
def fit_gains_and_foregrounds(g_r, g_i, fg_r, fg_i, data_r, data_i, wgts, fg_comps, corr_inds, use_min=False, tol=1e-14, maxsteps=10000, optimizer='Adamax', freeze_model=False, verbose=False, notebook_progressbar=False, dtype=np.float32, graph_mode=False, n_profile_steps=0, profile_log_dir='./logdir', sky_model_r=None,... | 4,280,224,059,098,685,400 | Run optimization loop to fit gains and foreground components.
Parameters
----------
g_r: tf.Tensor object.
tf.Tensor object holding real parts of gains.
g_i: tf.Tensor object.
tf.Tensor object holding imag parts of gains.
fg_r: list
list of tf.Tensor objects. Each has shape (nvecs, ngrps, 1, 1)
tf.Tens... | calamity/calibration.py | fit_gains_and_foregrounds | aewallwi/calamity | python | def fit_gains_and_foregrounds(g_r, g_i, fg_r, fg_i, data_r, data_i, wgts, fg_comps, corr_inds, use_min=False, tol=1e-14, maxsteps=10000, optimizer='Adamax', freeze_model=False, verbose=False, notebook_progressbar=False, dtype=np.float32, graph_mode=False, n_profile_steps=0, profile_log_dir='./logdir', sky_model_r=None,... |
def insert_model_into_uvdata_tensor(uvdata, time, polarization, ants_map, red_grps, model_r, model_i, scale_factor=1.0):
'Insert fitted tensor values back into uvdata object for tensor mode.\n\n Parameters\n ----------\n uvdata: UVData object\n uvdata object to insert model data into.\n time: flo... | -9,176,988,501,350,762,000 | Insert fitted tensor values back into uvdata object for tensor mode.
Parameters
----------
uvdata: UVData object
uvdata object to insert model data into.
time: float
JD of time to insert.
polarization: str
polarization to insert.
ants_map: dict mapping integers to integers
map between each antenna numb... | calamity/calibration.py | insert_model_into_uvdata_tensor | aewallwi/calamity | python | def insert_model_into_uvdata_tensor(uvdata, time, polarization, ants_map, red_grps, model_r, model_i, scale_factor=1.0):
'Insert fitted tensor values back into uvdata object for tensor mode.\n\n Parameters\n ----------\n uvdata: UVData object\n uvdata object to insert model data into.\n time: flo... |
def insert_gains_into_uvcal(uvcal, time, polarization, gains_re, gains_im):
'Insert tensorized gains back into uvcal object\n\n Parameters\n ----------\n uvdata: UVData object\n uvdata object to insert model data into.\n time: float\n JD of time to insert.\n polarization: str\n p... | 5,082,459,756,504,565,000 | Insert tensorized gains back into uvcal object
Parameters
----------
uvdata: UVData object
uvdata object to insert model data into.
time: float
JD of time to insert.
polarization: str
polarization to insert.
gains_re: dict with int keys and tf.Tensor object values
dictionary mapping i antenna numbers t... | calamity/calibration.py | insert_gains_into_uvcal | aewallwi/calamity | python | def insert_gains_into_uvcal(uvcal, time, polarization, gains_re, gains_im):
'Insert tensorized gains back into uvcal object\n\n Parameters\n ----------\n uvdata: UVData object\n uvdata object to insert model data into.\n time: float\n JD of time to insert.\n polarization: str\n p... |
def tensorize_fg_coeffs(data, wgts, fg_model_comps, notebook_progressbar=False, verbose=False):
'Initialize foreground coefficient tensors from uvdata and modeling component dictionaries.\n\n\n Parameters\n ----------\n data: list\n list of tf.Tensor objects, each with shape (ngrps, nbls, nfreqs)\n ... | -1,908,258,831,102,641,400 | Initialize foreground coefficient tensors from uvdata and modeling component dictionaries.
Parameters
----------
data: list
list of tf.Tensor objects, each with shape (ngrps, nbls, nfreqs)
representing data
wgts: list
list of tf.Tensor objects, each with shape (ngrps, nbls, nfreqs)
representing weight... | calamity/calibration.py | tensorize_fg_coeffs | aewallwi/calamity | python | def tensorize_fg_coeffs(data, wgts, fg_model_comps, notebook_progressbar=False, verbose=False):
'Initialize foreground coefficient tensors from uvdata and modeling component dictionaries.\n\n\n Parameters\n ----------\n data: list\n list of tf.Tensor objects, each with shape (ngrps, nbls, nfreqs)\n ... |
def get_auto_weights(uvdata, delay_extent=25.0):
'\n inverse variance weights from interpolated autocorrelation data\n\n Parameters\n ----------\n uvdata: UVData object\n UVData object containing autocorrelation data to use for computing inverse noise weights.\n offset: float, optional\n ... | -1,132,488,393,028,415,200 | inverse variance weights from interpolated autocorrelation data
Parameters
----------
uvdata: UVData object
UVData object containing autocorrelation data to use for computing inverse noise weights.
offset: float, optional
Fit autocorrelation to delay components with this width.
Returns
-------
data_weights: U... | calamity/calibration.py | get_auto_weights | aewallwi/calamity | python | def get_auto_weights(uvdata, delay_extent=25.0):
'\n inverse variance weights from interpolated autocorrelation data\n\n Parameters\n ----------\n uvdata: UVData object\n UVData object containing autocorrelation data to use for computing inverse noise weights.\n offset: float, optional\n ... |
def calibrate_and_model_tensor(uvdata, fg_model_comps_dict, gains=None, freeze_model=False, optimizer='Adamax', tol=1e-14, maxsteps=10000, include_autos=False, verbose=False, sky_model=None, dtype=np.float32, use_min=False, use_redundancy=False, notebook_progressbar=False, correct_resid=False, correct_model=True, weigh... | 6,425,277,343,294,833,000 | Perform simultaneous calibration and foreground fitting using tensors.
Parameters
----------
uvdata: UVData object
uvdata objet of data to be calibrated.
fg_model_comps_dict: dictionary
dictionary with keys that are tuples of tuples of 2-tuples (thats right, 3 levels)
in the first level, each tuple repres... | calamity/calibration.py | calibrate_and_model_tensor | aewallwi/calamity | python | def calibrate_and_model_tensor(uvdata, fg_model_comps_dict, gains=None, freeze_model=False, optimizer='Adamax', tol=1e-14, maxsteps=10000, include_autos=False, verbose=False, sky_model=None, dtype=np.float32, use_min=False, use_redundancy=False, notebook_progressbar=False, correct_resid=False, correct_model=True, weigh... |
def calibrate_and_model_mixed(uvdata, horizon=1.0, min_dly=0.0, offset=0.0, ant_dly=0.0, include_autos=False, verbose=False, red_tol=1.0, red_tol_freq=0.5, n_angle_bins=200, notebook_progressbar=False, use_redundancy=False, use_tensorflow_to_derive_modeling_comps=False, eigenval_cutoff=1e-10, dtype_matinv=np.float64, r... | 2,490,183,869,147,370,000 | Simultaneously solve for gains and model foregrounds with a mix of DPSS vectors
for baselines with no frequency redundancy and simple_cov components for
groups of baselines that have some frequency redundancy.
Parameters
----------
uvdata: UVData object.
dataset to calibrate and filter.
horizon: float, op... | calamity/calibration.py | calibrate_and_model_mixed | aewallwi/calamity | python | def calibrate_and_model_mixed(uvdata, horizon=1.0, min_dly=0.0, offset=0.0, ant_dly=0.0, include_autos=False, verbose=False, red_tol=1.0, red_tol_freq=0.5, n_angle_bins=200, notebook_progressbar=False, use_redundancy=False, use_tensorflow_to_derive_modeling_comps=False, eigenval_cutoff=1e-10, dtype_matinv=np.float64, r... |
def calibrate_and_model_dpss(uvdata, horizon=1.0, min_dly=0.0, offset=0.0, include_autos=False, verbose=False, red_tol=1.0, notebook_progressbar=False, fg_model_comps_dict=None, **fitting_kwargs):
"Simultaneously solve for gains and model foregrounds with DPSS vectors.\n\n Parameters\n ----------\n uvdata:... | -4,381,372,808,634,719,000 | Simultaneously solve for gains and model foregrounds with DPSS vectors.
Parameters
----------
uvdata: UVData object.
dataset to calibrate and filter.
horizon: float, optional
fraction of baseline delay length to model with dpss modes
unitless.
default is 1.
min_dly: float, optional
minimum delay to... | calamity/calibration.py | calibrate_and_model_dpss | aewallwi/calamity | python | def calibrate_and_model_dpss(uvdata, horizon=1.0, min_dly=0.0, offset=0.0, include_autos=False, verbose=False, red_tol=1.0, notebook_progressbar=False, fg_model_comps_dict=None, **fitting_kwargs):
"Simultaneously solve for gains and model foregrounds with DPSS vectors.\n\n Parameters\n ----------\n uvdata:... |
def read_calibrate_and_model_dpss(input_data_files, input_model_files=None, input_gain_files=None, resid_outfilename=None, gain_outfilename=None, model_outfilename=None, fitted_info_outfilename=None, x_orientation='east', clobber=False, bllen_min=0.0, bllen_max=np.inf, bl_ew_min=0.0, ex_ants=None, select_ants=None, gpu... | -2,364,376,502,582,659,600 | Driver function for using calamity with DPSS modeling.
Parameters
----------
input_data_files: list of strings or UVData object.
list of paths to input files to read in and calibrate.
input_model_files: list of strings or UVData object, optional
list of paths to model files for overal phase/amp reference.
... | calamity/calibration.py | read_calibrate_and_model_dpss | aewallwi/calamity | python | def read_calibrate_and_model_dpss(input_data_files, input_model_files=None, input_gain_files=None, resid_outfilename=None, gain_outfilename=None, model_outfilename=None, fitted_info_outfilename=None, x_orientation='east', clobber=False, bllen_min=0.0, bllen_max=np.inf, bl_ew_min=0.0, ex_ants=None, select_ants=None, gpu... |
def render_formset(formset, **kwargs):
'Render a formset to a Bootstrap layout.'
renderer_cls = get_formset_renderer(**kwargs)
return renderer_cls(formset, **kwargs).render() | 3,676,325,694,860,855,300 | Render a formset to a Bootstrap layout. | src/bootstrap4/forms.py | render_formset | Natureshadow/django-bootstrap4 | python | def render_formset(formset, **kwargs):
renderer_cls = get_formset_renderer(**kwargs)
return renderer_cls(formset, **kwargs).render() |
def render_formset_errors(formset, **kwargs):
'Render formset errors to a Bootstrap layout.'
renderer_cls = get_formset_renderer(**kwargs)
return renderer_cls(formset, **kwargs).render_errors() | -6,894,594,435,518,397,000 | Render formset errors to a Bootstrap layout. | src/bootstrap4/forms.py | render_formset_errors | Natureshadow/django-bootstrap4 | python | def render_formset_errors(formset, **kwargs):
renderer_cls = get_formset_renderer(**kwargs)
return renderer_cls(formset, **kwargs).render_errors() |
def render_form(form, **kwargs):
'Render a form to a Bootstrap layout.'
renderer_cls = get_form_renderer(**kwargs)
return renderer_cls(form, **kwargs).render() | -2,290,790,819,255,574,500 | Render a form to a Bootstrap layout. | src/bootstrap4/forms.py | render_form | Natureshadow/django-bootstrap4 | python | def render_form(form, **kwargs):
renderer_cls = get_form_renderer(**kwargs)
return renderer_cls(form, **kwargs).render() |
def render_form_errors(form, type='all', **kwargs):
'Render form errors to a Bootstrap layout.'
renderer_cls = get_form_renderer(**kwargs)
return renderer_cls(form, **kwargs).render_errors(type) | -6,262,209,671,540,802,000 | Render form errors to a Bootstrap layout. | src/bootstrap4/forms.py | render_form_errors | Natureshadow/django-bootstrap4 | python | def render_form_errors(form, type='all', **kwargs):
renderer_cls = get_form_renderer(**kwargs)
return renderer_cls(form, **kwargs).render_errors(type) |
def render_field(field, **kwargs):
'Render a field to a Bootstrap layout.'
renderer_cls = get_field_renderer(**kwargs)
return renderer_cls(field, **kwargs).render() | 212,413,380,482,624,060 | Render a field to a Bootstrap layout. | src/bootstrap4/forms.py | render_field | Natureshadow/django-bootstrap4 | python | def render_field(field, **kwargs):
renderer_cls = get_field_renderer(**kwargs)
return renderer_cls(field, **kwargs).render() |
def render_label(content, label_for=None, label_class=None, label_title=''):
'Render a label with content.'
attrs = {}
if label_for:
attrs['for'] = label_for
if label_class:
attrs['class'] = label_class
if label_title:
attrs['title'] = label_title
return render_tag('label... | 4,835,622,836,655,177,000 | Render a label with content. | src/bootstrap4/forms.py | render_label | Natureshadow/django-bootstrap4 | python | def render_label(content, label_for=None, label_class=None, label_title=):
attrs = {}
if label_for:
attrs['for'] = label_for
if label_class:
attrs['class'] = label_class
if label_title:
attrs['title'] = label_title
return render_tag('label', attrs=attrs, content=content) |
def render_button(content, button_type=None, button_class='btn-primary', size='', href='', name=None, value=None, title=None, extra_classes='', id=''):
'Render a button with content.'
attrs = {}
classes = add_css_class('btn', button_class)
size = text_value(size).lower().strip()
if (size == 'xs'):
... | 5,598,860,407,885,194,000 | Render a button with content. | src/bootstrap4/forms.py | render_button | Natureshadow/django-bootstrap4 | python | def render_button(content, button_type=None, button_class='btn-primary', size=, href=, name=None, value=None, title=None, extra_classes=, id=):
attrs = {}
classes = add_css_class('btn', button_class)
size = text_value(size).lower().strip()
if (size == 'xs'):
classes = add_css_class(classes,... |
def render_field_and_label(field, label, field_class='', label_for=None, label_class='', layout='', **kwargs):
'Render a field with its label.'
if (layout == 'horizontal'):
if (not label_class):
label_class = get_bootstrap_setting('horizontal_label_class')
if (not field_class):
... | 2,039,437,234,522,795,500 | Render a field with its label. | src/bootstrap4/forms.py | render_field_and_label | Natureshadow/django-bootstrap4 | python | def render_field_and_label(field, label, field_class=, label_for=None, label_class=, layout=, **kwargs):
if (layout == 'horizontal'):
if (not label_class):
label_class = get_bootstrap_setting('horizontal_label_class')
if (not field_class):
field_class = get_bootstrap_set... |
def render_form_group(content, css_class=FORM_GROUP_CLASS):
'Render a Bootstrap form group.'
return f'<div class="{css_class}">{content}</div>' | -311,337,625,377,371,400 | Render a Bootstrap form group. | src/bootstrap4/forms.py | render_form_group | Natureshadow/django-bootstrap4 | python | def render_form_group(content, css_class=FORM_GROUP_CLASS):
return f'<div class="{css_class}">{content}</div>' |
def is_widget_with_placeholder(widget):
'\n Return whether this widget should have a placeholder.\n\n Only text, text area, number, e-mail, url, password, number and derived inputs have placeholders.\n '
return isinstance(widget, (TextInput, Textarea, NumberInput, EmailInput, URLInput, PasswordInput)) | 119,742,989,659,189,860 | Return whether this widget should have a placeholder.
Only text, text area, number, e-mail, url, password, number and derived inputs have placeholders. | src/bootstrap4/forms.py | is_widget_with_placeholder | Natureshadow/django-bootstrap4 | python | def is_widget_with_placeholder(widget):
'\n Return whether this widget should have a placeholder.\n\n Only text, text area, number, e-mail, url, password, number and derived inputs have placeholders.\n '
return isinstance(widget, (TextInput, Textarea, NumberInput, EmailInput, URLInput, PasswordInput)) |
def _init_features(self):
'Set up the repository of available Data ONTAP features.'
self.features = na_utils.Features() | 3,182,935,898,800,352,000 | Set up the repository of available Data ONTAP features. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | _init_features | sapcc/cinder | python | def _init_features(self):
self.features = na_utils.Features() |
def get_ontap_version(self, cached=True):
'Gets the ONTAP version.'
if cached:
return self.connection.get_ontap_version()
ontap_version = netapp_api.NaElement('system-get-version')
result = self.connection.invoke_successfully(ontap_version, True)
version_tuple = (result.get_child_by_name('ve... | -6,697,713,649,390,994,000 | Gets the ONTAP version. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_ontap_version | sapcc/cinder | python | def get_ontap_version(self, cached=True):
if cached:
return self.connection.get_ontap_version()
ontap_version = netapp_api.NaElement('system-get-version')
result = self.connection.invoke_successfully(ontap_version, True)
version_tuple = (result.get_child_by_name('version-tuple') or netapp_a... |
def get_ontapi_version(self, cached=True):
'Gets the supported ontapi version.'
if cached:
return self.connection.get_api_version()
ontapi_version = netapp_api.NaElement('system-get-ontapi-version')
res = self.connection.invoke_successfully(ontapi_version, False)
major = res.get_child_conten... | 7,650,952,949,425,096,000 | Gets the supported ontapi version. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_ontapi_version | sapcc/cinder | python | def get_ontapi_version(self, cached=True):
if cached:
return self.connection.get_api_version()
ontapi_version = netapp_api.NaElement('system-get-ontapi-version')
res = self.connection.invoke_successfully(ontapi_version, False)
major = res.get_child_content('major-version')
minor = res.g... |
def check_is_naelement(self, elem):
'Checks if object is instance of NaElement.'
if (not isinstance(elem, netapp_api.NaElement)):
raise ValueError('Expects NaElement') | 2,203,974,980,253,227,500 | Checks if object is instance of NaElement. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | check_is_naelement | sapcc/cinder | python | def check_is_naelement(self, elem):
if (not isinstance(elem, netapp_api.NaElement)):
raise ValueError('Expects NaElement') |
def create_lun(self, volume_name, lun_name, size, metadata, qos_policy_group_name=None):
'Issues API request for creating LUN on volume.'
path = ('/vol/%s/%s' % (volume_name, lun_name))
space_reservation = metadata['SpaceReserved']
initial_size = size
ontap_version = self.get_ontap_version()
if ... | -900,611,365,462,998,100 | Issues API request for creating LUN on volume. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | create_lun | sapcc/cinder | python | def create_lun(self, volume_name, lun_name, size, metadata, qos_policy_group_name=None):
path = ('/vol/%s/%s' % (volume_name, lun_name))
space_reservation = metadata['SpaceReserved']
initial_size = size
ontap_version = self.get_ontap_version()
if (ontap_version < '9.5'):
initial_size = ... |
def set_lun_space_reservation(self, path, flag):
'Sets the LUN space reservation on ONTAP.'
lun_modify_space_reservation = netapp_api.NaElement.create_node_with_children('lun-set-space-reservation-info', **{'path': path, 'enable': str(flag)})
self.connection.invoke_successfully(lun_modify_space_reservation,... | 3,347,695,314,018,310,700 | Sets the LUN space reservation on ONTAP. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | set_lun_space_reservation | sapcc/cinder | python | def set_lun_space_reservation(self, path, flag):
lun_modify_space_reservation = netapp_api.NaElement.create_node_with_children('lun-set-space-reservation-info', **{'path': path, 'enable': str(flag)})
self.connection.invoke_successfully(lun_modify_space_reservation, True) |
def destroy_lun(self, path, force=True):
'Destroys the LUN at the path.'
lun_destroy = netapp_api.NaElement.create_node_with_children('lun-destroy', **{'path': path})
if force:
lun_destroy.add_new_child('force', 'true')
self.connection.invoke_successfully(lun_destroy, True)
seg = path.split(... | 7,368,047,698,348,012,000 | Destroys the LUN at the path. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | destroy_lun | sapcc/cinder | python | def destroy_lun(self, path, force=True):
lun_destroy = netapp_api.NaElement.create_node_with_children('lun-destroy', **{'path': path})
if force:
lun_destroy.add_new_child('force', 'true')
self.connection.invoke_successfully(lun_destroy, True)
seg = path.split('/')
LOG.debug('Destroyed L... |
def map_lun(self, path, igroup_name, lun_id=None):
'Maps LUN to the initiator and returns LUN id assigned.'
lun_map = netapp_api.NaElement.create_node_with_children('lun-map', **{'path': path, 'initiator-group': igroup_name})
if lun_id:
lun_map.add_new_child('lun-id', lun_id)
try:
result... | 5,147,786,705,441,598,000 | Maps LUN to the initiator and returns LUN id assigned. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | map_lun | sapcc/cinder | python | def map_lun(self, path, igroup_name, lun_id=None):
lun_map = netapp_api.NaElement.create_node_with_children('lun-map', **{'path': path, 'initiator-group': igroup_name})
if lun_id:
lun_map.add_new_child('lun-id', lun_id)
try:
result = self.connection.invoke_successfully(lun_map, True)
... |
def unmap_lun(self, path, igroup_name):
'Unmaps a LUN from given initiator.'
lun_unmap = netapp_api.NaElement.create_node_with_children('lun-unmap', **{'path': path, 'initiator-group': igroup_name})
try:
self.connection.invoke_successfully(lun_unmap, True)
except netapp_api.NaApiError as e:
... | 682,668,600,462,337,700 | Unmaps a LUN from given initiator. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | unmap_lun | sapcc/cinder | python | def unmap_lun(self, path, igroup_name):
lun_unmap = netapp_api.NaElement.create_node_with_children('lun-unmap', **{'path': path, 'initiator-group': igroup_name})
try:
self.connection.invoke_successfully(lun_unmap, True)
except netapp_api.NaApiError as e:
exc_info = sys.exc_info()
... |
def create_igroup(self, igroup, igroup_type='iscsi', os_type='default'):
'Creates igroup with specified args.'
igroup_create = netapp_api.NaElement.create_node_with_children('igroup-create', **{'initiator-group-name': igroup, 'initiator-group-type': igroup_type, 'os-type': os_type})
self.connection.invoke_s... | 4,476,615,876,935,663,000 | Creates igroup with specified args. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | create_igroup | sapcc/cinder | python | def create_igroup(self, igroup, igroup_type='iscsi', os_type='default'):
igroup_create = netapp_api.NaElement.create_node_with_children('igroup-create', **{'initiator-group-name': igroup, 'initiator-group-type': igroup_type, 'os-type': os_type})
self.connection.invoke_successfully(igroup_create, True) |
def add_igroup_initiator(self, igroup, initiator):
'Adds initiators to the specified igroup.'
igroup_add = netapp_api.NaElement.create_node_with_children('igroup-add', **{'initiator-group-name': igroup, 'initiator': initiator})
self.connection.invoke_successfully(igroup_add, True) | -6,145,192,281,599,506,000 | Adds initiators to the specified igroup. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | add_igroup_initiator | sapcc/cinder | python | def add_igroup_initiator(self, igroup, initiator):
igroup_add = netapp_api.NaElement.create_node_with_children('igroup-add', **{'initiator-group-name': igroup, 'initiator': initiator})
self.connection.invoke_successfully(igroup_add, True) |
def do_direct_resize(self, path, new_size_bytes, force=True):
'Resize the LUN.'
seg = path.split('/')
LOG.info('Resizing LUN %s directly to new size.', seg[(- 1)])
lun_resize = netapp_api.NaElement.create_node_with_children('lun-resize', **{'path': path, 'size': new_size_bytes})
if force:
lu... | 6,683,164,055,784,541,000 | Resize the LUN. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | do_direct_resize | sapcc/cinder | python | def do_direct_resize(self, path, new_size_bytes, force=True):
seg = path.split('/')
LOG.info('Resizing LUN %s directly to new size.', seg[(- 1)])
lun_resize = netapp_api.NaElement.create_node_with_children('lun-resize', **{'path': path, 'size': new_size_bytes})
if force:
lun_resize.add_new_... |
def get_lun_geometry(self, path):
'Gets the LUN geometry.'
geometry = {}
lun_geo = netapp_api.NaElement('lun-get-geometry')
lun_geo.add_new_child('path', path)
try:
result = self.connection.invoke_successfully(lun_geo, True)
geometry['size'] = result.get_child_content('size')
... | -6,749,439,803,077,511,000 | Gets the LUN geometry. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_lun_geometry | sapcc/cinder | python | def get_lun_geometry(self, path):
geometry = {}
lun_geo = netapp_api.NaElement('lun-get-geometry')
lun_geo.add_new_child('path', path)
try:
result = self.connection.invoke_successfully(lun_geo, True)
geometry['size'] = result.get_child_content('size')
geometry['bytes_per_sec... |
def get_volume_options(self, volume_name):
'Get the value for the volume option.'
opts = []
vol_option_list = netapp_api.NaElement('volume-options-list-info')
vol_option_list.add_new_child('volume', volume_name)
result = self.connection.invoke_successfully(vol_option_list, True)
options = result... | -4,522,512,593,235,644,400 | Get the value for the volume option. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_volume_options | sapcc/cinder | python | def get_volume_options(self, volume_name):
opts = []
vol_option_list = netapp_api.NaElement('volume-options-list-info')
vol_option_list.add_new_child('volume', volume_name)
result = self.connection.invoke_successfully(vol_option_list, True)
options = result.get_child_by_name('options')
if o... |
def move_lun(self, path, new_path):
'Moves the LUN at path to new path.'
seg = path.split('/')
new_seg = new_path.split('/')
LOG.debug('Moving LUN %(name)s to %(new_name)s.', {'name': seg[(- 1)], 'new_name': new_seg[(- 1)]})
lun_move = netapp_api.NaElement('lun-move')
lun_move.add_new_child('pat... | -297,760,326,579,492,900 | Moves the LUN at path to new path. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | move_lun | sapcc/cinder | python | def move_lun(self, path, new_path):
seg = path.split('/')
new_seg = new_path.split('/')
LOG.debug('Moving LUN %(name)s to %(new_name)s.', {'name': seg[(- 1)], 'new_name': new_seg[(- 1)]})
lun_move = netapp_api.NaElement('lun-move')
lun_move.add_new_child('path', path)
lun_move.add_new_child... |
def get_iscsi_target_details(self):
'Gets the iSCSI target portal details.'
raise NotImplementedError() | 5,800,743,555,444,232,000 | Gets the iSCSI target portal details. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_iscsi_target_details | sapcc/cinder | python | def get_iscsi_target_details(self):
raise NotImplementedError() |
def get_fc_target_wwpns(self):
'Gets the FC target details.'
raise NotImplementedError() | 2,441,588,315,056,165,400 | Gets the FC target details. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_fc_target_wwpns | sapcc/cinder | python | def get_fc_target_wwpns(self):
raise NotImplementedError() |
def get_iscsi_service_details(self):
'Returns iscsi iqn.'
raise NotImplementedError() | -6,930,080,783,860,774,000 | Returns iscsi iqn. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_iscsi_service_details | sapcc/cinder | python | def get_iscsi_service_details(self):
raise NotImplementedError() |
def check_iscsi_initiator_exists(self, iqn):
'Returns True if initiator exists.'
raise NotImplementedError() | 8,060,813,294,559,359,000 | Returns True if initiator exists. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | check_iscsi_initiator_exists | sapcc/cinder | python | def check_iscsi_initiator_exists(self, iqn):
raise NotImplementedError() |
def set_iscsi_chap_authentication(self, iqn, username, password):
"Provides NetApp host's CHAP credentials to the backend."
raise NotImplementedError() | 3,464,418,870,571,428,400 | Provides NetApp host's CHAP credentials to the backend. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | set_iscsi_chap_authentication | sapcc/cinder | python | def set_iscsi_chap_authentication(self, iqn, username, password):
raise NotImplementedError() |
def get_lun_list(self):
'Gets the list of LUNs on filer.'
raise NotImplementedError() | 5,492,762,630,089,887,000 | Gets the list of LUNs on filer. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_lun_list | sapcc/cinder | python | def get_lun_list(self):
raise NotImplementedError() |
def get_igroup_by_initiators(self, initiator_list):
'Get igroups exactly matching a set of initiators.'
raise NotImplementedError() | 7,145,069,194,903,305,000 | Get igroups exactly matching a set of initiators. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_igroup_by_initiators | sapcc/cinder | python | def get_igroup_by_initiators(self, initiator_list):
raise NotImplementedError() |
def _has_luns_mapped_to_initiator(self, initiator):
'Checks whether any LUNs are mapped to the given initiator.'
lun_list_api = netapp_api.NaElement('lun-initiator-list-map-info')
lun_list_api.add_new_child('initiator', initiator)
result = self.connection.invoke_successfully(lun_list_api, True)
lun_... | 9,150,061,350,151,996,000 | Checks whether any LUNs are mapped to the given initiator. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | _has_luns_mapped_to_initiator | sapcc/cinder | python | def _has_luns_mapped_to_initiator(self, initiator):
lun_list_api = netapp_api.NaElement('lun-initiator-list-map-info')
lun_list_api.add_new_child('initiator', initiator)
result = self.connection.invoke_successfully(lun_list_api, True)
lun_maps_container = (result.get_child_by_name('lun-maps') or ne... |
def has_luns_mapped_to_initiators(self, initiator_list):
'Checks whether any LUNs are mapped to the given initiator(s).'
for initiator in initiator_list:
if self._has_luns_mapped_to_initiator(initiator):
return True
return False | 7,278,811,898,778,940,000 | Checks whether any LUNs are mapped to the given initiator(s). | cinder/volume/drivers/netapp/dataontap/client/client_base.py | has_luns_mapped_to_initiators | sapcc/cinder | python | def has_luns_mapped_to_initiators(self, initiator_list):
for initiator in initiator_list:
if self._has_luns_mapped_to_initiator(initiator):
return True
return False |
def get_lun_by_args(self, **args):
'Retrieves LUNs with specified args.'
raise NotImplementedError() | -3,744,136,302,429,453,300 | Retrieves LUNs with specified args. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_lun_by_args | sapcc/cinder | python | def get_lun_by_args(self, **args):
raise NotImplementedError() |
def get_performance_counter_info(self, object_name, counter_name):
'Gets info about one or more Data ONTAP performance counters.'
api_args = {'objectname': object_name}
result = self.connection.send_request('perf-object-counter-list-info', api_args, enable_tunneling=False)
counters = (result.get_child_b... | -1,821,368,557,504,805,400 | Gets info about one or more Data ONTAP performance counters. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_performance_counter_info | sapcc/cinder | python | def get_performance_counter_info(self, object_name, counter_name):
api_args = {'objectname': object_name}
result = self.connection.send_request('perf-object-counter-list-info', api_args, enable_tunneling=False)
counters = (result.get_child_by_name('counters') or netapp_api.NaElement('None'))
for co... |
def delete_snapshot(self, volume_name, snapshot_name):
'Deletes a volume snapshot.'
api_args = {'volume': volume_name, 'snapshot': snapshot_name}
self.connection.send_request('snapshot-delete', api_args) | 9,135,002,389,939,195,000 | Deletes a volume snapshot. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | delete_snapshot | sapcc/cinder | python | def delete_snapshot(self, volume_name, snapshot_name):
api_args = {'volume': volume_name, 'snapshot': snapshot_name}
self.connection.send_request('snapshot-delete', api_args) |
def create_cg_snapshot(self, volume_names, snapshot_name):
'Creates a consistency group snapshot out of one or more flexvols.\n\n ONTAP requires an invocation of cg-start to first fence off the\n flexvols to be included in the snapshot. If cg-start returns\n success, a cg-commit must be execute... | -1,963,789,309,185,197,600 | Creates a consistency group snapshot out of one or more flexvols.
ONTAP requires an invocation of cg-start to first fence off the
flexvols to be included in the snapshot. If cg-start returns
success, a cg-commit must be executed to finalized the snapshot and
unfence the flexvols. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | create_cg_snapshot | sapcc/cinder | python | def create_cg_snapshot(self, volume_names, snapshot_name):
'Creates a consistency group snapshot out of one or more flexvols.\n\n ONTAP requires an invocation of cg-start to first fence off the\n flexvols to be included in the snapshot. If cg-start returns\n success, a cg-commit must be execute... |
def get_snapshot(self, volume_name, snapshot_name):
'Gets a single snapshot.'
raise NotImplementedError() | 2,336,373,799,148,635,000 | Gets a single snapshot. | cinder/volume/drivers/netapp/dataontap/client/client_base.py | get_snapshot | sapcc/cinder | python | def get_snapshot(self, volume_name, snapshot_name):
raise NotImplementedError() |
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