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def __cluster_distance(self, cluster1, cluster2):
"""!
@brief Calculate minimal distance between clusters using representative points.
@param[in] cluster1 (cure_cluster): The first cluster.
@param[in] cluster2 (cure_cluster): The second cluster.
@return (... |
def allocate_observation_matrix(self):
"""!
@brief Allocates observation matrix in line with output dynamic of the network.
@details Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each iteration.
@return (list) Observation matrix... |
def __allocate_neuron_patterns(self, start_iteration, stop_iteration):
"""!
@brief Allocates observation transposed matrix of neurons that is limited by specified periods of simulation.
@details Matrix where state of each neuron is denoted by zero/one in line with Heaviside function on each i... |
def allocate_sync_ensembles(self, steps):
"""!
@brief Allocate clusters in line with ensembles of synchronous neurons where each synchronous ensemble corresponds to only one cluster.
@param[in] steps (double): Amount of steps from the end that is used for analysis. During spe... |
def show_dynamic_matrix(cnn_output_dynamic):
"""!
@brief Shows output dynamic as matrix in grey colors.
@details This type of visualization is convenient for observing allocated clusters.
@param[in] cnn_output_dynamic (cnn_dynamic): Output dynamic of the chaotic neural netw... |
def show_observation_matrix(cnn_output_dynamic):
"""!
@brief Shows observation matrix as black/white blocks.
@details This type of visualization is convenient for observing allocated clusters.
@param[in] cnn_output_dynamic (cnn_dynamic): Output dynamic of the chaotic neural... |
def simulate(self, steps, stimulus):
"""!
@brief Simulates chaotic neural network with extrnal stimulus during specified steps.
@details Stimulus are considered as a coordinates of neurons and in line with that weights
are initialized.
@param[in] steps (ui... |
def __calculate_states(self):
"""!
@brief Calculates new state of each neuron.
@detail There is no any assignment.
@return (list) Returns new states (output).
"""
output = [ 0.0 for _ in range(self.__num_osc) ]
for i ... |
def __neuron_evolution(self, index):
"""!
@brief Calculates state of the neuron with specified index.
@param[in] index (uint): Index of neuron in the network.
@return (double) New output of the specified neuron.
"""
value = 0.0
... |
def __create_weights(self, stimulus):
"""!
@brief Create weights between neurons in line with stimulus.
@param[in] stimulus (list): External stimulus for the chaotic neural network.
"""
self.__average_distance = average_neighbor_distance(stimulu... |
def __create_weights_all_to_all(self, stimulus):
"""!
@brief Create weight all-to-all structure between neurons in line with stimulus.
@param[in] stimulus (list): External stimulus for the chaotic neural network.
"""
for i in range(len(stimulus)... |
def __create_weights_delaunay_triangulation(self, stimulus):
"""!
@brief Create weight Denlauny triangulation structure between neurons in line with stimulus.
@param[in] stimulus (list): External stimulus for the chaotic neural network.
"""
poin... |
def __calculate_weight(self, stimulus1, stimulus2):
"""!
@brief Calculate weight between neurons that have external stimulus1 and stimulus2.
@param[in] stimulus1 (list): External stimulus of the first neuron.
@param[in] stimulus2 (list): External stimulus of the second neur... |
def show_network(self):
"""!
@brief Shows structure of the network: neurons and connections between them.
"""
dimension = len(self.__location[0])
if (dimension != 3) and (dimension != 2):
raise NameError('Network that is located in different ... |
def __create_surface(self, dimension):
"""!
@brief Prepares surface for showing network structure in line with specified dimension.
@param[in] dimension (uint): Dimension of processed data (external stimulus).
@return (tuple) Description of surface for drawing net... |
def show_pattern(syncpr_output_dynamic, image_height, image_width):
"""!
@brief Displays evolution of phase oscillators as set of patterns where the last one means final result of recognition.
@param[in] syncpr_output_dynamic (syncpr_dynamic): Output dynamic of a syncpr network.
... |
def animate_pattern_recognition(syncpr_output_dynamic, image_height, image_width, animation_velocity = 75, title = None, save_movie = None):
"""!
@brief Shows animation of pattern recognition process that has been preformed by the oscillatory network.
@param[in] syncpr_output_dynamic (s... |
def __show_pattern(ax_handle, syncpr_output_dynamic, image_height, image_width, iteration):
"""!
@brief Draws pattern on specified ax.
@param[in] ax_handle (Axis): Axis where pattern should be drawn.
@param[in] syncpr_output_dynamic (syncpr_dynamic): Output dynamic of a syncpr n... |
def train(self, samples):
"""!
@brief Trains syncpr network using Hebbian rule for adjusting strength of connections between oscillators during training.
@param[in] samples (list): list of patterns where each pattern is represented by list of features that are equal to [-1; 1].
... |
def simulate_dynamic(self, pattern, order = 0.998, solution = solve_type.RK4, collect_dynamic = False, step = 0.1, int_step = 0.01, threshold_changes = 0.0000001):
"""!
@brief Performs dynamic simulation of the network until stop condition is not reached.
@details In other words network performs... |
def simulate_static(self, steps, time, pattern, solution = solve_type.FAST, collect_dynamic = False):
"""!
@brief Performs static simulation of syncpr oscillatory network.
@details In other words network performs pattern recognition during simulation.
@param[in] steps (uint): Nu... |
def memory_order(self, pattern):
"""!
@brief Calculates function of the memorized pattern.
@details Throws exception if length of pattern is not equal to size of the network or if it consists feature with value that are not equal to [-1; 1].
@param[in] pattern (list): Pattern fo... |
def __calculate_memory_order(self, pattern):
"""!
@brief Calculates function of the memorized pattern without any pattern validation.
@param[in] pattern (list): Pattern for recognition represented by list of features that are equal to [-1; 1].
@return (double) Order of ... |
def _phase_kuramoto(self, teta, t, argv):
"""!
@brief Returns result of phase calculation for specified oscillator in the network.
@param[in] teta (double): Phase of the oscillator that is differentiated.
@param[in] t (double): Current time of simulation.
@param[in] argv... |
def __validate_pattern(self, pattern):
"""!
@brief Validates pattern.
@details Throws exception if length of pattern is not equal to size of the network or if it consists feature with value that are not equal to [-1; 1].
@param[in] pattern (list): Pattern for recognition represe... |
def process(self):
"""!
@brief Performs cluster analysis in line with rules of K-Medians algorithm.
@return (kmedians) Returns itself (K-Medians instance).
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
... |
def __update_clusters(self):
"""!
@brief Calculate Manhattan distance to each point from the each cluster.
@details Nearest points are captured by according clusters and as a result clusters are updated.
@return (list) updated clusters as list of clusters where each cluste... |
def __update_medians(self):
"""!
@brief Calculate medians of clusters in line with contained objects.
@return (list) list of medians for current number of clusters.
"""
medians = [[] for i in range(len(self.__clusters))]
for... |
def cleanup_old_versions(
src, keep_last_versions,
config_file='config.yaml', profile_name=None,
):
"""Deletes old deployed versions of the function in AWS Lambda.
Won't delete $Latest and any aliased version
:param str src:
The path to your Lambda ready project (folder must contain a vali... |
def deploy(
src, requirements=None, local_package=None,
config_file='config.yaml', profile_name=None,
preserve_vpc=False
):
"""Deploys a new function to AWS Lambda.
:param str src:
The path to your Lambda ready project (folder must contain a valid
config.yaml and handler... |
def deploy_s3(
src, requirements=None, local_package=None,
config_file='config.yaml', profile_name=None,
preserve_vpc=False
):
"""Deploys a new function via AWS S3.
:param str src:
The path to your Lambda ready project (folder must contain a valid
config.yaml and handler module (e.g... |
def upload(
src, requirements=None, local_package=None,
config_file='config.yaml', profile_name=None,
):
"""Uploads a new function to AWS S3.
:param str src:
The path to your Lambda ready project (folder must contain a valid
config.yaml and handler module (e.g.: service.py).
... |
def invoke(
src, event_file='event.json',
config_file='config.yaml', profile_name=None,
verbose=False,
):
"""Simulates a call to your function.
:param str src:
The path to your Lambda ready project (folder must contain a valid
config.yaml and handler module (e.g.: service.py).
:... |
def init(src, minimal=False):
"""Copies template files to a given directory.
:param str src:
The path to output the template lambda project files.
:param bool minimal:
Minimal possible template files (excludes event.json).
"""
templates_path = os.path.join(
os.path.dirname(... |
def build(
src, requirements=None, local_package=None,
config_file='config.yaml', profile_name=None,
):
"""Builds the file bundle.
:param str src:
The path to your Lambda ready project (folder must contain a valid
config.yaml and handler module (e.g.: service.py).
:param str local_pa... |
def get_callable_handler_function(src, handler):
"""Tranlate a string of the form "module.function" into a callable
function.
:param str src:
The path to your Lambda project containing a valid handler file.
:param str handler:
A dot delimited string representing the `<module>.<function name... |
def _install_packages(path, packages):
"""Install all packages listed to the target directory.
Ignores any package that includes Python itself and python-lambda as well
since its only needed for deploying and not running the code
:param str path:
Path to copy installed pip packages to.
:pa... |
def pip_install_to_target(path, requirements=None, local_package=None):
"""For a given active virtualenv, gather all installed pip packages then
copy (re-install) them to the path provided.
:param str path:
Path to copy installed pip packages to.
:param str requirements:
If set, only th... |
def get_role_name(region, account_id, role):
"""Shortcut to insert the `account_id` and `role` into the iam string."""
prefix = ARN_PREFIXES.get(region, 'aws')
return 'arn:{0}:iam::{1}:role/{2}'.format(prefix, account_id, role) |
def get_account_id(
profile_name, aws_access_key_id, aws_secret_access_key,
region=None,
):
"""Query STS for a users' account_id"""
client = get_client(
'sts', profile_name, aws_access_key_id, aws_secret_access_key,
region,
)
return client.get_caller_identity().get('Account') |
def get_client(
client, profile_name, aws_access_key_id, aws_secret_access_key,
region=None,
):
"""Shortcut for getting an initialized instance of the boto3 client."""
boto3.setup_default_session(
profile_name=profile_name,
aws_access_key_id=aws_access_key_id,
aws_secret_access_... |
def create_function(cfg, path_to_zip_file, use_s3=False, s3_file=None):
"""Register and upload a function to AWS Lambda."""
print('Creating your new Lambda function')
byte_stream = read(path_to_zip_file, binary_file=True)
profile_name = cfg.get('profile')
aws_access_key_id = cfg.get('aws_access_key... |
def update_function(
cfg, path_to_zip_file, existing_cfg, use_s3=False, s3_file=None, preserve_vpc=False
):
"""Updates the code of an existing Lambda function"""
print('Updating your Lambda function')
byte_stream = read(path_to_zip_file, binary_file=True)
profile_name = cfg.get('profile')
a... |
def upload_s3(cfg, path_to_zip_file, *use_s3):
"""Upload a function to AWS S3."""
print('Uploading your new Lambda function')
profile_name = cfg.get('profile')
aws_access_key_id = cfg.get('aws_access_key_id')
aws_secret_access_key = cfg.get('aws_secret_access_key')
client = get_client(
... |
def get_function_config(cfg):
"""Check whether a function exists or not and return its config"""
function_name = cfg.get('function_name')
profile_name = cfg.get('profile')
aws_access_key_id = cfg.get('aws_access_key_id')
aws_secret_access_key = cfg.get('aws_secret_access_key')
client = get_clie... |
def cached_download(url, name):
"""Download the data at a URL, and cache it under the given name.
The file is stored under `pyav/test` with the given name in the directory
:envvar:`PYAV_TESTDATA_DIR`, or the first that is writeable of:
- the current virtualenv
- ``/usr/local/share``
- ``/usr/... |
def fate(name):
"""Download and return a path to a sample from the FFmpeg test suite.
Data is handled by :func:`cached_download`.
See the `FFmpeg Automated Test Environment <https://www.ffmpeg.org/fate.html>`_
"""
return cached_download('http://fate.ffmpeg.org/fate-suite/' + name,
... |
def curated(name):
"""Download and return a path to a sample that is curated by the PyAV developers.
Data is handled by :func:`cached_download`.
"""
return cached_download('https://docs.mikeboers.com/pyav/samples/' + name,
os.path.join('pyav-curated', name.replace('/', os.pa... |
def get_library_config(name):
"""Get distutils-compatible extension extras for the given library.
This requires ``pkg-config``.
"""
try:
proc = Popen(['pkg-config', '--cflags', '--libs', name], stdout=PIPE, stderr=PIPE)
except OSError:
print('pkg-config is required for building PyA... |
def update_extend(dst, src):
"""Update the `dst` with the `src`, extending values where lists.
Primiarily useful for integrating results from `get_library_config`.
"""
for k, v in src.items():
existing = dst.setdefault(k, [])
for x in v:
if x not in existing:
... |
def dump_config():
"""Print out all the config information we have so far (for debugging)."""
print('PyAV:', version, git_commit or '(unknown commit)')
print('Python:', sys.version.encode('unicode_escape' if PY3 else 'string-escape'))
print('platform:', platform.platform())
print('extension_extra:')... |
def _CCompiler_spawn_silent(cmd, dry_run=None):
"""Spawn a process, and eat the stdio."""
proc = Popen(cmd, stdout=PIPE, stderr=PIPE)
out, err = proc.communicate()
if proc.returncode:
raise DistutilsExecError(err) |
def new_compiler(*args, **kwargs):
"""Create a C compiler.
:param bool silent: Eat all stdio? Defaults to ``True``.
All other arguments passed to ``distutils.ccompiler.new_compiler``.
"""
make_silent = kwargs.pop('silent', True)
cc = _new_compiler(*args, **kwargs)
# If MSVC10, initialize ... |
def iter_cython(path):
'''Yield all ``.pyx`` and ``.pxd`` files in the given root.'''
for dir_path, dir_names, file_names in os.walk(path):
for file_name in file_names:
if file_name.startswith('.'):
continue
if os.path.splitext(file_name)[1] not in ('.pyx', '.pxd'... |
def cleanup_text (text):
"""
It scrubs the garbled from its stream...
Or it gets the debugger again.
"""
x = " ".join(map(lambda s: s.strip(), text.split("\n"))).strip()
x = x.replace('“', '"').replace('”', '"')
x = x.replace("‘", "'").replace("’", "'").replace("`", "'")
x = x.replace('... |
def split_grafs (lines):
"""
segment the raw text into paragraphs
"""
graf = []
for line in lines:
line = line.strip()
if len(line) < 1:
if len(graf) > 0:
yield "\n".join(graf)
graf = []
else:
graf.append(line)
if... |
def filter_quotes (text, is_email=True):
"""
filter the quoted text out of a message
"""
global DEBUG
global PAT_FORWARD, PAT_REPLIED, PAT_UNSUBSC
if is_email:
text = filter(lambda x: x in string.printable, text)
if DEBUG:
print("text:", text)
# strip off q... |
def get_word_id (root):
"""
lookup/assign a unique identify for each word root
"""
global UNIQ_WORDS
# in practice, this should use a microservice via some robust
# distributed cache, e.g., Redis, Cassandra, etc.
if root not in UNIQ_WORDS:
UNIQ_WORDS[root] = len(UNIQ_WORDS)
ret... |
def fix_microsoft (foo):
"""
fix special case for `c#`, `f#`, etc.; thanks Microsoft
"""
i = 0
bar = []
while i < len(foo):
text, lemma, pos, tag = foo[i]
if (text == "#") and (i > 0):
prev_tok = bar[-1]
prev_tok[0] += "#"
prev_tok[1] += "#"... |
def fix_hypenation (foo):
"""
fix hyphenation in the word list for a parsed sentence
"""
i = 0
bar = []
while i < len(foo):
text, lemma, pos, tag = foo[i]
if (tag == "HYPH") and (i > 0) and (i < len(foo) - 1):
prev_tok = bar[-1]
next_tok = foo[i + 1]
... |
def parse_graf (doc_id, graf_text, base_idx, spacy_nlp=None):
"""
CORE ALGORITHM: parse and markup sentences in the given paragraph
"""
global DEBUG
global POS_KEEPS, POS_LEMMA, SPACY_NLP
# set up the spaCy NLP parser
if not spacy_nlp:
if not SPACY_NLP:
SPACY_NLP = spacy... |
def parse_doc (json_iter):
"""
parse one document to prep for TextRank
"""
global DEBUG
for meta in json_iter:
base_idx = 0
for graf_text in filter_quotes(meta["text"], is_email=False):
if DEBUG:
print("graf_text:", graf_text)
grafs, new_bas... |
def get_tiles (graf, size=3):
"""
generate word pairs for the TextRank graph
"""
keeps = list(filter(lambda w: w.word_id > 0, graf))
keeps_len = len(keeps)
for i in iter(range(0, keeps_len - 1)):
w0 = keeps[i]
for j in iter(range(i + 1, min(keeps_len, i + 1 + size))):
... |
def build_graph (json_iter):
"""
construct the TextRank graph from parsed paragraphs
"""
global DEBUG, WordNode
graph = nx.DiGraph()
for meta in json_iter:
if DEBUG:
print(meta["graf"])
for pair in get_tiles(map(WordNode._make, meta["graf"])):
if DEBUG:
... |
def write_dot (graph, ranks, path="graph.dot"):
"""
output the graph in Dot file format
"""
dot = Digraph()
for node in graph.nodes():
dot.node(node, "%s %0.3f" % (node, ranks[node]))
for edge in graph.edges():
dot.edge(edge[0], edge[1], constraint="false")
with open(path,... |
def render_ranks (graph, ranks, dot_file="graph.dot"):
"""
render the TextRank graph for visual formats
"""
if dot_file:
write_dot(graph, ranks, path=dot_file) |
def text_rank (path):
"""
run the TextRank algorithm
"""
graph = build_graph(json_iter(path))
ranks = nx.pagerank(graph)
return graph, ranks |
def find_chunk (phrase, np):
"""
leverage noun phrase chunking
"""
for i in iter(range(0, len(phrase))):
parsed_np = find_chunk_sub(phrase, np, i)
if parsed_np:
return parsed_np |
def enumerate_chunks (phrase, spacy_nlp):
"""
iterate through the noun phrases
"""
if (len(phrase) > 1):
found = False
text = " ".join([rl.text for rl in phrase])
doc = spacy_nlp(text.strip(), parse=True)
for np in doc.noun_chunks:
if np.text != text:
... |
def collect_keyword (sent, ranks, stopwords):
"""
iterator for collecting the single-word keyphrases
"""
for w in sent:
if (w.word_id > 0) and (w.root in ranks) and (w.pos[0] in "NV") and (w.root not in stopwords):
rl = RankedLexeme(text=w.raw.lower(), rank=ranks[w.root]/2.0, ids=[w.... |
def collect_entities (sent, ranks, stopwords, spacy_nlp):
"""
iterator for collecting the named-entities
"""
global DEBUG
sent_text = " ".join([w.raw for w in sent])
if DEBUG:
print("sent:", sent_text)
for ent in spacy_nlp(sent_text).ents:
if DEBUG:
print("NER:"... |
def collect_phrases (sent, ranks, spacy_nlp):
"""
iterator for collecting the noun phrases
"""
tail = 0
last_idx = sent[0].idx - 1
phrase = []
while tail < len(sent):
w = sent[tail]
if (w.word_id > 0) and (w.root in ranks) and ((w.idx - last_idx) == 1):
# keep c... |
def normalize_key_phrases (path, ranks, stopwords=None, spacy_nlp=None, skip_ner=True):
"""
collect keyphrases, named entities, etc., while removing stop words
"""
global STOPWORDS, SPACY_NLP
# set up the stop words
if (type(stopwords) is list) or (type(stopwords) is set):
# explicit co... |
def mh_digest (data):
"""
create a MinHash digest
"""
num_perm = 512
m = MinHash(num_perm)
for d in data:
m.update(d.encode('utf8'))
return m |
def rank_kernel (path):
"""
return a list (matrix-ish) of the key phrases and their ranks
"""
kernel = []
if isinstance(path, str):
path = json_iter(path)
for meta in path:
if not isinstance(meta, RankedLexeme):
rl = RankedLexeme(**meta)
else:
rl... |
def top_sentences (kernel, path):
"""
determine distance for each sentence
"""
key_sent = {}
i = 0
if isinstance(path, str):
path = json_iter(path)
for meta in path:
graf = meta["graf"]
tagged_sent = [WordNode._make(x) for x in graf]
text = " ".join([w.raw f... |
def limit_keyphrases (path, phrase_limit=20):
"""
iterator for the most significant key phrases
"""
rank_thresh = None
if isinstance(path, str):
lex = []
for meta in json_iter(path):
rl = RankedLexeme(**meta)
lex.append(rl)
else:
lex = path
... |
def limit_sentences (path, word_limit=100):
"""
iterator for the most significant sentences, up to a specified limit
"""
word_count = 0
if isinstance(path, str):
path = json_iter(path)
for meta in path:
if not isinstance(meta, SummarySent):
p = SummarySent(**meta)
... |
def make_sentence (sent_text):
"""
construct a sentence text, with proper spacing
"""
lex = []
idx = 0
for word in sent_text:
if len(word) > 0:
if (idx > 0) and not (word[0] in ",.:;!?-\"'"):
lex.append(" ")
lex.append(word)
idx += 1
... |
def json_iter (path):
"""
iterator for JSON-per-line in a file pattern
"""
with open(path, 'r') as f:
for line in f.readlines():
yield json.loads(line) |
def pretty_print (obj, indent=False):
"""
pretty print a JSON object
"""
if indent:
return json.dumps(obj, sort_keys=True, indent=2, separators=(',', ': '))
else:
return json.dumps(obj, sort_keys=True) |
def get_object(cls, api_token, snapshot_id):
"""
Class method that will return a Snapshot object by ID.
"""
snapshot = cls(token=api_token, id=snapshot_id)
snapshot.load()
return snapshot |
def load(self):
"""
Fetch data about tag
"""
tags = self.get_data("tags/%s" % self.name)
tag = tags['tag']
for attr in tag.keys():
setattr(self, attr, tag[attr])
return self |
def create(self, **kwargs):
"""
Create the tag.
"""
for attr in kwargs.keys():
setattr(self, attr, kwargs[attr])
params = {"name": self.name}
output = self.get_data("tags", type="POST", params=params)
if output:
self.name = output['ta... |
def __get_resources(self, resources, method):
""" Method used to talk directly to the API (TAGs' Resources) """
tagged = self.get_data(
'tags/%s/resources' % self.name, params={
"resources": resources
},
type=method,
)
return tagged |
def __extract_resources_from_droplets(self, data):
"""
Private method to extract from a value, the resources.
It will check the type of object in the array provided and build
the right structure for the API.
"""
resources = []
if not isinstance(data, l... |
def add_droplets(self, droplet):
"""
Add the Tag to a Droplet.
Attributes accepted at creation time:
droplet: array of string or array of int, or array of Droplets.
"""
droplets = droplet
if not isinstance(droplets, list):
droplets = [... |
def remove_droplets(self, droplet):
"""
Remove the Tag from the Droplet.
Attributes accepted at creation time:
droplet: array of string or array of int, or array of Droplets.
"""
droplets = droplet
if not isinstance(droplets, list):
dr... |
def get_object(cls, api_token, action_id):
"""
Class method that will return a Action object by ID.
"""
action = cls(token=api_token, id=action_id)
action.load_directly()
return action |
def wait(self, update_every_seconds=1):
"""
Wait until the action is marked as completed or with an error.
It will return True in case of success, otherwise False.
Optional Args:
update_every_seconds - int : number of seconds to wait before
... |
def get_object(cls, api_token, droplet_id):
"""Class method that will return a Droplet object by ID.
Args:
api_token (str): token
droplet_id (int): droplet id
"""
droplet = cls(token=api_token, id=droplet_id)
droplet.load()
return droplet |
def get_data(self, *args, **kwargs):
"""
Customized version of get_data to perform __check_actions_in_data
"""
data = super(Droplet, self).get_data(*args, **kwargs)
if "type" in kwargs:
if kwargs["type"] == POST:
self.__check_actions_in_data(data)
... |
def load(self):
"""
Fetch data about droplet - use this instead of get_data()
"""
droplets = self.get_data("droplets/%s" % self.id)
droplet = droplets['droplet']
for attr in droplet.keys():
setattr(self, attr, droplet[attr])
for net in self.networ... |
def _perform_action(self, params, return_dict=True):
"""
Perform a droplet action.
Args:
params (dict): parameters of the action
Optional Args:
return_dict (bool): Return a dict when True (default),
otherwise return an Act... |
def resize(self, new_size_slug, return_dict=True, disk=True):
"""Resize the droplet to a new size slug.
https://developers.digitalocean.com/documentation/v2/#resize-a-droplet
Args:
new_size_slug (str): name of new size
Optional Args:
return_dict (bool): Return a... |
def take_snapshot(self, snapshot_name, return_dict=True, power_off=False):
"""Take a snapshot!
Args:
snapshot_name (str): name of snapshot
Optional Args:
return_dict (bool): Return a dict when True (default),
otherwise return an Action.
power... |
def rebuild(self, image_id=None, return_dict=True):
"""Restore the droplet to an image ( snapshot or backup )
Args:
image_id (int): id of image
Optional Args:
return_dict (bool): Return a dict when True (default),
otherwise return an Action.
Ret... |
def change_kernel(self, kernel, return_dict=True):
"""Change the kernel to a new one
Args:
kernel : instance of digitalocean.Kernel.Kernel
Optional Args:
return_dict (bool): Return a dict when True (default),
otherwise return an Action.
Returns ... |
def __get_ssh_keys_id_or_fingerprint(ssh_keys, token, name):
"""
Check and return a list of SSH key IDs or fingerprints according
to DigitalOcean's API. This method is used to check and create a
droplet with the correct SSH keys.
"""
ssh_keys_id = list()
... |
def create(self, *args, **kwargs):
"""
Create the droplet with object properties.
Note: Every argument and parameter given to this method will be
assigned to the object.
"""
for attr in kwargs.keys():
setattr(self, attr, kwargs[attr])
# P... |
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