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dropbox/stone | stone/frontend/parser.py | https://github.com/dropbox/stone/blob/2e95cbcd1c48e05cca68c919fd8d24adec6b0f58/stone/frontend/parser.py#L718-L727 | def p_examples_add(self, p):
'examples : examples example'
p[0] = p[1]
if p[2].label in p[0]:
existing_ex = p[0][p[2].label]
self.errors.append(
("Example with label '%s' already defined on line %d." %
(existing_ex.label, existing_ex.lineno),
p[2].lineno, p[2].path))
p[0][p[2].label] = p[2] | [
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chuck1/codemach | codemach/machine.py | https://github.com/chuck1/codemach/blob/b0e02f363da7aa58de7d6ad6499784282958adeb/codemach/machine.py#L374-L380 | def pop(self, n):
"""
Pop the **n** topmost items from the stack and return them as a ``list``.
"""
poped = self.__stack[len(self.__stack) - n:]
del self.__stack[len(self.__stack) - n:]
return poped | [
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iamteem/redisco | redisco/containers.py | https://github.com/iamteem/redisco/blob/a7ba19ff3c38061d6d8bc0c10fa754baadcfeb91/redisco/containers.py#L145-L147 | def update(self, *others):
"""Update the set, adding elements from all others."""
self.db.sunionstore(self.key, [self.key] + [o.key for o in others]) | [
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DLR-RM/RAFCON | source/rafcon/gui/mygaphas/tools.py | https://github.com/DLR-RM/RAFCON/blob/24942ef1a904531f49ab8830a1dbb604441be498/source/rafcon/gui/mygaphas/tools.py#L750-L777 | def on_button_press(self, event):
"""Handle button press events.
If the (mouse) button is pressed on top of a Handle (item.Handle), that handle is grabbed and can be
dragged around.
"""
if not event.get_button()[1] == 1: # left mouse button
return False
view = self.view
item, handle = HandleFinder(view.hovered_item, view).get_handle_at_point((event.x, event.y))
# Handle must be the end handle of a connection
if not handle or not isinstance(item, ConnectionView) or handle not in item.end_handles():
return False
if handle is item.from_handle():
self._start_port_v = item.from_port
else:
self._start_port_v = item.to_port
self._parent_state_v = item.parent
self._end_handle = handle
if isinstance(item, TransitionView):
self._is_transition = True
self._connection_v = item
return True | [
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If the (mouse) button is pressed on top of a Handle (item.Handle), that handle is grabbed and can be
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] | python | train |
rikrd/inspire | inspirespeech/htk_model.py | https://github.com/rikrd/inspire/blob/e281c0266a9a9633f34ab70f9c3ad58036c19b59/inspirespeech/htk_model.py#L85-L103 | def load_model(*args):
"""Load an HTK model from one ore more files.
:param args: Filenames of the model (e.g. macros hmmdefs)
:return: The model as an OrderedDict()
"""
text = ''
for fnm in args:
text += open(fnm).read()
text += '\n'
parser = htk_model_parser.htk_modelParser()
model = HtkModelSemantics()
return parser.parse(text,
rule_name='model',
ignorecase=True,
semantics=model,
comments_re="\(\*.*?\*\)",
trace=False) | [
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Alignak-monitoring/alignak | alignak/macroresolver.py | https://github.com/Alignak-monitoring/alignak/blob/f3c145207e83159b799d3714e4241399c7740a64/alignak/macroresolver.py#L450-L496 | def _get_type_of_macro(macros, objs):
r"""Set macros types
Example::
ARG\d -> ARGN,
HOSTBLABLA -> class one and set Host in class)
_HOSTTOTO -> HOST CUSTOM MACRO TOTO
SERVICESTATEID:srv-1:Load$ -> MACRO SERVICESTATEID of the service Load of host srv-1
:param macros: macros list in a dictionary
:type macros: dict
:param objs: objects list, used to tag object macros
:type objs: list
:return: None
"""
for macro in macros:
# ARGN Macros
if re.match(r'ARG\d', macro):
macros[macro]['type'] = 'ARGN'
continue
# USERN macros
# are managed in the Config class, so no
# need to look that here
elif re.match(r'_HOST\w', macro):
macros[macro]['type'] = 'CUSTOM'
macros[macro]['class'] = 'HOST'
continue
elif re.match(r'_SERVICE\w', macro):
macros[macro]['type'] = 'CUSTOM'
macros[macro]['class'] = 'SERVICE'
# value of macro: re.split('_HOST', '_HOSTMAC_ADDRESS')[1]
continue
elif re.match(r'_CONTACT\w', macro):
macros[macro]['type'] = 'CUSTOM'
macros[macro]['class'] = 'CONTACT'
continue
# On demand macro
elif len(macro.split(':')) > 1:
macros[macro]['type'] = 'ONDEMAND'
continue
# OK, classical macro...
for obj in objs:
if macro in obj.macros:
macros[macro]['type'] = 'object'
macros[macro]['object'] = obj
continue | [
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SERVICESTATEID:srv-1:Load$ -> MACRO SERVICESTATEID of the service Load of host srv-1
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:return: None | [
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AltSchool/dynamic-rest | dynamic_rest/routers.py | https://github.com/AltSchool/dynamic-rest/blob/5b0338c3dd8bc638d60c3bb92645857c5b89c920/dynamic_rest/routers.py#L227-L246 | def get_canonical_path(resource_key, pk=None):
"""
Return canonical resource path.
Arguments:
resource_key - Canonical resource key
i.e. Serializer.get_resource_key().
pk - (Optional) Object's primary key for a single-resource URL.
Returns: Absolute URL as string.
"""
if resource_key not in resource_map:
# Note: Maybe raise?
return None
base_path = get_script_prefix() + resource_map[resource_key]['path']
if pk:
return '%s/%s/' % (base_path, pk)
else:
return base_path | [
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google/apitools | apitools/gen/service_registry.py | https://github.com/google/apitools/blob/f3745a7ea535aa0e88b0650c16479b696d6fd446/apitools/gen/service_registry.py#L371-L421 | def __ComputeMethodInfo(self, method_description, request, response,
request_field):
"""Compute the base_api.ApiMethodInfo for this method."""
relative_path = self.__names.NormalizeRelativePath(
''.join((self.__client_info.base_path,
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method_id = method_description['id']
ordered_params = []
for param_name in method_description.get('parameterOrder', []):
param_info = method_description['parameters'][param_name]
if param_info.get('required', False):
ordered_params.append(param_name)
method_info = base_api.ApiMethodInfo(
relative_path=relative_path,
method_id=method_id,
http_method=method_description['httpMethod'],
description=util.CleanDescription(
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response_type_name=self.__names.ClassName(response),
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flat_path = method_description.get('flatPath', None)
if flat_path is not None:
flat_path = self.__names.NormalizeRelativePath(
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if flat_path != relative_path:
method_info.flat_path = flat_path
if method_description.get('supportsMediaUpload', False):
method_info.upload_config = self.__ComputeUploadConfig(
method_description.get('mediaUpload'), method_id)
method_info.supports_download = method_description.get(
'supportsMediaDownload', False)
self.__all_scopes.update(method_description.get('scopes', ()))
for param, desc in method_description.get('parameters', {}).items():
param = self.__names.CleanName(param)
location = desc['location']
if location == 'query':
method_info.query_params.append(param)
elif location == 'path':
method_info.path_params.append(param)
else:
raise ValueError(
'Unknown parameter location %s for parameter %s' % (
location, param))
method_info.path_params.sort()
method_info.query_params.sort()
return method_info | [
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dswah/pyGAM | pygam/pygam.py | https://github.com/dswah/pyGAM/blob/b3e5c3cd580f0a3ad69f9372861624f67760c325/pygam/pygam.py#L1075-L1115 | def _estimate_r2(self, X=None, y=None, mu=None, weights=None):
"""
estimate some pseudo R^2 values
currently only computes explained deviance.
results are stored
Parameters
----------
y : array-like of shape (n_samples,)
output data vector
mu : array-like of shape (n_samples,)
expected value of the targets given the model and inputs
weights : array-like shape (n_samples,) or None, optional
containing sample weights
if None, defaults to array of ones
Returns
-------
None
"""
if mu is None:
mu = self.predict_mu(X=X)
if weights is None:
weights = np.ones_like(y).astype('float64')
null_mu = y.mean() * np.ones_like(y).astype('float64')
null_d = self.distribution.deviance(y=y, mu=null_mu, weights=weights)
full_d = self.distribution.deviance(y=y, mu=mu, weights=weights)
null_ll = self._loglikelihood(y=y, mu=null_mu, weights=weights)
full_ll = self._loglikelihood(y=y, mu=mu, weights=weights)
r2 = OrderedDict()
r2['explained_deviance'] = 1. - full_d.sum()/null_d.sum()
r2['McFadden'] = full_ll/null_ll
r2['McFadden_adj'] = 1. - (full_ll - self.statistics_['edof'])/null_ll
return r2 | [
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weights : array-like shape (n_samples,) or None, optional
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radjkarl/imgProcessor | imgProcessor/interpolate/interpolate2dStructuredCrossAvg.py | https://github.com/radjkarl/imgProcessor/blob/7c5a28718f81c01a430152c60a686ac50afbfd7c/imgProcessor/interpolate/interpolate2dStructuredCrossAvg.py#L7-L19 | def interpolate2dStructuredCrossAvg(grid, mask, kernel=15, power=2):
'''
#######
usefull if large empty areas need to be filled
'''
vals = np.empty(shape=4, dtype=grid.dtype)
dist = np.empty(shape=4, dtype=np.uint16)
weights = np.empty(shape=4, dtype=np.float32)
valid = np.empty(shape=4, dtype=bool)
return _calc(grid, mask, power, kernel, vals, dist, weights, valid) | [
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NYUCCL/psiTurk | psiturk/psiturk_shell.py | https://github.com/NYUCCL/psiTurk/blob/7170b992a0b5f56c165929cf87b3d3a1f3336c36/psiturk/psiturk_shell.py#L1029-L1051 | def do_worker(self, arg):
"""
Usage:
worker approve (--all | --hit <hit_id> ... | <assignment_id> ...) [--all-studies] [--force]
worker reject (--hit <hit_id> | <assignment_id> ...)
worker unreject (--hit <hit_id> | <assignment_id> ...)
worker bonus (--amount <amount> | --auto) (--hit <hit_id> | <assignment_id> ...)
worker list [--submitted | --approved | --rejected] [(--hit <hit_id>)] [--all-studies]
worker help
"""
if arg['approve']:
self.worker_approve(arg['--all'], arg['<hit_id>'], arg['<assignment_id>'], arg['--all-studies'], arg['--force'])
elif arg['reject']:
self.amt_services_wrapper.worker_reject(arg['<hit_id>'], arg['<assignment_id>'])
elif arg['unreject']:
self.amt_services_wrapper.worker_unreject(arg['<hit_id>'], arg['<assignment_id>'])
elif arg['list']:
self.worker_list(arg['--submitted'], arg['--approved'], arg['--rejected'], arg['<hit_id>'], arg['--all-studies'])
elif arg['bonus']:
self.amt_services_wrapper.worker_bonus(arg['<hit_id>'], arg['--auto'], arg['<amount>'], '',
arg['<assignment_id>'])
else:
self.help_worker() | [
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worker reject (--hit <hit_id> | <assignment_id> ...)
worker unreject (--hit <hit_id> | <assignment_id> ...)
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shoebot/shoebot | lib/graph/__init__.py | https://github.com/shoebot/shoebot/blob/d554c1765c1899fa25727c9fc6805d221585562b/lib/graph/__init__.py#L725-L797 | def create(iterations=1000, distance=1.0, layout=LAYOUT_SPRING, depth=True):
""" Returns a new graph with predefined styling.
"""
#global _ctx
_ctx.colormode(_ctx.RGB)
g = graph(iterations, distance, layout)
# Styles for different types of nodes.
s = style.style
g.styles.append(s(style.LIGHT , _ctx, fill = _ctx.color(0.0, 0.0, 0.0, 0.20)))
g.styles.append(s(style.DARK , _ctx, fill = _ctx.color(0.3, 0.5, 0.7, 0.75)))
g.styles.append(s(style.BACK , _ctx, fill = _ctx.color(0.5, 0.8, 0.0, 0.50)))
g.styles.append(s(style.IMPORTANT, _ctx, fill = _ctx.color(0.3, 0.6, 0.8, 0.75)))
g.styles.append(s(style.HIGHLIGHT, _ctx, stroke = _ctx.color(1.0, 0.0, 0.5), strokewidth=1.5))
g.styles.append(s(style.MARKED , _ctx))
g.styles.append(s(style.ROOT , _ctx, text = _ctx.color(1.0, 0.0, 0.4, 1.00),
stroke = _ctx.color(0.8, 0.8, 0.8, 0.60),
strokewidth = 1.5,
fontsize = 16,
textwidth = 150))
# Important nodes get a double stroke.
def important_node(s, node, alpha=1.0):
style.style(None, _ctx).node(s, node, alpha)
r = node.r * 1.4
_ctx.nofill()
_ctx.oval(node.x-r, node.y-r, r*2, r*2)
# Marked nodes have an inner dot.
def marked_node(s, node, alpha=1.0):
style.style(None, _ctx).node(s, node, alpha)
r = node.r * 0.3
_ctx.fill(s.stroke)
_ctx.oval(node.x-r, node.y-r, r*2, r*2)
g.styles.important.node = important_node
g.styles.marked.node = marked_node
g.styles.depth = depth
# Styling guidelines. All nodes have the default style, except:
# 1) a node directly connected to the root gets the LIGHT style.
# 2) a node with more than 4 edges gets the DARK style.
# 3) a node with a weight of 0.75-1.0 gets the IMPORTANT style.
# 4) the graph.root node gets the ROOT style.
# 5) the node last clicked gets the BACK style.
g.styles.guide.append(style.LIGHT , lambda graph, node: graph.root in node.links)
g.styles.guide.append(style.DARK , lambda graph, node: len(node.links) > 4)
g.styles.guide.append(style.IMPORTANT , lambda graph, node: node.weight > 0.75)
g.styles.guide.append(style.ROOT , lambda graph, node: node == graph.root)
g.styles.guide.append(style.BACK , lambda graph, node: node == graph.events.clicked)
# An additional rule applies every node's weight to its radius.
def balance(graph, node):
node.r = node.r*0.75 + node.r*node.weight*0.75
g.styles.guide.append("balance", balance)
# An additional rule that keeps leaf nodes closely clustered.
def cluster(graph, node):
if len(node.links) == 1:
node.links.edge(node.links[0]).length *= 0.5
g.styles.guide.append("cluster", cluster)
g.styles.guide.order = [
style.LIGHT, style.DARK, style.IMPORTANT, style.ROOT, style.BACK, "balance", "nurse"
]
return g | [
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log2timeline/dfvfs | dfvfs/resolver/resolver.py | https://github.com/log2timeline/dfvfs/blob/2b3ccd115f9901d89f383397d4a1376a873c83c4/dfvfs/resolver/resolver.py#L42-L66 | def OpenFileEntry(cls, path_spec_object, resolver_context=None):
"""Opens a file entry object defined by path specification.
Args:
path_spec_object (PathSpec): path specification.
resolver_context (Optional[Context]): resolver context, where None
represents the built in context which is not multi process safe.
Returns:
FileEntry: file entry or None if the path specification could not be
resolved.
"""
file_system = cls.OpenFileSystem(
path_spec_object, resolver_context=resolver_context)
if resolver_context is None:
resolver_context = cls._resolver_context
file_entry = file_system.GetFileEntryByPathSpec(path_spec_object)
# Release the file system so it will be removed from the cache
# when the file entry is destroyed.
resolver_context.ReleaseFileSystem(file_system)
return file_entry | [
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log2timeline/plaso | plaso/analysis/browser_search.py | https://github.com/log2timeline/plaso/blob/9c564698d2da3ffbe23607a3c54c0582ea18a6cc/plaso/analysis/browser_search.py#L117-L135 | def _ExtractGMailSearchQuery(self, url):
"""Extracts a search query from a GMail search URL.
GMail: https://mail.google.com/mail/u/0/#search/query[/?]
Args:
url (str): URL.
Returns:
str: search query or None if no query was found.
"""
if 'search/' not in url:
return None
_, _, line = url.partition('search/')
line, _, _ = line.partition('/')
line, _, _ = line.partition('?')
return line.replace('+', ' ') | [
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url (str): URL.
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lowandrew/OLCTools | coreGenome/core.py | https://github.com/lowandrew/OLCTools/blob/88aa90ac85f84d0bbeb03e43c29b0a9d36e4ce2a/coreGenome/core.py#L169-L193 | def annotatedcore(self):
"""
Calculates the core genome of organisms using custom databases
"""
logging.info('Calculating annotated core')
# Determine the total number of core genes
self.total_core()
# Iterate through all the samples, and process all Escherichia
for sample in self.metadata:
if sample.general.bestassemblyfile != 'NA':
# Create a set to store the names of all the core genes in this strain
sample[self.analysistype].coreset = set()
if sample.general.referencegenus == 'Escherichia':
# Add the Escherichia sample to the runmetadata
self.runmetadata.samples.append(sample)
# Parse the BLAST report
try:
report = sample[self.analysistype].report
self.blastparser(report=report,
sample=sample,
fieldnames=self.fieldnames)
except KeyError:
sample[self.analysistype].coreset = list()
# Create the report
self.reporter() | [
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PmagPy/PmagPy | pmagpy/pmag.py | https://github.com/PmagPy/PmagPy/blob/c7984f8809bf40fe112e53dcc311a33293b62d0b/pmagpy/pmag.py#L2827-L3374 | def PintPars(datablock, araiblock, zijdblock, start, end, accept, **kwargs):
"""
calculate the paleointensity magic parameters make some definitions
"""
if 'version' in list(kwargs.keys()) and kwargs['version'] == 3:
meth_key = 'method_codes'
beta_key = 'int_b_beta'
temp_key, min_key, max_key = 'treat_temp', 'meas_step_min', 'meas_step_max'
dc_theta_key, dc_phi_key = 'treat_dc_field_theta', 'treat_dc_field_phi'
# convert dataframe to list of dictionaries
datablock = datablock.to_dict('records')
z_key = 'int_z'
drats_key = 'int_drats'
drat_key = 'int_drat'
md_key = 'int_md'
dec_key = 'dir_dec'
inc_key = 'dir_inc'
mad_key = 'int_mad_free'
dang_key = 'int_dang'
ptrm_key = 'int_n_ptrm'
theta_key = 'int_theta'
gamma_key = 'int_gamma'
delta_key = 'int_delta'
frac_key = 'int_frac'
gmax_key = 'int_gmax'
scat_key = 'int_scat'
else:
beta_key = 'specimen_b_beta'
meth_key = 'magic_method_codes'
temp_key, min_key, max_key = 'treatment_temp', 'measurement_step_min', 'measurement_step_max'
z_key = 'specimen_z'
drats_key = 'specimen_drats'
drat_key = 'specimen_drat'
md_key = 'specimen_md'
dec_key = 'specimen_dec'
inc_key = 'specimen_inc'
mad_key = 'specimen_int_mad'
dang_key = 'specimen_dang'
ptrm_key = 'specimen_int_ptrm_n'
theta_key = 'specimen_theta'
gamma_key = 'specimen_gamma'
delta_key = 'specimen_delta'
frac_key = 'specimen_frac'
gmax_key = 'specimen_gmax'
scat_key = 'specimen_scat'
first_Z, first_I, zptrm_check, ptrm_check, ptrm_tail = [], [], [], [], []
methcode, ThetaChecks, DeltaChecks, GammaChecks = "", "", "", ""
zptrm_check = []
first_Z, first_I, ptrm_check, ptrm_tail, zptrm_check, GammaChecks = araiblock[
0], araiblock[1], araiblock[2], araiblock[3], araiblock[4], araiblock[5]
if len(araiblock) > 6:
# used only for perpendicular method of paleointensity
ThetaChecks = araiblock[6]
# used only for perpendicular method of paleointensity
DeltaChecks = araiblock[7]
xi, yi, diffcum = [], [], 0
xiz, xzi, yiz, yzi = [], [], [], []
Nptrm, dmax = 0, -1e-22
# check if even zero and infield steps
if len(first_Z) > len(first_I):
maxe = len(first_I) - 1
else:
maxe = len(first_Z) - 1
if end == 0 or end > maxe:
end = maxe
# get the MAD, DANG, etc. for directional data
bstep = araiblock[0][start][0]
estep = araiblock[0][end][0]
zstart, zend = 0, len(zijdblock)
for k in range(len(zijdblock)):
zrec = zijdblock[k]
if zrec[0] == bstep:
zstart = k
if zrec[0] == estep:
zend = k
PCA = domean(zijdblock, zstart, zend, 'DE-BFL')
D, Diz, Dzi, Du = [], [], [], [] # list of NRM vectors, and separated by zi and iz
for rec in zijdblock:
D.append((rec[1], rec[2], rec[3]))
Du.append((rec[1], rec[2]))
if rec[4] == 1:
Dzi.append((rec[1], rec[2])) # if this is ZI step
else:
Diz.append((rec[1], rec[2])) # if this is IZ step
# calculate the vector difference sum
vds = dovds(D)
b_zi, b_iz = [], []
# collect data included in ZigZag calculation
if end + 1 >= len(first_Z):
stop = end - 1
else:
stop = end
for k in range(start, end + 1):
for l in range(len(first_I)):
irec = first_I[l]
if irec[0] == first_Z[k][0]:
xi.append(irec[3])
yi.append(first_Z[k][3])
pars, errcode = int_pars(xi, yi, vds)
if errcode == 1:
return pars, errcode
# for k in range(start,end+1):
for k in range(len(first_Z) - 1):
for l in range(k):
# only go down to 10% of NRM.....
if old_div(first_Z[k][3], vds) > 0.1:
irec = first_I[l]
if irec[4] == 1 and first_I[l + 1][4] == 0: # a ZI step
xzi = irec[3]
yzi = first_Z[k][3]
xiz = first_I[l + 1][3]
yiz = first_Z[k + 1][3]
slope = np.arctan2((yzi - yiz), (xiz - xzi))
r = np.sqrt((yzi - yiz)**2 + (xiz - xzi)**2)
if r > .1 * vds:
b_zi.append(slope) # suppress noise
elif irec[4] == 0 and first_I[l + 1][4] == 1: # an IZ step
xiz = irec[3]
yiz = first_Z[k][3]
xzi = first_I[l + 1][3]
yzi = first_Z[k + 1][3]
slope = np.arctan2((yiz - yzi), (xzi - xiz))
r = np.sqrt((yiz - yzi)**2 + (xzi - xiz)**2)
if r > .1 * vds:
b_iz.append(slope) # suppress noise
#
ZigZag, Frat, Trat = -1, 0, 0
if len(Diz) > 2 and len(Dzi) > 2:
ZigZag = 0
dizp = fisher_mean(Diz) # get Fisher stats on IZ steps
dzip = fisher_mean(Dzi) # get Fisher stats on ZI steps
dup = fisher_mean(Du) # get Fisher stats on all steps
#
# if directions are TOO well grouped, can get false positive for ftest, so
# angles must be > 3 degrees apart.
#
if angle([dizp['dec'], dizp['inc']], [dzip['dec'], dzip['inc']]) > 3.:
F = (dup['n'] - 2.) * (dzip['r'] + dizp['r'] - dup['r']) / \
(dup['n'] - dzip['r'] - dizp['r']
) # Watson test for common mean
nf = 2. * (dup['n'] - 2.) # number of degees of freedom
ftest = fcalc(2, nf)
Frat = old_div(F, ftest)
if Frat > 1.:
ZigZag = Frat # fails zigzag on directions
methcode = "SM-FTEST"
# now do slopes
if len(b_zi) > 2 and len(b_iz) > 2:
bzi_m, bzi_sig = gausspars(b_zi) # mean, std dev
biz_m, biz_sig = gausspars(b_iz)
n_zi = float(len(b_zi))
n_iz = float(len(b_iz))
b_diff = abs(bzi_m - biz_m) # difference in means
#
# avoid false positives - set 3 degree slope difference here too
if b_diff > 3 * np.pi / 180.:
nf = n_zi + n_iz - 2. # degrees of freedom
svar = old_div(((n_zi - 1.) * bzi_sig**2 +
(n_iz - 1.) * biz_sig**2), nf)
T = old_div((b_diff), np.sqrt(
svar * (old_div(1.0, n_zi) + old_div(1.0, n_iz)))) # student's t
ttest = tcalc(nf, .05) # t-test at 95% conf.
Trat = old_div(T, ttest)
if Trat > 1 and Trat > Frat:
ZigZag = Trat # fails zigzag on directions
methcode = "SM-TTEST"
pars[z_key] = ZigZag
pars[meth_key] = methcode
# do drats
if len(ptrm_check) != 0:
diffcum, drat_max = 0, 0
for prec in ptrm_check:
step = prec[0]
endbak = end
zend = end
while zend > len(zijdblock) - 1:
zend = zend - 2 # don't count alteration that happens after this step
if step < zijdblock[zend][0]:
Nptrm += 1
for irec in first_I:
if irec[0] == step:
break
diffcum += prec[3] - irec[3]
if abs(prec[3] - irec[3]) > drat_max:
drat_max = abs(prec[3] - irec[3])
pars[drats_key] = (100 * abs(diffcum) / first_I[zend][3])
pars[drat_key] = (100 * abs(drat_max) / first_I[zend][3])
elif len(zptrm_check) != 0:
diffcum = 0
for prec in zptrm_check:
step = prec[0]
endbak = end
zend = end
while zend > len(zijdblock) - 1:
zend = zend - 1
if step < zijdblock[zend][0]:
Nptrm += 1
for irec in first_I:
if irec[0] == step:
break
diffcum += prec[3] - irec[3]
pars[drats_key] = (100 * abs(diffcum) / first_I[zend][3])
else:
pars[drats_key] = -1
pars[drat_key] = -1
# and the pTRM tails
if len(ptrm_tail) != 0:
for trec in ptrm_tail:
step = trec[0]
for irec in first_I:
if irec[0] == step:
break
if abs(trec[3]) > dmax:
dmax = abs(trec[3])
pars[md_key] = (100 * dmax / vds)
else:
pars[md_key] = -1
pars[min_key] = bstep
pars[max_key] = estep
pars[dec_key] = PCA["specimen_dec"]
pars[inc_key] = PCA["specimen_inc"]
pars[mad_key] = PCA["specimen_mad"]
pars[dang_key] = PCA["specimen_dang"]
pars[ptrm_key] = Nptrm
# and the ThetaChecks
if ThetaChecks != "":
t = 0
for theta in ThetaChecks:
if theta[0] >= bstep and theta[0] <= estep and theta[1] > t:
t = theta[1]
pars[theta_key] = t
else:
pars[theta_key] = -1
# and the DeltaChecks
if DeltaChecks != "":
d = 0
for delta in DeltaChecks:
if delta[0] >= bstep and delta[0] <= estep and delta[1] > d:
d = delta[1]
pars[delta_key]
else:
pars[delta_key] = -1
pars[gamma_key] = -1
if GammaChecks != "":
for gamma in GammaChecks:
if gamma[0] <= estep:
pars['specimen_gamma'] = gamma[1]
# --------------------------------------------------------------
# From here added By Ron Shaar 11-Dec 2012
# New parameters defined in Shaar and Tauxe (2012):
# FRAC (specimen_frac) - ranges from 0. to 1.
# SCAT (specimen_scat) - takes 1/0
# gap_max (specimen_gmax) - ranges from 0. to 1.
# --------------------------------------------------------------
# --------------------------------------------------------------
# FRAC is similar to Fvds, but the numerator is the vds fraction:
# FRAC= [ vds (start,end)] / total vds ]
# gap_max= max [ (vector difference) / vds (start,end)]
# --------------------------------------------------------------
# collect all zijderveld data to arrays and calculate VDS
z_temperatures = [row[0] for row in zijdblock]
zdata = [] # array of zero-fields measurements in Cartezian coordinates
# array of vector differences (for vds calculation)
vector_diffs = []
NRM = zijdblock[0][3] # NRM
for k in range(len(zijdblock)):
DIR = [zijdblock[k][1], zijdblock[k][2], old_div(zijdblock[k][3], NRM)]
cart = dir2cart(DIR)
zdata.append(np.array([cart[0], cart[1], cart[2]]))
if k > 0:
vector_diffs.append(
np.sqrt(sum((np.array(zdata[-2]) - np.array(zdata[-1]))**2)))
# last vector difference: from the last point to the origin.
vector_diffs.append(np.sqrt(sum(np.array(zdata[-1])**2)))
vds = sum(vector_diffs) # vds calculation
zdata = np.array(zdata)
vector_diffs = np.array(vector_diffs)
# calculate the vds within the chosen segment
vector_diffs_segment = vector_diffs[zstart:zend]
# FRAC calculation
FRAC = old_div(sum(vector_diffs_segment), vds)
pars[frac_key] = FRAC
# gap_max calculation
max_FRAC_gap = max(
old_div(vector_diffs_segment, sum(vector_diffs_segment)))
pars[gmax_key] = max_FRAC_gap
# ---------------------------------------------------------------------
# Calculate the "scat box"
# all data-points, pTRM checks, and tail-checks, should be inside a "scat box"
# ---------------------------------------------------------------------
# intialization
# fail scat due to arai plot data points
pars["fail_arai_beta_box_scatter"] = False
pars["fail_ptrm_beta_box_scatter"] = False # fail scat due to pTRM checks
pars["fail_tail_beta_box_scatter"] = False # fail scat due to tail checks
pars[scat_key] = "t" # Pass by default
# --------------------------------------------------------------
# collect all Arai plot data points in arrays
x_Arai, y_Arai, t_Arai, steps_Arai = [], [], [], []
NRMs = araiblock[0]
PTRMs = araiblock[1]
ptrm_checks = araiblock[2]
ptrm_tail = araiblock[3]
PTRMs_temperatures = [row[0] for row in PTRMs]
NRMs_temperatures = [row[0] for row in NRMs]
NRM = NRMs[0][3]
for k in range(len(NRMs)):
index_pTRMs = PTRMs_temperatures.index(NRMs[k][0])
x_Arai.append(old_div(PTRMs[index_pTRMs][3], NRM))
y_Arai.append(old_div(NRMs[k][3], NRM))
t_Arai.append(NRMs[k][0])
if NRMs[k][4] == 1:
steps_Arai.append('ZI')
else:
steps_Arai.append('IZ')
x_Arai = np.array(x_Arai)
y_Arai = np.array(y_Arai)
# --------------------------------------------------------------
# collect all pTRM check to arrays
x_ptrm_check, y_ptrm_check, ptrm_checks_temperatures, = [], [], []
x_ptrm_check_starting_point, y_ptrm_check_starting_point, ptrm_checks_starting_temperatures = [], [], []
for k in range(len(ptrm_checks)):
if ptrm_checks[k][0] in NRMs_temperatures:
# find the starting point of the pTRM check:
for i in range(len(datablock)):
rec = datablock[i]
if "LT-PTRM-I" in rec[meth_key] and float(rec[temp_key]) == ptrm_checks[k][0]:
starting_temperature = (float(datablock[i - 1][temp_key]))
try:
index = t_Arai.index(starting_temperature)
x_ptrm_check_starting_point.append(x_Arai[index])
y_ptrm_check_starting_point.append(y_Arai[index])
ptrm_checks_starting_temperatures.append(
starting_temperature)
index_zerofield = zerofield_temperatures.index(
ptrm_checks[k][0])
x_ptrm_check.append(old_div(ptrm_checks[k][3], NRM))
y_ptrm_check.append(
old_div(zerofields[index_zerofield][3], NRM))
ptrm_checks_temperatures.append(ptrm_checks[k][0])
break
except:
pass
x_ptrm_check_starting_point = np.array(x_ptrm_check_starting_point)
y_ptrm_check_starting_point = np.array(y_ptrm_check_starting_point)
ptrm_checks_starting_temperatures = np.array(
ptrm_checks_starting_temperatures)
x_ptrm_check = np.array(x_ptrm_check)
y_ptrm_check = np.array(y_ptrm_check)
ptrm_checks_temperatures = np.array(ptrm_checks_temperatures)
# --------------------------------------------------------------
# collect tail checks to arrays
x_tail_check, y_tail_check, tail_check_temperatures = [], [], []
x_tail_check_starting_point, y_tail_check_starting_point, tail_checks_starting_temperatures = [], [], []
for k in range(len(ptrm_tail)):
if ptrm_tail[k][0] in NRMs_temperatures:
# find the starting point of the pTRM check:
for i in range(len(datablock)):
rec = datablock[i]
if "LT-PTRM-MD" in rec[meth_key] and float(rec[temp_key]) == ptrm_tail[k][0]:
starting_temperature = (float(datablock[i - 1][temp_key]))
try:
index = t_Arai.index(starting_temperature)
x_tail_check_starting_point.append(x_Arai[index])
y_tail_check_starting_point.append(y_Arai[index])
tail_checks_starting_temperatures.append(
starting_temperature)
index_infield = infield_temperatures.index(
ptrm_tail[k][0])
x_tail_check.append(
old_div(infields[index_infield][3], NRM))
y_tail_check.append(
old_div(ptrm_tail[k][3], NRM) + old_div(zerofields[index_infield][3], NRM))
tail_check_temperatures.append(ptrm_tail[k][0])
break
except:
pass
x_tail_check = np.array(x_tail_check)
y_tail_check = np.array(y_tail_check)
tail_check_temperatures = np.array(tail_check_temperatures)
x_tail_check_starting_point = np.array(x_tail_check_starting_point)
y_tail_check_starting_point = np.array(y_tail_check_starting_point)
tail_checks_starting_temperatures = np.array(
tail_checks_starting_temperatures)
# --------------------------------------------------------------
# collect the chosen segment in the Arai plot to arrays
x_Arai_segment = x_Arai[start:end + 1] # chosen segent in the Arai plot
y_Arai_segment = y_Arai[start:end + 1] # chosen segent in the Arai plot
# --------------------------------------------------------------
# collect pTRM checks in segment to arrays
# notice, this is different than the conventional DRATS.
# for scat calculation we take only the pTRM checks which were carried out
# before reaching the highest temperature in the chosen segment
x_ptrm_check_for_SCAT, y_ptrm_check_for_SCAT = [], []
for k in range(len(ptrm_checks_temperatures)):
if ptrm_checks_temperatures[k] >= pars[min_key] and ptrm_checks_starting_temperatures <= pars[max_key]:
x_ptrm_check_for_SCAT.append(x_ptrm_check[k])
y_ptrm_check_for_SCAT.append(y_ptrm_check[k])
x_ptrm_check_for_SCAT = np.array(x_ptrm_check_for_SCAT)
y_ptrm_check_for_SCAT = np.array(y_ptrm_check_for_SCAT)
# --------------------------------------------------------------
# collect Tail checks in segment to arrays
# for scat calculation we take only the tail checks which were carried out
# before reaching the highest temperature in the chosen segment
x_tail_check_for_SCAT, y_tail_check_for_SCAT = [], []
for k in range(len(tail_check_temperatures)):
if tail_check_temperatures[k] >= pars[min_key] and tail_checks_starting_temperatures[k] <= pars[max_key]:
x_tail_check_for_SCAT.append(x_tail_check[k])
y_tail_check_for_SCAT.append(y_tail_check[k])
x_tail_check_for_SCAT = np.array(x_tail_check_for_SCAT)
y_tail_check_for_SCAT = np.array(y_tail_check_for_SCAT)
# --------------------------------------------------------------
# calculate the lines that define the scat box:
# if threshold value for beta is not defined, then scat cannot be calculated (pass)
# in this case, scat pass
if beta_key in list(accept.keys()) and accept[beta_key] != "":
b_beta_threshold = float(accept[beta_key])
b = pars[b_key] # best fit line
cm_x = np.mean(np.array(x_Arai_segment)) # x center of mass
cm_y = np.mean(np.array(y_Arai_segment)) # y center of mass
a = cm_y - b * cm_x
# lines with slope = slope +/- 2*(specimen_b_beta)
two_sigma_beta_threshold = 2 * b_beta_threshold
two_sigma_slope_threshold = abs(two_sigma_beta_threshold * b)
# a line with a shallower slope (b + 2*beta*b) passing through the center of mass
# y=a1+b1x
b1 = b + two_sigma_slope_threshold
a1 = cm_y - b1 * cm_x
# bounding line with steeper slope (b - 2*beta*b) passing through the center of mass
# y=a2+b2x
b2 = b - two_sigma_slope_threshold
a2 = cm_y - b2 * cm_x
# lower bounding line of the 'beta box'
# y=intercept1+slop1x
slop1 = old_div(a1, ((old_div(a2, b2))))
intercept1 = a1
# higher bounding line of the 'beta box'
# y=intercept2+slop2x
slop2 = old_div(a2, ((old_div(a1, b1))))
intercept2 = a2
pars['specimen_scat_bounding_line_high'] = [intercept2, slop2]
pars['specimen_scat_bounding_line_low'] = [intercept1, slop1]
# --------------------------------------------------------------
# check if the Arai data points are in the 'box'
# the two bounding lines
ymin = intercept1 + x_Arai_segment * slop1
ymax = intercept2 + x_Arai_segment * slop2
# arrays of "True" or "False"
check_1 = y_Arai_segment > ymax
check_2 = y_Arai_segment < ymin
# check if at least one "True"
if (sum(check_1) + sum(check_2)) > 0:
pars["fail_arai_beta_box_scatter"] = True
# --------------------------------------------------------------
# check if the pTRM checks data points are in the 'box'
if len(x_ptrm_check_for_SCAT) > 0:
# the two bounding lines
ymin = intercept1 + x_ptrm_check_for_SCAT * slop1
ymax = intercept2 + x_ptrm_check_for_SCAT * slop2
# arrays of "True" or "False"
check_1 = y_ptrm_check_for_SCAT > ymax
check_2 = y_ptrm_check_for_SCAT < ymin
# check if at least one "True"
if (sum(check_1) + sum(check_2)) > 0:
pars["fail_ptrm_beta_box_scatter"] = True
# --------------------------------------------------------------
# check if the tail checks data points are in the 'box'
if len(x_tail_check_for_SCAT) > 0:
# the two bounding lines
ymin = intercept1 + x_tail_check_for_SCAT * slop1
ymax = intercept2 + x_tail_check_for_SCAT * slop2
# arrays of "True" or "False"
check_1 = y_tail_check_for_SCAT > ymax
check_2 = y_tail_check_for_SCAT < ymin
# check if at least one "True"
if (sum(check_1) + sum(check_2)) > 0:
pars["fail_tail_beta_box_scatter"] = True
# --------------------------------------------------------------
# check if specimen_scat is PASS or FAIL:
if pars["fail_tail_beta_box_scatter"] or pars["fail_ptrm_beta_box_scatter"] or pars["fail_arai_beta_box_scatter"]:
pars[scat_key] = 'f'
else:
pars[scat_key] = 't'
return pars, 0 | [
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"]",
"]",
")",
">",
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":",
"F",
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"(",
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"[",
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"-",
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")",
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"(",
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"[",
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"]",
"+",
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"(",
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"-",
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"[",
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"(",
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"[",
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"]",
"-",
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")",
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":",
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")",
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":",
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")",
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".05",
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"# --------------------------------------------------------------",
"# From here added By Ron Shaar 11-Dec 2012",
"# New parameters defined in Shaar and Tauxe (2012):",
"# FRAC (specimen_frac) - ranges from 0. to 1.",
"# SCAT (specimen_scat) - takes 1/0",
"# gap_max (specimen_gmax) - ranges from 0. to 1.",
"# --------------------------------------------------------------",
"# --------------------------------------------------------------",
"# FRAC is similar to Fvds, but the numerator is the vds fraction:",
"# FRAC= [ vds (start,end)] / total vds ]",
"# gap_max= max [ (vector difference) / vds (start,end)]",
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"[",
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"[",
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"NRM",
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"0",
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"[",
"3",
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"# Calculate the \"scat box\"",
"# all data-points, pTRM checks, and tail-checks, should be inside a \"scat box\"",
"# ---------------------------------------------------------------------",
"# intialization",
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gem/oq-engine | openquake/hazardlib/gsim/atkinson_boore_1995.py | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/atkinson_boore_1995.py#L75-L105 | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
C = self.COEFFS[imt]
# clip rhypo at 10 (this is the minimum distance used in
# deriving the equation), see page 22, this avoids singularity
# in mean value equation
rhypo = dists.rhypo.copy()
rhypo[rhypo < 10] = 10
# convert magnitude from Mblg to Mw
mag = rup.mag * 0.98 - 0.39 if rup.mag <= 5.5 else \
2.715 - 0.277 * rup.mag + 0.127 * rup.mag * rup.mag
# functional form as explained in 'Youngs_fit_to_AB95lookup.doc'
f1 = np.minimum(np.log(rhypo), np.log(70.))
f2 = np.maximum(np.log(rhypo / 130.), 0)
mean = (
C['c1'] + C['c2'] * mag + C['c3'] * mag ** 2 +
(C['c4'] + C['c5'] * mag) * f1 +
(C['c6'] + C['c7'] * mag) * f2 +
C['c8'] * rhypo
)
stddevs = self._get_stddevs(stddev_types, dists.rhypo.shape[0])
return mean, stddevs | [
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globality-corp/microcosm-flask | microcosm_flask/formatting/base.py | https://github.com/globality-corp/microcosm-flask/blob/c2eaf57f03e7d041eea343751a4a90fcc80df418/microcosm_flask/formatting/base.py#L50-L74 | def build_etag(self, response, include_etag=True, **kwargs):
"""
Add an etag to the response body.
Uses spooky where possible because it is empirically fast and well-regarded.
See: http://blog.reverberate.org/2012/01/state-of-hash-functions-2012.html
"""
if not include_etag:
return
if not spooky:
# use built-in md5
response.add_etag()
return
# use spooky
response.headers["ETag"] = quote_etag(
hexlify(
spooky.hash128(
response.get_data(),
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fananimi/pyzk | zk/base.py | https://github.com/fananimi/pyzk/blob/1a765d616526efdcb4c9adfcc9b1d10f6ed8b938/zk/base.py#L1349-L1395 | def __recieve_tcp_data(self, data_recv, size):
""" data_recv, raw tcp packet
must analyze tcp_length
must return data, broken
"""
data = []
tcp_length = self.__test_tcp_top(data_recv)
if self.verbose: print ("tcp_length {}, size {}".format(tcp_length, size))
if tcp_length <= 0:
if self.verbose: print ("Incorrect tcp packet")
return None, b""
if (tcp_length - 8) < size:
if self.verbose: print ("tcp length too small... retrying")
resp, bh = self.__recieve_tcp_data(data_recv, tcp_length - 8)
data.append(resp)
size -= len(resp)
if self.verbose: print ("new tcp DATA packet to fill misssing {}".format(size))
data_recv = bh + self.__sock.recv(size + 16 )
if self.verbose: print ("new tcp DATA starting with {} bytes".format(len(data_recv)))
resp, bh = self.__recieve_tcp_data(data_recv, size)
data.append(resp)
if self.verbose: print ("for misssing {} recieved {} with extra {}".format(size, len(resp), len(bh)))
return b''.join(data), bh
recieved = len(data_recv)
if self.verbose: print ("recieved {}, size {}".format(recieved, size))
response = unpack('HHHH', data_recv[8:16])[0]
if recieved >= (size + 32):
if response == const.CMD_DATA:
resp = data_recv[16 : size + 16]
if self.verbose: print ("resp complete len {}".format(len(resp)))
return resp, data_recv[size + 16:]
else:
if self.verbose: print("incorrect response!!! {}".format(response))
return None, b""
else:
if self.verbose: print ("try DATA incomplete (actual valid {})".format(recieved-16))
data.append(data_recv[16 : size + 16 ])
size -= recieved - 16
broken_header = b""
if size < 0:
broken_header = data_recv[size:]
if self.verbose: print ("broken", (broken_header).encode('hex'))
if size > 0:
data_recv = self.__recieve_raw_data(size)
data.append(data_recv)
return b''.join(data), broken_header | [
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tanwanirahul/django-batch-requests | batch_requests/settings.py | https://github.com/tanwanirahul/django-batch-requests/blob/9c5afc42f7542f466247f4ffed9c44e1c49fa20d/batch_requests/settings.py#L27-L34 | def import_class(class_path):
'''
Imports the class for the given class name.
'''
module_name, class_name = class_path.rsplit(".", 1)
module = import_module(module_name)
claz = getattr(module, class_name)
return claz | [
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RedisJSON/rejson-py | rejson/client.py | https://github.com/RedisJSON/rejson-py/blob/55f0adf3adc40f5a769e28e541dbbf5377b90ec6/rejson/client.py#L205-L213 | def jsonarrinsert(self, name, path, index, *args):
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Inserts the objects ``args`` to the array at index ``index`` under the
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"""
pieces = [name, str_path(path), index]
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vecnet/vecnet.openmalaria | vecnet/openmalaria/scenario/interventions.py | https://github.com/vecnet/vecnet.openmalaria/blob/795bc9d1b81a6c664f14879edda7a7c41188e95a/vecnet/openmalaria/scenario/interventions.py#L516-L523 | def anophelesParams(self):
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list_of_anopheles = []
for anophelesParams in self.gvi.findall("anophelesParams"):
list_of_anopheles.append(AnophelesParams(anophelesParams))
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juicer/juicer | juicer/utils/__init__.py | https://github.com/juicer/juicer/blob/0c9f0fd59e293d45df6b46e81f675d33221c600d/juicer/utils/__init__.py#L837-L844 | def repo_def_matches_reality(juicer_def, pulp_def):
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return the update necessary to make `pulp_def` match `juicer_def`.
`juicer_def` - A JuicerRepo() object representing a juicer repository
`pulp_def` - A PulpRepo() object representing a pulp repository
"""
return juicer.common.Repo.RepoDiff(juicer_repo=juicer_def, pulp_repo=pulp_def) | [
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sebp/scikit-survival | sksurv/kernels/clinical.py | https://github.com/sebp/scikit-survival/blob/cfc99fd20454cdd6f4f20fe331b39f2191ccaabc/sksurv/kernels/clinical.py#L35-L50 | def _get_continuous_and_ordinal_array(x):
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nominal_columns = x.select_dtypes(include=['object', 'category']).columns
ordinal_columns = pandas.Index([v for v in nominal_columns if x[v].cat.ordered])
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x_out = numpy.column_stack((x_num, x))
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x_out = x_num
return x_out, nominal_columns | [
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django-fluent/django-fluent-blogs | fluent_blogs/admin/abstractbase.py | https://github.com/django-fluent/django-fluent-blogs/blob/86b148549a010eaca9a2ea987fe43be250e06c50/fluent_blogs/admin/abstractbase.py#L72-L85 | def formfield_for_dbfield(self, db_field, **kwargs):
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Allow formfield_overrides to contain field names too.
"""
overrides = self.formfield_overrides.get(db_field.name)
if overrides:
kwargs.update(overrides)
field = super(AbstractEntryBaseAdmin, self).formfield_for_dbfield(db_field, **kwargs)
# Pass user to the form.
if db_field.name == 'author':
field.user = kwargs['request'].user
return field | [
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Qiskit/qiskit-terra | qiskit/validation/base.py | https://github.com/Qiskit/qiskit-terra/blob/d4f58d903bc96341b816f7c35df936d6421267d1/qiskit/validation/base.py#L201-L224 | def _create_validation_schema(schema_cls):
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Model validation is not part of Marshmallow. Schemas have a ``validate``
method but this delegates execution on ``load`` and discards the result.
Similarly, ``load`` will call ``_deserialize`` on every field in the
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"""
validation_schema = schema_cls()
for _, field in validation_schema.fields.items():
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validate_function = field.__class__.check_type
field._deserialize = MethodType(validate_function, field)
return validation_schema | [
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robinandeer/puzzle | puzzle/models/sql/models.py | https://github.com/robinandeer/puzzle/blob/9476f05b416d3a5135d25492cb31411fdf831c58/puzzle/models/sql/models.py#L55-L59 | def case_comments(self):
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comments = (comment for comment in self.comments if
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inasafe/inasafe | safe/utilities/gis.py | https://github.com/inasafe/inasafe/blob/831d60abba919f6d481dc94a8d988cc205130724/safe/utilities/gis.py#L356-L363 | def qgis_version_detailed():
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:rtype: list
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version = str(Qgis.QGIS_VERSION_INT)
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webrecorder/pywb | pywb/apps/frontendapp.py | https://github.com/webrecorder/pywb/blob/77f8bb647639dd66f6b92b7a9174c28810e4b1d9/pywb/apps/frontendapp.py#L105-L130 | def _init_routes(self):
"""Initialize the routes and based on the configuration file makes available
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self.url_map = Map()
self.url_map.add(Rule('/static/_/<coll>/<path:filepath>', endpoint=self.serve_static))
self.url_map.add(Rule('/static/<path:filepath>', endpoint=self.serve_static))
self.url_map.add(Rule('/collinfo.json', endpoint=self.serve_listing))
if self.is_valid_coll('$root'):
coll_prefix = ''
else:
coll_prefix = '/<coll>'
self.url_map.add(Rule('/', endpoint=self.serve_home))
self.url_map.add(Rule(coll_prefix + self.cdx_api_endpoint, endpoint=self.serve_cdx))
self.url_map.add(Rule(coll_prefix + '/', endpoint=self.serve_coll_page))
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if self.recorder_path:
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self.url_map.add(Rule('/proxy-fetch/<path:url>', endpoint=self.proxy_fetch,
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pysal/giddy | giddy/markov.py | https://github.com/pysal/giddy/blob/13fae6c18933614be78e91a6b5060693bea33a04/giddy/markov.py#L811-L830 | def summary(self, file_name=None):
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A summary method to call the Markov homogeneity test to test for
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To learn more about the properties of the tests, refer to
:cite:`Rey2016a` and :cite:`Kang2018`.
"""
class_names = ["C%d" % i for i in range(self.k)]
regime_names = ["LAG%d" % i for i in range(self.k)]
ht = homogeneity(self.T, class_names=class_names,
regime_names=regime_names)
title = "Spatial Markov Test"
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ht.summary(file_name=file_name, title=title)
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Alignak-monitoring/alignak | alignak/objects/item.py | https://github.com/Alignak-monitoring/alignak/blob/f3c145207e83159b799d3714e4241399c7740a64/alignak/objects/item.py#L587-L611 | def fill_data_brok_from(self, data, brok_type):
"""
Add properties to 'data' parameter with properties of this object when 'brok_type'
parameter is defined in fill_brok of these properties
:param data: object to fill
:type data: object
:param brok_type: name of brok_type
:type brok_type: var
:return: None
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cls = self.__class__
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for prop, entry in list(cls.properties.items()):
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if brok_type in entry.fill_brok:
data[prop] = self.get_property_value_for_brok(prop, cls.properties)
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if hasattr(cls, 'running_properties'):
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for prop, entry in list(cls.running_properties.items()):
# if 'fill_brok' in cls.running_properties[prop]:
if brok_type in entry.fill_brok:
data[prop] = self.get_property_value_for_brok(prop, cls.running_properties) | [
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miguelmoreto/pycomtrade | src/pyComtrade.py | https://github.com/miguelmoreto/pycomtrade/blob/1785ebbc96c01a60e58fb11f0aa4848be855aa0d/src/pyComtrade.py#L300-L306 | def getAnalogID(self,num):
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Returns the COMTRADE ID of a given channel number.
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listidx = self.An.index(num) # Get the position of the channel number.
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marteinn/genres | genres/db.py | https://github.com/marteinn/genres/blob/4bbc90f7c2c527631380c08b4d99a4e40abed955/genres/db.py#L89-L102 | def _parse_entry(entry, limit=10):
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entry = entry.split(",")
label = entry[0]
points = limit
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ddorn/GUI | GUI/math.py | https://github.com/ddorn/GUI/blob/e1fcb5286d24e0995f280d5180222e51895c368c/GUI/math.py#L109-L117 | def normnorm(self):
"""
Return a vecor noraml to this one with a norm of one
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n = self.norm()
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pip-services3-python/pip-services3-commons-python | pip_services3_commons/reflect/RecursiveObjectWriter.py | https://github.com/pip-services3-python/pip-services3-commons-python/blob/22cbbb3e91e49717f65c083d36147fdb07ba9e3b/pip_services3_commons/reflect/RecursiveObjectWriter.py#L93-L107 | def copy_properties(dest, src):
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numba/llvmlite | llvmlite/binding/targets.py | https://github.com/numba/llvmlite/blob/fcadf8af11947f3fd041c5d6526c5bf231564883/llvmlite/binding/targets.py#L47-L69 | def get_host_cpu_features():
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Returns a dictionary-like object indicating the CPU features for current
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are the feature name as string and a boolean indicating whether the feature
is available. The returned value is an instance of ``FeatureMap`` class,
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with ffi.OutputString() as out:
outdict = FeatureMap()
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raise RuntimeError("failed to get host cpu features.")
flag_map = {'+': True, '-': False}
content = str(out)
if content: # protect against empty string
for feat in content.split(','):
if feat: # protect against empty feature
outdict[feat[1:]] = flag_map[feat[0]]
return outdict | [
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inspirehep/harvesting-kit | harvestingkit/utils.py | https://github.com/inspirehep/harvesting-kit/blob/33a7f8aa9dade1d863110c6d8b27dfd955cb471f/harvestingkit/utils.py#L376-L392 | def convert_date_from_iso_to_human(value):
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try:
year, month, day = value.split("-")
except ValueError:
# Not separated by "-". Space?
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year, month, day = value.split(" ")
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Jammy2211/PyAutoLens | autolens/model/profiles/mass_profiles.py | https://github.com/Jammy2211/PyAutoLens/blob/91e50369c7a9c048c83d217625578b72423cd5a7/autolens/model/profiles/mass_profiles.py#L353-L356 | def einstein_radius_rescaled(self):
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Falkonry/falkonry-python-client | falkonryclient/service/falkonry.py | https://github.com/Falkonry/falkonry-python-client/blob/0aeb2b00293ee94944f1634e9667401b03da29c1/falkonryclient/service/falkonry.py#L438-L446 | def get_status(self, tracker_id):
"""
Get status of a task
:param tracker_id: string
"""
url = '/app/track/' + tracker_id
response = self.http.get(url)
return response | [
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touilleMan/marshmallow-mongoengine | marshmallow_mongoengine/conversion/fields.py | https://github.com/touilleMan/marshmallow-mongoengine/blob/21223700ea1f1d0209c967761e5c22635ee721e7/marshmallow_mongoengine/conversion/fields.py#L143-L155 | def register_field(mongo_field_cls, marshmallow_field_cls,
available_params=()):
"""
Bind a marshmallow field to it corresponding mongoengine field
:param mongo_field_cls: Mongoengine Field
:param marshmallow_field_cls: Marshmallow Field
:param available_params: List of :class marshmallow_mongoengine.cnoversion.params.MetaParam:
instances to import the mongoengine field config to marshmallow
"""
class Builder(MetaFieldBuilder):
AVAILABLE_PARAMS = available_params
MARSHMALLOW_FIELD_CLS = marshmallow_field_cls
register_field_builder(mongo_field_cls, Builder) | [
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Azure/azure-sdk-for-python | azure-cognitiveservices-vision-face/azure/cognitiveservices/vision/face/operations/large_face_list_operations.py | https://github.com/Azure/azure-sdk-for-python/blob/d7306fde32f60a293a7567678692bdad31e4b667/azure-cognitiveservices-vision-face/azure/cognitiveservices/vision/face/operations/large_face_list_operations.py#L778-L855 | def add_face_from_stream(
self, large_face_list_id, image, user_data=None, target_face=None, custom_headers=None, raw=False, callback=None, **operation_config):
"""Add a face to a large face list. The input face is specified as an
image with a targetFace rectangle. It returns a persistedFaceId
representing the added face, and persistedFaceId will not expire.
:param large_face_list_id: Id referencing a particular large face
list.
:type large_face_list_id: str
:param image: An image stream.
:type image: Generator
:param user_data: User-specified data about the face for any purpose.
The maximum length is 1KB.
:type user_data: str
:param target_face: A face rectangle to specify the target face to be
added to a person in the format of "targetFace=left,top,width,height".
E.g. "targetFace=10,10,100,100". If there is more than one face in the
image, targetFace is required to specify which face to add. No
targetFace means there is only one face detected in the entire image.
:type target_face: list[int]
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param callback: When specified, will be called with each chunk of
data that is streamed. The callback should take two arguments, the
bytes of the current chunk of data and the response object. If the
data is uploading, response will be None.
:type callback: Callable[Bytes, response=None]
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:return: PersistedFace or ClientRawResponse if raw=true
:rtype: ~azure.cognitiveservices.vision.face.models.PersistedFace or
~msrest.pipeline.ClientRawResponse
:raises:
:class:`APIErrorException<azure.cognitiveservices.vision.face.models.APIErrorException>`
"""
# Construct URL
url = self.add_face_from_stream.metadata['url']
path_format_arguments = {
'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True),
'largeFaceListId': self._serialize.url("large_face_list_id", large_face_list_id, 'str', max_length=64, pattern=r'^[a-z0-9-_]+$')
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
if user_data is not None:
query_parameters['userData'] = self._serialize.query("user_data", user_data, 'str', max_length=1024)
if target_face is not None:
query_parameters['targetFace'] = self._serialize.query("target_face", target_face, '[int]', div=',')
# Construct headers
header_parameters = {}
header_parameters['Accept'] = 'application/json'
header_parameters['Content-Type'] = 'application/octet-stream'
if custom_headers:
header_parameters.update(custom_headers)
# Construct body
body_content = self._client.stream_upload(image, callback)
# Construct and send request
request = self._client.post(url, query_parameters, header_parameters, body_content)
response = self._client.send(request, stream=False, **operation_config)
if response.status_code not in [200]:
raise models.APIErrorException(self._deserialize, response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('PersistedFace', response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized | [
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:param callback: When specified, will be called with each chunk of
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:type callback: Callable[Bytes, response=None]
:param operation_config: :ref:`Operation configuration
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:return: PersistedFace or ClientRawResponse if raw=true
:rtype: ~azure.cognitiveservices.vision.face.models.PersistedFace or
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:raises:
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DataDog/integrations-core | tokumx/datadog_checks/tokumx/vendor/pymongo/collection.py | https://github.com/DataDog/integrations-core/blob/ebd41c873cf9f97a8c51bf9459bc6a7536af8acd/tokumx/datadog_checks/tokumx/vendor/pymongo/collection.py#L2359-L2454 | def find_one_and_update(self, filter, update,
projection=None, sort=None, upsert=False,
return_document=ReturnDocument.BEFORE, **kwargs):
"""Finds a single document and updates it, returning either the
original or the updated document.
>>> db.test.find_one_and_update(
... {'_id': 665}, {'$inc': {'count': 1}, '$set': {'done': True}})
{u'_id': 665, u'done': False, u'count': 25}}
By default :meth:`find_one_and_update` returns the original version of
the document before the update was applied. To return the updated
version of the document instead, use the *return_document* option.
>>> from pymongo import ReturnDocument
>>> db.example.find_one_and_update(
... {'_id': 'userid'},
... {'$inc': {'seq': 1}},
... return_document=ReturnDocument.AFTER)
{u'_id': u'userid', u'seq': 1}
You can limit the fields returned with the *projection* option.
>>> db.example.find_one_and_update(
... {'_id': 'userid'},
... {'$inc': {'seq': 1}},
... projection={'seq': True, '_id': False},
... return_document=ReturnDocument.AFTER)
{u'seq': 2}
The *upsert* option can be used to create the document if it doesn't
already exist.
>>> db.example.delete_many({}).deleted_count
1
>>> db.example.find_one_and_update(
... {'_id': 'userid'},
... {'$inc': {'seq': 1}},
... projection={'seq': True, '_id': False},
... upsert=True,
... return_document=ReturnDocument.AFTER)
{u'seq': 1}
If multiple documents match *filter*, a *sort* can be applied.
>>> for doc in db.test.find({'done': True}):
... print(doc)
...
{u'_id': 665, u'done': True, u'result': {u'count': 26}}
{u'_id': 701, u'done': True, u'result': {u'count': 17}}
>>> db.test.find_one_and_update(
... {'done': True},
... {'$set': {'final': True}},
... sort=[('_id', pymongo.DESCENDING)])
{u'_id': 701, u'done': True, u'result': {u'count': 17}}
:Parameters:
- `filter`: A query that matches the document to update.
- `update`: The update operations to apply.
- `projection` (optional): A list of field names that should be
returned in the result document or a mapping specifying the fields
to include or exclude. If `projection` is a list "_id" will
always be returned. Use a dict to exclude fields from
the result (e.g. projection={'_id': False}).
- `sort` (optional): a list of (key, direction) pairs
specifying the sort order for the query. If multiple documents
match the query, they are sorted and the first is updated.
- `upsert` (optional): When ``True``, inserts a new document if no
document matches the query. Defaults to ``False``.
- `return_document`: If
:attr:`ReturnDocument.BEFORE` (the default),
returns the original document before it was updated, or ``None``
if no document matches. If
:attr:`ReturnDocument.AFTER`, returns the updated
or inserted document.
- `**kwargs` (optional): additional command arguments can be passed
as keyword arguments (for example maxTimeMS can be used with
recent server versions).
.. versionchanged:: 3.4
Added the `collation` option.
.. versionchanged:: 3.2
Respects write concern.
.. warning:: Starting in PyMongo 3.2, this command uses the
:class:`~pymongo.write_concern.WriteConcern` of this
:class:`~pymongo.collection.Collection` when connected to MongoDB >=
3.2. Note that using an elevated write concern with this command may
be slower compared to using the default write concern.
.. versionadded:: 3.0
"""
common.validate_ok_for_update(update)
kwargs['update'] = update
return self.__find_and_modify(filter, projection,
sort, upsert, return_document, **kwargs) | [
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{u'_id': 665, u'done': False, u'count': 25}}
By default :meth:`find_one_and_update` returns the original version of
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>>> from pymongo import ReturnDocument
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{u'_id': u'userid', u'seq': 1}
You can limit the fields returned with the *projection* option.
>>> db.example.find_one_and_update(
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The *upsert* option can be used to create the document if it doesn't
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>>> db.example.delete_many({}).deleted_count
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>>> db.example.find_one_and_update(
... {'_id': 'userid'},
... {'$inc': {'seq': 1}},
... projection={'seq': True, '_id': False},
... upsert=True,
... return_document=ReturnDocument.AFTER)
{u'seq': 1}
If multiple documents match *filter*, a *sort* can be applied.
>>> for doc in db.test.find({'done': True}):
... print(doc)
...
{u'_id': 665, u'done': True, u'result': {u'count': 26}}
{u'_id': 701, u'done': True, u'result': {u'count': 17}}
>>> db.test.find_one_and_update(
... {'done': True},
... {'$set': {'final': True}},
... sort=[('_id', pymongo.DESCENDING)])
{u'_id': 701, u'done': True, u'result': {u'count': 17}}
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- `filter`: A query that matches the document to update.
- `update`: The update operations to apply.
- `projection` (optional): A list of field names that should be
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to include or exclude. If `projection` is a list "_id" will
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- `sort` (optional): a list of (key, direction) pairs
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- `upsert` (optional): When ``True``, inserts a new document if no
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- `return_document`: If
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- `**kwargs` (optional): additional command arguments can be passed
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.. versionchanged:: 3.4
Added the `collation` option.
.. versionchanged:: 3.2
Respects write concern.
.. warning:: Starting in PyMongo 3.2, this command uses the
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softlayer/softlayer-python | SoftLayer/managers/ipsec.py | https://github.com/softlayer/softlayer-python/blob/9f181be08cc3668353b05a6de0cb324f52cff6fa/SoftLayer/managers/ipsec.py#L179-L187 | def remove_remote_subnet(self, context_id, subnet_id):
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apache/incubator-heron | third_party/python/cpplint/cpplint.py | https://github.com/apache/incubator-heron/blob/ad10325a0febe89ad337e561ebcbe37ec5d9a5ac/third_party/python/cpplint/cpplint.py#L739-L754 | def IsErrorSuppressedByNolint(category, linenum):
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return (_global_error_suppressions.get(category, False) or
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Consults the global error_suppressions map populated by
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Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
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chrisspen/burlap | burlap/vm.py | https://github.com/chrisspen/burlap/blob/a92b0a8e5206850bb777c74af8421ea8b33779bd/burlap/vm.py#L644-L654 | def respawn(name=None, group=None):
"""
Deletes and recreates one or more VM instances.
"""
if name is None:
name = get_name()
delete(name=name, group=group)
instance = get_or_create(name=name, group=group)
env.host_string = instance.public_dns_name | [
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amperser/proselint | proselint/checks/misc/illogic.py | https://github.com/amperser/proselint/blob/cb619ee4023cc7856f5fb96aec2a33a2c9f1a2e2/proselint/checks/misc/illogic.py#L52-L60 | def check_without_your_collusion(text):
"""Check the textself."""
err = "misc.illogic.collusion"
msg = "It's impossible to defraud yourself. Try 'aquiescence'."
regex = "without your collusion"
return existence_check(
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aws/aws-encryption-sdk-python | doc/conf.py | https://github.com/aws/aws-encryption-sdk-python/blob/d182155d5fb1ef176d9e7d0647679737d5146495/doc/conf.py#L21-L27 | def get_version():
"""Reads the version (MAJOR.MINOR) from this module."""
release = get_release()
split_version = release.split(".")
if len(split_version) == 3:
return ".".join(split_version[:2])
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gitpython-developers/GitPython | git/objects/submodule/base.py | https://github.com/gitpython-developers/GitPython/blob/1f66e25c25cde2423917ee18c4704fff83b837d1/git/objects/submodule/base.py#L287-L308 | def _write_git_file_and_module_config(cls, working_tree_dir, module_abspath):
"""Writes a .git file containing a (preferably) relative path to the actual git module repository.
It is an error if the module_abspath cannot be made into a relative path, relative to the working_tree_dir
:note: will overwrite existing files !
:note: as we rewrite both the git file as well as the module configuration, we might fail on the configuration
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if it becomes one
:param working_tree_dir: directory to write the .git file into
:param module_abspath: absolute path to the bare repository
"""
git_file = osp.join(working_tree_dir, '.git')
rela_path = osp.relpath(module_abspath, start=working_tree_dir)
if is_win:
if osp.isfile(git_file):
os.remove(git_file)
with open(git_file, 'wb') as fp:
fp.write(("gitdir: %s" % rela_path).encode(defenc))
with GitConfigParser(osp.join(module_abspath, 'config'),
read_only=False, merge_includes=False) as writer:
writer.set_value('core', 'worktree',
to_native_path_linux(osp.relpath(working_tree_dir, start=module_abspath))) | [
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gem/oq-engine | openquake/hazardlib/gsim/zhao_2016.py | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/zhao_2016.py#L82-L102 | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# extracting dictionary of coefficients specific to required
# intensity measure type.
C = self.COEFFS[imt]
C_SITE = self.SITE_COEFFS[imt]
s_c, idx = self._get_site_classification(sites.vs30)
sa_rock = (self.get_magnitude_scaling_term(C, rup) +
self.get_sof_term(C, rup) +
self.get_depth_term(C, rup) +
self.get_distance_term(C, dists, rup))
sa_soil = self.add_site_amplification(C, C_SITE, sites,
sa_rock, idx, rup)
stddevs = self.get_stddevs(C, sites.vs30.shape, idx, stddev_types)
return sa_soil, stddevs | [
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COLORFULBOARD/revision | revision/client.py | https://github.com/COLORFULBOARD/revision/blob/2f22e72cce5b60032a80c002ac45c2ecef0ed987/revision/client.py#L225-L233 | def tmp_file_path(self):
"""
:return:
:rtype: str
"""
return os.path.normpath(os.path.join(
TMP_DIR,
self.filename
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PythonCharmers/python-future | src/future/types/newbytes.py | https://github.com/PythonCharmers/python-future/blob/c423752879acc05eebc29b0bb9909327bd5c7308/src/future/types/newbytes.py#L324-L332 | def rindex(self, sub, *args):
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S.rindex(sub [,start [,end]]) -> int
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pos = self.rfind(sub, *args)
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dhermes/bezier | src/bezier/surface.py | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L322-L352 | def edges(self):
"""The edges of the surface.
.. doctest:: surface-edges
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5 , 1.0, 0.1875, 0.625, 0.0],
... [0.0, -0.1875, 0.0, 0.5 , 0.625, 1.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> edge1, _, _ = surface.edges
>>> edge1
<Curve (degree=2, dimension=2)>
>>> edge1.nodes
array([[ 0. , 0.5 , 1. ],
[ 0. , -0.1875, 0. ]])
Returns:
Tuple[~bezier.curve.Curve, ~bezier.curve.Curve, \
~bezier.curve.Curve]: The edges of
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"""
edge1, edge2, edge3 = self._get_edges()
# NOTE: It is crucial that we return copies here. Since the edges
# are cached, if they were mutable, callers could
# inadvertently mutate the cached value.
edge1 = edge1._copy() # pylint: disable=protected-access
edge2 = edge2._copy() # pylint: disable=protected-access
edge3 = edge3._copy() # pylint: disable=protected-access
return edge1, edge2, edge3 | [
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un33k/django-ipware | ipware/utils.py | https://github.com/un33k/django-ipware/blob/dc6b754137d1bb7d056ac206a6e0443aa3ed68dc/ipware/utils.py#L91-L100 | def get_ip_info(ip_str):
"""
Given a string, it returns a tuple of (IP, Routable).
"""
ip = None
is_routable_ip = False
if is_valid_ip(ip_str):
ip = ip_str
is_routable_ip = is_public_ip(ip)
return ip, is_routable_ip | [
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log2timeline/dfvfs | dfvfs/vfs/tar_file_system.py | https://github.com/log2timeline/dfvfs/blob/2b3ccd115f9901d89f383397d4a1376a873c83c4/dfvfs/vfs/tar_file_system.py#L165-L190 | def GetTARInfoByPathSpec(self, path_spec):
"""Retrieves the TAR info for a path specification.
Args:
path_spec (PathSpec): a path specification.
Returns:
tarfile.TARInfo: TAR info or None if it does not exist.
Raises:
PathSpecError: if the path specification is incorrect.
"""
location = getattr(path_spec, 'location', None)
if location is None:
raise errors.PathSpecError('Path specification missing location.')
if not location.startswith(self.LOCATION_ROOT):
raise errors.PathSpecError('Invalid location in path specification.')
if len(location) == 1:
return None
try:
return self._tar_file.getmember(location[1:])
except KeyError:
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senaite/senaite.core | bika/lims/content/calculation.py | https://github.com/senaite/senaite.core/blob/7602ce2ea2f9e81eb34e20ce17b98a3e70713f85/bika/lims/content/calculation.py#L237-L262 | def getCalculationDependencies(self, flat=False, deps=None):
""" Recursively calculates all dependencies of this calculation.
The return value is dictionary of dictionaries (of dictionaries...)
{service_UID1:
{service_UID2:
{service_UID3: {},
service_UID4: {},
},
},
}
set flat=True to get a simple list of AnalysisService objects
"""
if deps is None:
deps = [] if flat is True else {}
for service in self.getDependentServices():
calc = service.getCalculation()
if calc:
calc.getCalculationDependencies(flat, deps)
if flat:
deps.append(service)
else:
deps[service.UID()] = {}
return deps | [
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set flat=True to get a simple list of AnalysisService objects | [
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hydraplatform/hydra-base | hydra_base/lib/data.py | https://github.com/hydraplatform/hydra-base/blob/9251ff7946505f7a272c87837390acd1c435bc6e/hydra_base/lib/data.py#L853-L866 | def check_dataset_in_collection(dataset_id, collection_id, **kwargs):
"""
Check whether a dataset is contained inside a collection
:param dataset ID
:param collection ID
:returns 'Y' or 'N'
"""
_get_collection(collection_id)
collection_item = _get_collection_item(collection_id, dataset_id)
if collection_item is None:
return 'N'
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return 'Y' | [
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wavycloud/pyboto3 | pyboto3/rds.py | https://github.com/wavycloud/pyboto3/blob/924957ccf994303713a4eed90b775ff2ab95b2e5/pyboto3/rds.py#L6300-L6729 | def modify_db_instance(DBInstanceIdentifier=None, AllocatedStorage=None, DBInstanceClass=None, DBSubnetGroupName=None, DBSecurityGroups=None, VpcSecurityGroupIds=None, ApplyImmediately=None, MasterUserPassword=None, DBParameterGroupName=None, BackupRetentionPeriod=None, PreferredBackupWindow=None, PreferredMaintenanceWindow=None, MultiAZ=None, EngineVersion=None, AllowMajorVersionUpgrade=None, AutoMinorVersionUpgrade=None, LicenseModel=None, Iops=None, OptionGroupName=None, NewDBInstanceIdentifier=None, StorageType=None, TdeCredentialArn=None, TdeCredentialPassword=None, CACertificateIdentifier=None, Domain=None, CopyTagsToSnapshot=None, MonitoringInterval=None, DBPortNumber=None, PubliclyAccessible=None, MonitoringRoleArn=None, DomainIAMRoleName=None, PromotionTier=None, EnableIAMDatabaseAuthentication=None):
"""
Modifies settings for a DB instance. You can change one or more database configuration parameters by specifying these parameters and the new values in the request.
See also: AWS API Documentation
Examples
This example immediately changes the specified settings for the specified DB instance.
Expected Output:
:example: response = client.modify_db_instance(
DBInstanceIdentifier='string',
AllocatedStorage=123,
DBInstanceClass='string',
DBSubnetGroupName='string',
DBSecurityGroups=[
'string',
],
VpcSecurityGroupIds=[
'string',
],
ApplyImmediately=True|False,
MasterUserPassword='string',
DBParameterGroupName='string',
BackupRetentionPeriod=123,
PreferredBackupWindow='string',
PreferredMaintenanceWindow='string',
MultiAZ=True|False,
EngineVersion='string',
AllowMajorVersionUpgrade=True|False,
AutoMinorVersionUpgrade=True|False,
LicenseModel='string',
Iops=123,
OptionGroupName='string',
NewDBInstanceIdentifier='string',
StorageType='string',
TdeCredentialArn='string',
TdeCredentialPassword='string',
CACertificateIdentifier='string',
Domain='string',
CopyTagsToSnapshot=True|False,
MonitoringInterval=123,
DBPortNumber=123,
PubliclyAccessible=True|False,
MonitoringRoleArn='string',
DomainIAMRoleName='string',
PromotionTier=123,
EnableIAMDatabaseAuthentication=True|False
)
:type DBInstanceIdentifier: string
:param DBInstanceIdentifier: [REQUIRED]
The DB instance identifier. This value is stored as a lowercase string.
Constraints:
Must be the identifier for an existing DB instance
Must contain from 1 to 63 alphanumeric characters or hyphens
First character must be a letter
Cannot end with a hyphen or contain two consecutive hyphens
:type AllocatedStorage: integer
:param AllocatedStorage: The new storage capacity of the RDS instance. Changing this setting does not result in an outage and the change is applied during the next maintenance window unless ApplyImmediately is set to true for this request.
MySQL
Default: Uses existing setting
Valid Values: 5-6144
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value.
Type: Integer
MariaDB
Default: Uses existing setting
Valid Values: 5-6144
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value.
Type: Integer
PostgreSQL
Default: Uses existing setting
Valid Values: 5-6144
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value.
Type: Integer
Oracle
Default: Uses existing setting
Valid Values: 10-6144
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value.
SQL Server
Cannot be modified.
If you choose to migrate your DB instance from using standard storage to using Provisioned IOPS, or from using Provisioned IOPS to using standard storage, the process can take time. The duration of the migration depends on several factors such as database load, storage size, storage type (standard or Provisioned IOPS), amount of IOPS provisioned (if any), and the number of prior scale storage operations. Typical migration times are under 24 hours, but the process can take up to several days in some cases. During the migration, the DB instance will be available for use, but might experience performance degradation. While the migration takes place, nightly backups for the instance will be suspended. No other Amazon RDS operations can take place for the instance, including modifying the instance, rebooting the instance, deleting the instance, creating a Read Replica for the instance, and creating a DB snapshot of the instance.
:type DBInstanceClass: string
:param DBInstanceClass: The new compute and memory capacity of the DB instance. To determine the instance classes that are available for a particular DB engine, use the DescribeOrderableDBInstanceOptions action. Note that not all instance classes are available in all regions for all DB engines.
Passing a value for this setting causes an outage during the change and is applied during the next maintenance window, unless ApplyImmediately is specified as true for this request.
Default: Uses existing setting
Valid Values: db.t1.micro | db.m1.small | db.m1.medium | db.m1.large | db.m1.xlarge | db.m2.xlarge | db.m2.2xlarge | db.m2.4xlarge | db.m3.medium | db.m3.large | db.m3.xlarge | db.m3.2xlarge | db.m4.large | db.m4.xlarge | db.m4.2xlarge | db.m4.4xlarge | db.m4.10xlarge | db.r3.large | db.r3.xlarge | db.r3.2xlarge | db.r3.4xlarge | db.r3.8xlarge | db.t2.micro | db.t2.small | db.t2.medium | db.t2.large
:type DBSubnetGroupName: string
:param DBSubnetGroupName: The new DB subnet group for the DB instance. You can use this parameter to move your DB instance to a different VPC. If your DB instance is not in a VPC, you can also use this parameter to move your DB instance into a VPC. For more information, see Updating the VPC for a DB Instance .
Changing the subnet group causes an outage during the change. The change is applied during the next maintenance window, unless you specify true for the ApplyImmediately parameter.
Constraints: Must contain no more than 255 alphanumeric characters, periods, underscores, spaces, or hyphens.
Example: mySubnetGroup
:type DBSecurityGroups: list
:param DBSecurityGroups: A list of DB security groups to authorize on this DB instance. Changing this setting does not result in an outage and the change is asynchronously applied as soon as possible.
Constraints:
Must be 1 to 255 alphanumeric characters
First character must be a letter
Cannot end with a hyphen or contain two consecutive hyphens
(string) --
:type VpcSecurityGroupIds: list
:param VpcSecurityGroupIds: A list of EC2 VPC security groups to authorize on this DB instance. This change is asynchronously applied as soon as possible.
Constraints:
Must be 1 to 255 alphanumeric characters
First character must be a letter
Cannot end with a hyphen or contain two consecutive hyphens
(string) --
:type ApplyImmediately: boolean
:param ApplyImmediately: Specifies whether the modifications in this request and any pending modifications are asynchronously applied as soon as possible, regardless of the PreferredMaintenanceWindow setting for the DB instance.
If this parameter is set to false , changes to the DB instance are applied during the next maintenance window. Some parameter changes can cause an outage and will be applied on the next call to RebootDBInstance , or the next failure reboot. Review the table of parameters in Modifying a DB Instance and Using the Apply Immediately Parameter to see the impact that setting ApplyImmediately to true or false has for each modified parameter and to determine when the changes will be applied.
Default: false
:type MasterUserPassword: string
:param MasterUserPassword: The new password for the DB instance master user. Can be any printable ASCII character except '/', ''', or '@'.
Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible. Between the time of the request and the completion of the request, the MasterUserPassword element exists in the PendingModifiedValues element of the operation response.
Default: Uses existing setting
Constraints: Must be 8 to 41 alphanumeric characters (MySQL, MariaDB, and Amazon Aurora), 8 to 30 alphanumeric characters (Oracle), or 8 to 128 alphanumeric characters (SQL Server).
Note
Amazon RDS API actions never return the password, so this action provides a way to regain access to a primary instance user if the password is lost. This includes restoring privileges that might have been accidentally revoked.
:type DBParameterGroupName: string
:param DBParameterGroupName: The name of the DB parameter group to apply to the DB instance. Changing this setting does not result in an outage. The parameter group name itself is changed immediately, but the actual parameter changes are not applied until you reboot the instance without failover. The db instance will NOT be rebooted automatically and the parameter changes will NOT be applied during the next maintenance window.
Default: Uses existing setting
Constraints: The DB parameter group must be in the same DB parameter group family as this DB instance.
:type BackupRetentionPeriod: integer
:param BackupRetentionPeriod: The number of days to retain automated backups. Setting this parameter to a positive number enables backups. Setting this parameter to 0 disables automated backups.
Changing this parameter can result in an outage if you change from 0 to a non-zero value or from a non-zero value to 0. These changes are applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request. If you change the parameter from one non-zero value to another non-zero value, the change is asynchronously applied as soon as possible.
Default: Uses existing setting
Constraints:
Must be a value from 0 to 35
Can be specified for a MySQL Read Replica only if the source is running MySQL 5.6
Can be specified for a PostgreSQL Read Replica only if the source is running PostgreSQL 9.3.5
Cannot be set to 0 if the DB instance is a source to Read Replicas
:type PreferredBackupWindow: string
:param PreferredBackupWindow: The daily time range during which automated backups are created if automated backups are enabled, as determined by the BackupRetentionPeriod parameter. Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible.
Constraints:
Must be in the format hh24:mi-hh24:mi
Times should be in Universal Time Coordinated (UTC)
Must not conflict with the preferred maintenance window
Must be at least 30 minutes
:type PreferredMaintenanceWindow: string
:param PreferredMaintenanceWindow: The weekly time range (in UTC) during which system maintenance can occur, which might result in an outage. Changing this parameter does not result in an outage, except in the following situation, and the change is asynchronously applied as soon as possible. If there are pending actions that cause a reboot, and the maintenance window is changed to include the current time, then changing this parameter will cause a reboot of the DB instance. If moving this window to the current time, there must be at least 30 minutes between the current time and end of the window to ensure pending changes are applied.
Default: Uses existing setting
Format: ddd:hh24:mi-ddd:hh24:mi
Valid Days: Mon | Tue | Wed | Thu | Fri | Sat | Sun
Constraints: Must be at least 30 minutes
:type MultiAZ: boolean
:param MultiAZ: Specifies if the DB instance is a Multi-AZ deployment. Changing this parameter does not result in an outage and the change is applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request.
Constraints: Cannot be specified if the DB instance is a Read Replica.
:type EngineVersion: string
:param EngineVersion: The version number of the database engine to upgrade to. Changing this parameter results in an outage and the change is applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request.
For major version upgrades, if a non-default DB parameter group is currently in use, a new DB parameter group in the DB parameter group family for the new engine version must be specified. The new DB parameter group can be the default for that DB parameter group family.
For a list of valid engine versions, see CreateDBInstance .
:type AllowMajorVersionUpgrade: boolean
:param AllowMajorVersionUpgrade: Indicates that major version upgrades are allowed. Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible.
Constraints: This parameter must be set to true when specifying a value for the EngineVersion parameter that is a different major version than the DB instance's current version.
:type AutoMinorVersionUpgrade: boolean
:param AutoMinorVersionUpgrade: Indicates that minor version upgrades will be applied automatically to the DB instance during the maintenance window. Changing this parameter does not result in an outage except in the following case and the change is asynchronously applied as soon as possible. An outage will result if this parameter is set to true during the maintenance window, and a newer minor version is available, and RDS has enabled auto patching for that engine version.
:type LicenseModel: string
:param LicenseModel: The license model for the DB instance.
Valid values: license-included | bring-your-own-license | general-public-license
:type Iops: integer
:param Iops: The new Provisioned IOPS (I/O operations per second) value for the RDS instance. Changing this setting does not result in an outage and the change is applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request.
Default: Uses existing setting
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value. If you are migrating from Provisioned IOPS to standard storage, set this value to 0. The DB instance will require a reboot for the change in storage type to take effect.
SQL Server
Setting the IOPS value for the SQL Server database engine is not supported.
Type: Integer
If you choose to migrate your DB instance from using standard storage to using Provisioned IOPS, or from using Provisioned IOPS to using standard storage, the process can take time. The duration of the migration depends on several factors such as database load, storage size, storage type (standard or Provisioned IOPS), amount of IOPS provisioned (if any), and the number of prior scale storage operations. Typical migration times are under 24 hours, but the process can take up to several days in some cases. During the migration, the DB instance will be available for use, but might experience performance degradation. While the migration takes place, nightly backups for the instance will be suspended. No other Amazon RDS operations can take place for the instance, including modifying the instance, rebooting the instance, deleting the instance, creating a Read Replica for the instance, and creating a DB snapshot of the instance.
:type OptionGroupName: string
:param OptionGroupName: Indicates that the DB instance should be associated with the specified option group. Changing this parameter does not result in an outage except in the following case and the change is applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request. If the parameter change results in an option group that enables OEM, this change can cause a brief (sub-second) period during which new connections are rejected but existing connections are not interrupted.
Permanent options, such as the TDE option for Oracle Advanced Security TDE, cannot be removed from an option group, and that option group cannot be removed from a DB instance once it is associated with a DB instance
:type NewDBInstanceIdentifier: string
:param NewDBInstanceIdentifier: The new DB instance identifier for the DB instance when renaming a DB instance. When you change the DB instance identifier, an instance reboot will occur immediately if you set Apply Immediately to true, or will occur during the next maintenance window if Apply Immediately to false. This value is stored as a lowercase string.
Constraints:
Must contain from 1 to 63 alphanumeric characters or hyphens
First character must be a letter
Cannot end with a hyphen or contain two consecutive hyphens
:type StorageType: string
:param StorageType: Specifies the storage type to be associated with the DB instance.
Valid values: standard | gp2 | io1
If you specify io1 , you must also include a value for the Iops parameter.
Default: io1 if the Iops parameter is specified; otherwise standard
:type TdeCredentialArn: string
:param TdeCredentialArn: The ARN from the Key Store with which to associate the instance for TDE encryption.
:type TdeCredentialPassword: string
:param TdeCredentialPassword: The password for the given ARN from the Key Store in order to access the device.
:type CACertificateIdentifier: string
:param CACertificateIdentifier: Indicates the certificate that needs to be associated with the instance.
:type Domain: string
:param Domain: The Active Directory Domain to move the instance to. Specify none to remove the instance from its current domain. The domain must be created prior to this operation. Currently only a Microsoft SQL Server instance can be created in a Active Directory Domain.
:type CopyTagsToSnapshot: boolean
:param CopyTagsToSnapshot: True to copy all tags from the DB instance to snapshots of the DB instance; otherwise false. The default is false.
:type MonitoringInterval: integer
:param MonitoringInterval: The interval, in seconds, between points when Enhanced Monitoring metrics are collected for the DB instance. To disable collecting Enhanced Monitoring metrics, specify 0. The default is 0.
If MonitoringRoleArn is specified, then you must also set MonitoringInterval to a value other than 0.
Valid Values: 0, 1, 5, 10, 15, 30, 60
:type DBPortNumber: integer
:param DBPortNumber: The port number on which the database accepts connections.
The value of the DBPortNumber parameter must not match any of the port values specified for options in the option group for the DB instance.
Your database will restart when you change the DBPortNumber value regardless of the value of the ApplyImmediately parameter.
MySQL
Default: 3306
Valid Values: 1150-65535
MariaDB
Default: 3306
Valid Values: 1150-65535
PostgreSQL
Default: 5432
Valid Values: 1150-65535
Type: Integer
Oracle
Default: 1521
Valid Values: 1150-65535
SQL Server
Default: 1433
Valid Values: 1150-65535 except for 1434 , 3389 , 47001 , 49152 , and 49152 through 49156 .
Amazon Aurora
Default: 3306
Valid Values: 1150-65535
:type PubliclyAccessible: boolean
:param PubliclyAccessible: Boolean value that indicates if the DB instance has a publicly resolvable DNS name. Set to True to make the DB instance Internet-facing with a publicly resolvable DNS name, which resolves to a public IP address. Set to False to make the DB instance internal with a DNS name that resolves to a private IP address.
PubliclyAccessible only applies to DB instances in a VPC. The DB instance must be part of a public subnet and PubliclyAccessible must be true in order for it to be publicly accessible.
Changes to the PubliclyAccessible parameter are applied immediately regardless of the value of the ApplyImmediately parameter.
Default: false
:type MonitoringRoleArn: string
:param MonitoringRoleArn: The ARN for the IAM role that permits RDS to send enhanced monitoring metrics to CloudWatch Logs. For example, arn:aws:iam:123456789012:role/emaccess . For information on creating a monitoring role, go to To create an IAM role for Amazon RDS Enhanced Monitoring .
If MonitoringInterval is set to a value other than 0, then you must supply a MonitoringRoleArn value.
:type DomainIAMRoleName: string
:param DomainIAMRoleName: The name of the IAM role to use when making API calls to the Directory Service.
:type PromotionTier: integer
:param PromotionTier: A value that specifies the order in which an Aurora Replica is promoted to the primary instance after a failure of the existing primary instance. For more information, see Fault Tolerance for an Aurora DB Cluster .
Default: 1
Valid Values: 0 - 15
:type EnableIAMDatabaseAuthentication: boolean
:param EnableIAMDatabaseAuthentication: True to enable mapping of AWS Identity and Access Management (IAM) accounts to database accounts; otherwise false.
You can enable IAM database authentication for the following database engines
For MySQL 5.6, minor version 5.6.34 or higher
For MySQL 5.7, minor version 5.7.16 or higher
Default: false
:rtype: dict
:return: {
'DBInstance': {
'DBInstanceIdentifier': 'string',
'DBInstanceClass': 'string',
'Engine': 'string',
'DBInstanceStatus': 'string',
'MasterUsername': 'string',
'DBName': 'string',
'Endpoint': {
'Address': 'string',
'Port': 123,
'HostedZoneId': 'string'
},
'AllocatedStorage': 123,
'InstanceCreateTime': datetime(2015, 1, 1),
'PreferredBackupWindow': 'string',
'BackupRetentionPeriod': 123,
'DBSecurityGroups': [
{
'DBSecurityGroupName': 'string',
'Status': 'string'
},
],
'VpcSecurityGroups': [
{
'VpcSecurityGroupId': 'string',
'Status': 'string'
},
],
'DBParameterGroups': [
{
'DBParameterGroupName': 'string',
'ParameterApplyStatus': 'string'
},
],
'AvailabilityZone': 'string',
'DBSubnetGroup': {
'DBSubnetGroupName': 'string',
'DBSubnetGroupDescription': 'string',
'VpcId': 'string',
'SubnetGroupStatus': 'string',
'Subnets': [
{
'SubnetIdentifier': 'string',
'SubnetAvailabilityZone': {
'Name': 'string'
},
'SubnetStatus': 'string'
},
],
'DBSubnetGroupArn': 'string'
},
'PreferredMaintenanceWindow': 'string',
'PendingModifiedValues': {
'DBInstanceClass': 'string',
'AllocatedStorage': 123,
'MasterUserPassword': 'string',
'Port': 123,
'BackupRetentionPeriod': 123,
'MultiAZ': True|False,
'EngineVersion': 'string',
'LicenseModel': 'string',
'Iops': 123,
'DBInstanceIdentifier': 'string',
'StorageType': 'string',
'CACertificateIdentifier': 'string',
'DBSubnetGroupName': 'string'
},
'LatestRestorableTime': datetime(2015, 1, 1),
'MultiAZ': True|False,
'EngineVersion': 'string',
'AutoMinorVersionUpgrade': True|False,
'ReadReplicaSourceDBInstanceIdentifier': 'string',
'ReadReplicaDBInstanceIdentifiers': [
'string',
],
'ReadReplicaDBClusterIdentifiers': [
'string',
],
'LicenseModel': 'string',
'Iops': 123,
'OptionGroupMemberships': [
{
'OptionGroupName': 'string',
'Status': 'string'
},
],
'CharacterSetName': 'string',
'SecondaryAvailabilityZone': 'string',
'PubliclyAccessible': True|False,
'StatusInfos': [
{
'StatusType': 'string',
'Normal': True|False,
'Status': 'string',
'Message': 'string'
},
],
'StorageType': 'string',
'TdeCredentialArn': 'string',
'DbInstancePort': 123,
'DBClusterIdentifier': 'string',
'StorageEncrypted': True|False,
'KmsKeyId': 'string',
'DbiResourceId': 'string',
'CACertificateIdentifier': 'string',
'DomainMemberships': [
{
'Domain': 'string',
'Status': 'string',
'FQDN': 'string',
'IAMRoleName': 'string'
},
],
'CopyTagsToSnapshot': True|False,
'MonitoringInterval': 123,
'EnhancedMonitoringResourceArn': 'string',
'MonitoringRoleArn': 'string',
'PromotionTier': 123,
'DBInstanceArn': 'string',
'Timezone': 'string',
'IAMDatabaseAuthenticationEnabled': True|False
}
}
:returns:
CreateDBInstance
DeleteDBInstance
ModifyDBInstance
"""
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See also: AWS API Documentation
Examples
This example immediately changes the specified settings for the specified DB instance.
Expected Output:
:example: response = client.modify_db_instance(
DBInstanceIdentifier='string',
AllocatedStorage=123,
DBInstanceClass='string',
DBSubnetGroupName='string',
DBSecurityGroups=[
'string',
],
VpcSecurityGroupIds=[
'string',
],
ApplyImmediately=True|False,
MasterUserPassword='string',
DBParameterGroupName='string',
BackupRetentionPeriod=123,
PreferredBackupWindow='string',
PreferredMaintenanceWindow='string',
MultiAZ=True|False,
EngineVersion='string',
AllowMajorVersionUpgrade=True|False,
AutoMinorVersionUpgrade=True|False,
LicenseModel='string',
Iops=123,
OptionGroupName='string',
NewDBInstanceIdentifier='string',
StorageType='string',
TdeCredentialArn='string',
TdeCredentialPassword='string',
CACertificateIdentifier='string',
Domain='string',
CopyTagsToSnapshot=True|False,
MonitoringInterval=123,
DBPortNumber=123,
PubliclyAccessible=True|False,
MonitoringRoleArn='string',
DomainIAMRoleName='string',
PromotionTier=123,
EnableIAMDatabaseAuthentication=True|False
)
:type DBInstanceIdentifier: string
:param DBInstanceIdentifier: [REQUIRED]
The DB instance identifier. This value is stored as a lowercase string.
Constraints:
Must be the identifier for an existing DB instance
Must contain from 1 to 63 alphanumeric characters or hyphens
First character must be a letter
Cannot end with a hyphen or contain two consecutive hyphens
:type AllocatedStorage: integer
:param AllocatedStorage: The new storage capacity of the RDS instance. Changing this setting does not result in an outage and the change is applied during the next maintenance window unless ApplyImmediately is set to true for this request.
MySQL
Default: Uses existing setting
Valid Values: 5-6144
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value.
Type: Integer
MariaDB
Default: Uses existing setting
Valid Values: 5-6144
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value.
Type: Integer
PostgreSQL
Default: Uses existing setting
Valid Values: 5-6144
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value.
Type: Integer
Oracle
Default: Uses existing setting
Valid Values: 10-6144
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value.
SQL Server
Cannot be modified.
If you choose to migrate your DB instance from using standard storage to using Provisioned IOPS, or from using Provisioned IOPS to using standard storage, the process can take time. The duration of the migration depends on several factors such as database load, storage size, storage type (standard or Provisioned IOPS), amount of IOPS provisioned (if any), and the number of prior scale storage operations. Typical migration times are under 24 hours, but the process can take up to several days in some cases. During the migration, the DB instance will be available for use, but might experience performance degradation. While the migration takes place, nightly backups for the instance will be suspended. No other Amazon RDS operations can take place for the instance, including modifying the instance, rebooting the instance, deleting the instance, creating a Read Replica for the instance, and creating a DB snapshot of the instance.
:type DBInstanceClass: string
:param DBInstanceClass: The new compute and memory capacity of the DB instance. To determine the instance classes that are available for a particular DB engine, use the DescribeOrderableDBInstanceOptions action. Note that not all instance classes are available in all regions for all DB engines.
Passing a value for this setting causes an outage during the change and is applied during the next maintenance window, unless ApplyImmediately is specified as true for this request.
Default: Uses existing setting
Valid Values: db.t1.micro | db.m1.small | db.m1.medium | db.m1.large | db.m1.xlarge | db.m2.xlarge | db.m2.2xlarge | db.m2.4xlarge | db.m3.medium | db.m3.large | db.m3.xlarge | db.m3.2xlarge | db.m4.large | db.m4.xlarge | db.m4.2xlarge | db.m4.4xlarge | db.m4.10xlarge | db.r3.large | db.r3.xlarge | db.r3.2xlarge | db.r3.4xlarge | db.r3.8xlarge | db.t2.micro | db.t2.small | db.t2.medium | db.t2.large
:type DBSubnetGroupName: string
:param DBSubnetGroupName: The new DB subnet group for the DB instance. You can use this parameter to move your DB instance to a different VPC. If your DB instance is not in a VPC, you can also use this parameter to move your DB instance into a VPC. For more information, see Updating the VPC for a DB Instance .
Changing the subnet group causes an outage during the change. The change is applied during the next maintenance window, unless you specify true for the ApplyImmediately parameter.
Constraints: Must contain no more than 255 alphanumeric characters, periods, underscores, spaces, or hyphens.
Example: mySubnetGroup
:type DBSecurityGroups: list
:param DBSecurityGroups: A list of DB security groups to authorize on this DB instance. Changing this setting does not result in an outage and the change is asynchronously applied as soon as possible.
Constraints:
Must be 1 to 255 alphanumeric characters
First character must be a letter
Cannot end with a hyphen or contain two consecutive hyphens
(string) --
:type VpcSecurityGroupIds: list
:param VpcSecurityGroupIds: A list of EC2 VPC security groups to authorize on this DB instance. This change is asynchronously applied as soon as possible.
Constraints:
Must be 1 to 255 alphanumeric characters
First character must be a letter
Cannot end with a hyphen or contain two consecutive hyphens
(string) --
:type ApplyImmediately: boolean
:param ApplyImmediately: Specifies whether the modifications in this request and any pending modifications are asynchronously applied as soon as possible, regardless of the PreferredMaintenanceWindow setting for the DB instance.
If this parameter is set to false , changes to the DB instance are applied during the next maintenance window. Some parameter changes can cause an outage and will be applied on the next call to RebootDBInstance , or the next failure reboot. Review the table of parameters in Modifying a DB Instance and Using the Apply Immediately Parameter to see the impact that setting ApplyImmediately to true or false has for each modified parameter and to determine when the changes will be applied.
Default: false
:type MasterUserPassword: string
:param MasterUserPassword: The new password for the DB instance master user. Can be any printable ASCII character except '/', ''', or '@'.
Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible. Between the time of the request and the completion of the request, the MasterUserPassword element exists in the PendingModifiedValues element of the operation response.
Default: Uses existing setting
Constraints: Must be 8 to 41 alphanumeric characters (MySQL, MariaDB, and Amazon Aurora), 8 to 30 alphanumeric characters (Oracle), or 8 to 128 alphanumeric characters (SQL Server).
Note
Amazon RDS API actions never return the password, so this action provides a way to regain access to a primary instance user if the password is lost. This includes restoring privileges that might have been accidentally revoked.
:type DBParameterGroupName: string
:param DBParameterGroupName: The name of the DB parameter group to apply to the DB instance. Changing this setting does not result in an outage. The parameter group name itself is changed immediately, but the actual parameter changes are not applied until you reboot the instance without failover. The db instance will NOT be rebooted automatically and the parameter changes will NOT be applied during the next maintenance window.
Default: Uses existing setting
Constraints: The DB parameter group must be in the same DB parameter group family as this DB instance.
:type BackupRetentionPeriod: integer
:param BackupRetentionPeriod: The number of days to retain automated backups. Setting this parameter to a positive number enables backups. Setting this parameter to 0 disables automated backups.
Changing this parameter can result in an outage if you change from 0 to a non-zero value or from a non-zero value to 0. These changes are applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request. If you change the parameter from one non-zero value to another non-zero value, the change is asynchronously applied as soon as possible.
Default: Uses existing setting
Constraints:
Must be a value from 0 to 35
Can be specified for a MySQL Read Replica only if the source is running MySQL 5.6
Can be specified for a PostgreSQL Read Replica only if the source is running PostgreSQL 9.3.5
Cannot be set to 0 if the DB instance is a source to Read Replicas
:type PreferredBackupWindow: string
:param PreferredBackupWindow: The daily time range during which automated backups are created if automated backups are enabled, as determined by the BackupRetentionPeriod parameter. Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible.
Constraints:
Must be in the format hh24:mi-hh24:mi
Times should be in Universal Time Coordinated (UTC)
Must not conflict with the preferred maintenance window
Must be at least 30 minutes
:type PreferredMaintenanceWindow: string
:param PreferredMaintenanceWindow: The weekly time range (in UTC) during which system maintenance can occur, which might result in an outage. Changing this parameter does not result in an outage, except in the following situation, and the change is asynchronously applied as soon as possible. If there are pending actions that cause a reboot, and the maintenance window is changed to include the current time, then changing this parameter will cause a reboot of the DB instance. If moving this window to the current time, there must be at least 30 minutes between the current time and end of the window to ensure pending changes are applied.
Default: Uses existing setting
Format: ddd:hh24:mi-ddd:hh24:mi
Valid Days: Mon | Tue | Wed | Thu | Fri | Sat | Sun
Constraints: Must be at least 30 minutes
:type MultiAZ: boolean
:param MultiAZ: Specifies if the DB instance is a Multi-AZ deployment. Changing this parameter does not result in an outage and the change is applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request.
Constraints: Cannot be specified if the DB instance is a Read Replica.
:type EngineVersion: string
:param EngineVersion: The version number of the database engine to upgrade to. Changing this parameter results in an outage and the change is applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request.
For major version upgrades, if a non-default DB parameter group is currently in use, a new DB parameter group in the DB parameter group family for the new engine version must be specified. The new DB parameter group can be the default for that DB parameter group family.
For a list of valid engine versions, see CreateDBInstance .
:type AllowMajorVersionUpgrade: boolean
:param AllowMajorVersionUpgrade: Indicates that major version upgrades are allowed. Changing this parameter does not result in an outage and the change is asynchronously applied as soon as possible.
Constraints: This parameter must be set to true when specifying a value for the EngineVersion parameter that is a different major version than the DB instance's current version.
:type AutoMinorVersionUpgrade: boolean
:param AutoMinorVersionUpgrade: Indicates that minor version upgrades will be applied automatically to the DB instance during the maintenance window. Changing this parameter does not result in an outage except in the following case and the change is asynchronously applied as soon as possible. An outage will result if this parameter is set to true during the maintenance window, and a newer minor version is available, and RDS has enabled auto patching for that engine version.
:type LicenseModel: string
:param LicenseModel: The license model for the DB instance.
Valid values: license-included | bring-your-own-license | general-public-license
:type Iops: integer
:param Iops: The new Provisioned IOPS (I/O operations per second) value for the RDS instance. Changing this setting does not result in an outage and the change is applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request.
Default: Uses existing setting
Constraints: Value supplied must be at least 10% greater than the current value. Values that are not at least 10% greater than the existing value are rounded up so that they are 10% greater than the current value. If you are migrating from Provisioned IOPS to standard storage, set this value to 0. The DB instance will require a reboot for the change in storage type to take effect.
SQL Server
Setting the IOPS value for the SQL Server database engine is not supported.
Type: Integer
If you choose to migrate your DB instance from using standard storage to using Provisioned IOPS, or from using Provisioned IOPS to using standard storage, the process can take time. The duration of the migration depends on several factors such as database load, storage size, storage type (standard or Provisioned IOPS), amount of IOPS provisioned (if any), and the number of prior scale storage operations. Typical migration times are under 24 hours, but the process can take up to several days in some cases. During the migration, the DB instance will be available for use, but might experience performance degradation. While the migration takes place, nightly backups for the instance will be suspended. No other Amazon RDS operations can take place for the instance, including modifying the instance, rebooting the instance, deleting the instance, creating a Read Replica for the instance, and creating a DB snapshot of the instance.
:type OptionGroupName: string
:param OptionGroupName: Indicates that the DB instance should be associated with the specified option group. Changing this parameter does not result in an outage except in the following case and the change is applied during the next maintenance window unless the ApplyImmediately parameter is set to true for this request. If the parameter change results in an option group that enables OEM, this change can cause a brief (sub-second) period during which new connections are rejected but existing connections are not interrupted.
Permanent options, such as the TDE option for Oracle Advanced Security TDE, cannot be removed from an option group, and that option group cannot be removed from a DB instance once it is associated with a DB instance
:type NewDBInstanceIdentifier: string
:param NewDBInstanceIdentifier: The new DB instance identifier for the DB instance when renaming a DB instance. When you change the DB instance identifier, an instance reboot will occur immediately if you set Apply Immediately to true, or will occur during the next maintenance window if Apply Immediately to false. This value is stored as a lowercase string.
Constraints:
Must contain from 1 to 63 alphanumeric characters or hyphens
First character must be a letter
Cannot end with a hyphen or contain two consecutive hyphens
:type StorageType: string
:param StorageType: Specifies the storage type to be associated with the DB instance.
Valid values: standard | gp2 | io1
If you specify io1 , you must also include a value for the Iops parameter.
Default: io1 if the Iops parameter is specified; otherwise standard
:type TdeCredentialArn: string
:param TdeCredentialArn: The ARN from the Key Store with which to associate the instance for TDE encryption.
:type TdeCredentialPassword: string
:param TdeCredentialPassword: The password for the given ARN from the Key Store in order to access the device.
:type CACertificateIdentifier: string
:param CACertificateIdentifier: Indicates the certificate that needs to be associated with the instance.
:type Domain: string
:param Domain: The Active Directory Domain to move the instance to. Specify none to remove the instance from its current domain. The domain must be created prior to this operation. Currently only a Microsoft SQL Server instance can be created in a Active Directory Domain.
:type CopyTagsToSnapshot: boolean
:param CopyTagsToSnapshot: True to copy all tags from the DB instance to snapshots of the DB instance; otherwise false. The default is false.
:type MonitoringInterval: integer
:param MonitoringInterval: The interval, in seconds, between points when Enhanced Monitoring metrics are collected for the DB instance. To disable collecting Enhanced Monitoring metrics, specify 0. The default is 0.
If MonitoringRoleArn is specified, then you must also set MonitoringInterval to a value other than 0.
Valid Values: 0, 1, 5, 10, 15, 30, 60
:type DBPortNumber: integer
:param DBPortNumber: The port number on which the database accepts connections.
The value of the DBPortNumber parameter must not match any of the port values specified for options in the option group for the DB instance.
Your database will restart when you change the DBPortNumber value regardless of the value of the ApplyImmediately parameter.
MySQL
Default: 3306
Valid Values: 1150-65535
MariaDB
Default: 3306
Valid Values: 1150-65535
PostgreSQL
Default: 5432
Valid Values: 1150-65535
Type: Integer
Oracle
Default: 1521
Valid Values: 1150-65535
SQL Server
Default: 1433
Valid Values: 1150-65535 except for 1434 , 3389 , 47001 , 49152 , and 49152 through 49156 .
Amazon Aurora
Default: 3306
Valid Values: 1150-65535
:type PubliclyAccessible: boolean
:param PubliclyAccessible: Boolean value that indicates if the DB instance has a publicly resolvable DNS name. Set to True to make the DB instance Internet-facing with a publicly resolvable DNS name, which resolves to a public IP address. Set to False to make the DB instance internal with a DNS name that resolves to a private IP address.
PubliclyAccessible only applies to DB instances in a VPC. The DB instance must be part of a public subnet and PubliclyAccessible must be true in order for it to be publicly accessible.
Changes to the PubliclyAccessible parameter are applied immediately regardless of the value of the ApplyImmediately parameter.
Default: false
:type MonitoringRoleArn: string
:param MonitoringRoleArn: The ARN for the IAM role that permits RDS to send enhanced monitoring metrics to CloudWatch Logs. For example, arn:aws:iam:123456789012:role/emaccess . For information on creating a monitoring role, go to To create an IAM role for Amazon RDS Enhanced Monitoring .
If MonitoringInterval is set to a value other than 0, then you must supply a MonitoringRoleArn value.
:type DomainIAMRoleName: string
:param DomainIAMRoleName: The name of the IAM role to use when making API calls to the Directory Service.
:type PromotionTier: integer
:param PromotionTier: A value that specifies the order in which an Aurora Replica is promoted to the primary instance after a failure of the existing primary instance. For more information, see Fault Tolerance for an Aurora DB Cluster .
Default: 1
Valid Values: 0 - 15
:type EnableIAMDatabaseAuthentication: boolean
:param EnableIAMDatabaseAuthentication: True to enable mapping of AWS Identity and Access Management (IAM) accounts to database accounts; otherwise false.
You can enable IAM database authentication for the following database engines
For MySQL 5.6, minor version 5.6.34 or higher
For MySQL 5.7, minor version 5.7.16 or higher
Default: false
:rtype: dict
:return: {
'DBInstance': {
'DBInstanceIdentifier': 'string',
'DBInstanceClass': 'string',
'Engine': 'string',
'DBInstanceStatus': 'string',
'MasterUsername': 'string',
'DBName': 'string',
'Endpoint': {
'Address': 'string',
'Port': 123,
'HostedZoneId': 'string'
},
'AllocatedStorage': 123,
'InstanceCreateTime': datetime(2015, 1, 1),
'PreferredBackupWindow': 'string',
'BackupRetentionPeriod': 123,
'DBSecurityGroups': [
{
'DBSecurityGroupName': 'string',
'Status': 'string'
},
],
'VpcSecurityGroups': [
{
'VpcSecurityGroupId': 'string',
'Status': 'string'
},
],
'DBParameterGroups': [
{
'DBParameterGroupName': 'string',
'ParameterApplyStatus': 'string'
},
],
'AvailabilityZone': 'string',
'DBSubnetGroup': {
'DBSubnetGroupName': 'string',
'DBSubnetGroupDescription': 'string',
'VpcId': 'string',
'SubnetGroupStatus': 'string',
'Subnets': [
{
'SubnetIdentifier': 'string',
'SubnetAvailabilityZone': {
'Name': 'string'
},
'SubnetStatus': 'string'
},
],
'DBSubnetGroupArn': 'string'
},
'PreferredMaintenanceWindow': 'string',
'PendingModifiedValues': {
'DBInstanceClass': 'string',
'AllocatedStorage': 123,
'MasterUserPassword': 'string',
'Port': 123,
'BackupRetentionPeriod': 123,
'MultiAZ': True|False,
'EngineVersion': 'string',
'LicenseModel': 'string',
'Iops': 123,
'DBInstanceIdentifier': 'string',
'StorageType': 'string',
'CACertificateIdentifier': 'string',
'DBSubnetGroupName': 'string'
},
'LatestRestorableTime': datetime(2015, 1, 1),
'MultiAZ': True|False,
'EngineVersion': 'string',
'AutoMinorVersionUpgrade': True|False,
'ReadReplicaSourceDBInstanceIdentifier': 'string',
'ReadReplicaDBInstanceIdentifiers': [
'string',
],
'ReadReplicaDBClusterIdentifiers': [
'string',
],
'LicenseModel': 'string',
'Iops': 123,
'OptionGroupMemberships': [
{
'OptionGroupName': 'string',
'Status': 'string'
},
],
'CharacterSetName': 'string',
'SecondaryAvailabilityZone': 'string',
'PubliclyAccessible': True|False,
'StatusInfos': [
{
'StatusType': 'string',
'Normal': True|False,
'Status': 'string',
'Message': 'string'
},
],
'StorageType': 'string',
'TdeCredentialArn': 'string',
'DbInstancePort': 123,
'DBClusterIdentifier': 'string',
'StorageEncrypted': True|False,
'KmsKeyId': 'string',
'DbiResourceId': 'string',
'CACertificateIdentifier': 'string',
'DomainMemberships': [
{
'Domain': 'string',
'Status': 'string',
'FQDN': 'string',
'IAMRoleName': 'string'
},
],
'CopyTagsToSnapshot': True|False,
'MonitoringInterval': 123,
'EnhancedMonitoringResourceArn': 'string',
'MonitoringRoleArn': 'string',
'PromotionTier': 123,
'DBInstanceArn': 'string',
'Timezone': 'string',
'IAMDatabaseAuthenticationEnabled': True|False
}
}
:returns:
CreateDBInstance
DeleteDBInstance
ModifyDBInstance | [
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] | python | train |
djgagne/hagelslag | hagelslag/evaluation/ObjectEvaluator.py | https://github.com/djgagne/hagelslag/blob/6fb6c3df90bf4867e13a97d3460b14471d107df1/hagelslag/evaluation/ObjectEvaluator.py#L62-L87 | def load_forecasts(self):
"""
Loads the forecast files and gathers the forecast information into pandas DataFrames.
"""
forecast_path = self.forecast_json_path + "/{0}/{1}/".format(self.run_date.strftime("%Y%m%d"),
self.ensemble_member)
forecast_files = sorted(glob(forecast_path + "*.json"))
for forecast_file in forecast_files:
file_obj = open(forecast_file)
json_obj = json.load(file_obj)
file_obj.close()
track_id = json_obj['properties']["id"]
obs_track_id = json_obj['properties']["obs_track_id"]
forecast_hours = json_obj['properties']['times']
duration = json_obj['properties']['duration']
for f, feature in enumerate(json_obj['features']):
area = np.sum(feature["properties"]["masks"])
step_id = track_id + "_{0:02d}".format(f)
for model_type in self.model_types:
for model_name in self.model_names[model_type]:
prediction = feature['properties'][model_type + "_" + model_name.replace(" ", "-")]
if model_type == "condition":
prediction = [prediction]
row = [track_id, obs_track_id, self.ensemble_name, self.ensemble_member, forecast_hours[f],
f + 1, duration, area] + prediction
self.forecasts[model_type][model_name].loc[step_id] = row | [
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budacom/trading-bots | trading_bots/bots/registry.py | https://github.com/budacom/trading-bots/blob/8cb68bb8d0b5f822108db1cc5dae336e3d3c3452/trading_bots/bots/registry.py#L199-L254 | def populate(self, installed_bots=None):
"""
Load bots.
Import each bot module.
It is thread-safe and idempotent, but not re-entrant.
"""
if self.ready:
return
# populate() might be called by two threads in parallel on servers
# that create threads before initializing the WSGI callable.
with self._lock:
if self.ready:
return
# An RLock prevents other threads from entering this section. The
# compare and set operation below is atomic.
if self.loading:
# Prevent re-entrant calls to avoid running AppConfig.ready()
# methods twice.
raise RuntimeError("populate() isn't re-entrant")
self.loading = True
# Phase 1: Initialize bots
for entry in installed_bots or {}:
if isinstance(entry, Bot):
cls = entry
entry = '.'.join([cls.__module__, cls.__name__])
bot_reg = BotRegistry.create(entry)
if bot_reg.label in self.bots:
raise ImproperlyConfigured(
"Bot labels aren't unique, "
"duplicates: %s" % bot_reg.label)
self.bots[bot_reg.label] = bot_reg
bot_reg.bots = self
# Check for duplicate bot names.
counts = Counter(
bot_reg.name for bot_reg in self.bots.values())
duplicates = [
name for name, count in counts.most_common() if count > 1]
if duplicates:
raise ImproperlyConfigured(
"Bot names aren't unique, "
"duplicates: %s" % ", ".join(duplicates))
self.bots_ready = True
# Phase 2: import config files
for bot in self.bots.values():
bot.import_configs()
self.configs_ready = True
self.ready = True | [
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Import each bot module.
It is thread-safe and idempotent, but not re-entrant. | [
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titusjan/argos | argos/config/qtctis.py | https://github.com/titusjan/argos/blob/20d0a3cae26c36ea789a5d219c02ca7df21279dd/argos/config/qtctis.py#L54-L65 | def createPenWidthCti(nodeName, defaultData=1.0, zeroValueText=None):
""" Creates a FloatCti with defaults for configuring a QPen width.
If specialValueZero is set, this string will be displayed when 0.0 is selected.
If specialValueZero is None, the minValue will be 0.1
"""
# A pen line width of zero indicates a cosmetic pen. This means that the pen width is
# always drawn one pixel wide, independent of the transformation set on the painter.
# Note that line widths other than 1 may be slow when anti aliasing is on.
return FloatCti(nodeName, defaultData=defaultData, specialValueText=zeroValueText,
minValue=0.1 if zeroValueText is None else 0.0,
maxValue=100, stepSize=0.1, decimals=1) | [
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robin900/gspread-dataframe | gspread_dataframe.py | https://github.com/robin900/gspread-dataframe/blob/b64fef7ec196bfed69362aa35c593f448830a735/gspread_dataframe.py#L59-L77 | def _resize_to_minimum(worksheet, rows=None, cols=None):
"""
Resize the worksheet to guarantee a minimum size, either in rows,
or columns, or both.
Both rows and cols are optional.
"""
# get the current size
current_cols, current_rows = (
worksheet.col_count,
worksheet.row_count
)
if rows is not None and rows <= current_rows:
rows = None
if cols is not None and cols <= current_cols:
cols = None
if cols is not None or rows is not None:
worksheet.resize(rows, cols) | [
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grantmcconnaughey/Lintly | lintly/builds.py | https://github.com/grantmcconnaughey/Lintly/blob/73c1ee36740ac5bb2a32d3f24fca2a27f4d4e466/lintly/builds.py#L82-L97 | def find_diff_violations(self, patch):
"""
Uses the diff for this build to find changed lines that also have violations.
"""
violations = collections.defaultdict(list)
for line in patch.changed_lines:
file_violations = self._all_violations.get(line['file_name'])
if not file_violations:
continue
line_violations = [v for v in file_violations if v.line == line['line_number']]
for v in line_violations:
violations[line['file_name']].append(v)
return violations | [
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mandeep/Travis-Encrypt | travis/cli.py | https://github.com/mandeep/Travis-Encrypt/blob/0dd2da1c71feaadcb84bdeb26827e6dfe1bd3b41/travis/cli.py#L53-L107 | def cli(username, repository, path, password, deploy, env, clipboard, env_file):
"""Encrypt passwords and environment variables for use with Travis CI.
Travis Encrypt requires as arguments the user's GitHub username and repository name.
Once the arguments are passed, a password prompt will ask for the password that needs
to be encrypted. The given password will then be encrypted via the PKCS1v15 padding
scheme and printed to standard output. If the path to a .travis.yml file
is given as an argument, the encrypted password is added to the .travis.yml file.
"""
key = retrieve_public_key('{}/{}' .format(username, repository))
if env_file:
if path:
config = load_travis_configuration(path)
for env_var, value in dotenv_values(env_file).items():
encrypted_env = encrypt_key(key, value.encode())
config.setdefault('env', {}).setdefault('global', {})[env_var] = {'secure': encrypted_env}
dump_travis_configuration(config, path)
print('Encrypted variables from {} added to {}'.format(env_file, path))
else:
print('\nPlease add the following to your .travis.yml:')
for env_var, value in dotenv_values(env_file).items():
encrypted_env = encrypt_key(key, value.encode())
print("{}:\n secure: {}".format(env_var, encrypted_env))
else:
encrypted_password = encrypt_key(key, password.encode())
if path:
config = load_travis_configuration(path)
if config is None:
config = OrderedDict()
if deploy:
config.setdefault('deploy', {}).setdefault('password', {})['secure'] = encrypted_password
elif env:
try:
config.setdefault('env', {}).setdefault('global', {})['secure'] = encrypted_password
except TypeError:
for item in config['env']['global']:
if isinstance(item, dict) and 'secure' in item:
item['secure'] = encrypted_password
else:
config.setdefault('password', {})['secure'] = encrypted_password
dump_travis_configuration(config, path)
print('Encrypted password added to {}' .format(path))
elif clipboard:
pyperclip.copy(encrypted_password)
print('\nThe encrypted password has been copied to your clipboard.')
else:
print('\nPlease add the following to your .travis.yml:\nsecure: {}' .format(encrypted_password)) | [
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saltstack/salt | salt/states/boto_secgroup.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/boto_secgroup.py#L603-L663 | def absent(
name,
vpc_id=None,
vpc_name=None,
region=None,
key=None,
keyid=None,
profile=None):
'''
Ensure a security group with the specified name does not exist.
name
Name of the security group.
vpc_id
The ID of the VPC to remove the security group from, if any. Exclusive with vpc_name.
vpc_name
The name of the VPC to remove the security group from, if any. Exclusive with vpc_name.
.. versionadded:: 2016.3.0
region
Region to connect to.
key
Secret key to be used.
keyid
Access key to be used.
profile
A dict with region, key and keyid, or a pillar key (string)
that contains a dict with region, key and keyid.
.. versionadded:: 2016.3.0
'''
ret = {'name': name, 'result': True, 'comment': '', 'changes': {}}
sg = __salt__['boto_secgroup.get_config'](name=name, group_id=None, region=region, key=key,
keyid=keyid, profile=profile, vpc_id=vpc_id,
vpc_name=vpc_name)
if sg:
if __opts__['test']:
ret['comment'] = 'Security group {0} is set to be removed.'.format(name)
ret['result'] = None
return ret
deleted = __salt__['boto_secgroup.delete'](name=name, group_id=None, region=region, key=key,
keyid=keyid, profile=profile, vpc_id=vpc_id,
vpc_name=vpc_name)
if deleted:
ret['changes']['old'] = {'secgroup': sg}
ret['changes']['new'] = {'secgroup': None}
ret['comment'] = 'Security group {0} deleted.'.format(name)
else:
ret['result'] = False
ret['comment'] = 'Failed to delete {0} security group.'.format(name)
else:
ret['comment'] = '{0} security group does not exist.'.format(name)
return ret | [
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name
Name of the security group.
vpc_id
The ID of the VPC to remove the security group from, if any. Exclusive with vpc_name.
vpc_name
The name of the VPC to remove the security group from, if any. Exclusive with vpc_name.
.. versionadded:: 2016.3.0
region
Region to connect to.
key
Secret key to be used.
keyid
Access key to be used.
profile
A dict with region, key and keyid, or a pillar key (string)
that contains a dict with region, key and keyid.
.. versionadded:: 2016.3.0 | [
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] | python | train |
LogicalDash/LiSE | allegedb/allegedb/__init__.py | https://github.com/LogicalDash/LiSE/blob/fe6fd4f0a7c1780e065f4c9babb9bc443af6bb84/allegedb/allegedb/__init__.py#L972-L983 | def new_multidigraph(self, name, data=None, **attr):
"""Return a new instance of type MultiDiGraph, initialized with the given
data if provided.
:arg name: a name for the graph
:arg data: dictionary or NetworkX graph object providing initial state
"""
self._init_graph(name, 'MultiDiGraph')
mdg = MultiDiGraph(self, name, data, **attr)
self._graph_objs[name] = mdg
return mdg | [
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bcbio/bcbio-nextgen | scripts/utils/plink_to_vcf.py | https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/scripts/utils/plink_to_vcf.py#L119-L154 | def fix_nonref_positions(in_file, ref_file):
"""Fix Genotyping VCF positions where the bases are all variants.
The plink/pseq output does not handle these correctly, and
has all reference/variant bases reversed.
"""
ignore_chrs = ["."]
ref2bit = twobit.TwoBitFile(open(ref_file))
out_file = in_file.replace("-raw.vcf", ".vcf")
with open(in_file) as in_handle:
with open(out_file, "w") as out_handle:
for line in in_handle:
if line.startswith("#"):
out_handle.write(line)
else:
parts = line.rstrip("\r\n").split("\t")
pos = int(parts[1])
# handle chr/non-chr naming
if parts[0] not in ref2bit.keys() and parts[0].replace("chr", "") in ref2bit.keys():
parts[0] = parts[0].replace("chr", "")
# handle X chromosome
elif parts[0] not in ref2bit.keys() and parts[0] == "23":
for test in ["X", "chrX"]:
if test in ref2bit.keys():
parts[0] == test
ref_base = None
if parts[0] not in ignore_chrs:
try:
ref_base = ref2bit[parts[0]].get(pos-1, pos).upper()
except Exception as msg:
print "Skipping line. Failed to retrieve reference base for %s\n%s" % (str(parts), msg)
parts = fix_vcf_line(parts, ref_base)
if parts is not None:
out_handle.write("\t".join(parts) + "\n")
return out_file | [
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iotile/coretools | transport_plugins/websocket/iotile_transport_websocket/generic/async_client.py | https://github.com/iotile/coretools/blob/2d794f5f1346b841b0dcd16c9d284e9bf2f3c6ec/transport_plugins/websocket/iotile_transport_websocket/generic/async_client.py#L89-L104 | async def stop(self):
"""Stop this websocket client and disconnect from the server.
This method is idempotent and may be called multiple times. If called
when there is no active connection, it will simply return.
"""
if self._connection_task is None:
return
try:
await self._connection_task.stop()
finally:
self._con = None
self._connection_task = None
self._manager.clear() | [
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JukeboxPipeline/jukebox-core | src/jukeboxcore/gui/widgets/reftrackwidget.py | https://github.com/JukeboxPipeline/jukebox-core/blob/bac2280ca49940355270e4b69400ce9976ab2e6f/src/jukeboxcore/gui/widgets/reftrackwidget.py#L59-L73 | def select(self, ):
"""Store the selected taskfileinfo self.selected and accept the dialog
:returns: None
:rtype: None
:raises: None
"""
s = self.browser.selected_indexes(self.browser.get_depth()-1)
if not s:
return
i = s[0].internalPointer()
if i:
tfi = i.internal_data()
self.selected = tfi
self.accept() | [
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pypa/pipenv | pipenv/patched/notpip/_vendor/html5lib/treeadapters/sax.py | https://github.com/pypa/pipenv/blob/cae8d76c210b9777e90aab76e9c4b0e53bb19cde/pipenv/patched/notpip/_vendor/html5lib/treeadapters/sax.py#L13-L50 | def to_sax(walker, handler):
"""Call SAX-like content handler based on treewalker walker
:arg walker: the treewalker to use to walk the tree to convert it
:arg handler: SAX handler to use
"""
handler.startDocument()
for prefix, namespace in prefix_mapping.items():
handler.startPrefixMapping(prefix, namespace)
for token in walker:
type = token["type"]
if type == "Doctype":
continue
elif type in ("StartTag", "EmptyTag"):
attrs = AttributesNSImpl(token["data"],
unadjustForeignAttributes)
handler.startElementNS((token["namespace"], token["name"]),
token["name"],
attrs)
if type == "EmptyTag":
handler.endElementNS((token["namespace"], token["name"]),
token["name"])
elif type == "EndTag":
handler.endElementNS((token["namespace"], token["name"]),
token["name"])
elif type in ("Characters", "SpaceCharacters"):
handler.characters(token["data"])
elif type == "Comment":
pass
else:
assert False, "Unknown token type"
for prefix, namespace in prefix_mapping.items():
handler.endPrefixMapping(prefix)
handler.endDocument() | [
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eventbrite/eventbrite-sdk-python | eventbrite/access_methods.py | https://github.com/eventbrite/eventbrite-sdk-python/blob/f2e5dc5aa1aa3e45766de13f16fd65722163d91a/eventbrite/access_methods.py#L425-L460 | def post_series_publish(self, id, **data):
"""
POST /series/:id/publish/
Publishes a repeating event series and all of its occurrences that are not already canceled or deleted. Once a date is cancelled it can still be uncancelled and can be viewed by the public. A deleted date cannot be undeleted and cannot by viewed by the public. In order for
publish to be permitted, the event must have all necessary information, including a name and description, an organizer,
at least one ticket, and valid payment options. This API endpoint will return argument errors for event fields that
fail to validate the publish requirements. Returns a boolean indicating success or failure of the publish.
field_error event.name MISSING
Your event must have a name to be published.
field_error event.start MISSING
Your event must have a start date to be published.
field_error event.end MISSING
Your event must have an end date to be published.
field_error event.start.timezone MISSING
Your event start and end dates must have matching time zones to be published.
field_error event.organizer MISSING
Your event must have an organizer to be published.
field_error event.currency MISSING
Your event must have a currency to be published.
field_error event.currency INVALID
Your event must have a valid currency to be published.
field_error event.tickets MISSING
Your event must have at least one ticket to be published.
field_error event.tickets.N.name MISSING
All tickets must have names in order for your event to be published. The N will be the ticket class ID with the
error.
field_error event.tickets.N.quantity_total MISSING
All non-donation tickets must have an available quantity value in order for your event to be published. The N
will be the ticket class ID with the error.
field_error event.tickets.N.cost MISSING
All non-donation tickets must have a cost (which can be ``0.00`` for free tickets) in order for your event to
be published. The N will be the ticket class ID with the error.
.. _unpublish-series-by-id:
"""
return self.post("/series/{0}/publish/".format(id), data=data) | [
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Your event must have an organizer to be published.
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Your event must have a currency to be published.
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Your event must have a valid currency to be published.
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.. _unpublish-series-by-id: | [
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SITools2/pySitools2_1.0 | sitools2/core/pySitools2.py | https://github.com/SITools2/pySitools2_1.0/blob/acd13198162456ba401a0b923af989bb29feb3b6/sitools2/core/pySitools2.py#L373-L379 | def __parseColumns(self):
"""Returns the list of columns related to the dataset."""
columns = []
if self.__dataItem.has_key('columnModel'):
for column in self.__dataItem['columnModel']:
columns.append(Column(column))
return columns | [
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kwikteam/phy | phy/stats/ccg.py | https://github.com/kwikteam/phy/blob/7e9313dc364304b7d2bd03b92938347343703003/phy/stats/ccg.py#L19-L26 | def _increment(arr, indices):
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benley/butcher | butcher/util.py | https://github.com/benley/butcher/blob/8b18828ea040af56b7835beab5fd03eab23cc9ee/butcher/util.py#L91-L102 | def flatten(listish):
"""Flatten an arbitrarily-nested list of strings and lists.
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for elem in listish:
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DataBiosphere/toil | src/toil/wdl/wdl_synthesis.py | https://github.com/DataBiosphere/toil/blob/a8252277ff814e7bee0971139c2344f88e44b644/src/toil/wdl/wdl_synthesis.py#L560-L598 | def write_function(self, job):
'''
Writes out a python function for each WDL "task" object.
Each python function is a unit of work written out as a string in
preparation to being written out to a file. In WDL, each "job" is
called a "task". Each WDL task is written out in multiple steps:
1: Header and inputs (e.g. 'def mapping(self, input1, input2)')
2: Log job name (e.g. 'job.fileStore.logToMaster('initialize_jobs')')
3: Create temp dir (e.g. 'tempDir = fileStore.getLocalTempDir()')
4: import filenames and use readGlobalFile() to get files from the
jobStore
5: Reformat commandline variables (like converting to ' '.join(files)).
6: Commandline call using subprocess.Popen().
7: Write the section returning the outputs. Also logs stats.
:return: a giant string containing the meat of the job defs for the toil script.
'''
# write the function header
fn_section = self.write_function_header(job)
# write out commandline keywords
fn_section += self.write_function_cmdline(job)
if self.needsdocker(job):
# write a bash script to inject into the docker
fn_section += self.write_function_bashscriptline(job)
# write a call to the docker API
fn_section += self.write_function_dockercall(job)
else:
# write a subprocess call
fn_section += self.write_function_subprocesspopen()
# write the outputs for the definition to return
fn_section += self.write_function_outputreturn(job, docker=self.needsdocker(job))
return fn_section | [
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7: Write the section returning the outputs. Also logs stats.
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jazzband/django-push-notifications | push_notifications/wns.py | https://github.com/jazzband/django-push-notifications/blob/c4a0d710711fa27bfb6533c0bf3468cb67a62679/push_notifications/wns.py#L259-L322 | def dict_to_xml_schema(data):
"""
Input a dictionary to be converted to xml. There should be only one key at
the top level. The value must be a dict with (required) `children` key and
(optional) `attrs` key. This will be called the `sub-element dictionary`.
The `attrs` value must be a dictionary; each value will be added to the
element's xml tag as attributes. e.g.:
{"example": {
"attrs": {
"key1": "value1",
...
},
...
}}
would result in:
<example key1="value1" key2="value2"></example>
If the value is a dict it must contain one or more keys which will be used
as the sub-element names. Each sub-element must have a value of a sub-element
dictionary(see above) or a list of sub-element dictionaries.
If the value is not a dict, it will be the value of the element.
If the value is a list, multiple elements of the same tag will be created
from each sub-element dict in the list.
:param data: dict: Used to create an XML tree. e.g.:
example_data = {
"toast": {
"attrs": {
"launch": "param",
"duration": "short",
},
"children": {
"visual": {
"children": {
"binding": {
"attrs": {"template": "ToastText01"},
"children": {
"text": [
{
"attrs": {"id": "1"},
"children": "text1",
},
{
"attrs": {"id": "2"},
"children": "text2",
},
],
},
},
},
},
},
},
}
:return: ElementTree.Element
"""
for key, value in data.items():
root = _add_element_attrs(ET.Element(key), value.get("attrs", {}))
children = value.get("children", None)
if isinstance(children, dict):
_add_sub_elements_from_dict(root, children)
return root | [
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iotile/coretools | transport_plugins/awsiot/iotile_transport_awsiot/device_adapter.py | https://github.com/iotile/coretools/blob/2d794f5f1346b841b0dcd16c9d284e9bf2f3c6ec/transport_plugins/awsiot/iotile_transport_awsiot/device_adapter.py#L364-L381 | def _find_connection(self, topic):
"""Attempt to find a connection id corresponding with a topic
The device is found by assuming the topic ends in <slug>/[control|data]/channel
Args:
topic (string): The topic we received a message on
Returns:
int: The internal connect id (device slug) associated with this topic
"""
parts = topic.split('/')
if len(parts) < 3:
return None
slug = parts[-3]
return slug | [
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MediaFire/mediafire-python-open-sdk | mediafire/uploader.py | https://github.com/MediaFire/mediafire-python-open-sdk/blob/8f1f23db1b16f16e026f5c6777aec32d00baa05f/mediafire/uploader.py#L353-L363 | def _upload_check(self, upload_info, resumable=False):
"""Wrapper around upload/check"""
return self._api.upload_check(
filename=upload_info.name,
size=upload_info.size,
hash_=upload_info.hash_info.file,
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path=upload_info.path,
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NuGrid/NuGridPy | nugridpy/data_plot.py | https://github.com/NuGrid/NuGridPy/blob/eee8047446e398be77362d82c1d8b3310054fab0/nugridpy/data_plot.py#L1540-L1935 | def abu_chart(self, cycle, mass_range=None ,ilabel=True,
imlabel=True, imlabel_fontsize=8, imagic=False,
boxstable=True, lbound=(-12, 0),
plotaxis=[0, 0, 0, 0], show=True, color_map='jet',
ifig=None,data_provided=False,thedata=None,
savefig=False,drawfig=None,drawax=None,mov=False,
path=None):
'''
Plots an abundance chart
Parameters
----------
cycle : string, integer or list
The cycle we are looking in. If it is a list of cycles,
this method will then do a plot for each of these cycles
and save them all to a file.
mass_range : list, optional
A 1x2 array containing the lower and upper mass range. If
this is an instance of abu_vector this will only plot
isotopes that have an atomic mass within this range. This
will throw an error if this range does not make sence ie
[45,2] if None, it will plot over the entire range. The
default is None.
ilabel : boolean, optional
Elemental labels off/on. The default is True.
imlabel : boolean, optional
Label for isotopic masses off/on. The default is True.
imlabel_fontsize : integer, optional
Fontsize for isotopic mass labels. The default is 8.
imagic : boolean, optional
Turn lines for magic numbers off/on. The default is False.
boxstable : boolean, optional
Plot the black boxes around the stable elements. The
defaults is True.
lbound : tuple, optional
Boundaries for colour spectrum ploted. The default is
(-12,0).
plotaxis : list, optional
Set axis limit. If [0, 0, 0, 0] the complete range in (N,Z)
will be plotted. It equates to [xMin, xMax, Ymin, Ymax].
The default is [0, 0, 0, 0].
show : boolean, optional
Boolean of if the plot should be displayed. Useful with
saving multiple plots using abu_chartMulti. The default is
True.
color_map : string, optional
Color map according to choices in matplotlib
(e.g. www.scipy.org/Cookbook/Matplotlib/Show_colormaps).
The default is 'jet'.
ifig : integer, optional
Figure number, if ifig is None it wiil be set to the cycle
number. The defaults is None.
savefig : boolean, optional
Whether or not to save the figure.
The default is False
drawfig, drawax, mov : optional, not necessary for user to set these variables
The figure and axes containers to be drawn on, and whether or not a movie is
being made (only True when se.movie is called, which sets mov to True
automatically
path: path where to save figure
'''
if ifig == None and not mov:
ifig=cycle
if type(cycle)==type([]):
self.abu_chartMulti(cycle, mass_range,ilabel,imlabel,imlabel_fontsize,imagic,boxstable,\
lbound,plotaxis,color_map, path=path)
return
plotType=self._classTest()
if mass_range!=None and mass_range[0]>mass_range[1]:
raise IOError("Please input a proper mass range")
if plotType=='se':
if not data_provided:
cycle=self.se.findCycle(cycle)
# nin=zeros(len(self.se.A))
# zin=zeros(len(self.se.Z))
yin=self.get(cycle, 'iso_massf')
isom=self.se.isomeric_states
masses = self.se.get(cycle,'mass')
else:
cycle=cycle # why so serious?
yin=thedata[0]
isom=self.se.isomeric_states
masses = thedata[1]
# for i in xrange(len(nin)):
# zin[i]=self.se.Z[i]
# nin[i]=self.se.A[i]-zin[i]
# SJONES implicit loop instead:
zin=array([el for el in self.se.Z])
nin=array([el for el in self.se.A])-zin
#Test if the mass cell order is inverted
#and hence mass[-1] the center.
if masses[0]>masses[-1]:
#invert
print('Inverted order of mass cells will be taken into account.')
yin=yin[::-1]
masses=masses[::-1]
if mass_range != None:
# trim out only the zones needed:
tmpyps=[]
masses.sort() # SJ: not sure why this sort if necessary
# for i in xrange(len(masses)):
# if (masses[i] >mass_range[0] and masses[i]<mass_range[1]) or\
# (masses[i]==mass_range[0] or masses[i]==mass_range[1]):
# tmpyps.append(yin[i])
# yin=tmpyps
# find lower and upper indices and slice instead:
idxl=np.abs(masses-mass_range[0]).argmin()
if masses[idxl] < mass_range[0]: idxl+=1
idxu=np.abs(masses-mass_range[1]).argmin()
if masses[idxu] > mass_range[1]: idxu-=1
yin=yin[idxl:idxu+1]
#tmp=zeros(len(yin[0]))
#for i in xrange(len(yin)):
# for j in xrange(len(yin[i])):
# tmp[j]+=yin[i][j]
tmp2=sum(yin,axis=0) # SJONES sum along axis instead of nested loop
tmp=old_div(tmp2,len(yin))
yin=tmp
elif plotType=='PPN':
ain=self.get('A',cycle)
zin=self.get('Z',cycle)
nin=ain-zin
yin=self.get('ABUNDANCE_MF',cycle)
isom=self.get('ISOM',cycle)
if mass_range != None:
tmpA=[]
tmpZ=[]
tmpIsom=[]
tmpyps=[]
for i in range(len(nin)):
if (ain[i] >mass_range[0] and ain[i]<mass_range[1])\
or (ain[i]==mass_range[0] or ain[i]==mass_range[1]):
tmpA.append(nin[i])
tmpZ.append(zin[i])
tmpIsom.append(isom[i])
tmpyps.append(yin[i])
zin=tmpZ
nin=tmpA
yin=tmpyps
isom=tmpIsom
else:
raise IOError("This method, abu_chart, is not supported by this class")
# in case we call from ipython -pylab, turn interactive on at end again
turnoff=False
if not show:
try:
ioff()
turnoff=True
except NameError:
turnoff=False
nnmax = int(max(nin))+1
nzmax = int(max(zin))+1
nzycheck = zeros([nnmax,nzmax,3])
for i in range(len(nin)):
if isom[i]==1:
ni = int(nin[i])
zi = int(zin[i])
nzycheck[ni,zi,0] = 1
nzycheck[ni,zi,1] = yin[i]
#######################################################################
# elemental names: elname(i) is the name of element with Z=i
elname=self.elements_names
#### create plot
## define axis and plot style (colormap, size, fontsize etc.)
if plotaxis==[0,0,0,0]:
xdim=10
ydim=6
else:
dx = plotaxis[1]-plotaxis[0]
dy = plotaxis[3]-plotaxis[2]
ydim = 6
xdim = ydim*dx/dy
params = {'axes.labelsize': 12,
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 12,
'ytick.labelsize': 12,
'text.usetex': True}
#pl.rcParams.update(params) #May cause Error, someting to do with tex
if mov:
fig=drawfig
fig.set_size_inches(xdim,ydim)
artists=[]
else:
fig=pl.figure(ifig,figsize=(xdim,ydim),dpi=100)
axx = 0.10
axy = 0.10
axw = 0.85
axh = 0.8
if mov:
ax=drawax
else:
ax=pl.axes([axx,axy,axw,axh])
# Tick marks
xminorlocator = MultipleLocator(1)
xmajorlocator = MultipleLocator(5)
ax.xaxis.set_major_locator(xmajorlocator)
ax.xaxis.set_minor_locator(xminorlocator)
yminorlocator = MultipleLocator(1)
ymajorlocator = MultipleLocator(5)
ax.yaxis.set_major_locator(ymajorlocator)
ax.yaxis.set_minor_locator(yminorlocator)
# color map choice for abundances
cmapa = cm.get_cmap(name=color_map)
# color map choice for arrows
cmapr = cm.autumn
# if a value is below the lower limit its set to white
cmapa.set_under(color='w')
cmapr.set_under(color='w')
# set value range for abundance colors (log10(Y))
norma = colors.Normalize(vmin=lbound[0],vmax=lbound[1])
# set x- and y-axis scale aspect ratio to 1
ax.set_aspect('equal')
#print time,temp and density on top
temp = ' '#'%8.3e' %ff['temp']
time = ' '#'%8.3e' %ff['time']
dens = ' '#'%8.3e' %ff['dens']
#May cause Error, someting to do with tex
'''
#box1 = TextArea("t : " + time + " s~~/~~T$_{9}$ : " + temp + "~~/~~$\\rho_{b}$ : " \
# + dens + ' g/cm$^{3}$', textprops=dict(color="k"))
anchored_box = AnchoredOffsetbox(loc=3,
child=box1, pad=0.,
frameon=False,
bbox_to_anchor=(0., 1.02),
bbox_transform=ax.transAxes,
borderpad=0.,
)
ax.add_artist(anchored_box)
'''
## Colour bar plotted
patches = []
color = []
for i in range(nzmax):
for j in range(nnmax):
if nzycheck[j,i,0]==1:
xy = j-0.5,i-0.5
rect = Rectangle(xy,1,1,)
# abundance
yab = nzycheck[j,i,1]
if yab == 0:
yab=1e-99
col =log10(yab)
patches.append(rect)
color.append(col)
p = PatchCollection(patches, cmap=cmapa, norm=norma)
p.set_array(array(color))
p.set_zorder(1)
if mov:
artist1=ax.add_collection(p)
artists.append(artist1)
else:
ax.add_collection(p)
if not mov:
cb = pl.colorbar(p)
# colorbar label
cb.set_label('log$_{10}$(X)')
# plot file name
graphname = 'abundance-chart'+str(cycle)
# Add black frames for stable isotopes
if boxstable:
for i in range(len(self.stable_el)):
if i == 0:
continue
tmp = self.stable_el[i]
try:
zz= self.elements_names.index(tmp[0]) #charge
except:
continue
for j in range(len(tmp)):
if j == 0:
continue
nn = int(tmp[j]) #atomic mass
nn=nn-zz
xy = nn-0.5,zz-0.5
rect = Rectangle(xy,1,1,ec='k',fc='None',fill='False',lw=3.)
rect.set_zorder(2)
ax.add_patch(rect)
# decide which array to take for label positions
iarr = 0
# plot element labels
if ilabel:
for z in range(nzmax):
try:
nmin = min(argwhere(nzycheck[:,z,iarr]))[0]-1
ax.text(nmin,z,elname[z],horizontalalignment='center',verticalalignment='center',\
fontsize='x-small',clip_on=True)
except ValueError:
continue
# plot mass numbers
if imlabel:
for z in range(nzmax):
for n in range(nnmax):
a = z+n
if nzycheck[n,z,iarr]==1:
ax.text(n,z,a,horizontalalignment='center',verticalalignment='center',\
fontsize=imlabel_fontsize,clip_on=True)
# plot lines at magic numbers
if imagic:
ixymagic=[2, 8, 20, 28, 50, 82, 126]
nmagic = len(ixymagic)
for magic in ixymagic:
if magic<=nzmax:
try:
xnmin = min(argwhere(nzycheck[:,magic,iarr]))[0]
xnmax = max(argwhere(nzycheck[:,magic,iarr]))[0]
line = ax.plot([xnmin,xnmax],[magic,magic],lw=3.,color='r',ls='-')
except ValueError:
dummy=0
if magic<=nnmax:
try:
yzmin = min(argwhere(nzycheck[magic,:,iarr]))[0]
yzmax = max(argwhere(nzycheck[magic,:,iarr]))[0]
line = ax.plot([magic,magic],[yzmin,yzmax],lw=3.,color='r',ls='-')
except ValueError:
dummy=0
# set axis limits
if plotaxis==[0,0,0,0]:
xmax=max(nin)
ymax=max(zin)
ax.axis([-0.5,xmax+0.5,-0.5,ymax+0.5])
else:
ax.axis(plotaxis)
# set x- and y-axis label
ax.set_xlabel('neutron number (A-Z)')
ax.set_ylabel('proton number Z')
if not mov:
pl.title('Isotopic Chart for cycle '+str(int(cycle)))
if savefig:
if path is not None:
graphname = os.path.join(path, graphname)
fig.savefig(graphname)
print(graphname,'is done')
if show:
pl.show()
if turnoff:
ion()
if mov:
return p,artists
else:
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Parameters
----------
cycle : string, integer or list
The cycle we are looking in. If it is a list of cycles,
this method will then do a plot for each of these cycles
and save them all to a file.
mass_range : list, optional
A 1x2 array containing the lower and upper mass range. If
this is an instance of abu_vector this will only plot
isotopes that have an atomic mass within this range. This
will throw an error if this range does not make sence ie
[45,2] if None, it will plot over the entire range. The
default is None.
ilabel : boolean, optional
Elemental labels off/on. The default is True.
imlabel : boolean, optional
Label for isotopic masses off/on. The default is True.
imlabel_fontsize : integer, optional
Fontsize for isotopic mass labels. The default is 8.
imagic : boolean, optional
Turn lines for magic numbers off/on. The default is False.
boxstable : boolean, optional
Plot the black boxes around the stable elements. The
defaults is True.
lbound : tuple, optional
Boundaries for colour spectrum ploted. The default is
(-12,0).
plotaxis : list, optional
Set axis limit. If [0, 0, 0, 0] the complete range in (N,Z)
will be plotted. It equates to [xMin, xMax, Ymin, Ymax].
The default is [0, 0, 0, 0].
show : boolean, optional
Boolean of if the plot should be displayed. Useful with
saving multiple plots using abu_chartMulti. The default is
True.
color_map : string, optional
Color map according to choices in matplotlib
(e.g. www.scipy.org/Cookbook/Matplotlib/Show_colormaps).
The default is 'jet'.
ifig : integer, optional
Figure number, if ifig is None it wiil be set to the cycle
number. The defaults is None.
savefig : boolean, optional
Whether or not to save the figure.
The default is False
drawfig, drawax, mov : optional, not necessary for user to set these variables
The figure and axes containers to be drawn on, and whether or not a movie is
being made (only True when se.movie is called, which sets mov to True
automatically
path: path where to save figure | [
"Plots",
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] | python | train |
gem/oq-engine | openquake/commands/plot.py | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commands/plot.py#L120-L140 | def make_figure_source_geom(extractors, what):
"""
Extract the geometry of a given sources
Example:
http://127.0.0.1:8800/v1/calc/30/extract/source_geom/1,2,3
"""
import matplotlib.pyplot as plt
fig = plt.figure()
[ex] = extractors
sitecol = ex.get('sitecol')
geom_by_src = vars(ex.get(what))
ax = fig.add_subplot(1, 1, 1)
ax.grid(True)
ax.set_xlabel('Source')
bmap = basemap('cyl', sitecol)
for src, geom in geom_by_src.items():
if src != 'array':
bmap.plot(geom['lon'], geom['lat'], label=src)
bmap.plot(sitecol['lon'], sitecol['lat'], 'x')
ax.legend()
return plt | [
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Example:
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cloudtools/stacker | stacker/actions/build.py | https://github.com/cloudtools/stacker/blob/ad6013a03a560c46ba3c63c4d153336273e6da5d/stacker/actions/build.py#L41-L59 | def should_update(stack):
"""Tests whether a stack should be submitted for updates to CF.
Args:
stack (:class:`stacker.stack.Stack`): The stack object to check.
Returns:
bool: If the stack should be updated, return True.
"""
if stack.locked:
if not stack.force:
logger.debug("Stack %s locked and not in --force list. "
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return False
else:
logger.debug("Stack %s locked, but is in --force "
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quora/qcore | qcore/asserts.py | https://github.com/quora/qcore/blob/fa5cd438eea554db35fd29cbc8dfbde69f09961c/qcore/asserts.py#L104-L130 | def assert_eq(expected, actual, message=None, tolerance=None, extra=None):
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If tolerance is specified, raises an AssertionError if either
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PMEAL/OpenPNM | openpnm/utils/petsc.py | https://github.com/PMEAL/OpenPNM/blob/0547b5724ffedc0a593aae48639d36fe10e0baed/openpnm/utils/petsc.py#L49-L97 | def _initialize_A(self):
r"""
This method creates the petsc sparse coefficients matrix from the
OpenPNM scipy one. The method also equally decomposes the matrix at
certain rows into different blocks (each block contains all the
columns) and distributes them over the pre-assigned cores for parallel
computing. The method can be used in serial.
"""
# Matrix of coefficients size
self.m, self.n = (self.A).shape
# Create a petsc sparse matrix
self.petsc_A = PETSc.Mat()
self.petsc_A.create(PETSc.COMM_WORLD)
self.petsc_A.setSizes([self.m, self.n])
self.petsc_A.setType('aij') # sparse
self.petsc_A.setUp()
# Pre-allocate memory for the coefficients matrix.
# Optional, but needed in the case where the
# matrix does not already exist.
# self.petsc_A.setPreallocationNNZ([ \
# sp.sparse.csr_matrix.getnnz(A,axis=0)])
# Loop over owned block of rows on this processor
# and insert entry values (for parallel computing).
self.Istart, self.Iend = self.petsc_A.getOwnershipRange()
# Assign values to the coefficients matrix from the scipy
# sparse csr one: petscMat = \
# PETSc.Mat().createAIJ(size=existingMat.shape, csr= \
# (existingMat.indptr,existingMat.indices,existingMat.data))
size_tmp = self.A.shape
csr1 = (self.A.indptr[self.Istart:self.Iend+1] -
self.A.indptr[self.Istart])
ind1 = self.A.indptr[self.Istart]
ind2 = self.A.indptr[self.Iend]
csr2 = self.A.indices[ind1:ind2]
csr3 = self.A.data[ind1:ind2]
self.petsc_A = PETSc.Mat().createAIJ(size=size_tmp,
csr=(csr1, csr2, csr3))
# Communicate off-processor values
# and setup internal data structures
# for performing parallel operations
self.petsc_A.assemblyBegin()
self.petsc_A.assemblyEnd() | [
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mikedh/trimesh | trimesh/exchange/ply.py | https://github.com/mikedh/trimesh/blob/25e059bf6d4caa74f62ffd58ce4f61a90ee4e518/trimesh/exchange/ply.py#L289-L419 | def elements_to_kwargs(elements, fix_texture, image):
"""
Given an elements data structure, extract the keyword
arguments that a Trimesh object constructor will expect.
Parameters
------------
elements: OrderedDict object, with fields and data loaded
Returns
-----------
kwargs: dict, with keys for Trimesh constructor.
eg: mesh = trimesh.Trimesh(**kwargs)
"""
kwargs = {'metadata': {'ply_raw': elements}}
vertices = np.column_stack([elements['vertex']['data'][i]
for i in 'xyz'])
if not util.is_shape(vertices, (-1, 3)):
raise ValueError('Vertices were not (n,3)!')
try:
face_data = elements['face']['data']
except (KeyError, ValueError):
# some PLY files only include vertices
face_data = None
faces = None
# what keys do in-the-wild exporters use for vertices
index_names = ['vertex_index',
'vertex_indices']
texcoord = None
if util.is_shape(face_data, (-1, (3, 4))):
faces = face_data
elif isinstance(face_data, dict):
# get vertex indexes
for i in index_names:
if i in face_data:
faces = face_data[i]
break
# if faces have UV coordinates defined use them
if 'texcoord' in face_data:
texcoord = face_data['texcoord']
elif isinstance(face_data, np.ndarray):
face_blob = elements['face']['data']
# some exporters set this name to 'vertex_index'
# and some others use 'vertex_indices' but we really
# don't care about the name unless there are multiple
if len(face_blob.dtype.names) == 1:
name = face_blob.dtype.names[0]
elif len(face_blob.dtype.names) > 1:
# loop through options
for i in face_blob.dtype.names:
if i in index_names:
name = i
break
# get faces
faces = face_blob[name]['f1']
try:
texcoord = face_blob['texcoord']['f1']
except (ValueError, KeyError):
# accessing numpy arrays with named fields
# incorrectly is a ValueError
pass
# PLY stores texture coordinates per- face which is
# slightly annoying, as we have to then figure out
# which vertices have the same position but different UV
expected = (faces.shape[0], faces.shape[1] * 2)
if (image is not None and
texcoord is not None and
texcoord.shape == expected):
# vertices with the same position but different
# UV coordinates can't be merged without it
# looking like it went through a woodchipper
# in- the- wild PLY comes with things merged that
# probably shouldn't be so disconnect vertices
if fix_texture:
# reshape to correspond with flattened faces
uv = texcoord.reshape((-1, 2))
# round UV to OOM 10^4 as they are pixel coordinates
# and more precision is not necessary or desirable
search = np.column_stack((
vertices[faces.reshape(-1)],
(uv * 1e4).round()))
# find vertices which have the same position AND UV
unique, inverse = grouping.unique_rows(search)
# set vertices, faces, and UV to the new values
vertices = search[:, :3][unique]
faces = inverse.reshape((-1, 3))
uv = uv[unique]
else:
# don't alter vertices, UV will look like crap
# if it was exported with vertices merged
uv = np.zeros((len(vertices), 2))
uv[faces.reshape(-1)] = texcoord.reshape((-1, 2))
# create the visuals object for the texture
kwargs['visual'] = visual.texture.TextureVisuals(
uv=uv, image=image)
# kwargs for Trimesh or PointCloud
kwargs.update({'faces': faces,
'vertices': vertices})
# if both vertex and face color are defined pick the one
# with the most going on
colors = []
signal = []
if kwargs['faces'] is not None:
f_color, f_signal = element_colors(elements['face'])
colors.append({'face_colors': f_color})
signal.append(f_signal)
if kwargs['vertices'] is not None:
v_color, v_signal = element_colors(elements['vertex'])
colors.append({'vertex_colors': v_color})
signal.append(v_signal)
# add the winning colors to the result
kwargs.update(colors[np.argmax(signal)])
return kwargs | [
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-----------
kwargs: dict, with keys for Trimesh constructor.
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alvarogzp/telegram-bot-framework | bot/action/standard/userinfo.py | https://github.com/alvarogzp/telegram-bot-framework/blob/7b597a415c1901901c677976cb13100fc3083107/bot/action/standard/userinfo.py#L26-L30 | def get_instance(cls, state):
""":rtype: UserStorageHandler"""
if cls.instance is None:
cls.instance = UserStorageHandler(state)
return cls.instance | [
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aliyun/aliyun-log-python-sdk | aliyun/log/logclient.py | https://github.com/aliyun/aliyun-log-python-sdk/blob/ac383db0a16abf1e5ef7df36074374184b43516e/aliyun/log/logclient.py#L1691-L1710 | def get_machine_group(self, project_name, group_name):
""" get machine group in a project
Unsuccessful opertaion will cause an LogException.
:type project_name: string
:param project_name: the Project name
:type group_name: string
:param group_name: the group name to get
:return: GetMachineGroupResponse
:raise: LogException
"""
headers = {}
params = {}
resource = "/machinegroups/" + group_name
(resp, headers) = self._send("GET", project_name, None, resource, params, headers)
return GetMachineGroupResponse(resp, headers) | [
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KartikTalwar/Duolingo | duolingo.py | https://github.com/KartikTalwar/Duolingo/blob/0f7e9a0d4bfa864ade82890fca3789679ef38bee/duolingo.py#L237-L255 | def get_languages(self, abbreviations=False):
"""
Get praticed languages.
:param abbreviations: Get language as abbreviation or not
:type abbreviations: bool
:return: List of languages
:rtype: list of str
"""
data = []
for lang in self.user_data.languages:
if lang['learning']:
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data.append(lang['language'])
else:
data.append(lang['language_string'])
return data | [
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:param abbreviations: Get language as abbreviation or not
:type abbreviations: bool
:return: List of languages
:rtype: list of str | [
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pystorm/pystorm | pystorm/bolt.py | https://github.com/pystorm/pystorm/blob/0f853e007c79e03cefdb4a0794423f84dce4c2f3/pystorm/bolt.py#L496-L503 | def _batch_entry_run(self):
"""The inside of ``_batch_entry``'s infinite loop.
Separated out so it can be properly unit tested.
"""
time.sleep(self.secs_between_batches)
with self._batch_lock:
self.process_batches() | [
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sidecars/python-quickbooks | quickbooks/mixins.py | https://github.com/sidecars/python-quickbooks/blob/4cb2b6da46423bad8b32b85d87f9a97b698144fd/quickbooks/mixins.py#L211-L228 | def query(cls, select, qb=None):
"""
:param select: QBO SQL query select statement
:param qb:
:return: Returns list
"""
if not qb:
qb = QuickBooks()
json_data = qb.query(select)
obj_list = []
if cls.qbo_object_name in json_data["QueryResponse"]:
for item_json in json_data["QueryResponse"][cls.qbo_object_name]:
obj_list.append(cls.from_json(item_json))
return obj_list | [
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saltstack/salt | salt/modules/azurearm_network.py | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/azurearm_network.py#L240-L269 | def security_rules_list(security_group, resource_group, **kwargs):
'''
.. versionadded:: 2019.2.0
List security rules within a network security group.
:param security_group: The network security group to query.
:param resource_group: The resource group name assigned to the
network security group.
CLI Example:
.. code-block:: bash
salt-call azurearm_network.security_rules_list testnsg testgroup
'''
netconn = __utils__['azurearm.get_client']('network', **kwargs)
try:
secrules = netconn.security_rules.list(
network_security_group_name=security_group,
resource_group_name=resource_group
)
result = __utils__['azurearm.paged_object_to_list'](secrules)
except CloudError as exc:
__utils__['azurearm.log_cloud_error']('network', str(exc), **kwargs)
result = {'error': str(exc)}
return result | [
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:param resource_group: The resource group name assigned to the
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CLI Example:
.. code-block:: bash
salt-call azurearm_network.security_rules_list testnsg testgroup | [
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rochacbruno/manage | manage/auto_import.py | https://github.com/rochacbruno/manage/blob/e904c451862f036f4be8723df5704a9844103c74/manage/auto_import.py#L13-L21 | def import_submodules(name, submodules=None):
"""Import all submodules for a package/module name"""
sys.path.insert(0, name)
if submodules:
for submodule in submodules:
import_string('{0}.{1}'.format(name, submodule))
else:
for item in pkgutil.walk_packages([name]):
import_string('{0}.{1}'.format(name, item[1])) | [
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NicolasLM/spinach | spinach/brokers/redis.py | https://github.com/NicolasLM/spinach/blob/0122f916643101eab5cdc1f3da662b9446e372aa/spinach/brokers/redis.py#L100-L118 | def enqueue_jobs(self, jobs: Iterable[Job]):
"""Enqueue a batch of jobs."""
jobs_to_queue = list()
for job in jobs:
if job.should_start:
job.status = JobStatus.QUEUED
else:
job.status = JobStatus.WAITING
jobs_to_queue.append(job.serialize())
if jobs_to_queue:
self._run_script(
self._enqueue_job,
self._to_namespaced(NOTIFICATIONS_KEY),
self._to_namespaced(RUNNING_JOBS_KEY.format(self._id)),
self.namespace,
self._to_namespaced(FUTURE_JOBS_KEY),
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metagriffin/asset | asset/plugin.py | https://github.com/metagriffin/asset/blob/f2c5e599cd4688f82216d4b5cfa87aab96d8bb8c/asset/plugin.py#L112-L184 | def plugins(group, spec=None):
# TODO: share this documentation with `../doc/plugin.rst`...
'''
Returns a `PluginSet` object for the specified setuptools-style
entrypoint `group`. This is just a wrapper around
`pkg_resources.iter_entry_points` that allows the plugins to sort
and override themselves.
The optional `spec` parameter controls how and what plugins are
loaded. If it is ``None`` or the special value ``'*'``, then the
normal plugin loading will occur, i.e. all registered plugins will
be loaded and their self-declared ordering and dependencies will be
applied.
Otherwise, the `spec` is taken as a comma- or whitespace-separated
list of plugins to load. In this mode, the `spec` can either specify
an exact list of plugins to load, in the specified order, referred
to as an "absolute" spec. Otherwise, it is a "relative" spec, which
indicates that it only adjusts the standard registered plugin
loading. A spec is a list of either absolute or relative
instructions, and they cannot be mixed.
In either mode, a plugin is identified either by name for registered
plugins (e.g. ``foo``), or by fully-qualified Python module and
symbol name for unregistered plugins
(e.g. ``package.module.symbol``).
Plugins in an absolute spec are loaded in the order specified and
can be optionally prefixed with the following special characters:
* ``'?'`` : the specified plugin should be loaded if available. If
it is not registered, cannot be found, or cannot be loaded, then
it is ignored (a DEBUG log message will be emitted, however).
Plugins in a relative spec are always prefixed with at least one of
the following special characters:
* ``'-'`` : removes the specified plugin; this does not affect
plugin ordering, it only removes the plugin from the loaded
list. If the plugin does not exist, no error is thrown.
* ``'+'`` : adds or requires the specified plugin to the loaded
set. If the plugin is not a named/registered plugin, then it will
be loaded as an asset-symbol, i.e. a Python-dotted module and
symbol name. If the plugin does not exist or cannot be loaded,
this will throw an error. It does not affect the plugin ordering
of registered plugins.
* ``'/'`` : the plugin name is taken as a regular expression that
will be used to match plugin names and it must terminate in a
slash. Note that this must be the **last** element in the spec
list.
Examples:
* ``'*'`` : load all registered plugins.
* ``'foo,bar'`` : load the "foo" plugin, then the "bar" plugin.
* ``'foo,?bar'`` : load the "foo" plugin and if the "bar" plugin
exists, load it too.
* ``'-zig'`` : load all registered plugins except the "zig" plugin.
* ``'+pkg.foo.bar'`` : load all registered plugins and then load
the "pkg.foo.bar" Python symbol.
* ``'pkg.foo.bar'`` : load only the "pkg.foo.bar" Python symbol.
'''
pspec = _parse_spec(spec)
plugs = list(_get_registered_plugins(group, pspec))
plugs += list(_get_unregistered_plugins(group, plugs, pspec))
return PluginSet(group, spec, list(_sort_plugins(group, plugs, pspec, spec))) | [
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* ``'+'`` : adds or requires the specified plugin to the loaded
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* ``'-zig'`` : load all registered plugins except the "zig" plugin.
* ``'+pkg.foo.bar'`` : load all registered plugins and then load
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* ``'pkg.foo.bar'`` : load only the "pkg.foo.bar" Python symbol. | [
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jleclanche/fireplace | fireplace/game.py | https://github.com/jleclanche/fireplace/blob/d0fc0e97e185c0210de86631be20638659c0609e/fireplace/game.py#L193-L201 | def queue_actions(self, source, actions, event_args=None):
"""
Queue a list of \a actions for processing from \a source.
Triggers an aura refresh afterwards.
"""
source.event_args = event_args
ret = self.trigger_actions(source, actions)
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joyent/python-manta | manta/auth.py | https://github.com/joyent/python-manta/blob/f68ef142bdbac058c981e3b28e18d77612f5b7c6/manta/auth.py#L246-L310 | def ssh_key_info_from_key_data(key_id, priv_key=None):
"""Get/load SSH key info necessary for signing.
@param key_id {str} Either a private ssh key fingerprint, e.g.
'b3:f0:a1:6c:18:3b:42:63:fd:6e:57:42:74:17:d4:bc', or the path to
an ssh private key file (like ssh's IdentityFile config option).
@param priv_key {str} Optional. SSH private key file data (PEM format).
@return {dict} with these keys:
- type: "agent"
- signer: Crypto signer class (a PKCS#1 v1.5 signer for RSA keys)
- fingerprint: key md5 fingerprint
- algorithm: See ALGO_FROM_SSH_KEY_TYPE for supported list.
- ... some others added by `load_ssh_key()`
"""
if FINGERPRINT_RE.match(key_id) and priv_key:
key_info = {"fingerprint": key_id, "priv_key": priv_key}
else:
# Otherwise, we attempt to load necessary details from ~/.ssh.
key_info = load_ssh_key(key_id)
# Load a key signer.
key = None
try:
key = serialization.load_pem_private_key(
key_info["priv_key"],
password=None,
backend=default_backend())
except TypeError as ex:
log.debug("could not import key without passphrase (will "
"try with passphrase): %s", ex)
if "priv_key_path" in key_info:
prompt = "Passphrase [%s]: " % key_info["priv_key_path"]
else:
prompt = "Passphrase: "
for i in range(3):
passphrase = getpass(prompt)
if not passphrase:
break
try:
key = serialization.load_pem_private_key(
key_info["priv_key"],
password=passphrase,
backend=default_backend())
except ValueError:
continue
else:
break
if not key:
details = ""
if "priv_key_path" in key_info:
details = " (%s)" % key_info["priv_key_path"]
raise MantaError("could not import key" + details)
# If load_ssh_key() wasn't run, set the algorithm here.
if 'algorithm' not in key_info:
if isinstance(key, ec.EllipticCurvePrivateKey):
key_info['algorithm'] = ECDSA_ALGO_FROM_KEY_SIZE[str(key.key_size)]
elif isinstance(key, rsa.RSAPrivateKey):
key_info['algorithm'] = RSA_STR
else:
raise MantaError("Unsupported key type for: {}".format(key_id))
key_info["signer"] = key
key_info["type"] = "ssh_key"
return key_info | [
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@param priv_key {str} Optional. SSH private key file data (PEM format).
@return {dict} with these keys:
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mosdef-hub/mbuild | mbuild/utils/conversion.py | https://github.com/mosdef-hub/mbuild/blob/dcb80a2becd5d0e6a7e3e7bcb1b59793c46a2dd3/mbuild/utils/conversion.py#L4-L23 | def RB_to_OPLS(c0, c1, c2, c3, c4, c5):
"""Converts Ryckaert-Bellemans type dihedrals to OPLS type.
Parameters
----------
c0, c1, c2, c3, c4, c5 : Ryckaert-Belleman coefficients (in kcal/mol)
Returns
-------
opls_coeffs : np.array, shape=(4,)
Array containing the OPLS dihedrals coeffs f1, f2, f3, and f4
(in kcal/mol)
"""
f1 = (-1.5 * c3) - (2 * c1)
f2 = c0 + c1 + c3
f3 = -0.5 * c3
f4 = -0.25 * c4
return np.array([f1, f2, f3, f4]) | [
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rodluger/everest | everest/inject.py | https://github.com/rodluger/everest/blob/6779591f9f8b3556847e2fbf761bdfac7520eaea/everest/inject.py#L28-L334 | def Inject(ID, inj_model='nPLD', t0=None, per=None, dur=0.1, depth=0.001,
mask=False, trn_win=5, poly_order=3, make_fits=False, **kwargs):
'''
Run one of the :py:obj:`everest` models with injected transits and attempt
to recover the transit depth at the end with a simple linear regression
with a polynomial baseline. The depth is stored in the
:py:obj:`inject` attribute of the model (a dictionary) as
:py:obj:`rec_depth`. A control injection is also performed, in which the
transits are injected into the de-trended data; the recovered depth in
the control run is stored in :py:obj:`inject`
as :py:obj:`rec_depth_control`.
:param int ID: The target id
:param str inj_model: The name of the :py:obj:`everest` model to run. \
Default `"nPLD"`
:param float t0: The transit ephemeris in days. Default is to draw from \
the uniform distributon [0., :py:obj:`per`)
:param float per: The injected planet period in days. Default is to draw \
from the uniform distribution [2, 10]
:param float dur: The transit duration in days. Must be in the range \
[0.05, 0.5]. Default 0.1
:param float depth: The fractional transit depth. Default 0.001
:param bool mask: Explicitly mask the in-transit cadences when computing \
the PLD model? Default :py:obj:`False`
:param float trn_win: The size of the transit window in units of the \
transit duration
:param int poly_order: The order of the polynomial used to fit the \
continuum
'''
# Randomize the planet params
if per is None:
a = 3.
b = 10.
per = a + (b - a) * np.random.random()
if t0 is None:
t0 = per * np.random.random()
# Get the actual class
_model = eval(inj_model)
inject = {'t0': t0, 'per': per, 'dur': dur, 'depth': depth, 'mask': mask,
'poly_order': poly_order, 'trn_win': trn_win}
# Define the injection class
class Injection(_model):
'''
The :py:obj:`Injection` class is a special subclass of a
user-selected :py:obj:`everest` model.
See :py:func:`Inject` for more details.
'''
def __init__(self, *args, **kwargs):
'''
'''
self.inject = kwargs.pop('inject', None)
self.parent_class = kwargs.pop('parent_class', None)
self.kwargs = kwargs
super(Injection, self).__init__(*args, **kwargs)
@property
def name(self):
'''
'''
if self.inject['mask']:
maskchar = 'M'
else:
maskchar = 'U'
return '%s_Inject_%s%g' % (self.parent_class,
maskchar, self.inject['depth'])
def load_tpf(self):
'''
Loads the target pixel files and injects transits at the pixel level.
'''
# Load the TPF
super(Injection, self).load_tpf()
log.info("Injecting transits...")
# Inject the transits into the regular data
transit_model = Transit(
self.time, t0=self.inject['t0'], per=self.inject['per'],
dur=self.inject['dur'], depth=self.inject['depth'])
for i in range(self.fpix.shape[1]):
self.fpix[:, i] *= transit_model
self.fraw = np.sum(self.fpix, axis=1)
if self.inject['mask']:
self.transitmask = np.array(list(set(np.concatenate(
[self.transitmask, np.where(transit_model < 1.)[0]]))),
dtype=int)
# Update the PLD normalization
self.get_norm()
def recover_depth(self):
'''
Recovers the injected transit depth from the long
cadence data with a simple LLS solver.
The results are all stored in the :py:obj:`inject`
attribute of the model.
'''
# Control run
transit_model = Transit(
self.time, t0=self.inject['t0'], per=self.inject['per'],
dur=self.inject['dur'], depth=self.inject['depth'])
kwargs = dict(self.kwargs)
kwargs.update({'clobber': False})
control = eval(self.parent_class)(
self.ID, is_parent=True, **kwargs)
control.fraw *= transit_model
# Get params
log.info("Recovering transit depth...")
t0 = self.inject['t0']
per = self.inject['per']
dur = self.inject['dur']
depth = self.inject['depth']
trn_win = self.inject['trn_win']
poly_order = self.inject['poly_order']
for run, tag in zip([self, control], ['', '_control']):
# Compute the model
mask = np.array(
list(set(np.concatenate([run.badmask, run.nanmask]))),
dtype=int)
flux = np.delete(run.flux / np.nanmedian(run.flux), mask)
time = np.delete(run.time, mask)
transit_model = (Transit(time, t0=t0, per=per,
dur=dur, depth=depth) - 1) / depth
# Count the transits
t0 += np.ceil((time[0] - dur - t0) / per) * per
ttimes0 = np.arange(t0, time[-1] + dur, per)
tinds = []
for tt in ttimes0:
# Get indices for this chunk
inds = np.where(np.abs(time - tt) < trn_win * dur / 2.)[0]
# Ensure there's a transit in this chunk, and that
# there are enough points for the polynomial fit
if np.any(transit_model[inds] < 0.) and \
len(inds) > poly_order:
tinds.append(inds)
# Our design matrix
sz = (poly_order + 1) * len(tinds)
X = np.empty((0, 1 + sz), dtype=float)
Y = np.array([], dtype=float)
T = np.array([], dtype=float)
# Loop over all transits
for i, inds in enumerate(tinds):
# Get the transit model
trnvec = transit_model[inds].reshape(-1, 1)
# Normalize the time array
t = time[inds]
t = (t - t[0]) / (t[-1] - t[0])
# Cumulative arrays
T = np.append(T, time[inds])
Y = np.append(Y, flux[inds])
# Polynomial vector
polyvec = np.array(
[t ** o for o in range(0, poly_order + 1)]).T
# Update the design matrix with this chunk
lzeros = np.zeros((len(t), i * (poly_order + 1)))
rzeros = np.zeros(
(len(t), sz - (i + 1) * (poly_order + 1)))
chunk = np.hstack((trnvec, lzeros, polyvec, rzeros))
X = np.vstack((X, chunk))
# Get the relative depth
A = np.dot(X.T, X)
B = np.dot(X.T, Y)
C = np.linalg.solve(A, B)
rec_depth = C[0]
# Get the uncertainties
sig = 1.4826 * \
np.nanmedian(np.abs(flux - np.nanmedian(flux))
) / np.nanmedian(flux)
cov = sig ** 2 * np.linalg.solve(A, np.eye(A.shape[0]))
err = np.sqrt(np.diag(cov))
rec_depth_err = err[0]
# Store the results
self.inject.update(
{'rec_depth%s' % tag: rec_depth,
'rec_depth_err%s' % tag: rec_depth_err})
# Store the detrended, folded data
D = (Y - np.dot(C[1:], X[:, 1:].T) +
np.nanmedian(Y)) / np.nanmedian(Y)
T = (T - t0 - per / 2.) % per - per / 2.
self.inject.update(
{'fold_time%s' % tag: T, 'fold_flux%s' % tag: D})
def plot_final(self, ax):
'''
Plots the injection recovery results.
'''
from mpl_toolkits.axes_grid.inset_locator import inset_axes
ax.axis('off')
ax1 = inset_axes(ax, width="47%", height="100%", loc=6)
ax2 = inset_axes(ax, width="47%", height="100%", loc=7)
# Plot the recovered folded transits
ax1.plot(self.inject['fold_time'],
self.inject['fold_flux'], 'k.', alpha=0.3)
x = np.linspace(np.min(self.inject['fold_time']), np.max(
self.inject['fold_time']), 500)
try:
y = Transit(
x, t0=0., per=self.inject['per'], dur=self.inject['dur'],
depth=self.inject['rec_depth'])
except:
# Log the error, and carry on
exctype, value, tb = sys.exc_info()
for line in traceback.format_exception_only(exctype, value):
l = line.replace('\n', '')
log.error(l)
y = np.ones_like(x) * np.nan
ax1.plot(x, y, 'r-')
ax1.annotate('INJECTED', xy=(0.98, 0.025),
xycoords='axes fraction',
ha='right', va='bottom', fontsize=10, alpha=0.5,
fontweight='bold')
ax1.annotate('True depth:\nRecovered depth:',
xy=(0.02, 0.025),
xycoords='axes fraction',
ha='left', va='bottom', fontsize=6, color='r')
ax1.annotate('%.6f\n%.6f' % (self.inject['depth'],
self.inject['rec_depth']),
xy=(0.4, 0.025),
xycoords='axes fraction',
ha='left', va='bottom', fontsize=6, color='r')
ax1.margins(0, None)
ax1.ticklabel_format(useOffset=False)
# Plot the recovered folded transits (control)
ax2.plot(self.inject['fold_time_control'],
self.inject['fold_flux_control'], 'k.', alpha=0.3)
x = np.linspace(np.min(self.inject['fold_time_control']), np.max(
self.inject['fold_time_control']), 500)
try:
y = Transit(
x, t0=0., per=self.inject['per'], dur=self.inject['dur'],
depth=self.inject['rec_depth_control'])
except:
# Log the error, and carry on
exctype, value, tb = sys.exc_info()
for line in traceback.format_exception_only(exctype, value):
l = line.replace('\n', '')
log.error(l)
y = np.ones_like(x) * np.nan
ax2.plot(x, y, 'r-')
ax2.annotate('CONTROL', xy=(0.98, 0.025), xycoords='axes fraction',
ha='right', va='bottom', fontsize=10, alpha=0.5,
fontweight='bold')
ax2.annotate('True depth:\nRecovered depth:',
xy=(0.02, 0.025),
xycoords='axes fraction',
ha='left', va='bottom', fontsize=6, color='r')
ax2.annotate('%.6f\n%.6f' % (self.inject['depth'],
self.inject['rec_depth_control']),
xy=(0.4, 0.025),
xycoords='axes fraction',
ha='left', va='bottom', fontsize=6, color='r')
ax2.margins(0, None)
ax2.ticklabel_format(useOffset=False)
N = int(0.995 * len(self.inject['fold_flux_control']))
hi, lo = self.inject['fold_flux_control'][np.argsort(
self.inject['fold_flux_control'])][[N, -N]]
fsort = self.inject['fold_flux_control'][np.argsort(
self.inject['fold_flux_control'])]
pad = (hi - lo) * 0.2
ylim = (lo - 2 * pad, hi + pad)
ax2.set_ylim(ylim)
ax1.set_ylim(ylim)
ax2.set_yticklabels([])
for tick in ax1.get_xticklabels() + ax1.get_yticklabels() + \
ax2.get_xticklabels():
tick.set_fontsize(5)
def finalize(self):
'''
Calls the depth recovery routine at the end
of the de-trending step.
'''
super(Injection, self).finalize()
self.recover_depth()
return Injection(ID, inject=inject, parent_class=inj_model,
make_fits=make_fits, **kwargs) | [
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] | Run one of the :py:obj:`everest` models with injected transits and attempt
to recover the transit depth at the end with a simple linear regression
with a polynomial baseline. The depth is stored in the
:py:obj:`inject` attribute of the model (a dictionary) as
:py:obj:`rec_depth`. A control injection is also performed, in which the
transits are injected into the de-trended data; the recovered depth in
the control run is stored in :py:obj:`inject`
as :py:obj:`rec_depth_control`.
:param int ID: The target id
:param str inj_model: The name of the :py:obj:`everest` model to run. \
Default `"nPLD"`
:param float t0: The transit ephemeris in days. Default is to draw from \
the uniform distributon [0., :py:obj:`per`)
:param float per: The injected planet period in days. Default is to draw \
from the uniform distribution [2, 10]
:param float dur: The transit duration in days. Must be in the range \
[0.05, 0.5]. Default 0.1
:param float depth: The fractional transit depth. Default 0.001
:param bool mask: Explicitly mask the in-transit cadences when computing \
the PLD model? Default :py:obj:`False`
:param float trn_win: The size of the transit window in units of the \
transit duration
:param int poly_order: The order of the polynomial used to fit the \
continuum | [
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theislab/scvelo | scvelo/tools/rank_velocity_genes.py | https://github.com/theislab/scvelo/blob/c7a96d70edfe705e86bf364434a9527d4fd8df11/scvelo/tools/rank_velocity_genes.py#L39-L52 | def select_groups(adata, groups='all', key='louvain'):
"""Get subset of groups in adata.obs[key].
"""
strings_to_categoricals(adata)
if isinstance(groups, list) and isinstance(groups[0], int): groups = [str(n) for n in groups]
categories = adata.obs[key].cat.categories
groups_masks = np.array([categories[i] == adata.obs[key].values for i, name in enumerate(categories)])
if groups == 'all':
groups = categories.values
else:
groups_ids = [np.where(categories.values == name)[0][0] for name in groups]
groups_masks = groups_masks[groups_ids]
groups = categories[groups_ids].values
return groups, groups_masks | [
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jrderuiter/pybiomart | src/pybiomart/dataset.py | https://github.com/jrderuiter/pybiomart/blob/7802d45fe88549ab0512d6f37f815fc43b172b39/src/pybiomart/dataset.py#L88-L92 | def filters(self):
"""List of filters available for the dataset."""
if self._filters is None:
self._filters, self._attributes = self._fetch_configuration()
return self._filters | [
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spyder-ide/spyder | spyder/plugins/editor/plugin.py | https://github.com/spyder-ide/spyder/blob/f76836ce1b924bcc4efd3f74f2960d26a4e528e0/spyder/plugins/editor/plugin.py#L1831-L1847 | def print_file(self):
"""Print current file"""
editor = self.get_current_editor()
filename = self.get_current_filename()
printer = Printer(mode=QPrinter.HighResolution,
header_font=self.get_plugin_font('printer_header'))
printDialog = QPrintDialog(printer, editor)
if editor.has_selected_text():
printDialog.setOption(QAbstractPrintDialog.PrintSelection, True)
self.redirect_stdio.emit(False)
answer = printDialog.exec_()
self.redirect_stdio.emit(True)
if answer == QDialog.Accepted:
self.starting_long_process(_("Printing..."))
printer.setDocName(filename)
editor.print_(printer)
self.ending_long_process() | [
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qacafe/cdrouter.py | cdrouter/devices.py | https://github.com/qacafe/cdrouter.py/blob/aacf2c6ab0b987250f7b1892f4bba14bb2b7dbe5/cdrouter/devices.py#L312-L321 | def connect(self, id): # pylint: disable=invalid-name,redefined-builtin
"""Open proxy connection to a device's management interface.
:param id: Device ID as an int.
:return: :class:`devices.Connection <devices.Connection>` object
:rtype: devices.Connection
"""
schema = ConnectionSchema()
resp = self.service.post(self.base+str(id)+'/connect/')
return self.service.decode(schema, resp) | [
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] | python | train |
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