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def getSpaceUse(self):
"""Get disk space usage.
@return: Dictionary of filesystem space utilization stats for filesystems.
"""
stats = {}
try:
out = subprocess.Popen([dfCmd, "-Pk"],
stdout=subprocess.PIPE).communic... |
def retrieveVals(self):
"""Retrieve values for graphs."""
stats = self._dbconn.getDatabaseStats()
databases = stats.get('databases')
totals = stats.get('totals')
if self.hasGraph('pg_connections'):
limit = self._dbconn.getParam('max_connections')
... |
def connect(self, host, port):
"""Connects via a RS-485 to Ethernet adapter."""
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect((host, port))
self._reader = sock.makefile(mode='rb')
self._writer = sock.makefile(mode='wb') |
def process(self, data_changed_callback):
"""Process data; returns when the reader signals EOF.
Callback is notified when any data changes."""
# pylint: disable=too-many-locals,too-many-branches,too-many-statements
while True:
byte = self._reader.read(1)
while Tr... |
def send_key(self, key):
"""Sends a key."""
_LOGGER.info('Queueing key %s', key)
frame = self._get_key_event_frame(key)
# Queue it to send immediately following the reception
# of a keep-alive packet in an attempt to avoid bus collisions.
self._send_queue.put({'frame': f... |
def states(self):
"""Returns a set containing the enabled states."""
state_list = []
for state in States:
if state.value & self._states != 0:
state_list.append(state)
if (self._flashing_states & States.FILTER) != 0:
state_list.append(States.FILTER... |
def get_state(self, state):
"""Returns True if the specified state is enabled."""
# Check to see if we have a change request pending; if we do
# return the value we expect it to change to.
for data in list(self._send_queue.queue):
desired_states = data['desired_states']
... |
def set_state(self, state, enable):
"""Set the state."""
is_enabled = self.get_state(state)
if is_enabled == enable:
return True
key = None
desired_states = [{'state': state, 'enabled': not is_enabled}]
if state == States.FILTER_LOW_SPEED:
if no... |
def trace(function, *args, **k) :
"""Decorates a function by tracing the begining and
end of the function execution, if doTrace global is True"""
if doTrace : print ("> "+function.__name__, args, k)
result = function(*args, **k)
if doTrace : print ("< "+function.__name__, args, k, "->", result)
return result |
def geocode(self, location) :
url = QtCore.QUrl("http://maps.googleapis.com/maps/api/geocode/xml")
url.addQueryItem("address", location)
url.addQueryItem("sensor", "false")
"""
url = QtCore.QUrl("http://maps.google.com/maps/geo/")
url.addQueryItem("q", location)
url.addQueryItem("output", "csv")
url.add... |
def correlate(params, corrmat):
"""
Force a correlation matrix on a set of statistically distributed objects.
This function works on objects in-place.
Parameters
----------
params : array
An array of of uv objects.
corrmat : 2d-array
The correlation matrix to b... |
def induce_correlations(data, corrmat):
"""
Induce a set of correlations on a column-wise dataset
Parameters
----------
data : 2d-array
An m-by-n array where m is the number of samples and n is the
number of independent variables, each column of the array corresponding
... |
def plotcorr(X, plotargs=None, full=True, labels=None):
"""
Plots a scatterplot matrix of subplots.
Usage:
plotcorr(X)
plotcorr(..., plotargs=...) # e.g., 'r*', 'bo', etc.
plotcorr(..., full=...) # e.g., True or False
plotc... |
def chol(A):
"""
Calculate the lower triangular matrix of the Cholesky decomposition of
a symmetric, positive-definite matrix.
"""
A = np.array(A)
assert A.shape[0] == A.shape[1], "Input matrix must be square"
L = [[0.0] * len(A) for _ in range(len(A))]
for i in range(len(A)):
... |
def get(self, uri, params={}):
'''A generic method to make GET requests to the OpenDNS Investigate API
on the given URI.
'''
return self._session.get(urljoin(Investigate.BASE_URL, uri),
params=params, headers=self._auth_header, proxies=self.proxies
) |
def post(self, uri, params={}, data={}):
'''A generic method to make POST requests to the OpenDNS Investigate API
on the given URI.
'''
return self._session.post(
urljoin(Investigate.BASE_URL, uri),
params=params, data=data, headers=self._auth_header,
... |
def get_parse(self, uri, params={}):
'''Convenience method to call get() on an arbitrary URI and parse the response
into a JSON object. Raises an error on non-200 response status.
'''
return self._request_parse(self.get, uri, params) |
def post_parse(self, uri, params={}, data={}):
'''Convenience method to call post() on an arbitrary URI and parse the response
into a JSON object. Raises an error on non-200 response status.
'''
return self._request_parse(self.post, uri, params, data) |
def categorization(self, domains, labels=False):
'''Get the domain status and categorization of a domain or list of domains.
'domains' can be either a single domain, or a list of domains.
Setting 'labels' to True will give back categorizations in human-readable
form.
For more de... |
def cooccurrences(self, domain):
'''Get the cooccurrences of the given domain.
For details, see https://investigate.umbrella.com/docs/api#co-occurrences
'''
uri = self._uris["cooccurrences"].format(domain)
return self.get_parse(uri) |
def related(self, domain):
'''Get the related domains of the given domain.
For details, see https://investigate.umbrella.com/docs/api#relatedDomains
'''
uri = self._uris["related"].format(domain)
return self.get_parse(uri) |
def security(self, domain):
'''Get the Security Information for the given domain.
For details, see https://investigate.umbrella.com/docs/api#securityInfo
'''
uri = self._uris["security"].format(domain)
return self.get_parse(uri) |
def rr_history(self, query, query_type="A"):
'''Get the RR (Resource Record) History of the given domain or IP.
The default query type is for 'A' records, but the following query types
are supported:
A, NS, MX, TXT, CNAME
For details, see https://investigate.umbrella.com/docs/a... |
def domain_whois(self, domain):
'''Gets whois information for a domain'''
uri = self._uris["whois_domain"].format(domain)
resp_json = self.get_parse(uri)
return resp_json |
def domain_whois_history(self, domain, limit=None):
'''Gets whois history for a domain'''
params = dict()
if limit is not None:
params['limit'] = limit
uri = self._uris["whois_domain_history"].format(domain)
resp_json = self.get_parse(uri, params)
return res... |
def ns_whois(self, nameservers, limit=DEFAULT_LIMIT, offset=DEFAULT_OFFSET, sort_field=DEFAULT_SORT):
'''Gets the domains that have been registered with a nameserver or
nameservers'''
if not isinstance(nameservers, list):
uri = self._uris["whois_ns"].format(nameservers)
p... |
def search(self, pattern, start=None, limit=None, include_category=None):
'''Searches for domains that match a given pattern'''
params = dict()
if start is None:
start = datetime.timedelta(days=30)
if isinstance(start, datetime.timedelta):
params['start'] = int... |
def samples(self, anystring, limit=None, offset=None, sortby=None):
'''Return an object representing the samples identified by the input domain, IP, or URL'''
uri = self._uris['samples'].format(anystring)
params = {'limit': limit, 'offset': offset, 'sortby': sortby}
return self.get_par... |
def sample(self, hash, limit=None, offset=None):
'''Return an object representing the sample identified by the input hash, or an empty object if that sample is not found'''
uri = self._uris['sample'].format(hash)
params = {'limit': limit, 'offset': offset}
return self.get_parse(uri, pa... |
def as_for_ip(self, ip):
'''Gets the AS information for a given IP address.'''
if not Investigate.IP_PATTERN.match(ip):
raise Investigate.IP_ERR
uri = self._uris["as_for_ip"].format(ip)
resp_json = self.get_parse(uri)
return resp_json |
def prefixes_for_asn(self, asn):
'''Gets the AS information for a given ASN. Return the CIDR and geolocation associated with the AS.'''
uri = self._uris["prefixes_for_asn"].format(asn)
resp_json = self.get_parse(uri)
return resp_json |
def timeline(self, uri):
'''Get the domain tagging timeline for a given uri.
Could be a domain, ip, or url.
For details, see https://docs.umbrella.com/investigate-api/docs/timeline
'''
uri = self._uris["timeline"].format(uri)
resp_json = self.get_parse(uri)
retu... |
def abs(x):
"""
Absolute value
"""
if isinstance(x, UncertainFunction):
mcpts = np.abs(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.abs(x) |
def acos(x):
"""
Inverse cosine
"""
if isinstance(x, UncertainFunction):
mcpts = np.arccos(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.arccos(x) |
def acosh(x):
"""
Inverse hyperbolic cosine
"""
if isinstance(x, UncertainFunction):
mcpts = np.arccosh(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.arccosh(x) |
def asin(x):
"""
Inverse sine
"""
if isinstance(x, UncertainFunction):
mcpts = np.arcsin(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.arcsin(x) |
def asinh(x):
"""
Inverse hyperbolic sine
"""
if isinstance(x, UncertainFunction):
mcpts = np.arcsinh(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.arcsinh(x) |
def atan(x):
"""
Inverse tangent
"""
if isinstance(x, UncertainFunction):
mcpts = np.arctan(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.arctan(x) |
def atanh(x):
"""
Inverse hyperbolic tangent
"""
if isinstance(x, UncertainFunction):
mcpts = np.arctanh(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.arctanh(x) |
def ceil(x):
"""
Ceiling function (round towards positive infinity)
"""
if isinstance(x, UncertainFunction):
mcpts = np.ceil(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.ceil(x) |
def cos(x):
"""
Cosine
"""
if isinstance(x, UncertainFunction):
mcpts = np.cos(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.cos(x) |
def cosh(x):
"""
Hyperbolic cosine
"""
if isinstance(x, UncertainFunction):
mcpts = np.cosh(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.cosh(x) |
def degrees(x):
"""
Convert radians to degrees
"""
if isinstance(x, UncertainFunction):
mcpts = np.degrees(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.degrees(x) |
def exp(x):
"""
Exponential function
"""
if isinstance(x, UncertainFunction):
mcpts = np.exp(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.exp(x) |
def expm1(x):
"""
Calculate exp(x) - 1
"""
if isinstance(x, UncertainFunction):
mcpts = np.expm1(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.expm1(x) |
def fabs(x):
"""
Absolute value function
"""
if isinstance(x, UncertainFunction):
mcpts = np.fabs(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.fabs(x) |
def floor(x):
"""
Floor function (round towards negative infinity)
"""
if isinstance(x, UncertainFunction):
mcpts = np.floor(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.floor(x) |
def hypot(x, y):
"""
Calculate the hypotenuse given two "legs" of a right triangle
"""
if isinstance(x, UncertainFunction) or isinstance(x, UncertainFunction):
ufx = to_uncertain_func(x)
ufy = to_uncertain_func(y)
mcpts = np.hypot(ufx._mcpts, ufy._mcpts)
return UncertainF... |
def log(x):
"""
Natural logarithm
"""
if isinstance(x, UncertainFunction):
mcpts = np.log(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.log(x) |
def log10(x):
"""
Base-10 logarithm
"""
if isinstance(x, UncertainFunction):
mcpts = np.log10(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.log10(x) |
def log1p(x):
"""
Natural logarithm of (1 + x)
"""
if isinstance(x, UncertainFunction):
mcpts = np.log1p(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.log1p(x) |
def radians(x):
"""
Convert degrees to radians
"""
if isinstance(x, UncertainFunction):
mcpts = np.radians(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.radians(x) |
def sin(x):
"""
Sine
"""
if isinstance(x, UncertainFunction):
mcpts = np.sin(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.sin(x) |
def sinh(x):
"""
Hyperbolic sine
"""
if isinstance(x, UncertainFunction):
mcpts = np.sinh(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.sinh(x) |
def sqrt(x):
"""
Square-root function
"""
if isinstance(x, UncertainFunction):
mcpts = np.sqrt(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.sqrt(x) |
def tan(x):
"""
Tangent
"""
if isinstance(x, UncertainFunction):
mcpts = np.tan(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.tan(x) |
def tanh(x):
"""
Hyperbolic tangent
"""
if isinstance(x, UncertainFunction):
mcpts = np.tanh(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.tanh(x) |
def trunc(x):
"""
Truncate the values to the integer value without rounding
"""
if isinstance(x, UncertainFunction):
mcpts = np.trunc(x._mcpts)
return UncertainFunction(mcpts)
else:
return np.trunc(x) |
def lhd(
dist=None,
size=None,
dims=1,
form="randomized",
iterations=100,
showcorrelations=False,
):
"""
Create a Latin-Hypercube sample design based on distributions defined in the
`scipy.stats` module
Parameters
----------
dist: array_like
frozen scipy.stat... |
def to_uncertain_func(x):
"""
Transforms x into an UncertainFunction-compatible object,
unless it is already an UncertainFunction (in which case x is returned
unchanged).
Raises an exception unless 'x' belongs to some specific classes of
objects that are known not to depend on UncertainFunctio... |
def Beta(alpha, beta, low=0, high=1, tag=None):
"""
A Beta random variate
Parameters
----------
alpha : scalar
The first shape parameter
beta : scalar
The second shape parameter
Optional
--------
low : scalar
Lower bound of the distribution support (... |
def BetaPrime(alpha, beta, tag=None):
"""
A BetaPrime random variate
Parameters
----------
alpha : scalar
The first shape parameter
beta : scalar
The second shape parameter
"""
assert (
alpha > 0 and beta > 0
), 'BetaPrime "alpha" and "beta" paramete... |
def Bradford(q, low=0, high=1, tag=None):
"""
A Bradford random variate
Parameters
----------
q : scalar
The shape parameter
low : scalar
The lower bound of the distribution (default=0)
high : scalar
The upper bound of the distribution (default=1)
"""
ass... |
def Burr(c, k, tag=None):
"""
A Burr random variate
Parameters
----------
c : scalar
The first shape parameter
k : scalar
The second shape parameter
"""
assert c > 0 and k > 0, 'Burr "c" and "k" parameters must be greater than zero'
return uv(ss.burr(c, k), ... |
def ChiSquared(k, tag=None):
"""
A Chi-Squared random variate
Parameters
----------
k : int
The degrees of freedom of the distribution (must be greater than one)
"""
assert int(k) == k and k >= 1, 'Chi-Squared "k" must be an integer greater than 0'
return uv(ss.chi2(k), tag=... |
def Erlang(k, lamda, tag=None):
"""
An Erlang random variate.
This distribution is the same as a Gamma(k, theta) distribution, but
with the restriction that k must be a positive integer. This
is provided for greater compatibility with other simulation tools, but
provides no advantage over ... |
def Exponential(lamda, tag=None):
"""
An Exponential random variate
Parameters
----------
lamda : scalar
The inverse scale (as shown on Wikipedia). (FYI: mu = 1/lamda.)
"""
assert lamda > 0, 'Exponential "lamda" must be greater than zero'
return uv(ss.expon(scale=1.0 / lamda... |
def ExtValueMax(mu, sigma, tag=None):
"""
An Extreme Value Maximum random variate.
Parameters
----------
mu : scalar
The location parameter
sigma : scalar
The scale parameter (must be greater than zero)
"""
assert sigma > 0, 'ExtremeValueMax "sigma" must be greater t... |
def Fisher(d1, d2, tag=None):
"""
An F (fisher) random variate
Parameters
----------
d1 : int
Numerator degrees of freedom
d2 : int
Denominator degrees of freedom
"""
assert (
int(d1) == d1 and d1 >= 1
), 'Fisher (F) "d1" must be an integer greater than 0... |
def Gamma(k, theta, tag=None):
"""
A Gamma random variate
Parameters
----------
k : scalar
The shape parameter (must be positive and non-zero)
theta : scalar
The scale parameter (must be positive and non-zero)
"""
assert (
k > 0 and theta > 0
), 'Gamma "k... |
def LogNormal(mu, sigma, tag=None):
"""
A Log-Normal random variate
Parameters
----------
mu : scalar
The location parameter
sigma : scalar
The scale parameter (must be positive and non-zero)
"""
assert sigma > 0, 'Log-Normal "sigma" must be positive'
return uv(s... |
def Normal(mu, sigma, tag=None):
"""
A Normal (or Gaussian) random variate
Parameters
----------
mu : scalar
The mean value of the distribution
sigma : scalar
The standard deviation (must be positive and non-zero)
"""
assert sigma > 0, 'Normal "sigma" must be greater... |
def Pareto(q, a, tag=None):
"""
A Pareto random variate (first kind)
Parameters
----------
q : scalar
The scale parameter
a : scalar
The shape parameter (the minimum possible value)
"""
assert q > 0 and a > 0, 'Pareto "q" and "a" must be positive scalars'
p = Uni... |
def Pareto2(q, b, tag=None):
"""
A Pareto random variate (second kind). This form always starts at the
origin.
Parameters
----------
q : scalar
The scale parameter
b : scalar
The shape parameter
"""
assert q > 0 and b > 0, 'Pareto2 "q" and "b" must be positive sc... |
def PERT(low, peak, high, g=4.0, tag=None):
"""
A PERT random variate
Parameters
----------
low : scalar
Lower bound of the distribution support
peak : scalar
The location of the distribution's peak (low <= peak <= high)
high : scalar
Upper bound of the distribut... |
def StudentT(v, tag=None):
"""
A Student-T random variate
Parameters
----------
v : int
The degrees of freedom of the distribution (must be greater than one)
"""
assert int(v) == v and v >= 1, 'Student-T "v" must be an integer greater than 0'
return uv(ss.t(v), tag=tag) |
def Triangular(low, peak, high, tag=None):
"""
A triangular random variate
Parameters
----------
low : scalar
Lower bound of the distribution support
peak : scalar
The location of the triangle's peak (low <= peak <= high)
high : scalar
Upper bound of the distribu... |
def Uniform(low, high, tag=None):
"""
A Uniform random variate
Parameters
----------
low : scalar
Lower bound of the distribution support.
high : scalar
Upper bound of the distribution support.
"""
assert low < high, 'Uniform "low" must be less than "high"'
retur... |
def Weibull(lamda, k, tag=None):
"""
A Weibull random variate
Parameters
----------
lamda : scalar
The scale parameter
k : scalar
The shape parameter
"""
assert (
lamda > 0 and k > 0
), 'Weibull "lamda" and "k" parameters must be greater than zero'
re... |
def Bernoulli(p, tag=None):
"""
A Bernoulli random variate
Parameters
----------
p : scalar
The probability of success
"""
assert (
0 < p < 1
), 'Bernoulli probability "p" must be between zero and one, non-inclusive'
return uv(ss.bernoulli(p), tag=tag) |
def Binomial(n, p, tag=None):
"""
A Binomial random variate
Parameters
----------
n : int
The number of trials
p : scalar
The probability of success
"""
assert (
int(n) == n and n > 0
), 'Binomial number of trials "n" must be an integer greater than zero'... |
def Geometric(p, tag=None):
"""
A Geometric random variate
Parameters
----------
p : scalar
The probability of success
"""
assert (
0 < p < 1
), 'Geometric probability "p" must be between zero and one, non-inclusive'
return uv(ss.geom(p), tag=tag) |
def Hypergeometric(N, n, K, tag=None):
"""
A Hypergeometric random variate
Parameters
----------
N : int
The total population size
n : int
The number of individuals of interest in the population
K : int
The number of individuals that will be chosen from the popul... |
def Poisson(lamda, tag=None):
"""
A Poisson random variate
Parameters
----------
lamda : scalar
The rate of an occurance within a specified interval of time or space.
"""
assert lamda > 0, 'Poisson "lamda" must be greater than zero.'
return uv(ss.poisson(lamda), tag=tag) |
def covariance_matrix(nums_with_uncert):
"""
Calculate the covariance matrix of uncertain variables, oriented by the
order of the inputs
Parameters
----------
nums_with_uncert : array-like
A list of variables that have an associated uncertainty
Returns
-------
cov_m... |
def correlation_matrix(nums_with_uncert):
"""
Calculate the correlation matrix of uncertain variables, oriented by the
order of the inputs
Parameters
----------
nums_with_uncert : array-like
A list of variables that have an associated uncertainty
Returns
-------
cor... |
def var(self):
"""
Variance value as a result of an uncertainty calculation
"""
mn = self.mean
vr = np.mean((self._mcpts - mn) ** 2)
return vr |
def skew(self):
r"""
Skewness coefficient value as a result of an uncertainty calculation,
defined as::
_____ m3
\/beta1 = ------
std**3
where m3 is the third central moment and std is the standard deviation
... |
def kurt(self):
"""
Kurtosis coefficient value as a result of an uncertainty calculation,
defined as::
m4
beta2 = ------
std**4
where m4 is the fourth central moment and std is the standard deviation
"""
... |
def stats(self):
"""
The first four standard moments of a distribution: mean, variance, and
standardized skewness and kurtosis coefficients.
"""
mn = self.mean
vr = self.var
sk = self.skew
kt = self.kurt
return [mn, vr, sk, kt] |
def percentile(self, val):
"""
Get the distribution value at a given percentile or set of percentiles.
This follows the NIST method for calculating percentiles.
Parameters
----------
val : scalar or array
Either a single value or an array of values be... |
def describe(self, name=None):
"""
Cleanly show what the four displayed distribution moments are:
- Mean
- Variance
- Standardized Skewness Coefficient
- Standardized Kurtosis Coefficient
For a standard Normal distribution, these are [0, 1... |
def plot(self, hist=False, show=False, **kwargs):
"""
Plot the distribution of the UncertainFunction. By default, the
distribution is shown with a kernel density estimate (kde).
Optional
--------
hist : bool
If true, a density histogram is displayed (... |
def plot(self, hist=False, show=False, **kwargs):
"""
Plot the distribution of the UncertainVariable. Continuous
distributions are plotted with a line plot and discrete distributions
are plotted with discrete circles.
Optional
--------
hist : bool
... |
def load_hat(self, path): # pylint: disable=no-self-use
"""Loads the hat from a picture at path.
Args:
path: The path to load from
Returns:
The hat data.
"""
hat = cv2.imread(path, cv2.IMREAD_UNCHANGED)
if hat is None:
raise ValueErr... |
def find_faces(self, image, draw_box=False):
"""Uses a haarcascade to detect faces inside an image.
Args:
image: The image.
draw_box: If True, the image will be marked with a rectangle.
Return:
The faces as returned by OpenCV's detectMultiScale method for
... |
def find_resources(self, rsrc_type, sort=None, yield_pages=False, **kwargs):
"""Find instances of `rsrc_type` that match the filter in `**kwargs`"""
return rsrc_type.find(self, sort=sort, yield_pages=yield_pages, **kwargs) |
def changed(self, message=None, *args):
"""Marks the object as changed.
If a `parent` attribute is set, the `changed()` method on the parent
will be called, propagating the change notification up the chain.
The message (if provided) will be debug logged.
"""
if message ... |
def register(cls, origin_type):
"""Decorator for mutation tracker registration.
The provided `origin_type` is mapped to the decorated class such that
future calls to `convert()` will convert the object of `origin_type`
to an instance of the decorated class.
"""
def decor... |
def convert(cls, obj, parent):
"""Converts objects to registered tracked types
This checks the type of the given object against the registered tracked
types. When a match is found, the given object will be converted to the
tracked type, its parent set to the provided parent, and returne... |
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