"""ARMA process and estimation with scipy.signal.lfilter
Notes
-----
* written without textbook, works but not sure about everything
briefly checked and it looks to be standard least squares, see below
* theoretical autocorrelation function of general ARMA
Done, relatively easy to guess solution, time consuming to get
theoretical test cases, example file contains explicit formulas for
acovf of MA(1), MA(2) and ARMA(1,1)
Properties:
Judge, ... (1985): The Theory and Practise of Econometrics
Author: josefpktd
License: BSD
"""
import warnings
from statsmodels.compat.pandas import Appender
import numpy as np
from scipy import linalg, optimize, signal
from statsmodels.tools.docstring import Docstring, remove_parameters
from statsmodels.tools.validation import array_like
__all__ = [
"arma_acf",
"arma_acovf",
"arma_generate_sample",
"arma_impulse_response",
"arma2ar",
"arma2ma",
"deconvolve",
"lpol2index",
"index2lpol",
]
NONSTATIONARY_ERROR = """\
The model's autoregressive parameters (ar) indicate that the process
is non-stationary. arma_acovf can only be used with stationary processes.
"""
[docs]def arma_generate_sample(
ar, ma, nsample, scale=1, distrvs=None, axis=0, burnin=0
):
"""
Simulate data from an ARMA.
Parameters
----------
ar : array_like
The coefficient for autoregressive lag polynomial, including zero lag.
ma : array_like
The coefficient for moving-average lag polynomial, including zero lag.
nsample : int or tuple of ints
If nsample is an integer, then this creates a 1d timeseries of
length size. If nsample is a tuple, creates a len(nsample)
dimensional time series where time is indexed along the input
variable ``axis``. All series are unless ``distrvs`` generates
dependent data.
scale : float
The standard deviation of noise.
distrvs : function, random number generator
A function that generates the random numbers, and takes ``size``
as argument. The default is np.random.standard_normal.
axis : int
See nsample for details.
burnin : int
Number of observation at the beginning of the sample to drop.
Used to reduce dependence on initial values.
Returns
-------
ndarray
Random sample(s) from an ARMA process.
Notes
-----
As mentioned above, both the AR and MA components should include the
coefficient on the zero-lag. This is typically 1. Further, due to the
conventions used in signal processing used in signal.lfilter vs.
conventions in statistics for ARMA processes, the AR parameters should
have the opposite sign of what you might expect. See the examples below.
Examples
--------
>>> import numpy as np
>>> np.random.seed(12345)
>>> arparams = np.array([.75, -.25])
>>> maparams = np.array([.65, .35])
>>> ar = np.r_[1, -arparams] # add zero-lag and negate
>>> ma = np.r_[1, maparams] # add zero-lag
>>> y = sm.tsa.arma_generate_sample(ar, ma, 250)
>>> model = sm.tsa.ARIMA(y, (2, 0, 2), trend='n').fit(disp=0)
>>> model.params
array([ 0.79044189, -0.23140636, 0.70072904, 0.40608028])
"""
distrvs = np.random.standard_normal if distrvs is None else distrvs
if np.ndim(nsample) == 0:
nsample = [nsample]
if burnin:
# handle burin time for nd arrays
# maybe there is a better trick in scipy.fft code
newsize = list(nsample)
newsize[axis] += burnin
newsize = tuple(newsize)
fslice = [slice(None)] * len(newsize)
fslice[axis] = slice(burnin, None, None)
fslice = tuple(fslice)
else:
newsize = tuple(nsample)
fslice = tuple([slice(None)] * np.ndim(newsize))
eta = scale * distrvs(size=newsize)
return signal.lfilter(ma, ar, eta, axis=axis)[fslice]
[docs]def arma_acovf(ar, ma, nobs=10, sigma2=1, dtype=None):
"""
Theoretical autocovariances of stationary ARMA processes
Parameters
----------
ar : array_like, 1d
The coefficients for autoregressive lag polynomial, including zero lag.
ma : array_like, 1d
The coefficients for moving-average lag polynomial, including zero lag.
nobs : int
The number of terms (lags plus zero lag) to include in returned acovf.
sigma2 : float
Variance of the innovation term.
Returns
-------
ndarray
The autocovariance of ARMA process given by ar, ma.
See Also
--------
arma_acf : Autocorrelation function for ARMA processes.
acovf : Sample autocovariance estimation.
References
----------
.. [*] Brockwell, Peter J., and Richard A. Davis. 2009. Time Series:
Theory and Methods. 2nd ed. 1991. New York, NY: Springer.
"""
if dtype is None:
dtype = np.common_type(np.array(ar), np.array(ma), np.array(sigma2))
p = len(ar) - 1
q = len(ma) - 1
m = max(p, q) + 1
if sigma2.real < 0:
raise ValueError("Must have positive innovation variance.")
# Short-circuit for trivial corner-case
if p == q == 0:
out = np.zeros(nobs, dtype=dtype)
out[0] = sigma2
return out
elif p > 0 and np.max(np.abs(np.roots(ar))) >= 1:
raise ValueError(NONSTATIONARY_ERROR)
# Get the moving average representation coefficients that we need
ma_coeffs = arma2ma(ar, ma, lags=m)
# Solve for the first m autocovariances via the linear system
# described by (BD, eq. 3.3.8)
A = np.zeros((m, m), dtype=dtype)
b = np.zeros((m, 1), dtype=dtype)
# We need a zero-right-padded version of ar params
tmp_ar = np.zeros(m, dtype=dtype)
tmp_ar[: p + 1] = ar
for k in range(m):
A[k, : (k + 1)] = tmp_ar[: (k + 1)][::-1]
A[k, 1 : m - k] += tmp_ar[(k + 1) : m]
b[k] = sigma2 * np.dot(ma[k : q + 1], ma_coeffs[: max((q + 1 - k), 0)])
acovf = np.zeros(max(nobs, m), dtype=dtype)
try:
acovf[:m] = np.linalg.solve(A, b)[:, 0]
except np.linalg.LinAlgError:
raise ValueError(NONSTATIONARY_ERROR)
# Iteratively apply (BD, eq. 3.3.9) to solve for remaining autocovariances
if nobs > m:
zi = signal.lfiltic([1], ar, acovf[:m:][::-1])
acovf[m:] = signal.lfilter(
[1], ar, np.zeros(nobs - m, dtype=dtype), zi=zi
)[0]
return acovf[:nobs]
[docs]def arma_acf(ar, ma, lags=10):
"""
Theoretical autocorrelation function of an ARMA process.
Parameters
----------
ar : array_like
Coefficients for autoregressive lag polynomial, including zero lag.
ma : array_like
Coefficients for moving-average lag polynomial, including zero lag.
lags : int
The number of terms (lags plus zero lag) to include in returned acf.
Returns
-------
ndarray
The autocorrelations of ARMA process given by ar and ma.
See Also
--------
arma_acovf : Autocovariances from ARMA processes.
acf : Sample autocorrelation function estimation.
acovf : Sample autocovariance function estimation.
"""
acovf = arma_acovf(ar, ma, lags)
return acovf / acovf[0]
[docs]def arma_pacf(ar, ma, lags=10):
"""
Theoretical partial autocorrelation function of an ARMA process.
Parameters
----------
ar : array_like, 1d
The coefficients for autoregressive lag polynomial, including zero lag.
ma : array_like, 1d
The coefficients for moving-average lag polynomial, including zero lag.
lags : int
The number of terms (lags plus zero lag) to include in returned pacf.
Returns
-------
ndarrray
The partial autocorrelation of ARMA process given by ar and ma.
Notes
-----
Solves yule-walker equation for each lag order up to nobs lags.
not tested/checked yet
"""
# TODO: Should use rank 1 inverse update
apacf = np.zeros(lags)
acov = arma_acf(ar, ma, lags=lags + 1)
apacf[0] = 1.0
for k in range(2, lags + 1):
r = acov[:k]
apacf[k - 1] = linalg.solve(linalg.toeplitz(r[:-1]), r[1:])[-1]
return apacf
[docs]def arma_periodogram(ar, ma, worN=None, whole=0):
"""
Periodogram for ARMA process given by lag-polynomials ar and ma.
Parameters
----------
ar : array_like
The autoregressive lag-polynomial with leading 1 and lhs sign.
ma : array_like
The moving average lag-polynomial with leading 1.
worN : {None, int}, optional
An option for scipy.signal.freqz (read "w or N").
If None, then compute at 512 frequencies around the unit circle.
If a single integer, the compute at that many frequencies.
Otherwise, compute the response at frequencies given in worN.
whole : {0,1}, optional
An options for scipy.signal.freqz/
Normally, frequencies are computed from 0 to pi (upper-half of
unit-circle. If whole is non-zero compute frequencies from 0 to 2*pi.
Returns
-------
w : ndarray
The frequencies.
sd : ndarray
The periodogram, also known as the spectral density.
Notes
-----
Normalization ?
This uses signal.freqz, which does not use fft. There is a fft version
somewhere.
"""
w, h = signal.freqz(ma, ar, worN=worN, whole=whole)
sd = np.abs(h) ** 2 / np.sqrt(2 * np.pi)
if np.any(np.isnan(h)):
# this happens with unit root or seasonal unit root'
import warnings
warnings.warn(
"Warning: nan in frequency response h, maybe a unit " "root",
RuntimeWarning,
)
return w, sd
[docs]def arma_impulse_response(ar, ma, leads=100):
"""
Compute the impulse response function (MA representation) for ARMA process.
Parameters
----------
ar : array_like, 1d
The auto regressive lag polynomial.
ma : array_like, 1d
The moving average lag polynomial.
leads : int
The number of observations to calculate.
Returns
-------
ndarray
The impulse response function with nobs elements.
Notes
-----
This is the same as finding the MA representation of an ARMA(p,q).
By reversing the role of ar and ma in the function arguments, the
returned result is the AR representation of an ARMA(p,q), i.e
ma_representation = arma_impulse_response(ar, ma, leads=100)
ar_representation = arma_impulse_response(ma, ar, leads=100)
Fully tested against matlab
Examples
--------
AR(1)
>>> arma_impulse_response([1.0, -0.8], [1.], leads=10)
array([ 1. , 0.8 , 0.64 , 0.512 , 0.4096 ,
0.32768 , 0.262144 , 0.2097152 , 0.16777216, 0.13421773])
this is the same as
>>> 0.8**np.arange(10)
array([ 1. , 0.8 , 0.64 , 0.512 , 0.4096 ,
0.32768 , 0.262144 , 0.2097152 , 0.16777216, 0.13421773])
MA(2)
>>> arma_impulse_response([1.0], [1., 0.5, 0.2], leads=10)
array([ 1. , 0.5, 0.2, 0. , 0. , 0. , 0. , 0. , 0. , 0. ])
ARMA(1,2)
>>> arma_impulse_response([1.0, -0.8], [1., 0.5, 0.2], leads=10)
array([ 1. , 1.3 , 1.24 , 0.992 , 0.7936 ,
0.63488 , 0.507904 , 0.4063232 , 0.32505856, 0.26004685])
"""
impulse = np.zeros(leads)
impulse[0] = 1.0
return signal.lfilter(ma, ar, impulse)
[docs]def arma2ma(ar, ma, lags=100):
"""
A finite-lag approximate MA representation of an ARMA process.
Parameters
----------
ar : ndarray
The auto regressive lag polynomial.
ma : ndarray
The moving average lag polynomial.
lags : int
The number of coefficients to calculate.
Returns
-------
ndarray
The coefficients of AR lag polynomial with nobs elements.
Notes
-----
Equivalent to ``arma_impulse_response(ma, ar, leads=100)``
"""
return arma_impulse_response(ar, ma, leads=lags)
[docs]def arma2ar(ar, ma, lags=100):
"""
A finite-lag AR approximation of an ARMA process.
Parameters
----------
ar : array_like
The auto regressive lag polynomial.
ma : array_like
The moving average lag polynomial.
lags : int
The number of coefficients to calculate.
Returns
-------
ndarray
The coefficients of AR lag polynomial with nobs elements.
Notes
-----
Equivalent to ``arma_impulse_response(ma, ar, leads=100)``
"""
return arma_impulse_response(ma, ar, leads=lags)
# moved from sandbox.tsa.try_fi
[docs]def ar2arma(ar_des, p, q, n=20, mse="ar", start=None):
"""
Find arma approximation to ar process.
This finds the ARMA(p,q) coefficients that minimize the integrated
squared difference between the impulse_response functions (MA
representation) of the AR and the ARMA process. This does not check
whether the MA lag polynomial of the ARMA process is invertible, neither
does it check the roots of the AR lag polynomial.
Parameters
----------
ar_des : array_like
The coefficients of original AR lag polynomial, including lag zero.
p : int
The length of desired AR lag polynomials.
q : int
The length of desired MA lag polynomials.
n : int
The number of terms of the impulse_response function to include in the
objective function for the approximation.
mse : str, 'ar'
Not used.
start : ndarray
Initial values to use when finding the approximation.
Returns
-------
ar_app : ndarray
The coefficients of the AR lag polynomials of the approximation.
ma_app : ndarray
The coefficients of the MA lag polynomials of the approximation.
res : tuple
The result of optimize.leastsq.
Notes
-----
Extension is possible if we want to match autocovariance instead
of impulse response function.
"""
# TODO: convert MA lag polynomial, ma_app, to be invertible, by mirroring
# TODO: roots outside the unit interval to ones that are inside. How to do
# TODO: this?
# p,q = pq
def msear_err(arma, ar_des):
ar, ma = np.r_[1, arma[: p - 1]], np.r_[1, arma[p - 1 :]]
ar_approx = arma_impulse_response(ma, ar, n)
return ar_des - ar_approx # ((ar - ar_approx)**2).sum()
if start is None:
arma0 = np.r_[-0.9 * np.ones(p - 1), np.zeros(q - 1)]
else:
arma0 = start
res = optimize.leastsq(msear_err, arma0, ar_des, maxfev=5000)
arma_app = np.atleast_1d(res[0])
ar_app = (np.r_[1, arma_app[: p - 1]],)
ma_app = np.r_[1, arma_app[p - 1 :]]
return ar_app, ma_app, res
_arma_docs = {"ar": arma2ar.__doc__, "ma": arma2ma.__doc__}
[docs]def lpol2index(ar):
"""
Remove zeros from lag polynomial
Parameters
----------
ar : array_like
coefficients of lag polynomial
Returns
-------
coeffs : ndarray
non-zero coefficients of lag polynomial
index : ndarray
index (lags) of lag polynomial with non-zero elements
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore", np.ComplexWarning)
ar = array_like(ar, "ar")
index = np.nonzero(ar)[0]
coeffs = ar[index]
return coeffs, index
[docs]def index2lpol(coeffs, index):
"""
Expand coefficients to lag poly
Parameters
----------
coeffs : ndarray
non-zero coefficients of lag polynomial
index : ndarray
index (lags) of lag polynomial with non-zero elements
Returns
-------
ar : array_like
coefficients of lag polynomial
"""
n = max(index)
ar = np.zeros(n + 1)
ar[index] = coeffs
return ar
[docs]def lpol_fima(d, n=20):
"""MA representation of fractional integration
.. math:: (1-L)^{-d} for |d|<0.5 or |d|<1 (?)
Parameters
----------
d : float
fractional power
n : int
number of terms to calculate, including lag zero
Returns
-------
ma : ndarray
coefficients of lag polynomial
"""
# hide import inside function until we use this heavily
from scipy.special import gammaln
j = np.arange(n)
return np.exp(gammaln(d + j) - gammaln(j + 1) - gammaln(d))
# moved from sandbox.tsa.try_fi
[docs]def lpol_fiar(d, n=20):
"""AR representation of fractional integration
.. math:: (1-L)^{d} for |d|<0.5 or |d|<1 (?)
Parameters
----------
d : float
fractional power
n : int
number of terms to calculate, including lag zero
Returns
-------
ar : ndarray
coefficients of lag polynomial
Notes:
first coefficient is 1, negative signs except for first term,
ar(L)*x_t
"""
# hide import inside function until we use this heavily
from scipy.special import gammaln
j = np.arange(n)
ar = -np.exp(gammaln(-d + j) - gammaln(j + 1) - gammaln(-d))
ar[0] = 1
return ar
# moved from sandbox.tsa.try_fi
[docs]def lpol_sdiff(s):
"""return coefficients for seasonal difference (1-L^s)
just a trivial convenience function
Parameters
----------
s : int
number of periods in season
Returns
-------
sdiff : list, length s+1
"""
return [1] + [0] * (s - 1) + [-1]
[docs]def deconvolve(num, den, n=None):
"""Deconvolves divisor out of signal, division of polynomials for n terms
calculates den^{-1} * num
Parameters
----------
num : array_like
signal or lag polynomial
denom : array_like
coefficients of lag polynomial (linear filter)
n : None or int
number of terms of quotient
Returns
-------
quot : ndarray
quotient or filtered series
rem : ndarray
remainder
Notes
-----
If num is a time series, then this applies the linear filter den^{-1}.
If both num and den are both lag polynomials, then this calculates the
quotient polynomial for n terms and also returns the remainder.
This is copied from scipy.signal.signaltools and added n as optional
parameter.
"""
num = np.atleast_1d(num)
den = np.atleast_1d(den)
N = len(num)
D = len(den)
if D > N and n is None:
quot = []
rem = num
else:
if n is None:
n = N - D + 1
input = np.zeros(n, float)
input[0] = 1
quot = signal.lfilter(num, den, input)
num_approx = signal.convolve(den, quot, mode="full")
if len(num) < len(num_approx): # 1d only ?
num = np.concatenate((num, np.zeros(len(num_approx) - len(num))))
rem = num - num_approx
return quot, rem
_generate_sample_doc = Docstring(arma_generate_sample.__doc__)
_generate_sample_doc.remove_parameters(["ar", "ma"])
_generate_sample_doc.replace_block("Notes", [])
_generate_sample_doc.replace_block("Examples", [])
[docs]class ArmaProcess(object):
r"""
Theoretical properties of an ARMA process for specified lag-polynomials.
Parameters
----------
ar : array_like
Coefficient for autoregressive lag polynomial, including zero lag.
Must be entered using the signs from the lag polynomial representation.
See the notes for more information about the sign.
ma : array_like
Coefficient for moving-average lag polynomial, including zero lag.
nobs : int, optional
Length of simulated time series. Used, for example, if a sample is
generated. See example.
Notes
-----
Both the AR and MA components must include the coefficient on the
zero-lag. In almost all cases these values should be 1. Further, due to
using the lag-polynomial representation, the AR parameters should
have the opposite sign of what one would write in the ARMA representation.
See the examples below.
The ARMA(p,q) process is described by
.. math::
y_{t}=\phi_{1}y_{t-1}+\ldots+\phi_{p}y_{t-p}+\theta_{1}\epsilon_{t-1}
+\ldots+\theta_{q}\epsilon_{t-q}+\epsilon_{t}
and the parameterization used in this function uses the lag-polynomial
representation,
.. math::
\left(1-\phi_{1}L-\ldots-\phi_{p}L^{p}\right)y_{t} =
\left(1+\theta_{1}L+\ldots+\theta_{q}L^{q}\right)\epsilon_{t}
Examples
--------
ARMA(2,2) with AR coefficients 0.75 and -0.25, and MA coefficients 0.65 and 0.35
>>> import statsmodels.api as sm
>>> import numpy as np
>>> np.random.seed(12345)
>>> arparams = np.array([.75, -.25])
>>> maparams = np.array([.65, .35])
>>> ar = np.r_[1, -arparams] # add zero-lag and negate
>>> ma = np.r_[1, maparams] # add zero-lag
>>> arma_process = sm.tsa.ArmaProcess(ar, ma)
>>> arma_process.isstationary
True
>>> arma_process.isinvertible
True
>>> arma_process.arroots
array([1.5-1.32287566j, 1.5+1.32287566j])
>>> y = arma_process.generate_sample(250)
>>> model = sm.tsa.ARIMA(y, (2, 0, 2), trend='n').fit(disp=0)
>>> model.params
array([ 0.79044189, -0.23140636, 0.70072904, 0.40608028])
The same ARMA(2,2) Using the from_coeffs class method
>>> arma_process = sm.tsa.ArmaProcess.from_coeffs(arparams, maparams)
>>> arma_process.arroots
array([1.5-1.32287566j, 1.5+1.32287566j])
"""
# TODO: Check unit root behavior
def __init__(self, ar=None, ma=None, nobs=100):
if ar is None:
ar = np.array([1.0])
if ma is None:
ma = np.array([1.0])
with warnings.catch_warnings():
warnings.simplefilter("ignore", np.ComplexWarning)
self.ar = array_like(ar, "ar")
self.ma = array_like(ma, "ma")
self.arcoefs = -self.ar[1:]
self.macoefs = self.ma[1:]
self.arpoly = np.polynomial.Polynomial(self.ar)
self.mapoly = np.polynomial.Polynomial(self.ma)
self.nobs = nobs
[docs] @classmethod
def from_roots(cls, maroots=None, arroots=None, nobs=100):
"""
Create ArmaProcess from AR and MA polynomial roots.
Parameters
----------
maroots : array_like
Roots for the MA polynomial
1 + theta_1*z + theta_2*z^2 + ..... + theta_n*z^n
arroots : array_like
Roots for the AR polynomial
1 - phi_1*z - phi_2*z^2 - ..... - phi_n*z^n
nobs : int, optional
Length of simulated time series. Used, for example, if a sample
is generated.
Returns
-------
ArmaProcess
Class instance initialized with arcoefs and macoefs.
Examples
--------
>>> arroots = [.75, -.25]
>>> maroots = [.65, .35]
>>> arma_process = sm.tsa.ArmaProcess.from_roots(arroots, maroots)
>>> arma_process.isstationary
True
>>> arma_process.isinvertible
True
"""
arpoly = np.polynomial.polynomial.Polynomial.fromroots(arroots)
mapoly = np.polynomial.polynomial.Polynomial.fromroots(maroots)
# As from_coeffs will create a polynomial with constant 1/-1,(MA/AR)
# we need to scale the polynomial coefficients accordingly
return cls(np.r_[1, -np.asarray(-1 * arpoly.coef[1:] / arpoly.coef[0])],
np.r_[1, np.asarray(mapoly.coef[1:] / mapoly.coef[0])],
nobs=nobs)
[docs] @classmethod
def from_coeffs(cls, arcoefs=None, macoefs=None, nobs=100):
"""
Create ArmaProcess from an ARMA representation.
Parameters
----------
arcoefs : array_like
Coefficient for autoregressive lag polynomial, not including zero
lag. The sign is inverted to conform to the usual time series
representation of an ARMA process in statistics. See the class
docstring for more information.
macoefs : array_like
Coefficient for moving-average lag polynomial, excluding zero lag.
nobs : int, optional
Length of simulated time series. Used, for example, if a sample
is generated.
Returns
-------
ArmaProcess
Class instance initialized with arcoefs and macoefs.
Examples
--------
>>> arparams = [.75, -.25]
>>> maparams = [.65, .35]
>>> arma_process = sm.tsa.ArmaProcess.from_coeffs(ar, ma)
>>> arma_process.isstationary
True
>>> arma_process.isinvertible
True
"""
arcoefs = [] if arcoefs is None else arcoefs
macoefs = [] if macoefs is None else macoefs
return cls(
np.r_[1, -np.asarray(arcoefs)],
np.r_[1, np.asarray(macoefs)],
nobs=nobs,
)
[docs] @classmethod
def from_estimation(cls, model_results, nobs=None):
"""
Create an ArmaProcess from the results of an ARIMA estimation.
Parameters
----------
model_results : ARIMAResults instance
A fitted model.
nobs : int, optional
If None, nobs is taken from the results.
Returns
-------
ArmaProcess
Class instance initialized from model_results.
See Also
--------
statsmodels.tsa.arima.model.ARIMA
The models class used to create the ArmaProcess
"""
nobs = nobs or model_results.nobs
return cls(
model_results.polynomial_reduced_ar,
model_results.polynomial_reduced_ma,
nobs=nobs,
)
def __mul__(self, oth):
if isinstance(oth, self.__class__):
ar = (self.arpoly * oth.arpoly).coef
ma = (self.mapoly * oth.mapoly).coef
else:
try:
aroth, maoth = oth
arpolyoth = np.polynomial.Polynomial(aroth)
mapolyoth = np.polynomial.Polynomial(maoth)
ar = (self.arpoly * arpolyoth).coef
ma = (self.mapoly * mapolyoth).coef
except:
raise TypeError("Other type is not a valid type")
return self.__class__(ar, ma, nobs=self.nobs)
def __repr__(self):
msg = "ArmaProcess({0}, {1}, nobs={2}) at {3}"
return msg.format(
self.ar.tolist(), self.ma.tolist(), self.nobs, hex(id(self))
)
def __str__(self):
return "ArmaProcess\nAR: {0}\nMA: {1}".format(
self.ar.tolist(), self.ma.tolist()
)
[docs] @Appender(remove_parameters(arma_acovf.__doc__, ["ar", "ma", "sigma2"]))
def acovf(self, nobs=None):
nobs = nobs or self.nobs
return arma_acovf(self.ar, self.ma, nobs=nobs)
[docs] @Appender(remove_parameters(arma_acf.__doc__, ["ar", "ma"]))
def acf(self, lags=None):
lags = lags or self.nobs
return arma_acf(self.ar, self.ma, lags=lags)
[docs] @Appender(remove_parameters(arma_pacf.__doc__, ["ar", "ma"]))
def pacf(self, lags=None):
lags = lags or self.nobs
return arma_pacf(self.ar, self.ma, lags=lags)
[docs] @Appender(
remove_parameters(
arma_periodogram.__doc__, ["ar", "ma", "worN", "whole"]
)
)
def periodogram(self, nobs=None):
nobs = nobs or self.nobs
return arma_periodogram(self.ar, self.ma, worN=nobs)
[docs] @Appender(remove_parameters(arma_impulse_response.__doc__, ["ar", "ma"]))
def impulse_response(self, leads=None):
leads = leads or self.nobs
return arma_impulse_response(self.ar, self.ma, leads=leads)
[docs] @Appender(remove_parameters(arma2ma.__doc__, ["ar", "ma"]))
def arma2ma(self, lags=None):
lags = lags or self.lags
return arma2ma(self.ar, self.ma, lags=lags)
[docs] @Appender(remove_parameters(arma2ar.__doc__, ["ar", "ma"]))
def arma2ar(self, lags=None):
lags = lags or self.lags
return arma2ar(self.ar, self.ma, lags=lags)
@property
def arroots(self):
"""Roots of autoregressive lag-polynomial"""
return self.arpoly.roots()
@property
def maroots(self):
"""Roots of moving average lag-polynomial"""
return self.mapoly.roots()
@property
def isstationary(self):
"""
Arma process is stationary if AR roots are outside unit circle.
Returns
-------
bool
True if autoregressive roots are outside unit circle.
"""
if np.all(np.abs(self.arroots) > 1.0):
return True
else:
return False
@property
def isinvertible(self):
"""
Arma process is invertible if MA roots are outside unit circle.
Returns
-------
bool
True if moving average roots are outside unit circle.
"""
if np.all(np.abs(self.maroots) > 1):
return True
else:
return False
[docs] def invertroots(self, retnew=False):
"""
Make MA polynomial invertible by inverting roots inside unit circle.
Parameters
----------
retnew : bool
If False (default), then return the lag-polynomial as array.
If True, then return a new instance with invertible MA-polynomial.
Returns
-------
manew : ndarray
A new invertible MA lag-polynomial, returned if retnew is false.
wasinvertible : bool
True if the MA lag-polynomial was already invertible, returned if
retnew is false.
armaprocess : new instance of class
If retnew is true, then return a new instance with invertible
MA-polynomial.
"""
# TODO: variable returns like this?
pr = self.maroots
mainv = self.ma
invertible = self.isinvertible
if not invertible:
pr[np.abs(pr) < 1] = 1.0 / pr[np.abs(pr) < 1]
pnew = np.polynomial.Polynomial.fromroots(pr)
mainv = pnew.coef / pnew.coef[0]
if retnew:
return self.__class__(self.ar, mainv, nobs=self.nobs)
else:
return mainv, invertible
[docs] @Appender(str(_generate_sample_doc))
def generate_sample(
self, nsample=100, scale=1.0, distrvs=None, axis=0, burnin=0
):
return arma_generate_sample(
self.ar, self.ma, nsample, scale, distrvs, axis=axis, burnin=burnin
)


How many days until Christmas 2016?
We provide you the very best Christmas countdown clock and hope you find this countdown useful. You can watch the countdown will show you how many days, hours, minutes and seconds ticking down to 0. Besides, We bring you the best advice and tips about how to decorate a Christmas tree, how to prepare Christmas meals, how to choose Christmas gifts, how to enjoin Christmas games party and so on. We hope that howmanydaystillchristmas.net will bring you the best information that helps you have a happy and warm Christmas.
Wish you are happy and merry Christmas.
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Buying the best gift for boys on Christmas Holiday is a quite task. Every type of boy has a different hobby in choosing...


-
Conference Rooms
In sed nibh mauris. Curabitur scelerisque dignissim viverra. Etiam interdum enim nec turpis.
Inclusions:
1 long wooden table 10 cushioned office chairs 1 round of coffee with pastry per person
Rates:
$100 per 3 hours $25 per succeeding hour
-
Conference Rooms
Donec odio nunc, consectetur fringilla tincidunt nec, cursus vitae ipsum.
Inclusions:
Theatre with up to 300 seating Projector Surround sound system
Rates:
z $500 for 3 hours $200 per succeeding hour
-
Birthday Parties
In sed nibh mauris. Curabitur scelerisque dignissim viverra. Etiam interdum enim nec turpis.
Inclusions:
50 seats and 5 round tables Sound system DJ Decorations
Rates:
$100 per 3 hours $25 per succeeding hour
-
Plays
Donec odio nunc, consectetur fringilla tincidunt nec, cursus vitae ipsum.
Inclusions:
Theatre with up to 300 seating Projector Surround sound system
Rates:
$500 for 3 hours $200 per succeeding hour


--- title: Jacobs VAULT keywords: fastai sidebar: home_sidebar nb_path: "nbs/index.ipynb" ---
VAULT contains scripts and notebooks that (a) ingest and characterize the provided AIS shipping tracks and satellite TLE data, (b) find "hits" - satellites are visible for a given ship position, and (c) highlight coverage gaps / flaws in the data. Here is an example starmap showing satellites visible from one point on track, colored by quality of the satellite's TLE data.
Note: we still need to build these packages so this works.
We recommend installing from conda, but any of these should work:
| Anaconda | Pip | Git |
|---|---|---|
conda install -c <CONDA CHANNEL> jacobs-vault gh anaconda |
pip install jacobs-vault |
git clone git@github.com:cmorris-jacobs/jacobs-vault.git |
Alternatives:
conda install -c <CHANNEL> -c jacobs-vault
git clone https://github.com/cmorris-jacobs/jacobs-vault.git
Change to the jacobs-vault
folder and ensure that data/
contains or points to the VAULT data. (SAY MORE!) Then explore these options:
cd demo
. run.sh
May require linking demo/data
to the data folder, e.g. ln -s ../data ./
.
Activate the vault
Python virtual environment and start a new jupyter kernel.
conda env -f environment.yml
conda activate vault
jupyter notebook
You can now explore and run the notebooks in the nbs/
folder.
Jacobs-VAULT is the result of a hackathon challenge, so in addition to a working demo and analysis notebooks, it still has exploratory paths and alternate approaches. Folders are in three rough groups:
etl
- Original ETL scripts, mostly Spark SQL and Hive.ais-analytics
- Subproject Spark to analyze AIS data. Alternate ETL.geotransformer
- Subproject using Spark to analyze TLE data. Alternate ETL. Directly calls sgp4
and the astropy
package, instead of skyfield
.A mix of exploratory notebooks and literate programming notebooks that generate Python modules and documentation (including this README) via the nbdev
package. Controlled by the toplevel Makefile
, using the vault
virtual environment captured in environment.yml
.
nbs
- Toplevel notebooks, generate docs, modules, and README.jacobs_vault
- Python modules generated from nbs/
by nbdev
packagedocs
- Documentation generated from nbs/
by nbdev
packagedata
- (See "Demo Folders".)The demo supports a notebook with an interactive map-based walktrhough of getting AIS tracks, and querying a track for satellite coverage, using the Skyfield
package for ephemeris calculations.
demo
- As much of the demo as possible lives under here, for completeness.data
- Daily satellite files stored (or linked) asdata/VAULT_Data/TLE_daily/
year/
MM/
nn.tab.gz
. Used by the demo and other notebooks & scripts. autoencoder
- Exploratory work using a PyTorch deep network to discover high-level features and pattersn in the AIS data.ais-kml
- Concurrent visualization attempt using OpenSphere.hitttestservice
- First attempt to wrap HitTest code into a web service.scripts
- A collection of scripts, esp. SQL queries.Some code expects an Apache Spark setup with Hive and Hadoop available. The ais-analytics
and geotransformer
folders contain cookie-cutter
setups with scripts that
will start Spark-enabled jupyter notebooks, or launch a spark job with the required virtual environment.
After cloning the repository, use the conda
package manager to install the main dependencies. (We provide files for pip
, but we recommend conda.)
conda env create -f environment.yml
Key top-level packages fall in three broad categories:
Tests are automatically extracted from notebooks in nbs/
. To run the tests in parallel, launch:
nbdev_test_nbs
or make test
For all the tests to pass, you'll need to install the following optional dependencies:
pip install ...
Tests are written using nbdev
, for example see the documentation for hit_quality
or viz
.
You can clone the repository and install dependencies with ...
git clone https://github.com/cmorris-jacobs/jacobs-vault
pip install -e "jacobs-vault[dev]"
After you clone this repository, please run nbdev_install_git_hooks
in your terminal. This sets up git hooks, which clean up the notebooks to remove the extraneous stuff stored in the notebooks (e.g. which cells you ran) which causes unnecessary merge conflicts.
Before submitting a PR, check that the local library and notebooks match. The script nbdev_diff_nbs
can let you know if there is a difference between the local library and the notebooks.
nbdev_build_lib
or make jacobs-vault
.nbdev_update_lib
.We do not yet have official docker containers, but when done they should be available from the github site.


Parallaxed
Easily apply parallax effects to page elements as you scroll.
Selectors
Pass in a string, or array, of the selectors you’d like to target
Enter / Exit
Setup callbacks for when an element enters or exits the viewport.
Progress
Progress information is also supplied in case there are styles that need to be set based on the elements position within/through the viewport.
Resize
In addition to scroll
events triggering the callbacks, resize
events can trigger the callbacks as well.
Simple
There could be a lot of other default stuff that this could do, but I was trying to keep this simple.
Let me know if there’s something else you’d like to see this do, @ryanhefner.


Hello World!
Welcome to this website, I hope you are enjoying your time here !
I am really happy to provide this webste to you all !
Being my first attempt to create website using Angular2, I am happy with the result
Doing this website was really enjoyable, working to produce content about a game I really like was nice too
I will keep working on it, don't forget to pass by sometimes so :D
Well, I didn't introduce myself,
My name is Laurent, for you on internet I will be Lismeth, actually 22 years old, and in the end of my first year of computer science study
Until now I have only code using C, I saw a bit of Ruby, and next year I am going to learn C++ in my classes.
Before entering my school, I was playing a lot, not too many games
I was more the kind who start a game and stuck to it until it annoy me
As for now, I would like in the future to work as a developer in the video games industry
As main hobbies, I have :
And of course coding


In addition to displaying data in a chart, the loading, failed, and empty states should be accounted for. The interactive example below demonstrates an artifically long loading time, with options to render a failed or empty template.


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Hi! I'm a parrot. I'll repeat back what you say!
What would you like me to say?
How many times should I say it?
Submit


Taking the Coursera Deep Learning Specialization, Structuring Machine Learning Projects course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details.
Table of Contents
ML Strategy
Introduction to ML Strategy
Why ML Strategy
Important to know how to structure your machine learning project. This will prevent time wasted pursuing ineffective optimization.
Ideas:
- Collect more data
- Collect more diverse training set
- Train algorithm longer with gradient descent
- Try Adam instead of Gradient Descent
- Try a bigger/smaller network
- Try Dropout
- Add $L_2$ regularization
- Network Architecture
- Activation functions
- # hidden units
- etc…
These are ways of analyzing your problem to determine what idea to try.
Orthogonalization
There are so many things to try and change! Orthogonalization is the understanding of what tuning changes what effect.
Chain of Assumptions in Machine Learning
- Fit Training Set Well on Cost Function
- Bigger Network
- ADAM instead of Gradient Descent
- Fit Dev Set Well on Cost Function
- Regularization
- Getting a bigger training set
- Fit Test Set Well on Cost Function
- Getting a bigger dev set
- Performs Well in Real World
- Change dev set or cost function
Setting Up Your Goal
Single Number Evaluation Metric
Machine Learning is an emperical process. It consistantly iterates between Idea > Code > Experiment
.
Dog Classification Example
| Precision | Recall |
|---|---|
| Of examples recognized as dog, what % are actually dogs? | What % of actual dogs are correctly recognized? |
Use an $F_1$ Score. “Average” of Precision P and Recall R.
$$ \dfrac{2}{(1/p) + (1/r)} \leftarrow \text{“Harmonic Mean”} $$
| Classifier | Precision | Recall | F1 Score |
|---|---|---|---|
| A | 95% | 90% | 92.4% |
| B | 98% | 85% | 91.0% |
Satisficing and Optimizing Metric
Another classification example. Accuracy may be an F1 Score.
| Classifier | Accuracy | Running Time |
|---|---|---|
| A | 90% | 80ms |
| B | 92% | 95ms |
| C | 95% | 1500ms |
$$ \text{Cost} = \text{Accuracy} - 0.5 * \text{Running Time} $$
Maximize accuracy, subject to running time $\leq$ 100 ms.
In this example, Accuracy is the optimizing metric. Running Time is the satisficing metric.
Given N metrics: Pick 1 optimizing metric, pick N-1 satisficing metrics.
Train/Dev/Test Distributions
Cat classification example development (hold out cross validation set) and test sets.
Example: Regions
- US
- UK
- Other Europe
- South America
- India
- China
- Other Asia
- Australia
Make sure your dev and test sets come from the same distribution.
Rather than making dev sets certain regions and test set certain regions, take all of the regions and randomly shuffle the data into the dev/test set.
Takeaway
Chose a dev set and test set to reflect data you expect to get in the future and consider important to do well on.
Size of the Dev and Test Sets
Old way of splitting data is approximately 70% Train, 30% Test. (or 60% Train, 20% Dev, 20% Test). Dataset size is around 100, 1000, 10000.
Given a million training examples, might be reasonable to have (98% Train, 1% Dev, 1% Test). Once again, ensure distribution is random.
Set your test set to be big enough to give high condifence in the overall performance of your system.
When to Change Dev/Test Sets and Metrics
Classification Example.
Metric: Classification Error
- Algorithm A: 3% error
- lets through a lot of pornographic images (classifies boobs as a cat, for instance)
- Even though it’s 2% better, it’s a worse algorithm
- Alforithm B: 5% error
Metric + Dev prefer A. You and Users prefer B.
$$ \text{Error} \rightarrow \dfrac{1}{\text{M}_{\text{dev}}} \sum \limits ^{\text{M}_{\text{dev}}} _ {i=1} \mathcal{L} \{ y^{(i)}_{\text{pred}} \neq y^{(i)} \} $$
- Predicted Value is (0/1)
May add a weight value:
$$ \text{Error} \rightarrow \dfrac{1}{\text{M}_{\text{dev}}} \sum \limits ^{\text{M}_{\text{dev}}} _ {i=1} w^{(i)} \mathcal{L} \{ y^{(i)}_{\text{pred}} \neq y^{(i)} \} $$
$w^{(i)}$ is 1 if $x^{(i)}$ is non-porn or set $w^{(i)}$ is 10 if $x^{(i)}$ is porn.
- So far we’ve only discussed ho to define a metric to evaluate classifiers. (Place the target)
- Worry separately about how to do well on this metric afterwards. (Hit the target.)
Comparing to Human-Level Performance
Why Human-level Performance?
Progress tends to be relatively rapid towards human level performance, then after surpassing human level performance the accuracy gains tend to plateau.
The hope is this achieves theoretical optimal performance (Bayes optimal error). Bayes Optimal Error is the best possible error such that there is no way a function mapping $x \rightarrow y$ exists that can perform better.
Human level performance, for many cases, is usually very close to Bayes optimal error.
Also, below human level performance, you can perform the following:
- Get labeled data from humans.
- Gain insight from manual error analysis: Why did a person get this right?
- Better analysis of bias/variance.
Avoidable Bias
Classification Example:
| Metric | Example A | Example B |
|---|---|---|
| Humans | 1% | 7.5% |
| Training Error | 8% | 8% |
| Dev Error | 10% | 10% |
| Reccomendation | Focus on Bias | Focus on Variance |
Example A, learning algorithm isn’t even fitting the training data well.
In Example B, learning algorithm performs very close to human performance. Might be a variance problem.
Think of Human-Level error as a proxy, or estimate, for Bayes error.
- Difference between Humans (Bayes) and Training Error is Avoidable Bias.
- If this value is large, maybe use a bigger neural network?
- Difference between Training and Error is Variance.
- If this value is large, you’re overfitting your training data.
Understanding Human-level Performance
Metical Image classification Example
Suppose:
- Typical Human: 3% error
- Typical Doctor: 1% error
- Experienced doctor: 0.7% error
- Team of experienced doctors: 0.5% error
What is “human-level” error? Recall that the “human-error” is a proxy for Bayes error. It is the team of experienced doctors. $\text{Bayes error} \leq 0.5%$
Use the appropraite metric for your application requirements. Maybe it is sufficent to beat the typical doctor. Maybe you must optimize for the team of experienced doctors.
Surpassing Human-level Performance
| Metric | Example A | Example B |
|---|---|---|
| Team of Humans | 0.5% | 0.5% |
| Humans | 1% | 1% |
| Training Error | 0.6% | 0.3% |
| Dev Error | 0.8% | 0.4% |
In Example B, you don’t have enough information to tell if you are overfitting or if you’re not fitting enough. No longer have objective metric to determine Bayes error.
Problems where ML significantly surpasses human-level performance
- Online advertising
- Product recommendations
- Logistics (predicting transit time)
- Loan approvals
All of these examples learn from structured data. These are not natural perception problems. There are some natural perception problems that machines have solved.
- Speech recognition
- Some image recognition
- Medical (ECG, Skin Cancer)
Improving your Model Performance
The Two Fundamental Assumptions of Supervised Learning
- You can fit the training set pretty well. You acheive low avoidable bias.
- The training set performance generalizes pretty well to the dev/test set. (Variance)


Cooking
Have you ever considered cooking as a series of patterns? That when a cook follows a recipe he/she is folowing an algorithm?
Computational thinking is a necessary fundamental skill for everyone. It's as important as reading, writing, and arithmetic. The need is evidenced by MIT's recent report on computational thinking. The president's Computer Science for All initiative recognizes the same in k-12, "Our economy is rapidly shifting, and both educators and business leaders are increasingly recognizing that computer science (CS) is a “new basic” skill necessary for economic opportunity and social mobility." Join us in a journey to the fun world of computation!
Have you ever considered cooking as a series of patterns? That when a cook follows a recipe he/she is folowing an algorithm?
Have you ever considered that packing a suitcase is an optimization problem? To pack we must decide between weight, size, and volume.
Have you ever considered that finding a coin is a search pattern? To find a coin you must first identify the type of coin, the size, and the color. Each question establishing a different search parameter.
The event will last a week, Jul 31 - Aug 4
How do you make sure the shuttle bus follows the shortest route to pick up all the students?
How to organize the rotation of the schedules in a restaurant to ensure a good service and profit.
How to optimize an interview schedule?
Time dedicated to the project
The event will last a week, Jul 31 - Aug 4
Education is the igniting of a flame, not the filling of a vessel. Our goal is to be the spark.
My name is Isabella Voelkl and I am a senior at MIT, graduating in June. I am majoring in environmental engineering and management science. I will be working for Anheuser Busch after graduation, in a rotational management program. I am very interested in getting more women involved in computer science programs as I think it is a field that is only growing and gaining importance. I would love to see more females appear in this male dominated field, especially those with a diverse background.
My name is Silvia Vázquez and I graduated from MIT with a specialization in Mechanical Engineering with a concentration in Entrepreneurship and a secondary specialization in Latin American Studies. I like to teach and work with students interested in STEM fields!
Abel Sánchez is a scientist, director of research, and director of the MIT Geospatial Laboratory. His specialty areas are cybersecurity, Internet of Things (IOT), and data science.
I am a senior at Wellesley High School. For the past two years I have worked at the Broad Institute for Cancer Research at MIT and Harvard where I created visualizations and worked on infrastructure for cancer analysis and data management. I am very excited about the opportunity to share my passion for computational thinking with secondary and high school students.
If you have any questions, you can contact us using the following email address.


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THE MOTHER AND THE WHORE (director: Jean Eustache; cast: Jean-Pierre Léaud (Alexandre), Bernadette Lafont (Marie), Françoise Lebrun (Veronika), Isabelle Weingarten (Gilberte), Jacques Renard (friend of Alexandre), 1973-France)
Having lived through and somehow survived the cultural revolutions of the late 60s and early 70s American style, not fully aware of what the French upheaval was all about, except to notice that there was more of an identification over there between the working-class and intellectuals, and not the same usage of hallucinogenic drugs and anti-Vietnam protests that was sweeping the American landscape. I was therefore most interested that this film tackled the subject of revolution totally by looking through the eyes of sexual change, following the trail of a professional idler, in his late 20s, Alexandre (Léaud), who manages not to work, own an apartment or possess many material things, doing this by living off the women he has affairs with, who supply him with food, an apartment to share, clothes, and perhaps even some money.
We first meet Alexandre as he arrives at the Sorbonne to get confirmation that his relationship is all over with the coed who just dumped him for another man, Gilberte (Isabelle), who tells him it is definetly over between them, she is marrying this other man. Alexandre pleads his case, professing his love for her, cunningly trying to win her back, but to no avail. We subsequently learn that Gilberte was a virgin when he met her, and even though he treated her badly, he still believes he loves her very much. Despondent with her decision, he comes out of the bohemian cafe he hangs out in, Deux Magots, and eyes this very attractive blond. His pick-up line to her is, "I don't have time to have a drink with you but can I have your telephone number."   Alexandre chimes in, that he also likes her when she smiles.
When Alexandre returns to the apartment he shares with his lover, the mother of the story, the 30ish, well-endowed boutique owner, Marie (Lafont), who in real-life was the ex-lover of the director of the film, who never acted before, but has a strong sexual presence in this film, he tells her most everything about what he does, including his new lady friend.
After we see how rakish and supposedly cool Alexandre is, and hear his amusing rantings about life, philosophy and love, (he sure loves to talk nonstop) we also discover that he has a double-standard about sex, being not so free and easy when it comes to having his women folk do the same thing he does to them.
This "take me seriously" three and a half-hour film is surprisingly never dull, and is always diverting and titillating in its sexual characterization of these three rather likable and vulnerable participants in experimental living, that has its ups and downs as far as their compulsive libertine tendencies go. We follow the relationship over a few weeks grow between Veronika and him, in-between bouts of too much drinking, chain smoking and sharing a bed with the at-first antagonistic Marie, but a soon to be more accepting Marie, until her true feelings emerge again, as she attempts suicide but is stopped by Alexandre, and then we are treated to hearing one of the best "I am a whore" speeches I ever heard, as Veronike fesses up to wanting to be loved, that sex is really nothing to her even though she enjoys coming the maximum number of times, (she has some kind of thing about saying maximum whenever she can) but is not ashamed that she is free enough to go to bed with whomever she desires, claiming that she is better than the hypocrite housewife who dreams of screwing the plumber or some stranger, but never acts on her desire.
Confessions and half-baked philosophies come pouring out of this film at the drop of a fancy scarf, as Alexandre states he can't love a woman who doesn't love him. While Marie demands that she could have other relationships also, just like he does. With all the so-called sexual freedom they have, it is discernible that there seems to be a lot of tension around them, making one wonder really if they are enjoying themselves more than their bourgeoisie counterparts are.
 All the quirkiness and nonsensical palaver of childish 1970s rebellion is seen here, along with a strong sense of connecting with others of the similar mind-set, allowing sex to be the liberating experience that it should be for them, if what they are into is to be taken seriously as some kind of revoluntionary statement.
The film culminates with Alexandre forcing himself into making a choice between the two women who love him, and choosing the more youthful blond when she is at her most vulnerable. He asks Veronika to marry him as she is vomiting into a bucket, as the scene fades out with him sitting in her apartment, about to become a married man. This is not your typical Hollywood romantic happy ending for a film, but one that is very fitting for this film.
It is worth noting, that the director of this seminal work, Jean Eustache, killed himself at age 42 in 1981, and that certainly some parts of this film were based on his real-life experiences.
The acting is superb.  The only thing that is impressive about his intellect, was that he is often seen carrying a book by Proust under his arms as he goes to the cafe to read, which is bookmarked to the story about "The Captive." All the women are special in their roles, making us look at them bodily-wise as well as at their mind. I see very little differences between the 1973 sexual mores and the 1999 ones. The later year is when I saw this film on video for the first time.
How this film plays out, depends on one's perspective about sex and relationships, and how important they are; if, perhaps, they are the most important worldly concerns one has, even more important than money or career or philosophy, then this film should prove to not only be entertaining but mindful. If thought of, as otherwise, then the film could still prove to be a most revealing study of how women look at men, especially men who think they are real smoothies. It also should indicate that women often choose these smoothies over other men because they like to play games with men and feel comfortable being around such men.
What the film accomplishes, is to not rely on its plot and on any particular message, but to allow things to happen as they normally would, relying on the compelling nature of the actors to garner our interest, and our natural curiousity about what makes us love one another until it reaches the surface of some common understanding. That this was successfully done, can be attested by me from my own experiences and how important sex was for me in forming a relationship during the 1970s, it also seemed to shape my attitudes in love and in the sense I had of the world. Sex always seems to be revolutionary because it is never static, it is never quite done in the same way.
This film shows no signs of its age, it is impressive enough for me to watch today as it was for me to live vicariously through it in my thoughts of the 1970s. I think that the filmmaker could do no wrong in this film, every gesture and endless chat seemed so terribly right. And if the film is not an optimistic one, then that is just the way the filmmaker saw the situation of the lovers.
REVIEWED ON 4/17/99 GRADE: A
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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We understand that shopping can be a hassle some times, and shopping for good deals is even harder. That is why we designed Metagrocer - to help you plan your shopping trip, get coupons for your items, and use them at the store. Whether you are making a quick run to the store for a few items, planning your weekly grocery shopping, or spontaneously deciding to buy yourself a snack as you walk by the store, Metagrocer will help you find the best deals.
Observed and interviewed 3 UW students while they went on their grocery shopping trips.
Developed six tasks that could be performed with our app.
Sketched three interface designs that supported the tasks, and selected one to be further refined.
Created two storyboards to convey the functionality of the design.
Created paper prototype that conveyed the critical aspects of our design.
Received two heuristic evaluations and revised the paper prototype based on the evaluation results.
Conducted three usability testing sessions with the paper prototype.
Updated prototype with the feedback and created digital mockup.
Includes design research, task development, design generation, and final design selection.
Includes paper prototyping, inspection, user testing, and final digital mockup.
This poster displays the key parts of our design, and the process we used to create it.
Civil & Environmental Engineering
Computer Science & Engineering
Computer Science & Engineering
Human Centered Design & Engineering
We come from a range of disciples, experiences,
and backgrounds, and together we form a powerful team.


Apply for a basic criminal record check
Use this service to apply for a basic criminal record check.
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Anyone over 16 can apply, unless you:
- live in Scotland or Northern Ireland
- have been asked to get a standard or enhanced DBS check for a job
How much it costs
It costs £25 to get a basic criminal record check.What you will need
Before you apply make sure you have:
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Transaction Request
Order No.
Total Amount
Currency Name
Transaction Type (S/P/R)
Recurring Period(NA/W/M)
Recurring Day
No Of Recurring
Merchant ID
Encryption Key
Add Field 1
Add Field 2
Add Field 3
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Response Url


Spring is here!
Click here for 10% off your Spring order of $50 or more.
Welcome
Welcome to Florana's! Our owners are a team of flower-lovers who have over 50 years combined experience in the floral business. We provide a wide variety of succulents, cactus plants, orchids and floral arrangements.
From a simple "Thank you" gift to more formal occasions such as weddings, we are bound to have something to fit your needs, so please browse our site and stop by our shop some time. We hope to sell arrangements online in the near future, so stay tuned!


§49-21 General laws applicable. The provisions of part III of chapter 47, relating to lost, stolen, destroyed, or defaced bonds, and to lost, stolen, destroyed, or defaced coupons, to the extent that they are applicable, shall apply to revenue bonds issued pursuant to this chapter. [L 1989, c 80, pt of §3]


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include <mztuni.hpp>
s = mztuni_seed(seed)
u = mztuni()
CD = 7654321./167777216.
Using this value in mztuni
does not produce the values in Table 4 of the reference.
Replacing the value above
by the value below does produce the values in Table 4:
CD = 7654321./16777216.
mat2cpp
, this routine
is not in the mat2cpp
namespace and has its own include file.
seed
has prototype
size_t seed
and is used to seed the random number generator.
The function call
CALL RSTART(I, J, K, L)
in the reference above corresponds to
seed = (((L-1)*168 + K-1)*168 + J-1)*168 + I + 1
Note the restriction that
I
,
J
,
K
and
L
are between 1
and 168
and not all equal to 1
; i.e.,
3 <= seed <= 796594177
seed
is equal to one,
the current time is used to choose the actual seed between three
and 796594177.
This actual seed is then used to seed the random number generator.
seed
is equal to zero,
no action is taken.
This case is useful for retrieving the previous seed used
to seed the random number generator.
s
has prototype
size_t s
It is the previous seed that was used to seed the random number generator.
If
seed > 0
,
the
s
corresponds to this call to mztuni_seed
.
u
has prototype
double u
It is sample of a random variable that is uniformly distributed between
the values zero and one.
mztuni
.


Money Play. Should Red double? If doubled, should White take?
pips Red=163 White=168
(CorrectAnswer= 0/0, ErrorScore= 0)
Red’s position looks threatening and given the right roll such as 44 he could do a lot of damage but even then, White is guaranteed an ace-point game.
From here the game could take many courses given it is so early.
Positions with only two points made in the attacker’s home board are very rarely passes and this one is no exception. In fact, passing would nearly be a quadruple blunder.
It is the double that is more in question. This is a double but only just because there are only a few market-losing sequences and because White cannot be blitzed.
From here the game could take many courses given it is so early.
Positions with only two points made in the attacker’s home board are very rarely passes and this one is no exception. In fact, passing would nearly be a quadruple blunder.
It is the double that is more in question. This is a double but only just because there are only a few market-losing sequences and because White cannot be blitzed.
Cube analysis 2-ply cubeless equity +0.492 0.639 0.269 0.033 - 0.361 0.084 0.005 Cubeful equities: 1. Double, take +0.739 2. Double, pass +1.000 (+0.261) 3. No double +0.675 (-0.064) Proper cube action: Double, take


Big Brother-style facial recognition installed at Bejing tube station
A metro station in Bejing has begun using a facial-recognition system to sort passengers during rush hour.
The Big Brother-esque technology has been set up at the entrance to the station in the capital city and collects data on individuals as they enter.
According to local media, it will be able to sort passengers into different channels based on a ‘credit system’ and therefore ease congestion at crowded times during the day.
Any bags larger than a sheet of A4 paper have to be scanned separately.
Zhan Minghui, director of the Beijing Rail Traffic Control Center, told an urban transportation forum that it will improve efficiency.
‘The technique aims to improve the efficiency of security checks and includes both body checks and luggage screening when large numbers of passengers enter the station,’ he said, according to the Hong Kong Free Press.
Advertisement Advertisement
Although the move is in keeping with China’s increased rollout of surveillance technology, not everyone is sold on the idea. Even some of China’s tightly-regulated media have raised some concerns. China Central Television has called for laws to be introduced to regulate the proliferation of surveillance tech.
The company behind the system at the metro station, Beijing Ruubypay Science and Technology Co, hasn’t confirmed whether or not it will be rolled out to other stations in the city.


open task manager of Chrome, click link button, observe the memory surge from 27M to 278M, and click unload, observe the memory will drop back to 27M in a minute. How fast the memory drop might be different in your machine .


I'm an ambitious person who has developed mature and responsible approach to any task that I undertake or situation I’m in.
interested in technology,thinks about software development as meaningful way to introduce knowledge .
Education :
Code Fellows,
Certificate - Advanced Software Development in Full-Stack JavaScript
Al-Balqa’a Applied University/ Princess Alia College,
Certificate – B.A. in English language literature


Welcome to Admin Panel, date :
Logout
Home
Security Panel
Rising
To Security Panel
To delete All Registered People
To delete All Entries
For Face Recognition
@Deep Learning
Press to Start
Only allow those person who are registered, with help of face recognition and stop those having no information in the Database
Train The Model
QR CODE cccccccccccccccc
@Python Library
Press to Start
Check people`s identity with QR Code.


Amabie: A twitter bot that tweets hope
Hope is lost. People are scared of the unknown virus, uncertain about what is to come, and are scared of deaths. People lost faith in the government /1 1/ From managing health infrastructure to managing migrant workers . State governments, although are pulling all-nighters and trying their best, are failing in the face of calamity. Amabie Amabie is a yokai with three legs, a body with scales and a beaked face.  Hope is lost.
Social isolation has taken a personal toll on me. My primal mind is not able to comprehend about the impact of uncertainty on me, but time will tell that.  It is a Japanese powerful creature called Yokai who said to a farmer that the coming three years will bear good harvest or an epidemic. Worried farmer asked if anything can be done in case of a pandemic. Kind Amabie instructed him to show people her picture to spare them from the disease.
This myth was adopted by Japanese artists and started drawing images of amabie and posting it on twitter. And what followed was beautiful. Artists, graphic designers, people who can draw started drawing pictures of Amabie and posted in bulk on twitter. These posts came along with prayers, blessings, and hopeful invocations to amabie. It is just beautiful /2 2/ The Japanese have this amazing way of using repetition to get rid of anxiety. At an individual level, the psycho-mythology of folding 2000 paper cranes gives person a wish is on the similar lines. It frees one from worry, anxiety, shows new direction and makes one hopeful. and was the exact thing I needed–Hope without rational convinces about vaccines or promises to get back to normal life. I decided to collect all the hope in one place and spread it to as many people as I can.
I made a twitter-bot to represent this collective hope.
In case the hyperlink does not work, here is the url https://twitter.com/amabie14 The automation of this interaction of retweeting every image of amabie fills me up with joy, as I made something that spreads hope! However mythical it be! Endnote: A rhetorical question. Can a twitter bot be art?
The initiation
One of the first tweets with a beautiful image of amabie:
新型コロナウィルス対策#アマビエ pic.twitter.com/aAXUZlwoNS
— しげおか秀満 (@shigeokahide) February 29, 2020


{% extends "layout.html" %} {% block content %} {{ super() }}
Please note that while dashboard functionality is open to the public, the ability to customize dashboards and surveys are available to employees of Miami-Dade County only. A miamidade.gov e-mail address is required.


| October 29th 2015 |
LLVM Developers’ Meeting |
slides |
video |
JF Bastien and Dan Gohman |
| November 10th 2015 |
BlinkOn 5: WebAssembly |
|
video |
Nick Bray |
| December 9th 2015 |
Emscripten and WebAssembly |
slides |
|
Alon Zakai |
| January 31st 2016 |
FOSDEM LLVM room |
slides |
|
JF Bastien and Dan Gohman |
| June 13th 2016 |
NYLUG Presents: WebAssembly: A New Compiler Target For The Web |
|
video |
Luke Wagner |
| July 7th 2016 |
VMSS16: A Little on V8 and WebAssembly |
slides |
video |
Ben L. Titzer |
| September 14th 2016 |
WebAssembly: birth of a virtual ISA |
|
video |
Ben Smith |
| September 21st 2016 |
C++ on the Web: Let’s have some serious fun |
|
video |
Dan Gohman |
| September 22nd 2016 |
WebAssembly: high speed at low cost for everyone |
paper |
|
Andreas Rossberg |
| November 7th 2016 |
Empire Node: How Webassembly Will Change the Way You Write Javascript |
|
video |
Seth Samuel |
| November 12th 2016 |
Chrome Dev Summit Advanced JS performance with V8 and WebAssembly |
|
video |
Seth Thompson |


 Then you've come to the right place. By entering in a URL in the text field below, we will create a tiny URL that
will not break in email postings
and
never expires
.
An example
Turn this URL:
https://www.amazon.com/Kindle-Wireless-Reading-Display-Globally/dp/B003FSUDM4/ref=amb_link_353259562_2?pf_rd_m=ATVPDKIK
X0DER&pf_rd_s=center-10&pf_rd_r=11EYKTN682A79T370AM3&pf_rd_t=201&pf_rd_p=1270985982&pf_rd_i=B002Y27P3M
into this TinyURL:
Which one would you rather cut and paste into your browser? That's the power of TinyURL!
Click and drag the following link to your
links
toolbar.
Once this is on your toolbar, you'll be able to make a TinyURL at the click of a button. By clicking on the toolbar button, a TinyURL will be created for the page you are currently at.
This is compatible with most web browsers and platforms as long as your bookmarks or favorites allow javascript. The links toolbar may not be visible in all setups and in most browsers, you can enable it in the View->Toolbars menu of your web browser. You can also put it in your bookmarks instead of the links toolbar.
Redirection to any page in your site!
With TinyURL, you can also make a smaller URL that will work for any page on your site. Let's say that you have a website with the homepage that is at:
https://www.my-internet-isp.com/~myusername
Entering that URL into TinyURL will create a URL like
https://tinyurl.com/3
With this you can then redirect someone to anywhere within your site by appending a slash and the pages filename to it. So if you have a page at https://www.my-internet-isp.com/~myusername/my-links-page.html, you can use the URL https://tinyurl.com/3/my-links-page.html and going to this URL will forward the visitor to the page in your website.
Link to us!
Please link to us! Just make a link to https://tinyurl.com or use the following code to make a URL input box:
Terms of use
TinyURL was created as a free service to make posting long URLs easier, and may only be used for actual URLs. Using it for spamming or illegal
purposes is forbidden and any such use will result in the TinyURL being disabled and you may be reported to all ISPs involved and to the proper
governmental agencies. This service is provided without warranty of any kind.


REPORT INFO
| Action Type | Submission Month | Submission Year |
|---|---|---|
| segmentActionType | Month of Tape | Year of Tape |
ARREST AGENCY
| STATE | ORI | NAME | CITY INDICATOR |
|---|---|---|---|
| State | ORI | Name | City Indicator |
ARREST
| ATS | ARREST DATE | ARREST TYPE | OFFENSE CODE |
|---|---|---|---|
| arrestTransactionNumber | arrestDate | typeOfArrestType | ucrOffenseCodeType |
ARRESTEE
| SEX | sexOfPersonType | RACE | raceOfPersonType |
|---|---|---|---|
| ETHNICITY | ethnicityOfPersonType | RESIDENT STATUS | residentStatusOfPersonType |
| AVERAGE AGE | averageAge | AGE UNKNOWN | ageUnknown |
| MIN AGE | ageOfArresteeMin | MAX | ageOfArresteeMax |
| NONNUMERIC AGE | nonNumericAge | JUVENILE | juvenile |
| DISPOSITION UNDER 18 | dispositionOfArresteeUnder18Type | ARMED WITH |
- |
FBI SUBMISSION
| STATUS | FBI submission Status | ||||||
|---|---|---|---|---|---|---|---|
| SUBMISSION TIMESTAMP | submissionTimestamp | ||||||
| REQUEST FILE PATH | requestFilePath | ||||||
| RESPONSE TIMESTAMP | responseTimestamp | ||||||
| RESPONSE FILE PATH | responseFilePath | ||||||
| FAULT CODE | faultCode | ||||||
| FAULT DESCRIPTION | faultDescription | ||||||
|
VIOLATIONS
|


Pluto is a dwarf planet in the Kuiper belt, a ring of bodies beyond Neptune. It was the first Kuiper belt object to be discovered. It is the largest and second-most-massive known dwarf planet in the Solar System and the ninth-largest and tenth-most-massive known object directly orbiting the Sun. It is the largest known trans-Neptunian object by volume but is less massive than Eris, a dwarf planet in the scattered disc. Like other Kuiper belt objects, Pluto is primarily made of ice and rock[13] and is relatively small—about one-sixth the mass of Earth's Moon and one-third its volume. It has a moderately eccentric and inclined orbit during which it ranges from 30 to 49 astronomical units or AU (4.4–7.4 billion km) from the Sun. This means that Pluto periodically comes closer to the Sun than Neptune, but a stable orbital resonance with Neptune prevents them from colliding. Light from the Sun takes about 5.5 hours to reach Pluto at its average distance (39.5 AU).
Pluto was discovered by Clyde Tombaugh in 1930, and was originally considered the ninth planet from the Sun. After 1992, its status as a planet fell into question following the discovery of several objects of similar size in the Kuiper belt. In 2005, Eris, which is 27% more massive than Pluto, was discovered, which led the International Astronomical Union (IAU) to define the term "planet" formally for the first time the following year.[14] This definition excluded Pluto and reclassified it as a member of the new "dwarf planet" category.


If you want to add a contribution to this project, you are welcome to add your data at https://github.com/ignatandrei/netCoreAlphabet
Site Web : https://ignatandrei.github.io/netCoreAlphabet/
Android https://play.google.com/store/apps/details?id=com.github.ignatandrei.netcorealphabet&hl=en
Windows Store : https://www.microsoft.com/en-us/p/netcorealphabet/9pdx9rv0xhds
Done by Andrei Ignat, http://msprogrammer.serviciipeweb.ro/
Contributors:
WE DO NOT STORE ANY DATA. PERIOD.
We physically can’t.
We have nowhere to store it.
We don’t even have a server database to store it.
So even if Justin Bieber asked nicely to see your data, we wouldn’t have anything to show him.
(Thanks to http://ecquire.com/blog/privacy-policy/ )


This Policy explains what happens to any personal data that you provide to us, or that we collect from you whilst you visit our site and how we use cookies on this website. We do update this Policy from time to time so please do review this Policy regularly.
In running and maintaining our website we may collect and process the following data about you:
Cookies provide information regarding the computer used by a visitor. We may use cookies where appropriate to gather information about your computer in order to assist us in improving our website. We may gather information about your general internet use by using the cookie. Where used, these cookies are downloaded to your computer and stored on the computer’s hard drive. Such information will not identify you personally; it is statistical data which does not identify any personal details whatsoever. Our advertisers may also use cookies, over which we have no control. Such cookies (if used) would be downloaded once you click on advertisements on our website. You can adjust the settings on your computer to decline any cookies if you wish. This can be done within the “settings” section of your computer. For more information please read the advice at AllAboutCookies.org.
We use the information that we collect from you to provide our services to you. In addition to this we may use the information for one or more of the following purposes:
If you have previously purchased goods or services from us we may provide to you details of similar goods or services, or other goods and services, that you may be interested in.
We never give your details to third parties to use your data to enable them to provide you with information regarding unrelated goods or services.
In operating our website it may become necessary to transfer data that we collect from you to locations outside of the European Union for processing and storing. By providing your personal data to us, you agree to this transfer, storing and processing. We do our utmost to ensure that all reasonable steps are taken to make sure that your data is stored securely.
Unfortunately the sending of information via the internet is not totally secure and on occasion such information can be intercepted. We cannot guarantee the security of data that you choose to send us electronically, sending such information is entirely at your own risk.
We will not disclose your personal information to any other party other than in accordance with this Privacy Policy and in the circumstances detailed below:
On occasion we include links to third parties on this website. Where we provide a link it does not mean that we endorse or approve that site’s policy towards visitor privacy. You should review their privacy policy before sending them any personal data.
In accordance with the Data Protection Act 1998 you have the right to access any information that we hold relating to you. Please note that we reserve the right to charge a fee of £10 to cover costs incurred by us in providing you with the information.
Please do not hesitate to contact us regarding any matter relating to this Privacy and Cookies Policy via the contact form on the main site.
This Privacy & Cookie Policy has been adapted from an open template from http://www.diylegals.co.uk/ for use on http://www.itguyjosh.com.


Why use awk?
Awk is a great tool to handle log and other text file.
Basic syntax
Awk allows you to write two special routines that can be executed before any input is read and after all input is read. These are the procedures associated with the BEGIN and END rules, respectively.In other words, you can do some preprocessing before the main input loop is ever executed and you can do some postprocessing after the main input loop has terminated. The BEGIN and ENDprocedures are optional. like this:
$ awk "BEGIN {do some preprocessing} { do the work, worked on the input file} END {do some postprocessing}"
Some Constants
| Item | Description | | :——– | ——–| |FS | field separator. by default, its value is a single space| |OFS | output field separator | |NF |number of fields for the current input record| |RS |record separator| |ORS |output reacord sepatator| |FILENAME |the name of the current input file| CONVFMT : number-to-string conversions
- default value is “%.6g” $ awk ‘BEGIN {str = (5.5 + 3.2) “ is a nice value”; print str; }’ 8.7 is a nice value
- change its value to “%d” $ awk ‘BEGIN {CONVFMT=”%d”;str = (5.5 + 3.2) “ is a nice value”;print str; }’ 8 is a nice value |
NOTE: In POSIX awk, assigning a new value to FS has no effect on the current input line; it only affects the next input line.
Escape Sequences
| Sequence |Description | | :——– | ——– | |\a| Alert character, usually ASCII BEL character |\b| Backspace |\f| Formfeed |\n| Newline |\r| Carriage return |\t| Horizontal tab |\v| Vertical tab |\ ddd| Character represented as 1 to 3 digit octal value |\x| hex Character represented as hexadecimal value |\ c| Any literal character c (e.g., " for “ )
Relational Operators
| Operator |Description | | :——– | ——– | |< |Less than |>| Greater than |<=| Less than or equal to |>=| Greater than or equal to |==| Equal to |!=| Not equal to |~| Matches |!~| Does not match
Format Specifiers Used in printf
| Character | Description | | :——– | ——– | |c| ASCII character |d| Decimal integer |i| Decimal integer. (Added in POSIX) |e| Floating-point format ([-] d . precision e [+-] dd ) |E| Floating-point format ([-] d . precision E [+-] dd ) |f| Floating-point format ([-] ddd . precision ) |g| e or f conversion, whichever is shortest, with trailing zeros removed |G| E or f conversion, whichever is shortest, with trailing zeros removed |o| Unsigned octal value |s| String |x| Unsigned hexadecimal number. Uses a - f for 10 to 15 |X| Unsigned hexadecimal number. Uses A - F for 10 to 15 |% |Literal %
Awk’s Built-In String Functions
| Awk Function |Description | | :————– | ——– | |gsub ( r , s , t ) |Globally substitutes s for each match of the regular expression r in the string t . Returns the number of substitutions. If t is not supplied, defaults to $0 . |index ( s , t ) | Returns position of substring t in string s or zero if not present. | length ( s ) | Returns length of string s or length of $0 if no string is supplied. | match ( s , r ) | Returns either the position in s where the regular expression r begins, or 0 if no occurrences are found. Sets the values of RSTARTand RLENGTH . | split ( s , a , sep) | Parses string s into elements of array a using field separator sep ; returns number of elements. If sep is not supplied, FS is used. Array splitting works the same way as field splitting. | sprintf (“ fmt “,expr ) | Uses printf format specification for expr . | sub ( r , s , t ) | Substitutes s for first match of the regular expression r in the string t . Returns 1 if successful; 0 otherwise. If t is not supplied, defaults to $0 . | substr ( s , p , n) | Returns substring of string s at beginning position p up to a maximum length of n . If n is not supplied, the rest of the string from p is used. | tolower ( s ) | Translates all uppercase characters in string s to lowercase and returns the new string. | toupper ( s ) | Translates all lowercase characters in string s to uppercase and returns the new string.
Limitations
| Item | Limit | | :——– | ——– | |Number of fields per record |100 |Characters per input record | 3000 |Characters per output record |3000 |Characters per field | 1024 |Characters per printf string | 3000 |Characters in literal string |400 |Characters in character class |400 |Files open | 15 |Pipes open | 1
Examples
# filesum: list files and total size in bytes
# input: long listing produced by "ls -l"
#1 output column headers
BEGIN {print "BYTES", "\t", "FILE"}
#2 test for 9 fields; files begins with "-"
NF == 9 && /^-/ {
sum += $5 # accumulate size of file
++filenum # count number of files
print $5, "\t", $9
}
#3 test for 9 fields; directory begins with "d"
NF == 9 && /^d/ {
sum += $5
++filenum
print $5, "\t", $9
}
#4 test for ls -lR line ./dir
$1 ~ /^\..*:$/{
print "\t" $0 # print <dir> and name
}
#5 once all is done
END {
# print total file size and number of files
print "Total: ", sum, "bytes (" filenum " files)"
}


Our second, full-length album is now available to purchase and stream. Click the links below to give it a listen!
Get Started!Our limited-edition women's graphic tee is quite the show-stopper.
White Buffalo Woman is a young, howling mess that is crouching on all fours and readying itself to pounce on the world of rock ’n’ roll.
The band started in Minerva, Ohio in drummer Joe’s basement, below their family-run funeral home. Members include Evan Rutledge (Vocals), Adam Murphy (Guitar), Alex Leggett (Bass) Devin Bezeredi (Guitar) and Joe Wales (Drums). The band fuses rock, blues, psychedelic, souland punk into an explosion of new music.
They have played across the US and opened for acts like Dex Romweber, Patrick Sweany, The Buffalo Killers, Dan Sartain, Jacuzzi Boys, The Ettes, James Leg and The Stone Foxes.
Their first full-length album, White Buffalo Woman, was released by Grimtale in early 2016. They have released a 7” vinyl record “My Days/ This Town” (released on Grimtale Records) in 2012, followed by a self released four song EP in 2013 and a 7″ single “Like You Used To Do/Always on My Mind” released on Grimtale records in 2014. Their music has earned them airplay on Little Steven’s “Underground Garage” on SiriusXM and a thumbs-up from J. Geils' frontman/singer Peter Wolf. Their musical style ranges from organ-laced garage rock to Southern-fried soul to uncannily Stonesy, all great fun.
Ready to start your next project with us? That's great! Give us a call or send us an email and we will get back to you as soon as possible!
123-456-6789


{% extends "layout_picker.html" %} {% set serviceName = "Apply for a resident's parking permit" %} {% set pageTitle = "New address not yet verified" %} {% block content %}
We received your new and old addresses from GOV.UK Verify.
Your new address isn't yet verified because you haven't lived there long enough.
{#
Jane Smith
7 Lower Drive
Passington
PS3 6HO
Jane Smith
30 Thornhill Gardens
{{council.shortName}}
SR2 7LE
This means you can't get a 12 month resident's permit today.
However, you can get a 3 month permit today. Just before it expires, you'll be able to apply for a full 12 month permit with GOV.UK Verify – you'll have lived there long enough by then.
Apply for a 3 month resident's permit


Inspired by the islands of Southeast Asia, Sanhok is a compact battleground a quarter the size of Erangel
and Miramar at just 4km x 4km. The map might be smaller, but the battles won’t be.
You’ll find more (and better) gear and ammo on Sanhok. Spawn rates have been adjusted so you can
gear up for battles quickly.
Smaller island, speedier games. Circle dynamics have been adjusted to bring battles on Sanhok to a
dramatic finish more quickly than on the bigger maps.
In the months after Sanhok’s launch, we’ll continue to add map-specific vehicles (including the Tukshai!)
and weapons.
CELEBRATE THE LAUNCH OF SANHOK
WITH LIMITED-TIME MISSIONS AND REWARDS!
Event Period: June 22 – July 21, 2018 (UTC)
PLAY GAMES, LEVEL UP
Gain experience
just by playing games to level up.
INTRODUCING MISSIONS
Take on dozens of daily and weekly challenges
both on Sanhok and other maps.
EARN SWAG
By completing missions and leveling up,
you’ll earn a special parachute skin
(the Snowflower) for free.
UNLOCK FREE LOOT!
Temporarily unlock some items (and earn a couple of permanent rewards) for free by leveling up!
*We've decided to add two extra permanent rewards for all participating players! Find out more HERE
(All items obtained through the Event Pass are non-tradable and non-marketable)
Violent
Violet Jacket
Temporary Trial Item
Turquoise
Delight-kar98K
Temporary Trial Item
Tracksuit Top
(Yellow)
Temporary Trial Item
Gold Plate
S686
Temporary Trial Item
Tracksuit Pants
(Yellow)
Temporary Trial Item
Pleated Mini-Skirt
(Black)
Temporary Trial Item
Hotpants
(Blue)
Temporary Trial Item
Free Nickname
Change
Permanent
Parachute Skin
'Snowflower'
EVENT PASS: SANHOK
GET EVEN MORE MISSIONS AND LOOT!
With the launch of Sanhok, we’re introducing an exciting new way to
unlock cosmetic rewards in PUBG. Purchase Event Pass: Sanhok from the Steam Store
to open up more missions, more levels, and tons of permanent rewards.
(All items obtained through the Event Pass are non-tradable and non-marketable)
We’ll track all of your progress toward the event pass missions even if you don’t purchase the pass, so if you purchase it midway through the event you’ll still be able to claim all the rewards you’ve earned!
If you’re playing without purchasing the event pass, you’ll receive the Nickname Change item and permanently unlock the parachute (obtainable at level 20), but all other earned cosmetic items will expire. However, if you purchase the event pass, you will be able to obtain a lot more cosmetic items that will not expire even after the event period. All temporary items (marked with the timer icon) will expire regardless of purchasing the event pass.
Accomplish missions to gain experience points, or simply accumulate survival time to rack up experience points (2 XP every five minutes in-game, up to 80 XP per day). Those who have purchased the Event Pass can rack up 4 XP every five minutes of survival time up to a total of 80 XP per day.


--- title: Home icon: home order: 1 --- {% assign first = true %} {% for post in site.posts limit:3 %} {% unless first %}


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WITNESS LIST Natural Resources Committee April 29, 2003 - 1:00P HB 1150 FOR: Crane, Susan Special Project Manager (City of San Antonio), San Antonio, TX McFarland, Bob Attorney (Cities of Arlington and Grand Prairie), Austin, TX ON: Rocha, Susan Attorney (City of San Antonio), San Antonio, TX Registering, but not testifying: For: Cooper, Paige (The Trust for Public Land), Austin, TX HB 1567 FOR: Gibbs, Gary (Association of Electric Companies of TX), Austin, TX Woodward, Michael Attorney (Waste Control Specialists), Austin, TX AGAINST: Barnidge, Kate (Nuke Free Texas), Austin, TX Bening, Rita (Self), Addison, TX Hadden, Karen (Sustainable Energy & Economic Development Coalition), Austin, TX Harrison, Dan (Self), Dallas, TX Hubener, Ann (Self), Duncanville, TX Mendoza, Tristan Director (Texas Radiation Online), Austin, TX Mohr, Isabella (Self), Dallas, TX Moore, Jayme (Nuke Free Texas), Austin, TX Rogers, Erin (Lone Star Sierra Club), Austin, TX Smith, Tom "Smitty" Director (Public Citizen), Austin, TX Wales, Matthew (Self), Dallas, TX Weston-Reza, Michele (Women's Action Coalition of Austin & Nuke Free Texas), Austin, TX ON: Jablonski, Susan (TCEQ), Austin, TX McBurney, Ruth Division Director (Texas Department of Health), Austin, TX Registering, but not testifying: For: Clayton, Bill Consultant (Waste Control Specialists), Austin, TX Fainter, Jr., John President (Association of Electric Companies of Texas), Austin, TX Glacken, Shawn (TXU), Dallas, TX McCollough, Parker V.P. Government Affairs (Entergy Corporation), Austin, TX Miller, Tami Regulatory Specialist (CenterPoint Energy), Houston, TX Selig, Edward (Advocates for Responsible Disposal in Texas), Austin, TX Stewart, Jay Attorney (Waste Control Specialists), Austin, TX Against: Anderson, Sparky Program Director (Clean Water Action), Austin, TX Bornhorst, Rebecca (Downwinders at Risk), DeSoto, TX Campbell, Tammy (Self), Longview, TX Carter, Gloria (Self), Garland, TX Harp, Peggy (Self), Farmersville, TX Hubener, James (Self), Duncanville, TX Hubener, Katy Executive Director (Blue Skies Alliance), Duncanville, TX Irby, Harriet (Self), Pantego, TX Mein, Joenie Executive Committee (Dallas Sierra Club), Dallas, TX Noble, Jessica (Self), Longview, TX Parten, Ed (S.M.A.R.T., TBBU), Kingwood, TX Richards, Judy (Self), Duncanville, TX Thoms, John (Self), Austin, TX Tinker, Dierdre (Blue Skies Alliance, Downwinders at Risk), Flower Mound, TX Williams, Margaret (Self), Dallas, TX HB 2021 Registering, but not testifying: On: Johnson, Boyd Director of Enforcement (TX Railroad Commission), Austin, TX SB 24 FOR: DiQuinzio, Joe (Self), Austin, TX SB 767 FOR: Lary, Trey Attorney (Beamer Road Partners), Houston, TX SB 970 ON: Gipson, David Attorney (Texas Department of Agriculture), Austin, TX SB 1494 FOR: Bailey, Chuck Lawyer (Bexar Metropolitan Water District), Austin, TX Registering, but not testifying: For: Milleda, Nancy Lobbyist (Bexar Metropolitan Water District), Austin, TX Rosenberg, Louis Attorney (Bexar Metropolitan Water District), San Antonio, TX SB 1570 ON: Contreras III, Carlos Attorney (Water Exploration Co., Ltd.), San Antonio, TX SB 1899 FOR: Stover, John Lawyer (Pineywood Groundwater Conservation District), Lufkin, TX


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Get data for analysis using Chromium / Telemetry patches
Read
this
for an explanation of CT's accuracy of results.


The Paragraph toolbar of the TelerikEditorProvider RTE has the following tools:
| Tool | Icon | Description |
|---|---|---|
| SuperScript | Transforms selected content to superscript. | |
| Subscript | Transforms selected content to subscript. | |
| New Paragraph | Inserts a new paragraph. | |
| Insert Groupbox | Inserts a group box. Use to layout forms and to label sections of your content. | |
| Horizontal Rule | Inserts a horizontal line. | |
| Insert Date | Inserts the current date according to your computer. | |
| Insert Time | Inserts the current time according to your computer. | |
| Format Code Block | Enables you to insert and format a block of code and have it render as text. |


When \(a \ne 0\), there are two solutions to \(ax^2 + bx + c = 0\) and they are \[x = {-b \pm \sqrt{b^2-4ac} \over 2a}.\]

Source Code Highlighting!

Or through third-party tools.

Inline code highlighting
background-color: rgba(0, 0, 0, 0.9);
is also possible.


Better HAC
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Most Recent Grade
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Lowest Grade
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Courses
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{% endfor %}
Grades
Assignment Name
Course
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{% for assignment in assignments %}
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{% endif %}