Custom statespace models¶
The true power of the state space model is to allow the creation and estimation of custom models. This notebook shows various statespace models that subclass sm.tsa.statespace.MLEModel
.
Remember the general state space model can be written in the following general way:
You can check the details and the dimensions of the objects in this link
Most models won’t include all of these elements. For example, the design matrix \(Z_t\) might not depend on time (\(\forall t \;Z_t = Z\)), or the model won’t have an observation intercept \(d_t\).
We’ll start with something relatively simple and then show how to extend it bit by bit to include more elements.
Model 1: time-varying coefficients. One observation equation with two state equations
Model 2: time-varying parameters with non identity transition matrix
Model 3: multiple observation and multiple state equations
Bonus: pymc3 for Bayesian estimation
[ ]:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from collections import OrderedDict
plt.rc("figure", figsize=(16,8))
plt.rc("font", size=15)
Model 1: time-varying coefficients¶
The observed data is \(y_t, x_t, w_t\). With \(x_t, w_t\) being the exogenous variables. Notice that the design matrix is time-varying, so it will have three dimensions (k_endog x k_states x nobs
)
The states are \(\beta_{x,t}\) and \(\beta_{w,t}\). The state equation tells us these states evolve with a random walk. Thus, in this case the transition matrix is a 2 by 2 identity matrix.
We’ll first simulate the data, the construct a model and finally estimate it.
[ ]:
def gen_data_for_model1():
nobs = 1000
rs = np.random.RandomState(seed=93572)
d = 5
var_y = 5
var_coeff_x = 0.01
var_coeff_w = 0.5
x_t = rs.uniform(size=nobs)
w_t = rs.uniform(size=nobs)
eps = rs.normal(scale=var_y**0.5, size=nobs)
beta_x = np.cumsum(rs.normal(size=nobs, scale=var_coeff_x**0.5))
beta_w = np.cumsum(rs.normal(size=nobs, scale=var_coeff_w**0.5))
y_t = d + beta_x * x_t + beta_w * w_t + eps
return y_t, x_t, w_t, beta_x, beta_w
y_t, x_t, w_t, beta_x, beta_w = gen_data_for_model1()
_ = plt.plot(y_t)
[ ]:
class TVRegression(sm.tsa.statespace.MLEModel):
def __init__(self, y_t, x_t, w_t):
exog = np.c_[x_t, w_t] # shaped nobs x 2
super(TVRegression, self).__init__(
endog=y_t, exog=exog, k_states=2,
initialization='diffuse')
# Since the design matrix is time-varying, it must be
# shaped k_endog x k_states x nobs
# Notice that exog.T is shaped k_states x nobs, so we
# just need to add a new first axis with shape 1
self.ssm['design'] = exog.T[np.newaxis, :, :] # shaped 1 x 2 x nobs
self.ssm['selection'] = np.eye(self.k_states)
self.ssm['transition'] = np.eye(self.k_states)
#Which parameters need to be positive?
self.positive_parameters = slice(1, 4)
@property
def param_names(self):
return ['intercept', 'var.e', 'var.x.coeff', 'var.w.coeff']
@property
def start_params(self):
"""
Defines the starting values for the parameters
The linear regression gives us reasonable starting values for the constant
d and the variance of the epsilon error
"""
exog = sm.add_constant(self.exog)
res = sm.OLS(self.endog, exog).fit()
params = np.r_[res.params[0], res.scale, 0.001, 0.001]
return params
def transform_params(self, unconstrained):
"""
We constraint the last three parameters
('var.e', 'var.x.coeff', 'var.w.coeff') to be positive,
because they are variances
"""
constrained = unconstrained.copy()
constrained[self.positive_parameters] = constrained[self.positive_parameters]**2
return constrained
def untransform_params(self, constrained):
"""
Need to unstransform all the parameters you transformed
in the `transform_params` function
"""
unconstrained = constrained.copy()
unconstrained[self.positive_parameters] = unconstrained[self.positive_parameters]**0.5
return unconstrained
def update(self, params, **kwargs):
params = super(TVRegression, self).update(params, **kwargs)
self['obs_intercept', 0, 0] = params[0]
self['obs_cov', 0, 0] = params[1]
self['state_cov'] = np.diag(params[2:4])
And then estimate it with our custom model class¶
[ ]:
mod = TVRegression(y_t, x_t, w_t)
res = mod.fit()
print(res.summary())
The values that generated the data were:
intercept = 5
var.e = 5
var.x.coeff = 0.01
var.w.coeff = 0.5
As you can see, the estimation recovered the real parameters pretty well.
We can also recover the estimated evolution of the underlying coefficients (or states in Kalman filter talk)
[ ]:
fig, axes = plt.subplots(2, figsize=(16, 8))
ss = pd.DataFrame(res.smoothed_state.T, columns=['x', 'w'])
axes[0].plot(beta_x, label='True')
axes[0].plot(ss['x'], label='Smoothed estimate')
axes[0].set(title='Time-varying coefficient on x_t')
axes[0].legend()
axes[1].plot(beta_w, label='True')
axes[1].plot(ss['w'], label='Smoothed estimate')
axes[1].set(title='Time-varying coefficient on w_t')
axes[1].legend()
fig.tight_layout();
Model 2: time-varying parameters with non identity transition matrix¶
This is a small extension from Model 1. Instead of having an identity transition matrix, we’ll have one with two parameters (\(\rho_1, \rho_2\)) that we need to estimate.
What should we modify in our previous class to make things work? + Good news: not a lot! + Bad news: we need to be careful about a few things
1) Change the starting parameters function¶
We need to add names for the new parameters \(\rho_1, \rho_2\) and we need to start corresponding starting values.
The param_names
function goes from:
def param_names(self):
return ['intercept', 'var.e', 'var.x.coeff', 'var.w.coeff']
to
def param_names(self):
return ['intercept', 'var.e', 'var.x.coeff', 'var.w.coeff',
'rho1', 'rho2']
and we change the start_params
function from
def start_params(self):
exog = sm.add_constant(self.exog)
res = sm.OLS(self.endog, exog).fit()
params = np.r_[res.params[0], res.scale, 0.001, 0.001]
return params
to
def start_params(self):
exog = sm.add_constant(self.exog)
res = sm.OLS(self.endog, exog).fit()
params = np.r_[res.params[0], res.scale, 0.001, 0.001, 0.8, 0.8]
return params
Change the
update
function
It goes from
def update(self, params, **kwargs):
params = super(TVRegression, self).update(params, **kwargs)
self['obs_intercept', 0, 0] = params[0]
self['obs_cov', 0, 0] = params[1]
self['state_cov'] = np.diag(params[2:4])
to
def update(self, params, **kwargs):
params = super(TVRegression, self).update(params, **kwargs)
self['obs_intercept', 0, 0] = params[0]
self['obs_cov', 0, 0] = params[1]
self['state_cov'] = np.diag(params[2:4])
self['transition', 0, 0] = params[4]
self['transition', 1, 1] = params[5]
(optional) change
transform_params
anduntransform_params
This is not required, but you might wanna restrict \(\rho_1, \rho_2\) to lie between -1 and 1. In that case, we first import two utility functions from statsmodels
.
from statsmodels.tsa.statespace.tools import (
constrain_stationary_univariate, unconstrain_stationary_univariate)
constrain_stationary_univariate
constraint the value to be within -1 and 1. unconstrain_stationary_univariate
provides the inverse function. The transform and untransform parameters function would look like this (remember that \(\rho_1, \rho_2\) are in the 4 and 5th index):
def transform_params(self, unconstrained):
constrained = unconstrained.copy()
constrained[self.positive_parameters] = constrained[self.positive_parameters]**2
constrained[4] = constrain_stationary_univariate(constrained[4:5])
constrained[5] = constrain_stationary_univariate(constrained[5:6])
return constrained
def untransform_params(self, constrained):
unconstrained = constrained.copy()
unconstrained[self.positive_parameters] = unconstrained[self.positive_parameters]**0.5
unconstrained[4] = unconstrain_stationary_univariate(constrained[4:5])
unconstrained[5] = unconstrain_stationary_univariate(constrained[5:6])
return unconstrained
I’ll write the full class below (without the optional changes I have just discussed)
[ ]:
class TVRegressionExtended(sm.tsa.statespace.MLEModel):
def __init__(self, y_t, x_t, w_t):
exog = np.c_[x_t, w_t] # shaped nobs x 2
super(TVRegressionExtended, self).__init__(
endog=y_t, exog=exog, k_states=2,
initialization='diffuse')
# Since the design matrix is time-varying, it must be
# shaped k_endog x k_states x nobs
# Notice that exog.T is shaped k_states x nobs, so we
# just need to add a new first axis with shape 1
self.ssm['design'] = exog.T[np.newaxis, :, :] # shaped 1 x 2 x nobs
self.ssm['selection'] = np.eye(self.k_states)
self.ssm['transition'] = np.eye(self.k_states)
#Which parameters need to be positive?
self.positive_parameters = slice(1, 4)
@property
def param_names(self):
return ['intercept', 'var.e', 'var.x.coeff', 'var.w.coeff',
'rho1', 'rho2']
@property
def start_params(self):
"""
Defines the starting values for the parameters
The linear regression gives us reasonable starting values for the constant
d and the variance of the epsilon error
"""
exog = sm.add_constant(self.exog)
res = sm.OLS(self.endog, exog).fit()
params = np.r_[res.params[0], res.scale, 0.001, 0.001, 0.7, 0.8]
return params
def transform_params(self, unconstrained):
"""
We constraint the last three parameters
('var.e', 'var.x.coeff', 'var.w.coeff') to be positive,
because they are variances
"""
constrained = unconstrained.copy()
constrained[self.positive_parameters] = constrained[self.positive_parameters]**2
return constrained
def untransform_params(self, constrained):
"""
Need to unstransform all the parameters you transformed
in the `transform_params` function
"""
unconstrained = constrained.copy()
unconstrained[self.positive_parameters] = unconstrained[self.positive_parameters]**0.5
return unconstrained
def update(self, params, **kwargs):
params = super(TVRegressionExtended, self).update(params, **kwargs)
self['obs_intercept', 0, 0] = params[0]
self['obs_cov', 0, 0] = params[1]
self['state_cov'] = np.diag(params[2:4])
self['transition', 0, 0] = params[4]
self['transition', 1, 1] = params[5]
To estimate, we’ll use the same data as in model 1 and expect the \(\rho_1, \rho_2\) to be near 1.
The results look pretty good! Note that this estimation can be quite sensitive to the starting value of \(\rho_1, \rho_2\). If you try lower values, you’ll see it fails to converge.
[ ]:
mod = TVRegressionExtended(y_t, x_t, w_t)
res = mod.fit(maxiter=2000) #it doesn't converge with 50 iters
print(res.summary())
Model 3: multiple observation and state equations¶
We’ll keep the time-varying parameters, but this time we’ll also have two observation equations.
Observation equations¶
\(\hat{i_t}, \hat{M_t}, \hat{s_t}\) are observed each period.
The model for the observation equation has two equations:
Following the general notation from state space models, the endogenous part of the observation equation is \(y_t = (\hat{i_t}, \hat{M_t})\) and we only have one exogenous variable \(\hat{s_t}\)
State equations¶
Matrix notation for the state space model¶
I’ll simulate some data, talk about what we need to modify and finally estimate the model to see if we’re recovering something reasonable.
[ ]:
true_values = {'var_e1': 0.01, 'var_e2': 0.01,
'var_w1': 0.01, 'var_w2': 0.01,
'delta1': 0.8, 'delta2': 0.5, 'delta3': 0.7}
def gen_data_for_model3():
#Starting values
alpha1_0 = 2.1
alpha2_0 = 1.1
t_max = 500
def gen_i(alpha1, s):
return alpha1*s + np.sqrt(true_values['var_e1'])*np.random.randn()
def gen_m_hat(alpha2):
return 1*alpha2 + np.sqrt(true_values['var_e2'])*np.random.randn()
def gen_alpha1(alpha1, alpha2):
w1 = np.sqrt(true_values['var_w1'])*np.random.randn()
return true_values['delta1'] * alpha1 + true_values['delta2'] * alpha2 + w1
def gen_alpha2(alpha2):
w2 = np.sqrt(true_values['var_w2'])*np.random.randn()
return true_values['delta3'] * alpha2 + w2
s_t = 0.3 + np.sqrt(1.4)*np.random.randn(t_max)
i_hat = np.empty(t_max)
m_hat = np.empty(t_max)
current_alpha1 = alpha1_0
current_alpha2 = alpha2_0
for t in range(t_max):
#Obs eqns
i_hat[t] = gen_i(current_alpha1, s_t[t])
m_hat[t] = gen_m_hat(current_alpha2)
#state eqns
new_alpha1 = gen_alpha1(current_alpha1, current_alpha2)
new_alpha2 = gen_alpha2(current_alpha2)
#Update states for next period
current_alpha1 = new_alpha1
current_alpha2 = new_alpha2
return i_hat, m_hat, s_t
i_hat, m_hat, s_t = gen_data_for_model3()
What do we need to modify?¶
Once again, we don’t need to change much, but we need to be careful about the dimensions.
1) The __init__
function changes from¶
def __init__(self, y_t, x_t, w_t):
exog = np.c_[x_t, w_t]
super(TVRegressionExtended, self).__init__(
endog=y_t, exog=exog, k_states=2,
initialization='diffuse')
self.ssm['design'] = exog.T[np.newaxis, :, :] # shaped 1 x 2 x nobs
self.ssm['selection'] = np.eye(self.k_states)
self.ssm['transition'] = np.eye(self.k_states)
to
def __init__(self, i_t: np.array, s_t: np.array, m_t: np.array):
exog = np.c_[s_t, np.repeat(1, len(s_t))] # exog.shape => (nobs, 2)
super(MultipleYsModel, self).__init__(
endog=np.c_[i_t, m_t], exog=exog, k_states=2,
initialization='diffuse')
self.ssm['design'] = np.zeros((self.k_endog, self.k_states, self.nobs))
self.ssm['design', 0, 0, :] = s_t
self.ssm['design', 1, 1, :] = 1
Note that we did not have to specify k_endog
anywhere. The initialization does this for us after checking the dimensions of the endog
matrix.
2) The update()
function¶
changes from
def update(self, params, **kwargs):
params = super(TVRegressionExtended, self).update(params, **kwargs)
self['obs_intercept', 0, 0] = params[0]
self['obs_cov', 0, 0] = params[1]
self['state_cov'] = np.diag(params[2:4])
self['transition', 0, 0] = params[4]
self['transition', 1, 1] = params[5]
to
def update(self, params, **kwargs):
params = super(MultipleYsModel, self).update(params, **kwargs)
#The following line is not needed (by default, this matrix is initialized by zeroes),
#But I leave it here so the dimensions are clearer
self['obs_intercept'] = np.repeat([np.array([0, 0])], self.nobs, axis=0).T
self['obs_cov', 0, 0] = params[0]
self['obs_cov', 1, 1] = params[1]
self['state_cov'] = np.diag(params[2:4])
#delta1, delta2, delta3
self['transition', 0, 0] = params[4]
self['transition', 0, 1] = params[5]
self['transition', 1, 1] = params[6]
The rest of the methods change in pretty obvious ways (need to add parameter names, make sure the indexes work, etc). The full code for the function is right below
[ ]:
starting_values = {'var_e1': 0.2, 'var_e2': 0.1,
'var_w1': 0.15, 'var_w2': 0.18,
'delta1': 0.7, 'delta2': 0.1, 'delta3': 0.85}
class MultipleYsModel(sm.tsa.statespace.MLEModel):
def __init__(self, i_t: np.array, s_t: np.array, m_t: np.array):
exog = np.c_[s_t, np.repeat(1, len(s_t))] # exog.shape => (nobs, 2)
super(MultipleYsModel, self).__init__(
endog=np.c_[i_t, m_t], exog=exog, k_states=2,
initialization='diffuse')
self.ssm['design'] = np.zeros((self.k_endog, self.k_states, self.nobs))
self.ssm['design', 0, 0, :] = s_t
self.ssm['design', 1, 1, :] = 1
#These have ok shape. Placeholders since I'm changing them
#in the update() function
self.ssm['selection'] = np.eye(self.k_states)
self.ssm['transition'] = np.eye(self.k_states)
#Dictionary of positions to names
self.position_dict = OrderedDict(var_e1=1, var_e2=2,
var_w1=3, var_w2=4,
delta1=5, delta2=6, delta3=7)
self.initial_values = starting_values
self.positive_parameters = slice(0, 4)
@property
def param_names(self):
return list(self.position_dict.keys())
@property
def start_params(self):
"""
Initial values
"""
#(optional) Use scale for var_e1 and var_e2 starting values
params = np.r_[self.initial_values['var_e1'],
self.initial_values['var_e2'],
self.initial_values['var_w1'],
self.initial_values['var_w2'],
self.initial_values['delta1'],
self.initial_values['delta2'],
self.initial_values['delta3']]
return params
def transform_params(self, unconstrained):
"""
If you need to restrict parameters
For example, variances should be > 0
Parameters maybe have to be within -1 and 1
"""
constrained = unconstrained.copy()
constrained[self.positive_parameters] = constrained[self.positive_parameters]**2
return constrained
def untransform_params(self, constrained):
"""
Need to reverse what you did in transform_params()
"""
unconstrained = constrained.copy()
unconstrained[self.positive_parameters] = unconstrained[self.positive_parameters]**0.5
return unconstrained
def update(self, params, **kwargs):
params = super(MultipleYsModel, self).update(params, **kwargs)
#The following line is not needed (by default, this matrix is initialized by zeroes),
#But I leave it here so the dimensions are clearer
self['obs_intercept'] = np.repeat([np.array([0, 0])], self.nobs, axis=0).T
self['obs_cov', 0, 0] = params[0]
self['obs_cov', 1, 1] = params[1]
self['state_cov'] = np.diag(params[2:4])
#delta1, delta2, delta3
self['transition', 0, 0] = params[4]
self['transition', 0, 1] = params[5]
self['transition', 1, 1] = params[6]
[ ]:
mod = MultipleYsModel(i_hat, s_t, m_hat)
res = mod.fit()
print(res.summary())
Bonus: pymc3 for fast Bayesian estimation¶
In this section I’ll show how you can take your custom state space model and easily plug it to pymc3
and estimate it with Bayesian methods. In particular, this example will show you an estimation with a version of Hamiltonian Monte Carlo called the No-U-Turn Sampler (NUTS).
I’m basically copying the ideas contained in this notebook, so make sure to check that for more details.
[ ]:
#Extra requirements
import theano
import theano.tensor as tt
import pymc3 as pm
We need to define some helper functions to connect theano to the likelihood function that is implied in our model
[ ]:
class Loglike(tt.Op):
itypes = [tt.dvector] # expects a vector of parameter values when called
otypes = [tt.dscalar] # outputs a single scalar value (the log likelihood)
def __init__(self, model):
self.model = model
self.score = Score(self.model)
def perform(self, node, inputs, outputs):
theta, = inputs # contains the vector of parameters
llf = self.model.loglike(theta)
outputs[0][0] = np.array(llf) # output the log-likelihood
def grad(self, inputs, g):
# the method that calculates the gradients - it actually returns the
# vector-Jacobian product - g[0] is a vector of parameter values
theta, = inputs # our parameters
out = [g[0] * self.score(theta)]
return out
class Score(tt.Op):
itypes = [tt.dvector]
otypes = [tt.dvector]
def __init__(self, model):
self.model = model
def perform(self, node, inputs, outputs):
theta, = inputs
outputs[0][0] = self.model.score(theta)
We’ll simulate again the data we used for model 1. We’ll also fit
it again and save the results to compare them to the Bayesian posterior we get.
[ ]:
y_t, x_t, w_t, beta_x, beta_w = gen_data_for_model1()
plt.plot(y_t)
[ ]:
mod = TVRegression(y_t, x_t, w_t)
res_mle = mod.fit(disp=False)
print(res_mle.summary())
Bayesian estimation¶
We need to define a prior for each parameter and the number of draws and burn-in points
[ ]:
# Set sampling params
ndraws = 3000 # 3000 number of draws from the distribution
nburn = 600 # 600 number of "burn-in points" (which will be discarded)
[ ]:
# Construct an instance of the Theano wrapper defined above, which
# will allow PyMC3 to compute the likelihood and Jacobian in a way
# that it can make use of. Here we are using the same model instance
# created earlier for MLE analysis (we could also create a new model
# instance if we preferred)
loglike = Loglike(mod)
with pm.Model():
# Priors
intercept = pm.Uniform('intercept', 1, 10)
var_e = pm.InverseGamma('var.e', 2.3, 0.5)
var_x_coeff = pm.InverseGamma('var.x.coeff', 2.3, 0.1)
var_w_coeff = pm.InverseGamma('var.w.coeff', 2.3, 0.1)
# convert variables to tensor vectors
theta = tt.as_tensor_variable([intercept, var_e, var_x_coeff, var_w_coeff])
# use a DensityDist (use a lamdba function to "call" the Op)
pm.DensityDist('likelihood', lambda v: loglike(v), observed={'v': theta})
# Draw samples
trace = pm.sample(ndraws, tune=nburn, discard_tuned_samples=True, cores=4)
How does the posterior distribution compare with the MLE estimation?¶
The clearly peak around the MLE estimate.
[ ]:
results_dict = {'intercept': res_mle.params[0], 'var.e': res_mle.params[1],
'var.x.coeff': res_mle.params[2], 'var.w.coeff': res_mle.params[3]}
plt.tight_layout()
_ = pm.traceplot(trace,
lines=[(k, {}, [v]) for k, v in dict(results_dict).items()],
combined=True,
figsize=(12, 12))


{% include /head.html %}
Welcome to the temporary home of the
Support for data and software required by 21st-century research


Update ():
- Added statistic: current positive/negative count streak
- Added statistic: current positive/negative get streak
- Added statistics for "get counts", meaning counts that reached a get
You can also check the changelog for more information.
- Added statistic: current positive/negative count streak
- Added statistic: current positive/negative get streak
- Added statistics for "get counts", meaning counts that reached a get
You can also check the changelog for more information.
Loading...
Stats in a nutshell
All charts are dynamic. You can click and drag to zoom on a specific portion of a chart.
Note: Values between parenthesis are variations since the last time I posted those stats.
General
There are total counts.This is of the main thread's number of counts.
Counts are ranging from to , making a total range of , which is 1/ of the maximum possible range with that many counts.
The average count value is , and the median count value is .
Most common counts
| # | The number ... | was counted ... times |
|---|
Least common counts
| # | The number ... | was counted ... times |
|---|
Reply time
The first Tug of War count was counted ago, on 2015-11-05 17:13:39 UTC.Since then, the time between two consecutive counts has ranged from to .
The average reply time is . The median reply time is .
For comparison, the reply time for counts reaching a get has ranged from to .
The average reply time for counts reaching a get is . The median reply time for counts reaching a get is .
Count direction
The longest increasing streak lasted counts. This is of the entire range.This streak went up from to .
The longest decreasing streak lasted counts. This is of the entire range.
This streak went down from to .
On average, the count changes direction every counts.
On average, increasing streaks last counts longer than decreasing ones.
Count sign
Out of all the counts, are strictly positive, while are strictly negative.This means of all counts are strictly positive, and are strictly negative.
Zero was counted times, which is 1 count in .
The longest positive streak lasted counts.
The longest negative streak lasted counts.
The current streak is and is counts long.
Other
There were deleted or otherwise unreadable counts that I had to interpolate based on what seemed more likely.We have forked times off the oldest count.
Stalemates
The counts that were reached the most consecutives times in a row are:One-value stalemates
| # | The number ... | was counted ... consecutives times |
|---|
Two-value stalemates
| # | The numbers ... | were counted for ... comments in a row |
|---|
Note: Values between parenthesis are variations since the last time I posted those stats.
General
There are total gets.The average get value is , and the median get value is .
Most common gets
| # | The get ... | was reached ... times |
|---|
Least common gets
| # | The get ... | was reached ... times |
|---|
Get time
The fastest get lasted , and went from to .The slowest get lasted , and went from to .
The average get duration is .
The median get duration is .
Get direction
The longest increasing get streak lasted gets.This streak went up from to .
The longest decreasing get streak lasted gets.
This streak went down from to .
On average, the get changes direction every gets.
On average, increasing get streaks last gets longer than decreasing ones.
Get sign
Out of all the gets, are strictly positive, while are strictly negative.This means of all gets are strictly positive, and are strictly negative.
The get reached was zero times, which is 1 get in .
The longest positive get streak lasted gets.
The longest negative get streak lasted gets.
The current get streak is and is counts long.
Get length
The shortest get took 200 counts. This happened several times over the course of the Tug of War, and can't be beaten, since gets are every 200 counts.The longest get took counts, and went from to .
The average get length is comments.
The median get length is comments.
The last get was longer than of other gets.
Note: Values between parenthesis are variations since the last time I posted those stats.
General
There are reddit users taking part in the Tug of War.On average, each user has counted times, with a median of counts.
| # | The user ... | has counted ... times | Variation |
|---|
Gets and assists
Users with the most gets
Here are displayed the counters who have obtained at least gets.| # | The user ... | has obtained ... gets |
|---|
Users with the most assists
Here are displayed the counters who have obtained at least assists.| # | The user ... | has obtained ... assists |
|---|
Reply time
Users with the lowest median reply time
Here are the 100 fastest users amongst the users who have counted at least times.| # | The user ... | has a median reply time of ... seconds | over ... counts |
|---|
Users with the highest score
To give a more meaningful ranking of the fastest and most active users, here are the 100 users with the highest "counting score", determined as the sum of the inverse square roots of this user's reply times in seconds.This allows us to include users with very few counts but an overall high speed.
For example, 100 16-second counts are worth as many points as 50 4-seconds counts, or 25 1-second counts.
| # | The user ... | has a score of ... |
|---|
Users with the lowest average reply time
Here are the 100 fastest users amongst the users who have counted at least times. Note that this calculation uses the average reply time, which might not be the best indicator of speed overall.| # | The user ... | has an average reply time of ... seconds | over ... counts |
|---|
Teams
Highest scoring teams
Here are the 20 pairs of users who have counted together the most often. The number of points a team has is the number of comments posted by this team that have been replied to by the other team member.| # | The users ... | have shared ... counts |
|---|
Top 100 counters
| # | The user ... | has counted ... times | Variation |
|---|
- How does it work?
- Regularly (usually when a get is reached), I fetch all the recent count data from Reddit using a small script that I wrote. After making sure everything is right, I then use another script to generate the statistics, that I simply paste into a file. This website then reads this data and displays the statistics in a more aesthetically pleasing way. If you want to see what the raw stat file looks like, take a look here.
- What's next?
- I plan on adding more statistics. In particular, more user-based stats (who participated in most threads, etc). Since I already have the complete dataset, it is easy to generate those statistics.
- Is the data reliable?
- All the counts were gathered automatically, and then checked, so this isn't a source of error. However, some slight alterations had to be made to the data. For example, it sometimes happened that someone missed a count or counted twice the same number, and it wasn't always noticed by the users. I could unfortunately not leave that as is, due to how the scripts I'm using to generate statistics work, and I had to manually "fix" a few counts here and there, by changing a digit or sometimes adding or removing a count. This is why if you examine the data carefully, you will find a few discrepancies. I did my best to keep that editing to a minimum and to do it in the most coherent way possible, but if you notice that something looks wrong, feel free to message me and I will see if I can change it. For reference, I estimate about 0.2% of the data (500 counts) were altered this way.
- Contact
- Have a suggestion on how to improve the website or add more stats? Found a typo? Something looks wrong? Feel free to shoot me an e-mail or a message on Reddit!
- Raw data & scripts
- The raw data of the Tug of War threads (list of threads, forks, complete formatted file with usernames, counts and comment submission dates) will soon be available directly on the website. For now, just message me if you want access to this data, or to the scripts I used to generate it.
25 May 2019:
- Added statistic: current positive/negative count streak- Added statistic: current positive/negative get streak
- Added statistics for "get counts", meaning counts that reached a get
21 May 2019:
- Two new charts are available at the bottom of the "Charts" tab: The first one shows how many counts were posted above or below each number. The second one shows how much time the count spent above or below each number- Slight formatting and visual improvements
5 May 2019:
- Charts are now dynamic! You can click and drag on them to zoom. A legend in the top right corner also gives you the exact values.- Statistics generation is now dynamic as well! This changes nothing for users, but it means I no longer have to copy paste every single statistic, as they are generated directly in the right format into this file. This means that data updates are now about three times faster than earlier.
29 Apr 2019:
- Charts are now generated automatically (no inconsistent crop that varies between updates)- Charts are now saved in SVG, which makes them zoomable and provides you with the raw data if you're willing to parse it
02 Jun 2018:
- Graphs now have a higher size and resolution- User-based charts now display stat variations in addition to rank variations (re-implemented)
17 Dec 2017:
- Added statistic: last get length in comparison to other gets- Added chart: cumulative contribution of top users
- Added chart: top teams by score
-
08 Nov 2017:
- Added chart: cumulative contribution of top users- Added chart: top teams by score
- User-based charts now display stat variations in addition to rank variations
07 Nov 2017:
- Added statistic: last get length in comparison to other gets28 Sep 2017:
- Ties are now clearly displayed in ranking tables, and are taken into account when calculating ranking variations27 Sep 2017:
- Changelog pop-up will now be dismissed until next version when closed- Added increasing/decreasing get streaks, and fixed increasing/decreasing count streaks stats that were wrong
- Added users with the lowest median reply time
- Added ratio to main counting thread
- Ranking tables now support ties
- Optimized layout, fixed typos and made things hopefully more readable overall
24 Sep 2017:
- Added users with the highest "counting score"- Site is now easier to maintain (lots of useless code removed)
23 Sep 2017:
- Added users with the most gets- Added users with the most assists
- Added fastest users
09 Sep 2017:
- Added Hall of Counters (top 100 counters)24 Aug 2017:
- Added reply time histogram23 Aug 2017:
- Added basic use stats- Added chart for get length distribution
- Added chart for posting time


Simple Interest Calculator
Amount: $
Interest rate:
10.25 %
Number of years:
1
2
3
4
5
6
7
8
9
10
Compute Interest
Interest:


{% extends "layout.html" %} {% block content %}
How to play:
You create a hypothetical tweet and pick two users.
The app will tell you which user would be more likely to tweet that!
Add a user yourself
Then make predictions with them!


Appointment Service JSP Pages
Simple MVC Example
click on
testHeartbeat
to open application
Rest end points
http://localhost:8084/projectfacadeweb/rest/appointmentService/
http://localhost:8084/projectfacadeweb/rest/appointmentService/getHeartbeat


| 022:005 Khan | : |
O mankind! If you are in doubt about the Resurrection, then verily! We have created you (i.e. Adam) from dust, then from a Nutfah (mixed drops of male and female sexual discharge i.e. offspring of Adam), then from a clot (a piece of thick coagulated blood) then from a little lump of flesh, some formed and some unformed (miscarriage), that We may make (it) clear to you (i.e. to show you Our Power and Ability to do what We will). And We cause whom We will to remain in the wombs for an appointed term, then We bring you out as infants, then (give you growth) that you may reach your age of full strength.  And you see the earth barren, but when We send down water (rain) on it, it is stirred (to life), it swells and puts forth every lovely kind (of growth). |
| 022:005 Maulana | : |
O people, if you are in doubt about the Resurrection, then surely We created you from dust, then from a small life-germ, then from a clot, then from a lump of flesh, complete in make and incomplete, that We may make clear to you. And We cause what We please to remain in the wombs till an appointed time, then We bring you forth as babies, then that you may attain your maturity.  And thou seest the earth barren, but when We send down thereon water, it stirs and swells and brings forth a beautiful (growth) of every kind. |
| 022:005 Pickthal | : |
O mankind! if ye are in doubt concerning the Resurrection, then lo! We have created you from dust, then from a drop of seed, then from a clot, then from a little lump of flesh shapely and shapeless, that We may make (it) clear for you. And We cause what We will to remain in the wombs for an appointed time, and afterward We bring you forth as infants, then (give you growth) that ye attain your full strength.  And thou (Muhammad) seest the earth barren, but when We send down water thereon, it doth thrill and swell and put forth every lovely kind (of growth). |
| 022:005 Rashad | : |
O people, if you have any doubt about resurrection, (remember that) we created you from dust, and subsequently from a tiny drop, which turns into a hanging (embryo), then it becomes a fetus that is given life or deemed lifeless. We thus clarify things for you. We settle in the wombs whatever we will for a predetermined period.   Also, you look at a land that is dead, then as soon as we shower it with water, it vibrates with life and grows all kinds of beautiful plants. |
| 022:005 Sarwar | : |
People, if you have doubts about the Resurrection, you must know that We created you from clay that was turned into a living germ. This was developed into a clot of blood, which was made into a well formed and partly shapeless lump of flesh. This is how We show you that resurrection is not more difficult for Us than your creation. We cause whatever We want to stay in the womb for an appointed time, We then take you out of the womb as a baby, so that you may grow up and receive strength. Some of you may then die and others may grow to a very old age and lose your memory. You may see the earth as a barren land, but when we send rain, it starts to stir and swell and produce various pairs of attractive herbs. |
| 022:005 Shakir | : |
O people! if you are in doubt about the raising, then surely We created you from dust, then from a small seed, then from a clot, then from a lump of flesh, complete in make and incomplete, that We may make clear to you; and We cause what We please to stay in the wombs till an appointed time, then We bring you forth as babies, then that you may attain your maturity; and of you is he who is caused to die, and of you is he who is brought back to the worst part of life, so that after having knowledge he does not know anything; and you see the earth sterile land, but when We send down on it the water, it stirs and swells and brings forth of every kind a beautiful herbage. |
| 022:005 Sherali | : |
O people, if you are in doubt concerning the Resurrection, then consider that WE have indeed created you from dust, then from a sperm drop, then from clotted blood, then from a lump of flesh, partly formed and partly unformed, in order that WE make Our power manifest to you. And WE cause what WE will to remain in the wombs for an appointed term; then WE bring you forth as babes; then WE rear you that you may attain to your age of full strength. And there are some of you who are caused to die in the normal course, and there are others among you are kept back till the worst part of life with the result that they know nothing after having had knowledge. And thou seest the earth lifeless, but when WE send down water thereon it stirs and swells and grows every kind of beauteous vegetation. |
| 022:005 Yusufali | : |
O mankind! if ye have a doubt about the Resurrection, (consider) that We created you out of dust, then out of sperm, then out of a leech-like clot, then out of a morsel of flesh, partly formed and partly unformed, in order that We may manifest (our power) to you; and We cause whom We will to rest in the wombs for an appointed term, then do We bring you out as babes, then (foster you) that ye may reach your age of full strength; and some of you are called to die, and some are sent back to the feeblest old age, so that they know nothing after having known (much), and (further), thou seest the earth barren and lifeless, but when We pour down rain on it, it is stirred (to life), it swells, and it puts forth every kind of beautiful growth (in pairs). |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الملحدون المنكرون ليوم البعث Pillars of Islam> Islamic> The Apostates Who Denies the Resurrection الإيمان > اليوم الآخر > البعث Faith> The Hereafter> The Resurrection الإنسان والعلاقات الاجتماعية > الإنسان > خلقه Man and The Social Relations>Man>Being Created الإنسان والعلاقات الاجتماعية > الإنسان > شرفه ودنوه Man and The Social Relations>Man>When Honourable and When Unhonourable الإنسان والعلاقات الاجتماعية > الإنسان > طول عمره يضعفه ويعجزه Man and The Social Relations>Man>His Long Life Weakens him and Making him Incapable الإنسان والعلاقات الاجتماعية > الرجل والمرأة Man and The Social Relations>Man and Woman الإنسان والعلاقات الاجتماعية > المجتمعات > خلقهم من نفس واحدة Man and The Social Relations>The Societies>Their Creation from One Self التجارة و الزراعة و الصناعة و الصيد > الزارعة Trade, Agriculture, Industry and Hunting>Agriculture العلوم والفنون > الحقائق العلمية و الإشاة إلى وقائع أيدتهاالإكتشافات العلمية > الإنسان وخلقه Science and Art> The Scientific Facts and the Indication to Facts which have been supported by the Scientific Discoveries>Man and his Creation العلوم والفنون > الحقائق العلمية و الإشاة إلى وقائع أيدتهاالإكتشافات العلمية > الزراعة Science and Art> The Scientific Facts and the Indication to Facts which have been supported by the Scientific Discoveries>Agriculture العلوم والفنون > الحقائق العلمية و الإشاة إلى وقائع أيدتهاالإكتشافات العلمية > النبات Science and Art> The Scientific Facts and the Indication to Facts which have been supported by the Scientific Discoveries>Plants
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Josh Shepherd CV
<EMAIL_ADDRESS>• linkedin.com/in/joshshepherd-
Personal Profile:
Skilled in Computer Science, can quickly adapt to any new situation, very interested in gaming and the communities within, spend most my free time either gaming or contributing to open source projects to help the community, always full of energy and put all my effort into my work
Areas of Expertise:
- Can quickly adapt to different situations
- Massive interest in helping others
- Confident, punctual, reliable, formal, smart
- Able to listen and respond accordingly
- Can work well under pressure
- Experienced working in a good team
Education:
University of Wolverhampton
Wolverhampton, UK
Degree in Computer Science (Games Development) (September 2017 - July 2021)
TDM Wyre Academy
Birmingham, UK
Apprenticeship at Simworx Ltd. (September 2015 - September 2017)
Hillcrest School & Community College
Dudley, UK
Secondary School (2007 - 2012)
Work Experience:
Junior Software Developer
Simworx Ltd. - Kingswinford, UK (September 2015 - March 2018)
- Creating & maintaining in-house WPF C# applications (using MVVM) and ride experience
projects made inside Unreal Engine 4 & Unity 3D
- Produced documentation for programs & code to be given to ride operators and future
developers, including screenshots, step by step guides & F.A.Q's
- Maintained & managed IT for the whole company alone. Specified and ordered all IT
equipment, including setup & installation for new employees.
YouTube Content Creator
youtube.com/JoshLmao (April 2012 – September 2015)
- Created gaming related videos for audiences around the world
- Specialised in gaming walkthroughs, entertaining gaming content videos & gaming guides
and tutorials
McDonalds Crew Member
McDonalds – Upper Gornal, UK (December 2013 – February 2014)
- Served as a member of the staff working front tills & and serving customers
- Helped with preparing food in the back and organizing orders
Projects:
Avengers: Endings website
AvengersEndings.com
Spotify4Unity - Unity Asset
Unity asset which provides and improves the experience of using the Spotify Web API inside Unity.
Lumière - Game Jam
A game I created by myself for the Ludum Dare game jam. All assets and code were done by myself in one weekend.
As the theme was "Running out of power", I made an very simple infinite runner game with enemies, that deplete your
healty the more you move and get hit.


November 1
The Power of Love
But faith...works by love. Galatians 5:6
I used to wonder why we believers didn't see more of the power of God operating among us than we do. With what we know about faith and the Word, it seemed to me signs and wonders and miracles should be happening all the time. So, one day I asked, "Lord, why isn't the power of God turned up to a higher volume in the Church?" Do you know what He said? He told me we weren't walking in enough love yet. God wants us to have power. But first, He has to be sure we'll use that power in love. He wants to know that we won't take it and mix it with judgment and criticism and blast people out of the water. "Kenneth," He said, "I can't afford to back your words with supernatural power in a church service on Sunday morning and then have you get on the freeway that night and lash out at someone because he pulled over into your lane. You'd blow him off the highway. I can't leave the power of Almighty God at a high volume in the mouth of an unmerciful fool."
Then He reminded me of a time I took my son, John, hunting. He was just a little guy at the time, barely big enough to keep his gun from knocking him on his back every time he'd pull the trigger. I was teaching him how to shoot, and he was coming along pretty well. That day we were walking along out at Gloria's grandparents' farm and John spotted one of the biggest tarantula spiders I'd ever seen. It was clinging to the wall of the barn. When John saw that spider, he took aim. He was going to blow that bug away.
If I hadn't stopped him, he would have too. And it wouldn't have dawned on him until it was all over that he was going to blow a hole in the barn at the same time. From my grown-up perspective, I could see that would be foolishness. He couldn't. He was looking through the eyes of a child.
Do you want God to put a shotgun of spiritual power in your hand, so you can blow the works of the devil to kingdom come? Then focus on love. Pursue it. Practice it. Study it. Grow up in it. Then you'll see the power of God operating through you.
Scripture Reading: Romans 13:8-14


With great responsibility comes great power - Mark Manson.
I have over 2-years of IT experience in Software Engineering and Web
application design & development. I love to design and develop resilient
and fault-tolerant applications using the essence of Software Engineering
and with discrete programming languages, design patterns, and frameworks.
I predominantly work with radiant user interface design & development and
reliable business web service advancement.
Software Engineering, Web application development, and DevOps are my cup
of coffee, apart from these Computer Science areas. I love to concentrate
on Cloud Computing, Human-computer interaction (HCI), and Neurosciences as
my research.
Wipro Limited
Senior Project Engineer, Full-time employee
Hyderabad, Telangana, India (December, 2021 - Current)
• User Interface design using Angular framework and Angular Material UI
component library.
• Agile methodology.
Full Stack Developer, Full-time employee
Hyderabad, Telangana, India (February, 2021 - Current)
• Developed producers and subscribers for JMS (Java Message Service)
APIs.
• Worked with Microservices architecture.
• Scrum software development.
• Collaboration with cross-functional teams across different
geographies.
Back-end Developer, Full-time employee
Hyderabad, Telangana, India (June 2020 - February 2021)
• Modernization of legacy business model.
• Implementation of server-side business logic.
• Development of RESTful stateless web service APIs using the Spring
Boot framework to overcome security vulnerabilities.
• Manual deployment and automated deployment using Jenkins.
• Agile Delivery Framework (ADF).
Front-end Developer, Full-time employee
Hyderabad, Telangana, India (January 2020 - June 2020)
• Digitalisation of naïve business model.
• Design of user-interface using Angular framework to modernize the look
and feel of the web application.
• Reduction in a large effort of manual work.
• Scaled agile framework (SAFe) development.
Automation Engineer, Full-time employee
Hyderabad, Telangana, India (August 2019 – January 2020)
Bengaluru, Karnataka, India (May 2019 – August 2019)
• Quality and assurance of the web application developed.
• Automating testing process by using Selenium and Cucumber frameworks.
• On-time Delivery and quality of product according to business by
adopting TDD (Test-driven development) dialects.
• Agile testing.
Elite Intern (Project Engineer Intern), Internship
Hyderabad, Telangana, India (January 2019 - April 2019)
• Designing a web application using the Angular framework.
• Automating server monitoring alerts.
GeeksforGeeks, A computer science portal for geeks
K L University, Guntur, Andhra Pradesh, India (2018 - 2019)
A Wikipedia of Computer Science
• Internet marketing.
• Public speaking (Declamation).
• Conducting and managing hackathons, coding, workshops, quizzes, and
other series of events that are exclusive to Computer Science.
• A Bridge between GeeksforGeeks and programming clubs, University
placement cell.
Undergraduate
Bachelor of Technology (B. Tech) in Computer Science and Engineering
K L University
Guntur, Andhra Pradesh, India (July 2015 – May 2019)
• Specialisation in Cloud Computing.
• Public speaking (Declamation).
• Research work concentrated on Cloud Computing and Neural Networks.
Senior secondary (APBIE)
Intermediate (HSC - Higher Secondary Certificate)
Narayana Junior College
Vijayawada, Andhra Pradesh, India (June 2013 - March 2015)
• Major in Maths, Physics, and Chemistry.
K L University
Analysis of cloud computing security and strategies using neural networks.
• System which can adopt the Neural Networks algorithm and study the
different Cloud Computing security systems and strategies for the
improvement of cloud security.
Analysis of Dynamic Task Scheduling Based on A Two-Stage Strategy in Cloud
Computing.
• Mainly concentrating on the performance efficacy, taking parameters
such as Task types and its parameters and VM types and its parameters to
make our assumptions on quality of algorithm performance using Cloud
simulation tools.


Pitch Form
Write your name and your partner's name here:
Request for 3rd
Choose your team/app name:
App Name
Team Name
Submit


SUNY and CUNY to waive application fees for veterans, Cuomo says
by APNEW YORK -- New York Gov. Andrew Cuomo says application fees for the State University of New York and City University of New York systems will be waived for veterans and their spouses.
The Democrat announced the new measure Sunday. He says he hopes this change will allow veterans and their families to "take the next steps in adapting back to civilian life."
The SUNY and CUNY systems require applicants to pay a fee ranging from $50 to $65.
The Board of Trustees for both CUNY and SUNY will include resolutions to implement the change at their next board meetings.
Cuomo also says several state landmarks will be lit red, white and blue on Nov. 11 in recognition of Veteran's Day.


Issue #29 - expando named submit breaks validation.
Your Name:
Username: (not "foo")
Email Address:
Will you submit to receiving our newsletter?
Sign Up:


Influence tokens which give you more control over post payouts and allow you to earn on curation rewards. Part of hightouch's STEEM POWER is currently delegated. Delegation is donated for influence or to help new users perform actions on steemit. Your delegation amount can fluctuate.
STEEM and SBDs are tradeable tokens that may be transferred anywhere at anytime to any other cryptocurrency or FIAT. SBD can be exchanged on the market for STEEM. Steem can be converted to STEEM POWER in a process called powering up.
Balances subject to 3 day withdraw waiting period.
The estimated value is based on an average value of Steem in US dollars.


GAMUT Sensor Data Workflow
This is a workflow illustrating the steps in the flow of GAMUT data and the CI tools deployed to support it. The workflow ensures that data are continuously ingested with essential metadata. It also addresses the challenges related to tracking physical infrastructure, managing data streaming from multiple watersheds, and data quality assurance and quality control (QA/QC). A detailed description of the workflow can be found here.
1. Monitoring Site Design and Communications
The suite of sensors at each site are connected to a datalogger, which provides programming logic to operate the site and onsite data storage. A single connected server enables automated communication, scheduled download of data, functionality to send new programs and instructions to the site, and a variety of communication and data collection diagnostic utilities.
2. Server Infrastructure
The GAMUT data workflow is spread across three virtual servers:
- A server running Campbell Scientific’s Loggernet software that manages communication with each site.
- A server running Microsoft SQL Server hosts the operational databases into which the streaming sensor data are loaded.
- A web server on which several applications are hosted for sharing GAMUT data on the Internet.
3. Streaming Data Loading
Data are downloaded as text files from each of the monitoring sites and then the Streaming Data Loader, which is installed on the Loggernet server, is used to load the data into Observations Data Model (ODM) databases (one for each watershed), housed on the Microsoft SQL Server. The Streaming Data Loader and the ODM databases are components of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) .
4. Operational Databases and Data Quality Assurance
In addition to the field protocols specified in the GAMUT QA/QC Plan (e.g., site visits, calibrations, maintenance, etc.), we have developed automated data checks as stored procedures on the databases. New data are screened for battery voltages below acceptable thresholds, values that are outside acceptable ranges, values that are persistent, etc. When problematic conditions are detected, email alerts notify watershed technicians so that they can investigate.
5. Data Quality Control
ODM Tools Python is an open source software application that enables the watershed technicians to perform quality control edits on the data stored in the ODM database. Features include the export of data series and associated metadata, plotting and summarizing multiple data series, and the recording of quality control edits performed on data series through an integrated Python script editor and console. This important functionality moves raw data to quality controlled products while capturing information about transformations to ensure that the full provenance of the data is recorded. ODM Tools Python is available at https://github.com/UCHIC/ODMToolsPython.
6. Backups
The GAMUT data management workflow contains multiple levels of backups.
- Storage on the datalogger at each monitoring site ensures that if the centralized data management system goes offline, data collection will continue uninterrupted.
- The Loggernet network is backed up regularly using the built-in backup utility.
- The individual text files downloaded from each site are regularly archived ensuring that the original data files will always be available exactly as they were collected.
- The operational SQL Server databases have regular file-based backups.
- Each of the virtual servers on which the system runs are scheduled for backups using the capabilities of the virtualization software ensuring continuity if the physical hardware were to fail.
7. Data Sharing on the Internet
The data stored in the databases are shared on the Internet using multiple mechanisms. The data are published using the CUAHSI HIS WaterOneFlow web services, which can be accessed via web service requests. We developed a web-based interface including the ability to browse available data series, an interactive map, and a variety of plot types and descriptive statistics. The web-based data visualization is found at http://data.iutahepscor.org/tsa/.
8. Equipment Management
We developed a database for storing information on the inventory of data collection equipment (e.g., sensors, dataloggers, etc.), deployments of the equipment, and records of field activities and service events. This database is linked to a web application interface enabling the watershed technicians to enter information about the equipment, where it is deployed, and field activities performed on the equipment. The web application is available via GitHUB: https://github.com/UCHIC/ODM2Sensor/.


Preparation time
30 mins to 1 hour
Cooking time
1 to 2 hours
Serves
Makes 1 cake
Dietary
Mich's 'skinnylicious' vanilla cake is fat-free and dairy-free, yet doesn't compromise on flavour. This cake should be eaten on the day it is made.
Equipment: you will need a sugar thermometer and a blowtorch
To make the compote, put the fruit, sugar, orange juice and zest in a heavy-based saucepan over a medium heat. Bring to a gentle simmer, then cook at a gentle simmer for 25–30 minutes, stirring occasionally, until the fruit has softened and reduced to a thick pulp.
Remove from the heat and strain the fruit through a sieve, using the back of a spoon to push as much through as possible. Taste and adjust the sugar or orange juice if necessary. Leave to cool, then store in an airtight container in the fridge until ready to use.
For the cakes, preheat the oven to 160C/140C Fan/Gas 3. Grease and line the base and sides of three 20cm/8in loose-bottomed sandwich tins with baking paper.
Put the eggs, sugar and vanilla bean paste in a large heatproof bowl set over a saucepan of gently simmering water, making sure the bowl doesn't touch the water. Beat together using a whisk until the sugar has dissolved and the mixture has reached 40–45C (it should feel like a hot bath). Remove from the heat.
Pour the mixture into a standing mixer and whisk at maximum speed for 15 minutes, or until the batter is cool and thick and has tripled in volume.  Gently fold in the flour, 2 tablespoons at a time, using a metal spoon or rubber spatula, until fully combined. Pour the batter into the tins and bake for 25–30 minutes, or until the cakes are risen, golden brown and spring back when pressed. Transfer to a wire cooling rack and leave to cool for 5 minutes. Turn the cakes out of the tins and leave to cool completely.
Meanwhile, make the Italian meringue. Heat the sugar, glucose and 4 tablespoons water in a saucepan over a medium–high heat, stirring gently. Using a sugar thermometer, check when the solution reaches 80C and then stop stirring. Continue to heat the mixture, without stirring. When the sugar syrup reaches 110C, put the egg whites in a clean glass bowl and start whisking on full speed. When the sugar syrup reaches 119C, remove the pan from the heat and add the syrup to the egg whites in a slow, steady stream, while continuing to whisk them. Once all the syrup has been added, whisk until the meringue has cooled completely.
To serve, place one cake on a stand and spread with half the compote. Attach an open star nozzle to a large piping bag, then fill with the meringue. Pipe a third of the meringue in a large circle from the centre outwards. Place the second sponge on top and repeat the process.
Once the third and final sponge is in place, finish with the remaining Italian meringue. Use a blowtorch to colour and caramelise the meringue on top and that is peeping out of the cake. Serve the day you make it.


Rebellion has acquired the Bitmap Brothers, new games coming based on its licenses
by John PapadopoulosRebellion announced today its acquisition of the iconic The Bitmap Brothers brand and portfolio. The Bitmap Brothers were responsible for what many regard as some of the stand-out games of the late 80s and early 90s, including titles such as Xenon, Z: Steel Soldiers, Speedball and Chaos Engine.
As the press release reads, Rebellion plans to bring classic The Bitmap Brothers games to new platforms. Furthermore, it plans to create new titles based on its beloved licenses.
Rebellion CEO and Co-founder Jason Kingsley OBE said:
“We’re delighted with the addition of The Bitmap Brothers to the ever-growing Rebellion portfolio. The Bitmap Brothers are renowned for making great games and for bringing gaming into the mainstream with inimitable style. We’ve known Mike Montgomery for many years, and we’re honoured by the faith and trust that he has shown in us by passing on the torch. We’ll strive to be vigilant custodians of one of gaming’s great names.”


Use of cookies by Travelville
You are advised that the company Travelville uses cookies or other tracers on its online booking sites Travelville.com
These tracers may be installed on your device depending on the preferences that you expressed or may express at any time in accordance with this policy.
1. Why have a cookies policy?
With a view to provide information and ensure transparency, Travelville established this policy so that you can learn more about:
2. What is a cookie?
Cookies and other similar tracers are packets of data used by servers to send status information to a user's browser and return status information to the original server through this same browser.
The status information can be a session identifier, a language, an expiration date, a response field or other types of information.
During their validity period, cookies are used to store status information when a browser accesses various pages of a website or when the browser returns to this website at a later point.
There are different types of cookies:
3. Why use cookies?
We use cookies and other tracers primarily for the following purposes:
Cookies strictly necessary for browsing the Travelville Websites and the ability to use all of their features, and intended in particular to:
Cookies for features intended in particular to:
Cookies for visitor tracking are aimed at improving the comfort of users by helping us understand your interactions with Travelville Websites (most visited pages, applications used, etc.); these cookies may collect statistics or test different ways of displaying information in order to improve the relevance and usability of our services.
Advertising cookies are intended to (i) offer you, in advertising spaces, relevant, targeted content that may be of interest to you (best offers, other destinations, etc.) based on your interests, browsing behaviour, preferences, and other factors, and (ii) reduce the number of times that the advertisements appear.
Affiliate cookies identify the third-party website that redirected a visitor to Travelville websites.
Social network cookies, set by third parties, allow you to share your opinion about and content from Travelville websites on social networks (for example, the "Share" or "Like" application buttons for social networks).
The social network applications on Travelville websites as mentioned above can in some cases allow the social networks concerned to identify you even if you did not click on the application button.
This type of button can allow a social network to track your browsing on Travelville websites, simply because your account in the social network concerned is enabled on your device (open session) while you are browsing.
We recommend that you read the policies of these social networks to familiarize yourself with how they use the browsing information they may collect, especially with regard to advertising. These policies must specifically allow you to make choices on these social networks, particularly by configuring your user accounts for each of them.
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The installation of certain cookies is subject to your consent. Also, when you first visit the Travelville websites, you are asked whether you agree to the installation of this type of cookie, which is only activated after your acceptance.
This process is supported by means of an information banner on the home page of the Travelville websites, which informs you that by continuing to browse, you are agreeing to the installation of cookies that require consent on your device.
You can change your mind at any time using the various methods described in section "Deleting and/or blocking cookies".
5. Deleting and/or blocking cookies
You have several options for deleting cookies and other tracers.
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Although most browsers are set by default to accept cookies, you can, if you desire, choose to accept all cookies, always block cookies, or choose which cookies to accept based on their senders.
You can also set your browser to accept or block cookies on a case-by-case basis before they are installed. Your browser also allows you to regularly delete cookies from your device. Remember to configure all the browsers in your different devices (tablets, smartphones, computers, etc.).
Regarding management of cookies and your preferences, configuration varies for each browser. This is described in the Help menu of your browser, as well as how to edit your preferences with regard to cookies. For example:
Saving a cookie to your device depends on your wishes, which you can exercise and change at any time and free of charge using the settings offered by your browser software.
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For example, you can visit the website http://www.youronlinechoices.com to prohibit the installation of these cookies on your device. This website is offered by digital advertising professionals brought together within the European Digital Advertising Alliance (EDAA) and managed in France by Interactive Advertising Bureau France.


Git,Github and Version Control System
Version control is a way of tracking changes made on a file, it remembers all the changes made and when the changes are made. There are many benefits for using version control. To mention some, version control can be a way of storing changes made in a file. we are able to navigate through the versions of the project we are making and that gives us security knowing we can go back and forth through the versions. Version control can also let us to work collaboratively in one file, anybody on the team can work freely on any file at any time. we can write a description of what we changed briefly so the other user able to understand simply what we changed.
Git is a fast and modern implementation of version control. Git is the system that saves the contents of the file in your project. Git allows us to work on project from our computer locally and every time we commit using a git command git commit, we are saving the changes that we made. I believe, git is a drive for version control to track the changes.
Github is a social coding website that allows developer to share their code and keep track of changes using git. Github is a platform to push on the changes you made locally in your computer using git to a remote repository in Github. Everyone who is working on the same project able to see the changes you made using Github and also you are able to merge your files together to a common final repository.


Before Change
LIMIT 100
""")
row1_avg = bdb.execute("SELECT AVG(y) FROM row1").fetchall()
row1_avg = row1_avg[0][0]
row5_avg = bdb.execute("SELECT AVG(y) FROM row5").fetchall()
row5_avg = row5_avg[0][0]
// Mean of simulations for row 1 should be "significantly" larger.
assert row1_avg > row5_avg + 10
After Change
assert row5_avg + 10 < row1_avg
// Multiple specified rowids should produce an error.
with pytest.raises(bayeslite.BQLError):
bdb.execute("""
SIMULATE y FROM t_p
GIVEN oid = 5, rowid = 2 LIMIT 10;
""")
with pytest.raises(bayeslite.BQLError):
bdb.execute("""
SIMULATE y FROM t_p
GIVEN _rowid_ = 5, OID = 1, w = 3 LIMIT 10;


Before Change
keepdims=True).expand_as(point_a)
norm_point_b = gs.sum(point_b ** 2, axis=-1,
keepdims=True).expand_as(point_a)
sum_prod_a_b = (point_a * point_b).sum(
-1, keepdims=True).expand_as(point_a)
add_nominator = ((1 + 2 * sum_prod_a_b + norm_point_b) * point_a +
(1 - norm_point_a) * point_b)
After Change
sum_prod_a_b = (point_a * point_b).sum(
-1, keepdims=True)
sum_prod_a_b = gs.repeat(sum_prod_a_b, point_a.shape[-1], -1)
add_nominator = ((1 + 2 * sum_prod_a_b + norm_point_b) * point_a +
(1 - norm_point_a) * point_b)


This step will allow you to block all future Wii U updates, without blocking access to the eShop like the DNS method did.
Blocking updates is not necessary, but recommended on 5.5.2 to prevent future updates breaking your Haxchi/CBHC setup.
If you do not have Haxchi/CBHC, it is recommended that you install it now.
If you are on 5.5.1 with Haxchi/CBHC, it is recommended to update to 5.5.2.
Windows instructions are compatible with Wine on Mac/Linux
If you don't know what this means, ignore this.


| java.lang.Object | ||
| ↳ | android.content.BroadcastReceiver | |
| ↳ | com.esri.android.geotrigger.GeotriggerBroadcastReceiver |
A BroadcastReceiver
that handles communication with the
GeotriggerService
.
Provides interfaces that can be implemented on an Activity or Service to receive callbacks when Geotrigger events occur.
| Constants | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| String | ACTION_LOCATION_UPLOADED | Broadcast Action: The GeotriggerService has successfully pushed all batched location fixes to the server. |
|||||||||
| String | ACTION_SEND_UPDATES_FAIL | Broadcast Action: The service attempted to send location updates to the server, but failed to do so. | |||||||||
| String | ACTION_TRACKING_PROFILE_CHANGED | Broadcast Action: The tracking service has switched to a new tracking profile. | |||||||||
| String | EXTRA_COUNT | An integer representing the number of objects affected. | |||||||||
| String | EXTRA_NEW_PROFILE | The new tracking profile. | |||||||||
| String | EXTRA_OLD_PROFILE | The previous tracking profile. |
| Public Constructors | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| GeotriggerBroadcastReceiver() |
| Public Methods | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| static IntentFilter |
getDefaultIntentFilter()
Get a default
IntentFilter that will match all actions declared by
GeotriggerBroadcastReceiver . | ||||||||||
| final void | onReceive(Context context, Intent intent) |
| Protected Methods | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| void |
onDeviceRegisterFail(Context context)
Override this method to implement your own behavior to occur when the device fails
to register with ArcGIS Online.
| ||||||||||
| void |
onGeofenceCreation(Context context, String triggerId, double latitude, double longitude, float radius, boolean isHop)
Override this method to implement your own behavior when a geofence is created.
| ||||||||||
| void |
onGeofenceRemoved(Context context, String triggerId)
Override this method to implement your own behavior when a non-hop geofence is removed (and no longer being tracked).
| ||||||||||
| void |
onLocationUpdate(Context context, Location location, boolean isOnDemand)
Override this method to implement your own behavior to occur when a new location fix is obtained.
| ||||||||||
| void |
onLocationUpload(Context context, int count)
Override this method to implement your own behavior to occur when locations are uploaded to the Esri
Geotrigger Service API.
| ||||||||||
| void |
onLocationUploadFail(Context context)
This method attempts to call the method of the same name on the associated listener by
casting Context to that listener type.
| ||||||||||
| void |
onPushMessage(Context context, Bundle data)
Override this method to implement your own behavior to occur when push messages are received via GCM.
| ||||||||||
| void |
onPushTokenRegister(Context context, String pushToken)
Override this method to implement your own behavior to occur when the device is ready to receive push notifications. | ||||||||||
| void |
onPushTokenRegisterFail(Context context, String pushToken)
Override this method to implement your own behavior to occur when the device fails to update
the server with its push message token.
| ||||||||||
| void |
onReady(Context context)
Override this method to implement your own behavior to occur when the device has
registered with ArcGIS Online and is ready to make API calls.
| ||||||||||
| void |
onTrackingProfileChange(Context context, String oldProfile, String newProfile)
Override this method to implement your own behavior to occur when the tracking profile changes.
|
|
[Expand]
Inherited Methods | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
From class
android.content.BroadcastReceiver
| |||||||||||
|
From class
java.lang.Object
|
Broadcast Action: The GeotriggerService
has successfully pushed all batched location fixes to the server.
The intent will have the following extra values:
EXTRA_COUNT
the number of fixes pushed to the server.
Broadcast Action: The service attempted to send location updates to the server, but failed to do so.
This could be due to a local exception, or an error code being returned from the server.
Broadcast Action: The tracking service has switched to a new tracking profile.
The intent will have the following extra values:
An integer representing the number of objects affected.
The new tracking profile.
The previous tracking profile.
Get a default IntentFilter
that will match all actions declared by
GeotriggerBroadcastReceiver
.
This method is intended to be used as a shortcut when registering a new broadcast receiver in your Activity
or Service classes:
registerReceiver(receiver, GeotriggerBroadcastReceiver.getDefaultIntentFilter());
IntentFilter
.
Override this method to implement your own behavior to occur when the device fails to register with ArcGIS Online.
This method attempts to call the method of the same name on the associated listener by casting Context to that listener type.
| context | a Context object.
|
|---|
Override this method to implement your own behavior when a geofence is created.
| context | a Context object. |
|---|---|
| triggerId | Should match what was set when the trigger was created, unless isHop is equal to true. |
| latitude | latitude of the center of the geofence. |
| longitude | longitude of the center of the geofence. |
| radius | the radius of the circular geofence. |
| isHop | indicates whether this is an internal geofence (true) or a geofence representing a trigger (false). |
Override this method to implement your own behavior when a non-hop geofence is removed (and no longer being tracked).
| context | a Context object. |
|---|---|
| triggerId | Should match what was set when the trigger was created, but may also be an internal id. |
Override this method to implement your own behavior to occur when a new location fix is obtained. This method attempts to call the method of the same name on the associated listener by casting Context to that listener type.
| context | a Context object. |
|---|---|
| location | a Location object. |
| isOnDemand | whether or not this location was recorded as a result of calling
requestOnDemandUpdate(Context) .
|
Override this method to implement your own behavior to occur when locations are uploaded to the Esri Geotrigger Service API.
This method attempts to call the method of the same name on the associated listener by casting Context to that listener type.
| context | a Context object. |
|---|---|
| count | the number of location fixes that were uploaded. |
This method attempts to call the method of the same name on the associated listener by casting Context to that listener type.
Override this method to implement your own behavior to occur when the device fails to upload location fixes to the server.
| context | a Context object.
|
|---|
Override this method to implement your own behavior to occur when push messages are received via GCM.
This method attempts to call the method of the same name on the associated listener by casting Context to that listener type.
| context | a Context object. |
|---|---|
| data | message data. |
Override this method to implement your own behavior to occur when the device is ready to receive push notifications.
This method attempts to call the method of the same name on the associated listener by casting Context to that listener type.
| context | a Context object. |
|---|---|
| pushToken | GCM Token |
Override this method to implement your own behavior to occur when the device fails to update the server with its push message token.
This method attempts to call the method of the same name on the associated listener by casting Context to that listener type.
| context | a Context object. |
|---|---|
| pushToken | a GCM push token. |
Override this method to implement your own behavior to occur when the device has registered with ArcGIS Online and is ready to make API calls.
This method attempts to call the method of the same name on the associated listener by casting Context to that listener type.
| context | a Context object.
|
|---|
Override this method to implement your own behavior to occur when the tracking profile changes.
This method attempts to call the method of the same name on the associated listener by casting Context to that listener type.
Tracking profile values may be:
| context | a Context object. |
|---|---|
| oldProfile | the previous tracking profile. |
| newProfile | the new tracking profile. |


You can use this tool to send BitcoinZ to your friends using their eMail addresses. The give.btcz.app BTCz Giveaway Tool is an free service that provides you end-to-end-user BTCz payment control. It generates a new address (and QR) for each payment. A expiration date is set in case your freinds do not take the payment, in which case it will be returned to your address. This is an open-sourced solution available as a baseline for further development by anyone. Intended to push user acceptance, this project can be a great lever for entrepreneurship people to use in their BTCz promotion efforts. The BTCz Giveaway Tool allows anyone and everyone to promote BTCz in the easiest possible way.
For any comment, suggestion or issues about this tool, feel free to contact us via the below community channels. You can also contact us directly by using the contact form. Please visit first the FAQFAQ page for any usage related questions. Thank you!
This is a free and open-source service, please support us by donating here.
[BTCz.app] BTCz: t1VYSo8VtpKMm1SUwp1KJHbqrtfqj7tgpaE[Community] BTCz: t1fHHnAXxoPWGY77sG5Zw2sFfGUTpW6BcSZ[Increase Gift] BTCz: <%= GiftAddress %>


Hey! Myself Mahedi Hasan Niloy.I am a Software Engineer. Currently work as an Intern in Object Canvas Technology.Hands on experience on different Programing Language like C, C#, Java, Python,Java Script. Markup language like HTML and CSS and Framework like Dot Net and Entity Framework.I am ready to work as a junior Software Engineer.
In Web Design i have knowledge in HTML5, CSS3, Java Script. Framework: Ajax, React JS
I can Develop Software in many Programing platform like C Sharp, Java, Python and using framework Dot net, Entity framework.
My Final year project was based on Machine Learning and Data Science. I have a knowledge on Python and work with Panda, Numpy, tensorflow API
I am also a Researcher. In university life i reaserch on Artifical intiligence and data science. I have one reaserch papaer which is published on Scope Index.Me and my teammate present our paper in An inetrnational Conference which is held on New Delli,India. Our another paper also selected in an International Conference held in Madina, Soudi Arabia. Beacuse of COVID-19 we are not able to attend on it.
Detecting Bangladesh Vegetable using Tensorflow Object Detection API
Hospital Managment System Using HTML and CSS
Detection of Hate Speech in Bangla language using AI
Criminal Database
MVC, Desktop CRUD Operation using Entity Framework
Computer Science Graduate. Junior Software Engineer in Object Canvas Technology
Download Resume


Within the Nephological semantics project, I use vector space models to look at the semasiological variation within a type (e.g. polysemy) and a visualization tool developed within the project (mainly by Thomas Wielfaert) to assess the effect of parameter tuning in our models.
Click here to take a look!


Word:
Start Game
Users ready:
READY
(You are NOT READY)
START GAME!


About
Plugins give very low access to each net work request through two main methods; option modification and request modification. Before starting the server, all options are free to be modified by plugin.options and then every request can be modified through plugin.modifications. Parameters for plugin.options are the server options and parameters for modifications is http.Request and http.ServerResponse All plugins are passed as an array through the plugins option.
Example
plugin.js
/**
* an example plugin to error every request and overwrite a return function
*/
/*
* ops is are the server options
*/
module.exports.options = function(ops)
{
ops.returnFunctions.returnTest=()=>'test plugin returned function';
}
/*
* req is the raw http request and res is the raw http response
*/
module.exports.modifications = function(req, res)
{
res.statusCode=302;
}
lightspeed options
plugins:[
require('./plugin')
]


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The following is a list of compile dependencies for this project. These dependencies are required to compile and run the application:
The following is a list of test dependencies for this project. These dependencies are only required to compile and run unit tests for the application:
| GroupId | ArtifactId | Version | Type | License |
|---|---|---|---|---|
| junit | junit | 4.11 | jar | Common Public License Version 1.0 |
The following is a list of provided dependencies for this project. These dependencies are required to compile the application, but should be provided by default when using the library:
| GroupId | ArtifactId | Version | Type | License |
|---|---|---|---|---|
| org.apache.maven.plugin-tools | maven-plugin-annotations | 3.2 | jar | The Apache Software License, Version 2.0 |
The following is a list of transitive dependencies for this project. Transitive dependencies are the dependencies of the project dependencies.
The following is a list of compile dependencies for this project. These dependencies are required to compile and run the application:
The following is a list of test dependencies for this project. These dependencies are only required to compile and run unit tests for the application:
| GroupId | ArtifactId | Version | Type | License |
|---|---|---|---|---|
| org.hamcrest | hamcrest-core | 1.3 | jar | New BSD License |
Eclipse Public License, Version 1.0: Aether :: API, Aether :: Implementation, Aether :: SPI, Aether :: Utilities, Sisu-Inject-Bean : Aggregate OSGi bundle, Sisu-Inject-Plexus : Aggregate OSGi bundle
Apache Public License 2.0: Plexus Cipher: encryption/decryption Component, Plexus Security Dispatcher Component, plexus-build-api
Unknown: Default Plexus Container, ant-launcher, classworlds, org.apache.tools.ant
Apache License, Version 2.0: Native ARchive plugin for Maven
Public Domain: XZ for Java
New BSD License: Hamcrest Core
The Apache Software License, Version 2.0: Apache Maven Wagon :: API, Commons Compress, Commons IO, FindBugs-bcel, Maven Aether Provider, Maven Artifact, Maven Artifact Manager, Maven Common Artifact Filters, Maven Compat, Maven Core, Maven Model, Maven Model Builder, Maven Plugin API, Maven Plugin Java 5 Annotations, Maven Plugin Registry Model, Maven Profile Model, Maven Project Builder, Maven Repository Metadata Model, Maven Settings, Maven Settings Builder, Plexus :: Component Annotations, Plexus Archiver Component, Plexus Classworlds, Plexus Common Utilities, Plexus Compiler Api, Plexus IO Components, Plexus Interpolation API, Sisu Guava - Core Library, Sisu Guice - Core Library, Sisu-Inject-Bean : Aggregate OSGi bundle, SureFire API, SureFire Booter, XML Commons External Components XML APIs, Xerces2 Java Parser
Common Public License Version 1.0: JUnit
| Filename | Size | Entries | Classes | Packages | JDK Rev | Debug | Sealed |
|---|---|---|---|---|---|---|---|
| classworlds-1.1-alpha-2.jar | 36.64 kB | 32 | 22 | 3 | 1.1 | debug | - |
| bcel-findbugs-6.0.jar | 598.02 kB | 438 | 419 | 8 | 1.5 | debug | - |
| commons-io-2.4.jar | 180.80 kB | 128 | 110 | 6 | 1.6 | debug | - |
| junit-4.11.jar | 239.30 kB | 266 | 233 | 28 | 1.5 | debug | - |
| ant-1.8.1.jar | 1.44 MB | 924 | 873 | 30 | 1.4 | debug | - |
| ant-launcher-1.8.1.jar | 12.02 kB | 12 | 5 | 1 | 1.4 | debug | - |
| commons-compress-1.5.jar | 250.24 kB | 162 | 135 | 14 | 1.5 | debug | - |
| maven-aether-provider-3.0.4.jar | 55.17 kB | 40 | 23 | 1 | 1.5 | debug | - |
| maven-artifact-3.0.4.jar | 50.87 kB | 57 | 32 | 11 | 1.5 | debug | - |
| maven-artifact-manager-2.0.8.jar | 55.34 kB | 46 | 24 | 7 | 1.4 | debug | - |
| maven-compat-3.0.4.jar | 274.90 kB | 211 | 159 | 29 | 1.5 | debug | - |
| maven-core-3.0.4.jar | 545.94 kB | 428 | 350 | 43 | 1.5 | debug | - |
| maven-model-3.0.4.jar | 159.72 kB | 67 | 50 | 3 | 1.5 | debug | - |
| maven-model-builder-3.0.4.jar | 147.06 kB | 142 | 109 | 16 | 1.5 | debug | - |
| maven-plugin-api-3.0.4.jar | 48.16 kB | 46 | 25 | 6 | 1.5 | debug | - |
| maven-plugin-registry-2.0.8.jar | 28.41 kB | 28 | 10 | 2 | 1.4 | debug | - |
| maven-profile-2.0.8.jar | 34.54 kB | 33 | 16 | 2 | 1.4 | debug | - |
| maven-project-2.0.8.jar | 113.93 kB | 76 | 51 | 10 | 1.4 | debug | - |
| maven-repository-metadata-3.0.4.jar | 29.37 kB | 25 | 7 | 2 | 1.5 | debug | - |
| maven-settings-3.0.4.jar | 45.57 kB | 33 | 17 | 2 | 1.5 | debug | - |
| maven-settings-builder-3.0.4.jar | 40.41 kB | 53 | 32 | 5 | 1.5 | debug | - |
| maven-plugin-annotations-3.2.jar | 14.73 kB | 23 | 8 | 1 | 1.5 | debug | - |
| maven-common-artifact-filters-1.4.jar | 30.86 kB | 36 | 19 | 2 | 1.5 | debug | - |
| surefire-api-2.6.jar | 518.40 kB | 298 | 245 | 23 | 1.3 | debug | - |
| surefire-booter-2.6.jar | 270.34 kB | 150 | 112 | 12 | 1.3 | debug | - |
| wagon-provider-api-2.2.jar | 51.31 kB | 61 | 39 | 8 | 1.5 | debug | - |
| plexus-archiver-2.4.4.jar | 160.12 kB | 133 | 108 | 12 | 1.5 | debug | - |
| plexus-classworlds-2.4.jar | 45.96 kB | 52 | 37 | 5 | 1.4 | debug | - |
| plexus-compiler-api-2.3.jar | 24.42 kB | 34 | 20 | 4 | 1.5 | debug | - |
| plexus-component-annotations-1.5.5.jar | 4.11 kB | 15 | 3 | 1 | 1.5 | release | - |
| plexus-container-default-1.0-alpha-9-stable-1.jar | 189.63 kB | 215 | 175 | 28 | 1.1 | debug | - |
| plexus-interpolation-1.14.jar | 59.64 kB | 60 | 44 | 6 | 1.4 | debug | - |
| plexus-io-2.0.10.jar | 57.41 kB | 63 | 44 | 5 | 1.5 | debug | - |
| plexus-utils-3.0.17.jar | 245.89 kB | 129 | 104 | 9 | 1.6 | debug | - |
| hamcrest-core-1.3.jar | 43.97 kB | 52 | 45 | 3 | 1.5 | debug | - |
| aether-api-1.13.1.jar | 87.56 kB | 116 | 96 | 11 | 1.5 | debug | - |
| aether-impl-1.13.1.jar | 127.10 kB | 87 | 73 | 2 | 1.5 | debug | - |
| aether-spi-1.13.1.jar | 15.07 kB | 33 | 17 | 5 | 1.5 | debug | - |
| aether-util-1.13.1.jar | 126.98 kB | 116 | 92 | 14 | 1.5 | debug | - |
| plexus-build-api-0.0.7.jar | 8.28 kB | 19 | 4 | 1 | 1.4 | debug | - |
| plexus-cipher-1.4.jar | 13.18 kB | 20 | 6 | 1 | 1.4 | debug | - |
| plexus-sec-dispatcher-1.3.jar | 27.89 kB | 31 | 13 | 3 | 1.4 | debug | - |
| sisu-guava-0.9.9.jar | 1.44 MB | 1,183 | 1,163 | 10 | 1.5 | debug | - |
| sisu-guice-3.1.0-no_aop.jar | 349.05 kB | 310 | 295 | 8 | 1.5 | debug | - |
| sisu-inject-bean-2.3.0.jar | 282.29 kB | 256 | 224 | 15 | 1.5 | debug | - |
| sisu-inject-plexus-2.3.0.jar | 199.64 kB | 207 | 163 | 27 | 1.5 | debug | - |
| xz-1.2.jar | 92.59 kB | 115 | 102 | 9 | 1.4 | release | sealed |
| Total | Size | Entries | Classes | Packages | JDK Rev | Debug | Sealed |
| 47 | 8.73 MB | 7,061 | 5,953 | 454 | 1.6 | 45 | 1 |
| compile: 44 | compile: 8.44 MB | compile: 6,720 | compile: 5,667 | compile: 422 | - | compile: 42 | compile: 1 |
| test: 2 | test: 283.26 kB | test: 318 | test: 278 | test: 31 | - | test: 2 | - |
| provided: 1 | provided: 14.73 kB | provided: 23 | provided: 8 | provided: 1 | - | provided: 1 | - |


Audio-Video-Programming - Prof. Dr. Plaß & Sudau - WiSe 17/18
Project Descrpition:
„SoundSpace“ is a tangible sound application which allows you to transfer into different acoustic settings. As a user you are able to adjust every audio source the way you would like it to be.
You can use different shapes as well as a webinterface to adjust your SoundSpace. The shapes can be placed on a given plane, which represents the real room as a white box.
Using the webinterface the you can chooses a setting and set different parameters for each shape. Wherever you place the shapes, a Sound according to your Settings
in the web Interface will appear.
Chooses a Setting as well as different Parameters for each shape.
By default every shape has already assigned an audiosource
Just feel free to Experiment and enjoy your personal 3D SoundSpace!


1 Samuel 17:57
And as David returned from the slaughter of the Philistine, Abner took him, and brought him before Saul with the head of the Philistine in his hand.


Labour says 'horrific' job centre death shows 'stark reality of Tory Britain'
A 65-year-old mancollapsed at Llanelli Job Centre in Carmarthenshire, southwest Wales, as he reportedly waited for an appointment to discuss his Jobseeker's Allowance
by Bradley Jolly, https://www.mirror.co.uk/authors/bradley-jolly/Labour's Ian Lavery said that the "horrific" death of a 65-year-old in a job centre after being declared fit to work showed the "stark reality of Tory Britain".
The 65-year-old, who has not been named, was sat in the waiting area of Llanelli Job Centre Plus in Carmarthenshire, Wales, when he fell unwell.
Fellow customers and staff rushed to help him and started CPR but he died at the scene on Friday morning.
It is believed he was declared fit for work by the job centre earlier this year, and was waiting for an appointment about Jobseeker's Allowance.
Mr Lavery said:"This horrific incident shows the stark reality of Tory Britain.
"By forcing unwell people to be ‘fit for work’, the Conservatives have shown they are unfit to govern.
"The lives and well-being of vulnerable people depend on us removing them from power."
One witness said: "I didn't know him myself, but the man who was sat next to me told me that he had grown up with the guy.
"The man next to me told me that the poorly guy had diabetes and had been declared fit for work by the job centre earlier in the year but he was obviously ill.
"The ambulance came but he sadly died and they had to take his body out of the job centre, it was awful."
Have you been affected by this story? Contact<EMAIL_ADDRESS>Everyone in the building was asked to leave after the man collapsed.
It remained closed for the rest of the day.
The witness added: "This lady who worked there walked over to him after she thought he looked quite unwell and that's when they realised he'd collapsed.
"Another woman and a man helped her put him on the floor and start CPR.
"We were all very shook up, it was a horrible situation that should have never happened.
A spokesperson for the Welsh Ambulance Service said: "We were called on Friday 15 November at approximately 09:33 am to reports of a person needing urgent medical attention on Stepney Street in central Llanelli.
"We responded with one rapid response vehicle, one specialised medical vehicle and one emergency ambulance."
A spokesperson for Dyfed-Powys Police said: "Police in Llanelli responded to a report of the sudden death of a 65-year-old man at Llanelli Job Centre this morning (Friday, November 15).
"There are no suspicious circumstances. Next of kin and HM Coroner have been informed."
A DWP spokesman said: “Our thoughts and those of the staff at Llanelli Jobcentre are with the family and friends at this time."


JINGLE ALL THE WAY A film review by Walter Frith Copyright 1996 Walter Frith
Starring Arnold Schwarzenegger, Sinbad, Rita Wilson and Phil Hartman.
Arnold Schwarzenegger's attempts at comedy have been pretty successful with 'Twins' (1988) 'Kindergarten Cop' (1990) and 'Junior' (1994). 'Junior' is certainly the least of these three films in the opinion of this reviewer but Arnold has struck pure gold with 'Jingle All the Way.' This is an absolutely dizzying and rambunctious physical comedy which puts Arnold in good standing with the genre of slapstick. The big guy plays a workaholic dad and husband who is always breaking promises he's made to his young son and is now feeling the resentment not only from the boy but from his wife. To try and make up for things Arnie promises to get the boy a Turbo Man action figure which is the hottest toy on the market. The only problem is that he procrastinates and on Christmas Eve day is forced to rush all over town to find one which is practically impossible. He is in competition with Sinbad who is trying to find one for his own son and Sinbad delivers some good physical comedy but lays on the laughs mostly with his gift of gab. Director Bryan Levant ('Beethoven') uses some very unattractive characterizations which add an extra dose of laughing gas to the formula of this already fast paced and loony looking farce which plays like a cross between a comic book and Saturday morning cartoon with delightful results. Rita Wilson (Mrs. Tom Hanks) plays Arnold's wife and Phil Hartman is Arnold's seemingly nice guy next door neighbor with a hidden agenda for pursuing Schwarzenegger's wife in romantic fashion. >From devilish Santa Claus con men to a sadistic reindeer to a climax which puts Arnold in Turbo Man's shoes, this is a sheer feel good and hilarious film which had me laughing in all the right places and offers some touching moments as well. 'Jingle All the Way' is the best escapist fare Christmas film since 'Home Alone' and is far superior in every way. This movie's title doesn't only deliver but you will definitely find yourself laughing all the way and if you don't then shame on you, Scrooge!
OUT OF 5> * * * *
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Grand Magister Tifaren:
The necromantic rite of communion is a secret rite performed only by the children of Shilen. It is our secret heritage. Not everyone may receive it. Who sent you?
"High Priest Innocentin sent me."


Non-clickable icons¶
By default, any point icon that you configure in Fusion displays a pop-up balloon whenever EC users click it. You can configure the pop-up balloons to display information about the point. An example is shown below.
However, if you do not want an icon to display a pop-up balloon, you can use one of Fusion’s Advanced options to make it a non-clickable icon. (Non-clickable icons are point icons that do not change when clicked.)
To create a non-clickable icon:
Add a new point vector resource to a vector project. In the example below, the point vector resource is called USA_Cities.
Assign filters, icon types, and minimum and maximum visibility display rule settings to the point layer.
In the Balloon section, change the Style from Default to Advanced.
Leave the Text field and the Advanced field empty.
Click Advanced. The Balloon Style Text window appears.
Click Insert Default Text.
Remove all text except: $[description].
Click OK twice to exit all windows.
Save and Build the vector project.
Build the 3D database, then publish to Fusion.
You can now view the non-clickable icon in EC.


Scroll to target
Another target that should not trigger a scroll
Scroll to target (to attribute)
I'm the target
Scroll to top


{% include "includes/_nav.html" %}
Flask
Student Database
Powered by Python & Firebase
{% block content %} {% endblock %}


This module provides a high-level interface to the functionality of the modulegraph package.
High-level interface, takes iterables for: scripts, includes, packages, excludes
And returns a modulegraph.modulegraph.ModuleGraph instance, python_files, and extensions
python_files is a list of pure python dependencies as modulegraph.Module objects,
extensions is a list of platform-specific C extension dependencies as modulegraph.Module objects
Return two lists: the first one contains the python files in the graph, the second the C extensions.
| Parameters: | mf – a modulegraph.modulegraph.ModuleGraph instance |
|---|
The functionality in this section is much lower level and should probably not be used. It’s mostly documented as a convenience for maintainers.
Return a mapping of implied dependencies. The key is a, possibly dotted, module name and the value a list of dependencies.
This contains hardcoded list of hard dependencies, for example for C extensions in the standard libary that perform imports in C code, which the generic dependency finder cannot locate.
Updates the lists of includes, packages and excludes for the current platform. This will add items to these lists based on hardcoded platform information.
Feeds the given ModuleGraph with the scripts, includes and packages and returns the resulting graph. This function will create a new graph when mf is not specified or None.


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