utils
¶SVD (X, \*\*kwargs) |
Robust SVD decomposition. | ||
absmax (arr) |
Find the absolute maximum of an array. | ||
analytic_expected_correlation (noise_level) |
Expected correlation coefficient of simulated i.i.d. | ||
columnwise_correlation (ypred, y[, zscorea, …]) |
Compute the correlation coefficients | ||
columnwise_rsquared (ypred, y, \*\*kwargs) |
Compute the R2 | ||
delay2slice (delay) |
Get a slicer for an array at a desired delay (e.g. | ||
delay_signal (mat[, delays, fill]) |
Create a temporally shifted version of the data | ||
determinant_normalizer (mat[, thresh]) |
Compute scalar to normalize covariance matrix determinant to 1. | ||
difference_operator (order, nobs) |
Get a finite difference operator matrix of size nobs. | ||
explainable_variance (repeats[, ncorrection, …]) |
Compute the explainable variance in the recorded signals. | ||
fast_indexing (a, rows[, cols]) |
Extract row and column entries from a 2D np.ndarray. | ||
generate_data ([n, p, v, noise, testsize, …]) |
Get some B,X,Y data generated from gaussian (0,1). | ||
generate_trnval_folds (N[, sampler, nchunks, …]) |
Split dataset into training and validation folds | ||
hrf_convolution (input_responses[, HRF, …]) |
Convolve a series of impulses in a matrix with a given HRF | ||
hrf_default_basis ([dt, duration]) |
Hemodynamic response function basis set. | ||
hyperopt_make_trial_data (tid, vals, loss) |
Generate a valid dictionary as a trial for hyperopt.Trials. | ||
hyperopt_make_trials (values, losses[, …]) |
|
||
isdiag (mat) |
Determine whether matrix is diagonal. | ||
mult_diag (d, mat[, left]) |
Efficient multiply a full matrix by a diagonal matrix. | ||
noise_ceiling_correction (repeats, yhat[, …]) |
Noise ceiling corrected correlation coefficient. |
tikreg.utils.
SVD
(X, **kwargs)¶Robust SVD decomposition.
First uses scipy.linalg.svd by default. If the SVD does not converge, it will use a slower more robust SVD algorithm (DGESVD).
See scipy.linalg.svd for full documentation.
| Parameters: |
|
|---|---|
| Returns: |
|
tikreg.utils.
absmax
(arr)¶Find the absolute maximum of an array.
This is somewhat more efficient than e.g. np.nanmax(np.abs(arr))
| Returns: |
|
|---|
Examples
>>> arr = np.random.randn(20,20)
>>> maxval = absmax(arr)
>>> direct_maxval = np.nanmax(np.abs(arr))
>>> assert np.allclose(maxval, direct_maxval)
tikreg.utils.
analytic_expected_correlation
(noise_level)¶Expected correlation coefficient of simulated i.i.d. normal data.
Compute the expectation on the correlation coefficient given the amount of noise in the data. Assumes signal and noise are i.i.d. normal with a fixed noise_level.
| Parameters: |
|
|---|---|
| Returns: |
|
Notes
Assumes both signal and noise are generated from a MVN distribution with zero-mean and the noise variance is determined by \(\sigma\) (noise_level).
If our model is perfect (i.e. \(\hat{y} = y\)), then the maximum correlation coefficient we can achieve is determined by \(\sigma\). Concretely:
where \(\rho\) is the correlation coefficient.
Examples
>>> nstim, noise_level = 100000, 1.0
>>> ydata = np.random.randn(nstim)
>>> noise = np.random.randn(nstim)*noise_level
>>> ypred = zscore(ydata) + noise
>>> empirical_correlation = columnwise_correlation(ydata, ypred)
>>> print(empirical_correlation) # doctest: +SKIP
0.7073279476259667
>>> # As nstim -> inf, this converges to the analytic solution
>>> expected_correlation = analytic_expected_correlation(noise_level)
>>> print(round(expected_correlation, 6))
0.707107
>>> assert np.allclose(expected_correlation, empirical_correlation, atol=1e-02)
tikreg.utils.
columnwise_correlation
(ypred, y, zscorea=True, zscoreb=True, axis=0)¶Compute the correlation coefficients
Predictions and actual responses are matrices whose columns correspond to the units sampled (e.g. voxels, neurons, etc).
| Parameters: |
|
|---|---|
| Returns: |
|
Notes
Recall that the correlation cofficient is defined as
Since it is scale invariant, we can zscore and get the same
Examples
>>> x = np.random.randn(100,2)
>>> y = np.random.randn(100,2)
>>> cc = columnwise_correlation(x, y)
>>> cc.shape
(2,)
>>> c1 = np.corrcoef(x[:,0], y[:,0])[0,1]
>>> c2 = np.corrcoef(x[:,1], y[:,1])[0,1]
>>> assert np.allclose(cc, np.r_[c1, c2])
tikreg.utils.
columnwise_rsquared
(ypred, y, **kwargs)¶Compute the R2
Predictions and actual responses are matrices whose columns correspond to the units sampled (e.g. voxels, neurons, etc).
| Parameters: |
|
|---|---|
| Returns: |
|
References
https://en.wikipedia.org/wiki/Coefficient_of_determination#Extensions
tikreg.utils.
delay2slice
(delay)¶Get a slicer for an array at a desired delay (e.g. a[delay2slice(3)]).
| Parameters: |
|
|---|---|
| Returns: |
|
See also
delay_signal
Examples
>>> # e.g. 5 timepoints and 3 features
>>> mat = np.arange(5*3).reshape(5,3)
>>> print(mat)
[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]
[ 9 10 11]
[12 13 14]]
>>> slicer = delay2slice(2) # delay by 2 samples
>>> # starts at `delay` and has (n - delay) samples
>>> print(mat[slicer])
[[0 1 2]
[3 4 5]
[6 7 8]]
>>> # starts at 0th sample and shifts data explicitly
>>> delayed_mat = delay_signal(mat, [2])
>>> print(delayed_mat)
[[0 0 0]
[0 0 0]
[0 1 2]
[3 4 5]
[6 7 8]]
tikreg.utils.
delay_signal
(mat, delays=[0, 1, 2, 3], fill=0)¶Create a temporally shifted version of the data
| Parameters: |
|
|---|---|
| Returns: |
|
Notes
The data is delayed such that each p columns correspond to one delay. The order of the delays is preserved.
where \(X_{\delta \left(j \right)}\) corresponds to the original data delayed by j samples.
Examples
>>> mat = np.arange(5*3).reshape(5,3)
>>> print(mat)
[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]
[ 9 10 11]
[12 13 14]]
>>> delayed_mat = delay_signal(mat, [0,1,2])
>>> print(delayed_mat.shape)
(5, 9)
>>> print(delayed_mat) #
[[ 0 1 2 0 0 0 0 0 0]
[ 3 4 5 0 1 2 0 0 0]
[ 6 7 8 3 4 5 0 1 2]
[ 9 10 11 6 7 8 3 4 5]
[12 13 14 9 10 11 6 7 8]]
>>> delayed_mat_neg = delay_signal(mat, [-1,0,1]) # negative delays
>>> print(delayed_mat_neg)
[[ 3 4 5 0 1 2 0 0 0]
[ 6 7 8 3 4 5 0 1 2]
[ 9 10 11 6 7 8 3 4 5]
[12 13 14 9 10 11 6 7 8]
[ 0 0 0 12 13 14 9 10 11]]
tikreg.utils.
determinant_normalizer
(mat, thresh=1e-08)¶Compute scalar to normalize covariance matrix determinant to 1.
Uses the pseudo-determinant for numerical robustness. This is implemented by ignoring the smallest eigenvalues.
| Parameters: |
|
|---|---|
| Returns: |
|
Notes
The determinant can be thought of as the variance of a covariance matrix in high-dimensions. Analogous to standardizing data to have unit variance (e.g. z-scoring), one can standardize a matrix to have a determinant of 1. Setting the determinant to 1 allows the covariance structure to vary while keeping the ‘variance’ constant.
This function is useful when sampling covariance matrices from Wishart distributions or when the generating covariance matrix function has hyper-parameters of its own.
Examples
>>> mat = np.random.randn(20,20)
>>> cov = np.dot(mat.T, mat)
>>> assert np.allclose(np.linalg.det(cov), 1.0) is False
>>> scale = determinant_normalizer(cov)
>>> assert np.allclose(np.linalg.det(cov/scale), 1.0)
tikreg.utils.
difference_operator
(order, nobs)¶Get a finite difference operator matrix of size nobs.
| Parameters: |
|
|---|---|
| Returns: |
|
tikreg.utils.
explainable_variance
(repeats, ncorrection=True, dozscore=True)¶Compute the explainable variance in the recorded signals.
| Parameters: |
|
|---|---|
| Returns: |
|
Notes
Explainable variance can be interpreted as the \(R^2\) of a model that predicts each repetition with the mean across repetitions.
References
Schoppe, et al. (2016), Hsu, et al. (2004), David, et al. (2005).
Examples
First, simulate some repeated data for 50 units (e.g. voxels, neurons).
>>> nreps, ntpts, nunits, noise = 10, 100, 50, 2.0
>>> _, _, Y = generate_data(n=ntpts, testsize=0, v=nunits, noise=0.0)
>>> repeats = np.asarray([Y for i in range(nreps)])
>>> # Add i.i.d. gaussian noise to each copy of the data
>>> repeats += np.random.randn(nreps, ntpts, nunits)*noise
>>> print(repeats.shape)
(10, 100, 50)
The repeats can be used to compute the explainable variance for each simulated unit.
>>> EV = explainable_variance(repeats)
>>> EV.shape
(50,)
>>> print(EV.mean()) # doctest: +SKIP
0.20099454817453574
>>> analytic_R2 = analytic_expected_correlation(2.0)**2
>>> print(round(analytic_R2, 6))
0.2
tikreg.utils.
fast_indexing
(a, rows, cols=None)¶Extract row and column entries from a 2D np.ndarray.
Much faster and memory efficient than fancy indexing for rows and cols from a matrix. Slightly faster for row indexing.
| Parameters: |
|
|---|---|
| Returns: |
|
tikreg.utils.
generate_data
(n=100, p=10, v=2, noise=1.0, testsize=None, dozscore=False, feature_sparsity=0.0)¶Get some B,X,Y data generated from gaussian (0,1).
| Parameters: |
|
|---|---|
| Returns: |
|
Examples
>>> B, (Xtrain, Xtest), (Ytrain, Ytest) = generate_data(n=100, p=10, v=2,testsize=20)
>>> B.shape, (Xtrain.shape, Xtest.shape), (Ytrain.shape, Ytest.shape)
((10, 2), ((100, 10), (20, 10)), ((100, 2), (20, 2)))
>>> B, X, Y = generate_data(n=100, p=10, v=2,noise=1.0,testsize=0) # No test data
>>> B.shape, X.shape, Y.shape
((10, 2), (100, 10), (100, 2))
tikreg.utils.
generate_trnval_folds
(N, sampler='cv', nchunks=5, nfolds=5, testpct=0.2)¶Split dataset into training and validation folds
| Parameters: |
|
|---|---|
| Yields: |
|
Notes
By default, this function is optimized for autocorrelated signals. If sampled signals are not autocorrelated, set nchunks=1
Examples
>>> folds = generate_trnval_folds(100, sampler='cv')
>>> fold_sizes = [(len(trnidx),len(validx)) for trnidx, validx in folds]
>>> print(fold_sizes)
[(80, 20), (80, 20), (80, 20), (80, 20), (80, 20)]
>>> folds = generate_trnval_folds(100, sampler='bcv', nfolds=(2,5))
>>> print(len(list(folds)))
10
>>> folds = generate_trnval_folds(127, sampler='cv', nchunks=10, nfolds=5)
>>> fold_sizes = [(len(trnidx),len(validx)) for trnidx, validx in folds]
>>> print(fold_sizes) # doctest: +SKIP
[(97, 30), (97, 30), (107, 20), (107, 20), (107, 20)]
>>> folds = generate_trnval_folds(100, sampler='nbb')
>>> fold_sizes = [(len(np.unique(trnidx)),len(validx)) for trnidx, validx in folds]
>>> print(fold_sizes) # doctest: +SKIP
[(50, 50), (60, 40), (55, 45), (60, 40), (55, 45)]
>>> folds = generate_trnval_folds(100, sampler='mbb')
>>> fold_sizes = [(len(np.unique(trnidx)),len(validx)) for trnidx, validx in folds]
>>> print(fold_sizes) # doctest: +SKIP
[(57, 43), (60, 40), (60, 40), (65, 35), (51, 49)]
tikreg.utils.
hrf_convolution
(input_responses, HRF=None, do_convolution=True, dt=None)¶Convolve a series of impulses in a matrix with a given HRF
| Parameters: |
|
|---|---|
| Returns: |
|
Examples
>>> impulses = np.zeros((100, 2))
>>> impulses[5, 0] = 1
>>> impulses[25, 1] = 1
>>> bold = hrf_convolution(impulses, dt=1.0) # Default peak at 6s
tikreg.utils.
hrf_default_basis
(dt=2.0, duration=32)¶Hemodynamic response function basis set.
Wrapper to hrf_estimation package.
| Parameters: |
|
|---|---|
| Returns: |
|
tikreg.utils.
hyperopt_make_trial_data
(tid, vals, loss)¶Generate a valid dictionary as a trial for hyperopt.Trials.
| Parameters: |
|
|---|---|
| Returns: |
|
tikreg.utils.
hyperopt_make_trials
(values, losses, parameter_names=None)¶| Parameters: |
|
|---|---|
| Returns: |
|
tikreg.utils.
isdiag
(mat)¶Determine whether matrix is diagonal.
| Parameters: |
|
|---|---|
| Returns: |
|
tikreg.utils.
mult_diag
(d, mat, left=True)¶Efficient multiply a full matrix by a diagonal matrix.
This function should always be faster than dot.
| Parameters: |
|
|---|---|
| Returns: |
|
Notes
This code by: Pietro Berkes<EMAIL_ADDRESS>Mon Mar 26 11:55:47 CDT 2007 http://mail.scipy.org/pipermail/numpy-discussion/2007-March/026807.html
Examples
>>> mat = np.random.randn(20,20)
>>> d = np.random.randn(20)
>>> assert np.allclose(mult_diag(d, mat, left=True), np.dot(np.diag(d), mat))
>>> assert np.allclose(mult_diag(d, mat, left=False), np.dot(mat, np.diag(d)))
tikreg.utils.
noise_ceiling_correction
(repeats, yhat, dozscore=True)¶Noise ceiling corrected correlation coefficient.
Correlation coefficient estimate that better reflects the error in the predictions that is due to the inaccuracy of the model, rather than the noise intrinsic in the responses. This is achieved by removing the non-stationary part of the noise in the measured signals across multiple repetitions of the same stimulus/task/conditions.
| Parameters: |
|
|---|---|
| Returns: |
|
Notes
Repeats are used to compute the amount of explainable variance in each one of the units (e.g. voxels, neurons, etc.). This is equivalent to estimating the adjusted \(R^2\) of an OLS model that predicts each individual repeat timecourse with the mean timecourse computed across repetitions. This process is performed individually for each unit.
The mean timecouse of each unit is computed. The product between the predicted responses and the mean timecourse is then computed. This value is then divided by the amount of explainable variance.
\(r_{ncc}\) is misbehaved if \(R^2\) is very low.
References
Schoppe, et al. (2016), Hsu, et al. (2004), David, et al. (2005).
Examples
First, simulate some repeated data for 50 units (e.g. voxels, neurons).
>>> nreps, ntpts, nunits, noise = 10, 100, 50, 2.0
>>> _, _, Y = generate_data(n=ntpts, testsize=0, v=nunits, noise=0.0, dozscore=True)
>>> repeats = np.asarray([Y for i in range(nreps)])
>>> # Add i.i.d. gaussian noise to each copy of the data
>>> repeats += np.random.randn(nreps, ntpts, nunits)*noise
>>> print(repeats.shape)
(10, 100, 50)
Compute the noise ceiling corrected correlation coefficient using random predictions. Because the predictions are unrelated to the data, we expected a value of 0.
>>> mean_nccr = noise_ceiling_correction(repeats, np.random.randn(ntpts, nunits)).mean()
>>> mean_nccR2 = (mean_nccr**2)*np.sign(mean_nccr)
>>> # As ntpts -> inf, this converges to 0 because predictions are random
>>> assert np.allclose(round(mean_nccR2, 2), 0)
Next, we produce more accurate predictions.
>>> Yhat = Y + np.random.randn(ntpts, nunits)*0.5 # little noise
>>> # raw correlation reflects both noise in signals (2.0) and predictions (0.5)
>>> raw_perf = columnwise_correlation(repeats.mean(0), Yhat).mean()
>>> print(raw_perf) # doctest: +SKIP
0.7534268012539665
The raw correlation coefficient computed reflects the amount of noise in the signals (2.0) and the amount of noise in our model (0.5). The noise ceiling corrected correlation coefficient can be used to obtain an estimate that more closely reflects the error due to predictions alone.
>>> accurate_mean_nccr = noise_ceiling_correction(repeats, Yhat).mean()
>>> print(accurate_mean_nccr) # doctest: +SKIP
0.8954483753448146
>>> # As nreps, ntpts -> inf, the nccr is determined by the prediction error alone (0.5)
>>> # Analytically, the error in the predictions is 0.5 so the expected correlation is ~0.89
>>> print(round(analytic_expected_correlation(0.5), 6))
0.894427
>>> assert np.allclose(accurate_mean_nccr, analytic_expected_correlation(0.5), rtol=1e-01) # Small simulation


To go beyond a one-column list and provide a customized drop-down, use the base class and write your own control with the correct column set up and formatting. This page shows two examples.
Example 1: Use some CSS to coerce the contents of the auto complete to layout as a HTML table.
Example 2: Use an Angular Material mat-table inside the drop-down.


As the space of correct programs has infinitesimal measure on the space of possible programs, expanding the program space to include threads ensures at least one profanity-inducing threading bug.


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Click here to download the full example code
2D Robot SLAM - Example¶
Goals of this script:
apply the UKF for performing 2D SLAM (Simultaneous Localization And Mapping).
discover a computationally alternative way for performing UKF inspired from [HMR13]. This alternative leads to computational speed improvement when only a part of the state is involved in a propagation or in update step.
augment the state when a new landmark is observed in a UKF derivative-free way.
We assume the reader is already familiar with the approach described in the tutorial.
This script considers the 2D robot SLAM problem where the robot is equipped with wheel odometry and observes unknown landmark measurements. The robot state is propagated through the odometry model and landmark observations are used in the UKF measurement step. Landmarks are static and we assume no error coming from data association. We reproduce the simulations that are described in [HMR10] , [HMR13].
Import¶
import ukfm
from ukfm import SLAM2D as MODEL
import numpy as np
import matplotlib
ukfm.set_matplotlib_config()
Model and Simulation¶
This script uses the SLAM2D()
model that requires sequence time
and odometry frequency.
# sequence time (s)
T = 2500
# odometry frequency (Hz)
odo_freq = 1
# create the model
model = MODEL(T, odo_freq)
The trajectory of the robot consists in turning at constant speed.
# true speed of robot (m/s)
v = 0.25
# true angular velocity (rad/s)
gyro = 1.5 / 180 * np.pi
# odometry noise standard deviation
odo_std = np.array([0.05*v/np.sqrt(2), # speed (v/m)
0.05*v*np.sqrt(2)*2]) # angular speed (rad/s)
When simulating data, we generate a map. The map is defined as landmarks constantly spaced on a circle with slightly higher radius than the radius of the robot trajectory.
# simulate true trajectory and noisy input
states, omegas, ldks = model.simu_f(odo_std, v, gyro)
# observation noise standard deviation (m)
obs_std = 0.1
# plot the map
model.plot_traj(states, ldks)
The state and the input contain the following variables:
states[n].Rot # orientation (matrix)
states[n].p # robot position
states[n].p_l # landmark positions
omegas[n].gyro # robot angular velocity
omegas[n].v # robot speed
Landmark positions are a 2D array where we get the k-th landmark as
states[n].p_l[k]
. The number of landmarks in the state starts from zero
and increases when the robot observes a new landmark.
We compute noisy landmark measurements based on the true states.
ys = model.simu_h(states, obs_std, ldks)
A measurement contains the observations of all visible landmarks as:
y_n = ys[n] # measurement at timestamp n
y_n_k = y_n[k] # k-th observed landmark at instant n, where y_n_k[2] is the
# landmark index (-1 if the landmark is not observed)
Filter Design and Initialization¶
We embed the robot state in \(SO(2) \times \mathbb{R}^2\) and each landmark position in \(\mathbb{R}^2\), such that:
the retraction \(\varphi(.,.)\) is the \(SO(2)\) exponential for orientation, and the vector addition for positions.
the inverse retraction \(\varphi^{-1}_.(.)\) is the \(SO(2)\) logarithm for orientation and the vector subtraction for positions.
Remaining parameter setting is standard.
# propagation noise covariance matrix
Q = np.diag(odo_std**2)
# measurement noise covariance matrix
R = obs_std**2*np.eye(2)
# sigma point parameters
alpha = np.array([1e-3, 1e-3, 1e-3, 1e-3, 1e-3])
# initial uncertainty matrix
P0 = np.zeros((3, 3)) # The state is perfectly initialized without
Regarding implementation, we use the Jacobian UKF (JUKF()
) that
spares time when only a part of the space is involved in a propagation or
update step.
How it works ? Consider the propagation of the covariance in an extended Kalman filter as
where the robot state uncertainty is put in the first indices of the covariance matrix \(\mathbf{P}_{n}\). As landmarks are statics, the Jacobian take the forms
The JUKF allows to compute \(\mathbf{F}^R\) and \(\mathbf{G}^R\) by
only using the required sigma points. Here it corresponds to the sigma points
of the robot state. This requires to set the reduced retraction red_phi
and inverse retraction red_phi_inv
that compute the required subpart of
the full retraction \(\varphi(.,.)\) and inverse retraction
\(\varphi^{-1}_.(.)\), and to define corresponding indices red_idx
in
\(\mathbf{P}_n\).
Similarly for the observation of a landmark, e.g. the first landmark, the observation matrix has the form
The JUKF computes \(\mathbf{H}^1\) by only using the required sigma points
of the robot state and the observed landmark. This requires to set another
function up_phi
using during update to compute a subpart of the retraction
\(\varphi(.,.)\), as corresponding indices up_idx
in
\(\mathbf{P}_n\).
Finally, we require to define a new function \(z(.,.)\) to augment the state such that
where \(\boldsymbol{\chi}_n^{\mathrm{aug}}\) is the augmented state and
the \(\mathbf{y}_n\) the measurement used to augment the state. Here this
measurement is a landmark observation. To make the augmentation efficient we
need to compute sigma points for only the state involved in \(z(.,.)\),
aug_phi
is thus only a subpart of \(\varphi(.,.)\) and aug_inv_phi
is the inverse retraction of \(\varphi(.,.)^{-1}\) corresponding to the
novel part of the state only.
# reduced weights during propagation
red_idxs = np.array([0, 1, 2]) # indices corresponding to the robot state in P
# weights during update
aug_idxs = np.array([0, 1, 2]) # indices corresponding to the robot state in P
state0 = model.STATE(Rot=states[0].Rot, p=states[0].p, p_l=np.zeros((0, 2)))
ukf = ukfm.JUKF(state0=state0, P0=P0, f=model.f, h=model.h, Q=Q, phi=model.phi,
alpha=alpha,
red_phi=model.red_phi,
red_phi_inv=model.red_phi_inv,
red_idxs=red_idxs,
up_phi=model.up_phi,
up_idxs=np.arange(5), # it will changes during the sequence
aug_z=model.aug_z,
aug_phi=model.aug_phi,
aug_phi_inv=model.aug_phi_inv,
aug_idxs=aug_idxs,
aug_q=2)
# set variables for recording estimates along the full trajectory
ukf_states = [states[0]]
ukf_Ps = [P0]
# indices of already observed landmarks
ukf_lmk = np.array([])
Filtering¶
The UKF proceeds as a standard Kalman filter with a for loop.
for n in range(1, model.N):
# propagation
ukf.propagation(omegas[n-1], model.dt)
y_n = ys[n]
# observed landmarks
idxs = np.where(y_n[:, 2] >= 0)[0]
# update each landmark already in the filter
p_ls = ukf.state.p_l
for idx0 in idxs:
idx = np.where(ukf_lmk == y_n[idx0, 2])[0]
if idx.shape[0] == 0:
continue
# indices of the robot and observed landmark in P
up_idxs = np.hstack([0, 1, 2, 3+2*idx, 4+2*idx])
ukf.state.p_l = np.squeeze(p_ls[idx])
# compute observability matrices and residual
ukf.H_num(np.squeeze(y_n[idx0, :2]), up_idxs, R)
ukf.state.p_l = p_ls
# update only if some landmarks have been observed
if ukf.H.shape[0] > 0:
ukf.state_update()
# augment the state with new landmark
for idx0 in idxs:
idx = np.where(ukf_lmk == y_n[idx0, 2])[0]
if not idx.shape[0] == 0:
continue
# augment the landmark state
ukf_lmk = np.hstack([ukf_lmk, int(y_n[idx0, 2])])
# indices of the new landmark
idx = ukf_lmk.shape[0]-1
# new landmark position
p_l = np.expand_dims(ukf.state.p + ukf.state.Rot.dot(y_n[idx0, :2]), 0)
p_ls = np.vstack([ukf.state.p_l, p_l])
ukf.state.p_l = p_l
# get Jacobian and then covariance following [2]
R_n = obs_std ** 2 * np.eye(2)
ukf.aug(y_n[idx0, :2], aug_idxs, R)
ukf.state.p_l = p_ls
# save estimates
ukf_states.append(ukf.state)
ukf_Ps.append(ukf.P)
Results¶
We plot the trajectory, the position of the landmarks and the estimated trajectory in the same plot, the attitude error, the position error, and their confidence interval.
model.plot_results(ukf_states, ukf_Ps, states, ldks)
We note the \(3\sigma\) confidence interval decreases along time.
Conclusion¶
This script shows how the UKF on parallelizable manifolds can be used for 2D SLAM. By leveraging numerical Jacobian inference, one obtains a computationally more efficient filter. The UKF works for this example, but consistency issues happear at the end of the trajectory.
You can now:
consider non-linear range and bearing measurement.
benchmark the UKF with different retractions and compare it to the extended Kalman filter and the invariant extended Kalman filter of [BB17].
Total running time of the script: ( 0 minutes 15.807 seconds)


Add a tag to a page
You can add tags to pages by adding tags
in the frontmatter with values inside brackets, like this:
---
title: 5.0 Release Notes
permalink: release_notes_5_0.html
tags: [formatting, single_sourcing]
---
Tags overview
posts.tags.tagname
, where tagname
is the name of the tag. You can then list all posts in that tag namespace. But pages don’t off this same tag namespace, so you could actually use another key instead of tags
. Nevertheless, I’m using the same tags
approach for posts as with pages.To prevent tags from getting out of control and inconsistent, first make sure the tag appears in the _data/tags.yml file. If it’s not there, the tag you add to a page won’t be read. I added this check just to make sure I’m using the same tags consistently and not adding new tags that don’t have tag archive pages.
Additionally, you must create a tag archive page similar to the other pages named tag_{tagname}.html folder. This theme doesn’t auto-create tag archive pages.
For simplicity, make all your tags single words (connect them with hyphens if necessary).
Setting up tags
Tags have a few components.
-
In the _data/tags.yml file, add the tag names you want to allow. For example:
allowed-tags: - getting_started - overview - formatting - publishing - single_sourcing - special_layouts - content types
-
Create a tag archive file for each tag in your tags_doc.yml list. Name the file following the same pattern in the tags folder, like this: tag_collaboration.html.
Each tag archive file needs only this:
--- title: "Collaboration pages" tagName: collaboration search: exclude permalink: tag_collaboration.html sidebar: mydoc_sidebar --- {% include taglogic.html %}
Note: In the _includes/mydoc folder, there’s a taglogic.html file. This file (included in each tag archive file) has common logic for getting the tags and listing out the pages containing the tag in a table with summaries or truncated excerpts. You don’t have to do anything with the file — just leave it there because the tag archive pages reference it. -
Change the title, tagName, and permalink values to be specific to the tag name you just created.
By default, the _layouts/page.html file will look for any tags on a page and insert them at the bottom of the page using this code:

{% if page.tags != null %}
Tags:
{% assign projectTags = site.data.tags.allowed-tags %}
{% for tag in page.tags %}
{% if projectTags contains tag %}
{{page.tagName}}{{tag}}
{% endif %}
{% endfor %}
{% endif %}

Because this code appears on the _layouts/page.html file by default, you don’t need to do anything in your page to get the tags to appear. However, if you want to alter the placement or change the button color, you can do so within the _includes/taglogic.html file.
You can change the button color by changing the class on the button from btn-info
to one of the other button classes bootstrap provides. See Labels for more options on button class names.
Retrieving pages for a specific tag
If you want to retrieve pages outside of a particular tag_archive page, you could use this code:
Getting started pages:

{% for page in site.pages %}
{% for tag in page.tags %}
{% if tag == "getting_started" %}

{{page.title}}

{% endif %}
{% endfor %}
{% endfor %}

Here’s how that code renders:
Getting started pages:
- About the theme author
- About Ruby, Gems, Bundler, and other prerequisites
- Install Jekyll on Mac
- Pages
- Posts
- Release notes 5.0
- Release notes 6.0
- Sidebar Navigation
- Support
- Supported features
If you want to sort the pages alphabetically, you have to apply a sort
filter:
Getting started pages:

{% assign sorted_pages = (site.pages | sort: 'title') %}
{% for page in sorted_pages %}
{% for tag in page.tags %}
{% if tag == "getting_started" %}

{{page.title}}

{% endif %}
{% endfor %}
{% endfor %}

Here’s how that code renders:
Getting started pages:
- About Ruby, Gems, Bundler, and other prerequisites
- About the theme author
- Install Jekyll on Mac
- Pages
- Posts
- Release notes 5.0
- Release notes 6.0
- Sidebar Navigation
- Support
- Supported features
Efficiency
Although the tag approach here uses for
loops, these are somewhat inefficient on a large site. Most of my tech doc projects don’t have hundreds of pages (like my blog does). If your project does have hundreds of pages, this for
loop approach with tags is going to slow down your build times.
Without the ability to access pages inside a universal namespace with the page type, there aren’t many workarounds here for faster looping.
With posts (instead of pages), since you can access just the posts inside posts.tag.tagname
, you can be a lot more efficient with the looping.
Still, if the build times are getting long (e.g., 1 or 2 minutes per build), look into reducing the number of for
loops on your site.
Empty tags?
If your page shows “tags:” at the bottom without any value, it could mean a couple of things:
- You’re using a tag that isn’t specified in your allowed tags list in your tags.yml file.
- You have an empty
tags: []
property in your frontmatter.
If you don’t want tags to appear at all on your page, remove the tags property from your frontmatter.
Remembering the right tags
Since you may have many tags and find it difficult to remember what tags are allowed, I recommend creating a template that prepopulates all your frontmatter with all possible tags. Then just remove the tags that don’t apply.
See WebStorm Text Editor for tips on creating file templates in WebStorm.


Wolfton, Magus Leon
| Birth Name | Wolfton, Magus Leon |
| Gender | male |
| Age at Death | unknown |
Narrative
Magnus Wolfson was married 4th Qtr 1842 Whitechapel London, there was also a Meyer Wolfson married in the same Quarter, maybe his brother ???
Jan 2006 - Raelene told me she found Magnus living with his sister-in-law on the 1861 Census, after his wife had died.
Events
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Birth | about 1815 | |||
|
|
||||
| Death | 1861 Census 46 yrs old | |||
|
|
Families
Family of Wolfton, Magus Leon and Cook, Hannah |
||||||||||||||||
| Married | Wife | Cook, Hannah ( * 1819-04-29 + 1858-01-17 ) | ||||||||||||||
|
||||||||||||||||
| Children |
| Name | Birth Date | Death Date |
|---|---|---|
| Wolfton, Heindrick John | 1844-07-10 | 1844-12-17 |
| Wolfton, Edward William | ||
| Wolfton, Herbert |
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | BFE2B193F0DCD8119522D869BB0402FE51BD |
Attributes
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | BEE2B193F0DCD8119522D869BB0402FE50AD |
Pedigree
-
- Wolfton, Magus Leon


Akan, ethnolinguistic grouping of peoples of the Guinea Coast who speak Akan languages (of the Kwa branch of the Niger-Congo family).
Most Akan peoples live in Ghana, where they settled in successive waves of migration between the 11th and 18th centuries.
Others inhabit the eastern part of Côte d’Ivoire and parts of Togo.
In Ghana, children are given their first name as a "day-name" which corresponds to the day in the week they were born. This web application will help you,find out the day of the week you were born or someone was born.
It will also help determine to you your Akan Name, based on your gender and birthday month.
Click here to check your akan name.


Taught by
Alexandra Ackerman
Code available at
https://github.com/newschool-webfundamentals
Presentations available at
newschool-webfundamentals.github.io
This class is specially designed for people who think code, math, and computers in general are intimidating. Through a series of playful challenges, you will learn how computers, code, and the Web actually work. Along the way, you will pick up valuable skills and knowledge that will allow you to do more complex interactive projects in the future. It’s strongly recommended that this class be taken along with the appropriate News, Narrative & Design class.
What's a developer and what do I do?
This course will focus on the fundamentals of frontend web development from the perspective of a journalist.
Monthly retrospectives to hear what's working in the class, what's not working, and what you'd like me to start doing to make this experience as helpful as possible for you.
Toward the end of the semester, I will devote one or two classes to topics you want to learn. Start thinking about that and we'll take a vote.
Throughout the course, you will be required to post assignments to GitHub.
Since we have some time, let's set up your GitHub accounts and go over this week's assignment.
Now it's time to have some fun...


Scratch3D is a 3-D game design extension currently under development for Scratch. This extension empowers Scratch users to create and think in three dimensions, with natural and realistic lighting, perspective, object creation and physics that simulate real life.
Scratch Is a visual based programming language developed by MIT's Media Lab's Lifelong Kindergarten group. Scratch allows user to create fun and interactive programs using code blocks the user can drag and drop into place. Scratch's simplicity and easy of use make it an ideal introductory language for kids. Not only does Scratch3D allow Scratch users to create fun interactive 3-D games; it also introduces the user to basic fundamentals of programming and computational thinking. Scratch3D is an engaging and exciting way to introduce and inspire the next generation of computer programmers to the magic of creating games and stories using computer simulated 3D space and 3D geometries.


Letters: Protest at ballot box; Jalousie windows don’t mix; Minimum wage bill | Honolulu Star-Advertiser
Protest at ballot box, not with blockades; Vote for extreme change in political leadership; Minimum wage bill should be top priority. Read more
Mahalo for reading the Honolulu Star-Advertiser!
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--- layout: post esbma_id: 1777 title: Twitter! Curse your sudden but inevitable betrayal! date: 2010-03-31 13:27:48 ---
Last night I saw this article, which basically says the one day soon computers won't present the user with the ability to manipulate "files" anymore.
Suddenly, I had stuff to say. But I've been using Twitter, which limits me to 140 characters. So, I Tweeted this:
We're heading toward two classes of computers: one for people like me, and one for people like my Mom.
(And BTW, Mom, usually when I mention you on the Internet, I'm not really talking about you.
Anyway, I thought the tweet would be enough. It wasn't.
In the beginning, we were the only ones here. Normal people didn't use computers at all. Only the geeks used computers, and we certainly weren't using them to get anything done.
I remember my Mom saying that she would never use a computer. (Mom, this one is actually you.) And I certainly can't blame her for thinking that at the time. She had no reason to see computers as a way of getting things done. All she knew is that I would periodically run into the family room to announce to my parents that I had just shaved three more instructions out of the main loop so now my graphics move faster. And Mom just wanted me to at least stack my Byte magazines in the corner so she could get the vacuum cleaner through.
Fast forward to today. Computers, by and large, are still designed for geeks. This is why we all buy T-shirts that say "No, I will not fix your computer". The genius of the iPad is that it cannot get things like viruses. It is a closed platform. You can't put apps on it. You can't write and distribute software for it without Apple's permission. This is why geeks hate it and normal people will love it.
Your Mom wants a computer she doesn't have to ask you to fix. She is willing to trade power and flexibility to get simplicity. The iPad is another major step.
I find this interesting because it raises all kinds of questions:
This is a major wave of change. I don't know the answers to these questions. The only thing that seems clear to me is that Microsoft will miss this wave just like they missed the last one.


Dangote: First phase construction of Apapa Port road to be ready by December
by Oluseyi AwojulugbeAliko Dangote, president of the Dangote Group, says the first phase construction of the road leading from the seaport in Apapa to the Lagos-Ibadan Expressway Toll Gate will be completed before the end of December 2019.
Dangote was speaking during an inspection of the construction site with Godwin Emefiele, governor of the Central Bank of Nigeria.
According to the billionaire businessman, the 40-kilometre road will have a lifespan of 100 years upon completion.
In his remarks, Emefiele commended President Muhammadu Buhari for signing the executive order 7, which opened the opportunity for private companies to intervene in infrastructure.
He also advised corporate bodies in the country to take advantage of the presidential order to raise infrastructure bonds at concessionary rates to support the federal government in its desire to develop infrastructure in Nigeria.
“It is a good thing for this country. When we talk about the Ease of Doing Business, you can imagine a situation whereby people have been complaining about the ease of transporting their goods out of the Apapa port,” he said during the inspection on Saturday.
While commending Dangote for taking the initiative, Emefiele expressed hope that the road would ease the traffic challenge on the route, particularly for trucks conveying cleared goods from the ports.
He further commended the Dangote Group for using local content, saying “this is 100 % local content. Alhaji Dangote uses his cement on this road, the rods I understand are coming from African Foundries that smelted waste steel, which is converted into iron bars and is being used for the road”.


Comics via RSS?
Here. Has anyone built a comics.com to RSS gateway? I'd like to subscribe to my favorite comics w/o being forced to actually visit the site on a daily basis (I don't do that anymore [*]).
A dilbert.com to RSS gateway would be nice, too.
[*] https://csells.github.io/sb6/spout/#No_huggy,_no_kissy,_until_I_get_an_RSS_feed


Online Documentation is available for help with this tool's User Interface, Results, and Error Messages.
Translate Options


Snookes1516.github.io
On BA (Hons) Hair, Make-up and Prosthetics for Performance you will learn research, design, technical and production skills. Experience is built across performance.
Importance of creative writing in english - royal holloway. The Department of English, encompasses our undergraduate English programme as well as more specialist disciplines such as Creative Writing and Publishing which. Williams at the Royal Albert Hall for the 2014 BBC Radio 2 Folk Awards. Royal Holloway, University of London Creative Writing. This writing can also alumni you an distinctive, creative edge in careers such as publishing.
Find your light at the University of Hull - where teaching and research combine to change lives. Royal holloway creative writing alumni - Tenetsofleyki. A comic story inspired by his upbringing; history s not just for the boys, he wrote the book in the evenings and at weekends because of his day job. Shtum.
Autumn open days booking now University The University for the Creative Arts is a specialist art and design university in the south of England. It was formed in 2005 as University College for the Creative. Passages - Pacific Western Airlines PWA Employee Reunion. Goldsmiths creative writing alumni - Tastefulventure. Creative Writing - MA at Royal Holloway, These scholarships are normally writing to applicants alumni a one writing Masters programme excluding royal.
BibMe: Free Bibliography Citation Maker The Spread the Word Life Writing Prize is the annual prize for narrative non fiction, run by Spread the Word in partnership with Goldsmiths Writers Centre
Higher The magazine for the alumni of Royal Holloway All the latest buzz from the Royal Holloway and Bedford College Alumni Network. Creative Writing alumnae.
BA (Hons) Hair, Make-up and Prosthetics for Performance. Summer Reading for EN2215 Creative Writing: Structure and Style. Kate Williams (historian) - Wikipedia.
Alumni Network - Higher Online The magazine 11 September 2018. Creative Writing alumnae highly commended in the Forward Prizes for Poetry. In Memoriam We will always remember these exceptional people who were our co-workers and friends. A tribute to PW314. A special mention to Captain Harry. The University of Lincoln, based in the City of Lincoln UK offers students degree courses underpinned by research engagement. Ma Creative Writing Royal Holloway : Creative Writing. Royal holloway creative writing alumni. Meet 100 universities at the largest UK University Fair for international students in London. Register for free entry today and learn more about studying Alumni News Archive - Higher Online The magazine
BibMe Free Bibliography Citation Maker - MLA, APA, Chicago, Harvard.
Manchester Writing Competition 2017 · Manchester. English and Creative Writing Search Royal Holloway. Site search. English and Creative Writing BA. Our alumni. We’ve played a role in thousands of careers. BA (Hons) 3D Effects for Performance and Fashion. Universities Attending UK University Fair London. A TEF Gold Standard University University of Lincoln. Ma Creative Writing Royal Holloway - Postgraduate.
Importance of creative writing in english - royal holloway creative writing alumni. General. Share Tweet. All of my vanderbilt essays are done. #partayyyy. The winners of the 2017 Manchester Writing Competition have been announced: Sakinah Hofler has been awarded the £10,000 Manchester Fiction Prize while. University for the Creative Arts - Wikipedia. Department of English City, University of London.
English and Creative Writing BA Royal Holloway. About. On BA (Hons) 3D Effects for Performance and Fashion you will learn how to interpret text for a performance context while acquiring specialised design and craft. Goldsmiths creative writing alumni - professional writers, Royal holloway, university of writing alumni have recently been facilitating creative. EN2215 Creative Writing: Structure and Style - Royal.


pipes {{name | uppercase}} {{name | lowercase}} {{age|currency}} {{age|currency:'INR'}} {{today}} {{today | date}} {{today | date:'dd-MM-yyyy'}} {{15| hexa}} {{2| power:2}}


Man in his 50s arrested after inmate found murdered in Cloverhill Prison in Dublin
Reports suggest warders had to pull a fellow prisoner off him when they entered the cell following an altercation
by Emma McMenamy, Edel Hughes, Darragh BerryA man in his 50s has been arrested after an inmate at a Dublin prison was found murdered in his cell.
Inmate Mark Lawlor was found dead shortly after midnight on Saturday following reports of a vicious row with a fellow prisoner.
Mr Lawlor, who was in his 30s, was a petty criminal who had been in custody in Cloverhill Prison for just over a week facing theft charges.
Prison staff tried to resuscitate Lawlor but he could not be revived.
Reports suggest warders had to pull a fellow prisoner off him when they entered the cell following an altercation.
Gardai launched a murder inquiry following the death.
A garda spokesperson said: "The man was arrested at Cloverhill Prison on suspicion of murder.
"He is currently detained at Clondalkin Garda Station under Section 4 of the Criminal Justice Act 1984."
Gardai also revealed a post-mortem examination on Mr Lawlor's body has been completed but the results are not being released for operational reasons.
A source said: “Inmates in the cell next to Lawlor’s heard a commotion and notified warders but Lawlor was already dead by the time they got to him.
“They had to pull the other inmate off him.
“It’s understood that a fight broke out between the two inmates after midnight and Lawlor was strangled. A reason for the row is not yet known.
How to follow the Irish Mirror on social media
You can like our main Facebook page here.
Our Irish Mirror Sports page - which brings you all your Irish and UK sports news - can be found here.
You can also check out our Irish Mirror GAA sports page.
The Irish Mirror Twitter account is @IrishMirror while our sports account is @MirrorSportIE.
Our Instagram account can be found here.
“A full investigation into the incident will now take place but there’s no rhyme or reason as to why it started yet.”
A spokesman from the Irish Prison Service said: “All practicable measures are taken by management and staff to ensure the safety of those in custody.
“However, regrettably no prison system can be completely free of violence and inter-prisoner assaults do occur.”
A Garda spokesman added: “Gardai are following a definite line of inquiry and the investigation is ongoing.”


I am passionate about solving complex problems and building excellent software that will help improve the lives around me. I have a bachelor’s degree in Computer Science and years of experience with a variety of skills. I love learning new technologies to improve the development of software that will turn ideas into reality.
HTML5
CSS3|SASS
JavaScript
Bootstrap
JQuery
Angular
Ionic
React/Redux
SQL
MySQL
Firebase
MongoDB
Java
Python
C|C++
Node
Git
GCP
HTML
Angular
Ionic
Firebase
Typescript
Node
GCP
Python
Plaid API
The Odds API
HTML
Angular
Ionic
Firebase
Node
GCP
Python
Plaid Api
The Odds API
Plaid API
HTML
Angular
Ionic
Firebase
Node
GCP
Python
Plaid Api
The Odds Api
Plaid Api


Creates a new instance of the AdaptiveLightingController. Refer to the AdaptiveLightingController documentation on how to use it.
The lightbulb to which Adaptive Lighting support should be added.
Optional options to define the operating mode (automatic vs manual).
This method can be called to manually disable the current active Adaptive Lighting transition. When using AdaptiveLightingControllerMode.AUTOMATIC you won't need to call this method. In AdaptiveLightingControllerMode.MANUAL you must call this method when Adaptive Lighting should be disabled. This is the case when the user manually changes the value of Hue, Saturation or ColorTemperature characteristics (or if any of those values is changed by physical interaction with the lightbulb).
Returns the minimum interval threshold (in milliseconds) a accessory may notify HomeKit controllers about a new color temperature value via event notifications (what happens when you call Characteristic.updateValue). Meaning the accessory should only send event notifications to subscribed HomeKit controllers at the specified interval.
Typically this evaluates to 600000 milliseconds (10 minutes).
Returns the time where the current transition curve was started in epoch time millis. A transition curves is active for 24 hours typically and is renewed every 24 hours by a HomeHub. Additionally see getAdaptiveLightingTimeOffset.
It is not necessarily given, that we have the same time (or rather the correct time) as the HomeKit controller
who set up the transition schedule.
Thus we record the delta between our current time and the the time send with the setup request.
timeOffset
is defined as Date.now() - getAdaptiveLightingStartTimeOfTransition();
.
So in the case were we actually have a correct local time, it most likely will be positive (due to network latency).
But of course it can also be negative.
This method returns the interval (in milliseconds) in which the light should update its internal color temperature (aka changes it physical color). A lightbulb should ideally change this also when turned of in oder to have a smooth transition when turning the light on.
Typically this evaluates to 60000 milliseconds (60 seconds).
Retrieve the AdaptiveLightingTransitionPoint for the current timestamp. Returns undefined if the current transition schedule reached its end.
Returns if a Adaptive Lighting transition is currently active.
This class allows adding Adaptive Lighting support to Lightbulb services. The Lightbulb service MUST have the Characteristic.ColorTemperature characteristic AND the Characteristic.Brightness characteristic added. The light may also expose Characteristic.Hue and Characteristic.Saturation characteristics (though additional work is required to keep them in sync with the color temperature characteristic. see below)
How Adaptive Lighting works: When enabling AdaptiveLighting the iDevice will send a transition schedule for the next 24 hours. This schedule will be renewed all 24 hours by a HomeHub in your home (updating the schedule according to your current day/night situation). Once enabled the lightbulb will execute the provided transitions. The color temperature value set is always dependent on the current brightness value. Meaning brighter light will be colder and darker light will be warmer. HomeKit considers Adaptive Lighting to be disabled as soon a write happens to either the Hue/Saturation or the ColorTemperature characteristics. The AdaptiveLighting state must persist across reboots.
The AdaptiveLightingController can be operated in two modes: AdaptiveLightingControllerMode.AUTOMATIC and AdaptiveLightingControllerMode.MANUAL with AUTOMATIC being the default. The goal would be that the color transition is done DIRECTLY on the light itself, thus not creating any additional/heavy traffic on the network. So if your light hardware/API supports transitions please go the extra mile and use MANUAL mode.
Below is an overview what you need to or consider when enabling AdaptiveLighting (categorized by mode). The AdaptiveLightingControllerMode can be defined with the second constructor argument.
AUTOMATIC (Default mode):
This is the easiest mode to setup and needs less to no work form your side for AdaptiveLighting to work. The AdaptiveLightingController will go through setup procedure with HomeKit and automatically update the color temperature characteristic base on the current transition schedule. It is also adjusting the color temperature when a write to the brightness characteristic happens. Additionally it will also handle turning off AdaptiveLighting, when it detects a write happening to the ColorTemperature, Hue or Saturation characteristic (though it can only detect writes coming from HomeKit and can't detect changes done to the physical devices directly! See below).
So what do you need to consider in automatic mode:
MANUAL mode:
Manual mode is recommended for any accessories which support transitions natively on the devices end. Like for example ZigBee lights which support sending transitions directly to the lightbulb which then get executed ON the lightbulb itself reducing unnecessary network traffic. Here is a quick overview what you have to consider to successfully implement AdaptiveLighting support. The AdaptiveLightingController will also in manual mode do all of the setup procedure. It will also save the transition schedule to disk to keep AdaptiveLighting enabled across reboots. The "only" thing you have to do yourself is handling the actual transitions, check that event notifications are only sent in the defined interval threshold, adjust the color temperature when brightness is changed and signal that Adaptive Lighting should be disabled if ColorTemperature, Hue or Saturation is changed manually.
First step is to setup up a event handler for the AdaptiveLightingControllerEvents.UPDATE, which is called when AdaptiveLighting is enabled, the HomeHub updates the schedule for the next 24 hours or AdaptiveLighting is restored from disk on startup. In the event handler you can get the current schedule via AdaptiveLightingController.getAdaptiveLightingTransitionCurve, retrieve current intervals like AdaptiveLightingController.getAdaptiveLightingUpdateInterval or AdaptiveLightingController.getAdaptiveLightingNotifyIntervalThreshold and get the date in epoch millis when the current transition curve started using AdaptiveLightingController.getAdaptiveLightingStartTimeOfTransition. Additionally AdaptiveLightingController.getAdaptiveLightingBrightnessMultiplierRange can be used to get the valid range for the brightness value to calculate the brightness adjustment factor. The method AdaptiveLightingController.isAdaptiveLightingActive can be used to check if AdaptiveLighting is enabled. Besides actually running the transition (see AdaptiveLightingTransitionCurveEntry) you must correctly update the color temperature when the brightness of the lightbulb changes (see AdaptiveLightingTransitionCurveEntry.brightnessAdjustmentFactor), and signal when AdaptiveLighting got disabled by calling AdaptiveLightingController.disableAdaptiveLighting when ColorTemperature, Hue or Saturation where changed manually. Lastly you should set up a event handler for the AdaptiveLightingControllerEvents.DISABLED event. In yet unknown circumstances HomeKit may also send a dedicated disable command via the control point characteristic. Be prepared to handle that.


DRACULA: DEAD AND LOVING IT A film review by Christopher Null Copyright 1995 Christopher Null
After the vastly disappointing ROBIN HOOD: MEN IN TIGHTS, Mel Brooks really needed to prove himself by getting back to his YOUNG FRANKENSTEIN and BLAZING SADDLES-type comedy. The Dracula legend seemed like the perfect way to do it. But sadly, Brooks manages to hack it up like he did to poor Robin Hood, thanks to some very stale jokes and overly repetitious gags.
Brooks basically takes BRAM STOKER'S DRACULA (Francis Ford Coppola's film) and gives it the once-over, recreating the plot and characters almost directly from that movie, and giving them supposedly funny lines. The problem is that BRAM STOKER'S DRACULA was pretty silly to begin with, and Brooks' version comes off as poking fun at a film that was already doing a good job of it all by itself.
There are thankfully a few good lines in the film (my favorite: Van Helsing: "She's Nosferatu!" Renfield: "She's Italian?"), but overall, the movie sinks. Casting Leslie Nielsen as Dracula was a good move, as his antics are always good for a chuckle or two, but the continual overexposure of people like Amy Yasbeck (as Mina) in films like this leaves nothing new for the viewer to expect. And while Lysette Anthony's sultry Lucy is fun to watch...they kill her after 45 minutes.
Most of the movie's gags involve tripping, falling down, running into a wall or a window, or some other slapstick device. But the most tedious of the jokes is the nearly constant mocking of accents and overdramatic speech the various characters use. This gets old after the second scene, where the stereotypical trill in a gypsy's voice is made by the manual quivering of the skin around her throat. Ha ha.
Brooks fans won't be pleased, and neither will the vampire aficionado. Stay away from this film, and let it bury itself. Figuratively speaking, that is.
RATING: **
\-------------------------------\ |* Unquestionably awful | |** Sub-par on many levels | |*** Average, hits and misses | |**** Good, memorable film | |***** Perfection | \-------------------------------\
-Christopher Null /<EMAIL_ADDRESS>-Movie Emporium (reviews) / http://www.notes.tpoint.net/emporium/ -Contributing Editor, FEEDBACK / http://www.eden.com/~feedback/ -E-mail requests to join the movie review mailing list
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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Returns a proxy object that delegates method calls to a parent or sibling class of type.
#TODO
#TODO
super() only works for new-style classes.
super() is useful for accessing inherited methods that have been overridden in a class. The search order is same as that used by getattr() except that the type itself is skipped.
The __mro__ attribute of the type lists the method resolution search order used by both getattr() and super(). The attribute is dynamic and can change whenever the inheritance hierarchy is updated.
If the second argument is omitted, the super object returned is unbound. If the second argument is an object, isinstance(obj, type) must be true. If the second argument is a type, issubclass(type2, type) must be true (this is useful for classmethods).
There are two typical use cases for super. In a class hierarchy with single inheritance, super can be used to refer to parent classes without naming them explicitly, thus making the code more maintainable. This use closely parallels the use of super in other programming languages.
The second use case is to support cooperative multiple inheritance in a dynamic execution environment. This use case is unique to Python and is not found in statically compiled languages or languages that only support single inheritance. This makes it possible to implement “diamond diagrams” where multiple base classes implement the same method. Good design dictates that this method have the same calling signature in every case (because the order of calls is determined at runtime, because that order adapts to changes in the class hierarchy, and because that order can include sibling classes that are unknown prior to runtime).
For both use cases, a typical superclass call looks like this:
>>> class C(B):
>>> def method(self, arg):
>>> super(C, self).method(arg)
Note that super() is implemented as part of the binding process for explicit dotted attribute lookups such as super().__getitem__(name). It does so by implementing its own __getattribute__() method for searching classes in a predictable order that supports cooperative multiple inheritance. Accordingly, super() is undefined for implicit lookups using statements or operators such as super()[name]. Also note that super() is not limited to use inside methods. The two argument form specifies the arguments exactly and makes the appropriate references.
For practical suggestions on how to design cooperative classes using super(), see guide to using super().
>>> class Foo(object):
... def fb(self):
... print 'foo'
...
>>> class Bar(Foo):
... def fb(self):
... super(Bar, self).fb()
... print 'bar'
...
>>> f = Foo()
>>> f.fb()
foo
>>> b = Bar()
>>> b.fb()
foo
bar
#TODO


SKYLINKConnection Class Reference
| Inherits from | NSObject |
|---|---|
| Declared in | SKYLINKConnection.h |
Overview
@class
You should make sure this objects does not get released as long as you need it, for example by storing it as a strong property.
Delegates
lifeCycleDelegate
delegate related to life cycle, implementing the SKYLINKConnectionLifeCycleDelegate protocol.
@property (nonatomic, weak) id<SKYLINKConnectionLifeCycleDelegate> _Null_unspecified lifeCycleDelegate
Declared In
SKYLINKConnection.h
remotePeerDelegate
delegate related to remote peer activities, implementing the SKYLINKConnectionRemotePeerDelegate protocol.
@property (nonatomic, weak) id<SKYLINKConnectionRemotePeerDelegate> _Null_unspecified remotePeerDelegate
Declared In
SKYLINKConnection.h
mediaDelegate
delegate related to audio/video media, implementing the SKYLINKConnectionMediaDelegate protocol.
@property (nonatomic, weak) id<SKYLINKConnectionMediaDelegate> _Null_unspecified mediaDelegate
Declared In
SKYLINKConnection.h
messagesDelegate
delegate related to various type of custom messages, implementing the SKYLINKConnectionMessagesDelegate protocol.
@property (nonatomic, weak) id<SKYLINKConnectionMessagesDelegate> _Null_unspecified messagesDelegate
Declared In
SKYLINKConnection.h
fileTransferDelegate
delegate related to file transfer, implementing the SKYLINKConnectionFileTransferDelegate protocol.
@property (nonatomic, weak) id<SKYLINKConnectionFileTransferDelegate> _Null_unspecified fileTransferDelegate
Declared In
SKYLINKConnection.h
recordingDelegate
delegate related to room recording, implementing the SKYLINKConnectionRecordingDelegate protocol.
@property (nonatomic, weak) id<SKYLINKConnectionRecordingDelegate> _Null_unspecified recordingDelegate
Declared In
SKYLINKConnection.h
Peer Id
myPeerId
peer id of the current local user
@property (nonatomic, readonly) NSString *myPeerId
Declared In
SKYLINKConnection.h
maxPeerCount
Maximun number of peers.
@property (nonatomic, assign) NSInteger maxPeerCount
Discussion
The default value depends on the configuration.
Declared In
SKYLINKConnection.h
Lifecycle
– initWithConfig:appKey:
Initialize and return a newly allocated connection object.
- (null_unspecified id)initWithConfig:(null_unspecified SKYLINKConnectionConfig *)config appKey:(null_unspecified NSString *)appKey
Parameters
config |
The connection configuration object. |
|---|---|
appKey |
APP key. |
Discussion
Changes in config after creating the object won’t affect the connection.
Declared In
SKYLINKConnection.h
– connectToRoomWithSecret:roomName:userInfo:
Join the room specifiying the shared secret, room name and user info.
- (BOOL)connectToRoomWithSecret:(null_unspecified NSString *)secret roomName:(null_unspecified NSString *)roomName userInfo:(null_unspecified id)userInfo
Parameters
secret |
Shared secret. |
|---|---|
roomName |
Name of the room to join. |
userInfo |
User defined information (relating to oneself). May be an NSString, NSDictionary or NSArray. |
Return Value
NO if a connection is already established.
Discussion
It is recommended to use connectToRoomWithCredentials:roomName:userInfo: after calculating the credentials on a server, but if the client application has no server implementation then this one should be used.
Declared In
SKYLINKConnection.h
– connectToRoomWithCredentials:roomName:userInfo:
Join the room specifiying the calculated credential info, room name and user info.
- (nonnull NSString *)connectToRoomWithCredentials:(null_unspecified NSDictionary *)credInfo roomName:(null_unspecified NSString *)roomName userInfo:(null_unspecified id)userInfo
Parameters
credInfo |
A dictionary containing a credential, startTime and duration. |
|---|---|
roomName |
Name of the room to join. |
userInfo |
User defined information (relating to oneself). May be an NSString, NSDictionary or NSArray. |
Return Value
nil if connection can be established otherwise a message specifying reason for connection denial.
Discussion
The dictionary ‘credInfo’ is expected to have 3 non-Null parameters: an NSString type ‘credential’, an NSDate type ‘startTime’ and an NSNumber type ‘duration’ in hours. The ‘startTime’ must be a correct time of the client application’s timezone. Both the ‘startTime’ and ‘duration’ must be the same as the ones that were used to calculate the credential. Failing to provide any of them will result in a connection denial.
Declared In
SKYLINKConnection.h
– connectToRoomWithStringURL:userInfo:
Join the room specifiying the calculated string URL and user info.
- (BOOL)connectToRoomWithStringURL:(null_unspecified NSString *)stringURL userInfo:(null_unspecified id)userInfo
Parameters
stringURL |
Generated with room name, appKey, secret, startTime and duration. Typed NSString (not NSURL). |
|---|---|
userInfo |
User defined information (relating to oneself). May be an NSString, NSDictionary or NSArray. |
Return Value
YES (success) if connection can be established. NO if a connection is already established.
Discussion
Use this method when you calculate the URL on your server with your API key, secret and room name. Allows you to avoid having those parameters in the iOS app code.
Declared In
SKYLINKConnection.h
– disconnect:
Leave the room.
- (void)disconnect:(void ( ^ _Null_unspecified ) ( void ))completion
Parameters
completion |
The completion block called on the UI thread after leaving the room. This block is a good place to deallocate SKYLINKConnection if desired. Leave as empty block if not required. |
|---|
Discussion
Leave the room and remove any video renderers and PeerConnections.
Declared In
SKYLINKConnection.h
Room Control.
– refreshConnection:
Refresh peer connection with a specified peer.
- (void)refreshConnection:(null_unspecified NSString *)peerId
Parameters
peerId |
The unique id of the peer with whom the connection is being refreshed. |
|---|
Discussion
This method is provided as a convenience method. So that one can call if a peer streams are not behaving correctly.
Declared In
SKYLINKConnection.h
Media
– muteAudio:
Mute/unmute own audio and trigger mute/unmute audio call back for all other peers.
- (void)muteAudio:(BOOL)isMuted
Parameters
isMuted |
Flag to set if audio should be muted. Set to true to mute and false to unmute. |
|---|
Declared In
SKYLINKConnection.h
– muteVideo:
Mute/unmute own video and trigger mute/unmute video call back for all other peers.
- (void)muteVideo:(BOOL)isMuted
Parameters
isMuted |
Flag to set if video should be muted. Set to true to mute and false to unmute. |
|---|
Declared In
SKYLINKConnection.h
– isAudioMuted
Checks if own audio is currently muted.
- (BOOL)isAudioMuted
Return Value
true if audio is muted and false otherwise.
Declared In
SKYLINKConnection.h
– isVideoMuted
Checks if own video is currently muted.
- (BOOL)isVideoMuted
Return Value
true if video is muted and false otherwise.
Declared In
SKYLINKConnection.h
– switchCamera
Switches between front and back camera. By default the front camera input is captured.
- (void)switchCamera
Declared In
SKYLINKConnection.h
Recording (Beta)
– startRecording
Start the recording of the room.
- (nullable NSString *)startRecording
Return Value
The NSString return value is an error description. A nil value means no error occured.
Discussion
Warning: This feature is in BETA.
This is a Skylink Media Relay only feature, it needs to be enable for the API Key in Temasys developer console.
Declared In
SKYLINKConnection.h
– stopRecording
Stop the recording of the room.
- (nullable NSString *)stopRecording
Return Value
The NSString return value is an error description. A nil value means no error occured.
Discussion
Warning: This feature is in BETA.
This is a Skylink Media Relay only feature, it needs to be enable for the API Key in Temasys developer console.
Declared In
SKYLINKConnection.h
Messaging
– sendCustomMessage:peerId:
Send a custom message (dictionary, array or string) to a peer via signaling server.
- (void)sendCustomMessage:(null_unspecified id)message peerId:(null_unspecified NSString *)peerId
Parameters
message |
User defined message to be sent. May be an NSString, NSDictionary or NSArray. |
|---|---|
peerId |
The unique id of the peer to whom the message is sent. |
Discussion
If the ‘peerId’ is not given then the message is broadcasted to all the peers.
Declared In
SKYLINKConnection.h
– sendDCMessage:peerId:
Send a message (dictionary, array or string) to a peer via data channel.
- (BOOL)sendDCMessage:(null_unspecified id)message peerId:(null_unspecified NSString *)peerId
Parameters
message |
User defined message to be sent. May be an NSString, NSDictionary, NSArray. |
|---|---|
peerId |
The unique id of the peer to whom the message is sent. |
Return Value
YES if the message has been succesfully sent to all targeted peers, if NO is returned and verbose is enabled then informations will be logged.
Discussion
If the ‘peerId’ is not given then the message is broadcasted to all the peers.
Declared In
SKYLINKConnection.h
– sendBinaryData:peerId:
Send binary data to a peer via data channel.
- (void)sendBinaryData:(null_unspecified NSData *)data peerId:(null_unspecified NSString *)peerId
Parameters
data |
Binary data to be sent to the peer. The maximum size the method expects is 65456 bytes. |
|---|---|
peerId |
The unique id of the peer to whom the data is sent. |
Discussion
If the ‘peerId’ is not given then the data is sent to all the peers. If the caller passes data object exceeding the maximum length i.e. 65456, excess bytes are truncated to the limit before sending the data on to the channel.
Declared In
SKYLINKConnection.h
File Transfer
– sendFileTransferRequest:assetType:peerId:
This will trigger a file permission event at a peer.
- (void)sendFileTransferRequest:(null_unspecified NSURL *)fileURL assetType:(SKYLINKAssetType)assetType peerId:(null_unspecified NSString *)peerId
Parameters
fileURL |
The url of the file to send. |
|---|---|
assetType |
The type of the asset to send. |
peerId |
The unique id of the peer to whom the file would be sent. |
Exceptions
exception |
An exception will be raised if there is already a file transfer being done with the same peer. |
|---|
Declared In
SKYLINKConnection.h
– sendFileTransferRequest:assetType:
This will trigger a broadcast file permission event at all peers in the room.
- (void)sendFileTransferRequest:(null_unspecified NSURL *)fileURL assetType:(SKYLINKAssetType)assetType
Parameters
fileURL |
The url of the file to send. |
|---|---|
assetType |
The type of the asset to send. |
Discussion
If all the data channel connections are busy in some file transfer then this message will be ignored. If one or more data channel connections are not busy in some file transfer then this will trigger a broadcast file permission event at the available peers.
Declared In
SKYLINKConnection.h
– acceptFileTransfer:filename:peerId:
Accept or reject the file transfer request from a peer.
- (void)acceptFileTransfer:(BOOL)accept filename:(null_unspecified NSString *)filename peerId:(null_unspecified NSString *)peerId
Parameters
accept |
Flag to specify whether the request is accepted. |
|---|---|
filename |
The name of the file in request. |
peerId |
The unique id of the peer who sent the file transfer request. |
Declared In
SKYLINKConnection.h
– cancelFileTransfer:peerId:
Cancel the existing on going transfer at anytime.
- (void)cancelFileTransfer:(null_unspecified NSString *)filename peerId:(null_unspecified NSString *)peerId
Parameters
filename |
The name of the file in request (optional). |
|---|---|
peerId |
The unique id of the peer with whom file is being transmitted. |
Declared In
SKYLINKConnection.h
Miscellaneous
– sendUserInfo:
Update user information for every other peer and triggers user info call back at all the other peer’s end.
- (void)sendUserInfo:(null_unspecified id)userInfo
Parameters
userInfo |
User defined information. May be an NSString, NSDictionary or NSArray. |
|---|
Declared In
SKYLINKConnection.h
– getUserInfo:
Get the cached user info for a particular peer.
- (nonnull id)getUserInfo:(null_unspecified NSString *)peerId
Parameters
peerId |
The unique id of the peer. |
|---|
Return Value
User defined information. May be an NSString, NSDictionary or NSArray.
Declared In
SKYLINKConnection.h
– roomId
Get room ID.
- (nonnull NSString *)roomId
Return Value
Room ID.
Discussion
This is generally not needed.
Declared In
SKYLINKConnection.h
Utility
+ getSkylinkVersion
Get the version string of this Skylink SDK for iOS.
+ (nonnull NSString *)getSkylinkVersion
Return Value
Version string of this Skylink SDK for iOS.
Declared In
SKYLINKConnection.h
+ setVerbose:
Enable/disable verbose logs for all the connections.
+ (void)setVerbose:(BOOL)verbose
Parameters
verbose |
enable/disable verbose logs. Default is NO. |
|---|
Discussion
Warning: You should always disable logs in RELEASE mode.
Declared In
SKYLINKConnection.h
+ calculateCredentials:duration:startTime:secret:
Calculate credentials to be used by the connection.
+ (nonnull NSString *)calculateCredentials:(null_unspecified NSString *)roomName duration:(null_unspecified NSNumber *)duration startTime:(null_unspecified NSDate *)startTime secret:(null_unspecified NSString *)secret
Parameters
roomName |
Name of the room. |
|---|---|
duration |
Duration of the call in hours. |
startTime |
Start time of the call as per client application time zone. |
secret |
The shared secret. |
Return Value
The calculated credential string.
Declared In
SKYLINKConnection.h


I need to know, doesn't WF and EWF:unarmed or possibly epic prowess help you in shifted forms, like bear, dragon? And does ability stats stack, f.ex 2 str from armor, and two strength from shield?
Edited By Grimnir77 on 07/02/05 06:41
IIRC statistic bonuses from different sources all carry over into a shifted form, I think that epic prowess also carries over as well, but Im fairly sure that epic weapon focus unarmed strike dosen't carry over, since when shifted into a creature you are actually using a "creature weapon" like a claw or bite attack.
M1ke is correct. For some strange reason though, improved critical: unarmed carries over to your unarmed shapes. Never quite figured out why, but I know it does. _________________ If everyone is thinking alike, then someone is failing to think.
Thats what I saw, thats why I asked. And all other abilities, like monk attack schedule. Well, that compromises my build somewhat, but it being in a world where most people have only +5 weps, the DR in dragonform gets good. Wish they had fixed this....


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Brazil top court ruling could free jailed former president Lula
Thousands could potentially benefit, including Lula, after Supreme Court ends mandatory imprisonment rule.
Brazil's Supreme Court on Thursday overturned a law requiring convicted criminals to go to jail after losing their first appeal, a politically-charged ruling that may lead to the release of former left-wing President Luiz Inacio Lula da Silva.
The reinterpretation of Brazil's penal code, allowing convicts to exhaust their appeal options before being locked up, could benefit dozens of high-profile prisoners, among them Lula, jailed last year for corruption.
The 74-year-old left-wing leader, who was president between 2003 and 2010, had been favoured to win the 2018 election but his imprisonment barred him from running.
More:
Nearly 5,000 convicts could potentially benefit from the ruling that has been criticised by the country's justice minister.
By a 6-5 vote, the court overturned a three-year-old rule that contributed to the success of Brazil's biggest corruption investigation, the so-called Car Wash operation that put dozens of company executives and politicians in jail for bribes and kickbacks.
The prospect of serving immediate prison time after losing a first appeal encouraged suspects to negotiate plea deals with prosecutors, providing them information that helped unravel the biggest corruption scheme in Brazil's history.
The Car Wash prosecutors said the ruling would make their job harder and favour impunity because of Brazil's "excessive" appeal processes. They said in a statement that the court's decision was out of sync with a country that wants an end to corruption.
Lula's lawyers said they would seek his immediate release by the lower court that convicted him. He was jailed in 2018 for eight years and 10 months after he was found guilty of taking bribes from engineering firms in return for government contracts.
Polarised country
Lula, Brazil's first working-class president, left office with sky-high popularity ratings thanks to social policies that raised millions from poverty, but his critics say he ruined the country by allowing corruption to flourish.
His release would heighten tensions in a polarised nation that elected far-right President Jair Bolsonaro last year.
If and when he is released, Lula would be free to engage in politics but would not be eligible under Brazil's Clean Record law to seek elected office for eight years after his first conviction in June 2017.
"Lula is not electable, but that is not needed for him to take centre stage," political analyst Alberto Almeida said.
"He can draw more politicians from the centre and end up as a dealmaker for his party's candidate in the 2022 presidential elections."
His Workers Party welcomed the court's decision. Its leader, Gleisi Hoffmann, called it "a very important step to strengthen democracy and the constitution at a time when they are under threat from an extreme-right government."
Sergio Moro, the Car Wash trials judge who sentenced Lula and is now justice minister in Bolsonaro's government, warned before the court's decision that overturning the rule would be a big setback for the fight against corruption.
According to the National Council of Justice, some 4,895 convicts could potentially benefit from the rule change.


THE ECONOMY THAT PROPELS
Release: {{appState.version}} Network: {{appState.network}} Mode: {{appState.mode}} Peers: {{status?.inboundPeers.length + status?.outboundPeers.length}}
or
your account


OpenOffice Internal Spots
The following is a list of projects or documents which are in a close relation to the API project and
provides additionally useful information.
- UNO Development Kit Project
- This project provides information about UNO as the fundamental base for the OpenOffice API.
- Scripting Project
- This project provides information about the upcoming scripting framework.
- OpenOffice Wiki
- The OpenOffice Wiki provides a lot of useful information for developers and can probably be seen as the most important place for developers.
- Extensions Project
- The OpenOffice Extensions project with focus on extensions development and an extensions repository.
- OpenOffice for Developers
- This provides an useful entrance point for all who are interested in developing for
OpenOffice or with the OpenOffice API.
- OpenOffice Community Forum
- The forum provides a lot of information for OpenOffice users and developers.
Anything missing here? Write us!


ONE WAY PASSAGE (director: Tay Garnett; screenwriters: from a novel by Robert Lord/Wilson Mizner/Joe Jackson; cinematographer: Robert Kurrle; editor: Ralph Dawson; cast: William Powell (Dan Hardesty), Kay Francis (Joan Ames), Warren Hymer (Steve Burke), Frank McHugh (Skippy), Aline MacMahon (Countess Barilhaus/Barrel House Betty), Frederick Burton (Doctor), Roscoe Karns (Ship's Bartender); Runtime: 69; Warner Bros.; 1932)
Reviewed by Dennis Schwartz
Robert Lord won an Oscar for Best Original Screenplay. The director, Tay Garnett, allowed for satire to carry the comedic load and his instincts were correct, as the film worked, easily overcoming the story's melodramatics. For the popular stars, William Powell and Kay Francis, this was the sixth and last film they were teamed together, and is possibly their best film. It is interesting to note that Kay had a speech impediment, not being able to pronounce r's, therefore the script tried to give her as few of those sounds in her dialogue as possible.
This is a breezy story (believe it or not!) about doomed lovers who sail together from Hong Kong to San Francisco. Kay is a wealthy socialite who is terminally ill. William is a convicted murderer on his way to the slammer, where he is to be executed.
Joan (Kay Francis) and Don (William Powell) first meet in an international bar in Hong Kong where she bumps Don, whereby he spills his precious 'paradise cocktail,' which upsets him, but when their eyes meet they fall in love. They drink a farewell toast and break their glasses, leaving the stems crossed as a sign of good luck, figuring it is unlikely that they will ever meet again.
When Don steps outside the bar, he is arrested by San Francisco Detective Steve Burke (Warren Hymer). He cuffs him and tells him they're sailing to San Francisco together. Don tries to escape by loosening the ship's railing and falling overboard, but when he realizes that Steve can't swim he rescues him rather than let him drown. The grateful Steve decides not to cuff him for the three week ocean voyage, one that has a stopover in Honolulu.
Don meets Joan in the ship's bar and the two star-crossed lovers begin their doomed romance in earnest, without telling each other their deadly concern. She's traveling with a doctor (Frederick Burton) to look after her, while he meets on board two con artists he knew from home who are working the boat. Barrel House Betty (Aline MacMahon) poses as Countess Barilhaus, as she tries to rope in a wealthy Englishman, while Skippy (Frank McHugh) laughs his way through the minor cons he pulls. In a funny skit, he rushes into the ship's bar and tells the bartender: "Hurry up give me a drink before the fight begins." After two drinks, the bartender asks: "What fight?" Skippy: "The fight between you and me when I tell you I can't pay for the drink."
With the help of his cronies, Don makes his escape in Honolulu. But when he tries to tell Joan, she has a heart attack and faints before he can tell her why he must leave. He then rushes her back to the ship, where he is recaptured. As a subplot, Countess Barilhaus gets smitten over Steve and the promise that they could settle down in his chicken ranch, as the two become an item, to the laughter of Skippy.
In the last scene, when Don is taken off the boat by Steve, he greets Joan to say goodbye, as they both learned about each other's condition but don't realize the other knows it. They make plans to meet again one year later in Agua Caliente--a rendezvous that neither will survive to keep. At a bar in Agua Caliente one year later, two cocktail glasses suddenly shatter, as if having been joined in a toast by invisible hands.
REVIEWED ON 4/8/2001 GRADE: B
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
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Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Test the setTransform of the Scene. the lime cube and yellow polygon are grouped in one Scene, and they are moved in one shot.
Test Viewport.invalidate with Scene.
That's all Folks!


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|
ssm
|
A SISR implementation of the classic stochastic volatility model of Taylor. More...
#include <svol_filter.h>
Public Member Functions | |
| SVolFilter (unsigned numParts, double beta, double phi, double sigma, unsigned pLen=0) | |
| The constructor. More... | |
| ~SVolFilter () | |
| The destructor. | |
| double | logQ1Ev (const Vec &x1, const Vec &y1) |
| Evaluates the log of q1. More... | |
| double | logMuEv (const Vec &x1) |
| Evaluates the log of mu. More... | |
| double | logGEv (const Vec &yt, const Vec &xt) |
| Evaluates the log of g. More... | |
| double | logQEv (const Vec &xt, const Vec &xtm1, const Vec &yt) |
| Evaluates the log of q. More... | |
| double | logFEv (const Vec &xt, const Vec &xtm1) |
| Evaluates the log of f. More... | |
| Vec | qSamp (const Vec &xtm1, const Vec &yt) |
| Samples from q (assumed constant throughout time). More... | |
| Vec | q1Samp (const Vec &y1) |
| Samples from q1. More... | |
| Public Member Functions inherited from SISRFilter | |
| SISRFilter (int numParts, SISRResampStyle resampTechnique=SISRResampStyle::everytime_multinomial, unsigned int pathLength=0, double essPerc=1.0) | |
| Constructor. More... | |
| ~SISRFilter () | |
| Destructor. | |
| double | getLogCondLike () const |
| get log p(y_t|y_{1:t-1})$ or log p(y_1). More... | |
| double | getESS () const |
| get effective sample size. More... | |
| std::vector< std::vector< Vec > > | getFullParts () const |
| get $p(x_{1:t}|y_{1:t})$ More... | |
| std::vector< Mat > | getExpectations () const |
| get all stored expectations. With respect to $p(x_t|y_{1:t})$ More... | |
| std::vector< double > | getLogUWeights () const |
| Get log-un-normalized weights. More... | |
| void | filterOrSmooth (const Vec &data, const std::vector< std::function< const Mat(const Vec &)> > &fs=std::vector< std::function< const Mat(const Vec &)> >()) |
| If storing whole paths, performs smoothing. If not storing whole paths, performs filtering. More... | |
A SISR implementation of the classic stochastic volatility model of Taylor.
| SVolFilter::SVolFilter | ( | unsigned | numParts, |
| double | beta, | ||
| double | phi, | ||
| double | sigma, | ||
| unsigned | pLen = 0 |
||
| ) |
The constructor.
| numParts | the number of particles you want. |
| beta | |
| phi | |
| sigma | |
| pLen | the length of particles. only set to non-zero number if you want to pre-allocate. |
Evaluates the log of f.
| xt | a Vec of time t's state. |
| xtm1 | a Vec of time (t-1)'s state. |
Implements SISRFilter.
Evaluates the log of g.
| yt | a Vec of time t's data. |
| xt | a Vec of time t's state. |
Implements SISRFilter.
|
virtual |
Evaluates the log of mu.
| x1 | Vec of time 1's state. |
Implements SISRFilter.
Evaluates the log of q1.
| x1 | a Vec of time 1 state. |
| y1 | time one data Vec. |
Implements SISRFilter.
Evaluates the log of q.
| xt | a Vec of time t's state. |
| xtm1 | a Vec of time (t-1)'s state. |
| yt | a Vec pf time t's datum. |
Implements SISRFilter.
Samples from q1.
| y1 | a Vec of time 1's data observation. |
Implements SISRFilter.
Samples from q (assumed constant throughout time).
| xtm1 | a Vec of the previous time's state. |
| yt | a Vec of the most recent data observation. |
Implements SISRFilter.


|
|
IFXContour Class ReferenceThis class provides a means to store a set of contours.
More...
Inheritance diagram for IFXContour:
List of all members.
Detailed Description
This class provides a means to store a set of contours.
It is used by IFXGlyph3DGenerator. It is an internal API. See IFXGlyph3DGenerator for documentation.
Member Function Documentation
| virtual IFXRESULT IFXAPI IFXContour::Delete |
( |
U32 |
uIndex |
) |
[pure virtual] |
|
| virtual IFXRESULT IFXAPI IFXContour::GetCount |
( |
U32 * |
pCount |
) |
[pure virtual] |
|
| virtual IFXRESULT IFXAPI IFXContour::GetNormal |
( |
U32 |
uIndex, |
|
|
SIFXContourPoint * |
pNormal |
|
) |
[pure virtual] |
|
| virtual IFXRESULT IFXAPI IFXContour::GetPosition |
( |
U32 |
uIndex, |
|
|
SIFXContourPoint * |
pPosition |
|
) |
[pure virtual] |
|
| virtual IFXRESULT IFXAPI IFXContour::Initialize |
( |
U32 |
uSize |
) |
[pure virtual] |
|
| virtual IFXRESULT IFXAPI IFXContour::SetNormal |
( |
U32 |
uIndex, |
|
|
SIFXContourPoint * |
pNormal |
|
) |
[pure virtual] |
|
| virtual IFXRESULT IFXAPI IFXContour::SetPosition |
( |
U32 |
uIndex, |
|
|
SIFXContourPoint * |
pPosition |
|
) |
[pure virtual] |
|
© 1999 - 2006 Intel Corporation. All rights reserved.
* Other names and brands may be claimed as the property of others.
|
|


{% extends 'btax/input_base.html' %} {% load staticfiles %} {% load flatblocks %} {% block style %} {{block.super}} {% endblock %} {% block content %}
Tables of Effective Tax Rates or Their Components by Asset or Industry
These results were generated by PolicyBrain version {{ webapp_version }} on {{ created_on|date:"D, M jS Y \a\t g:iA" }} UTC using B-Tax version {{ btax_version }}.


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Definition at line 6 of file toy.py.
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# Authors: Cedric Rommel<EMAIL_ADDRESS>#
# License: BSD (3-clause)
import torch
from torch import nn
from .modules import Expression, Ensure4d
[docs]class EEGInceptionMI(nn.Module):
"""EEG Inception for Motor Imagery, as proposed in [1]_
The model is strongly based on the original InceptionNet for computer
vision. The main goal is to extract features in parallel with different
scales. The network has two blocks made of 3 inception modules with a skip
connection.
The model is fully described in [1]_.
Notes
-----
This implementation is not guaranteed to be correct, has not been checked
by original authors, only reimplemented bosed on the paper [1]_.
Parameters
----------
in_channels : int
Number of EEG channels.
n_classes : int
Number of classes.
input_window_s : float, optional
Size of the input, in seconds. Set to 4.5 s as in [1]_ for dataset
BCI IV 2a.
sfreq : float, optional
EEG sampling frequency in Hz. Defaults to 250 Hz as in [1]_ for dataset
BCI IV 2a.
n_convs : int, optional
Number of convolution per inception wide branching. Defaults to 5 as
in [1]_ for dataset BCI IV 2a.
n_filters : int, optional
Number of convolutional filters for all layers of this type. Set to 48
as in [1]_ for dataset BCI IV 2a.
kernel_unit_s : float, optional
Size in seconds of the basic 1D convolutional kernel used in inception
modules. Each convolutional layer in such modules have kernels of
increasing size, odd multiples of this value (e.g. 0.1, 0.3, 0.5, 0.7,
0.9 here for `n_convs`=5). Defaults to 0.1 s.
activation: nn.Module
Activation function. Defaults to ReLU activation.
References
----------
.. [1] Zhang, C., Kim, Y. K., & Eskandarian, A. (2021).
EEG-inception: an accurate and robust end-to-end neural network
for EEG-based motor imagery classification.
Journal of Neural Engineering, 18(4), 046014.
"""
def __init__(
self,
in_channels,
n_classes,
input_window_s=4.5,
sfreq=250,
n_convs=5,
n_filters=48,
kernel_unit_s=0.1,
activation=nn.ReLU(),
):
super().__init__()
self.in_channels = in_channels
self.n_classes = n_classes
self.input_window_s = input_window_s
self.input_window_samples = int(input_window_s * sfreq)
self.sfreq = sfreq
self.n_convs = n_convs
self.n_filters = n_filters
self.kernel_unit_s = kernel_unit_s
self.activation = activation
self.ensuredims = Ensure4d()
self.dimshuffle = Expression(_transpose_to_b_c_1_t)
# ======== Inception branches ========================
self.initial_inception_module = _InceptionModuleMI(
in_channels=self.in_channels,
n_filters=self.n_filters,
n_convs=self.n_convs,
kernel_unit_s=self.kernel_unit_s,
sfreq=self.sfreq,
activation=self.activation,
)
intermediate_in_channels = (self.n_convs + 1) * self.n_filters
self.intermediate_inception_modules_1 = nn.ModuleList([
_InceptionModuleMI(
in_channels=intermediate_in_channels,
n_filters=self.n_filters,
n_convs=self.n_convs,
kernel_unit_s=self.kernel_unit_s,
sfreq=self.sfreq,
activation=self.activation,
) for _ in range(2)
])
self.residual_block_1 = _ResidualModuleMI(
in_channels=self.in_channels,
n_filters=intermediate_in_channels,
activation=self.activation,
)
self.intermediate_inception_modules_2 = nn.ModuleList([
_InceptionModuleMI(
in_channels=intermediate_in_channels,
n_filters=self.n_filters,
n_convs=self.n_convs,
kernel_unit_s=self.kernel_unit_s,
sfreq=self.sfreq,
activation=self.activation,
) for _ in range(3)
])
self.residual_block_2 = _ResidualModuleMI(
in_channels=intermediate_in_channels,
n_filters=intermediate_in_channels,
activation=self.activation,
)
# XXX The paper mentions a final average pooling but does not indicate
# the kernel size... The only info available is figure1 showing a
# final AveragePooling layer and the table3 indicating the spatial and
# channel dimensions are unchanged by this layer... This could indicate
# a stride=1 as for MaxPooling layers. Howevere, when we look at the
# number of parameters of the linear layer following the average
# pooling, we see a small number of parameters, potentially indicating
# that the whole time dimension is averaged on this stage for each
# channel. We follow this last hypothesis here to comply with the
# number of parameters reported in the paper.
self.ave_pooling = nn.AvgPool2d(
kernel_size=(1, self.input_window_samples),
)
self.flat = nn.Flatten()
self.fc = nn.Linear(
# in_features=self.input_window_samples * intermediate_in_channels,
in_features=intermediate_in_channels,
out_features=self.n_classes,
bias=True,
)
self.softmax = nn.LogSoftmax(dim=1)
[docs] def forward(
self,
X: torch.Tensor,
) -> torch.Tensor:
X = self.ensuredims(X)
X = self.dimshuffle(X)
res1 = self.residual_block_1(X)
out = self.initial_inception_module(X)
for layer in self.intermediate_inception_modules_1:
out = layer(out)
out = out + res1
res2 = self.residual_block_2(out)
for layer in self.intermediate_inception_modules_2:
out = layer(out)
out = res2 + out
out = self.ave_pooling(out)
out = self.flat(out)
out = self.fc(out)
return self.softmax(out)
class _InceptionModuleMI(nn.Module):
def __init__(
self,
in_channels,
n_filters,
n_convs,
kernel_unit_s=0.1,
sfreq=250,
activation=nn.ReLU(),
):
super().__init__()
self.in_channels = in_channels
self.n_filters = n_filters
self.n_convs = n_convs
self.kernel_unit_s = kernel_unit_s
self.sfreq = sfreq
self.bottleneck = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.n_filters,
kernel_size=1,
bias=True,
)
kernel_unit = int(self.kernel_unit_s * self.sfreq)
# XXX Maxpooling is usually used to reduce spatial resolution, with a
# stride equal to the kernel size... But it seems the authors use
# stride=1 in their paper according to the output shapes from Table3,
# although this is not clearly specified in the paper text.
self.pooling = nn.MaxPool2d(
kernel_size=(1, kernel_unit),
stride=1,
padding=(0, int(kernel_unit // 2)),
)
self.pooling_conv = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.n_filters,
kernel_size=1,
bias=True,
)
self.conv_list = nn.ModuleList([
nn.Conv2d(
in_channels=self.n_filters,
out_channels=self.n_filters,
kernel_size=(1, (n_units * 2 + 1) * kernel_unit),
padding="same",
bias=True,
) for n_units in range(self.n_convs)
])
self.bn = nn.BatchNorm2d(self.n_filters * (self.n_convs + 1))
self.activation = activation
def forward(
self,
X: torch.Tensor,
) -> torch.Tensor:
X1 = self.bottleneck(X)
X1 = [conv(X1) for conv in self.conv_list]
X2 = self.pooling(X)
X2 = self.pooling_conv(X2)
out = torch.cat(X1 + [X2], 1)
out = self.bn(out)
return self.activation(out)
class _ResidualModuleMI(nn.Module):
def __init__(
self,
in_channels,
n_filters,
activation=nn.ReLU()
):
super().__init__()
self.in_channels = in_channels
self.n_filters = n_filters
self.activation = activation
self.bn = nn.BatchNorm2d(self.n_filters)
self.conv = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.n_filters,
kernel_size=1,
bias=True,
)
def forward(
self,
X: torch.Tensor,
) -> torch.Tensor:
out = self.conv(X)
out = self.bn(out)
return self.activation(out)
def _transpose_to_b_c_1_t(x):
return x.permute(0, 1, 3, 2)


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