Choose the correct anser for the following statements.(20-marks)
UML is informationed by a vision of the sturcture of software systems known as the ________ model.
(a)1 + 4 view (b)4 + 1 view (c)5+ 1 view (d)3 + 1 view
_______ features can also be used in descendants of the owing class.
(a)protected (b)private (c)public (d)package
Multiplicity iformation should be shown on _________.
(a)associations (b)generalizations (c)specialization (d)none of these
Two kinds of diagrams are defined in UML for showing interactions, _____ diagrams.
(a)class and object (b)sequence and collaboration
(c)use case and activity (d)component adn deployment
Composition is a strong form of _______ in which the 'part' objects are dependent on the 'whole' objects.
(a)association (b)operation (c)aggregation (d)generalization
The design view desctibes the _________ structure that support the functional requirements expressed in the use case view.
(a)external (b)logical (c)physical (d)none of these
Component diagrams sow components and the relationships between them, modeled by________.
(a)artefacts (b)dependencies (c)interfaces (d)none of these
Insequence dagram, the period of time during which an object is processing a message is known as _______.
(a)Lifeline (b)Activation (c)Return (d)Transition
______ means that one case is included whenever one of the other use case is performed.
(a)Use case inclusion (b)Use case entension (c)Generalization (d)Use case description
_________ can be used to simplify the sturcture of a state chart, by factoring out common behavior.
(a)Composit States (b)Maper (c)Observer (d)Component
Abstract classes are classes that are intended to have instances. (True/False)
Realization of the use case is an object.(True/False)
A class describes a set of objects that share the same structure and properties.(True/False)
Guard conditions can be used to specify which one out of a set of transitions actually fires on a particular occasion.(True/False)
Exit actions are performed every time a state becomes active.(Ture/False)
Association roles connect classifier roles in collaborations and indicate how objects can be linked, and hence exchange messages, in interactions.(True/False)
Classes are defined at compile-time, but objects are createed at run-time, as instances of classes.(True/False)
Data types can also be defined as enumerations, which are essentially the same as the enumerated types provided in many programming langurages.(True/False)
The 'trace' stereotype denote a dependecy between two model elements that represent the different concept.(True/False)
The general UML notation allows and association to connect any number of classes, the majority of associations used are binary, connecting only two classes.(True/False)
Write the short notes on the differences between ANY FOUR of the following pairs:(20 marks)
Class and Abstract
Boundary object and Control object
Classifier role and Association role
Entry action and Exist action
Include dependecy and Extend dependecy
Aggregation and Composition
Of-the-Month (OTMC) is an innovation young firm that sells membershis to people who have an interest in certain products/services. People pay membership fees for one year and the club sends them a special product of theri interest each month. For example, OTMC has a coffee-of-the month club that sends members one pound of special coffee each month. OTMC currently has six products of memberships; coffee, wine, beer, cigars, flowers, adn computer games and each of which cost a different amount. Customers usually belong to just one product, but some belong to two or more products. When people join OTMC, the telephone operatior records the name, mailing address, phone, number, email address, credit-card information, start date, adn membership product(s),e.g.,coffee.
Draw the UML use case diagram for the above system.(10 marks)
Define the major tasks and write use case diagram description of (a).(5 marks)
Consider the world of libraries. A library has books, videos and CDs that it loans to its users.All library material has an id and a title. In additions, books have one or more authors, videos have one or more actors, while CDs have one or more entertatiners. The library maintains one or more copies of each library item (books, video, or CD). Copies of all library materials can be loaned to users. references-only material is loaned for 2hrs and can't be removed from the library. Other materials
can be loaned fro 2 weeks. For ever loan, the library records the user's id, the loan date and teh return date. The library maintains users' names, address and phone number.(15 marks)
Draw a class diagram for the library case above. Make sure to show attributes, multiplicities and aggregation/compositions, where appropriate. No need to show any operations.
Draw object diagram for the book is loaned from the library, with dummy data members and the relation between them.
User user = new User ('U1022', "Mr. Brown", "23 Avenu, Buston", "859985637");
Book Lbook = new Book ('BSc2256', "Science of Computing", "Copies-2");
Loan loanl = new loanl ('U1022','BSc2256', 'Copies-2', "12/03/2017", "26/03/2017");
Assume that you have to develop a computer system for the library described in No.(4) above. Only members of the library may borrow books. The system checks whether the potential borrower is a member of the library, and whether there is no reservation on the book. If both check succeed the system records that the book is on loan. Otherwise if refuses the loan. A member may ask to extend the loan of the book. The system then checks whether there is a reservation on the book. If so, the system refuses to extend the loan. Otherwise it records the extension of the loan.
Draw the sequence diagram and collaboration diagram that shws scenario of borrowing books
(15 marks)
Draw a state chart diagram and write a scenario summariing the information given in the following description of an automatic washing machine.
The washing machine is in idle state, when power on otherwise it is in off state. In this state, user can choose washing plans such as regular or delicate or super delicate. When user press Start after setting the washine plan, the machine starts to soak in the water (pump in) for 30 mins, then it starts rinsing (washing) by stirring for another 30 mins. If user sets the delicate or super delicate washing plan, the machine skips teh soak in process and starts rinsing (washing) by stirring for 30 mins. After the rinse process is stopped and it starts draining (pump out) for 5 mins.
If the washing plan is super delicate, after drainng, it turns the light off and back to idle state. Other washing plans continue to dry process by stirring for 10 mins.
After drying, it turns light off and back to idle state.(15 marks)


Pedagogy (V) by Clare Louise Harmon
[Redacted] plays automatic saw-toothed
forward and back
her elbow sufficiently low
and wrist supple.

[Redacted] plays automatic
saw-toothed forward back
elbow down with weight
of a body all up
in the finger
just as [Redacted] just as
he taught her.
I know you are
just girl but still
you have a little power.
[Redacted] teaches
automatic: Play
saw-toothed forward
back. She demonstrates Look
she demonstrates are you looking?
See how my weight
is channeled She demonstrates
here into my
finger?
Just as [Redacted]
just as he taught her
she demonstrates I know you are
just eight she demonstrates
but still still
you have a little
power eh?
She demonstrates
just as [Redacted] just as he
taught her touching
hesitant
and ever so slight.
Clare Louise Harmon is the author of THE THINGBODY (Instar Books 2015) and the chapbook, IF WISHES WERE HORSES THE POOR WOULD RIDE (Finishing Line Press 2016). Her work has appeared in numerous publications including Sixth Finch, The Feminist Wire, PANK, Quaint, Tammy, and The Stockholm Review of Literature, among others. Prior to her poet-life, she taught violin, viola, and chamber music at Drake University. Currently, she lives and writes in New Orleans in Alex Chilton’s former apartment in Treme. more at cargocollective.com/clarelouiseharmon


Compute DICS source power in a grid of time-frequency windows and display results.
The original reference is: Dalal et al. Five-dimensional neuroimaging: Localization of the time-frequency dynamics of cortical activity. NeuroImage (2008) vol. 40 (4) pp. 1686-1700
# Author: Roman Goj<EMAIL_ADDRESS>#
# License: BSD (3-clause)
import mne
from mne.event import make_fixed_length_events
from mne.datasets import sample
from mne.time_frequency import compute_epochs_csd
from mne.beamformer import tf_dics
from mne.viz import plot_source_spectrogram
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
noise_fname = data_path + '/MEG/sample/ernoise_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
subjects_dir = data_path + '/subjects'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name
Read raw data
raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw.info['bads'] = ['MEG 2443'] # 1 bad MEG channel
# Pick a selection of magnetometer channels. A subset of all channels was used
# to speed up the example. For a solution based on all MEG channels use
# meg=True, selection=None and add mag=4e-12 to the reject dictionary.
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, meg='mag', eeg=False, eog=False,
stim=False, exclude='bads',
selection=left_temporal_channels)
raw.pick_channels([raw.ch_names[pick] for pick in picks])
reject = dict(mag=4e-12)
# Re-normalize our empty-room projectors, which should be fine after
# subselection
raw.info.normalize_proj()
# Setting time windows. Note that tmin and tmax are set so that time-frequency
# beamforming will be performed for a wider range of time points than will
# later be displayed on the final spectrogram. This ensures that all time bins
# displayed represent an average of an equal number of time windows.
tmin, tmax, tstep = -0.55, 0.75, 0.05 # s
tmin_plot, tmax_plot = -0.3, 0.5 # s
# Read epochs
event_id = 1
events = mne.read_events(event_fname)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
baseline=None, preload=True, proj=True, reject=reject)
# Read empty room noise raw data
raw_noise = mne.io.read_raw_fif(noise_fname, preload=True)
raw_noise.info['bads'] = ['MEG 2443'] # 1 bad MEG channel
raw_noise.pick_channels([raw_noise.ch_names[pick] for pick in picks])
raw_noise.info.normalize_proj()
# Create noise epochs and make sure the number of noise epochs corresponds to
# the number of data epochs
events_noise = make_fixed_length_events(raw_noise, event_id)
epochs_noise = mne.Epochs(raw_noise, events_noise, event_id, tmin_plot,
tmax_plot, baseline=None, preload=True, proj=True,
reject=reject)
epochs_noise.info.normalize_proj()
epochs_noise.apply_proj()
# then make sure the number of epochs is the same
epochs_noise = epochs_noise[:len(epochs.events)]
# Read forward operator
forward = mne.read_forward_solution(fname_fwd, surf_ori=True)
# Read label
label = mne.read_label(fname_label)
Out:
Opening raw data file /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
Read a total of 3 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Current compensation grade : 0
Range : 25800 ... 192599 = 42.956 ... 320.670 secs
Ready.
Adding average EEG reference projection.
1 projection items deactivated
Reading 0 ... 166799 = 0.000 ... 277.714 secs...
72 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 3)
4 projection items activated
Loading data for 72 events and 781 original time points ...
0 bad epochs dropped
Opening raw data file /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/ernoise_raw.fif...
Isotrak not found
Read a total of 3 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Current compensation grade : 0
Range : 19800 ... 85867 = 32.966 ... 142.965 secs
Ready.
Reading 0 ... 66067 = 0.000 ... 109.999 secs...
110 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Loading data for 110 events and 481 original time points ...
1 bad epochs dropped
Projections have already been applied. Setting proj attribute to True.
Reading forward solution from /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
2 source spaces read
Desired named matrix (kind = 3523) not available
Read MEG forward solution (7498 sources, 306 channels, free orientations)
Desired named matrix (kind = 3523) not available
Read EEG forward solution (7498 sources, 60 channels, free orientations)
MEG and EEG forward solutions combined
Source spaces transformed to the forward solution coordinate frame
Converting to surface-based source orientations...
Average patch normals will be employed in the rotation to the local surface coordinates....
[done]
Time-frequency beamforming based on DICS
# Setting frequency bins as in Dalal et al. 2008
freq_bins = [(4, 12), (12, 30), (30, 55), (65, 300)] # Hz
win_lengths = [0.3, 0.2, 0.15, 0.1] # s
# Then set FFTs length for each frequency range.
# Should be a power of 2 to be faster.
n_ffts = [256, 128, 128, 128]
# Subtract evoked response prior to computation?
subtract_evoked = False
# Calculating noise cross-spectral density from empty room noise for each
# frequency bin and the corresponding time window length. To calculate noise
# from the baseline period in the data, change epochs_noise to epochs
noise_csds = []
for freq_bin, win_length, n_fft in zip(freq_bins, win_lengths, n_ffts):
noise_csd = compute_epochs_csd(epochs_noise, mode='fourier',
fmin=freq_bin[0], fmax=freq_bin[1],
fsum=True, tmin=-win_length, tmax=0,
n_fft=n_fft)
noise_csds.append(noise_csd)
# Computing DICS solutions for time-frequency windows in a label in source
# space for faster computation, use label=None for full solution
stcs = tf_dics(epochs, forward, noise_csds, tmin, tmax, tstep, win_lengths,
freq_bins=freq_bins, subtract_evoked=subtract_evoked,
n_ffts=n_ffts, reg=0.001, label=label)
# Plotting source spectrogram for source with maximum activity
# Note that tmin and tmax are set to display a time range that is smaller than
# the one for which beamforming estimates were calculated. This ensures that
# all time bins shown are a result of smoothing across an identical number of
# time windows.
plot_source_spectrogram(stcs, freq_bins, tmin=tmin_plot, tmax=tmax_plot,
source_index=None, colorbar=True)
Out:
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
Computing time-frequency DICS beamformer for time window -550 to -250 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -500 to -200 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -450 to -150 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -400 to -100 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -350 to -50 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -300 to 0 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -250 to 49 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -200 to 99 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -150 to 149 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -100 to 199 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -50 to 249 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 0 to 300 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 50 to 350 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 99 to 399 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 150 to 450 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 199 to 499 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 250 to 550 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 300 to 600 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 350 to 649 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 400 to 700 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 449 to 750 ms, in frequency range 4 to 12 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -550 to -350 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -500 to -300 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -450 to -250 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -400 to -200 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -350 to -150 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -300 to -100 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -250 to -49 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -200 to 0 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -150 to 49 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -100 to 99 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -50 to 149 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 0 to 200 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 50 to 250 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 99 to 300 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 150 to 350 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 199 to 399 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 250 to 450 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 300 to 500 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 350 to 550 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 400 to 600 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 449 to 649 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 500 to 700 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 550 to 750 ms, in frequency range 12 to 30 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -550 to -400 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -500 to -350 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -450 to -300 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -400 to -250 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -350 to -200 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -300 to -150 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -250 to -100 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -200 to -50 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -150 to 0 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -100 to 49 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -50 to 99 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 0 to 150 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 50 to 200 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 99 to 249 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 150 to 300 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 199 to 350 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 250 to 400 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 300 to 450 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 350 to 500 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 400 to 550 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 449 to 600 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 500 to 650 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 550 to 700 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 600 to 750 ms, in frequency range 30 to 55 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -550 to -450 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -500 to -400 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -450 to -350 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -400 to -300 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -350 to -250 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -300 to -200 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -250 to -150 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -200 to -100 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -150 to -50 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -100 to 0 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window -50 to 49 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 0 to 100 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 50 to 150 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 99 to 199 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 150 to 250 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 199 to 299 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 250 to 350 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 300 to 400 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 350 to 449 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 400 to 500 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 449 to 549 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 500 to 600 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 550 to 650 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 600 to 700 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Computing time-frequency DICS beamformer for time window 650 to 750 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Adding a time window to cover last time points
Computing time-frequency DICS beamformer for time window 650 to 750 ms, in frequency range 65 to 300 Hz
Computing cross-spectral density from epochs...
using FFT with a Hanning window to estimate spectra
[done]
13 out of 366 channels remain after picking
Computing DICS source power...
[done]
Total running time of the script: (0 minutes 25.546 seconds)
plot_tf_dics.py
plot_tf_dics.ipynb


White Castle announces plans to open world’s largest location in Orlando
by Joel FrancoORLANDO, FLA. (WSVN) - The world’s largest White Castle is coming to Florida.
The company revealed a new location will be opening in the Orlando area.
White Castle, known for their sliders, has not had a presence in the Sunshine State in more than half a century.
The new location is expected to break ground near Walt Disney World in early 2020. According to the Orlando Sentinel, the location will be at Village at O-Town West, near Disney World on Daryl Carter Parkway.
Around 145 workers will be employed at the location.
Currently, White Castle has locations in 13 states, operating more than 375 restaurants in total.
The restaurant is known for its variety of sliders. According to Fox 35, a ‘Crave Mobile’ was at the site of the announcement serving up White Castle burgers.


Google News rolling out support for bilingual news feed
by Joe FedewaBack in September, app teardown expert Jane Manchun Wong discovered a handful of features headed to Google apps. Among those features was the ability to choose multiple languages in Google News. Today, Google officially announced the ability to view stories in two languages in your news feed.
You can now go into the Settings of the Google News app and select two languages in the “Languages & regions” section. Currently, you can select up to two languages/regions. This means, for example, that you can keep up with news in English and Hindi, or you can read news from the two countries in the same language. You’ll see all the stories in the same feed in their respective languages. Stories will appear more frequently in the language/region you have selected as the Primary.
Google says the feature is available in Google News for Android and iOS across 141 countries and 41 languages. All of your previous personalization preferences carry over to the added languages. As mentioned above, go into the Settings and find the “Languages & regions” section. Tap the “Add language and region” button and search. This is a great feature for people who speak multiple languages and had to rely on separate apps to get news. Now you can do it all in one single place.
Source: Google


{% extends 'layout-lab-3-pregnant.html' %} {% block pageTitle %} Pregnancy - The start of labour - NHS.UK {% endblock %} {% block beforeContent %} {% from 'details/macro.njk' import details %} {{ details({ "classes": "alpha-lab-breadcrumbs", "text": "Labour and birth", "HTML": "
Labour begins with the latent phase. This can take hours, or sometimes days.


MouseWheel()
Syntax
Result = MouseWheel()Description
Returns the number of ticks the mouse wheel has moved since the last function call. ExamineMouse() should be called before this function to update the mouse status.
Parameters
None.
Return value
The number of ticks the mouse wheel has moved since the last function call. The value is positive if the wheel has moved forward and negative is the wheel has moved backwards.
See Also
ExamineMouse()
Supported OS
Windows


|
MiniGFX
0.13.5
Helper classes, abstractions, and interfaces to assist in OpenGL rendering applications.
|
An STL-like vector for OpenGL buffered data. More...
#include <glVector.hpp>
Public Member Functions | |
| ~glVector () | |
| Destroy this GL Vector. | |
| glVector (const size_t &capacity=1) | |
| Construct a GL Vector. More... | |
| glVector (glVector &&other) noexcept | |
| Move constructor. More... | |
| glVector (const glVector &other) noexcept | |
| Copy constructor. More... | |
| glVector & | operator= (glVector &&other) noexcept |
| Movement operator, for moving another buffer into this one. More... | |
| glVector & | operator= (const glVector &other) noexcept |
| Copy operator, for copying another buffer into this one. More... | |
| T & | operator[] (const size_t index) noexcept |
| Retrieve a reference to the element at the index specified. More... | |
| void | resize (const size_t newCapacity) noexcept |
| Resizes the internal capacity of this vector. More... | |
| size_t | getLength () const noexcept |
| Retrieve the length of this vector (number of elements in it). More... | |
| Public Member Functions inherited from mini::glBuffer | |
| virtual | ~glBuffer ()=default |
| Virtual Destructor. | |
| glBuffer ()=default | |
| Default constructor. | |
| glBuffer (glBuffer &&) noexcept=default | |
| Move constructor. | |
| glBuffer (const glBuffer &) noexcept=default | |
| Copy constructor. | |
| glBuffer & | operator= (glBuffer &&) noexcept=default |
| Move assignment. | |
| glBuffer & | operator= (const glBuffer &) noexcept=default |
| Copy assignment. | |
| void | beginWriting () const noexcept |
| Prepare this buffer for writing, waiting on any reads. | |
| void | endWriting () const noexcept |
| Signal that this multi-buffer is finished being written to. | |
| void | endReading () noexcept |
| Signal that this multi-buffer is finished being read from. | |
| void | bindBuffer (const GLenum target) const noexcept |
| Bind this buffer to the target specified. More... | |
| void | bindBufferBase (const GLenum target, const GLuint index) const noexcept |
| Bind this buffer to a particular shader binding point. More... | |
Additional Inherited Members | |
| Static Public Member Functions inherited from mini::glBuffer | |
| static void | WaitForFence (GLsync &fence) noexcept |
| Wait for the fence at the supplied index to pass. More... | |
| Protected Attributes inherited from mini::glBuffer | |
| GLsync | m_writeFence = nullptr |
| Protected Attributes. More... | |
| GLsync | m_readFence = nullptr |
| Fence for safely reading data. | |
| GLuint | m_bufferID = 0 |
| OpenGL object ID for this buffer. | |
An STL-like vector for OpenGL buffered data.
| T | the type of element to construct an array of. |
|
inline |
Construct a GL Vector.
| capacity | the starting capacity (1 or more). |
|
inlinenoexcept |
Move constructor.
| other | another buffer to move the data from, to here. |
|
inlinenoexcept |
Copy constructor.
| other | another buffer to move the data from, to here. |
|
inlinenoexcept |
Retrieve the length of this vector (number of elements in it).
|
inlinenoexcept |
Movement operator, for moving another buffer into this one.
| other | another buffer to move the data from, to here. |
|
inlinenoexcept |
Copy operator, for copying another buffer into this one.
| other | another buffer to copy the data from, to here. |
|
inlinenoexcept |
Retrieve a reference to the element at the index specified.
| index | an index to the element desired. |
|
inlinenoexcept |
Resizes the internal capacity of this vector.
| newCapacity | the new desired capacity. |


Netizens ROFL as #InternationalMensDay Coincides With #WorldToiletDay
New Delhi (Sputnik): 19 November marks #InternationalMensDay, promoting gender equality and raising awareness about men’s health.
This year the celebration is themed “Making a Difference for Men and Boys” – focusing on the need to value men across the globe, while pledging to make practical improvements in their mental health and well-being.
Incidentally, International Men’s Day falls on the same day as World Toilet Day.
Netizens were quick to mock this and inundated Twitter with hilarious reactions and rib-tickling memes.
“International Men's Day” was first coined back in 1999 by a West Indies-based professor called Dr Jerome Teelucksingh.
Later, Indian Men’s advocate Uma Challa played a significant role in promoting the day to a global level, trying to associate the occasion as an awareness programme.
Unfortunately today, men themselves have fallen prey to what has widely been perceived as the toxicity of patriarchy at many levels.
For adults, current rape laws in India have no room for male victims who fear to come forward because of a lack of legal aid and stigma shadowing the idea of masculinity
Citizens from around the world took to social networking platforms to celebrate and shower love on fathers, brothers, friends, boyfriends, and husbands on #InternationalMensDay.


Herberon Fields [ak12], plain, in Mandoria, peasants: 102
Last updated Turn #134
Herberon Fields [ak12], plain, in Mandoria, peasants: 102
------------------------------------------------------------------------
30: The population grows by one peasant.
Routes leaving Herberon Fields:
North, plain, to Herberon Fields [aj12], 7 days
Garrison [bg3j], garrison, uncontrolled, with ten peasants
East, plain, to Herberon Fields [ak13], 7 days
South, plain, to Herberon Fields [al12], 7 days
West, plain, to Herberon Fields [ak11], 7 days
Seen here:
Stack of cyclops [iv7v], cyclops, number: 10
Crowd [jn0l], angry peasants, number: 28, on guard
Stack of cyclops [gs0z], cyclops, number: 8


info-window(<InfoWindowOption>, <InfoWindowEvent>)
Initialize a Google map InfoWindow and set a scope method showInfoWindow
Requires: map directive
Restrict To: Element Or Attribute
Parameters:
| Name | Type | Description |
|---|---|---|
<InfoWindowOption> |
String | Any InfoWindow options, https://developers.google.com/maps/documentation/javascript/reference?csw=1#InfoWindowOptions |
<InfoWindowEvent> |
String | Any InfoWindow events, https://developers.google.com/maps/documentation/javascript/reference |
- Source:
Requires:
- module:Attr2Options
Example
Example:
<map center="[40.74, -74.18]">
<marker position="the cn tower" on-click="showInfoWindow(event, 'marker-info'"></marker>
<info-window id="marker-info" style="display: none;">
<h1> I am an InfoWindow </h1>
I am here at [[this.getPosition()]]
</info-window>
</map>
For working example, please visit:
https://rawgit.com/allenhwkim/angularjs-google-maps/master/build/marker_with_info_window.html
Requires
- module:Attr2Options


Board class, class used to display and do calculations on boards.
More...
#include <Board.h>
|
|
bool | checkIfWon () |
| | Slot to check if all the ships were destoryed.
|
| |
| void | setSquares (int x, int y, int size, int flip, int state) |
| | Slot to set square(s) to certain state. More...
|
| |
|
|
void | won () |
| | Signal emited when all the ships were destroyed.
|
| |
|
| | Board (int size, bool allowClicks) |
| | Constructor responsible for creating new board. More...
|
| |
|
int | getSize () |
| | Function repsonsible for returning size of a board, both width and height are the same.
|
| |
| void | display (QGraphicsScene *scene, int x, int y) |
| | Function responsible for displaying object on scene. More...
|
| |
| Battleships::Square * | getSquare (int x, int y) |
| | Function responsible for returning Square pointer. More...
|
| |
| QList< Battleships::Square * > | getSquares () |
| | Function responsible for returning list of all Square pointer. More...
|
| |
| bool | checkIfEmpty (int x, int y, int size, int flip) |
| | Function responsible for checking if squares are empty. More...
|
| |
| void | generateRandomShips (int big, int medium, int small) |
| | Function responsible for generating random ships on board. More...
|
| |
Board class, class used to display and do calculations on boards.
◆ Board()
| Battleships::Board::Board |
( |
int |
size, |
|
|
bool |
allowClicks |
|
) |
| |
Constructor responsible for creating new board.
- Parameters
-
| size | - size of board |
| allowClicks | - should board receive click events |
◆ checkIfEmpty()
| bool Battleships::Board::checkIfEmpty |
( |
int |
x, |
|
|
int |
y, |
|
|
int |
size, |
|
|
int |
flip |
|
) |
| |
Function responsible for checking if squares are empty.
- Parameters
-
| x | - position on x axis on board |
| y | - position on y axis on board |
| size | - amount of squares to check |
| flip | - check horizontally(false) or vertically(true) |
- Returns
- True if squares are empty, else False
◆ display()
| void Battleships::Board::display |
( |
QGraphicsScene * |
scene, |
|
|
int |
x, |
|
|
int |
y |
|
) |
| |
Function responsible for displaying object on scene.
- Parameters
-
| scene | - pointer to scene on which object will be displayed |
| x | - position on x axis on scene |
| y | - position on y axis on scene |
◆ generateRandomShips()
| void Battleships::Board::generateRandomShips |
( |
int |
big, |
|
|
int |
medium, |
|
|
int |
small |
|
) |
| |
Function responsible for generating random ships on board.
- Parameters
-
| big | - amount of big ships |
| medium | - amount of medium ships |
| small | - amount of small ships |
◆ getSquare()
| Square * Battleships::Board::getSquare |
( |
int |
x, |
|
|
int |
y |
|
) |
| |
Function responsible for returning Square pointer.
- Parameters
-
| x | - position on x axis on board |
| y | - position on y axis on board |
- Returns
- Square pointer
◆ getSquares()
Function responsible for returning list of all Square pointer.
- Returns
- QList of Square pointers
◆ setSquares
| void Battleships::Board::setSquares |
( |
int |
x, |
|
|
int |
y, |
|
|
int |
size, |
|
|
int |
flip, |
|
|
int |
state |
|
) |
| |
|
slot |
Slot to set square(s) to certain state.
- Parameters
-
| x | - position on x axis on board |
| y | - position on y axis on board |
| size | - amount of squares to set |
| flip | - set horizontally(false) or vertically(true) |
| state | - state to be set |
The documentation for this class was generated from the following files:


| Stage Manager | .................... | Chevis LaBelle |
| Costume Alterations & Flag Designer | .................... | Leslie Wilson |
| Follow Spots | .................... | Leigh Ann Chandler & Jay Swindle |
| Hair and Wigs | .................... | Marian Latimer |
| Lights | .................... | George Jackson |
| Lobby Decoration | .................... | Sandra Boutwell |
| Lobby Workers & Consessions | .................... | Cindy Robinson, Robbie Robinson, Marian Latimer, Teri Myers, Stacy Wansley |
| Make-Up | .................... | Lacy Hinton & Marian Latimer |
| Programs | .................... | Robbie Robinson, Juna Broome, Marian Latimer |
| Publicity | .................... | Cynthia Welborn |
| Set Designer | .................... | Joey Lay |
| Set Construction | .................... | Gerald Wansley, Bob Kyzar, Juna Broome, Lance Broome, Cindy Robinson, Robbie Robinson, Lacy Hinton, Teresa Rayborn, Sandra Boutwell, Alisha Powell, Joey Lay, Brent Holifield, Mike Travis, Cindy McNair |
| Sound | .................... | Gerald Wansley |


Data Processing Functions¶
Supported processing operations are described below. Functions are organized categorically.
In most cases, a “safe” version of each function can be invoked to create a new SeisData object with the processed output.
Any function that can logically operate on a SeisChannel object will do so. Any
function that operates on a SeisData object will also operate on a SeisEvent
object by applying itself to the SeisData object in the :data
field.
Signal Processing¶
Remove the mean from all channels i with S.fs[i] > 0.0. Specify irr=true to also remove the mean from irregularly sampled channels. Ignores NaNs.
Remove the polynomial trend of degree n from every regularly-sampled channel i in S using a least-squares polynomial fit. Ignores NaNs.
-
equalize_resp!(S, resp_new::Array[, hc_new=HC, C=CH])
Translate all data in SeisData structure S to instrument response resp_new. Expected structure of resp_new is a complex Float64 2d array with zeros in resp[:,1], poles in resp[:,2]. If channel i has key S.misc[i][“hc”], the corresponding value is used as the critical damping constant; otherwise a value of 1.0 is assumed.
-
filtfilt!(S::SeisData[; KWs])
Apply a zero-phase filter to data in S.x.
-
filtfilt!(Ev::SeisEvent[; KWs])
Apply zero-phase filter to Ev.data.x.
-
filtfilt!(C::SeisChannel[; KWs])
Apply zero-phase filter to C.x
Filtering is applied to each contiguous data segment of each channel separately.
### Keywords Keywords control filtering behavior; specify e.g. filtfilt!(S, fl=0.1, np=2, rt=”Lowpass”). Default values can be changed by adjustin the shared keywords, e.g., SeisIO.KW.Filt.np = 2 changes the default number of poles to 2.
| KW | Default | Type | Description |
|---|---|---|---|
| fl | 1.0 | Float64 | lower corner frequency [Hz] (a) |
| fh | 15.0 | Float64 | upper corner frequency [Hz] (a) |
| np | 4 | Int64 | number of poles |
| rp | 10 | Int64 | pass-band ripple (dB) |
| rs | 30 | Int64 | stop-band ripple (dB) |
| rt | “Bandpass” | String | response type (type of filter) |
| dm | “Butterworth” | String | design mode (name of filter) |
(a) By convention, the lower corner frequency (fl) is used in a Highpass filter, and fh is used in a Lowpass filter.
-
taper!(S)
Cosine taper each channel in S around time gaps.
-
unscale!(S[, irr=false])
Divide the gains from all channels i with S.fs[i] > 0.0. Specify irr=true to also remove gains of irregularly-sampled channels.
Merge and Synchronize¶
-
merge!(S::SeisData, U::SeisData)
Merge two SeisData structures. For timeseries data, a single-pass merge-and-prune operation is applied to value pairs whose sample times are separated by less than half the sampling interval.
-
merge!(S::SeisData)
“Flatten” a SeisData structure by merging data from identical channels.
Merge Behavior¶
Two (or more) channels are merged if they have the same values for each of
these fields:
+ :id
+ :fs
+ :resp
+ :units
+ :loc
Unset (empty) values for :resp
, :units
, and :loc
are treated as a
match to any non-empty value in the corresponding field.
How Merges Work¶
- Non-overlapping data are concatenated and time gaps are adjusted as needed.
- Redundant data are removed.
- Data that overlap in time and have different values are averaged pairwise if xi, xj : | ti-tj | < (0.5*S.fs). Warnings are thrown when this situation is encountered.
It’s best to merge only unprocessed data. Merging data segments that were processed independently (e.g. detrended) throws many warnings if values differ at overlapping times.
When SeisIO Won’t Merge¶
SeisIO does not combine data channels if any of the five fields above are non-empty and different. For example, if a SeisData object S contains two channels, each with id “XX.FOO..BHZ”, but one has fs=100 Hz and the other fs=50 Hz, merge! does nothing.
-
sync!(S::SeisData)
Synchronize the start times of all data in S to begin at or after the last start time in S.
-
sync!(S::SeisData[, s=ST, t=EN, v=VV])
Synchronize all data in S to start at ST and terminate at EN with verbosity level VV.
For regularly-sampled channels, gaps between the specified and true times are filled with the mean; this isn’t possible with irregularly-sampled data.
Specifying start time (s)¶
- s=”last”: (Default) sync to the last start time of any channel in S.
- s=”first”: sync to the first start time of any channel in S.
- A numeric value is treated as an epoch time (?time for details).
- A DateTime is treated as a DateTime. (see Dates.DateTime for details.)
- Any string other than “last” or “first” is parsed as a DateTime.
Specifying end time (t)¶
- t=”none”: (Default) end times are not synchronized.
- t=”last”: synchronize all channels to end at the last end time in S.
- t=”first” synchronize to the first end time in S.
- numeric, datetime, and non-reserved strings are treated as for -s.
-
mseis!(S::SeisData, U::SeisData, ...)
Merge multiple SeisData structures into S.
Other Processing Functions¶
-
nanfill!(S)
For each channel i in S, replace all NaNs in S.x[i] with the mean of non-NaN values.
-
ungap!(S[, m=true])
For each channel i in S, fill time gaps in S.t[i] with the mean of non-NAN data in S.x[i]. If m=false, gaps are filled with NANs.


Mountains
Mountains often serve as geographic features that define natural
borders of countries. Their height can influence weather patterns,
stalling storms that roll off the oceans and squeezing water from
the clouds. The other side is often much drier. The rugged
landscapes even provide refuge—and protection—for fleeing and
invading armies.
Forest
Forest ecosystems can be found in all regions capable of sustaining
tree growth, at altitudes up to the tree line, except where natural
fire frequency or other disturbance is too high, or where the
environment has been altered by human activity.
Lakes
Hutchinson presented in his publication a comprehensive analysis of
the origin of lakes and proposed what is a widely accepted
classification of lakes according to their origin. This
classification recognizes 11 major lake types that are divided into
76 subtypes. The 3 of 11 major lake types are:
- tectonic lakes
- solution lakes
- organic lakes


| createWidget {htmlwidgets} | R Documentation |
Create an HTML widget based on widget YAML and JavaScript contained within the specified package.
createWidget( name, x, width = NULL, height = NULL, sizingPolicy = htmlwidgets::sizingPolicy(), package = name, dependencies = NULL, elementId = NULL, preRenderHook = NULL )
name |
Widget name (should match the base name of the YAML and JavaScript files used to implement the widget) |
x |
Widget instance data (underlying data to render and options that
govern how it's rendered). This value will be converted to JSON using
|
width |
Fixed width for widget (in css units). The default is
|
height |
Fixed height for widget (in css units). The default is
|
sizingPolicy |
Options that govern how the widget is sized in various
containers (e.g. a standalone browser, the RStudio Viewer, a knitr figure,
or a Shiny output binding). These options can be specified by calling the
|
package |
Package where the widget is defined (defaults to the widget name). |
dependencies |
Additional widget HTML dependencies (over and above those defined in the widget YAML). This is useful for dynamic dependencies that only exist when selected widget options are enabled (e.g. sets of map tiles or projections). |
elementId |
Use an explicit element ID for the widget (rather than an automatically generated one). Useful if you have other JavaScript that needs to explicitly discover and interact with a specific widget instance. |
preRenderHook |
A function to be run on the widget, just prior to rendering. It accepts the entire widget object as input, and should return a modified widget object. |
For additional details on developing widgets, see package vignettes:
vignette("develop_intro", package = "htmlwidgets")
.
An object of class htmlwidget
that will intelligently print
itself into HTML in a variety of contexts including the R console, within R
Markdown documents, and within Shiny output bindings.


BERKSHIRE HATHAWAY INC.3555 Farman StreetOmaha, NE 68131Official Home Page
FOR A FREE CAR INSURANCE QUOTE THAT COULD SAVE YOU SUBSTANTIAL MONEYWWW.GEICO.COM OR CALL 1-888-395-6349, 24 HOURS A DAY
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If you have any comments about our WEB page, you can either write us at the address shown above or e-mail us at<EMAIL_ADDRESS>However, due to the limited number of personnel in our corporate office, we are unable to provide a direct response.


|
|
Boost.RegexRedistributables and Library Names |
|
If you are using Microsoft or Borland C++ and link to a dll version of the run
time library, then you can choose to also link to a dll version of boost.regex
by defining the symbol BOOST_REGEX_DYN_LINK when you compile your code. While
these dll's are redistributable, there are no "standard" versions, so when
installing on the users PC, you should place these in a directory private to
your application, and not in the PC's directory path. Note that if you link to
a static version of your run time library, then you will also link to a static
version of boost.regex and no dll's will need to be distributed. The possible
boost.regex dll and library names are computed according to the following
formula:
BOOST_LIB_PREFIX
+ "boost_regex_"
+ BOOST_LIB_TOOLSET
+ "_"
+ BOOST_LIB_THREAD_OPT
+ BOOST_LIB_RT_OPT
+ BOOST_LIB_DEBUG_OPT
These are defined as:
BOOST_LIB_PREFIX: Either "lib" if this is a static link library,
or nothing for dynamic / shared libraries.
BOOST_LIB_TOOLSET: The compiler toolset name (vc6, vc7, bcb5 etc).
BOOST_LIB_THREAD_OPT: "s" for single thread builds,
"m" for multithread builds.
BOOST_LIB_RT_OPT: "s" for static runtime,
"d" for dynamic runtime.
BOOST_LIB_LINK_OPT: "s" for static link,
"i" for dynamic link.
BOOST_LIB_DEBUG_OPT: nothing for release builds,
"d" for debug builds,
"dd" for debug-diagnostic builds (_STLP_DEBUG).
Note: you can disable automatic library selection by defining the symbol BOOST_REGEX_NO_LIB when compiling, this is useful if you want to statically link even though you're using the dll version of your run time library, or if you need to debug boost.regex.
Revised 24 Oct 2003
© Copyright John Maddock 1998- 2003
Use, modification and distribution are subject to the Boost Software License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)


Unsupported Cyclers - Writing your own BEEPDatapath
¶
If you are using a cycler not supported by BEEP, you can still use BEEP to structure, featurize, and run models on your data!
To do this, you simply inherit from the BEEPDatapath
base class described on the Advanced Tutorial to create your own Datapath.
BEEPDatapath
handles all structuring of battery cycler files by taking them from raw cycler output files (usually csvs or text) and converting them
into consistent interfaces for structuring.
Your custom datapath will work with BEEP's capabilities similarly to all existing cyclers/datapaths.
The Simplest Case: Using from_file
¶
To put your cycler data in a format BEEP can understand, inherit from the BEEPDatapath
class and implement the from_file
classmethod.
Requirements¶
Your from file method will need to produce the following data to work correctly with BEEP.
1. A dataframe of the battery cycler data, in a standard format¶
The dataframe should have at least the following columns, named exactly as described:
test_time
: Time of the test, in secondscycle_index
: Integer index of the cycle numbercurrent
: Current drawn to/from battery, in ampsvoltage
: Voltage, in voltscharge_capacity
: Charge capacity of the battery, in amp-hoursdischarge_capacity
: Discharge capacity of the battery, in amp-hourscharge_energy
: Charge energy of the battery, in watt-hoursdischarge_energy
: Discharge energy of the battery, in watt-hoursstep_index
: Index integer of the charge-step, e.g., resting = 1, charging = 2, etc.step_time
: amount of time spent in this charge-step, in seconds.
(Optional):
temperature
: Temperature of the cell itselfdate_time
: Date time, as timestamp (ms from unix epoch)date_time_iso
: Date time in UTC time zone, formatted using.isoformat()
internal_resistance
: Internal resistance of battery, in ohm
The dataframe may contain other data, if available from your cycler output.
2. Metadata dictionary¶
All available metadata from the cycler run should be gathered by from_file
. This can include things like:
barcode
protocol
channel_id
- and other cycler-specific metadata.
The metadata should be a dictionary.
3. Paths to raw input files¶
Finally, paths to all raw input files should be collected as a dictionary, mapping file type to the absolute path. For example, if each run of your cycler requires a time series file and a metadata file, the paths dictionary would look like:
paths = {
"raw": "/path/to/raw/timeseries.csv",
"metadata": "/path/to/metadata.json"
}
Note raw
and metadata
are special keys. While having these two exact paths is recommended, arbitrary other paths to supporting files
can be passed in the paths dictionary without any special naming convention. For example:
paths = {
"raw": "/path/to/raw/timeseries.csv",
"metadata": None,
"my_other_required_filetype_path": "/path/to/somefile.hd5"
}
Column Mapping¶
To transparently keep consistent data types and column names, we recommend making the following class attributes in your BEEPDatapath
child class:
COLUMN_MAPPING
: Maps raw column names to BEEP canonical namesCOLUMNS_IGNORE
: Raw column names to ignore, if they are not needed (for example,Environmental Temperature (C)
)DATA_TYPES
: Mapping of BEEP canoncial column name to data type, in pandas-parsable format. For example, if your cycle index should be 32-pt integer, you can include the key-value"cycle_index": "int32"
in yourDATA_TYPES
class attribute.
Code Example - putting it all together¶
Once your from_file
method is able to extract the three requirements in the correct format, you should be able to pass those
objects to the cls
constructor inside of from_file
. For example:
import os
import json
import pytz
import pandas as pd
from beep.structure.base import BEEPDatapath
class MyCyclerDatapath(BEEPDatapath):
COLUMN_MAPPING = {
"test_time (s)": "test_time",
"cycle_index": "cycle_index",
"current (a)": "current",
"voltage (v)": "voltage",
"charge_capacity (ah)": "charge_capacity",
"discharge_capacity (ah)": "discharge_capacity",
"charge_energy (wh)": "charge_energy",
"discharge_energy (wh)": "discharge_energy",
"cell_temperature (c)": "temperature",
"date_time": "date_time",
"steptime": "step_time",
"stepix": "step_index"
}
# Columns to ignore
COLUMNS_IGNORE = ["environment_temperature (c)"]
# Mapping of data types for BEEP columns
DATA_TYPES = {
"test_time": "float32",
"cycle_index": "int32",
"current": "float32",
"voltage": "float32",
"charge_capacity": "float64",
"discharge_capacity": "float64",
"charge_energy": "float64",
"discharge_energy": "float64",
"temperature": "float32",
"date_time": "float32",
"step_time": "float32",
"step_index": "int32"
}
@classmethod
def from_file(cls, path, metadata_path=None):
# some code to get the raw data in BEEP format
# assuming it does not need to be further augmented
df = pd.read_csv(path)
df = df.drop(cls.COLUMNS_IGNORE)
df.rename(columns=cls.COLUMN_MAPPING, inplace=True)
# For example, adding a date_time_iso column if not already present
df["date_time_iso"] = df["date_time"].apply(
lambda x: x.from_timestamp().replace(tzinfo=pytz.UTC).isoformat()
)
# Cast all data types to those specified as class attrs
for column, dtype in cls.DATA_TYPES.items():
if column in df:
if not df[column].isnull().values.any():
df[column] = df[column].astype(dtype)
# Read in metadata from a separate json file, for example
if metadata_path:
with open(metadata_path, "r") as f:
metadata = json.load(f)
else:
metadata = {}
# specify all paths absolutely
paths = {
"raw": os.path.abspath(path),
"metadata": os.path.abspath(metadata_path)
}
# Return the 3 required objects to BEEPDatapath
return cls(df, metadata, paths)
After your BEEPDatapath is working¶
Once your BEEPDatapath is able to load raw files using from_file
, all of BEEP's other modules and methods should work with it like they do with any
other Datapath/cycler.
For example, structuring your BEEPDatapath requires only calling the parent BEEPDatapath
's .structure
method.
For more info on the capabilities of BEEPDatapath
, see the Advanced Tutorial.
Advanced usage¶
Your cycler may possess capabilities for data or structuring outside of base BEEPDatapath
's capabilities. In this case,
it may be needed to implement additional methods or override BEEPDatapath
methods beyond from_file
. The specific implementation will depend
on your cycler's capabilities; however, it is recommended not to override the following methods in particular:
BEEPDatapath.structure
BEEPDatapath.autostructure
BEEPDatapath.as_dict
BEEPDatapath.from_dict
If these methods are overridden in an incompatible way, it is likely they will break further downstream BEEP tasks, such as diagnostic structuring or featurization.


Tourism Trinidad to provide cruise visitors with an authentic experience
by Tourism Trinidad Limited(PRESS RELEASE) – On Thursday November 14, 2019, Tourism Trinidad Limited (TTL) held an official welcome ceremony to launch the 2019/2020 cruise season as the Caribbean Princess cruise liner docked at the port of Port of Spain with some 3,600 passengers.
For this November 2019 to April 2020 cruise season, Destination Trinidad will be receiving 70,000 passengers from twenty-seven (27) vessel calls. Two (2) new cruise lines and three (3) new cruise vessels will also be calling at the port of Port of Spain. In 2018, Destination Trinidad received 59,000 passengers.
Internationally, the cruise season has been on an upward trend; declaring a record high of 28.5 million passengers in 2018; with a significant number of those passengers coming from North America – 14.2 million passengers to be exact. In fact, North American travellers dominated the Caribbean, for cruise travel – recording a seven (7%) percent increase…up to 9.8 million passengers in 2018.
At the official cruise reception, the Chairman of Tourism Trinidad, Howard Chin Lee, pointed out that “today’s travellers are looking for new experiences; especially millennials (or as we like to call them…the disrupters) who are actively seeking out authentic experiences in exotic, far flung destinations and want something cool to share with their friends on social media”.
Cruise ships are responding to this competitive environment, offering lavish features such as underwater lounges with submarine type experiences to entice the new age traveller. Recently Virgin – one of the most prominent players in the airline industry has taken a bold step to ‘reinvent the cruise experience.’ As explained by Richard Branson, owner of Virgin Cruise, “We have taken the time to think about every detail and craft an experience that brings the Virgin brand to life and disrupts the travel industry.”
It is against this background that Tourism Trinidad welcomed the Caribbean Princess guests with an immersive and authentic experience that celebrated the vibrant and diverse spirit of Trinidad through enticing cuisine, exhilarating entertainment, and authentic regional experiences, bringing the destination to life and disrupting traditional welcomes.
The sweet sounds of parang, steelpan, colourful Moko Jumbies and Maypole dancing greeted the passengers on the quayside as they disembarked. Inside the Cruise Ship Hall, sampling stations of Maracas Bake and Shark, Doubles, Coconut Water as well as an exhibition of Lopinot’s cocoa and coffee heritage (with chocolate and cocoa tea tasting) were set up.
Chairman Howard Chin Lee emphasised the need for Trinidad to find innovative ways to further enhance and use its tourism assets to disrupt Caribbean Cruising and capture significant market share beyond the 4% increase of last year. “Our Port is one of a few to offer Cruise ship docking in the capital city, with immediate access to duty free shopping, gift shops and boutiques, and easy access to our financial district. This undoubtedly presents a competitive advantage for Trinidad. Now, add to this our leisure, eco, and year-round festivals and you have a one-of-a-kind tourism experience like no other Caribbean island”.
In the coming months Tourism Trinidad will be working hand-in-hand with regional corporations, tourism action groups and industry partners to address major issues impacting upon the visitor experience such as destination improvement and skills development. The company will be undertaking hospitality and service quality training across communities, agencies and service departments, sharing best management practices; and focusing on all the individuals who come into direct contact with international visitors (immigration, customs, transport, shops, restaurants and hotels and even within the communities). These are the people who will feel the direct benefits of increased business opportunities and greater demand for their products and services.
The long-term aim of Tourism Trinidad is to build a sustainable service culture thorough continuous training, product development and enhanced service delivery – thereby creating the right customer experience to sustain and grow Trinidad’s tourism business.
Tourism Trinidad will be providing cultural entertainment throughout the 2019/2020 season from the moment our international guests arrive to their departure.
More Caribbean Stories
-
Bishop of Roseau describes situation in Dominica after unrest as “sad” November 20, 2019
-
Jamaica: Woman raped five times November 20, 2019
-
CARICOM delegation discussing corresponding banking in the US November 20, 2019
-
Statement by CCJ President on the passing of former Chief Justice of Guyana, Mr Justice Ian Chang CCH November 20, 2019
-
OECS educators become literacy changemakers November 20, 2019
-
Jamaican Rose Hudson-Wilkin becomes Church of England’s first black female bishop November 20, 2019
-
Barbados hit by two days of power outages November 20, 2019
-
US government warns of travel to Dominica; calls for restraint November 19, 2019
-
Dominica’s opposition leader strikes back; blames police for unrest in Roseau November 19, 2019


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What Is a Collection?
A collection is a user-defined name from 1 to 128 characters that the programmer must specify for every package. A collection is not an actual, physical database object.
NOTE | Prior to DB2 V8, the maximum length of a collection name was 18 characters; this was increased to a maximum of 128 characters as of DB2 V8. |
You can compare collections to databases. A DB2 database is not actually a physical object (ignoring, for the moment, the DBD). In much the same way that a database is a grouping of DB2 objects, a collection is a grouping of DB2 packages.
By specifying a different collection identifier for a package, the same DBRM can be bound into different packages. This capability permits program developers to use the same program DBRM for different packages, enabling easy access to tables that have the same structure (DDL) but different owners.
Assume, for example, that you have created copies of the DB2 sample tables and given them an authid of DSNCLONE. You now have a DSN8810.DEPT table and a DSNCLONE.DEPT table with the same physical construction (such as the same columns and keys). Likewise, assume that you have duplicated all the other sample tables. You then could write a single program, using unqualified embedded SQL, to access either the original or the cloned tables.
The trick is to use unqualified SQL. You could simply bind a program into one package with a collection identifier of ORIG and into another package with a collection identifier of CLONE. The bind for the package with the ORIG collection identifier specifies the DSN8810 qualifier, and the bind for the CLONE collection package specifies the DSNCLONE qualifier. You would store both of these in the DB2 Catalog.
But how do you access these packages? Assume that both packages were generated from a DBRM named SAMPPROG. This would give you packages named ORIG.SAMPPROG and CLONE.SAMPPROG. You can bind both these packages into a plan called SAMPPLAN, for example, as in the following:
BIND PLAN (SAMPPLAN)
PKLIST(ORIG.SAMPPROG, CLONE.SAMPPROG)
The program then specifies which collection to use with the SET CURRENT PACKAGESET statement. By issuing the following statement, the plan is instructed to use the package identified by the value of the host variable (in this example, either ORIG or CLONE).
EXEC SQL
SET CURRENT PACKAGESET = :HOST-VAR
END-EXEC.
Another use of packages is to identify and relate a series of programs to a given plan. You can bind a plan and specify a wildcard for the package identifier. This effectively ties to the plan all valid packages for the specified collection. Consider the following BIND statement, for example:
BIND PLAN(SAMPLE) PKLIST(ORIG.*)
All valid packages in the ORIG collection are bound to the SAMPLE plan. If new packages are bound specifying the ORIG collection identifier, they are included automatically in the SAMPLE plan; no bind or rebind is necessary.
CURRENT PACKAGE PATH Special Register
| DB2 V8 adds the CURRENT PACKAGE PATH special register to ease distributed development, because not every environment supports package list specification using PKLIST. You can use CURRENT PACKAGE PATH to specify the collections to use for package resolution. CURRENT PACKAGE PATH differs from CURRENT PACKAGESET, because the package path contains a list of collection names, whereas the PACKAGESET is a single collection. Whichever was set last is used to determine the package that is to be invoked. |
Consider setting CURRENT PACKAGE PATH for DB2 for z/OS Application Requestors connected via DRDA and requesting the execution of a package on a remote DB2 for z/OS. Doing so can reduce network traffic and improve performance.
Another useful purpose for CURRENT PACKAGE PATH is to set the list of collections in programs that run from a workstation to access DB2 for z/OS. Such programs do not have a plan, and therefore have no PKLIST. Such programs had to issue a new SET CURRENT PACKAGESET statement each time the program needed to switch to a different collection. With CURRENT PACKAGE PATH multiple collections may be specified once.
Finally, stored procedure and user-defined functions may benefit from using CURRENT PACKAGE PATH to specify multiple collections.
Collection Size
Do not concern yourself with collection size. Bind as many packages into a single collection as you want. Remember, a collection is not a physical entity. It is merely a method of referencing packages.
Quite often people confuse collections with package lists. The size of a collection is irrelevant. The size of a package list can be relevant—the smaller the better.
Package List Size
You do not need to go to extraordinary means to limit the size of the package list as the performance gain realized due to smaller package lists usually is not significant. A recently conducted test shows that the difference between accessing the first entry in a package list is only milliseconds faster than accessing the one hundredth entry in the package list. Of course, milliseconds can sometimes make a difference.
The length of the PKLIST starts to become a performance issue only when the list contains thousands of packages. A better reason to limit the size of the package list is to enhance maintainability. The fewer entries in the package list, the easier maintenance will be.
Consider using wildcarding to limit the size of the package list, thereby simplifying maintenance.
|


The Gift a film review by Chad Polenz
WHAT IT'S ABOUT:
Annie Wilson (Cate Blanchett), a widow who struggles to raise her children in a small town in Georgia is asked for help by local authorities in solving the case of a missing woman. Annie is something of a psychic, she has involuntary bouts with the supernatural where she can see the past and the future and physically feel the actions happenning to her she envisions. Her "gift" leads to the arrest of a nasty wife beater who may or may not have killed the pretty rich girl found in the swamp on his property. In an ultra conservative backwoods town will her testimony based on her psychic visions hold up in court? Do they even have the right man?
WHAT'S GOOD ABOUT IT:
Aside from Keanu Reeves giving one of the best performances of his career as the loathable redneck Donnie Barksdale, there is little else going for the film.
WHAT'S NOT SO GOOD ABOUT IT:
Lame, unoriginal screenplay. Predictable ending. Suspense scenes that are just plain boring. Ridicously trite characterization makes the entire South out to be ingnorant, hateful goobers. Relentless in its emotional sensationaliztion.
OVERALL CRITIQUE:
"The Gift" is one of those movies that makes you wonder where you've seen this story before. It's a collection of every cliche and stereotype of the South ever made, and coming from a Yankee like me who dislikes the South that's really saying something.
For an established, talented director Sam Raimi is unable to provide any kind of originality to this film. It's predictable from the first scene, the screenplay is written like a rip-off of a photocopied bootlegged play. You know that as nasty as the murder suspect is he didn't actually do it. It's no surprise either who the real villain turns out to be and what their motive was. Sigh, how trite.
For a supernatural thriller this is neither scary nor intense because it's so cliche. "The Gift" is basically a glorified b-movie.
RATING: * (out of **** = poor)
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Sample HTML Page with Javascript
This page contains a simple page with examples that can be edited by live edit.


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Discover the new
MapStore 2
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GeoSolutions
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ChefStudio.it
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The following requirements must be met before installing and using PeopleFinder Offline for Palm OS:
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Anime or Cartoon
I'm working on a program to distinguish between Anime and Cartoon based on their
style.
I'm using Deep Learning and I need a big dataset. I've collected a lot of images and now I need Your help to sort it.
I'm using Deep Learning and I need a big dataset. I've collected a lot of images and now I need Your help to sort it.


import copy
import logging
import threading
from .nlu_benchmarker import NLUBenchmarker
# Other imports only for the type hints
from typing import Tuple, List, Dict
from .types import ConfusionMatrix, Intent
from .nlu_data_set import NLUDataSet
from .nlu_framework import NLUFramework
from .nlu_intent_rating import NLUIntentRating
# This setting prevents cut-off labels in plots created by matplotlib
from matplotlib import rcParams; rcParams.update({ "figure.autolayout": True }); del rcParams
import matplotlib.pyplot as plt
[docs]class IntentThresholdOptimizer:
[docs] @classmethod
async def optimize(
cls,
framework: NLUFramework,
data_set: NLUDataSet,
iterations: int,
grid_step_size: float
) -> float:
"""
Find the optimal confidence threshold for interpreting an intent classification result as
the None-intent.
The optimal threshold is found by running multiple iterations of the benchmark and searching
for the threshold that maximizes the F1 score for each iteration. The optimal threshold is
found by a grid search. The grids of each iteration are averaged and the threshold with the
maximum F1 score found that way is returned.
Args:
framework: An NLU framework which is already prepared for the data set.
data_set: The data set to optimize the threshold for.
iterations: The number of iterations to repeat and average the threshold optimization.
Returns:
The optimized threshold.
"""
grids: Dict[float, List[float]] = {}
for i in range(iterations):
logging.getLogger(cls.__name__).info(
"Optimizing %s, iteration %s out of %s.",
framework.title,
i + 1,
iterations
)
# Collect all grids
grid = await cls.__iteration(framework, data_set, grid_step_size)
for thresh, score in grid.items():
grids[thresh] = grids.get(thresh, [])
grids[thresh].append(score)
grids_avg: Dict[float, Dict[str, float]] = {}
# Calculate the means and variances of all entries in the grids
for thresh, scores in grids.items():
mean = sum(scores) / len(scores)
var = sum(map(
lambda x, mean=mean: (x - mean) ** 2, # type: ignore
scores)) / len(scores)
grids_avg[thresh] = { "mean": mean, "var": var }
# Find the threshold with the highest F1 score
t_max = { "score": 0., "thresh": 0. }
for thresh, score_avg in grids_avg.items():
if score_avg["mean"] > t_max["score"]:
t_max = { "score": score_avg["mean"], "thresh": thresh }
print("Threshold: F1 score mean (variance)")
for thresh, score_avg in grids_avg.items():
print("{:.2f}: {:.2f} ({:.2f})".format(thresh, score_avg["mean"], score_avg["var"]))
print("Best: {:.2f}".format(t_max["thresh"]))
cls.__plot(grids_avg, "Threshold Optimization of {} on {}".format(
framework.title,
data_set.title
))
return t_max["thresh"]
@classmethod
async def __iteration(
cls,
framework: NLUFramework,
data_set: NLUDataSet,
grid_step_size: float
) -> Dict[float, float]:
try:
# Train
await framework.train(data_set.training_data)
# Classify (without applying any threshold)
ratings = [
(datum.intent, await framework.rateIntents(datum.sentence))
for datum
in data_set.validation_data
]
finally:
# Guarantee the cleanup
await framework.cleanupTraining()
# Re-shuffle the data set
data_set.reshuffle()
def calc_f1_for_threshold(threshold: float) -> float:
thresholded_ratings = copy.deepcopy(ratings)
for (_, rating) in thresholded_ratings:
rating.noneIfBelow(threshold)
confusion_matrix = cls.__toConfusionMatrix(thresholded_ratings)
f1_scores = list(NLUBenchmarker.confusionMatrixToF1Scores(confusion_matrix).values())
return sum(f1_scores) / len(f1_scores)
# Run a grid search over the threshold
grid: Dict[float, float] = {}
thresh = 0.
while thresh < 1.:
grid[thresh] = calc_f1_for_threshold(thresh)
thresh += grid_step_size
return grid
@staticmethod
def __toConfusionMatrix(ratings: List[Tuple[Intent, NLUIntentRating]]) -> ConfusionMatrix:
confusion_matrix: ConfusionMatrix = {}
for (intent, rating) in ratings:
confusion_matrix[intent] = confusion_matrix.get(intent, {})
confusion_matrix[intent][rating.detected_intent] = (
confusion_matrix[intent].get(rating.detected_intent, 0) + 1
)
return confusion_matrix
@staticmethod
def __plot(grids: Dict[float, Dict[str, float]], title: str) -> None:
sorted_coord_pairs = sorted(grids.items(), key=lambda x: x[0])
def _plot() -> None:
plt.errorbar(
[ x[0] for x in sorted_coord_pairs ],
[ x[1]["mean"] for x in sorted_coord_pairs ],
yerr=[ x[1]["var"] for x in sorted_coord_pairs ],
capsize=5,
capthick=2,
errorevery=len(sorted_coord_pairs) // 20
)
plt.ylim(bottom=0)
plt.ylabel("F1 score * 100 (mean over all iterations)")
plt.xlabel("threshold")
plt.title(title)
plt.show()
threading.Thread(target=_plot).start()


Hilaria Baldwin ‘doing better’ after second miscarriage with husband Alec
Hilaria Baldwin revealed she is ‘doing better’ after suffering her second miscarriage.
The actress – who is married to Alec Baldwin – confirmed her tragic news earlier this week, after attending her 20-week scan.
And the 35-year-old has given fans an update on how she’s doing, following a tough few days.
Taking to her Instagram story, she explained: ‘Little update: I’m doing better. I have my moments when I feel better then I have moments when I will get really sad.
‘But I feel like time and healing and everyone’s support is helping me.
‘Spending time with my babies and husband helps so much,’ she added. ‘I’m so lucky to have them.
‘My heart goes out to those of you who suffer like this without support. You are true warriors. I send you my love and admiration.’
Hilaria and Alec announced their pregnancy earlier this year, after previously suffering a miscarriage back in April.
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Just last month, the author confirmed they were welcoming a baby girl into their brood, alongside their four kids, Leonardo, Rafael, Carmen and Romeo.
Sadly she told fans on Tuesday that their little one died at the four-month mark.
‘We are very sad to share that today we learned that our baby passed away at 4 months,’ she captioned an emotional video of herself and Carmen.
‘We also want you to know that even though we are not okay right now, we will be.
‘We are so lucky with our four healthy babies—and we will never lose sight of this. I told Carmen and took this so I could send it to Alec.
‘I guess this is a good way to share it with you too. I told her that this baby isn’t going to come after all…but we will try very hard to give her a little sister another time.
‘I’m really devastated right now…I was not expecting this when I went to my scan today. I don’t know what else to say.
‘I’m still in shock and don’t have this all quite clear. Please no paparazzi…that’s all I ask.’
Need support? Contact the Samaritans
For emotional support you can call the Samaritans 24-hour helpline on 116 123, email<EMAIL_ADDRESS>visit a Samaritans branch in person or go to the Samaritans website.


My favorite color is blue red! Build a future where people live in harmony with nature.
Build a future where people live in harmony with nature.
This is my first paragraph.


Support Vector Machines have really nice math behind it. Let's take a look how this algorithm will fare in our classfication problem.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, svm
from sklearn.cross_validation import train_test_split, KFold
from sklearn.grid_search import GridSearchCV
digits = datasets.load_digits()
# split dataset into train and test
X_train, X_test, y_train, y_test = train_test_split( digits.data,
digits.target,
test_size=0.33 )
clf = svm.LinearSVC(penalty='l1', loss='squared_hinge',
dual=False, tol=1e-3 )
# parameters for grid search
param_grid = dict( C=np.logspace(-2.3, -1.3, 10) )
# we will use grid search to choose "optimal" hyperparameters
grid = GridSearchCV(clf, refit=False, param_grid=param_grid,
cv=KFold( len( X_train ), 30 ) )
grid.fit( X_train, y_train )
# fit over entire train dataset
clf.C = grid.best_params_[ 'C' ]
clf.fit( X_train, y_train )
# run tests
print( 'Test score:{0}'.format( clf.score( X_test, y_test ) ) )