The premise of Doctor Who is that he is a Time Lord, capable of ridiculously epic travels throughout time and space - even if it is billions of years into the past or future and/or numerous time jumps in a single episode / story arc.
This dataset represents a fraction of his Earth-based Time Travel. The Time Jumps are the Sum of DateTo and DateFrom per the episode Date references themselves as scripted) ... the glory of Science Fiction!


Country and state-level stats are from the Covid Tracking Project. County-level statistics are from the New York Times covid-19-data data set.
Note: Hospitalization data should be considered the lower bound for total hospitalizations. State-level hospitalization data from the Covid Tracking Project is currently only available for 0 states. IHME's Hospital Projections appear to be more accurate.
Note: This is a beta version. All of the code is on github. Code review me, PLEASE! If you find a bug, please email me!


This interactive video describes the interactive diagram below and shows how to interact with it, and gives examples of relationships that can be discovered when doing so. Remember, turn on your audio!


WITNESS LIST Prompt Payment of Health Care Providers Committee January 24, 2002 - 9:30A Prompt Payment of Health Care Provi FOR: Haney, David Pharmacist (True Care Pharmacy), Vidor, TX Koepsel, DPM, Kirk Podiatrist (Texas Podiatric Medical Association), Seabrook, TX Rodriguez, Anna M. (Anthony Chavez, M.D.), Houston, TX Sitton, Chip Managed Healthcare Director (Tomball Regional Hospital), Spring, TX Steward, Randy Director, Managed Care (Tenet Healthcare), Houston, TX Swisher, Debra Financial Coordinator (Life Flight), Missouri City, TX Taylor, M.D., William Medical Director (Aetna), Austin, TX Vaughan, Ron (Tenet Helath Systems), Colleyville, TX Weigum, Eileen Director of Claims (Community First Health Plan), San Antonio, TX AGAINST: Quick, Thomas Insurance Executive (United Healthcare), Plano, TX ON: Chavez, Paulette Office Manager (Anthony Chavez, M.D.), Houston, TX DeCell, Denise (Christus Health Gulf Coast), Houston, TX DeLargy, Betty Attorney (Texas Health Insurance Pool & Health Insurance Companies), Austin, TX Durant, Tom Director (Managed Care-Medical Income Concepts, Inc.), Houston, TX Fix, Jennifer Pharmacist (Texas Pharmacy Association), Fort Worth, TX Flaum, M.D., Morris Associate Dean for Clinical Affairs (University of Texas Medical School - Houston), Houston, TX Grigsby, Katherine Provider (Oncology Consultants, P.A.), Houston, TX Hand-Burns, Sharon Provider Consultant (Clients), Houston, TX Harris, Mollie Insurance Specialist (Dr. Michael Sweeney), Houston, TX Hill, Jane Administrator (Drs. Aldama, Ehni, Cech, Harper, and Parker), Houston, TX Kajander, John Chief Administrative Officer (TIRR Systems), Houston, TX Kirby, Tim B. Senior Vice President (Methodist Hospital of Dallas), Dallas, TX Lowery, Kerry COO (Greater Houston Anesthesiology), Houston, TX Mahon, William J. Executive Director (National Health Care Anti-Fraud Association), Washington, DC Montemayor, Jose Commissioner (Texas Department of Insurance), Austin, TX Paschal, Melissa L. (Health Care Administration Gulf Coast Division), Houston, TX Rodgers, J. Darren Vice President (Blue Cross Blue Shield of Texas), Dallas, TX Rummel, Leah Executive Director (Texas Association of Health Plans), Austin, TX Schauer, Tim Director of Government Relations (Memorial Hermann Healthcare System), Katy, TX Selden, Audrey Sr. Associate Commissioner (Texas Department of Insurance), Austin, TX Southerland, Frances Business Manager (Memorial City Surgical Associates), Houston, TX Speight, Susan Family Therapist (Texas Association for Marriage and Family Therapy), Dickinson, TX Sulgrove-Hernandez, Gale Director of Business (Memorial Clinic Associates), Rosharon, TX Vanzant, M.D., Robert Physician (Harris County Medical Society), Houston, TX White, Jeff Director of Managed Care (Christus Santa Rosa Health Care), San Antonio, TX Registering, but not testifying: For: Pavia, H. Allan Pharmacist (True Care Pharmacy and Independents), Linden, TX On: Davis, Victoria (Garden Oaks Adult Activity Center), Houston, TX Harracksingh, Rachel B. President (Texas Ambulance Association), Canutillo, TX Valenta, Anna M. Director, Managed Care (HCA), Houston, TX Walli, Steve C. CEO (United Healthcare), Houston, TX Wilks-Bingham, Shannon Executive Director (Trauma Regional Advisory Council), Raymondville, TX Providing written testimony: For: Moore, Walter Physician (Self), Kingwood, TX Tietjen, John A. Health Care Administrator (MD Anderson Cancer Center), Missouri City, TX On: Herndon, Paul Chief Financial Officer (Texas Children's Pediatric Associates), Houston, TX Smith, Gary K. Director, Managed Care (US Imaging Inc.), Houston, TX


Scales
Wiki
▸ [[API Reference]] ▸
Scales
See one of:
[[Quantitative Scales]] - for continuous input domains, such as numbers.
[[Ordinal Scales]] - for discrete input domains, such as names or categories.
[[Time Scales]] - for time domains.


{% extends '_base.html' %} {% block title %}Choose Trello list{% endblock %} {% block main %}
Select the list that you use to indicate that stories your team work on have been reviewed and accepted by a product manager.


Product Categories
Check Out The Latest Products On Our Website. Exciting Discounts, Hurry!!
Move The Cursor Over The Product Image To View The Product Details.


Overview
Distributed Energy-Grid Optimization for Demand and Storage Utilisation involves the use of strategies and technologies to manage and optimize the generation, distribution, and use of energy in a distributed energy grid. This can help to improve the efficiency, sustainability, and resilience of the energy system.
Method
To address the complexities of the modern electric grid, particularly with the rise of distributed energy resources (DERs) and renewables. A potential solution should involve five key steps: 1. Shift towards predictive and autonomous grid optimization: By using predictive capabilities and autonomous controls, utilities can make proactive decisions, employing adaptive algorithms and predictive analytics for increased efficiency. 2. Maximize grid reliability: This step focuses on assessing weather events and preparing repair actions, leading to efficient outage response. 3. Ramp up renewables and DERs: Utilities must manage and orchestrate these resources, providing end-to-end management. 4. Form a connected data fabric: This involves creating an end-to-end communication channel for managing data across the network. Seamless integration between systems ensures real-time data readiness. 5. Predict intermittency for transmission and distribution: Managing DERs and renewables requires comprehensive load forecasting to reduce surprises for control operators. At last, the method should empower utilities to optimize the modern grid with greater visibility, improved controls, and enhanced connectivity, enabling a move towards a Net-Zero future.
Reference


|
cds
1.2.0
|
Static buffer (opt::buffer option) More...
#include <cds/opt/buffer.h>
Data Structures | |
| struct | rebind |
| Rebind buffer for other template parameters. More... |
Public Types | |
| typedef T | value_type |
| value type |
Public Member Functions | |
| static_buffer () | |
| Construct static buffer. | |
| static_buffer (size_t nCapacity) | |
| Construct buffer of given capacity. | |
| value_type & | operator[] (size_t i) |
Get item i . | |
| const value_type & | operator[] (size_t i) const |
Get item i , const version. | |
| size_t | capacity () const noexcept() |
| Returns buffer capacity. | |
| void | zeroize () |
| Zeroize the buffer. |
Static Public Attributes | |
| static const size_t | c_nCapacity = Capacity |
| Capacity. |
Static buffer (opt::buffer option)
One of available type for opt::buffer type-option.
This buffer maintains static array. No dynamic memory allocation performed.
T
- item type the buffer stores Capacity
- the capacity of buffer. The value must be power of two.
|
inline |
Construct buffer of given capacity.
This ctor ignores nCapacity
argument. The capacity of static buffer is defined by template argument Capacity


A simple example¶
This example is contained in fit_example.py
. It walks you through how to run a fit on an example spectrum located in data/example_spectrum.txt
. This file has four columns; the wavelength of each value, the fluxes, the error and the instrumental resolution at that wavelength. It’s made from the SSP models themselves, with added Gaussian noise.
Setting Up¶
Packages¶
You’ll need the following packages to run this simple example, many of which should come as standard in the scientific python library.
numpy
,scipy
andmatplotlib
lmfit
for itsParameters
object- A fitting code- we’ll use
emcee
Load in our data¶
We first read the datafile and get the relevant wavelength array, flux spectrum and error spectrum, as well as the instrumental resolution.
datafile='data/example_spectrum.txt'
lamdas, flux, errors, instrumental_resolution=np.genfromtxt(datafile, unpack=True)
You can optionally load a number of sky spectra here too.
Mask out any sections we want to ignore¶
We can give a list of two-component arrays with ‘start’ and ‘stop’ wavelengths around regions of the spectrum we don’t want to fit. These might be areas of residual telluric absorption, particularly bad sky-lines, etc. These should be observed wavelengths, so can be read straight from the spectrum.
telluric_lam_1=np.array([[6862, 6952]])
telluric_lam_2=np.array([[7586, 7694]])
skylines=np.array([[8819, 8834], [8878.0, 8893], [8911, 8925], [8948, 8961]])
Select the regions we want to fit between¶
During the fit, we have to compare the model and our data and use multiplicative Legendre polynomials to correct for small differences in continuum shape caused by the instrument or poor flux calibration. Ideally we’d fit the entire spectrum at once, but this tends to make finding these polynomials too slow. Here, we compromise by splitting our spectrum into four sections, with each one getting its own set of polynomials. These are then combined in the likelihood function.
These wavelength are rest frame wavelengths. They’ll be the values which we plot on the x-axis at the end.
fit_wavelengths=np.array([[4750, 5600], [5600, 6800], [6800, 8000], [8000, 9200]])
TODO: Sort out what FWML_gal is
Load the class¶
We can now load our SpectralFit class:
fit=SpectralFit(lamdas, flux, errors, pixel_weights, fit_wavelengths, FWHM_gal, instrumental_resolution=instrumental_resolution, skyspecs=skyspecs, element_imf=element_imf)
fit.set_up_fit()
which will read in all of our SSP templates, log-rebin them and get everything read to fit.
An example fitting process¶
We now have our list of free parameters in the model. This is the lmfit
Parameters object, which starts:
theta=LM.Parameters()
#LOSVD parameters
theta.add('Vel', value=0.0, min=-1000.0, max=10000.0)
theta.add('sigma', value=300.0, min=10.0, max=500.0)
#Abundance of Na. Treat this separately, since it can vary up to +1.0 dex
theta.add('Na', value=0.5, min=-0.45, max=1.0, vary=True)
#Abundance of elements which can vary positively and negatively
theta.add('Ca', value=0.0, min=-0.45, max=0.45, vary=True)
theta.add('Fe', value=0.0, min=-0.45, max=0.45, vary=True)
# and so on...
Each of these variables can be switched on and off using the vary
keyword. The min
and max
keywords give the range of the (flat) prior. More details can be found at the lmfit
documentation here
We now have a way to make templates at various model parameters, and compare them to our data! Try changing a few values of theta
and seeing what happens to the model. You can plot the fit at any time by calling SF.plot_fit(theta, fit.fit_settings)
.
Once we’re happy with the variables we want to fit for, we select the starting positions of our walkers. Here we’re just assuming a small ball around the initial parameter positions, but you should check that starting in different (or random) areas of parameter space gives you the same results!
These functions make this ball, using a different standard deviation in each dimension:
#Now get the starting values for each parameter, as well as the prior bounds
start_values, bounds=SF.get_start_vals_and_bounds(theta)
p0=SF.get_starting_poitions_for_walkers(start_values, stds, nwalkers)
We can now run the fit using emcee
(or any program of your choice: other MCMC samplers are available!):
print("Running the fitting with {} walkers for {} steps".format(nwalkers, nsteps))
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=[theta, variables, bounds], pool=None)
result = sampler.run_mcmc(p0, nsteps, progress=True)
The emcee
documentation can be found here.
Results¶
Once our fit has finished (which may take a while!), we can get the samples, find various sample statistics of our posterior and plot the fit itself:
#get rid of the burn-in
burnin=nsteps-5000
samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))
print("\tDone")
#Get the 16th, 50th and 84th percentiles of the marginalised posteriors for each parameter
best_results = np.array(map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]), zip(*np.percentile(samples, [16, 50, 84], axis=0))))
for v, r in zip(variables, best_results):
print "{}: {:.3f} +{:.2f}/-{:.2f}".format(v, r[0], r[1], r[2])
#Make a set of parameters with the results
results_theta=LM.Parameters()
for v, r in zip(variables, best_results):
results_theta.add('{}'.format(v), value=r, vary=False)
#... and plot
SF.plot_fit(results_theta, fit.fit_settings)
It’s always a good idea to look at a corner plot of your results (each parameter plotted against the others, as well as a one dimensional marginalised histogram). Finally, inspecting the residuals as a function of wavelength is also very important! A lot of issues with the fitting can be diagnosed this way, as well as getting a feeling for how reliable the results may be.
As an aside, the v3.0.0dev version of emcee
has some really nice features- such as incrementally saving your progress to a h5
file and showing a built in progress bar. I’d highly recommend keeping an eye on it!


Demo page for SVGuitar. Try to create some SVGs here.
For a more elaborate chord editor that also uses SVGuitar check out ChordPic.


USGS: Magnitude 5.9 earthquake strikes Mindanao
MANILA, Nov 18 — An earthquake of 5.9 magnitude struck 92 kilometres east northeast of Cotabato, Mindanao in the Philippines on Monday, according to the United States Geological Survey.
There were no immediate reports of damage. — Reuters


2023 Curr. Biol. An environmentally induced multicellular life cycle of a unicellular cyanobacterium Tang, S.*, Pichugin, Y.*, and Hammerschmidt, K. Current Biology, in press 2023 HTML PDF 2022 Nat Genet The evolutionary dynamics of extrachromosomal DNA in human cancers Lange, J.T.*, Rose, J.C., Chen, C.Y.*, Pichugin, Y.*, Xie, L., Tang, J., Hung, K.L., Yost, K.E., Shi, Q., Erb, M.L., Rajkumar, U., Wu, S., Taschner-Mandl, S., Bernkopf, M., Swanton, C., Liu, Z., Huang, W., Chang, H.Y., Bafna, V., Henssen, A.G., Werner, B., and Mischel, P.S. Nature Genetics 2022 HTML PDF Supp eLife Eco-evolutionary dynamics of clonal multicellular life cycles Ress, V., Traulsen, A., and Pichugin, Y.# eLife 2022 HTML PDF PNAS The possible modes of microbial reproduction are fundamentally restricted by distribution of mass between parent and offspring Pichugin, Y., and Traulsen, A. Proceedings of the National Academy of Sciences 2022 HTML PDF Supp J R Soc Interface Evolution of reproductive strategies in incipient multicellularity Gao, Y., Pichugin, Y., Gokhale, C. S., and Traulsen, A. Journal of the Royal Society Interface 2022 HTML PDF Supp 2021 eLife Evolution of irreversible somatic differentiation Gao, Y., Park, H.J., Traulsen, A., and Pichugin, Y.# eLife 2021 HTML PDF Dev Dyn Limb reduction in squamate reptiles correlates with the reduction of the chondrocranium: a case study on serpentiform anguids Yaryhin, O., Klembara, J., Pichugin, Y., Kaucka, M., and Werneburg, I. Developmental Dynamics 2021 HTML PDF Supp 2020 eLife Why is cyclic dominance so rare? Park, H.-J., Pichugin, Y., and Traulsen, A. eLife 2020 HTML PDF PLOS CB Evolution of multicellular life cycles under costly fragmentation Pichugin, Y.#, and Traulsen, A. PLOS Computational Biology 2020 HTML PDF Supp Code Ecol Lett Meta-population structure and the evolutionary transition to multicellularity Rose, C.J., Hammerschmidt, K., Pichugin, Y., and Rainey, P.B. Ecology Letters 2020 HTML PDF Supp 2019 PLOS CB Interacting cells driving the evolution of multicellular life cycles Gao, Y., Traulsen, A., and Pichugin, Y.# PLoS Computational Biology 2019 HTML PDF Supp bioRxiv Stochastic dynamics of extra-chromosomal DNA Pichugin, Y., Huang, W., and Werner, B. bioRxiv 2019 HTML PDF J R Soc Interface Evolution of simple multicellular life cycles in dynamic environments Pichugin, Y.#, Park, H.J., and Traulsen, A. Journal of the Royal Society Interface 2019 HTML PDF Supp Phys Rev E Population size changes and extinction risk of populations driven by mutant interactors Park, H.-J., Pichugin, Y., Huang, W., and Traulsen, A. Phys. Rev. E 2019 HTML PDF 2017 PLOS CB Fragmentation modes and the evolution of life cycles Pichugin, Y.#, Peña, J., Rainey, P.B., and Traulsen, A. PLoS Computational Biology 2017 HTML PDF Supp 2015 J Theor Biol Modes of migration and multilevel selection in evolutionary multiplayer games Pichugin, Y.#, Gokhale, C. S., Garcia, J., Traulsen, A., and Rainey, P. B. Journal of Theoretical Biology 2015 HTML PDF 2012 Peculiarities of cytometrical methods of DNA content determination in the nucleus Pichugin, Y., Semiyanov, K.A., Chernyshev, A.V., Palchikova, I.G., Omelyanchyuk, L.V., and Maltsev, V.P. Cell and Tissue Biology 2012 HTML PDF


Jacked 12hrs. +
I purchased a fair amout of Dexies for a relatively CHEAP price. I remember asking my dad questions about it and joking with my aunt (who is in the medical field as well as my family) how she should run marathons on it. I had never dreamed of ever finding any in my life.....
Afer an exam (I'm 14 and in the 9th grade) on a friday, I got home to a pill that I was told was Oxycocet (Oxycodone). After checking everywhere in PH Desk R. It was nowhere to be found. So I dug up an old doccument on a drug Forum and the pill was found out to be a Canadian version of Percocet. Well I live in Canada and was sure happy that they matched.
T-0:00 (11:35am)- Down one Oxycocet (Oxycodone 5mg and acetominophen 500mg) Anxious, this is my first time with Oxycodone. I've had much experience will Codeine and used to it's effects.
T-0:05- Start cooking some lunch in case I can't while under the influence.
T-0:30- Typical opiate like symtoms. I start typing slower, and feeling in a good happy mood.
T-1:00 (12:00pm)- I'm getting rolling eyes and feeling slightly euphoric. Better than codeine. Codeine tends to make me heavy and I must lay down. Though walking is fun on codeine. But on Oxy its not as fun, but the mental effects are better.
T-1:15-I notice that functioning is easier with Oxycodone than with Codeine. I'm chatting with a friend. She tells me her mother has plenty of Oxycontin and Morphine. I want to buy, but she cannot access them due to a locked door. No problems... Than the most intense momment happends, a direct quote 'What is Dexedrine?'. I freak I tell her all about it and what it does. Her brother takes it for his ADHD and has quite a bit. I want to buy, she agrees. We set up a deal, and I will bike to her house when her parents leave.
T-1:50- I'm so exited, my Oxy effects seem to disappear. Though I am still feeling sort of loose.
T-2:30 (1:30pm)- The deal is made, I'm biking home with a pocket full of Dexdrine, amazing. This was so unbelievable.
T-2:45- I check the pill's ingredients (5mg of Dextro-amphetamine Sulfate) I let excitement take over me. I cut it up with a spoon and knife, make a few lines, get a straw and shoot the orange coloured powder into my nasal passage. Dumb idea.
T-2:50- Not quite feeling anything yet, methylphenidate takes a least 20 mins to kick in, so I figure a Sulfate salt form of amphetamine would work somewhat as easy. Obviously hydrochloride salt is better. I feel like my nose is runnning, and I feel drip. I check in the mirror and I find orange dye running out my nose! Fuck. I forgot about that... excitement is deadly. I am spitting up dye also. I notice my breathing is depressed and I wonder if the non-active ingredients had contained chalk or something.
T-3:15- The dye has stopped running and I'm no longer spitting it up. I don't notice a slight high. I boost with 2 more Dexedrine tablets orally.
T-3:45- I'm jacked, my eyes are wide open and this beats any Methylphenidate high easily. I'm typing to friends very fast and then tell me to slow down. I notice unlike ephedrine and methamphetamine (which I have heard does, but I've never tried it) my hair on my head doesn't seem to be feeling stuck up. I'm quite calm talkative and cetainly euphoric.
T-4:30- This is what my estimated time that I thought the Oxy would wear off. The effects have not been apparent for over 3hrs. Damn.
T-5:00- It like no time has gone by. I don't notice that I've been on my computer for 3hrs. typing. Whew, this is good stuff. I have the feeling I'll be jacked for most of the night. My eyes are forced open, I can't close my eyes, let alone barely blink. I start conversing to a friend that has tried MDMA to not try it again (I have never tried MDMA and I don't beleive I ever will). I tell her of negative effects.  She is very agressive, and my emotions are increased. I start freaking, she doesn't seem to be real.  But I was real, I just couldn't take it in. Crazyness... Well we made up and stopped fighting. She was also quite jealous of the Dexies. I sort of like her, she's cool. But now I'm scared, seeing her like that under the influence of Dexedrine is insane. I'm sort of freaked and do not have the same opinion of her as before.
T-6:00 to 8:45 (5:00pm-7:45pm)- My father and I decide to go out to dinner. He is an Anesthesiologist and knows a great deal about 'substances', well so I thought. He had no idea I was under the influence. Well maybe since I ate very little, was pissing every 20mins and eyes wide open he might have suspected something. Amazing, we talked about drugs at dinner for a long time. I notice I was quite constipated from the Oxycodone.
T-9:30 (8:30pm)- I'm still very jacked, not freaked anymore, but just jacked. I'm grinding straws in my teeth, my jaw is slightly clenched. We visit his parents, I have nice conversations with them.
T-10:50-13:00 (9:50pm to 12am)- Return home, I'm still jacked and constipated. I decide for more chat.  Time flys by so fast, but I feel this day has been the equivlent of two.
T-13:01-13:30- I'm being agressive and mad at people for their own stupidnesses. I fight with potheads, and I try to understand a good friend with an eating disorder. This is sad. She has been in the hospital for 2 weeks for anorexia and she now back at a normal weight. She tells me how she is still anorexic and is so prideful. I tottaly understand the irony of the situation, I'm so sad for her that I become very agressive and bring back many past things to try and change her. I feel useless not to be able to help her. I must help her. I get off the chat and I want to rest, my body is tired, but my mind isn't. I'm hungry, but I can't eat. This seems normal for an amphetamine.
T-13:31-14:30)- I listen to a full CD in bed. I can't sleep. I decide to force myself to eat wondering if that will have effect. I eat 2 bananas, cheese and some chips.
T-15:00-15:30? or 16:00?)- Sleep is still not easy, but I'm so exhausted I belive I just passed out. Jacked for 12hrs. WOW.
I wake up feeling tired, I didn't get much sleep. I woke up a few times. I wasn't much constipated in the morning from the Oxycodone.
Now normally I usually do a new substance I have never tried before alone, but on this occasion I took a chance. Nothing bad went wrong and I learned valuable things:
1. Be prepared----> What is bad about combining a narcotics and stimulants is that I wouldn't be able to take a depressent to make you pass out. I wouldn't take the chance, people die that way.
2. Narcotic withdrawl----> Strangely it didnt happen. This may have been avoided with Dexedrine.
3. Addicting----> Both of these drugs have obvious abuse potential. I would probly be more suseptible to amphetamine addiction. I do have an urge to repeat the Oxycodone experience but in higher dose. I will try to avoid possible abuse as best as I can.
I believe this was a positive and moving experience, I will never repeat this combination. I do plan to use these drugs sometime later, but in moderation.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Hi! I am Joey Young. I am a highschool graduate who is looking at getting into a programming career. I am an interesting person I would say. I like computers and pretty much all types of music. I like a lot of things. I don't have much job experience, I worked in a warehouse for a summer and a tshirt shop for a little over a week. I am looking to grow a lot through the classes I am taking and would love if you kept checking up on my work every once in a while!


Scene II.-A lobby in the castle of Wolfenstein. [in 1)
Enter BARBARA R.H., followed by CARLINE.
Bar.
Don't talk to me, girl, remember your station and consider
mine. Who am I and what am I that I should be lodged in a
wing of the castle overlooking the dog kennels.
Car.
Your ladyship is quite right to rebel, and when I advised your
ladyship to calm yourself, it was out of consideration for
your ladyships eyes--any little flurry does so spoil the
natural beauty of their expression.
Bar.
 I'll not be imposed on.
Where's Mynheer Von Puffengruntz? Where's his lordship's
chamberlain?
Car.
[Looking L.H.]
Here he comes down the great stairway, as full of flesh and
scant of breath as ever.
[aside],
and as the waddling old porpoise appears to have a liquorish
tooth for this silly old buzzard, I'll leave them to bill and
coo, while I run off to comfort dear 'Mina who has passed the
whole night in tears for poor Rodolphe.
[Von Puff. enters L.H., Carline courteseys and exits L.H.]
Puf.
Eh! what! madam Barbara, stirring so early? Why, bless my
soul, the mist of the mountain is yet hanging upon the turrets
of the castle.
Bar.
Then let it hang, and be hanged to it! Stirring, indeed! Havn't
I been stirring all night? Hasn't her ladyship been stirring
all night, and wouldn't the Seven Sleepers have
been stirring all night, too, if they had lodged where I did?
Puf.
Surely nothing has had the audacity to disturb the quiet of
your slumbers?
Bar.
Nothing! To begin with, do you call such a storm as that of
last night nothing? Why, the thunder creashed over the castle
loud enough to wake the dead. That was bad enough, but only
to think that I, the right honorable foster-mother that is
to be, to her right honorable ladyship that is to be, passing
the night in apartments overlooking a dog-kennel.
Bar.
Aye, a kennel of great, savage hounds, fed late at night with
raw meat by their one-eyed keeper, on purpose to give them the night-mare
and make them
dream all night long of chasing wild boars in the forest.
Ugh!  I promise you that
they shall be soundly punished and instantly removed. But
I, too, am criminal. The apartments were selected by me because
they commanded the best view of the mountain.
Bar.
But I don't want to look at the mountain. I got a glimpse
of it from one of the north windows last night during the
storm, and it looked for all the world as if Belzebub and his
imps were holding a jubileee there.
Puf.
Enough, my dear Madam Barbara, enough! If the mountain is
unpleasant, it shall be removed. No, no, I--I mean
you shall
be removed.
[Approaching her in a wheedling manner.]
Will it please you to accompany me and choose for yourself?
Bar.
[Simperingly.]
Really, mynheer Von Puffingruntz, you have such mollifying
ways that--that--
Bar.
Would it be prudent! Dare I trust myself?
Puf.
Confiding innocence, rely upon the honor of a Von Puffengruntz.
[Looks around, sighs, kisses her.


| Total net profit | -170.67 | Gross profit | 318.97 | Gross loss | -489.64 |
| Profit factor | 0.65 | Expected payoff | -0.43 | ||
| Absolute drawdown | -192.19 | Maximal drawdown (%) | 210.76 (1134.95%) | ||
| Total trades | 396 | Profit trades (% of total) | 139 (35.1%) | Loss trades (% of total) | 257 (64.9%) |
| Long positions (won %) | 205 (35.61%) | Short positions (won %) | 191 (34.55%) | ||
| Largest | profit trade | 9.78 | loss trade | -12.56 | |
| Average | profit trade | 2.29 | loss trade | -1.91 | |
| Minimum | profit trade | 0.0 | loss trade | -0.03 | |
| Maximum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Average | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Minimum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Maximum | Reward-Risk | 0.0 | Duration in minutes | 5699 | |
| Average | Reward-Risk | 0.0 | Duration in minutes | 1141.3 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 8.0 (12.2) | consecutive losses (loss in money) | 26.0 (-57.59) | |
| Average | consecutive wins | 3.0 | consecutive losses | 6.0 |
| Total net profit | -108.4 | Gross profit | 157.25 | Gross loss | -265.65 |
| Profit factor | 0.59 | Expected payoff | -0.53 | ||
| Absolute drawdown | -141.5 | Maximal drawdown (%) | 142.65 (12404.35%) | ||
| Total trades | 205 | Profit trades (% of total) | 73 (35.61%) | Loss trades (% of total) | 132 (64.39%) |
| Largest | profit trade | 9.55 | loss trade | -12.56 | |
| Average | profit trade | 2.15 | loss trade | -2.01 | |
| Minimum | profit trade | 0.01 | loss trade | -0.06 | |
| Maximum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Average | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Minimum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Maximum | Reward-Risk | 0.0 | Duration in minutes | 5699.0 | |
| Average | Reward-Risk | 0.0 | Duration in minutes | 1057.1 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0.0 | |
| Maximum | consecutive wins (profit in money) | 8.0 (22.61) | consecutive losses (loss in money) | 23.0 (-61.56) | |
| Average | consecutive wins | 3.0 | consecutive losses | 7.0 |
| Total net profit | -62.27 | Gross profit | 161.72 | Gross loss | -223.99 |
| Profit factor | 0.72 | Expected payoff | -0.33 | ||
| Absolute drawdown | -66.31 | Maximal drawdown (%) | 127.26 (208.79%) | ||
| Total trades | 191 | Profit trades (% of total) | 66 (34.55%) | Loss trades (% of total) | 125 (65.45%) |
| Largest | profit trade | 9.78 | loss trade | -10.67 | |
| Average | profit trade | 2.45 | loss trade | -1.79 | |
| Minimum | profit trade | 0.0 | loss trade | -0.03 | |
| Maximum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Average | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Minimum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Maximum | Reward-Risk | 0.0 | Duration in minutes | 4251.0 | |
| Average | Reward-Risk | 0.0 | Duration in minutes | 1231.7 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 8.0 (19.52) | consecutive losses (loss in money) | 32.0 (-74.55) | |
| Average | consecutive wins | 3.0 | consecutive losses | 6.0 |


Let’s make the event as grand as your culture.
We at SIPA Events understand that when people attend a cultural show, they have a lot of expectations from the whole experience. We work on building and augmenting that experience. It is manifested in several ways. One of the ways is to express care for our audiences. Proper seating arrangement, organising an event with high-quality, good lighting and sound system and other factors contribute to giving them an amazing experience.
Technology has greatly increased the level of cultural events organised these days. As conscientious cultural event organisers, we make optimal use of technology that enhances event experience to a significant extent. We use event applications that allow attendees to enjoy free Wi-Fi facilities, beat the queues to check-in at the venue, and offer exclusive entertainment value using tech wonders like VR. It not only helps in boosting the attention of the audience but also make them happier and satisfied with the services.
At SIPA Events one of the best cultural events organising companies, we take pride in offering high-end event management solutions to our clients. Our team of highly expert and experienced professionals leaves no stone unturned to organise a successful event.
We are efficient in handling every conceivable type of event, whether corporate, party, wedding, or any other event. Events range from small group meetings to arena-size conferences, concert sound systems, or anything else; nothing is difficult for us to handle.


LQ is a prototype for doing live discussions in the classroom and providing a safe, anonymized back-channel to conversations.
A prototype that was piloted in multiple courses for science education. It is currently on hold.


Karaoke Templater
From Aegisub Manual
Karaoke Templater is an Automation script that ships with Aegisub. Its primary purpose is to help creating karaoke effects with a specially designed template language. Karaoke Templater is already installed and ready to use along with Aegisub.
Tutorials: Introducing Karaoke Templater
- A simple example
- Using math expressions
- Using multiple template lines
- More advanced effects with positioned syllables
Reference
- Declaring template and code lines
- Rules for when and in what order templates are run
- Template modifiers
- Inline variables (dollar-variables)
- Rules for code blocks and code lines
- Contents of the code block/line execution environment
Also see the Automation 4 karaskel.lua section for more information on what's in the line and syl variables, and more.
For users of multi-template
If you have used the multi-template script from Aegisub 1.10 you should recognise several similar concepts in the karaoke templater, but there are also several pitfalls. Some of them are:
- You no longer declare template lines in the Actor field but in the Effect field instead. You can also put much more than just template in there. Read the tutorials above for an introduction, or the reference below if you feel adventurous.
- Instead of using percent-signs to write Lua code blocks you use exclamation marks. So write !$start+$i*30! instead of %$start+$i*30%.
- The A global is gone, but line and syl are directly accesible. The escaped Lua code is no longer run in the true global environment but instead in its own environment, so clashes between your templates and Karaoke Templater itself is much less probable.
- The return false hack to cancel execution of a template no longer works. Neither does having multi-statement Lua blocks and returning from them in general. For the first purpose the fxgroup functionality has been introduced, and for your multi-statement needs code lines have been introduced.
- Instead of working with newline and line (for being-generated and original line) you now work with line and orgline for being-generated and original lines.
- The retime function has been introduced to make it much easier to control the start and end times of your generated lines.
- Lots of more fancy features. Check the tutorials or read the reference to learn about it all.
| Overview: |
Automation Manager • Running macros • Using export filters • Standard macros • Changes from Automation 3 • Moving from Automation 3 |
|---|---|
| Karaoke Templater reference: |
Declaring templates • Execution order • Modifiers • Inline-variables ($-variables) • Code lines and blocks • Execution envirionment |
| Lua reference: |
Registration • Subtitles object • Progress reporting • Config dialogues • Misc. APIs • karaskel.lua • utils.lua • unicode.lua • cleantags.lua |
| Karaskel concepts: |
Style tables • Dialogue line tables • Syllable tables • Inline effects • Furigana |


You find the source code of this page on my Github repo.
To compile the source code you need:
clone git repo with git clone https://github.com/bitborder/itec-f1601.git
git clone https://github.com/bitborder/itec-f1601.git


9.Masson-Delmotte, V. et al. In
Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Ch. 5 (Cambridge University Press, 2013).


April 28
And Moses said unto God, Who am I, that I should go unto Pharaoh, and that I should bring forth the children of Israel out of Egypt? And he said, Certainly I will be with thee.
Exodus 3:11-12
Have you ever had a Holy Spirit inspired dream, a dream of doing something really great for Jesus? A dream of being so prosperous, for instance, that you can finance a nationwide revival? A dream of leading thousands of people to the Lord? At one time or another, you probably have, but perhaps you backed away from it. Maybe you thought, "Oh my, I couldn't do that. Satan's kept me defeated for so long I have a poor self-image."
If so, I have some good news for you. If you'll believe God, even a poor selfimage won't keep you from success. Look in the book of Exodus and you'll see a man who proved that. His name was Moses. Moses didn't have a very good selfimage. He'd made a terrible mistake early in his career. It was a mistake that drove him into the wilderness and kept him there for 40 years herding somebody else's sheep.
He'd once dreamed of being a deliverer of God's people, but no more. As far as he was concerned, he was finished...a failure...a flop! But God didn't think so. In fact, when God came to Moses in that wilderness, He didn't ask for Moses' credentials. He didn't mention his shady history. He just told him to go see Pharaoh and tell him to let God's people go.
Moses, however, was still wrestling with his poor self-image. "Who am I that I should go to Pharaoh?" he stammered. You know what God said in response? He just said, "Certainly, I will be with thee."
You see, it didn't matter who Moses was. What mattered was that the living God was with him. The same thing is true for you today. You don't need a history of successes behind you to answer God's call. You don't need a string of spiritual credentials. All you need is the presence of the Lord.
 Put him in his place. Tell him it doesn't matter who you are because the living God is with you. Then dare to follow your dream!
Scripture Reading: Exodus 3:1-14


jmlr jmlr2008 jmlr2008-82 jmlr2008-82-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Kristin P. Bennett
Abstract: unkown-abstract
J. Friedman. Greedy function approximation: The gradient boosting machine. Annals of Statistics, 29:1189–1232, 2001. 179 F RIEDMAN , H ASTIE AND T IBSHIRANI J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting (with discussion). Annals of Statistics, 28:337–407, 2000. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer Verlag, New York, 2001. T. Hastie, J. Taylor, R. Tibshirani, and G. Walther. Forward stagewise regression and the monotone lasso. Electronic Journal of Statistics, 1:1–29 (electronic), 2007. DOI: 10.1214/07-EJS004. 180


Note: During scale-down instances will wait in a Terminating state for 10 minutes to allow for connection draining before termination.
Please Note: AWS will always keep instances balanced across availability zones.
Make sure that the minimum number of required up-to-date instances are present in each AZ before using scale-in to remove out-of-date instances. Protected instances are never terminated!


{% extends "scrum/base.html" %} {% block main_content %}
Sorry. Not much here yet. Choose a project on the left, and go. Just go.


- pympler.muppy.get_objects(remove_dups=True, include_frames=False)¶
Return a list of all known objects excluding frame objects.
If (outer) frame objects shall be included, pass include_frames=True. In order to prevent building reference cycles, the current frame object (of the caller of get_objects) is ignored. This will not prevent creating reference cycles if the object list is passed up the call-stack. Therefore, frame objects are not included by default.
Keyword arguments: remove_dups – if True, all duplicate objects will be removed. include_frames – if True, includes frame objects.
- pympler.muppy.get_size(objects)¶
Compute the total size of all elements in objects.
- pympler.muppy.get_diff(left, right)¶
Get the difference of both lists.
The result will be a dict with this form {‘+’: [], ‘-‘: []}. Items listed in ‘+’ exist only in the right list, items listed in ‘-‘ exist only in the left list.
- pympler.muppy.sort(objects)¶
Sort objects by size in bytes.
- pympler.muppy.filter(objects, Type=None, min=-1, max=-1)¶
Filter objects.
The filter can be by type, minimum size, and/or maximum size.
Keyword arguments: Type – object type to filter by min – minimum object size max – maximum object size
- pympler.muppy.get_referents(object, level=1)¶
Get all referents of an object up to a certain level.
The referents will not be returned in a specific order and will not contain duplicate objects. Duplicate objects will be removed.
Keyword arguments: level – level of indirection to which referents considered.
This function is recursive.


I'm building a library for web coding which many developer need and use so much in their projects.
This page is under construction. You are welcome to my projects on GitHub and my exciting project vLexikon.com (Swedish-Vietnamese dictionary). I'm also on LinkedIn. I would like to receive feedback from you on what is about web programming. I always love it! (^_~)
Sincerely, Khoi Le


Idukki is one of the 14 districts of Kerala state, India, created on 26 January 1972. This beautiful High range district of Kerala is geographically known for its Mountainous Hills and Dense Forests. For the people of Kerala, Idukki is always associated with Power Generation. About 66% of the State’s Power needs come from the Hydroelectric Power Projects in Idukki. Idukki which lies in the Western Ghats of Kerala is the second largest district in area but has the lowest population density. Idukki has a vast forest reserve area; more than a half of the district is covered by forests. The urban areas are densely populated whereas villages are sparsely populated. Idukki is also known as the spice garden of Kerala.Idukki District came into existence on the 26th January, 1972 as per the notification No. 54131/C2/71/RD dated the 24th January, 1972, carving out Devikulam, Udumbanchola and Peerumade Taluks from erstwhile Kottayam District and Thodupuzha Taluk (excluding Kalloorkad Village and portion of Manjalloor Village included in Kalloorkad and Manjalloor Panchayats) of erstwhile Ernakulam District. The jurisdiction was slightly altered by a subsequent notification No.7754/C2/72/RD dated the 14th February, 1972. The name Idukki was derived from the word ‘Idukku’ which means a gorge.


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Flight
Guests of Beautiful Bora Bora Bungalows first fly to Faa'a International Airport (PPT) in Papeete, Tahiti, the only international airport serving French Polynesia. From Papeete, it is a short 45-minute flight to Bora Bora, where we will meet you upon arrival and escort you to the Resort.
Destination
In partnership with Aircraft Logix, this new Beautiful Bora Bora Bungalows service provides non-stop flights from the executive terminal of Los Angeles’ Van Nuys Airport (VNY) – bypassing the hassles of commercial air travel, and boarding directly onto a Gulfstream G550 private charter. Guests will relax in a truly luxurious flight experience and enjoy first-class dining and cocktails as they are flown directly to Bora Bora. Upon arrival, guests will be escorted by yacht to the breathtaking Beautiful Bora Bora Bungalows. Guests have the option of an exclusive private charter (one party of up to 12 guests) year-round, or a shared private charter (with other Four Seasons guests) over select dates throughout 2017.
This VIP experience starts at USD 27,500 per person based on double occupancy, for an average seven-night stay (approximately USD 4,000 per night). This includes complimentary invitation-only access to our shared private chartered aircraft and accommodations in an overwater bungalow suite.
Exclusive charter flights start at USD 160,000, plus accommodations.


Compute the probability of a set of segments (signal
envelopes), given a set of events. This is doen by either
maximizing or integrating the probability over the template
parameters describing the signals.
-
get_method(method_str, segment=True)[source]
-
log_likelihood_mode(segment, event)[source]
-
log_likelihood_montecarlo(segment, event)[source]
-
log_likelihood_optimize(segment, event)[source]
-
wave_log_likelihood_mode(wave, event)[source]
-
wave_log_likelihood_montecarlo(wave, event, n=50)[source]
-
wave_log_likelihood_optimize(wave, event, use_leb_phases=False, optim_params=None)[source]


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#include <PointerType.h>
Inheritance diagram for PointerType:
Examples for pointer types are the select pointer and the horizontal resize pointer. Pointer types are defined by the skin.
Definition at line 24 of file PointerType.h.
Public Member Functions | |
| virtual void | draw (DrawingContext &context, Vector position)=NULL |
| Draws the pointer at a given position. | |
| virtual void | onPointerMoved (Vector newPosition, Vector oldPosition) |
| Handles the event that the pointer has moved, e.g. | |
| virtual | ~PointerType () |
| Destroys the pointer type. |
|
|
Destroys the pointer type.
|
|
|
Draws the pointer at a given position.
Reimplemented in SimplePointerType. |
|
|
Handles the event that the pointer has moved, e.g. by creating an effect. The default implementation does nothing.
|


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Anyone who knows anything about sports knows it's not just about the total output of a player, rather, it's about how much they can affect the team. In the above graph, instead of just looking at overall HPR in the Elite Eight round (Robinson finished with the fourth highest total of any hitter with a 5.05 in a loss to Texas), we look at the impact each hitter had on her team. When looking at percentage of team's HPR, Robinson was reponsible for 80 percent of the output among Nebraska's three hitters, the highest percentage of any hitter of the eight teams competing.
And not only did Robinson come up clutch for the Huskers, but her HPR in the match, despite being swept, turned out to be one of the top marks posted by any player in the round of eight.
Outside of the 5.50 of Haley Eckerman to cap an impressive day for Texas' hitters, Robinson's effort against the Longhorns was third best from all eight teams, falling behind a pair of big numbers posted in the game noted below as 'the outlier'. Of the Big Ten teams in the round of eight, Robinson's HPR was the best by a third of a point, showing her ability to produce even in the face of the sweep.
To further show the value of the HPR, you can see the affect of All-Big Ten setter Lauren Carlini in the fact Wisconsin's three hitters finished with totals of 3.67, 3.56 and 3.36 in advancing to the Final Four.
By this point, in addition to the HPR showing signs of withstanding the test of actual data, there's also another trend emerging: Kelsey Robinson delivered when the Huskers needed it the most.
The Outlier: The one issue that does need to be addressed here, especially when you see the two numbers posted in the match, is the existence of an outlier in the form of the Washington vs. USC match. In the third matchup this season of the two teams, both Krista Vansant and Nwanebu used that familiarity to their advantage to take turns lighting each other up while playing on the same pin. Yes, both players put up impressive numbers, but given the circumstances (three total blocks between the two players) it is easy to see where all of the kills and digs came from in what turned into a mano-y-mano showdown in LA.
| Player | HPR | HPR % |
| Texas | ||
| Haley Eckerman ^ | 5.50 | 41.4% |
| Bailey Webster ' | 4.94 | 37.2% |
| Khat Bell $ | 2.84 | 21.4% |
| Washington | ||
| Krista Vansant ^ | 6.90 | 62.3% |
| Kaleigh Nelson @ | 1.87 | 16.9% |
| Cassie Strickland | 2.30 | 20.8% |
| Wisconsin | ||
| Dominique Thompson | 3.56 | 33.6% |
| Ellen Chapman | 3.36 | 31.8% |
| Deme Morales | 3.67 | 34.6% |
| Penn State | ||
| Ariel Scott ^ | 3.40 | 37.7% |
| Deja McClendon ' | 4.70 | 52.1% |
| Megan Courtney | 0.92 | 10.2% |
| Player | HPR | HPR % |
| Nebraska | ||
| Kelsey Robinson ^ | 5.04 | 80.3% |
| Kadie Rolfzen @ | 1.37 | 21.9% |
| Amber Rolfzen | -.14 | -2.2% |
| USC | ||
| Ebony Nwanebu ^ | 6.36 | 76.6% |
| Samantha Bricio | 0.85 | 10.3% |
| Sara Shaw | 1.09 | 13.1% |
| Purdue | ||
| Val Nichol @ | 2.21 | 55.2% |
| Sam Epenesa | 0.79 | 19.8% |
| Catherine Rebarchak | 1.00 | 25.0% |
| Stanford | ||
| Jordan Burgess $ | 3.40 | 48.4% |
| Brittany Howard | 2.53 | 36.0% |
| Rachel Williams | 1.10 | 15.6% |
^ AVCA First Team All-American
' AVCA Second Team All-American
@ AVCA Third Team All-American
$ AVCA Honorable Mention All-American


# heading 1
## heading 2
### heading 3
#### heading 4
##### heading 5
###### heading 6
Results in the following:
## heading 2
### heading 3
#### heading 4
##### heading 5
###### heading 6
Results in the following:
heading 1
heading 2
heading 3
heading 4
heading 5
heading 6
*This text will be italic*
_This will also be italic_
**This text will be bold**
__This will also be bold__
_You **can** combine them_
Results in the following:
This text will be italic
This will also be italic
This text will be bold
This will also be bold
You can combine them
_This will also be italic_
**This text will be bold**
__This will also be bold__
_You **can** combine them_
Results in the following:
This text will be italic
This will also be italic
This text will be bold
This will also be bold
You can combine them
* Item 1
* Item 2
* Item 2a
* Item 2b
Results in the following:
* Item 2
* Item 2a
* Item 2b
Results in the following:
- Item 1
- Item 2
- Item 2a
- Item 2b
1. Item 1
2. Item 2
3. Item 2a
4. Item 2b
Results in the following:
2. Item 2
3. Item 2a
4. Item 2b
Results in the following:
- Item 1
- Item 2
- Item 2a
- Item 2b
![DOGE](https://upload.wikimedia.org/wikipedia/en/5/5f/Original_Doge_meme.jpg)
i.e. this format: ![Alt Text](image url)
Results in the following:
i.e. this format: ![Alt Text](image url)
Results in the following:
https://about.gitlab.com/
[GITLAB](https://about.gitlab.com/)
i.e. these formats:
link url
[Text](link url)
Results in the following:
https://about.gitlab.com/
GITLAB
[GITLAB](https://about.gitlab.com/)
i.e. these formats:
link url
[Text](link url)
Results in the following:
https://about.gitlab.com/
GITLAB
For a more extensive explanation
Simple overview of Github-flavored Markdown, which all should work on this site too
Official Markdown Documentation
Simple overview of Github-flavored Markdown, which all should work on this site too
Official Markdown Documentation


Advanced Big Data Analytics - Final Project
Analysis and Predictions on NYC Motor Vehicle Collision Data
Anshuman Singh (as4916) Diksha Vanvari (dhv2108)
Heat Map of all accidents across all five boroughs
Distribution of accidents across all boroughs over time of the day
To see the causes of accidents in a borough, click on its bar.
Causes of accidents
Frequency of fatal accidents over time of the day
Causes of accidents across all 5 boroughs
To see the distribution of a cause across boroughs, click on its sector
Distribution across boroughs
Relative distribution of accidents by vehicle type
To see the distribution of a vehicle type across boroughs, click on its sector
Distribution across boroughs
Weather Conditions on the days when most number of accidents occurred in the last three years
Gaussian Mixture Model on Accident Data - 4 Clusters


Server API Responses
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Week02 Team Activity
First div
Second div
Third div
Change color
Click Me
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Assignment Index


SummApp processes your calls into a neat collection of extracted information and suggested actions that you can view, edit, search for and share.
Simply call someone or answer a call
Share relevant data with others
After the call, you will see the extracted information and suggested actions
From the summary, you can add an event with all relevant information to your calendar, share it or invoke other actions
We started the Treecio team as three vigorous students. Enrolled at Hackprague 2017, eager to get some skills and meet peers. Created SummApp prototype in 24 hours, placed 2nd, and kept going. Our current team has 6 members.


139. Warszawa JUG - Open source brag
:
async-retry
33rd Degree -
Saiku – taking OLAP databases into 21st century
JInkubator -
Monitoring Java applications
Scalar Conference -
The Dark Side of Scala


public interface InstanceCallbacks extends ClearCallback, DeleteCallback, LoadCallback, StoreCallback
PersistenceCapable
class that provides callback methods for life
cycle events implements this interface.
For JDO 2.0, InstanceCallbacks
has been refactored to extend
four other interfaces, without changing any of the methods or semantics.
This allows fine-grained control over callbacks, for
example to allow a class to implement the load callback without
implementing any of the other callbacks. For backward compatibility
with JDO 1.0, the InstanceCallbacks
interface is preserved.
Classes which include non-persistent fields whose values depend on the values of persistent fields require callbacks on specific JDO instance life cycle events in order to correctly populate the values in these fields.
The callbacks might also be used if the persistent instances need to be put into the runtime infrastructure of the application. For example, a persistent instance might notify other instances on changes to state. The persistent instance might be in a list of managed instances. When the persistent instance is made hollow, it can no longer generate change events, and the persistent instance should be removed from the list of managed instances.
To implement this, the application programmer would implement
jdoPostLoad
to put itself into the list of managed
instances, and implement jdoPreClear
to remove itself from
the list. With JDO 1.0, the domain class would be declared to implement
InstanceCallbacks
. With JDO 2.0, the domain class
would be declared to implement
javax.jdo.listener.LoadCallback
and
javax.jdo.listener.ClearCallback
.
Note that JDO does not manage the state of non-persistent fields, and when a JDO instance transitions to hollow, JDO clears the persistent fields. It is the programmer's responsibility to clear non-persistent fields so that garbage collection of referred instances can occur.
jdoPreClear
jdoPreDelete
jdoPostLoad
jdoPreStore
Copyright © 2005–1970 Apache Software Foundation. All rights reserved.


- characters(char[], int, int) - Method in class bpmImport.smParser.ServiceObjectContentHandler
-
- child - Variable in class bpmImport.smParser.Edge
-
The child.
- COMMENTS - Static variable in class bpmImport.smParser.Parser
-
view comments
- COMMENTS - Static variable in class bpmnCore.ServiceGraph
-
view comments
- componentName - Variable in class bpmImport.smParser.Rule
-
The component name.
- CONN_TYPE_Gateway2EndEvent - Static variable in class bpmImport.SMImport
-
The Constant CONN_TYPE_Gateway2EndEvent.
- CONN_TYPE_Gateway2Gateway - Static variable in class bpmImport.SMImport
-
The Constant CONN_TYPE_Gateway2Gateway.
- CONN_TYPE_Gateway2Task - Static variable in class bpmImport.SMImport
-
The Constant CONN_TYPE_Gateway2Task.
- CONN_TYPE_StartEvent2Gateway - Static variable in class bpmImport.SMImport
-
The Constant CONN_TYPE_StartEvent2Gateway.
- CONN_TYPE_StartEvent2Task - Static variable in class bpmImport.SMImport
-
The Constant CONN_TYPE_StartEvent2Task.
- CONN_TYPE_Task2EndEvent - Static variable in class bpmImport.SMImport
-
The Constant CONN_TYPE_Task2EndEvent.
- CONN_TYPE_Task2Gateway - Static variable in class bpmImport.SMImport
-
The Constant CONN_TYPE_Task2Gateway.
- CONN_TYPE_Task2Task - Static variable in class bpmImport.SMImport
-
The Constant CONN_TYPE_Task2Task.
- connect(int, String, String) - Method in class bpmImport.SMImport
-
Creates a directed connection between 2 Objects
by inserting a SequenceFlow between them
and linking each of Objects with this SequenceFlow.
- content - Variable in class bpmnCore.Documentation
-
The content.
- createAssociationRef() - Method in class bpmnCore.MemoryModel
-
Creates the association ref.
- createdAt - Variable in class bpmImport.smParser.ModelInfo
-
The created at.
- createEdges(IdGenerator) - Method in class bpmnCore.MemoryModel
-
Creates the edges.
- createServiceGraph(ArrayList<DTO_ServiceModelRule>) - Method in class bpmnCore.ServiceGraph
-
creates the temporal service graph by applying temporal conditions.
- createShapes(IdGenerator) - Method in class bpmnCore.MemoryModel
-
Creates the shapes.
- createTypeMap() - Method in class bpmnCore.MemoryModel
-
Creates the type map.
- currentValue - Variable in class bpmImport.smParser.ServiceObjectContentHandler
-
The current value.


The login details for this user are as follows. Be sure to write the temporary password down, it will not be recoverable later!


pymatgen.analysis.structure_prediction.substitutor module¶
-
class
Substitutor
(threshold=0.001, symprec=0.1, **kwargs)[source]¶ Bases:
monty.json.MSONable
This object uses a data mined ionic substitution approach to propose compounds likely to be stable. It relies on an algorithm presented in Hautier, G., Fischer, C., Ehrlacher, V., Jain, A., and Ceder, G. (2011). Data Mined Ionic Substitutions for the Discovery of New Compounds. Inorganic Chemistry, 50(2), 656-663. doi:10.1021/ic102031h
This substitutor uses the substitution probability class to find good substitutions for a given chemistry or structure.
Parameters: - threshold – probability threshold for predictions
- symprec – symmetry precision to determine if two structures are duplicates
- kwargs – kwargs for the SubstitutionProbability object lambda_table, alpha
-
get_allowed_species
()[source]¶ returns the species in the domain of the probability function any other specie will not work
-
pred_from_comp
(composition)[source]¶ Similar to pred_from_list except this method returns a list after checking that compositions are charge balanced.
-
pred_from_list
(species_list)[source]¶ There are an exceptionally large number of substitutions to look at (260^n), where n is the number of species in the list. We need a more efficient than brute force way of going through these possibilities. The brute force method would be:
output = [] for p in itertools.product(self._sp.species_list , repeat = len(species_list)): if self._sp.conditional_probability_list(p, species_list) > self._threshold: output.append(dict(zip(species_list,p))) return output
Instead of that we do a branch and bound.
Parameters: species_list – list of species in the starting structure Returns: list of dictionaries, each including a substitutions dictionary, and a probability value
-
pred_from_structures
(target_species, structures_list, remove_duplicates=True, remove_existing=False)[source]¶ performs a structure prediction targeting compounds containing all of the target_species, based on a list of structure (those structures can for instance come from a database like the ICSD). It will return all the structures formed by ionic substitutions with a probability higher than the threshold
Notes: If the default probability model is used, input structures must be oxidation state decorated. See AutoOxiStateDecorationTransformation
This method does not change the number of species in a structure. i.e if the number of target species is 3, only input structures containing 3 species will be considered.
Parameters: - target_species – a list of species with oxidation states e.g., [Specie(‘Li’,1),Specie(‘Ni’,2), Specie(‘O’,-2)]
- structures_list – a list of dictionnary of the form {‘structure’:Structure object ,’id’:some id where it comes from} the id can for instance refer to an ICSD id.
- remove_duplicates – if True, the duplicates in the predicted structures will be removed
- remove_existing – if True, the predicted structures that already exist in the structures_list will be removed
Returns: a list of TransformedStructure objects.


The Body Sensor Location Characteristic is an optional characteristic of the Heart Rate Profile. If your device supports it, press the Read button to read the Body Sensor Location from your device.


Hey There!
Im a Fullstack Developer
Date of Birth: 03/07/1990
Current Residence: center, Israel
Status:married
Used Angular 2+ (angular 8) as framework to create a Single Page Application (SPA) which can bind data to specific views and synchronize data with server. Involved in Node.js & Express architecture implementation and client side validations. Used Node JS architectural concepts like event loop, callbacks, and event emitters. Worked on Rest Webservices as well as Node Rest framework for backend services, used Mongo DB for database services. Responsibilities: Develop/program new functionality and features in line with functional specifications and requirements. Designed and implemented the user interface using HTML5, CSS3, JavaScript and Angular 8. Used NPM (Node Package Manager) for Node.js development.
playing chess, football, computer games, TED Lectures, discovery, walking a lot with wife and friend


Home
About
Contact
Search
Can you spot the item that doesn't belong?
Lorem
Ipsum
Dolor
Sit
Bumblebees
Aenean
Consectetur


Currently the software only features a module for mission analysis which was inherited from the previous Space Trajectory Analysis Tool by ESA which has already been updated to compile despite an untouched code base since 2011 on current machines. The source code already compiles on Gentoo Linux and Ubuntu Linux. It is planned to revise the code to also support other Linux distributions, Windows and Mac OS X. Starting from successful builds on various platforms, the code base will be upgraded to replace the currently used Qt4 with the latest Qt5 library. Further modifications will include the seperation of the code base into two parts, one handling the computations involved in the software, and the other providing a graphical user interface. Based on this framework more modules will be developed to cover more aspects of the design of spacecrafts and space missions. A non-exclusive list of considered modules include:
Currently the software is distributed in the form of the git repository hosted at Github. An experimental ebuild for Gentoo based systems is available on the gentoo-overlay of the Dwarf-Planet-Project. Packages for Debian based systems, Windows and Mac OS X are currently work in progress and will be provided here when the packaging process is completed.
The current alpha build is provided under the EUPL 1.1. New relases will be published under the AGPLv3.
Project Manager
Software Developer
Quality Assurance
Particle Radiation module for the SDT project
Evaluation of Computational Platforms
GUI Designer for the SDT project


Plotting basics
- Histograms and density plots
- Scatter plots
- Controls:
- titles
- line width, color, and type
- point size, color, and type
- x and y axis labels
- Controls:
Daniel Anderson
CourseR
d <- read.delim("./data/ratebeer_beerjobber.txt")
head(d)
## name brewer
## 1 Abbey Monks Ale Abbey Beverage Company
## 2 Abbey Monks Tripel Abbey Beverage Company
## 3 Abbey Monks Wit Abbey Beverage Company
## 4 Alameda Barn Owl Imperial Brown Ale Alameda Brewing Co.
## 5 Alameda Black Bear XX Stout Alameda Brewing Co.
## 6 Alameda El Torero Organic IPA Alameda Brewing Co.
## style abv ratings score.overall score.by.style
## 1 Belgian Ale 5.2 96 50 49
## 2 Abbey Tripel 8.0 3 NA NA
## 3 Belgian White (Witbier) 5.1 46 23 19
## 4 Brown Ale 7.9 13 74 81
## 5 Foreign Stout 6.8 172 94 76
## 6 India Pale Ale (IPA) 7.2 56 74 43
Primary purpose of a histogram: See the distrbution of a variable
hist(d$abv)
hist(d$ratings)
?hist
hist(d$ratings, breaks = 35)
hist(d$ratings, breaks = 100)
hist(d$abv)
dens <- density(d$abv); plot(dens)
dens <- density(d$abv)
library(sfsmisc)
integrate.xy(dens$x, dens$y)
## [1] 1.000879
abv57 <- cbind(dens$x, dens$y)
head(abv57, n = 2)
## [,1] [,2]
## [1,] -1.276634 0.0007432923
## [2,] -1.246588 0.0009135057
abv57 <- subset(abv57, abv57[ ,1] >= 5 &
abv57[ ,1] <= 7)
integrate.xy(abv57[ ,1], abv57[ ,2])
## [1] 0.4565267
Change the binwidth to control the smoothing fact. The smaller the binwidth, the closer the function will resemble the observed data. The larger the binwidth, the closer the function will resemble a standard normal.
dens1 <- density(d$abv, bw = .001)
plot(dens1)
dens2 <- density(d$abv, bw = 100)
plot(dens2)
probability = TRUE
to a histogram. What
do you notice?plot()
functionGeneric x-y plotting. Multiple types of plots are possible. From the documentation:
Two specifications
plot(outcome ~ predictor, data = d) # Just like the lm function
plot(x-variable, y-variable)
plot(score.overall ~ abv, data = d)
plot(d$abv, d$score.overall)
| Argument | Description |
|---|---|
| main | Main title of the Plot |
| sub | Subtitle, added at the bottom of the plot |
| xlab | x-axis label |
| ylab | y-axis lable |
| col | Color (can be multiple things, depending on call) |
| pch | Point type |
| xlim | Limits of x-axis (vector of length 2) |
| ylim | Limits of y-axis (vector of length 2) |
| bg | Background color (can be multiple things, depending on call) |
| cex | Size of points |
| cex.SPEC | Specific call to axis/lab/main/sub to change size of those SPEC (specifications) |
| col.SPEC | Color specific calls |
plot(d$score.by.style, d$score.overall)
plot(d$score.by.style, d$score.overall,
main = "Relation Between Style Rating and Overall Rating",
xlab = "Beer Style Rating",
ylab = "Overall Beer Rating")
plot(d$score.by.style, d$score.overall,
main = "Relation Between Style
Rating and Overall Rating",
xlab = "Beer Style Rating",
ylab = "Overall Beer Rating",
pch = 3,
cex = 2,
col = "purple")
plot(1:20, 1:20, pch = 1:20, cex = 3)
mod <-
lm(score.overall ~ score.by.style,
data = d)
plot(d$score.by.style, d$score.overall,
main = "Relation Between Style
Rating and Overall Rating",
xlab = "Beer Style Rating",
ylab = "Overall Beer Rating",
pch = 3,
cex = 2,
col = "purple")
abline(coef(mod)[1], coef(mod)[2],
lty = 3,
lwd = 5,
col = "aquamarine3")
plot(1:6, 1:6, type = "n")
for(i in 1:6) abline(i, 0, lty = i, lwd = 3)
Produce the following plot (the colors can be different, but it should not be black)
Overlay a density plot on a histogram, maintain frequency y-axis
First, plot the histogram, suppressing all axes, labels, and plot border
hist(d$abv,
probability = TRUE,
axes = FALSE,
main = "",
xlab = "",
ylab = "")
Next, use lines
to add the density line
lines(density(d$abv),
col = "darkgray",
lty = 3,
lwd = 3)
Call a new plot to overaly on the current plot
par(new = TRUE)
Plot the histogram again, labeling the axes how you'd like (note, this process is neccessary to keep the frequency y-axis)
hist(d$abv,
main = "Distribution of Beer Alcohol
by Volume",
xlab = "Alcohol by Volume"
)
Produce the following plots. Again, the colors, line types, etc., can be different, but they should not be the default (where the defaults have been changed).


I was born in Cedar Rapids, Iowa in 1990. I've spent most of my life living
on the South East side of the city. In school growing up I played the saxaphone,
ran hurdle events in track, and participated in show choir.
After high school I briefly attended Iowa State University.
I also have taken some classes at Kirkwood. My jobs have mostly been in food and
customer service. I'm hoping this class will allow me to get a job that
I like and that will allow me to have the life I want.


§352-12 Segregation of committed persons. Persons committed to the youth correctional facilities shall be kept segregated based on considerations including age, maturity level, attitude, behavior, offense committed, commitment period, and rehabilitation status. [L 1980, c 303, pt of §8(1)]


documentation
Getting Started
To start developing with francisco, all you need to do is link the current stylesheet:
<link rel="stylesheet" href="https://cdn.rawgit.com/kjhx/francisco/v1.3.1/francisco.css">
That's it! All there's left to do is read the rest of the documentation.
Typography
The default typeface is determined by the browser and the operating system, helping create a more comfortable viewing experience. Using the system provided fonts often makes a webpage feel more like it fits in with other UI your device. Additionally, relying on system provided fonts vastly improves webpage performance.
All type is set in rems so that a single property, html
font-size, can be used to globally modify all type and spacing. Obviously, both emphasis and strong text is supported.
Heading 14.4rem
Heading 23.8rem
Heading 31.6rem
Grid
Francisco uses a flexible, fully responsive three column grid system. You build up the grid by placing

tags inside of a

. You'll typically want to place these (or your whole webpage) inside of a

, which acts as a formatted container. This is what the standard three column grid looks like:
column
column
column

column

column

column

We can also use the second type of column to take up two columns worth of space, creating a 60/40 split. All you have to do is replace the column
attribute with double
. This works in both orientations:
double
column
column
double
Navigation
You can create simple horizontal navigation with the
tag. By using a
tag, the first link will become bolder and darker than the rest of the navigation, making a title of sorts.
Additionally, you can use the
tag to create a vertical navigation with the same properties as the above.

Title
Page One
Page Two

Code
Both inline code and complete code blocks are supported. Just wrap whatever you need in
tags, or wrap those in

tags for a block.
Code blocks do wrap on smaller screens — this is necessary to prevent breaking the entire mobile experience.

Utilities
Included are a handful of utility classes to make dealing with floats, clearing, and tables a little easier. The current ones are u-fl-right
, u-fl-left
, u-cf
, and u-table-odd
.
These will float right, float left, and clear floats respectively. The fourth is used on every odd row of a table to highlight it, like so:
| Name | Age | Location |
|---|---|---|
| Timothy Halloway | 19 | St. Louis |
| Zach Halvorson | 21 | Friendswood |
| Noah Lockhart | 19 | Kansas City |
| Alex Ohrmundt | 20 | Houston |
| Ellen Finke | 19 | Taipei |
| Name | Age | Location |
|---|---|---|
| Timothy Halloway | 19 | St. Louis |
| Zach Halvorson | 21 | Friendswood |
| Noah Lockhart | 19 | Kansas City |
| Alex Ohrmundt | 20 | Houston |
| Ellen Finke | 19 | Taipei |
Things of note
- Global attribution
- Element declaration was intended to be used outside of the class attribute (such as

and

), you can still declare their class normally as

.
- Element declaration was intended to be used outside of the class attribute (such as
- Link colors
- Links are recolored and animated with a fade-in effect by default. In the light themed variant this translates to a light red, while in the dark theme this translates to light blue.
- Utility classes
- The above utility classes are not actually included as a part of this css framework. That was only added to the base css to fulfill an assignment requirement for including tables.


--- layout: default title: Language Reference ---
TODO: This page still needs to be written. Things to include:
$
local
Let D, E, F range over arbitrary expressions. Let ≡ mean contextual equivalence, i.e if D ≡ E then substituting D for E in any context will manifest to the same JSON.
super
:super
and have no
common fields:


Aaina From The Body Is Out! Emraan Hashmi, Shobita Dhulipala, Vedhika’s Romantic Track Strikes The Right Chords
Featuring Emraan Hashmi, Shobita Dhulipala and Vedika, the song is a romantic track where we see Emraan reminiscing about Vedika while he spends some time with his on-screen wife Shobita.
by Koimoi.com TeamThe Body starring Emraan Hashmi, Rishi Kapoor, Shobita Dhulipala and Vedika has the audience hooked to its updates. After a thrilling trailer, the makers of the film have released the first track from the film titled Aaina.
Featuring Emraan Hashmi, Shobita Dhulipala and Vedika, the song is a romantic track where we see Emraan reminiscing about Vedika while he spends some time with his on-screen wife Shobita. The lyrics are soothing but what made the look brilliant is the picturesque scenery where the song is shot. Emraan can be seen romancing both the actress in Aaina.
Voiced by Arko, Tulsi Kumar, and Neha Kakkar, the lyrics have been penned down by Arko. The singer-lyricist Arko has also composed the music of the song. Emraan took to his Twitter account to share the video of the song with his fans. He wrote, “Banaa le mujhe Aaina ke mere har lafz ka…Tu hi ek maaina.”
Directed by Jeethu Joseph, ‘The Body’ is slated to release on December 13. The director is well known for his Malayalam film Drishyam that was remade in Hindi with Ajay Devgn, Tabu.
As for Emraan Hashmi, he was last seen in Netflix series ‘Bard of Blood’, which also starred Shobita Dhulipala. Now, Hashmi is busy working on his next project ‘Chehra’ which also stars Amitabh Bachchan.


assert.equal(2, this.v1.x);
assert.equal(2, this.v1.y);
assert.equal(107, this.v2.x);
assert.equal(2, this.v2.y);
assert.equal(202, this.v3.x);
assert.equal(2, this.v3.y);
assert.equal(2, this.v4.x);
assert.equal(23, this.v4.y);
assert.equal(107, this.v5.x);
assert.equal(23, this.v5.y);
assert.equal(232, this.v6.x);
assert.equal(23, this.v6.y);
assert.equal(2, this.v7.x);
assert.equal(44, this.v7.y);
assert.equal(112, this.v8.x);
assert.equal(44, this.v8.y);
assert.equal(213, this.v9.x);
assert.equal(44, this.v9.y);
// Change alignment
this.layout.setAttribute('align', 'right');
assert.equal(32, this.v1.x);
assert.equal(2, this.v1.y);
assert.equal(137, this.v2.x);
assert.equal(2, this.v2.y);
assert.equal(232, this.v3.x);
assert.equal(2, this.v3.y);
assert.equal(2, this.v4.x);
assert.equal(23, this.v4.y);
assert.equal(107, this.v5.x);
assert.equal(23, this.v5.y);
assert.equal(232, this.v6.x);
assert.equal(23, this.v6.y);
assert.equal(21, this.v7.x);
assert.equal(44, this.v7.y);
assert.equal(131, this.v8.x);
assert.equal(44, this.v8.y);
assert.equal(232, this.v9.x);
assert.equal(44, this.v9.y);
// Change a hint
this.v5.setAttribute('layouthint', '{"spacingbefore":0, "spacingafter":0}');
assert.equal(32, this.v1.x);
assert.equal(2, this.v1.y);
assert.equal(137, this.v2.x);
assert.equal(2, this.v2.y);
assert.equal(232, this.v3.x);
assert.equal(2, this.v3.y);
assert.equal(32, this.v4.x);
assert.equal(23, this.v4.y);
assert.equal(132, this.v5.x);
assert.equal(23, this.v5.y);
assert.equal(232, this.v6.x);
assert.equal(23, this.v6.y);
assert.equal(21, this.v7.x);
assert.equal(44, this.v7.y);
assert.equal(131, this.v8.x);
assert.equal(44, this.v8.y);
assert.equal(232, this.v9.x);
assert.equal(44, this.v9.y);
// Justify
this.layout.setAttribute('justify', true);
assert.equal(2, this.v1.x);
assert.equal(2, this.v1.y);
assert.equal(117, this.v2.x);
assert.equal(2, this.v2.y);
assert.equal(232, this.v3.x);
assert.equal(2, this.v3.y);
assert.equal(2, this.v4.x);
assert.equal(23, this.v4.y);
assert.equal(117, this.v5.x);
assert.equal(23, this.v5.y);
assert.equal(232, this.v6.x);
assert.equal(23, this.v6.y);
assert.equal(2, this.v7.x);
assert.equal(44, this.v7.y);
assert.equal(117, this.v8.x);
assert.equal(44, this.v8.y);
assert.equal(232, this.v9.x);
assert.equal(44, this.v9.y);
assert.equal(2, this.v1.x);
assert.equal(4, this.v1.y);
assert.equal(2, this.v2.x);
assert.equal(41, this.v2.y);
assert.equal(53, this.v3.x);
assert.equal(41, this.v3.y);
assert.closeTo(7.66, this.v4.x, 0.01);
assert.equal(78, this.v4.y);
assert.closeTo(84.32, this.v5.x, 0.01);
assert.equal(78, this.v5.y);
assert.equal(2, this.v6.x);
assert.closeTo(143.16, this.v6.y, 0.01);
assert.equal(73, this.v7.x);
assert.closeTo(143.16, this.v7.y, 0.01);
assert.equal(134, this.v8.x);
assert.closeTo(143.16, this.v8.y, 0.01);
assert.equal(2, this.v9.x);
assert.closeTo(190.16, this.v9.y, 0.01);
assert.closeTo(231.16, this.height, 0.01);
var layout = this.layout,
v1 = this.v1,
v2 = this.v2,
v3 = this.v3,
v4 = this.v4,
v5 = this.v5,
v6 = this.v6,
v7 = this.v7,
v8 = this.v8,
v9 = this.v9;
assert.equal(2, v1.y);
assert.equal(2, v2.y);
assert.equal(2, v3.y);
assert.closeTo(56.16, v4.y, 0.1);
assert.equal(38, v5.y);
assert.closeTo(120.32, v6.y, 0.1);
assert.closeTo(120.32, v7.y, 0.1);
assert.closeTo(120.32, v8.y, 0.1);
assert.closeTo(175.33, v9.y, 0.1);
layout.setAttribute('linealign', 'middle');
assert.equal(2, v1.y);
assert.equal(7, v2.y);
assert.equal(2, v3.y);
assert.closeTo(56.16, v4.y, 0.1);
assert.closeTo(48.65, v5.y, 0.1);
assert.closeTo(130.32, v6.y, 0.1);
assert.closeTo(125.32, v7.y, 0.1);
assert.closeTo(120.32, v8.y, 0.1);
assert.closeTo(175.33, v9.y, 0.1);
layout.setAttribute('linealign', 'bottom');
assert.equal(2, v1.y);
assert.equal(12, v2.y);
assert.equal(2, v3.y);
assert.closeTo(56.16, v4.y, 0.1);
assert.closeTo(59.32, v5.y, 0.1);
assert.closeTo(140.32, v6.y, 0.1);
assert.closeTo(130.32, v7.y, 0.1);
assert.closeTo(120.32, v8.y, 0.1);
assert.closeTo(175.33, v9.y, 0.1);
// Flip axis
layout.setAttribute('axis', 'y');
assert.closeTo(43.32, v1.x, 0.1);
assert.closeTo(33.32, v2.x, 0.1);
assert.closeTo(23.32, v3.x, 0.1);
assert.closeTo(7.66, v4.x, 0.1);
assert.closeTo(84.32, v5.x, 0.1);
assert.closeTo(94.32, v6.x, 0.1);
assert.closeTo(104.32, v7.x, 0.1);
assert.closeTo(114.32, v8.x, 0.1);
assert.closeTo(171.84, v9.x, 0.1);
this.parent.linecount = linecount;
this.parent.maxsize = maxsize;
this.parent.lineCalls = [];
this.super();
this.parent.lineCalls.push('start ' + lineindex, 'value ' + value);
this.super();
this.parent.lineCalls.push('end ' + lineindex, 'item ' + items.length, 'value ' + value);
this.super();
assert.equal(3, this.linecount, 'The line count is 3.')
assert.equal(337, this.maxsize, 'The max size is 337.')
assert.equal(this.lineCalls.join(', '), 'start 1, value 2, end 1, item 4, value 305, start 2, value 2, end 2, item 3, value 334, start 3, value 2, end 3, item 2, value 213');
// Make another line occur.
this.v2.setAttribute('layouthint', '{"break":true}')
assert.equal(4, this.linecount, 'The line count is 4.')
assert.equal(348, this.maxsize, 'The max size is 348.')
// Switch axis
this.layout.setAttribute('axis', 'y')
assert.equal(3, this.linecount, 'The line count is 3.')
assert.equal(88, this.maxsize, 'The max size is 88.')
this.v9.setAttribute('height', 25);
assert.equal(3, this.linecount, 'The line count is 3.')
assert.equal(93, this.maxsize, 'The max size is 93.')
assert.equal(this.lineCalls.join(', '), 'start 1, value 2, end 1, item 1, value 22, start 2, value 2, end 2, item 4, value 85, start 3, value 2, end 3, item 4, value 90');
this.parent.linecount = linecount;
this.parent.maxsize = maxsize;
this.parent.lineCalls = [];
this.super();
this.parent.lineCalls.push('start ' + lineindex, 'value ' + value);
this.super();
this.parent.lineCalls.push('end ' + lineindex, 'item ' + items.length, 'value ' + value);
this.super();
var layout = this.layout,
v1 = this.v1,
v2 = this.v2,
v3 = this.v3,
v4 = this.v4,
v5 = this.v5,
v6 = this.v6,
v7 = this.v7,
v8 = this.v8,
v9 = this.v9;
assert.equal(88, v1.x);
assert.equal(149, v2.x);
assert.equal(220, v3.x);
assert.equal(301, v4.x);
assert.equal(48, v5.x);
assert.equal(149, v6.x);
assert.equal(240, v7.x);
assert.equal(321, v8.x);
assert.equal(331, v9.x);
layout.setAttribute('align', 'center')
assert.equal(45, v1.x);
assert.equal(106, v2.x);
assert.equal(177, v3.x);
assert.equal(258, v4.x);
assert.equal(25, v5.x);
assert.equal(126, v6.x);
assert.equal(217, v7.x);
assert.equal(298, v8.x);
assert.closeTo(166.5, v9.x, 0.1);
layout.setAttribute('align', 'top')
assert.equal(2, v1.x);
assert.equal(63, v2.x);
assert.equal(134, v3.x);
assert.equal(215, v4.x);
assert.equal(2, v5.x);
assert.equal(103, v6.x);
assert.equal(194, v7.x);
assert.equal(275, v8.x);
assert.equal(2, v9.x);
// Justify and then verify the single line item is positioned correctly
layout.setAttribute('justify', true);
assert.equal(2, v9.x);
layout.setAttribute('align', 'middle')
assert.closeTo(166.5, v9.x, 0.1);
layout.setAttribute('align', 'bottom')
assert.equal(331, v9.x);
// flip axis and force a single lines
layout.setAttribute('axis', 'y')
v2.setAttribute('layouthint', '{"break":true}')
assert.equal(51, v1.y);
layout.setAttribute('align', 'middle')
assert.closeTo(26.5, v1.y, 0.1);
layout.setAttribute('align', 'top')
assert.equal(2, v1.y);
if @canUpdate()
if !@updateCount? then @updateCount = 0
@updateCount++
@super()
var layout = this.layout,
v1 = this.v1,
v2 = this.v2,
v3 = this.v3,
v4 = this.v4,
v5 = this.v5,
v6 = this.v6,
v7 = this.v7,
v8 = this.v8,
v9 = this.v9;
assert.equal(2, v1.x);
assert.closeTo(72.67, v2.x, 0.1);
assert.closeTo(153.33, v3.x, 0.1);
assert.equal(244, v4.x);
assert.equal(2, v5.x);
assert.equal(103, v6.x);
assert.equal(214, v7.x);
assert.equal(2, v8.x);
assert.equal(234, v9.x);
assert.equal(1, layout.updateCount, 'The layout should only be updated 1 time so far.');
v1.setAttribute('width', 80);
assert.equal(2, layout.updateCount, 'The layout should only be updated 2 times so far.');
assert.equal(2, v1.x);
assert.equal(86, v2.x);
assert.equal(160, v3.x);
assert.equal(244, v4.x);
var container = this.container,
layout = container.layout,
v1 = container.v1,
v2 = container.v2,
v3 = container.v3,
v4 = container.v4,
v5 = container.v5,
v6 = container.v6,
v7 = container.v7,
v8 = container.v8,
v9 = container.v9;
// Verify updateparent works
assert.equal(74, container.innerheight, 'The innerheight of the container should be (row heights + linespacing + lineinset + lineoutset).');
// Verify all views are positioned correctly
assert.equal(2, v1.x, 'The first view has an x position of the inset.');
assert.equal(4, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(103, v2.x, 'The second view has an x position of the inset + first view width + spacing.');
assert.equal(4, v2.y, 'The second view has a y position of the lineinset.');
assert.equal(2, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(48, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
// Change view width to be smaller but without changing wrap.
v1.setAttribute('width', 80);
assert.equal(2, v1.x, 'The first view has an x position of the inset.');
assert.equal(4, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(83, v2.x, 'The second view has an x position of the inset + first view width + spacing.');
assert.equal(4, v2.y, 'The second view has a y position of the lineinset.');
assert.equal(2, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(48, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
// Change view width to be larger so that wrap will change
v1.setAttribute('width', 101);
assert.equal(2, v1.x, 'The first view has an x position of the inset.');
assert.equal(4, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(104, v2.x, 'The second view has an x position of the inset + first view width + spacing.');
assert.equal(4, v2.y, 'The second view has a y position of the lineinset.');
assert.equal(103, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(48, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
// Make view height smaller such that row height won't change
v2.setAttribute('height', 15);
assert.equal(2, v1.x, 'The first view has an x position of the inset.');
assert.equal(4, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(104, v2.x, 'The second view has an x position of the inset + first view width + spacing.');
assert.equal(4, v2.y, 'The second view has a y position of the lineinset.');
assert.equal(103, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(48, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
// Change view height such that row height will change
v2.setAttribute('height', 25);
assert.equal(2, v1.x, 'The first view has an x position of the inset.');
assert.equal(4, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(104, v2.x, 'The second view has an x position of the inset + first view width + spacing.');
assert.equal(4, v2.y, 'The second view has a y position of the lineinset.');
assert.equal(103, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(53, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
assert.equal(79, container.innerheight, 'The innerheight of the container should be (row heights + linespacing + lineinset + lineoutset).');
// Make several view heights smaller such that row height will change
v1.setAttribute('height', 15);
v2.setAttribute('height', 15);
v3.setAttribute('height', 15);
assert.equal(2, v1.x, 'The first view has an x position of the inset.');
assert.equal(4, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(104, v2.x, 'The second view has an x position of the inset + first view width + spacing.');
assert.equal(4, v2.y, 'The second view has a y position of the lineinset.');
assert.equal(103, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(43, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
assert.equal(69, container.innerheight, 'The innerheight of the container should be (row heights + linespacing + lineinset + lineoutset).');
// Change inset so that line will unwrap
layout.setAttribute('inset', 1);
assert.equal(1, v1.x, 'The first view has an x position of the inset.');
assert.equal(4, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(1, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(48, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
assert.equal(74, container.innerheight, 'The innerheight of the container should be (row heights + linespacing + lineinset + lineoutset).');
// Change outset so that line will wrap
layout.setAttribute('outset', 4);
assert.equal(1, v1.x, 'The first view has an x position of the inset.');
assert.equal(4, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(103, v2.x, 'The second view has an x position of the inset + first view width + spacing.');
assert.equal(4, v2.y, 'The second view has a y position of the lineinset.');
assert.equal(102, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(43, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
assert.equal(69, container.innerheight, 'The innerheight of the container should be (row heights + linespacing + lineinset + lineoutset).');
// Change spacing
layout.setAttribute('spacing', 2);
assert.equal(1, v1.x, 'The first view has an x position of the inset.');
assert.equal(4, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(104, v2.x, 'The second view has an x position of the inset + first view width + spacing.');
assert.equal(4, v2.y, 'The second view has a y position of the lineinset.');
assert.equal(205, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(43, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
// Change line inset
layout.setAttribute('lineinset', 2);
assert.equal(1, v1.x, 'The first view has an x position of the inset.');
assert.equal(2, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(104, v2.x, 'The second view has an x position of the inset + first view width + spacing.');
assert.equal(2, v2.y, 'The second view has a y position of the lineinset.');
assert.equal(205, v9.x, 'The ninth view has an x position of the inset.');
assert.equal(41, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
// Change line outset
layout.setAttribute('lineoutset', 1);
assert.equal(62, container.innerheight, 'The innerheight of the container should be (row heights + linespacing + lineinset + lineoutset).');
// Change line spacing
layout.setAttribute('linespacing', 1);
assert.equal(2, v1.y, 'The first view has a y position of the lineinset.');
assert.equal(39, v9.y, 'The ninth view has a y position of the lineinset + first two row heights + spacing.');
assert.equal(60, container.innerheight, 'The innerheight of the container should be (row heights + linespacing + lineinset + lineoutset).');
// Reverse the layout
layout.setAttribute('reverse', true);
assert.equal(205, v1.x, 'The first view has an x position of 205.');
assert.equal(44, v1.y, 'The first view has a y position of 44.');
assert.equal(103, v2.x, 'The second view has an x position of 103.');
assert.equal(44, v2.y, 'The second view has a y position of 44.');
assert.equal(1, v9.x, 'The ninth view has an x position of 1.');
assert.equal(2, v9.y, 'The ninth view has a y position of 2.');
// Unreverse
layout.setAttribute('reverse', false);
// Change axis
layout.setAttribute('axis', 'y');
assert.equal(2, v1.x, 'The first view has an x position of 2.');
assert.equal(1, v1.y, 'The first view has a y position of 1.');
assert.equal(104, v4.x, 'The fourth view has an x position of 104.');
assert.equal(1, v4.y, 'The fourth view has a y position of 1.');
assert.equal(306, v9.x, 'The ninth view has an x position of 306.');
assert.equal(23, v9.y, 'The ninth view has a y position of 23.');
assert.equal(407, container.innerwidth, 'The innerwidth of the container should be 407.');
// Change height of the container so 1 row is removed
container.setAttribute('height', 100);
assert.equal(2, v1.x, 'The first view has an x position of 2.');
assert.equal(1, v1.y, 'The first view has a y position of 1.');
assert.equal(205, v9.x, 'The ninth view has an x position of 205.');
assert.equal(1, v9.y, 'The ninth view has a y position of 1.');
assert.equal(306, container.innerwidth, 'The innerwidth of the container should be 306.');
// Hide a view
v9.setAttribute('visible', false);
assert.equal(205, container.innerwidth, 'The innerwidth of the container should be 205.');
// Show a view
v9.setAttribute('visible', true);
assert.equal(306, container.innerwidth, 'The innerwidth of the container should be 306.');
// Force a few breaks
v2.setAttribute('layouthint', '{"break":true}');
v3.setAttribute('layouthint', '{"break":true}');
assert.equal(104, v2.x, 'The second view is now at 104 since it is a break.');
assert.equal(205, v3.x, 'The third view is now at 205 since it is a break.');
assert.equal(407, container.innerwidth, 'The innerwidth of the container should be 407.');
// Remove a break
v2.setAttribute('layouthint', null);
assert.equal(2, v2.x, 'The second view is now at 2 since it is no longer a break.');
assert.equal(104, v3.x, 'The third view is now at 104.');
assert.equal(306, container.innerwidth, 'The innerwidth of the container should be 306.');


Independently Owned Eating in Oxford
MAMMA MIA PIZZERIA ££
DESCRIPTION
EDAMAME £££
DESCRIPTION
PARSONAGE GRILL ££££
DESCRIPTION
THE MAGDALEN ARMS ££
DESCRIPTION
PINT SHOP £££
DESCRIPTION
ARBEQUINA ££
DESCRIPTION
TURL STREET KITCHEN ££
DESCRIPTION
PORTABELLO £££
DESCRIPTION
TURL STREET KITCHEN ££
DESCRIPTION
GEES RESTAURANT AND BAR £££
DESCRIPTION


Type: number
Enum for DirectionWay Way values.
Type: number
Get Direction Way by name
(string)
DirectionWay
:
Get Direction Way name
(DirectionWay)
String
:
Get reverse direction way
(DirectionWay)
DirectionWay
:
Reversed direction
ICE information for a peer
RID info
(String)
rid value
(DirectionWay)
direction
Get rid direction
DirectionWay
:
Get rid direction
DirectionWay
:
Set direction setup
(DirectionWay)
Set direction setup
(DirectionWay)
Get pt formats for rid
Array.Number
:
Enum for Setup values.
Type: number
Simulcast information
Expands a plain JSON object containing an SimulcastInfo
(Object)
JSON object
SimulcastInfo
:
Parsed Simulcast info
Create a clone of this track info object
SimulcastInfo
:
Add a simulcast alternative streams for the specific direction
(DirectionWay)
Which direction you want the streams for
(Array<SimulcastStreamInfo>)
Stream info of all the alternatives
Add a single simulcast stream for the specific direction
(DirectionWay)
Which direction you want the streams for
(Array<SimulcastStreamInfo>)
Stream info of all the alternatives
Get all simulcast streams by direction
(DirectionWay)
Which direction you want the streams for
Array<Array<SimulcastStreamInfo>>
:
Media Track information
Add source group to track
(SourceGroupInfo)
Get the source group fot the desired type
(String)
Group type
SourceGroupInfo
:
Get all source groups for this track
Array<SourceGroupInfo>
:
Get simulcast encoding information for this track (if any)
Array<Array<TrackEncodingInfo>>
:
Add simulcast encoding information for this track
(TrackEncodingInfo)
Simulcast encoding info
Add simulcast encoding information for this track
(Array<TrackEncodingInfo>)
Simulcast encoding info
Add simulcast encoding information for this track
(Array<Array<TrackEncodingInfo>>)
Simulcast encoding info
Media information (relates to a m-line in SDP)
Helper factory for creating media info objects.
(any)
(Object)
Supported media capabilities to be included on media info
| Name | Description |
|---|---|
supported.codecs (Map<String, CodecInfo> | Array<String>)
|
Map or codecInfo or list of strings with the supported codec names |
(String)
Media type
(Boolean)
If rtx is supported for codecs (only needed if passing codec names instead of CodecInfo)
(Object)
MediaInfo
:
Helper usefull for creating media info answers.
(Object)
Supported codecs and extensions to be included on answer
| Name | Description |
|---|---|
supported.codecs Map<String, CodecInfo>
|
List of strings with the supported codec names |
supported.extensions Set<String>
|
List of strings with the supported codec names |
supported.simulcast Boolean
|
Simulcast is enabled |
supported.rtcpfbs Array<String>
|
Supported RTCP feedback params |
MediaInfo
:
Get Simulcast info
SimulcastInfo
:
Set stream simulcast info
(SimulcastInfo)
Simulcast stream info
SDP semantic info object This object represent the minimal information of an WebRTC SDP in a semantic hierarchy
(Number)
SDP version attribute
Get SDES info for the transport bundle
SDESInfo
:
DTLS info object
Set SDES info object for the transport bundle
(SDESInfo)
DTLS info object
Add ICE candidate for transport
(CandidateInfo)
ICE candidate
Add ICE candidates for transport
(any)
Get all ICE candidates for this transport
Array<CandidateInfo>
:
Get announced stream
(String)
StreamInfo
:
Get all announced stream
Array<StreamInfo>
:
Get first announced stream
StreamInfo
:
Announce a new stream in SDP
(StreamInfo)
Remove an announced stream from SDP
(StreamInfo)
boolean
:
Remove all streams


Ahesive
HOT MELT
Hot melt adhesive (HMA), also known as hot glue. The gun uses a continuous-duty heating element to melt the plastic glue, which the user pushes through the gun either with a mechanical trigger mechanism on the gun, or with direct
finger pressure. The glue squeezed out of the heated nozzle is initially hot enough to burn and even blister skin. The glue is tacky when hot, and solidifies in a few seconds to one minute.


CodeSonar Hub server certificate.
[Optional, HTTPS hubs only]
A Credentials parameter of type secret file, where the file contains the hub server certificate in PEM format (Base-64 encoded ASCII).
This setting is useful if your hub is not automatically trusted by your Jenkins server, for example, if your hub uses a self-signed certificate.


DataFrame.
stack
Stack the prescribed level(s) from columns to index.
Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:
if the columns have a single level, the output is a Series; if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.
if the columns have a single level, the output is a Series;
if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.
The new index levels are sorted.
Stacked dataframe or series.
See also
DataFrame.unstack
Unstack prescribed level(s) from index axis onto column axis.
DataFrame.pivot
Reshape dataframe from long format to wide format.
DataFrame.pivot_table
Create a spreadsheet-style pivot table as a DataFrame.
Notes
The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).
Examples
Single level columns
>>> df_single_level_cols = ps.DataFrame([[0, 1], [2, 3]], ... index=['cat', 'dog'], ... columns=['weight', 'height'])
Stacking a dataframe with a single level column axis returns a Series:
>>> df_single_level_cols weight height cat 0 1 dog 2 3 >>> df_single_level_cols.stack().sort_index() cat height 1 weight 0 dog height 3 weight 2 dtype: int64
Multi level columns: simple case
>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'), ... ('weight', 'pounds')]) >>> df_multi_level_cols1 = ps.DataFrame([[1, 2], [2, 4]], ... index=['cat', 'dog'], ... columns=multicol1)
Stacking a dataframe with a multi-level column axis:
>>> df_multi_level_cols1 weight kg pounds cat 1 2 dog 2 4 >>> df_multi_level_cols1.stack().sort_index() weight cat kg 1 pounds 2 dog kg 2 pounds 4
Missing values
>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'), ... ('height', 'm')]) >>> df_multi_level_cols2 = ps.DataFrame([[1.0, 2.0], [3.0, 4.0]], ... index=['cat', 'dog'], ... columns=multicol2)
It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe.