Changeset 747
- Timestamp:
- 08/11/14 17:58:45 (22 months ago)
- Location:
- mystic
- Files:
-
- 2 edited
-
mystic/munge.py (modified) (2 diffs)
-
scripts/mystic_model_plotter.py (modified) (11 diffs)
Legend:
- Unmodified
- Added
- Removed
-
mystic/mystic/munge.py
r742 r747 30 30 elif isinstance(source, Null): 31 31 return [],[] #XXX: or source.x, source.y (e.g. Null(),Null())? or Error? 32 else: #XXX: what about taking a logfile instance?32 else: 33 33 raise IOError("a history filename or instance is required") 34 34 try: # read standard logfile (or monitor) … … 43 43 except: #KeyError 44 44 _step, params, cost = logfile_reader(source) 45 #FIXME: doesn't work for multi-id logfile; select id? 45 46 params, cost = raw_to_support(params, cost) 46 47 except: -
mystic/scripts/mystic_model_plotter.py
r746 r747 10 10 11 11 generate surface contour plots for model, specified by full import path 12 generate model trajectory from logfile , if provided12 generate model trajectory from logfile (or solver restart file), if provided 13 13 14 14 The option "bounds" takes an indicator string, where the bounds should … … 49 49 50 50 from mystic.munge import read_history 51 from mystic.munge import logfile_reader, raw_to_support 52 53 #XXX: better if reads single id only? (e.g. same interface as read_history) 54 def get_history(source, ids=None): 55 """get params and cost from the given source 56 57 source is the name of the trajectory logfile (or solver instance) 58 if provided, ids are the list of 'run ids' to select 59 """ 60 try: # if it's a logfile, it might be multi-id 61 step, param, cost = logfile_reader(source) 62 except: # it's not a logfile, so read and return 63 param, cost = read_history(source) 64 return [param],[cost] 65 66 # split (i,id) into iteration and id 67 multinode = len(step[0]) - 1 #XXX: what if step = []? 68 if multinode: id = [i[1] for i in step] 69 else: id = [0 for i in step] 70 71 params = [[] for i in range(max(id) + 1)] 72 costs = [[] for i in range(len(params))] 73 # populate params for each id with the corresponding (param,cost) 74 for i in range(len(id)): 75 if ids is None or id[i] in ids: # take only the selected 'id' 76 params[id[i]].append(param[i]) 77 costs[id[i]].append(cost[i]) 78 params = [r for r in params if len(r)] # only keep selected 'ids' 79 costs = [r for r in costs if len(r)] # only keep selected 'ids' 80 81 # convert to support format 82 for i in range(len(params)): 83 params[i], costs[i] = raw_to_support(params[i], costs[i]) 84 return params, costs 85 51 86 52 87 def get_instance(location, *args, **kwds): … … 138 173 139 174 140 def draw_projection( file, select=0, scale=True, shift=False, style=None, figure=None):175 def draw_projection(x, cost, scale=True, shift=False, style=None, figure=None): 141 176 """draw a solution trajectory (for overlay on a 1D plot) 142 177 143 file is monitor or logfile of solution trajectories 144 select is the parameter index (e.g. 0 -> param[0]) selected for plotting 178 x is the sequence of values for one parameter (i.e. a parameter trajectory) 179 cost is the sequence of costs (i.e. the solution trajectory) 145 180 if scale is provided, scale the intensity as 'z = log(4*z*scale+1)+2' 146 181 if shift is provided, shift the intensity as 'z = z+shift' (useful for -z's) … … 148 183 if figure is provided, plot to an existing figure 149 184 """ 150 # params are the parameter trajectories151 # cost is the solution trajectory152 params, cost = read_history(file)153 d = {'x':0, 'y':1, 'z':2} #XXX: remove the easter egg?154 if select in d: select = d[select]155 x = params[int(select)] # requires one parameter156 157 185 if not figure: figure = plt.figure() 158 186 ax = figure.gca() … … 175 203 176 204 177 def draw_trajectory( file, select=None, surface=False, scale=True, shift=False, style=None, figure=None):205 def draw_trajectory(x, y, cost=None, scale=True, shift=False, style=None, figure=None): 178 206 """draw a solution trajectory (for overlay on a contour plot) 179 207 180 file is monitor or logfile of solution trajectories 181 select is a len-2 list of parameter indicies (e.g. 0,1 -> param[0],param[1])182 if surface is True, plot the trajectories as a 3D projection 208 x is a sequence of values for one parameter (i.e. a parameter trajectory) 209 y is a sequence of values for one parameter (i.e. a parameter trajectory) 210 cost is the solution trajectory (i.e. costs); if provided, plot a 3D contour 183 211 if scale is provided, scale the intensity as 'z = log(4*z*scale+1)+2' 184 212 if shift is provided, shift the intensity as 'z = z+shift' (useful for -z's) … … 186 214 if figure is provided, plot to an existing figure 187 215 """ 188 # params are the parameter trajectories189 # cost is the solution trajectory190 params, cost = read_history(file)191 if select is None: select = (0,1)192 d = {'x':0, 'y':1, 'z':2} #XXX: remove the easter egg?193 for ind,val in enumerate(select):194 if val in d: select[ind] = d[val]195 params = [params[int(i)] for i in select[:2]]196 #XXX: take 'params,cost=None' instead of 'file,surface=False'?197 x,y = params # requires two parameters198 199 216 if not figure: figure = plt.figure() 200 217 201 if surface: kwds = {'projection':'3d'} # 3D 202 elif surface is None: # 1D 203 raise NotImplementedError('need to add an option string parser') 204 else: kwds = {} # 2D 218 if cost: kwds = {'projection':'3d'} # 3D 219 else: kwds = {} # 2D 205 220 ax = figure.gca(**kwds) 206 221 207 222 if style in [None, False]: 208 223 style = 'w-o' #if not scale else 'k-o' 209 if surface: # is 3D, cost is needed224 if cost: # is 3D, cost is needed 210 225 import numpy 211 226 if shift: … … 311 326 312 327 if __name__ == '__main__': 313 #FIXME: for a script, need to:314 # - enable 'skip' plotting points (points or line or both)?315 328 #FIXME: should be able to: 316 329 # - apply a constraint as a region of NaN -- apply when 'xx,yy=x[ij],y[ij]' 317 330 # - apply a penalty by shifting the surface (plot w/alpha?) -- as above 318 # - read logfile with multiple trajectories (i.e. parallel batch)319 331 # - build an appropriately-sized default grid (from logfile info) 332 # - move all mulit-id param/cost reading into read_history 320 333 #FIXME: current issues: 321 334 # - 1D slice and projection work for 2D function, but aren't "pretty" 322 # - 1D slice and projection for 1D function needs further testing...335 # - 1D slice and projection for 1D function, is it meaningful and correct? 323 336 # - should be able to plot from solver.genealogy (multi-monitor?) [1D,2D,3D?] 324 337 # - should be able to scale 'z-axis' instead of scaling 'z' itself 325 338 # (see https://github.com/matplotlib/matplotlib/issues/209) 339 # - if trajectory outside contour grid, will increase bounds 340 # (see support_hypercube.py for how to fix bounds) 326 341 327 342 #XXX: note that 'argparse' is new as of python2.7 … … 334 349 metavar="STR",default=",,", 335 350 help="string to assign label to axis") 336 #parser.add_option("-n","--nid",action="store",dest="id",\337 #metavar="INT",default=None,338 #help="id # of the nth simultaneous points to plot")339 # parser.add_option("-i","--iters",action="store",dest="iters",\340 # metavar="STR",default=":",341 # help="indicator string to select iterationsto plot")351 parser.add_option("-n","--nid",action="store",dest="id",\ 352 metavar="INT",default=None, 353 help="id # of the nth simultaneous points to plot") 354 parser.add_option("-i","--iter",action="store",dest="stop",\ 355 metavar="INT",default=None, 356 help="the largest iteration to plot") 342 357 parser.add_option("-r","--reduce",action="store",dest="reducer",\ 343 358 metavar="STR",default="None", … … 361 376 # get the import path for the model 362 377 model = parsed_args[0] # e.g. 'mystic.models.rosen' 378 if "None" == model: model = None #XXX: 'required'... allow this? 363 379 364 380 try: # get the name of the parameter log file … … 418 434 label = ['','',''] 419 435 420 #try: # select which 'id' to plot results for421 # id= (int(parsed_opts.id),) #XXX: allow selecting more than one id ?422 #except:423 # id = None # i.e. 'all' **or** use id=0, which should be 'best' energy ? 424 425 # try: # select which iterations to plot426 # iters = parsed_opts.iters.split(',') # format is ":2, 2:4, 5, 6:" 427 #except:428 # iters = [':'] 436 try: # select which 'id' to plot results for 437 ids = (int(parsed_opts.id),) #XXX: allow selecting more than one id ? 438 except: 439 ids = None # i.e. 'all' 440 441 try: # select which iteration to stop plotting at 442 stop = int(parsed_opts.stop) 443 except: 444 stop = None 429 445 430 446 ################################################# … … 480 496 model = masked(mask)(model) 481 497 482 # project trajectory on a 1D slice of the model surface #XXX: useful?483 # fig0 = draw_slice(model, x=x, y=sol[-1], scale=scale, shift=shift) 484 # draw_projection(source, select=0, style=style, scale=scale, shift=shift, figure=fig0) 485 486 # plot the trajectory on the model surface (2D or 3D)487 if model: # plot the surface498 ## plot the surface in 1D 499 #if solver: v=sol[-1] 500 #elif source: v=cost[-1] 501 #else: v=None 502 #fig0 = draw_slice(model, x=x, y=v, scale=scale, shift=shift) 503 # plot the surface in 2D or 3D 488 504 fig = draw_contour(model, x, y, surface=surface, fill=fill, scale=scale, shift=shift) 489 505 else: 506 #fig0 = None 490 507 fig = None 491 if source: # plot the trajectory 492 fig = draw_trajectory(source, select=select, surface=surface, style=style, scale=scale, shift=shift, figure=fig) 508 509 if source: 510 # params are the parameter trajectories 511 # cost is the solution trajectory 512 params, cost = get_history(source, ids) 513 if len(cost) > 1: style = style[1:] # 'auto-color' #XXX: or grayscale? 514 515 for p,c in zip(params, cost): 516 ## project trajectory on a 1D slice of model surface #XXX: useful? 517 #s = select[0] if len(select) else 0 518 #px = p[int(s)] # draw_projection requires one parameter 519 ## ignore everything after 'stop' 520 #_c = c[:stop] 521 #_x = px[:stop] 522 #fig0 = draw_projection(_x,_c, style=style, scale=scale, shift=shift, figure=fig0) 523 524 # plot the trajectory on the model surface (2D or 3D) 525 # get two selected params #XXX: what if len(select)<2? or len(p)<2? 526 p = [p[int(i)] for i in select[:2]] 527 px,py = p # draw_trajectory requires two parameters 528 # ignore everything after 'stop' 529 _x = px[:stop] 530 _y = py[:stop] 531 _c = c[:stop] if surface else None 532 fig = draw_trajectory(_x,_y,_c, style=style, scale=scale, shift=shift, figure=fig) 493 533 494 534 # add labels to the axes
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Hong Kong’s candyman turns sweets into an art form
HONG KONG, Nov 1 — Using a pair of scissors, a blow pipe and small metal tools, Hong Kong shopkeeper Louis To whittles a chunk of molten sugar into a dragon, a talent he began honing during China’s Cultural Revolution.
To’s shop on Cheung Chau, a small island in the waters west of Hong Kong’s famous Victoria Harbour, has become a must-see for visiting tourists.
There they can watch a man locals dub “The Candyman” sculpt sugar into a host of animals and fantastical creatures.
The technique is a centuries-old folk art form in mainland China but there are few people in Hong Kong with the knowledge or skill set.
The origins of To’s art go back to the deprivation of the Cultural Revolution when he learned to make his own toys.
“During this period, there was no food, there was nothing,” recalled To, who was born in Chaozhou, southern China. “So when I saw other children with toys, I tried to make replicas for myself.”
He remembers being transfixed by the local sugar sculptor who would come to his neighbourhood on a bike and using little more than a kerosene lamp and a few chunks of coal would magic up candied works of art.
“I had no money to buy candies, but I watched the craftsmen make it,” he told AFP. “I remember very well how he proceeded, in every detail.”
To’s family moved to Hong Kong in the 1980s when China began to open up after Mao Zedong’s death.
He wasn’t a good student and dropped out of school to pursue a career as an artist.
But it was only when he moved to Cheung Chau island with his own family in the early 2000s that he switched to sculpting sugar.
While Hong Kong is best known for its dizzyingly dense collection of skyscrapers, many of the territory’s outlying islands have a distinctly laid-back, rural vibe to them.
Cheung Chau has few buildings higher than four stories and life is more traditional, built around the local fishing community.
“In Cheung Chau, I saw many things that reminded me of my childhood — ﬂying kites, lion dances... things that you don’t see in the city, only here,” To said. “And I remembered that I wanted to craft candies.”
With no recipes to guide him, To started experimenting himself, melting all kinds of sugars.
“For six months, I tried at least a dozen of them,” he explained.
Eventually he struck upon the right technique to get the sugar pliable enough to sculpt.
“I had blisters on my hands. I almost gave up,” he recalled.
Now he is able to knead sugar dough at 80C with his bare hands. — AFP


- Global adv_seg_print (uint8_t *ptr, uint32_t len)
- Display flag information based on the following bits
0 LE Limited Discoverable Mode
1 LE General Discoverable Mode
2 BR/EDR Not Supported
3 Simultaneous LE and BR/EDR to Same Device is Capable (Controller)
4 Simultaneous LE and BR/EDR to Same Device is Capable (Host)
5..7 Reserved
- File clock-nrf.c
- In all functions make sure the parameters passed are valid with proper error codes
- Group Definitions for marking the beginning and ending of code to profile
- Make the PROFILE_START and PROFILE_STOP self configuring based on prescalar used
- Group Definitions for marking the beginning and ending of code to profile
- Make the PROFILE_START and PROFILE_STOP self configuring based on prescalar used
- Global packets [LOGGER_NUMBER][MAX_PDU_SIZE+8]
- Use dynamic memory with check if still within heap
- Global PRINT_TIME
- Make the PRINT_TIME self configuring based on prescalar used
- Global read_time_us (void)
- Take care of overflow of time
- Warning
- The time overflows after 283 seconds
- Global start_ms_timer (timer_num id, timer_mode mode, uint32_t ticks, void(*handler)(void))
- Take care of values of ticks passed which are greater than 2^24, now it is suppressed to 2^24 when greater
- Warning
- Works only for input less than 512000 milli-seconds, or 8.5 min without when RTC_PRESCALER is 0
- Global uart_putchar (uint8_t cr)
- Add interrupt and WFE to save power


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A channel to discuss trading. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
ComputerGenie 👴👴👴03-Apr-19 11:17 PM
if the sole saving grace of currencyX were that currencyY had value against currencyA, then currencyX actually has a 0 value
p.03-Apr-19 11:17 PM
guys he might be satoshi
1
ComputerGenie 👴👴👴03-Apr-19 11:18 PM
or he might just hate that something so useless runs his financial life
PHBA206103-Apr-19 11:18 PM
I won't believe anyone is Satoshi unless he send me 10000 btc
1
p.03-Apr-19 11:19 PM
Seems useless when it makes people rich for life
no reason to exist
ComputerGenie 👴👴👴03-Apr-19 11:19 PM
if I had 10k btc I would have cashed out at $20k and you'd not know who tf I am (edited)
richardt03-Apr-19 11:19 PM
lol. I'm a huge supporter of KMD, don't get me wrong. Just don't get what's with all the hate for BTC around here
Caglio03-Apr-19 11:19 PM
@ComputerGenie 👴👴👴 you wouldnt change the sex right ?
richardt03-Apr-19 11:20 PM
Without BTC, there wouldn't even be a KMD....
p.03-Apr-19 11:20 PM
i dont see people swapping KMD for large amounts of FIAT in swap groups, I do see BTC though. maybe one day there bud
Caglio03-Apr-19 11:20 PM
I would like to change all my KMD for fiat
Ill do it if KMD hits 50$
You have my word on it
p.03-Apr-19 11:21 PM
you wont keep it for the tech???\
Caglio03-Apr-19 11:21 PM
Not really
I will buy them back if they hit 30$ tho
ComputerGenie 👴👴👴03-Apr-19 11:21 PM
Just don't get what's with all the hate for BTC around here
for me it's on a technical level, but what I initially said has no animus (just a simple stating that for crypto to work then there needs to not being an absolute cross-pegging of value to any one crypto)
Caglio03-Apr-19 11:21 PM
Or 20$
ComputerGenie 👴👴👴03-Apr-19 11:22 PM
Without BTC, there wouldn't even be a KMD....
And without 15 other cryptos before it, there would be no BTC
Bitcoin solved 1 problem, long ago, it's solved now move on(edited)
p.03-Apr-19 11:23 PM
move on
good timing
richardt03-Apr-19 11:23 PM
Ok. Can you tell the rest of the market to also move on so our KMD's can be worth much more?
p.03-Apr-19 11:23 PM
must be rich beyond measure
richardt03-Apr-19 11:23 PM
Caglio03-Apr-19 11:23 PM
move on - sounds similiar as moon on
when lambo sir ?
ComputerGenie 👴👴👴03-Apr-19 11:24 PM
Can you tell the rest of the market to also move on so our KMD's can be worth much more?
I try one by one to get people to that point
1
richardt03-Apr-19 11:24 PM
and make the world a better place?
Alright03-Apr-19 11:24 PM
do believe we will see BTC's market share continue to drop as long as core is at the helm
ComputerGenie 👴👴👴03-Apr-19 11:25 PM
well that's forever then
p.03-Apr-19 11:25 PM
bunch of maximalists in here
Caglio03-Apr-19 11:25 PM
Hi my favorite muppet
p.03-Apr-19 11:25 PM
lots of trade talk
not pleb talk
Caglio03-Apr-19 11:25 PM
p. I like you
We should go to Thailand together
To Phuket
p.03-Apr-19 11:25 PM
Caglio03-Apr-19 11:26 PM
Spend cash on cocaine and first class ladies of the night
ComputerGenie 👴👴👴03-Apr-19 11:26 PM
richardt03-Apr-19 11:26 PM
Well in that case, if we're looking for a de-coupling then may not happen in our lifetime
Caglio03-Apr-19 11:26 PM
I like their sex tourism
When KMD hits 50$ im going to thailand
richardt03-Apr-19 11:27 PM
Till then, I'm also rooting for BTC to go up.
Alright03-Apr-19 11:33 PM
s2x is the real bitcoin.
ComputerGenie 👴👴👴03-Apr-19 11:33 PM
and csw is the real satoshi
P.S. I read your proposal and you type pretty good for a 3yr old
Alright03-Apr-19 11:35 PM
mommy help me
ComputerGenie 👴👴👴03-Apr-19 11:35 PM
lol
Deleted User03-Apr-19 11:35 PM
so cute
always plenty of love here.(edited)
1
richardt03-Apr-19 11:39 PM
I've always told people who are skeptics about crypto that in the end, it will not be our generation that will mass adopt crypto, it will likely be the next generations. The reality is crypto is aiming to replace institutions that have been in place for generations. I think it's wrong to compare it to the internet or email.
Alright04-Apr-19 12:00 AM
BCH is the closest thing to the "original bitcoin" tech wise
Gordon Komodo04-Apr-19 12:01 AM
it has increased my fiat value (edited)
ComputerGenie 👴👴👴04-Apr-19 12:01 AM
Hollowman900004-Apr-19 12:02 AM
tf is going on here
Talk of ladyboys in the night paid with btcCore
2
ComputerGenie 👴👴👴04-Apr-19 12:03 AM
when core collects all their pay in BTC and not USD, then I might reverse my position on the value of BTC
Hollowman900004-Apr-19 12:04 AM
I just need to know what currency is most advantageous to pay my coke dealer with
Alright04-Apr-19 12:04 AM
xmr ofc
Hollowman900004-Apr-19 12:05 AM
Googolchain is of no interest to me
I don’t have a black hole SSD
ComputerGenie 👴👴👴04-Apr-19 12:05 AM
for coke dealers, you use DGB; everyone knows this


|
Elite Insights JSON
<IP_ADDRESS>
Start your reading with the JsonLog class. The documentation is in Camel Case, as dictated by C# conventions, the generated json will be in Pascal Case
|
Class corresponding to the regrouping of the same type of minions More...
Public Attributes | |
| string | Name |
| Name of the minion More... | |
| List< int > | TotalDamage |
| Total Damage done by minions Length == # of phases More... | |
| List< int > [] | TotalTargetDamage |
| Damage done by minions against targets Length == # of targets and the length of each sub array is equal to # of phases More... | |
| List< int > | TotalShieldDamage |
| Total Shield Damage done by minions Length == # of phases More... | |
| List< int > [] | TotalTargetShieldDamage |
| Shield Damage done by minions against targets Length == # of targets and the length of each sub array is equal to # of phases More... | |
| List< JsonDamageDist > [] | TotalDamageDist |
| Total Damage distribution array Length == # of phases More... | |
| List< JsonDamageDist > [][] | TargetDamageDist |
| Per Target Damage distribution array Length == # of targets and the length of each sub array is equal to # of phases More... | |
| List< JsonRotation > | Rotation |
| Rotation data More... | |
Class corresponding to the regrouping of the same type of minions
Definition at line 8 of file JsonMinions.cs.
| string LuckParser.Builders.JsonModels.JsonMinions.Name |
Name of the minion
Definition at line 13 of file JsonMinions.cs.
| List<JsonRotation> LuckParser.Builders.JsonModels.JsonMinions.Rotation |
| List<JsonDamageDist> [][] LuckParser.Builders.JsonModels.JsonMinions.TargetDamageDist |
Per Target Damage distribution array
Length == # of targets and the length of each sub array is equal to # of phases
Definition at line 45 of file JsonMinions.cs.
| List<int> LuckParser.Builders.JsonModels.JsonMinions.TotalDamage |
Total Damage done by minions
Length == # of phases
Definition at line 18 of file JsonMinions.cs.
| List<JsonDamageDist> [] LuckParser.Builders.JsonModels.JsonMinions.TotalDamageDist |
Total Damage distribution array
Length == # of phases
Definition at line 39 of file JsonMinions.cs.
| List<int> LuckParser.Builders.JsonModels.JsonMinions.TotalShieldDamage |
Total Shield Damage done by minions
Length == # of phases
Definition at line 28 of file JsonMinions.cs.
| List<int> [] LuckParser.Builders.JsonModels.JsonMinions.TotalTargetDamage |
Damage done by minions against targets
Length == # of targets and the length of each sub array is equal to # of phases
Definition at line 23 of file JsonMinions.cs.
| List<int> [] LuckParser.Builders.JsonModels.JsonMinions.TotalTargetShieldDamage |
Shield Damage done by minions against targets
Length == # of targets and the length of each sub array is equal to # of phases
Definition at line 33 of file JsonMinions.cs.


{% extends "medicalforms/form_base.html" %} {% block title %} Authorization and Direction {% endblock title %} {% block form_content %}
I, {{form.authorize_name}}, AUTHORIZE AND DIRECT you as follows:
1. Full and Final Settlement:
In the event of Full and Final Settlement, I authorize and direct you to remit payment directly to Bloor West Rehabilitation Centre, in satisfaction of its accounts first, prior to remitting payment for the reminder of settlement funds to either myself or my legal representative.
2. Release of Information:
I authorize Bloor West Rehabilitation Centre to furnish to my Insurance Company, Physician, Case Worker, Adjuster, Employer or Lawyer all information in your possession directly relating to my medical condition and treatment received at the above clinic.
3. Direct Billing:
I authorize and direct you to accept direct billing from Bloor West Rehabilitation Centre, for any and all documentation pertaining to benefits paid.
4. Negotiation and Meditation:
I authorize Bloor West Rehabilitation Centre and its agents to negotiate, mediate and litigate on my behalf and to receive payment for treatment and services provided by Bloor West Rehabilitation Centre.
5. Denied and Incurred Treatment:
In the event of Denied and Incurred Treatment, I authorize and direct you to remit payment directly to Bloor West Rehabilitation Centre, in satisfaction of its outstanding accounts for any services rendered.
All prior Direction and Authorizations, Instructions and Powers of Attorneys are hereby revoked.
This shall be your full, complete, sufficient and irrevocable authorization for so doing.


To meet all corporate employees need, Lazeez-E-Niyaz is one Show stopper. We offer the dishes in such a way
that, whenever you think about hosting an event, Lazeez-E-Niyaz is the one on which you want to bank on.
What is Corporate Catering?
Our corporate packages not only tickles the taste buds but also makes worth to remember it for a long
time. We set our benchmarks periodically and try to break it on regular periods which is in ultimate
pursuit of bringing the quality, taste and quantity that would make corporate people exuberant on daily
basis.
Crystal Menu
This is very basic package offered by Lazeez-E- Niyaz, which includes limited number of Dishes with
reasonable price. But the quality standard will be same as set by Lazeez-E- Niyaz.
Silver Menu
This package includes all the dishes offered in Crystal package but few extra delicious dishes will
included as well to make your appetite worthier.
Gold Menu
As the name suggests, The Gold package will offer you the tastiest and more delicious dishes which
on tasting, you will feel the Love with which our highly experienced chefs put in to prepare the
dishes.


Enriching your career through your personal interests
After reading my colleague @richardfayers post about taking time out of your day to reflect. I realised I’d been doing something similar; taking my hat off as a technologist and learning about other topics.
I’ve been reading lots of books about urban planning and architecture, such as ‘Cities for People’ by Jan Gehl and ‘Happy City’ by Charles Montgomery. I started reading these books because I was interested in the topic, but what I’ve found is that I can’t help but widen the view I take in my work as a technologist.
- I’ve learnt from the early modernist movement, that you can’t design and plan for every eventuality and it’s best to empower people to shape their own surroundings.
- I’ve learnt to appreciate how ‘cheap’ prototyping can be with technology, if mistakes are made we only lose time and don’t leave a scar on the physical landscape.
- I’ve reinforced my view to not blindly follow trends, but to use the correct tools to understand the root cause of the problem and implement the right solution.
- Similarly I’ve learnt that “It’s just the way it is” isn’t a valid answer and as humans, our physical and digital surroundings shape us as people and encourage our behaviours. Why not encourage good and productive behaviours over negative and wasteful ones?
Using empathy as a tool in technology isn’t new. It has been a crucial part of the user experience movement which has vastly improved the systems we interact with over the last 5–10 years, pushing technology further into our lives. Now I believe it’s time for us to empathise with other disciplines and professions, to help us become less technology-biased.
Technologists are often (rightly) accused of thinking that technology can solve every problem. The Silicon Valley arrogance of saving the world with ‘Smart Cities’ based on the assumption they’ll transform cities when in reality people and cultures have the ultimate impact.
A successful urban planning strategy which has been implemented in Holland and Denmark, chooses to understand the needs of the person and and not the technology. Jan Gehl created methods for urban planners to measure people and not just density of vehicle traffic, resulting in pedestrianisation and cycle infrastructure opposed to roads.
Blindly adopting technology for the sake of it can have long term negative impacts and sometimes even kill your business. Instead, it is better to take a step back, make sure you understand the root cause of the problem and are measuring the right things in order to implement the right solution.
So what can you do?
Spend time learning from other industries, how they are solving problems, their perception of technology and hopefully you’ll find common ground because we’re trying to solve human problems after all. Attend talks, read books and integrate with communities.
In doing the above, you’ll not only learn to approach problems in a different way but also as barriers between technologists and other professions are broken down, new and interesting opportunities will appear.
You’ll need to choose something you are interested in, experimenting and “dipping your toe in” can help you find this. I’ll leave you with a few ideas:
- Artists are experts at interpreting the world around us, see the ignored or unknown and replaying it in a way that people can understand and enjoy.
- Writers and teachers can help us to tell stories and share our skills with others.
- Linguists can help us understand how the language we use shapes the way we think.
- Actors can help us to empathise in order to portray the problems of those we are working for and Anthropologists can help us to measure and quantify this in a scientific way.
As for me, I’ll continue to read about the interesting world of urban planning and architecture. If you feel the same about another topic, I’d love to hear your insights.
Originally posted on Medium


QInfer: Bayesian inference for quantum information
Cassandra E. Granade
Centre for Engineered Quantum Systems
$
\newcommand{\ee}{\mathrm{e}}
\newcommand{\ii}{\mathrm{i}}
\newcommand{\dd}{\mathrm{d}}
\newcommand{\id}{𝟙}
\newcommand{\TT}{\mathrm{T}}
\newcommand{\defeq}{\mathrel{:=}}
\newcommand{\Tr}{\operatorname{Tr}}
\newcommand{\Var}{\operatorname{Var}}
\newcommand{\Cov}{\operatorname{Cov}}
\newcommand{\rank}{\operatorname{rank}}
\newcommand{\expect}{\mathbb{E}}
\newcommand{\sket}[1]{|#1\rangle\negthinspace\rangle}
\newcommand{\sbraket}[1]{\langle\negthinspace\langle#1\rangle\negthinspace\rangle}
\newcommand{\Gini}{\operatorname{Ginibre}}
\newcommand{\supp}{\operatorname{supp}}
\newcommand{\ket}[1]{\left|#1\right\rangle}
\newcommand{\bra}[1]{\left\langle#1\right|}
\newcommand{\braket}[1]{\left\langle#1\right\rangle}
$
joint work with Christopher Ferrie
contributions from
Steven Casagrande, Ian Hincks, Jonathan Gross, Michal Kononenko, Thomas Alexander, and Yuval Sanders
Characterization plays a number of different roles in
quantum information experiments.
- Calibration
- (Rabi/Ramsey/phase est., crosstalk learning)
- Diagnosis and Debugging
- (Tomography)
- Verification and Validation
- (RB, quantum Hamiltonian learning)
All of these are examples of parameter estimation.
## Parameter Estimation ##
Given data $D$, and a model $\vec{x}$,
what should we estimate $\vec{x}$ as?
- Rabi/Ramsey: $\vec{x} = (\omega)$
- Crosstalk/Hamiltonian learning: $\vec{x} = \operatorname{vec}(H)$
- Tomography: $\vec{x} = \operatorname{vec}(\rho)$
---
From an experimental perspective, parameter estimation
isn't the point, but a tool to get things done.
### **Example**: Ramsey Estimation ###
Suppose $H = \omega \sigma_z / 2$ for some unknown $\omega$.
To learn $\omega$:
- Prepare $\ket{+} \propto \ket{0} + \ket{1}$, measure “click” w/ pr.:
$
\|\bra{+} \ee^{\ii \omega t \sigma_z / 2} \ket{+}\|^2 = \cos^2(\omega t / 2)
$.
- Repeat for many “shots” to estimate click pr.
- Repeat for many times to estimate signal.
You'll get something that looks a bit like this:
What's $\omega$? Fourier transform and look at the peak.
We can do better.
Our goal is to make useful tools for parameter estimation
that work in practice, in a statistically-principled
fashion.
Make it easier to get experiments done:
- Reduce data collection costs
- Provide accurate estimates
- Support reproducible research practices.
...but first, an aside on scientific software in quantum information.
Our field is very well publicly supported.
Thus, our science should support the public.
- **Reproducible**: open data, source
- **Accessible**: e.g. green/gold
Perhaps most of all, we must make sure that our
results are **reusable**.
---
Our work should enable further research,
not prevent it.
This flies in the face of how physicists are taught to program
and present numerical results.
*(e.g. dependent on closed software, no data, source or documentation provided)*
Let's do **reproducible**, **accessible** and **reusable**
research instead.
Our theoretical basis will be
Bayesian Inference with Particle Filtering.
Represent our beliefs about the model by a set of
hypotheses $\{\vec{x}_i\}$, along with their weights
$\{w_i\}$.
\begin{align*}
\text{Estimate: } \hat{x} & = \sum_i w_i \vec{x}_i \\
\text{Update: } w_i' & \propto w_i \times \Pr(D | \vec{x}_i)
\end{align*}
Numerical stability is provided by resampling:
- Contract hypotheses towards center of mass
- Convolve with Gaussian
Preserves estimates and errors of hypotheses $\{\vec{x}\}$,
while restoring stability of the approximation.
- Statistically principled: approximates Bayesian
posteriors, makes dependence on prior *explicit*.
- Very general *and extensible* approach.
- Provides rich error reporting, model selection and other
diagnostics.
>>> import qinfer
Implements particle filtering, with support for common
quantum information models:
- Hamiltonian learning / phase estimation
- Randomized benchmarking
- State and process tomography
### Scientific Software is for People, not Computers ###
Thus, **QInfer** is:
- **Accessible**: written in Python,
usable from Python, Julia, MATLAB.
- **Open source**: modifiable and reproducible.
- **Portable**: Windows/Linux/OS X.
- **Legible**: well-documented (guide & examples).
- **Expressive**: represents the scientific concepts.
### Installing **QInfer** ###
```bash
$ pip install qinfer
```
Works on Python 2.7, 3.3, 3.4, and 3.5 with the Anaconda
Distribution.
---
For Julia, need one additional step:
```julia
julia> Pkg.add("PyCall")
```
### Using **QInfer**: Simple Estimation ###
Make the data...
```python
import numpy as np
true_omega = 70.3
n_shots = 100
ts = np.pi * np.arange(1, 101) / (2 * 100.0);
signal = np.sin(true_omega * ts / 2) ** 2;
counts = np.random.binomial(n=n_shots, p=ideal_signal)
```
...then process it.
```python
import qinfer as qi
data = np.column_stack([counts, ts, n_shots * np.ones(len(ts))])
est_mean, est_cov = qi.simple_est_prec(data, freq_min=0, freq_max=100)
```
### Using **QInfer** from MATLAB 2016a ###
Same idea: make the data...
```matlab
true_omega = 70.3;
n_shots = 400;
ts = pi * (1:1:100) / (2 * 100);
signal = sin(true_omega * ts / 2) .^ 2;
counts = binornd(n_shots, signal);
```
... then process it.
```matlab
setenv MKL_NUM_THREADS 1
data = py.numpy.column_stack({counts ts ...
n_shots * ones(1, size(ts, 2))});
est = py.qinfer.simple_est_prec(data, ...
pyargs('freq_min', 0, 'freq_max', 100));
```
### Using **QInfer** from Julia ###
Same for Julia: make the data...
```julia
true_omega = 70.3
n_shots = 100
ts = pi * (1:1:100) / (2 * 100)
signal = sin(true_omega * ts / 2) .^ 2
counts = map(p -> rand(Binomial(n_shots, p)), signal);
```
...then process it.
```julia
@pyimport numpy as np
@pyimport qinfer as qi
data = [counts'; ts'; n_shots * ones(length(ts))']'
est_mean, est_cov = qi.simple_est_prec(data, freq_min=0, freq_max=100)
```
### Breaking it Down ###
**QInfer** is built up of several main components:
- ``Model``: Specifies a model for what parameters describe
an experiment.
- ``Distribution``: Specifies what is known about those
parameters at the start.
- ``SMCUpdater``: Uses sequential Monte Carlo to update
knowledge based on data.
- ``Heuristic``: Selects new experiments to perform.
### **QInfer** Concepts: Models ###
Parameter esimation problems are specified as **models**,
defining parameters of interest, what data looks like, etc.
```python
>>> SimplePrecessionModel()
>>> BinomialModel(RandomizedBenchmarkingModel())
>>> BinomialModel(TomographyModel(basis))
```
### **QInfer** Concepts: Models ###
Models expose two very important functionalities:
- ``simulate_experiment``: Simulates data $d$
from an experiment $e$, according
to a set of model parameters $\vec{x}$.
- ``likelihood``: Returns the probability $\Pr(d | \vec{x}; e)$
of observing $d$ in an experiment $e$
given model parameters $\vec{x}$.
The ``likelihood`` function returns a *tensor*
$$
L_{ijk} = \Pr(d_i | \vec{x}_j; e_k).
$$
```python
L = SimplePrecessionModel().likelihood(
1, # outcomes
np.array([w]), # models
ts # experiments
)
```
Enables:
- Vectorization over data, models and experiments
- Parallelization
- Caching of intermediate results
# Index along data and models to get a vector over experiments.
plt.plot(ts, L[0, 0, :])
### **QInfer** Concepts: Distributions ###
A `Distribution` is an object that produces *samples*.
**QInfer** comes with several built-in...
```python
>>> UniformDistribution([0, omega_max])
>>> plt.hist(NormalDistribution(0, 2).sample(n=100000), bins=20)
```
![](figures/normal-dist.png)
### **QInfer** Concepts: Distributions ###
...including exotic distributions such as redit priors.
```python
>>> plot_rebit_prior(
... GinibreReditDistribution(pauli_basis(1)),
... rebit_axes=[1, 3]
... )
```
### **QInfer** Concepts: Distributions ###
Distributions can also be combined in different ways:
```python
>>> ProductDistribution(
... NormalDistribution([0.9, 0.1 ** 2]),
... UniformDistribution([0, 1]),
... ConstantDistribution(0)
... )
```
### Using **QInfer**: Updating ###
Typically, once you have a model and a prior, learning
parameters then proceeds by looping over data:
```python
>>> updater = SMCUpdater(model, n_particles, prior)
>>> for idx in range(n_measurements):
... experiment = # select the next experiment
... datum = # make a measurement
... updater.update(datum, experiment)
>>> est = updater.est_mean()
```
### Using **QInfer**: Updating ###
The updated distribution provides estimates,
error bars, and plots.
#### **Example**: Randomized Benchmarking ####
```python
>>> mean, cov, extra = qi.simple_est_rb(
... # Use return_all=True to get the updater.
... data, p_min=0.8, return_all=True
... )
>>> print(mean[0], "±", np.sqrt(cov)[0, 0])
0.991188359708 ± 0.0012933975599
>>> extra['updater'].plot_posterior_marginal(idx_param=0)
>>> extra['updater'].plot_covariance(corr=True)
```
### **QInfer** Concepts: Heuristics ###
*Heuristics* can be used to design measurements.
For example, $t_k = ab^k$ is optimal for non-adaptive
Rabi/Ramsey/phase estimation.
```
>>> heuristic = ExpSparseHeuristic(scale=a, base=b)
```
**QInfer** implements heuristics as functions which provide
new experiments.
### Updater Loops Revisited ###
For instance, using a heuristic ``heuristic_class`` and
simulating data, we can flesh out the updater loop.
```python
>>> updater = SMCUpdater(model, n_particles, prior)
>>> heuristic = heuristic_class(updater)
>>> for idx in range(n_measurements):
... experiment = heuristic()
... datum = model.simulate_experiment(true_model, experiment)
... updater.update(datum, experiment)
>>> est = updater.est_mean()
```
### Tomography Updater Loop ###
```python
from qinfer.tomography import *
basis = pauli_basis(1)
prior = GinibreReditDistribution(basis)
model = BinomialModel(TomographyModel(basis))
updater = SMCUpdater(model, 2000, prior)
heuristic = RandomPauliHeuristic(updater,
other_fields={'n_meas': 40}
)
for idx_exp in xrange(50):
experiment = heuristic()
datum = model.simulate_experiment(true_state, experiment)
updater.update(datum, experiment)
```
## **QInfer** 💖 **QuTiP** ##
Tomography support in **QInfer** is backed by
the quantum object representation and prior
distributions
provided by **QuTiP**
3.2.
```python
>>> modelparams = basis.state_to_modelparams(rho)
>>> rho = basis.modelparams_to_state(rho)
>>> cov_superop = basis.covariance_mtx_to_superop(
... updater.est_covariance_mtx()
... )
```
Makes it easy to integrate with other QIP concepts: fidelity, Schatten and diamond norms, etc.
### **QInfer** Concepts: Performance Testing ###
In both of these examples, we assumed that the true model
was known. This lets us quickly assess how well **QInfer**
works for a given model.
```python
>>> performance = perf_test_multiple(
... n_trials=400,
... model=BinomialModel(SimplePrecessionModel()),
... n_particles=2000,
... prior=UniformDistribution([0, 1]),
... n_exp=200,
... # partial lets us quickly make new heuristic classes.
... heuristic_class=partial(
... ExpSparseHeuristic, other_fields={'n_meas': 40}
... )
... )
```
### Simple Parallelization ###
First, we connect to our engines...
```python
from ipyparallel import *
c = Client()
load_balanced_view = c.load_balanced_view()
```
...then we can easily delegate trials.
```python
basis = pauli_basis(1)
performance = perf_test_multiple(
n_trials=400,
model=BinomialModel(TomographyModel(basis)),
n_particles=2000,
prior=GinibreDistribution(basis),
n_exp=100,
heuristic_class=partial(
RandomPauliHeuristic, other_fields={'n_meas': 40}
),
apply=load_balanced_view.apply, # ← parallel!
progressbar=IPythonProgressBar # ← Jupyter!
)
```
### Diffusive Models ###
**QInfer** also supports time-dependent parameter estimation
by adding an update rule to hypothesis positions as well as
weights:
$$
\vec{x}(t_{k + 1}) - \vec{x}(t_k) \sim \mathcal{N}(0, \sigma^2).
$$
Diffusive estimation can still work even if the underlying trajectory
is deterministic.
For example, suppose a coin bias evolves as $\Pr(\text{heads}) = p = \frac12 (\cos^2(\omega_1 t / 2) + \cos^2(\omega_2 t) / 2))$.
How can we use QInfer to do reproducible
research?
Practical adaptive quantum tomography
1605.05039
• joint work with Chris Ferrie and Steve Flammia
Self-guided tomography (Ferrie 10/bchr) as a heuristic
for adaptive tomography.
- Based on QInfer and QuTiP.
- All figures generated from a single Jupyter Notebook.
- Source and data available on Figshare 10/bhfk.
Our hope is that **QInfer** will thus be a useful tool for theory and experiment alike.
---
- Source: https://github.com/QInfer/python-qinfer
- Docs: http://docs.qinfer.org
- Try it out online: https://goo.gl/zWt9Du
*Version 1.0 coming soon.*


We are a dedicated food restaurant and company that is focused on providing and creating scrumptious dishes that ranges from our very own pizzas to other cuisines provided in the menu for our customers.
Our foods are verified and tested by health practitioners to ensure that we provide the best for our dearly customers


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Not connected to socket.io live updates. Please check your configuration and consult the documentation.
To get the current status, please refresh this page manually.


{% extends "base.html" %} {% block title%}Light control demo{% endblock%} {% block extra_js %} {% load socketio_tags %} {% socketio %} {% endblock %} {% block main %}
By changing the orientation of the phone you can change the colour of an RGB light. This will work on the X/Y axis from a desktop using a mouse but you can't control z which will be static.
X:
Y:
Z:


These are the algorithms that are used in Computer Vision-Based Door Detection for Accessibility of Unfamiliar Environments to Blind Persons [Ann 1].
Ideally, the model consists of four corners (90 degrees each) and four lines (the doorframe). However, since the door may be occluded or the door may not be exactly in front of the camera, the model used in this paper just includes four corners and four lines.
Now, the assumptions:
This model along with the presented assumptions for the algorithm allows the door detection to be robust to variations in color, texture, occlusion, and whether the door is opened or closed.
Gaussian Smoothing, also known as Gaussian blur, is used to reduce image noise and it eliminates the unnecessary corners for door detection.
[Img 4] A discrete version of a filter you can apply to every pixel to enact Gaussian Smoothing.
Developed in 1986, the Canny Edge Detector is used to obtain the edges in the image and is known as the optimal detector by many. The algorithm consists of the following steps [Ann 8]:
*More information can be found on the OpenCV implementation of the Canny Edge Detector can be found here.
The corner extraction method was developed from [Ann 10] by calculating global and local curvature properties from the edge map produced by Step 2. Endpoints of contours also count as corners, which gives us corners even if they are occluded.
* See Algorithms Part II for the rest of the algorithms, here.


As an intern at FullStack Labs, I was able to work with Wyndow to design the Grosvenor Concierge mobile app. Hilton had commissioned Wyndow, a company who provides an API to let hotels display nearby events in real-time. The Hilton Edinburgh Grosvenor wanted to use this API to build an app specifically for their hotel so that guests could use it to not just see nearby events, but primarily get deals for the hotels restaurants and hopefully boost sales.
How could we design an app to help Hilton boost sales in food and beverage, and how could we design efficiently and effectively to ensure minimum work for developers and thus a smaller budget for the client?
Fortunately, Wyndow already had an existing app, so it was decided that the design would be as identical as possible, changing only what was needed, such as color and removing certain features so that it fit Hilton's brand standards. After all, this was a Hilton App. This would ensure as little design and development time as possible, but still allow us to build an app that would meet Hilton's needs
Since earlier research phases such as user stories had already been established by the existing app, we were able to jump right into wireframing. A style guide also had to be created; since this was an app to be officially used by Hilton, it had to match Hilton's branding guidelines. Components and icons were based on Google Material Design, again to save time and money for the client.
The colors that we decided to use came straight from Hilton's own style guide, which they provided for us. While Hilton did have their own typography, we went ahead and used native fonts for the app. Once wireframes and a style guide were approved we were able to move on to hi-fidelity mockups and prototyping.
Of course, as designers, we want to put forth our best work and create beautifully designed apps. Yet sometimes the needs of clients just don't allow us to do that. With the case of this app, while it may be a bummer that the client didn't have the budget to create something much more visually stunning, what was important was to swallow pride and design what the client wanted and at the same time meet the needs of users. In the case of this project, that was a win.


//= template/head.html
Thank you for your request
One of us will get back in touch with you soon!
//= template/footer.html


| Term | (p.o.s.) | Definition | Synonyms/Antonyms | Sentences |
| pedestrian | (adj.) | lacking inspiration or excitement |
Syn: dull, boring, uninteresting
Ant: opulent, lavish, extraordinary |
The pedestrian television program was not worth watching
because it was boring and uneventful.
That chess move was nothing but pedestrian; prepare for my ultimate attack! |
| bona fide | (adj.) | genuine or real |
Syn: authentic, legitimate
Ant: fake, invalid |
Ralph wanted a bona fide New York cheesecake, so
he travelled all the way to New York just so he could taste
the real deal.
John didn't know that his song had become a bona fide music hit, for he had believed the news had been a scam. |
| adventitious | (adj.) | happening or occurring by chance rather than nature |
Syn: unplanned, coincidental, involuntary
Ant: organized, consequential |
The formation of a pearl is an adventitious effect of
coating a foreign body in the shell of the mollusk, which just so randomly
happens to form into a luxurious pearl.
The weird-looking plant is adventitious and not native to this country. |
| fecund | (adj.) | capable of producing offspring or growth |
Syn: fertile, productive, prolific
Ant: barren, infertile |
The fecund nature of the jungle
allowed for its expansive growth and development of diverse
species of inhabitants.
The fecund engineer knew he had to quickly find a way to fix the collapsing bridge. |
| deviate | (v.) | to depart from the established course |
Syn: diverge, digress, vary
Ant: converge, stay the same |
Try not to deviate from your central argument while you
are trying to prove a specific claim.
My current grade in Physics has deviated from its normal score due to my inept studying technique for the last test. |
| obfuscate | (v.) | to render unclear or unintelligible |
Syn: obscure, confuse, complicate
Ant: clarify |
The car salesman obfuscated the dissatisfied customer so much that the customer
hit himself in the face due to the confusion.
Surprisingly, the efforts of the translator only served to obfuscate an already confusing conversation. |
| impale | (v.) | to pierce (with a sharp point) |
Syn: stab, strike, break
Ant: mend, heal |
Nathan impaled an earthworm on a book before he
split it open and revealed its innards to the world.
Tetsu took his twin daggers and impaled Orion in the chest, tearing open his innards. |
| extenuate | (v.) | to make something less serious |
Syn: mitigate, vindicate, explain
Ant: amplify, exaggerate |
You cannot extenuate your bad habit of
procrastinating until the last day because it gives you so much stress
and many sleepless nights.
The woman’s troubled childhood wasn’t enough to extenuate or excuse her of killing her husband. |
| parochial | (adj.) | having a limited or narrow outlook |
Syn: restricted, introverted
Ant: free, liberal, broad-minded |
Martin had a parochial fear of travel, so it surprised everyone when he
finally expressed his disdain as he had for such a little thing.
Since the professor tends to be parochial, he is often unwilling to listen to theories other than his own. |
| glower | (v.) | to have an angry or sullen look |
Syn: scowl, glare, frown
Ant: smile, laugh |
Sheila, covered in food, glowered at the waiter after he
tripped and dropped the tray of food onto her without a sincere apology.
Kiyo glowered at Aykesano as he stumbled around with a stupid grin on his face. |
| edify | (v.) | to instruct or improve morally or intellectually |
Syn: educate, teach, train
Ant: confuse, obfuscate |
Alice thought that she could edify her son, but she
knew she could not when she realized that she herself had not
learned the proper methods of teaching.
According to my therapist, the best way for me to edify my mind is by spending a small portion of my day in personal reflection. |
| ambiguous | (adj.) | open to interpretation |
Syn: unclear, unknown, debatable
Ant: clear, denotative, precise |
The ambiguous quote caused controversy in the class because
it was quite interpretable and many inferred it to be inherently evil
while others believed it was a snide mockery of that evil.
With an ambiguous plot, no one could understand the film's premise. |
| cataclysm | (n.) | a large-scale and violent event |
Syn: disaster, catastrophe, crisis
Ant: salvation, godsend, liberation |
A cataclysm caused by an asteroid hitting the Earth would probably
result in the mass extinction of the world's animals.
When the Milky Way collides with the Andromeda Galaxy, the planets and stars within them will not fall to catclysm because of how far apart they are. |
| optimum | (adj.) | best, highest quality |
Syn: optimal, most advantageous
Ant: worst |
If you do not wait for optimum sailing conditions, you might
end up in the middle of a storm and become either stranded at sea or veer
off course.
In order to score an optimum SAT score, you must be patient and practice well. |
| importune | (v.) | to harrass persistently |
Syn: beg, solicit
Ant: hint, suggest |
The uninsured woman importuned the hospital
to let her in so that she could acquire the treatment
she wanted.
Realizing that the boss was in one of his rare good moods, I seized the opportunity to importune him for a raise. |


Run the statements in the right panel.
The following SQL demonstrates many of the new arithmetic functions that are available to the user.
You should see a result set in the lower right panel.


@@include('./_head.html', { "path": "../..", "title": "Pixel Documentation - Blog cards" })
MenuDocumentation and examples for blog-cards. Cards are built with as little markup and styles as possible, but still manage to deliver a ton of control and customization.
Below is an example of a basic blog-card with mixed content and a fixed width. Cards have no fixed width to start, so they’ll naturally fill the full width of its parent element. This is easily customized with bootstrap 4 various sizing options
Use the .shadow
, .border
and .bg-*
modifier classes to make blog cards more creative.
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Similar to headers and footers, cards can include top
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Using a combination of grid and utility classes, cards can be made horizontal in a mobile-friendly and responsive way. In the example below, we remove the grid gutters with .no-gutters
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Using a combination of grid and utility classes, cards can be made horizontal in a mobile-friendly and responsive way. In the example below, we remove the grid gutters with .no-gutters
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.col-md-*
classes to make the card horizontal at the md
breakpoint. Further adjustments may be needed depending on your card content.
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According to some historical records, some people out there have boundless energy, loads of free time, and ambition...
According to some historical records, some people out there have boundless energy, loads of free time, and ambition...

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Remote workers, here's how to dodge distractions

According to some historical records, some people out there have boundless energy, loads of free time, and ambition...

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Eight tips for improving contact form conversions

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According to some historical records, some people out there have boundless energy, loads of free time, and ambition...
According to some historical records, some people out there have boundless energy, loads of free time, and ambition...

Design

Remote workers, here's how to dodge distractions

According to some historical records, some people out there have boundless energy, loads of free time, and ambition...

Learn more

Design

Remote workers, here's how to dodge distractions

According to some historical records, some people out there have boundless energy, loads of free time, and ambition...

Learn more


SNL mocks Warren's $52T 'Medicare-for-all': 'When the numbers are this big they’re just pretend!'
by Brie StimsonWith the remarkably versatile Kate McKinnon at the helm, this weekend’s "Saturday Night Live" cold open took aim at Sen. Elizabeth Warren’s $52 trillion "Medicare-for-all" health care plan.
“I am in my natural habitat – a public school on a weekend,” McKinnon's excitable Warren quipped at an Iowa town hall, complete with fist pumps, some "whoos" and the senator’s signature raspy voice.
She also took a moment to give former Rep. Beto O’Rourke a sendoff after he dropped out of the race last week.
“Let me know how my dust tastes,” she said.
After mentioning that she pays taxes in every state “out of principle,” she took questions from cast members playing ambivalent voters.
Asked why it took her so long to release her health care plan, McKinnon’s Warren answered, “When Bernie [Sanders] was talking 'Medicare-for-all', everybody was like, ‘Oh cool,’ and then they turned to me and said, ‘Fix it, Mom.’”
She added that her plan “compares favorably” to former Vice President Joe Biden’s “in that it exists.”
“No one asks how we’re going to pay for ‘Remember Obama,” she said, referring to Biden’s tendency to frequently cozy up to the former president.
She then answered a question about estimates of how much her plan would cost.
“We’re talking trillions,” she answered. “When the numbers are this big they’re just pretend.”
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Warren has surged in polls recently as Biden has faded and is in the lead in a new Iowa poll.


Things I have learned this year in Scratch:
For this scratch project I tried to create a fun and interactive game that was also visually appealing. I enjoyed using the scratch abilities to format for loops, timed functions, and more. This was one of my first projects in computer science and I find it interesting to see how I have improved and the abilities that coding provides me.
Timeline:
3 weeks
For this scratch project I tried to create a fun and interactive game that was also visually appealing. I enjoyed using the scratch abilities to format for loops, timed functions, and more. I used my creativity to create an intersting game that was also fun to play and funny as well as visually appealing.
Timeline:
4 weeks
I have learned in the beginning of this year how the logic of coding worked and I believe that scratch showed me the way coding should be used. It was a good application to start the year with. In this project I tried to recreate the famous cartoon show Tom and Jerry. I believe this is an interesting project and extremely fun as well as silly. This project also showed my ability to create for loops, functions, and more on scratch.
Timeline:
3 weeks
I have learned this year that scratch can produce multiple different stamps on a page. For this project I created a for loop and produced an interesting project that was different and showed my ability to produce loops.
Timeline:
2 weeks
For this project I tried to produce a fashion show; however, I wasn't as successful as in my other projects but I learned how to improve my skills. This was the last scratch project I made before I moved onto codepen and codeacademy; therefore, I did not have as much time to work on this project.
Timeline:
1 weeks


| example | run | view library | syntax | UI library | model | router | others |
|---|---|---|---|---|---|---|---|
| Setup : the Meiosis pattern setup for several virual DOM libraries. | view library list | Various | JSX, template literals | ||||
| Wiki Examples : examples from the Wiki. | example list | React, Mithril | JSX, hyperscript | Bootstrap |
url-mapper, Universal Router |
||
| 1. Temperatures : components managing their own state, nested within the single-state tree. | run example | Preact | JSX | PureCSS | Lodash | Joi | |
| 2. Random-Gif : scalable front-end architecture. | run example | Mithril | hyperscript | Tachyons | Ramda | ||
| 3. TodoMVC1 : the canonical TodoMVC example. Written in TypeScript. | run example | Snabbdom | hyperscript | TodoMVC CSS | Lodash |
history crossroads |
|
| 4. Todo List With Server : demonstrates making AJAX calls to the server. | run example | Inferno | JSX | Semantic UI | Ramda | Validate.js | |
| 5. Library : server-side rendering and progressive loading. | run example | domvm | hyperscript | mini.css | crocks | hapi |


| Rstpm2-package | Flexible parametric survival models. |
| addModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| aft | Parametric accelerated failure time model with smooth time functions |
| aft-class | Class "stpm2" ~~~ |
| aftModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| AIC-method | Class "pstpm2" |
| AICc-method | Class "pstpm2" |
| anova-method | Class "pstpm2" |
| as.data.frame.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| as.data.frame.markov_msm_diff | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| as.data.frame.markov_msm_ratio | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| BIC-method | Class "pstpm2" |
| brcancer | German breast cancer data from Stata. |
| coef<- | Generic method to update the coef in an object. |
| collapse_markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| colon | Colon cancer. |
| confint.predictnl | Estimation of standard errors using the numerical delta method. |
| cox.tvc | Test for a time-varying effect in the 'coxph' model |
| diff | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| diff.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| eform | S3 method for to provide exponentiated coefficents with confidence intervals. |
| eform-method | Class "pstpm2" |
| eform-method | Class "stpm2" ~~~ |
| eform.stpm2 | S3 method for to provide exponentiated coefficents with confidence intervals. |
| grad | gradient function (internal function) |
| gsm | Parametric and penalised generalised survival models |
| gsm.control | Defaults for the gsm call |
| hazFun | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| hrModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| incrVar | Utility that returns a function to increment a variable in a data-frame. |
| legendre.quadrature.rule.200 | Legendre quadrature rule for n=200. |
| lhs | Internal functions for the rstpm2 package. |
| lhs<- | Internal functions for the rstpm2 package. |
| lines-method | Class "pstpm2" |
| lines-method | Class "stpm2" ~~~ |
| lines.pstpm2 | S3 methods for lines |
| lines.stpm2 | S3 methods for lines |
| markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| nsx | Generate a Basis Matrix for Natural Cubic Splines (with eXtensions) |
| nsxD | Generate a Basis Matrix for the first derivative of Natural Cubic Splines (with eXtensions) |
| numDeltaMethod | Calculate numerical delta method for non-linear predictions. |
| plot-method | Class "stpm2" ~~~ |
| plot-method | plots for an stpm2 fit |
| plot-method | Class "pstpm2" |
| plot-method | Class "stpm2" ~~~ |
| plot-method | Class '"tvcCoxph"' |
| plot-methods | plots for an stpm2 fit |
| plot.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| popmort | Background mortality rates for the colon dataset. |
| predict-method | Class "stpm2" ~~~ |
| predict-method | Predicted values for an stpm2 or pstpm2 fit |
| predict-methods | Predicted values for an stpm2 or pstpm2 fit |
| predict.formula | Estimation of standard errors using the numerical delta method. |
| predict.nsx | Evaluate a Spline Basis |
| predictnl | Estimation of standard errors using the numerical delta method. |
| predictnl-method | Class "stpm2" ~~~ |
| predictnl-method | ~~ Methods for Function predictnl ~~ |
| predictnl-method | Class "pstpm2" |
| predictnl-method | Class "stpm2" ~~~ |
| predictnl-methods | ~~ Methods for Function predictnl ~~ |
| predictnl.default | Estimation of standard errors using the numerical delta method. |
| predictnl.lm | Estimation of standard errors using the numerical delta method. |
| print.predictnl | Estimation of standard errors using the numerical delta method. |
| pstpm2 | Parametric and penalised generalised survival models |
| pstpm2-class | Class "pstpm2" |
| qAICc-method | Class "pstpm2" |
| ratio_markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| rbind.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| residuals-method | Residual values for an stpm2 or pstpm2 fit |
| residuals-methods | Residual values for an stpm2 or pstpm2 fit |
| rhs | Internal functions for the rstpm2 package. |
| rhs<- | Internal functions for the rstpm2 package. |
| Rstpm2 | Flexible parametric survival models. |
| splineFun | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| standardise | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| standardise.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| stpm2 | Parametric and penalised generalised survival models |
| stpm2-class | Class "stpm2" ~~~ |
| subset.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| summary-method | Class "pstpm2" |
| summary-method | Class "stpm2" ~~~ |
| transform.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| tvcCoxph-class | Class '"tvcCoxph"' |
| vcov.markov_msm | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |
| vuniroot | Vectorised One Dimensional Root (Zero) Finding |
| zeroModel | Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models. |


v.1.0.0
First copy "imagebrowser" directory to TinyMCE plugins folder.
While initializing TinyMCE:
<script>
tinymce.init({
...
plugins: "imagebrowser",
toolbar: "imagebrowser",
imagebrowser: {
windowcaption: "",
root: "/",
overwrite: false
}
...
});
</script>
Supports English and Turkish.
CC0 1.0 Universal Licence
Dependecies have their own licence information. Please view them before use.


[This Courseware | Basic Concepts | How We Use These Materials | QSC Computing Policy]
Queen Salote College is responsible for securing its network and
computing systems in a reasonable and economically feasible degree
against unauthorized access and/or abuse, while making them
accessible for authorized and legitimate users. This
responsibility includes informing users of expected standards of
conduct and the punitive measures for not adhering to them. Any attempt to violate the provisions
of this policy will result in disciplinary action in the form of
temporary revocation of user accounts, regardless of the success or
failure of the attempt. Permanent revocations can result from
disciplinary actions taken by a panel judiciary board called upon
to investigate network abuses.
The users of the network are responsible for respecting and
adhering to school regulations, local and international laws. Any attempt to break those regulations
and laws through the use of the network may result in litigation
against the offender by the proper authorities. If such an event
should occur, this organization will fully comply with the
authorities to provide any information necessary for the litigation
process.
Use of computer facilities is acceptance of the terms of this Computing Policy.
Once a user receives a userID to be used to access the network and computer systems on that network, they are solely responsible for all actions taken while using that userID. Therefore:
| 1.1 | Applying for an userid under false pretenses is a punishable disciplinary offense. |
| 1.2 | Sharing your userid with any other person is prohibited. In the result that you do share your userid with another person, you will be solely responsible for the actions that other person appropriated. |
| 1.3 | Deletion, examination, copying, or modification of files and/or data belonging to other users without their prior consent is prohibited. |
| 1.4 | Attempts to evade or change resource quotas are prohibited. |
| 1.5 | Continued impedance of other users through mass consumption of system resources, after receipt of a request to cease such activity, is prohibited. |
| 1.6 | Use of facilities and/or services for commercial purposes is prohibited. |
| 1.7 | Any unauthorized, deliberate action which damages or disrupts a computing system, alters it normal performance, or causes it to malfunction is a violation regardless of system location or time duration. |
Whenever you send electronic mail, your name and userid are included in each mail message. You are responsible for all electronic mail originating from your userid. Therefore:
| 2.1 | Forgery (or attempted forgery) of electronic mail messages is prohibited. |
| 2.2 | Attempts to read, delete, copy, or modify the electronic mail of other users are prohibited. |
| 2.3 | Attempts at sending harassing, obscene and/or other threatening email to another user is prohibited. |
| 2.4 | Attempts at sending unsolicited junk mail, "for-profit" messages or chain letters is prohibited. |
As a user of the network, you may be allowed to access other networks (and/or the computer systems attached to those networks). Therefore:
| 3.1 | Use of systems and/or networks in attempts to gain unauthorized access to remote systems is prohibited. |
| 3.2 | Use of systems and/or networks to connect to other systems, in evasion of the physical limitations of the remote system/local, is prohibited. |
| 3.3 | Decryption of system or user passwords is prohibited. |
| 3.4 | The copying of system files is prohibited. |
| 3.5 | The copying of copyrighted materials, such as third-party software, without the express written permission of the owner or the proper license, is prohibited. |
| 3.6 | Intentional attempts to "crash" Network systems or programs are punishable disciplinary offenses. |
| 3.7 | Any attempts to secure a higher level of privilege on Network systems are punishable disciplinary offenses. |
| 3.8 | The willful introduction of computer "viruses" or other disruptive/destructive programs into the organization network or into external networks is prohibited. |
This document is an adaptation of original work (c) Copyright 1998, Arlene Rinaldi + Florida Atlantic University


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welcome to spack editor
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# qed > Quandora markdown editor The editor aim is to provide an easy to use markdown editor for both developers and regular users. It still remainins a code source editor but it tries to give an apercu of the final HTML rendering while typing. For more demanding users there is also a live HTML preview available with scroll synchronization. Its primary goal is to write comments or small / medium documents. It is not especially designed to deal with large markdown documents. The editor is implemented in pure javascript and it is based on the [Content Editable](https://developer.mozilla.org/en-US/docs/Web/Guide/HTML/Content_Editable) browsers capability. ## Dependencies There are no dependencies for the core part of the editor. Only two optional dependencies are needed when using the preview and live preview feature: * [marked.js](https://github.com/chjj/marked) - a Markdown parser which is needed to generate the HTML preview (only needed when using the preview feature) * [MutationObserver.js](https://github.com/polymer/MutationObservers) - A Mutation Observers Polyfill which is needed for the live preview feature on IE9 ## Requirements: Any *evergreen* browser (i.e. Chrome, Safari, Firefox, IE10+). To work in IE 9 it requires the `MutationObserver.js` dependency. ## Features * Markdown syntax highlighting and HTML apercu or hints when possible. * Markdown syntax autocompletion. * Live preview (requires marked.js dependency) * Native spellcheck (uses the browser *content editable* capability). * Copy / Paste and customizable drag / drop. * HTML pasted content is converted to markdown (not supported on IE). * Fully configurable suggest / autocompletion support. * Display a configurable alert when leaving the editor page which content was modified and not saved. * Extensibility through custom actions. * Minimal mode for inline editing (with no toolbar). * Editor mode with a customizable toolbar (you can remove or add custom actions as you want). * Full Page mode - maximize to the size of the browser client area. * Customizable stylesheet for both toolbar and editor content. **To be implemented**: * Automatic reference-style management: keep link definitions and references in sync. This will make possible to insert *human friendly* links by inserting link references and automatically manage the link definitions. ## Distribution files The editor is distributed as one default css file and a javascript file that comes in 3 different flavors depending on your needs: * Default CSS file: `src/editor.css` * JS files: * `build/qed-all.js` - include the editor code and all dependencies (marked.js and MutationObserver.js). * `build/qed-core.js` - include the editor code and the MutationObserver.js dependency. * `build/qed.js`- include only the editor code. No dependencies are included. The minified versions of these files are ending in `.min.js`. The `build/qed.min.js` file is about **57K** and gziped is about **15K**. **Note**: Usually when using the markdown editor you will want to use the preview too - in that case it is better to define a custom stylesheet for the preview - oherwise you will end up with the browser defaults which are not very pretty. We have an example of such a stylesheet in `demo/preview.css` that you can use - it is not the best one - but surely better than the browser defaults. ## Building To build the editor you need [Grunt](http://gruntjs.com/). To install grunt and get started with, see http://gruntjs.com/getting-started You need first to install the project build dependencies before running the build. Go to the project directory and run: (you should do this only the once to locally download dependencies) ```shell npm install ``` Then to build simply run: ```shell grunt ``` ## Usage To use the editor you should include in your HTML page the editor javascript file (which one depends on your needs) and a styleshhet to configure the editor and optionally a stylesheet for the preview. You can use the default stylesheets from `src/editor.css` and `demo/preview.css`. For the example below we will use the complete qed build (containing all the dependencies): `build/qed-all.min.js`. ### Including the required scripts and sylesheets First you need to declare the editor and the stylesheets in your page. Put this in the page `head`: ```html
``` ### Attach the editor to a `textarea` element The simplest method to use the editor is to attach it to an existing `textarea` element: ### More? For more details look into the sources and in the `demo` directory. ## Demos * See this README in the editor [here](http://quandora.github.io/qed/demo.html). The defaut toolbar theme and actions are used. The editor is opened in **full page** mode. **Note** that actions like insert link or insert image or any other actions can be customized. The insert image action is not available in the demo since it requires storage to upload images etc. But drag dropping images or inserting them through a navbar button is possible. You should just write a little bit of javascript code to enable and customize it. Also, it is easy to define your own actions and add them to the toolbar. If you don't want a default action you just ommit her when defining the toolbar. * See the suggest feature at work [here](http://quandora.github.io/qed/suggest-demo.html). This editor is using a *blue theme* fo the toolbar (look in the `head` for the style override) and it adds a simple **suggest** functionality. When you type @{{LETTER}} the editor will try to suggest you countries starting with {{LETTER}}. This example is using an internal array of countries. But you can also use **AJAX** to fetch the suggest matchings you want to display. I will provide you soon an example on using ajax (through JQuery) on doing this.


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Unity Overlay Toolkit
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The utility class that observes the other object and invoke callbacks when changed property fits the description More...
Public Member Functions | |
| PropertyChangeHandler (T observed) | |
| Ctor. More... | |
| void | Setup () |
| Sets the this object according to the current state of the observed object More... | |
| void | Dispose () |
The utility class that observes the other object and invoke callbacks when changed property fits the description
| T | : | class | |
| T | : | INotifyPropertyChanged |
|
inline |
Ctor.
| observed | Object to boserve |
| refreshAtStart | Should object call all passed hadlers at start |
|
inline |
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inline |
Sets the this object according to the current state of the observed object