Sampling surface simulation is useful in the comparison of different areal sampling methods in forestry, ecology and natural resources. The sampSurf package allows the simulation of numerous sampling methods for standing trees and downed woody debris in a spatial context. It also provides an S4 class and method structure that facilitates the addition of new sampling methods.
| Version: | 0.7-4 |
| Depends: | R (≥ 2.13.1), methods (≥ 2.13.1), sp (≥ 0.9-84), raster (≥ 1.9-5), boot (≥ 1.3-3) |
| Imports: | lattice (≥ 0.20-35), latticeExtra (≥ 0.6-28), rasterVis (≥ 0.10-3) |
| Suggests: | rgl, rgeos (≥ 0.2-9) |
| Published: | 2017-09-27 |
| Author: | Jeffrey H. Gove |
| Maintainer: | Jeffrey H. Gove <jgove at fs.fed.us> |
| License: | GPL (≥ 3) |
| URL: | http://sampsurf.r-forge.r-project.org/ |
| NeedsCompilation: | no |
| CRAN checks: | sampSurf results |
| Reference manual: | sampSurf.pdf |
| Vignettes: |
Getting Started |
| Package source: | sampSurf_0.7-4.tar.gz |
| Windows binaries: | r-devel: sampSurf_0.7-4.zip, r-release: sampSurf_0.7-4.zip, r-oldrel: sampSurf_0.7-4.zip |
| OS X binaries: | r-release: sampSurf_0.7-4.tgz, r-oldrel: sampSurf_0.7-4.tgz |
| Old sources: | sampSurf archive |
Please use the canonical form https://CRAN.R-project.org/package=sampSurf to link to this page.


| Application version | 4.0 |
| Application licence | BSD |
| Trad4 version | 4.0 |
| Document version | 4.0 |
| Author | schevans |
| Date | 31-01-12 |
This application is based on the ISDA CDS Standard Model (version 1.7), developed and supported in collaboration with Markit Group Ltd. The original code is available here: http://www.cdsmodel.com/
This application is simply a set of wrappers round the ISDA function calls. This library never intended for this purpose and was actually released as a reference CDS implementation to facilitate the so-called SNAC convention which allowed credit default swaps to be netted in clearing houses after the 2007 crisis. As such, it presents us with a couple of problems in this context - specifically with thread-safety and memory management. This will be discussed below in the section Porting, and is required reading for anyone considering the trad4/imperaflow way.
The model is very similar to that of bond_risk, discussed at length in the trad4 manual. The idea is to get the computationally expensive parts of pricing a trade (in this case the ir and credit curve bootstrapping) to persist so that any objects that depend on this result can re-use them. Also, this being trad4, different parts of the problem can run concurrently.
From the abstract diagram we can see that the two legs are valued concurrently, and their pvs are aggregated in standard_risk.
We can see the advantages of the trad4 approach more clearly in the concrete diagram. This shows one ir_curve (LIBOR.GBP), two credit_curves (Safeway and Vodafone) and three trades against those curves, two against Safeway (trade_1 and trade_2) and one against Vodafone (trade_3). The objects fee_leg_1 and contingent_leg_1 are the fee and contingent legs corresponding to trade_1, and standard_risk_1 is the standard_risk (aka pv) of trade_1. In practise you would have a handfull of ir_curves, hundreds of credit_curves and thousands of trades.
From this we can see that should the Safeway credit_curve move (or 'be marked' in the parlance) then the Safeway curve would need to be re-calculated once, the fee_leg_1, contingent_leg_1, fee_leg_2 and contingent_leg_2 would all run concurrently. Lastly standard_risk_1 and standard_risk_2 would fire, producing the new pvs of these trades.
The t4 files are available here
Now assume the ISDA code is that of an existing library which we want to port to trad4. We have our model and method, now what of the practicalities?
The first issue we'll look at is thread safety - can we safely call a random 3rd party lib through the api and expect it to be thread safe? The answer is a qualified Yes.
The second issue is memory management. The way most of the ISDA api calls behave is to new-off the relevant structure and hands a pointer back to the calculate function. This involves handing over heap management to the 3rd party library which - while not part of the trad4 philosophy until recently - rewards the leap of faith.
So, the first thing you ask when you've just hooked up your library to a trad4 calling framework is: Is this going to be thread-safe? The answer is: There's no such thing as thread-safe code, only code that's thread-safe in a particular threading context.
Let's first look at two different threading contexts that might apply to a 3rd party lib.
In this case your lib might do matrix operations that spin up threads internally to the library and invisible to the api. These must be avoided in a trad4 app for the same reason they should be avoided for grid based HPC - you'll lose control of which threads are doing what.
Solution: Compile/runtime switch to single-thread your lib.
In this case, the architect of the library (or, more likely, multiple architects at various stages of enlightenment) have tried to anticipate our threading context. This usually means mutex locks scattered liberally about whenever a resource looks a bit shared. These mutexes are insidious because they assume every threading context, and in my experience serve none.
Solution: Remove all existing mutex locks before you proceed. Note where they were.
Now, how does any of this apply to us here? The answer is none of it, as this lib makes no claim of thread safety. As discussed above, this is a good thing.
So is jpm_cds thread-safe in a trad4 threading context? The answer is: Why wouldn't it be? By calling the api through a trad4 application you are sure you'll never be reading from and writing to the same object at the same time.
However, there are cases where you will find threading problems in the trad4 context. The first is a practical one from jpm_cds - JpmcdsErrMsg mechanism - which writes to a global buffer which is written to a file on exit. Beyond a garbled log file this isn't a big deal, but it's illustrative.
A more common problem you'll encounter is with any internal caching your lib uses. A common cashing mechanism has at it's heart a hash. Hashes are great and are perfectly thread-safe in a trad4 context, until they are resized, in which case they become neither. Best to replace the hash with a lock-free array of pointers, say about MAX_OBJECTS long? (#defined in trad4.h). Again we're sure of a) the trad4 calling convention where no two objects will be simultaneously read from and written to and b) we know how many objects are expected for this application, and we'll want to keep them in memory so we'll know in advance how big a cache we'll need.
Previously, all memory management was handled by trad4. The space required for each object was described by the t4 files, and these were newed-off and populated (in the case of static and sub) or zeroed (in the case of pub). In fact in older versions of The Manual I explicitly stated that if you feel the need to use the heap you're doing it wrong. This meant any 3rd party lib required re-implementation in terms of trad4. While noble purity has it's place (not that I've ever found it), pragmatism wins every time.
By handing over memory management to a library that already does memory management, you can use that library (almost) unchanged. The almost covers caches and logging and such which might need a tweak, but reusing existing libraries without the requirement of a re-write makes life far more exiting.
Now this takes a fair amount of trust in your 3rd party lib, but if you're already relying on it for memory management you already have that trust.
Now let's look at how this affects jpm_cds. We'll start with the pub section of ir_curve.t4:
pub TCurve* pTCurve
Here's the calculate function:
if ( ir_curve_pTCurve ) { free( ir_curve_pTCurve ); // malloced on line 34 of isda_cds_model_c_v1/lib/src/cmemory.c } ir_curve_pTCurve = (JpmcdsBuildIRZeroCurve( ir_curve_baseDate, ir_curve_st_observables_types, ir_curve_st_observables_dates, ir_curve_st_observables_rates, NUM_RATES, ir_curve_mmDCC, ir_curve_fixedSwapFreq, ir_curve_floatSwapFreq, ir_curve_fixedSwapDCC, ir_curve_floatSwapDCC, ir_curve_badDayConv, local_holidays));
So you can see JpmcdsBuildIRZeroCurve news off the structure then handed over to the objects to which this r_curve subscribes. If the pointer in question - ir_curve_pTCurve - is pointing to another structure from a previous run it gets deleted.
This may not be the ideal arrangement - object construction and destruction is unnecessary expensive when the object hasn't fundamentally changed, but that's how this 3rd party lib works, so we have to go with it. The point is it works.
The IDSA model only produced pv. As we're already bootstrapping the credit_curve, there's little overhead of also bootstrapping the credit_curve perturbed by +-1bp and using these curves to calculate the cr01 (cr01 being the shift in pv given a +-1bp shift in the credit_curve). This would involve extending the TCurve structure to add:
TRatePt* fArray_p01 TRatePt* fArray_m01
These arrays would hold the credit_curve (for cr01) or ir_curve (for dv01) perturbed up and down by 1bp. Then the legs would also produce pvcrp01, pvcrm01, pvdvp01 and pvdvm01 which would be used in standard_risk to aggregate into trade-level cr01 and dv01.
Likewise we could produce cr01 by gridpoint, by introducing a range of fArrays corresponding to a range of curves perturbed by gridpoint. This would produce a ladder of cr01s. This would come at the expense of a fair bit of RAM, but as the number of curves is relatively low compared to the number of trades this should not present a problem in practice.
To run the application:
1) Download and unpack the distribution3) Source jpm_cds.conf:$ cd trad4_v4_0_full/jpm_cds
4) Start jpm_cds:jpm_cds$ . ./jpm_cds.conf
jpm_cds$ jpm_cds
To increase or decrease the number of threads used (the default is 4), set NUM_THREADS and re-start the application:
$ export NUM_THREADS=64 $ jpm_cds
To update a running system, log into the database:
$ t4db SQL>
And make any required updates. Then instruct the running application to collect these changes by using send_reload.sh:
$ send_reload.sh
Firstly, should you run the precompiler, expect to see a warning about the Makefile under jpm_cds/lib. This is due to the partial nature of the integration between the two systems in the Phase1 release. Please ignore this warning.
Secondly, should you wish to rebuild the ISDA binaries, do so as follows:
jpm_cds$ cd objects/isda_cds_model_c_v1.7/lib/build/lib/linux linux$ make clean linux$ make


4.2. Response to Stochastic Ground Motion¶
The purpose of this analysis is to verify that EE-UQ app is able to reproduce the correct pseudo-acceleration response spectrum when using synthetic acceleration time histories generated using the stochastic ground motion model option as the seismic event. The maximum pseudo-acceleration for a single-degree-of-freedom system with varying natural frequencies calculated by EE-UQ app is compared to the values predicted by smelt as well as the geometric mean of the four NGA-West2 ground motion prediction equations (GPMEs) that account for soil sites. The scenario considered in this verification example is an event with a Moment Magnitude (\(M_W\)) of 6.5, closest-to-site rupture distance (\(R_{rupt}\)) of 20 km, and average shear-wave velocity in the top 30 m of soil (\(V_{s30}\)) equal to 400 m/s.
The single-degree-of-freedom system was input using the MDOF option as the Building Model Input in EE-UQ app. Here, the mass was set to unity and the damping ratio to 5%. The story stiffness was modified to set the natural frequency of the system in order to calculate the response spectrum. The structural response was calculated for 10 sample synthetic acceleration time histories for each structural period and compared to those from smelt and the GMPEs, as shown in Fig. 4.2.1. As can be seen in this figure, the spectral response calculated by EE-UQ app falls within the mean plus/minus one sigma bounds of the GMPEs and smelt while tending toward the mean. This produces the expected result as EE-UQ app is calling smelt in the backend to generate the synthetic motions. The full validation of smelt in implementing the predictive stochastic model proposed by Vlachos et al. (2018) vlachos2018predictive can be found in the smelt library documentation.
Note
The smelt and GMPE spectra show the mean and mean plus/minus one logarithmic standard deviation. The GMPE spectra are based on the geometric mean of the four NGA-West2 models that account for site soil conditions. The smelt spectra are based on an ensemble of 1000 synthetic ground motions. EE-UQ app response values are based on the mean pseudo-acceleration for 10 synthetic ground motion samples per period, \(T_n\).


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DUKE
UNIVERSITY
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c.
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abundant.
M.
R.
CROSBY
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Legend -
Level of confidence that token is an accurately-transcribed word
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very low
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Notebooks
Story: A note about the porcupine
A note about the porcupine
Title
A note about the porcupine
Collection
Summary
More about what Dia!kwain's parents instructed him about the porcupine; children should not eat the things that are at the back of the porcupine's neck.
Comments
1) This story is found in Book V-6
Contributors
Date
March 1875
Categories
Custom and daily life, The Rain and Rain's water, Plants and animals
Keywords
porcupine (a |xam belief about) , porcupine (the back of its neck) , porcupine (eating) , porcupine (and the hail) , porcupine (lessons regarding) , porcupine (avoidance behaviour relating to) , porcupine (is a rain's thing or animal) , porcupine (children fear) , prohibitions (about eating the porcupine's neck) , prohibitions (and the rain) , prohibitions (and porcupines) , prohibitions (and what children eat) , rain (its anger) , rain (its things) , rain (its animals) , rain (about the porcupine) , rain (and hail) , rain (and eating the porcupine's neck) , teaching (avoidance behaviour) , teaching (about avoiding eating the porcupine's neck) , food (eating porcupine) , food (what children eat) , education (about avoiding eating the porcupine's neck) , animal (about the porcupine) , animal (avoiding eating the porcupine's neck) , children (fear rain's things) , children (fear the porcupine) , children (must avoid eating the porcupine's neck) , hail (and avoiding eating the porcupine's neck) , hail (and the things on the back of the porcupine's neck)
Story Pages
4387v-4389v


Located in the Central on Jakarta, Gren Alia Cikini Hotel is a perfect place to venue a meeting. With excellent meeting rooms, enjoyable menus and dedicated services, we guarantee a great meeting for everyone. What you’ll receive: fine services and amenities, excellent location in Central of Jakarta, variety of dining, coffee break and beverages selections, capacity up to 300 pax, high speed internet access.
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Jupiter Ascending
The Avengers - Age of Ultron
Fast & Furious 7
Insurgent
Into the woods
Interstellar
Fifty shades of gray
Exodus - Gods and kings
Jurassic World


--- layout: member_profile title: "Vikram Radhakrishnan" permalink: /members/vradhakrishnan/ name: "Vikram Radhakrishnan" affiliation: "Leiden University" jointaffiliation: "Netherlands Organisation for Applied Scientific Research" country: "Netherlands" position: "PhD Student" researchtitle: "Nonlinear Control of Adaptive Optics for Exoplanet Imaging " summary: prior: orcid: "https://orcid.org/0000-0003-4122-4046" website: linkedin: "https://www.linkedin.com/in/vikram-mark-radhakrishnan-90038660/" rgate: "https://www.researchgate.net/profile/Vikram_Radhakrishnan" research: ["Adaptive Optics" , "Exoplanets" , "Coronagraphy" , "Nonlinear Control", "Machine Learning"] keywords: "Adaptive Optics -- Exoplanets -- Coronagraphy -- Nonlinear Control -- Machine Learning" email<EMAIL_ADDRESS>image_path: /assets/img/member/vradhakrishnan.jpeg ---
Current Research Summary:
This research aims at the dedicated shaping of the stellar PSF in an exoplanet imager to create an image-plane dark area at the position of an exoplanet. In this approach, we do not distinguish between the role of the coronagraph and AO. Instead we treat the combined system as an adaptive coronagraph, where the coronagraph imposes a static phase addition on the wavefront, and the AO system imposes dynamic phase additions with closed-loop feedback from the wavefront sensor measurements and periodic updates from the focal plane imaging camera. This direct, nonlinear approach of minimization of stellar light at the exoplanet's position will significantly enhance the achievable contrast of exoplanet imagers by correcting all remaining wavefront aberrations and component imperfections.
Prior Research:
Master's degree in Astronomy and Instrumentation at Leiden University (2015-2017)


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stk.calculators.ga.selectors module¶
Defines Selector
classes.
Selectors are objects with a select()
method, which is used to select molecules from a Population
.
Examples of how Selector
classes can be used is given their
documentation, for example Fittest
, Roulette
or
AboveAverage
.
Selector
can be combined to generate more complex selection
processes, such as stochastic sampling, using
SelectorSequence
or SelectorFunnel
. Examples of how
this can happen are given in the documentation of these two classes.
Extending stk: Making new selectors.¶
When a new Selector
class is made it must inherit
Selector
and define a select()
method.
select()
must take a single argument, which is the
Population
from which molecules are selected.
-
class
stk.calculators.ga.selectors.
AboveAverage
(duplicate_batches=False, num=None, duplicates=True, use_rank=False, batch_size=1)¶ Bases:
stk.calculators.ga.selectors.Selector
Yields above average batches of molecules.
The fitness of a batch is the sum of all fitness values of the molecules in the batch. Contrary to the name, this selector will also yield a batch which has exactly average fitness.
-
duplicate_batches
¶ If
True
, the same batch may be yielded more than once. For example, if the batch has a fitness twice above average it will be yielded twice, if it has a fitness three times above average, it will be yielded three times and so on.Type: bool
Examples
Yielding molecules one at a time. For example, if molecules need to be selected for mutation or the next generation.
# Make a population holding some molecules. pop = Population(...) # Make the selector. above_avg = AboveAverage() # Select the molecules. for selected, in above_avg.select(pop): # Do stuff with each selected molecule, like apply a # mutation to it to generate a mutant. mutant = mutator.mutate(selected)
Yielding multiple molecules at once. For example, if molecules need to be selected for crossover.
# Make a population holding some molecules. pop = Population(...) # Make the selector. above_avg = AboveAverage(batch_size=2) # Select the molecules. for selected in above_avg.select(pop): # selected is a tuple of length 2, holding the selected # molecules. You can do stuff with the selected molecules # Like apply crossover operations on them. offspring = list(crosser.crossover(*selectd))
-
_batch
(population)¶ Batch molecules of population together.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
which is the sum of all fitness values of the molecules in the batch.
-
_no_duplicates
(batches)¶ Makes sure that no molecule is yielded in more than one batch.
Parameters: batches ( iterable
) – Aniterable
yieldingtuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
.
-
-
class
stk.calculators.ga.selectors.
Fittest
(num=None, duplicates=True, batch_size=1)¶ Bases:
stk.calculators.ga.selectors.Selector
Selects batches of molcules, fittest first.
The fitness of a batch is the sum of the fitness values of the molecules in the batch.
Examples
Yielding molecules one at a time. For example, if molecules need to be selected for mutation or the next generation.
# Make a population holding some molecules. pop = Population(...) # Make the selector. fittest = Fittest() # Select the molecules. for selected, in fittest.select(pop): # Do stuff with each selected molecule, like apply a # mutation to it to generate a mutant. mutant = mutator.mutate(selected)
Yielding multiple molecules at once. For example, if molecules need to be selected for crossover.
# Make a population holding some molecules. pop = Population(...) # Make the selector. fittest = Fittest(batch_size=2) # Select the molecules. for selected in fittest.select(pop): # selected is a tuple of length 2, holding the selected # molecules. You can do stuff with the selected molecules # Like apply crossover operations on them. offspring = list(crosser.crossover(*selectd))
-
_batch
(population)¶ Batch molecules of population together.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
which is the sum of all fitness values of the molecules in the batch.
-
_no_duplicates
(batches)¶ Makes sure that no molecule is yielded in more than one batch.
Parameters: batches ( iterable
) – Aniterable
yieldingtuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
.
-
select
(population)¶ Yields batches of molecules, fittest first.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
ofMolecule
– A batch of selected molecules.
-
-
class
stk.calculators.ga.selectors.
Roulette
(num=None, duplicates=True, use_rank=False, batch_size=1)¶ Bases:
stk.calculators.ga.selectors.Selector
Uses roulette selection to select batches of molecules.
In roulette selection the probability a batch is selected is given by its fitness. If the total fitness is the sum of all fitness values, the chance a batch is selected is given by:
p = batch fitness / total fitness,
where
p
is the probability of selection and the batch fitness is the sum of all fitness values of molecules in the batch [1].References
[1] http://tinyurl.com/csc3djm Examples
Yielding molecules one at a time. For example, if molecules need to be selected for mutation or the next generation.
# Make a population holding some molecules. pop = Population(...) # Make the selector. roulette = Roulette() # Select the molecules. for selected, in roulette.select(pop): # Do stuff with each selected molecule, like apply a # mutation to it to generate a mutant. mutant = mutator.mutate(selected)
Yielding multiple molecules at once. For example, if molecules need to be selected for crossover.
# Make a population holding some molecules. pop = Population(...) # Make the selector. roulette = Roulette(batch_size=2) # Select the molecules. for selected in roulette.select(pop): # selected is a tuple of length 2, holding the selected # molecules. You can do stuff with the selected molecules # Like apply crossover operations on them. offspring = list(crosser.crossover(*selectd))
-
_batch
(population)¶ Batch molecules of population together.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
which is the sum of all fitness values of the molecules in the batch.
-
_no_duplicates
(batches)¶ Makes sure that no molecule is yielded in more than one batch.
Parameters: batches ( iterable
) – Aniterable
yieldingtuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
.
-
select
(population)¶ Yields batches of molecules using roulette selection.
Parameters: population ( Populaion
) – APopulation
from which batches of molecules are selected.Yields: tuple
ofMolecule
– A batch of selected molecules.
-
-
class
stk.calculators.ga.selectors.
Selector
(num, duplicates, use_rank, batch_size)¶ Bases:
object
Selects batches of molecules from a population.
Molecules are selected in batches, and each batch is selected based on its fitness. The fitness of a batch is the sum of all fitness values of the molecules in the batch. Batches may be of size 1, if single molecules are to be yielded.
-
num
¶ The number of batches to yield. If
None
then yielding will continue forever or until the generator is exhausted, whichever comes first.Type: int
-
duplicates
¶ If
True
the same member can be yielded in more than one batch.Type: bool
-
use_rank
¶ When
True
the fitness value of a molecule is calculated asf = 1/rank
.Type: bool
-
batch_size
¶ The number of molecules yielded at once.
Type: int
-
_batch
(population)¶ Batch molecules of population together.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
which is the sum of all fitness values of the molecules in the batch.
-
_no_duplicates
(batches)¶ Makes sure that no molecule is yielded in more than one batch.
Parameters: batches ( iterable
) – Aniterable
yieldingtuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
.
-
select
(population)¶ Selects batches of molecules from population.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
ofMolecule
– A batch of selected molecules.
-
-
class
stk.calculators.ga.selectors.
SelectorFunnel
(*selectors)¶ Bases:
stk.calculators.ga.selectors.Selector
Applies
Selector
objects in order.Each
Selector
inselectors
is used until exhaustion. The molecules selected by eachSelector
are passed to the next one for selection. As a result, only the finalSelector
can yield batches of size greater than 1.-
selectors
¶ The
Selector
objects used to select molecules. For all except the lastnum
must be1
.Type: tuple
ofSelector
Examples
Use
Roulette
on only the 10 molecules with the highest fitness.# Make a population with 20 molecules. pop = Population(...) # Create a Selector which yields the top 10 molecules according # to roulette selection. fittest = Fittest(num=10) roulette = Roulette() elitist_roulette = SelectorFunnel(fittest, roulette) # Use the selector to yield molecules. for selected_mol in elitist_roulette.select(pop): # Do something with the selected molecules. ...
-
_batch
(population)¶ Batch molecules of population together.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
which is the sum of all fitness values of the molecules in the batch.
-
_no_duplicates
(batches)¶ Makes sure that no molecule is yielded in more than one batch.
Parameters: batches ( iterable
) – Aniterable
yieldingtuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
.
-
select
(population)¶ Yields batches of molecules.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
ofMolecule
– A batch of selected molecules.
-
-
class
stk.calculators.ga.selectors.
SelectorSequence
(*selectors)¶ Bases:
stk.calculators.ga.selectors.Selector
Yields from selectors in order.
Examples
First use
Fittest
to select the 10 fittest batches and then useRoulette
to select batches in proportion to their fitness.# Make a population. pop = Population(...) # Create a Selector which yields 10 batches of molecules and # then uses roulette selection indefinitely. fittest = Fittest(batch_size=3) roulette = Roulette(batch_size=3) elitist_roulette = SelectorSequence(fittest, roulette) # Use the selector to yield molecules. for selected_mol in elitist_roulette.select(pop): # Do something with the selected molecules. ...
-
_batch
(population)¶ Batch molecules of population together.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
which is the sum of all fitness values of the molecules in the batch.
-
_no_duplicates
(batches)¶ Makes sure that no molecule is yielded in more than one batch.
Parameters: batches ( iterable
) – Aniterable
yieldingtuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
.Yields: tuple
– Atuple
of form(batch, fitness)
wherebatch
is atuple
ofMolecule
andfitness
is afloat
.
-
select
(population)¶ Yields batches of molecules.
Parameters: population ( Population
) – APopulation
from which batches of molecules are selected.Yields: tuple
ofMolecule
– A batch of selected molecules.
-


没什么好说的，自从回来之后，GFW的影子便无处不在，好好的网络不停的reset。真使人挺无奈的。更让人无奈的事实是：不论是什么内容放到互联网上都能形成对骂，quite magical.
.r003 September 16
After the using rb file to import the posts from Textpattern, which I find a little bit tricky is that all posts’ detailed post time kinda lost, like original post time is “Mar 15th, 2011 21:59:00”, only the date were available exists, but the detail hour/minute got lost, kinda pity.
And for Jekyll part, I personally cloned this repository , it definitely meet regular requirement for blogging. But if the pagination is a demand, add this line in _config.yml
paginate: 3
3 is the number of posts for each page. More information could be grab from here . See? It is in the wiki! But you can’t find it on the wiki’s home page.
On jekyll’s page structure, basically, it start from the index.html in the root container, and uses the file _layout folder. Extremely like what I have in Textpattern :)
And for code highlighting, pygments is great, and also which is part of github’s default setting(github uses jekyll --pygments --safe), you may need put pygments: true in _config.xml. Pygments does a bit slow
Well, I always said to myself, leave it tomorrow, you’ll have time to do it. In my face: I already forget details that have been done in this setup…
.r002 September 12
As may noticed, this blog was moved to full static pages, thanks to Jekyll , the fantastic page generator.
As well, this procedure also evolves ruby stuffs, which cost about half and day for a newbie like me to sort all these things out. Trying to list all the problems I met below, 所谓以儆效尤啊, lol:
rvm things, so following the topic in last article, version thing does cost a lot of time to deal with. At first, I would like to think that put
rvm list
should list all ruby versions in the system, also include the version that come that from the system. But actually it’s NOT. so use this:
rvm system
ruby -v
ruby 1.8.7 (2010-01-10 patchlevel 249) [universal-darwin11.0]
Voila.
For import textpattern blog posts to Jekyll, this part is pretty funny though. First, you’ll need to copy the import utility(textpattern.rb) to local disk, and use the ruby file to import all you posts. Grab the open_gem to meet the prerequisite:
gem install open_gem
than
gem open jekyll
On my laptop, this will let vim to open the package structure, open that file and save it into wherever you want. then import data from txp database
ruby -r './_import/textpattern.rb' -e 'Jekyll::TextPattern.process("database_name", "username", "password", "hostname")'
This file’s operation could be tricky, it’s database name was hard coded as “textpattern”, in my previous environment, the database name is txp_textpattern, so be aware of that. And also you may need add a new line
include 'yaml'
in the rb file, cause I met this problem.
TBD.


{% extends "layout.html" %} {% block title %}Assignments{% endblock %} {% block content %}
See Course page by Jānis Lazovskis with worksheets. Many more worksheets are in ORTUS.
These are short paper-based algorithmic tasks done in class; about 10-15 minutes long; people who write them from home get 3 extra minutes of work to download the paper, to photograph and to send their solution. We had 15 such exercises. (Sample assignments are just a preview for the real thing, they do not affect grade).
| Grading ID | Title | Due Date | Time Est. | Max | Average |
|---|---|---|---|---|---|
| MK01 | Bitwise operations, flowcharts Sample |
2020-09-14 | 12 min. | 10‰ | TBD |
| MK02 | Arrays, Pointers Sample |
2020-09-18 | 12 min. | 10‰ | TBD |
| MK03 | Using STL Sample |
2020-10-01 | 12 min. | 10‰ | 8.28‰ |
| MK04 | Pointer Operations Sample |
2020-10-07 | 12 min. | 10‰ | TBD |
| MK05 | Big-O-Notation Sample |
2020-10-14 | 12 min. | 10‰ | 4.61‰ |
| MK06 | Heaps Sample |
2020-10-28 | 12 min. | 10‰ | TBD |
| MK07 | Hashtables Sample |
2020-11-05 | 12 min. | 10‰ | TBD |
| MK08 | Binary Trees Sample |
2020-11-11 | 12 min. | 10‰ | 8.39‰ |
| MK09 | Quicksort Sample |
2020-11-12 | 12 min. | 10‰ | 8.78‰ |
| MK10 | Red-Black Trees | 2020-11-30 | 12 min. | 10‰ | TBD |
| MK11 | Topological Sort | 2020-12-10 | 30 min. | 20‰ | TBD |
| MK12 | Strongly Connected Components | 2020-12-10 | 30 min. | 20‰ | TBD |
| MK13 | Dijkstra's Algorithm | 2020-12-10 | 30 min. | 20‰ | TBD |
| MK14 | Bellman-Ford Algorithm | 2020-12-10 | 30 min. | 20‰ | TBD |
| MK15 | MST Prim's Algorithm | 2020-12-10 | 30 min. | 20‰ | TBD |
Due to the epidemic both exams were remote; notes and external references could be used.
| Grading ID | Title | Due Date | Time Est. | Max | Average |
|---|---|---|---|---|---|
| MIDTERM | Midterm Midterm Solution Sample (Stacks and Queues) |
2020-10-22 | 100 min. | 150‰ | 110.28‰ |
| FINAL | Final Sample Final |
2020-12-17 | 120 min. | 150‰ | TBD |
These activities are not graded. Most tools introduced here are not mandatory. We appreciate, if you inform others about your preferred C++ coding practices.
These exercises are meant to write simple C++ programs. We plan 7 or 8 such exercises.
| Ortus grade ID | Title | Additional Info | Due Date | Time Est. | Max | Average |
|---|---|---|---|---|---|---|
| EX1 | Periodic strings | Palindromes (Sample code) | 2020-09-09 | 4h | 20‰ | 12.75‰ |
| EX2 | Finding extreme students | N/A | 2020-09-11 | 4h | 20‰ | 16.94‰ |
| EX3 | Reverse an ordered tree | Inverted Trees in advertising; What is a stub; Encapsulation in programming. |
2020-09-25 | 12h | 20‰ | 8.88‰ |
| EX4 | Matrix Operations | Operators/Templates (Sample code) | 2020-10-05 | 4h | 30‰ | 21.19‰ |
| EX5 | Circularly Linked Lists | Makefile (also for unit tests); Catch2TestRunner.cpp. |
2020-10-12 | 4h | 30‰ | 24.38‰ |
| EX6 | Shapes and Transformations | Testcases (ZIP) | 2020-10-26 | 12h | 30‰ | 15.00‰ |
| EX7 | Aliens | Testcases (ZIP) | 2020-11-09 | 12h | 100‰ | 71.50‰ |
| EX8 | Balancing Binary Trees | Testcases (ZIP) | 2020-11-30 | 12h | 100‰ | 59.38‰ |
| EX9 | Code Reviews (style, leaks, performance) | 2020-12-04 | 12h | 150‰ | 123.33‰ |


My own demo
Istanbul Code Coverage Report
This demo uses
Flickr's Public Feed API
to search public photos.


Koliko High School Survey Form
How can you help us improve our school.
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What option best describes your position?
Select Current Position
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What do you like most about Koliko High School?
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All of the mentioned
Would you recommend Koliko High School to Friends and Family?
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Any comments or suggestions to what we should improve?
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An attempt at visualizing this
Article
.
Graph Legend :
- person
- business
- university
- hospital
This page is using
D3js
and
dagre-d3
plugin.


The array method reduce is used to iterate through an array and condense it into one value. To use reduce you pass in a callback whose arguments are an accumulator (in this case, previousVal) and the current value (currentVal). The accumulator is like a total that reduce keeps track of after each operation. The current value is just the next element in the array you're iterating through. reduce has an optional second argument which can be used to set the initial value of the accumulator. If no initial value is specified it(accumulator) will be the first array element and currentVal will start with the second array element. Here is an example of reduce being used to subtract all the values of an array: var singleVal = array.reduce(function(previousVal, currentVal) { return previousVal - currentVal; }, 0); Use the reduce method to sum all the values in array and assign it to singleVal.


or |
||
Select the trump suit: |
|||
| Bidding: | Generate a maximum willing bid by picking a random number. Increment the bid until it is above the maximum willing bid, then pass. |
| Declaring Trump: | Declare trump suit with whichever suit has the highest sum of card values. |
| Passing Cards To Declarer: | Pass a random set of cards. |
| Passing Cards As The Declarer: | Pass a random set of cards. |
| Trick-Taking: | Play a random legal card. |
| Bidding: | Simulate many rounds with cards randomly dealt to standard strategy players. Set the maximum willing bid at the round score achieved in more than 83% of the simulations. |
| Declaring Trump: | Simulate many rounds with cards randomly dealt to standard strategy players. Pick the trump suit that resulted in the highest willing bid. |
| Passing Cards To Declarer: | First prefer not to pass any cards that result in a meld score for your hand. Then, prefer to first pass highest trump cards. Then prefer highest non-trump cards. |
| Passing Cards As The Declarer: | First prefer not to pass any cards that result in a meld score for our hand. Then, prefer to first pass non-ace cards that would make us void in a suit, then prefer non-trump kings, then non-trump queens, then jack of diamonds (except if just received from partner), then lowest non-trump cards, then lowest trump cards. Finally, if necessary, we would choose to pass the cards that contribute the least amount of points to our meld score. |
| Trick-Taking When Leading: | Remembering what has been played and what was shown during passing and melding, play the highest trump card that is guaranteed to take the trick. Otherwise, play the highest non trump card. |
| Trick-Taking When Following Lead: | If we don't have a play that can beat what is currently in the trick pile, then if our partner played the current highest trick card then we play our lowest counter. Otherwise we play our lowest card. If we do have play options that could beat what is currently in the trick pile, then we play our highest winning card unless we are playing last. If we are playing last and we can take the trick then we play our lowest card that can take the trick. |
| Bidding: | Simulate many rounds with cards randomly dealt to standard strategy players. Set the maximum willing bid at the round score achieved in more than 75% of the simulations. |
| Declaring Trump: | Simulate many rounds with cards randomly dealt to standard strategy players. Pick the trump suit that resulted in the highest willing bid. |
| Passing Cards To Declarer: | First prefer not to pass any cards that result in a meld score for your hand. Then, prefer to pass highest trump cards (except 9's of trump). Then prefer aces. Then prefer 9s of trump. Then prefer queen of spades or jack of diamonds. Then prefer jacks. Then prefer nines. Then pass the cards that contribute the least amount of points to our meld score. |
| Passing Cards As The Declarer: | First prefer not to pass any cards that result in a meld score for our hand. Then prefer non-trump kings, then non-trump queens, then jack of diamonds (except if just received from partner), then lowest non-trump cards, then lowest trump cards. Finally, if necessary, we would choose to pass the cards that contribute the least amount of points to our meld score. |
| Trick-Taking When Leading: | First remove bad options: For each legal play, simulate the rest of the round 200 times with random deals of the unseen cards. If any play results in an average round score that is more than half a point lower than the highest average play, then remove it from the options considered. Remembering what has been played and what was shown during passing and melding, play the highest trump card that is guaranteed to take the trick. Otherwise, play the highest non trump card that is guaranteed to take the trick. Otherwise, play the lowest card. |
| Trick-Taking When Following Lead: | First remove bad options: For each legal play, simulate the rest of the round 200 times with random deals of the unseen cards. If any play results in an average round score that is more than half a point lower than the highest average play, then remove it from the options considered. If we don't have a play that can beat what is currently in the trick pile, then if our partner played the current highest trick card then we play our lowest counter. Otherwise we play our lowest card. If we do have play options that could beat what is currently in the trick pile, then we play our highest winning card unless we are playing the last trick card. If we are playing last and we can take the trick then we play our lowest card that can take the trick. |
| Hint/Analyze button on all levels: | |
| Undo button on all levels: | |
| Sort cards left to right: | |
| Score in multiples of ten: | |
| Card deck count: | |
| Winning score: | |
| Bidding speed: | |
| Minimum bid: | |
| Passing cards count: | |
| Trick-taking speed: | |
| Trick-taking display: | |
| Preferred difficulty: |
| Easy | Standard | Pro | Total |
| Games Played | 0 | |||
| Wins | 0 | |||
| Win Percentage |
| Avg Bid Contract | 0 | |||
| Avg Meld | 0 | |||
| Avg Counters | ||||
| Avg Positive Round Score | ||||
| Make Contract % |
| Avg Meld | 0 | |||
| Avg Counters | ||||
| Avg Round Score |
|
|
| Type | Combination | Points | |||
| Single | Double | x3 | x4 | ||
| Runs | A,10,K,Q,J Trump | 15 | 150 | 225 | 300 |
| Marriages | K,Q, Trump | 4 | 8 | 12 | 16 |
| K,Q | 2 | 4 | 6 | 8 | |
| Arounds | |||||
| A each suit | 10 | 100 | 150 | 200 | |
| K each suit | 8 | 80 | 120 | 160 | |
| Q each suit | 6 | 60 | 90 | 120 | |
| J each suit | 4 | 40 | 60 | 90 | |
| Pinochle | Q♤,J♢ | 4 | 30 | 60 | 90 |
| Dix | 9 Trump | 1 | 2 |


|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.mindswap.pellet.utils.Timer
Class used to keep track how much time is spent for a specific operation. Timers are primarily used to display info about performance. A timer is started at the beginning of a function and is stopped at the end of that function (special care needed when there are multiple return commands in a function because the status of unstopped timers is undefined). A timer also stores how many times the timer has been started so average time spent in a function can be computed.
When a timer is used in a recursive function it will typically be started multiple times. Timer class will only measure the time spent in the first call. This is done by counting how many times a timer is started and time spent is computed only when the number of stop() calls evens out the start() calls. It is the programmer's responsibility to make sure each start() is stopped by a stop() call.
Each timer may be associated with a timeout limit. This means that time spent between start() and stop() calls should be less than the timeout specified. Timeouts will only be checked when check() function is called. If check() function is not called setting timeouts has no effect. It is up to the programmer to decide when and how many times a timer will be checked.
There may be a dependancy between timers. For example, classification, realization and entailment operations all use consistency checks. If something goes wrong inside a consistency check and that operation does not finish in a reasonable time, the timeout on the dependant timer may expire. To handle such cases, a timer may be asscoaited with a dependant timer so every time a timer is checked for a timeout, its dependant timer will also be checked. Normally, we would like to associate many dependants with a timer but for efficency reasons (looping over an array each time is expensive) each timer is allowed to have only one dependant.
Timers
class stores a set of timers and provides functions to start, stop and
check timers.
Timers
| Field Summary | |
static long |
NO_TIMEOUT
|
static long |
NOT_STARTED
|
| Constructor Summary | |
Timer(java.lang.String name)
Create a timer with no dependant. |
|
Timer(java.lang.String name,
Timer dependant)
Create a timer that has the specified dependant timer. |
| Method Summary | |
void |
add(Timer timer)
Update the total time elapsed and number of counts by by adding the values from another timer. |
void |
check()
Check if the elapsed time is greater than the timeout limit and throw a TimeoutException if that is the case. |
double |
getAverage()
Retutn the total time spent (in milliseconds) divided by the number of times this timer has been ran. |
long |
getCount()
Return the total number of times this timer has been started and stopped. |
long |
getElapsed()
Return the time elapsed (in miliseconds) since the last time this timer was started. |
long |
getLast()
Return the total time spent between last start()-stop() period. |
java.lang.String |
getName()
Return the name of this timer. |
long |
getTimeout()
Return the timeout associated with this timer. |
long |
getTotal()
Return the total time (in miliseconds) spent while this timer was running. |
boolean |
isStarted()
Return true if timer has been started with a start() call but not has been
stopped with a stop() call. |
void |
reset()
Reset all the counters associated with this timer. |
void |
setTimeout(long timeout)
Set a timeout limit for this timer. |
void |
start()
Start time timer by recording the time this function is called. |
void |
stop()
Stop the timer, increment the count and update the total time spent. |
java.lang.String |
toString()
|
| Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
public static final long NOT_STARTED
public static final long NO_TIMEOUT
| Constructor Detail |
public Timer(java.lang.String name)
name
- public Timer(java.lang.String name, Timer dependant)
name
- dependant
- | Method Detail |
public void add(Timer timer)
timer
- public void start()
public void stop()
public void reset()
public void check() throws TimeoutException
TimeoutException
public boolean isStarted()
start()
call but not has been
stopped with a stop()
call.
public java.lang.String getName()
public long getElapsed()
public long getTotal()
public long getCount()
start()
function is called may be greater than this amount.
public long getTimeout()
public double getAverage()
public long getLast()
public void setTimeout(long timeout)
timeout
- public java.lang.String toString()
|
|||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


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|
Assimp
5.0.1, master @ cd42b955
|
Data structure for a LWO file surface (= material) More...
Public Member Functions | |
| Surface () | |
Public Attributes | |
| bool | bDoubleSided |
| true for two-sided materials More... | |
| float | mAdditiveTransparency |
| Intensity of additive blending. More... | |
| float | mBumpIntensity |
| Bump intensity scaling. More... | |
| TextureList | mBumpTextures |
| aiColor3D | mColor |
| Color of the surface. More... | |
| float | mColorHighlights |
| TextureList | mColorTextures |
| Textures - the first entry in the list is evaluated first. More... | |
| TextureList | mDiffuseTextures |
| float | mDiffuseValue |
| Various material parameters. More... | |
| float | mGlossiness |
| TextureList | mGlossinessTextures |
| float | mIOR |
| Index of refraction. More... | |
| float | mLuminosity |
| float | mMaximumSmoothAngle |
| Maximum angle between two adjacent triangles that they can be smoothed - in degrees. More... | |
| std::string | mName |
| Name of the surface. More... | |
| TextureList | mOpacityTextures |
| TextureList | mReflectionTextures |
| ShaderList | mShaders |
| Names of the special shaders to be applied to the surface. More... | |
| TextureList | mSpecularTextures |
| float | mSpecularValue |
| float | mTransparency |
| std::string | mVCMap |
| Vertex color map to be used to color the surface. More... | |
| uint32_t | mVCMapType |
| bool | mWireframe |
| Wireframe flag. More... | |
Data structure for a LWO file surface (= material)
|
inline |
| bool Assimp::LWO::Surface::bDoubleSided |
true for two-sided materials
| float Assimp::LWO::Surface::mAdditiveTransparency |
Intensity of additive blending.
| float Assimp::LWO::Surface::mBumpIntensity |
Bump intensity scaling.
| TextureList Assimp::LWO::Surface::mBumpTextures |
| aiColor3D Assimp::LWO::Surface::mColor |
Color of the surface.
| float Assimp::LWO::Surface::mColorHighlights |
| TextureList Assimp::LWO::Surface::mColorTextures |
Textures - the first entry in the list is evaluated first.
| TextureList Assimp::LWO::Surface::mDiffuseTextures |
| float Assimp::LWO::Surface::mDiffuseValue |
Various material parameters.
| float Assimp::LWO::Surface::mGlossiness |
| TextureList Assimp::LWO::Surface::mGlossinessTextures |
| float Assimp::LWO::Surface::mIOR |
Index of refraction.
| float Assimp::LWO::Surface::mLuminosity |
| float Assimp::LWO::Surface::mMaximumSmoothAngle |
Maximum angle between two adjacent triangles that they can be smoothed - in degrees.
| std::string Assimp::LWO::Surface::mName |
Name of the surface.
| TextureList Assimp::LWO::Surface::mOpacityTextures |
| TextureList Assimp::LWO::Surface::mReflectionTextures |
| ShaderList Assimp::LWO::Surface::mShaders |
Names of the special shaders to be applied to the surface.
| TextureList Assimp::LWO::Surface::mSpecularTextures |
| float Assimp::LWO::Surface::mSpecularValue |
| float Assimp::LWO::Surface::mTransparency |
| std::string Assimp::LWO::Surface::mVCMap |
Vertex color map to be used to color the surface.
| uint32_t Assimp::LWO::Surface::mVCMapType |
| bool Assimp::LWO::Surface::mWireframe |
Wireframe flag.


Deer (venison)
Deer (venison) is a Green food. Green foods are safe to eat as a regular part of a diet.
Category Quotes
Guideline related quotes related to Meat-Game and Green throughout Geoff Bond's publications.
Examples of one serving include 3 oz (size of a deck of cards) cooked meat or poultry; 3 oz grilled fish; 1 egg. ~Paleo in a Nutshell p.95
For the meat, poultry, eggs, and fish group, Preferably consume two servings a day of “Green-Green” foods. If unavailable, you can consume two servings a day of “Green” foods. ~Paleo in a Nutshell p.102
For the meat, poultry, eggs, and fish group Restrict total food group servings per session to one. ~Paleo in a Nutshell p.102
For the meat, poultry, eggs, and fish group, Restrict total food group servings per day to two. ~Paleo in a Nutshell p.102
Additional Quotes
Specific references to Deer (venison) throughout Geoff Bond's publications.
. . . if you can get them, the exotic meats like venison, crocodile, ostrich, and caribou are good . . . ~Paleo in a Nutshell p.48
We are beginning to see the introduction of some "managed" wild animals on the market, such as venison (from deer), kangaroo, antelope, and bison (Plains buffalo). These creatures are not strictly speaking domesticated— they breed according to their own inclinations and are allowed to roam relatively freely on a range that closely resembles their natural habitat. Their numbers are culled in a sustainable way and their meat is introduced into the food chain. The American researcher Loren Cordain considers that the meat from these animals is similar to the Savanna Model, with the proviso that they browse the naturally occurring vegetation and are not given commercial feed. ~Paleo in a Nutshell p.59
30,000 years ago, the Cro-Magnons of Europe ate fish, turtles, shellfish, and birds. Meanwhile the Neanderthals, who lived alongside them, ate reindeer, mammoth, and other large herbivores. ~Deadly Harvest p.22
The amount of wild meat that the average person in the developed world consumes in a year is close to zero. However, both in North America and in parts of Europe, the hunting of wild animals is still possible on a controlled, recreational basis. In this way, the meat of bear, moose, caribou, deer, wild boar, elk, and similar creatures enters the diets of some hunters' families and the diners at specialist restaurants. This meat corresponds quite closely to the hunted big game of the Savanna Model. The same applies to small game such as the squirrel, hare, and rabbit. ~Deadly Harvest p.59


QML Camera Example
This example demonstrates how to use the Qt Multimedia QML API to access camera functions. It shows how to change settings and to capture images.
Running the Example
To run the example from Qt Creator, open the Welcome mode and select the example from Examples. For more information, visit Building and Running an Example.
Application Structure
Most of the QML code supports the user interface for this application with the camera types being mostly found in declarative-camera.qml and CaptureControls.qml.
CaptureControls, which is implemented in CaptureControls.qml, generates a column on the right hand side of the screen which includes control buttons for focus (not initially visible), capture, flash modes, white balance, exposure compensation, and if a preview is available, a preview button. The last button exits from the application.
Files:
- multimedia/declarative-camera/CameraButton.qml
- multimedia/declarative-camera/CameraListButton.qml
- multimedia/declarative-camera/CameraListPopup.qml
- multimedia/declarative-camera/CameraPropertyButton.qml
- multimedia/declarative-camera/CameraPropertyPopup.qml
- multimedia/declarative-camera/FocusButton.qml
- multimedia/declarative-camera/PhotoCaptureControls.qml
- multimedia/declarative-camera/PhotoPreview.qml
- multimedia/declarative-camera/Popup.qml
- multimedia/declarative-camera/VideoCaptureControls.qml
- multimedia/declarative-camera/VideoPreview.qml
- multimedia/declarative-camera/ZoomControl.qml
- multimedia/declarative-camera/declarative-camera.qml
- multimedia/declarative-camera/qmlcamera.cpp
- multimedia/declarative-camera/declarative-camera.pro
- multimedia/declarative-camera/declarative-camera.qmlproject
- multimedia/declarative-camera/declarative-camera.qrc
Images:
- multimedia/declarative-camera/images/camera_auto_mode.png
- multimedia/declarative-camera/images/camera_camera_setting.png
- multimedia/declarative-camera/images/camera_flash_auto.png
- multimedia/declarative-camera/images/camera_flash_fill.png
- multimedia/declarative-camera/images/camera_flash_off.png
- multimedia/declarative-camera/images/camera_flash_redeye.png
- multimedia/declarative-camera/images/camera_white_balance_cloudy.png
- multimedia/declarative-camera/images/camera_white_balance_flourescent.png
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- multimedia/declarative-camera/images/camera_white_balance_sunny.png
- multimedia/declarative-camera/images/toolbutton.png


Heya there. Myran here, feel free to check out these Snazzy links filled with stuffz up top :) .It is all about me so go crazy and get to know a bit about me. Enjoy :)


Download the BB8 Starter Files
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To merge motivating ideas & thoughts, techniques, knowledge and experiences for positive contribution towards the system development in the rapidly changing scenario of ICT where my conceptual and analytical skills can be utilized and to further enhance my knowledge and experiences.


This div has been skewed - note that the text is still selectable.
This div has been scaled - again, the text is real text.
This div has been rotated - you get the idea about the text!
This div has been translated 10px down, and 20px across.
This div has all four types!
hover


If you can see it, hear it, or feel it. tothefront.co is a blog for anyone who is enthusiastic about making creative stuff on the web.


EFCC has become a monster, says Owoseni
by Oladimeji RamonOladimeji Ramon
A former Commissioner of Police in Lagos State, Fatai Owoseni, said on Thursday that the Economic and Financial Crimes Commission has become a monster.
Owoseni, while describing Nigeria as a banana republic, said the EFCC had become a willing tool in the hands of whoever was in power.
The retired police officer spoke in Lagos at the 12th Annual Business Luncheon of a commercial law firm, SPA Ajibade & Co.
The luncheon with theme, ‘The forum for resolution of civil disputes: The courts v law enforcement agencies,’ was chaired by Justice Amina Augie of the Supreme Court, who was represented by Justice Helen Ogunwumiju of the Court of Appeal, while a former Attorney General of Lagos State, Mr Olasupo Shasore (SAN), presented the lead paper.
Owoseni was on the panel of discussants alongside Justice Biobele George will of the Court of Appeal and the Chairman of the Nigerian Bar Association, Lagos branch, Mr Lateef Akangbe.
While responding to the lead paper, Owoseni bemoaned what he described as abuse of office and disregard for the rule of law, saying the EFCC had become a monster.
He said, “The rule of law and the application and the enforcement is interpreted based on which side of the divide you are. If you are any of these governors today, the rule of law is something to you; if you become the Vice-President today, the rule of law is another thing to you; and we are seeing it.”
Owoseni traced the origin of the EFCC to the Task Force on Trade Malpractices, which he described as a self-serving body created purposefully to criminalise civil disputes.
He said, “That task force was used to seize properties, to do all sorts of things and that was what metamorphosed into the EFCC. Depending on which side of the divide you belong, to some of us, the EFCC has become a monster.”
In his contribution, Justice Georgewill knocked the police and the EFCC for the practice of making arrest before investigation.
The Justice said, “In civilised societies, you don’t arrest, detain and investigate; no! You investigate, arrest and detain.  You are supposed to conduct preliminary investigation before you arrest somebody. It is so unfortunate, the police and the EFCC, you have to stop.”
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Contact: theeditor@punchng.com


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--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/simpa.20using.20h.20vs.20simp.20at.20h.2C.20simp.20.5Bh.5D.html ---
On a few occasions, I've found that simp at h, simp [h]
works when simpa using h
does not. I thought the latter was intended to replace the former. Is this not the case?
They are not equivalent. simpa using h
is equivalent to simp at h |-, exact h
in particular, simp at h, simp [h]
may use h
to rewrite in subterms, while simpa
is just preparing to match it directly against the goal
This makes simpa
less powerful but more controlled, especially when quantifiers get involved
Ah, okay. Thanks.
Sean -- do you fancy editing the simp docs to explain this? I never really feel on top of this subtlety so never feel equipped to change them to explain it. I am still at the stage where I just try random combinations of simp, simpa, simp!, simp * at *
and just keep going until it works.
https://github.com/leanprover/mathlib/blob/master/docs/extras/simp.md
Just edit the community one I guess
Hmm, I don't know. I'm just looking at that document for the first time. I don't find it very easy to follow for reference, and I wouldn't know where to put what. I think Mario answering Zulip questions in a document would be better. :wink:
To make matters even more interesting, I have a goal that isn't solved by by simp at h, simp [h]
but is solved by by simp at h; simp; exact h
or by simp at h; simp [h] {contextual := tt}
or λ x, by simpa using h x
. After by simp at h; simp
, h
and the goal look exactly the same (with set_option pp.all true
). I'm not clear on why it can't be solved.
Last updated: Apr 24 2020 at 19:02 UTC


This will let Galileo connect to your home wifi network. Then you can connect with Galileo and use the internet simultaneously.
Fields do not match!
Leave blank if you have an unsecured network.


With the advent of multi-core processors reaching the mainstream user along with the gradual tapering of the increase of individual core speeds, there is a growing need for high-level tools to simplify the creation of multi-threaded applications in order to allow for scalability of software over the years to come. Preferably, a solution should also allow existing code-bases to be easily and incrementally updated to take advantage of modern day processors, and as well, code which may be targetting both multi-core and single-core processors should not have to suffer by strictly using one model of execution. Rather, behavior should be toggleable on a variety of levels such that alterations in a small number of locations in code could change the technique used for execution over a large portion of a given project.
Boost.Act attempts to supply a solution to this problem in the form of a portable library which provides STL-style algorithms that can be toggled via template policies to execute either synchronously or in parallel, and by providing templates and macros to allow the creation of asynchronous functions, active objects, and atomic objects. In addition, Boost.Act also allows the decoupling of signaling function calls from their actual invocations in such a way that one may switch between immediate, concurrent, lazy evaluation, or a user-provided evaluation strategy on a case-by-case basis with potentially no runtime cost to the application.
Interlocked Types
In order to be able to construct the high-level concepts presented in this
library and to allow users to be able to portably and efficiently implement
their own shared-memory datastructures and algorithms, portable low-level interaction
must be made both possible and efficient. In Boost.Act
,
this is accomplished through the use of the low-level abstraction of an Interlocked
Type
, which are types that support both thread-unsafe and
thread-safe operations with semantics specified by the user at the call-site.
Polymorphic Algorithms
For the scope of this library, a polymorphic algorithm is considered to be an algorithm whose model of execution may vary depending on policies passed prior to invocation. For example, a polymorphic algorithm may run serially with one policy and may internally run in parallel and rejoin with a different policy. Regardless of which policies are used, the same logical operation is performed, allowing users to switch between different execution models with minimal changes to code.
One of the fundamental components of Boost.Act is its collection of STL-style polymorphic algorithms. Those familiar with the STL will find themselves right at home, as the majority of the algorithms correspond directly with those in the STL and are callable in a similar manner, with the additional ability to toggle the execution model of the algorithm. Along with the standard algorithms provided by the STL, other templates are also included for the sole purpose of the creation of user-defined polymorphic algorithms.
Algorithm Models
By default, Boost.Act algorithms are performed in parallel whenever possible. Algorithm models are types which are used as policies to alter this behavior when required on both a global and call-by-call level. In addition to the parallel algorithm model, a serial algorithm model is provided which forces calls to be made in a single thread. Users may also create their own algorithm models if the desired behavior is not provided by either of those included with the library.
Actions
It is often desirable to be able to signal a function, perform other unrelated
operations, and then eventually wait for the signaled function's completion.
In order to provide such functionality, Boost.Act introduces the concept of
an action
, which is similar in many ways to a future
.
An action
is a type which represents a signaled operation.
With a stored action
, you have access to that operation
such that you may wait for the function's completion if you need the effects
to have taken place prior to reaching a further point in code. Actions also
provide an indirect interface to the result of a signaled function which allows
you to queue operations that may act upon the result of the signalled function
and which execute in the same thread as the original call.
Active Objects
Much like we have a way of decoupling the signaling of a function from the actual invocation of that function, Boost.Act also provides a way to separate the signaling of intrinsic functions of a type from the invocation of such functions, yet guarantees the same order of execution of such functions relative to one another. Since the invocations of such functions are serialized, it makes it easy to signal several functions which all deal with the same object without having to worry that one call may occur while another is executing, and with the guarantee that they are called in the same order as they were signaled.


Design
After one month...
Visual comparison to see the effect of the changes I introduced
- Humans are bad at noticing change
- On big and complex project, changing CSS is RISKY
- Adding external dependencies can pollute CSS global namespace
Example:
Heatmap
Screenshot-based Regression Testing can offer:
- Quick iteration
- Safe refactoring
- No time spent on writing layout tests
- Integration in CI/CD Pipeline
- No more complaints from QA 😁
Visual Regression Testing Tools
Galen Framework
https://github.com/galenframework/galen
- Uses Selenium to interact with the page
- Runs in Selenium Grid
- Easy to setup responsive tests
- Can be run in the cloud (SauceLabs, BrowserStack, PerfectoMobile)
To run this code you will need chromedriver
brew install chromedriver


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Resources
David Maguire
The David Maguire programme work with people who stammer, not simply with their speech behaviours. Their approach is not just about learning physical techniques that deal effectively in the short term with dysfluencies. They identifies and teach the mechanics and dynamics of speaking. The result, in most cases, is an immediate, improved control of speech.
To ensure continuing control in the long term, these physical techniques are supplemented by an understanding of the psychology of stammering and the benefits of self-acceptance.
Together, these strategies combine to sustain consistent improvement.
Through techniques of concentration, assertiveness and non-avoidance, the McGuire Programme helps tackle the many fears of speaking experienced by people who stammer.
As part of our long-term strategy, they explain the process of relapse and how to deal with it, should it occur, and we demonstrate how to challenge and begin to change lifelong negative traits. A sports mentality is basic to the Maguire Programme. Their positive attitude is for people to think of themelves as "athletes" training to become accomplished at a sport – the sport of speaking. Click
here for more details.
Mach 1
Mastering Change Ltd. was set up by Geoff Brown and Barbara Gommersall, specifically to develop the MACH 1 programme to help people who stammer.
- Geoff, as a client has attended a multiplicity of stammering therapy courses in the UK and abroad and brings a wealth of personal experience.
- Barbara is an NLP Master Practitioner and INLPTA certified NLP Trainer with fifteen years experience in training, personal development and therapeutic practice.
- Daniel (assistant trainer) is also an NLP Master Practitioner and has been using NLP with individuals and in a corporate setting for six years.
The MACH 1 programme takes you through a process that deals with who you are, your patterns of thinking and the belief structures that support stammering behaviour. The programme will help you make important changes to the way you think and behave - changes that will continue to have positive effects long after the course is ended.
You will learn more about yourself, find out how to detach from negative experiences in the past and discover how to create outcomes that help you to move towards the things that are really important to you.
You will create and maintain positive and resourceful feelings and states of mind that are not dependent on external experiences.
Click
here for report from our 2009 Mach 1 session
The Michael Palin Centre for stammering children
Stammering is one of the most common speech disorders, but recent research shows that many teachers don't know how to deal with it – something the Michael Palin centre aims to change. Here children at the centre explain what it is like to live with a stammer in the classroom - Click
here for Guardian Article.
Neuro Lingustic Programming (NLP) – Mind over matter
"A field of study that attempts to build a set of transferable skills by programming the unconscious mind"
"NLP is a collection of models, tools and techniques that enables its practitioners to:
- Communicate more effectively
- Achieve excellence in their chosen field
- Overcome performance-limiting behaviours
- Programme their unconscious mind
- Programme themselves to improve performance and results"
"NLP is a therapeutic technique to detect and reprogramme unconscious patterns of thought and behaviour in order to alter psychological responses. The basic principle of NLP is that it is in an individual`s power to change their own subconscious programming for the better" Click
here
and
here for more details.
Neuro Semantics
Click
here for article - 'How I am mastering using NLP and Neuro Semantics
What is the world of Neuro-Semantics?
Neuro-Semantics is actualizing your highest and best. It is making real your highest meanings and visions, and it is implementing your best values and ethics.
Neuro-Semantics is a field wherein we model the essential structure of experiences using positive psychology models that focus on accessing the best resources and the highest meanings.
As such, Neuro-Semantics is about performance—performing your highest meanings so that what you know you can do. It is about meanings—adding the most robust meanings to your life to make things more meaningful and significant.
Neuro-Semantics is Winning the Inner Game to Achieve Peak Performance in the Outer Game
Your inner games are your semantics. Your inner game is your inner matrix of meaning frames. Your layers upon layers of meanings make up your inner world. Your outer games is your neurology. Here you actualize your inner meanings.  Click here for details.
SpeechEasy
People who stutter have a voice and deserve to be heard. Like you, they want to be able to express their thoughts, feelings and ideas freely. You are not alone.
SpeechEasy can help.
SpeechEasy is a combination of proven technology and techniques which can be used to reduce stuttering and to help increase fluency. By using a device which mimics the choral effect paired with traditional fluency techniques, you can have the confidence to say what you want to say when you want to say it.
SpeechEasy is a tool which can help your message be heard. The thoughts and ideas you have to offer the world around you are no longer hidden, giving you the confidence in communication and the freedom to live your life.
This devise is recommended for people with severe stammeres.
Click here for more details.
StarFish
Supportive Training And Recovery For Individual Stammerer's Harmony.
The Starfish Project has had over 10 years experience helping and supporting people with Stammers gain control of their Speech and Lives. Click here for more information.


MeltDown
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Throttling Futures
Note:
I just went into a lot of detail about some of the subtilties of how to avoid some pitfalls with Fures. Let's take a step and look at their usage.
map and flatMap:
```scala
def fetchOldDoc(id: Int): Future[String]
def transform(oldDoc: String): String
def upload(doc: String): Future[Unit]
def update(id: Int): Future[Unit] = {
fetchOldDoc(id).map(transform).flatMap(upload)
}
```
for-comprehensions:
```scala
def fetchOldDoc(id: Int): Future[String]
def transform(oldDoc: String): String
def upload(doc: String): Future[Unit]
def update(id: Int): Future[Unit] = {
for {
old <- fetchOldDoc(id)
updated = transform(old)
_ <- upload(updated)
} yield ()
}
```
working with multiple futures:
```scala
def update(id: Int): Future[Unit]
val ids: Seq[int]
id.foreach(update)
```
## Parallel Futures
```scala
for {
_ <- upload(docA)
_ <- upload(docB)
} yield ()
//versus
val eventualA = upload(docA)
val eventualB = upload(docB)
for {
_ <- eventualA
_ <- eventualB
} yield ()
```
## Future.traverse
```scala
def upload(doc: String): Unit = ...
val docs: Seq[String] = ...
def uploadAll(docs: List[String]): Future[Unit] = {
???
}
```
What if there are a lot of documents?
Note:
- We're using async libraries for calling webservices or inserting to cassandra.
- And say we have a lot of documents (let's say 1000 or 10k).
- Futures are eager. If I traverse a list of 1000 items, we'll immediately kick off 1000 Futures.
- We often don't actually want to do that. The web service or database you're calling might not handle that.
Use case: Batch job
"Fetch the latest review score for 300k hotels and write to a database"]
Note:
- Let's say I have a batch job to update the review score for the whole inventory of hotels.
- This is a usecase I've come across a number of times.
```scala
def fetchIds(): Future[Seq[Id]] = ... //100k IDs
def fetchScore(id: Id): Future[(Id, Score)] = ...
def persist(idScorePair: (Id, Score)): Future[Unit] = ...
for {
ids <- fetchIds()
scores <- Future.traverse(ids)(fetchScore)
_ <- Future.traverse(scores)(persist)
} yield ()
```
However:
- Future.traverse is eager
- Nothing in standard library to throttle it.
Note:
- Future.traverse eagerly schedules kicks off every Future immediately!
- For blocking code wrapped in Futures, you can manage concurrency through the execution context.
- But what about with real async libraries?
- Then I have a problem. If I fetch scores from a web service, it may not be able to handle that much traffic, and my Futures will fail.
- Or by doing lots of database writes at the end, you can degrade your datbase's read performance. That's an issue I've had with Cassandra.
## Akka Actors
Good for working with state in a concurrent system...
...Which is a problem I try to avoid.
Note:
- Solves concurrency issues by not processing messages concurrently.
- helps solves issues related to concurrent access/modification around state.
- Often you don't have state in your application; instead you an push it out to a database.
- Actors don't tackle long-running IO and problems associated with it.
- So although I've used it before, I've not really had a usecase where it's needed (instead of offloading to a DB).
...Then I changed jobs...
Note:
- Until a couple of years ago, that was my understanding: "Use Futures, avoid blocking IO. I don't need Akka."
- Then I started working in a team using async libraries for all IO, and Akka Streams to read from kafka topics.
- I ended up revisiting both Akka and Futures, and so I want to share how my understanding evolved.
RateLimiter


Class Overlay
Reprents the overlay definition.
Inheritance
Inherited Members
Namespace: DevZest.Windows.Docking.Primitives
Assembly: DevZest.WpfDocking.dll
Syntax
public sealed class Overlay : Freezable, ISealable
Remarks
You can attach Overlay object to System.Windows.FrameworkElement by setting DockManager.Overlay attached property. Once Overlay object attached, a System.Windows.Controls.ContentPresenter will be generated on top of the associated element.
Fields
| Name | Description |
|---|---|
| AssociatedElementProperty | Identifies the AssociatedElement dependency property. |
| ContentProperty | Identifies the Content dependency property. |
| ContentTemplateProperty | Identifies the ContentTemplate dependency property. |
| ContentTemplateSelectorProperty | Identifies the ContentTemplateSelector dependency property. |
| IsFloatingWindowPreviewProperty | Identifies the IsFloatingWindowPreview dependency property. |
Properties
| Name | Description |
|---|---|
| AssociatedElement | Gets the associated element. |
| Content | Gets or sets the data used to generate the overlay UI. |
| ContentTemplate | Gets or sets the template used to display the content of the overlay. |
| ContentTemplateSelector | Gets or sets the System.Windows.Controls.DataTemplateSelector, which allows the application writer to provide custom logic for choosing the template that is used to display the content of the overlay. |
| IsFloatingWindowPreview | Gets or sets the value indicates whether the overlay is floating window preview. |
Methods
| Name | Description |
|---|---|
| CreateInstanceCore() | |
| FreezeCore(Boolean) |


Afghanistan upset West Indies in T20 clash
Lucknow - Karim Janat came in from the cold to take a starring role Saturday as Afghanistan beat West Indies by 41 runs to level their three-match Twenty20 series.
The 21-year-old was only a substitute in the first clash in Lucknow that West Indies easily won. But he took five wickets as West Indies were restricted to 106-8 in the chase for Afghanistan's 147-7 in the second game.
Janat's 26 from 18 balls, including five fours, was also the joint-highest score for Afghanistan along with opener Hazratullah Zazai.
"I was the 12th man for the last game and that was the best way for me to see what the line should be. I had a plan in mind and I kept to it," said Janat, whose change of pace and skillful choice of length claimed valuable scalps including Evin Lewis and Shimron Hetmyer.
"On this wicket, change of pace is a good weapon to have."
Afghanistan had looked on course for a bigger total but Kesrick Williams took two wickets in two balls to claim openers Zazai and Rahmanullah Gurbaz after they put on 42.
Some of Afghanistan's mid-order wickets went cheaply and with dew making bowling difficult, West Indies captain Kieron Pollard must have thought his side's unbeaten record in the tour so far was safe.
But medium pace bowler Janat was well-supported by the other Afghan bowlers and the West Indies batsmen looked sluggish as they chased a run-rate that quickly got out of control.
"At the halfway stage, we were happy with 147," said Pollard. "Their batsmen came hard at us, but we came back well, especially Kesrick. We cannot fault the bowlers.
"We didn't get a good start in the powerplay, and we lost wickets every time we were trying to be aggressive.
"We just have 24 hours to wrong the rights, and hopefully we can come back and do those things right tomorrow."
The final game is also in Lucknow, where Afghanistan play their home games, on Sunday. West Indies beat Afghanistan 3-0 in the one-day series before the T20s.


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{% extends 'extender.html' %} {% load crispy_forms_tags %} {% block content %}
Log in your physical activity!
(Duration in Minutes, Weight in kg)


WITNESS LIST Economic Development Committee February 15, 1999-10:30A Texas Aerospace Commission On: Cunningham, Walter (Self and Texas Aerospace Commission) Garcia, Jesse "Chuy" (Self and Texas Aerospace Commission) McKenzie, Gregg (Self and Texas Aerospace Commission) Moser, Thomas L. (Tom) (Texas Aerospace Commission) Texas Council on Workforce and Eco On: Curry, Ronald E. (Texas Council on Workforce and Economic Competitiveness) Texas Workforce Commission On: Rath, Diane (Texas Workforce Commission) Sheridan, Mike (Texas Workforce Commission)


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Installing ASP.NET 5 On Mac OS X¶
ASP.NET 5 runs on the .NET Execution Environment (DNX), which is available on multiple platforms, including OS X. This article describes how to install DNX, and therefore ASP.NET 5, on OS X using .NET Core and Mono.
- In this article:
Install the .NET Version Manager (DNVM)¶
Use the .NET Version Manager (DNVM) to install different versions of the .NET Execution Environment (DNX) on OS X.
To install DNVM run the following:
curl -sSL https://raw.githubusercontent.com/aspnet/Home/dev/dnvminstall.sh | DNX_BRANCH=dev sh && source ~/.dnx/dnvm/dnvm.sh
Once this step is complete you should be able to run dnvm
and see some help text.
Install the .NET Execution Environment (DNX)¶
The .NET Execution Environment (DNX) is used to build and run .NET projects. Use DNVM to install DNX for Mono or .NET Core (see Choosing the Right .NET For You on the Server).
To install DNX for .NET Core:
Install the DNX prerequisites using Homebrew:
brew update brew install icu4c
Use DNVM to install DNX for .NET Core:
dnvm upgrade -r coreclr
Note
.NET Core on OS X is still in early preview. Please refer to the latest Release Notes for known issues and limitations.
To install DNX for Mono:
Install Mono for OS X. Alternatively you can install Mono via Homebrew.
Use DNVM to install DNX for Mono:
dnvm upgrade -r mono
By default DNVM will install DNX for Mono if no runtime is specified.
Note
Restoring packages using DNX on Mono may fail with multiple canceled requests. You may be able to work around this issue by setting MONO_THREADS_PER_CPU
to a larger number (ex. 2000).
Summary¶
ASP.NET 5 is built on the cross-platform .NET Execution Environment (DNX), which can be installed on OS X as well as Linux and Windows. Installing DNX and ASP.NET 5 on OS X takes just a few minutes, using a few simple commands. You’re now ready to build your first ASP.NET application!


E-Academy is an amazing place for everyone who is interested in learning. Here you may find solutions to your problems that have already been solved by another users. You can also add new materials and share them with everyone. E-Academy is here. Now. Freely.
Sign upOur platform is made to be easily used. Everything is possible just in a single click.
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This is an example of the type of file that must be used to generate a map based on the data submitted.
Each State must be abbreviated when creating the table. Only CSV files will be accepted when creating maps.
Download a template file to get started today!


--- layout: post title: brush Sketches date: '2015-07-13T18:30:33+02:00' categories: - Sketches picture_url: https://film-Storyboards.tumblr.com/post/124018919143/brush-Sketches ---
Brush Sketches.


Беременность и нимесил
из курса Нимесил: правда и выдумка
Tousled food truck polaroid, salvia bespoke small batch Pinterest Marfa. Fingerstache authentic craft beer, food truck Banksy Carles kale chips hoodie. Trust fund artisan master cleanse fingerstache post-ironic, fashion axe art party Etsy direct trade retro organic.
Cliche Shoreditch Odd Future Pinterest, pug disrupt photo booth VHS literally occupy gluten-free polaroid Intelligentsia PBR mustache. Locavore fashion axe chia, iPhone cardigan disrupt Etsy dreamcatcher. Craft beer selvage fanny pack, 8-bit post-ironic keffiyeh iPhone mlkshk pop-up. Pug blog asymmetrical ethnic, stumptown shabby chic chillwave ugh before they sold out.


--- layout: post title: "Brand New On My City's New Logo" ---
Brand New On My City's New Logo
Boring.


sg_ecg_tool package¶
Submodules¶
sg_ecg_tool.sg_ecg_tool module¶
Python package that generates statistics on higher education and employment, for Singaporean students.
This package will allow you to enter Subjects you are interested in, or Universities you are keen on, so that you can access statistics/information on the latest employment rates and salaries associated with certain courses. This package also contains information on the latest openings, for various industries.
To that end, there are 7 functions in this package that provide different capabilities, to empower you on your education and career guidance journey.
There is important information in this documentation, as there are some key permissible values that must be keyed into certain slots (‘arguments’). It will otherwise not be possible to use this package. Where necessary, these will be reiterated throughout the documentation.
Values for university_shortname¶
This is a comprehensive list of the six autonomous universities in Singapore.
You will need to key in the values on the left of the assignment operator (‘=’), for the university_shortname argument. The full names of the universities are to the right of the assignment operator.
The ‘sg’ in front of the acronyms is an alias that I recommend. This can change depending on how you import this package.
sg.NTU = ‘Nanyang Technological University’
sg.NUS = ‘National University of Singapore’
sg.SUTD = ‘Singapore University of Technology and Design’
sg.SUSS = ‘Singapore University of Social Sciences’
sg.SIT = ‘Singapore Institute of Technology’
sg.SMU = ‘Singapore Management University’
Values for subject¶
This is a list of potential subjects you may be interested in pursuing.
These subjects have been titled in a self-explanatory way. You just need to key them in as they are rendered here.
The ‘sg’ in front of the acronyms is an alias that I recommend. This can change depending on how you import this package.
sg.Business
sg.Engineering
sg.Computing
sg.Education
sg.Law
sg.Humanities_SocialSci
sg.Healthcare
sg.Science
sg.Design_Art
Values for industry¶
This is a list of potential industries you may be keen on pursuing.
These values, unlike for the two lists above, must be entered as strings, therefore the inverted commas around them. There will be a reminder, for specific modules that utilize industry.
‘accommodation and food services’,
‘administrative and support services’,
‘business and real estate services’,
‘community, social and personal services’,
‘construction’,
‘electrical products’,
‘electronic products’,
‘electronic, computer and optical products’,
‘fabricated metal products’,
‘fabricated metal products, machinery and equipment’,
‘financial and insurance services’,
‘financial intermediation’,
‘financial services’,
‘food, beverages and tobacco’,
‘hotels and restaurants’,
‘information and communications’,
‘machinery and equipment’,
‘medical and precision instruments’,
‘other manufacturing industries’,
‘others’,
‘paper products and printing’,
‘paper products and publishing’,
‘paper,rubber,plastic products and printing’,
‘petroleum and chemical products’,
‘petroleum, chemical and pharmaceutical products’,
‘professional services’,
‘real estate and leasing services’,
‘real estate services’,
‘rubber and plastic products’,
‘textile and wearing apparel’,
‘transport and storage’,
‘transport equipment’,
‘transport, storage and communications’,
‘transportation and storage’,
‘wholesale and retail trade’
Enjoy!
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sg_ecg_tool.sg_ecg_tool.
explore_opening
(industry2, csv=False)[source]¶ If you are particularly interested in how many available job openings there are in a particular industry, this function can provide you with that information. This can be used in conjunction with see_opening(). You can key in an industry that you have some form of interest in, and get a full list of job vacancy rates, over quarters.
- Parameters
industry2 (str. One of the preset strings, which signify a given industry. This is a compulsory argument to enter.) –
csv (bool. If set to true, a csv file with this information will be saved to your working directory. It is False by default.) –
- Returns
A Pandas DataFrame, containing the queried information regarding job vacancies for a given industry.
If csv=True, a csv file will also be generated.
Examples
>>> from sg_ecg_tool import sg_ecg_tool as sg >>> sg.explore_opening('transportation and storage') [DataFrame of 59 rows x 5 columns]
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sg_ecg_tool.sg_ecg_tool.
explore_study
(subject, csv=False)[source]¶ If you do not know what you would like to study, this is a good start. You can key in a subject that you have some form of interest in, and get a full list of options.
- Parameters
subject (List of Dicts. One of the preset list comprehensions that have been pre-defined. This is a compulsory argument to enter.) –
csv (bool. If set to true, a csv file with this information will be saved to your working directory. It is False by default.) –
- Returns
A Pandas DataFrame, containing the queried information regarding the subject of study.
If csv=True, a csv file will also be generated.
Examples
>>> from sg_ecg_tool import sg_ecg_tool as sg >>> sg.explore_study(sg.Business) [Dataframe of 120 rows x 13 columns] >>> sg.explore_stdy(sg.Healthcare) [DataFrame of 49 rows x 13 columns]
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sg_ecg_tool.sg_ecg_tool.
get_employed
(subject, university_shortname, csv=False)[source]¶ If you already have some sense of what you might like to do, and where you might like to do it, this function can be especially helpful. This can be used in conjunction with see_employ().
You can key in a subject and university that you have some form of interest in, and get a full list of options.
- Parameters
subject (List of Dicts. One of the preset list comprehensions that have been pre-defined. This is a compulsory argument to enter.) –
university_shortname (str. However, you can simply key in the variables that have been created as part of this package (e.g., sg.NUS, sg.NTU) for convenience. This is a compulsory argument to enter.) –
csv: bool. If set to true, a csv file with this information will be saved to your working directory. It is False by default.
- Returns
A Pandas DataFrame, containing the queried information regarding the subject, school of study and the employment rates.
If csv=True, a csv file will also be generated.
Examples
>>> from sg_ecg_tool import sg_ecg_tool as sg >>> sg.get_employed(sg.Law, sg.SMU) [DataFrame of 12 rows x 6 columns]
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sg_ecg_tool.sg_ecg_tool.
get_paid
(subject, university_shortname, csv=False)[source]¶ If you are particularly interested in how much you can expect to be paid, after graduating from your intended course, this function can be especially helpful. This can be used in conjunction with see_paid(). You can key in a subject and university that you have some form of interest in, and get a full list of courses and the salary statistics of their graduates.
- Parameters
subject (List of Dicts. One of the preset list comprehensions that have been pre-defined. This is a compulsory argument to enter.) –
university_shortname (str. However, you can simply key in the variables that have been created as part of this package (e.g., sg.NUS, sg.NTU) for convenience. This is a compulsory argument to enter.) –
csv (bool. If set to true, a csv file with this information will be saved to your working directory. It is False by default.) –
- Returns
A Pandas DataFrame, containing the queried information regarding the subject, school of study and the anticipated salary rates.
If csv=True, a csv file will also be generated.
Examples
>>> from sg_ecg_tool import sg_ecg_tool as sg >>> sg.get_paid(sg.Humanities_SocialSci, sg.NTU) [DataFrame of 96 rows x 7 columns]
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sg_ecg_tool.sg_ecg_tool.
see_employ
(subject, university_shortname)[source]¶ If you already have some sense of what you might like to do, and where you might like to do it, this function can be especially helpful. This can be used in conjunction with get_employed(). You can key in a subject and university that you have some form of interest in, and get a visualization of overall employment rate, over time.
- Parameters
subject (List of Dicts. One of the preset list comprehensions that have been pre-defined. This is a compulsory argument to enter.) –
university_shortname (str. However, you can simply key in the variables that have been created as part of this package (e.g., sg.NUS, sg.NTU) for convenience. This is a compulsory argument to enter.) –
- Returns
- Return type
A matplotlib.pyplot visualization, with year as the x-axis and mean employment rate as the y-axis.
Examples
>>> from sg_ecg_tool import sg_ecg_tool as sg >>> sg.see_employ(sg.Law, sg.SMU) [Visualization]
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sg_ecg_tool.sg_ecg_tool.
see_opening
(industry2)[source]¶ If you are particularly interested in how many available job openings there are in a particular industry, this function can provide you with that information. This can be used in conjunction with explore_opening(). You can key in an industry that you have some form of interest in, and get visualization of job vacancy rates, over 7 recent quarters.
- Parameters
industry2 (str. One of the preset strings, which signify a given industry. This is a compulsory argument to enter.) –
- Returns
- Return type
A matplotlib.pyplot visualization, with quarters as the x-axis and job vacancy rate as the y-axis.
Examples
>>> from sg_ecg_tool import sg_ecg_tool as sg >>> sg.see_opening('paper products and publishing') [Visualization]
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sg_ecg_tool.sg_ecg_tool.
see_paid
(subject, university_shortname)[source]¶ If you are particularly interested in how much you can expect to be paid, after graduating from your intended course, this function can be especially helpful. This can be used in conjunction with get_paid().
You can key in a subject and university that you have some form of interest in, and get a visualization of gross median salary of graduates from a particular type of course and university, over time.
subject: List of Dicts. One of the preset list comprehensions that have been pre-defined. This is a compulsory argument to enter.
university_shortname: str. However, you can simply key in the variables that have been created as part of this package (e.g., sg.NUS, sg.NTU) for convenience. This is a compulsory argument to enter.
A matplotlib.pyplot visualization, with year as the x-axis and average gross monthly median salary as the y-axis.
>>> from sg_ecg_tool import sg_ecg_tool as sg >>> sg.see_paid(sg.Education, sg.NTU)
[Visualization]


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