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intprim
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The MixtureModel class is an extension of BasisModel to multiple basis spaces for multiple degrees of freedom. More...
Public Member Functions | |
| def | __init__ (self, basis_models) |
| Initialization method for the MixtureModel class. More... | |
| def | get_block_diagonal_basis_matrix (self, x, out_array=None, start_row=0, start_col=0) |
| Gets the block diagonal basis matrix for the given phase value(s). More... | |
| def | get_block_diagonal_basis_matrix_derivative (self, x, out_array=None, start_row=0, start_col=0) |
| Gets the block diagonal basis derivative matrix for the given phase value(s). More... | |
| def | get_weighted_vector_derivative (self, x, weights, out_array=None, start_row=0, start_col=0) |
| Gets the weighted vector derivatives corresponding to this basis model for the given basis state. More... | |
| def | fit_basis_functions_linear_closed_form (self, x, y) |
| Fits the given trajectory to this basis model via least squares. More... | |
| def | apply_coefficients (self, x, coefficients, deriv=False) |
| Applies the given weights to this basis model. More... | |
| def | observed_to_state_indices (self, observed_indices) |
| def | observed_indices_related (self, observed_indices) |
| def | __init__ (self, basis_models) |
| Initialization method for the MixtureModel class. More... | |
| def | get_block_diagonal_basis_matrix (self, x, out_array=None, start_row=0, start_col=0) |
| Gets the block diagonal basis matrix for the given phase value(s). More... | |
| def | get_block_diagonal_basis_matrix_derivative (self, x, out_array=None, start_row=0, start_col=0) |
| Gets the block diagonal basis derivative matrix for the given phase value(s). More... | |
| def | get_weighted_vector_derivative (self, x, weights, out_array=None, start_row=0, start_col=0) |
| Gets the weighted vector derivatives corresponding to this basis model for the given basis state. More... | |
| def | fit_basis_functions_linear_closed_form (self, x, y) |
| Fits the given trajectory to this basis model via least squares. More... | |
| def | apply_coefficients (self, x, coefficients, deriv=False) |
| Applies the given weights to this basis model. More... | |
| def | observed_to_state_indices (self, observed_indices) |
| def | observed_indices_related (self, observed_indices) |
| Public Member Functions inherited from intprim.basis.basis_model.BasisModel | |
| def | __init__ (self, degree, observed_dof_names) |
| Initialization method for the BasisModel class. More... | |
| def | get_block_diagonal_basis_matrix (self, x, out_array=None, start_row=0, start_col=0) |
| Gets the block diagonal basis matrix for the given phase value(s). More... | |
| def | get_block_diagonal_basis_matrix_derivative (self, x, out_array=None, start_row=0, start_col=0) |
| Gets the block diagonal basis derivative matrix for the given phase value(s). More... | |
| def | get_weighted_vector_derivative (self, x, weights, out_array=None, start_row=0, start_col=0) |
| Gets the weighted vector derivatives corresponding to this basis model for the given basis state. More... | |
| def | fit_basis_functions_linear_closed_form (self, x, y) |
| Fits the given trajectory to this basis model via least squares. More... | |
| def | apply_coefficients (self, x, coefficients, deriv=False) |
| Applies the given weights to this basis model. More... | |
| def | plot (self) |
| def | plot_derivative (self) |
| def | plot_weighted (self, coefficients, coefficient_names) |
| def | observed_to_state_indices (self, observed_indices) |
| def | observed_indices_related (self, observed_indices) |
| def | __init__ (self, degree, observed_dof_names) |
| Initialization method for the BasisModel class. More... | |
| def | get_block_diagonal_basis_matrix (self, x, out_array=None, start_row=0, start_col=0) |
| Gets the block diagonal basis matrix for the given phase value(s). More... | |
| def | get_block_diagonal_basis_matrix_derivative (self, x, out_array=None, start_row=0, start_col=0) |
| Gets the block diagonal basis derivative matrix for the given phase value(s). More... | |
| def | get_weighted_vector_derivative (self, x, weights, out_array=None, start_row=0, start_col=0) |
| Gets the weighted vector derivatives corresponding to this basis model for the given basis state. More... | |
| def | fit_basis_functions_linear_closed_form (self, x, y) |
| Fits the given trajectory to this basis model via least squares. More... | |
| def | apply_coefficients (self, x, coefficients, deriv=False) |
| Applies the given weights to this basis model. More... | |
| def | plot (self) |
| def | plot_derivative (self) |
| def | plot_weighted (self, coefficients, coefficient_names) |
| def | observed_to_state_indices (self, observed_indices) |
| def | observed_indices_related (self, observed_indices) |
Public Attributes | |
| observed_dof_names | |
| num_observed_dof | |
| observed_dof_to_model_map | |
| observed_dof_to_degree_map | |
| basis_models | |
| model_degrees | |
| block_prototype | |
| Public Attributes inherited from intprim.basis.basis_model.BasisModel | |
| observed_dof_names | |
| num_observed_dof | |
| block_prototype | |
The MixtureModel class is an extension of BasisModel to multiple basis spaces for multiple degrees of freedom.
This allows a user to define separate models for each degree of freedom. For example, in a 3 DoF model the first 2 DoFs might both belong to a GaussianModel while the 3rd belongs to a SigmoidalModel.
| def intprim.basis.mixture_model.MixtureModel.__init__ | ( | self, | |
| basis_models | |||
| ) |
Initialization method for the MixtureModel class.
| basis_models | array-like, shape(num_basis_models, ). Contains a list of (non-MixtureModel) basis models that are encapsulated in this model. |
| def intprim.basis.mixture_model.MixtureModel.__init__ | ( | self, | |
| basis_models | |||
| ) |
Initialization method for the MixtureModel class.
| basis_models | array-like, shape(num_basis_models, ). Contains a list of (non-MixtureModel) basis models that are encapsulated in this model. |
| def intprim.basis.mixture_model.MixtureModel.apply_coefficients | ( | self, | |
| x, | |||
| coefficients, | |||
deriv = False |
|||
| ) |
Applies the given weights to this basis model.
Projects a basis state to the measurement space.
| x | Scalar of vector of dimension T containing the phase values to project at. |
| coefficients | Vector of dimension degree * num_observed_dof containing the basis weights. |
| deriv | True to use basis function derivative, False to use regular basis functions. |
| def intprim.basis.mixture_model.MixtureModel.apply_coefficients | ( | self, | |
| x, | |||
| coefficients, | |||
deriv = False |
|||
| ) |
Applies the given weights to this basis model.
Projects a basis state to the measurement space.
| x | Scalar of vector of dimension T containing the phase values to project at. |
| coefficients | Vector of dimension degree * num_observed_dof containing the basis weights. |
| deriv | True to use basis function derivative, False to use regular basis functions. |
| def intprim.basis.mixture_model.MixtureModel.fit_basis_functions_linear_closed_form | ( | self, | |
| x, | |||
| y | |||
| ) |
Fits the given trajectory to this basis model via least squares.
| x | Vector of dimension T containing the phase values of the trajectory. |
| y | Matrix of dimension num_observed_dof x T containing the observations of the trajectory. |
| coefficients | Vector of dimension degree * num_observed_dof containing the fitted basis weights. |
| def intprim.basis.mixture_model.MixtureModel.fit_basis_functions_linear_closed_form | ( | self, | |
| x, | |||
| y | |||
| ) |
Fits the given trajectory to this basis model via least squares.
| x | Vector of dimension T containing the phase values of the trajectory. |
| y | Matrix of dimension num_observed_dof x T containing the observations of the trajectory. |
| coefficients | Vector of dimension degree * num_observed_dof containing the fitted basis weights. |
| def intprim.basis.mixture_model.MixtureModel.get_block_diagonal_basis_matrix | ( | self, | |
| x, | |||
out_array = None , |
|||
start_row = 0 , |
|||
start_col = 0 |
|||
| ) |
Gets the block diagonal basis matrix for the given phase value(s).
Used to transform vectors from the basis space to the measurement space.
| x | Scalar of vector of dimension T containing the phase values to use in the creation of the block diagonal matrix. |
| out_array | Matrix of dimension greater to or equal than (degree * num_observed_dof * T) x num_observed_dof in which the results are stored. If none, an internal matrix is used. |
| start_row | A row offset to apply to results in the block diagonal matrix. |
| start_col | A column offset to apply to results in the block diagonal matrix. |
| def intprim.basis.mixture_model.MixtureModel.get_block_diagonal_basis_matrix | ( | self, | |
| x, | |||
out_array = None , |
|||
start_row = 0 , |
|||
start_col = 0 |
|||
| ) |
Gets the block diagonal basis matrix for the given phase value(s).
Used to transform vectors from the basis space to the measurement space.
| x | Scalar of vector of dimension T containing the phase values to use in the creation of the block diagonal matrix. |
| out_array | Matrix of dimension greater to or equal than (degree * num_observed_dof * T) x num_observed_dof in which the results are stored. If none, an internal matrix is used. |
| start_row | A row offset to apply to results in the block diagonal matrix. |
| start_col | A column offset to apply to results in the block diagonal matrix. |
| def intprim.basis.mixture_model.MixtureModel.get_block_diagonal_basis_matrix_derivative | ( | self, | |
| x, | |||
out_array = None , |
|||
start_row = 0 , |
|||
start_col = 0 |
|||
| ) |
Gets the block diagonal basis derivative matrix for the given phase value(s).
Used to transform vectors from the derivative basis space to the measurement space.
| x | Scalar containing the phase value to use in the creation of the block diagonal matrix. |
| out_array | Matrix of dimension greater to or equal than (degree * num_observed_dof) x num_observed_dof in which the results are stored. If none, an internal matrix is used. |
| start_row | A row offset to apply to results in the block diagonal matrix. |
| start_col | A column offset to apply to results in the block diagonal matrix. |
| def intprim.basis.mixture_model.MixtureModel.get_block_diagonal_basis_matrix_derivative | ( | self, | |
| x, | |||
out_array = None , |
|||
start_row = 0 , |
|||
start_col = 0 |
|||
| ) |
Gets the block diagonal basis derivative matrix for the given phase value(s).
Used to transform vectors from the derivative basis space to the measurement space.
| x | Scalar containing the phase value to use in the creation of the block diagonal matrix. |
| out_array | Matrix of dimension greater to or equal than (degree * num_observed_dof) x num_observed_dof in which the results are stored. If none, an internal matrix is used. |
| start_row | A row offset to apply to results in the block diagonal matrix. |
| start_col | A column offset to apply to results in the block diagonal matrix. |
| def intprim.basis.mixture_model.MixtureModel.get_weighted_vector_derivative | ( | self, | |
| x, | |||
| weights, | |||
out_array = None , |
|||
start_row = 0 , |
|||
start_col = 0 |
|||
| ) |
Gets the weighted vector derivatives corresponding to this basis model for the given basis state.
| x | Scalar containing the phase value to use in the creation of the block diagonal matrix. |
| weights | Vector of dimension degree * num_observed_dof containing the weights for this basis model. |
| out_array | Matrix of dimension greater to or equal than 1 x degree in which the results are stored. If none, an internal matrix is used. |
| start_row | A row offset to apply to results in the block diagonal matrix. |
| start_col | A column offset to apply to results in the block diagonal matrix. |
| def intprim.basis.mixture_model.MixtureModel.get_weighted_vector_derivative | ( | self, | |
| x, | |||
| weights, | |||
out_array = None , |
|||
start_row = 0 , |
|||
start_col = 0 |
|||
| ) |
Gets the weighted vector derivatives corresponding to this basis model for the given basis state.
| x | Scalar containing the phase value to use in the creation of the block diagonal matrix. |
| weights | Vector of dimension degree * num_observed_dof containing the weights for this basis model. |
| out_array | Matrix of dimension greater to or equal than 1 x degree in which the results are stored. If none, an internal matrix is used. |
| start_row | A row offset to apply to results in the block diagonal matrix. |
| start_col | A column offset to apply to results in the block diagonal matrix. |


Controls window updating when you have more than on window open on the same buffer and you make changes to a part of the buffer visible in multiple windows.
If quick-redisplay is non-zero, Emacs will not keep all windows updated as you type -- this makes display updating much faster. All changes will eventually be applied to all windows.
When quick-redisplay it is set to zero, all windows are always accurate.
query-replace-use-region-for-search « 0
Controls how the query replace functions default the search string. When ON and there is a region selected it is used as the search string defualt.


Stale-While-Revalidate
I use the service worker to implement the "Stale-While-Revalidate" strategy on this page.
First of all, the image below loads from the cache then be revalidated for the next use.
To see the effect, keep refreshing the page after the service worker activation!


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ChangeLogThis file summarizes all types of changes to the OpenSSL toolkit, i.e. changes between each patchlevel. Take this list as a reference for concrete and detailed information about every significant change. The presented contents reflects the current state of the CHANGES file inside the git repository.


☰ Topics
Please open the navigation pane to the left. These topics will include examples and links to their respective website, if you wish to learn more.
asdfasdfjkla;sdhvp;loqahjsdfv;lkahnsdv;lkjahdf;klvjbnsd;klfjbns;dfnb;vsadnfb;lskdnfb
Please open the navigation pane to the left. These topics will include examples and links to their respective website, if you wish to learn more.
asdfasdfjkla;sdhvp;loqahjsdfv;lkahnsdv;lkjahdf;klvjbnsd;klfjbns;dfnb;vsadnfb;lskdnfb


Percentages and discount
Calculate the price to pay of your discounted products
Test
Write the price of your product:
Write the discount of your product:
Result:
Calculate the discounted price


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#
Created
Vendor ID
Label
Runs
Description
{% if object_list.count > 0 %}
{% for sequencer in object_list %}
{% include 'sequencers/_sequencer_item.html' with item=sequencer details_card_mode=False %}
{% endfor %}
{% else %}


Lendroid is a 0x-inspired, non-rent seeking, trust-independent, open protocol enabling decentralized lending, margin trading and short selling on the Ethereum blockchain. It aims to solve the shortcomings of centralized exchanges by creating a global shared lending pool, and a symbiotic off-chain infrastructure supported by incentivized participants - Relayers and Wranglers. Simply put, Lenders contribute their offers to the lending pool through relayers, who then match the offers with appropriate traders. The traders can use the borrowed funds to margin trade, make a profit, and repay the lenders.
If a trader wishes to monetize his confidence in the price of an asset increasing/decreasing, he would take a position by purchasing/selling the asset. If he is extremely certain, he would build a leveraged position by borrowing additional funds from a lender to magnify his profits.
Lending - A borrower pledges digital assets into an escrow account, with specific terms for the loan set in a smart contract. If the lender’s and borrower’s terms match, the smart contract is executed, locking in the collateral and releasing the funds to the borrower funds. If the loan obligations are satisfied by the borrower, his collateral is unlocked automatically. If the borrower defaults or the collateral drops below the agreed loan to value, the collateral is liquidated.
Margin trading - A margin trader starts by depositing collateral. The trader is allowed to borrow up to a certain leverage to the point where the margin level is greater that the initial level (set through community governance). The borrowed funds come from the shared lending pool to which the lenders can submit offers. With the borrowed funds, the trader interacts with order-books to calibrate positions to reflect expectations from future price movements. Once the traders feel it’s time to unwind their position, the trader once again interacts with the order book.
If the trader made a profit, he can repay the lenders and withdraw the profit along with his collateral.
If a loss is incurred, the trader is expected to deposit the difference to compensate for the losses, repay the loans in full and only then withdraw collateral.
If the account reaches liquidation levels, the wranglers step in and take over the account/positions.
Lendroid is compatible with any ERC20-based token. This gives the margin trader a high level of flexibility with respect to the nature of collateral he can deposit against a loan. A Margin Trader is free to deposit not just one kind of collateral in a token of his choice, but multiple kinds of ERC20-based tokens at the same time, in the same margin account. In what is envisioned as a community-driven governance model, the protocol will add or remove support for a token based on the current volatility of the token.
Analogous to the Maker from the 0x protocol, the lender broadcasts the loan-offer to all decentralized exchanges (off-chain action). The lender deposits the funds into the Funding Account of the Lendroid Smart Contract System. To lubricate the inherent processes, the lender deposits some LST (Lendroid Support Token) meant for the Relayer at the end of the loan period. Some he deposits for the Wrangler in the event that it was he who identified the Margin Account at liquidation level and helped close the loan. The LSTs required for these processes are locked in at the time of initiation. If there is not enough LST for these processes, the loan is not initialized.
On Lendroid, the Margin Trader enjoys leveraged lending in a uniquely decentralized environment. A Margin Trader can avail a loan through his Relayer. Offers are signed by the respective Lenders who made them are available with the Relayer. He avails the loan, deposits collateral and engages in margin trading/short selling. He opens a margin account, within which he is free to change positions or add collateral (if the health of the margin account dips). He can withdraw his collateral or liquidate his Margin Account when there are no loans owed.
The Relayer is the first point of contact for both lender and trader on the Lendroid protocol. Off chain, this entity effectively takes up the liquidity related functions of an exchange, in a wholly decentralized manner. The one significant addition to the Lendroid Relayer over its 0x counterpart is the management of loan offers. Relayers provide an interface, and manage order books and offer books. They are key to creating the shared global lending pool, and have complete access to it. The collective of loan offers recorded with not just one particular Relayer, but with the entire community of Relayers on the protocol.
The Wrangler is an entity conceptualized for the Lendroid ecosystem. One of two incentivized actors within the protocol, the Wrangler is intended to perform a computationally intensive role, that of monitoring the Margin Accounts. By monitoring and ‘Wrangling’ terminal accounts (at liquidation level), the Wrangler maintains the general health of the ecosystem, while also protecting the interests of the Lenders. From the creation of a Margin Account, to every state change in position, adding of collateral, right until the account is liquidated, the Wrangler keeps tabs on each development. Whenever there is a change in state, the Wrangler notifies the Smart Contract System, which then triggers an Oracle call and subsequently checks the margin level of the account.
When a Wrangler identifies a terminal margin account, the claim is verified by the smart contract. If ratified, the Wrangler is awarded a bounty (part of the loan offer object, deposited by the Lender) and the auction is triggered. The Lendroid auction is not winner take all. The trader’s collateral and positions are taken over, but not by any single Wrangler. Every Wrangler chooses to repay a small portion of total outstanding loan and his bid would be proportionately considered.
The Lendroid Support Token, or LST, is the native token of the Lendroid protocol. Participation on the protocol requires a user to possess LSTs. Those that do not are incentivized to earn LSTs by engaging in activities that contribute to the health of the ecosystem. As with all ERC20 tokens, LSTs will be tradeable following the crowd sale.
The LST enables utility and lubricates processes on the protocol. There are four players on the protocol, two of which provide utility - the Relayers and the Wranglers, and two others who avail of these utilities and participate to maximize profits - the Lenders and the Borrowers/Margin Traders. On Lendroid, the toll for service or utility providers on the protocol is baked into the system. Even without possessing a single token of LST, a user might participate on Lendroid. As a Wrangler, he can monitor Margin Accounts and choose to earn LSTs by identifying those accounts at liquidation level. In other words, they can be part of the network even without LSTs, by pitching in to provide vital services like monitoring loan lifecycles and margin accounts.
The total supply of tokens is 6,000,000,000 LST. The token launch will take place on Novermber 25th, 2017. ETH is the only accepted currency during the launch.
Lendroid was founded in Canada, and is currently based in Singapore..
Email: connect@lendroid.com


{% extends "blog/admin/base.html" %} {% block title %}Admin: Publishing Post{% endblock %} {% block body %}
Your changes have been saved (but not published)
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{% endif %} {% else %}Your draft post has been saved.
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SARVA 3.0 is much more participative than its predecessors. Not only do we allow officials to generate region-specific asssessments of impact, but contributions of data in respect of mitigation and adaptation actions, disasters, and other important or noteworthy global change related events are also possible. We will shortly pilot contributions of media - such as photographs and videos - to be linked to our events datanbase via mobile phones.
SAEON's proposed Events Database will blend data from a variety of sources, including social media monitoring, news site monitoring, contributions of photographs and videos, and remotely sensed data. These data streams can be compared to historical data on declared disasters obtained from NDMC.
It is hoped that we can use these techniques to generate datasets that are otherwise very diofficult or impossible to obtain. As an example: urban fires are widespread in South Africa, and have huge impacts in terms of livelihoods and lives for the poorest of our population - but reliable data on the location, frequency and duration of these fires does not exist. The fires are often too small and short-lived to be recorded by satellites. We therefore see a major advantage in scraping news feeds and social media for urban fire information.
Similarly, many other events that are of interest to the Risk and Vulnerability community are poorly recorded. Examples include wildlife poaching, service delivery protests, and municipal service interruptions.


WITNESS LIST Veteran Affairs & Military Installations Committee March 14, 2001 - 3:00P SB 803 ON: Howdeshell, Doris Director, Travel Division (Texas Department of Transportation), Buda, TX SB 911 FOR: Brady, Christopher Assistant City Manager (City of San Antonio), San Antonio, TX Godfrey, Brendan Deputy Director (Brooks Air Force Base), San Antonio, TX Messbarger, Dick Vice Chair (Texas Defense, Aviation, & Aerospace Alliance), Kingsville, TX ON: Christopherson, James Director (Office of Defense Affairs), Austin, TX SB 950 Registering, but not testifying: On: Buerschinger, Charlie Director (Texas Veterans Commission), Austin, TX Hunt, Darrel Deputy Director (Texas Department of Transportation), Austin, TX SB 1089 ON: Brueschinger, Charlie Director (Texas Veterans Commission), Austin, TX Registering, but not testifying: On: Hunt, Darrel Deputy Director (Texas Department of Transportation), Austin, TX


Breakpoints are the sizes where you screen will change layouts. As you can see in the Rows section, at certain screen sizes the content will change to a different layout. With Bootstrap, this concept is made a lot more simple if the content is put into a column and container.
Just
col-md-4being the only one. So once this breakpoint is hit, all the content wrapped in the
class="container"the
class="row"and the
class="col-md-4"it will turn itself into one column.
We offer specialized care for reptiles, rodents, birds, and other exotic pets.
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Wellness and senior exams, ultrasound, x-ray, and dental cleanings are just a few of our general health services.
Let our nutrition experts review your pet's diet and prescribe a custom nutrition plan for optimum health and disease prevention.
We offer the latest advances in safe and effective prevention and treatment of fleas, ticks, worms, heart worm, and other parasites.
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Specifically has the classes of:
col-sm-6
col-md-4
col-lg-2
We offer specialized care for reptiles, rodents, birds, and other exotic pets.
Our therapeutic grooming treatments help battle fleas, allergic dermatitis, and other challenging skin conditions.
Wellness and senior exams, ultrasound, x-ray, and dental cleanings are just a few of our general health services.
Let our nutrition experts review your pet's diet and prescribe a custom nutrition plan for optimum health and disease prevention.
We offer the latest advances in safe and effective prevention and treatment of fleas, ticks, worms, heart worm, and other parasites.
Our veterinarians are experienced in modern vaccination protocols that prevent many of the deadliest diseases in pets.


Hiroshi Lockheimer answers the question, "How did you get here?"
I’m always curious to hear people’s answers to this question. I look around at my colleagues and I’m in constant awe of their qualifications, and more importantly, achievements. I feel the same way when I watch TED talks — my latest favorite is by a Japanese architect who built a new kind of kindergarten aimed at fostering fun, creativity and independence. And as I’m typing right now I’m listening to Sia — whatever your preferred genre, it’s hard to dispute that she’s in total command of her talent.
Lately, I’ve had a deepening sense of appreciation for all the talent that surrounds us in the world — whether that’s the chef who makes the best pizza in town or the construction crew that creates a building from seemingly nothing. And it always brings me back to the question: “How did they get here?”
Around my ten-year anniversary as a Google employee someone asked me the very same question: “How did you get here?”
My path to Google wasn’t a typical one. I don’t have a degree, I wasn’t involved in the invention of the internet, I didn’t pioneer the use of MapReduce, and — confession time — I have no idea how TensorFlow works (but I’m pretty sure there’s some sort of sorcery involved).
I’ve always had a curiosity about building things. I have fond memories of being a Master Planner for my very own Lego city. In fact, by my late teens, I thought I wanted to be an architect (of buildings). But I was also fascinated by the magic of technology: a magnet that turns tape into sound? Another — this time floppy — magnet that can be used to store information for your computer? How did they work? (Sorcery?)
My curiosity was particularly intense about computers. It was persistent enough that I decided to teach myself how to program on my parents’ Mac. I bought a book on Pascal and a copy of THINK Pascal. I still remember my very first app for System 6, a simple clock (by the way, I absolutely loved Inside Mac). It was incredibly satisfying to deconstruct the magic: a while loop here, a few if/thens there, and I had an interactive window that displayed the time.
But, wait, where did the window come from? How were the digits drawn on screen? What was responsible for all that magic? I had to go deeper. So eventually I delved into the world of operating systems.
Luckily, around that time, a tiny little startup in Menlo Park, California, released a really funky operating system called BeOS, along with an even funkier PC called the BeBox. I ordered mine as soon as I found out about it. It took them something like three months to ship it to Japan, but while I waited, I scoured the API docs every night.
When it finally arrived, I was off to the races. For an aspiring OS developer — and this will resonate if you’re familiar with it — the BeOS framework was, shall we say, opportunity rich. My first project was to create a text engine that could handle more than one font, size, and color at a time. Similar to the clock, I was driven by curiosity — how did text engines work?
I open-sourced this text engine, which eventually led to a phone call from a recruiter. A few months and several interviews later, I was headed to Tokyo Narita Airport with a backpack, a duffel bag, and a one-way ticket to San Francisco: I was going to work at Be.
The memory of the final interview leading up to that moment is still deeply etched in my mind. I met with the CEO, Jean-Louis Gassée. JLG (as he’s known) and I talked a lot about Japan and whether its culture fosters software entrepreneurship (I posited no; topic for another post). We had a 15-minute conversation, tops. He then did his throat-clearing thing (it’s a thing for those who know him), abruptly stood up, and walked me out of his office. The aforementioned recruiter greeted us. He looked at her and said, “I’m supportive.” And that was it.
To be honest, I didn’t know what he meant by that. Supportive of what?! That might have been a typical hiring moment for JLG — certainly was a head-scratcher moment for me — but in retrospect, that was the moment when a new path opened, the path from where I would make all future decisions.
It was kind of like in Star Trek IV, when Kirk travels back in time to save the whales — one path fades away and another set of paths materializes. And that’s happened over and over and over again — whether it’s when I met the ever-crazy (and wonderful) Andy Rubin and ended up working at Danger (and eventually Google), or when I accepted a job working for Danny Shader at Good (where I also met my wife), or the first time I talked to my now-boss Sundar Pichai.
So, how did I get here? Here are my takeaways:
Curiosity powers everything. Learning doesn’t end at a fixed point in time (like a graduation ceremony) — in fact, you can learn to learn even more efficiently over time. Corollary: nobody was born the world’s leading expert on anything, so you can make yourself that person if you work hard enough at it.
You never know if you’re in that “Kirk moment” RIGHT NOW, so treat every minute with care and passion. Corollary: be open to new paths, and don’t follow a rigid n-year plan.
While I consider myself a fiercely independent person, Kirk moments are created by and with others. I have so many people to thank. I would not have gotten here without them.
My goal is to create those moments for others — both the people I work with and everyone who uses our products. The platforms and products I work on touch the lives of billions of people every day. I don’t take that responsibility lightly. Every day I’m excited by what’s possible, and much like the teenage me, I’m eager to create and build products that feel like magic.
P.S. to the recruiter from Be: When I first arrived in San Francisco, you and your husband let me, practically a stranger, stay with you while I searched for my own apartment. You even took me to Bed Bath & Beyond so that I could buy essentials like sheets and towels. That was kinda amazing. In the haze of jet lag and my general freak-out over having just moved thousands of miles from home, I don’t think I thanked you enough. Thank you.
This story originally appeared on Medium.


Videos, blogposts, and useful content related to automatic mixing
Joseph Colonel and Christian Steinmetz
Slides


Revision 3.0
This is a short program meant to help you make graphs for the IA.  These text files list out where to draw points, lines, circles, etc.
Then, inside Windows Explorer, drag and drop your text file on top of
DROP_OVER_ME.bat
file. This will make a window appear with the graph. Using
the buttons on the bottom, you can zoom in or examine the graph, and save it as a
.png
file when you're happy.
This program was tailor-made for this IA, so it's deliberately designed to be somewhat basic.
Warning!
Do not delete or change anything inside the bin
folder. That'll stop
the program from working. This readme/the batch file also uses stuff from inside
that folder, so moving the readme or batch file to a new location will break them.
Here is an example file, and the graph it will generate. A detailed breakdown of
how this file was produced is included below. You can see both this image and the
script file used to produce it inside the examples
folder.
% Any line that starts with '%' is a comment and is
% ignored by the program.
% These set the x and y axises, and the interval
% between each tick.
xrange -5, 7
xinterval 2.0
yrange -5, 7
yinterval 2.0
% Draws a circle of radius 2.
circle 2
% Draws a point at (1, 2). Note that I'm using square
% brackets for all cartesian coordinates.
point [1, 2]
% You can plug in arbitrary math expressions. The order
% of operations is respected. The constant 'pi' and the
% sqrt function are provided.
point [1 - (2 * 1.5), pi * sqrt(3)]
% You can also use complex numbers instead of cartesian
% coordinates. Note there is no implicit multiplication:
% you always have to do 3 * i, not 3i.
% Therefore, writing [2, 2] is equivalent to writing
% 2 + 2 * i
point -pi + sqrt(3) * i
% You can add color to any line, point, circle, or text
% you add by adding an exclamation mark and the color
% name. See:
% http://www.w3schools.com/html/html_colornames.asp
% for a list of all valid colors.
point 1 - i ! red
% You can draw lines between two points. Separate each
% complex number/coordinate with a comma.
line [0, 0], [2, 2]
line -i, 5 + i ! aqua
% You can draw circles. All circles are centered around
% the origin, and the number following is the radius.
circle 3.5 ! red
circle 2 ! green
% You can add text. The first argument is the coordinate
% where the text starts. The second argument is the text
% to display. Note that it's surrounded by quote marks.
% To make text display on more than one line, use the
% '\n' symbol whenever you want a newline.
text [3, -3.5], "This is some text\nBlah blah blah."
% You can define variables so you can refer to
% coordinates without constantly having to rewrite them.
% Here, I'm creating a variable called 'p1' which is
% equal to the cartesian coordinate [4, 4]. Note the
% comma between the variable name and the coordinate.
variable p1, [4, 4]
% To use a variable, place it where you would normally
% have a coordinate or complex number and prefix it
% with the '@' symbol.
% Don't forget about that '@' symbol!
point @p1 ! orange
line [5, 5], @p1 ! green
line [3, 5], @p1 ! green
line [5, 3], @p1 ! green
line [3, 3], @p1 ! green
As you can see, each line in general takes the basic form:
command argument1, argument2 ! optional_color
...where each line starts with a command, one or two arguments (separated by a comma), and an optional color.
Here is another example file to help get you started:
xrange -1.3, 1.3
yrange -1.3, 1.3
xinterval .2
yinterval .2
variable p1, 1
variable p2, -1/4 + sqrt(5)/4 + i*sqrt(sqrt(5)/8 + 5/8)
point @p1
point @p2
point -sqrt(5)/4 - 1/4 + i*sqrt(-sqrt(5)/8 + 5/8)
point -sqrt(5)/4 - 1/4 - i*sqrt(-sqrt(5)/8 + 5/8)
point -1/4 + sqrt(5)/4 - i*sqrt(sqrt(5)/8 + 5/8)
circle 1 ! green
line @p1, @p2 ! red
Michael Lee — This project is licensed under the MIT License


{% extends 'base.html' %} {% block content %}
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Message in a Bottle (1999)
A Review by Christian Pyle
Robin Wright Penn recently denounced the script for her new movie, "Message in a Bottle," as being too "in-your-face and precious." She's right-the script is a manipulative hanky-puller painted by the numbers, but it is redeemed by guileless performances and an ending that departs from the simplistic themes that, up to then, it seems to pursue.
The plot's a sentimental romance with syrupy overtones: On vacation, Theresa Osborne (Penn) finds a bottle on a beach. Inside is a type-written note from "G." to a departed love, Catherine. The note testifies to the sensitivity and depth of feeling of "G.," and Theresa, divorced and lonely, begins a quest to find the note's author. Her boss (Robbie Coltrane), a "Chicago Tribune" columnist, publishes the letter, and two other letters are revealed.
The clues in the letters lead Theresa to the Outer Banks of North Carolina and boat-restorer Garret Blake (Kevin Costner). His wife Catherine died two years earlier, and Garret has lived in limbo ever since. Theresa, of course, lies to Garret about why she's there (setting the stage for the inevitable "girl-loses-boy" development when he finds out the truth), but they soothe each other's wounds of lost love. Romance blooms.
Both Theresa and Garret abandoned their dreams when they lost their spouses. She dreamed of being a writer; he intended to build his own boats. Predictably, each inspires the other to begin writing/building again. Up to the end, the message in this bottle seems clear: move on after loss, learn to live and love again. I won't divulge the ending, of course, but it undercuts the predictable themes developed up to then. It led me to rethink my assumptions about the movie and its subject matter.
This is the sort of movie that has to sell itself to the audience. I was willing to buy into it because the performances were open and honest. Both Penn and Costner seem quite earnest and genuine in their roles, which is a surprising turn for Costner, who usually alternates between his "Eliot Ness" and "Crash Davis" personas. Paul Newman, who plays Garret's dad, Dodge Blake, is a national treasure. He's never given a bad performance or put forth less than a full effort. Even in a small supporting role, Newman shines. By the time it was over, I found myself caring most about Dodge, a cranky recovering alcoholic who dotes on his son.
The familiar plot of "Message in a Bottle" shatters easily with slightest application of cynicism or even realism, but its cast puts forth an admirable effort to defuse those reactions.
Grade: B-
© 1999 Christian L. Pyle
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Story Mode
Custom Game
Credits
Best in:
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Start
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Construct Invaders
by
github.com/errorsfordays
Music courtesy of Kevin MacLeod
(www.smartsound.com/royalty-free-music/incompetech)
Sounds from Leshy SFDesigner
(www.leshylabs.com)
and www.freesfx.co.uk and talkingwav.com
Images from flickr.com/commons
Back


AccorHotels Arena (originally known as Palais Omnisports de Paris-Bercy and formerly Bercy Arena) is an indoor sports arena and concert hall located on boulevard de Bercy in the 12th arrondissement of Paris, France. The closest métro station is Bercy.
Designed by the architectural firm Andrault-Parat, Prouve and Guvan, it has a pyramidal shape and walls covered with sloping lawn. It is the main venue for the Paris Masters, ATP Tour tennis tournament and for many sports: handball, basketball, boxing, gymnastics, track cycling, show jumping and more.
It has a seating capacity ranging from 7,000 to 20,300, depending on the event. POPB hosted the European gymnastics championship in 2000, the 1991 and 1996[1] FIBA European Championships Final Fours and the European Basketball Championship in 1999 among others.
"Don't underestimate the Handball. I suggest you try it again, Luke."
The POPB has also staged WWE Raw on September 28, 2008. It also hosted the 2010 Euroleague Final Four. It also hosted the Masters Karting Paris Bercy star race from 1993 to 2001, and again in 2011.
As part of the 2014–15 renovations efforts, the arena was renamed Bercy Arena on 1 January 2015. It was again renamed AccorHotels Arena in October 2015.[2]


|
| void | OnError (ShopifyError error) |
| | Called when a request initiated by the controller returns an error More...
|
| |
| void | OnLoadingFinished () |
| | Called when the controller has finished loading data from shopify. Shops can use this to dismiss loading states or spinners. More...
|
| |
| void | OnLoadingStarted () |
| | Called when the controller has started loading data from shopify. Shops can use this to know when to show loading states or spinners. More...
|
| |
| void | OnPurchaseCancelled () |
| | Called when an ongoing purchase is cancelled. This either occurs when a native payment method is cancelled or the purchase is cancelled manually. More...
|
| |
| void | OnPurchaseCompleted () |
| | Called when an ongoing purchase completes. When using the Web purchase method, it's possible this will never be called if the player navigates away from the browser and doesn't complete the purchase. In this case you should always show a UI for cancelling the purchase in-game. More...
|
| |
| void | OnPurchaseStarted () |
| | Called when a purchase has started in the controller. Useful for changing the UI when a purchase is ongoing. More...
|
| |
| void | OnPurchaseFailed (ShopifyError error) |
| | Called when an ongoing purchase fails to complete. This can occur if the cart is invalid and a checkout cannot be created from it. If it's a native payment (ie: Apple Pay), then this can also occur if the payment fails to process. More...
|
| |
| void | OnProductLoaded (Product product, ProductVariant[] variants) |
| | Called when the controller is finished loading the product that the shop is selling. More...
|
| |
◆ OnError()
| void Shopify.UIToolkit.Shops.DebugSingleProductShop.OnError |
( |
ShopifyError |
error | ) |
|
|
inline |
Called when a request initiated by the controller returns an error
- Parameters
-
| error | The error that occured |
Implements Shopify.UIToolkit.IShop.
◆ OnLoadingFinished()
| void Shopify.UIToolkit.Shops.DebugSingleProductShop.OnLoadingFinished |
( |
| ) |
|
|
inline |
Called when the controller has finished loading data from shopify. Shops can use this to dismiss loading states or spinners.
Implements Shopify.UIToolkit.IShop.
◆ OnLoadingStarted()
| void Shopify.UIToolkit.Shops.DebugSingleProductShop.OnLoadingStarted |
( |
| ) |
|
|
inline |
Called when the controller has started loading data from shopify. Shops can use this to know when to show loading states or spinners.
Implements Shopify.UIToolkit.IShop.
◆ OnProductLoaded()
| void Shopify.UIToolkit.Shops.DebugSingleProductShop.OnProductLoaded |
( |
Product |
product, |
|
|
ProductVariant[] |
variants |
|
) |
| |
|
inline |
Called when the controller is finished loading the product that the shop is selling.
- Parameters
-
| product | The product that was loaded |
| variants | The variants of the product that was loaded |
Implements Shopify.UIToolkit.ISingleProductShop.
◆ OnPurchaseCancelled()
| void Shopify.UIToolkit.Shops.DebugSingleProductShop.OnPurchaseCancelled |
( |
| ) |
|
|
inline |
Called when an ongoing purchase is cancelled. This either occurs when a native payment method is cancelled or the purchase is cancelled manually.
Implements Shopify.UIToolkit.IShop.
◆ OnPurchaseCompleted()
| void Shopify.UIToolkit.Shops.DebugSingleProductShop.OnPurchaseCompleted |
( |
| ) |
|
|
inline |
Called when an ongoing purchase completes. When using the Web purchase method, it's possible this will never be called if the player navigates away from the browser and doesn't complete the purchase. In this case you should always show a UI for cancelling the purchase in-game.
Implements Shopify.UIToolkit.IShop.
◆ OnPurchaseFailed()
| void Shopify.UIToolkit.Shops.DebugSingleProductShop.OnPurchaseFailed |
( |
ShopifyError |
error | ) |
|
|
inline |
Called when an ongoing purchase fails to complete. This can occur if the cart is invalid and a checkout cannot be created from it. If it's a native payment (ie: Apple Pay), then this can also occur if the payment fails to process.
Implements Shopify.UIToolkit.IShop.
◆ OnPurchaseStarted()
| void Shopify.UIToolkit.Shops.DebugSingleProductShop.OnPurchaseStarted |
( |
| ) |
|
|
inline |
Called when a purchase has started in the controller. Useful for changing the UI when a purchase is ongoing.
Implements Shopify.UIToolkit.IShop.
The documentation for this class was generated from the following file:
- Assets/Shopify/UIToolkit/Shops/Debug/DebugSingleProductShop.cs


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Classes
The following classes are available globally.
-
A mitigator tries to make a problem less servere. This is a default implementation that implements all the required protocols:
Mitigator
-> capture any thrown errors and rethrow them if needed.ResponseMitigatable
-> Try to handle response errors. By default errors are printed and rethrown.RequestMitigatable
-> Try to handle request errors. By default errors are printed and rethrown.
Declaration
Swift
public class DefaultMitigator: Mitigator, ResponseMitigatable, RequestMitigatable
-
Use class functions to easily change queue’s.
See moreDeclaration
Swift
public class dispatch
-
Air
handles interactions with a model of a specific Type calledRivet
.This class is intentionally stateless.
Tasks
Save
Type
is converted to JSON and send as the body of a requestRetrieve
You can retreive a single instance or an array of objects
Handle response via
ResponseController
The response controllers does the actual parsing. In theory you can parse any kind of reponse, for now we only support JSON.
Pass errors to the
Mitigator
Any type can decide to handle error in a specific way that is suited for that
Type
by conforming to protocolMitigatable
.You can inspect how error handling is expected to behave by looking at
DefaultMitigatorSpec
in the tests of the Example project.Mocking
You can also mock this class via its Type. Take a look at the
See moreGameScoreTest
in example to know how.Declaration
Swift
public class Air
-
Deal with the errors of the response and interpret the response.
Tasks
Handle errors in response
Errors cause an throw
Pass response to the
TransformController
Responses are interpretted in the
See moreTransFormController
Declaration
Swift
public class ResponseController
-
Transformations of data to concrete objects. This implementation expects data to be valid JSON. Any Type using these functions should be :
Parsable
Mitigatable
-> Try to solve problems found in the data provided.
Tasks
Transform data to
See moreRivetable
instances.Declaration
Swift
public class TransformController


And I'm a Cloud Software Programmer
This website is a catologe of What I learned in Fresno city college cit - 82 class!
From the basics of what the internet is to the node package manager, we sure have been through a lot in this short time, I wish we had more time together to delve deeper into many topics but what can you do? I wish I had more time to work on this, One of the most useful topics we covered was definatly the build tools, after GIT of course.
WEB: These are a few of the topics we covered that interested me.
GIT: GIT a vitial tool for any programmer, this tool allows versioning for all your code.
Security: An important topic for web development security, though we only touched the surface these are a few topics I would like to go over.
Editors: We where interduced to a few of the editors for writing code with the main one we focused on was Vs-code which is great because it is my favorite editor.


{% extends "main.html" %} {% block body %}
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Drag and drop the programmers into the teams! Each team must have one lead programmer and one trainee.
Double-click on a programmer (not in training) to make them a team lead.
Drag and drop the programmers into the teams! Each team must have one lead programmer and one trainee.
Double-click on a programmer (not in training) to make them a team lead.


Simon says, "Stand" . Nick stands. Simon says, "Tap" . Nick taps. Simon says, "Stamp" . Nick stamps. Simon says, "Spin" . Nick spins.82 press-ups" . Nick did 2 press-ups. He is out. Nick sits. Nick rests. Simon says, "Stand, tap, stamp, spin, press-ups" .
in-context


LightBox Slide Show Part 1
Utilities for presenting a slide show in a single page as a series of clickable thumbnails.
Using the slideshow utilities, you can create a webpage that includes a set of images grouped together into a single LightBox collection. Any or all of those images can be displayed as thumbnails on the page. Clicking any of the thumbnails will use LightBox to bring up the full size image and allow the user to cycle through the entire collection. Setting up the page is a matter of specifying a container element (a DIV is preferred) in the HTML for every slide in the set and either calling the function addSlide to fill in the details or by specifying all of the info for the page in a JSON file. If the file is named slides.json, it will be automatically loaded on document load.SlideShow vs SlideDeck
The slide show tools provide two ways to present slides. The first is the slide show
which allows you to sprinkle your slides throughout a document placing them anywhere you like.
This is a good way to, for example, write a paper about something and provide small thumbnails
throughout the paper that you can click on for the larger image. What separates this from the
simpleImage function described in Gp.Basic is that multiple images can be grouped
together so that if you click one, the entire collection opens in LightBox and you can
cycle through them. In addition, you can hide images. In other words, consider you have a page
and you have twenty slides to go with it. You want all twenty slides to be available in the
collection when you're using LightBox to cycle but maybe you only want to display six
image thumbnails in the page. With the slide show you can put all twenty in the page but the
ones you don't want to see can be hidden.
So how does this all work?
So how does this all work?
Gp.Slideshow.addSlide(id, group, slide);
Inserts an image into a container element and also adds the image into a lightbox collection.
Of course, this is still a lot of code in your HTML file. While the HTML is fairly simple, the JS calls are pretty involved and if you have a lot of slides in the file, this script section is going to get messy and hard to maintain.
id - id of the container element where the image will be inserted.
group - the name of the LightBox collection to put the image in.
slide - JSON slide object.
group - the name of the LightBox collection to put the image in.
slide - JSON slide object.
{
/* [optional] Url to image file.
* @type String (default = "id.jpg")
* Default is created by using the input id and assuming the file type is JPG.
*/
"url" : null,
/* [optional] Thumbnail caption.
* @type String
*/
"caption" : null,
/* [optional] Full window description.
* @type String
*/
"description" : null,
/* [optional] Thumbnail float property.
* @type String (default = "none")
* Possible values: "left", "right", "none", "inherit", "hidden"
* If "hidden", then no thumbnail is displayed.
*/
"float" : null,
/* [optional] Scale applied to original image for thumbnail.
* @type Number (default = none).
* If no thumbscale is given, it is assumed that the thumbnail image will be in a
* separate file named the same as the original with "_tn" appended to the filename.
*/
"thumbscale" : null
}
Usage
To place a slide in your HTML, you need to put a container element in your HTML and then make a JS function call to fill in the rest of the details. Let's suppose that you want to put the images "gp-slide-1.jpg" and "gp-slide-2.jpg" into a collection and display a thumbnail for just the first image. You start by putting two container elements in your HTML and specify ids for the elements. The ids can be anything you like as long as they are unique.

At the bottom of your page after you've pulled in all of the requisite JS files, you would
start another script section and make the function calls to actually fill in the details.
Gp.Slideshow.addSlide("my-picture-1", "addSlide",
{
"url" : "gp-slide-1.jpg",
"caption" : "Dandelions",
"description" : "Two Dandelions",
"float" : "right"
"thumbscale" : "0.5"
}
);
Gp.Slideshow.addSlide("my-picture-2", "addSlide",
{
"url" : "gp-slide-2.jpg",
"description" : "Two ducks in the grass",
"float" : "hidden"
}
);
Note the absence of some of the JSON properties in those calls. The second slide has the float
property set to "hidden" which means there will be no thumbnail display. Since there is
no thumbnail display, there is no reason for a "caption" or "thumbscale" property as these
properties only apply to the thumbnail display.
Of course, this is still a lot of code in your HTML file. While the HTML is fairly simple, the JS calls are pretty involved and if you have a lot of slides in the file, this script section is going to get messy and hard to maintain.
Gp.Slideshow.fromJSON(jsonURL);
jsonURL - URL of the JSON file to load.
{
/* [required] Group/collection title.
* @type String
* Will be used as the LightBox collection name.
*/
"title" : null,
/* [optional] Group/collection description.
* @type String
*/
"description" : null,
/* [optional] Common id of all container elements minus the index.
* @type String (default = "gp-slide")
*/
"prefix" : null,
/* [optional] Scale applied to all slides for thumbnail display.
* @type Number (default = none)
* Will apply to any slide that doesn't have its own "thumbscale".
*/
"thumbscale" : null,
/* [required] Slide data.
* @type Array slide objects
* Same format as slide object in addSlide
*/
"data" :
[
{
slide object 1
},
{
slide object n
}
]
}
Using the fromJSON function, you can layout all of the data for an entire collection
of slides in a separate JSON file and then make just one JS call in our HTML page to load that
JSON file.
Usage
The setup in your HTML is pretty much the same as in addSlide except that the formatting of the container element ids is a little more strict. Ideally you will have as many container elements in your page as there are slide objects in the JSON file. The id of each element must be "prefix-n" where "prefix" is a JSON property and "n" is the 1-based index of the slide object in the array. Suppose then that you wanted to put the same two slides as in the "addSlide" sample but use a JSON file instead. The JSON in the file would then look as follows:
{
"title" : "fromJSON",
"thumbscale" : "0.4",
"data" :
[
{
"url" : "gp-slide-1.jpg",
"caption" : "Dandelions",
"description" : "Two dandelions",
"float" : "right"
},
{
"url" : "gp-slide-2.jpg",
"description" : "Two ducks in the grass",
"float" : "hidden"
}
]
}
This is a good place to point out a handy shortcut. If you look back at the documentation for
the slide JSON object, note that the "url" property is optional. If no url is given,
it is assumed that the image file is a JPG file and is named the same as the container element
id. So in this case, we're already taking the default "gp-slide" prefix and our files
are JPG files named using the prefix. Taking that into account, the JSON can be thinned out a
bit removing the "url" property.
{
"title" : "fromJSON",
"thumbscale" : "0.4",
"data" :
[
{
"caption" : "Dandelions",
"description" : "Two dandelions",
"float" : "right"
},
{
"description" : "Two ducks in the grass",
"float" : "hidden"
}
]
}
The next step would be to put the container elements in your HTML. The elements don't have to
be grouped together. The only thing that matters is that they exist and have the correct id
format. Assuming that you've taken the "gp-slide" default as the prefix, the container would
look like:

With the JSON file and the container elements all in place, putting it all together is now
simply a matter of making a single JavaScript call at the bottom of your page to load the
file and insert the slides.
Gp.SlideShow.fromJSON("myslides.json");
slides.json
By taking special care in naming your JSON file, you can make things even easier. When the
file gp.tools.js is loaded, the location of the page is automatically scanned for a
file named slides.json. If it is found, then it is loaded automatically.
That's it!
Usage
The HTML and the contents of the file are exactly the same as with the fromJSON description. Here, however, you make sure the name of the JSON file is slides.json. Remove the call to fromJSON from your script section as this will now happen automatically.That's it!
Customization
Like the other utilities, there are some styles included in gp.tools.css that are used
in the slideshow code. You can use these styles as is or override them where you see fit.
When a slide is added to the page, the style "gp-slide-image" is automatically applied to the container element (the DIV). The default implementations include in the stock CSS file are:
Of course, you are free to change any and all of these properties as you see fit.
When a slide is added to the page, the style "gp-slide-image" is automatically applied to the container element (the DIV). The default implementations include in the stock CSS file are:
.gp-slide-image
{
display : inline-block;
padding : 8px;
text-align : center;
}
.gp-slide-image img
{
border : 0;
}
The display is set to inline-block in order to make the floating work better. A little
bit of padding is added so that text wrapped around the image doesn't butt right up against
the image. The text alignment is centered so as to center the caption in the thumbnail. An
additional style is set such that any images within the container do not display a border.
Of course, you are free to change any and all of these properties as you see fit.


ghost
sign up
home
notification
menu
sign in :
ghost_name:
password:
......you are going to ......
"" enter a nother world ""


Some outer text
Some outer text
Some div1 text
Some div1 text
Some div1 text
Some div2 text
Some div2 text
Some outer text
Some outer text
All Paragraphs
First Div
Second Div
1
2


Goal
Participants are asked to submit automated predictions of lesion segmentation boundaries from dermoscopic images.
Data
Lesion segmentation data includes the original image, paired with the expert manual tracing of the lesion boundaries in the form of a binary mask.
Training Data
The Training Data file is a ZIP file,
containing 900 dermoscopic lesion images in JPEG format. All images
are named using the scheme ISIC_<image_id>.jpg
,
where <image_id>
is a 7-digit unique identifier.
EXIF tags in the images have been removed; any remaining EXIF tags
should not be relied upon to provide accurate metadata.
Training Ground Truth
Download Training Ground Truth
The Training Ground Truth file is a ZIP file,
containing 900 binary mask images in PNG format. All masks are
named using the scheme
ISIC_<image_id>_Segmentation.png
, where
<image_id>
matches the corresponding Training
Data image for the mask. All mask images will have the exact same
dimensions as their corresponding lesion image. Mask images are
encoded as single-channel (grayscale) 8-bit PNGs (to provide
lossless compression), where each pixel is either:
0
: representing the background of the image, or areas outside the lesion255
: representing the foreground of the image, or areas inside the lesion
Notes
Masks were created by an expert clinician, using either a semi-automated process (using a user-provided seed point, a user-tuned flood-fill algorithm, and morphological filtering) or a manual process (from a series of user-provided polyline points).
Participants are not strictly required to utilize the training data in the development of their lesion segmentation algorithm and are free to train their algorithm using external data sources.
Submission Format
Test Data
Given the Test Data file, a ZIP file of 379 images of the exact same format as the Training Data, participants are expected to generate and submit a file of Test Results.
The Test Data file should be downloaded via the "Download test dataset" button below, which becomes available once a participant is signed-in and opts to participate in this phase of the challenge.
Test Results
The submitted Test Results file should be in the exact same format as the Training Ground Truth file. Specifically, the Test Results file should be a ZIP file of 379 binary mask images in PNG format. Each mask should contain the participant's best attempt at a fully automated lesion segmentation of the corresponding image in the Test Data. Each mask should be named and encoded according to the conventions of the Training Ground Truth.
Submission Process
Shortly after being submitted, participants will receive a confirmation email to their registered email address to confirm that their submission was parsed and scored, or to provide a notification that parsing of their submission failed (with a link to details as to the cause of the failure). Participants should not consider their submission complete until receiving a confirmation email.
Multiple submissions may be made with absolutely no penalty. Only the most recent submission will be used to determine a participant's final score. Indeed, participants are encouraged to provide trial submissions early to ensure that the format of their submission is parsed and evaluated successfully, even if final results are not yet ready for submission.
Evaluation
Submitted Test Results segmentations will be compared to private (until after the challenge ends) Test Ground Truth. The Test Ground Truth was produced from the exact same source and methodology as the Training Ground Truth (both sets were randomly sub-sampled from a larger data pool).
Submitted segmentations will be compared using a variety of metrics, all computed at the level of single pixels, including:
However, participants will be ranked and awards granted based only on the Jaccard index.
Some useful resources for metrics computation include:
{% endblock %}


1. Managing widgets in the Hypercube
2. Managing widgets on the Desktop
The Hypercube is an off-desktop environment where you can open, manage and run widgets, similar to Apple's Dashboard layer. It is made up of one or more Cubes, each with its own set of widgets. Only one Cube may be visible at a time.
Widget management in the Hypercube is accomplished by using the Control Bubble Icons in the top right corner of your main monitor. The following table describes the main Control Bubble Icons available in the current release of Amnesty Hypercube:
| Opens the Widget Gallery, a list of every widget installed in Amnesty Hypercube on your machine. Clicking a widget icon in the gallery adds the widget to your current Cube. If the widget already exists in your Cube, it will come to the front. Clicking the widget icon again hides the widget, though its position and settings in your Cube will be saved. | |
| Opens the Cube Gallery, a list of the Cubes that make up your Hypercube. Clicking a cube icon in the gallery switches the Hypercube's portal to that Cube, meaning that the current Cube is hidden as the newly-selected Cube and its widgets appear in its place. | |
| Opens the Provider Gallery, a list of web widget providers on the Internet whose web widget HTML codes are recognized by Amnesty Hypercube's NoClick widget creation technology. NoClick automatically creates a widget when HTML code is copied to the clipboard. Installing a new widget into Amnesty Hypercube is as easy as finding a widget via the Provider Gallery and copying its HTML code – NoClick will take care of the rest. |
Widget management on the desktop is accomplished by clicking on the Amnesty Hypercube icon which is installed in your system menubar (or system tray). This will reveal a pull down menu with various commands:
| The first section of the menu lists any widgets that you have added to the desktop. Selecting a widget from this list makes the widget visible (a check will appear next to its name indicating that it is visible). Selecting a widget again will hide the widget. Hide Widgets hides any visible widgets on the desktop and Show Widgets makes all the widgets listed in the menu visible. Add Widgets to Desktop opens the Widget Gallery. It behaves exactly like the Widget Gallery in the Hypercube (see previous section), except that widgets are added to the desktop.  It behaves exactly like the Provider Gallery in the Hypercube (see previous section) except that newly installed widgets are created on the desktop. Create Widget from Code reveals a window in which you may paste HTML code from any website, including sites that do not appear in the Provider Gallery. The pasted code must be valid HTML in order for the widget creation to succeed. |
|
A widget can be controlled by clicking the blue Info icon that appears in the top right corner of the widget when you mouse into it. This will reveal a pop up menu with various commands:
|
Display Settings opens a panel in which you can control the widget's desktop level, opacity, dragging preference and background color. These settings apply only to the current location (Desktop or Cube) in which the widget is visible. Global Properties opens a panel in which you can set the widget name and advanced properties properties such as how the widget uses your computer's processor and whether the widget is loaded from a local or remote page. These settings apply to all the locations in which the widget may appear. Uninstall opens a panel that asks for confirmation to uninstall the widget. When a widget is uninstalled it is permanently removed from Amnesty Hypercube on your machine. Copy Code to Clipboard copies the widget's HTML code to the clipboard for publishing outside of Amnesty Hypercube. Push into Dashboard hides the widget and "pushes" it into your OS X Dashboard. Remove from Desktop/Cube removes the widget from its current location (Desktop or Cube). Its position and settings will be lost. |
|


Meeting Duration
15 minutes
30 minutes
45 minutes
1 hour
1 ½ hours
2 hours
2 ½ hours
3 hours
3 ½ hours
4 hours
4 ½ hours
5 Hours
Enter your primary email address


This web page introduces the jQuery Javascript library as part of the three essential computer languages used for developing websites: HTML, CSS and Javascript. You can obtain documentation for the framework from the following links.
RESERVED


FROM THE DESK OF: MRS JULLIET ABRAMS AFRICAN DEVELOPMENT BANK HEADQUARTERS COTONOU REPUBLIC OF BENIN. I am MRS JULLIET ABRAMS, credit officer of African Development Bank. I have an urgent and very confidential business proposition for you. In June, 1998, a British Oil consultant/contractor with the West African's oil and Solid Minerals Corporation, Mr. David Van Buskirk made a numbered time (Fixed) Deposit for twelve calendar months, valued at US$35,000,000.00(Thirty five Million United States Dollars only) in my branch. Upon maturity,I sent a routine notification to his forwarding address but got no reply. After a month, we sent a reminder and finally we discovered from his contract employers, the West African oil and Solid Minerals Corporation that Mr.David Van Buskirk died in an air crash along with his wife on the 31st October, 1999 in an Egyptian airline 990 with other passengers on board. On further investigation,I found out that he died without making a WILL, and hitherto, all attempts to trace his next of kin was fruitless. Mr.David Van Buskirk was residing at No 1 Rue de Avenida, G.R.A. Cotonou, Benin Republic. I therefore made further investigation and discovered that Mr.David Van Buskirk did not declare any kin or relations in all his official documents, including his Bank Deposit paperwork in my Bank. This sum of US$35,000,000.00 is still sitting in my Bank and the interest is being rolled over with the principal sum at the end of each year. No one will ever come forward to claim it. According to Laws of Benin, at the expiration of 5 (five) years, the money will be declared frozen and forfeited to the Benin Government if nobody applies to claim the fund. Consequently, my proposal is that I will like you as a foreigner to stand in as the next of kin or business partner to Mr.David Van Buskirk so that the fruits of this old man's labor will not get into the hands of some corrupt government officials. This is simple, I will like you to provide immediately your full names and address,phone and fax numbers so that the attorney will prepare the necessary documents and affidavits that will put you in place as the next of kin or business partner. We shall employ the services of an attorney for drafting and notarization of the WILL and to obtain the necessary documents and letter of probate/administration in your favor for the transfer. A bank account in any part of the world that you will provide will then facilitate the transfer of this money to you as the beneficiary/next of kin. The money will be paid into your account for us to share in the ratio of 65% for me, 30% for you and 5% for expenses incurred in the course of the transaction. We could also use an ASSET MANAGEMENT COMPANY in Europe to secure this funds and transfer to your account in your country. There is no risk at all as all the paperwork for this transaction will be done legitimately and with my position as the credit officer guarantees the successful execution of this transaction. If you are interested, please reply immediately to my email box. Please send me your confidential telephone and fax numbers for easy communication.  Awaiting your urgent reply. Thanks and regards. Mrs Julliet Abrams Direct Line: 00229 07 14 19 Alternative email address: <jullietabrams@yahoo.ca>


--- title: "AAPI Data Community Facts" author: "Sono Shah" date: "2018-12-01T00:00:00" output: html_document ---
As part of my work at AAPI Data, I also create tools to increase the accessibility of demographic data, my most recent project was to create a Community Factsheet tool using shiny, check it out here.
Community facts is a simple Shiny App that displays demographic data from the ACS.


A variety of treatments are designed for different age groups, including adults. Ceramic braces and Invisalign® clear aligners are popular options that offer a comfortable design and translucent appearance for those with orthodontic conditions that qualify for these types of treatment. Even if traditional metal braces are what is required or what is in your budget, today’s technologies can help to speed up treatment time reducing the number of months you might feel self-conscious about wearing braces.
Equally important is improving the health of your teeth and gums. If your gums and bone structure are healthy, tooth alignment can be changed at any age. Correcting crooked teeth and a bad bite can eliminate gum and bone loss, tooth decay, abnormal wear of tooth enamel, headaches and TMJ.
Adult braces and orthodontic treatment is a growing trend that is likely to continue because in most cases, orthodontic problems can be treated as easily for adults as for children.


Tutorial¶
This tutorial walks you through some of the fundamental Airflow concepts, objects, and their usage while writing your first pipeline.
Example Pipeline definition¶
Here is an example of a basic pipeline definition. Do not worry if this looks complicated, a line by line explanation follows below.
"""
Code that goes along with the Airflow tutorial located at:
https://github.com/apache/incubator-airflow/blob/master/airflow/example_dags/tutorial.py
"""
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2015, 6, 1),
'email'<EMAIL_ADDRESS>'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
}
dag = DAG('tutorial', default_args=default_args, schedule_interval=timedelta(days=1))
# t1, t2 and t3 are examples of tasks created by instantiating operators
t1 = BashOperator(
task_id='print_date',
bash_command='date',
dag=dag)
t2 = BashOperator(
task_id='sleep',
bash_command='sleep 5',
retries=3,
dag=dag)
templated_command = """
{% for i in range(5) %}
echo "{{ ds }}"
echo "{{ macros.ds_add(ds, 7)}}"
echo "{{ params.my_param }}"
{% endfor %}
"""
t3 = BashOperator(
task_id='templated',
bash_command=templated_command,
params={'my_param': 'Parameter I passed in'},
dag=dag)
t2.set_upstream(t1)
t3.set_upstream(t1)
It’s a DAG definition file¶
One thing to wrap your head around (it may not be very intuitive for everyone
at first) is that this Airflow Python script is really
just a configuration file specifying the DAG’s structure as code.
The actual tasks defined here will run in a different context from
the context of this script. Different tasks run on different workers
at different points in time, which means that this script cannot be used
to cross communicate between tasks. Note that for this
purpose we have a more advanced feature called XCom
.
People sometimes think of the DAG definition file as a place where they can do some actual data processing - that is not the case at all! The script’s purpose is to define a DAG object. It needs to evaluate quickly (seconds, not minutes) since the scheduler will execute it periodically to reflect the changes if any.
Importing Modules¶
An Airflow pipeline is just a Python script that happens to define an Airflow DAG object. Let’s start by importing the libraries we will need.
# The DAG object; we'll need this to instantiate a DAG
from airflow import DAG
# Operators; we need this to operate!
from airflow.operators.bash_operator import BashOperator
Default Arguments¶
We’re about to create a DAG and some tasks, and we have the choice to explicitly pass a set of arguments to each task’s constructor (which would become redundant), or (better!) we can define a dictionary of default parameters that we can use when creating tasks.
from datetime import datetime, timedelta
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2015, 6, 1),
'email'<EMAIL_ADDRESS>'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
}
For more information about the BaseOperator’s parameters and what they do,
refer to the airflow.models.BaseOperator
documentation.
Also, note that you could easily define different sets of arguments that would serve different purposes. An example of that would be to have different settings between a production and development environment.
Instantiate a DAG¶
We’ll need a DAG object to nest our tasks into. Here we pass a string
that defines the dag_id
, which serves as a unique identifier for your DAG.
We also pass the default argument dictionary that we just defined and
define a schedule_interval
of 1 day for the DAG.
dag = DAG(
'tutorial', default_args=default_args, schedule_interval=timedelta(days=1))
Tasks¶
Tasks are generated when instantiating operator objects. An object
instantiated from an operator is called a constructor. The first argument
task_id
acts as a unique identifier for the task.
t1 = BashOperator(
task_id='print_date',
bash_command='date',
dag=dag)
t2 = BashOperator(
task_id='sleep',
bash_command='sleep 5',
retries=3,
dag=dag)
Notice how we pass a mix of operator specific arguments (bash_command
) and
an argument common to all operators (retries
) inherited
from BaseOperator to the operator’s constructor. This is simpler than
passing every argument for every constructor call. Also, notice that in
the second task we override the retries
parameter with 3
.
The precedence rules for a task are as follows:
- Explicitly passed arguments
- Values that exist in the
default_args
dictionary - The operator’s default value, if one exists
A task must include or inherit the arguments task_id
and owner
,
otherwise Airflow will raise an exception.
Templating with Jinja¶
Airflow leverages the power of Jinja Templating and provides the pipeline author with a set of built-in parameters and macros. Airflow also provides hooks for the pipeline author to define their own parameters, macros and templates.
This tutorial barely scratches the surface of what you can do with
templating in Airflow, but the goal of this section is to let you know
this feature exists, get you familiar with double curly brackets, and
point to the most common template variable: {{ ds }}
(today’s “date
stamp”).
templated_command = """
{% for i in range(5) %}
echo "{{ ds }}"
echo "{{ macros.ds_add(ds, 7) }}"
echo "{{ params.my_param }}"
{% endfor %}
"""
t3 = BashOperator(
task_id='templated',
bash_command=templated_command,
params={'my_param': 'Parameter I passed in'},
dag=dag)
Notice that the templated_command
contains code logic in {% %}
blocks,
references parameters like {{ ds }}
, calls a function as in
{{ macros.ds_add(ds, 7)}}
, and references a user-defined parameter
in {{ params.my_param }}
.
The params
hook in BaseOperator
allows you to pass a dictionary of
parameters and/or objects to your templates. Please take the time
to understand how the parameter my_param
makes it through to the template.
Files can also be passed to the bash_command
argument, like
bash_command='templated_command.sh'
, where the file location is relative to
the directory containing the pipeline file (tutorial.py
in this case). This
may be desirable for many reasons, like separating your script’s logic and
pipeline code, allowing for proper code highlighting in files composed in
different languages, and general flexibility in structuring pipelines. It is
also possible to define your template_searchpath
as pointing to any folder
locations in the DAG constructor call.
Using that same DAG constructor call, it is possible to define
user_defined_macros
which allow you to specify your own variables.
For example, passing dict(foo='bar')
to this argument allows you
to use {{ foo }}
in your templates. Moreover, specifying
user_defined_filters
allow you to register you own filters. For example,
passing dict(hello=lambda name: 'Hello %s' % name)
to this argument allows
you to use {{ 'world' | hello }}
in your templates. For more information
regarding custom filters have a look at the
Jinja Documentation
For more information on the variables and macros that can be referenced in templates, make sure to read through the Macros section
Setting up Dependencies¶
We have tasks t1, t2 and t3 that do not depend on each other. Here’s a few ways you can define dependencies between them:
t1.set_downstream(t2)
# This means that t2 will depend on t1
# running successfully to run.
# It is equivalent to:
t2.set_upstream(t1)
# The bit shift operator can also be
# used to chain operations:
t1 >> t2
# And the upstream dependency with the
# bit shift operator:
t2 << t1
# Chaining multiple dependencies becomes
# concise with the bit shift operator:
t1 >> t2 >> t3
# A list of tasks can also be set as
# dependencies. These operations
# all have the same effect:
t1.set_downstream([t2, t3])
t1 >> [t2, t3]
[t2, t3] << t1
Note that when executing your script, Airflow will raise exceptions when it finds cycles in your DAG or when a dependency is referenced more than once.
Recap¶
Alright, so we have a pretty basic DAG. At this point your code should look something like this:
"""
Code that goes along with the Airflow tutorial located at:
https://github.com/apache/incubator-airflow/blob/master/airflow/example_dags/tutorial.py
"""
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2015, 6, 1),
'email'<EMAIL_ADDRESS>'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
}
dag = DAG(
'tutorial', default_args=default_args, schedule_interval=timedelta(days=1))
# t1, t2 and t3 are examples of tasks created by instantiating operators
t1 = BashOperator(
task_id='print_date',
bash_command='date',
dag=dag)
t2 = BashOperator(
task_id='sleep',
bash_command='sleep 5',
retries=3,
dag=dag)
templated_command = """
{% for i in range(5) %}
echo "{{ ds }}"
echo "{{ macros.ds_add(ds, 7)}}"
echo "{{ params.my_param }}"
{% endfor %}
"""
t3 = BashOperator(
task_id='templated',
bash_command=templated_command,
params={'my_param': 'Parameter I passed in'},
dag=dag)
t2.set_upstream(t1)
t3.set_upstream(t1)
Testing¶
Running the Script¶
Time to run some tests. First let’s make sure that the pipeline
parses. Let’s assume we’re saving the code from the previous step in
tutorial.py
in the DAGs folder referenced in your airflow.cfg
.
The default location for your DAGs is ~/airflow/dags
.
python ~/airflow/dags/tutorial.py
If the script does not raise an exception it means that you haven’t done anything horribly wrong, and that your Airflow environment is somewhat sound.
Command Line Metadata Validation¶
Let’s run a few commands to validate this script further.
# print the list of active DAGs
airflow list_dags
# prints the list of tasks the "tutorial" dag_id
airflow list_tasks tutorial
# prints the hierarchy of tasks in the tutorial DAG
airflow list_tasks tutorial --tree
Testing¶
Let’s test by running the actual task instances on a specific date. The
date specified in this context is an execution_date
, which simulates the
scheduler running your task or dag at a specific date + time:
# command layout: command subcommand dag_id task_id date
# testing print_date
airflow test tutorial print_date 2015-06-01
# testing sleep
airflow test tutorial sleep 2015-06-01
Now remember what we did with templating earlier? See how this template gets rendered and executed by running this command:
# testing templated
airflow test tutorial templated 2015-06-01
This should result in displaying a verbose log of events and ultimately running your bash command and printing the result.
Note that the airflow test
command runs task instances locally, outputs
their log to stdout (on screen), doesn’t bother with dependencies, and
doesn’t communicate state (running, success, failed, …) to the database.
It simply allows testing a single task instance.
Backfill¶
Everything looks like it’s running fine so let’s run a backfill.
backfill
will respect your dependencies, emit logs into files and talk to
the database to record status. If you do have a webserver up, you’ll be able
to track the progress. airflow webserver
will start a web server if you
are interested in tracking the progress visually as your backfill progresses.
Note that if you use depends_on_past=True
, individual task instances
will depend on the success of the preceding task instance, except for the
start_date specified itself, for which this dependency is disregarded.
The date range in this context is a start_date
and optionally an end_date
,
which are used to populate the run schedule with task instances from this dag.
# optional, start a web server in debug mode in the background
# airflow webserver --debug &
# start your backfill on a date range
airflow backfill tutorial -s 2015-06-01 -e 2015-06-07
What’s Next?¶
That’s it, you’ve written, tested and backfilled your very first Airflow pipeline. Merging your code into a code repository that has a master scheduler running against it should get it to get triggered and run every day.
Here’s a few things you might want to do next:
Take an in-depth tour of the UI - click all the things!
Keep reading the docs! Especially the sections on:
- Command line interface
- Operators
- Macros
Write your first pipeline!


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--- layout: page title: About me subtitle: null css: /css/aboutme.css published: true ---
Data Science and Machine Learning enthusiast, Currently working as a Senior Data Scientist at Analytics Vidhya.
Completed my Bachelors from Indian Institute of Technology(B.H.U) Varanasi in Mining Engineering.
I enjoy writing python scripts for machine learning problems. In the past several years I've become an experienced Python user.
I like music, travelling at any given (and non-given) moment, meeting new people and playing any game.
The projects page is a nice place to see some of my work, but you can also view or download my resume.
Grew up in Najibabad,India, currently at Gurgaon, India as a Senior Data Scientist at Fractal Analytics.
I also have a blog, on Analytics Vidhya, that I'll leave here just because I want to have a reference to it somewhere.


The unlock progress for the rpk-74m is not displayed in bf3 battlelog
as in "# 15 of 16", plus a picture and a progress bar
btw, i absolutely love the bblog plugin!
BrainFooLong
Administrator
I don't think that this is a bblog bug.
Could you verify it with deactivated bblog also?
the7thguest
Hmm could you pls be a little more detailled, maybe post a screenshot?
If I surf on my profile for example, I see the progress of the RPK regulary, only the little pics in the main overview seem to be missing (or buggy since Battlelog is beeing modified a lot lately).


#include <stListPriorityQueue.h>
Public Member Functions | |
| void | SetPageID (stPageID pageID) |
| stPageID | GetPageID () |
| void | SetDistance (stDistance distance) |
| stDistance | GetDistance () |
| void | SetRadius (stDistance radius) |
| stDistance | GetRadius () |
| void | SetNext (stEntry *next) |
| stEntry * | GetNext () |
This class defines node type of stListPriorityQueue.
Tests.
Documentation update.
Definition at line 80 of file stListPriorityQueue.h.
|
inline |
Returns the Distance.
Definition at line 111 of file stListPriorityQueue.h.
|
inline |
Returns the next Entry.
Definition at line 143 of file stListPriorityQueue.h.
|
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Returns the PageID.
Definition at line 95 of file stListPriorityQueue.h.
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Returns the Radius.
Definition at line 127 of file stListPriorityQueue.h.
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Set the Distance of this entry.
| distance | distance of this entry. |
Definition at line 104 of file stListPriorityQueue.h.
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Set the Next entry.
| next | next entry. |
Definition at line 136 of file stListPriorityQueue.h.
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Set the PageID of this entry.
| pageID | the pageID. |
Definition at line 88 of file stListPriorityQueue.h.
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Set the Radius of this entry.
| radius | subtree radius of this entry. |
Definition at line 120 of file stListPriorityQueue.h.


blowout.particles¶
-
blowout.
particles
(m_tot, d, vf, profile, oil, yk, x0, y0, z0, Tj, lambda_1, lag_time)[source]¶ Create particles to add to a bent plume model simulation
Creates bent_plume_model.Particle objects for the given particle properties so that they can be added to the total list of particles in the simulation.
- Parameters
- m_totfloat
Total mass flux of this fluid phase in the simulation (kg/s)
- dnp.array
Array of particle sizes for this fluid phase (m)
- vfnp.array
Array of volume fractions for each particle size for this fluid phase (–). This array should sum to 1.0.
- profileambient.Profile
An ambient.Profile object with the ambient ocean water column data
- oildbm.FluidParticle
A dbm.FluidParticle object that contains the desired oil database composition
- yknp.array
Mole fractions of each compound in the chemical database of the oil dbm.FluidParticle object (–).
- x0, y0, z0floats
Initial position of the particles in the simulation domain (m). Note that x0 and y0 should be zero for particles starting on the plume centerline.
- Tjfloat
Initial temperature of the particles in the jet (K)
- lambda_1float
Value of the dispersed phase spreading parameter of the jet integral model (–).
- lag_timebool
Flag that indicates whether (True) or not (False) to use the biodegradation lag times data.
- Returns
- disp_phaseslist of bent_plume_model.Particle objects
List of bent_plume_model.Particle objects to be added to the present bent plume model simulation based on the given input data.
Notes
See the documentation for the bent_plume_model for more information on the Particle object.


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Name (Year) |
{{row.name}}
({{row.yearPublished}}) |
Published | {{row.yearPublished}} | Num. Players | {{row.numPlayersRecommended | json}} | Time | {{row.playingTime}} min | Age | {{row.playerAge}}+ | Rating | {{row.ratingAverage}} | Weight | {{row.weightAverage}} | Rank | {{row.rank}} |
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