by
Kay Cao and Robin Johnson1
This paper is based on an econometric model of the New Zealand meat export trade for the period 1990-2003. The model is known as a gravity model because it describes trade flows as being balanced between the respective pull of domestic and importing country size and incomes. The meat trade is characterised by strict hygiene regulations, quota markets and tariff charges in different markets. In addition, meat is not a homogeneous commodity but is characterised by breed, cut and presentation, and differentiated market destinations. An econometric model not only identifies general trends in demand and supply which might already be fairly obvious to the trade preactitioner but it also puts confidence intervals on the economic relationships being observed. We estimate income elasticities of demand, price elasticities, distance factors, tariff rate quota restrictions, and the impact of Hazard Analysis Critical Control Point (HACCP) regulations. Results are available for all meat, beef and veal and lamb exports to 9 countries.
Meat is a major export earner for New Zealand and a source of considerable national income. The markets for meat are all at a long distance and involve heavy storage, preservation and transport costs. Since the British entry into the EU, the preferred position of the British market has declined, though tariff rate quota (TRQ) entitlements maintain a lucrative market in the EU for lamb. Since the late 1950s access to the US market for beef has opened up new opportunities. More recently the US has imposed tariff rate quotas on Australia and New Zealand and restricted imports in some years. As a result of these restrictions market diversification has been encouraged in the trade and a wide array of products are sent to widely different markets.
The second characteristic of the export meat market is the wide prevalence of non-tariff barriers in importing countries (NTBs). These trade barriers are based on food safety and animal health grounds in importing countries which create a high cost hurdle to trade. NTBs are regulated by the WTO Agreement on Sanitary and Phytosanitary Measures signed in 1994. There are common systems in place for maintaining hygiene standards which may have deletarious or positive impacts on trade in meat.
The paper is based on the period since the Meat Board regulatorary powers were lifted, though the Board's successor, Meat New Zealand, continues to manage the tariff rate quota markets on the behalf of the industry.
In this paper, we want to model trade flows to different destinations and look for income and price relationships in the various markets. The model is a combined cross-section and time series data model subject to problems with the distribution of the error terms. We overcome these problems by using a statistical package, POOL command in SHAZAM, to redistribute the error terms (Cao and Scrimgeour 2003).
From a policy point of view we are interested in changes in the direction of trade, changes in product mix and new markets, changes in quota conditions, commercial relationships, regulatory relationships and food safety and animal health restrictions. The kind of analysis we employ gives very good estimates of the overall price and income elasticities though we haven't separated them out for different markets.
The basic market situation is one of regulated trade characterised by the tariff rate quotas and hygiene restrictions. Private shippers in New Zealand work through agents in the main country markets and are generally fully aware of current price expectations in each market. An annual model of market relationships can therefore be regarded as fully reflecting current supply and demand conditions, even though shipping times to the west coast of the US is two weeks and four weeks to Rotterdam.
Where possible we will model food safety and animal health requirements as represented by the Hazard Analysis Critical Control Point (HACCP) system devised in the US. There are a number of problems in assessing technical barriers to trade such as hygiene restrictions and we turn to these next.
Our main research objective is to quantify changes in trade patterns that result from the use of different policy instruments for technical standards. Gravity regression models offer some hope in isolating the particular effects on trade in goods by changes in technical standards and health measures. Gravity models use cross-section and time series data to examine trade flows between countries in the context of changing population and income trends. They can be based on single commodities or a SITC industry group. Our hypothesis would be to ask if health and technical standards make a difference to such trade flows between countries?
Some approaches set the trade instrument as the dependent variable in the context of data from a number of countries and look for different responses between countries to the particular instrument concerned. Other approaches use the flow of trade in an SITC category between pairs of countries, and look for a trade response to different policy instruments for food safety over time. As an example of the first approach, Rose (2002a, 2002b) takes a wide sample of countries in 1987 and examines whether frequency and coverage measures of NTBs differ between Members and Non-members of WTO after allowing for remoteness, population size and income per capita. In samples covering intermediate goods and capital goods, manufacturing goods, agricultural goods and resources Rose found no significant differences between Members and Non-members. That is, the mix of non-tariff protection measures in each country is not particularly related to membership of WTO in the period concerned.
A technical policy instrument approach is
examined by Otsuki and Wilson (2000) using SITC categories for dried grains
(wheat, rice, maize, dried and preserved fruit and nuts) for 31 exporting
countries and 15 importing countries between 1995 and 1998. The standard tested
was the permitted level of aflatoxin B allowed by importing countries as
measured in parts/m. After allowing for GNP, population, distance, and membership of MERCOSUR, ASEAN and NAFTA, there was a negative [and significant] trade response to lower permitted levels of
contaminant as would be expected. If the Codex standard for afloxin B were imposed across all countries [some are above and some are below] there would be considerable increases in the grain trade, and if countries were
confined to the [more stringent] EU standard there would be a 6% fall in trade.
In another study by Wilson et al (2003), the presence or absence of hormone residues is tested. The test is for minimum levels of tetracycline in parts per million in bovine meat, and the model encompasses 16 exporting countries and 5 importing countries for the period 1995-2000. In this case the aflatoxin result is repeated [at a significant level] with an elasticity of less than one. At the CODEX MRL, trade would increase by 57% and at the EU MRL, trade would decrease by 34%. In the earlier result, Australia would increase its exports to Canada, EU, New Zealand and Japan, but decrease them to the United States. New Zealand would increase exports to Australia and Canada, EU and Japan, but lose exports to the United States [The US has the highest permitted MRL].
Our research concerns the impact of the hazard analysis critical control point (HACCP) regulations on the meat trade between New Zealand and its main customers (as shown in Table 1). HACCP is a systematic approach to the identification, evaluation, and control of food safety hazards . Previous to its becoming mandatory in 1999, inspection of meat products was by visual and organoleptic [sensory] systems. The hypothesis is that the regression coefficient for the dummy variable representing mandatory inspection will be positive and probably less than unity (small percentage effect).
The international market for chilled and frozen meat products is characterised by regulated trade between countries, by tariff rate import quotas in some countries (EU the USA and Canada), and strict import hygiene regulations in all countries. Before the WTO Agreement in 1995, there was a bilateral agreement with the EU for a certain quota at zero tariff (see Table 3). Since 1995, there has been a tariff rate quota of 226.7 th tonnes with the tariff set at 12.8% plus a variable rate between 90-308 euros/kg depending on cut. For the beef quota in the US the out-of-quota tariff is 4.4 cents/kg. For Canada, exporters have to apply to Agriculture Canada for permits totalling up to 20,000 tonnes per year. We do not see a straightforward way to model these tariff and quota arrangements apart from identifying the quota markets.
Hygiene standards tend to be set by the US Department of Agriculture and the EU animal health authorities (Petrey and Johnson 1993). Private shippers in New Zealand work through agents in the main country markets and are generally fully aware of current price expectations in each market. An annual model of market relationships can therefore be regarded as fully reflecting current supply and demand conditions, even though shipping times to the west coast of the US is two weeks and four weeks to Rotterdam.
The New Zealand meat trade is dominated by lamb exports to the EU and beef exports to the USA. Significant amounts of lamb are also exported to China and the USA; significant amounts of beef are exported to Canada, Japan and Korea (Table 1). In terms of all nine countries covered by our models (Australia, Canada, China, France, Germany, Japan, Korea, Britain and the USA), 74% of total exports of lamb, 77% of exports of beef, and 76 % of all meat exports were going to these 9 destinations in the 2002-03 September year.
| Table 1: Shifts in New Zealand Meat Trade 1991-2003 | ||||||
|---|---|---|---|---|---|---|
| (% of total trade) | ||||||
| Destination | LAMB | BEEF | ALL MEAT | |||
| 1991-92 | 2002-03 | 1991-92 | 2002-03 | 1991-92 | 2002-03 | |
| Australia | 0.1 | 0.1 | 0.8 | 0.4 | 0.4 | 0.3 |
| Canada | 2.3 | 3.4 | 6.3 | 10.8 | 3.5 | 6.4 |
| China | -- | 10.0 | -- | 0.3 | 0.2 | 4.4 |
| France | 5.7 | 9.2 | 0.1 | 0.1 | 3.0 | 3.8 |
| Germany | 6.6 | 7.7 | 0.1 | 0.1 | 3.5 | 3.5 |
| Japan | 5.5 | 2.7 | 2.8 | 4.3 | 4.4 | 3.8 |
| Korea | 5.2 | 0.2 | 3.8 | 6.1 | 4.4 | 3.5 |
| UK | 30.5 | 32.9 | 0.1 | 0.1 | 14.8 | 11.0 |
| USA | 2.3 | 7.9 | 74.5 | 54.9 | 30.3 | 39.8 |
| Other | 41.8 | 35.9 | 11.5 | 22.9 | 35.5 | 23.5 |
| Total | 100 | 100 | 100 | 100 | 100 | 100 |
| Coverage % | 58.2 | 74.1 | 88.5 | 77.1 | 64.5 | 76.5 |
| Source. MWI |
While there were considerable shifts in the meat trade in the 1990s, the total size of the trade was not expanding. Total meat exports were 736,000 tonnes in 1987-88, reached 785,000 tonns in 1992-93, and were only 629,000 tonnes in 2002-03. Similarly unit returns were static through the early 1990s but slowly rose after 1997-98. One could say that this situation was production led, as other land uses became profitable, pointing to static terms of trade for livestock production in the country.
Table 2 shows the quota limitation and utilization for beef into the US for the last 10 years; Table 3 shows the EU sheepmeat and goatmeat quota utilization into the EU for recent years; and Table 4 shows details of the quality beef quota into the EU. It is characteristic that NZ fills the EU lamb quota every year but does not always fill the US beef quota.
| Table 2: US Beef and Veal Quota Utilization (tonnes product weight) | |||
|---|---|---|---|
| Calendar year | US TRG | Imports from NZ | Per cent |
| 1994 | 184400 | 176174 | 95.5 |
| 1995 | 213402 | 185762 | 87.0 |
| 1996 | 213402 | 162939 | 76.4 |
| 1997 | 213402 | 190079 | 89.1 |
| 1998 | 213402 | 191242 | 89.6 |
| 1999 | 213402 | 179142 | 83.9 |
| 2000 | 213402 | 213402 | 100.0 |
| 2001 | 213402 | 209681 | 98.3 |
| 2002 | 213402 | 199163 | 93.3 |
| 2003 | 213402 | 211549 | 99.1 |
| 2004 | 213402 | 211655 | 99.2 |
| Source: Meat New Zealand |
| Table 3: EU Sheepmeat and Goatmeat Quota Utilization (tonnes c.w.e.) | |||
|---|---|---|---|
| Calendar year | EU TRQ | Imports from NZ | Per cent |
| 1995 | 216150 | 210529 | 97.4 |
| 1996 | 226700 | 221675 | 97.8 |
| 1997 | 226700 | 222622 | 98.2 |
| 1998 | 226700 | 222722 | 98.3 |
| 1999 | 226700 | 220868 | 97.4 |
| 2000 | 226700 | 226672 | 99.9 |
| 2001 | 226700 | 226585 | 99.9 |
| 2002 | 226700 | 226638 | 99.9 |
| 2003 | 226700 | 226216 | 99.8 |
| Source Meat New Zealand |
| Table 4: EU High Quality Beef Quota Utilization (tonnes product weight) | |||
|---|---|---|---|
| June year | EU TRG | Imports from NZ | Per cent |
| 1999-00 | 300 | 299.6 | 99.9 |
| 2000-01 | 300 | 299.7 | 99.9 |
| 2001-02 | 300 | 224.2 | 74.7 |
| 2002-03 | 300 | 286.0 | 95.3 |
| Source: Meat New Zealand |
The commodity-specific gravity model, as derived by Bergstrand (1985 and 1989), explains bilateral trade flows as a function of the two countries' income, per capita income (or population), transportation costs, and other factors that may be aiding or restricting trade such as tariffs, exchange rate, prices and health regulations.
Xij = b0 Yib1 Yjb2 Lib3 Ljb4 Cijb5 Aijb6 eUij (1)
Where Xij is the value (or volume) of real trade flows of a specific commodity from country i to country j, Yi and Yj are GDP of the two countries, Li and Lj are their populations, Cij is the transportation cost between i and j, Aij comprises the other factors, and eis an error term normally distributed.
In its estimation form, and using more descriptive independent variable nomenclature, we can write:
ln Xijt = b0 + b1lnYit + b2lnYjt +b3lnLit + b4lnLjt + b5lnDISTij + b6QUOTAij + b7lnERijt + b8lnPijt + b9lnPRODjt + b10HACCPij + Uijt (2)
where
Xijtis tonnes of meat exported to each country market.
DISTij is the distance between the two countries, which is used as a proxy for transportation costs.
QUOTAij is a dummy variable which takes into account tariff policy between any two countries. As the market for NZ meats is dominated by the presence of quotas, we include a dummy variable which takes value 1 when there is a quota in place.
ERijt is the exchange rate which is the value of NZ dollars expressed in terms of the foreign currency.
Pijt is unit meat export price at the f.o.b. level to destination j.
PRODjt is the volume of meat production in country j which is used as a proxy for changes in price of meats in each country.
HACCPij is a dummy variable which takes value 1 when it is mandated, zero otherwise.
It is normally expected that the coefficients of income variables are positive as countries with higher incomes tend to trade more. Similarly, coefficients of population2 and distance are normally expected to be negative. However, for a single exporting country and a single commodity, these expectations may be subject to change. As stated in Bergstrand (1989), income coefficients are positive if the commodity is the luxury end of consumption, capital-intensive in production, and having an elasticity of substitution exceeding unity. This may not be the case always for NZ meat products. The QUOTA coefficient is expected to have a positive sign as the quotas generally allow for importing a certain quantity of NZ meats (beef or sheepmeat) at zero or concessional tariff rates. The coefficient for exchange rate is expected to have a negative sign as an appreciation of the NZ dollar tends to have a negative impact on its exports. The export price coefficient, however, is expected to have a negative sign as exports have not responded to higher prices in recent years and NZ is a price-taker for meat. The production coefficient is expected to be negative as an increase in meat production of importing countries may have a negative impact on NZ meat exports. Finally, HACCP is expected to have a positive sign as better food safety practises could enhance market access. That is, HACCP is a restriction on trade in one period, but an opportunity in the next period.
Data of bilateral trade in meat products between NZ and major trading partners (8 countries3) over the period 1990-2003 are used to estimate the model (as specified in equation 2). These data are provided by Statistics NZ as well as Meat and Wool Economic Service (now MWI Economic Service). GDP and population data are taken from International Financial Statistics Yearbook (IMF, 2003). All financial data have been converted to their real values. Production data is taken from FAO Statistical Database (http://apps.fao.org). Distances between countries are calculated as distances between capital cities, data is taken from http://www.geobytes.com/CityDistance Tool.htm?loadpage.
As only the US and UK market have tariff rate quotas for NZ meat products administered by MeatNZ, QUOTA takes value 1 for these 2 countries over the whole period. In New Zealand, HACCP was mandated in 1999 so HACCP takes value 1 from the year 20004 when the changes take effect. Although voluntary HACCP maybe present at some plants before year 2000, we do not include this fact as our focus is on the effect of a uniform adoption of HACCP on the total industry's export performance.
Our diagnostic tests showed that there is evidence of autocorrelation and heteroscedasticity in the data set. Therefore to estimate the gravity model we used POOL command in SHAZAM. The POOL command applies a generalised least square procedure (GLS) to first estimate the model by Ordinary Least Square (OLS) then transform the observations using the estimated residuals and apply OLS to the transformed model. The estimation results for 8 countries (excluding China) are reported in Table 5.
Most of the estimated coefficients are significant, except for the coefficient of importing country's GDP. Income coefficients have negative signs which reflect the fact that NZ meats are not capital-intensive nor luxury products. NZ GDP is negative with respect to trade indicating that on average marketed quantities have reduced over the period to most destinations while incomes rose by 2.5% per year. On the other hand, population rise in NZ is positively associated with the meat trade as NZ's population growth has been very small over the period (1.1% per year).. Distance sign is also positive indicating that growth markets for NZ meats are at the longer distances. Exchange rates have expected sign (negative) which suggests that an appreciation of the NZ dollars will have a negative and significant impact on its meat exports. Importing countries' meat production has a negative impact on NZ meat
| Table 5. Estimation results for gravity model (2) | ||
|---|---|---|
| Variable | Coefficient | Standard Error |
| b0 | 9.25 | 2.41*** |
| GDP.nz | -2.31 | -0.55*** |
| GDP.imp | -0.071 | -0.14 |
| POP.nz | 5.66 | 1.44*** |
| POP.imp | 1.022 | 0.28*** |
| DISTnz.imp | 0.55 | 0.18*** |
| ER nz.imp | -0.088 | -0.041** |
| PRODUCTION.imp | -0.28 | -0.12** |
| PRICE nz.imp | -0.81 | -0.08*** |
| QUOTA EFFECT | 0.89 | 0.19*** |
| HACCP EFFECT | 0.088 | 0.044** |
| BUSE R-SQUARE | 0.891 | |
| **, *** denote significance at a = 0.05 and 0.01 respectively |
exports as expected. The price coefficient is negative which reflects the fact that export prices were relatively static over the 1990s (see further discussion below). Quota has a positive and significant effect on export direction indicating that both the EU and US take more exports after introduction than would otherwise be the case. Finally, the HACCP coefficient is positive and significant which shows that adopting a food safety management program like HACCP has had a positive impact on export direction since it was introduced.
This estimation excludes China where demand for lamb has been rapidly increasing. If China is included, GDP.imp becomes significant and positive, POPnz is non-significant, POPimp becomes negative, the PROD effect is reduced, QUOTA and HACCP change in size a bit. Significantly, the rapid growth of incomes in China tips the result towards a more income elastic model for the whole meat trade and suggests income elasticities > unity but price elasticities from -0.8 to -1.75 (including China). Our interpretation of the high negative price coefficient is that China was buying at the high price end of the market in the early 1990s (possibly for the hotel trade) as well as continuing low returns in other markets as reported above. Since this result is replicated in other specifications, we deduce that NZ producers have been fighting a declining terms of trade phenomenon through most of the 1990s although traded prices have risen since 1997.
If beef and lamb are run separately, the broad pattern of results in Table 2 is repeated. Beef and lamb quantities exported are reported separately and we have calculated f.o.b. prices for each from the values and quantities reported. Both have an importer's income elasticity around 0.7, the price elasticity is -0.7 to -0.3, both have a positive response to quota; but DIST is negative for beef and positive for lamb, beef is affected by importers PROD while lamb is not, and beef has a negative response to HACCP whereas lamb is positive.(all including China). Omitting China does not change any sign for lamb though the elasticities vary. For beef, the high income elasticity disappears, DIST turns positive, PRICE and PROD stay the same, but HACCP turns positive, and QUOTA margin is positive but non-significant.
So we end with a dilemma. Include more data or be selective in our choice of countries? The problem with China in the early 90s is the lack of a record of trade. Some missing plot technique is probably required as the inclusion of China reflects the recent quick growth of lamb exports as shown in Table 1. This is part of the total policy mix and we need to reflect it. There are also markets which once were important (e.g. Iran and Russia) but have since dropped away.
This paper picks up a theme from last year's conference. It establishes more thoroughly the broad market conditions faced by NZ for meat products. We like the broad presentation of market trends because it is presented in traditional framework of elasticities and destinations. Desk policy officers tend to think of the crisis of the day and not the broad picture. This information adds to the quality of the informaton on the market as a whole. The model comes with statistical tests that spell out that the main coefficients (and hence trends shown) have a high degree of repeatibility and therefore reliance for policy positions.
We like the high response for income elasticity when new markets are included, and the terms of trade implication arising from the unit price variable. HACCP is a small effect but significant in most cases though not for the beef estimation. The latter result tends to show that in the US market there was no significant shift in supply after HACCP becoming mandatory. Perhaps compliance with the US standards was already very high?.
Bergstrand J H (1985), The Gravity Equation in International Trade: Some Microeconomic Foundations and Empirical Evidence, The Review of Economics and Statistics.
Bergstrand J H (1989), The Generalised Gravity Equation, Monopolistic Competition, and the Factor-Proportions Theory in International Trade, The Review of Economics and Statistics.
Cao K and Scrimgeour F (2003), Assessing the Competitiveness of the New Zealand Meat Industry: Does Food Safety Standards Help?, Paper presented to Winter Conference of New Zealand Agricultural and Resource Economics Society, Blenheim.
International Monetary Fund (2003), International Financial Statistics Yearbook, Washington.
Otsuki T and Wilson J S (2000), Food Safety Regulations and Global Food Trade Patterns, American Agricultural Economics Association Annual Meeting.
Petrey L A and Johnson R W M (1993), Agriculture in the Uruguay Round: Sanitary and Phytosanitary Measures, Review of Marketing and Agricultural Economics 61(3), 433-442.

Wilson J S, Otsuki T and Majumbar B (2003), Balancing Food Safety and Risk: Do Drug Residue Limits Affect International Trade in Beef?, American Agricultural Association Annual Meeting, Montreal.
(1) Graduate Student, Waikato University, and Consulting Economist, Wellington, respectively. Assistance from Richard Wallace (MAF), Matthew Newman (MWI), and Scott Gallacher (MeatNZ) is acknowledged. We also would like to acknowledge the financial support from the Department of Economics, University of Waikato for the purchase of some parts of the data in the analysis. Comments from two reviewers have been taken into account.
(2) As per capita income (Y/L) is expected to have positive sign, L is expected to have negative sign
(3) China is excluded due to some data missing during the 1900-2003 period
(4) This is year ending June 2000. Also please note that we do not include the effects of disease outbreak (eg. BSE in Canada and USA) as this just happened recently, outside the studied period.


Welcome to my programming portfolio. This is a portfolio showcasing some of my work. Here are some of my work.
My name is Anzal Mohamed. I completed my High School education in 2018 in Ghanima el marzuq girls secondary As for hobbies I can name probably alot write but the main once are in my list of hobbies. I have always had this goal of being a software engineer but somehow when I completed my high school education my parents pulled out the you will do medicine and Nothing else! card on me.somehow I do not blame them as the fault kind of also is mine...see I had been singing that I want to be a neurosurgeon song to them since I could sing my ABC song in class one. all that changed when I took computer as a subject in high school and instantly started building castle in the air of being the next Elon Musk and here we are today... .After litrally convincing my parents for the last two years they let me finally take this course in Moringa School. My journey begins here.


25 Ventures is a collection of startup veterans who take ideas from scratch or existing startups in their infancy and turn them into independent companies using shared expertise and resources.
WE ARE THE FOUNDERS SECRET WEAPON!
Having been part of more than 100 startups, we speak “Founder” fluently. We deeply understand the challenges of explaining YOUR IDEAS to the world, building products, launching companies, building teams, raising money, marketing and the all the other stuff you deal with on a daily basis.
Because of this, we have seen (and made) all the mistakes in the book. In fact, we are writing a book about them right now! It is our mission, rather our calling, to help other entrepreneurs avoid errors, save time and grow faster at all costs.
Unlike incubators or accelerators, the main goal of a venture studio is to actively build a startup as part of the team, not an outside investor or advisor. We provide active involvement and resources dedicated to working operationally on the project, for an important period of time.
We provide a pool of instantly available resources that create an internal culture of trust, deal flow, attentiveness, and determination. We are leading the fight to end poor entrepreneurship. The Daily Drop is an online forum where we disclose everything the 25 Ventures team did and heard from the previous day.
Instead of building one project at a time we focus in on creating a portfolio of companies that can help one another during their growth phases. This community approaches greatly reduces errors being made by more than one company.
Ready to start your next project with us? Give us a call or send us an email and we will get back to you as soon as possible!
747-245-9198


VideoFrameBuffer |
VideoFrameBuffer is a buffer which contains a single video buffer's raw data. Typically owned by a VideoFrame which includes additional metadata. interface VideoFrameBuffer |
VideoFrameI420Buffer |
VideoFrameI420Buffer provides an reference counted wrapper of a YUV where planes are natively (i.e. in JNI) allocated direct byte buffers. class VideoFrameI420Buffer : VideoFrameBuffer |
VideoFrameRGBABuffer |
VideoFrameRGBABuffer provides an reference counted wrapper of an RGBA natively (i.e. in JNI) allocated direct byte buffer. class VideoFrameRGBABuffer : VideoFrameBuffer |
VideoFrameTextureBuffer |
VideoFrameTextureBuffer provides an reference counted wrapper of an OpenGLES texture and related metadata class VideoFrameTextureBuffer : VideoFrameBuffer |


# It's Just Text What if there was a way to convey an idea over a long distance such that anyone regardless of age, physical condition, available equipment or bandwidth can access it? Introducing: __It's Just Text__. The lowest common denominator of the communication on the Internet. ## Only 3 Rules To Go By - Content is what matters. Use plain text with no formatting other than emphasis and headers which facilitate reading. - Wrapping and whitespace are part of your content: control them. Structure your ideas with whitespace. Wrap at the 72nd character; it reads well, zooms well and fits multiple documents side-by-side. - Forgo all interaction. Don't store state, don't use dynamic elements, don't listen to events. ## Getting Started __It's Just Text__ is a manifesto. You can literally upload a *.txt file to your server and you're good. But what if you want to embed an image in your document? Or make it easier for the reader to refer to other hyperlinked documents? It's the World Wide Web after all! I wrote a tiny JavaScript library which parses plain text files using a minimal Markdown-like syntax and produces a valid HTML document: [itsjusttext]. It's licensed under the MIT license. You're welcome to create your own variation which best serves your needs. Put the following HTML at the top of your text file. Edit the title and make sure the file is served as HTML (usually by changing its extension to *.html). <!doctype html><html lang=en><meta charset=utf-8> <script defer src="itsjusttext.js"></script> <title>It's Just Text</title> That's it! The JavaScript code is fast and extremely lean, weighing in at just 0.5 KB. It will mark up your content in a minimal way as described in the next section. ## What's Included - Your content, exactly how you wrote it. - Three levels of headings: # hello - Emphasis: __hi there__, - Links with references: [howdy]. - Images via the <img> element. - And any other HTML as well, actually. See the source of this page for a complete example. -- [@stas] [itsjusttext]: https://github.com/stasm/itsjusttext [@stas]: https://twitter.com/stas


We make things with code (HTML, CSS and JavaScript) and computer graphics. We've been doing this for a while and we know our tools like the back of our hands.
We make things with code (HTML, CSS and JavaScript) and computer graphics. We've been doing this for a while and we know our tools like the back of our hands.


This sample is created using IBM Verify javascript SDK
IBM Security Identity and Access: Developer Portal


Missing Features
- Window Rollers support
Set / read status from the interface
- Interface: Temperature endpoints setup
Set / read from the interface
- Interface/API: Multiroom audio system integration
Using node-sonos-http-api
- Rule-engine (CEP)
To implement alarm and human presence simulator


#assign elastic_overview = "true">
For how to configure for testing goto docs / testing / elasticsearch.
There are quite a few different reasons why ElasticSearch integration is useful to applications using Ebean ORM.
Many modern applications provide a single search input field to operate in a 'Google search' fashion. For example, searching for customers including their name and address (street, city, country etc).
ElasticSearch uses analysers and inverted indexes to make "text search" fast and efficient. OLTP databases generally provide very different indexing via BTree and BitMap indexes which are orientated towards the needs of OLTP (short transactions, self balancing etc).
Many databases provide materialised views which are generally derived / calculated views used to provide faster views of the data. Materialised views are often refreshed periodically and used by applications knowing that the derived / calculated data is only refreshed periodically and potentially stale. For example, product pricing might be refreshed every midnight.
ElasticSearch indexes can viewed in a similar way to DB materialised views. They represent a materialised (document orientated) view of the data which can be updated in near real time with each commit or updated in a more periodic batch fashion.
ElasticSearch architecturally has built in horizontal scaling and this can be used to provide an easier / cheaper way to scale read access when compared to scaling a more traditional OLTP database. Note that the access is not equivalent to that provided by most OLTP databases in that it does not provide the same transactional read consistency guarantees and is 'near' real time (usually lags by a second etc) - however, these limitations are often acceptable given the improved read scalability.
ElasticSearch can also be viewed as a document store and often the 'documents' stored are denormalised and contain derived and joined information. This means a retrieve of a single document in elastic search can compete with a SQL query would involve many joins. For example, an Order document might be denormalised and otherwise in a SQL query join Order, OrderDetails, Customer and Product tables.
The traditional downsides of denormalised data exist but when your OLTP database is still the primary data store and ElasticSearch is a secondary store then storing 'denormalised documents' is nicely manageable.
ElasticSearch can be used as an effective replacement of the L2 query cache on types that experience a reasonable amount of insert, update or delete changes.
Essentially if an application is reliant on the L2 Query cache
on a bean type that is
frequently changed (Inserts, updates or deletes) then this will result in load on the database
due to ineffective utilisation of the L2 Query cache (as it is frequently invalidated).
ElasticSearch provides many "exact value / term" predicates that map well to "relational" predicates and many relational/orm queries can be reasonably executed against ElasticSearch. If there is a need to scale "find many" queries against frequently updated tables then ElasticSearch can provide a good solution with the caveat that ElasticSearch indexes might be slightly out of date.
For Ebean we have the term L3 cache
which means the remote part of an L2 cache
.
That is, the L2 cache has a "Near cache" and "Remote cache" and "L3 cache" here means the remote part of an
L2 cache.
Ebean can use ElasticSearch as an L3 cache (instead of the more traditional Hazelcast/Infinispan/etc distributed caches). If there is a miss in the local L2 cache Ebean can go and hit ElasticSearch rather than the database with the benefits that ElasticSearch is known to have all the data and have it in denormalised form (so a single hit against ElasticSearch might populate more of the graph than a traditional DB hit).
ORM graphs naturally map to JSON documents. Ebean also has built in support for marshalling/unmarshalling to and from JSON as well as executing large queries efficiently. This makes it is completely natural and reasonable for example to execute a large query over all the objects of a certain type and covert those object graphs to JSON and send to ElasticSearch.
The caveat is that ORM graphs are typically well structured (semi-structured if you include storing JSON in the DB which is well supported by Postgres and Oracle). In terms of mapping ORM graphs to ElasticSearch documents we typically need to take mapping into account such as identifying properties that are 'codes' (and should not be analysed) and identifying properties that we want in analysed and raw form (to support the order by clause for queries).
The core integration function is to provide good mechanisms to sync changes to ElasticSearch. That is, when Ebean processes changes to the database it automatically knows what updates it needs to make to the indexes and providing features such that you can control how and when those updates occur.
Ebean determines from the mapping what index update events are required and you can control if these updates are sent to ElasticSearch immediately in the background or queued for later processing or ignored (when you want full control over the indexing).
You can always query ElasticSearch directly without using Ebean and that will fit well with some use cases. In addition Ebean provides good support for executing queries with a number of benefits.
Ebean will automatically translate expressions to use associated 'raw' fields for sort (order by) clauses and in the case of term expressions. That is, Ebean knows via mapping which properties have associated non-analyzed (raw) fields and will use these where needed (order by and term expression).
Used when we want to fetch from ElasticSearch and then persist to our OLTP DB. The Persistence context is used to de-duplicate beans when the object graph is built from JSON documents giving us a consistent graph (which is what we typically want for persisting). The persistence context also plays a part in query joins (merging multiple JSON sources of the same bean together).
Like ORM queries we can use the Load Context to provide efficient batch lazy loading and query joins. Object graphs built from ElasticSearch can invoke lazy loading (to L2, other indexes and fallback to the OLTP DB when a bean type is mapped to an Elastic index). The Load context also provides "query joins" such that we can build a result by joining to other indexes. For example, fetch from the customer index and join the contacts index (In ElasticSearch terminology this would be described as an "application side join". We can do this efficiently by batch loading as we do in traditional ORM query joins (to avoid N + 1).
We can use query beans to build the query in a type safe manor which is good for long term maintenance with compile time protection against schema and type changes.
The ability to write an ORM query and then painlessly change it to hit ElasticSearch gives application developers the ability to delay the decision and fairly painlessly change between hitting the DB or Elastic. This is not a 'perfect' swap with considerations for case insensitivity of predicates and read consistency (and no support for sub-queries) but for a great many cases this ability to easily swap between the two allows developers to get on with building with the knowledge they can later revisit (and change the implementation to hit Elastic without changing the semantics in terms of persistence context, lazy loading, saving back to the DB etc).
That is, this provides a low barrier path to horizontally scaling reads.
On an Ebean query you just need to set useDocStore(true)
and then the query will execute
against ElasticSearch. The only query expressions that can't be translated to ElasticSearch are
sub-query expressions - otherwise all the expressions can be translated to ElasticSearch expressions.
Most "relational/exact value" expressions map directly to ElasticSearch expressions with some exceptions.
Just like ORM queries you can optimise an ElasticSearch query to only fetch the part of the document that you need. You can use the normal Ebean query select() and fetch() to define what part of the document to fetch back and Ebean will apply that to the ElasticSearch query.
```java ListLike ORM queries you can "query join" multiple indexes.
```java // fetch from the customer index // ... "join" the contacts index ListSupport for this can change the decisions on how much denormalisation/embedded documents is used for specific indexes. For example, in an "Order" index you need to decide how much of the customer will be included/embedded into the order. You can embedded the customer(id,name) or customer(id,name,billingAddress(*)) etc. The ability to easily join the "customer index" (to fetch whatever customer details is required) means that you may choose to embed less of the customer into the order document.
Full text query expressions are also exposed by Ebean. These are only used for "document store" queries and are not used for normal ORM queries that go to the OLTP database.
Instead of using where()
you can use text()
to add "full text" search expressions
to the query like match
, multi-match
, common terms
,
text simple query
and text query
. These map directly to the corresponding ElasticSearch
full text expressions.
Note that when you add a "text expression" the query is automatically set to useDocStore(true)
.
The following features are currently considered out of scope.
ElasticSearch provides a parent/child mapping as an alternative to nested/embedded/denormalised approach. At this stage support for this is out of scope and instead the nesting/denormalisation approach is preferred.
Note that Ebean can use "query joins" to fetch join multiple indexes into a single result.


Let’s pretend I’m writing some
inline code
just to see how it comes out.
Then, a line or two about how lines work.
Let’s say two.
Two more.
just for fun and curiosity…which is the point if there is one.
Entia non sunt multiplicanda praeter necessitatem.


HalideOS - An Experimental OS from DSC KIIT #
DSC KIIT is thrilled to open source HalideOS, our first experimental operating system written entirely from scratch.
- You can find the source code at github.com/DSC-KIIT/project-halide
- Instructions on how to run/build HalideOS are available here
About HalideOS #
HalideOS is an experimental operating system written entirely from scratch. It supports a basic shell with some commands including a command-line calculator. You can boot it up and run it in a virtual machine.
It is a very basic and bare bones operating system with a total of around 2,500 lines of code developed in a few weeks of time. The main motive of this project was to demonstrate some basic principles and ideas of operating systems. Something small and understandable that beginners can read and make sense out of.
The bulk of HalideOS is written using C++17 with some x86 assembly code for the bootloader. We have also implemented a very basic standard library that developers can use to add commands to the shell.
Screenshots #
Please refer the Getting Started and Documentation sections of the site to read more about the design and the code.
Operating Systems Development #
The modern operating systems we use are a marvel of computer science and software engineering. If you are someone who takes a keen interest in understanding how computers ultimately work, you will really enjoy learning about developing operating systems.
Developing an operating system from scratch is one of the hardest tasks in computer science. It is considered a pinnacle of software engineering. It requires a good grasp of C/C++, x86 assembly language, processor architecture and memory. But it is not something that undergraduate students can’t do as we learn all these subjects as a part of our coursework.
This project is an attempt by DSC KIIT to introduce students to the fascinating domain of operating systems development.
Get Involved #
HalideOS is open-sourced under the MIT License, feel free to contribute interesting features and improvements on the GitHub Repository.
KIIT students who want to learn more about this domain can reach out to DSC KIIT or the original project members on Twitter or Instagram.
About the Creators #
This project was developed by Ujjwal Singh Shekhawat and Junaid Rahim. Both of them are second-year students in the CSE branch and members of the OS Team here at DSC KIIT.
Special thanks to Priyansi from the Content Team for helping out with this site and the documentation.


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Generic Office Items
Item 1
Some info on Item 1...
Item 2
Some info on Item 2...
Item 3
Some info on Item 3...
New Office Item
What's this?


Range Queries
This post is just for revising range queries. Have highlighted the topics and problems. Might write solutions (my approach) for these problems as well for future reference.
to do : create collapsible for solutions + also to put the same in DP post
Table of Contents (segment tree)
Link: cp-algorithms - Segment tree
- Simplest form of a Segment Tree ✔
- Structure of the Segment Tree ✔
- Construction ✔
- Sum queries ✔
- Update queries ✔
- Implementation ✔
- Memory efficient implementation ✔
- Advanced versions of Segment Trees
- More complex queries ✔
- Saving the entire subarrays in each vertex ✔
- Range updates (Lazy Propagation) ✔
- Generalization to higher dimensions 📝
- Preserving the history of its values (Persistent Segment Tree)
- Implicit segment tree
Table of Contents (fenwick tree or BIT)
Link: cp-algorithms - Fenwick tree
- Description
- Overview ✔
- Definition of g(i) ✔
- Implementation
- Finding sum in one-dimensional array ✔
- Finding minimum of [0,r] in one-dimensional array ✔
- Finding sum in two-dimensional array ✔
- One-based indexing approach ✔
- Range operations
- Point Update and Range Query ✔
- Range Update and Point Query 📝
- Range Updates and Range Queries 📝
CSES.fi problemSet (Range Queries)
- Range Sum Queries I ✔
- Range Sum Queries I ✔
- Range Minimum Queries I ✔
- Range Sum Queries II ✔
- Range Minimum Queries II ✔
- Range Xor Queries ✔
- Range Update Queries ✔
- Forest Queries ✔
- Hotel Queries ✔
- List Removals ✔
- Salary Queries
- Subarray Sum Queries
- Distinct Values Queries
- Forest Queries II
- Range Updates and Sums
- Polynomial Queries
- Range Queries and Copies
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2015121
{% load static %} {% block respondtorequests %}
{% comment %} TODO: Add notification counters! {% endcomment %}| Student | Type | Purpose | From | To | Approve |
|---|---|---|---|---|---|
2015121
|
Casual | Visit to Home! | 10 Aug'17 | 10 Jan'17 | |
2015121
|
Vacation | Visit to Home! | 10th Aug'17 | 10th Jan'17 |
| Student | Reason | From | To | Approve |
|---|---|---|---|---|
2015121
|
I have decided to go Vegetarian! | CM #1 | CM #2 | |
2015121
|
Mata rani paap lagayegi! | CM #1 | CM #2 |


Bookies laugh at Alberto Del Rio as Tito Ortiz swells to massive betting favorite for Combate showdown
by Jesse HollandIf you decide to bet on the upcoming Combate Americas mixed martial arts (MMA) event, which takes place Sat., Dec. 7, 2019 inside Payne Arena in Hidalgo, Texas, and lay $100 on Tito Ortiz, you’ll walk away with about seven bucks in profit.
That’s because the former UFC light heavyweight champion recently swelled to a massive -1386 betting favorite over at BookMaker. Probably because his opponent, former WWE superstar Alberto Del Rio, hasn’t competed in nearly a decade.
A $100 bet on the 42 year-old PRIDE veteran, however, will net you $700.
In case you’re wondering ... yes, that’s the biggest betting line Ortiz, 44, has enjoyed in his combat sports career. The second closest came against Chuck Liddell ahead of their Golden Boy laugher in late 2018, a fight Ortiz won by first-round knockout.
Keep in mind we’re still roughly two weeks away from fight night, so that line will continue to shift in both directions. But if you’re thinking of taking Del Rio for the bookie-busting upset, best to strike while the iron is hot.
For much more on Combate Americas click here.


E-mail us<EMAIL_ADDRESS>if you have any questions and we’ll get back to you as soon as possible.
Material lists and price.
Borrow the space for your events exhibits.


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Chai supports: IE 9+, Chrome 7+, FireFox 4+ ' Safari 5+ only. For others use:
compatibility-assignation.html
compatibility-native.html


Contains characters from 3 groups: upper case, lower case and symbols. Easy to remember, if
contains up to 6 syllables (13 chars), but still hard to hack using dictionary, since it is
not a real word.
Parameterized
You can tune up different char groups propotions using the multipliers below. Specifying
the zero value will exclude corresponding group from the generation.


Web App – Simple Interest Calculator
| Amount | |
| Interest Rate | % |
| No. of Years | |
If you deposit
at an interest rate of %.
You will receive an amount of ,
in the year
| Amount | |
| Interest Rate | % |
| No. of Years | |
If you deposit
at an interest rate of %.
You will receive an amount of ,
in the year


Our data set for this example will consist of gross domestic product (GDP), a measure of how wealthy a country is, and CO2 emissions. Both of these numbers will naturally be larger for larger countries, because more people will produce more wealth and more CO2, so we consider the per capita numbers, meaning the amount of money or CO2 per person. We can get these numbers by taking the total amount and dividing by the population of the country. If you already know that there is a relationship between data sets because of something like population, dividing out by the value is an easy way to eliminate that relationship. This is called normalizing the data set.
in-context


You are requesting temporarily access to Chameleon in order to
reproduce '{{ artifact.title }}'. By submitting this form,
an email will be sent to the PI of the project. If you have any
information relevant to your request, please include it below.
Use this to help the PI understand that you are a real person with
an interest in their research.
After the PI reviews your application, you will receive an email
with the approval decision. If approved, the email will contain
a link that starts the daypass. Approved access for this artifact
is for {{ artifact.reproducibility.access_hours }} hours.
{% bootstrap_field form.name %}
{% bootstrap_field form.email %}
{% bootstrap_field form.institution %}
{% bootstrap_field form.reason %}


The web is the most popular medium for sharing interactive data visualizations thanks to the portability of the web browser and the accessibility of the internet. Unfortunately, creating interactive web graphics often requires a working knowledge of a number of complicated web technologies. In recent years, we've seen a number of projects to alleviate this burden on users, particularly for statisticians and people who analyze data. I will present some examples and lessons learned from my work on the following R packages for creating interactive web graphics: plotly, LDAvis, and animint.


--- notes: | A simple navigation structure **Modifiers:** - `.nav--inline-up`: lays out the navigation horizontally at a breakpoint ---


1217 px
User Agent is set correctly.
Viewport width is 412px.
Viewport height is 603px.
Device Pixel Ratio is 3.5.


Triple H reveals why popular WWE faction split up prematurely
by Pratyay GhoshNXT TakeOver: WarGames took place last night and once again, the black and yellow brand did not disappoint. Following TakeOver, Triple H spoke to Cathy Kelley in an exclusive interview. During the interview, 'The Game' revealed why SAnitY broke up so soon after being called up to the main roster.
Triple H on why SAnitY broke up
SAnitY were called up to the main roster during the 2018 Superstar Shake-Up. Despite being a popular act in NXT, the faction failed to have any impact on the main roster, and being called up without Nikki Cross didn't help. Their appearances on WWE television grew fewer and further apart until they were officially disbanded.
Triple H addressed the SAnitY situation during his interview after NXT TakeOver: WarGames.Triple H said that the faction didn't click and fell apart from the inside which led to WWE splitting them up.
Meanwhile, Rhea Ripley joined Triple H during the media call and revealed the women's NXT team for Survivor Series tonight. Ripley will be joined by Bianca Belair, Io Shirai, Toni Storm and Candice LeRae.
Check out the interview below:
Now you can rate RAW, SmackDown and NXT matches on Sportskeeda !


In this scenario, the app will subscribe to events so that it can respond to the actions a user takes with a toast.
When users click on your toast, dismiss it, or the toast times out, events are raised. You can attach listeners for these events to the toast object.
In addition, you can listen to the "activated" event from either the toast object or the Windows.UI.WebUI.WebUIApplication object to tell if the user clicked on your toast. The difference is that the WebUIApplication event is raised by toasts that arise from either local or push notifications, while the toast object event is raised only by local notifications.
You can also include a context in the toast notification payload that is specific to your app. This context can be accessed by your app when your app is activated from the toast notification. The context information included in the toast should be used to inform your app what action to take when it is launched through the toast. For example, if the toast represents a breaking news article in a news app, the context information in the toast should provide enough information so that the app can take the user directly to that story.
Whatever action you take on the displayed toast will be displayed here


Name: Robert Downey Jr
Email<EMAIL_ADDRESS>Phone : +1<PHONE_NUMBER>
Address :Dia's Residence , Lance Street, Malibu Club Road
Carerr Objective : To Establish a Software Company
| COURSE |
Institution |
Aggrigate |
| B.Tech |
Pepperdine University |
10.0 |
| Intermediate |
Malibu High School |
99.8% |
| 10Th |
Malibu High School |
10.0 |
Technical Skills :
- C
- C++
- Python
- Java
- R-Programming
- HTML
- CSS
- JS
- PROLOG
Achivements :
- Best Programmer
- Code Chef 4Satrs
- Oracle Intern


The Earthquake library offers access to the United States Geological Survey’s Earthquake feed. This is international data about earthquakes happening all over. They offer a lot of information, including magnitude and coordinates. You can get information as far back as the past month, or as most recent as the past hour. Note that this data stream has a very high velocity - if you check every five minutes, you’ll find it may have already changed.
>>> from earthquakes import earthquakes
You can get a Report about the latest earthquakes.
>>> report = earthquakes.get_report()
>>> report
<Report USGS Significant Earthquakes, Past Hour, 2 Quakes>
By default, it only gets the significant earthquakes from the past hour. There are usually not that many significant earthquakes in the world, though.
>>> earthquakes.get_report('week', 'all')
<Report USGS All Earthquakes, Past Week, 1612 Quakes>
>>> report = earthquakes.get_report('hour', '1.0')
>>> report
<Report USGS Magnitude 1.0+ Earthquakes, Past Hour, 4 Quakes>
Reports have a title, a list of earthquakes, and a boundary box that fits all the earthquakes.
>>> report.title
'USGS Magnitude 1.0+ Earthquakes, Past Hour'
>>> report.earthquakes
[<Earthquake nc72216991>, <Earthquake ak11254626>, <Earthquake ak11254622>, <Earthquake ak11254614>]
>>> report.area
<Box (-151.35, 38.76), (-122.78, 63.12)>
A given earthquake has many properties available.
>>> quake = report.earthquakes[0]
>>> quake
<Earthquake nc72216991>
>>> quake.magnitude
1.1
>>> quake.location
<Coordinate -122.776, 38.761>
>>> quake.location_description
'2km SW of The Geysers, California'
>>> quake.time # epoch time
1399423359500
Other fields: alert_level, distance, event_source, felt_reports, gap, id, maximum_estimated_intensity, maximum_reported_intensity, root_mean_square, significance, url.
Warning
Sometimes earthquake information is released before it is fully compiled, such as the number of felt_reports. Therefore, data may change between calls to the service. Moreover, older earthquakes in a Report may have more data available to them than newer ones.
The built-in cache allows you to work online:
>>> earthquakes.connect() # unnecessary: default is connected
or offline:
>>> earthquakes.disconnect()
>>> earthquakes.get_report('day', 'all')
<Report USGS All Earthquakes, Past Day, 237 Quakes>
But remember there must be data in the cache already!
report = earthquakes.get_report(‘hour’, ‘1.0’) earthquakes.earthquakes.USGSException: No data was in the cache for this time and threshold (‘hour’, ‘1.0’).
The cache can be configured to handle repeated calls differently. For example, if you want, you could make it return new results every time you call:
>>> earthquakes.get_report('hour', 'all')
<Report USGS All Earthquakes, Past Day, 9 Quakes>
>>> earthquakes.get_report('hour', 'all')
<Report USGS All Earthquakes, Past Day, 12 Quakes>
>>> earthquakes.get_report('hour', 'all')
<Report USGS All Earthquakes, Past Day, 6 Quakes>
Say you want to add the latest earthquakes to the cache:
>>> earthquakes._start_editing()
>>> earthquakes.get_posts('hour', '1.0')
>>> earthquakes._save_cache()
Now the file “cache.json” file will have an entry for (“hour”, “1.0”), and you can use that as an input to the function when disconnected.
You can also create a different cache file by passing a filename to the _save_cache() method, and use that cache by passing its name to the disconnect() method.
For example, this will populate a file called “violent.json”, ideally taken during a time of high geologic activity.
>>> earthquakes._start_editing()
>>> earthquakes.get_posts('hour', '1.0')
>>> earthquakes._save_cache('violent.json')
To use that cached file, specify the json file name when you call disconnect():
>>> earthquakes.disconnect("violent.json")
Finally, you can put multiple entries into the cache for a given input, simulating multiple calls. These items will be appended. If the cache runs out, it will start returning empty reports.
>>> earthquakes.connect()
>>> earthquakes._start_editing()
>>> report = earthquakes.get_posts('hour', '1.0')
<Report USGS Magnitude 1.0+ Earthquakes, Past Hour, 9 Quakes>
>>> time.sleep(1000 * 60) # 1 hour
>>> earthquakes.get_posts('hour', '1.0')
<Report USGS Magnitude 1.0+ Earthquakes, Past Hour, 12 Quakes>
>>> earthquakes._save_cache()
>>> earthquakes.disconnect()
>>> earthquakes.get_posts('hour', '1.0')
<Report USGS Magnitude 1.0+ Earthquakes, Past Hour, 9 Quakes>
>>> earthquakes.get_posts('hour', '1.0')
<Report USGS Magnitude 1.0+ Earthquakes, Past Hour, 12 Quakes>
>>> earthquakes.get_posts('hour', '1.0')
<Report USGS Magnitude 1.0+ Earthquakes, Past Hour, 0 Quakes>
>>> earthquakes.get_posts('hour', '1.0')
<Report USGS Magnitude 1.0+ Earthquakes, Past Hour, 0 Quakes>
To run the unit tests from the command line:
>>> python -m tests.test
Further documentation is available in the docs/_builds/index.html file.
| Parameters: | dict (dict) – the dictionary to parse |
|---|---|
| Returns: | the iterable dictionary |
Internal method to convert a URL into it’s response (a str).
| Parameters: | url (str) – the url to request a response from |
|---|---|
| Returns: | the str response |
Internal method to combine the url and params into a single url string.
| Parameters: |
|
|---|---|
| Returns: | a str of the full url |
Internal method to get a dict of the latitude and longitude from the JSON
| Parameters: | json_res (dict) – the JSON response to parse |
|---|---|
| Returns: | a dict containing the coordinates |
| Parameters: | json_res (dict) – the JSON response to parse |
|---|
Internal method to form and query the server
| Parameters: | params (dict) – the parameters to pass to the server |
|---|---|
| Returns: | a dict of the JSON response |
Convert an address to it’s respective longitude and latitude
| Parameters: | address (str) – the address to get coordinates of |
|---|---|
| Returns: | the GPS coordinates of the location |


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Transitionable
protocol Transitionable : AnyObject
A protocol comformed by viewController classes. This is the boundary between the view model and the controller for view state changes or navigation
-
Typically an enum with different screen states or actions
Declaration
Swift
associatedtype State
-
Call this for pushing view controllers in the navigation stack
Declaration
Swift
func push(to newState: State)
Parameters
to
To a given state
-
Call this for displaying other views in the same screen
Declaration
Swift
func display(to newState: State)
Parameters
to
To a given state
-
A general purpose alert view
Declaration
Swift
func showAlert(title: String, message: String)
Parameters
title
Title of the the alert
message
The alert message


Company Motto: Lorem ipsum dolor sit amet, consectetur adipisicing elit.
So your business isn't running as much as haulin' arse. So trucks are passing you on the freeway. Sew, a needle pulling thread.
Have you heard geese in your attic? Is turning it off and on again not helping? Are you tired of me asking questions?
Silvern Software will take your business to the next level without losing any of it's personality. We can help your operation to keep track of those pesky little day to day things. The things that fall through the cracks, so to speak.
These guys are guys!
-Lady, 99Birds are birds are birds are birds. Ducks just dont give a f-
-Chuck, 12Things with twelve purple its twice again but not always behind that where mine wont ever been there thing what with how then?
-Bob Bobson, 54 (R)Tap a photo to learn more.


| Field Summary | ||
|---|---|---|
const Number | PLAIN_TEXT | |
| PlainText | ||
const Number | STOP | |
| Stop | ||
const Number | TRIANGLE_UP | |
| TriangleUp | ||
const Number | TRIANGLE_DOWN | |
| TriangleDown | ||
const Number | CHEVRON_UP | |
| ChevronUp | ||
const Number | CHEVRON_DOWN | |
| ChevronDown | ||
const Number | RING_INSIDE | |
| RingInside | ||
const Number | RING_OUTSIDE | |
| RingOutside | ||
const Number | ARCH_UP_CURVE | |
| ArchUpCurve | ||
const Number | ARCH_DOWN_CURVE | |
| ArchDownCurve | ||
const Number | CIRCLE_CURVE | |
| CircleCurve | ||
const Number | BUTTON_CURVE | |
| ButtonCurve | ||
const Number | ARCH_UP_POUR | |
| ArchUpPour | ||
const Number | ARCH_DOWN_POUR | |
| ArchDownPour | ||
const Number | CIRCLE_POUR | |
| CirclePour | ||
const Number | BUTTON_POUR | |
| ButtonPour | ||
const Number | CURVE_UP | |
| CurveUp | ||
const Number | CURVE_DOWN | |
| CurveDown | ||
const Number | CAN_UP | |
| CanUp | ||
const Number | CAN_DOWN | |
| CanDown | ||
const Number | WAVE_1 | |
| Wave1 | ||
const Number | WAVE_2 | |
| Wave2 | ||
const Number | DOUBLE_WAVE_1 | |
| DoubleWave1 | ||
const Number | DOUBLE_WAVE_2 | |
| DoubleWave2 | ||
const Number | INFLATE | |
| Inflate | ||
const Number | DEFLATE | |
| Deflate | ||
const Number | INFLATE_BOTTOM | |
| InflateBottom | ||
const Number | DEFLATE_BOTTOM | |
| DeflateBottom | ||
const Number | INFLATE_TOP | |
| InflateTop | ||
const Number | DEFLATE_TOP | |
| DeflateTop | ||
const Number | DEFLATE_INFLATE | |
| DeflateInflate | ||
const Number | DEFLATE_INFLATE_DEFLATE | |
| DeflateInflateDeflate | ||
const Number | FADE_RIGHT | |
| FadeRight | ||
const Number | FADE_LEFT | |
| FadeLeft | ||
const Number | FADE_UP | |
| FadeUp | ||
const Number | FADE_DOWN | |
| FadeDown | ||
const Number | SLANT_UP | |
| SlantUp | ||
const Number | SLANT_DOWN | |
| SlantDown | ||
const Number | CASCADE_UP | |
| CascadeUp | ||
const Number | CASCADE_DOWN | |
| CascadeDown | ||
const Number | MIXED | |
| Mixed |
| Field Detail |
|---|
PLAIN_TEXT | |
const Number PLAIN_TEXT |
STOP | |
const Number STOP |
TRIANGLE_UP | |
const Number TRIANGLE_UP |
TRIANGLE_DOWN | |
const Number TRIANGLE_DOWN |
CHEVRON_UP | |
const Number CHEVRON_UP |
CHEVRON_DOWN | |
const Number CHEVRON_DOWN |
RING_INSIDE | |
const Number RING_INSIDE |
RING_OUTSIDE | |
const Number RING_OUTSIDE |
ARCH_UP_CURVE | |
const Number ARCH_UP_CURVE |
ARCH_DOWN_CURVE | |
const Number ARCH_DOWN_CURVE |
CIRCLE_CURVE | |
const Number CIRCLE_CURVE |
BUTTON_CURVE | |
const Number BUTTON_CURVE |
ARCH_UP_POUR | |
const Number ARCH_UP_POUR |
ARCH_DOWN_POUR | |
const Number ARCH_DOWN_POUR |
CIRCLE_POUR | |
const Number CIRCLE_POUR |
BUTTON_POUR | |
const Number BUTTON_POUR |
CURVE_UP | |
const Number CURVE_UP |
CURVE_DOWN | |
const Number CURVE_DOWN |
CAN_UP | |
const Number CAN_UP |
CAN_DOWN | |
const Number CAN_DOWN |
WAVE_1 | |
const Number WAVE_1 |
WAVE_2 | |
const Number WAVE_2 |
DOUBLE_WAVE_1 | |
const Number DOUBLE_WAVE_1 |
DOUBLE_WAVE_2 | |
const Number DOUBLE_WAVE_2 |
INFLATE | |
const Number INFLATE |
DEFLATE | |
const Number DEFLATE |
INFLATE_BOTTOM | |
const Number INFLATE_BOTTOM |
DEFLATE_BOTTOM | |
const Number DEFLATE_BOTTOM |
INFLATE_TOP | |
const Number INFLATE_TOP |
DEFLATE_TOP | |
const Number DEFLATE_TOP |
DEFLATE_INFLATE | |
const Number DEFLATE_INFLATE |
DEFLATE_INFLATE_DEFLATE | |
const Number DEFLATE_INFLATE_DEFLATE |
FADE_RIGHT | |
const Number FADE_RIGHT |
FADE_LEFT | |
const Number FADE_LEFT |
FADE_UP | |
const Number FADE_UP |
FADE_DOWN | |
const Number FADE_DOWN |
SLANT_UP | |
const Number SLANT_UP |
SLANT_DOWN | |
const Number SLANT_DOWN |
CASCADE_UP | |
const Number CASCADE_UP |
CASCADE_DOWN | |
const Number CASCADE_DOWN |
MIXED | |
const Number MIXED |


comments welcome
Thoughts on Peer Pairing and Feedback
Wednesday, April 15th, 2015
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When DBC’s Phase 0 was starting, we were quickly exposed to the concept of peer pairing in programming. Most everything that I read - and this article in particular - suggested that it leads to great developers and great code. It can be slow-going at times, and tough to adjust to, but the benefits are many. So when I realized we’d be doing a lot of it every week, I was excited.
Five weeks later… I love pairing. It’s great to work with others to solve challenges. I’ve realized that even if I go into a session thinking that I know the best way to do something, I can learn so much more by coming with no strong opinions on how to approach the problem. Inevitably, something will be done in a way that I hadn’t considered, and I will have learned something. It has been incredibly rewarding. Even if I don’t learn a totally new concept, I always learn some little trick that makes my own work easier. And it’s also nice to have someone to laugh with over mistakes, and grumble with when something is particularly tedious. Few parts of pairing have been difficult.
That said, we do a lot of feedback. Reading that feedback can be unnerving, and it’s tough when you’re anticipating its arrival. I have to admit, I was nervous when perusing the first few selections. I don’t want to hear bad feedback any more than anyone else. But I think if you have a good understanding of your own strengths and weaknesses, constructive feedback can always be seen as something that’s helpful.
And so far, it’s been pretty good. But the process doesn’t come without struggles. I have a very hard time being impolite, even a little. I try to be very aware of others’ feelings. So when pairing, I pay special attention to the opinions of my partner, and I try not to make decisions without first getting their approval. As I expected, this has shown up in my feedback. People say that I know what I’m doing, so I should just be confident and “take the reins.” This is a struggle for me. In college, when my grades depended on my work, I was hard on partners in group settings, and I re-did a lot of their work on my own. And I felt bad about that.
Now that I’m more aware of my actions, I want to try to make decisions and changes a little more tactfully. But I err on the side of caution probably a little too much, especially in Phase 0, when there’s no grade or evaluation on the line. I just want us both to learn. But as I move forward, I do need to be aware of this. Once I start making projects that matter, I’ll need to stand up and say “No, that won’t work,” and not waste time coming to a democratic decision on every little thing.
It can also be tough to give feedback. We are supposed to include something “constructive” (negative), but that’s hard to do. Usually, there really aren’t any glaring issues, and even if there are, it’s tough to be hard on someone, when the new material seems like enough to deal with.
The whole topic of feedback reminds me of a video of Linus Torvalds - the creator of Linux and git - speaking to a group of students at a University. The topic of Torvalds’s “rough” demeanor came up. He admits to giving blunt feedback, and he says there’s a reason:
I believe that when you work with a lot of people, it's better to be really open about your feelings, so that you don't have people that by mistake misread you.
He goes on to describe a fitting anecdote, when he was initially supportive of someone when they were building a project that he knew he didn’t like. He didn’t want to tell them it wasn’t good enough, but eventually he had to tell them anyway, and then their time had been wasted. Because of situations like that, he believes that it’s best to be open and honest right away.
It’s a fine line to balance. It’s hard to put your stamp of approval on something you aren’t 100% supportive of, to work in ways that you aren’t used to, and to give feedback on someone else’s work. But these are all skills that are invaluable. I’m glad we’re getting opportunities to practice this now, and I’m glad the industry is moving toward pairing in general. I believe the opportunities for joint growth are limitless, and I’m excited to do more.
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Data Analysis
Data analysis aims to manipulate, clean, and process data in Python.
Dataset Source
The dataset source is from the UCI Machine Learning Repository. It has 303 records, of which 139 has heart disease, and the rate of illness is 45.87%.
Missing Value
The original data set has missing values and redundant attributes. The data source provided on Kaggle has solved this problem.
Among the 13 attributes, 6 attributes are numeric values and there
are 7 categorical values. The main point is to encode the categorical value, so the method of one-hot coding is adopted.
Data Preprocessing
In the process of data analysis, data preparation is a particularly important task, including data loading, data cleaning, data normalization, data transformation, feature extraction and selection.
The
dataset contains categorical and numeric features separately, with many columns. The correlation matrix can be visualized as a heat map in Figure, to detect correlations between columns. After computing
the pairwise correlation
of them, excluding empty values.
It is evident that the relationship between the target value and other values is very high. Therefore, green is positively correlated
and red is negatively correlated. The darker the color inside the grid, the higher the correlation magnitude.