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An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

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w3-button" href="/w3css/default.asp">Learn W3.CSS</a> <a class="w3-bar-item w3-button" href="/colors/default.asp">Learn Colors</a> <a class="w3-bar-item w3-button" href="/icons/default.asp">Learn Icons</a> <a class="w3-bar-item w3-button" href="/graphics/default.asp">Learn Graphics</a> <a class="w3-bar-item w3-button" href="/graphics/svg_intro.asp">Learn SVG</a> <a class="w3-bar-item w3-button" href="/graphics/canvas_intro.asp">Learn Canvas</a> <a class="w3-bar-item w3-button" href="/howto/default.asp">Learn How To</a> <a class="w3-bar-item w3-button" href="/sass/default.php">Learn Sass</a> <div class="w3-hide-large w3-hide-small"> <h3 class="w3-margin-top">Data Analytics</h3> <a class="w3-bar-item w3-button" href="/ai/default.asp">Learn AI</a> <a class="w3-bar-item w3-button" href="/python/python_ml_getting_started.asp">Learn Machine Learning</a> <a class="w3-bar-item w3-button" href="/datascience/default.asp">Learn Data Science</a> <a class="w3-bar-item w3-button" href="/python/numpy/default.asp">Learn NumPy</a> <a class="w3-bar-item w3-button" href="/python/pandas/default.asp">Learn Pandas</a> <a class="w3-bar-item w3-button" href="/python/scipy/index.php">Learn SciPy</a> <a class="w3-bar-item w3-button" href="/python/matplotlib_intro.asp">Learn Matplotlib</a> <a class="w3-bar-item w3-button" href="/statistics/index.php">Learn Statistics</a> <a class="w3-bar-item w3-button" href="/excel/index.php">Learn Excel</a> <h3 class="w3-margin-top">XML Tutorials</h3> <a class="w3-bar-item w3-button" href="/xml/default.asp">Learn XML</a> <a class="w3-bar-item w3-button" href="/xml/ajax_intro.asp">Learn XML AJAX</a> <a class="w3-bar-item w3-button" href="/xml/dom_intro.asp">Learn XML DOM</a> <a class="w3-bar-item w3-button" href="/xml/xml_dtd_intro.asp">Learn XML DTD</a> <a class="w3-bar-item w3-button" href="/xml/schema_intro.asp">Learn XML Schema</a> <a class="w3-bar-item w3-button" href="/xml/xsl_intro.asp">Learn XSLT</a> <a class="w3-bar-item w3-button" href="/xml/xpath_intro.asp">Learn XPath</a> <a class="w3-bar-item w3-button" href="/xml/xquery_intro.asp">Learn XQuery</a> </div> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">JavaScript</h3> <a class="w3-bar-item w3-button" href="/js/default.asp">Learn JavaScript</a> <a class="w3-bar-item w3-button" href="/jquery/default.asp">Learn jQuery</a> <a class="w3-bar-item w3-button" href="/react/default.asp">Learn React</a> <a class="w3-bar-item w3-button" href="/angular/default.asp">Learn AngularJS</a> <a class="w3-bar-item w3-button" href="/js/js_json_intro.asp">Learn JSON</a> <a class="w3-bar-item w3-button" href="/js/js_ajax_intro.asp">Learn AJAX</a> <a class="w3-bar-item w3-button" href="/appml/default.asp">Learn AppML</a> <a class="w3-bar-item w3-button" href="/w3js/default.asp">Learn W3.JS</a> <h3 class="w3-margin-top">Programming</h3> <a class="w3-bar-item w3-button" href="/python/default.asp">Learn Python</a> <a class="w3-bar-item w3-button" href="/java/default.asp">Learn Java</a> <a class="w3-bar-item w3-button" href="/c/index.php">Learn C</a> <a class="w3-bar-item w3-button" href="/cpp/default.asp">Learn C++</a> <a class="w3-bar-item w3-button" href="/cs/index.php">Learn C#</a> <a class="w3-bar-item w3-button" href="/r/default.asp">Learn R</a> <a class="w3-bar-item w3-button" href="/kotlin/index.php">Learn Kotlin</a> <a class="w3-bar-item w3-button" href="/go/index.php">Learn Go</a> <a class="w3-bar-item w3-button" href="/django/index.php">Learn Django</a> <a class="w3-bar-item w3-button" href="/typescript/index.php">Learn TypeScript</a> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">Server Side</h3> <a class="w3-bar-item w3-button" href="/sql/default.asp">Learn SQL</a> <a class="w3-bar-item w3-button" href="/mysql/default.asp">Learn MySQL</a> <a class="w3-bar-item w3-button" href="/php/default.asp">Learn PHP</a> <a class="w3-bar-item w3-button" href="/asp/default.asp">Learn ASP</a> <a class="w3-bar-item w3-button" href="/nodejs/default.asp">Learn Node.js</a> <a class="w3-bar-item w3-button" href="/nodejs/nodejs_raspberrypi.asp">Learn Raspberry Pi</a> <a class="w3-bar-item w3-button" href="/git/default.asp">Learn Git</a> <a class="w3-bar-item w3-button" href="/aws/index.php">Learn AWS Cloud</a> <h3 class="w3-margin-top">Web Building</h3> <a class="w3-bar-item w3-button" href="https://www.w3schools.com/spaces" target="_blank" onclick="ga('send', 'event', 'spacesFromTutorialsAcc', 'click');" title="Get Your Own Website With W3schools Spaces">Create a Website <span class="ribbon-topnav ws-yellow">NEW</span></a> <a class="w3-bar-item w3-button" href="/where_to_start.asp">Where To Start</a> <a class="w3-bar-item w3-button" href="/w3css/w3css_templates.asp">Web Templates</a> <a class="w3-bar-item w3-button" href="/browsers/default.asp">Web Statistics</a> <a class="w3-bar-item w3-button" href="/cert/default.asp">Web Certificates</a> <a class="w3-bar-item w3-button" href="/whatis/default.asp">Web Development</a> <a class="w3-bar-item w3-button" href="/tryit/default.asp">Code Editor</a> <a class="w3-bar-item w3-button" href="/typingspeed/default.asp">Test Your Typing Speed</a> <a class="w3-bar-item w3-button" href="/codegame/index.html" target="_blank">Play a Code Game</a> <a class="w3-bar-item w3-button" href="/cybersecurity/index.php">Cyber Security</a> <a class="w3-bar-item w3-button" href="/accessibility/index.php">Accessibility</a> </div> <div class="w3-col l3 m6 w3-hide-medium"> <h3 class="w3-margin-top">Data Analytics</h3> <a class="w3-bar-item w3-button" href="/ai/default.asp">Learn AI</a> <a class="w3-bar-item w3-button" href="/python/python_ml_getting_started.asp">Learn Machine Learning</a> <a class="w3-bar-item w3-button" href="/datascience/default.asp">Learn Data Science</a> <a class="w3-bar-item w3-button" href="/python/numpy/default.asp">Learn NumPy</a> <a class="w3-bar-item w3-button" href="/python/pandas/default.asp">Learn Pandas</a> <a class="w3-bar-item w3-button" href="/python/scipy/index.php">Learn SciPy</a> <a class="w3-bar-item w3-button" href="/python/matplotlib_intro.asp">Learn Matplotlib</a> <a class="w3-bar-item w3-button" href="/statistics/index.php">Learn Statistics</a> <a class="w3-bar-item w3-button" href="/excel/index.php">Learn Excel</a> <a class="w3-bar-item w3-button" href="/googlesheets/index.php">Learn Google Sheets</a> <h3 class="w3-margin-top">XML Tutorials</h3> <a class="w3-bar-item w3-button" href="/xml/default.asp">Learn XML</a> <a class="w3-bar-item w3-button" href="/xml/ajax_intro.asp">Learn XML AJAX</a> <a class="w3-bar-item w3-button" href="/xml/dom_intro.asp">Learn XML DOM</a> <a class="w3-bar-item w3-button" href="/xml/xml_dtd_intro.asp">Learn XML DTD</a> <a class="w3-bar-item w3-button" href="/xml/schema_intro.asp">Learn XML Schema</a> <a class="w3-bar-item w3-button" href="/xml/xsl_intro.asp">Learn XSLT</a> <a class="w3-bar-item w3-button" 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target="_top" href="tag_cite.asp">&lt;cite&gt;</a> <a target="_top" href="tag_code.asp">&lt;code&gt;</a> <a target="_top" href="tag_col.asp">&lt;col&gt;</a> <a target="_top" href="tag_colgroup.asp">&lt;colgroup&gt;</a> <a target="_top" href="tag_data.asp">&lt;data&gt;</a> <a target="_top" href="tag_datalist.asp">&lt;datalist&gt;</a> <a target="_top" href="tag_dd.asp">&lt;dd&gt;</a> <a target="_top" href="tag_del.asp">&lt;del&gt;</a> <a target="_top" href="tag_details.asp">&lt;details&gt;</a> <a target="_top" href="tag_dfn.asp">&lt;dfn&gt;</a> <a target="_top" href="tag_dialog.asp">&lt;dialog&gt;</a> <a target="_top" href="tag_dir.asp">&lt;dir&gt;</a> <a target="_top" href="tag_div.asp">&lt;div&gt;</a> <a target="_top" href="tag_dl.asp">&lt;dl&gt;</a> <a target="_top" href="tag_dt.asp">&lt;dt&gt;</a> <a target="_top" href="tag_em.asp">&lt;em&gt;</a> <a target="_top" href="tag_embed.asp">&lt;embed&gt;</a> <a target="_top" href="tag_fieldset.asp">&lt;fieldset&gt;</a> <a target="_top" href="tag_figcaption.asp">&lt;figcaption&gt;</a> <a target="_top" href="tag_figure.asp">&lt;figure&gt;</a> <a target="_top" href="tag_font.asp">&lt;font&gt;</a> <a target="_top" href="tag_footer.asp">&lt;footer&gt;</a> <a target="_top" href="tag_form.asp">&lt;form&gt;</a> <a target="_top" href="tag_frame.asp">&lt;frame&gt;</a> <a target="_top" href="tag_frameset.asp">&lt;frameset&gt;</a> <a target="_top" href="tag_hn.asp">&lt;h1&gt; - &lt;h6&gt;</a> <a target="_top" href="tag_head.asp">&lt;head&gt;</a> <a target="_top" href="tag_header.asp">&lt;header&gt;</a> <a target="_top" href="tag_hr.asp">&lt;hr&gt;</a> <a target="_top" href="tag_html.asp">&lt;html&gt;</a> <a target="_top" href="tag_i.asp">&lt;i&gt;</a> <a target="_top" href="tag_iframe.asp">&lt;iframe&gt;</a> <a target="_top" href="tag_img.asp">&lt;img&gt;</a> <a target="_top" href="tag_input.asp">&lt;input&gt;</a> <a target="_top" href="tag_ins.asp">&lt;ins&gt;</a> <a target="_top" href="tag_kbd.asp">&lt;kbd&gt;</a> <a target="_top" href="tag_label.asp">&lt;label&gt;</a> <a target="_top" href="tag_legend.asp">&lt;legend&gt;</a> <a target="_top" href="tag_li.asp">&lt;li&gt;</a> <a target="_top" href="tag_link.asp">&lt;link&gt;</a> <a target="_top" href="tag_main.asp">&lt;main&gt;</a> <a target="_top" href="tag_map.asp">&lt;map&gt;</a> <a target="_top" href="tag_mark.asp">&lt;mark&gt;</a> <a target="_top" href="tag_meta.asp">&lt;meta&gt;</a> <a target="_top" href="tag_meter.asp">&lt;meter&gt;</a> <a target="_top" href="tag_nav.asp">&lt;nav&gt;</a> <a target="_top" href="tag_noframes.asp">&lt;noframes&gt;</a> <a target="_top" href="tag_noscript.asp">&lt;noscript&gt;</a> <a target="_top" href="tag_object.asp">&lt;object&gt;</a> <a target="_top" href="tag_ol.asp">&lt;ol&gt;</a> <a target="_top" href="tag_optgroup.asp">&lt;optgroup&gt;</a> <a target="_top" href="tag_option.asp">&lt;option&gt;</a> <a target="_top" href="tag_output.asp">&lt;output&gt;</a> <a target="_top" href="tag_p.asp" class="active">&lt;p&gt;</a> <a target="_top" href="tag_param.asp">&lt;param&gt;</a> <a target="_top" href="tag_picture.asp">&lt;picture&gt;</a> <a target="_top" href="tag_pre.asp">&lt;pre&gt;</a> <a target="_top" href="tag_progress.asp">&lt;progress&gt;</a> <a target="_top" href="tag_q.asp">&lt;q&gt;</a> <a target="_top" href="tag_rp.asp">&lt;rp&gt;</a> <a target="_top" href="tag_rt.asp">&lt;rt&gt;</a> <a target="_top" href="tag_ruby.asp">&lt;ruby&gt;</a> <a target="_top" href="tag_s.asp">&lt;s&gt;</a> <a target="_top" href="tag_samp.asp">&lt;samp&gt;</a> <a target="_top" href="tag_script.asp">&lt;script&gt;</a> <a target="_top" href="tag_section.asp">&lt;section&gt;</a> <a target="_top" href="tag_select.asp">&lt;select&gt;</a> <a target="_top" href="tag_small.asp">&lt;small&gt;</a> <a target="_top" href="tag_source.asp">&lt;source&gt;</a> <a target="_top" href="tag_span.asp">&lt;span&gt;</a> <a target="_top" href="tag_strike.asp">&lt;strike&gt;</a> <a target="_top" href="tag_strong.asp">&lt;strong&gt;</a> <a target="_top" href="tag_style.asp">&lt;style&gt;</a> <a target="_top" href="tag_sub.asp">&lt;sub&gt;</a> <a target="_top" href="tag_summary.asp">&lt;summary&gt;</a> <a target="_top" href="tag_sup.asp">&lt;sup&gt;</a> <a target="_top" href="tag_svg.asp">&lt;svg&gt;</a> <a target="_top" href="tag_table.asp">&lt;table&gt;</a> <a target="_top" href="tag_tbody.asp">&lt;tbody&gt;</a> <a target="_top" href="tag_td.asp">&lt;td&gt;</a> <a target="_top" href="tag_template.asp">&lt;template&gt;</a> <a target="_top" href="tag_textarea.asp">&lt;textarea&gt;</a> <a target="_top" href="tag_tfoot.asp">&lt;tfoot&gt;</a> <a target="_top" href="tag_th.asp">&lt;th&gt;</a> <a target="_top" href="tag_thead.asp">&lt;thead&gt;</a> <a target="_top" href="tag_time.asp">&lt;time&gt;</a> <a target="_top" href="tag_title.asp">&lt;title&gt;</a> <a target="_top" href="tag_tr.asp">&lt;tr&gt;</a> <a target="_top" href="tag_track.asp">&lt;track&gt;</a> <a target="_top" href="tag_tt.asp">&lt;tt&gt;</a> <a target="_top" href="tag_u.asp">&lt;u&gt;</a> <a target="_top" href="tag_ul.asp">&lt;ul&gt;</a> <a target="_top" href="tag_var.asp">&lt;var&gt;</a> <a target="_top" href="tag_video.asp">&lt;video&gt;</a> <a target="_top" href="tag_wbr.asp">&lt;wbr&gt;</a> </div> <br><br> </div> </div> </div> <div class="w3-main w3-light-grey" id="belowtopnav" style="margin-left: 220px; padding-top: 44px;"> <div class="w3-row w3-white"> <div class="w3-col l10 m12" id="main"> <div id="mainLeaderboard" style="overflow:hidden;"> <!-- MainLeaderboard--> <!--<pre>main_leaderboard, all: [728,90][970,90][320,50][468,60]</pre>--> <div id="adngin-main_leaderboard-0" data-google-query-id="CJPA_sueqvcCFXiOSwUd2fYBLg"><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1__container__" style="border: 0pt none;"><iframe id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1" name="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1" title="3rd party ad content" width="728" height="90" scrolling="no" marginwidth="0" marginheight="0" frameborder="0" role="region" aria-label="Advertisement" tabindex="0" srcdoc="" data-google-container-id="7" style="border: 0px; vertical-align: bottom;" data-load-complete="true"><div style="position: absolute; width: 0px; height: 0px; border: 0px; padding: 0px; margin: 0px; overflow: hidden;"><button></button><a href="https://yahoo.com"></a><input></div></iframe></div></div> <!-- adspace leaderboard --> </div> <h1>HTML <span class="color_h1">&lt;p&gt;</span> Tag</h1> <div class="w3-clear w3-center nextprev"> <a class="w3-left w3-btn" href="tag_output.asp">❮<span class="w3-hide-small"> Previous</span></a> <a class="w3-btn" href="default.asp"><span class="w3-hide-small">Complete HTML </span>Reference</a> <a class="w3-right w3-btn" href="tag_param.asp"><span class="w3-hide-small">Next </span>❯</a> </div> <br> <div class="w3-example"> <h3>Example</h3> <p>A paragraph is marked up as follows:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs1" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <p>More "Try it Yourself" examples below.</p> <hr> <h2>Definition and Usage</h2> <p>The <code class="w3-codespan">&lt;p&gt;</code> tag defines a paragraph.</p> <p>Browsers automatically add a single blank line before and after each <code class="w3-codespan">&lt;p&gt;</code> element.</p> <p><strong>Tip:</strong> Use CSS to <a href="/html/html_css.asp">style paragraphs</a>.</p> <hr> <h2>Browser Support</h2> <table class="browserref notranslate"> <tbody><tr> <th style="width:20%;font-size:16px;text-align:left;">Element</th> <th style="width:16%;" class="bsChrome" title="Chrome"></th> <th style="width:16%;" class="bsEdge" title="Internet Explorer / Edge"></th> <th style="width:16%;" class="bsFirefox" title="Firefox"></th> <th style="width:16%;" class="bsSafari" title="Safari"></th> <th style="width:16%;" class="bsOpera" title="Opera"></th> </tr><tr> <td style="text-align:left;">&lt;p&gt;</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> </tr> </tbody></table> <hr> <h2>Global Attributes</h2> <p>The <code class="w3-codespan">&lt;p&gt;</code> tag also supports the <a href="ref_standardattributes.asp">Global Attributes in HTML</a>.</p> <hr> <h2>Event Attributes</h2> <p>The <code class="w3-codespan">&lt;p&gt;</code> tag also supports the <a href="ref_eventattributes.asp">Event Attributes in HTML</a>.</p> <hr> <div id="midcontentadcontainer" style="overflow:auto;text-align:center"> <!-- MidContent --> <!-- <p class="adtext">Advertisement</p> --> <div id="adngin-mid_content-0" data-google-query-id="CKfs_8ueqvcCFXiOSwUd2fYBLg"><div id="sn_ad_label_adngin-mid_content-0" class="sn_ad_label" style="color:#000000;font-size:12px;margin:0;text-align:center;">ADVERTISEMENT</div><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1__container__" style="border: 0pt none; display: inline-block; width: 300px; height: 250px;"><iframe frameborder="0" src="https://56d0da6c34aaa471db22bb4266aac656.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html" id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1" title="3rd party ad content" name="" scrolling="no" marginwidth="0" marginheight="0" width="300" height="250" data-is-safeframe="true" sandbox="allow-forms allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-top-navigation-by-user-activation" role="region" aria-label="Advertisement" tabindex="0" data-google-container-id="8" style="border: 0px; vertical-align: bottom;" data-load-complete="true"></iframe></div></div> </div> <hr> <h2>More Examples</h2> <div class="w3-example"> <h3>Example</h3> <p>Align text in a paragraph (with CSS):</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="attributecolor" style="color:red"> style<span class="attributevaluecolor" style="color:mediumblue">="text-align:right"</span></span><span class="tagcolor" style="color:mediumblue">&gt;</span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_align_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Style paragraphs with CSS:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>html<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>head<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>style<span class="tagcolor" style="color:mediumblue">&gt;</span></span><span class="cssselectorcolor" style="color:brown"><br>p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br>&nbsp; color<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> navy<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; text-indent<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 30px<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; text-transform<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> uppercase<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span><br></span><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/style<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/head<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>body<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/body<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/html<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_style_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>&nbsp;More on paragraphs:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br>This paragraph<br>contains a lot of lines<br>in the source code,<br> but the browser <br>ignores it.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs2" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Poem problems in HTML:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br>My Bonnie lies over the ocean.<br>My Bonnie lies over the sea.<br>My Bonnie lies over the ocean.<br>Oh, bring back my Bonnie to me.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_poem" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <hr> <h2>Related Pages</h2> <p>HTML tutorial: <a href="/html/html_paragraphs.asp">HTML Paragraphs</a></p> <p>HTML DOM reference: <a href="/jsref/dom_obj_paragraph.asp">Paragraph Object</a></p> <hr> <h2>Default CSS Settings</h2> <p>Most browsers will display the <code class="w3-codespan">&lt;p&gt;</code> element with the following default values:</p> <div class="w3-example"> <h3>Example</h3> <div class="w3-code notranslate cssHigh"><span class="cssselectorcolor" style="color:brown"> p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br>&nbsp; display<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> block<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; margin-top<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; margin-bottom<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; margin-left<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp;&nbsp;margin-right<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span> 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<!DOCTYPE html> <html> <head> <meta charset="utf-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="description" content="Eat cells smaller than you and don't get eaten by the bigger ones, as an MMO"> <meta name="keywords" content="agario, agar, io, cell, cells, virus, bacteria, blob, game, games, web game, html5, fun, flash"> <meta name="robots" content="index, follow"> <meta name="viewport" content="minimal-ui, width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no"> <meta name="apple-mobile-web-app-capable" content="yes"> <meta property="fb:app_id" content="677505792353827"/> <meta property="og:title" content="Agar.io"/> <meta property="og:description" content="Eat cells smaller than you and don't get eaten by the bigger ones, as an MMO"/> <meta property="og:url" content="http://agar.io"/> <meta property="og:image" content="http://agar.io/img/1200x630.png"/> <meta property="og:image:width" content="1200"/> <meta property="og:image:height" content="630"/> <meta property="og:type" content="website"/> <title>Agar.io</title> <link id="favicon" rel="icon" type="image/png" href="favicon-32x32.png"/> <!-- Área de anuncio --> <link href='https://fonts.googleapis.com/css?family=Ubuntu:700' rel='stylesheet' type='text/css'> <link href="css/bootstrap.min.css" rel="stylesheet"> <link href="css/glyphicons-social.css" rel="stylesheet"> <link href="css/animate.css" rel="stylesheet"> <style>body{padding:0;margin:0;overflow:hidden;}#canvas{position:absolute;left:0;right:0;top:0;bottom:0;width:100%;height:100%;}form{margin-bottom:0px;}.btn-play,.btn-settings,.btn-spectate,.btn-play-guest,.btn-login,.btn-logout{display:block;float:left;height:35px;}.btn-spectate,.btn-logout{height:35px;display:block;width:110px;margin-left:10px;margin-bottom:5px;}#helloContainer[data-logged-in="0"] .btn-play-guest{margin-left:5px;width:125px;}#helloContainer[data-logged-in="0"] .btn-login{margin-left:5px;width:145px;}#helloContainer[data-logged-in="0"] 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top:42.5%;}#nicks {width: 10%;float: left; position: absolute; left: 46%; top: 42.5%;}#nick{width:10%;padding: 0px; left: 46%; top: -12px;position: relative;}#locationKnown #region{width:100%;}#locationUnknown #region{margin-bottom:15px;}#gamemode{width:10%;float:right;top: -42.5%;right: 44%;position: relative;}.agario-panel{display:inline-block;width:350px;background-color:rgba(25, 28, 29, 0.72);margin:2px;border-radius:10px;padding:5px 15px 5px 15px;vertical-align:top;}.agario-side-panel{display:inline-block;width:220px;}#helloContainer,.connecting-panel{position:absolute;top:50%;left:50%;margin-right:-50%;-webkit-transform:translate(-50%,-50%);-ms-transform:translate(-50%,-50%);transform:translate(-50%,-50%);}#a300x250{width:300px;height:250px;background-repeat:no-repeat;background-size:contain;background-position:center center;}.agario-exp-bar{height:30px;position:relative;border:2px solid #01612B;}.agario-exp-bar .progress-bar{background-color:#338833;border-radius:0px 4px 4px 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.agario-party-4{display:block;}#helloContainer[data-party-state="5"] .agario-party-5{display:block;}#helloContainer[data-party-state="6"] .agario-party-6{display:block;}.partyToken{margin-bottom:10px;}.side-container{vertical-align:top;display:inline-block;width:224px;}.cell-spinner{display:block;margin:0;}.creating-party-text{position:absolute;cursor:default;top:0;bottom:0;left:0;right:0;width:100%;height:100%;text-align:center;color:#FFF;font-size:24px;line-height:100px;text-shadow:0px 0px 3px #000000,-1px 0px 0px #000000,1px 0px 0px #000000,0px 1px 0px #000000,0px -1px 0px #000000,-1px -1px 0px #000000,1px 1px 0px #000000,-1px 1px 0px #000000,1px -1px 0px #000000;}.agario-results-0,.agario-results-1,.agario-results-2{display:none;}#helloContainer[data-results-state="0"] .agario-results-0{display:block;}#helloContainer[data-results-state="1"] .agario-results-1{display:block;}#helloContainer[data-results-state="2"] .agario-results-2{display:block;}#options>label{display:block;width:94px;float:left;}#stats{position:relative;width:350px;height:581px;padding:0px 0px 300px 0px;overflow:hidden;}#statsPelletsContainer,#statsTimeAliveContainer,#statsHighestMassContainer,#statsTimeLeaderboardContainer,#statsPlayerCellsEatenContainer,#statsTopPositionContainer{position:absolute;width:100px;height:100px;}#statsPelletsContainer{top:30px;left:50px;}#statsHighestMassContainer{top:30px;right:50px;}#statsTimeAliveContainer{top:85px;left:50px;}#statsTimeLeaderboardContainer{top:85px;right:50px;}#statsPlayerCellsEatenContainer{top:140px;left:50px;}#statsTopPositionContainer{top:140px;right:50px;}#statsPellets{position:absolute;top:0;left:0;bottom:0;right:0;margin:auto;}#statsText{position:absolute;top:0;bottom:0;left:0;right:0;line-height:100px;font-size:23px;}#statsSubtext{position:absolute;bottom:0;left:0;right:0;line-height:60px;font-size:12px;color:#000;text-align:center;}#statsChartText{position:absolute;left:20px;bottom:250px;line-height:40px;font-size:40px;}#statsChartText,#statsText{cursor:default;color:#444;text-align:center;font-weight:bold;}#statsContinue{position:absolute;left:25px;right:25px;width:300px;bottom:295px;}#statsGraph{position:absolute;bottom:350px;left:0px;right:0px;opacity:0.4;}#s300x250{position:absolute;bottom:10px;left:25px;right:25px;width:300px;height:250px;}.tosBox{z-index:1000;position:absolute;bottom:0;right:0;background-color:#FFF;border-radius:5px 0px 0px 0px;padding:5px 10px;}</style> <script src="js/jquery.js"></script> <script src="js/bootstrap.min.js"></script> <script> i18n_lang = 'en'; i18n_dict = { 'en': { 'connecting': 'Connecting', 'connect_help': 'If you cannot connect to the servers, check if you have some anti virus or firewall blocking the connection.', 'play': 'Jogar', 'spectate': 'Observar O Jogo', 'login_and_play': 'Logar No Facebook', 'play_as_guest': 'Play as guest', 'share': 'Share', 'advertisement': 'Advertisement', 'privacy_policy': 'Privacy Policy', 'terms_of_service': 'Terms of Service', 'changelog': 'Changelog', 'instructions_mouse': 'Move your mouse to control your cell', 'instructions_space': 'Pressiona <b>Space</b> Para Duplica', 'instructions_w': 'Pressiona <b>W</b> Para Da Massa', 'gamemode_ffa': 'FFA', 'gamemode_teams': 'Time', 'gamemode_experimental': 'Experimental', 'region_select': ' -- Select a Region -- ', 'region_us_east': 'US East', 'region_us_west': 'US West', 'region_north_america': 'North America', 'region_south_america': 'South America', 'region_europe': 'Europe', 'region_turkey': 'Turkey', 'region_poland': 'Poland', 'region_east_asia': 'East Asia', 'region_russia': 'Russia', 'region_china': 'China', 'region_oceania': 'Oceania', 'region_australia': 'Australia', 'region_players': 'players', 'option_no_skins': 'Remover skins', 'option_no_names': 'Sem Nome', 'option_dark_theme': 'Tema Escuro', 'option_no_colors': 'Sem Cores', 'option_show_mass': 'Most. Massa', 'leaderboard': 'Leaderboard', 'unnamed_cell': 'Célula sem nome !"', 'last_match_results': 'Last match results', 'score': 'Pontos', 'leaderboard_time': '', 'mass_eaten': 'Mass Eaten', 'top_position': 'Top Position', 'position_1': 'Primeiro', 'position_2': 'Segundo', 'position_3': 'Terceiro', 'position_4': 'Quarto', 'position_5': 'Quinto', 'position_6': 'Sexto', 'position_7': 'Setimo', 'position_8': 'Oitavo', 'position_9': 'Nono', 'position_10': 'Decimo', 'player_cells_eaten': 'Player Cells Eaten', 'survival_time': 'Survival Time', 'games_played': 'Games played', 'highest_mass': 'Massa Total', 'total_cells_eaten': 'Total cells eaten', 'total_mass_eaten': 'Total mass eaten', 'longest_survival': 'Longest survival', 'logout': 'Sair', 'stats': 'Stats', 'shop': 'Shop', 'party': 'Jogar Com Os Amigos', 'party_description': 'Play with your friends in the same map', 'create_party': 'Create', 'creating_party': 'Criando Ah partida...', 'join_party': 'Criar Partoda', 'back_button': 'Sair', 'joining_party': 'Connectando Na Sala ...', 'joined_party_instructions': 'You are now playing with this Sala:', 'party_join_error': 'There was a problem joining that party, please make sure the code is correct, or try creating another party', 'login_tooltip': 'Login with Facebook and get:<br\xA0/><br /><br />Jogar the game with more mass!<br />Level up to get even more starting mass!', 'create_party_instructions': 'Give this link to your friends:', 'join_party_instructions': 'Your friend should have given you a code, type it here:', 'continue': 'Continuar', 'option_skip_stats': 'Pular Estatísticas', 'stats_food_eaten': 'Alim. ingeridos', 'stats_highest_mass': 'highest mass', 'stats_time_alive': 'Tempo Vivo', 'stats_leaderboard_time': 'Tempo no Rank', 'stats_cells_eaten': 'Células Ingeridas', 'stats_top_position': 'Posição Rankeada?', '': '' }, '?': {} }; i18n_lang = (window.navigator.userLanguage || window.navigator.language || 'en').split('-')[0]; if (!i18n_dict.hasOwnProperty(i18n_lang)) { i18n_lang = 'en'; } i18n = i18n_dict[i18n_lang]; (function(window, $) { function Init() { g_drawLines = true; PlayerStats(); setInterval(PlayerStats, 180000); g_canvas = g_canvas_ = document.getElementById('canvas'); g_context = g_canvas.getContext('2d'); g_canvas.onmousedown = function(event) { if (g_touchCapable) { var deltaX = event.clientX - (5 + g_protocol / 5 / 2); var deltaY = event.clientY - (5 + g_protocol / 5 / 2); if (Math.sqrt(deltaX * deltaX + deltaY * deltaY) <= g_protocol / 5 / 2) { SendPos(); SendCmd(17); return; } } g_mouseX = event.clientX; g_mouseY = event.clientY; UpdatePos(); SendPos(); }; g_canvas.onmousemove = function(event) { g_mouseX = event.clientX; g_mouseY = event.clientY; UpdatePos(); }; g_canvas.onmouseup = function() {}; if (/firefox/i.test(navigator.userAgent)) { document.addEventListener('DOMMouseScroll', WheelHandler, false); } else { document.body.onmousewheel = WheelHandler; } var spaceDown = false; var cachedSkin = false; var wkeyDown = false; var keyEPressed = false; //EDITED window.onkeydown = function(event) { if (!(32 != event.keyCode || spaceDown)) { SendPos(); SendCmd(17); spaceDown = true; } if (!(81 != event.keyCode || cachedSkin)) { SendCmd(18); cachedSkin = true; } if (!(87 != event.keyCode || wkeyDown)) { SendPos(); SendCmd(21); wkeyDown = true; } if (69 == event.keyCode) { //EDITED if (!keyEPressed) { keyEPressed = true; timerE(); } } if (27 == event.keyCode) { __unmatched_10(300); } }; window.onkeyup = function(event) { if (32 == event.keyCode) { spaceDown = false; } if (87 == event.keyCode) { wkeyDown = false; } if (81 == event.keyCode && cachedSkin) { SendCmd(19); cachedSkin = false; } if (69 == event.keyCode) { //EDITED if (keyEPressed) { keyEPressed = false; } } }; window.onblur = function() { SendCmd(19); wkeyDown = cachedSkin = spaceDown = keyEPressed = false; //EDITED }; function timerE () { //EDITED if (keyEPressed) { SendPos(); SendCmd(21); setInterval(timerE, 200); } } window.onresize = ResizeHandler; window.requestAnimationFrame(__unmatched_130); setInterval(SendPos, 40); if (g_region) { $('#region').val(g_region); } SyncRegion(); SetRegion($('#region').val()); $.each(g_skinNamesA, function(v, node) { //EDITED $("#nicks").append($("<option></option>").attr("value", v).text(node)); }); if (0 == __unmatched_112 && g_region) { Start(); } __unmatched_10(0); ResizeHandler(); if (window.location.hash && 6 <= window.location.hash.length) { RenderLoop(window.location.hash); } } function WheelHandler(event) { g_zoom *= Math.pow(0.9, event.wheelDelta / -120 || event.detail || 0); if(!isUnlimitedZoom) { if (1 > g_zoom) { g_zoom = 1; } if (g_zoom > 4 / g_scale) { g_zoom = 4 / g_scale; } } } function UpdateTree() { if (0.4 > g_scale) { g_pointTree = null; } else { for (var minX = Number.POSITIVE_INFINITY, minY = Number.POSITIVE_INFINITY, maxX = Number.NEGATIVE_INFINITY, maxY = Number.NEGATIVE_INFINITY, maxSize = 0, i = 0; i < g_cells.length; i++) { var cell = g_cells[i]; if (!(!cell.N() || cell.R || 20 >= cell.size * g_scale)) { maxSize = Math.max(cell.size, maxSize); minX = Math.min(cell.x, minX); minY = Math.min(cell.y, minY); maxX = Math.max(cell.x, maxX); maxY = Math.max(cell.y, maxY); } } g_pointTree = QTreeFactory.la({ ca: minX - (maxSize + 100), da: minY - (maxSize + 100), oa: maxX + (maxSize + 100), pa: maxY + (maxSize + 100), ma: 2, na: 4 }); for (i = 0; i < g_cells.length; i++) { if (cell = g_cells[i], cell.N() && !(20 >= cell.size * g_scale)) { for (minX = 0; minX < cell.a.length; ++minX) { minY = cell.a[minX].x; maxX = cell.a[minX].y; if (!(minY < g_viewX - g_protocol / 2 / g_scale || maxX < g_viewY - __unmatched_60 / 2 / g_scale || minY > g_viewX + g_protocol / 2 / g_scale || maxX > g_viewY + __unmatched_60 / 2 / g_scale)) { g_pointTree.m(cell.a[minX]); } } } } } } function UpdatePos() { g_moveX = (g_mouseX - g_protocol / 2) / g_scale + g_viewX; g_moveY = (g_mouseY - __unmatched_60 / 2) / g_scale + g_viewY; } function PlayerStats() { if (null == g_regionLabels) { g_regionLabels = {}; $('#region').children().each(function() { var $this = $(this); var val = $this.val(); if (val) { g_regionLabels[val] = $this.text(); } }); } $.get('https://m.agar.io/info', function(data) { var regionNumPlayers = {}; var region; for (region in data.regions) { var region_ = region.split(':')[0]; regionNumPlayers[region_] = regionNumPlayers[region_] || 0; regionNumPlayers[region_] += data.regions[region].numPlayers; } for (region in regionNumPlayers) { $('#region option[value="' + region + '"]').text(g_regionLabels[region] + ' (' + regionNumPlayers[region] + ' players)'); } }, 'json'); } function HideOverlay() { $('#adsBottom').hide(); $('#overlays').hide(); $('#stats').hide(); $('#mainPanel').hide(); __unmatched_141 = g_playerCellDestroyed = false; SyncRegion(); if (window.googletag && window.googletag.pubads && window.googletag.pubads().clear) { window.googletag.pubads().clear(window.aa.concat(window.ab)); } } function SetRegion(val) { if (val && val != g_region) { if ($('#region').val() != val) { $('#region').val(val); } g_region = window.localStorage.location = val; $('.region-message').hide(); $('.region-message.' + val).show(); $('.btn-needs-server').prop('disabled', false); if (g_drawLines) { Start(); } } } function __unmatched_10(char) { if (!(g_playerCellDestroyed || __unmatched_141)) { $('#adsBottom').show(); g_nick = null; __unmatched_13(window.aa); if (1000 > char) { qkeyDown = 1; } g_playerCellDestroyed = true; $('#mainPanel').show(); if (0 < char) { $('#overlays').fadeIn(char); } else { $('#overlays').show(); } } } function Render(__unmatched_174) { $('#helloContainer').attr('data-gamemode', __unmatched_174); __unmatched_95 = __unmatched_174; $('#gamemode').val(__unmatched_174); } function SyncRegion() { if ($('#region').val()) { window.localStorage.location = $('#region').val(); } else if (window.localStorage.location) { $('#region').val(window.localStorage.location); } if ($('#region').val()) { $('#locationKnown').append($('#region')); } else { $('#locationUnknown').append($('#region')); } } function __unmatched_13(__unmatched_175) { if (window.googletag) { window.googletag.cmd.push(function() { if (g_canRefreshAds) { g_canRefreshAds = false; setTimeout(function() { g_canRefreshAds = true; }, 60000 * g_refreshAdsCooldown); if (window.googletag && window.googletag.pubads && window.googletag.pubads().refresh) { window.googletag.pubads().refresh(__unmatched_175); } } }); } } function __unmatched_14(i_) { return window.i18n[i_] || window.i18n_dict.en[i_] || i_; } function FindGame() { var __unmatched_177 = ++__unmatched_112; console.log('Find ' + g_region + __unmatched_95); $.ajax('https://m.agar.io/', { error: function() { setTimeout(FindGame, 1000); }, success: function(__unmatched_178) { __unmatched_178 = __unmatched_178.split('\n'); Connect('ws://' + __unmatched_178[0], __unmatched_178[1]); }, dataType: 'text', method: 'POST', cache: false, crossDomain: true, data: (g_region + __unmatched_95 || '?') + '\n154669603' }); } function Start() { if (g_drawLines && g_region) { $('#connecting').show(); FindGame(); } } function Connect(address, ticket) { if (points) { points.onopen = null; points.onmessage = null; points.onclose = null; try { points.close(); } catch (exception) {} points = null; } if (__unmatched_113.ip) { address = 'ws://' + __unmatched_113.ip; } if (null != __unmatched_121) { var __unmatched_181 = __unmatched_121; __unmatched_121 = function() { __unmatched_181(ticket); }; } if (g_secure) { var parts = address.split(':'); address = parts[0] + 's://ip-' + parts[1].replace(/\./g, '-').replace(/\//g, '') + '.tech.agar.io:' + (+parts[2] + 2000); } g_playerCellIds = []; g_playerCells = []; g_cellsById = {}; g_cells = []; g_destroyedCells = []; g_scoreEntries = []; g_leaderboardCanvas = g_scorePartitions = null; g_maxScore = 0; g_connectSuccessful = false; console.log('Connecting to ' + address); points = new WebSocket(address); points.binaryType = 'arraybuffer'; points.onopen = function() { var data; console.log('socket open'); data = GetBuffer(5); data.setUint8(0, 254); data.setUint32(1, 5, true); SendBuffer(data); data = GetBuffer(5); data.setUint8(0, 255); data.setUint32(1, 154669603, true); SendBuffer(data); data = GetBuffer(1 + ticket.length); data.setUint8(0, 80); for (var i = 0; i < ticket.length; ++i) { data.setUint8(i + 1, ticket.charCodeAt(i)); } SendBuffer(data); RefreshAds(); }; points.onmessage = MessageHandler; points.onclose = CloseHandler; points.onerror = function() { console.log('socket error'); }; } function GetBuffer(size) { return new DataView(new ArrayBuffer(size)); } function SendBuffer(data) { points.send(data.buffer); } function CloseHandler() { if (g_connectSuccessful) { g_retryTimeout = 500; } console.log('socket close'); setTimeout(Start, g_retryTimeout); g_retryTimeout *= 2; } function MessageHandler(data) { Receive(new DataView(data.data)); } function Receive(data) { function __unmatched_190() { for (var string = '';;) { var char = data.getUint16(pos, true); pos += 2; if (0 == char) { break; } string += String.fromCharCode(char); } return string; } var pos = 0; if (240 == data.getUint8(pos)) { pos += 5; } switch (data.getUint8(pos++)) { case 16: ParseCellUpdates(data, pos); break; case 17: g_viewX_ = data.getFloat32(pos, true); pos += 4; g_viewY_ = data.getFloat32(pos, true); pos += 4; g_scale_ = data.getFloat32(pos, true); pos += 4; break; case 20: g_playerCells = []; g_playerCellIds = []; break; case 21: g_linesY_ = data.getInt16(pos, true); pos += 2; g_linesX_ = data.getInt16(pos, true); pos += 2; if (!g_ready) { g_ready = true; g_linesX = g_linesY_; g_linesY = g_linesX_; } break; case 32: g_playerCellIds.push(data.getUint32(pos, true)); pos += 4; break; case 49: if (null != g_scorePartitions) { break; } var num = data.getUint32(pos, true); var pos = pos + 4; g_scoreEntries = []; for (var i = 0; i < num; ++i) { var id = data.getUint32(pos, true); var pos = pos + 4; g_scoreEntries.push({ id: id, name: __unmatched_190() }); } UpdateLeaderboard(); break; case 50: g_scorePartitions = []; num = data.getUint32(pos, true); pos += 4; for (i = 0; i < num; ++i) { g_scorePartitions.push(data.getFloat32(pos, true)); pos += 4; } UpdateLeaderboard(); break; case 64: g_minX = data.getFloat64(pos, true); pos += 8; g_minY = data.getFloat64(pos, true); pos += 8; g_maxX = data.getFloat64(pos, true); pos += 8; g_maxY = data.getFloat64(pos, true); pos += 8; g_viewX_ = (g_maxX + g_minX) / 2; g_viewY_ = (g_maxY + g_minY) / 2; g_scale_ = 1; if (0 == g_playerCells.length) { g_viewX = g_viewX_; g_viewY = g_viewY_; g_scale = g_scale_; } break; case 81: var x = data.getUint32(pos, true); var pos = pos + 4; var __unmatched_196 = data.getUint32(pos, true); var pos = pos + 4; var __unmatched_197 = data.getUint32(pos, true); var pos = pos + 4; setTimeout(function() { __unmatched_43({ e: x, f: __unmatched_196, d: __unmatched_197 }); }, 1200); } } function ParseCellUpdates(data, pos) { function __unmatched_202() { for (var string = '';;) { var id = data.getUint16(pos, true); pos += 2; if (0 == id) { break; } string += String.fromCharCode(id); } return string; } function __unmatched_203() { for (var __unmatched_218 = '';;) { var r = data.getUint8(pos++); if (0 == r) { break; } __unmatched_218 += String.fromCharCode(r); } return __unmatched_218; } __unmatched_107 = g_time = Date.now(); if (!g_connectSuccessful) { g_connectSuccessful = true; __unmatched_24(); } __unmatched_88 = false; var num = data.getUint16(pos, true); pos += 2; for (var i = 0; i < num; ++i) { var cellA = g_cellsById[data.getUint32(pos, true)]; var cellB = g_cellsById[data.getUint32(pos + 4, true)]; pos += 8; if (cellA && cellB) { cellB.X(); cellB.s = cellB.x; cellB.t = cellB.y; cellB.r = cellB.size; cellB.J = cellA.x; cellB.K = cellA.y; cellB.q = cellB.size; cellB.Q = g_time; __unmatched_49(cellA, cellB); } } for (i = 0;;) { num = data.getUint32(pos, true); pos += 4; if (0 == num) { break; } ++i; var size; var cellA = data.getInt32(pos, true); pos += 4; cellB = data.getInt32(pos, true); pos += 4; size = data.getInt16(pos, true); pos += 2; var flags = data.getUint8(pos++); var y = data.getUint8(pos++); var b = data.getUint8(pos++); var y = __unmatched_40(flags << 16 | y << 8 | b); var b = data.getUint8(pos++); var isVirus = !!(b & 1); var isAgitated = !!(b & 16); var __unmatched_214 = null; if (b & 2) { pos += 4 + data.getUint32(pos, true); } if (b & 4) { __unmatched_214 = __unmatched_203(); } var name = __unmatched_202(); var flags = null; if (g_cellsById.hasOwnProperty(num)) { flags = g_cellsById[num]; flags.P(); flags.s = flags.x; flags.t = flags.y; flags.r = flags.size; flags.color = y; } else { flags = new Cell(num, cellA, cellB, size, y, name); g_cells.push(flags); g_cellsById[num] = flags; flags.ta = cellA; flags.ua = cellB; } flags.h = isVirus; flags.n = isAgitated; flags.J = cellA; flags.K = cellB; flags.q = size; flags.Q = g_time; flags.ba = b; flags.fa = __unmatched_214; if (name) { flags.B(name); } if (-1 != g_playerCellIds.indexOf(num) && -1 == g_playerCells.indexOf(flags)) { g_playerCells.push(flags); if (1 == g_playerCells.length) { g_viewX = flags.x; g_viewY = flags.y; __unmatched_136(); document.getElementById('overlays').style.display = 'none'; cached = []; __unmatched_139 = 0; __unmatched_140 = g_playerCells[0].color; __unmatched_142 = true; __unmatched_143 = Date.now(); g_mode = __unmatched_146 = __unmatched_145 = 0; } } } cellA = data.getUint32(pos, true); pos += 4; for (i = 0; i < cellA; i++) { num = data.getUint32(pos, true); pos += 4; flags = g_cellsById[num]; if (null != flags) { flags.X(); } } if (__unmatched_88 && 0 == g_playerCells.length) { __unmatched_144 = Date.now(); __unmatched_142 = false; if (!(g_playerCellDestroyed || __unmatched_141)) { if (__unmatched_148) { __unmatched_13(window.ab); ShowOverlay(); __unmatched_141 = true; $('#overlays').fadeIn(3000); $('#stats').show(); } else { __unmatched_10(3000); } } } } function __unmatched_24() { $('#connecting').hide(); SendNick(); if (__unmatched_121) { __unmatched_121(); __unmatched_121 = null; } if (null != __unmatched_123) { clearTimeout(__unmatched_123); } __unmatched_123 = setTimeout(function() { if (window.ga) { ++__unmatched_124; window.ga('set', 'dimension2', __unmatched_124); } }, 10000); } function SendPos() { if (IsConnected()) { var deltaY = g_mouseX - g_protocol / 2; var delta = g_mouseY - __unmatched_60 / 2; if (!(64 > deltaY * deltaY + delta * delta || 0.01 > Math.abs(g_lastMoveY - g_moveX) && 0.01 > Math.abs(g_lastMoveX - g_moveY))) { g_lastMoveY = g_moveX; g_lastMoveX = g_moveY; deltaY = GetBuffer(21); deltaY.setUint8(0, 16); deltaY.setFloat64(1, g_moveX, true); deltaY.setFloat64(9, g_moveY, true); deltaY.setUint32(17, 0, true); SendBuffer(deltaY); } } } function SendNick() { if (IsConnected() && g_connectSuccessful && null != g_nick) { var data = GetBuffer(1 + 2 * g_nick.length); data.setUint8(0, 0); for (var i = 0; i < g_nick.length; ++i) { data.setUint16(1 + 2 * i, g_nick.charCodeAt(i), true); } SendBuffer(data); g_nick = null; } } function IsConnected() { return null != points && points.readyState == points.OPEN; } function SendCmd(cmd) { if (IsConnected()) { var data = GetBuffer(1); data.setUint8(0, cmd); SendBuffer(data); } } function RefreshAds() { if (IsConnected() && null != __unmatched_108) { var __unmatched_226 = GetBuffer(1 + __unmatched_108.length); __unmatched_226.setUint8(0, 81); for (var y = 0; y < __unmatched_108.length; ++y) { __unmatched_226.setUint8(y + 1, __unmatched_108.charCodeAt(y)); } SendBuffer(__unmatched_226); } } function ResizeHandler() { g_protocol = window.innerWidth; __unmatched_60 = window.innerHeight; g_canvas_.width = g_canvas.width = g_protocol; g_canvas_.height = g_canvas.height = __unmatched_60; var $dialog = $('#helloContainer'); $dialog.css('transform', 'none'); var dialogHeight = $dialog.height(); var height = window.innerHeight; if (dialogHeight > height / 1.1) { $dialog.css('transform', 'translate(-50%, -50%) scale(' + height / dialogHeight / 1.1 + ')'); } else { $dialog.css('transform', 'translate(-50%, -50%)'); } GetScore(); } function ScaleModifier() { var scale; scale = 1 * Math.max(__unmatched_60 / 1080, g_protocol / 1920); return scale *= g_zoom; } function __unmatched_32() { if (0 != g_playerCells.length) { for (var scale = 0, i = 0; i < g_playerCells.length; i++) { scale += g_playerCells[i].size; } scale = Math.pow(Math.min(64 / scale, 1), 0.4) * ScaleModifier(); g_scale = (9 * g_scale + scale) / 10; } } function GetScore() { var x; var time = Date.now(); ++__unmatched_75; g_time = time; if (0 < g_playerCells.length) { __unmatched_32(); for (var y = x = 0, i = 0; i < g_playerCells.length; i++) { g_playerCells[i].P(); x += g_playerCells[i].x / g_playerCells.length; y += g_playerCells[i].y / g_playerCells.length; } g_viewX_ = x; g_viewY_ = y; g_scale_ = g_scale; g_viewX = (g_viewX + x) / 2; g_viewY = (g_viewY + y) / 2; } else { g_viewX = (29 * g_viewX + g_viewX_) / 30; g_viewY = (29 * g_viewY + g_viewY_) / 30; g_scale = (9 * g_scale + g_scale_ * ScaleModifier()) / 10; } UpdateTree(); UpdatePos(); if (!g_showTrails) { g_context.clearRect(0, 0, g_protocol, __unmatched_60); } if (g_showTrails) { g_context.fillStyle = g_showMass ? '#111111' : '#F2FBFF'; g_context.globalAlpha = 0.05; g_context.fillRect(0, 0, g_protocol, __unmatched_60); g_context.globalAlpha = 1; } else { DrawGrid(); } g_cells.sort(function(A, B) { return A.size == B.size ? A.id - B.id : A.size - B.size; }); g_context.save(); g_context.translate(g_protocol / 2, __unmatched_60 / 2); g_context.scale(g_scale, g_scale); g_context.translate(-g_viewX, -g_viewY); drawBorders(); drawLogo(); myMass = Math.min.apply(null, g_playerCells.map(function(r) { return r.N(); })) for (i = 0; i < g_destroyedCells.length; i++) { g_destroyedCells[i].w(g_context); } for (i = 0; i < g_cells.length; i++) { g_cells[i].w(g_context); } if (g_ready) { g_linesX = (3 * g_linesX + g_linesY_) / 4; g_linesY = (3 * g_linesY + g_linesX_) / 4; g_context.save(); g_context.strokeStyle = '#FFAAAA'; g_context.lineWidth = 10; g_context.lineCap = 'round'; g_context.lineJoin = 'round'; g_context.globalAlpha = 0.5; g_context.beginPath(); for (i = 0; i < g_playerCells.length; i++) { g_context.moveTo(g_playerCells[i].x, g_playerCells[i].y); g_context.lineTo(g_linesX, g_linesY); } g_context.stroke(); g_context.restore(); } g_context.restore(); if (g_leaderboardCanvas && g_leaderboardCanvas.width) { g_context.drawImage(g_leaderboardCanvas, g_protocol - g_leaderboardCanvas.width - 10, 10); } g_maxScore = Math.max(g_maxScore, __unmatched_36()); if (0 != g_maxScore) { if (null == g_cachedScore) { g_cachedScore = new CachedCanvas(24, '#FFFFFF'); } g_cachedScore.C(__unmatched_14('score') + ': ' + ~~(g_maxScore / 100)); y = g_cachedScore.L(); x = y.width; g_context.globalAlpha = 0.2; g_context.fillStyle = '#000000'; g_context.fillRect(10, __unmatched_60 - 10 - 24 - 10, x + 10, 34); g_context.globalAlpha = 1; g_context.drawImage(y, 15, __unmatched_60 - 10 - 24 - 5); } DrawSplitImage(); time = Date.now() - time; if (time > 1000 / 60) { g_pointNumScale -= 0.01; } else if (time < 1000 / 65) { g_pointNumScale += 0.01; } if (0.4 > g_pointNumScale) { g_pointNumScale = 0.4; } if (1 < g_pointNumScale) { g_pointNumScale = 1; } time = g_time - __unmatched_77; if (!IsConnected() || g_playerCellDestroyed || __unmatched_141) { qkeyDown += time / 2000; if (1 < qkeyDown) { qkeyDown = 1; } } else { qkeyDown -= time / 300; if (0 > qkeyDown) { qkeyDown = 0; } } if (0 < qkeyDown) { g_context.fillStyle = '#000000'; g_context.globalAlpha = 0.5 * qkeyDown; g_context.fillRect(0, 0, g_protocol, __unmatched_60); g_context.globalAlpha = 1; } __unmatched_77 = g_time; } function DrawGrid() { g_context.fillStyle = g_showMass ? '#111111' : '#F2FBFF'; g_context.fillRect(0, 0, g_protocol, __unmatched_60); g_context.save(); g_context.strokeStyle = g_showMass ? '#AAAAAA' : '#000000'; g_context.globalAlpha = 0.2 * g_scale; for (var width = g_protocol / g_scale, height = __unmatched_60 / g_scale, g_width = (-g_viewX + width / 2) % 50; g_width < width; g_width += 50) { g_context.beginPath(); g_context.moveTo(g_width * g_scale - 0.5, 0); g_context.lineTo(g_width * g_scale - 0.5, height * g_scale); g_context.stroke(); } for (g_width = (-g_viewY + height / 2) % 50; g_width < height; g_width += 50) { g_context.beginPath(); g_context.moveTo(0, g_width * g_scale - 0.5); g_context.lineTo(width * g_scale, g_width * g_scale - 0.5); g_context.stroke(); } g_context.restore(); } function DrawSplitImage() { if (g_touchCapable && g_splitImage.width) { var size = g_protocol / 5; g_context.drawImage(g_splitImage, 5, 5, size, size); } } function __unmatched_36() { for (var score = 0, i = 0; i < g_playerCells.length; i++) { score += g_playerCells[i].q * g_playerCells[i].q; } return score; } function UpdateLeaderboard() { g_leaderboardCanvas = null; if (null != g_scorePartitions || 0 != g_scoreEntries.length) { if (null != g_scorePartitions || g_showNames) { g_leaderboardCanvas = document.createElement('canvas'); var context = g_leaderboardCanvas.getContext('2d'); var height = 60; var height = null == g_scorePartitions ? height + 24 * g_scoreEntries.length : height + 180; var scale = Math.min(200, 0.3 * g_protocol) / 200; g_leaderboardCanvas.width = 200 * scale; g_leaderboardCanvas.height = height * scale; context.scale(scale, scale); context.globalAlpha = 0.4; context.fillStyle = '#000000'; context.fillRect(0, 0, 200, height); context.globalAlpha = 1; context.fillStyle = '#FFFFFF'; scale = null; scale = __unmatched_14('leaderboard'); context.font = '30px Ubuntu'; context.fillText(scale, 100 - context.measureText(scale).width / 2, 40); if (null == g_scorePartitions) { for (context.font = '20px Ubuntu', height = 0; height < g_scoreEntries.length; ++height) { scale = g_scoreEntries[height].name || __unmatched_14('unnamed_cell'); if (!g_showNames) { scale = __unmatched_14('unnamed_cell'); } if (-1 != g_playerCellIds.indexOf(g_scoreEntries[height].id)) { if (g_playerCells[0].name) { scale = g_playerCells[0].name; } context.fillStyle = '#FFAAAA'; } else { context.fillStyle = '#FFFFFF'; } scale = height + 1 + '. ' + scale; context.fillText(scale, 100 - context.measureText(scale).width / 2, 70 + 24 * height); } } else { for (height = scale = 0; height < g_scorePartitions.length; ++height) { var end = scale + g_scorePartitions[height] * Math.PI * 2; context.fillStyle = g_teamColors[height + 1]; context.beginPath(); context.moveTo(100, 140); context.arc(100, 140, 80, scale, end, false); context.fill(); scale = end; } } } } } function __unmatched_38(__unmatched_250, __unmatched_251, __unmatched_252, __unmatched_253, __unmatched_254) { this.V = __unmatched_250; this.x = __unmatched_251; this.y = __unmatched_252; this.i = __unmatched_253; this.b = __unmatched_254; } function Cell(id, x, y, size, color, name) { this.id = id; this.s = this.x = x; this.t = this.y = y; this.r = this.size = size; this.color = color; this.a = []; this.W(); this.B(name); } function __unmatched_40(__unmatched_261) { for (__unmatched_261 = __unmatched_261.toString(16); 6 > __unmatched_261.length;) { __unmatched_261 = '0' + __unmatched_261; } return '#' + __unmatched_261; } function drawBorders() { g_context.save() g_context.beginPath(); g_context.lineWidth = 1; g_context.strokeStyle = "#F87B32"; g_context.moveTo(getMapStartX(), getMapStartY()); g_context.lineTo(getMapStartX(), getMapEndY()); g_context.stroke(); g_context.moveTo(getMapStartX(), getMapStartY()); g_context.lineTo(getMapEndX(), getMapStartY()); g_context.stroke(); g_context.moveTo(getMapEndX(), getMapStartY()); g_context.lineTo(getMapEndX(), getMapEndY()); g_context.stroke(); g_context.moveTo(getMapStartX(), getMapEndY()); g_context.lineTo(getMapEndX(), getMapEndY()); g_context.stroke(); g_context.restore(); } function drawLogo(){ var logoimage = new Image(); logoimage.src = "img/split.png"; var width = this.j / 2; var dim = width / 2; g_context.save(); g_context.beginPath(); g_context.strokeStyle = "#F87B32"; g_context.moveTo(getMapStartX()/2, getMapStartX()/2); g_context.lineTo(getMapStartX()/2, getMapStartX()/2); g_context.stroke(); g_context.restore(); } function CachedCanvas(size, color, stroke, strokeColor) { if (size) { this.u = size; } if (color) { this.S = color; } this.U = !!stroke; if (strokeColor) { this.v = strokeColor; } } function __unmatched_42(__unmatched_266) { for (var size_ = __unmatched_266.length, __unmatched_268, __unmatched_269; 0 < size_;) { __unmatched_269 = Math.floor(Math.random() * size_); size_--; __unmatched_268 = __unmatched_266[size_]; __unmatched_266[size_] = __unmatched_266[__unmatched_269]; __unmatched_266[__unmatched_269] = __unmatched_268; } } function __unmatched_43(g_socket, __unmatched_271) { var noClip = '1' == $('#helloContainer').attr('data-has-account-data'); $('#helloContainer').attr('data-has-account-data', '1'); if (null == __unmatched_271 && window.localStorage.loginCache) { var rand = JSON.parse(window.localStorage.loginCache); rand.f = g_socket.f; rand.d = g_socket.d; rand.e = g_socket.e; window.localStorage.loginCache = JSON.stringify(rand); } if (noClip) { var __unmatched_274 = +$('.agario-exp-bar .progress-bar-text').first().text().split('/')[0]; var noClip = +$('.agario-exp-bar .progress-bar-text').first().text().split('/')[1].split(' ')[0]; var rand = $('.agario-profile-panel .progress-bar-star').first().text(); if (rand != g_socket.e) { __unmatched_43({ f: noClip, d: noClip, e: rand }, function() { $('.agario-profile-panel .progress-bar-star').text(g_socket.e); $('.agario-exp-bar .progress-bar').css('width', '100%'); $('.progress-bar-star').addClass('animated tada').one('webkitAnimationEnd mozAnimationEnd MSAnimationEnd oanimationend animationend', function() { $('.progress-bar-star').removeClass('animated tada'); }); setTimeout(function() { $('.agario-exp-bar .progress-bar-text').text(g_socket.d + '/' + g_socket.d + ' XP'); __unmatched_43({ f: 0, d: g_socket.d, e: g_socket.e }, function() { __unmatched_43(g_socket, __unmatched_271); }); }, 1000); }); } else { var __unmatched_275 = Date.now(); var name = function() { var deltaX; deltaX = (Date.now() - __unmatched_275) / 1000; deltaX = 0 > deltaX ? 0 : 1 < deltaX ? 1 : deltaX; deltaX = deltaX * deltaX * (3 - 2 * deltaX); $('.agario-exp-bar .progress-bar-text').text(~~(__unmatched_274 + (g_socket.f - __unmatched_274) * deltaX) + '/' + g_socket.d + ' XP'); $('.agario-exp-bar .progress-bar').css('width', (88 * (__unmatched_274 + (g_socket.f - __unmatched_274) * deltaX) / g_socket.d).toFixed(2) + '%'); if (1 > deltaX) { window.requestAnimationFrame(name); } else if (__unmatched_271) { __unmatched_271(); } }; window.requestAnimationFrame(name); } } else { $('.agario-profile-panel .progress-bar-star').text(g_socket.e); $('.agario-exp-bar .progress-bar-text').text(g_socket.f + '/' + g_socket.d + ' XP'); $('.agario-exp-bar .progress-bar').css('width', (88 * g_socket.f / g_socket.d).toFixed(2) + '%'); if (__unmatched_271) { __unmatched_271(); } } } function __unmatched_44(__unmatched_278) { if ('string' == typeof __unmatched_278) { __unmatched_278 = JSON.parse(__unmatched_278); } if (Date.now() + 1800000 > __unmatched_278.ka) { $('#helloContainer').attr('data-logged-in', '0'); } else { window.localStorage.loginCache = JSON.stringify(__unmatched_278); __unmatched_108 = __unmatched_278.ha; $('.agario-profile-name').text(__unmatched_278.name); RefreshAds(); __unmatched_43({ f: __unmatched_278.f, d: __unmatched_278.d, e: __unmatched_278.e }); $('#helloContainer').attr('data-logged-in', '1'); } } function __unmatched_45(data) { data = data.split('\n'); __unmatched_44({ name: data[0], sa: data[1], ha: data[2], ka: 1000 * +data[3], e: +data[4], f: +data[5], d: +data[6] }); } function UpdateScale(__unmatched_280) { if ('connected' == __unmatched_280.status) { var x = __unmatched_280.authResponse.accessToken; window.FB.api('/me/picture?width=180&height=180', function(__unmatched_282) { window.localStorage.fbPictureCache = __unmatched_282.data.url; $('.agario-profile-picture').attr('src', __unmatched_282.data.url); }); $('#helloContainer').attr('data-logged-in', '1'); if (null != __unmatched_108) { $.ajax('https://m.agar.io/checkToken', { error: function() { __unmatched_108 = null; UpdateScale(__unmatched_280); }, success: function(__unmatched_283) { __unmatched_283 = __unmatched_283.split('\n'); __unmatched_43({ e: +__unmatched_283[0], f: +__unmatched_283[1], d: +__unmatched_283[2] }); }, dataType: 'text', method: 'POST', cache: false, crossDomain: true, data: __unmatched_108 }); } else { $.ajax('https://m.agar.io/facebookLogin', { error: function() { __unmatched_108 = null; $('#helloContainer').attr('data-logged-in', '0'); }, success: __unmatched_45, dataType: 'text', method: 'POST', cache: false, crossDomain: true, data: x }); } } } function RenderLoop(x) { Render(':party'); $('#helloContainer').attr('data-party-state', '4'); x = decodeURIComponent(x).replace(/.*#/gim, ''); __unmatched_48('#' + window.encodeURIComponent(x)); $.ajax('https://m.agar.io/getToken', { error: function() { $('#helloContainer').attr('data-party-state', '6'); }, success: function(quick) { quick = quick.split('\n'); $('.partyToken').val('agar.io/#' + window.encodeURIComponent(x)); $('#helloContainer').attr('data-party-state', '5'); Render(':party'); Connect('ws://' + quick[0], x); }, dataType: 'text', method: 'POST', cache: false, crossDomain: true, data: x }); } function __unmatched_48(__unmatched_286) { if (window.history && window.history.replaceState) { window.history.replaceState({}, window.document.title, __unmatched_286); } } function __unmatched_49(__unmatched_287, __unmatched_288) { var playerOwned = -1 != g_playerCellIds.indexOf(__unmatched_287.id); var __unmatched_290 = -1 != g_playerCellIds.indexOf(__unmatched_288.id); var __unmatched_291 = 30 > __unmatched_288.size; if (playerOwned && __unmatched_291) { ++__unmatched_139; } if (!(__unmatched_291 || !playerOwned || __unmatched_290)) { ++__unmatched_146; } } function __unmatched_50(__unmatched_292) { __unmatched_292 = ~~__unmatched_292; var color = (__unmatched_292 % 60).toString(); __unmatched_292 = (~~(__unmatched_292 / 60)).toString(); if (2 > color.length) { color = '0' + color; } return __unmatched_292 + ':' + color; } function __unmatched_51() { if (null == g_scoreEntries) { return 0; } for (var i = 0; i < g_scoreEntries.length; ++i) { if (-1 != g_playerCellIds.indexOf(g_scoreEntries[i].id)) { return i + 1; } } return 0; } function ShowOverlay() { $('.stats-food-eaten').text(__unmatched_139); $('.stats-time-alive').text(__unmatched_50((__unmatched_144 - __unmatched_143) / 1000)); $('.stats-leaderboard-time').text(__unmatched_50(__unmatched_145)); $('.stats-highest-mass').text(~~(g_maxScore / 100)); $('.stats-cells-eaten').text(__unmatched_146); $('.stats-top-position').text(0 == g_mode ? ':(' : g_mode); var g_height = document.getElementById('statsGraph'); if (g_height) { var pointsAcc = g_height.getContext('2d'); var scale = g_height.width; var g_height = g_height.height; pointsAcc.clearRect(0, 0, scale, g_height); if (2 < cached.length) { for (var __unmatched_298 = 200, i = 0; i < cached.length; i++) { __unmatched_298 = Math.max(cached[i], __unmatched_298); } pointsAcc.lineWidth = 3; pointsAcc.lineCap = 'round'; pointsAcc.lineJoin = 'round'; pointsAcc.strokeStyle = __unmatched_140; pointsAcc.fillStyle = __unmatched_140; pointsAcc.beginPath(); pointsAcc.moveTo(0, g_height - cached[0] / __unmatched_298 * (g_height - 10) + 10); for (i = 1; i < cached.length; i += Math.max(~~(cached.length / scale), 1)) { for (var __unmatched_300 = i / (cached.length - 1) * scale, __unmatched_301 = [], __unmatched_302 = -20; 20 >= __unmatched_302; ++__unmatched_302) { if (!(0 > i + __unmatched_302 || i + __unmatched_302 >= cached.length)) { __unmatched_301.push(cached[i + __unmatched_302]); } } __unmatched_301 = __unmatched_301.reduce(function(__unmatched_303, __unmatched_304) { return __unmatched_303 + __unmatched_304; }) / __unmatched_301.length / __unmatched_298; pointsAcc.lineTo(__unmatched_300, g_height - __unmatched_301 * (g_height - 10) + 10); } pointsAcc.stroke(); pointsAcc.globalAlpha = 0.5; pointsAcc.lineTo(scale, g_height); pointsAcc.lineTo(0, g_height); pointsAcc.fill(); pointsAcc.globalAlpha = 1; } } } if (!window.agarioNoInit) { var __unmatched_53 = window.location.protocol; var g_secure = 'https:' == __unmatched_53; if (g_secure && -1 == window.location.search.indexOf('fb')) { window.location.href = 'http://agar.io/'; } else { var items = window.navigator.userAgent; if (-1 != items.indexOf('Android')) { if (window.ga) { window.ga('send', 'event', 'MobileRedirect', 'PlayStore'); } setTimeout(function() { window.location.href = 'https://play.google.com/store/apps/details?id=com.miniclip.agar.io'; }, 1000); } else if (-1 != items.indexOf('iPhone') || -1 != items.indexOf('iPad') || -1 != items.indexOf('iPod')) { if (window.ga) { window.ga('send', 'event', 'MobileRedirect', 'AppStore'); } setTimeout(function() { window.location.href = 'https://itunes.apple.com/app/agar.io/id995999703?mt=8&at=1l3vajp'; }, 1000); } else { var g_canvas_; var g_context; var g_canvas; var g_protocol; var __unmatched_60; var g_pointTree = null; var points = null; var g_viewX = 0; var g_viewY = 0; var g_playerCellIds = []; var g_playerCells = []; var g_cellsById = {}; var g_cells = []; var g_destroyedCells = []; var g_scoreEntries = []; var g_mouseX = 0; var g_mouseY = 0; var g_moveX = -1; var g_moveY = -1; var __unmatched_75 = 0; var g_time = 0; var __unmatched_77 = 0; var g_nick = null; var g_minX = 0; var g_minY = 0; var g_maxX = 10000; var g_maxY = 10000; var g_scale = 1; var g_region = null; var g_showSkins = true; var g_showNames = true; var g_noColors = false; var __unmatched_88 = false; var g_maxScore = 0; var g_showMass = false; var g_darkTheme = true; var g_viewX_ = g_viewX = ~~((g_minX + g_maxX) / 2); var g_viewY_ = g_viewY = ~~((g_minY + g_maxY) / 2); var g_scale_ = 1; var __unmatched_95 = ''; var g_scorePartitions = null; var g_drawLines = false; var g_ready = false; var g_linesY_ = 0; var g_linesX_ = 0; var g_linesX = 0; var g_linesY = 0; var g_ABGroup = 0; var g_teamColors = [ '#333333', '#FF3333', '#33FF33', '#3333FF' ]; var g_showTrails = false; var g_connectSuccessful = false; var __unmatched_107 = 0; var __unmatched_108 = null; var g_zoom = 1; var qkeyDown = 1; var g_playerCellDestroyed = false; var __unmatched_112 = 0; var __unmatched_113 = {}; (function() { var point = window.location.search; if ('?' == point.charAt(0)) { point = point.slice(1); } for (var point = point.split('&'), __unmatched_306 = 0; __unmatched_306 < point.length; __unmatched_306++) { var parts = point[__unmatched_306].split('='); __unmatched_113[parts[0]] = parts[1]; } }()); var g_touchCapable = 'ontouchstart' in window && /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(window.navigator.userAgent); var g_splitImage = new Image(); g_splitImage.src = 'img/split.png'; var canvasTest = document.createElement('canvas'); if ('undefined' == typeof console || 'undefined' == typeof DataView || 'undefined' == typeof WebSocket || null == canvasTest || null == canvasTest.getContext || null == window.localStorage) { alert('You browser does not support this game, we recommend you to use Firefox to play this'); } else { var g_regionLabels = null; window.setNick = function(val) { HideOverlay(); g_nick = val; SendNick(); g_maxScore = 0; }; window.setRegion = SetRegion; window.setSkins = function(val) { g_showSkins = val; }; window.setUnlimitedZoom = function(val) { isUnlimitedZoom = val; }; window.setNames = function(val) { g_showNames = val; }; window.setDarkTheme = function(val) { g_showMass = val; }; window.setColors = function(val) { g_noColors = val; }; window.setShowMass = function(val) { g_darkTheme = val; }; window.spectate = function(val) { isSpectating = val g_nick = null; SendCmd(1); HideOverlay(); }; window.setLargeBlobBorders = function(val) { isLargeBlobBorders = val; } window.setLargeNames = function(val) { isLargeNames = val; } window.setVirusTransparent = function(val){ isVirusTransparent = val; } window.nicksChange = function() { var name = $("#nicks").children("option").filter(":selected").text(); $("#nick").val(name); if (-1 != g_skinNamesA.indexOf(name)) { $("#preview").attr("src", "skins/" + name + ".png"); } }; window.getMapStartX = function() { return g_minX; } window.getMapStartY = function() { return g_minY; } window.getMapEndX = function() { return g_maxX; } window.getMapEndY = function() { return g_maxY; } window.setGameMode = function(val) { if (val != __unmatched_95) { if (':party' == __unmatched_95) { $('#helloContainer').attr('data-party-state', '0'); } Render(val); if (':party' != val) { Start(); } } }; window.setAcid = function(val) { g_showTrails = val; }; if (null != window.localStorage) { if (null == window.localStorage.AB9) { window.localStorage.AB9 = 0 + ~~(100 * Math.random()); } g_ABGroup = +window.localStorage.AB9; window.ABGroup = g_ABGroup; } $.get(__unmatched_53 + '//gc.agar.io', function(code) { var __unmatched_317 = code.split(' '); code = __unmatched_317[0]; __unmatched_317 = __unmatched_317[1] || ''; if (-1 == ['UA'].indexOf(code)) { g_skinNamesA.push('ussr'); } if (g_regionsByCC.hasOwnProperty(code)) { if ('string' == typeof g_regionsByCC[code]) { if (!g_region) { SetRegion(g_regionsByCC[code]); } else if (g_regionsByCC[code].hasOwnProperty(__unmatched_317)) { if (!g_region) { SetRegion(g_regionsByCC[code][__unmatched_317]); } } } } }, 'text'); if (window.ga) { window.ga('send', 'event', 'User-Agent', window.navigator.userAgent, { nonInteraction: 1 }); } var g_canRefreshAds = true; var g_refreshAdsCooldown = 0; var g_regionsByCC = { AF: 'JP-Tokyo', AX: 'EU-London', AL: 'EU-London', DZ: 'EU-London', AS: 'SG-Singapore', AD: 'EU-London', AO: 'EU-London', AI: 'US-Atlanta', AG: 'US-Atlanta', AR: 'BR-Brazil', AM: 'JP-Tokyo', AW: 'US-Atlanta', AU: 'SG-Singapore', AT: 'EU-London', AZ: 'JP-Tokyo', BS: 'US-Atlanta', BH: 'JP-Tokyo', BD: 'JP-Tokyo', BB: 'US-Atlanta', BY: 'EU-London', BE: 'EU-London', BZ: 'US-Atlanta', BJ: 'EU-London', BM: 'US-Atlanta', BT: 'JP-Tokyo', BO: 'BR-Brazil', BQ: 'US-Atlanta', BA: 'EU-London', BW: 'EU-London', BR: 'BR-Brazil', IO: 'JP-Tokyo', VG: 'US-Atlanta', BN: 'JP-Tokyo', BG: 'EU-London', BF: 'EU-London', BI: 'EU-London', KH: 'JP-Tokyo', CM: 'EU-London', CA: 'US-Atlanta', CV: 'EU-London', KY: 'US-Atlanta', CF: 'EU-London', TD: 'EU-London', CL: 'BR-Brazil', CN: 'CN-China', CX: 'JP-Tokyo', CC: 'JP-Tokyo', CO: 'BR-Brazil', KM: 'EU-London', CD: 'EU-London', CG: 'EU-London', CK: 'SG-Singapore', CR: 'US-Atlanta', CI: 'EU-London', HR: 'EU-London', CU: 'US-Atlanta', CW: 'US-Atlanta', CY: 'JP-Tokyo', CZ: 'EU-London', DK: 'EU-London', DJ: 'EU-London', DM: 'US-Atlanta', DO: 'US-Atlanta', EC: 'BR-Brazil', EG: 'EU-London', SV: 'US-Atlanta', GQ: 'EU-London', ER: 'EU-London', EE: 'EU-London', ET: 'EU-London', FO: 'EU-London', FK: 'BR-Brazil', FJ: 'SG-Singapore', FI: 'EU-London', FR: 'EU-London', GF: 'BR-Brazil', PF: 'SG-Singapore', GA: 'EU-London', GM: 'EU-London', GE: 'JP-Tokyo', DE: 'EU-London', GH: 'EU-London', GI: 'EU-London', GR: 'EU-London', GL: 'US-Atlanta', GD: 'US-Atlanta', GP: 'US-Atlanta', GU: 'SG-Singapore', GT: 'US-Atlanta', GG: 'EU-London', GN: 'EU-London', GW: 'EU-London', GY: 'BR-Brazil', HT: 'US-Atlanta', VA: 'EU-London', HN: 'US-Atlanta', HK: 'JP-Tokyo', HU: 'EU-London', IS: 'EU-London', IN: 'JP-Tokyo', ID: 'JP-Tokyo', IR: 'JP-Tokyo', IQ: 'JP-Tokyo', IE: 'EU-London', IM: 'EU-London', IL: 'JP-Tokyo', IT: 'EU-London', JM: 'US-Atlanta', JP: 'JP-Tokyo', JE: 'EU-London', JO: 'JP-Tokyo', KZ: 'JP-Tokyo', KE: 'EU-London', KI: 'SG-Singapore', KP: 'JP-Tokyo', KR: 'JP-Tokyo', KW: 'JP-Tokyo', KG: 'JP-Tokyo', LA: 'JP-Tokyo', LV: 'EU-London', LB: 'JP-Tokyo', LS: 'EU-London', LR: 'EU-London', LY: 'EU-London', LI: 'EU-London', LT: 'EU-London', LU: 'EU-London', MO: 'JP-Tokyo', MK: 'EU-London', MG: 'EU-London', MW: 'EU-London', MY: 'JP-Tokyo', MV: 'JP-Tokyo', ML: 'EU-London', MT: 'EU-London', MH: 'SG-Singapore', MQ: 'US-Atlanta', MR: 'EU-London', MU: 'EU-London', YT: 'EU-London', MX: 'US-Atlanta', FM: 'SG-Singapore', MD: 'EU-London', MC: 'EU-London', MN: 'JP-Tokyo', ME: 'EU-London', MS: 'US-Atlanta', MA: 'EU-London', MZ: 'EU-London', MM: 'JP-Tokyo', NA: 'EU-London', NR: 'SG-Singapore', NP: 'JP-Tokyo', NL: 'EU-London', NC: 'SG-Singapore', NZ: 'SG-Singapore', NI: 'US-Atlanta', NE: 'EU-London', NG: 'EU-London', NU: 'SG-Singapore', NF: 'SG-Singapore', MP: 'SG-Singapore', NO: 'EU-London', OM: 'JP-Tokyo', PK: 'JP-Tokyo', PW: 'SG-Singapore', PS: 'JP-Tokyo', PA: 'US-Atlanta', PG: 'SG-Singapore', PY: 'BR-Brazil', PE: 'BR-Brazil', PH: 'JP-Tokyo', PN: 'SG-Singapore', PL: 'EU-London', PT: 'EU-London', PR: 'US-Atlanta', QA: 'JP-Tokyo', RE: 'EU-London', RO: 'EU-London', RU: 'RU-Russia', RW: 'EU-London', BL: 'US-Atlanta', SH:

Simple inline form validation using HTML5 attributes that plays nicely with Bootstrap

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