A Swift library that uses the Accelerate framework to provide high-performance functions for matrix math, digital signal processing, and image manipulation.

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

Classification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at some of the objectives covered under this section of Machine Learning tutorial. Define Classification and list its algorithms Describe Logistic Regression and Sigmoid Probability Explain K-Nearest Neighbors and KNN classification Understand Support Vector Machines, Polynomial Kernel, and Kernel Trick Analyze Kernel Support Vector Machines with an example Implement the Naïve Bayes Classifier Demonstrate Decision Tree Classifier Describe Random Forest Classifier Classification: Meaning Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are 2 types of Classification: Binomial Multi-Class Classification: Use Cases Some of the key areas where classification cases are being used: To find whether an email received is a spam or ham To identify customer segments To find if a bank loan is granted To identify if a kid will pass or fail in an examination Classification: Example Social media sentiment analysis has two potential outcomes, positive or negative, as displayed by the chart given below. https://www.simplilearn.com/ice9/free_resources_article_thumb/classification-example-machine-learning.JPG This chart shows the classification of the Iris flower dataset into its three sub-species indicated by codes 0, 1, and 2. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-flower-dataset-graph.JPG The test set dots represent the assignment of new test data points to one class or the other based on the trained classifier model. Types of Classification Algorithms Let’s have a quick look into the types of Classification Algorithm below. Linear Models Logistic Regression Support Vector Machines Nonlinear models K-nearest Neighbors (KNN) Kernel Support Vector Machines (SVM) Naïve Bayes Decision Tree Classification Random Forest Classification Logistic Regression: Meaning Let us understand the Logistic Regression model below. This refers to a regression model that is used for classification. This method is widely used for binary classification problems. It can also be extended to multi-class classification problems. Here, the dependent variable is categorical: y ϵ {0, 1} A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc In this case, you model the probability distribution of output y as 1 or 0. This is called the sigmoid probability (σ). If σ(θ Tx) > 0.5, set y = 1, else set y = 0 Unlike Linear Regression (and its Normal Equation solution), there is no closed form solution for finding optimal weights of Logistic Regression. Instead, you must solve this with maximum likelihood estimation (a probability model to detect the maximum likelihood of something happening). It can be used to calculate the probability of a given outcome in a binary model, like the probability of being classified as sick or passing an exam. https://www.simplilearn.com/ice9/free_resources_article_thumb/logistic-regression-example-graph.JPG Sigmoid Probability The probability in the logistic regression is often represented by the Sigmoid function (also called the logistic function or the S-curve): https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-function-machine-learning.JPG In this equation, t represents data values * the number of hours studied and S(t) represents the probability of passing the exam. Assume sigmoid function: https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-probability-machine-learning.JPG g(z) tends toward 1 as z -> infinity , and g(z) tends toward 0 as z -> infinity K-nearest Neighbors (KNN) K-nearest Neighbors algorithm is used to assign a data point to clusters based on similarity measurement. It uses a supervised method for classification. The steps to writing a k-means algorithm are as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-distribution-graph-machine-learning.JPG Choose the number of k and a distance metric. (k = 5 is common) Find k-nearest neighbors of the sample that you want to classify Assign the class label by majority vote. KNN Classification A new input point is classified in the category such that it has the most number of neighbors from that category. For example: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-classification-machine-learning.JPG Classify a patient as high risk or low risk. Mark email as spam or ham. Keen on learning about Classification Algorithms in Machine Learning? Click here! Support Vector Machine (SVM) Let us understand Support Vector Machine (SVM) in detail below. SVMs are classification algorithms used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Once ideal hyperplanes are discovered, new data points can be easily classified. https://www.simplilearn.com/ice9/free_resources_article_thumb/support-vector-machines-graph-machine-learning.JPG The optimization objective is to find “maximum margin hyperplane” that is farthest from the closest points in the two classes (these points are called support vectors). In the given figure, the middle line represents the hyperplane. SVM Example Let’s look at this image below and have an idea about SVM in general. Hyperplanes with larger margins have lower generalization error. The positive and negative hyperplanes are represented by: https://www.simplilearn.com/ice9/free_resources_article_thumb/positive-negative-hyperplanes-machine-learning.JPG Classification of any new input sample xtest : If w0 + wTxtest > 1, the sample xtest is said to be in the class toward the right of the positive hyperplane. If w0 + wTxtest < -1, the sample xtest is said to be in the class toward the left of the negative hyperplane. When you subtract the two equations, you get: https://www.simplilearn.com/ice9/free_resources_article_thumb/equation-subtraction-machine-learning.JPG Length of vector w is (L2 norm length): https://www.simplilearn.com/ice9/free_resources_article_thumb/length-of-vector-machine-learning.JPG You normalize with the length of w to arrive at: https://www.simplilearn.com/ice9/free_resources_article_thumb/normalize-equation-machine-learning.JPG SVM: Hard Margin Classification Given below are some points to understand Hard Margin Classification. The left side of equation SVM-1 given above can be interpreted as the distance between the positive (+ve) and negative (-ve) hyperplanes; in other words, it is the margin that can be maximized. Hence the objective of the function is to maximize with the constraint that the samples are classified correctly, which is represented as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-machine-learning.JPG This means that you are minimizing ‖w‖. This also means that all positive samples are on one side of the positive hyperplane and all negative samples are on the other side of the negative hyperplane. This can be written concisely as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-formula.JPG Minimizing ‖w‖ is the same as minimizing. This figure is better as it is differentiable even at w = 0. The approach listed above is called “hard margin linear SVM classifier.” SVM: Soft Margin Classification Given below are some points to understand Soft Margin Classification. To allow for linear constraints to be relaxed for nonlinearly separable data, a slack variable is introduced. (i) measures how much ith instance is allowed to violate the margin. The slack variable is simply added to the linear constraints. https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-machine-learning.JPG Subject to the above constraints, the new objective to be minimized becomes: https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-formula.JPG You have two conflicting objectives now—minimizing slack variable to reduce margin violations and minimizing to increase the margin. The hyperparameter C allows us to define this trade-off. Large values of C correspond to larger error penalties (so smaller margins), whereas smaller values of C allow for higher misclassification errors and larger margins. https://www.simplilearn.com/ice9/free_resources_article_thumb/machine-learning-certification-video-preview.jpg SVM: Regularization The concept of C is the reverse of regularization. Higher C means lower regularization, which increases bias and lowers the variance (causing overfitting). https://www.simplilearn.com/ice9/free_resources_article_thumb/concept-of-c-graph-machine-learning.JPG IRIS Data Set The Iris dataset contains measurements of 150 IRIS flowers from three different species: Setosa Versicolor Viriginica Each row represents one sample. Flower measurements in centimeters are stored as columns. These are called features. IRIS Data Set: SVM Let’s train an SVM model using sci-kit-learn for the Iris dataset: https://www.simplilearn.com/ice9/free_resources_article_thumb/svm-model-graph-machine-learning.JPG Nonlinear SVM Classification There are two ways to solve nonlinear SVMs: by adding polynomial features by adding similarity features Polynomial features can be added to datasets; in some cases, this can create a linearly separable dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/nonlinear-classification-svm-machine-learning.JPG In the figure on the left, there is only 1 feature x1. This dataset is not linearly separable. If you add x2 = (x1)2 (figure on the right), the data becomes linearly separable. Polynomial Kernel In sci-kit-learn, one can use a Pipeline class for creating polynomial features. Classification results for the Moons dataset are shown in the figure. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-machine-learning.JPG Polynomial Kernel with Kernel Trick Let us look at the image below and understand Kernel Trick in detail. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-with-kernel-trick.JPG For large dimensional datasets, adding too many polynomial features can slow down the model. You can apply a kernel trick with the effect of polynomial features without actually adding them. The code is shown (SVC class) below trains an SVM classifier using a 3rd-degree polynomial kernel but with a kernel trick. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-equation-machine-learning.JPG The hyperparameter coefθ controls the influence of high-degree polynomials. Kernel SVM Let us understand in detail about Kernel SVM. Kernel SVMs are used for classification of nonlinear data. In the chart, nonlinear data is projected into a higher dimensional space via a mapping function where it becomes linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-machine-learning.JPG In the higher dimension, a linear separating hyperplane can be derived and used for classification. A reverse projection of the higher dimension back to original feature space takes it back to nonlinear shape. As mentioned previously, SVMs can be kernelized to solve nonlinear classification problems. You can create a sample dataset for XOR gate (nonlinear problem) from NumPy. 100 samples will be assigned the class sample 1, and 100 samples will be assigned the class label -1. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-graph-machine-learning.JPG As you can see, this data is not linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-non-separable.JPG You now use the kernel trick to classify XOR dataset created earlier. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-xor-machine-learning.JPG Naïve Bayes Classifier What is Naive Bayes Classifier? Have you ever wondered how your mail provider implements spam filtering or how online news channels perform news text classification or even how companies perform sentiment analysis of their audience on social media? All of this and more are done through a machine learning algorithm called Naive Bayes Classifier. Naive Bayes Named after Thomas Bayes from the 1700s who first coined this in the Western literature. Naive Bayes classifier works on the principle of conditional probability as given by the Bayes theorem. Advantages of Naive Bayes Classifier Listed below are six benefits of Naive Bayes Classifier. Very simple and easy to implement Needs less training data Handles both continuous and discrete data Highly scalable with the number of predictors and data points As it is fast, it can be used in real-time predictions Not sensitive to irrelevant features Bayes Theorem We will understand Bayes Theorem in detail from the points mentioned below. According to the Bayes model, the conditional probability P(Y|X) can be calculated as: P(Y|X) = P(X|Y)P(Y) / P(X) This means you have to estimate a very large number of P(X|Y) probabilities for a relatively small vector space X. For example, for a Boolean Y and 30 possible Boolean attributes in the X vector, you will have to estimate 3 billion probabilities P(X|Y). To make it practical, a Naïve Bayes classifier is used, which assumes conditional independence of P(X) to each other, with a given value of Y. This reduces the number of probability estimates to 2*30=60 in the above example. Naïve Bayes Classifier for SMS Spam Detection Consider a labeled SMS database having 5574 messages. It has messages as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-machine-learning.JPG Each message is marked as spam or ham in the data set. Let’s train a model with Naïve Bayes algorithm to detect spam from ham. The message lengths and their frequency (in the training dataset) are as shown below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-spam-detection.JPG Analyze the logic you use to train an algorithm to detect spam: Split each message into individual words/tokens (bag of words). Lemmatize the data (each word takes its base form, like “walking” or “walked” is replaced with “walk”). Convert data to vectors using scikit-learn module CountVectorizer. Run TFIDF to remove common words like “is,” “are,” “and.” Now apply scikit-learn module for Naïve Bayes MultinomialNB to get the Spam Detector. This spam detector can then be used to classify a random new message as spam or ham. Next, the accuracy of the spam detector is checked using the Confusion Matrix. For the SMS spam example above, the confusion matrix is shown on the right. Accuracy Rate = Correct / Total = (4827 + 592)/5574 = 97.21% Error Rate = Wrong / Total = (155 + 0)/5574 = 2.78% https://www.simplilearn.com/ice9/free_resources_article_thumb/confusion-matrix-machine-learning.JPG Although confusion Matrix is useful, some more precise metrics are provided by Precision and Recall. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-recall-matrix-machine-learning.JPG Precision refers to the accuracy of positive predictions. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-formula-machine-learning.JPG Recall refers to the ratio of positive instances that are correctly detected by the classifier (also known as True positive rate or TPR). https://www.simplilearn.com/ice9/free_resources_article_thumb/recall-formula-machine-learning.JPG Precision/Recall Trade-off To detect age-appropriate videos for kids, you need high precision (low recall) to ensure that only safe videos make the cut (even though a few safe videos may be left out). The high recall is needed (low precision is acceptable) in-store surveillance to catch shoplifters; a few false alarms are acceptable, but all shoplifters must be caught. Learn about Naive Bayes in detail. Click here! Decision Tree Classifier Some aspects of the Decision Tree Classifier mentioned below are. Decision Trees (DT) can be used both for classification and regression. The advantage of decision trees is that they require very little data preparation. They do not require feature scaling or centering at all. They are also the fundamental components of Random Forests, one of the most powerful ML algorithms. Unlike Random Forests and Neural Networks (which do black-box modeling), Decision Trees are white box models, which means that inner workings of these models are clearly understood. In the case of classification, the data is segregated based on a series of questions. Any new data point is assigned to the selected leaf node. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-machine-learning.JPG Start at the tree root and split the data on the feature using the decision algorithm, resulting in the largest information gain (IG). This splitting procedure is then repeated in an iterative process at each child node until the leaves are pure. This means that the samples at each node belonging to the same class. In practice, you can set a limit on the depth of the tree to prevent overfitting. The purity is compromised here as the final leaves may still have some impurity. The figure shows the classification of the Iris dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-graph.JPG IRIS Decision Tree Let’s build a Decision Tree using scikit-learn for the Iris flower dataset and also visualize it using export_graphviz API. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-machine-learning.JPG The output of export_graphviz can be converted into png format: https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-output.JPG Sample attribute stands for the number of training instances the node applies to. Value attribute stands for the number of training instances of each class the node applies to. Gini impurity measures the node’s impurity. A node is “pure” (gini=0) if all training instances it applies to belong to the same class. https://www.simplilearn.com/ice9/free_resources_article_thumb/impurity-formula-machine-learning.JPG For example, for Versicolor (green color node), the Gini is 1-(0/54)2 -(49/54)2 -(5/54) 2 ≈ 0.168 https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-sample.JPG Decision Boundaries Let us learn to create decision boundaries below. For the first node (depth 0), the solid line splits the data (Iris-Setosa on left). Gini is 0 for Setosa node, so no further split is possible. The second node (depth 1) splits the data into Versicolor and Virginica. If max_depth were set as 3, a third split would happen (vertical dotted line). https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-boundaries.JPG For a sample with petal length 5 cm and petal width 1.5 cm, the tree traverses to depth 2 left node, so the probability predictions for this sample are 0% for Iris-Setosa (0/54), 90.7% for Iris-Versicolor (49/54), and 9.3% for Iris-Virginica (5/54) CART Training Algorithm Scikit-learn uses Classification and Regression Trees (CART) algorithm to train Decision Trees. CART algorithm: Split the data into two subsets using a single feature k and threshold tk (example, petal length < “2.45 cm”). This is done recursively for each node. k and tk are chosen such that they produce the purest subsets (weighted by their size). The objective is to minimize the cost function as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/cart-training-algorithm-machine-learning.JPG The algorithm stops executing if one of the following situations occurs: max_depth is reached No further splits are found for each node Other hyperparameters may be used to stop the tree: min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes Gini Impurity or Entropy Entropy is one more measure of impurity and can be used in place of Gini. https://www.simplilearn.com/ice9/free_resources_article_thumb/gini-impurity-entrophy.JPG It is a degree of uncertainty, and Information Gain is the reduction that occurs in entropy as one traverses down the tree. Entropy is zero for a DT node when the node contains instances of only one class. Entropy for depth 2 left node in the example given above is: https://www.simplilearn.com/ice9/free_resources_article_thumb/entrophy-for-depth-2.JPG Gini and Entropy both lead to similar trees. DT: Regularization The following figure shows two decision trees on the moons dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/dt-regularization-machine-learning.JPG The decision tree on the right is restricted by min_samples_leaf = 4. The model on the left is overfitting, while the model on the right generalizes better. Random Forest Classifier Let us have an understanding of Random Forest Classifier below. A random forest can be considered an ensemble of decision trees (Ensemble learning). Random Forest algorithm: Draw a random bootstrap sample of size n (randomly choose n samples from the training set). Grow a decision tree from the bootstrap sample. At each node, randomly select d features. Split the node using the feature that provides the best split according to the objective function, for instance by maximizing the information gain. Repeat the steps 1 to 2 k times. (k is the number of trees you want to create, using a subset of samples) Aggregate the prediction by each tree for a new data point to assign the class label by majority vote (pick the group selected by the most number of trees and assign new data point to that group). Random Forests are opaque, which means it is difficult to visualize their inner workings. https://www.simplilearn.com/ice9/free_resources_article_thumb/random-forest-classifier-graph.JPG However, the advantages outweigh their limitations since you do not have to worry about hyperparameters except k, which stands for the number of decision trees to be created from a subset of samples. RF is quite robust to noise from the individual decision trees. Hence, you need not prune individual decision trees. The larger the number of decision trees, the more accurate the Random Forest prediction is. (This, however, comes with higher computation cost). Key Takeaways Let us quickly run through what we have learned so far in this Classification tutorial. Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. K-nearest Neighbors (KNN) algorithm uses similar features to classify data. Support Vector Machines (SVMs) classify data by detecting the maximum margin hyperplane between data classes. Naïve Bayes, a simplified Bayes Model, can help classify data using conditional probability models. Decision Trees are powerful classifiers and use tree splitting logic until pure or somewhat pure leaf node classes are attained. Random Forests apply Ensemble Learning to Decision Trees for more accurate classification predictions. Conclusion This completes ‘Classification’ tutorial. In the next tutorial, we will learn 'Unsupervised Learning with Clustering.'

NOTICE This repository contains the public FTC SDK for the SKYSTONE (2019-2020) competition season. If you are looking for the current season's FTC SDK software, please visit the new and permanent home of the public FTC SDK: FtcRobotController repository Welcome! This GitHub repository contains the source code that is used to build an Android app to control a FIRST Tech Challenge competition robot. To use this SDK, download/clone the entire project to your local computer. Getting Started If you are new to robotics or new to the FIRST Tech Challenge, then you should consider reviewing the FTC Blocks Tutorial to get familiar with how to use the control system: FTC Blocks Online Tutorial Even if you are an advanced Java programmer, it is helpful to start with the FTC Blocks tutorial, and then migrate to the OnBot Java Tool or to Android Studio afterwards. Downloading the Project If you are an Android Studio programmer, there are several ways to download this repo. Note that if you use the Blocks or OnBot Java Tool to program your robot, then you do not need to download this repository. If you are a git user, you can clone the most current version of the repository: git clone https://github.com/FIRST-Tech-Challenge/SKYSTONE.git Or, if you prefer, you can use the "Download Zip" button available through the main repository page. Downloading the project as a .ZIP file will keep the size of the download manageable. You can also download the project folder (as a .zip or .tar.gz archive file) from the Downloads subsection of the Releases page for this repository. Once you have downloaded and uncompressed (if needed) your folder, you can use Android Studio to import the folder ("Import project (Eclipse ADT, Gradle, etc.)"). Getting Help User Documentation and Tutorials FIRST maintains online documentation with information and tutorials on how to use the FIRST Tech Challenge software and robot control system. You can access this documentation using the following link: SKYSTONE Online Documentation Note that the online documentation is an "evergreen" document that is constantly being updated and edited. It contains the most current information about the FIRST Tech Challenge software and control system. Javadoc Reference Material The Javadoc reference documentation for the FTC SDK is now available online. Click on the following link to view the FTC SDK Javadoc documentation as a live website: FTC Javadoc Documentation Documentation for the FTC SDK is also included with this repository. There is a subfolder called "doc" which contains several subfolders: The folder "apk" contains the .apk files for the FTC Driver Station and FTC Robot Controller apps. The folder "javadoc" contains the JavaDoc user documentation for the FTC SDK. Online User Forum For technical questions regarding the Control System or the FTC SDK, please visit the FTC Technology forum: FTC Technology Forum Release Information Version 5.5 (20200824-090813) Version 5.5 requires Android Studio 4.0 or later. New features Adds support for calling custom Java classes from Blocks OpModes (fixes SkyStone issue #161). Classes must be in the org.firstinspires.ftc.teamcode package. Methods must be public static and have no more than 21 parameters. Parameters declared as OpMode, LinearOpMode, Telemetry, and HardwareMap are supported and the argument is provided automatically, regardless of the order of the parameters. On the block, the sockets for those parameters are automatically filled in. Parameters declared as char or java.lang.Character will accept any block that returns text and will only use the first character in the text. Parameters declared as boolean or java.lang.Boolean will accept any block that returns boolean. Parameters declared as byte, java.lang.Byte, short, java.lang.Short, int, java.lang.Integer, long, or java.lang.Long, will accept any block that returns a number and will round that value to the nearest whole number. Parameters declared as float, java.lang.Float, double, java.lang.Double will accept any block that returns a number. Adds telemetry API method for setting display format Classic Monospace HTML (certain tags only) Adds blocks support for switching cameras. Adds Blocks support for TensorFlow Object Detection with a custom model. Adds support for uploading a custom TensorFlow Object Detection model in the Manage page, which is especially useful for Blocks and OnBotJava users. Shows new Control Hub blink codes when the WiFi band is switched using the Control Hub's button (only possible on Control Hub OS 1.1.2) Adds new warnings which can be disabled in the Advanced RC Settings Mismatched app versions warning Unnecessary 2.4 GHz WiFi usage warning REV Hub is running outdated firmware (older than version 1.8.2) Adds support for Sony PS4 gamepad, and reworks how gamepads work on the Driver Station Removes preference which sets gamepad type based on driver position. Replaced with menu which allows specifying type for gamepads with unknown VID and PID Attempts to auto-detect gamepad type based on USB VID and PID If gamepad VID and PID is not known, use type specified by user for that VID and PID If gamepad VID and PID is not known AND the user has not specified a type for that VID and PID, an educated guess is made about how to map the gamepad Driver Station will now attempt to automatically recover from a gamepad disconnecting, and re-assign it to the position it was assigned to when it dropped If only one gamepad is assigned and it drops: it can be recovered If two gamepads are assigned, and have different VID/PID signatures, and only one drops: it will be recovered If two gamepads are assigned, and have different VID/PID signatures, and BOTH drop: both will be recovered If two gamepads are assigned, and have the same VID/PID signatures, and only one drops: it will be recovered If two gamepads are assigned, and have the same VID/PID signatures, and BOTH drop: neither will be recovered, because of the ambiguity of the gamepads when they re-appear on the USB bus. There is currently one known edge case: if there are two gamepads with the same VID/PID signature plugged in, but only one is assigned, and they BOTH drop, it's a 50-50 chance of which one will be chosen for automatic recovery to the assigned position: it is determined by whichever one is re-enumerated first by the USB bus controller. Adds landscape user interface to Driver Station New feature: practice timer with audio cues New feature (Control Hub only): wireless network connection strength indicator (0-5 bars) New feature (Control Hub only): tapping on the ping/channel display will switch to an alternate display showing radio RX dBm and link speed (tap again to switch back) The layout will NOT autorotate. You can switch the layout from the Driver Station's settings menu. Breaking changes Removes support for Android versions 4.4 through 5.1 (KitKat and Lollipop). The minSdkVersion is now 23. Removes the deprecated LinearOpMode methods waitOneFullHardwareCycle() and waitForNextHardwareCycle() Enhancements Handles RS485 address of Control Hub automatically The Control Hub is automatically given a reserved address Existing configuration files will continue to work All addresses in the range of 1-10 are still available for Expansion Hubs The Control Hub light will now normally be solid green, without blinking to indicate the address The Control Hub will not be shown on the Expansion Hub Address Change settings page Improves REV Hub firmware updater The user can now choose between all available firmware update files Version 1.8.2 of the REV Hub firmware is bundled into the Robot Controller app. Text was added to clarify that Expansion Hubs can only be updated via USB. Firmware update speed was reduced to improve reliability Allows REV Hub firmware to be updated directly from the Manage webpage Improves log viewer on Robot Controller Horizontal scrolling support (no longer word wrapped) Supports pinch-to-zoom Uses a monospaced font Error messages are highlighted New color scheme Attempts to force-stop a runaway/stuck OpMode without restarting the entire app Not all types of runaway conditions are stoppable, but if the user code attempts to talk to hardware during the runaway, the system should be able to capture it. Makes various tweaks to the Self Inspect screen Renames "OS version" entry to "Android version" Renames "WiFi Direct Name" to "WiFi Name" Adds Control Hub OS version, when viewing the report of a Control Hub Hides the airplane mode entry, when viewing the report of a Control Hub Removes check for ZTE Speed Channel Changer Shows firmware version for all Expansion and Control Hubs Reworks network settings portion of Manage page All network settings are now applied with a single click The WiFi Direct channel of phone-based Robot Controllers can now be changed from the Manage page WiFi channels are filtered by band (2.4 vs 5 GHz) and whether they overlap with other channels The current WiFi channel is pre-selected on phone-based Robot Controllers, and Control Hubs running OS 1.1.2 or later. On Control Hubs running OS 1.1.2 or later, you can choose to have the system automatically select a channel on the 5 GHz band Improves OnBotJava New light and dark themes replace the old themes (chaos, github, chrome,...) the new default theme is light and will be used when you first update to this version OnBotJava now has a tabbed editor Read-only offline mode Improves function of "exit" menu item on Robot Controller and Driver Station Now guaranteed to be fully stopped and unloaded from memory Shows a warning message if a LinearOpMode exists prematurely due to failure to monitor for the start condition Improves error message shown when the Driver Station and Robot Controller are incompatible with each other Driver Station OpMode Control Panel now disabled while a Restart Robot is in progress Disables advanced settings related to WiFi direct when the Robot Controller is a Control Hub. Tint phone battery icons on Driver Station when low/critical. Uses names "Control Hub Portal" and "Control Hub" (when appropriate) in new configuration files Improve I2C read performance Very large improvement on Control Hub; up to ~2x faster with small (e.g. 6 byte) reads Not as apparent on Expansion Hubs connected to a phone Update/refresh build infrastructure Update to 'androidx' support library from 'com.android.support:appcompat', which is end-of-life Update targetSdkVersion and compileSdkVersion to 28 Update Android Studio's Android plugin to latest Fix reported build timestamp in 'About' screen Add sample illustrating manual webcam use: ConceptWebcam Bug fixes Fixes SkyStone issue #248 Fixes SkyStone issue #232 and modifies bulk caching semantics to allow for cache-preserving MANUAL/AUTO transitions. Improves performance when REV 2M distance sensor is unplugged Improves readability of Toast messages on certain devices Allows a Driver Station to connect to a Robot Controller after another has disconnected Improves generation of fake serial numbers for UVC cameras which do not provide a real serial number Previously some devices would assign such cameras a serial of 0:0 and fail to open and start streaming Fixes ftc_app issue #638. Fixes a slew of bugs with the Vuforia camera monitor including: Fixes bug where preview could be displayed with a wonky aspect ratio Fixes bug where preview could be cut off in landscape Fixes bug where preview got totally messed up when rotating phone Fixes bug where crosshair could drift off target when using webcams Fixes issue in UVC driver on some devices (ftc_app 681) if streaming was started/stopped multiple times in a row Issue manifested as kernel panic on devices which do not have this kernel patch. On affected devices which do have the patch, the issue was manifest as simply a failure to start streaming. The Tech Team believes that the root cause of the issue is a bug in the Linux kernel XHCI driver. A workaround was implemented in the SDK UVC driver. Fixes bug in UVC driver where often half the frames from the camera would be dropped (e.g. only 15FPS delivered during a streaming session configured for 30FPS). Fixes issue where TensorFlow Object Detection would show results whose confidence was lower than the minimum confidence parameter. Fixes a potential exploitation issue of CVE-2019-11358 in OnBotJava Fixes changing the address of an Expansion Hub with additional Expansion Hubs connected to it Preserves the Control Hub's network connection when "Restart Robot" is selected Fixes issue where device scans would fail while the Robot was restarting Fix RenderScript usage Use androidx.renderscript variant: increased compatibility Use RenderScript in Java mode, not native: simplifies build Fixes webcam-frame-to-bitmap conversion problem: alpha channel wasn't being initialized, only R, G, & B Fixes possible arithmetic overflow in Deadline Fixes deadlock in Vuforia webcam support which could cause 5-second delays when stopping OpMode Version 5.4 (20200108-101156) Fixes SkyStone issue #88 Adds an inspection item that notes when a robot controller (Control Hub) is using the factory default password. Fixes SkyStone issue #61 Fixes SkyStone issue #142 Fixes ftc_app issue #417 by adding more current and voltage monitoring capabilities for REV Hubs. Fixes a crash sometimes caused by OnBotJava activity Improves OnBotJava autosave functionality ftc_app #738 Fixes system responsiveness issue when an Expansion Hub is disconnected Fixes issue where IMU initialization could prevent Op Modes from stopping Fixes issue where AndroidTextToSpeech.speak() would fail if it was called too early Adds telemetry.speak() methods and blocks, which cause the Driver Station (if also updated) to speak text Adds and improves Expansion Hub-related warnings Improves Expansion Hub low battery warning Displays the warning immediately after the hub reports it Specifies whether the condition is current or occurred temporarily during an OpMode run Displays which hubs reported low battery Displays warning when hub loses and regains power during an OpMode run Fixes the hub's LED pattern after this condition Displays warning when Expansion Hub is not responding to commands Specifies whether the condition is current or occurred temporarily during an OpMode run Clarifies warning when Expansion Hub is not present at startup Specifies that this condition requires a Robot Restart before the hub can be used. The hub light will now accurately reflect this state Improves logging and reduces log spam during these conditions Syncs the Control Hub time and timezone to a connected web browser programming the robot, if a Driver Station is not available. Adds bulk read functionality for REV Hubs A bulk caching mode must be set at the Hub level with LynxModule#setBulkCachingMode(). This applies to all relevant SDK hardware classes that reference that Hub. The following following Hub bulk caching modes are available: BulkCachingMode.OFF (default): All hardware calls operate as usual. Bulk data can read through LynxModule#getBulkData() and processed manually. BulkCachingMode.AUTO: Applicable hardware calls are served from a bulk read cache that is cleared/refreshed automatically to ensure identical commands don't hit the same cache. The cache can also be cleared manually with LynxModule#clearBulkCache(), although this is not recommended. (advanced users) BulkCachingMode.MANUAL: Same as BulkCachingMode.AUTO except the cache is never cleared automatically. To avoid getting stale data, the cache must be manually cleared at the beginning of each loop body or as the user deems appropriate. Removes PIDF Annotation values added in Rev 5.3 (to AndyMark, goBILDA and TETRIX motor configurations). The new motor types will still be available but their Default control behavior will revert back to Rev 5.2 Adds new ConceptMotorBulkRead sample Opmode to demonstrate and compare Motor Bulk-Read modes for reducing I/O latencies. Version 5.3 (20191004-112306) Fixes external USB/UVC webcam support Makes various bugfixes and improvements to Blocks page, including but not limited to: Many visual tweaks Browser zoom and window resize behave better Resizing the Java preview pane works better and more consistently across browsers The Java preview pane consistently gets scrollbars when needed The Java preview pane is hidden by default on phones Internet Explorer 11 should work Large dropdown lists display properly on lower res screens Disabled buttons are now visually identifiable as disabled A warning is shown if a user selects a TFOD sample, but their device is not compatible Warning messages in a Blocks op mode are now visible by default. Adds goBILDA 5201 and 5202 motors to Robot Configurator Adds PIDF Annotation values to AndyMark, goBILDA and TETRIX motor configurations. This has the effect of causing the RUN_USING_ENCODERS and RUN_TO_POSITION modes to use PIDF vs PID closed loop control on these motors. This should provide more responsive, yet stable, speed control. PIDF adds Feedforward control to the basic PID control loop. Feedforward is useful when controlling a motor's speed because it "anticipates" how much the control voltage must change to achieve a new speed set-point, rather than requiring the integrated error to change sufficiently. The PIDF values were chosen to provide responsive, yet stable, speed control on a lightly loaded motor. The more heavily a motor is loaded (drag or friction), the more noticable the PIDF improvement will be. Fixes startup crash on Android 10 Fixes ftc_app issue #712 (thanks to FROGbots-4634) Fixes ftc_app issue #542 Allows "A" and lowercase letters when naming device through RC and DS apps. Version 5.2 (20190905-083277) Fixes extra-wide margins on settings activities, and placement of the new configuration button Adds Skystone Vuforia image target data. Includes sample Skystone Vuforia Navigation op modes (Java). Includes sample Skystone Vuforia Navigation op modes (Blocks). Adds TensorFlow inference model (.tflite) for Skystone game elements. Includes sample Skystone TensorFlow op modes (Java). Includes sample Skystone TensorFlow op modes (Blocks). Removes older (season-specific) sample op modes. Includes 64-bit support (to comply with Google Play requirements). Protects against Stuck OpModes when a Restart Robot is requested. (Thanks to FROGbots-4634) (ftc_app issue #709) Blocks related changes: Fixes bug with blocks generated code when hardware device name is a java or javascript reserved word. Shows generated java code for blocks, even when hardware items are missing from the active configuration. Displays warning icon when outdated Vuforia and TensorFlow blocks are used (SkyStone issue #27) Version 5.1 (20190820-222104) Defines default PIDF parameters for the following motors: REV Core Hex Motor REV 20:1 HD Hex Motor REV 40:1 HD Hex Motor Adds back button when running on a device without a system back button (such as a Control Hub) Allows a REV Control Hub to update the firmware on a REV Expansion Hub via USB Fixes SkyStone issue #9 Fixes ftc_app issue #715 Prevents extra DS User clicks by filtering based on current state. Prevents incorrect DS UI state changes when receiving new OpMode list from RC Adds support for REV Color Sensor V3 Adds a manual-refresh DS Camera Stream for remotely viewing RC camera frames. To show the stream on the DS, initialize but do not run a stream-enabled opmode, select the Camera Stream option in the DS menu, and tap the image to refresh. This feature is automatically enabled when using Vuforia or TFOD—no additional RC configuration is required for typical use cases. To hide the stream, select the same menu item again. Note that gamepads are disabled and the selected opmode cannot be started while the stream is open as a safety precaution. To use custom streams, consult the API docs for CameraStreamServer#setSource and CameraStreamSource. Adds many Star Wars sounds to RobotController resources. Added SKYSTONE Sounds Chooser Sample Program. Switches out startup, connect chimes, and error/warning sounds for Star Wars sounds Updates OnBot Java to use a WebSocket for communication with the robot The OnBot Java page no longer has to do a full refresh when a user switches from editing one file to another Known issues: Camera Stream The Vuforia camera stream inherits the issues present in the phone preview (namely ftc_app issue #574). This problem does not affect the TFOD camera stream even though it receives frames from Vuforia. The orientation of the stream frames may not always match the phone preview. For now, these frames may be rotated manually via a custom CameraStreamSource if desired. OnBotJava Browser back button may not always work correctly It's possible for a build to be queued, but not started. The OnBot Java build console will display a warning if this occurs. A user might not realize they are editing a different file if the user inadvertently switches from one file to another since this switch is now seamless. The name of the currently open file is displayed in the browser tab. Version 5.0 (built on 19.06.14) Support for the REV Robotics Control Hub. Adds a Java preview pane to the Blocks editor. Adds a new offline export feature to the Blocks editor. Display wifi channel in Network circle on Driver Station. Adds calibration for Logitech C270 Updates build tooling and target SDK. Compliance with Google's permissions infrastructure (Required after build tooling update). Keep Alives to mitigate the Motorola wifi scanning problem. Telemetry substitute no longer necessary. Improves Vuforia error reporting. Fixes ftctechnh/ftc_app issues 621, 713. Miscellaneous bug fixes and improvements. Version 4.3 (built on 18.10.31) Includes missing TensorFlow-related libraries and files. Version 4.2 (built on 18.10.30) Includes fix to avoid deadlock situation with WatchdogMonitor which could result in USB communication errors. Comm error appeared to require that user disconnect USB cable and restart the Robot Controller app to recover. robotControllerLog.txt would have error messages that included the words "E RobotCore: lynx xmit lock: #### abandoning lock:" Includes fix to correctly list the parent module address for a REV Robotics Expansion Hub in a configuration (.xml) file. Bug in versions 4.0 and 4.1 would incorrect list the address module for a parent REV Robotics device as "1". If the parent module had a higher address value than the daisy-chained module, then this bug would prevent the Robot Controller from communicating with the downstream Expansion Hub. Added requirement for ACCESS_COARSE_LOCATION to allow a Driver Station running Android Oreo to scan for Wi-Fi Direct devices. Added google() repo to build.gradle because aapt2 must be downloaded from the google() repository beginning with version 3.2 of the Android Gradle Plugin. Important Note: Android Studio users will need to be connected to the Internet the first time build the ftc_app project. Internet connectivity is required for the first build so the appropriate files can be downloaded from the Google repository. Users should not need to be connected to the Internet for subsequent builds. This should also fix buid issue where Android Studio would complain that it "Could not find com.android.tools.lint:lint-gradle:26.1.4" (or similar). Added support for REV Spark Mini motor controller as part of the configuration menu for a servo/PWM port on the REV Expansion Hub. Provide examples for playing audio files in an Op Mode. Block Development Tool Changes Includes a fix for a problem with the Velocity blocks that were reported in the FTC Technology forum (Blocks Programming subforum). Change the "Save completed successfully." message to a white color so it will contrast with a green background. Fixed the "Download image" feature so it will work if there are text blocks in the op mode. Introduce support for Google's TensorFlow Lite technology for object detetion for 2018-2019 game. TensorFlow lite can recognize Gold Mineral and Silver Mineral from 2018-2019 game. Example Java and Block op modes are included to show how to determine the relative position of the gold block (left, center, right). Version 4.1 (released on 18.09.24) Changes include: Fix to prevent crash when deprecated configuration annotations are used. Change to allow FTC Robot Controller APK to be auto-updated using FIRST Global Control Hub update scripts. Removed samples for non supported / non legal hardware. Improvements to Telemetry.addData block with "text" socket. Updated Blocks sample op mode list to include Rover Ruckus Vuforia example. Update SDK library version number. Version 4.0 (released on 18.09.12) Changes include: Initial support for UVC compatible cameras If UVC camera has a unique serial number, RC will detect and enumerate by serial number. If UVC camera lacks a unique serial number, RC will only support one camera of that type connected. Calibration settings for a few cameras are included (see TeamCode/src/main/res/xml/teamwebcamcalibrations.xml for details). User can upload calibration files from Program and Manage web interface. UVC cameras seem to draw a fair amount of electrical current from the USB bus. This does not appear to present any problems for the REV Robotics Control Hub. This does seem to create stability problems when using some cameras with an Android phone-based Robot Controller. FTC Tech Team is investigating options to mitigate this issue with the phone-based Robot Controllers. Updated sample Vuforia Navigation and VuMark Op Modes to demonstrate how to use an internal phone-based camera and an external UVC webcam. Support for improved motor control. REV Robotics Expansion Hub firmware 1.8 and greater will support a feed forward mechanism for closed loop motor control. FTC SDK has been modified to support PIDF coefficients (proportional, integral, derivative, and feed forward). FTC Blocks development tool modified to include PIDF programming blocks. Deprecated older PID-related methods and variables. REV's 1.8.x PIDF-related changes provide a more linear and accurate way to control a motor. Wireless Added 5GHz support for wireless channel changing for those devices that support it. Tested with Moto G5 and E4 phones. Also tested with other (currently non-approved) phones such as Samsung Galaxy S8. Improved Expansion Hub firmware update support in Robot Controller app Changes to make the system more robust during the firmware update process (when performed through Robot Controller app). User no longer has to disconnect a downstream daisy-chained Expansion Hub when updating an Expansion Hub's firmware. If user is updating an Expansion Hub's firmware through a USB connection, he/she does not have to disconnect RS485 connection to other Expansion Hubs. The user still must use a USB connection to update an Expansion Hub's firmware. The user cannot update the Expansion Hub firmware for a downstream device that is daisy chained through an RS485 connection. If an Expansion Hub accidentally gets "bricked" the Robot Controller app is now more likely to recognize the Hub when it scans the USB bus. Robot Controller app should be able to detect an Expansion Hub, even if it accidentally was bricked in a previous update attempt. Robot Controller app should be able to install the firmware onto the Hub, even if if accidentally was bricked in a previous update attempt. Resiliency FTC software can detect and enable an FTDI reset feature that is available with REV Robotics v1.8 Expansion Hub firmware and greater. When enabled, the Expansion Hub can detect if it hasn't communicated with the Robot Controller over the FTDI (USB) connection. If the Hub hasn't heard from the Robot Controller in a while, it will reset the FTDI connection. This action helps system recover from some ESD-induced disruptions. Various fixes to improve reliability of FTC software. Blocks Fixed errors with string and list indices in blocks export to java. Support for USB connected UVC webcams. Refactored optimized Blocks Vuforia code to support Rover Ruckus image targets. Added programming blocks to support PIDF (proportional, integral, derivative and feed forward) motor control. Added formatting options (under Telemetry and Miscellaneous categories) so user can set how many decimal places to display a numerical value. Support to play audio files (which are uploaded through Blocks web interface) on Driver Station in addition to the Robot Controller. Fixed bug with Download Image of Blocks feature. Support for REV Robotics Blinkin LED Controller. Support for REV Robotics 2m Distance Sensor. Added support for a REV Touch Sensor (no longer have to configure as a generic digital device). Added blocks for DcMotorEx methods. These are enhanced methods that you can use when supported by the motor controller hardware. The REV Robotics Expansion Hub supports these enhanced methods. Enhanced methods include methods to get/set motor velocity (in encoder pulses per second), get/set PIDF coefficients, etc.. Modest Improvements in Logging Decrease frequency of battery checker voltage statements. Removed non-FTC related log statements (wherever possible). Introduced a "Match Logging" feature. Under "Settings" a user can enable/disable this feature (it's disabled by default). If enabled, user provides a "Match Number" through the Driver Station user interface (top of the screen). The Match Number is used to create a log file specifically with log statements from that particular Op Mode run. Match log files are stored in /sdcard/FIRST/matlogs on the Robot Controller. Once an op mode run is complete, the Match Number is cleared. This is a convenient way to create a separate match log with statements only related to a specific op mode run. New Devices Support for REV Robotics Blinkin LED Controller. Support for REV Robotics 2m Distance Sensor. Added configuration option for REV 20:1 HD Hex Motor. Added support for a REV Touch Sensor (no longer have to configure as a generic digital device). Miscellaneous Fixed some errors in the definitions for acceleration and velocity in our javadoc documentation. Added ability to play audio files on Driver Station When user is configuring an Expansion Hub, the LED on the Expansion Hub will change blink pattern (purple-cyan) to indicate which Hub is currently being configured. Renamed I2cSensorType to I2cDeviceType. Added an external sample Op Mode that demonstrates localization using 2018-2019 (Rover Ruckus presented by QualComm) Vuforia targets. Added an external sample Op Mode that demonstrates how to use the REV Robotics 2m Laser Distance Sensor. Added an external sample Op Mode that demonstrates how to use the REV Robotics Blinkin LED Controller. Re-categorized external Java sample Op Modes to "TeleOp" instead of "Autonomous". Known issues: Initial support for UVC compatible cameras UVC cameras seem to draw significant amount of current from the USB bus. This does not appear to present any problems for the REV Robotics Control Hub. This does seem to create stability problems when using some cameras with an Android phone-based Robot Controller. FTC Tech Team is investigating options to mitigate this issue with the phone-based Robot Controllers. There might be a possible deadlock which causes the RC to become unresponsive when using a UVC webcam with a Nougat Android Robot Controller. Wireless When user selects a wireless channel, this channel does not necessarily persist if the phone is power cycled. Tech Team is hoping to eventually address this issue in a future release. Issue has been present since apps were introduced (i.e., it is not new with the v4.0 release). Wireless channel is not currently displayed for WiFi Direct connections. Miscellaneous The blink indication feature that shows which Expansion Hub is currently being configured does not work for a newly created configuration file. User has to first save a newly created configuration file and then close and re-edit the file in order for blink indicator to work. Version 3.6 (built on 17.12.18) Changes include: Blocks Changes Uses updated Google Blockly software to allow users to edit their op modes on Apple iOS devices (including iPad and iPhone). Improvement in Blocks tool to handle corrupt op mode files. Autonomous op modes should no longer get switched back to tele-op after re-opening them to be edited. The system can now detect type mismatches during runtime and alert the user with a message on the Driver Station. Updated javadoc documentation for setPower() method to reflect correct range of values (-1 to +1). Modified VuforiaLocalizerImpl to allow for user rendering of frames Added a user-overrideable onRenderFrame() method which gets called by the class's renderFrame() method. Version 3.5 (built on 17.10.30) Changes with version 3.5 include: Introduced a fix to prevent random op mode stops, which can occur after the Robot Controller app has been paused and then resumed (for example, when a user temporarily turns off the display of the Robot Controller phone, and then turns the screen back on). Introduced a fix to prevent random op mode stops, which were previously caused by random peer disconnect events on the Driver Station. Fixes issue where log files would be closed on pause of the RC or DS, but not re-opened upon resume. Fixes issue with battery handler (voltage) start/stop race. Fixes issue where Android Studio generated op modes would disappear from available list in certain situations. Fixes problem where OnBot Java would not build on REV Robotics Control Hub. Fixes problem where OnBot Java would not build if the date and time on the Robot Controller device was "rewound" (set to an earlier date/time). Improved error message on OnBot Java that occurs when renaming a file fails. Removed unneeded resources from android.jar binaries used by OnBot Java to reduce final size of Robot Controller app. Added MR_ANALOG_TOUCH_SENSOR block to Blocks Programming Tool. Version 3.4 (built on 17.09.06) Changes with version 3.4 include: Added telemetry.update() statement for BlankLinearOpMode template. Renamed sample Block op modes to be more consistent with Java samples. Added some additional sample Block op modes. Reworded OnBot Java readme slightly. Version 3.3 (built on 17.09.04) This version of the software includes improves for the FTC Blocks Programming Tool and the OnBot Java Programming Tool. Changes with verion 3.3 include: Android Studio ftc_app project has been updated to use Gradle Plugin 2.3.3. Android Studio ftc_app project is already using gradle 3.5 distribution. Robot Controller log has been renamed to /sdcard/RobotControllerLog.txt (note that this change was actually introduced w/ v3.2). Improvements in I2C reliability. Optimized I2C read for REV Expansion Hub, with v1.7 firmware or greater. Updated all external/samples (available through OnBot and in Android project folder). Vuforia Added support for VuMarks that will be used for the 2017-2018 season game. Blocks Update to latest Google Blockly release. Sample op modes can be selected as a template when creating new op mode. Fixed bug where the blocks would disappear temporarily when mouse button is held down. Added blocks for Range.clip and Range.scale. User can now disable/enable Block op modes. Fix to prevent occasional Blocks deadlock. OnBot Java Significant improvements with autocomplete function for OnBot Java editor. Sample op modes can be selected as a template when creating new op mode. Fixes and changes to complete hardware setup feature. Updated (and more useful) onBot welcome message. Known issues: Android Studio After updating to the new v3.3 Android Studio project folder, if you get error messages indicating "InvalidVirtualFileAccessException" then you might need to do a File->Invalidate Caches / Restart to clear the error. OnBot Java Sometimes when you push the build button to build all op modes, the RC returns an error message that the build failed. If you press the build button a second time, the build typically suceeds. Version 3.2 (built on 17.08.02) This version of the software introduces the "OnBot Java" Development Tool. Similar to the FTC Blocks Development Tool, the FTC OnBot Java Development Tool allows a user to create, edit and build op modes dynamically using only a Javascript-enabled web browser. The OnBot Java Development Tool is an integrated development environment (IDE) that is served up by the Robot Controller. Op modes are created and edited using a Javascript-enabled browser (Google Chromse is recommended). Op modes are saved on the Robot Controller Android device directly. The OnBot Java Development Tool provides a Java programming environment that does NOT need Android Studio. Changes with version 3.2 include: Enhanced web-based development tools Introduction of OnBot Java Development Tool. Web-based programming and management features are "always on" (user no longer needs to put Robot Controller into programming mode). Web-based management interface (where user can change Robot Controller name and also easily download Robot Controller log file). OnBot Java, Blocks and Management features available from web based interface. Blocks Programming Development Tool: Changed "LynxI2cColorRangeSensor" block to "REV Color/range sensor" block. Fixed tooltip for ColorSensor.isLightOn block. Added blocks for ColorSensor.getNormalizedColors and LynxI2cColorRangeSensor.getNormalizedColors. Added example op modes for digital touch sensor and REV Robotics Color Distance sensor. User selectable color themes. Includes many minor enhancements and fixes (too numerous to list). Known issues: Auto complete function is incomplete and does not support the following (for now): Access via this keyword Access via super keyword Members of the super cloass, not overridden by the class Any methods provided in the current class Inner classes Can't handle casted objects Any objects coming from an parenthetically enclosed expression Version 3.10 (built on 17.05.09) This version of the software provides support for the REV Robotics Expansion Hub. This version also includes improvements in the USB communication layer in an effort to enhance system resiliency. If you were using a 2.x version of the software previously, updating to version 3.1 requires that you also update your Driver Station software in addition to updating the Robot Controller software. Also note that in version 3.10 software, the setMaxSpeed and getMaxSpeed methods are no longer available (not deprecated, they have been removed from the SDK). Also note that the the new 3.x software incorporates motor profiles that a user can select as he/she configures the robot. Changes include: Blocks changes Added VuforiaTrackableDefaultListener.getPose and Vuforia.trackPose blocks. Added optimized blocks support for Vuforia extended tracking. Added atan2 block to the math category. Added useCompetitionFieldTargetLocations parameter to Vuforia.initialize block. If set to false, the target locations are placed at (0,0,0) with target orientation as specified in https://github.com/gearsincorg/FTCVuforiaDemo/blob/master/Robot_Navigation.java tutorial op mode. Incorporates additional improvements to USB comm layer to improve system resiliency (to recover from a greater number of communication disruptions). Additional Notes Regarding Version 3.00 (built on 17.04.13) In addition to the release changes listed below (see section labeled "Version 3.00 (built on 17.04.013)"), version 3.00 has the following important changes: Version 3.00 software uses a new version of the FTC Robocol (robot protocol). If you upgrade to v3.0 on the Robot Controller and/or Android Studio side, you must also upgrade the Driver Station software to match the new Robocol. Version 3.00 software removes the setMaxSpeed and getMaxSpeed methods from the DcMotor class. If you have an op mode that formerly used these methods, you will need to remove the references/calls to these methods. Instead, v3.0 provides the max speed information through the use of motor profiles that are selected by the user during robot configuration. Version 3.00 software currently does not have a mechanism to disable extra i2c sensors. We hope to re-introduce this function with a release in the near future. Version 3.00 (built on 17.04.13) *** Use this version of the software at YOUR OWN RISK!!! *** This software is being released as an "alpha" version. Use this version at your own risk! This pre-release software contains SIGNIFICANT changes, including changes to the Wi-Fi Direct pairing mechanism, rewrites of the I2C sensor classes, changes to the USB/FTDI layer, and the introduction of support for the REV Robotics Expansion Hub and the REV Robotics color-range-light sensor. These changes were implemented to improve the reliability and resiliency of the FTC control system. Please note, however, that version 3.00 is considered "alpha" code. This code is being released so that the FIRST community will have an opportunity to test the new REV Expansion Hub electronics module when it becomes available in May. The developers do not recommend using this code for critical applications (i.e., competition use). *** Use this version of the software at YOUR OWN RISK!!! *** Changes include: Major rework of sensor-related infrastructure. Includes rewriting sensor classes to implement synchronous I2C communication. Fix to reset Autonomous timer back to 30 seconds. Implementation of specific motor profiles for approved 12V motors (includes Tetrix, AndyMark, Matrix and REV models). Modest improvements to enhance Wi-Fi P2P pairing. Fixes telemetry log addition race. Publishes all the sources (not just a select few). Includes Block programming improvements Addition of optimized Vuforia blocks. Auto scrollbar to projects and sounds pages. Fixed blocks paste bug. Blocks execute after while-opModeIsActive loop (to allow for cleanup before exiting op mode). Added gyro integratedZValue block. Fixes bug with projects page for Firefox browser. Added IsSpeaking block to AndroidTextToSpeech. Implements support for the REV Robotics Expansion Hub Implements support for integral REV IMU (physically installed on I2C bus 0, uses same Bosch BNO055 9 axis absolute orientation sensor as Adafruit 9DOF abs orientation sensor). - Implements support for REV color/range/light sensor. Provides support to update Expansion Hub firmware through FTC SDK. Detects REV firmware version and records in log file. Includes support for REV Control Hub (note that the REV Control Hub is not yet approved for FTC use). Implements FTC Blocks programming support for REV Expansion Hub and sensor hardware. Detects and alerts when I2C device disconnect. Version 2.62 (built on 17.01.07) Added null pointer check before calling modeToByte() in finishModeSwitchIfNecessary method for ModernRoboticsUsbDcMotorController class. Changes to enhance Modern Robotics USB protocol robustness. Version 2.61 (released on 16.12.19) Blocks Programming mode changes: Fix to correct issue when an exception was thrown because an OpticalDistanceSensor object appears twice in the hardware map (the second time as a LightSensor). Version 2.6 (released on 16.12.16) Fixes for Gyro class: Improve (decrease) sensor refresh latency. fix isCalibrating issues. Blocks Programming mode changes: Blocks now ignores a device in the configuration xml if the name is empty. Other devices work in configuration work fine. Version 2.5 (internal release on released on 16.12.13) Blocks Programming mode changes: Added blocks support for AdafruitBNO055IMU. Added Download Op Mode button to FtcBocks.html. Added support for copying blocks in one OpMode and pasting them in an other OpMode. The clipboard content is stored on the phone, so the programming mode server must be running. Modified Utilities section of the toolbox. In Programming Mode, display information about the active connections. Fixed paste location when workspace has been scrolled. Added blocks support for the android Accelerometer. Fixed issue where Blocks Upload Op Mode truncated name at first dot. Added blocks support for Android SoundPool. Added type safety to blocks for Acceleration. Added type safety to blocks for AdafruitBNO055IMU.Parameters. Added type safety to blocks for AnalogInput. Added type safety to blocks for AngularVelocity. Added type safety to blocks for Color. Added type safety to blocks for ColorSensor. Added type safety to blocks for CompassSensor. Added type safety to blocks for CRServo. Added type safety to blocks for DigitalChannel. Added type safety to blocks for ElapsedTime. Added type safety to blocks for Gamepad. Added type safety to blocks for GyroSensor. Added type safety to blocks for IrSeekerSensor. Added type safety to blocks for LED. Added type safety to blocks for LightSensor. Added type safety to blocks for LinearOpMode. Added type safety to blocks for MagneticFlux. Added type safety to blocks for MatrixF. Added type safety to blocks for MrI2cCompassSensor. Added type safety to blocks for MrI2cRangeSensor. Added type safety to blocks for OpticalDistanceSensor. Added type safety to blocks for Orientation. Added type safety to blocks for Position. Added type safety to blocks for Quaternion. Added type safety to blocks for Servo. Added type safety to blocks for ServoController. Added type safety to blocks for Telemetry. Added type safety to blocks for Temperature. Added type safety to blocks for TouchSensor. Added type safety to blocks for UltrasonicSensor. Added type safety to blocks for VectorF. Added type safety to blocks for Velocity. Added type safety to blocks for VoltageSensor. Added type safety to blocks for VuforiaLocalizer.Parameters. Added type safety to blocks for VuforiaTrackable. Added type safety to blocks for VuforiaTrackables. Added type safety to blocks for enums in AdafruitBNO055IMU.Parameters. Added type safety to blocks for AndroidAccelerometer, AndroidGyroscope, AndroidOrientation, and AndroidTextToSpeech. Version 2.4 (released on 16.11.13) Fix to avoid crashing for nonexistent resources. Blocks Programming mode changes: Added blocks to support OpenGLMatrix, MatrixF, and VectorF. Added blocks to support AngleUnit, AxesOrder, AxesReference, CameraDirection, CameraMonitorFeedback, DistanceUnit, and TempUnit. Added blocks to support Acceleration. Added blocks to support LinearOpMode.getRuntime. Added blocks to support MagneticFlux and Position. Fixed typos. Made blocks for ElapsedTime more consistent with other objects. Added blocks to support Quaternion, Velocity, Orientation, AngularVelocity. Added blocks to support VuforiaTrackables, VuforiaTrackable, VuforiaLocalizer, VuforiaTrackableDefaultListener. Fixed a few blocks. Added type checking to new blocks. Updated to latest blockly. Added default variable blocks to navigation and matrix blocks. Fixed toolbox entry for openGLMatrix_rotation_withAxesArgs. When user downloads Blocks-generated op mode, only the .blk file is downloaded. When user uploads Blocks-generated op mode (.blk file), Javascript code is auto generated. Added DbgLog support. Added logging when a blocks file is read/written. Fixed bug to properly render blocks even if missing devices from configuration file. Added support for additional characters (not just alphanumeric) for the block file names (for download and upload). Added support for OpMode flavor (“Autonomous” or “TeleOp”) and group. Changes to Samples to prevent tutorial issues. Incorporated suggested changes from public pull 216 (“Replace .. paths”). Remove Servo Glitches when robot stopped. if user hits “Cancels” when editing a configuration file, clears the unsaved changes and reverts to original unmodified configuration. Added log info to help diagnose why the Robot Controller app was terminated (for example, by watch dog function). Added ability to transfer log from the controller. Fixed inconsistency for AngularVelocity Limit unbounded growth of data for telemetry. If user does not call telemetry.update() for LinearOpMode in a timely manner, data added for telemetry might get lost if size limit is exceeded. Version 2.35 (released on 16.10.06) Blockly programming mode - Removed unnecesary idle() call from blocks for new project. Version 2.30 (released on 16.10.05) Blockly programming mode: Mechanism added to save Blockly op modes from Programming Mode Server onto local device To avoid clutter, blocks are displayed in categorized folders Added support for DigitalChannel Added support for ModernRoboticsI2cCompassSensor Added support for ModernRoboticsI2cRangeSensor Added support for VoltageSensor Added support for AnalogInput Added support for AnalogOutput Fix for CompassSensor setMode block Vuforia Fix deadlock / make camera data available while Vuforia is running. Update to Vuforia 6.0.117 (recommended by Vuforia and Google to close security loophole). Fix for autonomous 30 second timer bug (where timer was in effect, even though it appeared to have timed out). opModeIsActive changes to allow cleanup after op mode is stopped (with enforced 2 second safety timeout). Fix to avoid reading i2c twice. Updated sample Op Modes. Improved logging and fixed intermittent freezing. Added digital I/O sample. Cleaned up device names in sample op modes to be consistent with Pushbot guide. Fix to allow use of IrSeekerSensorV3. Version 2.20 (released on 16.09.08) Support for Modern Robotics Compass Sensor. Support for Modern Robotics Range Sensor. Revise device names for Pushbot templates to match the names used in Pushbot guide. Fixed bug so that IrSeekerSensorV3 device is accessible as IrSeekerSensor in hardwareMap. Modified computer vision code to require an individual Vuforia license (per legal requirement from PTC). Minor fixes. Blockly enhancements: Support for Voltage Sensor. Support for Analog Input. Support for Analog Output. Support for Light Sensor. Support for Servo Controller. Version 2.10 (released on 16.09.03) Support for Adafruit IMU. Improvements to ModernRoboticsI2cGyro class Block on reset of z axis. isCalibrating() returns true while gyro is calibration. Updated sample gyro program. Blockly enhancements support for android.graphics.Color. added support for ElapsedTime. improved look and legibility of blocks. support for compass sensor. support for ultrasonic sensor. support for IrSeeker. support for LED. support for color sensor. support for CRServo prompt user to configure robot before using programming mode. Provides ability to disable audio cues. various bug fixes and improvements. Version 2.00 (released on 16.08.19) This is the new release for the upcoming 2016-2017 FIRST Tech Challenge Season. Channel change is enabled in the FTC Robot Controller app for Moto G 2nd and 3rd Gen phones. Users can now use annotations to register/disable their Op Modes. Changes in the Android SDK, JDK and build tool requirements (minsdk=19, java 1.7, build tools 23.0.3). Standardized units in analog input. Cleaned up code for existing analog sensor classes. setChannelMode and getChannelMode were REMOVED from the DcMotorController class. This is important - we no longer set the motor modes through the motor controller. setMode and getMode were added to the DcMotor class. ContinuousRotationServo class has been added to the FTC SDK. Range.clip() method has been overloaded so it can support this operation for int, short and byte integers. Some changes have been made (new methods added) on how a user can access items from the hardware map. Users can now set the zero power behavior for a DC motor so that the motor will brake or float when power is zero. Prototype Blockly Programming Mode has been added to FTC Robot Controller. Users can place the Robot Controller into this mode, and then use a device (such as a laptop) that has a Javascript enabled browser to write Blockly-based Op Modes directly onto the Robot Controller. Users can now configure the robot remotely through the FTC Driver Station app. Android Studio project supports Android Studio 2.1.x and compile SDK Version 23 (Marshmallow). Vuforia Computer Vision SDK integrated into FTC SDK. Users can use sample vision targets to get localization information on a standard FTC field. Project structure has been reorganized so that there is now a TeamCode package that users can use to place their local/custom Op Modes into this package. Inspection function has been integrated into the FTC Robot Controller and Driver Station Apps (Thanks Team HazMat… 9277 & 10650!). Audio cues have been incorporated into FTC SDK. Swap mechanism added to FTC Robot Controller configuration activity. For example, if you have two motor controllers on a robot, and you misidentified them in your configuration file, you can use the Swap button to swap the devices within the configuration file (so you do not have to manually re-enter in the configuration info for the two devices). Fix mechanism added to all user to replace an electronic module easily. For example, suppose a servo controller dies on your robot. You replace the broken module with a new module, which has a different serial number from the original servo controller. You can use the Fix button to automatically reconfigure your configuration file to use the serial number of the new module. Improvements made to fix resiliency and responsiveness of the system. For LinearOpMode the user now must for a telemetry.update() to update the telemetry data on the driver station. This update() mechanism ensures that the driver station gets the updated data properly and at the same time. The Auto Configure function of the Robot Controller is now template based. If there is a commonly used robot configuration, a template can be created so that the Auto Configure mechanism can be used to quickly configure a robot of this type. The logic to detect a runaway op mode (both in the LinearOpMode and OpMode types) and to abort the run, then auto recover has been improved/implemented. Fix has been incorporated so that Logitech F310 gamepad mappings will be correct for Marshmallow users. Release 16.07.08 For the ftc_app project, the gradle files have been modified to support Android Studio 2.1.x. Release 16.03.30 For the MIT App Inventor, the design blocks have new icons that better represent the function of each design component. Some changes were made to the shutdown logic to ensure the robust shutdown of some of our USB services. A change was made to LinearOpMode so as to allow a given instance to be executed more than once, which is required for the App Inventor. Javadoc improved/updated. Release 16.03.09 Changes made to make the FTC SDK synchronous (significant change!) waitOneFullHardwareCycle() and waitForNextHardwareCycle() are no longer needed and have been deprecated. runOpMode() (for a LinearOpMode) is now decoupled from the system's hardware read/write thread. loop() (for an OpMode) is now decoupled from the system's hardware read/write thread. Methods are synchronous. For example, if you call setMode(DcMotorController.RunMode.RESET_ENCODERS) for a motor, the encoder is guaranteed to be reset when the method call is complete. For legacy module (NXT compatible), user no longer has to toggle between read and write modes when reading from or writing to a legacy device. Changes made to enhance reliability/robustness during ESD event. Changes made to make code thread safe. Debug keystore added so that user-generated robot controller APKs will all use the same signed key (to avoid conflicts if a team has multiple developer laptops for example). Firmware version information for Modern Robotics modules are now logged. Changes made to improve USB comm reliability and robustness. Added support for voltage indicator for legacy (NXT-compatible) motor controllers. Changes made to provide auto stop capabilities for op modes. A LinearOpMode class will stop when the statements in runOpMode() are complete. User does not have to push the stop button on the driver station. If an op mode is stopped by the driver station, but there is a run away/uninterruptible thread persisting, the app will log an error message then force itself to crash to stop the runaway thread. Driver Station UI modified to display lowest measured voltage below current voltage (12V battery). Driver Station UI modified to have color background for current voltage (green=good, yellow=caution, red=danger, extremely low voltage). javadoc improved (edits and additional classes). Added app build time to About activity for driver station and robot controller apps. Display local IP addresses on Driver Station About activity. Added I2cDeviceSynchImpl. Added I2cDeviceSync interface. Added seconds() and milliseconds() to ElapsedTime for clarity. Added getCallbackCount() to I2cDevice. Added missing clearI2cPortActionFlag. Added code to create log messages while waiting for LinearOpMode shutdown. Fix so Wifi Direct Config activity will no longer launch multiple times. Added the ability to specify an alternate i2c address in software for the Modern Robotics gyro. Release 16.02.09 Improved battery checker feature so that voltage values get refreshed regularly (every 250 msec) on Driver Station (DS) user interface. Improved software so that Robot Controller (RC) is much more resilient and “self-healing” to USB disconnects: If user attempts to start/restart RC with one or more module missing, it will display a warning but still start up. When running an op mode, if one or more modules gets disconnected, the RC & DS will display warnings,and robot will keep on working in spite of the missing module(s). If a disconnected module gets physically reconnected the RC will auto detect the module and the user will regain control of the recently connected module. Warning messages are more helpful (identifies the type of module that’s missing plus its USB serial number). Code changes to fix the null gamepad reference when users try to reference the gamepads in the init() portion of their op mode. NXT light sensor output is now properly scaled. Note that teams might have to readjust their light threshold values in their op modes. On DS user interface, gamepad icon for a driver will disappear if the matching gamepad is disconnected or if that gamepad gets designated as a different driver. Robot Protocol (ROBOCOL) version number info is displayed in About screen on RC and DS apps. Incorporated a display filter on pairing screen to filter out devices that don’t use the “-“ format. This filter can be turned off to show all WiFi Direct devices. Updated text in License file. Fixed formatting error in OpticalDistanceSensor.toString(). Fixed issue on with a blank (“”) device name that would disrupt WiFi Direct Pairing. Made a change so that the WiFi info and battery info can be displayed more quickly on the DS upon connecting to RC. Improved javadoc generation. Modified code to make it easier to support language localization in the future. Release 16.01.04 Updated compileSdkVersion for apps Prevent Wifi from entering power saving mode removed unused import from driver station Corrrected "Dead zone" joystick code. LED.getDeviceName and .getConnectionInfo() return null apps check for ROBOCOL_VERSION mismatch Fix for Telemetry also has off-by-one errors in its data string sizing / short size limitations error User telemetry output is sorted. added formatting variants to DbgLog and RobotLog APIs code modified to allow for a long list of op mode names. changes to improve thread safety of RobocolDatagramSocket Fix for "missing hardware leaves robot controller disconnected from driver station" error fix for "fast tapping of Init/Start causes problems" (toast is now only instantiated on UI thread). added some log statements for thread life cycle. moved gamepad reset logic inside of initActiveOpMode() for robustness changes made to mitigate risk of race conditions on public methods. changes to try and flag when WiFi Direct name contains non-printable characters. fix to correct race condition between .run() and .close() in ReadWriteRunnableStandard. updated FTDI driver made ReadWriteRunnableStanard interface public. fixed off-by-one errors in Command constructor moved specific hardware implmentations into their own package. moved specific gamepad implemnatations to the hardware library. changed LICENSE file to new BSD version. fixed race condition when shutting down Modern Robotics USB devices. methods in the ColorSensor classes have been synchronized. corrected isBusy() status to reflect end of motion. corrected "back" button keycode. the notSupported() method of the GyroSensor class was changed to protected (it should not be public). Release 15.11.04.001 Added Support for Modern Robotics Gyro. The GyroSensor class now supports the MR Gyro Sensor. Users can access heading data (about Z axis) Users can also access raw gyro data (X, Y, & Z axes). Example MRGyroTest.java op mode included. Improved error messages More descriptive error messages for exceptions in user code. Updated DcMotor API Enable read mode on new address in setI2cAddress Fix so that driver station app resets the gamepads when switching op modes. USB-related code changes to make USB comm more responsive and to display more explicit error messages. Fix so that USB will recover properly if the USB bus returns garbage data. Fix USB initializtion race condition. Better error reporting during FTDI open. More explicit messages during USB failures. Fixed bug so that USB device is closed if event loop teardown method was not called. Fixed timer UI issue Fixed duplicate name UI bug (Legacy Module configuration). Fixed race condition in EventLoopManager. Fix to keep references stable when updating gamepad. For legacy Matrix motor/servo controllers removed necessity of appending "Motor" and "Servo" to controller names. Updated HT color sensor driver to use constants from ModernRoboticsUsbLegacyModule class. Updated MR color sensor driver to use constants from ModernRoboticsUsbDeviceInterfaceModule class. Correctly handle I2C Address change in all color sensors Updated/cleaned up op modes. Updated comments in LinearI2cAddressChange.java example op mode. Replaced the calls to "setChannelMode" with "setMode" (to match the new of the DcMotor method). Removed K9AutoTime.java op mode. Added MRGyroTest.java op mode (demonstrates how to use MR Gyro Sensor). Added MRRGBExample.java op mode (demonstrates how to use MR Color Sensor). Added HTRGBExample.java op mode (demonstrates how to use HT legacy color sensor). Added MatrixControllerDemo.java (demonstrates how to use legacy Matrix controller). Updated javadoc documentation. Updated release .apk files for Robot Controller and Driver Station apps. Release 15.10.06.002 Added support for Legacy Matrix 9.6V motor/servo controller. Cleaned up build.gradle file. Minor UI and bug fixes for driver station and robot controller apps. Throws error if Ultrasonic sensor (NXT) is not configured for legacy module port 4 or 5. Release 15.08.03.001 New user interfaces for FTC Driver Station and FTC Robot Controller apps. An init() method is added to the OpMode class. For this release, init() is triggered right before the start() method. Eventually, the init() method will be triggered when the user presses an "INIT" button on driver station. The init() and loop() methods are now required (i.e., need to be overridden in the user's op mode). The start() and stop() methods are optional. A new LinearOpMode class is introduced. Teams can use the LinearOpMode mode to create a linear (not event driven) program model. Teams can use blocking statements like Thread.sleep() within a linear op mode. The API for the Legacy Module and Core Device Interface Module have been updated. Support for encoders with the Legacy Module is now working. The hardware loop has been updated for better performance.

YiShape-Math is a Java math library that provides NumPy-like functionalities including vector & matrix operations, data visualization, statistics, optimization, time series, signal processing, audio analysis, image processing and machine learning models.

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

Exploring the scalable matrix extension of the Apple M4 processor

Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

18.065/18.0651: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

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lecture notes of "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning"

Digital signal analysis library for python. The library includes such methods of the signal analysis, signal processing and signal parameter estimation as ARMA-based techniques; subspace-based techniques; matrix-pencil-based methods; singular-spectrum analysis (SSA); dynamic-mode decomposition (DMD); empirical mode decomposition; variational mode decomposition (EMD); empirical wavelet transform (EWT); Hilbert vibration decomposition (HVD) and many others.

adding LED splash effect with the provide QMK firmware, by editing matrix_init_user(), matrix_scan_user(), process_record_user(), led_instructions[]

SparseP is the first open-source Sparse Matrix Vector Multiplication (SpMV) software package for real-world Processing-In-Memory (PIM) architectures. SparseP is developed to evaluate and characterize the first publicly-available real-world PIM architecture, the UPMEM PIM architecture. Described by C. Giannoula et al. [https://arxiv.org/abs/2201.05072]

Auto-deployment process for matrix-org/synapse

A Snakemake workflow and MrBiomics module for processing and visualizing (multimodal) sc/snRNA-seq data generated with 10X Genomics Kits or in the MTX matrix file format powered by the R package Seurat.

# XFEM_Fracture2D ### Description This is a Matlab program that can be used to solve fracture problems involving arbitrary multiple crack propagations in a 2D linear-elastic solid based on the principle of minimum potential energy. The extended finite element method is used to discretise the solid continuum considering cracks as discontinuities in the displacement field. To this end, a strong discontinuity enrichment and a square-root singular crack tip enrichment are used to describe each crack. Several crack growth criteria are available to determine the evolution of cracks over time; apart from the classic maximum tension (or hoop-stress) criterion, the minimum total energy criterion and the local symmetry criterion are implemented implicitly with respect to the discrete time-stepping. ### Key features * *Fast:* The stiffness matrix and the force vector (i.e. the equations' system) and the enrichment tracking data structures are updated at each time step only with respect to the changes in the fracture topology. This ultimately results in the major part of the computational expense in the solution to the linear system of equations rather than in the post-processing of the solution or in the assembly and updating of the equations. As Matlab offers fast and robust direct solvers, the computational times are reasonably fast. * *Robust.* Suitable for multiple crack propagations with intersections. Furthermore, the stress intensity factors are computed robustly via the interaction integral approach (with the inclusion of the terms to account for crack surface pressure, residual stresses or strains). The minimum total energy criterion and the principle of local symmetry are implemented implicitly in time. The energy release rates are computed based on the stiffness derivative approach using algebraic differentiation (rather than finite differencing of the potential energy). On the other hand, the crack growth direction based on the local symmetry criterion is determined such that the local mode-II stress intensity factor vanishes; the change in a crack tip kink angle is approximated using the ratio of the crack tip stress intensity factors. * *Easy to run.* Each job has its own input files which are independent form those of all other jobs. The code especially lends itself to running parametric studies. Various results can be saved relating to the fracture geometry, fracture mechanics parameters, and the elastic fields in the solid domain. Extensive visualisation library is available for plotting results. ### Instructions 1. Get started by running the demo to showcase some of the capabilities of the program and to determine if it can be useful for you. At the Matlab's command line enter: ```Matlab >> RUN_JOBS.m ``` This will execute a series of jobs located inside the *jobs directory* `./JOBS_LIBRARY/`. These jobs do not take very long to execute (around 5 minutes in total). 2. Subsequently, you can pick one of the jobs inside `./JOBS_LIBRARY/` by defining the job title: ```Matlab >> job_title = 'several_cracks/edge/vertical_tension' ``` 3. Then you can open all the relevant scripts for this job as follows: ```Matlab >> open_job ``` The following input scripts for the *job* will be open in the Matlab's editor: 1. `JOB_MAIN.m`: This is the job's main script. It is called when executing `RUN_JOB` (or `RUN_JOBS`) and acts like a wrapper. Notably, it can serve as a convenient interface to run parametric studies and to save intermediate simulation results. 2. `Input_Scope.m`: This defines the scope of the simulation. From which crack growth criteria to use, to what to compute and what results to show via plots and/or movies. To put it simply, the script is a bunch of "switches" that tell the program what the user wants to be done. 3. `Input_Material.m`: Defines the material's elastic properties in different regions or layers (called "phases") of the computational domain. Moreover, it defines the fracture toughness of the material (assumed to be constant in all material phases). 4. `Input_Crack.m`: Defines the initial crack geometry. 5. `Input_BC.m`: Defines boundary conditions, such as displacements, tractions, crack surface pressure (assumed to be constant in all cracks), body loads (e.g. gravity, pre-stress or pre-strain). 6. `Mesh_make.m`: In-house structured mesh generator for rectangular domains using either linear triangle or bilinear quadrilateral elements. It is possible to mesh horizontal layers using different mesh sizes. 7. `Mesh_read.m`: Gmsh based mesh reader for version-1 mesh files. Of course you can use your own mesh reader provided the output variables are of the correct format (see later). 8. `Mesh_file.m`: Specifies the mesh input file (.msh). At the moment, only Gmsh mesh files of version-1 are allowed. ### Mesh_file.m A mesh file needs to be able to output the following data or variables: * `mNdCrd`: Node coordinates, size = `[nNdStd, 2]` * `mLNodS`: Element connectivities, size = `[nElemn,nLNodS]` * `vElPhz`: Element material phase (or region) ID's, size = `[nElemn,1]` * `cBCNod`: cell of boundary nodes, cell size = `{nBound,1}`, cell element size = `[nBnNod,2]` Example mesh files are located in `./JOBS_LIBRARY/`. Gmsh version-1 file format is described [here](http://www.manpagez.com/info/gmsh/gmsh-2.4.0/gmsh_60.php). ### Additional notes * global variables are defined in `.\Routines_AuxInput\Declare_Global.m` * External libraries are `.\Other_Libs\distmesh` and `.\Other_Libs\mesh2d` ### References Two external meshing libraries are used for the local mesh refinement and remeshing at the crack tip during crack propagation or prior to a crack intersection with another crack or with a boundary of the domain. Specifically, these libraries, which are located in `.\Other_Libs\`, are the following: * [*mesh2d*](https://people.sc.fsu.edu/~jburkardt/m_src/mesh2d/mesh2d.html) by Darren Engwirda * [*distmesh*](http://persson.berkeley.edu/distmesh/) by Per-Olof Persson and Gilbert Strang. ### Issues and Support For support or questions please email [sutula.danas@gmail.com](mailto:sutula.danas@gmail.com). ### Authors Danas Sutula, University of Luxembourg, Luxembourg. If you find this code useful, we kindly ask that you consider citing us. * [Minimum energy multiple crack propagation](http://hdl.handle.net/10993/29414)

The objective of this project is to scrape a corpus of news articles from a set of web pages, pre-process the corpus, and then to apply unsupervised clustering algorithms to explore and summarise the contents of the corpus. Part 1. Text Data Scraping This part of the project should be implemented as a Python script 1. Identify the URLs for all news articles listed on the website: http://mlg.ucd.ie/modules/COMP41680/news/index.html 2. Retrieve all web pages corresponding to these article URLs. 3. From the web pages, extract the main body text containing the content of each news article. Save the body of each article as plain text. Part 2. Corpus Exploration Tasks to be completed in your IPython notebook: 1. Load the text corpus generated in Part 1. Apply any appropriate pre-processing steps and construct a document-term matrix representation of the corpus. 2. Summarise the overall corpus by identifying the most characteristic terms and phrases in the corpus. 3. Apply two alternative clustering algorithms of your choice to the document-term matrix to produce clusters of related documents. This might require applying each algorithm several times with different parameter values. 4. For each clustering generated in Step 3, summarise the contents of the clusters. Based on your summary, suggest a topic/theme for each cluster.

This repo is a fork from main repo and will usually have new features bundled faster than main repo (and maybe bundle some bugs, too). # Unofficial Facebook Chat API <img alt="version" src="https://img.shields.io/github/package-json/v/ProCoderMew/fca-unofficial?label=github&style=flat-square"> Facebook now has an official API for chat bots [here](https://developers.facebook.com/docs/messenger-platform). This API is the only way to automate chat functionalities on a user account. We do this by emulating the browser. This means doing the exact same GET/POST requests and tricking Facebook into thinking we're accessing the website normally. Because we're doing it this way, this API won't work with an auth token but requires the credentials of a Facebook account. _Disclaimer_: We are not responsible if your account gets banned for spammy activities such as sending lots of messages to people you don't know, sending messages very quickly, sending spammy looking URLs, logging in and out very quickly... Be responsible Facebook citizens. See [below](#projects-using-this-api) for projects using this API. ## Install If you just want to use fca-unofficial, you should use this command: ```bash npm install procodermew/fca-unofficial ``` It will download `fca-unofficial` from NPM repositories ## Testing your bots If you want to test your bots without creating another account on Facebook, you can use [Facebook Whitehat Accounts](https://www.facebook.com/whitehat/accounts/). ## Example Usage ```javascript const login = require("fca-unofficial"); // Create simple echo bot login({email: "FB_EMAIL", password: "FB_PASSWORD"}, (err, api) => { if(err) return console.error(err); api.listen((err, message) => { api.sendMessage(message.body, message.threadID); }); }); ``` Result: <img width="517" alt="screen shot 2016-11-04 at 14 36 00" src="https://cloud.githubusercontent.com/assets/4534692/20023545/f8c24130-a29d-11e6-9ef7-47568bdbc1f2.png"> ## Documentation You can see it [here](DOCS.md). ## Main Functionality ### Sending a message #### api.sendMessage(message, threadID[, callback][, messageID]) Various types of message can be sent: * *Regular:* set field `body` to the desired message as a string. * *Sticker:* set a field `sticker` to the desired sticker ID. * *File or image:* Set field `attachment` to a readable stream or an array of readable streams. * *URL:* set a field `url` to the desired URL. * *Emoji:* set field `emoji` to the desired emoji as a string and set field `emojiSize` with size of the emoji (`small`, `medium`, `large`) Note that a message can only be a regular message (which can be empty) and optionally one of the following: a sticker, an attachment or a url. __Tip__: to find your own ID, you can look inside the cookies. The `userID` is under the name `c_user`. __Example (Basic Message)__ ```js const login = require("fca-unofficial"); login({email: "FB_EMAIL", password: "FB_PASSWORD"}, (err, api) => { if(err) return console.error(err); var yourID = "000000000000000"; var msg = "Hey!"; api.sendMessage(msg, yourID); }); ``` __Example (File upload)__ ```js const login = require("fca-unofficial"); login({email: "FB_EMAIL", password: "FB_PASSWORD"}, (err, api) => { if(err) return console.error(err); // Note this example uploads an image called image.jpg var yourID = "000000000000000"; var msg = { body: "Hey!", attachment: fs.createReadStream(__dirname + '/image.jpg') } api.sendMessage(msg, yourID); }); ``` ------------------------------------ ### Saving session. To avoid logging in every time you should save AppState (cookies etc.) to a file, then you can use it without having password in your scripts. __Example__ ```js const fs = require("fs"); const login = require("fca-unofficial"); var credentials = {email: "FB_EMAIL", password: "FB_PASSWORD"}; login(credentials, (err, api) => { if(err) return console.error(err); fs.writeFileSync('appstate.json', JSON.stringify(api.getAppState())); }); ``` Alternative: Use [c3c-fbstate](https://github.com/c3cbot/c3c-fbstate) to get fbstate.json (appstate.json) ------------------------------------ ### Listening to a chat #### api.listen(callback) Listen watches for messages sent in a chat. By default this won't receive events (joining/leaving a chat, title change etc…) but it can be activated with `api.setOptions({listenEvents: true})`. This will by default ignore messages sent by the current account, you can enable listening to your own messages with `api.setOptions({selfListen: true})`. __Example__ ```js const fs = require("fs"); const login = require("fca-unofficial"); // Simple echo bot. It will repeat everything that you say. // Will stop when you say '/stop' login({appState: JSON.parse(fs.readFileSync('appstate.json', 'utf8'))}, (err, api) => { if(err) return console.error(err); api.setOptions({listenEvents: true}); var stopListening = api.listenMqtt((err, event) => { if(err) return console.error(err); api.markAsRead(event.threadID, (err) => { if(err) console.error(err); }); switch(event.type) { case "message": if(event.body === '/stop') { api.sendMessage("Goodbye…", event.threadID); return stopListening(); } api.sendMessage("TEST BOT: " + event.body, event.threadID); break; case "event": console.log(event); break; } }); }); ``` ## FAQS 1. How do I run tests? > For tests, create a `test-config.json` file that resembles `example-config.json` and put it in the `test` directory. From the root >directory, run `npm test`. 2. Why doesn't `sendMessage` always work when I'm logged in as a page? > Pages can't start conversations with users directly; this is to prevent pages from spamming users. 3. What do I do when `login` doesn't work? > First check that you can login to Facebook using the website. If login approvals are enabled, you might be logging in incorrectly. For how to handle login approvals, read our docs on [`login`](DOCS.md#login). 4. How can I avoid logging in every time? Can I log into a previous session? > We support caching everything relevant for you to bypass login. `api.getAppState()` returns an object that you can save and pass into login as `{appState: mySavedAppState}` instead of the credentials object. If this fails, your session has expired. 5. Do you support sending messages as a page? > Yes, set the pageID option on login (this doesn't work if you set it using api.setOptions, it affects the login process). > ```js > login(credentials, {pageID: "000000000000000"}, (err, api) => { … } > ``` 6. I'm getting some crazy weird syntax error like `SyntaxError: Unexpected token [`!!! > Please try to update your version of node.js before submitting an issue of this nature. We like to use new language features. 7. I don't want all of these logging messages! > You can use `api.setOptions` to silence the logging. You get the `api` object from `login` (see example above). Do > ```js > api.setOptions({ > logLevel: "silent" > }); > ``` <a name="projects-using-this-api"></a> ## Projects using this API: - [c3c](https://github.com/lequanglam/c3c) - A bot that can be customizable using plugins. Support Facebook & Discord. - [Miraiv2](https://github.com/miraiPr0ject/miraiv2) - A simple Facebook Messenger Bot made by CatalizCS and SpermLord. ## Projects using this API (original repository, facebook-chat-api): - [Messer](https://github.com/mjkaufer/Messer) - Command-line messaging for Facebook Messenger - [messen](https://github.com/tomquirk/messen) - Rapidly build Facebook Messenger apps in Node.js - [Concierge](https://github.com/concierge/Concierge) - Concierge is a highly modular, easily extensible general purpose chat bot with a built in package manager - [Marc Zuckerbot](https://github.com/bsansouci/marc-zuckerbot) - Facebook chat bot - [Marc Thuckerbot](https://github.com/bsansouci/lisp-bot) - Programmable lisp bot - [MarkovsInequality](https://github.com/logicx24/MarkovsInequality) - Extensible chat bot adding useful functions to Facebook Messenger - [AllanBot](https://github.com/AllanWang/AllanBot-Public) - Extensive module that combines the facebook api with firebase to create numerous functions; no coding experience is required to implement this. - [Larry Pudding Dog Bot](https://github.com/Larry850806/facebook-chat-bot) - A facebook bot you can easily customize the response - [fbash](https://github.com/avikj/fbash) - Run commands on your computer's terminal over Facebook Messenger - [Klink](https://github.com/KeNt178/klink) - This Chrome extension will 1-click share the link of your active tab over Facebook Messenger - [Botyo](https://github.com/ivkos/botyo) - Modular bot designed for group chat rooms on Facebook - [matrix-puppet-facebook](https://github.com/matrix-hacks/matrix-puppet-facebook) - A facebook bridge for [matrix](https://matrix.org) - [facebot](https://github.com/Weetbix/facebot) - A facebook bridge for Slack. - [Botium](https://github.com/codeforequity-at/botium-core) - The Selenium for Chatbots - [Messenger-CLI](https://github.com/AstroCB/Messenger-CLI) - A command-line interface for sending and receiving messages through Facebook Messenger. - [AssumeZero-Bot](https://github.com/AstroCB/AssumeZero-Bot) – A highly customizable Facebook Messenger bot for group chats. - [Miscord](https://github.com/Bjornskjald/miscord) - An easy-to-use Facebook bridge for Discord. - [chat-bridge](https://github.com/rexx0520/chat-bridge) - A Messenger, Telegram and IRC chat bridge. - [messenger-auto-reply](https://gitlab.com/theSander/messenger-auto-reply) - An auto-reply service for Messenger. - [BotCore](https://github.com/AstroCB/BotCore) – A collection of tools for writing and managing Facebook Messenger bots. - [mnotify](https://github.com/AstroCB/mnotify) – A command-line utility for sending alerts and notifications through Facebook Messenger.

mtXcontrol is a LED Matrix editor written in Processing to create multiple frames sequences easily. By now, support for Novation Launchpad and the 8x8 RGB LED Rainbowduino Controller by Seeedstudio is given.

Matrix Trace Processor

Code accompanying the paper M. Sorel, F. Sroubek, "Fast convolutional sparse coding using matrix inversion lemma", Digital Signal Processing, vol. 55, pp.44-51, 2016

A set of instructions documenting the process of hosting matrix via synapse on Termux Android. Plus a script to automate the process. No root is needed.

MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithms Key Features Discover best practices for engineering and maintaining OpenCV projects Explore important deep learning tools for image classification Understand basic image matrix formats and filters Book Description OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch

Stop reading matrix, write on it ! Library to encrypt and decrypt data in colors of a picture. Process also known as steganography

Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.

Transfer-matrix method (TMM) is powerful analytical method to solve Maxwell equations in layered structures. However, standard TMM is limited by infinite plane waves (e.g no Gaussian beam excitation) and it is only limited to linear processes (i.e calculation of second-harmonic, sum-frequency, difference-frequency generation is not possible). The aim of this package is extand standard TMM to include those features. The physics of those extensions are described in the follwoing publications, first extends the standard TMM to nonlinear processes and the second extends to the beams with arbritary profiles.

Gear61 / Random-Number-Generator Code Issues 5 Pull requests 0 Projects 0 Wiki Pulse projectFilesBackup/.idea/workspace.xml <?xml version="1.0" encoding="UTF-8"?> <project version="4"> <component name="AndroidLayouts"> <shared> <config /> </shared> </component> <component name="AndroidLogFilters"> <option name="TOOL_WINDOW_CONFIGURED_FILTER" value="Show only selected application" /> </component> <component name="ChangeListManager"> <list default="true" id="f5e37520-ca17-4d94-bb6c-b4cbc9c73568" name="Default" comment="" /> <ignored path="rngplus.iws" /> <ignored path=".idea/workspace.xml" /> <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" /> <option name="TRACKING_ENABLED" value="true" /> <option name="SHOW_DIALOG" value="false" /> <option name="HIGHLIGHT_CONFLICTS" value="true" /> <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" /> <option name="LAST_RESOLUTION" value="IGNORE" /> </component> <component name="ChangesViewManager" flattened_view="true" 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name="name" value="preReleaseBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="preReleaseUnitTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:animated-vector-drawable:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportAnimatedVectorDrawable2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:appcompat-v7:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportAppcompatV72330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:cardview-v7:23.1.1" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportCardviewV72311Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:design:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportDesign2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:recyclerview-v7:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportRecyclerviewV72330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:support-v4:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportSupportV42330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:support-vector-drawable:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComAndroidSupportSupportVectorDrawable2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.github.afollestad.material-dialogs:core:0.8.5.8" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComGithubAfollestadMaterialDialogsCore0858Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.github.rey5137:material:1.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComGithubRey5137Material122Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconify222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify-fontawesome:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconifyFontawesome222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify-ionicons:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconifyIonicons222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareDebugAndroidTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareDebugDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareDebugUnitTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare me.zhanghai.android.materialprogressbar:library:1.1.5" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareMeZhanghaiAndroidMaterialprogressbarLibrary115Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareReleaseDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="prepareReleaseUnitTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugAndroidTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugAndroidTestManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processDebugUnitTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processReleaseJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processReleaseManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="processReleaseUnitTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the signing info for each variant." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="signingReport" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prints out all the source sets defined in this project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="sourceSets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for all variants." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="test" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for the debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="testDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for the release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="testReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformClassesWithDexForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformClassesWithDexForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformClassesWithDexForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformNative_libsWithMergeJniLibsForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformNative_libsWithMergeJniLibsForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformNative_libsWithMergeJniLibsForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="transformResourcesWithMergeJavaResForReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstall all applications." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="uninstallAll" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="uninstallDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the android (on device) tests for the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="uninstallDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="uninstallRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="validateDebugSigning" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="zipalignDebug" /> </ExternalTaskPojo> </list> </value> </entry> <entry key="$PROJECT_DIR$/app"> <value> <list> <ExternalTaskPojo> <option name="description" value="Displays the Android dependencies of the project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="androidDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all variants of all applications and secondary packages." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assemble" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all the Test applications." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all Debug builds." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all Release builds." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="assembleReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="build" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project and all projects that depend on it." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="buildDependents" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays all buildscript dependencies declared in project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="buildEnvironment" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project and all projects it depends on." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="buildNeeded" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all checks." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="check" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="checkDebugManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="checkReleaseManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Deletes the build directory." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="clean" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugAndroidTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugUnitTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileDebugUnitTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileLint" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseUnitTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="compileReleaseUnitTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the components produced by project ':app'. [incubating]" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="components" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs instrumentation tests for all flavors on connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="connectedAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all device checks on currently connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="connectedCheck" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs the tests for debug on connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="connectedDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays all dependencies declared in project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="dependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the insight into a specific dependency in project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="dependencyInsight" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs instrumentation tests using all Device Providers." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="deviceAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all device checks using Device Providers and Test Servers." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="deviceCheck" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAndroidTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateDebugSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="generateReleaseSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays a help message." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="help" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalDebugAndroidTestJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalDebugJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalDebugUnitTestJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalReleaseJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="incrementalReleaseUnitTestJavaCompilationSafeguard" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="installDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs the android (on device) tests for the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="installDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="jarDebugClasses" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="jarReleaseClasses" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on all variants." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="lint" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="lintDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="lintRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs lint on just the fatal issues in the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="lintVitalRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAndroidTestAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAndroidTestJniLibFolders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAndroidTestShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugJniLibFolders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeDebugShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeReleaseAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeReleaseJniLibFolders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mergeReleaseShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Creates a version of android.jar that's suitable for unit tests." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="mockableAndroidJar" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the configuration model of project ':app'. [incubating]" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="model" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="packageDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="packageDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="packageRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preDebugAndroidTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preDebugBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preDebugUnitTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prePackageMarkerForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prePackageMarkerForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prePackageMarkerForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preReleaseBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="preReleaseUnitTestBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:animated-vector-drawable:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportAnimatedVectorDrawable2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:appcompat-v7:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportAppcompatV72330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:cardview-v7:23.1.1" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportCardviewV72311Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:design:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportDesign2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:recyclerview-v7:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportRecyclerviewV72330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:support-v4:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportSupportV42330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.android.support:support-vector-drawable:23.3.0" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComAndroidSupportSupportVectorDrawable2330Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.github.afollestad.material-dialogs:core:0.8.5.8" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComGithubAfollestadMaterialDialogsCore0858Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.github.rey5137:material:1.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComGithubRey5137Material122Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconify222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify-fontawesome:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconifyFontawesome222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare com.joanzapata.iconify:android-iconify-ionicons:2.2.2" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareComJoanzapataIconifyAndroidIconifyIonicons222Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareDebugAndroidTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareDebugDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareDebugUnitTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prepare me.zhanghai.android.materialprogressbar:library:1.1.5" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareMeZhanghaiAndroidMaterialprogressbarLibrary115Library" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareReleaseDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="prepareReleaseUnitTestDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugAndroidTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugAndroidTestManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugAndroidTestResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processDebugUnitTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processReleaseJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processReleaseManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processReleaseResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="processReleaseUnitTestJavaRes" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the sub-projects of project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="projects" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the properties of project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="properties" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the signing info for each variant." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="signingReport" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Prints out all the source sets defined in this project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="sourceSets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the tasks runnable from project ':app'." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="tasks" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for all variants." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="test" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for the debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="testDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for the release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="testReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformClassesWithDexForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformClassesWithDexForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformClassesWithDexForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformNative_libsWithMergeJniLibsForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformNative_libsWithMergeJniLibsForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformNative_libsWithMergeJniLibsForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForDebugUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="transformResourcesWithMergeJavaResForReleaseUnitTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstall all applications." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="uninstallAll" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="uninstallDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the android (on device) tests for the Debug build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="uninstallDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Uninstalls the Release build." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="uninstallRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="validateDebugSigning" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$/app" /> <option name="name" value="zipalignDebug" /> </ExternalTaskPojo> </list> </value> </entry> </map> </option> <option name="modificationStamps"> <map> <entry key="$PROJECT_DIR$" value="4377878861000" /> </map> </option> <option name="projectBuildClasspath"> <map> <entry key="$PROJECT_DIR$"> <value> <ExternalProjectBuildClasspathPojo> <option name="modulesBuildClasspath"> <map> <entry key="$PROJECT_DIR$"> <value> <ExternalModuleBuildClasspathPojo> <option name="entries"> <list> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/gradle/2.1.0/gradle-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/gradle-core/2.1.0/gradle-core-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/jacoco/org.jacoco.core/0.7.4.201502262128/org.jacoco.core-0.7.4.201502262128-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/jacoco/org.jacoco.core/0.7.4.201502262128/org.jacoco.core-0.7.4.201502262128.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-commons/5.0.3/asm-commons-5.0.3-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-commons/5.0.3/asm-commons-5.0.3.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/gradle-api/2.1.0/gradle-api-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/lint/lint/25.1.0/lint-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/databinding/compilerCommon/2.1.0/compilerCommon-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm/5.0.3/asm-5.0.3-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm/5.0.3/asm-5.0.3.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/net/sf/proguard/proguard-gradle/5.2.1/proguard-gradle-5.2.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/net/sf/proguard/proguard-gradle/5.2.1/proguard-gradle-5.2.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/transform-api/2.0.0-deprecated-use-gradle-api/transform-api-2.0.0-deprecated-use-gradle-api.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/builder/2.1.0/builder-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-debug-all/5.0.1/asm-debug-all-5.0.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-debug-all/5.0.1/asm-debug-all-5.0.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-tree/5.0.3/asm-tree-5.0.3-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-tree/5.0.3/asm-tree-5.0.3.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/google/guava/guava/17.0/guava-17.0-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/google/guava/guava/17.0/guava-17.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/annotations/25.1.0/annotations-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/lint/lint-checks/25.1.0/lint-checks-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/eclipse/jdt/core/compiler/ecj/4.4.2/ecj-4.4.2-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/eclipse/jdt/core/compiler/ecj/4.4.2/ecj-4.4.2.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/googlecode/juniversalchardet/juniversalchardet/1.0.3/juniversalchardet-1.0.3.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/tunnelvisionlabs/antlr4/4.5/antlr4-4.5.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/commons-io/commons-io/2.4/commons-io-2.4-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/commons-io/commons-io/2.4/commons-io-2.4.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/databinding/baseLibrary/2.1.0/baseLibrary-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/net/sf/proguard/proguard-base/5.2.1/proguard-base-5.2.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/net/sf/proguard/proguard-base/5.2.1/proguard-base-5.2.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/builder-model/2.1.0/builder-model-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/common/25.1.0/common-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/ddms/ddmlib/25.1.0/ddmlib-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/bouncycastle/bcprov-jdk15on/1.48/bcprov-jdk15on-1.48-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/bouncycastle/bcprov-jdk15on/1.48/bcprov-jdk15on-1.48.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/sdklib/25.1.0/sdklib-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/squareup/javawriter/2.5.0/javawriter-2.5.0-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/squareup/javawriter/2.5.0/javawriter-2.5.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/builder-test-api/2.1.0/builder-test-api-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/sdk-common/25.1.0/sdk-common-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/manifest-merger/25.1.0/manifest-merger-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/jack/jack-api/0.10.0/jack-api-0.10.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/bouncycastle/bcpkix-jdk15on/1.48/bcpkix-jdk15on-1.48-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/bouncycastle/bcpkix-jdk15on/1.48/bcpkix-jdk15on-1.48.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/jill/jill-api/0.10.0/jill-api-0.10.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-analysis/5.0.3/asm-analysis-5.0.3-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/ow2/asm/asm-analysis/5.0.3/asm-analysis-5.0.3.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/lint/lint-api/25.1.0/lint-api-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/tunnelvisionlabs/antlr4-runtime/4.5/antlr4-runtime-4.5.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/tunnelvisionlabs/antlr4-annotations/4.5/antlr4-annotations-4.5.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/antlr/antlr-runtime/3.5.2/antlr-runtime-3.5.2.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/antlr/ST4/4.0.8/ST4-4.0.8.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/net/sf/kxml/kxml2/2.3.0/kxml2-2.3.0-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/net/sf/kxml/kxml2/2.3.0/kxml2-2.3.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/repository/25.1.0/repository-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/apache/httpcomponents/httpclient/4.1.1/httpclient-4.1.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/apache/httpcomponents/httpclient/4.1.1/httpclient-4.1.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/apache/httpcomponents/httpmime/4.1/httpmime-4.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/apache/httpcomponents/httpmime/4.1/httpmime-4.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/google/code/gson/gson/2.2.4/gson-2.2.4-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/google/code/gson/gson/2.2.4/gson-2.2.4.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/apache/commons/commons-compress/1.8.1/commons-compress-1.8.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/apache/commons/commons-compress/1.8.1/commons-compress-1.8.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/dvlib/25.1.0/dvlib-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/layoutlib/layoutlib-api/25.1.0/layoutlib-api-25.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/external/lombok/lombok-ast/0.2.3/lombok-ast-0.2.3-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/external/lombok/lombok-ast/0.2.3/lombok-ast-0.2.3.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/abego/treelayout/org.abego.treelayout.core/1.0.1/org.abego.treelayout.core-1.0.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/apache/httpcomponents/httpcore/4.1/httpcore-4.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/apache/httpcomponents/httpcore/4.1/httpcore-4.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/commons-logging/commons-logging/1.1.1/commons-logging-1.1.1-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/commons-logging/commons-logging/1.1.1/commons-logging-1.1.1.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/commons-codec/commons-codec/1.4/commons-codec-1.4-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/commons-codec/commons-codec/1.4/commons-codec-1.4.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/intellij/annotations/12.0/annotations-12.0-sources.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/intellij/annotations/12.0/annotations-12.0.jar" /> </list> </option> <option name="path" value="$PROJECT_DIR$" /> </ExternalModuleBuildClasspathPojo> </value> </entry> <entry key="$PROJECT_DIR$/app"> <value> <ExternalModuleBuildClasspathPojo> <option name="entries"> <list> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/gradle/2.1.0/gradle-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/com/android/tools/build/gradle-core/2.1.0/gradle-core-2.1.0.jar" /> <option value="$APPLICATION_HOME_DIR$/gradle/m2repository/org/jacoco/org.jacoco.core/0.7.4.201502262128/org.jacoco.core-0.7.4.201502262128-sources.jar" /> <option 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