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sayantann11 / All Classification Templetes For MLClassification - 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.'
chrisneagu / FTC Skystone Dark Angels Romania 2020NOTICE 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.
molyswu / Hand Detectionusing Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.
MGz-Staze / Tools TermuxCara Update dan Upgrade Termux pkg update && pkg upgrade Tools Pendukung untuk Termux Tools pendukung yang di perlukan agar tools yang lain work. <—-| PENTING pkg install git php curl -y pkg install python2 pkg install apache2 Trik Termux Untuk mematikan atau memberhentikan tools tekan tombol volume bawah (-) + C secara bersamaan Untuk memunculkan commands sebelumnya tekan tombol volume bawah + P Menampilkan Tombol ESC, HOME, CTRL dan lainnya Tekan Tombol volume atas + Q script termux Menampilkan Matrix Ini cuma menampilkan angka dan huruf berjatuhan, terlihat keren •pkg install cmatrix cmatrix mempercantik termux Note : Jika ada pertanyaan saat install, silahkan ketik tombol y lalu enter saja, agar proses install dapat berjalan dengan lancar. Ratusan Tools Termux Sebagian dari Kali Linux •pkg install git •git clone https://github.com/Mrcakil/mrcakil •cd mrcakil •chmod +x tools •./tools ratusan tools termux Bermain atau Memunculkan Kereta Api pkg install sl sl main termux Bot Auto Reaction Facebook •git clone https://github.com/AMVengeance/FB-React.git •chmod +x FB-React -R cd FB-React •./start fb react Bot Love Instagram •git clone https://github.com/indogram/indotagram.git •php indotagram/login.php Masukkan username dan password instagram untuk memunculkan cookie Kemudian copy dan paste kode cookie ke website penyedia bot instagram disini bot instagram NIK dan KK Gratis •pkg install php git •git clone https://github.com/IndonesiaSecurity/kkktp •cd kkktp php •kkktp.php Menjalankan Musik di Termux pkg install mpv mpv /lokasi/musik/musik.mp3 cara memutar musik di termux Menampilkan Informasi System pkg install neofetch neofetch tampilan informasi Membuat Tulisan atau Teks Besar di Termux pkg install figlet figlet initulisannya cara membuat tulisan nesar di termux Crack hash password git clone https://github.com/FajriHidayat088/FHX-Hash-Killer/ cd FHX-Hash-Killer python2 FHXHashKiller.py Cara Menginstall Metasploit di Termux Metasploit adalah tools hacking yang dapat di gunakan untuk kegiatan seperti remote pc atau hp orang lain, membuat virus, menyadap dan lain sebagainya. Cara Install dan Menjalankannya pkg install curl curl -LO https://raw.githubusercontent.com/Hax4us/Metasploit_termux/master/metasploit.sh chmod +x metasploit.sh ./metasploit.sh Cara Install Wifite di Termux Android Wifite adalah salah satu tool yang berfungsi untuk wireless attack, seperti mengcrack atau membobol password wifi yang terkunci dengan wpa/wpa2, Cara Install Wifite git clone https://github.com/derv82/wifite cd wifite chmod 777 wifite.py Cara Menjalankan Wifite python2 wifite.py Bruteforce Bruteforce ini metode hacking yang dimana kita akan membuat suatu wordlist yang berisi kumpulan tebakan password, jika ada salah satu password yang sesuai maka akun bisa di dapatkan. pkg install pip pip install wordlist pkg install worlist wordlist -h cd /sdcard cat pas.txt Redhawk Fitur Redhawk Server detection Cloudflare detector robots scanner Whois GEO-IP Scan NMAP Port Scan DNS Lookup SubNet Calculator Subdomain Finder Reverse IP Scanner CMS detection For Sites On the same server. Parameter Finder Error based SQLi Detector Dan lain – lain. Cara install dan menjalankan Redhawk git clone https://github.com/Tuhinshubhra/RED_HAWK cd RED_HAWK chmod +x rhawk.php php rhawk.php Install SQLMAP Tools ini berguna sekali untuk kegiatan mengetes keamanan website, deface, carding dan semacamnya gi clone https://github.com/sqlmapproject/sqlmap cd sqlmap python2 sqlmap.py Cara menjalankan python2 sqlmap.py -u website.com --dbs Mengetahui semua parameter Sqlmap python2 sqlmap.py -hh Melacak IP git clone https://github.com/maldevel/IPGeolocation cd IPGeolocation chmod +x ipgeolocation.py pip install -r requirements.txt python ipgeolocation.py -m python ipgeolocation.py -t IPnya Hack Akun Facebook •pkg install python2-dev •apt install wget dip2 •install mechanize •cd/sterage/emulated/0 python2 fbbrute.py •storage/emulated/0/password.txt Membuat Virus git clone https://github.com/viruz09/CreaterVirus cd CreaterVirus python2 creater.py Cara copy virus cp agents.apk /lokasi/folder DDOS DDOS berguna untuk menyerang website dengan memberikan traffic yang tinggi sehingga website tersebut down. git clone https://github.com/4L13199/LITEDDOS python2 LITEDDOS.py [ip target atau domain] [port] [jumlah traffic] Cara Install Santet Online Di Termux Fitur Create a Netcat Payload and Listener Facebook Group Hijack Attack SMS Bomber Attack Vectors SMS Spoof Attack Vectors Denial-of-Service Attack •git clone https://github.com/Gameye98/santet-online •cd santet-online •python2 santet.py Tools B4J1N94N Fitur Nyari CC buat carding Spam akun gmail Whois lookup Dan ratusan tools lainnya •git clone https://github.com/DarknessCyberTeam/B4J1N64Nv5 •cd B4J1N64Nv5 •sh B4J1N64N.sh
rramatchandran / Big O Performance Java# big-o-performance A simple html app to demonstrate performance costs of data structures. - Clone the project - Navigate to the root of the project in a termina or command prompt - Run 'npm install' - Run 'npm start' - Go to the URL specified in the terminal or command prompt to try out the app. # This app was created from the Create React App NPM. Below are instructions from that project. Below you will find some information on how to perform common tasks. You can find the most recent version of this guide [here](https://github.com/facebookincubator/create-react-app/blob/master/template/README.md). ## Table of Contents - [Updating to New Releases](#updating-to-new-releases) - [Sending Feedback](#sending-feedback) - [Folder Structure](#folder-structure) - [Available Scripts](#available-scripts) - [npm start](#npm-start) - [npm run build](#npm-run-build) - [npm run eject](#npm-run-eject) - [Displaying Lint Output in the Editor](#displaying-lint-output-in-the-editor) - [Installing a Dependency](#installing-a-dependency) - [Importing a Component](#importing-a-component) - [Adding a Stylesheet](#adding-a-stylesheet) - [Post-Processing CSS](#post-processing-css) - [Adding Images and Fonts](#adding-images-and-fonts) - [Adding Bootstrap](#adding-bootstrap) - [Adding Flow](#adding-flow) - [Adding Custom Environment Variables](#adding-custom-environment-variables) - [Integrating with a Node Backend](#integrating-with-a-node-backend) - [Proxying API Requests in Development](#proxying-api-requests-in-development) - [Deployment](#deployment) - [Now](#now) - [Heroku](#heroku) - [Surge](#surge) - [GitHub Pages](#github-pages) - [Something Missing?](#something-missing) ## Updating to New Releases Create React App is divided into two packages: * `create-react-app` is a global command-line utility that you use to create new projects. * `react-scripts` is a development dependency in the generated projects (including this one). You almost never need to update `create-react-app` itself: it’s delegates all the setup to `react-scripts`. When you run `create-react-app`, it always creates the project with the latest version of `react-scripts` so you’ll get all the new features and improvements in newly created apps automatically. To update an existing project to a new version of `react-scripts`, [open the changelog](https://github.com/facebookincubator/create-react-app/blob/master/CHANGELOG.md), find the version you’re currently on (check `package.json` in this folder if you’re not sure), and apply the migration instructions for the newer versions. In most cases bumping the `react-scripts` version in `package.json` and running `npm install` in this folder should be enough, but it’s good to consult the [changelog](https://github.com/facebookincubator/create-react-app/blob/master/CHANGELOG.md) for potential breaking changes. We commit to keeping the breaking changes minimal so you can upgrade `react-scripts` painlessly. ## Sending Feedback We are always open to [your feedback](https://github.com/facebookincubator/create-react-app/issues). ## Folder Structure After creation, your project should look like this: ``` my-app/ README.md index.html favicon.ico node_modules/ package.json src/ App.css App.js index.css index.js logo.svg ``` For the project to build, **these files must exist with exact filenames**: * `index.html` is the page template; * `favicon.ico` is the icon you see in the browser tab; * `src/index.js` is the JavaScript entry point. You can delete or rename the other files. You may create subdirectories inside `src`. For faster rebuilds, only files inside `src` are processed by Webpack. You need to **put any JS and CSS files inside `src`**, or Webpack won’t see them. You can, however, create more top-level directories. They will not be included in the production build so you can use them for things like documentation. ## Available Scripts In the project directory, you can run: ### `npm start` Runs the app in the development mode.<br> Open [http://localhost:3000](http://localhost:3000) to view it in the browser. The page will reload if you make edits.<br> You will also see any lint errors in the console. ### `npm run build` Builds the app for production to the `build` folder.<br> It correctly bundles React in production mode and optimizes the build for the best performance. The build is minified and the filenames include the hashes.<br> Your app is ready to be deployed! ### `npm run eject` **Note: this is a one-way operation. Once you `eject`, you can’t go back!** If you aren’t satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project. Instead, it will copy all the configuration files and the transitive dependencies (Webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own. You don’t have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it. ## Displaying Lint Output in the Editor >Note: this feature is available with `react-scripts@0.2.0` and higher. Some editors, including Sublime Text, Atom, and Visual Studio Code, provide plugins for ESLint. They are not required for linting. You should see the linter output right in your terminal as well as the browser console. However, if you prefer the lint results to appear right in your editor, there are some extra steps you can do. You would need to install an ESLint plugin for your editor first. >**A note for Atom `linter-eslint` users** >If you are using the Atom `linter-eslint` plugin, make sure that **Use global ESLint installation** option is checked: ><img src="http://i.imgur.com/yVNNHJM.png" width="300"> Then make sure `package.json` of your project ends with this block: ```js { // ... "eslintConfig": { "extends": "./node_modules/react-scripts/config/eslint.js" } } ``` Projects generated with `react-scripts@0.2.0` and higher should already have it. If you don’t need ESLint integration with your editor, you can safely delete those three lines from your `package.json`. Finally, you will need to install some packages *globally*: ```sh npm install -g eslint babel-eslint eslint-plugin-react eslint-plugin-import eslint-plugin-jsx-a11y eslint-plugin-flowtype ``` We recognize that this is suboptimal, but it is currently required due to the way we hide the ESLint dependency. The ESLint team is already [working on a solution to this](https://github.com/eslint/eslint/issues/3458) so this may become unnecessary in a couple of months. ## Installing a Dependency The generated project includes React and ReactDOM as dependencies. It also includes a set of scripts used by Create React App as a development dependency. You may install other dependencies (for example, React Router) with `npm`: ``` npm install --save <library-name> ``` ## Importing a Component This project setup supports ES6 modules thanks to Babel. While you can still use `require()` and `module.exports`, we encourage you to use [`import` and `export`](http://exploringjs.com/es6/ch_modules.html) instead. For example: ### `Button.js` ```js import React, { Component } from 'react'; class Button extends Component { render() { // ... } } export default Button; // Don’t forget to use export default! ``` ### `DangerButton.js` ```js import React, { Component } from 'react'; import Button from './Button'; // Import a component from another file class DangerButton extends Component { render() { return <Button color="red" />; } } export default DangerButton; ``` Be aware of the [difference between default and named exports](http://stackoverflow.com/questions/36795819/react-native-es-6-when-should-i-use-curly-braces-for-import/36796281#36796281). It is a common source of mistakes. We suggest that you stick to using default imports and exports when a module only exports a single thing (for example, a component). That’s what you get when you use `export default Button` and `import Button from './Button'`. Named exports are useful for utility modules that export several functions. A module may have at most one default export and as many named exports as you like. Learn more about ES6 modules: * [When to use the curly braces?](http://stackoverflow.com/questions/36795819/react-native-es-6-when-should-i-use-curly-braces-for-import/36796281#36796281) * [Exploring ES6: Modules](http://exploringjs.com/es6/ch_modules.html) * [Understanding ES6: Modules](https://leanpub.com/understandinges6/read#leanpub-auto-encapsulating-code-with-modules) ## Adding a Stylesheet This project setup uses [Webpack](https://webpack.github.io/) for handling all assets. Webpack offers a custom way of “extending” the concept of `import` beyond JavaScript. To express that a JavaScript file depends on a CSS file, you need to **import the CSS from the JavaScript file**: ### `Button.css` ```css .Button { padding: 20px; } ``` ### `Button.js` ```js import React, { Component } from 'react'; import './Button.css'; // Tell Webpack that Button.js uses these styles class Button extends Component { render() { // You can use them as regular CSS styles return <div className="Button" />; } } ``` **This is not required for React** but many people find this feature convenient. You can read about the benefits of this approach [here](https://medium.com/seek-ui-engineering/block-element-modifying-your-javascript-components-d7f99fcab52b). However you should be aware that this makes your code less portable to other build tools and environments than Webpack. In development, expressing dependencies this way allows your styles to be reloaded on the fly as you edit them. In production, all CSS files will be concatenated into a single minified `.css` file in the build output. If you are concerned about using Webpack-specific semantics, you can put all your CSS right into `src/index.css`. It would still be imported from `src/index.js`, but you could always remove that import if you later migrate to a different build tool. ## Post-Processing CSS This project setup minifies your CSS and adds vendor prefixes to it automatically through [Autoprefixer](https://github.com/postcss/autoprefixer) so you don’t need to worry about it. For example, this: ```css .App { display: flex; flex-direction: row; align-items: center; } ``` becomes this: ```css .App { display: -webkit-box; display: -ms-flexbox; display: flex; -webkit-box-orient: horizontal; -webkit-box-direction: normal; -ms-flex-direction: row; flex-direction: row; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } ``` There is currently no support for preprocessors such as Less, or for sharing variables across CSS files. ## Adding Images and Fonts With Webpack, using static assets like images and fonts works similarly to CSS. You can **`import` an image right in a JavaScript module**. This tells Webpack to include that image in the bundle. Unlike CSS imports, importing an image or a font gives you a string value. This value is the final image path you can reference in your code. Here is an example: ```js import React from 'react'; import logo from './logo.png'; // Tell Webpack this JS file uses this image console.log(logo); // /logo.84287d09.png function Header() { // Import result is the URL of your image return <img src={logo} alt="Logo" />; } export default function Header; ``` This works in CSS too: ```css .Logo { background-image: url(./logo.png); } ``` Webpack finds all relative module references in CSS (they start with `./`) and replaces them with the final paths from the compiled bundle. If you make a typo or accidentally delete an important file, you will see a compilation error, just like when you import a non-existent JavaScript module. The final filenames in the compiled bundle are generated by Webpack from content hashes. If the file content changes in the future, Webpack will give it a different name in production so you don’t need to worry about long-term caching of assets. Please be advised that this is also a custom feature of Webpack. **It is not required for React** but many people enjoy it (and React Native uses a similar mechanism for images). However it may not be portable to some other environments, such as Node.js and Browserify. If you prefer to reference static assets in a more traditional way outside the module system, please let us know [in this issue](https://github.com/facebookincubator/create-react-app/issues/28), and we will consider support for this. ## Adding Bootstrap You don’t have to use [React Bootstrap](https://react-bootstrap.github.io) together with React but it is a popular library for integrating Bootstrap with React apps. If you need it, you can integrate it with Create React App by following these steps: Install React Bootstrap and Bootstrap from NPM. React Bootstrap does not include Bootstrap CSS so this needs to be installed as well: ``` npm install react-bootstrap --save npm install bootstrap@3 --save ``` Import Bootstrap CSS and optionally Bootstrap theme CSS in the ```src/index.js``` file: ```js import 'bootstrap/dist/css/bootstrap.css'; import 'bootstrap/dist/css/bootstrap-theme.css'; ``` Import required React Bootstrap components within ```src/App.js``` file or your custom component files: ```js import { Navbar, Jumbotron, Button } from 'react-bootstrap'; ``` Now you are ready to use the imported React Bootstrap components within your component hierarchy defined in the render method. Here is an example [`App.js`](https://gist.githubusercontent.com/gaearon/85d8c067f6af1e56277c82d19fd4da7b/raw/6158dd991b67284e9fc8d70b9d973efe87659d72/App.js) redone using React Bootstrap. ## Adding Flow Flow typing is currently [not supported out of the box](https://github.com/facebookincubator/create-react-app/issues/72) with the default `.flowconfig` generated by Flow. If you run it, you might get errors like this: ```js node_modules/fbjs/lib/Deferred.js.flow:60 60: Promise.prototype.done.apply(this._promise, arguments); ^^^^ property `done`. Property not found in 495: declare class Promise<+R> { ^ Promise. See lib: /private/tmp/flow/flowlib_34952d31/core.js:495 node_modules/fbjs/lib/shallowEqual.js.flow:29 29: return x !== 0 || 1 / (x: $FlowIssue) === 1 / (y: $FlowIssue); ^^^^^^^^^^ identifier `$FlowIssue`. Could not resolve name src/App.js:3 3: import logo from './logo.svg'; ^^^^^^^^^^^^ ./logo.svg. Required module not found src/App.js:4 4: import './App.css'; ^^^^^^^^^^^ ./App.css. Required module not found src/index.js:5 5: import './index.css'; ^^^^^^^^^^^^^ ./index.css. Required module not found ``` To fix this, change your `.flowconfig` to look like this: ```ini [libs] ./node_modules/fbjs/flow/lib [options] esproposal.class_static_fields=enable esproposal.class_instance_fields=enable module.name_mapper='^\(.*\)\.css$' -> 'react-scripts/config/flow/css' module.name_mapper='^\(.*\)\.\(jpg\|png\|gif\|eot\|otf\|webp\|svg\|ttf\|woff\|woff2\|mp4\|webm\)$' -> 'react-scripts/config/flow/file' suppress_type=$FlowIssue suppress_type=$FlowFixMe ``` Re-run flow, and you shouldn’t get any extra issues. If you later `eject`, you’ll need to replace `react-scripts` references with the `<PROJECT_ROOT>` placeholder, for example: ```ini module.name_mapper='^\(.*\)\.css$' -> '<PROJECT_ROOT>/config/flow/css' module.name_mapper='^\(.*\)\.\(jpg\|png\|gif\|eot\|otf\|webp\|svg\|ttf\|woff\|woff2\|mp4\|webm\)$' -> '<PROJECT_ROOT>/config/flow/file' ``` We will consider integrating more tightly with Flow in the future so that you don’t have to do this. ## Adding Custom Environment Variables >Note: this feature is available with `react-scripts@0.2.3` and higher. Your project can consume variables declared in your environment as if they were declared locally in your JS files. By default you will have `NODE_ENV` defined for you, and any other environment variables starting with `REACT_APP_`. These environment variables will be defined for you on `process.env`. For example, having an environment variable named `REACT_APP_SECRET_CODE` will be exposed in your JS as `process.env.REACT_APP_SECRET_CODE`, in addition to `process.env.NODE_ENV`. These environment variables can be useful for displaying information conditionally based on where the project is deployed or consuming sensitive data that lives outside of version control. First, you need to have environment variables defined, which can vary between OSes. For example, let's say you wanted to consume a secret defined in the environment inside a `<form>`: ```jsx render() { return ( <div> <small>You are running this application in <b>{process.env.NODE_ENV}</b> mode.</small> <form> <input type="hidden" defaultValue={process.env.REACT_APP_SECRET_CODE} /> </form> </div> ); } ``` The above form is looking for a variable called `REACT_APP_SECRET_CODE` from the environment. In order to consume this value, we need to have it defined in the environment: ### Windows (cmd.exe) ```cmd set REACT_APP_SECRET_CODE=abcdef&&npm start ``` (Note: the lack of whitespace is intentional.) ### Linux, OS X (Bash) ```bash REACT_APP_SECRET_CODE=abcdef npm start ``` > Note: Defining environment variables in this manner is temporary for the life of the shell session. Setting permanent environment variables is outside the scope of these docs. With our environment variable defined, we start the app and consume the values. Remember that the `NODE_ENV` variable will be set for you automatically. When you load the app in the browser and inspect the `<input>`, you will see its value set to `abcdef`, and the bold text will show the environment provided when using `npm start`: ```html <div> <small>You are running this application in <b>development</b> mode.</small> <form> <input type="hidden" value="abcdef" /> </form> </div> ``` Having access to the `NODE_ENV` is also useful for performing actions conditionally: ```js if (process.env.NODE_ENV !== 'production') { analytics.disable(); } ``` ## Integrating with a Node Backend Check out [this tutorial](https://www.fullstackreact.com/articles/using-create-react-app-with-a-server/) for instructions on integrating an app with a Node backend running on another port, and using `fetch()` to access it. You can find the companion GitHub repository [here](https://github.com/fullstackreact/food-lookup-demo). ## Proxying API Requests in Development >Note: this feature is available with `react-scripts@0.2.3` and higher. People often serve the front-end React app from the same host and port as their backend implementation. For example, a production setup might look like this after the app is deployed: ``` / - static server returns index.html with React app /todos - static server returns index.html with React app /api/todos - server handles any /api/* requests using the backend implementation ``` Such setup is **not** required. However, if you **do** have a setup like this, it is convenient to write requests like `fetch('/api/todos')` without worrying about redirecting them to another host or port during development. To tell the development server to proxy any unknown requests to your API server in development, add a `proxy` field to your `package.json`, for example: ```js "proxy": "http://localhost:4000", ``` This way, when you `fetch('/api/todos')` in development, the development server will recognize that it’s not a static asset, and will proxy your request to `http://localhost:4000/api/todos` as a fallback. Conveniently, this avoids [CORS issues](http://stackoverflow.com/questions/21854516/understanding-ajax-cors-and-security-considerations) and error messages like this in development: ``` Fetch API cannot load http://localhost:4000/api/todos. No 'Access-Control-Allow-Origin' header is present on the requested resource. Origin 'http://localhost:3000' is therefore not allowed access. If an opaque response serves your needs, set the request's mode to 'no-cors' to fetch the resource with CORS disabled. ``` Keep in mind that `proxy` only has effect in development (with `npm start`), and it is up to you to ensure that URLs like `/api/todos` point to the right thing in production. You don’t have to use the `/api` prefix. Any unrecognized request will be redirected to the specified `proxy`. Currently the `proxy` option only handles HTTP requests, and it won’t proxy WebSocket connections. If the `proxy` option is **not** flexible enough for you, alternatively you can: * Enable CORS on your server ([here’s how to do it for Express](http://enable-cors.org/server_expressjs.html)). * Use [environment variables](#adding-custom-environment-variables) to inject the right server host and port into your app. ## Deployment By default, Create React App produces a build assuming your app is hosted at the server root. To override this, specify the `homepage` in your `package.json`, for example: ```js "homepage": "http://mywebsite.com/relativepath", ``` This will let Create React App correctly infer the root path to use in the generated HTML file. ### Now See [this example](https://github.com/xkawi/create-react-app-now) for a zero-configuration single-command deployment with [now](https://zeit.co/now). ### Heroku Use the [Heroku Buildpack for Create React App](https://github.com/mars/create-react-app-buildpack). You can find instructions in [Deploying React with Zero Configuration](https://blog.heroku.com/deploying-react-with-zero-configuration). ### Surge Install the Surge CLI if you haven't already by running `npm install -g surge`. Run the `surge` command and log in you or create a new account. You just need to specify the *build* folder and your custom domain, and you are done. ```sh email: email@domain.com password: ******** project path: /path/to/project/build size: 7 files, 1.8 MB domain: create-react-app.surge.sh upload: [====================] 100%, eta: 0.0s propagate on CDN: [====================] 100% plan: Free users: email@domain.com IP Address: X.X.X.X Success! Project is published and running at create-react-app.surge.sh ``` Note that in order to support routers that use html5 `pushState` API, you may want to rename the `index.html` in your build folder to `200.html` before deploying to Surge. This [ensures that every URL falls back to that file](https://surge.sh/help/adding-a-200-page-for-client-side-routing). ### GitHub Pages >Note: this feature is available with `react-scripts@0.2.0` and higher. Open your `package.json` and add a `homepage` field: ```js "homepage": "http://myusername.github.io/my-app", ``` **The above step is important!** Create React App uses the `homepage` field to determine the root URL in the built HTML file. Now, whenever you run `npm run build`, you will see a cheat sheet with a sequence of commands to deploy to GitHub pages: ```sh git commit -am "Save local changes" git checkout -B gh-pages git add -f build git commit -am "Rebuild website" git filter-branch -f --prune-empty --subdirectory-filter build git push -f origin gh-pages git checkout - ``` You may copy and paste them, or put them into a custom shell script. You may also customize them for another hosting provider. Note that GitHub Pages doesn't support routers that use the HTML5 `pushState` history API under the hood (for example, React Router using `browserHistory`). This is because when there is a fresh page load for a url like `http://user.github.io/todomvc/todos/42`, where `/todos/42` is a frontend route, the GitHub Pages server returns 404 because it knows nothing of `/todos/42`. If you want to add a router to a project hosted on GitHub Pages, here are a couple of solutions: * You could switch from using HTML5 history API to routing with hashes. If you use React Router, you can switch to `hashHistory` for this effect, but the URL will be longer and more verbose (for example, `http://user.github.io/todomvc/#/todos/42?_k=yknaj`). [Read more](https://github.com/reactjs/react-router/blob/master/docs/guides/Histories.md#histories) about different history implementations in React Router. * Alternatively, you can use a trick to teach GitHub Pages to handle 404 by redirecting to your `index.html` page with a special redirect parameter. You would need to add a `404.html` file with the redirection code to the `build` folder before deploying your project, and you’ll need to add code handling the redirect parameter to `index.html`. You can find a detailed explanation of this technique [in this guide](https://github.com/rafrex/spa-github-pages). ## Something Missing? If you have ideas for more “How To” recipes that should be on this page, [let us know](https://github.com/facebookincubator/create-react-app/issues) or [contribute some!](https://github.com/facebookincubator/create-react-app/edit/master/template/README.md)
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hbollon / GyroscopeControl🌀 Unity script used for smooth and customizable object rotation with gyroscope (initially configured to rotate x and z axis using x and y axis of gyro but can be easily edited). It include initial calibration with offset, rotation speed (Time.deltaTime * velocity), smoothing parameter editable in Unity inspector and debug overlay.
limingado / NSCThe code is an implementation of the Nystrӧm-based spectral clustering with the K-nearest neighbour-based sampling (KNNS) method (Pang et al. 2021). It is aimed for individual tree segmentation using airborne LiDAR point cloud data. When using the code, please cite as: Yong Pang, Weiwei Wang, Liming Du, Zhongjun Zhang, Xiaojun Liang, Yongning Li, Zuyuan Wang (2021) Nystrӧm-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation, International Journal of Digital Earth Code files: ‘segmentation.py’: the main function, including deriving local maximum from Canopy Height Model (CHM); ‘VNSC.py’: other functions for the algorithm, including mean-shift voxelization, similarity graph construction, KNNS sampling, eigendecomposition, k-means clustering, as well as the computation and writing of individual tree parameters. Key parameters: When using the code, users can adjust the values of local maximum window, gap (the upper limit of the number of final clusters), knn (the number of k-nearest neighbours in the similarity graph) and quantile in meanshift method based specific data characteristics. Currently, the value of local maximum window is 3m ×3m, the value of gap is defined as the 1.5 times of the local maximum detected from CHM. Parameter knn can be defined as a constant value (40 in the code) based on the data characteristics, or be determined through the relationship between it and the number of voxels. The default setting of quantile in meanshift method is the average density of point clouds. More details can be found in Pang et al. (2021). Test data: ‘ALS_pointclouds.txt’: point cloud data; ‘ALS_CHM.tif’: CHM of the point cloud data; ‘Reference_tree.csv’: field measurements for algorithm validation. The position was measured using differential GNSS. The tree height of each tree in this file is obtained by regression estimation. Outputs: ‘Data_seg.csv’: coordinate of each point (x, y, z) as well as its cluster label after segmentation; ‘Parameter.csv’: individual tree parameters (TreeID, Position_X, Position_Y, Crown, Height) based on the calculation described in Pang et al. (2021).
Saturnremabtc64 / Supreme Octo RoboticeyxGear61 / 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 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[incubating]" /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="wrapper" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Displays the Android dependencies of the project." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="androidDependencies" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all variants of all applications and secondary packages." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="assemble" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all the Test applications." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="assembleAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles all Debug builds." /> <option name="linkedExternalProjectPath" 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</ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project and all projects that depend on it." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="buildDependents" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Assembles and tests this project and all projects it depends on." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="buildNeeded" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all checks." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="check" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="checkDebugManifest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" 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</ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugUnitTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileDebugUnitTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileLint" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseAidl" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseNdk" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseRenderscript" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseUnitTestJavaWithJavac" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="compileReleaseUnitTestSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs instrumentation tests for all flavors on connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="connectedAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all device checks on currently connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="connectedCheck" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs the tests for debug on connected devices." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="connectedDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Installs and runs instrumentation tests using all Device Providers." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="deviceAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Runs all device checks using Device Providers and Test Servers." /> <option name="linkedExternalProjectPath" 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value="$PROJECT_DIR$" /> <option name="name" value="generateDebugAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugBuildConfig" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugResValues" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugResources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateDebugSources" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="generateReleaseAssets" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" 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/> <option name="name" value="mergeReleaseShaders" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Creates a version of android.jar that's suitable for unit tests." /> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="mockableAndroidJar" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="packageDebug" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="packageDebugAndroidTest" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="packageRelease" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" value="$PROJECT_DIR$" /> <option name="name" value="preBuild" /> </ExternalTaskPojo> <ExternalTaskPojo> <option name="linkedExternalProjectPath" 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</ExternalTaskPojo> <ExternalTaskPojo> <option name="description" value="Run unit tests for all variants." /> <option name="linkedExternalProjectPath" value="$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$" /> <option name="name" 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<option name="linkedExternalProjectPath" value="$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$/gradle/m2repository/org/jacoco/org.jacoco.core/0.7.4.201502262128/org.jacoco.core-0.7.4.201502262128-sources.jar" /> <option value="$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 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N30nHaCkZ / LinuxLinux kernel release 3.x <http://kernel.org/> These are the release notes for Linux version 3. Read them carefully, as they tell you what this is all about, explain how to install the kernel, and what to do if something goes wrong. WHAT IS LINUX? Linux is a clone of the operating system Unix, written from scratch by Linus Torvalds with assistance from a loosely-knit team of hackers across the Net. It aims towards POSIX and Single UNIX Specification compliance. It has all the features you would expect in a modern fully-fledged Unix, including true multitasking, virtual memory, shared libraries, demand loading, shared copy-on-write executables, proper memory management, and multistack networking including IPv4 and IPv6. It is distributed under the GNU General Public License - see the accompanying COPYING file for more details. ON WHAT HARDWARE DOES IT RUN? Although originally developed first for 32-bit x86-based PCs (386 or higher), today Linux also runs on (at least) the Compaq Alpha AXP, Sun SPARC and UltraSPARC, Motorola 68000, PowerPC, PowerPC64, ARM, Hitachi SuperH, Cell, IBM S/390, MIPS, HP PA-RISC, Intel IA-64, DEC VAX, AMD x86-64, AXIS CRIS, Xtensa, Tilera TILE, AVR32 and Renesas M32R architectures. Linux is easily portable to most general-purpose 32- or 64-bit architectures as long as they have a paged memory management unit (PMMU) and a port of the GNU C compiler (gcc) (part of The GNU Compiler Collection, GCC). Linux has also been ported to a number of architectures without a PMMU, although functionality is then obviously somewhat limited. Linux has also been ported to itself. You can now run the kernel as a userspace application - this is called UserMode Linux (UML). DOCUMENTATION: - There is a lot of documentation available both in electronic form on the Internet and in books, both Linux-specific and pertaining to general UNIX questions. I'd recommend looking into the documentation subdirectories on any Linux FTP site for the LDP (Linux Documentation Project) books. This README is not meant to be documentation on the system: there are much better sources available. - There are various README files in the Documentation/ subdirectory: these typically contain kernel-specific installation notes for some drivers for example. See Documentation/00-INDEX for a list of what is contained in each file. Please read the Changes file, as it contains information about the problems, which may result by upgrading your kernel. - The Documentation/DocBook/ subdirectory contains several guides for kernel developers and users. These guides can be rendered in a number of formats: PostScript (.ps), PDF, HTML, & man-pages, among others. After installation, "make psdocs", "make pdfdocs", "make htmldocs", or "make mandocs" will render the documentation in the requested format. INSTALLING the kernel source: - If you install the full sources, put the kernel tarball in a directory where you have permissions (eg. your home directory) and unpack it: gzip -cd linux-3.X.tar.gz | tar xvf - or bzip2 -dc linux-3.X.tar.bz2 | tar xvf - Replace "X" with the version number of the latest kernel. Do NOT use the /usr/src/linux area! This area has a (usually incomplete) set of kernel headers that are used by the library header files. They should match the library, and not get messed up by whatever the kernel-du-jour happens to be. - You can also upgrade between 3.x releases by patching. Patches are distributed in the traditional gzip and the newer bzip2 format. To install by patching, get all the newer patch files, enter the top level directory of the kernel source (linux-3.X) and execute: gzip -cd ../patch-3.x.gz | patch -p1 or bzip2 -dc ../patch-3.x.bz2 | patch -p1 Replace "x" for all versions bigger than the version "X" of your current source tree, _in_order_, and you should be ok. You may want to remove the backup files (some-file-name~ or some-file-name.orig), and make sure that there are no failed patches (some-file-name# or some-file-name.rej). If there are, either you or I have made a mistake. Unlike patches for the 3.x kernels, patches for the 3.x.y kernels (also known as the -stable kernels) are not incremental but instead apply directly to the base 3.x kernel. For example, if your base kernel is 3.0 and you want to apply the 3.0.3 patch, you must not first apply the 3.0.1 and 3.0.2 patches. Similarly, if you are running kernel version 3.0.2 and want to jump to 3.0.3, you must first reverse the 3.0.2 patch (that is, patch -R) _before_ applying the 3.0.3 patch. You can read more on this in Documentation/applying-patches.txt Alternatively, the script patch-kernel can be used to automate this process. It determines the current kernel version and applies any patches found. linux/scripts/patch-kernel linux The first argument in the command above is the location of the kernel source. Patches are applied from the current directory, but an alternative directory can be specified as the second argument. - Make sure you have no stale .o files and dependencies lying around: cd linux make mrproper You should now have the sources correctly installed. SOFTWARE REQUIREMENTS Compiling and running the 3.x kernels requires up-to-date versions of various software packages. Consult Documentation/Changes for the minimum version numbers required and how to get updates for these packages. Beware that using excessively old versions of these packages can cause indirect errors that are very difficult to track down, so don't assume that you can just update packages when obvious problems arise during build or operation. BUILD directory for the kernel: When compiling the kernel, all output files will per default be stored together with the kernel source code. Using the option "make O=output/dir" allow you to specify an alternate place for the output files (including .config). Example: kernel source code: /usr/src/linux-3.X build directory: /home/name/build/kernel To configure and build the kernel, use: cd /usr/src/linux-3.X make O=/home/name/build/kernel menuconfig make O=/home/name/build/kernel sudo make O=/home/name/build/kernel modules_install install Please note: If the 'O=output/dir' option is used, then it must be used for all invocations of make. CONFIGURING the kernel: Do not skip this step even if you are only upgrading one minor version. New configuration options are added in each release, and odd problems will turn up if the configuration files are not set up as expected. If you want to carry your existing configuration to a new version with minimal work, use "make oldconfig", which will only ask you for the answers to new questions. - Alternative configuration commands are: "make config" Plain text interface. "make menuconfig" Text based color menus, radiolists & dialogs. "make nconfig" Enhanced text based color menus. "make xconfig" X windows (Qt) based configuration tool. "make gconfig" X windows (Gtk) based configuration tool. "make oldconfig" Default all questions based on the contents of your existing ./.config file and asking about new config symbols. "make silentoldconfig" Like above, but avoids cluttering the screen with questions already answered. Additionally updates the dependencies. "make olddefconfig" Like above, but sets new symbols to their default values without prompting. "make defconfig" Create a ./.config file by using the default symbol values from either arch/$ARCH/defconfig or arch/$ARCH/configs/${PLATFORM}_defconfig, depending on the architecture. "make ${PLATFORM}_defconfig" Create a ./.config file by using the default symbol values from arch/$ARCH/configs/${PLATFORM}_defconfig. Use "make help" to get a list of all available platforms of your architecture. "make allyesconfig" Create a ./.config file by setting symbol values to 'y' as much as possible. "make allmodconfig" Create a ./.config file by setting symbol values to 'm' as much as possible. "make allnoconfig" Create a ./.config file by setting symbol values to 'n' as much as possible. "make randconfig" Create a ./.config file by setting symbol values to random values. "make localmodconfig" Create a config based on current config and loaded modules (lsmod). Disables any module option that is not needed for the loaded modules. To create a localmodconfig for another machine, store the lsmod of that machine into a file and pass it in as a LSMOD parameter. target$ lsmod > /tmp/mylsmod target$ scp /tmp/mylsmod host:/tmp host$ make LSMOD=/tmp/mylsmod localmodconfig The above also works when cross compiling. "make localyesconfig" Similar to localmodconfig, except it will convert all module options to built in (=y) options. You can find more information on using the Linux kernel config tools in Documentation/kbuild/kconfig.txt. - NOTES on "make config": - Having unnecessary drivers will make the kernel bigger, and can under some circumstances lead to problems: probing for a nonexistent controller card may confuse your other controllers - Compiling the kernel with "Processor type" set higher than 386 will result in a kernel that does NOT work on a 386. The kernel will detect this on bootup, and give up. - A kernel with math-emulation compiled in will still use the coprocessor if one is present: the math emulation will just never get used in that case. The kernel will be slightly larger, but will work on different machines regardless of whether they have a math coprocessor or not. - The "kernel hacking" configuration details usually result in a bigger or slower kernel (or both), and can even make the kernel less stable by configuring some routines to actively try to break bad code to find kernel problems (kmalloc()). Thus you should probably answer 'n' to the questions for "development", "experimental", or "debugging" features. COMPILING the kernel: - Make sure you have at least gcc 3.2 available. For more information, refer to Documentation/Changes. Please note that you can still run a.out user programs with this kernel. - Do a "make" to create a compressed kernel image. It is also possible to do "make install" if you have lilo installed to suit the kernel makefiles, but you may want to check your particular lilo setup first. To do the actual install, you have to be root, but none of the normal build should require that. Don't take the name of root in vain. - If you configured any of the parts of the kernel as `modules', you will also have to do "make modules_install". - Verbose kernel compile/build output: Normally, the kernel build system runs in a fairly quiet mode (but not totally silent). However, sometimes you or other kernel developers need to see compile, link, or other commands exactly as they are executed. For this, use "verbose" build mode. This is done by inserting "V=1" in the "make" command. E.g.: make V=1 all To have the build system also tell the reason for the rebuild of each target, use "V=2". The default is "V=0". - Keep a backup kernel handy in case something goes wrong. This is especially true for the development releases, since each new release contains new code which has not been debugged. Make sure you keep a backup of the modules corresponding to that kernel, as well. If you are installing a new kernel with the same version number as your working kernel, make a backup of your modules directory before you do a "make modules_install". Alternatively, before compiling, use the kernel config option "LOCALVERSION" to append a unique suffix to the regular kernel version. LOCALVERSION can be set in the "General Setup" menu. - In order to boot your new kernel, you'll need to copy the kernel image (e.g. .../linux/arch/i386/boot/bzImage after compilation) to the place where your regular bootable kernel is found. - Booting a kernel directly from a floppy without the assistance of a bootloader such as LILO, is no longer supported. If you boot Linux from the hard drive, chances are you use LILO, which uses the kernel image as specified in the file /etc/lilo.conf. The kernel image file is usually /vmlinuz, /boot/vmlinuz, /bzImage or /boot/bzImage. To use the new kernel, save a copy of the old image and copy the new image over the old one. Then, you MUST RERUN LILO to update the loading map!! If you don't, you won't be able to boot the new kernel image. Reinstalling LILO is usually a matter of running /sbin/lilo. You may wish to edit /etc/lilo.conf to specify an entry for your old kernel image (say, /vmlinux.old) in case the new one does not work. See the LILO docs for more information. After reinstalling LILO, you should be all set. Shutdown the system, reboot, and enjoy! If you ever need to change the default root device, video mode, ramdisk size, etc. in the kernel image, use the 'rdev' program (or alternatively the LILO boot options when appropriate). No need to recompile the kernel to change these parameters. - Reboot with the new kernel and enjoy. IF SOMETHING GOES WRONG: - If you have problems that seem to be due to kernel bugs, please check the file MAINTAINERS to see if there is a particular person associated with the part of the kernel that you are having trouble with. If there isn't anyone listed there, then the second best thing is to mail them to me (torvalds@linux-foundation.org), and possibly to any other relevant mailing-list or to the newsgroup. - In all bug-reports, *please* tell what kernel you are talking about, how to duplicate the problem, and what your setup is (use your common sense). If the problem is new, tell me so, and if the problem is old, please try to tell me when you first noticed it. - If the bug results in a message like unable to handle kernel paging request at address C0000010 Oops: 0002 EIP: 0010:XXXXXXXX eax: xxxxxxxx ebx: xxxxxxxx ecx: xxxxxxxx edx: xxxxxxxx esi: xxxxxxxx edi: xxxxxxxx ebp: xxxxxxxx ds: xxxx es: xxxx fs: xxxx gs: xxxx Pid: xx, process nr: xx xx xx xx xx xx xx xx xx xx xx or similar kernel debugging information on your screen or in your system log, please duplicate it *exactly*. The dump may look incomprehensible to you, but it does contain information that may help debugging the problem. The text above the dump is also important: it tells something about why the kernel dumped code (in the above example, it's due to a bad kernel pointer). More information on making sense of the dump is in Documentation/oops-tracing.txt - If you compiled the kernel with CONFIG_KALLSYMS you can send the dump as is, otherwise you will have to use the "ksymoops" program to make sense of the dump (but compiling with CONFIG_KALLSYMS is usually preferred). This utility can be downloaded from ftp://ftp.<country>.kernel.org/pub/linux/utils/kernel/ksymoops/ . Alternatively, you can do the dump lookup by hand: - In debugging dumps like the above, it helps enormously if you can look up what the EIP value means. The hex value as such doesn't help me or anybody else very much: it will depend on your particular kernel setup. What you should do is take the hex value from the EIP line (ignore the "0010:"), and look it up in the kernel namelist to see which kernel function contains the offending address. To find out the kernel function name, you'll need to find the system binary associated with the kernel that exhibited the symptom. This is the file 'linux/vmlinux'. To extract the namelist and match it against the EIP from the kernel crash, do: nm vmlinux | sort | less This will give you a list of kernel addresses sorted in ascending order, from which it is simple to find the function that contains the offending address. Note that the address given by the kernel debugging messages will not necessarily match exactly with the function addresses (in fact, that is very unlikely), so you can't just 'grep' the list: the list will, however, give you the starting point of each kernel function, so by looking for the function that has a starting address lower than the one you are searching for but is followed by a function with a higher address you will find the one you want. In fact, it may be a good idea to include a bit of "context" in your problem report, giving a few lines around the interesting one. If you for some reason cannot do the above (you have a pre-compiled kernel image or similar), telling me as much about your setup as possible will help. Please read the REPORTING-BUGS document for details. - Alternatively, you can use gdb on a running kernel. (read-only; i.e. you cannot change values or set break points.) To do this, first compile the kernel with -g; edit arch/i386/Makefile appropriately, then do a "make clean". You'll also need to enable CONFIG_PROC_FS (via "make config"). After you've rebooted with the new kernel, do "gdb vmlinux /proc/kcore". You can now use all the usual gdb commands. The command to look up the point where your system crashed is "l *0xXXXXXXXX". (Replace the XXXes with the EIP value.) gdb'ing a non-running kernel currently fails because gdb (wrongly) disregards the starting offset for which the kernel is compiled.
doans / MoDANetIn the paper, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The multi-task learning (MTL) efficiency of MoDANet was evaluated with different variants of Y-shaped connection and fine-tuning some hyper-parameters of the deep network. As a result, MoDANet with one shared residual module using more filters, larger filter size, and longer signal length can achieve better performance of modulation classification and DOA estimation, but those might result in higher computational complexity. Therefore, choosing these parameters to attain a good trade-off between accuracy and computational cost is important, especially for resource-constrained devices. The network is investigated with two typical propagation channel models, including Pedestrian A and Vehicular A, to show the affect of those channels on the efficiency of the network. Remarkably, our work is the first DL-based MTL model to handle two unrelated tasks of modulation classification and DOA estimation. Please cite the papar as:
Aryia-Behroziuan / NumpyQuickstart tutorial Prerequisites Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial. If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Please see https://scipy.org/install.html for instructions. Learner profile This tutorial is intended as a quick overview of algebra and arrays in NumPy and want to understand how n-dimensional (n>=2) arrays are represented and can be manipulated. In particular, if you don’t know how to apply common functions to n-dimensional arrays (without using for-loops), or if you want to understand axis and shape properties for n-dimensional arrays, this tutorial might be of help. Learning Objectives After this tutorial, you should be able to: Understand the difference between one-, two- and n-dimensional arrays in NumPy; Understand how to apply some linear algebra operations to n-dimensional arrays without using for-loops; Understand axis and shape properties for n-dimensional arrays. The Basics NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes. For example, the coordinates of a point in 3D space [1, 2, 1] has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3. [[ 1., 0., 0.], [ 0., 1., 2.]] NumPy’s array class is called ndarray. It is also known by the alias array. Note that numpy.array is not the same as the Standard Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are: ndarray.ndim the number of axes (dimensions) of the array. ndarray.shape the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim. ndarray.size the total number of elements of the array. This is equal to the product of the elements of shape. ndarray.dtype an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples. ndarray.itemsize the size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize. ndarray.data the buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities. An example >>> import numpy as np a = np.arange(15).reshape(3, 5) a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) a.shape (3, 5) a.ndim 2 a.dtype.name 'int64' a.itemsize 8 a.size 15 type(a) <class 'numpy.ndarray'> b = np.array([6, 7, 8]) b array([6, 7, 8]) type(b) <class 'numpy.ndarray'> Array Creation There are several ways to create arrays. For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. >>> >>> import numpy as np >>> a = np.array([2,3,4]) >>> a array([2, 3, 4]) >>> a.dtype dtype('int64') >>> b = np.array([1.2, 3.5, 5.1]) >>> b.dtype dtype('float64') A frequent error consists in calling array with multiple arguments, rather than providing a single sequence as an argument. >>> >>> a = np.array(1,2,3,4) # WRONG Traceback (most recent call last): ... TypeError: array() takes from 1 to 2 positional arguments but 4 were given >>> a = np.array([1,2,3,4]) # RIGHT array transforms sequences of sequences into two-dimensional arrays, sequences of sequences of sequences into three-dimensional arrays, and so on. >>> >>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b array([[1.5, 2. , 3. ], [4. , 5. , 6. ]]) The type of the array can also be explicitly specified at creation time: >>> >>> c = np.array( [ [1,2], [3,4] ], dtype=complex ) >>> c array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]) Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. The function zeros creates an array full of zeros, the function ones creates an array full of ones, and the function empty creates an array whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64. >>> >>> np.zeros((3, 4)) array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) >>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified array([[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int16) >>> np.empty( (2,3) ) # uninitialized array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], # may vary [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) To create sequences of numbers, NumPy provides the arange function which is analogous to the Python built-in range, but returns an array. >>> >>> np.arange( 10, 30, 5 ) array([10, 15, 20, 25]) >>> np.arange( 0, 2, 0.3 ) # it accepts float arguments array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) When arange is used with floating point arguments, it is generally not possible to predict the number of elements obtained, due to the finite floating point precision. For this reason, it is usually better to use the function linspace that receives as an argument the number of elements that we want, instead of the step: >>> >>> from numpy import pi >>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2 array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np.sin(x) See also array, zeros, zeros_like, ones, ones_like, empty, empty_like, arange, linspace, numpy.random.Generator.rand, numpy.random.Generator.randn, fromfunction, fromfile Printing Arrays When you print an array, NumPy displays it in a similar way to nested lists, but with the following layout: the last axis is printed from left to right, the second-to-last is printed from top to bottom, the rest are also printed from top to bottom, with each slice separated from the next by an empty line. One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices. >>> >>> a = np.arange(6) # 1d array >>> print(a) [0 1 2 3 4 5] >>> >>> b = np.arange(12).reshape(4,3) # 2d array >>> print(b) [[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]] >>> >>> c = np.arange(24).reshape(2,3,4) # 3d array >>> print(c) [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] See below to get more details on reshape. If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners: >>> >>> print(np.arange(10000)) [ 0 1 2 ... 9997 9998 9999] >>> >>> print(np.arange(10000).reshape(100,100)) [[ 0 1 2 ... 97 98 99] [ 100 101 102 ... 197 198 199] [ 200 201 202 ... 297 298 299] ... [9700 9701 9702 ... 9797 9798 9799] [9800 9801 9802 ... 9897 9898 9899] [9900 9901 9902 ... 9997 9998 9999]] To disable this behaviour and force NumPy to print the entire array, you can change the printing options using set_printoptions. >>> >>> np.set_printoptions(threshold=sys.maxsize) # sys module should be imported Basic Operations Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result. >>> >>> a = np.array( [20,30,40,50] ) >>> b = np.arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> b**2 array([0, 1, 4, 9]) >>> 10*np.sin(a) array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854]) >>> a<35 array([ True, True, False, False]) Unlike in many matrix languages, the product operator * operates elementwise in NumPy arrays. The matrix product can be performed using the @ operator (in python >=3.5) or the dot function or method: >>> >>> A = np.array( [[1,1], ... [0,1]] ) >>> B = np.array( [[2,0], ... [3,4]] ) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[5, 4], [3, 4]]) Some operations, such as += and *=, act in place to modify an existing array rather than create a new one. >>> >>> rg = np.random.default_rng(1) # create instance of default random number generator >>> a = np.ones((2,3), dtype=int) >>> b = rg.random((2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[3.51182162, 3.9504637 , 3.14415961], [3.94864945, 3.31183145, 3.42332645]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): ... numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int64') with casting rule 'same_kind' When operating with arrays of different types, the type of the resulting array corresponds to the more general or precise one (a behavior known as upcasting). >>> >>> a = np.ones(3, dtype=np.int32) >>> b = np.linspace(0,pi,3) >>> b.dtype.name 'float64' >>> c = a+b >>> c array([1. , 2.57079633, 4.14159265]) >>> c.dtype.name 'float64' >>> d = np.exp(c*1j) >>> d array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j, -0.54030231-0.84147098j]) >>> d.dtype.name 'complex128' Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the ndarray class. >>> >>> a = rg.random((2,3)) >>> a array([[0.82770259, 0.40919914, 0.54959369], [0.02755911, 0.75351311, 0.53814331]]) >>> a.sum() 3.1057109529998157 >>> a.min() 0.027559113243068367 >>> a.max() 0.8277025938204418 By default, these operations apply to the array as though it were a list of numbers, regardless of its shape. However, by specifying the axis parameter you can apply an operation along the specified axis of an array: >>> >>> b = np.arange(12).reshape(3,4) >>> b array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> b.sum(axis=0) # sum of each column array([12, 15, 18, 21]) >>> >>> b.min(axis=1) # min of each row array([0, 4, 8]) >>> >>> b.cumsum(axis=1) # cumulative sum along each row array([[ 0, 1, 3, 6], [ 4, 9, 15, 22], [ 8, 17, 27, 38]]) Universal Functions NumPy provides familiar mathematical functions such as sin, cos, and exp. In NumPy, these are called “universal functions”(ufunc). Within NumPy, these functions operate elementwise on an array, producing an array as output. >>> >>> B = np.arange(3) >>> B array([0, 1, 2]) >>> np.exp(B) array([1. , 2.71828183, 7.3890561 ]) >>> np.sqrt(B) array([0. , 1. , 1.41421356]) >>> C = np.array([2., -1., 4.]) >>> np.add(B, C) array([2., 0., 6.]) See also all, any, apply_along_axis, argmax, argmin, argsort, average, bincount, ceil, clip, conj, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, invert, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sort, std, sum, trace, transpose, var, vdot, vectorize, where Indexing, Slicing and Iterating One-dimensional arrays can be indexed, sliced and iterated over, much like lists and other Python sequences. >>> >>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) # equivalent to a[0:6:2] = 1000; # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, 1000, 27, 1000, 1, 1000]) >>> for i in a: ... print(i**(1/3.)) ... 9.999999999999998 1.0 9.999999999999998 3.0 9.999999999999998 4.999999999999999 5.999999999999999 6.999999999999999 7.999999999999999 8.999999999999998 Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas: >>> >>> def f(x,y): ... return 10*x+y ... >>> b = np.fromfunction(f,(5,4),dtype=int) >>> b array([[ 0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]]) >>> b[2,3] 23 >>> b[0:5, 1] # each row in the second column of b array([ 1, 11, 21, 31, 41]) >>> b[ : ,1] # equivalent to the previous example array([ 1, 11, 21, 31, 41]) >>> b[1:3, : ] # each column in the second and third row of b array([[10, 11, 12, 13], [20, 21, 22, 23]]) When fewer indices are provided than the number of axes, the missing indices are considered complete slices: >>> >>> b[-1] # the last row. Equivalent to b[-1,:] array([40, 41, 42, 43]) The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent the remaining axes. NumPy also allows you to write this using dots as b[i,...]. The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is an array with 5 axes, then x[1,2,...] is equivalent to x[1,2,:,:,:], x[...,3] to x[:,:,:,:,3] and x[4,...,5,:] to x[4,:,:,5,:]. >>> >>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays) ... [ 10, 12, 13]], ... [[100,101,102], ... [110,112,113]]]) >>> c.shape (2, 2, 3) >>> c[1,...] # same as c[1,:,:] or c[1] array([[100, 101, 102], [110, 112, 113]]) >>> c[...,2] # same as c[:,:,2] array([[ 2, 13], [102, 113]]) Iterating over multidimensional arrays is done with respect to the first axis: >>> >>> for row in b: ... print(row) ... [0 1 2 3] [10 11 12 13] [20 21 22 23] [30 31 32 33] [40 41 42 43] However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is an iterator over all the elements of the array: >>> >>> for element in b.flat: ... print(element) ... 0 1 2 3 10 11 12 13 20 21 22 23 30 31 32 33 40 41 42 43 See also Indexing, Indexing (reference), newaxis, ndenumerate, indices Shape Manipulation Changing the shape of an array An array has a shape given by the number of elements along each axis: >>> >>> a = np.floor(10*rg.random((3,4))) >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.shape (3, 4) The shape of an array can be changed with various commands. Note that the following three commands all return a modified array, but do not change the original array: >>> >>> a.ravel() # returns the array, flattened array([3., 7., 3., 4., 1., 4., 2., 2., 7., 2., 4., 9.]) >>> a.reshape(6,2) # returns the array with a modified shape array([[3., 7.], [3., 4.], [1., 4.], [2., 2.], [7., 2.], [4., 9.]]) >>> a.T # returns the array, transposed array([[3., 1., 7.], [7., 4., 2.], [3., 2., 4.], [4., 2., 9.]]) >>> a.T.shape (4, 3) >>> a.shape (3, 4) The order of the elements in the array resulting from ravel() is normally “C-style”, that is, the rightmost index “changes the fastest”, so the element after a[0,0] is a[0,1]. If the array is reshaped to some other shape, again the array is treated as “C-style”. NumPy normally creates arrays stored in this order, so ravel() will usually not need to copy its argument, but if the array was made by taking slices of another array or created with unusual options, it may need to be copied. The functions ravel() and reshape() can also be instructed, using an optional argument, to use FORTRAN-style arrays, in which the leftmost index changes the fastest. The reshape function returns its argument with a modified shape, whereas the ndarray.resize method modifies the array itself: >>> >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.resize((2,6)) >>> a array([[3., 7., 3., 4., 1., 4.], [2., 2., 7., 2., 4., 9.]]) If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated: >>> >>> a.reshape(3,-1) array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) See also ndarray.shape, reshape, resize, ravel Stacking together different arrays Several arrays can be stacked together along different axes: >>> >>> a = np.floor(10*rg.random((2,2))) >>> a array([[9., 7.], [5., 2.]]) >>> b = np.floor(10*rg.random((2,2))) >>> b array([[1., 9.], [5., 1.]]) >>> np.vstack((a,b)) array([[9., 7.], [5., 2.], [1., 9.], [5., 1.]]) >>> np.hstack((a,b)) array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to hstack only for 2D arrays: >>> >>> from numpy import newaxis >>> np.column_stack((a,b)) # with 2D arrays array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) >>> a = np.array([4.,2.]) >>> b = np.array([3.,8.]) >>> np.column_stack((a,b)) # returns a 2D array array([[4., 3.], [2., 8.]]) >>> np.hstack((a,b)) # the result is different array([4., 2., 3., 8.]) >>> a[:,newaxis] # view `a` as a 2D column vector array([[4.], [2.]]) >>> np.column_stack((a[:,newaxis],b[:,newaxis])) array([[4., 3.], [2., 8.]]) >>> np.hstack((a[:,newaxis],b[:,newaxis])) # the result is the same array([[4., 3.], [2., 8.]]) On the other hand, the function row_stack is equivalent to vstack for any input arrays. In fact, row_stack is an alias for vstack: >>> >>> np.column_stack is np.hstack False >>> np.row_stack is np.vstack True In general, for arrays with more than two dimensions, hstack stacks along their second axes, vstack stacks along their first axes, and concatenate allows for an optional arguments giving the number of the axis along which the concatenation should happen. Note In complex cases, r_ and c_ are useful for creating arrays by stacking numbers along one axis. They allow the use of range literals (“:”) >>> >>> np.r_[1:4,0,4] array([1, 2, 3, 0, 4]) When used with arrays as arguments, r_ and c_ are similar to vstack and hstack in their default behavior, but allow for an optional argument giving the number of the axis along which to concatenate. See also hstack, vstack, column_stack, concatenate, c_, r_ Splitting one array into several smaller ones Using hsplit, you can split an array along its horizontal axis, either by specifying the number of equally shaped arrays to return, or by specifying the columns after which the division should occur: >>> >>> a = np.floor(10*rg.random((2,12))) >>> a array([[6., 7., 6., 9., 0., 5., 4., 0., 6., 8., 5., 2.], [8., 5., 5., 7., 1., 8., 6., 7., 1., 8., 1., 0.]]) # Split a into 3 >>> np.hsplit(a,3) [array([[6., 7., 6., 9.], [8., 5., 5., 7.]]), array([[0., 5., 4., 0.], [1., 8., 6., 7.]]), array([[6., 8., 5., 2.], [1., 8., 1., 0.]])] # Split a after the third and the fourth column >>> np.hsplit(a,(3,4)) [array([[6., 7., 6.], [8., 5., 5.]]), array([[9.], [7.]]), array([[0., 5., 4., 0., 6., 8., 5., 2.], [1., 8., 6., 7., 1., 8., 1., 0.]])] vsplit splits along the vertical axis, and array_split allows one to specify along which axis to split. Copies and Views When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases: No Copy at All Simple assignments make no copy of objects or their data. >>> >>> a = np.array([[ 0, 1, 2, 3], ... [ 4, 5, 6, 7], ... [ 8, 9, 10, 11]]) >>> b = a # no new object is created >>> b is a # a and b are two names for the same ndarray object True Python passes mutable objects as references, so function calls make no copy. >>> >>> def f(x): ... print(id(x)) ... >>> id(a) # id is a unique identifier of an object 148293216 # may vary >>> f(a) 148293216 # may vary View or Shallow Copy Different array objects can share the same data. The view method creates a new array object that looks at the same data. >>> >>> c = a.view() >>> c is a False >>> c.base is a # c is a view of the data owned by a True >>> c.flags.owndata False >>> >>> c = c.reshape((2, 6)) # a's shape doesn't change >>> a.shape (3, 4) >>> c[0, 4] = 1234 # a's data changes >>> a array([[ 0, 1, 2, 3], [1234, 5, 6, 7], [ 8, 9, 10, 11]]) Slicing an array returns a view of it: >>> >>> s = a[ : , 1:3] # spaces added for clarity; could also be written "s = a[:, 1:3]" >>> s[:] = 10 # s[:] is a view of s. Note the difference between s = 10 and s[:] = 10 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Deep Copy The copy method makes a complete copy of the array and its data. >>> >>> d = a.copy() # a new array object with new data is created >>> d is a False >>> d.base is a # d doesn't share anything with a False >>> d[0,0] = 9999 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Sometimes copy should be called after slicing if the original array is not required anymore. For example, suppose a is a huge intermediate result and the final result b only contains a small fraction of a, a deep copy should be made when constructing b with slicing: >>> >>> a = np.arange(int(1e8)) >>> b = a[:100].copy() >>> del a # the memory of ``a`` can be released. If b = a[:100] is used instead, a is referenced by b and will persist in memory even if del a is executed. Functions and Methods Overview Here is a list of some useful NumPy functions and methods names ordered in categories. See Routines for the full list. Array Creation arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r_, zeros, zeros_like Conversions ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat Manipulations array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack Questions all, any, nonzero, where Ordering argmax, argmin, argsort, max, min, ptp, searchsorted, sort Operations choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum Basic Statistics cov, mean, std, var Basic Linear Algebra cross, dot, outer, linalg.svd, vdot Less Basic Broadcasting rules Broadcasting allows universal functions to deal in a meaningful way with inputs that do not have exactly the same shape. The first rule of broadcasting is that if all input arrays do not have the same number of dimensions, a “1” will be repeatedly prepended to the shapes of the smaller arrays until all the arrays have the same number of dimensions. The second rule of broadcasting ensures that arrays with a size of 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is assumed to be the same along that dimension for the “broadcast” array. After application of the broadcasting rules, the sizes of all arrays must match. More details can be found in Broadcasting. Advanced indexing and index tricks NumPy offers more indexing facilities than regular Python sequences. In addition to indexing by integers and slices, as we saw before, arrays can be indexed by arrays of integers and arrays of booleans. Indexing with Arrays of Indices >>> >>> a = np.arange(12)**2 # the first 12 square numbers >>> i = np.array([1, 1, 3, 8, 5]) # an array of indices >>> a[i] # the elements of a at the positions i array([ 1, 1, 9, 64, 25]) >>> >>> j = np.array([[3, 4], [9, 7]]) # a bidimensional array of indices >>> a[j] # the same shape as j array([[ 9, 16], [81, 49]]) When the indexed array a is multidimensional, a single array of indices refers to the first dimension of a. The following example shows this behavior by converting an image of labels into a color image using a palette. >>> >>> palette = np.array([[0, 0, 0], # black ... [255, 0, 0], # red ... [0, 255, 0], # green ... [0, 0, 255], # blue ... [255, 255, 255]]) # white >>> image = np.array([[0, 1, 2, 0], # each value corresponds to a color in the palette ... [0, 3, 4, 0]]) >>> palette[image] # the (2, 4, 3) color image array([[[ 0, 0, 0], [255, 0, 0], [ 0, 255, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 255], [255, 255, 255], [ 0, 0, 0]]]) We can also give indexes for more than one dimension. The arrays of indices for each dimension must have the same shape. >>> >>> a = np.arange(12).reshape(3,4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> i = np.array([[0, 1], # indices for the first dim of a ... [1, 2]]) >>> j = np.array([[2, 1], # indices for the second dim ... [3, 3]]) >>> >>> a[i, j] # i and j must have equal shape array([[ 2, 5], [ 7, 11]]) >>> >>> a[i, 2] array([[ 2, 6], [ 6, 10]]) >>> >>> a[:, j] # i.e., a[ : , j] array([[[ 2, 1], [ 3, 3]], [[ 6, 5], [ 7, 7]], [[10, 9], [11, 11]]]) In Python, arr[i, j] is exactly the same as arr[(i, j)]—so we can put i and j in a tuple and then do the indexing with that. >>> >>> l = (i, j) # equivalent to a[i, j] >>> a[l] array([[ 2, 5], [ 7, 11]]) However, we can not do this by putting i and j into an array, because this array will be interpreted as indexing the first dimension of a. >>> >>> s = np.array([i, j]) # not what we want >>> a[s] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: index 3 is out of bounds for axis 0 with size 3 # same as a[i, j] >>> a[tuple(s)] array([[ 2, 5], [ 7, 11]]) Another common use of indexing with arrays is the search of the maximum value of time-dependent series: >>> >>> time = np.linspace(20, 145, 5) # time scale >>> data = np.sin(np.arange(20)).reshape(5,4) # 4 time-dependent series >>> time array([ 20. , 51.25, 82.5 , 113.75, 145. ]) >>> data array([[ 0. , 0.84147098, 0.90929743, 0.14112001], [-0.7568025 , -0.95892427, -0.2794155 , 0.6569866 ], [ 0.98935825, 0.41211849, -0.54402111, -0.99999021], [-0.53657292, 0.42016704, 0.99060736, 0.65028784], [-0.28790332, -0.96139749, -0.75098725, 0.14987721]]) # index of the maxima for each series >>> ind = data.argmax(axis=0) >>> ind array([2, 0, 3, 1]) # times corresponding to the maxima >>> time_max = time[ind] >>> >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0],0], data[ind[1],1]... >>> time_max array([ 82.5 , 20. , 113.75, 51.25]) >>> data_max array([0.98935825, 0.84147098, 0.99060736, 0.6569866 ]) >>> np.all(data_max == data.max(axis=0)) True You can also use indexing with arrays as a target to assign to: >>> >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> a[[1,3,4]] = 0 >>> a array([0, 0, 2, 0, 0]) However, when the list of indices contains repetitions, the assignment is done several times, leaving behind the last value: >>> >>> a = np.arange(5) >>> a[[0,0,2]]=[1,2,3] >>> a array([2, 1, 3, 3, 4]) This is reasonable enough, but watch out if you want to use Python’s += construct, as it may not do what you expect: >>> >>> a = np.arange(5) >>> a[[0,0,2]]+=1 >>> a array([1, 1, 3, 3, 4]) Even though 0 occurs twice in the list of indices, the 0th element is only incremented once. This is because Python requires “a+=1” to be equivalent to “a = a + 1”. Indexing with Boolean Arrays When we index arrays with arrays of (integer) indices we are providing the list of indices to pick. With boolean indices the approach is different; we explicitly choose which items in the array we want and which ones we don’t. The most natural way one can think of for boolean indexing is to use boolean arrays that have the same shape as the original array: >>> >>> a = np.arange(12).reshape(3,4) >>> b = a > 4 >>> b # b is a boolean with a's shape array([[False, False, False, False], [False, True, True, True], [ True, True, True, True]]) >>> a[b] # 1d array with the selected elements array([ 5, 6, 7, 8, 9, 10, 11]) This property can be very useful in assignments: >>> >>> a[b] = 0 # All elements of 'a' higher than 4 become 0 >>> a array([[0, 1, 2, 3], [4, 0, 0, 0], [0, 0, 0, 0]]) You can look at the following example to see how to use boolean indexing to generate an image of the Mandelbrot set: >>> import numpy as np import matplotlib.pyplot as plt def mandelbrot( h,w, maxit=20 ): """Returns an image of the Mandelbrot fractal of size (h,w).""" y,x = np.ogrid[ -1.4:1.4:h*1j, -2:0.8:w*1j ] c = x+y*1j z = c divtime = maxit + np.zeros(z.shape, dtype=int) for i in range(maxit): z = z**2 + c diverge = z*np.conj(z) > 2**2 # who is diverging div_now = diverge & (divtime==maxit) # who is diverging now divtime[div_now] = i # note when z[diverge] = 2 # avoid diverging too much return divtime plt.imshow(mandelbrot(400,400)) ../_images/quickstart-1.png The second way of indexing with booleans is more similar to integer indexing; for each dimension of the array we give a 1D boolean array selecting the slices we want: >>> >>> a = np.arange(12).reshape(3,4) >>> b1 = np.array([False,True,True]) # first dim selection >>> b2 = np.array([True,False,True,False]) # second dim selection >>> >>> a[b1,:] # selecting rows array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[b1] # same thing array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[:,b2] # selecting columns array([[ 0, 2], [ 4, 6], [ 8, 10]]) >>> >>> a[b1,b2] # a weird thing to do array([ 4, 10]) Note that the length of the 1D boolean array must coincide with the length of the dimension (or axis) you want to slice. In the previous example, b1 has length 3 (the number of rows in a), and b2 (of length 4) is suitable to index the 2nd axis (columns) of a. The ix_() function The ix_ function can be used to combine different vectors so as to obtain the result for each n-uplet. For example, if you want to compute all the a+b*c for all the triplets taken from each of the vectors a, b and c: >>> >>> a = np.array([2,3,4,5]) >>> b = np.array([8,5,4]) >>> c = np.array([5,4,6,8,3]) >>> ax,bx,cx = np.ix_(a,b,c) >>> ax array([[[2]], [[3]], [[4]], [[5]]]) >>> bx array([[[8], [5], [4]]]) >>> cx array([[[5, 4, 6, 8, 3]]]) >>> ax.shape, bx.shape, cx.shape ((4, 1, 1), (1, 3, 1), (1, 1, 5)) >>> result = ax+bx*cx >>> result array([[[42, 34, 50, 66, 26], [27, 22, 32, 42, 17], [22, 18, 26, 34, 14]], [[43, 35, 51, 67, 27], [28, 23, 33, 43, 18], [23, 19, 27, 35, 15]], [[44, 36, 52, 68, 28], [29, 24, 34, 44, 19], [24, 20, 28, 36, 16]], [[45, 37, 53, 69, 29], [30, 25, 35, 45, 20], [25, 21, 29, 37, 17]]]) >>> result[3,2,4] 17 >>> a[3]+b[2]*c[4] 17 You could also implement the reduce as follows: >>> >>> def ufunc_reduce(ufct, *vectors): ... vs = np.ix_(*vectors) ... r = ufct.identity ... for v in vs: ... r = ufct(r,v) ... return r and then use it as: >>> >>> ufunc_reduce(np.add,a,b,c) array([[[15, 14, 16, 18, 13], [12, 11, 13, 15, 10], [11, 10, 12, 14, 9]], [[16, 15, 17, 19, 14], [13, 12, 14, 16, 11], [12, 11, 13, 15, 10]], [[17, 16, 18, 20, 15], [14, 13, 15, 17, 12], [13, 12, 14, 16, 11]], [[18, 17, 19, 21, 16], [15, 14, 16, 18, 13], [14, 13, 15, 17, 12]]]) The advantage of this version of reduce compared to the normal ufunc.reduce is that it makes use of the Broadcasting Rules in order to avoid creating an argument array the size of the output times the number of vectors. Indexing with strings See Structured arrays. Linear Algebra Work in progress. Basic linear algebra to be included here. Simple Array Operations See linalg.py in numpy folder for more. >>> >>> import numpy as np >>> a = np.array([[1.0, 2.0], [3.0, 4.0]]) >>> print(a) [[1. 2.] [3. 4.]] >>> a.transpose() array([[1., 3.], [2., 4.]]) >>> np.linalg.inv(a) array([[-2. , 1. ], [ 1.5, -0.5]]) >>> u = np.eye(2) # unit 2x2 matrix; "eye" represents "I" >>> u array([[1., 0.], [0., 1.]]) >>> j = np.array([[0.0, -1.0], [1.0, 0.0]]) >>> j @ j # matrix product array([[-1., 0.], [ 0., -1.]]) >>> np.trace(u) # trace 2.0 >>> y = np.array([[5.], [7.]]) >>> np.linalg.solve(a, y) array([[-3.], [ 4.]]) >>> np.linalg.eig(j) (array([0.+1.j, 0.-1.j]), array([[0.70710678+0.j , 0.70710678-0.j ], [0. -0.70710678j, 0. +0.70710678j]])) Parameters: square matrix Returns The eigenvalues, each repeated according to its multiplicity. The normalized (unit "length") eigenvectors, such that the column ``v[:,i]`` is the eigenvector corresponding to the eigenvalue ``w[i]`` . Tricks and Tips Here we give a list of short and useful tips. “Automatic” Reshaping To change the dimensions of an array, you can omit one of the sizes which will then be deduced automatically: >>> >>> a = np.arange(30) >>> b = a.reshape((2, -1, 3)) # -1 means "whatever is needed" >>> b.shape (2, 5, 3) >>> b array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]], [[15, 16, 17], [18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]]]) Vector Stacking How do we construct a 2D array from a list of equally-sized row vectors? In MATLAB this is quite easy: if x and y are two vectors of the same length you only need do m=[x;y]. In NumPy this works via the functions column_stack, dstack, hstack and vstack, depending on the dimension in which the stacking is to be done. For example: >>> >>> x = np.arange(0,10,2) >>> y = np.arange(5) >>> m = np.vstack([x,y]) >>> m array([[0, 2, 4, 6, 8], [0, 1, 2, 3, 4]]) >>> xy = np.hstack([x,y]) >>> xy array([0, 2, 4, 6, 8, 0, 1, 2, 3, 4]) The logic behind those functions in more than two dimensions can be strange. See also NumPy for Matlab users Histograms The NumPy histogram function applied to an array returns a pair of vectors: the histogram of the array and a vector of the bin edges. Beware: matplotlib also has a function to build histograms (called hist, as in Matlab) that differs from the one in NumPy. The main difference is that pylab.hist plots the histogram automatically, while numpy.histogram only generates the data. >>> import numpy as np rg = np.random.default_rng(1) import matplotlib.pyplot as plt # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2 mu, sigma = 2, 0.5 v = rg.normal(mu,sigma,10000) # Plot a normalized histogram with 50 bins plt.hist(v, bins=50, density=1) # matplotlib version (plot) # Compute the histogram with numpy and then plot it (n, bins) = np.histogram(v, bins=50, density=True) # NumPy version (no plot) plt.plot(.5*(bins[1:]+bins[:-1]), n) ../_images/quickstart-2.png Further reading The Python tutorial NumPy Reference SciPy Tutorial SciPy Lecture Notes A matlab, R, IDL, NumPy/SciPy dictionary © Copyright 2008-2020, The SciPy community. Last updated on Jun 29, 2020. Created using Sphinx 2.4.4.
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myflashlab / SurfaceVideoPlayer ANESurfacePlayer air native extension helps you play video files inside your air mobile projects and you can control the position of the player by setting x, y, width and height parameters.
mrc1234 / Liri Bot2019# LIRI Bot ### Overview In this assignment, you will make LIRI. LIRI is like iPhone's SIRI. However, while SIRI is a Speech Interpretation and Recognition Interface, LIRI is a _Language_ Interpretation and Recognition Interface. LIRI will be a command line node app that takes in parameters and gives you back data. ### Before You Begin 1. LIRI will search Spotify for songs, Bands in Town for concerts, and OMDB for movies. 2. Make a new GitHub repository called liri-node-app and clone it to your computer. 3. To retrieve the data that will power this app, you'll need to send requests to the Bands in Town, Spotify and OMDB APIs. You'll find these Node packages crucial for your assignment. * [Node-Spotify-API](https://www.npmjs.com/package/node-spotify-api) * [Request](https://www.npmjs.com/package/request) * You'll use Request to grab data from the [OMDB API](http://www.omdbapi.com) and the [Bands In Town API](http://www.artists.bandsintown.com/bandsintown-api) * [Moment](https://www.npmjs.com/package/moment) * [DotEnv](https://www.npmjs.com/package/dotenv) ## Submission Guide Make sure you use the normal GitHub. Because this is a CLI App, there will be no need to deploy it to Heroku. This time, though, you need to include screenshots, a gif, and/or a video showing us that you got the app working with no bugs. You can include these screenshots or a link to a video in a `README.md` file. * Include screenshots (or a video) of typical user flows through your application (for the customer and if relevant the manager/supervisor). This includes views of the prompts and the responses after their selection (for the different selection options). * Include any other screenshots you deem necessary to help someone who has never been introduced to your application understand the purpose and function of it. This is how you will communicate to potential employers/other developers in the future what you built and why, and to show how it works. * Because screenshots (and well-written READMEs) are extremely important in the context of GitHub, this will be part of the grading. If you haven't written a markdown file yet, [click here for a rundown](https://guides.github.com/features/mastering-markdown/), or just take a look at the raw file of these instructions. ### Submission on BCS * Please submit the link to the Github Repository! ### Instructions 1. Navigate to the root of your project and run `npm init -y` — this will initialize a `package.json` file for your project. The `package.json` file is required for installing third party npm packages and saving their version numbers. If you fail to initialize a `package.json` file, it will be troublesome, and at times almost impossible for anyone else to run your code after cloning your project. 2. Make a `.gitignore` file and add the following lines to it. This will tell git not to track these files, and thus they won't be committed to Github. ``` node_modules .DS_Store .env ``` 3. Make a JavaScript file named `keys.js`. * Inside keys.js your file will look like this: ```js console.log('this is loaded'); exports.spotify = { id: process.env.SPOTIFY_ID, secret: process.env.SPOTIFY_SECRET }; ``` 4. Next, create a file named `.env`, add the following to it, replacing the values with your API keys (no quotes) once you have them: ```js # Spotify API keys SPOTIFY_ID=your-spotify-id SPOTIFY_SECRET=your-spotify-secret ``` * This file will be used by the `dotenv` package to set what are known as environment variables to the global `process.env` object in node. These are values that are meant to be specific to the computer that node is running on, and since we are gitignoring this file, they won't be pushed to github — keeping our API key information private. * If someone wanted to clone your app from github and run it themselves, they would need to supply their own `.env` file for it to work. 5. Make a file called `random.txt`. * Inside of `random.txt` put the following in with no extra characters or white space: * spotify-this-song,"I Want it That Way" 6. Make a JavaScript file named `liri.js`. 7. At the top of the `liri.js` file, add code to read and set any environment variables with the dotenv package: ```js require("dotenv").config(); ``` 8. Add the code required to import the `keys.js` file and store it in a variable. * You should then be able to access your keys information like so ```js var spotify = new Spotify(keys.spotify); ``` 9. Make it so liri.js can take in one of the following commands: * `concert-this` * `spotify-this-song` * `movie-this` * `do-what-it-says` ### What Each Command Should Do 1. `node liri.js concert-this <artist/band name here>` * This will search the Bands in Town Artist Events API (`"https://rest.bandsintown.com/artists/" + artist + "/events?app_id=codingbootcamp"`) for an artist and render the following information about each event to the terminal: * Name of the venue * Venue location * Date of the Event (use moment to format this as "MM/DD/YYYY") 2. `node liri.js spotify-this-song '<song name here>'` * This will show the following information about the song in your terminal/bash window * Artist(s) * The song's name * A preview link of the song from Spotify * The album that the song is from * If no song is provided then your program will default to "The Sign" by Ace of Base. * You will utilize the [node-spotify-api](https://www.npmjs.com/package/node-spotify-api) package in order to retrieve song information from the Spotify API. * The Spotify API requires you sign up as a developer to generate the necessary credentials. You can follow these steps in order to generate a **client id** and **client secret**: * Step One: Visit <https://developer.spotify.com/my-applications/#!/> * Step Two: Either login to your existing Spotify account or create a new one (a free account is fine) and log in. * Step Three: Once logged in, navigate to <https://developer.spotify.com/my-applications/#!/applications/create> to register a new application to be used with the Spotify API. You can fill in whatever you'd like for these fields. When finished, click the "complete" button. * Step Four: On the next screen, scroll down to where you see your client id and client secret. Copy these values down somewhere, you'll need them to use the Spotify API and the [node-spotify-api package](https://www.npmjs.com/package/node-spotify-api). 3. `node liri.js movie-this '<movie name here>'` * This will output the following information to your terminal/bash window: ``` * Title of the movie. * Year the movie came out. * IMDB Rating of the movie. * Rotten Tomatoes Rating of the movie. * Country where the movie was produced. * Language of the movie. * Plot of the movie. * Actors in the movie. ``` * If the user doesn't type a movie in, the program will output data for the movie 'Mr. Nobody.' * If you haven't watched "Mr. Nobody," then you should: <http://www.imdb.com/title/tt0485947/> * It's on Netflix! * You'll use the request package to retrieve data from the OMDB API. Like all of the in-class activities, the OMDB API requires an API key. You may use `trilogy`. 4. `node liri.js do-what-it-says` * Using the `fs` Node package, LIRI will take the text inside of random.txt and then use it to call one of LIRI's commands. * It should run `spotify-this-song` for "I Want it That Way," as follows the text in `random.txt`. * Edit the text in random.txt to test out the feature for movie-this and concert-this. ### BONUS * In addition to logging the data to your terminal/bash window, output the data to a .txt file called `log.txt`. * Make sure you append each command you run to the `log.txt` file. * Do not overwrite your file each time you run a command. ### Reminder: Submission on BCS * Please submit the link to the Github Repository! - - - ### Minimum Requirements Attempt to complete homework assignment as described in instructions. If unable to complete certain portions, please pseudocode these portions to describe what remains to be completed. Adding a README.md as well as adding this homework to your portfolio are required as well and more information can be found below. - - - ### Create a README.md Add a `README.md` to your repository describing the project. Here are some resources for creating your `README.md`. Here are some resources to help you along the way: * [About READMEs](https://help.github.com/articles/about-readmes/) * [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) - - - ### Add To Your Portfolio After completing the homework please add the piece to your portfolio. Make sure to add a link to your updated portfolio in the comments section of your homework so the TAs can easily ensure you completed this step when they are grading the assignment. To receive an 'A' on any assignment, you must link to it from your portfolio. - - - ### One More Thing If you have any questions about this project or the material we have covered, please post them in the community channels in slack so that your fellow developers can help you! If you're still having trouble, you can come to office hours for assistance from your instructor and TAs. **Good Luck!**
ntotorica / SMP Passivity EnforcementAuthor: Nathan Totorica Date: 5/14/2021 # Singularity Matrix Pertubation (SMP) This code was written for a class project in the course entitled ECE 504: "ST: Passive Electromagnetic Systems" taught in Spring 2021 by Dr. Ata Zadehgol at the University of Idaho in Moscow. This code was developed, in part, based on the code developed by Jennifer Houle in ECE 504 "ST: Modern Circuit Synthesis Algorithms" taught in Spring 2020 by Dr. Ata Zadehgol. Jennifer's code is available online at https://github.com/JenniferEHoule/Circuit_Synthesis. ## Overview Singular Matrix Perturbation (SMP) as introduced in [11], is a passivity enforcement algorithm for use on fitted models. This robust method is fast, computationally inexpensive, and accurate in enforcing passivity. This implementation using Python can easily be used with Vector Fitting algorithm implemented in [12]. Example cases to demonstrate passivity enforcmment were taken from [7][15], as well as custom examples designed in simulation software based off of multiport circuit synthesis as described in [6]. ## I/O and Flow Description - Instructions for input and output and flow description can be found in flow_diagram.pdf. ## Files - SMP.py: Singularity matrix perturbation implementation of [11] - s_compare.py: Compare ADS simulated S matrices. Calculate RMS error and plot - eig_plot.py: Plotting functions. Eigenvalue plots based off of plot.py in [12] - smp_ex.py: Example of how to pull .sp file in, parse parameters, send to vector fitting, enforce passivity, and generate new passive circuit. Imported from [12] - Ex2_y.py - vectfit3.py - intercheig.py - rot.py - pass_check.py - fitcalc.py - FRPY.py - intercheig.py - violextrema.py - plots.py - quadprog.py - pr2ss.py - utils.py ## Licensing GPL-3.0 License In addition to licensing: Embedding any of (or parts from) the routines of the Matrix Fitting Toolbox in a commercial software, or a software requiring licensing, is strictly prohibited. This applies to all routines, see Section 2.1. If the code is used in a scientific work, then reference should me made as follows: VFdriver.m and/or vectfit3.m: References [1],[2],[3] RPdriver.m and/or FRPY.m applied to Y-parameters: [8],[9] ## References [1] B. Gustavsen and A. Semlyen, "Rational approximation of frequency domain responses by Vector Fitting", IEEE Trans. Power Delivery, vol. 14, no. 3, pp. 1052-1061, July 1999. [2] B. Gustavsen, "Improving the pole relocating properties of vector fitting", IEEE Trans. Power Delivery, vol. 21, no. 3, pp. 1587-1592, July 2006. [3] D. Deschrijver, M. Mrozowski, T. Dhaene, and D. De Zutter, "Macromodeling of Multiport Systems Using a Fast Implementation of the Vector Fitting Method", IEEE Microwave and Wireless Components Letters, vol. 18, no. 6, pp. 383-385, June 2008. [4] B. Gustavsen, VFIT3, The Vector Fitting Website. March 20, 2013. Accessed on: Jan. 21, 2020. [Online]. Available: https://www.sintef.no/projectweb/vectfit/downloads/vfit3/. [5] A. Zadehgol, "A semi-analytic and cellular approach to rational system characterization through equivalent circuits", Wiley IJNM, 2015. [Online]. https://doi.org/10.1002/jnm.2119 [6] V. Avula and A. Zadehgol, "A Novel Method for Equivalent Circuit Synthesis from Frequency Response of Multi-port Networks", EMC EUR, pp. 79-84, 2016. [Online]. Available: ://WOS:000392194100012. [7] B. Gustavsen, Matrix Fitting Toolbox, The Vector Fitting Website. March 20, 2013. Accessed on: Feb. 25, 2020. [Online]. Available: https://www.sintef.no/projectweb/vectorfitting/downloads/matrix-fitting-toolbox/. [8] B. Gustavsen, "Fast passivity enforcement for S-parameter models by perturbation of residue matrix eigenvalues", IEEE Trans. Advanced Packaging, vol. 33, no. 1, pp. 257-265, Feb. 2010. [9] B. Gustavsen, "Fast Passivity Enforcement for Pole-Residue Models by Perturbation of Residue Matrix Eigenvalues", IEEE Trans. Power Delivery, vol. 23, no. 4, pp. 2278-2285, Oct. 2008. [10] A. Semlyen, B. Gustavsen, "A Half-Size Singularity Test Matrix for Fast and Reliable Passivity Assessment of Rational Models," IEEE Trans. Power Delivery, vol. 24, no. 1, pp. 345-351, Jan. 2009. [11] E. Medina, A. Ramirez, J. Morales and K. Sheshyekani, "Passivity Enforcement of FDNEs via Perturbation of Singularity Test Matrix," in IEEE Transactions on Power Delivery, vol. 35, no. 4, pp. 1648-1655, Aug. 2020, doi: 10.1109/TPWRD.2019.2949216. [12] Houle, Jennifer, GitHub. May 10, 2020. Accessed on: February 3, 2021. [Online]. Available: https://github.com/jenniferEhoule/circuit_synthesis
CaptainEFFF / Liri Node App# LIRI Bot ### Overview In this assignment, you will make LIRI. LIRI is like iPhone's SIRI. However, while SIRI is a Speech Interpretation and Recognition Interface, LIRI is a _Language_ Interpretation and Recognition Interface. LIRI will be a command line node app that takes in parameters and gives you back data. ### Before You Begin 1. LIRI will search Spotify for songs, Bands in Town for concerts, and OMDB for movies. 2. Make a new GitHub repository called liri-node-app and clone it to your computer. 3. To retrieve the data that will power this app, you'll need to send requests using the `axios` package to the Bands in Town, Spotify and OMDB APIs. You'll find these Node packages crucial for your assignment. * [Node-Spotify-API](https://www.npmjs.com/package/node-spotify-api) * [Axios](https://www.npmjs.com/package/axios) * You'll use Axios to grab data from the [OMDB API](http://www.omdbapi.com) and the [Bands In Town API](http://www.artists.bandsintown.com/bandsintown-api) * [Moment](https://www.npmjs.com/package/moment) * [DotEnv](https://www.npmjs.com/package/dotenv) ## Submission Guide Create and use a standard GitHub repository. As this is a CLI App, it cannot be deployed to GitHub pages or Heroku. This time you'll need to include screenshots, a GIF, and/or a video showing us that you have the app working with no bugs. You can include these screenshots/GIFs or a link to a video in a `README.md` file. In order to meet the Employer Competitive standards and be ready to show your application to employers, the `README.md` file should meet the following criteria: 1. Clearly state the problem the app is trying to solve (i.e. what is it doing and why) 2. Give a high-level overview of how the app is organized 3. Give start-to-finish instructions on how to run the app 4. Include screenshots, gifs or videos of the app functioning 5. Contain a link to a deployed version of the app 6. Clearly list the technologies used in the app 7. State your role in the app development Because screenshots (and well-written READMEs) are extremely important in the context of GitHub, this will be part of the grading in this assignment. If you haven't written a markdown file yet, [click here for a rundown](https://guides.github.com/features/mastering-markdown/), or just take a look at the raw file of these instructions. ### Commits Having an active and healthy commit history on GitHub is important for your future job search. It is also extremely important for making sure your work is saved in your repository. If something breaks, committing often ensures you are able to go back to a working version of your code. * Committing often is a signal to employers that you are actively working on your code and learning. * We use the mantra “commit early and often.” This means that when you write code that works, add it and commit it! * Numerous commits allow you to see how your app is progressing and give you a point to revert to if anything goes wrong. * Be clear and descriptive in your commit messaging. * When writing a commit message, avoid vague messages like "fixed." Be descriptive so that you and anyone else looking at your repository knows what happened with each commit. * We would like you to have well over 200 commits by graduation, so commit early and often! ### Submission on BCS * Please submit the link to the Github Repository! ### Instructions 1. Navigate to the root of your project and run `npm init -y` — this will initialize a `package.json` file for your project. The `package.json` file is required for installing third party npm packages and saving their version numbers. If you fail to initialize a `package.json` file, it will be troublesome, and at times almost impossible for anyone else to run your code after cloning your project. 2. Make a `.gitignore` file and add the following lines to it. This will tell git not to track these files, and thus they won't be committed to Github. ``` node_modules .DS_Store .env ``` 3. Make a JavaScript file named `keys.js`. * Inside keys.js your file will look like this: ```js console.log('this is loaded'); exports.spotify = { id: process.env.SPOTIFY_ID, secret: process.env.SPOTIFY_SECRET }; ``` 4. Next, create a file named `.env`, add the following to it, replacing the values with your API keys (no quotes) once you have them: ```js # Spotify API keys SPOTIFY_ID=your-spotify-id SPOTIFY_SECRET=your-spotify-secret ``` * This file will be used by the `dotenv` package to set what are known as environment variables to the global `process.env` object in node. These are values that are meant to be specific to the computer that node is running on, and since we are gitignoring this file, they won't be pushed to github — keeping our API key information private. * If someone wanted to clone your app from github and run it themselves, they would need to supply their own `.env` file for it to work. 5. Make a file called `random.txt`. * Inside of `random.txt` put the following in with no extra characters or white space: * spotify-this-song,"I Want it That Way" 6. Make a JavaScript file named `liri.js`. 7. At the top of the `liri.js` file, add code to read and set any environment variables with the dotenv package: ```js require("dotenv").config(); ``` 8. Add the code required to import the `keys.js` file and store it in a variable. ```js var keys = require("./keys.js"); ``` * You should then be able to access your keys information like so ```js var spotify = new Spotify(keys.spotify); ``` 9. Make it so liri.js can take in one of the following commands: * `concert-this` * `spotify-this-song` * `movie-this` * `do-what-it-says` ### What Each Command Should Do 1. `node liri.js concert-this <artist/band name here>` * This will search the Bands in Town Artist Events API (`"https://rest.bandsintown.com/artists/" + artist + "/events?app_id=codingbootcamp"`) for an artist and render the following information about each event to the terminal: * Name of the venue * Venue location * Date of the Event (use moment to format this as "MM/DD/YYYY") 2. `node liri.js spotify-this-song '<song name here>'` * This will show the following information about the song in your terminal/bash window * Artist(s) * The song's name * A preview link of the song from Spotify * The album that the song is from * If no song is provided then your program will default to "The Sign" by Ace of Base. * You will utilize the [node-spotify-api](https://www.npmjs.com/package/node-spotify-api) package in order to retrieve song information from the Spotify API. * The Spotify API requires you sign up as a developer to generate the necessary credentials. You can follow these steps in order to generate a **client id** and **client secret**: * Step One: Visit <https://developer.spotify.com/my-applications/#!/> * Step Two: Either login to your existing Spotify account or create a new one (a free account is fine) and log in. * Step Three: Once logged in, navigate to <https://developer.spotify.com/my-applications/#!/applications/create> to register a new application to be used with the Spotify API. You can fill in whatever you'd like for these fields. When finished, click the "complete" button. * Step Four: On the next screen, scroll down to where you see your client id and client secret. Copy these values down somewhere, you'll need them to use the Spotify API and the [node-spotify-api package](https://www.npmjs.com/package/node-spotify-api). 3. `node liri.js movie-this '<movie name here>'` * This will output the following information to your terminal/bash window: ``` * Title of the movie. * Year the movie came out. * IMDB Rating of the movie. * Rotten Tomatoes Rating of the movie. * Country where the movie was produced. * Language of the movie. * Plot of the movie. * Actors in the movie. ``` * If the user doesn't type a movie in, the program will output data for the movie 'Mr. Nobody.' * If you haven't watched "Mr. Nobody," then you should: <http://www.imdb.com/title/tt0485947/> * It's on Netflix! * You'll use the `axios` package to retrieve data from the OMDB API. Like all of the in-class activities, the OMDB API requires an API key. You may use `trilogy`. 4. `node liri.js do-what-it-says` * Using the `fs` Node package, LIRI will take the text inside of random.txt and then use it to call one of LIRI's commands. * It should run `spotify-this-song` for "I Want it That Way," as follows the text in `random.txt`. * Edit the text in random.txt to test out the feature for movie-this and concert-this. ### BONUS * In addition to logging the data to your terminal/bash window, output the data to a .txt file called `log.txt`. * Make sure you append each command you run to the `log.txt` file. * Do not overwrite your file each time you run a command. ### Reminder: Submission on BCS * Please submit the link to the Github Repository! - - - ### Minimum Requirements Attempt to complete homework assignment as described in instructions. If unable to complete certain portions, please pseudocode these portions to describe what remains to be completed. Adding a README.md as well as adding this homework to your portfolio are required as well and more information can be found below. - - - ### Create a README.md Add a `README.md` to your repository describing the project. Here are some resources for creating your `README.md`. Here are some resources to help you along the way: * [About READMEs](https://help.github.com/articles/about-readmes/) * [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) - - - ### Add To Your Portfolio After completing the homework please add the piece to your portfolio. Make sure to add a link to your updated portfolio in the comments section of your homework so the TAs can easily ensure you completed this step when they are grading the assignment. To receive an 'A' on any assignment, you must link to it from your portfolio. - - - ### One More Thing If you have any questions about this project or the material we have covered, please post them in the community channels in slack so that your fellow developers can help you! If you're still having trouble, you can come to office hours for assistance from your instructor and TAs. **Good Luck!**
acraig5075 / Best FitLeast-squares adjustment of x,y input data to determine the best fitting line, circle or ellipse parameters
