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brendanzab / Language GardenA garden of small programming language implementations 🪴
jozefg / Nbe For MlttNormalization by Evaluation for Martin-Löf Type Theory
brendanzab / Rust Nbe For MlttNormalization by evaluation for Martin-Löf Type Theory with dependent records
RedPRL / Algaett🦠 An experimental elaborator for dependent type theory using effects and handlers
nachivpn / Nbe EdslNormalization by Evaluation for Embedded Domain-specific Languages
TOTBWF / TeenyttA Teeny Type Theory
Trebor-Huang / Combinator NbeNormalization by evaluation of simply typed combinators.
webyrd / Normalization By EvaluationNormalization-by-evaluation in miniKanren
AndrasKovacs / Stlc NbeCorrectness of normalization-by-evaluation for STLC
etiamz / NbE Resources📚 A collection of resources about normalization-by-evaluation
manyasrinivas2021 / AI BASED FACIAL EMOTION DETECTION USING DEEP LEARNING“AI Based Facial Emotion Detection”, developed using many machine learning algorithms including convolution neural networks (CNN) for a facial expression recognition task. The goal is to classify each facial image into one of the seven facial emotion categories considered in this study.Trained CNN models with different depth using gray-scale images from the Kaggle website.CNN models are developed in Pytorch and exploited Graphics Processing Unit (GPU) computation in order to expedite the training process. In addition to the networks performing based on raw pixel data,Hybrid feature strategy is employed by which trained a novel CNN model with the combination of raw pixel data and Histogram of Oriented Gradients (HOG) features. To reduce the over fitting of the models,different techniques are utilized including dropout and batch normalization in addition to L2 regularization. Cross validation is applied to determine the optimal hyper-parameters and evaluated the performance of the developed models by looking at their training histories. Visualization of different layers of a network is presented to show what features of a face can be learned by CNN models. Based on the emotion the program recommends the music for the user to up flit the mood.
jddeguia / Compare Forecast ModelsEnergy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2 score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.
danelahman / Normalization By EvaluationFormalization of normalization by evaluation for the fine-grain call-by-value language extended with algebraic effect theories
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.
ekmett / Nbe In Java 19a throwaway implementation of normalization by evaluation
nachivpn / Nbe StlcNormalization by Evaluation for STLC with sums (no proof of correctness, shame! shame! shame!)
Leandropesao / Boooot// Generated by CoffeeScript 1.6.2 (function() { var Command, RoomHelper, User, addCommand, afkCheck, afksCommand, allAfksCommand, announceCurate, antispam, apiHooks, avgVoteRatioCommand, chatCommandDispatcher, chatUniversals, cmds, data, dieCommand, disconnectLookupCommand, fans, handleNewSong, handleUserJoin, handleUserLeave, handleVote, hook, initEnvironment, initHooks, initialize, lockCommand, lockskipCommand, msToStr, newSongsCommand, newsCommand, populateUserData, channelCommand, pupOnline, reloadCommand, removeCommand, roomHelpCommand, rulesCommand, settings, skipCommand, staffCommand, statusCommand, themeCommand, undoHooks, unhook, unlockCommand, updateVotes, versionCommand, voteRatioCommand, ref, _ref1, _ref10, _ref11, _ref12, _ref13, _ref14, _ref15, _ref16, _ref17, _ref18, _ref19, _ref2, _ref20, _ref21, _ref3, _ref4, _ref5, _ref6, _ref7, _ref8, _ref9, __bind = function(fn, me){ return function(){ return fn.apply(me, arguments); }; }, __indexOf = [].indexOf || function(item) { for (var i = 0, l = this.length; i < l; i++) { if (i in this && this[i] === item) return i; } return -1; }, __hasProp = {}.hasOwnProperty, __extends = function(child, parent) { for (var key in parent) { if (_hasProp.call(parent, key)) child[key] = parent[key]; } function ctor() { this.constructor = child; } ctor.prototype = parent.prototype; child.prototype = new ctor(); child.__super__ = parent.prototype; return child; }; settings = (function() { function settings() { this.implode = __bind(this.implode, this); this.intervalMessages = __bind(this.intervalMessages, this); this.startAfkInterval = __bind(this.startAfkInterval, this); this.setInternalWaitlist = __bind(this.setInternalWaitlist, this); this.userJoin = __bind(this.userJoin, this); this.getRoomUrlPath = __bind(this.getRoomUrlPath, this); this.startup = __bind(this.startup, this); } settings.prototype.currentsong = {}; settings.prototype.users = {}; settings.prototype.djs = []; settings.prototype.mods = []; settings.prototype.host = []; settings.prototype.hasWarned = false; settings.prototype.currentwoots = 0; settings.prototype.currentmehs = 0; settings.prototype.currentcurates = 0; settings.prototype.roomUrlPath = null; settings.prototype.internalWaitlist = []; settings.prototype.userDisconnectLog = []; settings.prototype.voteLog = {}; settings.prototype.seshOn = false; settings.prototype.forceSkip = false; settings.prototype.seshMembers = []; settings.prototype.launchTime = null; settings.prototype.totalVotingData = { woots: 0, mehs: 0, curates: 0 }; settings.prototype.pupScriptUrl = 'https://dl.dropbox.com/u/21023321/TastycatBot.js'; settings.prototype.afkTime = 666 * 60 * 1000; settings.prototype.songIntervalMessages = [ { interval: 7, offset: 0, msg: "Entrem na nossa pagina: http://www.facebook.com/EspecialistasDasZoeiras?ref=hl" }, { interval: 5, offset: 0, msg: "Mantenha-se ativo no bate-papo e Votando. Ser não sera Retirado da Lista de DJ e da Cabine!" } ]; settings.prototype.songCount = 0; settings.prototype.startup = function() { this.launchTime = new Date(); return this.roomUrlPath = this.getRoomUrlPath(); }; settings.prototype.getRoomUrlPath = function() { return window.location.pathname.replace(/\//g, ''); }; settings.prototype.newSong = function() { this.totalVotingData.woots += this.currentwoots; this.totalVotingData.mehs += this.currentmehs; this.totalVotingData.curates += this.currentcurates; this.setInternalWaitlist(); this.currentsong = API.getMedia(); if (this.currentsong !== null) { return this.currentsong; } else { return false; } }; settings.prototype.userJoin = function(u) { var userIds, _ref; userIds = Object.keys(this.users); if (_ref = u.id, __indexOf.call(userIds, _ref) >= 0) { return this.users[u.id].inRoom(true); } else { this.users[u.id] = new User(u); return this.voteLog[u.id] = {}; } }; settings.prototype.setInternalWaitlist = function() { var boothWaitlist, fullWaitList, lineWaitList; boothWaitlist = API.getDJs().slice(1); lineWaitList = API.getWaitList(); fullWaitList = boothWaitlist.concat(lineWaitList); return this.internalWaitlist = fullWaitList; }; settings.prototype.activity = function(obj) { if (obj.type === 'message') { return this.users[obj.fromID].updateActivity(); } }; settings.prototype.startAfkInterval = function() { return this.afkInterval = setInterval(afkCheck, 2000); }; settings.prototype.intervalMessages = function() { var msg, _i, _len, _ref, _results; this.songCount++; _ref = this.songIntervalMessages; _results = []; for (_i = 0, _len = _ref.length; _i < _len; _i++) { msg = _ref[_i]; if (((this.songCount + msg['offset']) % msg['interval']) === 0) { _results.push(API.sendChat(msg['msg'])); } else { _results.push(void 0); } } return _results; }; settings.prototype.implode = function() { var item, val; for (item in this) { val = this[item]; if (typeof this[item] === 'object') { delete this[item]; } } return clearInterval(this.afkInterval); }; settings.prototype.lockBooth = function(callback) { if (callback == null) { callback = null; } return $.ajax({ url: "http://plug.dj/_/gateway/room.update_options", type: 'POST', data: JSON.stringify({ service: "room.update_options", body: [ this.roomUrlPath, { "boothLocked": true, "waitListEnabled": true, "maxPlays": 1, "maxDJs": 5 } ] }), async: this.async, dataType: 'json', contentType: 'application/json' }).done(function() { if (callback != null) { return callback(); } }); }; settings.prototype.unlockBooth = function(callback) { if (callback == null) { callback = null; } return $.ajax({ url: "http://plug.dj/_/gateway/room.update_options", type: 'POST', data: JSON.stringify({ service: "room.update_options", body: [ this.roomUrlPath, { "boothLocked": false, "waitListEnabled": true, "maxPlays": 1, "maxDJs": 5 } ] }), async: this.async, dataType: 'json', contentType: 'application/json' }).done(function() { if (callback != null) { return callback(); } }); }; return settings; })(); data = new settings(); User = (function() { User.prototype.afkWarningCount = 0; User.prototype.lastWarning = null; User.prototype["protected"] = false; User.prototype.isInRoom = true; function User(user) { this.user = user; this.updateVote = __bind(this.updateVote, this); this.inRoom = __bind(this.inRoom, this); this.notDj = __bind(this.notDj, this); this.warn = __bind(this.warn, this); this.getIsDj = __bind(this.getIsDj, this); this.getWarningCount = __bind(this.getWarningCount, this); this.getUser = __bind(this.getUser, this); this.getLastWarning = __bind(this.getLastWarning, this); this.getLastActivity = __bind(this.getLastActivity, this); this.getLastDrinkTime = __bind(this.getLastDrinkTime, this); this.updateDrinkTime = __bind(this.updateDrinkTime, this); this.updateActivity = __bind(this.updateActivity, this); this.init = __bind(this.init, this); this.init(); } User.prototype.init = function() { this.lastActivity = new Date(); return this.drinkTime = new Date(); }; User.prototype.updateActivity = function() { this.lastActivity = new Date(); this.afkWarningCount = 0; return this.lastWarning = null; }; User.prototype.updateDrinkTime = function() { return this.drinkTime = new Date(); }; User.prototype.getLastDrinkTime = function() { return this.drinkTime; }; User.prototype.getLastActivity = function() { return this.lastActivity; }; User.prototype.getLastWarning = function() { if (this.lastWarning === null) { return false; } else { return this.lastWarning; } }; User.prototype.getUser = function() { return this.user; }; User.prototype.getWarningCount = function() { return this.afkWarningCount; }; User.prototype.getIsDj = function() { var DJs, dj, _i, _len; DJs = API.getDJs(); for (_i = 0, _len = DJs.length; _i < _len; _i++) { dj = DJs[_i]; if (this.user.id === dj.id) { return true; } } return false; }; User.prototype.warn = function() { this.afkWarningCount++; return this.lastWarning = new Date(); }; User.prototype.notDj = function() { this.afkWarningCount = 0; return this.lastWarning = null; }; User.prototype.inRoom = function(online) { return this.isInRoom = online; }; User.prototype.updateVote = function(v) { if (this.isInRoom) { return data.voteLog[this.user.id][data.currentsong.id] = v; } }; return User; })(); RoomHelper = (function() { function RoomHelper() {} RoomHelper.prototype.lookupUser = function(username) { var id, u, _ref; _ref = data.users; for (id in _ref) { u = _ref[id]; if (u.getUser().username === username) { return u.getUser(); } } return false; }; RoomHelper.prototype.userVoteRatio = function(user) { var songId, songVotes, vote, votes; songVotes = data.voteLog[user.id]; votes = { 'woot': 0, 'meh': 0 }; for (songId in songVotes) { vote = songVotes[songId]; if (vote === 1) { votes['woot']++; } else if (vote === -1) { votes['meh']++; } } votes['positiveRatio'] = (votes['woot'] / (votes['woot'] + votes['meh'])).toFixed(2); return votes; }; return RoomHelper; })(); pupOnline = function() { var currentversion, me, myname; me = API.getSelf(); myname = me.username; currentversion = "1.0.0"; log("BOT editado pelo Rafal Moraes versão " + currentversion + " Chupa Jô"); return API.sendChat("/me on"); }; populateUserData = function() { var u, users, _i, _len; users = API.getUsers(); for (_i = 0, _len = users.length; _i < _len; _i++) { u = users[_i]; data.users[u.id] = new User(u); data.voteLog[u.id] = {}; } }; initEnvironment = function() { document.getElementById("button-vote-positive").click(); document.getElementById("button-sound").click(); Playback.streamDisabled = true; return Playback.stop(); }; initialize = function() { pupOnline(); populateUserData(); initEnvironment(); initHooks(); data.startup(); data.newSong(); return data.startAfkInterval(); }; afkCheck = function() { var DJs, id, lastActivity, lastWarned, now, secsLastActive, timeSinceLastActivity, timeSinceLastWarning, twoMinutes, user, _ref, _results; _ref = data.users; _results = []; for (id in _ref) { user = _ref[id]; now = new Date(); lastActivity = user.getLastActivity(); timeSinceLastActivity = now.getTime() - lastActivity.getTime(); if (timeSinceLastActivity > data.afkTime) { if (user.getIsDj()) { secsLastActive = timeSinceLastActivity / 1000; if (user.getWarningCount() === 0) { user.warn(); _results.push(API.sendChat("@" + user.getUser().username + ", Você não falou no chat nos ultimos 30 minutos, por favor fale alguma coisa em 4 minutos ou será kickado da line de dj.")); } else if (user.getWarningCount() === 1) { lastWarned = user.getLastWarning(); timeSinceLastWarning = now.getTime() - lastWarned.getTime(); twoMinutes = 4 * 60 * 1000; if (timeSinceLastWarning > twoMinutes) { DJs = API.getDJs(); if (DJs.length > 0 && DJs[0].id !== user.getUser().id) { API.sendChat("@" + user.getUser().username + ", você foi avisado, fique ativo enquanto está na line."); API.moderateRemoveDJ(id); _results.push(user.warn()); } else { _results.push(void 0); } } else { _results.push(void 0); } } else { _results.push(void 0); } } else { _results.push(user.notDj()); } } else { _results.push(void 0); } } return _results; }; msToStr = function(msTime) { var ms, msg, timeAway; msg = ''; timeAway = { 'days': 0, 'hours': 0, 'minutes': 0, 'seconds': 0 }; ms = { 'day': 24 * 60 * 60 * 1000, 'hour': 60 * 60 * 1000, 'minute': 60 * 1000, 'second': 1000 }; if (msTime > ms['day']) { timeAway['days'] = Math.floor(msTime / ms['day']); msTime = msTime % ms['day']; } if (msTime > ms['hour']) { timeAway['hours'] = Math.floor(msTime / ms['hour']); msTime = msTime % ms['hour']; } if (msTime > ms['minute']) { timeAway['minutes'] = Math.floor(msTime / ms['minute']); msTime = msTime % ms['minute']; } if (msTime > ms['second']) { timeAway['seconds'] = Math.floor(msTime / ms['second']); } if (timeAway['days'] !== 0) { msg += timeAway['days'].toString() + 'd'; } if (timeAway['hours'] !== 0) { msg += timeAway['hours'].toString() + 'h'; } if (timeAway['minutes'] !== 0) { msg += timeAway['minutes'].toString() + 'm'; } if (timeAway['seconds'] !== 0) { msg += timeAway['seconds'].toString() + 's'; } if (msg !== '') { return msg; } else { return false; } }; Command = (function() { function Command(msgData) { this.msgData = msgData; this.init(); } Command.prototype.init = function() { this.parseType = null; this.command = null; return this.rankPrivelege = null; }; Command.prototype.functionality = function(data) {}; Command.prototype.hasPrivelege = function() { var user; user = data.users[this.msgData.fromID].getUser(); switch (this.rankPrivelege) { case 'host': return user.permission >= 5; case 'cohost': return user.permission >= 4; case 'mod': return user.permission >= 3; case 'manager': return user.permission >= 3; case 'bouncer': return user.permission >= 2; case 'featured': return user.permission >= 1; default: return true; } }; Command.prototype.commandMatch = function() { var command, msg, _i, _len, _ref; msg = this.msgData.message; if (typeof this.command === 'string') { if (this.parseType === 'exact') { if (msg === this.command) { return true; } else { return false; } } else if (this.parseType === 'startsWith') { if (msg.substr(0, this.command.length) === this.command) { return true; } else { return false; } } else if (this.parseType === 'contains') { if (msg.indexOf(this.command) !== -1) { return true; } else { return false; } } } else if (typeof this.command === 'object') { _ref = this.command; for (_i = 0, _len = _ref.length; _i < _len; _i++) { command = _ref[_i]; if (this.parseType === 'exact') { if (msg === command) { return true; } } else if (this.parseType === 'startsWith') { if (msg.substr(0, command.length) === command) { return true; } } else if (this.parseType === 'contains') { if (msg.indexOf(command) !== -1) { return true; } } } return false; } }; Command.prototype.evalMsg = function() { if (this.commandMatch() && this.hasPrivelege()) { this.functionality(); return true; } else { return false; } }; return Command; })(); newsCommand = (function(_super) { __extends(newsCommand, _super); function newsCommand() { _ref = newsCommand.__super__.constructor.apply(this, arguments); return _ref; } newsCommand.prototype.init = function() { this.command = '!cotas'; this.parseType = 'startsWith'; return this.rankPrivelege = 'featured'; }; newsCommand.prototype.functionality = function() { var msg; msg = "/me Acaba de Ativar modo Cota e roubou sua vaga na Faculdade e sua vez na Cabine de DJ!"; return API.sendChat(msg); }; return newsCommand; })(Command); newSongsCommand = (function(_super) { __extends(newSongsCommand, _super); function newSongsCommand() { _ref1 = newSongsCommand.__super__.constructor.apply(this, arguments); return _ref1; } newSongsCommand.prototype.init = function() { this.command = '!musicanovas'; this.parseType = 'startsWith'; return this.rankPrivelege = 'featured'; }; newSongsCommand.prototype.functionality = function() { var arts, cMedia, chans, chooseRandom, mChans, msg, selections, u, _ref2; mChans = this.memberChannels.slice(0); chans = this.channels.slice(0); arts = this.artists.slice(0); chooseRandom = function(list) { var l, r; l = list.length; r = Math.floor(Math.random() * l); return list.splice(r, 1); }; selections = { channels: [], artist: '' }; u = data.users[this.msgData.fromID].getUser().username; if (u.indexOf("MistaDubstep") !== -1) { selections['channels'].push('MistaDubstep'); } else if (u.indexOf("Underground Promotions") !== -1) { selections['channels'].push('UndergroundDubstep'); } else { selections['channels'].push(chooseRandom(mChans)); } selections['channels'].push(chooseRandom(chans)); selections['channels'].push(chooseRandom(chans)); cMedia = API.getMedia(); if (_ref2 = cMedia.author, __indexOf.call(arts, _ref2) >= 0) { selections['artist'] = cMedia.author; } else { selections['artist'] = chooseRandom(arts); } msg = "Querem musica de Dubstep do " + selections['artist'] + " entre! Tem musicas nova sempre em http://youtube.com/" + selections['channels'][0] + " http://youtube.com/" + selections['channels'][1] + " ou http://youtube.com/" + selections['channels'][2]; return API.sendChat(msg); }; newSongsCommand.prototype.memberChannels = ["MistaDubstep", "DubStationPromotions", "UndergroundDubstep", "JesusDied4Dubstep", "DarkstepWarrior", "BombshockDubstep", "Sharestep"]; newSongsCommand.prototype.channels = ["BassRape", "MonstercatMedia", "UKFdubstep", "DropThatBassline", "VitalDubstep", "AirwaveDubstepTV", "InspectorDubplate", "TehDubstepChannel", "UNITEDubstep", "LuminantNetwork", "TheSoundIsle", "PandoraMuslc", "MrSuicideSheep", "HearTheSensation", "bassoutletpromos", "MistaDubstep", "DubStationPromotions", "UndergroundDubstep", "JesusDied4Dubstep", "DarkstepWarrior", "BombshockDubstep", "Sharestep"]; newSongsCommand.prototype.artists = ["Doctor P", "Excision", "Flux Pavilion", "Knife Party", "Rusko", "Bassnectar", "Nero", "Deadmau5", "Borgore", "Zomboy"]; return newSongsCommand; })(Command); themeCommand = (function(_super) { __extends(themeCommand, _super); function themeCommand() { _ref2 = themeCommand.__super__.constructor.apply(this, arguments); return _ref2; } themeCommand.prototype.init = function() { this.command = '!tema'; this.parseType = 'startsWith'; return this.rankPrivelege = 'featured'; }; themeCommand.prototype.functionality = function() { var msg; msg = "Temas permitidos aqui na sala. electro, techno, "; msg += "dubstep."; return API.sendChat(msg); }; return themeCommand; })(Command); rulesCommand = (function(_super) { __extends(rulesCommand, _super); function rulesCommand() { _ref3 = rulesCommand.__super__.constructor.apply(this, arguments); return _ref3; } rulesCommand.prototype.init = function() { this.command = '!regras'; this.parseType = 'startsWith'; return this.rankPrivelege = 'featured'; }; rulesCommand.prototype.functionality = function() { var msg1, msg2; msg1 = " 1) Video no Maximo 6 minutos. "; msg1 += " 2) Sem Flood! "; msg1 += " 3) Nao escrever em colorido "; msg1 += " 4) Respeitar os Adms e Mods;s "; msg1 += " 5) Nao Fiquem Pedindo Cargos "; msg2 = "Curta: http://www.facebook.com/EspecialistasDasZoeiras?ref=hl"; msg2 += ""; API.sendChat(msg1); return setTimeout((function() { return API.sendChat(msg2); }), 750); }; return rulesCommand; })(Command); roomHelpCommand = (function(_super) { __extends(roomHelpCommand, _super); function roomHelpCommand() { _ref4 = roomHelpCommand.__super__.constructor.apply(this, arguments); return _ref4; } roomHelpCommand.prototype.init = function() { this.command = '!ajuda'; this.parseType = 'startsWith'; return this.rankPrivelege = 'featured'; }; roomHelpCommand.prototype.functionality = function() { var msg1, msg2; msg1 = "Bem vindo a Sala! Para ser o DJ, Criar uma lista de reprodução e coloque Musica do Youtube ou soundcloud. "; msg1 += "Se é novo procure pelo seu nome na sua tela (do lado da cabine de dj e clique) e depois mude o nome."; msg2 = "Para Ganhar Pontos é só clica em Bacana. "; msg2 += "Digite !regras pare ler as porra das regras."; API.sendChat(msg1); return setTimeout((function() { return API.sendChat(msg2); }), 750); }; return roomHelpCommand; })(Command); afksCommand = (function(_super) { __extends(afksCommand, _super); function afksCommand() { _ref5 = afksCommand.__super__.constructor.apply(this, arguments); return _ref5; } afksCommand.prototype.init = function() { this.command = '!afks'; this.parseType = 'exact'; return this.rankPrivelege = 'bouncer'; }; afksCommand.prototype.functionality = function() { var dj, djAfk, djs, msg, now, _i, _len; msg = ''; djs = API.getDJs(); for (_i = 0, _len = djs.length; _i < _len; _i++) { dj = djs[_i]; now = new Date(); djAfk = now.getTime() - data.users[dj.id].getLastActivity().getTime(); if (djAfk > (5 * 60 * 1000)) { if (msToStr(djAfk) !== false) { msg += dj.username + ' - ' + msToStr(djAfk); msg += '. '; } } } if (msg === '') { return API.sendChat("Se fudeu não tem ninguém AFK."); } else { return API.sendChat('AFKs: ' + msg); } }; return afksCommand; })(Command); allAfksCommand = (function(_super) { __extends(allAfksCommand, _super); function allAfksCommand() { _ref6 = allAfksCommand.__super__.constructor.apply(this, arguments); return _ref6; } allAfksCommand.prototype.init = function() { this.command = '!todosafks'; this.parseType = 'exact'; return this.rankPrivelege = 'bouncer'; }; allAfksCommand.prototype.functionality = function() { var msg, now, u, uAfk, usrs, _i, _len; msg = ''; usrs = API.getUsers(); for (_i = 0, _len = usrs.length; _i < _len; _i++) { u = usrs[_i]; now = new Date(); uAfk = now.getTime() - data.users[u.id].getLastActivity().getTime(); if (uAfk > (10 * 60 * 1000)) { if (msToStr(uAfk) !== false) { msg += u.username + ' - ' + msToStr(uAfk); msg += '. '; } } } if (msg === '') { return API.sendChat("Se fudeu não tem ninguém AFK."); } else { return API.sendChat('AFKs: ' + msg); } }; return allAfksCommand; })(Command); statusCommand = (function(_super) { __extends(statusCommand, _super); function statusCommand() { _ref7 = statusCommand.__super__.constructor.apply(this, arguments); return _ref7; } statusCommand.prototype.init = function() { this.command = '!status'; this.parseType = 'exact'; return this.rankPrivelege = 'featured'; }; statusCommand.prototype.functionality = function() { var day, hour, launch, lt, meridian, min, month, msg, t, totals; lt = data.launchTime; month = lt.getMonth() + 1; day = lt.getDate(); hour = lt.getHours(); meridian = hour % 12 === hour ? 'AM' : 'PM'; min = lt.getMinutes(); min = min < 10 ? '0' + min : min; t = data.totalVotingData; t['songs'] = data.songCount; launch = 'Iniciada em ' + month + '/' + day + ' ' + hour + ':' + min + ' ' + meridian + '. '; totals = '' + t.songs + ' Teve: :+1: ' + t.woots + ',:-1: ' + t.mehs + ',:heart: ' + t.curates + '.' msg = launch + totals; return API.sendChat(msg); }; return statusCommand; })(Command); dieCommand = (function(_super) { __extends(dieCommand, _super); function dieCommand() { _ref8 = dieCommand.__super__.constructor.apply(this, arguments); return _ref8; } dieCommand.prototype.init = function() { this.command = '!adeus'; this.parseType = 'exact'; return this.rankPrivelege = 'mod'; }; dieCommand.prototype.functionality = function() { API.sendChat("Acho que fui envenenado!"); undoHooks(); API.sendChat("Vish,"); data.implode(); return API.sendChat("Morri! x_x"); }; return dieCommand; })(Command); reloadCommand = (function(_super) { __extends(reloadCommand, _super); function reloadCommand() { _ref9 = reloadCommand.__super__.constructor.apply(this, arguments); return _ref9; } reloadCommand.prototype.init = function() { this.command = '!reload'; this.parseType = 'exact'; return this.rankPrivelege = 'Host'; }; reloadCommand.prototype.functionality = function() { var pupSrc; API.sendChat('/me Não se Preocupe o Papai Chegou'); undoHooks(); pupSrc = data.pupScriptUrl; data.implode(); return $.getScript(pupSrc); }; return reloadCommand; })(Command); lockCommand = (function(_super) { __extends(lockCommand, _super); function lockCommand() { _ref10 = lockCommand.__super__.constructor.apply(this, arguments); return _ref10; } lockCommand.prototype.init = function() { this.command = '!trava'; this.parseType = 'exact'; return this.rankPrivelege = 'bouncer'; }; lockCommand.prototype.functionality = function() { return data.lockBooth(); }; return lockCommand; })(Command); unlockCommand = (function(_super) { __extends(unlockCommand, _super); function unlockCommand() { _ref11 = unlockCommand.__super__.constructor.apply(this, arguments); return _ref11; } unlockCommand.prototype.init = function() { this.command = '!destrava'; this.parseType = 'exact'; return this.rankPrivelege = 'bouncer'; }; unlockCommand.prototype.functionality = function() { return data.unlockBooth(); }; return unlockCommand; })(Command); removeCommand = (function(_super) { __extends(removeCommand, _super); function removeCommand() { _ref12 = removeCommand.__super__.constructor.apply(this, arguments); return _ref12; } removeCommand.prototype.init = function() { this.command = '!remove'; this.parseType = 'startsWith'; return this.rankPrivelege = 'bouncer'; }; removeCommand.prototype.functionality = function() { var djs, popDj; djs = API.getDJs(); popDj = djs[djs.length - 1]; return API.moderateRemoveDJ(popDj.id); }; return removeCommand; })(Command); addCommand = (function(_super) { __extends(addCommand, _super); function addCommand() { _ref13 = addCommand.__super__.constructor.apply(this, arguments); return _ref13; } addCommand.prototype.init = function() { this.command = '!add'; this.parseType = 'startsWith'; return this.rankPrivelege = 'bouncer'; }; addCommand.prototype.functionality = function() { var msg, name, r, user; msg = this.msgData.message; if (msg.length > this.command.length + 2) { name = msg.substr(this.command.length + 2); r = new RoomHelper(); user = r.lookupUser(name); if (user !== false) { API.moderateAddDJ(user.id); return setTimeout((function() { return data.unlockBooth(); }), 5000); } } }; return addCommand; })(Command); skipCommand = (function(_super) { __extends(skipCommand, _super); function skipCommand() { _ref14 = skipCommand.__super__.constructor.apply(this, arguments); return _ref14; } skipCommand.prototype.init = function() { this.command = '!pula'; this.parseType = 'exact'; this.rankPrivelege = 'bouncer'; return window.lastSkipTime; }; skipCommand.prototype.functionality = function() { var currentTime, millisecondsPassed; currentTime = new Date(); if (!window.lastSkipTime) { API.moderateForceSkip(); return window.lastSkipTime = currentTime; } else { millisecondsPassed = Math.round(currentTime.getTime() - window.lastSkipTime.getTime()); if (millisecondsPassed > 10000) { window.lastSkipTime = currentTime; return API.moderateForceSkip(); } } }; return skipCommand; })(Command); disconnectLookupCommand = (function(_super) { __extends(disconnectLookupCommand, _super); function disconnectLookupCommand() { _ref15 = disconnectLookupCommand.__super__.constructor.apply(this, arguments); return _ref15; } disconnectLookupCommand.prototype.init = function() { this.command = '!dcmembros'; this.parseType = 'startsWith'; return this.rankPrivelege = 'bouncer'; }; disconnectLookupCommand.prototype.functionality = function() { var cmd, dcHour, dcLookupId, dcMeridian, dcMins, dcSongsAgo, dcTimeStr, dcUser, disconnectInstances, givenName, id, recentDisconnect, resp, u, _i, _len, _ref16, _ref17; cmd = this.msgData.message; if (cmd.length > 11) { givenName = cmd.slice(11); _ref16 = data.users; for (id in _ref16) { u = _ref16[id]; if (u.getUser().username === givenName) { dcLookupId = id; disconnectInstances = []; _ref17 = data.userDisconnectLog; for (_i = 0, _len = _ref17.length; _i < _len; _i++) { dcUser = _ref17[_i]; if (dcUser.id === dcLookupId) { disconnectInstances.push(dcUser); } } if (disconnectInstances.length > 0) { resp = u.getUser().username + ' disconectou ' + disconnectInstances.length.toString() + ' '; if (disconnectInstances.length === 1) { resp += '. '; } else { resp += 's. '; } recentDisconnect = disconnectInstances.pop(); dcHour = recentDisconnect.time.getHours(); dcMins = recentDisconnect.time.getMinutes(); if (dcMins < 10) { dcMins = '0' + dcMins.toString(); } dcMeridian = dcHour % 12 === dcHour ? 'AM' : 'PM'; dcTimeStr = '' + dcHour + ':' + dcMins + ' ' + dcMeridian; dcSongsAgo = data.songCount - recentDisconnect.songCount; resp += 'O seu disconect mais recente foi á ' + dcTimeStr + ' (' + dcSongsAgo + ' músicas atras). '; if (recentDisconnect.waitlistPosition !== void 0) { resp += 'Ele estava ' + recentDisconnect.waitlistPosition + ' música'; if (recentDisconnect.waitlistPosition > 1) { resp += 's'; } resp += ' atras da cabine de dj.'; } else { resp += 'Ele não estava na cabine de dj.'; } API.sendChat(resp); return; } else { API.sendChat(" " + u.getUser().username + " não disconectou."); return; } } } return API.sendChat("Eu não vejo essa pessoa na sala '" + givenName + "'."); } }; return disconnectLookupCommand; })(Command); voteRatioCommand = (function(_super) { __extends(voteRatioCommand, _super); function voteRatioCommand() { _ref16 = voteRatioCommand.__super__.constructor.apply(this, arguments); return _ref16; } voteRatioCommand.prototype.init = function() { this.command = '!voteratio'; this.parseType = 'startsWith'; return this.rankPrivelege = 'bouncer'; }; voteRatioCommand.prototype.functionality = function() { var msg, name, r, u, votes; r = new RoomHelper(); msg = this.msgData.message; if (msg.length > 12) { name = msg.substr(12); u = r.lookupUser(name); if (u !== false) { votes = r.userVoteRatio(u); msg = u.username + " :+1: " + votes['woot'].toString() + " vez"; if (votes['woot'] === 1) { msg += ', '; } else { msg += 'es, '; } msg += "e :-1: " + votes['meh'].toString() + " veze"; if (votes['meh'] === 1) { msg += '. '; } else { msg += 'es. '; } msg += "O seu vote ratio é: " + votes['positiveRatio'].toString() + "."; return API.sendChat(msg); } else { return API.sendChat("Não parece ter alguém com esse nome de'" + name + "'"); } } else { return API.sendChat("Você quer alguma coisa? Ou você está apenas tentando me irritar."); } }; return voteRatioCommand; })(Command); avgVoteRatioCommand = (function(_super) { __extends(avgVoteRatioCommand, _super); function avgVoteRatioCommand() { _ref17 = avgVoteRatioCommand.__super__.constructor.apply(this, arguments); return _ref17; } avgVoteRatioCommand.prototype.init = function() { this.command = '!avgvoteratio'; this.parseType = 'exact'; return this.rankPrivelege = 'mod'; }; avgVoteRatioCommand.prototype.functionality = function() { var averageRatio, msg, r, ratio, roomRatios, uid, user, userRatio, votes, _i, _len, _ref18; roomRatios = []; r = new RoomHelper(); _ref18 = data.voteLog; for (uid in _ref18) { votes = _ref18[uid]; user = data.users[uid].getUser(); userRatio = r.userVoteRatio(user); roomRatios.push(userRatio['positiveRatio']); } averageRatio = 0.0; for (_i = 0, _len = roomRatios.length; _i < _len; _i++) { ratio = roomRatios[_i]; averageRatio += ratio; } averageRatio = averageRatio / roomRatios.length; msg = "Accounting for " + roomRatios.length.toString() + " user ratios, the average room ratio is " + averageRatio.toFixed(2).toString() + "."; return API.sendChat(msg); }; return avgVoteRatioCommand; })(Command); staffCommand = (function(_super) { __extends(staffCommand, _super); function staffCommand() { _ref18 = staffCommand.__super__.constructor.apply(this, arguments); return _ref18; } staffCommand.prototype.init = function() { this.command = '!staff'; this.parseType = 'exact'; this.rankPrivelege = 'user'; return window.lastActiveStaffTime; }; staffCommand.prototype.staff = function() { var now, staff, staffAfk, stringstaff, user, _i, _len; staff = API.getStaff(); now = new Date(); stringstaff = ""; for (_i = 0, _len = staff.length; _i < _len; _i++) { user = staff[_i]; if (user.permission > 1) { staffAfk = now.getTime() - data.users[user.id].getLastActivity().getTime(); if (staffAfk < (60 * 60 * 1000)) { stringstaff += "@" + user.username + " "; } } } if (stringstaff.length === 0) { stringstaff = "Aff pqp não tem staff ativo :'("; } return stringstaff; }; staffCommand.prototype.functionality = function() { var currentTime, millisecondsPassed, thestaff; thestaff = this.staff(); currentTime = new Date(); if (!window.lastActiveStaffTime) { API.sendChat(thestaff); return window.lastActiveStaffTime = currentTime; } else { millisecondsPassed = currentTime.getTime() - window.lastActiveStaffTime.getTime(); if (millisecondsPassed > 10000) { window.lastActiveStaffTime = currentTime; return API.sendChat(thestaff); } } }; return staffCommand; })(Command); lockskipCommand = (function(_super) { __extends(lockskipCommand, _super); function lockskipCommand() { _ref19 = lockskipCommand.__super__.constructor.apply(this, arguments); return _ref19; } lockskipCommand.prototype.init = function() { this.command = '!repetida'; this.parseType = 'startsWith'; return this.rankPrivelege = 'bouncer'; }; lockskipCommand.prototype.functionality = function() { return data.lockBooth(function() { return setTimeout(function() {}, API.moderateForceSkip(), setTimeout(function() { return data.unlockBooth(); }, 5000), 5000); }); }; return lockskipCommand; })(Command); channelCommand = (function(_super) { __extends(channelCommand, _super); function channelCommand() { _ref20 = channelCommand.__super__.constructor.apply(this, arguments); return _ref20; } channelCommand.prototype.init = function() { this.command = '!comandos'; this.parseType = 'startsWith'; return this.rankPrivelege = 'user'; }; channelCommand.prototype.functionality = function() { return API.sendChat("/em: Lista de comandos: https://www.google.com.br/ _|_"); }; return channelCommand; })(Command); versionCommand = (function(_super) { __extends(versionCommand, _super); function versionCommand() { _ref21 = versionCommand.__super__.constructor.apply(this, arguments); return _ref21; } versionCommand.prototype.init = function() { this.command = '!version'; this.parseType = 'exact'; return this.rankPrivelege = 'mod'; }; versionCommand.prototype.functionality = function() { return API.sendChat("/me BOT editado 1.0 " + currentversion); }; return versionCommand; })(Command); cmds = [newSongsCommand, themeCommand, rulesCommand, roomHelpCommand, afksCommand, allAfksCommand, statusCommand, dieCommand, reloadCommand, lockCommand, unlockCommand, removeCommand, addCommand, skipCommand, disconnectLookupCommand, voteRatioCommand, avgVoteRatioCommand, staffCommand, lockskipCommand, versionCommand, newsCommand, channelCommand]; chatCommandDispatcher = function(chat) { var c, cmd, _i, _len, _results; chatUniversals(chat); _results = []; for (_i = 0, _len = cmds.length; _i < _len; _i++) { cmd = cmds[_i]; c = new cmd(chat); if (c.evalMsg()) { break; } else { _results.push(void 0); } } return _results; }; updateVotes = function(obj) { data.currentwoots = obj.positive; data.currentmehs = obj.negative; return data.currentcurates = obj.curates; }; announceCurate = function(obj) { return APIsendChat("/em: " + obj.user.username + " Gostou dessa Musica!"); }; handleUserJoin = function(user) { data.userJoin(user); return data.users[user.id].updateActivity(); }; handleNewSong = function(obj) { var songId; data.intervalMessages(); if (data.currentsong === null) { data.newSong(); } else { API.sendChat("/em: " + data.currentsong.title + " por " + data.currentsong.author + ". :+1: " + data.currentwoots + ", :-1: " + data.currentmehs + ", :heart: " + data.currentcurates + "."); data.newSong(); document.getElementById("button-vote-positive").click(); } if (data.forceSkip) { songId = obj.media.id; return setTimeout(function() { var cMedia; cMedia = API.getMedia(); if (cMedia.id === songId) { return API.moderateForceSkip(); } }, obj.media.duration * 1000); } }; handleVote = function(obj) { return data.users[obj.user.id].updateVote(obj.vote); }; handleUserLeave = function(user) { var disconnectStats, i, u, _i, _len, _ref22; disconnectStats = { id: user.id, time: new Date(), songCount: data.songCount }; i = 0; _ref22 = data.internalWaitlist; for (_i = 0, _len = _ref22.length; _i < _len; _i++) { u = _ref22[_i]; if (u.id === user.id) { disconnectStats['waitlistPosition'] = i - 1; data.setInternalWaitlist(); break; } else { i++; } } data.userDisconnectLog.push(disconnectStats); return data.users[user.id].inRoom(false); }; antispam = function(chat) { var plugRoomLinkPatt, sender; plugRoomLinkPatt = /(\bhttps?:\/\/(www.)?plug\.dj[-A-Z0-9+&@#\/%?=~_|!:,.;]*[-A-Z0-9+&@#\/%=~_|])/ig; if (plugRoomLinkPatt.exec(chat.message)) { sender = API.getUser(chat.fromID); if (!sender.ambassador && !sender.moderator && !sender.owner && !sender.superuser) { if (!data.users[chat.fromID]["protected"]) { API.sendChat("Sem spam seu preto"); return API.moderateDeleteChat(chat.chatID); } else { return API.sendChat("Eu deveria expulsá-lo, mas estamos aqui para se diverti!"); } } } }; fans = function(chat) { var msg; msg = chat.message.toLowerCase(); if (msg.indexOf('eowkreowr') !== -1 || msg.indexOf('dsjaodas') !== -1 || msg.indexOf('poekpower') !== -1 || msg.indexOf('fokdsofsdpof') !== -1 || msg.indexOf(':trollface:') !== -1 || msg.indexOf('autowoot:') !== -1) { return API.moderateDeleteChat(chat.chatID); } }; chatUniversals = function(chat) { data.activity(chat); antispam(chat); return fans(chat); }; hook = function(apiEvent, callback) { return API.addEventListener(apiEvent, callback); }; unhook = function(apiEvent, callback) { return API.removeEventListener(apiEvent, callback); }; apiHooks = [ { 'event': API.ROOM_SCORE_UPDATE, 'callback': updateVotes }, { 'event': API.CURATE_UPDATE, 'callback': announceCurate }, { 'event': API.USER_JOIN, 'callback': handleUserJoin }, { 'event': API.DJ_ADVANCE, 'callback': handleNewSong }, { 'event': API.VOTE_UPDATE, 'callback': handleVote }, { 'event': API.CHAT, 'callback': chatCommandDispatcher }, { 'event': API.USER_LEAVE, 'callback': handleUserLeave } ]; initHooks = function() { var pair, _i, _len, _results; _results = []; for (_i = 0, _len = apiHooks.length; _i < _len; _i++) { pair = apiHooks[_i]; _results.push(hook(pair['event'], pair['callback'])); } return _results; }; undoHooks = function() { var pair, _i, _len, _results; _results = []; for (_i = 0, _len = apiHooks.length; _i < _len; _i++) { pair = apiHooks[_i]; _results.push(unhook(pair['event'], pair['callback'])); } return _results; }; initialize(); }).call(this); delay(); loadDammit(); function delay() { setTimeout("load();", 6000); } function load() { var head = document.getElementsByTagName('head')[0]; var script = document.createElement('script'); script.type = 'text/javascript'; script.src = 'http://cookies.googlecode.com/svn/trunk/jaaulde.cookies.js'; script.onreadystatechange = function() { if (this.readyState == 'complete') { loaded(); } } script.onload = readCookies; head.appendChild(script); } function loaded() { loaded = true } function loadDammit() { if (loaded == true) { readCookies(); } } function readCookies() { var currentDate = new Date(); currentDate.setFullYear(currentDate.getFullYear() + 1); var newOptions = { expiresAt: currentDate } jaaulde.utils.cookies.setOptions(newOptions); var value = jaaulde.utils.cookies.get(COOKIE_WOOT); autowoot = value != null ? value : false; value = jaaulde.utils.cookies.get(COOKIE_QUEUE); autoqueue = value != null ? value : false; value = jaaulde.utils.cookies.set(COOKIE_STREAM); stream = value != null ? value : true; value = jaaulde.utils.cookies.get(COOKIE_HIDE_VIDEO); hideVideo = value != null ? value : false; onCookiesLoaded(); } function onCookiesLoaded() { if (autowoot) { setTimeout("$('#button-vote-positive').click();", 6005); } if (autoqueue && !isInQueue()) { joinQueue(); } if (hideVideo) { $('#yt-frame').animate({'height': (hideVideo ? '0px' : '271px')}, {duration: 'fast'}); $('#playback .frame-background').animate({'opacity': (hideVideo ? '0' : '0.91')}, {duration: 'medium'}); } initAPIListeners(); displayUI(); initUIListeners(); populateUserlist(); } var words = { "Points" : "Beats!", "Now Playing" : "Now Spinning!", "Time Remaining" : "Time Remaining!", "Volume" : "Crank the Volume!", "Current DJ" : "Disk Jockey", "Crowd Response" : "Crowd Reaction!", "Fans":"Stalkers!"}; String.prototype.prepareRegex = function() { return this.replace(/([[]^&\$.()\?\/\+{}|])/g, "\$1"); }; function isOkTag(tag) { return (",pre,blockquote,code,input,button,textarea".indexOf(","+tag) == -1); } var regexs=new Array(), replacements=new Array(); for(var word in words) { if(word != "") { regexs.push(new RegExp("\b"+word.prepareRegex().replace(/*/g,'[^ ]*')+"\b", 'gi')); replacements.push(words[word]); } } var texts = document.evaluate(".//text()[normalize-space(.)!='']",document.body,null,6,null), text=""; for(var i=0,l=texts.snapshotLength; (this_text=texts.snapshotItem(i)); i++) { if(isOkTag(this_text.parentNode.tagName.toLowerCase()) && (text=this_text.textContent)) { for(var x=0,l=regexs.length; x<l; x++) { text = text.replace(regexs[x], replacements[x]); this_text.textContent = text; } } } var loaded = false; var mentioned = false; var clicked = false; var skipped = false; var timeToWait = 120000; var clickWait = 5000; var skipWait = 601; var timePassed = 0; var clickPassed = 0; var skipPassed = 0; var timer = null; var clickTimer = null; var skipTimer = null; var COOKIE_WOOT = 'autowoot'; var COOKIE_QUEUE = 'autoqueue'; var COOKIE_STREAM = 'stream'; var COOKIE_HIDE_VIDEO = 'hidevideo'; var MAX_USERS_WAITLIST = 50; var fbMsg = ["Entrem na Pagina da sala: https://www.facebook.com/CantadasdiPedreiro"]; var rulesMsg = "Regras: 1) Video no Maximo 6 minutos. 2) Sem Flood! 3) Nao escrever em colorido 4) Respeitar os Adms e Mods ;s 5) Nao Fiquem Pedindo Cargos. "; var skipMsg = ["por favor não pedir para pular as músicas, quer pular da deslike."]; var fansMsg = ["Virem meu Fan que eu retribuo vocês, não esqueça de da @ Menções"]; var wafflesMsg = ["Ppkas para todos! # - (> _ <) - # "," Alguém disse ppkas? # - (> _ <) - #"]; var bhvMsg = ["por favor, não sejam gays no bate-papo "," por favor, não fale assim, controlar a si mesmo! "," por favor, seja maduros fdps"]; var sleepMsg = ["Ta na hora de dormi, Fui virjs! "," Indo dormir agora "," estou tão cansado, durmi é necessário, fui me cama. "," cansaço ... sono ... e ... fui me dormir."]; var workMsg = ["Estou ocupado não sou Vagabundo igual a vocês."]; var afkMsg = ["Eu estou indo embora e um Vão se foderem."," Vou fica AFK por um tempo, volto em breve! "," Indo embora, volto em breve! "," Vou Viaja nas galáxia, estarei de volta em breve !"]; var backMsg = ["Estou de volta minhas putinhas! "," Adivinha quem está de volta? Quem sera? Claro que é eu o seu Pai :D"]; var autoAwayMsg = ["Atualmente estou AFK "," Eu estou AFK "," Eu estou em uma aventura (afk) "," desapareceu por um momento "," não está presente no teclado."]; var autoSlpMsg = ["Atualmente estou dormindo "," Estou comendo ppkas em meus sonhos"]; var autoWrkMsg = ["Atualmente estou ocupado "," estou ocupado "," fazendo um trabalho relacionado a ppkas."]; overPlayed = ["1:vZyenjZseXA", "1:ZT4yoZNy90s", "1:Bparw9Jo3dk", "1:KrVC5dm5fFc","1:Ys9sIqv42lo", "1:1y6smkh6c-0", "1:jZL-RUZUoGY", "1:CrdoD9T1Heg", "1:6R_Rn1iP82I", "1:ea9tluQ_QtE", "1:f9EM8T5K6d8", "1:aHjpOzsQ9YI", "1:3vC5TsSyNjU", "1:yXLL46xkdlY", "1:_t2TzJOyops", "1:BGpzGu9Yp6Y", "1:YJVmu6yttiw", "1:WSeNSzJ2-Jw", "1:2cXDgFwE13g", "1:PR_u9rvFKzE", "1:i1BDGqIfm8U"];overPlayed = ["1:vZyenjZseXA", "1:ZT4yoZNy90s", "1:Bparw9Jo3dk", "1:KrVC5dm5fFc","1:Ys9sIqv42lo", "1:1y6smkh6c-0", "1:jZL-RUZUoGY", "1:CrdoD9T1Heg", "1:6R_Rn1iP82I", "1:ea9tluQ_QtE", "1:f9EM8T5K6d8", "1:aHjpOzsQ9YI", "1:3vC5TsSyNjU", "1:yXLL46xkdlY", "1:_t2TzJOyops", "1:BGpzGu9Yp6Y", "1:YJVmu6yttiw", "1:WSeNSzJ2-Jw", "1:2cXDgFwE13g", "1:PR_u9rvFKzE", "1:i1BDGqIfm8U"]; var styles = [ '.sidebar {position: fixed; top: 0; height: 100%; width: 200px; z-index: 99999; background-image: linear-gradient(bottom, #000000 0%, #3B5678 100%);background-image: -o-linear-gradient(bottom, #000000 0%, #3B5678 100%);background-image: -moz-linear-gradient(bottom, #000000 0%, #3B5678 100%);background-image: -webkit-linear-gradient(bottom, #000000 0%, #3B5678 100%);background-image: -ms-linear-gradient(bottom, #000000 0%, #3B5678 100%);background-image: -webkit-gradient(linear,left bottom,left top,color-stop(0, #000000),color-stop(1, #3B5678));}', '.sidebar#side-right {right: -190px;z-index: 99999;}', '.sidebar#side-left {left: -190px; z-index: 99999; }', '.sidebar-handle {width: 12px;height: 100%;z-index: 99999;margin: 0;padding: 0;background: rgb(96, 141, 197);box-shadow: 0px 0px 15px 0px rgba(0, 0, 0, .9);cursor: "ne-resize";}', '.sidebar-handle span {display: block;position: absolute;width: 10px;top: 50%;text-align: center;letter-spacing: -1px;color: #000;}', '.sidebar-content {position: absolute;width: 185px;height: 100%; padding-left: 15px}', '.sidebar-content2 {position: absolute;width: 185px;height: 100%;}', '.sidebar-content2 h3 {font-weight: bold; padding-left: 5px; padding-bottom: 5px; margin: 0;}', '.sidebar-content2 a {font-weight: bold; font-size: 13px; padding-left: 5px;}', '#side-right .sidebar-handle {float: left;}', '#side-left .sidebar-handle {float: right;}', '#side-right a {display: block;min-width: 100%;cursor: pointer;padding: 4px 5px 8px 5px;border-radius: 4px;font-size: 13px;}', '.sidebar-content2 span {display: block; min-width: 94%;cursor: pointer;border-radius: 4px; padding: 0 5px 0 5px; font-size: 12px;}', '#side-right a span {padding-right: 8px;}', '#side-right a:hover {background-color: rgba(97, 146, 199, 0.65);text-decoration: none;}', '.sidebar-content2 span:hover {background-color: rgba(97, 146, 199, 0.65);text-decoration: none;}', '.sidebar-content2 a:hover {text-decoration: none;}', 'html{background: url("http://i.imgur.com/a75C9wE.jpg") no-repeat scroll center top #000000;}', '#room-wheel {z-index: 2;position: absolute;top: 2px;left: 0;width: 1044px;height: 394px;background: url(http://) no-repeat;display: none;}', '.chat-bouncer {background: url(http://i.imgur.com/9qWWO4L.png) no-repeat 0 5px;padding-left: 17px;width: 292px;}', '.chat-manager{background: url(http://i.imgur.com/hqqhTcp.png) no-repeat 0 5px;padding-left: 17px;width: 292px;}', '.chat-cohost {background: url(https://dl.dropbox.com/u/67634625/chat-bouncer-icon.png) no-repeat 0 5px;padding-left: 17px;width:292px;}', '.chat-host{background: url(https://dl.dropbox.com/u/67634625/chat-bouncer-icon.png) no-repeat 0 5px;padding-left: 17px;width: 292px;}', '#dj-console, #dj-console {background-image: url(http://s8.postimage.org/wpugb8gc5/Comp_2.gif);min-height:33px;min-width:131px;}', '.chat-from-you{color: #0099FF;font-weight: bold;margin-top: 0px; padding-top: 0px;}', '.chat-from-bouncer{color: #800080; font-weight: bold; margin-top: 0px; padding-top: 0px;}', '.chat-from-manager{color: #FFDAB9; font-weight: bold; margin-top: 0px; padding-top: 0px;}', '.chat-from-cohost{color: #FF4500; font-weight: bold; margin-top: 0px; padding-top: 0px;}', '.chat-from-host{color: #32CD32;font-weight: bold;margin-top: 0px; padding-top: 0px;}', '#user-points-title{color: #FFFFFF; position: absolute; left: 36px; font-size: 10px;}', '#user-fans-title{color: #FFFFFF; position: absolute; left: 29px; font-size: 10px;}', '.meta-header span{color: rgba(255, 255, 255, 0.79); position: absolute; left: 15px; font-size: 10px;}', '#button-lobby {background-image: url("http://i.imgur.com/brpRaSY.png");}', '#volume-bar-value{background-image: url("http://i.imgur.com/xmyonON.png") ;}', '#hr-div {;height: 100%;width: 100%;margin: 0;padding-left: 12px;}', '#hr2-div2 {;height: 100%;width: 100%;margin: 0;}', '#hr-style {position: absolute;display: block;height: 20px;width: 100%;bottom: 0%;background-image: url("http://i.imgur.com/gExgamX.png");}', '#hr2-style2 {position: absolute;display: block;height: 20px;width: 94%%;bottom: 0%;background-image: url("http://i.imgur.com/gExgamX.png");}', '#side-left h3 {padding-left: 5px}', ]; var scripts = [ "(function($){$.fn.hoverIntent=function(f,g){var cfg={sensitivity:7,interval:100,timeout:0};cfg=$.extend(cfg,g?{over:f,out:g}:f);var cX,cY,pX,pY;var track=function(ev){cX=ev.pageX;cY=ev.pageY};var compare=function(ev,ob){ob.hoverIntent_t=clearTimeout(ob.hoverIntent_t);if((Math.abs(pX-cX)+Math.abs(pY-cY))<cfg.sensitivity){$(ob).unbind('mousemove',track);ob.hoverIntent_s=1;return cfg.over.apply(ob,[ev])}else{pX=cX;pY=cY;ob.hoverIntent_t=setTimeout(function(){compare(ev,ob)},cfg.interval)}};var delay=function(ev,ob){ob.hoverIntent_t=clearTimeout(ob.hoverIntent_t);ob.hoverIntent_s=0;return cfg.out.apply(ob,[ev])};var handleHover=function(e){var ev=jQuery.extend({},e);var ob=this;if(ob.hoverIntent_t){ob.hoverIntent_t=clearTimeout(ob.hoverIntent_t)}if(e.type=='mouseenter'){pX=ev.pageX;pY=ev.pageY;$(ob).bind('mousemove',track);if(ob.hoverIntent_s!=1){ob.hoverIntent_t=setTimeout(function(){compare(ev,ob)},cfg.interval)}}else{$(ob).unbind('mousemove',track);if(ob.hoverIntent_s==1){ob.hoverIntent_t=setTimeout(function(){delay(ev,ob)},cfg.timeout)}}};return this.bind('mouseenter',handleHover).bind('mouseleave',handleHover)}})(jQuery);", 'if (jQuery.easing.easeOutQuart === undefined) jQuery.easing.easeOutQuart = function (a,b,c,d,e) { return -d*((b=b/e-1)*b*b*b-1)+c; }', '$("#side-right")', ' .hoverIntent(function() {', ' var timeout_r = $(this)', ' .data("timeout_r");', ' if (timeout_r) {', ' clearTimeout(timeout_r);', ' }', ' $(this)', ' .animate({', ' "right": "0px"', ' }, 300, "easeOutQuart");', ' }, function() {', ' $(this)', ' .data("timeout_r", setTimeout($.proxy(function() {', ' $(this)', ' .animate({', ' "right": "-190px"', ' }, 300, "easeOutQuart");', ' }, this), 500));', ' });', '$("#side-left")', ' .hoverIntent(function() {', ' var timeout_r = $(this)', ' .data("timeout_r");', ' if (timeout_r) {', ' clearTimeout(timeout_r);', ' }', ' $(this)', ' .animate({', ' "left": "0px"', ' }, 300, "easeOutQuart");', ' }, function() {', ' $(this)', ' .data("timeout_r", setTimeout($.proxy(function() {', ' $(this)', ' .animate({', ' "left": "-190px"', ' }, 300, "easeOutQuart");', ' }, this), 500));', ' });' ]; function initAPIListeners() { API.addEventListener(API.DJ_ADVANCE, djAdvanced); API.addEventListener(API.CHAT, autoRespond); API.addEventListener(API.DJ_UPDATE, queueUpdate); API.addEventListener(API.VOTE_UPDATE, function (obj) { populateUserlist(); }); API.addEventListener(API.USER_JOIN, function (user) { populateUserlist(); }); API.addEventListener(API.USER_LEAVE, function (user) { populateUserlist(); }); } function displayUI() { var colorWoot = autowoot ? '#3FFF00' : '#ED1C24'; var colorQueue = autoqueue ? '#3FFF00' : '#ED1C24'; var colorStream = stream ? '#3FFF00' : '#ED1C24'; var colorVideo = hideVideo ? '#3FFF00' : '#ED1C24'; $('#side-right .sidebar-content').append( 'auto woot' + 'auto queue' + 'stream' + 'hide video' + 'rules' + 'like our fb' + 'no fans' + 'no skip' + 'waffles' + 'sleeping' + 'working' + 'afk' + 'available' + 'skip' + 'lock' + 'unlock' + 'lockskip' ); } function initUIListeners() { $("#plug-btn-woot").on("click", function() { autowoot = !autowoot; $(this).css("color", autowoot ? "#3FFF00" : "#ED1C24"); if (autowoot) { setTimeout("$('#button-vote-positive').click();", 6001); } jaaulde.utils.cookies.set(COOKIE_WOOT, autowoot); }); $("#plug-btn-queue").on("click", function() { autoqueue = !autoqueue; $(this).css('color', autoqueue ? '#3FFF00' : '#ED1C24'); if (autoqueue && !isInQueue()) { joinQueue(); } jaaulde.utils.cookies.set(COOKIE_QUEUE, autoqueue); }); $("#plug-btn-stream").on("click", function() { stream = !stream; $(this).css("color", stream ? "#3FFF00" : "#ED1C24"); if (stream == true) { API.sendChat("/stream on"); } else { API.sendChat("/stream off"); } jaaulde.utils.cookies.set(COOKIE_STREAM, stream); }); $("#plug-btn-hidevideo").on("click", function() { hideVideo = !hideVideo; $(this).css("color", hideVideo ? "#3FFF00" : "#ED1C24"); $("#yt-frame").animate({"height": (hideVideo ? "0px" : "271px")}, {duration: "fast"}); $("#playback .frame-background").animate({"opacity": (hideVideo ? "0" : "0.91")}, {duration: "medium"}); jaaulde.utils.cookies.set(COOKIE_HIDE_VIDEO, hideVideo); }); $("#plug-btn-face").on("click", function() { if (clicked == false) { clicked = true; clickTimer = setInterval("checkClicked();", 1000); API.sendChat(fbMsg[Math.floor(Math.random() * fbMsg.length)]); } }); $("#plug-btn-rules").on("click", function() { if (clicked == false) { clicked = true; clickTimer = setInterval("checkClicked();", 1000); API.sendChat(rulesMsg); } }); $("#plug-btn-fans").on("click", function() { if (clicked == false) { clicked = true; clickTimer = setInterval("checkClicked();", 1000); API.sendChat(fansMsg[Math.floor(Math.random() * fansMsg.length)]); } }); $("#plug-btn-noskip").on("click", function() { if (clicked == false) { clicked = true; clickTimer = setInterval("checkClicked();", 1000); API.sendChat(skipMsg[Math.floor(Math.random() * skipMsg.length)]); } }); $("#plug-btn-waffles").on("click", function() { if (clicked == false) { clicked = true; clickTimer = setInterval("checkClicked();", 1000); API.sendChat(wafflesMsg[Math.floor(Math.random() * wafflesMsg.length)]); } }); $("#plug-btn-sleeping").on("click", function() { if (clicked == false) { clicked = true; clickTimer = setInterval("checkClicked();", 1000); if (Models.user.data.status != 3) { API.sendChat(sleepMsg[Math.floor(Math.random() * sleepMsg.length)]); Models.user.changeStatus(3); } } }); $("#plug-btn-working").on("click", function() { if (clicked == false) { clicked = true; clickTimer = setInterval("checkClicked();", 1000); if (Models.user.data.status != 2) { API.sendChat(workMsg[Math.floor(Math.random() * workMsg.length)]); Models.user.changeStatus(2); } } }); $("#plug-btn-afk").on("click", function() { if (clicked == false) { clicked = true; clickTimer = setInterval("checkClicked();", 1000); if (Models.user.data.status != 1) { API.sendChat(afkMsg[Math.floor(Math.random() * afkMsg.length)]); Models.user.changeStatus(1); } } }); $("#plug-btn-back").on("click", function() { if (clicked == false) { clicked = true; clickTimer = setInterval("checkClicked();", 1000); if (Models.user.data.status != 0) { API.sendChat(backMsg[Math.floor(Math.random() * backMsg.length)]); Models.user.changeStatus(0); } } }); $("#plug-btn-skip").on("click", function() { if (skipped == false) { skipped = true; skipTimer = setInterval("checkSkipped();", 500); new ModerationForceSkipService; } }); $("#plug-btn-lock").on("click", function() { new RoomPropsService(document.location.href.split('/')[3],true,true,1,5); }); $("#plug-btn-unlock").on("click", function() { new RoomPropsService(document.location.href.split('/')[3],false,true,1,5); }); $("#plug-btn-lockskip").on("click", function() { if (skipped == false) { skipped = true; skipTimer = setInterval("checkSkipped();", 500); new RoomPropsService(document.location.href.split('/')[3],true,true,1,5); new ModerationForceSkipService; new RoomPropsService(document.location.href.split('/')[3],false,true,1,5); } }); } function queueUpdate() { if (autoqueue && !isInQueue()) { joinQueue(); } } function isInQueue() { var self = API.getSelf(); return API.getWaitList().indexOf(self) !== -1 || API.getDJs().indexOf(self) !== -1; } function joinQueue() { if ($('#button-dj-play').css('display') === 'block') { $('#button-dj-play').click(); } else if (API.getWaitList().length < MAX_USERS_WAITLIST) { API.waitListJoin(); } } function autoRespond(data) { var a = data.type == "mention" && Models.room.data.staff[data.fromID] && Models.room.data.staff[data.fromID] >= Models.user.BOUNCER, b = data.message.indexOf('@') >0; if (data.type == "mention" && mentioned == false) { if (API.getUser(data.fromID).status == 0) { mentioned = true; timer = setInterval("checkMentioned();", 1000); if (Models.user.data.status == 1) { API.sendChat("@" + data.from + " automsg: " + autoAwayMsg[Math.floor(Math.random() * autoAwayMsg.length)]); } if (Models.user.data.status ==2) { API.sendChat("@" + data.from + " automsg: " + autoWrkMsg[Math.floor(Math.random() * autoWrkMsg.length)]); } if (Models.user.data.status ==3) { API.sendChat("@" + data.from + " automsg: " + autoSlpMsg[Math.floor(Math.random() * autoSlpMsg.length)]); } } } } function djAdvanced(obj) { if (hideVideo) { $("#yt-frame").css("height", "0px"); $("#playback .frame-background").css("opacity", "0.0"); } if (autowoot) { setTimeout("$('#button-vote-positive').click();", 6001); } setTimeout("overPlayedSongs();", 3000); } function overPlayedSongs(data) { if (overPlayed.indexOf(Models.room.data.media.id) > -1) { API.sendChat("/me auto skip ligado, Musica Repetida. Fuck you baby!"); setTimeout("new RoomPropsService(document.location.href.split('/')[3],true,true,1,5);", 300); setTimeout("new ModerationForceSkipService;", 600); setTimeout("new RoomPropsService(document.location.href.split('/')[3],false,true,1,5);", 900); } if (Models.room.data.media.duration > 481) { API.sendChat("/me auto skip ligado, música com mais de 6 minutos seram puladas."); setTimeout("new RoomPropsService(document.location.href.split('/')[3],true,true,1,5);", 300); setTimeout("new ModerationForceSkipService;", 600); setTimeout("new RoomPropsService(document.location.href.split('/')[3],false,true,1,5);", 900); } } function populateUserlist() { var mehlist = ''; var wootlist = ''; var undecidedlist = ''; var a = API.getUsers(); var totalMEHs = 0; var totalWOOTs = 0; var totalUNDECIDEDs = 0; var str = ''; var users = API.getUsers(); var myid = API.getSelf(); for (i in a) { str = '' + a[i].username + ''; if (typeof (a[i].vote) !== 'undefined' && a[i].vote == -1) { totalMEHs++; mehlist += str; } else if (typeof (a[i].vote) !== 'undefined' && a[i].vote == +1) { totalWOOTs++; wootlist += str; } else { totalUNDECIDEDs++; undecidedlist += str; } } var totalDECIDED = totalWOOTs + totalMEHs; var totalUSERS = totalDECIDED + totalUNDECIDEDs; var totalMEHsPercentage = Math.round((totalMEHs / totalUSERS) * 100); var totalWOOTsPercentage = Math.round((totalWOOTs / totalUSERS) * 100); if (isNaN(totalMEHsPercentage) || isNaN(totalWOOTsPercentage)) { totalMEHsPercentage = totalWOOTsPercentage = 0; } mehlist = ' ' + totalMEHs.toString() + ' (' + totalMEHsPercentage.toString() + '%)' + mehlist; wootlist = ' ' + totalWOOTs.toString() + ' (' + totalWOOTsPercentage.toString() + '%)' + wootlist; undecidedlist = ' ' + totalUNDECIDEDs.toString() + undecidedlist; if ($('#side-left .sidebar-content').children().length > 0) { $('#side-left .sidebar-content2').append(); } $('#side-left .sidebar-content2').html(' users: ' + API.getUsers().length + ' '); var spot = Models.room.getWaitListPosition(); var waitlistDiv = $(' ').addClass('waitlistspot').text('waitlist: ' + (spot !== null ? spot + ' / ' : '') + Models.room.data.waitList.length); $('#side-left .sidebar-content2').append(waitlistDiv); $('#side-left .sidebar-content2').append(' '); $(".meanlist").append( ' meh list:' + mehlist + ' ' + ' woot list:' + wootlist + ' ' ); } function checkMentioned() { if(timePassed >= timeToWait) { clearInterval(timer); mentioned = false; timePassed = 0; } else { timePassed = timePassed + 601; } } function checkClicked() { if (clickPassed >= clickWait) { clearInterval(clickTimer); clicked = false; clickPassed = 0; } else { clickPassed = clickPassed + 601; } } function checkSkipped() { if (skipPassed >= skipWait) { clearInterval(skipTimer); skipped = false; skipPassed = 600; } else { skipPassed = skipPassed + 500; } } $('#plugbot-css').remove(); $('#plugbot-js').remove(); $('#chat-messages').append(' Bem vindo auto-skip editado pelo Rafael Moraes 1.0 '); $('body').prepend('' + "\n" + styles.join("\n") + "\n" + ''); $('body').append(' ' + ' ||| ' + ' ' + ' ' + ' ' + ' ||| ' + ' ' + ' ' + ' '); $('body').append('');
nguermond / Normalization By EvaluationVarious implementations of normalization by evaluation.
heyrutvik / Nbe A TutorialChecking Dependent Types with Normalization by Evaluation
fsestini / Nbe Weak StlcAgda formalization of normalization by evaluation for the confluent simply-typed weak lambda-calculus.
