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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.
hawx / VodkaA functional, stack-based, concatenative programming language
thesephist / XiA dynamic, stack-based concatenative toy programming language.
bary12 / NikudA Stack-Based programming language, using Hebrew Niqqud diacritical marks
Vanka0051 / Speech Enhancementspeech enhancement using DNN: [1] Xu, Y., Du, J., Dai, L.R. and Lee, C.H., 2015. A regression approach to speech enhancement based on deep neural networks. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 23(1), pp.7-19. https://github.com/yongxuUSTC/sednn/tree/master/mixture2clean_dnn CRN: Park S R , Lee J . A Fully Convolutional Neural Network for Speech Enhancement[J]. 2016. DSN(still coding): Nie S , Zhang H , Zhang X L , et al. DEEP STACKING NETWORKS WITH TIME SERIES FOR SPEECH SEPARATION[J]. 2014.
crmsnbleyd / Uiua ModeEmacs mode for uiua, a stack-based array language
dzaima / Canvasa stack-based ASCII-art golfing language
tile-lang / Tile VmOfficial stack based virtual machine and backend for tile-language
trb-a / Cumalis LispCumalis Lisp is a stack-based implementation of R7RS Scheme Language in Typescript. Can be used as a library in web browsers or Node.js environment.
bangyen / EsolangsInterpreters and compilers for esoteric programming languages, from stack-based to modern register-based systems
tormaroe / RopyA stack-based, esoteric, 2D programming language.
canonware / OnyxStack-based, multi-threaded, interpreted, general purpose programming language similar to PostScript
nyzd / JslJust a stack based language
Altiruss / Altiruss############################################################ # +------------------------------------------------------+ # # | Notes | # # +------------------------------------------------------+ # ############################################################ # If you want to use special characters in this document, such as accented letters, you MUST save the file as UTF-8, not ANSI. # If you receive an error when Essentials loads, ensure that: # - No tabs are present: YAML only allows spaces # - Indents are correct: YAML hierarchy is based entirely on indentation # - You have "escaped" all apostrophes in your text: If you want to write "don't", for example, write "don''t" instead (note the doubled apostrophe) # - Text with symbols is enclosed in single or double quotation marks # If you have problems join the Essentials help support channel: http://tiny.cc/EssentialsChat ############################################################ # +------------------------------------------------------+ # # | Essentials (Global) | # # +------------------------------------------------------+ # ############################################################ # A color code between 0-9 or a-f. Set to 'none' to disable. ops-name-color: 'none' # The character(s) to prefix all nicknames, so that you know they are not true usernames. nickname-prefix: '~' # Disable this if you have any other plugin, that modifies the displayname of a user. change-displayname: true # When this option is enabled, the (tab) player list will be updated with the displayname. # The value of change-displayname (above) has to be true. #change-playerlist: true # When essentialschat.jar isnt used, force essentials to add the prefix and suffix from permission plugins to displayname # This setting is ignored if essentialschat.jar is used, and defaults to 'true' # The value of change-displayname (above) has to be true. # Do not edit this setting unless you know what you are doing! #add-prefix-suffix: false # The delay, in seconds, required between /home, /tp, etc. teleport-cooldown: 5 # The delay, in seconds, before a user actually teleports. If the user moves or gets attacked in this timeframe, the teleport never occurs. teleport-delay: 5 # The delay, in seconds, a player can't be attacked by other players after they have been teleported by a command # This will also prevent the player attacking other players teleport-invulnerability: 4 # The delay, in seconds, required between /heal attempts heal-cooldown: 60 # What to prevent from /i /give # e.g item-spawn-blacklist: 46,11,10 item-spawn-blacklist: # Set this to true if you want permission based item spawn rules # Note: The blacklist above will be ignored then. # Permissions: # - essentials.itemspawn.item-all # - essentials.itemspawn.item-[itemname] # - essentials.itemspawn.item-[itemid] # - essentials.give.item-all # - essentials.give.item-[itemname] # - essentials.give.item-[itemid] # For more information, visit http://wiki.ess3.net/wiki/Command_Reference/ICheat#Item.2FGive permission-based-item-spawn: false # Mob limit on the /spawnmob command per execution spawnmob-limit: 10 # Shall we notify users when using /lightning warn-on-smite: true # motd and rules are now configured in the files motd.txt and rules.txt # When a command conflicts with another plugin, by default, Essentials will try to force the OTHER plugin to take priority. # Commands in this list, will tell Essentials to 'not give up' the command to other plugins. # In this state, which plugin 'wins' appears to be almost random. # # If you have two plugin with the same command and you wish to force Essentials to take over, you need an alias. # To force essentials to take 'god' alias 'god' to 'egod'. # See http://wiki.bukkit.org/Bukkit.yml#aliases for more information overridden-commands: # - god # Disabled commands will be completely unavailable on the server. # Disabling commands here will have no effect on command conflicts. disabled-commands: # - nick # These commands will be shown to players with socialSpy enabled # You can add commands from other plugins you may want to track or # remove commands that are used for something you dont want to spy on socialspy-commands: - msg - w - r - mail - m - t - whisper - emsg - tell - er - reply - ereply - email - action - describe - eme - eaction - edescribe - etell - ewhisper - pm # If you do not wish to use a permission system, you can define a list of 'player perms' below. # This list has no effect if you are using a supported permissions system. # If you are using an unsupported permissions system simply delete this section. # Whitelist the commands and permissions you wish to give players by default (everything else is op only). # These are the permissions without the "essentials." part. player-commands: - afk - afk.auto - back - back.ondeath - balance - balance.others - balancetop - build - chat.color - chat.format - chat.shout - chat.question - clearinventory - compass - depth - delhome - getpos - geoip.show - help - helpop - home - home.others - ignore - info - itemdb - kit - kits.tools - list - mail - mail.send - me - motd - msg - msg.color - nick - near - pay - ping - protect - r - rules - realname - seen - sell - sethome - setxmpp - signs.create.protection - signs.create.trade - signs.break.protection - signs.break.trade - signs.use.balance - signs.use.buy - signs.use.disposal - signs.use.enchant - signs.use.free - signs.use.gamemode - signs.use.heal - signs.use.info - signs.use.kit - signs.use.mail - signs.use.protection - signs.use.repair - signs.use.sell - signs.use.time - signs.use.trade - signs.use.warp - signs.use.weather - spawn - suicide - time - tpa - tpaccept - tpahere - tpdeny - warp - warp.list - world - worth - xmpp # Note: All items MUST be followed by a quantity! # All kit names should be lower case, and will be treated as lower in permissions/costs. # Syntax: - itemID[:DataValue/Durability] Amount [Enchantment:Level].. [itemmeta:value]... # For Item meta information visit http://wiki.ess3.net/wiki/Item_Meta # 'delay' refers to the cooldown between how often you can use each kit, measured in seconds. # For more information, visit http://wiki.ess3.net/wiki/Kits kits: tools: delay: 10 items: - 272 1 - 273 1 - 274 1 - 275 1 dtools: delay: 600 items: - 278 1 efficiency:1 durability:1 fortune:1 name:&4Gigadrill lore:The_drill_that_&npierces|the_heavens - 277 1 digspeed:3 name:Dwarf lore:Diggy|Diggy|Hole - 298 1 color:255,255,255 name:Top_Hat lore:Good_day,_Good_day - 279:780 1 notch: delay: 6000 items: - 397:3 1 player:Notch color: delay: 6000 items: - 387 1 title:&4Book_&9o_&6Colors author:KHobbits lore:Ingame_color_codes book:Colors firework: delay: 6000 items: - 401 1 name:Angry_Creeper color:red fade:green type:creeper power:1 - 401 1 name:StarryNight color:yellow,orange fade:blue type:star effect:trail,twinkle power:1 - 401 2 name:SolarWind color:yellow,orange fade:red shape:large effect:twinkle color:yellow,orange fade:red shape:ball effect:trail color:red,purple fade:pink shape:star effect:trail power:1 # Essentials Sign Control # See http://wiki.ess3.net/wiki/Sign_Tutorial for instructions on how to use these. # To enable signs, remove # symbol. To disable all signs, comment/remove each sign. # Essentials Colored sign support will be enabled when any sign types are enabled. # Color is not an actual sign, it's for enabling using color codes on signs, when the correct permissions are given. enabledSigns: #- color #- balance #- buy #- sell #- trade #- free #- disposal #- warp #- kit #- mail #- enchant #- gamemode #- heal #- info #- spawnmob #- repair #- time #- weather # How many times per second can Essentials signs be interacted with per player. # Values should be between 1-20, 20 being virtually no lag protection. # Lower numbers will reduce the possibility of lag, but may annoy players. sign-use-per-second: 4 # Backup runs a batch/bash command while saving is disabled backup: # Interval in minutes interval: 30 # Unless you add a valid backup command or script here, this feature will be useless. # Use 'save-all' to simply force regular world saving without backup. #command: 'rdiff-backup World1 backups/World1' # Set this true to enable permission per warp. per-warp-permission: false # Sort output of /list command by groups sort-list-by-groups: false # More output to the console debug: false # Set the locale for all messages # If you don't set this, the default locale of the server will be used. # For example, to set language to English, set locale to en, to use the file "messages_en.properties" # Don't forget to remove the # in front of the line # For more information, visit http://wiki.ess3.net/wiki/Locale #locale: en # Turn off god mode when people exit remove-god-on-disconnect: false # Auto-AFK # After this timeout in seconds, the user will be set as afk. # Set to -1 for no timeout. auto-afk: 300 # Auto-AFK Kick # After this timeout in seconds, the user will be kicked from the server. # Set to -1 for no timeout. auto-afk-kick: -1 # Set this to true, if you want to freeze the player, if he is afk. # Other players or monsters can't push him out of afk mode then. # This will also enable temporary god mode for the afk player. # The player has to use the command /afk to leave the afk mode. freeze-afk-players: false # When the player is afk, should he be able to pickup items? # Enable this, when you don't want people idling in mob traps. disable-item-pickup-while-afk: false # This setting controls if a player is marked as active on interaction. # When this setting is false, you will need to manually un-AFK using the /afk command. cancel-afk-on-interact: true # Should we automatically remove afk status when the player moves? # Player will be removed from AFK on chat/command regardless of this setting. # Disable this to reduce server lag. cancel-afk-on-move: true # You can disable the death messages of Minecraft here death-messages: true # Add worlds to this list, if you want to automatically disable god mode there no-god-in-worlds: # - world_nether # Set to true to enable per-world permissions for teleporting between worlds with essentials commands # This applies to /world, /back, /tp[a|o][here|all], but not warps. # Give someone permission to teleport to a world with essentials.worlds.<worldname> # This does not affect the /home command, there is a separate toggle below for this. world-teleport-permissions: false # The number of items given if the quantity parameter is left out in /item or /give. # If this number is below 1, the maximum stack size size is given. If over-sized stacks # are not enabled, any number higher than the maximum stack size results in more than one stack. default-stack-size: -1 # Over-sized stacks are stacks that ignore the normal max stack size. # They can be obtained using /give and /item, if the player has essentials.oversizedstacks permission. # How many items should be in an over-sized stack? oversized-stacksize: 64 # Allow repair of enchanted weapons and armor. # If you set this to false, you can still allow it for certain players using the permission # essentials.repair.enchanted repair-enchanted: true # Allow 'unsafe' enchantments in kits and item spawning. # Warning: Mixing and overleveling some enchantments can cause issues with clients, servers and plugins. unsafe-enchantments: false #Do you want essentials to keep track of previous location for /back in the teleport listener? #If you set this to true any plugin that uses teleport will have the previous location registered. register-back-in-listener: false #Delay to wait before people can cause attack damage after logging in login-attack-delay: 5 #Set the max fly speed, values range from 0.1 to 1.0 max-fly-speed: 0.8 #Set the maximum amount of mail that can be sent within a minute. mails-per-minute: 1000 # Set the maximum time /tempban can be used for in seconds. # Set to -1 to disable, and essentials.tempban.unlimited can be used to override. max-tempban-time: -1 ############################################################ # +------------------------------------------------------+ # # | EssentialsHome | # # +------------------------------------------------------+ # ############################################################ # Allows people to set their bed at daytime update-bed-at-daytime: true # Set to true to enable per-world permissions for using homes to teleport between worlds # This applies to the /home only. # Give someone permission to teleport to a world with essentials.worlds.<worldname> world-home-permissions: false # Allow players to have multiple homes. # Players need essentials.sethome.multiple before they can have more than 1 home, defaults to 'default' below. # Define different amounts of multiple homes for different permissions, e.g. essentials.sethome.multiple.vip # People with essentials.sethome.multiple.unlimited are not limited by these numbers. # For more information, visit http://wiki.ess3.net/wiki/Multihome sethome-multiple: default: 3 # essentials.sethome.multiple.vip vip: 5 # essentials.sethome.multiple.staff staff: 10 # Set timeout in seconds for players to accept tpa before request is cancelled. # Set to 0 for no timeout tpa-accept-cancellation: 120 ############################################################ # +------------------------------------------------------+ # # | EssentialsEco | # # +------------------------------------------------------+ # ############################################################ # For more information, visit http://wiki.ess3.net/wiki/Essentials_Economy # Defines the balance with which new players begin. Defaults to 0. starting-balance: 0 # worth-# defines the value of an item when it is sold to the server via /sell. # These are now defined in worth.yml # Defines the cost to use the given commands PER USE # Some commands like /repair have sub-costs, check the wiki for more information. command-costs: # /example costs $1000 PER USE #example: 1000 # /kit tools costs $1500 PER USE #kit-tools: 1500 # Set this to a currency symbol you want to use. currency-symbol: '$' # Set the maximum amount of money a player can have # The amount is always limited to 10 trillion because of the limitations of a java double max-money: 10000000000000 # Set the minimum amount of money a player can have (must be above the negative of max-money). # Setting this to 0, will disable overdrafts/loans completely. Users need 'essentials.eco.loan' perm to go below 0. min-money: -10000 # Enable this to log all interactions with trade/buy/sell signs and sell command economy-log-enabled: false ############################################################ # +------------------------------------------------------+ # # | EssentialsHelp | # # +------------------------------------------------------+ # ############################################################ # Show other plugins commands in help non-ess-in-help: true # Hide plugins which do not give a permission # You can override a true value here for a single plugin by adding a permission to a user/group. # The individual permission is: essentials.help.<plugin>, anyone with essentials.* or '*' will see all help regardless. # You can use negative permissions to remove access to just a single plugins help if the following is enabled. hide-permissionless-help: true ############################################################ # +------------------------------------------------------+ # # | EssentialsChat | # # +------------------------------------------------------+ # ############################################################ chat: # If EssentialsChat is installed, this will define how far a player's voice travels, in blocks. Set to 0 to make all chat global. # Note that users with the "essentials.chat.spy" permission will hear everything, regardless of this setting. # Users with essentials.chat.shout can override this by prefixing text with an exclamation mark (!) # Users with essentials.chat.question can override this by prefixing text with a question mark (?) # You can add command costs for shout/question by adding chat-shout and chat-question to the command costs section." radius: 0 # Chat formatting can be done in two ways, you can either define a standard format for all chat # Or you can give a group specific chat format, to give some extra variation. # If set to the default chat format which "should" be compatible with ichat. # For more information of chat formatting, check out the wiki: http://wiki.ess3.net/wiki/Chat_Formatting format: '&l{DISPLAYNAME} &3➽ &f&l{MESSAGE}' Dziewczyna: '{DISPLAYNAME} &3➽ &5 {MESSAGE}' #format: '&7[{GROUP}]&r {DISPLAYNAME}&7:&r {MESSAGE}' group-formats: # Default: '{WORLDNAME} {DISPLAYNAME}&7:&r {MESSAGE}' # Admins: '{WORLDNAME} &c[{GROUP}]&r {DISPLAYNAME}&7:&c {MESSAGE}' # If you are using group formats make sure to remove the '#' to allow the setting to be read. ############################################################ # +------------------------------------------------------+ # # | EssentialsProtect | # # +------------------------------------------------------+ # ############################################################ protect: # Database settings for sign/rail protection # mysql or sqlite # We strongly recommend against using mysql here, unless you have a good reason. # Sqlite seems to be faster in almost all cases, and in some cases mysql can be much slower. datatype: 'sqlite' # If you specified MySQL above, you MUST enter the appropriate details here. # If you specified SQLite above, these will be IGNORED. username: 'root' password: 'root' mysqlDb: 'jdbc:mysql://localhost:3306/minecraft' # General physics/behavior modifications prevent: lava-flow: false water-flow: false water-bucket-flow: false fire-spread: true lava-fire-spread: true flint-fire: false lightning-fire-spread: true portal-creation: false tnt-explosion: false tnt-playerdamage: false fireball-explosion: false fireball-fire: false fireball-playerdamage: false witherskull-explosion: false witherskull-playerdamage: false wither-spawnexplosion: false wither-blockreplace: false creeper-explosion: false creeper-playerdamage: false creeper-blockdamage: false enderdragon-blockdamage: true enderman-pickup: false villager-death: false # Monsters won't follow players # permission essentials.protect.entitytarget.bypass disables this entitytarget: false # Prevent the spawning of creatures spawn: creeper: false skeleton: false spider: false giant: false zombie: false slime: false ghast: false pig_zombie: false enderman: false cave_spider: false silverfish: false blaze: false magma_cube: false ender_dragon: false pig: false sheep: false cow: false chicken: false squid: false wolf: false mushroom_cow: false snowman: false ocelot: false iron_golem: false villager: false wither: false bat: false witch: false # Maximum height the creeper should explode. -1 allows them to explode everywhere. # Set prevent.creeper-explosion to true, if you want to disable creeper explosions. creeper: max-height: -1 # Protect various blocks. protect: # Protect all signs signs: false # Prevent users from destroying rails rails: false # Blocks below rails/signs are also protected if the respective rail/sign is protected. # This makes it more difficult to circumvent protection, and should be enabled. # This only has an effect if "rails" or "signs" is also enabled. block-below: true # Prevent placing blocks above protected rails, this is to stop a potential griefing prevent-block-on-rails: false # Store blocks / signs in memory before writing memstore: false # Disable various default physics and behaviors disable: # Should fall damage be disabled? fall: false # Users with the essentials.protect.pvp permission will still be able to attack each other if this is set to true. # They will be unable to attack users without that same permission node. pvp: false # Should drowning damage be disabled? # (Split into two behaviors; generally, you want both set to the same value) drown: false suffocate: false # Should damage via lava be disabled? Items that fall into lava will still burn to a crisp. ;) lavadmg: false # Should arrow damage be disabled projectiles: false # This will disable damage from touching cacti. contactdmg: false # Burn, baby, burn! Should fire damage be disabled? firedmg: false # Should the damage after hit by a lightning be disabled? lightning: false # Should Wither damage be disabled? wither: false # Disable weather options weather: storm: false thunder: false lightning: false ############################################################ # +------------------------------------------------------+ # # | EssentialsAntiBuild | # # +------------------------------------------------------+ # ############################################################ # Disable various default physics and behaviors # For more information, visit http://wiki.ess3.net/wiki/AntiBuild # Should people with build: false in permissions be allowed to build # Set true to disable building for those people # Setting to false means EssentialsAntiBuild will never prevent you from building build: true # Should people with build: false in permissions be allowed to use items # Set true to disable using for those people # Setting to false means EssentialsAntiBuild will never prevent you from using use: true # Should we tell people they are not allowed to build warn-on-build-disallow: true # For which block types would you like to be alerted? # You can find a list of IDs in plugins/Essentials/items.csv after loading Essentials for the first time. # 10 = lava :: 11 = still lava :: 46 = TNT :: 327 = lava bucket alert: on-placement: 10,11,46,327 on-use: 327 on-break: blacklist: # Which blocks should people be prevented from placing placement: 10,11,46,327 # Which items should people be prevented from using usage: 327 # Which blocks should people be prevented from breaking break: # Which blocks should not be pushed by pistons piston: ############################################################ # +------------------------------------------------------+ # # | Essentials Spawn / New Players | # # +------------------------------------------------------+ # ############################################################ newbies: # Should we announce to the server when someone logs in for the first time? # If so, use this format, replacing {DISPLAYNAME} with the player name. # If not, set to '' #announce-format: '' announce-format: '&dWelcome {DISPLAYNAME}&d to the server!' # When we spawn for the first time, which spawnpoint do we use? # Set to "none" if you want to use the spawn point of the world. spawnpoint: newbies # Do we want to give users anything on first join? Set to '' to disable # This kit will be given regardless of cost, and permissions. #kit: '' kit: tools # Set this to lowest, if you want Multiverse to handle the respawning # Set this to high, if you want EssentialsSpawn to handle the respawning # Set this to highest, if you want to force EssentialsSpawn to handle the respawning respawn-listener-priority: high # When users die, should they respawn at their first home or bed, instead of the spawnpoint? respawn-at-home: false # End of File <-- No seriously, you're done with configuration.
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