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1. Top results related to broadcasting python

   • Broadcasting a python function on to numpy arrays

     Top Answer

     Answered Mar 04, 2011 · 17 votes

     np.vectorize is a general way to convert Python functions that operate on numbers into numpy functions that operate on ndarrays.

     However, as you point out, it isn't very fast, since it is using a Python loop "under the hood".

     To achieve better speed, you have to hand-craft a function that expects numpy arrays as input and takes advantage of that numpy-ness:

     import numpy as np-def func2(x, y): return np.where(x>y,x+y,x-y) -x = np.array([-2, -1, 0, 1, 2])y = np.array([-2, -1, 0, 1, 2])-xx = x[:, np.newaxis]yy = y[np.newaxis, :]-print(func2(xx, yy))# [[ 0 -1 -2 -3 -4]# [-3 0 -1 -2 -3]# [-2 -1 0 -1 -2]# [-1 0 1 0 -1]# [ 0 1 2 3 0]]-

     Regarding performance:

     test.py:

     import numpy as np-def func2a(x, y): return np.where(x>y,x+y,x-y) -def func2b(x, y): ind=x>y z=np.empty(ind.shape,dtype=x.dtype) z[ind]=(x+y)[ind] z[~ind]=(x-y)[~ind] return z-def func2c(x, y): # x, y= x[:, None], y[None, :] A, L= x+ y, x<= y A[L]= (x- y)[L] return A-N=40x = np.random.random(N)y = np.random.random(N)-xx = x[:, np.newaxis]yy = y[np.newaxis, :]-

     Running:

     With N=30:

     % python -mtimeit -s'import test' 'test.func2a(test.xx,test.yy)'1000 loops, best of 3: 219 usec per loop-% python -mtimeit -s'import test' 'test.func2b(test.xx,test.yy)'1000 loops, best of 3: 488 usec per loop-% python -mtimeit -s'import test' 'test.func2c(test.xx,test.yy)'1000 loops, best of 3: 248 usec per loop-

     With N=1000:

     % python -mtimeit -s'import test' 'test.func2a(test.xx,test.yy)'10 loops, best of 3: 93.7 msec per loop-% python -mtimeit -s'import test' 'test.func2b(test.xx,test.yy)'10 loops, best of 3: 367 msec per loop-% python -mtimeit -s'import test' 'test.func2c(test.xx,test.yy)'10 loops, best of 3: 186 msec per loop-

     This seems to suggest that func2a is slightly faster than func2c (and func2b is horribly slow).

     Check all answers on Stack Overflow

     1/5

   • Broadcasting socket server in python

     Top Answer

     Answered May 23, 2017 · 8 votes

     There is an auto-responded question in SO already talking about this: How to make a server discoverable to LAN clients

     You can use the same technique to ask for servers in the local network.

     The approach (YMMV) in video games is:

     To discover server over Internet, you need some kind of notification service. You have plenty of options in the network that work as generic discovery services, popular since the android networking games era.

     Check all answers on Stack Overflow

     2/5

   • Python broadcasting message to all clients in a socket

     Top Answer

     Answered Oct 14, 2014 · 0 votes

     There is nothing like what you're trying to do, because as pointed out in the commends, sendall() means "definitely send all my bytes and keep trying until you have," not "send these bytes to lots of clients."

     You will want to use either UDP multicast (if you're on a relatively reliable network which supports it, such as a LAN or corporate WAN), or you will simply need to send explicitly to every connected client. The other alternative is peer-to-peer: send to several clients and instruct those clients to send to more clients until all clients are taken care of. Obviously this requires more coding. :)

     Check all answers on Stack Overflow

     3/5

   • Broadcasting message from grpc server to all/some connected clients in python

     Top Answer

     Answered Aug 05, 2020 · 2 votes

     This is certainly possible. The problem that you're running into here is that each call to Scenario.Chat on the server side corresponds to a single client connection. That is, this function is called when the streaming RPC starts and as soon as the function exits, the RPC ends.

     So if you want n connected clients, you'll need n instances of Scenario.Chat running concurrently, each on its own thread. This does mean that the number of concurrently connected clients is limited by the size of the threadpool with which you instantiate your server.

     So, let's say you have n threads in your server process dedicated to maintaining client connections. Then you need another n+1th thread (perhaps the main thread) determining when the server will broadcast a message to all clients (maybe by looking for input from STDIN?). When this extra thread determines that a message should be broadcast, it needs to communicate this intent to all of the threads maintaining connections to a client. There are many ways to make this happen. A threading.Condition and a global collections.deque, or a collections.deque per client connection (somewhat like channels between goroutines) would be two ways. The tricky bit here is ensuring that each client connection will receive the message regardless of how long the client connection thread takes to wake up and how many messages the n+1th thread decides to send in the interim.

     If this is still unclear, I can follow up with some actual code demonstrating the idea.

     Check all answers on Stack Overflow

     4/5

   • Python Flask SocketIO broadcasting outside of @socketio context

     Top Answer

     Answered Feb 20, 2016 · 1 votes

     Yes, this is a known issue, but it has a very simple workaround:

     def update_image_data(pvname, value, **kw): # broadcast event print "Sending broadcast" with app.app_context(): socketio.emit('newimage')

     Check all answers on Stack Overflow

     5/5

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2. numpy.org › doc › stableBroadcasting — NumPy v1.26 Manual

   numpy.org › doc › stable

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   The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes.

   • Data Types

     Array Scalars#. NumPy generally returns elements of arrays...

   • Structured Arrays

     Assignment from Python Native Types (Tuples)# The simplest...

   • NumPy How-Tos

     NumPy how-tos#. These documents are intended as recipes to...

   • Using NumPy C-Api

     Broadcasting over multiple arrays; User-defined data-types....

3. www.geeksforgeeks.org › numpy-array-broadcastingNumPy Array Broadcasting - GeeksforGeeks

   www.geeksforgeeks.org › numpy-array-broadcasting

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   Feb 2, 2024 · Broadcasting is a very reliable feature offered by the NumPy library in Python. It lets you perform arithmetic operations on arrays of different shapes. In this tutorial, we have explained all about NumPy array broadcasting covering its meaning, rules, and examples.

4. www.programiz.com › python-programming › numpyNumpy Broadcasting (With Examples) - Programiz

   www.programiz.com › python-programming › numpy

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   Example: NumPy Broadcasting import numpy as np # create 1-D array array1 = np.array([1, 2, 3]) # create 2-D array array2 = np.array([[1], [2], [3]]) # add arrays of different dimension # size of array1 expands to match with array2 sum = array1 + array2 print(sum) Output [[2 3 4] [3 4 5] [4 5 6]]

5. People also ask

   What is broadcasting in NumPy?

   • The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes. Broadcasting provides a means of vectorizing array operations so that looping occurs in C instead of Python.

   Broadcasting — NumPy v1.26 Manual

   numpy.org/doc/stable/user/basics.broadcasting.html

   See all results for this question

   How to use NumPy array broadcasting in Python?

   • Below are some examples of NumPy array broadcasting in Python: In this example, the code uses NumPy to create a 1×3 array ‘a’ with elements [17, 11, 19]. It then defines a scalar ‘b’ with a value of 3. Broadcasting is employed when adding ‘a’ and ‘b’ (a + b), performing element-wise addition, resulting in a new array ‘c’ with elements [20, 14, 22].

   NumPy Array Broadcasting - GeeksforGeeks

   www.geeksforgeeks.org/numpy-array-broadcasting/

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   Why is broadcasting a good alternative to Python?

   • Broadcasting provides a means of vectorizing array operations, therefore eliminating the need for Python loops. This is because NumPy is implemented in C Programming, which is a very efficient language. It does this without making needless copies of data which leads to efficient algorithm implementations.

   NumPy Array Broadcasting - GeeksforGeeks

   www.geeksforgeeks.org/numpy-array-broadcasting/

   See all results for this question

   What is array broadcasting?

   • Array: Array Broadcasting: Combining arrays of different shapes by automatically aligning their dimensions. Broadcasting can occur when the arrays have compatible dimensions or when one of the arrays can be reshaped to match the other.

   NumPy Broadcasting with Examples - Python Geeks

   pythongeeks.org/numpy-broadcasting/

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6. pythongeeks.org › numpy-broadcastingNumPy Broadcasting with Examples - Python Geeks

   pythongeeks.org › numpy-broadcasting

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   import numpy as np. a = np.array( [ [1, 2, 3], [4, 5, 6]]) b = np.array( [10, 20, 30]) c = a + b. print(c) Output: [ [11 22 33] [14 25 36]] Here, the array b with shape (3,) is broadcasted across the first dimension of a to match its shape (2, 3), resulting in element-wise addition.

7. machinelearningmastery.com › broadcasting-withA Gentle Introduction to Broadcasting with NumPy Arrays

   machinelearningmastery.com › broadcasting-with

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   • Tutorial Overview
   • Limitation with Array Arithmetic
   • Array Broadcasting
   • Broadcasting in Numpy
   • Limitations of Broadcasting
   • Extensions
   • Summary

   This tutorial is divided into 4 parts; they are: 1. Limitation with Array Arithmetic 2. Array Broadcasting 3. Broadcasting in NumPy 4. Limitations of Broadcasting

   See full list on machinelearningmastery.com

   You can perform arithmetic directly on NumPy arrays, such as addition and subtraction. For example, two arrays can be added together to create a new array where the values at each index are added together. For example, an array a can be defined as [1, 2, 3] and array b can be defined as [1, 2, 3] and adding together will result in a new array with ...

   See full list on machinelearningmastery.com

   Broadcasting is the name given to the method that NumPy uses to allow array arithmetic between arrays with a different shape or size. Although the technique was developed for NumPy, it has also been adopted more broadly in other numerical computational libraries, such as Theano, TensorFlow, and Octave. Broadcasting solves the problem of arithmetic ...

   See full list on machinelearningmastery.com

   We can make broadcasting concrete by looking at three examples in NumPy. The examples in this section are not exhaustive, but instead are common to the types of broadcasting you may see or implement.

   See full list on machinelearningmastery.com

   Broadcasting is a handy shortcut that proves very useful in practice when working with NumPy arrays. That being said, it does not work for all cases, and in fact imposes a strict rule that must be satisfied for broadcasting to be performed. Arithmetic, including broadcasting, can only be performed when the shape of each dimension in the arrays are ...

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   This section lists some ideas for extending the tutorial that you may wish to explore. 1. Create three new and different examples of broadcasting with NumPy arrays. 2. Implement your own broadcasting function for manually broadcasting in one and two-dimensional cases. 3. Benchmark NumPy broadcasting and your own custom broadcasting functions with o...

   See full list on machinelearningmastery.com

   In this tutorial, you discovered the concept of array broadcasting and how to implement in NumPy. Specifically, you learned: 1. The problem of arithmetic with arrays with different sizes. 2. The solution of broadcasting and common examples in one and two dimensions. 3. The rule of array broadcasting and when broadcasting fails. Do you have any ques...

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8. www.pythontutorial.net › numpy-broadcastingNumPy Broadcasting - Python Tutorial

   www.pythontutorial.net › numpy-broadcasting

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   import numpy as np a = np.array([ [1, 2, 3], [4, 5, 6] ]) b = np.array([10, 20, 30]) c = a + b print(c) Code language: Python (python) Output: [[11 22 33] [14 25 36]] Code language: Python (python) In this example, NumPy broadcasts the 1D array b across the second dimension to match the shape of the array a. NumPy broadcasting rules

9. jakevdp.github.io › PythonDataScienceHandbook › 02Computation on Arrays: Broadcasting | Python Data Science ...

   jakevdp.github.io › PythonDataScienceHandbook › 02

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   Broadcasting is simply a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, etc.) on arrays of different sizes. Introducing Broadcasting ¶. Recall that for arrays of the same size, binary operations are performed on an element-by-element basis: In [1]: import numpy as np. In [2]: