Showing posts with the label Robotics

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The Quick and Easy Way to Analyze Numpy Arrays

The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Robotics These Skills You Need

Robotics is a combination of multiple skills. Out of those many skills similar to B.Tech Electronics skill sets. I am sharing for your quick reference the complete skillset. PROGRAMMING Mat lab - Familiarity with command-line and external functions using MATLAB library; import/export of data; graphing/plotting functions & data; rudimentary animation Python, C / C++ familiarity ROS- Robot Operating System (ROS) - Optional (Good to know) Program Constructs- Sequencing, Selection, Iteration & Recursion Data Organization- Arrays, Lists, Pointers COMPUTERS Tools Productivity: SW (MS Office - Excel / Word / PowerPoint / Project) Operating Systems Windows or Apple-OS - use of personal laptop computer Linux or Ubuntu MATHEMATICS Linear Algebra Inversion, Eigenvalues, Null-Space Linear Differential Eq. Matrix-Algebra & -Manipulation Basic Calculus Derivatives, Gradients, Chain Rule Numerical Integration Basic Computational Implementation, e.g. Runge-Kutta 4 Fourier Analysis Newtonia