Program a simplified Galton board in python. It is required that: The user must select the number of rows n of the board The distribution resulting from dropping the beads should be displayed on the screen. Display an animation where the number of rows n increases over time. All resulting distributions must be scaled to the interval [0, 1], so that it is possible to appreciate the convergence to the normal distribution. Consider n from 1 to 80.

Write the function that takes three dimensions of a brick: height(a), width(b) and depth(c) and returns true if this brick can fit into a hole with the width (w) and height(h). Examples doesBrickFit(1, 1, 1, 1, 1) → true doesBrickFit(1, 2, 1, 1, 1) → true doesBrickFit(1, 2, 2, 1, 1) false Notes • You can turn the brick with any side towards the hole. • We assume that the brick fits if its sizes equal the ones of the hole (i.e. brick size should be less than or equal to the size of the hole, not strictly less). • You can't put a brick in at a non-orthogonal angle.

[Python Language] Using loops of any kind, lists, or is not allowed. Angela loves reading books. She recently started reading an AI generated series called “Harry Trotter”. Angela is collecting books from the series at her nearest bookstore. Since the series is AI generated, the publishers have produced an infinite collection of the books where each book is identified by a unique integer. The bookstore has exactly one copy of each book. Angela wants to buy the books in the range [l,r], where l ≤ r. As an example, the range [−3,3] means that Angela wants to buy the books  − 3,  − 2,  − 1, 0, 1, 2, and 3. Dan also loves the series (or maybe annoying Angela – who knows, really), and he manages to sneak into the bookstore very early to buy all of the books in the range [d,u], where d ≤ u. When Angela later visits, sadly she will not find those books there anymore. For example, if Angela tries to buy books [−2,3] and Dan has bought books [0,2], Angela would only receive books  − 2,  − 1,…

Given a vector of real numbers r = (r1, V2, ..., rn). We can standardize the vector using the formulation: V; = "im, where m is the ri-m mean of the vector r, and s is the standard deviation of r. The vector v = (V1, V2, . , Un) will be the scaled vector. ... 9 Write a Python function scale_vec(r) that takes the vector r as input and returns the scaled vector v. Sample inputs and outputs: • Input: np.array([1, 3, 5]), output: [-1.22474487 0. 1.22474487] Input: np.array([3.3, 1.2, -2.7, −0.6]), output: [1.35457092 0.40637128 -1.35457092 -0.40637128] Hint: Use numpy.mean and numpy.std with default parameters. [ ] # Write your function here Let's test your function. [ ] import numpy as np print (scale_vec(np.array ( [1, 3, 5]))) print (scale_vec(np.array([3.3, 1.2, -2.7, -0.6])))