import numpy as np
import matplotlib.pyplot as plt

Exercice Numpy

1. Array creation

• Create a 1D array with values from 0 to 10 and in steps of 0.1. Check the shape of the array:

• Create an array of normally distributed numbers with mean $\mu=0$ and standard deviation $\sigma=0.5$. It should have 20 rows and as many columns as there are elements in xarray. Call it normal_array:

• Check the type of normal_array:

2. Array mathematics

• Using xarray as x-variable, create a new array yarray as y-variable using the function $y = 10* cos(x) * e^{-0.1x}$:

• Create array_abs by taking the absolute value of array_mul:

• Create a boolan array (logical array) where all positions $>0.3$ in array_abs are True and the others False

• Create a standard deviation projection along the second dimension (columns) of array_abs. Check that the dimensions are the ones you expected. Also are the values around the value you expect?

3. Plotting

• Use a line plot to plot yarray vs xarray:

• Try to change the color of the plot to red and to have markers on top of the line as squares:

• Plot the normal_array as an imagage and change the colormap to 'gray':

• Assemble the two above plots in a figure with one row and two columns grid:

4. Indexing

• Create new arrays where you select every second element from xarray and yarray. Plot them on top of xarray and yarray.

• Select all values of yarray that are larger than 0. Plot those on top of the regular xarray and yarrayplot.

• Flip the order of xarray use it to plot yarray:

5. Combining arrays

• Create an array filled with ones with the same shape as normal_array. Concatenate it to normal_array along the first dimensions and plot the result:

• yarray represents a signal. Each line of normal_array represents a possible random noise for that signal. Using broadcasting, try to create an array of noisy versions of yarray using normal_array. Finally, plot it: