Scipy has a function called roots that allows us to do so, but for polynomials only: we would like to find values of x that sufficiently minimize |x⁵ - x + 1|. However, we can find approximate values of x that will make the left side of the equation as close as we like to the right side, i.e. For example, the equation x⁵ - x + 1 = 0 has a real root by the intermediate value theorem, but this equation is not solvable in radicals, and the only way to express the root exactly is to specify that it satisfies that equation. It is only possible to exactly determine the roots of a small class of equations. The module scipy.optimize provides functions to deal with many such problems and we discuss some applications. For example, fitting a line to a set of experimentally obtained values requires minimizing the sum of squares of the residuals. We often need to solve problems that deal with minimizing the value of an expression under certain constraints.
For the time being we will focus on the aspects of scipy that relate to the following functionalities: optimization, statistics and I/O.
Many functions are shared between scipy and numpy and can be imported from either package. Pylab.title('About as simple as it gets, folks')īy now you should not have any problems understanding this code. S = cos(4*pi*t) #define the dependent variable T = arange(0.0, 1.0 + 0.01, 0.01) #generate the independent variable values
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