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from scipy import optimize, log

def function(x):

   return log(x) + x

root = optimize.brentq(function, 0.1, 2.0) print(root) }}

from scipy import optimize, log, exp, arccosh

def fvalue(R):

   def func(f,R):
       return (R - 1.0)/(R + 1.0) - ( f / log(2) ) * arccosh(exp( log(2) / f) / 2.0)
   return optimize.brentq(func, 0.2, 1.0, args=(R))

}}

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• http://people.duke.edu/~ccc14/sta-663/CalibratingODEs.html

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import zipfile file = zipfile.ZipFile('test.zip', 'r') print(string) file.colse() }}

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import numpy as np x = np.array([0,1,2,3,4,5]) y = np.array([1,3,6,9,7,4]) dx = np.gradient(x) dy = np.gradient(y) print(dy/dx) }}

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import os DirList = [] for i in os.listdir():

   if os.path.isdir(i):
       DirList.append(i)

}}