Text string to Excel

Sometimes we just want to handle some data, from or to excel.

Here is a tip that can help in your scripts, when you want to copy data from or to an Excel spreadsheet.

Microsoft Excel recognizes the tabulation character ('\t') as cell separation, and a line break character ('\n') as a normal line break.

For example, if you copy some string data to the clipboard, as explained in previous post, you can paste it to excel cells, if the data is separated by '\t' character.

Probability: How to do combinations without repetition

Command:
itertools.combinations(iterable, r)

Return r length subsequences of elements from the input iterable.

Combinations are emitted in lexicographic sort order. So, if the input iterable is sorted, the combination tuples will be produced in sorted order.

Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each combination.

Example:

from itertools import combinations

print list(combinations([1,2,3,4], 3))

itertools.combinations returns an iterator. An iterator is something that you can for on. So, that is why it is transformed into a list in the example.

Excelent tips for Beginners

Excelent tips !!!

https://www.codementor.io/python/tutorial/python-tricks-for-beginners

SciPy minimize example - Fitting IDF Curves

SciPy (pronounced “Sigh Pie”) is an open source Python library used by scientists, analysts, and engineers doing scientific computing and technical computing.

SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

In this post I will show how to use a powerful function of SciPy - minimize.

Minimize has some methods of minimizing functions. Its official documentation is shown here.

The example used is to find coefficients of a standard rainfall IDF (intensity-duration-frequency) equation. The format of the equation is as following:

We have to input the intensity data to be fitted, and the equation as a function.

import numpy as np
from scipy.optimize import minimize

# This is the IDF function, for return periods of 10 and 25 years
def func2(par, res):
    f10 =  (par[0] * 10  **par[1])/((res[0,:]+par[2])**par[3])
    f25 =  (par[0] * 25  **par[1])/((res[0,:]+par[2])**par[3])
    erroTotQ = np.sum((f10-res[1,:])**2+(f25-res[2,:])**2)
    return erroTotQ

#durations array - in minutes
d=[5,10,15,20,25,30,35,40,60,120,180,360,540,720,900,1260,1440]

# Rainfall intensities, mm/h, same lenght as minutes
r10=[236.1,174.0,145.6,128.3,116.3,107.3,100.3,94.5,79.1,48.4,34.2,18.9,13.4,10.5,8.7,6.5,5.8]
r25=[294.5,217.1,181.6,160.0,145.1,133.9,125.1,118.0,98.7,60.4,42.7,23.6,16.7,13.1,10.8,8.1,7.2]

# the following line only gather the duration and the intensities as a numpy array,
# in order to pass all of the idf constants as one single parameter, named "valid"
valid = np.vstack((d, r10, r25))

#initial guess
param1 = [5000, 0.1, 10, 0.9]

res2 = minimize(func2, param1, args=(valid,), method='Nelder-Mead')
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
print res2