## How to get Ctrl+Arrow working for programs running in tmux?

The key combination of Ctrl+arrow key is often used for skipping forward or backward whole words. This can be used in the bash command line, Emacs and many other programs. However, when I am using tmux, this will not work. You can fix this, by adding the following to your ~/.tmux.conf:

`set-window-option -g xterm-keys on`
This was explained in a nice superuser Q&A.
You can interactively try out tmux commands by hitting C-b : — this will enter the command mode. You can use tab to complete commands.

## How to use SciPy Least Squares to minimize multiple functions at once

SciPy comes with a least squares Levenberg-Marquardt implementation. This allows you to minimize functions. By defining your function as the difference between some measurements and your model function, you can fit a model to those measurements.

Sometimes your model contains multiple functions. You can also minimize for all functions using this approach:

• Define your functions that you like to minimize A(p0), B(P1), …
their cumulative paramaters will be a tuple (p0, p1, …).
• Define your function to be minimized as f(x0), where x0 is expanded to the parameter tuple.
• The function f returns a vector of differences between discrete measured sample and the individual functions A, B etc.
• Let SciPy minimize this function, starting with a reasonably selected initial parameter vector.

This is an example implementation:

`import mathimport scipy.optimizemeasured = {     1: [ 0, 0.02735, 0.47265 ],    6: [ 0.0041, 0.09335, 0.40255 ],    10: [ 0.0133, 0.14555, 0.34115 ],    20: [ 0.0361, 0.205, 0.2589 ],    30: [ 0.06345, 0.23425, 0.20225 ],    60: [ 0.132, 0.25395, 0.114 ],    90: [ 0.2046, 0.23445, 0.06095 ],    120: [ 0.2429, 0.20815, 0.04895 ],    180: [ 0.31755, 0.1618, 0.02065 ],    240: [ 0.3648, 0.121, 0.0142 ],    315: [ 0.3992, 0.0989, 0.00195 ]}def A( x, a, k ):    return a * math.exp( -x * k )def B( x, a, k, l ):    return k * a / ( l - k ) * ( math.exp( -k * x ) - math.exp( -l * x ) )def C( x, a, k, l ):    return a * ( 1 - l / ( l - k ) * math.exp( -x * k ) + k / ( l - k ) * math.exp( -x * l ) )def f( x0 ):    a, k, l = x0    error = []    for x in measured:        error += [ C( x, a, k, l ) - measured[ x ][ 0 ],                   B( x, a, k, l ) - measured[ x ][ 1 ],                   A( x, a, k ) - measured[ x ][ 2 ]               ]    return errordef main():    x0 = ( 0.46, 0.01, 0.001 ) # initial parameters for a, k and l    x, cov, infodict, mesg, ier = scipy.optimize.leastsq( f, x0, full_output = True, epsfcn = 1.0e-2 )    print xif __name__ == "__main__":    main()`

SciPy returns a lot more information, not only the final parameters. See their documentation for details. You also may want to tweak epsfcn for a better fit. This depends on your functions shape and properties.