Applying bounds to specific variable during curve_fit (scipy) leads to an error
<p>I am trying to apply bounds onto some of the parameters during curve fitting but I'm getting the following error message when I tried to do so:</p>

<p><em>ValueError: too many values to unpack</em></p>

<p>Doesn't each 2-tuple in the bound command corresponds to x0, k, lapse, guess in the sigmoidscaled function in my case respectively (Ie. corresponding to p0 too)? </p>

<p>I then tried playing around in hopes of trying to figure out how it works by reducing the bound command to the following to rid the 'too many values':</p>

<blockquote>
  <p>bounds=((-np.inf,np.inf), (0,1))</p>
</blockquote>

<p>Then I get the error message of:</p>

<p><em>ValueError: Inconsistent shapes between bounds and <code>x0</code>.</em></p>

<p>What am I getting wrong here?</p>

<pre><code>import pylab
from scipy.optimize import curve_fit
from matplotlib.pyplot import *

n = 20 #20 trials
ydata = [0/n, 9.0/n, 9.0/n, 14.0/n, 17.0/n] #Divided by n to fit to a plot of y =1
xdata = np.array([ 1.0, 2.0, 3.0, 4.0, 5.0])


#The scaled sigmoid function
def sigmoidscaled(x, x0, k, lapse, guess):
    F = (1 + np.exp(-k*(x-x0))) 
    z = guess + (1-guess-lapse)/F
    return z

p0=[1,1,0,0] 
popt, pcov = curve_fit(sigmoidscaled, xdata, ydata, p0, bounds=((-np.inf,np.inf), (-np.inf,np.inf), (0,1), (0,1))

#Start and End of x-axis, in spaces of n. The higher the n, the smoother the curve.
x = np.linspace(1,5,20)
#The sigmoid values along the y-axis, generated in relation to the x values and the 50% point.
y = sigmoidscaled(x, *popt)

pylab.plot(xdata, ydata, 'o', label='Psychometric Raw', color = 'blue')
pylab.plot(x,y, label='Psychometric Fit', color = 'blue')
#y axis range.
pylab.ylim(0, 1)
#Replace x-axis numbers as labels and y-axis numbers as percentage
xticks([1., 2., 3., 4., 5.], ['C1','CN2','N3','CN4','S5'])
yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0], ['0%','20%','40%','60%','80%','100%'])
pylab.legend(loc='best')
xlabel('Conditions')
ylabel('% perceived more sin like')
pylab.show() 
</code></pre>