title: Applying bounds to specific variable during curve_fit (scipy) leads to an error
body: <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>