<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def evaluate(self, x, derivative=0, smooth=0, simple='auto'):
 """ smooth=0 is how much to smooth the spline data simple='auto' is whether we should just use straight interpolation you may want smooth > 0 for this, when derivative=1 """

        if simple=='auto': simple = self.simple
        
        # make it into an array if it isn't one, and remember that we did
        is_array = True
        if not type(x) == type(_pylab.array([])):
            x = _pylab.array([x])              
            is_array = False

        if simple:
            # loop over all supplied x data, and come up with a y for each
            y = []
            for n in range(0, len(x)):
                # get a window of data around x
                if smooth:
                    [xtemp, ytemp, etemp] = _fun.trim_data(self.xdata, self.ydata, None, [x[n]-smooth, x[n]+smooth])
                else:
                    i1 = _fun.index_nearest(x[n], self.xdata)

                    # if the nearest data point is lower than x, use the next point to interpolate                    
                    if self.xdata[i1] <= x[n] or i1 <= 0: i2 = i1+1
                    else:                                 i2 = i1-1

                    # if we're at the max, extrapolate
                    if i2 >= len(self.xdata):
                        print(x[n], "is out of range. extrapolating")
                        i2 = i1-1

                    x1 = self.xdata[i1]
                    y1 = self.ydata[i1]
                    x2 = self.xdata[i2]
                    y2 = self.ydata[i2]
                    slope = (y2-y1)/(x2-x1)
                                        
                    xtemp = _numpy.array([x[n]])
                    ytemp = _numpy.array([y1 + (x[n]-x1)*slope])
                    
                # calculate the slope based on xtemp and ytemp (if smoothing)
                # or just use the raw slope if smoothing=0
                if derivative == 1:
                    if smooth:
                        y.append((_numpy.average(xtemp*ytemp)-_numpy.average(xtemp)*_numpy.average(ytemp)) /
                                 (_numpy.average(xtemp*xtemp)-_numpy.average(xtemp)**2))
                    else:
                        y.append(slope)
                        
                # otherwise just average (even with one element)
                elif derivative==0:
                    y.append(_numpy.average(ytemp))
    
            if is_array: return _numpy.array(y)
            else:        return y[0]
            
        
        if smooth:
            
            y = []
            for n in range(0, len(x)):
                # take 20 data points from x+/-smooth
                xlow = max(self.xmin,x[n]-smooth)
                xhi  = min(self.xmax,x[n]+smooth)
                xdata = _pylab.linspace(xlow, xhi, 20)
                ydata = _interpolate.splev(xdata, self.pfit, derivative)
                y.append(_numpy.average(ydata))

            if is_array: return _numpy.array(y)
            else:        return y[0]
            
        else:       
            return _interpolate.splev(x, self.pfit, derivative)