human
I am working on the file ".ipynb_checkpoints/car-lane-detection-checkpoint.ipynb".
The first task is:
Create a class to keep track of lane detections

Here we use the average of last maxSamples to identify the lane
Can you write Python code for it?
assistant
#Here is the Python code you requested.

class Line():
    """
        class to store detected lane stats
    """
    def __init__(self, maxSamples=15):
        
        self.maxSamples = maxSamples 
        # x values of the last n fits of the line
        self.recent_xfitted = deque(maxlen=self.maxSamples)
        #polynomial coefficients for the most recent fit
        self.current_fit = [np.array([False])]  
        #polynomial coefficients averaged over the last n iterations
        self.best_fit = None  
        #difference in fit coefficients between last and new fits
        self.diffs = np.array([0,0,0], dtype='float')
        #average x values of the fitted line over the last n iterations
        self.bestx = None
        # was the line detected in the last iteration?
        self.detected = False 
        #radius of curvature of the line in some units
        self.radius_of_curvature = None 
        #distance in meters of vehicle center from the line
        self.line_base_pos = None 
         
    def update_lane(self, ally, allx):
        """
            Function to update the stats
        """
        # get the mean as the best x 
        self.bestx = np.mean(allx, axis=0)
        # fit a 2 order polynomial
        new_fit = np.polyfit(ally, allx, 2)
        # calculate the difference between last fit and new fit
        self.diffs = np.subtract(self.current_fit, new_fit)
        # update current fit
        self.current_fit = new_fit
        # add the new fit to the queue
        self.recent_xfitted.append(self.current_fit)
        # Use the queue mean as the best fit
        self.best_fit = np.mean(self.recent_xfitted, axis=0)
        # meters per pixel in y dimension
        ym_per_pix = 30/720
        # meters per pixel in x dimension
        xm_per_pix = 3.7/700
        # Calculate radius of curvature
        fit_cr = np.polyfit(ally*ym_per_pix, allx*xm_per_pix, 2)
        y_eval = np.max(ally)
        self.radius_of_curvature = ((1 + (2*fit_cr[0]*y_eval*ym_per_pix + fit_cr[1])**2)**1.5) / np.absolute(2*fit_cr[0])

# Utility...