len

(options,args) = option_parser.parse_args()
 
if len(args) == 2:
    inhandle = open(args[0],'r')
    outhandle = open(args[1],'w')
elif len(args) == 1:
    inhandle = open(args[0],'r')
    outhandle = sys.stdout
elif len(args) == 0:

    idxs = sorted(random.sample(xrange(total_chains),options.num))
    for (i,chain) in enumerate(vdj.parse_imgt(inhandle)):
        if len(idxs) == 0:
            break
        if i == idxs[0]:

            clean_line = line.strip()
 
            if (len(clean_line) > 0) :
                ret_line = clean_line
 

    time_list = list()
 
    num_metadata = len(metadata_list)
 
    ## Get list of indexes for metadata objects that enclose the specified time

    dt_list = list()
 
    num_select_times = len(time_list)
 
    t_min = min_time

def get_vector_string(vect) :
 
    ret_val = ""
 
    num_elems = len(vect)

    opts, args = argparser.parse_args()
 
    argc = len(args)
 
    min_args = int(1)

    def random_choice(self):
        choices = len(self.empties)
        while choices:  
            i = int(random.random()*choices)
            pos = self.empties[i]

    def add(self, pos):
        self.empty_pos[pos] = len(self.empties)
        self.empties.append(pos)
 
    def remove(self, pos):
        self.set(self.empty_pos[pos], self.empties[len(self.empties)-1])

                    if neighbour.color == color:
                        surround += 1
                if surround == len(square.neighbours): 
                    count += 1
        return count

 
           assert members1 == members2
           assert len(liberties1) == liberties2, ('liberties differ at %r: %d %d' % (root, len(liberties1), liberties2))
 
           empties1 = set(self.emptyset.empties)

        self.global_composition = dict(((x, 0.0) for x in AMINOACIDS))
        for aminoacid in self.sequence:
            self.global_composition[aminoacid] += 1.0 / len(self.sequence)
 
    def create_vector(self):

        for candidate in feature_table:
            difference = 0.0
            for counter in range(len(row)):
                difference += (row[counter] - candidate[counter]) ** 2
            kernel_row.append(exp(-D*difference))

def train_adatron(kernel_table, label_table, h, c):
    tolerance = 0.5
    alphas = [([0.0] * len(kernel_table)) for _ in range(len(label_table[0]))]
    betas = [([0.0] * len(kernel_table)) for _ in range(len(label_table[0]))]
    bias = [0.0] * len(label_table[0])

                filename = "7-9_waterfall_t_pump", y_range = (0.0, 1.0e3) )
# ==============================================================================
if( len(sys.argv) > 1 and sys.argv[1] == 'animate' ):
   animated_plot( x, y, z, labels["t"], labels["P_t"], "$z = {0:7.3f} \, m$", 
                  (x[0], x[-1]), (0.0, 1.0e3), fps = 20, frame_prefix = "pump_", filename = "7-9_animation_t_pump.avi" )

                filename = "7-9_waterfall_t_probe", y_range = (0.0, 1.2) )
# ==============================================================================
if( len(sys.argv) > 1 and sys.argv[1] == 'animate' ):
   animated_plot( x, y, z, labels["t"], labels["P_t"], "$z = {0:7.3f} \, m$", 
                  (x[0], x[-1]), (0.0, 1.2), fps = 20, frame_prefix = "probe_",

        device = 0
 
    if len (sys.argv) == 1:
        # no argument on the command line, try to use the camera
        capture = highgui.cvCreateCameraCapture (device)

            cv.cvReleaseImage (temp)
 
        elif len (points [0]) > 0:
            # we have points, so display them
 
            # calculate the optical flow
            points [1], status = cv.cvCalcOpticalFlowPyrLK (
                prev_grey, grey, prev_pyramid, pyramid,
                points [0], len (points [0]),

                points [0], len (points [0]),
                cv.cvSize (win_size, win_size), 3,
                len (points [0]),
                None,
                cv.cvTermCriteria (cv.CV_TERMCRIT_ITER|cv.CV_TERMCRIT_EPS,

    def request_getsize(self, environ, bits):
        if len(bits) == 1:
             size = int(bits[0])
        else:
            raise HttpException(status=404)

    def request_redirect(self, environ, bits):
        if bits:
            if len(bits) > 2:
                raise HttpException(status=404)
            num = int(bits[0])

    def request_cookies_set(self, environ, bits):
        if len(bits) == 2:
            key = bits[0]
            value = bits[1]
            if key and value:

    def request_status(self, environ, bits):
        try:
            number = int(bits[0]) if len(bits) == 1 else 404
        except:
            raise HttpException(status=404)

    def request_challenge_auth(self, environ, bits):
        if len(bits) == 2:
            auth = environ.get('HTTP_AUTHORIZATION')
            if auth and auth.type == 'basic':
                if auth.authenticated(environ, *bits):

        if logs:
            logs.sort()
            print "%s: merge from %d logs:" % (section, len(logs))
            for l in logs:
                print "  " + l

    if not options.quiet:
        print "Splitting %s log..." % section
        print "Initialising %d log buffers..." % len(sources)
        source = LineDisplay(source)
 

    def tostring(row):
        l = len(row)
        return struct.pack('%dB' % l, *row)
else:
    def tostring(row):

    """
 
    itotal = len(ipixels)
    atotal = len(apixels)
    newtotal = itotal + atotal

 
    p = list(palette)
    if not (0 < len(p) <= 256):
        raise ValueError("a palette must have between 1 and 256 entries")
    seen_triple = False
    for i,t in enumerate(p):
        if len(t) not in (3,4):

        search_text = gtk.Entry()
        s = self.buffer.get_selection_bounds()
        if len(s) > 0:
            search_text.set_text(self.buffer.get_slice(s[0], s[1]))
        dialog = gtk.Dialog("Search", self,