#! /usr/bin/env python
#
def clenshaw_curtis_set ( n ):

#*****************************************************************************80
#
## CLENSHAW_CURTIS_SET sets a Clenshaw-Curtis quadrature rule.
#
#  Discussion:
#
#    The integral:
#
#      Integral ( -1 <= X <= 1 ) F(X) dX
#
#    The quadrature rule:
#
#      Sum ( 1 <= I <= N ) W(I) * F ( X(I) )
#
#    The abscissas for the rule of order N can be regarded 
#    as the cosines of equally spaced angles between 180 and 0 degrees:
#
#      X(I) = cos ( ( I - 1 ) * PI / ( N - 1 ) )
#
#    except for the basic case N = 1, when
#
#      X(1) = 0.
#
#    A Clenshaw-Curtis rule that uses N points will integrate
#    exactly all polynomials of degrees 0 through N-1.  If N
#    is odd, then by symmetry the polynomial of degree N will
#    also be integrated exactly.
#
#    If the value of N is increased in a sensible way, then
#    the new set of abscissas will include the old ones.  One such
#    sequence would be N(K) = 2*K+1 for K = 0, 1, 2, ...
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    12 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Reference:
#
#    Charles Clenshaw, Alan Curtis,
#    A Method for Numerical Integration on an Automatic Computer,
#    Numerische Mathematik,
#    Volume 2, Number 1, December 1960, pages 197-205.
#
#  Parameters:
#
#    Input, integer N, the order.
#    N must be between 1 and 17, 33, 65 or 129.
#
#    Output, real X(N), the abscissas.
#
#    Output, real W(N), the weights.
#
  import numpy as np
  from sys import exit

  if ( n == 1 ):

    x = np.array ( [ \
      0.00000000000000000000 \
    ] )

    w = np.array ( [ \
      2.00000000000000000000 \
    ] )

  elif ( n == 2 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      1.00000000000000000000, \
      1.00000000000000000000 \
    ] )

  elif ( n == 3 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
      0.00000000000000000000, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.33333333333333333333, \
      1.33333333333333333333, \
      0.33333333333333333333 \
    ] )

  elif ( n == 4 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.50000000000000000000, \
      0.50000000000000000000, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.11111111111111111111, \
      0.88888888888888888889, \
      0.88888888888888888889, \
      0.11111111111111111111 \
    ] )

  elif ( n == 5 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.70710678118654752440, \
      0.00000000000000000000, \
      0.70710678118654752440, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.06666666666666666667, \
      0.53333333333333333333, \
      0.80000000000000000000, \
      0.53333333333333333333, \
      0.06666666666666666667 \
    ] )

  elif ( n == 6 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.80901699437494742410, \
     -0.30901699437494742410, \
      0.30901699437494742410, \
      0.80901699437493732410, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.04000000000000000000, \
      0.36074304120001121619, \
      0.59925695879998878381, \
      0.59925695879998878381, \
      0.36074304120001121619, \
      0.04000000000000000000 \
    ] )

  elif ( n == 7 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.86602540378443864676, \
     -0.50000000000000000000, \
      0.00000000000000000000, \
      0.50000000000000000000, \
      0.86602540378443864676, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.02857142857142857143, \
      0.25396825396825396825, \
      0.45714285714285714286, \
      0.52063492063492063492, \
      0.45714285714285714286, \
      0.25396825396825396825, \
      0.02857142857142857143 \
    ] )

  elif ( n == 8 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.90096886790241912624, \
     -0.62348980185873353053, \
     -0.22252093395631440429, \
      0.22252093395631440429, \
      0.62348980185873353053, \
      0.90096886790241910624, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.02040816326530612245, \
      0.19014100721820835178, \
      0.35224242371815911533, \
      0.43720840579832641044, \
      0.43720840579832641044, \
      0.35224242371815911533, \
      0.19014100721820835178, \
      0.02040816326530612245 \
    ] )

  elif ( n == 9 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.92387953251128675613, \
     -0.70710678118654752440, \
     -0.38268343236508977173, \
      0.00000000000000000000, \
      0.38268343236508977173, \
      0.70710678118654752440, \
      0.92387953251128675613, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.01587301587301587302, \
      0.14621864921601815501, \
      0.27936507936507936508, \
      0.36171785872048978150, \
      0.39365079365079365079, \
      0.36171785872048978150, \
      0.27936507936507936508, \
      0.14621864921601815501, \
      0.01587301587301587302 \
    ] )

  elif ( n == 10 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.93969262078590838405, \
     -0.76604444311897903520, \
     -0.50000000000000000000, \
     -0.17364817766693034885, \
      0.17364817766693034885, \
      0.50000000000000000000, \
      0.76604444311897903520, \
      0.93969262078590838405, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.01234567901234567901, \
      0.11656745657203712296, \
      0.22528432333810440813, \
      0.30194003527336860670, \
      0.34386250580414418320, \
      0.34386250580414418320, \
      0.30194003527336860670, \
      0.22528432333810440813, \
      0.11656745657203712296, \
      0.01234567901234567901 \
    ] )

  elif ( n == 11 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.95105651629515357212, \
     -0.80901699437494742410, \
     -0.58778525229247312917, \
     -0.30901699437494742410, \
      0.00000000000000000000, \
      0.30901699437494742410, \
      0.58778525229247312917, \
      0.80901699437494742410, \
      0.95105651629515357212, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.01010101010101010101, \
      0.09457905488370156116, \
      0.18563521442424776529, \
      0.25358833328368660623, \
      0.29921327042423708320, \
      0.31376623376623376623, \
      0.29921327042423708320, \
      0.25358833328368660623, \
      0.18563521442424776529, \
      0.09457905488370156116, \
      0.01010101010101010101 \
    ] )

  elif ( n == 12 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.95949297361449738989, \
     -0.84125353283118116886, \
     -0.65486073394528506406, \
     -0.41541501300188642553, \
     -0.14231483827328514044, \
      0.14231483827328514044, \
      0.41541501300188642553, \
      0.65486073394528506406, \
      0.84125353283118116886, \
      0.95949297361449738989, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.00826446280991735537, \
      0.07856015374620000543, \
      0.15504045508256136552, \
      0.21556254600086858099, \
      0.25991734106691617602, \
      0.28265504129353651666, \
      0.28265504129353651666, \
      0.25991734106691617602, \
      0.21556254600086858099, \
      0.15504045508256136552, \
      0.07856015374620000543, \
      0.00826446280991735537 \
    ] )

  elif ( n == 13 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.96592582628906828675, \
     -0.86602540378443864676, \
     -0.70710678118654752440, \
     -0.50000000000000000000, \
     -0.25881904510252076235, \
      0.00000000000000000000, \
      0.25881904510252076235, \
      0.50000000000000000000, \
      0.70710678118654752440, \
      0.86602540378443864676, \
      0.96592582628906828675, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.00699300699300699301, \
      0.06605742495207439452, \
      0.13154253154253154253, \
      0.18476338476338476338, \
      0.22697302697302697303, \
      0.25267569378104433860, \
      0.26198986198986198986, \
      0.25267569378104433860, \
      0.22697302697302697303, \
      0.18476338476338476338, \
      0.13154253154253154253, \
      0.06605742495207439452, \
      0.00699300699300699301 \
    ] )

  elif ( n == 14 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.97094181742605202716, \
     -0.88545602565320989590, \
     -0.74851074817110109863, \
     -0.56806474673115580251, \
     -0.35460488704253562597, \
     -0.12053668025532305335, \
      0.12053668025532305335, \
      0.35460488704253562597, \
      0.56806474673115580251, \
      0.74851074817110109863, \
      0.88545602565320989590, \
      0.97094181742605202716, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.00591715976331360947, \
      0.05646531376341444627, \
      0.11276867248985655881, \
      0.16003802611671868523, \
      0.19899241036578321848, \
      0.22590304977856444935, \
      0.23991536772234903239, \
      0.23991536772234903239, \
      0.22590304977856444935, \
      0.19899241036578321848, \
      0.16003802611671868523, \
      0.11276867248985655881, \
      0.05646531376341444627, \
      0.00591715976331360947 \
    ] )

  elif ( n == 15 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.97492791218182360702, \
     -0.90096886790241912624, \
     -0.78183148246802980871, \
     -0.62348980185873353053, \
     -0.43388373911755812048, \
     -0.22252093395631440429, \
      0.00000000000000000000, \
      0.22252093395631440429, \
      0.43388373911755812048, \
      0.62348980185873353053, \
      0.78183148246802980871, \
      0.90096886790241912624, \
      0.97492791218182360702, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.00512820512820512821, \
      0.04869938729508823855, \
      0.09782039167605215913, \
      0.13966507849560431803, \
      0.17560578900106674677, \
      0.20205146748238357364, \
      0.21888151163057340180, \
      0.22429633858205286777, \
      0.21888151163057340180, \
      0.20205146748238357364, \
      0.17560578900106674677, \
      0.13966507849560431803, \
      0.09782039167605215913, \
      0.04869938729508823855, \
      0.00512820512820512821 \
    ] )

  elif ( n == 16 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.97814760073380563793, \
     -0.91354545764260089550, \
     -0.80901699437494742410, \
     -0.66913060635885821383, \
     -0.50000000000000000000, \
     -0.30901699437494742410, \
     -0.10452846326765347140, \
      0.10452846326765347140, \
      0.30901699437494742410, \
      0.50000000000000000000, \
      0.66913060635885821383, \
      0.80901699437494742410, \
      0.91354545764260089550, \
      0.97814760073380563793, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.00444444444444444444, \
      0.04251476624752508988, \
      0.08553884025933288291, \
      0.12294010082849361533, \
      0.15573317603967369176, \
      0.18132978132978132978, \
      0.19921478132638853955, \
      0.20828410952436040635, \
      0.20828410952436040635, \
      0.19921478132638853955, \
      0.18132978132978132978, \
      0.15573317603967369176, \
      0.12294010082849361533, \
      0.08553884025933288291, \
      0.04251476624752508988, \
      0.00444444444444444444 \
    ] )

  elif ( n == 17 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.98078528040323044913, \
     -0.92387953251128675613, \
     -0.83146961230254523708, \
     -0.70710678118654752440, \
     -0.55557023301960222474, \
     -0.38268343236508977173, \
     -0.19509032201612826785, \
      0.00000000000000000000, \
      0.19509032201612826785, \
      0.38268343236508977173, \
      0.55557023301960222474, \
      0.70710678118654752440, \
      0.83146961230254523708, \
      0.92387953251128675613, \
      0.98078528040323044913, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.00392156862745098039, \
      0.03736870283720561032, \
      0.07548233154315183441, \
      0.10890555258189093044, \
      0.13895646836823307412, \
      0.16317266428170330256, \
      0.18147378423649335700, \
      0.19251386461292564687, \
      0.19641012582189052777, \
      0.19251386461292564687, \
      0.18147378423649335700, \
      0.16317266428170330256, \
      0.13895646836823307412, \
      0.10890555258189093044, \
      0.07548233154315183441, \
      0.03736870283720561032, \
      0.00392156862745098039 \
    ] )

  elif ( n == 33 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.99518472667219688624, \
     -0.98078528040323044913, \
     -0.95694033573220886494, \
     -0.92387953251128675613, \
     -0.88192126434835502971, \
     -0.83146961230254523708, \
     -0.77301045336273696081, \
     -0.70710678118654752440, \
     -0.63439328416364549822, \
     -0.55557023301960222474, \
     -0.47139673682599764856, \
     -0.38268343236508977173, \
     -0.29028467725446236764, \
     -0.19509032201612826785, \
     -0.098017140329560601994, \
      0.000000000000000000000, \
      0.098017140329560601994, \
      0.19509032201612826785, \
      0.29028467725446236764, \
      0.38268343236508977173, \
      0.47139673682599764856, \
      0.55557023301960222474, \
      0.63439328416364549822, \
      0.70710678118654752440, \
      0.77301045336273696081, \
      0.83146961230254523708, \
      0.88192126434835502971, \
      0.92387953251128675613, \
      0.95694033573220886494, \
      0.98078528040323044913, \
      0.99518472667219688624, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.00097751710654936461, \
      0.00939319796295501470, \
      0.01923424513268114918, \
      0.02845791667723369009, \
      0.03759434191404720602, \
      0.04626276283775174949, \
      0.05455501630398031044, \
      0.06227210954529400455, \
      0.06942757563043545090, \
      0.07588380044138847048, \
      0.08163481765493851023, \
      0.08657753844182743544, \
      0.09070611286772099874, \
      0.09394324443876873573, \
      0.09629232594548817919, \
      0.09769818820805558182, \
      0.09817857778176829677, \
      0.09769818820805558182, \
      0.09629232594548817919, \
      0.09394324443876873573, \
      0.09070611286772099874, \
      0.08657753844182743544, \
      0.08163481765493851023, \
      0.07588380044138847048, \
      0.06942757563043545090, \
      0.06227210954529400455, \
      0.05455501630398031044, \
      0.04626276283775174949, \
      0.03759434191404720602, \
      0.02845791667723369009, \
      0.01923424513268114918, \
      0.00939319796295501470, \
      0.00097751710654936461 \
    ] )

  elif ( n == 65 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.99879545620517239271, \
     -0.99518472667219688624, \
     -0.98917650996478097345, \
     -0.98078528040323044913, \
     -0.97003125319454399260, \
     -0.95694033573220886494, \
     -0.94154406518302077841, \
     -0.92387953251128675613, \
     -0.90398929312344333159, \
     -0.88192126434835502971, \
     -0.85772861000027206990, \
     -0.83146961230254523708, \
     -0.80320753148064490981, \
     -0.77301045336273696081, \
     -0.74095112535495909118, \
     -0.70710678118654752440, \
     -0.67155895484701840063, \
     -0.63439328416364549822, \
     -0.59569930449243334347, \
     -0.55557023301960222474, \
     -0.51410274419322172659, \
     -0.47139673682599764856, \
     -0.42755509343028209432, \
     -0.38268343236508977173, \
     -0.33688985339222005069, \
     -0.29028467725446236764, \
     -0.24298017990326388995, \
     -0.19509032201612826785, \
     -0.14673047445536175166, \
     -0.098017140329560601994, \
     -0.049067674327418014255, \
      0.000000000000000000000, \
      0.049067674327418014255, \
      0.098017140329560601994, \
      0.14673047445536175166, \
      0.19509032201612826785, \
      0.24298017990326388995, \
      0.29028467725446236764, \
      0.33688985339222005069, \
      0.38268343236508977173, \
      0.42755509343028209432, \
      0.47139673682599764856, \
      0.51410274419322172659, \
      0.55557023301960222474, \
      0.59569930449243334347, \
      0.63439328416364549822, \
      0.67155895484701840063, \
      0.70710678118654752440, \
      0.74095112535495909118, \
      0.77301045336273696081, \
      0.80320753148064490981, \
      0.83146961230254523708, \
      0.85772861000027206990, \
      0.88192126434835502971, \
      0.90398929312344333159, \
      0.92387953251128675613, \
      0.94154406518302077841, \
      0.95694033573220886494, \
      0.97003125319454399260, \
      0.98078528040323044913, \
      0.98917650996478097345, \
      0.99518472667219688624, \
      0.99879545620517239271, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.00024420024420024420, \
      0.00235149067531170332, \
      0.00483146544879091264, \
      0.00719269316173611402, \
      0.00958233879528379039, \
      0.01192339471421277160, \
      0.01425206043235199679, \
      0.01653498765728958965, \
      0.01878652974179578354, \
      0.02098627442973743378, \
      0.02314069493435819848, \
      0.02523506498175476590, \
      0.02727225714146838686, \
      0.02924065319746833770, \
      0.03114129710406762447, \
      0.03296454656997632997, \
      0.03471049818092511427, \
      0.03637092028663918309, \
      0.03794545992128481711, \
      0.03942698871295609976, \
      0.04081501340035783384, \
      0.04210333111141810203, \
      0.04329151496169082935, \
      0.04437417923925731580, \
      0.04535110955166067221, \
      0.04621766751092557684, \
      0.04697395904661414870, \
      0.04761604458525019296, \
      0.04814443257251220341, \
      0.04855584485714105274, \
      0.04885125664306609371, \
      0.04902801843102555294, \
      0.04908762351494245585, \
      0.04902801843102555294, \
      0.04885125664306609371, \
      0.04855584485714105274, \
      0.04814443257251220341, \
      0.04761604458525019296, \
      0.04697395904661414870, \
      0.04621766751092557684, \
      0.04535110955166067221, \
      0.04437417923925731580, \
      0.04329151496169082935, \
      0.04210333111141810203, \
      0.04081501340035783384, \
      0.03942698871295609976, \
      0.03794545992128481711, \
      0.03637092028663918309, \
      0.03471049818092511427, \
      0.03296454656997632997, \
      0.03114129710406762447, \
      0.02924065319746833770, \
      0.02727225714146838686, \
      0.02523506498175476590, \
      0.02314069493435819848, \
      0.02098627442973743378, \
      0.01878652974179578354, \
      0.01653498765728958965, \
      0.01425206043235199679, \
      0.01192339471421277160, \
      0.00958233879528379039, \
      0.00719269316173611402, \
      0.00483146544879091264, \
      0.00235149067531170332, \
      0.00024420024420024420 \
    ] )

  elif ( n == 129 ):

    x = np.array ( [ \
     -1.00000000000000000000, \
     -0.99969881869620422012, \
     -0.99879545620517239271, \
     -0.99729045667869021614, \
     -0.99518472667219688624, \
     -0.99247953459870999816, \
     -0.98917650996478097345, \
     -0.98527764238894124477, \
     -0.98078528040323044913, \
     -0.97570213003852854446, \
     -0.97003125319454399260, \
     -0.96377606579543986669, \
     -0.95694033573220886494, \
     -0.94952818059303666720, \
     -0.94154406518302077841, \
     -0.93299279883473888771, \
     -0.92387953251128675613, \
     -0.91420975570353065464, \
     -0.90398929312344333159, \
     -0.89322430119551532034, \
     -0.88192126434835502971, \
     -0.87008699110871141865, \
     -0.85772861000027206990, \
     -0.84485356524970707326, \
     -0.83146961230254523708, \
     -0.81758481315158369650, \
     -0.80320753148064490981, \
     -0.78834642762660626201, \
     -0.77301045336273696081, \
     -0.75720884650648454758, \
     -0.74095112535495909118, \
     -0.72424708295146692094, \
     -0.70710678118654752440, \
     -0.68954054473706692462, \
     -0.67155895484701840063, \
     -0.65317284295377676408, \
     -0.63439328416364549822, \
     -0.61523159058062684548, \
     -0.59569930449243334347, \
     -0.57580819141784530075, \
     -0.55557023301960222474, \
     -0.53499761988709721066, \
     -0.51410274419322172659, \
     -0.49289819222978403687, \
     -0.47139673682599764856, \
     -0.44961132965460660005, \
     -0.42755509343028209432, \
     -0.40524131400498987091, \
     -0.38268343236508977173, \
     -0.35989503653498814878, \
     -0.33688985339222005069, \
     -0.31368174039889147666, \
     -0.29028467725446236764, \
     -0.26671275747489838633, \
     -0.24298017990326388995, \
     -0.21910124015686979723, \
     -0.19509032201612826785, \
     -0.17096188876030122636, \
     -0.14673047445536175166, \
     -0.12241067519921619850, \
     -0.098017140329560601994, \
     -0.073564563599667423529, \
     -0.049067674327418014255, \
     -0.024541228522912288032, \
      0.00000000000000000000, \
      0.024541228522912288032, \
      0.049067674327418014255, \
      0.073564563599667423529, \
      0.098017140329560601994, \
      0.12241067519921619850, \
      0.14673047445536175166, \
      0.17096188876030122636, \
      0.19509032201612826785, \
      0.21910124015686979723, \
      0.24298017990326388995, \
      0.26671275747489838633, \
      0.29028467725446236764, \
      0.31368174039889147666, \
      0.33688985339222005069, \
      0.35989503653498814878, \
      0.38268343236508977173, \
      0.40524131400498987091, \
      0.42755509343028209432, \
      0.44961132965460660005, \
      0.47139673682599764856, \
      0.49289819222978403687, \
      0.51410274419322172659, \
      0.53499761988709721066, \
      0.55557023301960222474, \
      0.57580819141784530075, \
      0.59569930449243334347, \
      0.61523159058062684548, \
      0.63439328416364549822, \
      0.65317284295377676408, \
      0.67155895484701840063, \
      0.68954054473706692462, \
      0.70710678118654752440, \
      0.72424708295146692094, \
      0.74095112535495909118, \
      0.75720884650648454758, \
      0.77301045336273696081, \
      0.78834642762660626201, \
      0.80320753148064490981, \
      0.81758481315158369650, \
      0.83146961230254523708, \
      0.84485356524970707326, \
      0.85772861000027206990, \
      0.87008699110871141865, \
      0.88192126434835502971, \
      0.89322430119551532034, \
      0.90398929312344333159, \
      0.91420975570353065464, \
      0.92387953251128675613, \
      0.93299279883473888771, \
      0.94154406518302077841, \
      0.94952818059303666720, \
      0.95694033573220886494, \
      0.96377606579543986669, \
      0.97003125319454399260, \
      0.97570213003852854446, \
      0.98078528040323044913, \
      0.98527764238894124477, \
      0.98917650996478097345, \
      0.99247953459870999816, \
      0.99518472667219688624, \
      0.99729045667869021614, \
      0.99879545620517239271, \
      0.99969881869620422012, \
      1.00000000000000000000 \
    ] )

    w = np.array ( [ \
      0.00006103888176768602, \
      0.00058807215382869754, \
      0.00120930061875273991, \
      0.00180308126695362360, \
      0.00240715327877140915, \
      0.00300345869904497128, \
      0.00360197835812614147, \
      0.00419553798718534675, \
      0.00478862143341336763, \
      0.00537724746840184621, \
      0.00596388034730799521, \
      0.00654590843862298928, \
      0.00712483332325489785, \
      0.00769875778896082811, \
      0.00826865154203087108, \
      0.00883303867470133581, \
      0.00939256583934814871, \
      0.00994602784923457905, \
      0.01049386202576892125, \
      0.01103504877427254184, \
      0.01156988348290849967, \
      0.01209748052807164113, \
      0.01261803597977743271, \
      0.01313076516693974630, \
      0.01363579321293772047, \
      0.01413241437853094133, \
      0.01462070254634350205, \
      0.01510001572479266783, \
      0.01557039073899425960, \
      0.01603123858745057916, \
      0.01648256956220377909, \
      0.01692383985846499368, \
      0.01735504125411394958, \
      0.01777566938875279997, \
      0.01818570377926339481, \
      0.01858467519566908661, \
      0.01897255587067948426, \
      0.01934890842392451844, \
      0.01971370183700155725, \
      0.02006652805198357604, \
      0.02040735612003867863, \
      0.02073580533490147816, \
      0.02105184759002011131, \
      0.02135512797425970725, \
      0.02164562356712882440, \
      0.02192300400598756892, \
      0.02218725355897195088, \
      0.02243806539722630184, \
      0.02267543270456671718, \
      0.02289907134390605882, \
      0.02310898491627407168, \
      0.02330491126131143273, \
      0.02348686571193163505, \
      0.02365460746057766523, \
      0.02380816473024258975, \
      0.02394731750476901502, \
      0.02407210792327850000, \
      0.02418233623893147567, \
      0.02427805942075745923, \
      0.02435909748927643184, \
      0.02442552306156708690, \
      0.02447717542743444284, \
      0.02451414358881568292, \
      0.02453628559651495473, \
      0.02454370750551418263, \
      0.02453628559651495473, \
      0.02451414358881568292, \
      0.02447717542743444284, \
      0.02442552306156708690, \
      0.02435909748927643184, \
      0.02427805942075745923, \
      0.02418233623893147567, \
      0.02407210792327850000, \
      0.02394731750476901502, \
      0.02380816473024258975, \
      0.02365460746057766523, \
      0.02348686571193163505, \
      0.02330491126131143273, \
      0.02310898491627407168, \
      0.02289907134390605882, \
      0.02267543270456671718, \
      0.02243806539722630184, \
      0.02218725355897195088, \
      0.02192300400598756892, \
      0.02164562356712882440, \
      0.02135512797425970725, \
      0.02105184759002011131, \
      0.02073580533490147816, \
      0.02040735612003867863, \
      0.02006652805198357604, \
      0.01971370183700155725, \
      0.01934890842392451844, \
      0.01897255587067948426, \
      0.01858467519566908661, \
      0.01818570377926339481, \
      0.01777566938875279997, \
      0.01735504125411394958, \
      0.01692383985846499368, \
      0.01648256956220377909, \
      0.01603123858745057916, \
      0.01557039073899425960, \
      0.01510001572479266783, \
      0.01462070254634350205, \
      0.01413241437853094133, \
      0.01363579321293772047, \
      0.01313076516693974630, \
      0.01261803597977743271, \
      0.01209748052807164113, \
      0.01156988348290849967, \
      0.01103504877427254184, \
      0.01049386202576892125, \
      0.00994602784923457905, \
      0.00939256583934814871, \
      0.00883303867470133581, \
      0.00826865154203087108, \
      0.00769875778896082811, \
      0.00712483332325489785, \
      0.00654590843862298928, \
      0.00596388034730799521, \
      0.00537724746840184621, \
      0.00478862143341336763, \
      0.00419553798718534675, \
      0.00360197835812614147, \
      0.00300345869904497128, \
      0.00240715327877140915, \
      0.00180308126695362360, \
      0.00120930061875273991, \
      0.00058807215382869754, \
      0.00006103888176768602 \
    ] )

  else:

    print ( '' )
    print ( 'CLENSHAW_CURTIS_SET - Fatal error!' )
    print ( '  Illegal value of N = %d' % ( n ) )
    print ( '  Legal values are 1 to 17, 33, 65 or 129.' )
    exit ( 'CLENSHAW_CURTIS_SET - Fatal error!' )

  return x, w

def clenshaw_curtis_set_test ( ):

#*****************************************************************************80
#
## CLENSHAW_CURTIS_SET_TEST tests CLENSHAW_CURTIS_SET.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license. 
#
#  Modified:
#
#    03 April 2015
#
#  Author:
#
#    John Burkardt
#
  import platform

  print ( '' )
  print ( 'CLENSHAW_CURTIS_SET_TEST' )
  print ( '  Python version: %s' % ( platform.python_version ( ) ) )
  print ( '  CLENSHAW_CURTIS_SET sets up a Clenshaw Curtis' )
  print ( '  quadrature rule over [-1,1].' )
  print ( '' )
  print ( '     Index       X                       W' )

  for n in range ( 1, 11 ):

    x, w = clenshaw_curtis_set ( n )

    print ( '' )

    for i in range ( 0, n ):
      print ( '  %2d  %24.16g  %24.16g' % ( i, x[i], w[i] ) )
#
#  Terminate.
#
  print ( '' )
  print ( 'CLENSHAW_CURTIS_SET_TEST:' )
  print ( '  Normal end of execution.' )
  return

if ( __name__ == '__main__' ):
  from timestamp import timestamp
  timestamp ( )
  clenshaw_curtis_set_test ( )
  timestamp ( )