Python numpy.polyint方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def check_partialquadraturewithQ(collclass, t_start, t_end):
    for M in range(2, 13):
        coll = collclass(M, t_start, t_end)
        Q = coll.Qmat[1:,1:]
        # as in TEST 1, create and integrate a polynomial with random coefficients, but now of degree M-1 (or less for splines)
        degree = min(coll.order,M-1)
        poly_coeff = np.random.rand(degree)
        poly_vals  = np.polyval(poly_coeff, coll.nodes)
        poly_int_coeff = np.polyint(poly_coeff)
        for i in range(0,M):
            int_ex = np.polyval(poly_int_coeff, coll.nodes[i]) - np.polyval(poly_int_coeff, t_start)
            int_coll = np.dot(poly_vals, Q[i,:])
            assert abs(int_ex - int_coll)<1e-12, "For node type " + coll.__class__.__name__ + ", partial quadrature from Qmat rule failed to integrate polynomial of degree M-1 exactly for M = " + str(M)

# TEST 3:
# Check that the partial quadrature rules from Smat entries have order equal to number of nodes M
# ----------------------------------------------------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def test_polyint_type(self):
        # Ticket #944
        msg = "Wrong type, should be complex"
        x = np.ones(3, dtype=complex)
        assert_(np.polyint(x).dtype == complex, msg)
        msg = "Wrong type, should be float"
        x = np.ones(3, dtype=int)
        assert_(np.polyint(x).dtype == float, msg) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def test_polyint_type(self):
        # Ticket #944
        msg = "Wrong type, should be complex"
        x = np.ones(3, dtype=np.complex)
        assert_(np.polyint(x).dtype == np.complex, msg)
        msg = "Wrong type, should be float"
        x = np.ones(3, dtype=np.int)
        assert_(np.polyint(x).dtype == np.float, msg) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def test_polyint_type(self) :
        """Ticket #944"""
        msg = "Wrong type, should be complex"
        x = np.ones(3, dtype=np.complex)
        assert_(np.polyint(x).dtype == np.complex, msg)
        msg = "Wrong type, should be float"
        x = np.ones(3, dtype=np.int)
        assert_(np.polyint(x).dtype == np.float, msg) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def check_canintegratepolynomials(collclass,t_start,t_end):

    for M in range(2,13):

        coll = collclass(M, t_start, t_end)

        # some basic consistency tests
        assert np.size(coll.nodes)==np.size(coll.weights), "For node type " + type[0] + ", number of entries in nodes and weights is different"
        assert np.size(coll.nodes)==M, "For node type " + type[0] + ", requesting M nodes did not produce M entries in nodes and weights"

        # generate random set of polynomial coefficients
        poly_coeff = np.random.rand(coll.order-1)
        # evaluate polynomial at collocation nodes
        poly_vals  = np.polyval(poly_coeff, coll.nodes)
        # use python's polyint function to compute anti-derivative of polynomial
        poly_int_coeff = np.polyint(poly_coeff)
        # Compute integral from 0.0 to 1.0
        int_ex = np.polyval(poly_int_coeff, t_end) - np.polyval(poly_int_coeff, t_start)
        # use quadrature rule to compute integral
        int_coll = coll.evaluate(coll.weights, poly_vals)
        # For large values of M, substantial differences from different round of error have to be considered
        assert abs(int_ex - int_coll) < 1e-13, "For node type " + coll.__class__.__name__ + ", failed to integrate polynomial of degree " + str(coll.order-1) + " exactly. Error: %5.3e" % abs(int_ex - int_coll)


# TEST 2:
# Check that the Qmat entries are equal to the sum of Smat entries
# ---------------------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def check_partialquadraturewithS(collclass, t_start, t_end):
    for M in range(2, 13):
        coll = collclass(M, t_start, t_end)
        S = coll.Smat[1:,1:]
        # as in TEST 1, create and integrate a polynomial with random coefficients, but now of degree M-1 (or less for splines)
        degree = min(coll.order, M - 1)
        poly_coeff = np.random.rand(degree)
        poly_vals  = np.polyval(poly_coeff, coll.nodes)
        poly_int_coeff = np.polyint(poly_coeff)
        for i in range(1,M):
            int_ex = np.polyval(poly_int_coeff, coll.nodes[i]) - np.polyval(poly_int_coeff, coll.nodes[i-1])
            int_coll = np.dot(poly_vals, S[i,:])
            assert abs(int_ex - int_coll)<1e-12, "For node type " + coll.__class__.__name__ + ", partial quadrature rule from Smat failed to integrate polynomial of degree M-1 exactly for M = " + str(M) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def BdRate(group1, group2):
  """Compute the BD-rate between two score groups.

  The returned object also contains the range of PSNR values used
  to compute the result.

  Bjontegaard's metric allows to compute the average % saving in bitrate
  between two rate-distortion curves [1].

  rate1,psnr1 - RD points for curve 1
  rate2,psnr2 - RD points for curve 2

  adapted from code from: (c) 2010 Giuseppe Valenzise
  copied from code by jzern@google.com, jimbankoski@google.com

  """
  # pylint: disable=too-many-locals
  metric_set1 = group1.dataPoints()
  metric_set2 = group2.dataPoints()

  # numpy plays games with its exported functions.
  # pylint: disable=no-member
  # pylint: disable=bad-builtin
  psnr1 = [x[1] for x in metric_set1]
  psnr2 = [x[1] for x in metric_set2]

  log_rate1 = map(math.log, [x[0] for x in metric_set1])
  log_rate2 = map(math.log, [x[0] for x in metric_set2])

  # Best cubic poly fit for graph represented by log_ratex, psrn_x.
  poly1 = numpy.polyfit(psnr1, log_rate1, 3)
  poly2 = numpy.polyfit(psnr2, log_rate2, 3)

  # Integration interval.
  min_int = max([min(psnr1), min(psnr2)])
  max_int = min([max(psnr1), max(psnr2)])

  # find integral
  p_int1 = numpy.polyint(poly1)
  p_int2 = numpy.polyint(poly2)

  # Calculate the integrated value over the interval we care about.
  int1 = numpy.polyval(p_int1, max_int) - numpy.polyval(p_int1, min_int)
  int2 = numpy.polyval(p_int2, max_int) - numpy.polyval(p_int2, min_int)

  # Calculate the average improvement.
  avg_exp_diff = (int2 - int1) / (max_int - min_int)

  # In really bad formed data the exponent can grow too large.
  # clamp it.
  if avg_exp_diff > 200:
    avg_exp_diff = 200

  # Convert to a percentage.
  avg_diff = (math.exp(avg_exp_diff) - 1) * 100

  return {'difference': avg_diff, 'psnr':[min_int, max_int]} 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def bdsnr(metric_set1, metric_set2):
  """
  BJONTEGAARD    Bjontegaard metric calculation
  Bjontegaard's metric allows to compute the average gain in psnr between two
  rate-distortion curves [1].
  rate1,psnr1 - RD points for curve 1
  rate2,psnr2 - RD points for curve 2

  returns the calculated Bjontegaard metric 'dsnr'

  code adapted from code written by : (c) 2010 Giuseppe Valenzise
  http://www.mathworks.com/matlabcentral/fileexchange/27798-bjontegaard-metric/content/bjontegaard.m
  """
  # pylint: disable=too-many-locals
  # numpy seems to do tricks with its exports.
  # pylint: disable=no-member
  # map() is recommended against.
  # pylint: disable=bad-builtin
  rate1 = [x[0] for x in metric_set1]
  psnr1 = [x[1] for x in metric_set1]
  rate2 = [x[0] for x in metric_set2]
  psnr2 = [x[1] for x in metric_set2]

  log_rate1 = map(math.log, rate1)
  log_rate2 = map(math.log, rate2)

  # Best cubic poly fit for graph represented by log_ratex, psrn_x.
  poly1 = numpy.polyfit(log_rate1, psnr1, 3)
  poly2 = numpy.polyfit(log_rate2, psnr2, 3)

  # Integration interval.
  min_int = max([min(log_rate1), min(log_rate2)])
  max_int = min([max(log_rate1), max(log_rate2)])

  # Integrate poly1, and poly2.
  p_int1 = numpy.polyint(poly1)
  p_int2 = numpy.polyint(poly2)

  # Calculate the integrated value over the interval we care about.
  int1 = numpy.polyval(p_int1, max_int) - numpy.polyval(p_int1, min_int)
  int2 = numpy.polyval(p_int2, max_int) - numpy.polyval(p_int2, min_int)

  # Calculate the average improvement.
  if max_int != min_int:
    avg_diff = (int2 - int1) / (max_int - min_int)
  else:
    avg_diff = 0.0
  return avg_diff 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def bdrate(metric_set1, metric_set2):
  """
  BJONTEGAARD    Bjontegaard metric calculation
  Bjontegaard's metric allows to compute the average % saving in bitrate
  between two rate-distortion curves [1].

  rate1,psnr1 - RD points for curve 1
  rate2,psnr2 - RD points for curve 2

  adapted from code from: (c) 2010 Giuseppe Valenzise

  """
  # numpy plays games with its exported functions.
  # pylint: disable=no-member
  # pylint: disable=too-many-locals
  # pylint: disable=bad-builtin
  rate1 = [x[0] for x in metric_set1]
  psnr1 = [x[1] for x in metric_set1]
  rate2 = [x[0] for x in metric_set2]
  psnr2 = [x[1] for x in metric_set2]

  log_rate1 = map(math.log, rate1)
  log_rate2 = map(math.log, rate2)

  # Best cubic poly fit for graph represented by log_ratex, psrn_x.
  poly1 = numpy.polyfit(psnr1, log_rate1, 3)
  poly2 = numpy.polyfit(psnr2, log_rate2, 3)

  # Integration interval.
  min_int = max([min(psnr1), min(psnr2)])
  max_int = min([max(psnr1), max(psnr2)])

  # find integral
  p_int1 = numpy.polyint(poly1)
  p_int2 = numpy.polyint(poly2)

  # Calculate the integrated value over the interval we care about.
  int1 = numpy.polyval(p_int1, max_int) - numpy.polyval(p_int1, min_int)
  int2 = numpy.polyval(p_int2, max_int) - numpy.polyval(p_int2, min_int)

  # Calculate the average improvement.
  avg_exp_diff = (int2 - int1) / (max_int - min_int)

  # In really bad formed data the exponent can grow too large.
  # clamp it.
  if avg_exp_diff > 200:
    avg_exp_diff = 200

  # Convert to a percentage.
  avg_diff = (math.exp(avg_exp_diff) - 1) * 100

  return avg_diff 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def bdsnr(metric_set1, metric_set2):
  """
  BJONTEGAARD    Bjontegaard metric calculation
  Bjontegaard's metric allows to compute the average gain in psnr between two
  rate-distortion curves [1].
  rate1,psnr1 - RD points for curve 1
  rate2,psnr2 - RD points for curve 2

  returns the calculated Bjontegaard metric 'dsnr'

  code adapted from code written by : (c) 2010 Giuseppe Valenzise
  http://www.mathworks.com/matlabcentral/fileexchange/27798-bjontegaard-metric/content/bjontegaard.m
  """
  # pylint: disable=too-many-locals
  # numpy seems to do tricks with its exports.
  # pylint: disable=no-member
  # map() is recommended against.
  # pylint: disable=bad-builtin
  rate1 = [x[0] for x in metric_set1]
  psnr1 = [x[1] for x in metric_set1]
  rate2 = [x[0] for x in metric_set2]
  psnr2 = [x[1] for x in metric_set2]

  log_rate1 = list(map(math.log, rate1))
  log_rate2 = list(map(math.log, rate2))

  # Best cubic poly fit for graph represented by log_ratex, psrn_x.
  poly1 = numpy.polyfit(log_rate1, psnr1, 3)
  poly2 = numpy.polyfit(log_rate2, psnr2, 3)

  # Integration interval.
  min_int = max([min(log_rate1), min(log_rate2)])
  max_int = min([max(log_rate1), max(log_rate2)])

  # Integrate poly1, and poly2.
  p_int1 = numpy.polyint(poly1)
  p_int2 = numpy.polyint(poly2)

  # Calculate the integrated value over the interval we care about.
  int1 = numpy.polyval(p_int1, max_int) - numpy.polyval(p_int1, min_int)
  int2 = numpy.polyval(p_int2, max_int) - numpy.polyval(p_int2, min_int)

  # Calculate the average improvement.
  if max_int != min_int:
    avg_diff = (int2 - int1) / (max_int - min_int)
  else:
    avg_diff = 0.0
  return avg_diff 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import polyint [as 別名]
def bdrate(metric_set1, metric_set2):
  """
  BJONTEGAARD    Bjontegaard metric calculation
  Bjontegaard's metric allows to compute the average % saving in bitrate
  between two rate-distortion curves [1].

  rate1,psnr1 - RD points for curve 1
  rate2,psnr2 - RD points for curve 2

  adapted from code from: (c) 2010 Giuseppe Valenzise

  """
  # numpy plays games with its exported functions.
  # pylint: disable=no-member
  # pylint: disable=too-many-locals
  # pylint: disable=bad-builtin
  rate1 = [x[0] for x in metric_set1]
  psnr1 = [x[1] for x in metric_set1]
  rate2 = [x[0] for x in metric_set2]
  psnr2 = [x[1] for x in metric_set2]

  log_rate1 = list(map(math.log, rate1))
  log_rate2 = list(map(math.log, rate2))

  # Best cubic poly fit for graph represented by log_ratex, psrn_x.
  poly1 = numpy.polyfit(psnr1, log_rate1, 3)
  poly2 = numpy.polyfit(psnr2, log_rate2, 3)

  # Integration interval.
  min_int = max([min(psnr1), min(psnr2)])
  max_int = min([max(psnr1), max(psnr2)])

  # find integral
  p_int1 = numpy.polyint(poly1)
  p_int2 = numpy.polyint(poly2)

  # Calculate the integrated value over the interval we care about.
  int1 = numpy.polyval(p_int1, max_int) - numpy.polyval(p_int1, min_int)
  int2 = numpy.polyval(p_int2, max_int) - numpy.polyval(p_int2, min_int)

  # Calculate the average improvement.
  avg_exp_diff = (int2 - int1) / (max_int - min_int)

  # In really bad formed data the exponent can grow too large.
  # clamp it.
  if avg_exp_diff > 200:
    avg_exp_diff = 200

  # Convert to a percentage.
  avg_diff = (math.exp(avg_exp_diff) - 1) * 100

  return avg_diff