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Update to Py3 #5

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a375a3a
Py3 compatibility
jameskermode Oct 16, 2019
d2429b5
fix unit tests for Py3
jameskermode Oct 16, 2019
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20 changes: 10 additions & 10 deletions demos/demo1.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,24 +26,24 @@
degree = 4

# The first way of doing it is by directly supplying the weight function.
wf = lambda(x): 1. / math.sqrt(2. * math.pi) * np.exp(-x ** 2 / 2.)
wf = lambda x: 1. / math.sqrt(2. * math.pi) * np.exp(-x ** 2 / 2.)
# Construct it:
p = orthpol.OrthogonalPolynomial(degree,
left=-np.inf, right=np.inf, # Domain
wf=wf)
# An orthogonal polynomial is though of as a function.
# Here is how to get the number of inputs and outputs of that function
print 'Number of inputs:', p.num_input
print 'Number of outputs:', p.num_output
print(('Number of inputs:', p.num_input))
print(('Number of outputs:', p.num_output))
# Test if the polynomials are normalized (i.e., their norm is 1.):
print 'Is normalized:', p.is_normalized
print(('Is normalized:', p.is_normalized))
# Get the degree of the polynomial:
print 'Polynomial degree:', p.degree
print(('Polynomial degree:', p.degree))
# Get the alpha-beta recursion coefficients:
print 'Alpha:', p.alpha
print 'Beta:', p.beta
print(('Alpha:', p.alpha))
print(('Beta:', p.beta))
# The following should print a description of the polynomial
print str(p)
print((str(p)))
# Now you can evaluate the polynomial at any points you want:
X = np.linspace(-2., 2., 100)
# Here is the actual evaluation
Expand All @@ -55,7 +55,7 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel('$p_i(x)$', fontsize=16)
plt.legend(['$p_{%d}(x)$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to continue...'
print('Close the window to continue...')
plt.show()
# You may also compute the derivatives of the polynomials:
dphi = p.d(X)
Expand All @@ -65,5 +65,5 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel(r'$\frac{dp_i(x)}{dx}$', fontsize=16)
plt.legend([r'$\frac{p_{%d}(x)}{dx}$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to end demo...'
print('Close the window to end demo...')
plt.show()
18 changes: 9 additions & 9 deletions demos/demo10.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,17 +37,17 @@
p = orthpol.OrthogonalPolynomial(degree, rv=rv)
# An orthogonal polynomial is though of as a function.
# Here is how to get the number of inputs and outputs of that function
print 'Number of inputs:', p.num_input
print 'Number of outputs:', p.num_output
print('Number of inputs:', p.num_input)
print('Number of outputs:', p.num_output)
# Test if the polynomials are normalized (i.e., their norm is 1.):
print 'Is normalized:', p.is_normalized
print('Is normalized:', p.is_normalized)
# Get the degree of the polynomial:
print 'Polynomial degree:', p.degree
print('Polynomial degree:', p.degree)
# Get the alpha-beta recursion coefficients:
print 'Alpha:', p.alpha
print 'Beta:', p.beta
print('Alpha:', p.alpha)
print('Beta:', p.beta)
# The following should print a description of the polynomial
print str(p)
print(str(p))
# Now you can evaluate the polynomial at any points you want:
X = np.linspace(lower, upper, 100)
# Here is the actual evaluation
Expand All @@ -59,7 +59,7 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel('$p_i(x)$', fontsize=16)
plt.legend(['$p_{%d}(x)$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to continue...'
print('Close the window to continue...')
plt.show()
# You may also compute the derivatives of the polynomials:
dphi = p.d(X)
Expand All @@ -69,5 +69,5 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel(r'$\frac{dp_i(x)}{dx}$', fontsize=16)
plt.legend([r'$\frac{p_{%d}(x)}{dx}$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to end demo...'
print('Close the window to end demo...')
plt.show()
10 changes: 5 additions & 5 deletions demos/demo11.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,15 +25,15 @@
# Then construct the product basis
p = orthpol.ProductBasis(degree=degree, rvs=rvs)
# Print info about the polynomials
print str(p)
print(str(p))
# Evaluate the polynomials at some points
X = np.hstack([rvs[0].rvs(size=(100, 1)), rvs[1].rvs(size=(100, 1))])
# Look at the shape of X, it should be 100x2:
print 'X shape:', X.shape
print('X shape:', X.shape)
# Evaluate the polynomials at X
phi = p(X)
# Look at the shape of phi, it should be 100xp.num_output
print 'phi shape:', phi.shape
print('phi shape:', phi.shape)
# Take a look at the phi's also
print 'phi:'
print phi
print('phi:')
print(phi)
20 changes: 10 additions & 10 deletions demos/demo2.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,24 +26,24 @@
degree = 4

# The first way of doing it is by directly supplying the weight function.
wf = lambda(x): np.exp(-x)
wf = lambda x: np.exp(-x)
# Construct it:
p = orthpol.OrthogonalPolynomial(degree,
left=0, right=np.inf, # Domain
wf=wf)
# An orthogonal polynomial is though of as a function.
# Here is how to get the number of inputs and outputs of that function
print 'Number of inputs:', p.num_input
print 'Number of outputs:', p.num_output
print('Number of inputs:', p.num_input)
print('Number of outputs:', p.num_output)
# Test if the polynomials are normalized (i.e., their norm is 1.):
print 'Is normalized:', p.is_normalized
print('Is normalized:', p.is_normalized)
# Get the degree of the polynomial:
print 'Polynomial degree:', p.degree
print('Polynomial degree:', p.degree)
# Get the alpha-beta recursion coefficients:
print 'Alpha:', p.alpha
print 'Beta:', p.beta
print('Alpha:', p.alpha)
print('Beta:', p.beta)
# The following should print a description of the polynomial
print str(p)
print(str(p))
# Now you can evaluate the polynomial at any points you want:
X = np.linspace(0., 2., 100)
# Here is the actual evaluation
Expand All @@ -55,7 +55,7 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel('$p_i(x)$', fontsize=16)
plt.legend(['$p_{%d}(x)$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to continue...'
print('Close the window to continue...')
plt.show()
# You may also compute the derivatives of the polynomials:
dphi = p.d(X)
Expand All @@ -65,5 +65,5 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel(r'$\frac{dp_i(x)}{dx}$', fontsize=16)
plt.legend([r'$\frac{p_{%d}(x)}{dx}$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to end demo...'
print('Close the window to end demo...')
plt.show()
20 changes: 10 additions & 10 deletions demos/demo3.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,24 +26,24 @@
degree = 4

# The first way of doing it is by directly supplying the weight function.
wf = lambda(x): 1. / np.sqrt(1. - x)
wf = lambda x: 1. / np.sqrt(1. - x)
# Construct it:
p = orthpol.OrthogonalPolynomial(degree,
left=-1., right=1., # Domain
wf=wf)
# An orthogonal polynomial is though of as a function.
# Here is how to get the number of inputs and outputs of that function
print 'Number of inputs:', p.num_input
print 'Number of outputs:', p.num_output
print('Number of inputs:', p.num_input)
print('Number of outputs:', p.num_output)
# Test if the polynomials are normalized (i.e., their norm is 1.):
print 'Is normalized:', p.is_normalized
print('Is normalized:', p.is_normalized)
# Get the degree of the polynomial:
print 'Polynomial degree:', p.degree
print('Polynomial degree:', p.degree)
# Get the alpha-beta recursion coefficients:
print 'Alpha:', p.alpha
print 'Beta:', p.beta
print('Alpha:', p.alpha)
print('Beta:', p.beta)
# The following should print a description of the polynomial
print str(p)
print(str(p))
# Now you can evaluate the polynomial at any points you want:
X = np.linspace(-1., 1., 100)
# Here is the actual evaluation
Expand All @@ -55,7 +55,7 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel('$p_i(x)$', fontsize=16)
plt.legend(['$p_{%d}(x)$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to continue...'
print('Close the window to continue...')
plt.show()
# You may also compute the derivatives of the polynomials:
dphi = p.d(X)
Expand All @@ -65,5 +65,5 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel(r'$\frac{dp_i(x)}{dx}$', fontsize=16)
plt.legend([r'$\frac{p_{%d}(x)}{dx}$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to end demo...'
print('Close the window to end demo...')
plt.show()
20 changes: 10 additions & 10 deletions demos/demo4.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -28,24 +28,24 @@
# Pick the alpha and beta of the Gegenbauer polynomials
alpha = .5
# The first way of doing it is by directly supplying the weight function.
wf = lambda(x): (1. - x ** 2.) ** (alpha - 0.5)
wf = lambda x: (1. - x ** 2.) ** (alpha - 0.5)
# Construct it:
p = orthpol.OrthogonalPolynomial(degree,
left=-1., right=1., # Domain
wf=wf)
# An orthogonal polynomial is though of as a function.
# Here is how to get the number of inputs and outputs of that function
print 'Number of inputs:', p.num_input
print 'Number of outputs:', p.num_output
print('Number of inputs:', p.num_input)
print('Number of outputs:', p.num_output)
# Test if the polynomials are normalized (i.e., their norm is 1.):
print 'Is normalized:', p.is_normalized
print('Is normalized:', p.is_normalized)
# Get the degree of the polynomial:
print 'Polynomial degree:', p.degree
print('Polynomial degree:', p.degree)
# Get the alpha-beta recursion coefficients:
print 'Alpha:', p.alpha
print 'Beta:', p.beta
print('Alpha:', p.alpha)
print('Beta:', p.beta)
# The following should print a description of the polynomial
print str(p)
print(str(p))
# Now you can evaluate the polynomial at any points you want:
X = np.linspace(-1., 1., 100)
# Here is the actual evaluation
Expand All @@ -57,7 +57,7 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel('$p_i(x)$', fontsize=16)
plt.legend(['$p_{%d}(x)$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to continue...'
print('Close the window to continue...')
plt.show()
# You may also compute the derivatives of the polynomials:
dphi = p.d(X)
Expand All @@ -67,5 +67,5 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel(r'$\frac{dp_i(x)}{dx}$', fontsize=16)
plt.legend([r'$\frac{p_{%d}(x)}{dx}$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to end demo...'
print('Close the window to end demo...')
plt.show()
20 changes: 10 additions & 10 deletions demos/demo5.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,24 +29,24 @@
alpha = 2.
beta = 5.
# The first way of doing it is by directly supplying the weight function.
wf = lambda(x): (1. - x) ** alpha * (1 + x) ** beta
wf = lambda x: (1. - x) ** alpha * (1 + x) ** beta
# Construct it:
p = orthpol.OrthogonalPolynomial(degree,
left=-1., right=1., # Domain
wf=wf)
# An orthogonal polynomial is though of as a function.
# Here is how to get the number of inputs and outputs of that function
print 'Number of inputs:', p.num_input
print 'Number of outputs:', p.num_output
print('Number of inputs:', p.num_input)
print('Number of outputs:', p.num_output)
# Test if the polynomials are normalized (i.e., their norm is 1.):
print 'Is normalized:', p.is_normalized
print('Is normalized:', p.is_normalized)
# Get the degree of the polynomial:
print 'Polynomial degree:', p.degree
print('Polynomial degree:', p.degree)
# Get the alpha-beta recursion coefficients:
print 'Alpha:', p.alpha
print 'Beta:', p.beta
print('Alpha:', p.alpha)
print('Beta:', p.beta)
# The following should print a description of the polynomial
print str(p)
print(str(p))
# Now you can evaluate the polynomial at any points you want:
X = np.linspace(-1., 1., 100)
# Here is the actual evaluation
Expand All @@ -58,7 +58,7 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel('$p_i(x)$', fontsize=16)
plt.legend(['$p_{%d}(x)$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to continue...'
print('Close the window to continue...')
plt.show()
# You may also compute the derivatives of the polynomials:
dphi = p.d(X)
Expand All @@ -68,5 +68,5 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel(r'$\frac{dp_i(x)}{dx}$', fontsize=16)
plt.legend([r'$\frac{p_{%d}(x)}{dx}$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to end demo...'
print('Close the window to end demo...')
plt.show()
20 changes: 10 additions & 10 deletions demos/demo6.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,24 +26,24 @@
degree = 4

# The first way of doing it is by directly supplying the weight function
wf = lambda(x): 1.
wf = lambda x: 1.
# Construct it:
p = orthpol.OrthogonalPolynomial(degree,
left=0., right=1., # Domain
wf=wf)
# An orthogonal polynomial is though of as a function.
# Here is how to get the number of inputs and outputs of that function
print 'Number of inputs:', p.num_input
print 'Number of outputs:', p.num_output
print('Number of inputs:', p.num_input)
print('Number of outputs:', p.num_output)
# Test if the polynomials are normalized (i.e., their norm is 1.):
print 'Is normalized:', p.is_normalized
print('Is normalized:', p.is_normalized)
# Get the degree of the polynomial:
print 'Polynomial degree:', p.degree
print('Polynomial degree:', p.degree)
# Get the alpha-beta recursion coefficients:
print 'Alpha:', p.alpha
print 'Beta:', p.beta
print('Alpha:', p.alpha)
print('Beta:', p.beta)
# The following should print a description of the polynomial
print str(p)
print(str(p))
# Now you can evaluate the polynomial at any points you want:
X = np.linspace(0., 1., 100)
# Here is the actual evaluation
Expand All @@ -55,7 +55,7 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel('$p_i(x)$', fontsize=16)
plt.legend(['$p_{%d}(x)$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to continue...'
print('Close the window to continue...')
plt.show()
# You may also compute the derivatives of the polynomials:
dphi = p.d(X)
Expand All @@ -65,5 +65,5 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel(r'$\frac{dp_i(x)}{dx}$', fontsize=16)
plt.legend([r'$\frac{p_{%d}(x)}{dx}$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to end demo...'
print('Close the window to end demo...')
plt.show()
18 changes: 9 additions & 9 deletions demos/demo7.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,17 +33,17 @@
p = orthpol.OrthogonalPolynomial(degree, rv=rv)
# An orthogonal polynomial is though of as a function.
# Here is how to get the number of inputs and outputs of that function
print 'Number of inputs:', p.num_input
print 'Number of outputs:', p.num_output
print('Number of inputs:', p.num_input)
print('Number of outputs:', p.num_output)
# Test if the polynomials are normalized (i.e., their norm is 1.):
print 'Is normalized:', p.is_normalized
print('Is normalized:', p.is_normalized)
# Get the degree of the polynomial:
print 'Polynomial degree:', p.degree
print('Polynomial degree:', p.degree)
# Get the alpha-beta recursion coefficients:
print 'Alpha:', p.alpha
print 'Beta:', p.beta
print('Alpha:', p.alpha)
print('Beta:', p.beta)
# The following should print a description of the polynomial
print str(p)
print(str(p))
# Now you can evaluate the polynomial at any points you want:
X = np.linspace(0., 1., 100)
# Here is the actual evaluation
Expand All @@ -55,7 +55,7 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel('$p_i(x)$', fontsize=16)
plt.legend(['$p_{%d}(x)$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to continue...'
print('Close the window to continue...')
plt.show()
# You may also compute the derivatives of the polynomials:
dphi = p.d(X)
Expand All @@ -65,5 +65,5 @@
plt.xlabel('$x$', fontsize=16)
plt.ylabel(r'$\frac{dp_i(x)}{dx}$', fontsize=16)
plt.legend([r'$\frac{p_{%d}(x)}{dx}$' % i for i in range(p.num_output)], loc='best')
print 'Close the window to end demo...'
print('Close the window to end demo...')
plt.show()
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