Chapter 8: Mathematics#
We have already been doing math throughout this book as Python is fundamentally performing mathematical operations through arithmetic, calculus, algebra, and Boolean logic among others, but this chapter will dive deeper into symbolic mathematics, matrix operations, and integration. Some of this chapter will rely on SciPy and NumPy, but for the symbolic mathematics, we will use the popular SymPy library.
SymPy is the main library in the SciPy ecosystem for performing symbolic mathematics, and it is suitable for a wide audience from high school students to scientific researchers. It is something like a free, open source Mathematica substitute that is built on Python and is arguably more accessible in terms of cost and ease of acquisition. All of the following SymPy code relies on the following import which makes all of the SymPy modules available.
import sympy
8.1 Symbolic Mathematics#
SymPy differentiates itself from the rest of Python and SciPy stack in that it returns exact or symbolic results whereas Python, SciPy, and NumPy will generate numerical answers which may not be exact. That is to say, not only does SymPy perform symbolic mathematical operations, but even if the result of an operation has a numerical answer, SymPy will return the value in exact form. For example, if we take the square root of 2 using the math module, we get a numerical value.
import math
math.sqrt(2)
1.4142135623730951
The value returned is a rounded approximation of the true answer. In contrast, if the same operation is performed using SymPy, we get a different result.
sympy.sqrt(2)
Because the square root of two is an irrational number, it cannot be represented exactly by a decimal, so SymPy leaves it in the exact form of \(\sqrt{2}\). If we absolutely need a numerical value, SymPy can be instructed to evaluate an imprecise, numerical value using the evalf() method.
sympy.sqrt(2).evalf()
One of the advantages of evalf() is that it also accepts a significant figures argument.
sympy.sqrt(2).evalf(30)
sympy.pi.evalf(40)
8.1.1 Symbols#
Before SymPy will accept a variable as a symbol, the variable must first be defined as a SymPy symbol using the symbols() function. It takes one or more symbols at a time and attaches them to variables.
x, c, m = sympy.symbols('x c m')
x
There is no value attached to x as it is a symbol, so now it can be used to generate symbolic mathematical expressions.
E = m*c**2
E
E**2
SymPy can also be used to solve expressions for numerical values, and there are times when ony certain ranges or types of numerical values make physical sense. For example, concentrations should only be nonnegative and real values. To constrain solutions of an expression to positive and nonnegative values, additional arguments known as predicates can be added to the sympy.symbols() function such as nonnegative=True or real=True.
y = sympy.symbols('y', real=True, nonnegative=True)
To see what constrains were placed on a variable, use the assumptions() function demonstrated below.
from sympy.core.assumptions import assumptions
assumptions(y)
{'real': True,
'extended_real': True,
'finite': True,
'hermitian': True,
'imaginary': False,
'infinite': False,
'commutative': True,
'complex': True,
'nonnegative': True,
'extended_negative': False,
'negative': False,
'extended_nonnegative': True}
A selection of predicate arguments for the sympy.symbols() function are listed below in Table 1. This is not an exhaustive list, but a complete list can be found on the SymPy website under “Predicates.”
Table 1 Predicates forsympy.symbols()
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8.1.2 Pretty Printing#
Depending upon settings and version of SymPy, the output may look like Python equations which are not always the easiest to read. If so, you can turn on pretty printing, shown below, which will instruct SymPy to render the expressions in more traditional mathematical representations that you might see in a math textbook. More recent versions of SymPy make this unnecessary, however, as it generates more traditional mathematical representations by default.
from sympy import init_printing
sympy.init_printing()
8.1.3 SymPy Mathematical Functions#
Similar to the math Python module, SymPy contains an assortment of standard mathematical operators such as square root and trigonometric functions. A table of common functions is below. Some of the functions start with a capital letter such as Abs(). This is important so that they do not collide with native Python functions if SymPy is imported into the global namespace.
Table 2 Common SymPy Functions
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It is important to note that any mathematical function operating on a symbol needs to be from the SymPy library. For example, using a math.cos() function from the math Python modeule will result in an error.
8.2 Algebra in SymPy#
SymPy is quite capable at algebraic operations and is knowledgeable of common identities such as \(sin(x)^2 + cos(x)^2 = 1\), but before we proceed with doing algebra in SymPy, we need to cover some basic algebraic methods. These are provided in Table 3 which includes polynomial expansion and factoring, expression simplification, and solving equations. The subsequent sections demonstrate each of these.
Table 3 Common Algebraic Methods
Method |
Description |
|---|---|
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Expand polynomials |
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Factors polynomials |
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Simplifies the expression |
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Equates the expression to zero and solves for the requested variable |
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Substitutes a variable for a value, expression, or another variable |
8.2.1 Polynomial Expansion and Factoring#
When dealing with polynomials, expansion and factoring are common operations that can be tedious and time-consuming by hand. SymPy makes these quick and easy. For example, we can expand the expression \((x - 1)(3x + 2)\) as demonstrated below.
expr = (x - 1)*(3*x + 2)
sympy.expand(expr)
The process can be reversed by factoring the polynomial.
sympy.factor(3*x**2 - x - 2)
8.2.2 Simplification#
SymPy may not always return a mathematical expression into the simplest form. Below is an expression with a simpler form, and if we feed this into SymPy, it is not automatically simplified.
3*x**2 - 4*x - 15 / (x - 3)
However, if we instruct SymPy to simplify the expression using the simplify() method, it will make a best attempt at finding a simpler form.
sympy.simplify((3*x**2 - 4*x - 15) / (x - 3))
8.2.3 Solving Equations#
SymPy can also solve equations for an unknown variable using the solve() function. The function requires a single expression that is equal to zero. For example, the following solves for \(x\) in \(x^2 + 1.4x – 5.76 = 0\).
sympy.solve(x**2 + 1.4*x - 5.76)
[-3.20000000000000, 1.80000000000000]
8.2.4 Equilibrium ICE Table#
A common chemical application of the above algebraic operations is solving equilibrium problems using the ICE (Initial, Change, and Equilibrium) method. As a penultimate step, the mathematical expressions are inserted into the equilibrium expression and often result in a polynomial equation. Below is an example problem with completed ICE table and equilibrium expression.
2 NH\(_3\) |
\(\rightleftharpoons\) |
3 H\(_2\) (g) |
+ |
N\(_2\) (g) |
|
|---|---|---|---|---|---|
Initial |
0.60 M |
0.60 M |
0.00 M |
||
Change, \(\Delta\) |
-2x |
+3x |
+x |
||
Equilibrium |
0.60 - 2x |
0.60 + 3x |
x |
To expand the right portion of the equation, we can use the expand() method. Notice that the variable x has been constrained below to real (real=True) and nonnegative (nonnegative=True) values here. This is because in this example, x is one of the equilibrium concentrations, so imaginary and negative values would make no physical sense. These constraints may not be appropriate for other examples.
x = sympy.symbols('x', real=True, nonnegative=True)
expr = (x) * (0.60 + 3*x)**3 / (0.60 - 2*x)**2
sympy.expand(expr)
This is probably not what you were expecting or hoping for. The polynomial has been expanded, but the result is still a fraction. We can instruct SymPy to simplify the results.
sympy.simplify(sympy.expand(expr))
This is much better. Ultimately, we want to solve for \(x\), but the solve() function requires an expression that equals zero. We can achieve this by subtracting 3.44.
sympy.solve(expr - 3.44)
[0.170006841512893]
If the variable x was not constrained to real and nonnegative values, a fourth-order polynomial would return four solutions, with only one making physical sense. Because we did constrain x, the solve() function conveniently only returns 0.17.
8.2.5 Substitutions#
Another common algebreic operation is the substitution of one variable in an expression for another variable, expression, or value. This is accomplished in SymPy using the sympy.subs() function which requires two pieces of information - the variable being replaced (x_old) and the new varible, expression, or value (x_new).
sympy.subs(x_old, x_new)
As an example, let’s determine the composition of a mixture of two enantiomers based on the net optical rotation of this mixture. The net rotation of a mixture, \([\alpha]_{mix}\), of two enantiomers \(d\) and \(l\) is described below as the linear combination of rotations of each enantiomer where \(d\) and \(l\) are the mole fractions and [\(\alpha\)]\(_d\) and [\(\alpha\)]\(_l\) are the specific rotations of each enantiomer.
If we have a mixture where the net rotation is +8.3\(^\circ\) and the \(d\) and \(l\) enantiomers have specific rotations of +32.4\(^\circ\) and -32.4\(^\circ\), respectively, we can insert these values into the above equation to get the below result.
We now have one equation with two unknowns, \(d\) and \(l\). To solve this, we need a second equation which we can generate by recognizing that the sum of the fractions equals 1 just as the sum of percentages total to 100%.
We rearrange the above equation to \(d = 1 - l\) and now need to substitute this expression for d in the first equation. We can let SymPy perform this substitution.
d, l =sympy.symbols('d, l')
net = (d * 32.4 + l * -32.4) - 8.3
net_new = net.subs(d, 1 - l)
net_new
We can then solve this expression for \(l\) using the sympy.solve() function being that it equals zero in the current form.
sympy.solve(net_new)
[0.371913580246914]
The sympy.subs() function can also substitute variables for numberical values. If we want to see the net rotation is \(l = 0.6\) and \(d = 0.4\), we can run the following.
net.subs([[l, 0.6], [d, 0.4]])
8.3 Matrices#
Matrices are an efficient method of working with larger amounts of data. When done by hand, as is the case in many classroom environments, it is likely slow and painful. The beauty and power of matrices is when they are used with computers because they simplify bulk calculations. SymPy, SciPy, and NumPy all support matrix operations. If you need to do symbolic math, SymPy should be your go-to, but for the numerical calculations that we will do here, we will use NumPy’s linalg module.
SciPy and NumPy both offer a matrix object, but the SciPy official documentation discourages their use as they offer little advantage over a standard NumPy array. We will stick with NumPy arrays here, but below demonstrates creating a matrix object if you feel that you absolutely must use them. See the NumPy documentation page for further details on attributes and methods for this class of object.
import numpy as np
mat = np.matrix([[1, 8], [3, 2]])
mat
matrix([[1, 8],
[3, 2]])
8.3.1 Mathematical Operations with Arrays#
Being that we are using NumPy arrays, the standard mathematical operations use the +, ‒, *, /, and ** operators as demonstrated in chapter 4. There are a few other operations and methods, however, that are important for matrices such as calculating the inverse, determinant, transpose, and dot product. For these operations, we have the following methods provided by NumPy’s linalg module, Table 4, which are demonstrated in the following sections.
Table 4 Common np.linalg Methods
Method |
Description |
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Calculates the dot product |
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Returns the inverse of an array (if it exists) |
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Returns the Moore-Penrose pseudoinverse of an array |
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Returns the determinant of an array |
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Solves a system of linear equations |
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Returns approximate solution to a system of linear equations |
In addition, it is worth reiterating that there is a general NumPy array method transpose() that will transpose or rotate the array around the diagonal. There is a convenient array.T shortcut that is often used. See section 4.2.3 for details.
8.3.2 Solving Systems of Equations#
Solving systems of equations can be a tedious process by hand, but solving them using matrices can save time and effort. Let us say we want to solve the following system of equations for \(x\), \(y\), and \(z\).
These equations can be rewritten in matrix or array form as follows with the left matrix holding the coefficients.
We will call the first array M, the second X, and the third y, so we get
We can solve for X by multiplying (dot product) both sides by the inverse of M, \(M^{-1}\). Anything multiplied by its inverse is the identity, so \(M^{-1} \cdot M\) is the identity matrix and can be ignored.
To get the inverse of a matrix or array, we can use the np.linalg.inv() function provided by NumPy’s linear algebra module and use the dot() method to take the dot product.
M = np.array([[6, 10, -5],
[-2, 7, 1],
[-10, -11, 11]])
y = np.array([21, 13, -21])
np.linalg.inv(M).dot(y)
array([1., 2., 1.])
This means that \(x\) = 1, \(y\) = 2, and \(z\) = 1.
As a chemical example, we can use the above mathematics and Beer’s law to determine the concentration of three light absorbing analytes in a solution. The mathematical representation of Beer’s law is written below where \(A\) is absorbance (unitless), \(b\) is path length (cm), \(C\) is concentration (M), and \(\epsilon\) is the molar absorptivity (cm\(^{-1}\)M\(^{-1}\)). The latter value is analyte dependent.
For a path length of 1.0 cm, which is quite common, the equation simplifies down to:
When there are three analytes, \(x\), \(y\), and \(z\), the absorption of light at a given wavelength equals the sum of the individual absorptions.
If we measure the absorbance of a three analyte solution at three different wavelengths (\(\lambda\)), we get the following three equations.
As long as we know the molar absorptivity of each analyte at each wavelength collected from pure samples, we have three unknowns and three equations, so we can calculate the concentration of each component. The above equations can be represented as matrices shown below.
If the absorbances at the three wavelengths are 0.6469, 0.2823, and 0.2221, respectively, and we know the molar absorptivities, we get the following matrices.
We simply solve for the concentration matrix as was done earlier. Again, this is solvable using NumPy as shown below.
E = np.array([[7.8, 1.1, 2.0],
[2.6, 3.2, 0.89],
[1.8, 1.0, 8.9]])
A = np.array([0.6469, 0.282274, 0.22214])
np.linalg.inv(E).dot(A)
array([0.078 , 0.023 , 0.0066])
The concentrations are \(C_x\) = 0.078 M, \(C_y\) = 0.023 M, and \(C_z\) = 0.0066 M.
Alternatively, there is a np.linalg.solve() function that accomplishes the same calculation in a single function call. This function requires two pieces of information: the coefficient matrix and the dependant variable matrix. In our example, these are E and A, respectively.
np.linalg.solve(E, A)
array([0.078 , 0.023 , 0.0066])
If you perform either the above calculations on other data and receive a LinAlgError: Singular matrix error, this means that the coefficient matrix does not have an inverse and cannot be solved by these methods. One possible reason is that the coefficient matrix is not square - a requirement for obtaining an inverse. Here are two possible solutions to working around this issue.
Substitute the
np.linalg.inv()function with the Moore-Penrose pseudoinverse functionnp.linalg.pinv(). This versatile function can work with non-square matrices.Substitute the
np.linalg.lstsq()for thenp.linalg.solve()function. The former can find approximate solutions when exact solutions do not exist or when the coefficient matrix is not square. This is not uncommon when dealing with linear fitting because not all data points may fall perfectly on the line of best fit or the number of data points does not equal the number of independent variables.
As an example inspired by J. Chem. Educ. 2000, 77, 185-187, let’s calculate the enthalpy of the following reaction
knowing the enthalpy of the following two subreactions.
Using Hess’s law, we need to multiply the subreactions 1 and 2 by coefficients and add them together to generate the overall net reaction. Remember that reversing a reaction results in reversing the sign of reaction enthalpy, so it’s the same as multiplying by -1. We can represent this calculation using matrices show below where r\(_1\) and r\(_2\) are the coefficients for each subreaction. The values in each row of the first matrix are the number of SO\(_2\), SO\(_3\), O\(_2\), and S\(_8\) molecules, respectively, in the two subreactions, and the numbers in the last matrix are the numbers of the same molecules in the net reaction. The gray molecular formulas in the equation below are not part of the equation but rather are simply labels for clarity. You may notice that the coefficients in the balance equations on the reactant side are negative while products are positive. This allows us keep track of the side they are on.
If the three matrices are called A, R, and Y, respectively, we can rewrite the above calculation as follows.
When solving for R in the past, we simply multiplied both sides by \(A^{-1}\) like below.
The problem we face is that matrix A is not square and thus the matrix inverse cannot be calculated. Instead, we can use the Moore-Penrose pseudoinverse in place of the regular inverse as demonstrated below.
A = np.array([[0, -2],
[8, 2],
[-12, -1],
[-1, 0]])
Y = np.array([8, 0, -8, -1])
R = np.linalg.pinv(A).dot(Y)
R
array([ 1., -4.])
This means we need to multiply the first subreaction by 1 and the second subreaction by -4 (i.e., reverse it and quadruple everything).
Alternatively, we can use the np.linalg.lstsq() similarly to how we use the np.linalg.solve() function. Set the keyword argument rcond=None to avoid an error.
np.linalg.lstsq(A, Y, rcond=None)
(array([ 1., -4.]),
array([1.85454492e-31]),
np.int32(2),
array([14.58924398, 2.27023349]))
For the final step of our calculation, we need to multiply the values r\(_1\) and r\(_1\) by the enthalpy values of the subreactions and add them together.
dH_sub = np.array([-3160, -196])
dH = R.dot(dH_sub)
dH
np.float64(-2375.9999999999995)
This means that the enthalpy of the overall net reaction is -2376 kJ.
8.3.3 Least-Square Minimization by the Normal Equation#
Finding the line of best fit through data points can be accomplished by least-square minimization. What we are essentially looking for is an equation of the form \(y = mx + b\) that is as close as possible to the data points, and the mean square error determines what qualifies as “close.” If we rewrite this problem in matrix or array form, it will look like the following for a series of four point (\(x_n\), \(y_n\)) on a two-dimensional plane. The first array contains a column of ones to multiply with b, so for the first row, we get \(mx_0 + b = y_0\).
We will call the leftmost matrix \(X\), the center matrix \(\theta\), and the rightmost matrix \(y\).
Ultimately, we are looking for the values of \(m\) and \(b\), so we need to solve for matrix \(\theta\). This can be accomplished through optimization algorithms (section 14.2), or in the case of linear regression, there is a direct solution known as the normal equation shown below where \(X^T\) is the transpose of \(X\).
As an example, below is a table of synthetic data for copper cuprizone absorbances at various concentrations at 591 nm. We can use a linear fit to create a calibration curve from this data.
Table 5 Beer-Lambert Law Data for Copper Cuprizone
Concentration (10\(^{-6}\) M) |
Absorbance |
|---|---|
1.0 |
0.0154 |
3.0 |
0.0467 |
6.0 |
0.0930 |
15 |
0.2311 |
25 |
0.3925 |
35 |
0.5413 |
C = np.array([1.0e-06, 3.0e-06, 6.0e-06, 1.5e-05, 2.5e-05, 3.5e-05])
A = np.array([0.0154, 0.0467, 0.0930 , 0.2311, 0.3975, 0.5413])
y = A
X = np.vstack((C, np.ones(6))).T
X
array([[1.0e-06, 1.0e+00],
[3.0e-06, 1.0e+00],
[6.0e-06, 1.0e+00],
[1.5e-05, 1.0e+00],
[2.5e-05, 1.0e+00],
[3.5e-05, 1.0e+00]])
For the sake of readability, the calculation using the normal equation has been split in half as shown below.
u = np.linalg.inv(X.T.dot(X))
v = X.T.dot(y)
theta = u.dot(v)
theta
array([ 1.55886203e+04, -5.45355390e-06])
A plot of the linear regression and the data points is shown below, and the linear regression returned a molar absorptivity of 1.55 \(\times\) 10\(^4\) cm\(^{-1}\)M\(^{-1}\).The regression also returned a \(y\)-intercept value of -5.45 \(\times\) 10\(^{-6}\), which is below the detection limits making it practically zero. This makes sense because the \(y\)-intercept should always be approximately zero if the background is subtracted.
import matplotlib.pyplot as plt
x = np.linspace(0, 4e-5, 10)
plt.plot(x, 1.55886e4 * x - 5.45355e-6, '-', label='Linear Regression')
plt.plot(C, A, 'o', label='Data Points')
plt.legend()
plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
plt.xlabel('Concentration, M')
plt.ylabel('Absorbance, au');
8.3.4 Balancing Chemical Equations#
Matricies can also be used to balance chemical equations as shown below where \(x_1\) through \(x_4\) are the coefficients for the balanced chemical equation.
We can then describe the number of carbon, hydrogen, and oxygen atoms in each compound using 3 \(\times\) 1 matricies \(\left[ \begin{array}{c} C \\ H \\ O \end{array} \right]\) as shown below.
Because the number of carbons, hydrogens, and oxygens should be the same on both sides of the balanced chemical equation, if we subtract the products from the reactants, we should get zero.
One potentiall issue with this set of linear equations is that making all the \(x\) variables zero is a valid solution, so to avoid this solution, we will set one of the \(x\) variables to one. Remember that a balanced chemical equation is about the appropriate ratio between the reactants and products, so setting a single coefficient to one can still generate a balanced equation. The one issue is that the coefficients generated by the software may not be integers, but this can be fixed by multiplying the fractions to get whole numbers as a final step demonstrated below.
Here we have set \(x_4\) = 1.
Now we move the last term to the right side.
These matricies can now be merged into one larger matrix. The left matrix below will be called M and the right matrix below is called b.
We can then solve for the \(x\) values to get our coefficients using the np.linalg.solve() function as demonstrated below.
M = np.array([[3, 0, -1], [8, 0, 0], [0, 2, -2]])
b = np.array([0, 2, 1]).T
sol = np.linalg.solve(M, b)
sol
array([0.25, 1.25, 0.75])
This means that \(x_1\)=0.25, \(x_2\)=1.25, and \(x_3\)=0.75. We can append \(x_4\) below and then multiply all the values by the same number to generate all integers.
sol = np.append(sol, 1)
sol * 4
array([1., 5., 3., 4.])
This means that the integer coefficients for the balanced chemical equation are \(x_1\)=1, \(x_2\)=5, \(x_3\)=3, and \(x_4\)=4.
8.3.5 Eigenvalues and Eigenvectors#
This section covers using np.linalg to calculate eigenvalues and eigenvectors which is useful in quantum mechanics among other applications. This topic will not be utilized later in this book, so feel free to skip over this section if you have no interest in this topic.
For a square matrix \(A\), there can exist a scalar \(\lambda\) and vector \(V\) that satisfy the following equation.
The vector and scalar are known as the eigenvector and eigenvalue, respectively, and there may be more than one solution for any given matrix \(A\).
The np.linalg module includes a function np.linalg.eig() that returns the eigenvalue(s) and eigenvector(s) for a given square matrix in this order
np.linalg.eig(matrix)
As an example, we can determine the eigenvalues and eigenvector for the following matrix.
A = np.array([[3, 1], [4, 3]])
np.linalg.eig(A)
EigResult(eigenvalues=array([5., 1.]), eigenvectors=array([[ 0.4472136 , -0.4472136 ],
[ 0.89442719, 0.89442719]]))
The first array contains the two eigenvalues while the second matrix contains the two eigenvector solutions.
Not every matrix has eigenvalues or eigenvectors. In the case of the following 90\(\circ\) rotation matrix, the solution generated includes \(j\) values which is Python’s notation used for imaginary and complex numbers.
A = np.array([[0, -1], [1, 0]])
np.linalg.eig(A)
EigResult(eigenvalues=array([0.+1.j, 0.-1.j]), eigenvectors=array([[0.70710678+0.j , 0.70710678-0.j ],
[0. -0.70710678j, 0. +0.70710678j]]))
8.4 Calculus#
SymPy and SciPy both contain functionality for performing calculus operations. We will start with SymPy for the symbolic math and switch over to SciPy for the strictly numerical work in section 8.4.3. In this section, we will be working with the radial density functions (\(\psi\)) for hydrogen atomic orbitals. The squares of these functions (\(\psi ^2\)) provide the probability of finding an electron with respect to distance from the nucleus. While these equations are available in various textbooks, SymPy provides a physics module with a R_nl() function for generating these equations based on the principle (n) quantum number, angular (l) quantum number, and the atomic number (Z). For example, to generate the function for the 2p orbital of hydrogen, n = 2, l = 1, and Z = 1.
from sympy.physics.hydrogen import R_nl
r = sympy.symbols('r')
R_21 = R_nl(2, 1, r, Z=1)
R_21
This provides the wavefunction equation with respect to the radius, r. We can also convert it to a Python function using the sympy.lambdify() method.
f = sympy.lambdify(r, R_21, modules='numpy')
This function is now callable by providing a value for r.
f(0.5)
np.float64(0.07948602207520471)
8.4.1 Differentiation#
SymPy can take the derivative of mathematical expression using the sympy.diff() function. This function requires a mathematical expression, the variable with respect the derivative is taken from, and the degree. The default behavior is to take the first derivative if a degree is not specified.
sympy.diff(expr, r, deg)
As an example problem, the radius of maximum density can be found by taking the first derivative of the radial equation and solving for zero slope.
dR_21 = sympy.diff(R_21, r, 1)
dR_21
mx = float(sympy.solve(dR_21)[0])
The solve() function returns an array, so we need to index it to get the single value out. We can plot the radial density and the maximum density point to see if it worked.
R = np.linspace(0,20,100)
plt.plot(R, f(R))
plt.plot(mx, f(mx), 'o')
plt.xlabel('Radius, $a_0$')
plt.ylabel('Probability Density');
The radius is in Bohrs (\(a_0\)) which is equal to approximately 0.53 angstroms.
8.4.2 Integration of Functions#
SymPy can also integrate expressions using the sympy.integrate() function which takes the mathematical expression and the variable plus integration range in the form of a tuple. If the integration range is omitted, then SymPy will return a symbolic expression.
The normalized (i.e., totals to one) density function is the squared wave function times \(r^2\) (i.e, \(\psi ^2 r^2\)). We can use this to determine the probability of finding an electron in a particular range of distances from the radius. Below, we integrate from the nucleus to the radius of maximum density.
sympy.integrate(R_21**2 * r**2, (r,0, mx)).evalf()
There is a 5.27% probability finding an electron between the nucleus and the radius of maximum probability. This is probably may be a bit surprising, but examination of the radial density plot reveals that the radius of maximum probabily is quite close to the nucleus with a significant amount of density beyond the maximum radius. Let’s see the probability of finding an electron between 0 and 10 Bohrs from the nucleus.
sympy.integrate(R_21**2 * r**2, (r,0,10)).evalf()
There is a 97.1% chance of finding the electron between 0 and 10 angstroms.
The SciPy library also includes functions in the integrate module for integrating mathematical functions. Information can be found on the SciPy documentation page listed at the end of this chapter under Further Reading.
8.4.3 Integrating Sampled Data#
The above integration assumes a mathematical function is known. There are times when there is no known function to describe the data such as spectra. This is common in NMR spectroscopy and gas chromatography (GC) among many other applications where integrations of peak areas are used to quantify different components of a spectrum.
In the following example, we will use a section of a \(^1\)H NMR spectrum where we want to determine the ratio of the three triplet peaks via integration. NMR spectra are typically stored in binary files that require a special library to read, which is covered in chapter 11. For simplicity in this example, the data for a section of the NMR spectrum has been converted to a CSV file titled Ar_NMR.csv.
nmr = np.genfromtxt('data/Ar_NMR.csv', delimiter=',')
nmr
array([[0.00000000e+00, 3.42490660e-03],
[1.00000000e+00, 4.52560300e-03],
[2.00000000e+00, 6.67372160e-03],
[3.00000000e+00, 8.58410100e-03],
[4.00000000e+00, 1.23892580e-02],
[5.00000000e+00, 2.12517060e-02],
[6.00000000e+00, 5.18062560e-02],
[7.00000000e+00, 1.23403220e-01],
[8.00000000e+00, 7.49717060e-02],
[9.00000000e+00, 1.12987520e-01],
[1.00000000e+01, 2.47482900e-01],
[1.10000000e+01, 1.04401566e-01],
[1.20000000e+01, 8.17907750e-02],
[1.30000000e+01, 1.38453960e-01],
[1.40000000e+01, 5.04080100e-02],
[1.50000000e+01, 2.00982630e-02],
[1.60000000e+01, 1.38752850e-02],
[1.70000000e+01, 1.28241135e-02],
[1.80000000e+01, 1.54948140e-02],
[1.90000000e+01, 1.70803180e-02],
[2.00000000e+01, 2.34651420e-02],
[2.10000000e+01, 4.76330930e-02],
[2.20000000e+01, 1.10299855e-01],
[2.30000000e+01, 7.56612400e-02],
[2.40000000e+01, 1.15097150e-01],
[2.50000000e+01, 1.85112450e-01],
[2.60000000e+01, 3.18055840e-01],
[2.70000000e+01, 2.44278220e-01],
[2.80000000e+01, 1.44489410e-01],
[2.90000000e+01, 6.10540140e-02],
[3.00000000e+01, 7.93569160e-02],
[3.10000000e+01, 7.98874400e-02],
[3.20000000e+01, 2.85217260e-02],
[3.30000000e+01, 1.68548040e-02],
[3.40000000e+01, 1.48743290e-02],
[3.50000000e+01, 1.39662160e-02],
[3.60000000e+01, 1.28568120e-02],
[3.70000000e+01, 1.59042850e-02],
[3.80000000e+01, 2.24487820e-02],
[3.90000000e+01, 5.66768200e-02],
[4.00000000e+01, 6.38733950e-02],
[4.10000000e+01, 4.39581830e-02],
[4.20000000e+01, 1.09901360e-01],
[4.30000000e+01, 9.82578100e-02],
[4.40000000e+01, 3.91401280e-02],
[4.50000000e+01, 6.37550060e-02],
[4.60000000e+01, 4.55453170e-02],
[4.70000000e+01, 1.38955860e-02],
[4.80000000e+01, 7.40419900e-03],
[4.90000000e+01, 4.81957300e-03]])
The imported data are stored in an array where the first column contains the index values and the second column contains the amplitudes.
plt.plot(nmr[:,0],nmr[:,1], '.-');
Above is a plot of the peaks with respect to the index values (not ppm). To integrate under each of the triplet peaks, first we need the index values for the edges of each peak. Below is a list, i, that provides reasonable boundaries, and a plot is below with these edges marked in orange squares.
i = [(4, 17), (19, 34), (36, 49)]
plt.plot(nmr[:,1], '.-')
for pair in i:
for point in pair:
plt.plot(point, nmr[point,1], 'C1s')
Integrations under sampled data do not include the values between data points, so these regions are estimated based on assumptions. The trapezoid() function assumes that any data point between known points lies directly between the known data points (i.e., linear interpolation) as shown below by the blue lines.
Alternatively, the simpson() function uses the Simpson’s rule which estimates the data between known points using quadratic interpolation shown below.
Below, both the trapezoidal and Simpson’s methods are demonstrated. Note that the trapezoid(x, y) function takes both the \(x\) and \(y\) values as required, positional arguments while simpson(y, x=) only requires the \(y\) data but will optionally accept the \(x\) data as a keyword argument.
from scipy.integrate import trapezoid, simpson
# trapezoid method
for peak in i:
x = nmr[peak[0]:peak[1], 0]
y = nmr[peak[0]:peak[1], 1]
print(trapezoid(y, x))
1.0401881535
1.529880057
0.5834871775
# simpson method (note the different arguments)
for peak in i:
x = nmr[peak[0]:peak[1], 0]
y = nmr[peak[0]:peak[1], 1]
print(simpson(y, x=x))
1.0405229256666666
1.5661107306666666
0.5839565783333334
The three peaks have areas of approximately 2:3:1 ratio. Using Simpson’s rule here gives approximately the same result.
8.4.4 Integrating Ordinary Differential Equations#
Ordinary differential equations (ODE) mathematically describe the change of one or more dependent variables with respect to an independent variable. Common chemical applications include chemical kinetics, diffusion, electric current, among others. The SciPy integrate module provides an ODE integrator called odeint() which can integrate ordinary differential equations. This is useful for, among other things, integrating under kinetic differential equations to determine the concentration of reactants and products over the course of a chemical reaction.
For example, the following is a first-order chemical reaction with starting material, A, and product, P.
The decay of a radioactive isotope is an example of a first-order reaction because the rate of decay is proportional to the amount of A. First-order reaction rates are described by
where [A] is the concentration (M) of A, \(k\) is the rate constant (1/s), and rate is the change in [A] versus time (M/s). The odeint() function below takes a differential equation in the form of a Python function, func, the initial values for A, A0, and a list or array of the times,t, to calculate the [A] .
scipy.integrate.odeint(func, A0, t)
The Python function can be defined by a def statement or a lambda expression. The former is used below.
def rate_1st(A, t):
return -k * A
The function should take the dependent variable(s) as the first positional argument and the independent variable as the second positional argument. In this example, A is the dependent variable and time, t, is the independent variable. If there are multiple dependent variables, they need to be provided inside a composite object like a list or tuple which can be unpacked through indexing or tuple unpacking once inside the function. You may also notice that t is an unused argument in our Python function. It is included and required to signal to odeint() that the independent variable is t. The function is integrated below at times defined by t, and the initial concentration of A and rate constant are A0 and k, respectively.
from scipy.integrate import odeint
t = np.arange(0,50,4) # time(seconds)
A0 = 1 # starting concentration (molarity)
k = 0.1 # rate constant in 1/s
A_t = odeint(rate_1st, A0, t)
P_t = A0 - A_t # concentration of product
The concentration of product (P_t) is calculated through the difference between the initial concentration of starting material and the current concentration. That is, we assume that whatever starting material was consumed has become product. The results of the simulation have been visualized below.
plt.plot(t, A_t, 'o-', label='A')
plt.plot(t, P_t, 'p-', label='P')
plt.xlabel('Time, s')
plt.ylabel('[X], M')
plt.legend();
This approach to kinetic simulations can be adapted to even more complex reactions which are demonstrated in section 9.1.4.
8.5 Mathematics in Python#
Between SymPy, NumPy, SciPy, and Python’s built-in functionality, there is often more than one way to carry out calculations in Python. For example, finding roots and derivatives of polynomials can be, along with the approaches demonstrated in this chapter, calculated by creating a NumPy Polynomial object and using NumPy’s roots() and deriv() methods, respectively. How you carry out a calculation can often come down to matter of person preference, though there are differences in terms of speed and the output format. Find what works for you and do not necessarily worry if others are doing the same calculations through a different library or set of functions.
Further Reading#
SymPy Website. http://www.sympy.org/en/index.html (free resource)
SciPy and NumPy Documentation Pages. https://docs.scipy.org/doc/ (free resource)
Exercises#
Complete the following exercises in a Jupyter notebook using the SymPy and SciPy libraries. Any data file(s) refered to in the problems can be found in the data folder in the same directory as this chapter’s Jupyter notebook. Alternatively, you can download a zip file of the data for this chapter from here by selecting the appropriate chapter file and then clicking the Download button.
Factor the following polynomial using SymPy: \(x^2 + x – 6\)
Simplify the following mathematical expression using SymPy: \( z = 3x + x^2 + 2xy \)
Expand the following expression using SymPy: \((x – 2)(x + 5)(x)\)
A 53.2 g block of lead (Cp = 0.128 J/g·°C) at 128 °C is dropped into a 238.1 g water (Cp = 4.18 J/g·°C) at 25.0 °C. What is the final temperature of both the lead and water? Hint: Assume this is an isolated system, so q\(_{lead}\) + q\(_{water}\) = 0. We also know that \(q = mCp \Delta T\).
The following equation relates the \(\Delta\)G with respect to the equilibrium constant K.
\[ \Delta G = \Delta G^o - RT ln(K) \]If \(\Delta G^o\) = -1.22 kJ/mol for a chemical reaction, what is the value for K for this reaction at 298 K? Use the
sympy.solve()function to solve this problem. Remember that equilibrium is when \(\Delta\)G = 0 kJ/mol, and watch your energy units. (R = 8.314 J/mol·K)A matrix or array of x,y coordinates can be rotated on a two-dimensional plane around the origion by multiplying by the following rotation matrix (M\(_R\)). The angle (\(\theta\)) is in radians, and the coordinates are rotated clockwise around the origin.
\[\begin{split} M_R = \left[ \begin{array}{cc} cos(\theta) & -sin(\theta) \\ sin(\theta) & cos(\theta) \end{array} \right] \end{split}\]Below is an example using three generic points on the x,y plane.
\[\begin{split} \left[ \begin{array}{cc} x_0 & y_0 \\ x_1 & y_1 \\ x_2 & y_2 \end{array} \right] \cdot \left[ \begin{array}{cc} cos(\theta) & -sin(\theta) \\ sin(\theta) & cos(\theta) \end{array} \right] = \left[ \begin{array}{cc} x'_0 & y'_0 \\ x'_1 & y'_1 \\ x'_2 & y'_2 \end{array} \right] \end{split}\]a) Given the following coordinates for the four atoms in carbonate (CO\(_3^{2-}\)) measured in angstroms, rotate them 90\(^o\) clockwise. Plot the initial and rotated points in different colors to show that it worked.
\[ C: (2.00, 2.00) \quad O1: (2.00, 3.28) \quad O2: (0.27, 1.50) \quad O3: (3.73, 1.50) \]b) Package the above code into a function that takes an array of points and an angle and performs the above rotation.
Using the rotation matrix described in the above problem, write a function that rotates the carbonate anion around its own center of mass. The suggested steps to complete this task are listed below.
a) Calculate the center of mass
b) Subtract the center of mass from all points to shift the cluster to the origin.
c) Rotate the cluster of points.
d) Add the center of mass back the cluster the shift the points back.
The following is the equation for the work performed by a reversible, isothermal (i.e., constant T) expansion of a piston by a fixed quantity of gas.
\[ w = \int_{v_i}^{v_f} -nRT \frac{1}{V}dV \]a) Using SymPy, integrate this expression symbolically for V\(_i\) \(\rightarrow\) V\(_f\). Try having SymPy simplify the answer to see if there is a simpler form.
b) Integrate the same express above for the expansion of 2.44 mol of He gas from 0.552 L to 1.32 L at 298 K. Feel free to use either SymPy or SciPy.
Using
odeint(), simulate the concentration of starting material for the second-order reaction below and overlay it with the second-order integrated rate law to show that they agree.\[ 2A \rightarrow P \]Below are the transformation matricies for an S\(_4\) and C\(_2\) operation used in group theory. Show that two S\(_4\) operations equal one C\(_2\) operation by multiplying two S\(_4\) operations together. That is, show that S\(_4\)S\(_4\) = C\(_2\).
\[\begin{split} S_4 = \left[ \begin{array}{ccc} 0 & -1 & 0 \\ 1 & 0 & 0 \\ 0 & 0 & -1 \end{array} \right] \quad C_2 = \left[ \begin{array}{ccc} -1 & 0 & 0 \\ 0 & -1 & 0 \\ 0 & 0 & 1 \end{array} \right] \end{split}\]Using dot product math, write your own linear regression function that accepts the x and y coordinates of data points as separate arrays and returns the slope and intercept of a line of best fit.