辅导program、辅导Python编程设计

日期：2023-12-18 08:42

1. Assignment guidance
In this coursework, you will use software libraries to explore di?erent numerical schemes
and you will analyse the methods and results.
2. Assessment tasks
In this coursework, you will be writing a report for your boss as BigNumComp Inc
helping them to choose a numerical method. You should assume she has the knowledge
of second year undergraduate Computer Science student at the University of Leeds.
Problem
Your boss wants to find good numerical methods for solving predator-prey models.
Predator-prey models describe the evolution of two di?erent co-existing species: one
is the predator and one is the prey.
The problem can be described through the following system of di?erential equations
for x(t), the prey, and y(t) the predator:
dx
dt = ?x  xy + f(t)
dy
dt = xy  y + g(t),
where ?, , ,  are positive real numbers and f and g are given functions of t which
relate to external migration into or out of the system.
Your boss, helpfully, suggests two test cases for you to consider:
(a) Use the parameter set ? =  =  =  = 1,
f(t) =  sin (t)  (cos (t))2  cos (t) g(t) = sin (t) + (cos (t))2  cos (t),
and initial condition x(t = 0) = 2 and y(t = 0) = 0, which has exact solution
x(t) = 1 + cos(t) y(t)=1  cos(t).
(b) Use the parameter set ? = 2/3,  = 4/3,  = 1,  = 1, f(t) = 0, g(t) = 0 and
x(t = 0) = 0.9, y(t = 0) = 0.9. For this case there is no nice form for the exact
solution but your boss tells you that the solution should repeat itself and the
maxima of each population should be consistent (i.e. not change) over time.
You are tasking with finding the best solver in terms of accuracy and eciency. You
should write a report (following the template laid out below) evidencing your recommendations and reasoning.
Task
You recognise this problem as being solvable as a system of di?erential equations. You
have seen some methods in the course but know there are better methods available. In
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your preliminary research you find a company internal solver for systems of di?erential
equations (attached as solvers.py). You will solve and analyse the results of solving
the predator prey model for two di?erent methods within solvers.py and one method
from the module which you think is the most appropriate for this problem.
You should include all the sections in the template in your report. There is guidance of
what to include in each section and a guidance word limit for each section too. Code,
tables and equations do not count in the word limit. The word limit is only guidance
and no penalties will be introduced for going over the limit. You should aim to write
less than the word count to ensure your writing is concise and understandable to your
audience.
You should submit a jupyter notebook including all computations as your report.
You should write in full sentences throughout to guide the reader through what you
are doing. There is no need to include the file solvers.py in your solution. You
should write text in Markdown blocks and include all code for the implementation and
generating results in Code blocks.
Library documentation
solvers Solve di?erential equations.
Solve the di?erential equation(s) specified by
y0
(t) = f(t, y) subject to y(t0) = y0.
The problem is solved using a specified method from t0 to a final time T using a time
step dt.
Parameters
rhs A python function describing the right hand side function f of the di?erential
equation. The function takes two arguments: the first represents t for time and
the second represents the solution y which may be either a floating point type or
a numpy array for the case of multiple di?erential equations.
y0 The starting value of y - accepts either a floating point type or a numpy array for
the case of multiple di?erential equations.
t0 The starting time t0.
dt The time step dt.
T The final or stopping time.
method The method used to advance the solver given as a string. The method should
be one of
? "Heun"
? "Ralston"
? "Van De Houwen"
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? "SSPRK3"
? "Runge-Kutta"
? "3/8-rule"
? "Ralston-4".
Returns
t The time points where the solution was found as a list
y The estimate of the solution at each time point as a list
Sample code
Download the file solver.py and place it in the same folder as your code.
An example for solving the a single di?erential equation:
from solvers import solver
def rhs (t, y) :
return -y
y0 = 1.0
t0 = 0.0
dt = 0.1
T = 1.0
t, y = solver (rhs , y0 , t0 , dt , T)
An example for solving the a system of di?erential equation:
import numpy as np
from solvers import solver
def rhs (t, y) :
return np. array ([-y[1], y [0]])
y0 = np. array ([1.0, 0.0])
t0 = 0.0
dt = 0.1
T = 1.0
t, y = solver (rhs , y0 , t0 , dt , T)
Solution template
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(a) Implementation. Write code to be able to run three methods you have chosen for
arbitrary initial conditions (x(t = 0), y(t = 0)), time step (dt), model parameters
(?, , , ) and functions f and g. [50 words]
(b) Results. Simulate and show results for each of the test cases suggested by your
boss for a range of time steps until a final time T. For test case 2a, you should use
T = 2.5? and (at least) dt = T/100,T/200,T/400,T/800,T/1600. For test case
2b, you should use T = 30 and (at least) dt = T/100,T/200,T/400,T/800,T/1600.
You should demonstrate how solutions look for each method, and the accuracy
and eciency of each approach. [50 words]
(c) Analysis. Comment on the eciency and accuracy of each approach. [250 words]
(d) Conclusion. Compare the methods that you have results for, and any other relevant methods from the module, and make a recommendation of which method
you think is best. [300 words]
3. General guidance and study support
Examples of how to approach each aspect of this coursework is given in lectures and
using the online notes.
You may wish to read more about predator prey models (also known as Lotka Volterra
equations) to help with your analysis:
? Wikipedia: Lotka Volterra equations
? Mathworld: Lotka Volterra equations
Further support for this assessment is given through the MS Class Team. Details of
further support sessions will be given closer to the deadline.
4. Assessment criteria and marking process
Your work will be assessed on your code implementation, your results and their presentation, your analysis of the method and results, and your writing quality. Work will
be marked as a final assessment for this module so your mark will only be given back
as part of your final grade.
5. Presentation and referencing
The quality of written English will be assessed in this work - further details in the
Rubric below. As a minimum, you must ensure:
? Paragraphs are used
? There are links between and within paragraphs although these may be ine?ective
at times
? There are (at least) attempts at referencing
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? Word choice and grammar do not seriously undermine the meaning and comprehensibility of the argument
? Word choice and grammar are generally appropriate to an academic text
These are pass/ fail criteria. So irrespective of marks awarded elsewhere, if you do not
meet these criteria you will fail overall.
6. Submission requirements
Please submit your work via Gradescope by the deadline given. You should submit a
jupyter notebook with all your code, text and results included in a single document.
You are recommended to “reset the kernel” and “Run all cells” again before you submit.
7. Academic misconduct and plagiarism
? Leeds students are part of an academic community that shares ideas and develops
new ones.
? You need to learn how to work with others, how to interpret and present other
people’s ideas, and how to produce your own independent academic work. It is
essential that you can distinguish between other people’s work and your own, and
correctly acknowledge other people’s work.
? All students new to the University are expected to complete an online Academic
Integrity tutorial and test, and all Leeds students should ensure that they are
aware of the principles of Academic integrity.
? When you submit work for assessment it is expected that it will meet the University’s academic integrity standards.
? If you do not understand what these standards are, or how they apply to your
work, then please ask the module teaching sta? for further guidance.
By submitting this assignment you are confirming that the work is a true
expression of your own work and ideas and that you have given credit to
others where their work has contributed to yours.
8. Assessment/marking criteria grid
The final assessment will be marked out of 50 according the following rubric.
Algorithm implementation (10 marks)
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Marks Description
9-10 Algorithm(s) implemented accurately and eciently.
Professional quality code (Uniform formatting, unit tests
where appropriate). No eciency problems. Informative
comments.
7-8 Algorithm(s) implemented with no errors. Some
eciency problems. Helpful comments throughout.
6-7 Algorithm(s) implemented with no errors. Some helpful
comments.
5-6 Algorithm(s) implemented with minor errors. Some
comments.
0-4 Serious issues with code implementation resulting in
inaccurate results.
Presentation of results (15 marks)
Marks Description
13-15 Results in a variety of appropriate formats (i.e. tables,
plots, etc). Results and extensive additional useful
information shown. Plots and tables labelled accurately.
10-12 Results in a variety of appropriate formats (i.e. tables,
plots, etc). Results and additional useful information
shown. Plots and tables labelled accurately.
7-9 Results in a variety of appropriate formats (i.e. tables,
plots, etc). Results accurately shown.
4-6 Attempts are carefully formatting results suitable for
technical audience. Results accurately shown.
0-3 Basic or very limited results shared.
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Analysis of results (20 marks)
Marks Description
17-20 Critical explanations using additional computational
experiments and making reference to appropriate
external literature covering all methods
13-16 Critical explanations using additional computational
experiments covering all methods
9-12 Descriptive explanations using further analysis of
suggested computational experiments covering all
methods
5-8 Descriptive explanations based purely on suggested
experiments covering all methods
0-4 No or very limited results explained
Writing (5 marks)
Marks Description
5 Outstanding structure and clarity of writing, all in a
suitable language. No errors.
4 Clear structure and writing in suitable language. Some
minor errors.
3 Well structured with mostly clear writing in suitable
language. Some errors.
2 Structure could have been improved. Some text required
careful reading. Language not appropriate for technical
report.
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Marks Description
1 Poor presentation and structure with unclear or
confusion descriptions. Many errors.
A External library reference
The source code for solvers.py:
from typing import Callable , List , Tuple , TypeVar
import numpy as np
# Butcher tables for each of the methods used
TABLEAU = {
" Heun ": (
np. array ([[0.0, 0.0], [1.0, 0.0]]),
np. array ([0.5, 0.5]),
np. array ([0.0, 1.0]),
) ,
" Ralston ": (
np. array ([[0.0, 0.0], [2 / 3, 0.0]]),
np. array ([0.25, 0.75]),
np. array ([0.0, 2 / 3]),
) ,
" Van der Houwen ": (
np. array ([[0.0, 0.0, 0.0], [1 / 2, 0.0, 0.0], [0.0,
0.75, 0.0]]),
np. array ([2 / 9, 1 / 3, 4 / 9]),
np. array ([0.0, 1 / 2, 3 / 4]),
) ,
" SSPRK3 ": (
np. array ([[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.25,
0.25, 0.0]]),
np. array ([1 / 6, 1 / 6, 2 / 3]),
np. array ([0.0, 1.0, 1 / 2]),
) ,
"Runge - Kutta ": (
np. array (
[
[0.0, 0.0, 0.0, 0.0],
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[0.5, 0.0, 0.0, 0.0],
[0.0, 0.5, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0],
]
) ,
np. array ([1 / 6, 1 / 3, 1 / 3, 1 / 6]),
np. array ([0.0, 0.5, 0.5, 1.0]),
) ,
"3/8- rule ": (
np. array (
[
[0.0, 0.0, 0.0, 0.0],
[1 / 3, 0.0, 0.0, 0.0],
[-1 / 3, 1.0, 0.0, 0.0],
[1.0, -1.0, 1.0, 0.0],
]
) ,
np. array ([1 / 8, 3 / 8, 3 / 8, 1 / 8]),
np. array ([0.0, 1 / 3, 2 / 3, 1]),
) ,
" Ralston -4": (
np. array (
[
[0.0, 0.0, 0.0, 0.0],
[0.4, 0.0, 0.0, 0.0],
[0.29697761 , 0.15875964 , 0.0, 0.0],
[0.21810040 , -3.05096516, 3.83286476, 0.0],
]
) ,
np. array ([0.17476028, -0.55148066, 1.20553560,
0.17118478]),
np. array ([0.0, 0.4, 0.45573725 , 1.0]),
) ,
}
# types for y variable in solver
y_type = TypeVar (" y_type ", np. ndarray , np. double )
def solver (
rhs : Callable [[ np. double , y_type ] , y_type ] ,
y0: y_type ,
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t0: np. double ,
dt: np. double ,
T: np. double ,
method : str ,
) -> Tuple [ List [np. double ] , List [ y_type ]]:
"""
Solve the differential equation (s).
Solve the differential equation specified by
y ’(t) = rhs (t, y) subject to y( t_0 ) = y_0 .
The problem is solved numerical using METHOD from t0 to T
using a time step dt.
Parameters
----------
rhs
A function describing the right hand side of the
differential equation (s)
y0
The starting value of y
t0
The starting value of t
dt
The time step
T
The final or stopping time
method
The method used to advance to solver . method
should be one of:
Heun , Ralston , Van De Houwen , SSPRK3 , Runge -Kutta
, 3/8- rule , Ralston -4
Returns
-------
t
The time points where the solution was found
y
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The estimate of the solution at each time point
"""
# set initial data into solution arrays
t_out = [t0]
y_out = [y0]
# extract method helpers
matrix , weights , nodes = TABLEAU [ method ]
s = len ( weights )
k: List [ y_type | None ]=[ None for _ in range (s) ]
# count steps
timesteps = int (T / dt)
# time loop
for step in range ( timesteps ):
# build k’s
for i in range (s):
temp = sum ( matrix [i, j] * k[j] for j in range (i) )
k[i] = rhs ( t_out [-1] + dt * nodes [i] , y_out [-1] +
dt * temp )
y_update = sum ([k[i] * weights [i] for i in range (s)])
y_new = y_out [-1] + dt * y_update
t_new = t_out [-1] + dt
t_out . append ( t_new )
y_out . append ( y_new )
return t_out , y_out
def example_code_1 ():
"""
Example code for single differential equation
The problem is y ’(t) = y subject to y (0) = 1.0.
The problem is solved with dt = 0.1 until T = 1.0 using
Heun ’s method
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"""
def rhs1 (t: np. double , y: np. double ) -> np. double :
return -y
t, y = solver (rhs1 , 1.0, 0.0, 0.1, 1.0, " Heun ")
def example_code_2 ():
"""
example code for system of differential equations
The problem is (x ’(t), y ’(t)) = ( -y(t), x(t)) subject to
(x (0) , y (0) ) = (1.0, 0.0)
The problem is solved with dt = 0.1 until T = 1.0 using
the Runge - Kutta method
"""
def rhs2 (t: np. double , y: np. ndarray ) -> np. ndarray :
return np. array ([-y[1], y [0]])
t, y = solver (rhs2 , np. array ([1.0, 0.0]), 0.0, 0.1, 1.0,
"Runge - Kutta ")
if __name__ == " __main__ ":
for method , ( matrix , weights , nodes ) in TABLEAU . items ():
# test methods are explicit
np. testing . assert_almost_equal (np. tril ( matrix ) ,
matrix )
# test methods are consistent
np. testing . assert_almost_equal ( sum ( weights ), 1.0)
# test dimensions match
n, m = matrix . shape
assert n == m
assert n == len ( weights )
assert n == len ( nodes )
example_code_1 ()
example_code_2 ()