Java程序辅导
C C++ Java Python Processing编程在线培训 程序编写 软件开发 视频讲解
C C++ Java Python Processing编程在线培训 程序编写 软件开发 视频讲解
School
of
Physics
and
Astronomy,
University
of
Manchester
PHYS20762
ComputaAonal
Physics
Hywel
Owen
Part
1:
MATLAB
and
Data
Analysis
Lecture
1:
IntroducAon
to
ComputaAonal
Physics
What
is
this
course?
• This
is
a
course
on
ComputaAonal
Physics:
– The
use
of
algorithms
and
numerical
methods
to
perform
data
analysis
and
simulaAon
in
support
of
experimental
and
theoreAcal
physics
• There
are
3
pillars
of
physics:
– Theory
– Experiment
– SimulaAon
PHYS20762
Lecture
1
Course
Informa3on
• The
real
course
website:
– hPp://theory.physics.manchester.ac.uk/~hywel/teaching/phys20762/
• But
material
is
also
posted
on
Blackboard
– Blackboard
is
linked
to
the
website
above
PHYS20762
Lecture
1
Should
you
be
here?
• If
you
are
a
TheoreAcal
Physics
student:
– Go
away.
You
should
be
on
PHYS20872
Theory
CompuAng
Project.
– Were
you
at
the
orientaAon
session
yesterday?
No?
Then
talk
to
Niels
Walet.
• If
you
not
a
TheoreAcal
Physics
student:
– CongratulaAons!
– You
should
be
here.
• Can
you
do
both
PHYS20762
and
PHYS20872?
– No.
• Can
you
do
both
AddiAonal
Lab
and
PHYS20762?
– No.
• What
is
the
difference
between
PHYS20762
and
PHYS20872?
– PHYS20762
is
for
Normal
students
– PHYS20872
is
for
Theory
students
PHYS20762
Lecture
1
Why
should
you
do
this
course?
• Technical
compuAng
underlies
prePy
much
all
of
the
jobs
you
are
likely
to
do
a]er
graduaAng
• You
are
about
90%
likely
to
be
doing
scienAfic
programming
a]er
you
leave
Manchester
Graduate
Des3na3ons
ScienAc/CompuAng
Finance/Management
Postgrad/Research
Other
PHYS20762
Lecture
1
A
couple
of
examples
• My
best
friend
ScoP
• Graduate
physics
(Edinburgh)
• PhD
in
crystallography,
including
wriAng
of
experimental
control
and
data
analysis
• Then
programming
GUIs
in
C++
for
mobile
phone
manufacturer
• Now
wriAng
trading
so]ware
in
C++
for
CiAbank
in
London
• ££££
• Me
• Graduate
physics
(Manchester!)
• PhD
in
liquid
crystals,
wriAng
data
acquisiAon
and
data
analysis
• WriAng
control
so]ware
for
diagnosAc
staAon
on
parAcle
accelerators
(e.g.
SRS);
VB,
LabVIEW,
etc.
• WriAng
so]ware
to
simulate
behaviour
of
new
parAcle
accelerators
(e.g.
DIAMOND)
www.diamond.ac.uk
• Carrying
this
on
at
Manchester
PHYS20762
Lecture
1
What
languages/plaAorms
are
actually
used
in
the
‘real
world’?
Commercial
• Compiled
– C/C++
(esp.
Finance)
• Vis-‐C,
C#,
Obj-‐C,
Java
• Interpreted
– Python
(e.g.
Google)
– (Ruby,
Javascript)
• Environments
– LabVIEW
(esp.
for
DAQ)
– Excel
Research/Scien3fic
• Compiled
– C/C++
– Fortran
• F90,
F95
etc.
• Interpreted
– Python
• Environments
– MATLAB
– MathemaAca
PHYS20762
Lecture
1
Other languages
(Pascal, Visual Basic, Perl, Tcl)
Other packages
(Maple, Mathcad, IDL, Origin)
Structure
of
the
Course
• Part
1:
MATLAB
and
Data
Analysis
– Lecture
1:
The
MATLAB
environment
– Lecture
2:
Working
with
data
in
MATLAB
– Lecture
3:
Data
analysis
and
fiong
in
MATLAB
– Lecture
4:
File
input
and
output
in
MATLAB
• Part
2:
Numerical
Methods
– Lecture
5:
IntroducAon
to
numerical
methods
– Lecture
6:
Numerical
soluAon
of
equaAons
– Lecture
7:
Numerical
methods
in
MATLAB
– Lecture
8:
User-‐defined
funcAons
in
MATLAB
• Part
3:
Monte-‐Carlo
Techniques
– Lecture
9:
Random
Numbers
and
Monte
Carlo
IntegraAon
– Lecture
10:
Monte
Carlo
ParAcle
Transport
• MATLAB
PracAce
(Week
1)
• Projects:
– Project
1
(Weeks
2-‐4):
Analysis
of
experimental
spectrum
data
– Project
2
(Weeks
5-‐8):
Numerical
integraAon
of
the
damped
oscillator
equaAon
– Project
3
(Weeks
9-‐12):
PenetraAon
of
neutron
through
shielding
• 12
Half
days
in
the
laboratory
3.58
(½
on
Tue,
½
on
Fri)
PHYS20762
Lecture
1
Course
Aims
and
Learning
Outcomes
• To
familiarise
the
students
with
pracAcal
techniques
for
using
computers
in
physics.
• On
compleAon
successful
students
will
be
able
to:
1. write
MATLAB
programs
involving
computaAons
and
graphics;
2. use
MATLAB
to
analyse
and
fit
experimental
data;
3. use
numerical
methods
(Euler,
Verlet)
to
find
soluAons
of
an
ordinary
differenAal
equaAon
and
thereby
to
analyse
the
behaviour
of
a
physical
system
(e.g.
damped
oscillator);
4. understand
the
principles
of
the
Monte
Carlo
method
and
its
applicaAon
to
parAcle
transport
5. use
the
Monte
Carlo
method
to
simulate
parAcle
transport
through
a
given
physical
system
(e.g.
neutrons
through
shielding)
PHYS20762
Lecture
1
Assessments
• You
will
write
3
project
reports
• Assessment
is
based
on
the
content
of
these
reports
• Read
each
project
brief
(on
the
module
website)
and
follow
it!
• All
the
project
briefs
and
assessment
forms
(with
criteria)
are
already
on
the
website.
• Each
project
has
a
bonus
secAon
–
you
are
encouraged
to
try
them,
they’re
not
that
hard.
(hint:
don’t
think
of
them
as
bonuses…)
• Extra
marks
may
be
awarded
for
invenAveness
–
we
want
you
to
think!
• Projects
are
to
be
submiPed
in
paper
form:
– Sorry,
what?
This
is
a
compuAng
course,
and
you
want
a
report,
in
paper
format???
– We
tried
electronic,
and
it’s
too
much
hassle
both
for
you
and
for
us
– We
have
to
print
them
out
to
mark
them
up
for
your
feedback
anyway
PHYS20762
Lecture
1
Some
Recommended
Texts
• Chapman,
S.J.
MATLAB
Programming
for
Engineers
(Brooks
and
Cole
2000)
• Higham,
D.J.
&
Higham,
N.J.
MATLAB
Guide
(Philadelphia:
Society
for
Industrial
and
Applied
MathemaAcs)
2nd
ediAon
(2005)
• Garcia,
A.L.
Numerical
Methods
for
Physics
(PrenAce
Hall
1994)
• Kerningham,
B.W.
&
Ritchie,
D.M.
The
C
programming
Language
(PrenAce
Hall)
2nd
ediAon
(1988)
• Barlow,
R.J.
&
BarneP,
A.R.
Compu>ng
for
Scien>sts,
Principles
of
Programming
with
Fortran
90
and
C++
(John
Wiley
and
Sons
Ltd
,
1998)
• There
are
not
a
lot
of
good
texts
for
what
we’re
doing.
PHYS20762
Lecture
1
Two
Basic
Messages
About
This
Course
• Not
everything
you
need
to
know
for
the
projects
is
in
the
lecture
notes
– APend
the
lectures,
ask
quesAons!
– You
will
learn
a
lot
by
doing
the
projects:
get
feedback
in
the
lab
sessions
• Not
everything
in
the
lectures
is
about
the
projects
– We’re
trying
to:
– a)
Make
the
rest
of
your
degree
easier
– b)
Expose
you
to
ideas
that
you
can
learn
more
about
by
yourselves
later
– c)
Generally
educate
you
in
this
topic
PHYS20762
Lecture
1
Rubin
Landau’s
Rules
of
Educa3on
1.
Most
of
educaAon
is
learning
what
the
words
mean;
the
concepts
are
usually
quite
simple
once
you
understand
what
you
are
being
told.
2.
Confusion
is
the
first
step
to
understanding.
3.
TraumaAc
experiences
tend
to
be
the
most
educaAonal
ones.
Why
MATLAB?
• MATLAB
is
a
terrible
programming
language
• You
could
do
this
course
in,
for
example,
C++
• BUT….
– This
is
not
a
course
in
programming
(you
did
that
last
semester)
– This
is
a
course
on
algorithms,
i.e.
implemenAng
physics
using
compuAng
– The
language
is
secondary
– Speed
does
not
maPer
for
our
course
(hint:
it
does
a
bit)
PHYS20762
Lecture
1
PHYS20762
Lecture
1
Interlude…
Interlude:
How
do
programmers
spend
their
3me?
• Q:
How
much
of
a
typical
programmer’s
day
is
spent
actually
programming?
PHYS20762
Lecture
1
Interlude:
How
do
programmers
spend
their
3me?
• Q:
How
much
of
a
typical
programmer’s
day
is
spent
actually
programming?
PHYS20762
Lecture
1
Programming
25%
Debugging
25%
Documenta3on
10%
Design
20%
Mee3ngs
20%
PHYS20762
Lecture
1
Onward…
C++
is
great,
but…
• There
is
an
overhead
of
‘structure’
needed
in
your
code
• There
are
no
‘scienAfic/math’
libraries
built
in
to
the
language
– Plenty
of
add-‐ons
• It’s
fairly
plaxorm-‐dependent
– You
can’t
send
the
source
code
to
your
friend
easily
• It’s
fast,
but
you
o]en
don’t
need
that
parAcularly
PHYS20762
Lecture
1
MATLAB
• MATLAB
is
the
most
widely-‐used
interpreted
scienAfic
compuAng
package
– i.e.
you
will
probably
need
to
know
it
later
– It’s
also
VERY
handy
to
know
how
to
use
it
for
your
degree
• A]er
this
course
I
don’t
want
to
hear
people
moaning
about
Easyplot!
• MATLAB’s
advantages
– Interpreted
–
no
messing
around
between
wriAng,
compiling
and
running
– Lots
of
built-‐in
libraries
for
scienAsts
– Plaxorm
independent.
‘Write
once,
run
anywhere’
(nearly)
• MATLAB’s
disadvantages
– Interpreted,
so
somewhat
slower
than
compiled
languages
for
big
tasks
– Like
many
library-‐heavy
commercial
packages,
it’s
expensive
– But
you
won’t
noAce
either
of
these!
PHYS20762
Lecture
1
Reminders:
Workspace/Command
Window
PHYS20762
Lecture
1
Reminders:
Edit
Window
PHYS20762
Lecture
1
Reminders:
Variables
in
MATLAB
• (Reminder:
A
variable
is
a
unit
of
data
with
a
name,
which
is
available
to
the
program)
• Simple
variables
– comment = ‘This is a string’; % Can put comments
after the definition!
– a = 1; % Integers!
– b = 2.883; % Reals!
– c = 1.2*a + b; % Formulae!
– x = 1; y = 2; % Multiple definitions per line!
– v = [1 5 2 6]; % vector(row vector)–square brackets!!
– m = [1 5;3 8]; % 2x2 matrix!
– n = [0 1+7]; % Expression in a definition!
– p = [y(2) 7 y(3)]; % Array indexing!
PHYS20762
Lecture
1
Reminders:
Defining
and
indexing
arrays
x = 1:2:10 % First:increment:last!
!
x = 1 3 5 7 9!
!
g = 1:4; % Row vector!
h = g’; % Transpose makes column vector!
i = [1;3;4;5]; % Or define column vector explicitly!
!
!
This is all quite different from other languages!
!
!
PHYS20762
Lecture
1
Reminders:
Indexing
x = [1.1 -2.2 3.3 -4.4 5.5];!
!
x(3) is 3.3!
x(1:2) is [1.1 -2.2]!
x(1:2:5) is [1.1 3.3 5.5]!
!
!
m = [1 2 3;-2 -3 -4;3 4 5];!
m(6) is 4 – weird but true…!
m(2,3) is -4!
m(3,:) is [3 4 5]!
m(:,2) is [2;-3;4]!
m(1:2,3:4) is [2 3;-3 -4]
PHYS20762
Lecture
1
1 2 3!
-2 -3 -4!
3 4 5!
1 2 3!
-2 -3 -4!
3 4 5!
Reminders:
Func3ons
There
are
lots
of
funcAons
and
constants
available
to
you:
Math:
+
-‐
*
/
^
()
Comparison:
==
<
>
~=
(these
return
1/0
for
‘true’/’false’)
Logical:
&
|
(also
return
1/0
for
‘true’/’false’)
Numerical:
Inf
j
NaN
pi
FuncAons:
abs
exp
log
log2
log10
mod
real
imag
round
sign
Trig:
sin
cos
tan
asin
acos
atan
(etc.)
Specialised:
besselj
factorial
legendre
(etc.)
Use
the
Quick
Reference
Booklets
BIG
TIP:
Someone
will
probably
have
wanted
to
do
the
same
job
as
you.
That
funcAon
already
exists,
you
just
have
to
FIND
IT.
PHYS20762
Lecture
1
Reminders:
Let’s
plot
some
data
x = 0:pi/20:2*pi; % Make some x values!
y = sin(x); % Make some y values as a fn. of x!
!
plot(x,y);
PHYS20762
Lecture
1
Hint: also try linspace
Reminders:
How
about
another
one?
x = 0:pi/20:2*pi; % Make some x values!
z = (x-2).^2+2; % Make some z!
% (x-2)^2+2 doesn’t work!!
plot(x,z); % Over-writes first figure
PHYS20762
Lecture
1
Matrix
and
Array
Mul3ply/Power
–
‘a
Gotcha’
This is something *very* particular to MATLAB:
• * is used for matrix multiplication
• .* is used for element-by-element (array) multiplication
• ^ is used for matrix powers
• .^ is used for element-by-element (array) powers
PHYS20762
Lecture
1
>> a = [1 2;3 4]!
a =!
1 2!
3 4
>> a^2!
ans =!
7 10!
15 22!
>> a.^2!
ans =!
1 4!
9 16!
a^2 is equivalent to a*a (matrix multiplication)
…and
for
mul3plica3on
>> a = [1 2;3 4]!
!
a =!
!
1 2!
3 4!
!
>> b=[1 1;1 1]!
!
b =!
!
1 1!
1 1!
PHYS20762
Lecture
1
>> a*b!
!
ans =!
!
3 3!
7 7!
!
>> a.*b!
!
ans =!
!
1 2!
3 4!
Some
other
plot
types
• (Use
the
Quick
Reference
Booklets)
PHYS20762
Lecture
1
polar(x,y) bar(x,y)
loglog(x,y) semilogx(x,y)
Code
recommenda3ons
• Microso]
Excel
–
part
of
Office,
okay
for
basic
work
and
data
collecAon
• MATLAB
(hPp://www.mathworks.co.uk/
)
– Students
can
use
this
anywhere
you’re
connected
to
the
Uni
network
– Including
VPN
(hPp://www.itservices.manchester.ac.uk/vpn/)
• MathemaAca
(hPp://www.wolfram.com/)
– Awesome
so]ware,
but
less
used
than
MATLAB
– Also
have
a
site
license,
but
easier
to
use
Cluster
computers
• Python
(hPp://www.python.org/
)
– Use
Python
2.7.x
(not
3.x)
as
there’s
more
code
made
for
it
– Get
the
excellent
Canopy
package
(any
OS):
hPps://www.enthought.com/products/canopy/
PHYS20762
Lecture
1
Lab
Sessions
• Today:
MATLAB
Refresher
and
PracAce
• Lab
Sessions
are
10am
to
1pm
on
Tuesdays
or
Fridays
– (about)
Half
of
you
on
Tuesdays,
Half
of
you
on
Fridays.
– Computer
Lab
3.58
– Demonstrators
are
on
hand
only
at
these
Ames;
make
use
of
them
• At
other
Ames
you
may
carry
out
work
on
any
of
the
Schuster
cluster
computers
• Refer
to
the
Ametable
and
deadlines
to
work
out
when
to
hand
in
your
project
work:
– hPp://theory.physics.manchester.ac.uk/~hywel/phys20762/phys20762-‐
Ametable/
PHYS20762
Lecture
1