👨💻 about me home CV/Resume 🖊️ Contact Github LinkedIn I’m a Haskeller 📝 Blog Freedom, privacy, tutorials… 🏆 Best of LuaX Calculadoira panda upp Haskell todo pwd TPG Nextcloud Git BitTorrent
22 August 2010
Christophe Delord
This software is released under the LGPL license.
Table of Contents
SP (Simple Parser) is a Python^{1} parser generator. It is aimed at easy usage rather than performance. SP produces TopDown Recursive descent parsers. SP also uses memoization to optimize parsers' speed when dealing with ambiguous grammars.
SP is available under the GNU Lesser General Public:
Simple Parser: A Python parser generator
Copyright (C) 20092016 Christophe Delord
Simple Parser is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Simple Parser is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with Simple Parser. If not, see <http://www.gnu.org/licenses/>.
starts smoothly with a gentle tutorial as an introduction. I think this tutorial may be sufficient to start with SP.
is a reference documentation. It will detail SP as much as possible.
gives the reader some examples to illustrate SP.
SP is freely available on its web page (http://cdelord.fr/sp).
SP is a pure Python package. It may run on any platform supported by Python. The only requirement of SP is Python 2.6, Python 3.1 or newer^{2}. Python can be downloaded at http://www.python.org.
This short tutorial presents how to make a simple calculator. The
calculator will compute basic mathematical expressions (+
,

, *
, /
) possibly nested in
parenthesis. We assume the reader is familiar with regular
expressions.
Expressions are defined with a grammar. For example an expression is a sum of terms and a term is a product of factors. A factor is either a number or a complete expression in parenthesis.
We describe such grammars with rules. A rule describes the
composition of an item of the language. In our grammar we have 3 items
(expr, term, factor). We will call these items symbols or
non terminal symbols. The decomposition of a symbol is
symbolized with >
.
Grammar for expressions:
Grammar rule  Description 

expr > term (('+''') term)* 
An expression is a term eventually
followed with a plus (+ ) or a minus ( ) sign
and an other term any number of times (* is a repetition of
an expression 0 or more times). 
term > fact (('*''/') fact)* 
A term is a factor eventually followed
with a * or / sign and an other factor any
number of times. 
fact > ('+''') fact  number  '(' expr ')' 
A factor is either a factor preceded by a sign, a number or an expression in parenthesis. 
We have defined here the grammar rules (i.e. the sentences of the
language). We now need to describe the lexical items (i.e. the words of
the language). These words  also called terminal symbols  are
described using regular expressions. In the rules we have written some
of these terminal symbols (+
, 
,
*
, /
, (
, )
). We have
to define number
. For sake of simplicity numbers are
integers composed of digits (the corresponding regular expression can be
[09]+
). To simplify the grammar and then the Python script
we define two terminal symbols to group the operators (additive and
multiplicative operators). We can also define a special symbol that is
ignored by SP. This symbol is used as a separator. This is generally
useful for white spaces and comments.
Terminal symbol definition for expressions:
Terminal symbol  Regular expression  Comment 

number 
[09]+ or \d+ 
One or more digits 
addop 
[+] 
a + or a  
mulop 
[*/] 
a * or a / 
spaces 
\s+ 
One or more spaces 
This is sufficient to define our parser with SP.
Grammar of the expression recognizer:
def Calc():
number = R(r'[09]+')
addop = R('[+]')
mulop = R('[*/]')
with Separator(r'\s+'):
expr = Rule()
fact = Rule()
fact = addop & fact
fact = '(' & expr & ')'
fact = number
term = fact & ( mulop & fact )[:]
expr = term & ( addop & term )[:]
return expr
Calc
is the name of the Python function that returns a
parser. This function returns expr
which is the
axiom^{3} of the grammar.
expr
and fact
are recursive rules. They are
first declared as empty rules (expr = Rule()
) and
alternatives are later added (expr = ...
).
Slices are used to implement repetitions. foo[:]
parses
foo
zero or more times, which is equivalent to
foo*
in a classical grammar notation.
The grammar can also be defined with the mini grammar language provided by SP:
def Calc():
return compile("""
number = r'[09]+' ;
addop = r'[+]' ;
mulop = r'[*/]' ;
separator: r'\s+' ;
!expr = term (addop term)* ;
term = fact (mulop fact)* ;
fact = addop fact ;
fact = '(' expr ')' ;
fact = number ;
""")
Here the axiom^{4} is identified by
!
.
With this small grammar we can only recognize a correct expression. We will see in the next sections how to read the actual expression and to compute its value.
The input of the grammar is a string. To do something useful we need to read this string in order to transform it into an expected result.
This string can be read by catching the return value of terminal
symbols. By default any terminal symbol returns a string containing the
current token. So the token '('
always returns the string
'('
. For some tokens it may be useful to compute a Python
object from the token. For example number
should return an
integer instead of a string, addop
and mulop
,
followed by a number, should return a function corresponding to the
operator. That's why we will add a function to the token and rule
definitions. So we associate int
to number
and
op1
and op2
to unary and binary operators.
int
is a Python function converting objects to integers
and op1
and op2
are user defined
functions.
op1
and op2
functions:
op1 = lambda f,x: {'+':pos, '':neg}[f](x)
op2 = lambda f,y: lambda x: {'+': add, '': sub, '*': mul, '/': div}[f](x,y)
# red applyies functions to a number
def red(x, fs):
for f in fs: x = f(x)
return x
/
or *
operators:/
applies a function to an object returned by a
(sub)parser.*
applies a function to an tuple of objects returned by
a sequence of (sub) parsers.Token and rule definitions with functions:
number = R(r'[09]+') / int
fact = (addop & fact) * op1
term = (fact & ( (mulop & fact) * op2 )[:]) * red
# R(r'[09]+') applyed on "42" will return "42".
# R(r'[09]+') / int will return int("42")
# addop & fact applyied on "+ 42" will return ('+', 42)
# (addop & fact) * op1 will return op1(*('+', 42)), i.e. op1('+', 42)
# so (addop & fact) * op1 returns +42
# (addop & fact) * op2 will return op2(*('+', 42)), i.e. op2('+', 42)
# so (addop & fact) * op2 returns lambda x: add(x, 42)
# fact & ( (mulop & fact) * op2 )[:] returns a number and a list of functions
# for instance (42, [(lambda x:mul(x, 43)), (lambda x:mul(x, 44))])
# so (fact & ( (mulop & fact) * op2 )[:]) * red applyied on "42*43*44"
# will return red(42, [(lambda x:mul(x, 43)), (lambda x:mul(x, 44))])
# i.e. 42*43*44
And with the SP language:
number = r'[09]+' : `int` ;
addop = r'[+]' ;
mulop = r'[*/]' ;
fact = addop fact :: `op1` ;
term = fact (mulop fact :: `op2`)* :: `red` ;
# r'[09]+' applyed on "42" will return "42".
# r'[09]+' : `int` will return int("42")
# "addop fact" applyied on "+ 42" will return ('+', 42)
# "addop fact :: `op1`" will return op1(*('+', 42)), i.e. op1('+', 42)
# so "addop fact :: `op1`" returns +42
# "addop fact :: `op2`" will return op2(*('+', 42)), i.e. op2('+', 42)
# so "addop fact :: `op2`" returns lambda x: add(x, 42)
# "fact (mulop fact :: `op2`)*" returns a number and a list of functions
# for instance (42, [(lambda x:mul(x, 43)), (lambda x:mul(x, 44))])
# so "fact (mulop fact :: `op2`)* :: `red`" applyied on "42*43*44"
# will return red(42, [(lambda x:mul(x, 43)), (lambda x:mul(x, 44))])
# i.e. 42*43*44
In the SP language, :
(as /
) applies a
Python function (more generally a callable object) to a value returned
by a sequence and ::
(as *
) applies a Python
function to several values returned by a sequence.
Here is finally the complete parser.
Expression recognizer and evaluator:
from sp import *
def Calc():
from operator import pos, neg, add, sub, mul, truediv as div
op1 = lambda f,x: {'+':pos, '':neg}[f](x)
op2 = lambda f,y: lambda x: {'+': add, '': sub, '*': mul, '/': div}[f](x,y)
def red(x, fs):
for f in fs: x = f(x)
return x
number = R(r'[09]+') / int
addop = R('[+]')
mulop = R('[*/]')
with Separator(r'\s+'):
expr = Rule()
fact = Rule()
fact = (addop & fact) * op1
fact = '(' & expr & ')'
fact = number
term = (fact & ( (mulop & fact) * op2 )[:]) * red
expr = (term & ( (addop & term) * op2 )[:]) * red
return expr
Or with SP language:
from sp import *
def Calc():
from operator import pos, neg, add, sub, mul, truediv as div
op1 = lambda f,x: {'+':pos, '':neg}[f](x)
op2 = lambda f,y: lambda x: {'+': add, '': sub, '*': mul, '/': div}[f](x,y)
def red(x, fs):
for f in fs: x = f(x)
return x
return compile("""
number = r'[09]+' : `int` ;
addop = r'[+]' ;
mulop = r'[*/]' ;
separator: r'\s+' ;
!expr = term (addop term :: `op2`)* :: `red` ;
term = fact (mulop fact :: `op2`)* :: `red` ;
fact = addop fact :: `op1` ;
fact = '(' expr ')' ;
fact = number ;
""")
A parser is a simple Python object. This example show how to write a function that returns a parser. The parser can be applied to strings by simply calling the parser.
Writing SP grammars in Python:
from sp import *
def MyParser():
parser = ...
return parser
# You can instanciate your parser here
my_parser = MyParser()
# and use it
parsed_object = my_parser(string_to_be_parsed)
To use this parser you now just need to instantiate an object.
Complete Python script with expression parser:
from sp import *
def Calc():
...
calc = Calc()
while True:
expr = input('Enter an expression: ')
try: print(expr, '=', calc(expr))
except Exception as e: print("%s:"%e.__class__.__name__, e)
This tutorial shows some of the possibilities of SP. If you have read it carefully you may be able to start with SP. The next chapters present SP more precisely. They contain more examples to illustrate all the features of SP.
Happy SP'ing!
SP is a package which main function is to provide basic objects to build a complete parser.
The grammar is a Python object.
Grammar embedding example:
def Foo():
bar = R('bar')
return bar
Then you can use the new generated parser. The parser is simply a Python object.
Parser usage example:
test = "bar"
my_parser = Foo()
x = my_parser(test) # Parses "bar"
print x
SP grammars are Python objects. SP grammars may contain two parts:
are built by the R
or K
keywords.
are described after tokens in a Separator
context.
Example of SP grammar structure:
def Foo():
# Tokens
number = R(r'\d+') / int
# Rules
with Separator(r'\s+'):
S = number[:]
return S
foo = Foo()
result = foo("42 43 44") # return [42, 43, 44]
The lexer is based on the re^{5} module. SP profits from the power of Python regular expressions. This document assumes the reader is familiar with regular expressions.
You can use the syntax of regular expressions as expected by the re^{6} module.
Tokens can be explicitly defined by the R
,
K
and Separator
keywords.
Expression  Usage 

R 
defines a regular token. The token is defined with a regular expression and returns a string (or a tuple of strings if the regular expression defines groups). 
K 
defines a token that returns nothing (useful for keywords for instance). The keyword is defined by an identifier (in this case word boundaries are expected around the keyword) or another string (in this case the pattern is not considered as a regular expression). The token just recognizes a keyword and returns nothing. 
Separator 
is a context manager used to define separators for the rules defined in the context. The token is defined with a regular expression and returns nothing. 
A token can be defined by:
which identifies the token. This name is used by the parser.
which describes what to match to recognize the token.
which can translate the matched text into a Python object. It can be a function of one argument or a non callable object. If it is not callable, it will be returned for each token otherwise it will be applied to the text of the token and the result will be returned. This action is optional. By default the token text is returned.
Token definition examples:
integer = R(r'\d+') / int
identifier = R(r'[azAZ]\w*\b')
boolean = R(r'(TrueFalse)\b') / (lambda b: b=='True')
spaces = K(r'\s+')
comments = K(r'#.*')
with Separator(spacescomments):
# rules defined here will use spaces and comments as separators
atom = '(' & expr & ')'
There are two kinds of tokens. Tokens defined by the R
or K
keywords are parsed by the parser and tokens defined
by the Separator
keyword are considered as separators
(white spaces or comments for example) and are wiped out by the
lexer.
The word boundary \b
can be used to avoid recognizing
"True" at the beginning of "Truexyz".
If the regular expression defines groups, the parser returns a tuple containing these groups:
couple = R('<(\d+)(\d+)>')
couple("<4243>") == ('42', '43')
If the regular expression defines only one group, the parser returns the value of this group:
first = R('<(\d+)\d+>')
first("<4243>") == '42'
Unwanted groups can be avoided using (?:...)
.
A name can be given to a token to make error messages easier to read:
couple = R('<(\d+)(\d+)>', name="couple")
Regular expressions can be compiled using specific compilation
options. Options are defined in the re
module:
token = R('...', flags=re.IGNORECASEre.DOTALL)
re
defines the following flags:
Perform caseinsensitive matching.
Make \w
, \W
, \b
,
\B
, dependent on the current locale.
"^"
matches the beginning of lines (after a newline) as
well as the string. "$"
matches the end of lines (before a
newline) as well as the end of the string.
"."
matches any character at all, including the
newline.
Ignore whitespace and comments for nicer looking RE's.
Make \w
, \W
, \b
,
\B
, dependent on the Unicode locale
Tokens can also be defined on the fly. Their definition are then inlined in the grammar rules. This feature may be useful for keywords or punctuation signs.
In this case tokens can be written without the R
or
K
keywords. They are considered as keywords (as defined by
K
).
Inline token definition examples:
IfThenElse = 'if' & Cond &
'then' & Statement &
'else' & Statement
A parser is declared as a Python object.
Rule declarations have two parts. The left side declares the symbol
associated to the rule. The right side describes the decomposition of
the rule. Both parts of the declaration are separated with an equal sign
(=
).
Rule declaration example:
SYMBOL = (A & B) * (lambda a, b: f(a, b))
Sequences in grammar rules describe in which order symbols should
appear in the input string. For example the sequence
A & B
recognizes an A
followed by a
B
.
For example to say that a sum
is a term
plus another term
you can write:
Sum = Term & '+' & Term
Alternatives in grammar rules describe several possible
decompositions of a symbol. The infix pipe operator (
) is
used to separate alternatives. A  B
recognizes either an
A
or a B
. If both A
and
B
can be matched only the first longest match is
considered. So the order of alternatives may be very important when two
alternatives can match texts of the same size.
For example to say that an atom
is an integer
or an expression in paranthesis you can write:
Atom = integer  '(' & Expr & ')'
Repetitions in grammar rules describe how many times an expression should be matched.
Expression  Usage 

A[:1] 
recognizes zero or one
A . 
A[:] 
recognizes zero or more
A . 
A[1:] 
recognizes one or more
A . 
A[m:n] 
recognizes at least m and at most n
A . 
A[m:n:s] 
recognizes at least m and at most n
A using s as a separator. 
Repetitions are greedy. Repetitions are implemented as Python loops. Thus whatever the length of the repetitions, the Python stack will not overflow.
The separator is useful to parse lists. For instance a comma
separated parameter list is parameter[::',']
.
The following table lists the different structures in increasing precedence order. To override the default precedence you can group expressions with parenthesis.
Precedence in SP expressions:
Structure  Example 

Alternative  A  B 
Sequence  A & B 
Repetitions  A[x:y] 
Symbol and grouping  A and
( ... ) 
Grammar rules can contain actions as Python functions.
Functions are applied to parsed objects using /
or
*
.
Expression  Value 

parser / function 
returns function(result of parser). 
parser * function 
returns function(*result of parser). 
*
can be used to analyse the result of a sequence.
An abstract syntax tree (AST) is an abstract representation of the structure of the input. A node of an AST is a Python object (there is no constraint about its class). AST nodes are completely defined by the user.
AST example (parsing a couple):
class Couple:
def __init__(self, a, b):
self.a = a
self.b = b
def Foo():
couple = ('(' & item & ',' & item & ')') * Couple
return couple
It is sometimes useful to return a constant. C
defines a
parser that matches an empty input and returns a constant.
Constant example:
number = ( '1' & C("one")
 '2' & C("two")
 '3' & C("three")
)
To know the current position in the input string, the
At()
parser returns an object containing the current index
(attribute index
) and the corresponding line and column
numbers (attributes line
and column
):
position = At() / `lambda p: (p.line, p.column)`
rule = ... & pos & ...
Backtracking has a cost. The parser may often try to parse again the same string at the same position. To improve the speed of the parser, some time consuming functions are memoized. This drastically fasten the parser but requires more memory. If a lot of string are parsed in a single script this mechanism can slow down the computer because of heavy swap disk usage or even lead to a memory error.
To avoid such problems it is recommended to clean the memoization
cache by calling the sp.clean
function:
import sp
...
for s in a_lot_of_strings:
parse(s)
sp.clean()
This document describes the usage of SP with Python 2.6 or Python 3.1. Grammars need some adaptations to work with Python 2.5. or older.
Separators use context managers which don't exist in Python 2.4.
Context managers have been introduced in Python 2.5
(from __future__ import with_statement
) and in Python 2.6
(as a standard feature). When the context managers are not available, it
may be possible to call the __enter__
and
__exit__
method explicitly (tested for Python 2.4).
Python 2.6 and later:
number = R(r'\d+') / int
with Separator('\s+'):
coord = number & ',' & number
Python 2.5 with with_statement
:
from __future__ import with_statement
number = R(r'\d+') / int
with Separator('\s+'):
coord = number & ',' & number
Python 2.5 or 2.4 (or older but not tested) without
with_statement
:
sep = Separator('\s+')
number = R(r'\d+') / int
sep.__enter__()
coord = number & ',' & number
sep.__exit__()
Instead of using Python expressions that can sometimes be difficult
to read, it's possible to write grammars in a cleaner syntax and compile
these grammar with the sp.compile
function. This function
takes the grammar as a string parameter. The
sp.compile_file
function reads the grammar in a separate
file.
Here the equivalence between Python expressions and the SP mini language:
SP Python expressions  SP mini language  Description 

`R(”
regular expression”) 
`r
“regular expression” 
Token defined by a regular expression 
K("plain text") K("plain text", name="name") 
"plain text" name."plain text" 
Keyword defined by a non interpreted string 
t = R('.. .', flags=re.Ire.S) 
le xer: I S; t = r'...' 
Regular expression options 
with Separator(...): 
separator: ... ; 
Separator definition 
C(object) 
`object ` 
Parses nothing and returns object 
... / function 
... : `function ` 
Parses ... and apply the result to function
(function(...) ) 
... * function 
... :: `function ` 
Parses ... and apply the result (multiple values) to
function (function(*...) ) 
... & At() & ... 
... @ ... 
Position in the input string 
(...)[:] 
(...)* 
Zero or more matches 
(...)[1:] 
(...)+ 
One or more matches 
(...)[:1] 
(...)? 
Zero or one matche 
(...)[::S] 
[.../S]* 
Zero or more matches separated by S 
(...)[1::S] 
[.../S]+ 
One or more matches separated by S 
A & B & C 
A B C 
Sequence 
A  B  C 
A  B  C 
Alternative 
(...) 
(...) 
Grouping 
rule_name = ... 
rule_name = ... ; 
Rule definition 
axiom_name = ... 
!axiom_name = ... ; 
Axiom definition 
In mathematics, Newick tree format (or Newick notation or New Hampshire tree format)
is a way to represent graphtheoretical trees with edge lengths using parentheses and
commas. It was created by James Archie, William H. E. Day, Joseph Felsenstein, Wayne
Maddison, Christopher Meacham, F. James Rohlf, and David Swofford, at two meetings in
1986, the second of which was at Newick's restaurant in Dover, New Hampshire, USA.
 Wikipedia, the free encyclopedia
The grammar given by Wikipedia is:
Tree > Subtree ";"  Branch ";"
Subtree > Leaf  Internal
Leaf > Name
Internal > "(" BranchSet ")" Name
BranchSet > Branch  Branch "," BranchSet
Branch > Subtree Length
Name > empty  string
Length > empty  ":" number
With very few transformation, this grammar can be converted to a
Simple Parser grammar. Only BranchSet
is rewritten to use a
comma separated list parser:
Tree = Subtree ';'  Branch ';' ;
Subtree = Leaf  Internal ;
Leaf = Name ;
Internal = '(' [Branch/',']+ ')' Name ;
Branch = Subtree Length ;
Name = r'[^;:,()]*';
Length = ''  ':' r'[09.]+' ;
Here is the complete parser (newick.py):
In the previous example, the parser computes the value of the expression on the fly, while parsing. It is also possible to build an abstract syntax tree to store an abstract representation of the input. This may be useful when several passes are necessary.
This example shows how to parse an expression (infix, prefix or postfix) and convert it in infix, prefix and postfix notation. The expression is saved in a tree. Each node of the tree correspond to an operator in the expression. Each leaf is a number. Then to write the expression in infix, prefix or postfix notation, we just need to walk through the tree in a particular order.
The AST of this converter has three types of node:
is used to store operators (+
, 
,
*
, /
, ^
). It has two sons
associated to the sub expressions.
is an atomic expression (a number or a symbolic name).
is used to store functions.
These classes are instantiated by the init method. The infix, prefix and postfix methods return strings containing the representation of the node in infix, prefix and postfix notation.
ident = r'\b(?!sincostanminmax)\w+\b' : `Atom` ;
func1 = r'sin'  r'cos'  r'tan' ;
func2 = r'min'  r'max' ;
op = op_add  op_mul  op_pow ;
op_add = r'[+]' ;
op_mul = r'[*/]' ;
op_pow = r'\^' ;
The grammar for infix expressions is similar to the grammar used in the previous example:
expr = term (op_add term :: `lambda op, y: lambda x: Op(op, x, y)`)* :: `red` ;
term = fact (op_mul fact :: `lambda op, y: lambda x: Op(op, x, y)`)* :: `red` ;
fact = atom (op_pow fact :: `lambda op, y: lambda x: Op(op, x, y)`)? :: `red` ;
atom = ident ;
atom = '(' expr ')' ;
atom = func1 '(' expr ')' :: `Func` ;
atom = func2 '(' expr ',' expr ')' :: `Func` ;
red
is a function that applies a list of functions to a
value:
def red(x, fs):
for f in fs:
x = f(x)
return x
The grammar for prefix expressions is very simple. A compound prefix expression is an operator followed by two subexpressions, or a binary function followed by two subexpressions, or a unary function followed by one subexpression:
expr_pre = ident ;
expr_pre = op expr_pre expr_pre :: `Op` ;
expr_pre = func1 expr_pre :: `Func` ;
expr_pre = func2 expr_pre expr_pre :: `Func` ;
At first sight postfix and infix grammars may be very similar. Only the position of the operators changes. So a compound postfix expression is a first expression followed by a second one and an operator. This rule is left recursive. As SP is a descendant recursive parser, such rules are forbidden to avoid infinite recursion. To remove the left recursion a classical solution is to rewrite the grammar like this:
expr_post = ident expr_post_rest :: `lambda x, f: f(x)` ;
expr_post_rest =
( expr_post op :: `lambda y, op: lambda x: Op(op, x, y)`
 expr_post func2 :: `lambda y, f: lambda x: Func(f, x, y)`
 func1 : `lambda f: lambda x: Func(f, x)`
) expr_post_rest :: `lambda f, g: lambda x: g(f(x))` ;
expr_post_rest = `lambda x: x` ;
The parser searches for an atomic expression and builds the AST
corresponding to the remaining subexpression.
expr_post_rest
returns a function that builds the complete
AST when applied to the first atomic expression. This is a way to
simulate inherited attributes.
Using the previous red
function and the repetitions,
this rule can be rewritten as:
expr_post = ident expr_post_rest* :: `red` ;
expr_post_rest =
( expr_post op :: `lambda y, op: lambda x: Op(op, x, y)`
 expr_post func2 :: `lambda y, f: lambda x: Func(f, x, y)`
 func1 : `lambda f: lambda x: Func(f, x)`
) ;
or simply:
expr_post = ident
( expr_post op :: `lambda y, op: lambda x: Op(op, x, y)`
 expr_post func2 :: `lambda y, f: lambda x: Func(f, x, y)`
 func1 : `lambda f: lambda x: Func(f, x)`
)* :: `red` ;
Here is the complete source code (notation.py):
This chapter presents an extension of the calculator described in the tutorial. This calculator has a memory.
The grammar has been rewritten using the SP language.
The calculator has memories. A memory cell is identified by a name.
For example, if the user types pi = 3.14
, the memory cell
named pi
will contain the value of pi
and
2*pi
will return 6.28
.
Note
Another calculator is available as a separate package. Calc is a full featured programmers' calculator. It is scriptable and allows user functions.
Here is the complete source code (calc.py):
Python is a wonderful object oriented programming language available at http://www.python.org↩︎
Older Python versions may work (tested with Python 2.4 and 2.5). See the Older Python versions chapter.↩︎
The axiom is the symbol from which the parsing starts↩︎
The axiom is the symbol from which the parsing starts↩︎
re is a standard Python module. It handles regular expressions. For further information about re you can read http://docs.python.org/library/re.html↩︎
Read the Python documentation for further information: http://docs.python.org/library/re.html#resyntax↩︎