Metadata-Version: 1.1
Name: cexprtk
Version: 0.3.3
Summary: Mathematical expression parser: cython wrapper around the 'C++ Mathematical Expression Toolkit Library' 
Home-page: https://bitbucket.org/mjdr/cexprtk
Author: M.J.D. Rushton
Author-email: m.j.d.rushton@gmail.com
License: CPL
Download-URL: https://bitbucket.org/mjdr/cexprtk/get/0.3.3.tar.gz
Description: #cexprtk: Mathematical Expression Parsing and Evaluation in Python
        
        `cexprtk` is a cython wrapper around the "[ExprTK: C++ Mathematical Expression  Toolkit Library ](http://www.partow.net/programming/exprtk/index.html)"  by Arash Partow. Using `cexprtk` a powerful mathematical expression engine can be incorporated into your python project.
        
        ## Table of Contents
        [TOC]
        
        ## Installation
        
        The latest version of `cexprtk` can be installed using [pip][pip] :
        
        ```bash
        	$ pip install cexprtk
        ```
        
        __Note:__ Installation requires a compatible C++ compiler to be installed (unless installing from a binary wheel).
        
        
        ## Usage
        
        The following examples show the major features of `cexprtk`. 
        
        ### Example: Evaluate a simple equation
        
        The following shows how the arithmetic expression `(5+5) * 23` can be evaluated:
        
        ```python
        	>>> import cexprtk
        	>>> cexprtk.evaluate_expression("(5+5) * 23", {})
        	230.0
        ```
        
        ### Example: Using Variables
        
        Variables can be used within expressions by passing a dictionary to the `evaluate_expression` function. This maps variable names to their values. The expression from the previous example can be re-calculated using variable values:
        
        ```python
        	>>> import cexprtk
        	>>> cexprtk.evaluate_expression("(A+B) * C", {"A" : 5, "B" : 5, "C" : 23})
        	230.0
        ```
        
        ### Example: Re-using expressions
        When using the `evaluate_expression()` function, the mathematical expression is parsed, evaluated and then immediately thrown away. This example shows how to re-use an `Expression` for multiple evaluations.
        
        * An expression will be defined to calculate the circumference of circle, this will then be re-used to calculate the value for several different radii.
        * First a `Symbol_Table` is created containing a variable `r` (for radius), it is also populated with some useful constants such as π.
        
        ```python
        	>>> import cexprtk
        	>>> st = cexprtk.Symbol_Table({'r' : 1.0}, add_constants= True)
        ```
        
        * Now an instance of `Expression` is created, defining our function:
        
        ```python
        	>>> circumference = cexprtk.Expression('2*pi*r', st)
        ```
        
        * The `Symbol_Table` was initialised with `r=1`, the expression can be evaluated for this radius simply by calling it:
        
        ```python
        	>>> circumference()
        	6.283185307179586
        ```
        
        * Now update the radius to a value of 3.0 using the dictionary like object returned by the `Symbol_Table`'s `.variables` property:
        
        ```python
        	>>> st.variables['r'] = 3.0
        	>>> circumference()
        	18.84955592153876
        ```
        
        ### Example: Defining custom functions
        Python functions can be registered with a `Symbol_Table` then used in an `Expression`. In this example a custom function will be defined which produces a random number within a given range.
        
        A suitable function exists in the `random` module, namely `random.uniform`. As this is an instance method it needs to be wrapped in function:
        
        ```python
        >>> import random
        >>> def rnd(low, high):
        ...   return random.uniform(low,high)
        ...
        ```
        
        Our `rnd` function now needs to be registered with a `Symbol_Table`:
        
        ```python
        >>> import cexprtk
        >>> st = cexprtk.Symbol_Table({})
        >>> st.functions["rand"] = rnd
        ```
        
        The `functions` property of the `Symbol_Table` is accessed like a dictionary. In the preceding code snippet, a symbol table is created and then the `rnd` function is assigned to the `rand` key. This key is used as the function's name in a `cexprtk` expression. The key cannot be the same as an existing variable, constant or reserved function name.
        
        The `rand` function will now be used in an expression. This expression chooses a random number between 5 and 8 and then multiplies it by 10. The followin snippet shows the instantiation of the `Expression` which is then evaluated a few times. You will probably get different numbers out of your expression than shown, this is because your random number generator will have been initialised with a different seed than used in the example.
        
        ```python
        >>> e = cexprtk.Expression("rand(5,8) * 10", st)
        >>> e()
        61.4668441077191
        >>> e()
        77.13523163246415
        >>> e()
        59.14881842716157
        >>> e()
        69.1476535568958
        ```
        
        ### Example: Defining an unknown symbol resolver
        A callback can be passed to the `Expression` constructor through the `unknown_symbol_resolver_callback` parameter. This callback is invoked during expression parsing when a variable or constant is encountered that isn't in the `Symbol_Table` associated with the `Expression`. 
        
        The callback can be used to provide some logic that leads to a new symbol being registered or for an error condition to be flagged.
        
        __The Problem:__ The following example shows a potential use for the symbol resolver:
        
        * An expression contains variables of the form `m_VARIABLENAME` and `f_VARIABLENAME`.
        * `m_` or `f_` prefix the  actual variable name (perhaps indicating gender).
        * `VARIABLENAME` should be used to look up the desired value in a dictionary.
        * The dictionary value of `VARIABLENAME` should then be weighted according to its prefix:
        	+ `m_` variables should be multiplied by 0.8.
        	+ `f_` variables should be multiplied by 1.1.
        
        __The Solution:__
        
        * First the `VARIABLENAME` dictionary is defined:
        	
        	```python
        	variable_values = { 'county_a' : 82, 'county_b' : 76}
        	```
        
        * Now the callback is defined. This takes a single argument, *symbol*, which gives the name of the missing variable found in the expression:
        
        	```python
        	def callback(symbol):
        		# Tokenize the symbol name into prefix and VARIABLENAME components.
        		prefix,variablename = symbol.split("_", 1)
        		# Get the value for this VARIABLENAME from the variable_values dict
        		value = variable_values[variablename]
        		# Find the correct weight for the prefix
        		if prefix == 'm':
        			weight = 0.8
        		elif prefix == 'f':
        			weight = 1.1
        		else:
        			# Flag an error condition if prefix not found.
        			errormsg = "Unknown prefix "+ str(prefix)
        			return (False, cexprtk.USRSymbolType.VARIABLE, 0.0, errormsg)
        		# Apply the weight to the 
        		value *= weight
        		# Indicate success and return value to cexprtk
        		return (True, cexprtk.USRSymbolType.VARIABLE, value, "")
        	```
        
        * All that remains is to register the callback with an instance of `Expression` and to evaluate an expression. The expression to be evaluated is:
        	- `(m_county_a - f_county_b)`
        	- This should give a value of `(0.8*82) - (1.1*76) = -18`
        
        	```python
        		>>> st = cexprtk.Symbol_Table({})
        		>>> e = cexprtk.Expression("(m_county_a - f_county_b)", st, callback)
        		>>> e.value()
        		-18.0
        	```
        
        ---
        
        ## API Reference
        
        For information about expressions supported by `cexprtk` please refer to the original C++ [ExprTK][] documentation:
        
        ### Class Reference
        
        #### class Expression:
        Class representing mathematical expression.
        
        * Following instantiation, the expression is evaluated calling the expression or invoking its `value()` method.
        * The variable values used by the Expression can be modified through the `variables` property of the `Symbol_Table` instance associated with the expression. The `Symbol_Table` can be accessed using the `Expression.symbol_table` property.
        
        ##### Defining unknown symbol-resolver:
        
        The `unknown_symbol_resolver_callback` argument  to the `Expression`
        constructor accepts a callable which is invoked  whenever a symbol (i.e. a
        variable or a constant), is not found in the `Symbol_Table` given by the
        `symbol_table` argument. The `unknown_symbol_resolver_callback` can be
        used to provide a value for the missing value or to set an error condition.
        
        The callable should have following signature:
        
        ```python
        	def callback(symbol_name):
        		...
        ```
        
        Where `symbol_name` is a string identifying the missing symbol.
        
        The callable should return a tuple of the form:
        
        ```python
        	(HANDLED_FLAG, USR_SYMBOL_TYPE, SYMBOL_VALUE, ERROR_STRING)
        ```
        
        Where:
        
        * `HANDLED_FLAG` is a boolean:
        	+ `True` indicates that callback was able handle the error condition and that `SYMBOL_VALUE` should be used for the missing symbol. 
        	+ `False`, flags and error condition, the reason why the unknown symbol could not be resolved by the callback is described by `ERROR_STRING`.
        * `USR_SYMBOL_TYPE` gives type of symbol (constant or variable) that should be added to the `symbol_table` when unkown symbol is resolved. Value should be one of those given in `cexprtk.USRSymbolType`. e.g.
        	+ `cexprtk.USRSymbolType.VARIABLE`  
        	+ `cexprtk.USRSymbolType.CONSTANT`  
        * `SYMBOL_VALUE`, floating point value that should be used when resolving missing symbol.
        * `ERROR_STRING` when `HANDLED_FLAG` is `False` this can be used to describe error condition.
        
        ##### def __init__(self, *expression*, *symbol_table*, *unknown_symbol_resolver_callback* = None):
        Instantiate `Expression` from a text string giving formula and `Symbol_Table`
        instance encapsulating variables and constants used by the expression.
        
        __Parameters:__
        
        * __expression__ (*str*) String giving expression to be calculated.
        * __symbol_table__ (*Symbol_Table*) Object defining variables and constants.
        * __unknown_symbol_resolver_callback__ (*callable*)  See description above.
        
        ##### def value(self):
        Evaluate expression using variable values currently set within associated `Symbol_Table`
        
        __Returns:__
        
        * (*float*) Value resulting from evaluation of expression.
        
        ##### def __call__(self):
        Equivalent to calling `value()` method.
        
        __Returns:__
        
        * (*float*) Value resulting from evaluation of expression.
        
        ##### symbol_table
        Read only property that returns `Symbol_Table` instance associated with this expression.
        
        __Returns:__
        
        * (*Symbol_Table*) `Symbol_Table` associated with this `Expression`.
        
        ---
        
        #### class Symbol_Table:
        Class for providing variable and constant values to `Expression` instances.
        
        
        ##### def __init__(self, *variables*, *constants* = {}, *add_constants* = False, functions = {}):
        Instantiate `Symbol_Table` defining variables and constants for use with `Expression` class.
        
        __Example:__
        
        * To instantiate a `Symbol_Table` with:
        	+ `x = 1`
        	+ `y = 5`
        	+ define a constant `k = 1.3806488e-23`
        * The following code would be used:
        
        	```python
        		st = cexprtk.Symbol_Table({'x' : 1, 'y' : 5}, {'k'= 1.3806488e-23})
        	```
        
        __Parameters:__
        
        * __variables__ (*dict*) Mapping between variable name and initial variable value.
        * __constants__ (*dict*) Dictionary containing values that should be added to `Symbol_Table` as constants. These can be used a variables within expressions but their values cannot be updated following `Symbol_Table` instantiation.
        * __add_constants__ (*bool*) If `True`, add the standard constants `pi`, `inf`, `epsilon` to the 'constants' dictionary before populating the `Symbol_Table`
        * __functions__ (*dict*) Dictionary containing custom functions to be made available to expressions. Dictionary keys specify function names and values should be functions.
        
        ##### variables
        Returns dictionary like object containing variable values. `Symbol_Table` values can be updated through this object.
        
        __Example:__
        
        ```python
        	>>> import cexprtk
        	>>> st = cexprtk.Symbol_Table({'x' : 5, 'y' : 5})
        	>>> expression = cexprtk.Expression('x+y', st)
        	>>> expression()
        	10.0
        ```
        
        Update the value of `x` in the symbol table and re-evaluate the expression:
        
        ```python
        	>>> expression.symbol_table.variables['x'] = 11.0
        	>>> expression()
        	16.0
        ```
        
        __Returns:__
        
        * Dictionary like giving variables stored in this `Symbol_Table`. Keys are variables names and these map to variable values.
        
        ##### constants
        Property giving constants stored in this `Symbol_Table`.
        
        __Returns:__
        
        * Read-only dictionary like object mapping constant names stored in `Symbol_Table` to their values.
        
        ##### functions
        Returns dictionary like object containing custom python functions to use in expressions. 
        
        __Returns:__
        
        * Dictionary like giving function stored in this `Symbol_Table`. Keys are function names (as used in `Expression`) and these map to python callable objects including functions, functors, and `functools.partial`.
        
        ---
        
        #### class USRSymbolType:
        Defines constant values used to determine symbol type returned by `unknown_symbol_resolver_callback` (see `Expression` constructor documentation for more).
        
        ##### VARIABLE
        Value that should be returned by an `unknown_symbol_resolver_callback` to define a variable.
        
        ##### CONSTANT
        Value that should be returned by an `unknown_symbol_resolver_callback` to define a constant.
        
        ---
        
        ### Utility Functions
        #### def check_expression (*expression*)
        
        Check that expression can be parsed. If successful do nothing, if unsuccessful raise `ParseException`.
        
        __Parameters:__
        
        * *expression* (*str*) Formula to be evaluated
        
        __Raises:__ 
        
        * `ParseException`: If expression is invalid.	
        
        
        #### def evaluate_expression (*expression*, *variables*)
        Evaluate a mathematical formula using the exprtk library and return result.
        
        For more information about supported functions and syntax see the
        [exprtk C++ library website](http://www.partow.net/programming/exprtk/index.html).
        
        __Parameters:__
        
        * __expression__ (*str*) Expression to be evaluated.
        * __variables__ (*dict*) Dictionary containing variable name, variable value pairs to be used in expression.
        
        __Returns:__ 
        
        * (*float*): Evaluated expression
        
        __Raises:__ 
        
        * `ParseException`: if *expression* is invalid.
        
        ---
        
        ## Authors
        
        Cython wrapper by Michael Rushton (m.j.d.rushton@gmail.com), although most credit should go to Arash Partow for creating the underlying [ExprTK](http://www.partow.net/programming/exprtk/index.html) library.
        
        
        ## License
        
        `cexprtk` is released under the same terms as the [ExprTK][] library the [Common Public License Version 1.0][] (CPL).
        
        [pip]: http://www.pip-installer.org/en/latest/index.html
        [Common Public License Version 1.0]: http://opensource.org/licenses/cpl1.0.php
        
Keywords: math,formula,parser,arithmetic,evaluate
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Common Public License
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Cython
Classifier: Topic :: Scientific/Engineering :: Mathematics
