influence 1.1a4

A utility package influenced by java, coded in python

Influence - The Python Extender You Asked For

---

With influence you can extend python with things like two-dimensional

lists, fractions, string subtractors, etc. You can also upgrade python

with things it doesn’t have like arrays!

Overview

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The influence python library was created with one sole purpose, helping

you do things that can’t be done in standard python with ease

Usage

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Below is how to install and use the influence library in your own

programs!

Installation

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$ pip install influence

or

$ python3 -m pip install -U influence

The influence package has two package dependencies, numpy and matplotlib

(used for grapher and agrapher classes)

Cout

---

Cout (common output) has only one class, printer, that helps print

tuples, lists, dicts, etc. nicely

Importing:

from influence import cout

#or

from influence.cout import Printer

Printer Class

Methods:

Printer.print_list(list) #prints a list nicely

Printer.print_tuple(tuple) #prints a tuple nicely

Printer.print_dictionary(dict) #prints a dict nicely

Printer.print_all(ender, *items)

#prints all of items, if ender is False, prints each item on new line

#else all items are printed on the same line

Cin

‘’’

Cin (common input), has one class, input, that handles input

specifically

Importing:

from influence import cin

#or

from influence.cin import Input

Input Class

Methods:

value = Input.input(t, prompt=None)

#stores input into value

#prompt will be printed, defaults to None

#raises ValueError if input does not match type t

#raises TypeError if t not able to be casted from input

List2D Class

Creates a 2D list of a square size

Importing:

from influence.list import multilist

#or

from influence.list.multilist import List2D

Initializing:

l = List2D(rows=1, cols=1)

#creates the list to have rows number of rows and cols number of cols

Methods:

l[r_index][c_index] = item

#sets value at r_index and c_index to item

#raises IndexError if index out of bounds

l[r_index].append(item)

#since this is a list, if you wish to append the list

#you can do it this way instead of settings

l[r_index][c_index]

#returns value at r_index and c_index

#raises IndexError if index out of bounds

l.print()

#prints the list

l.remove(r_index, c_index)

#removes the value at r_index and c_index

#returns true if removed, false if index out of bounds

item in l

#returns true if item in l, false otherwise

l.index(item)

#returns indices of item if found in list

#returns [-1] otherwise

l.__len__() / len(l)

#returns the length of l

l.__str__() / str(l)

#returns l as a str

l.__delitem__(key) / del l[key]

#deletes row key from l

#raises IndexError if key out of bounds

RaggedList Class

Creates a 2D list, but doesn’t need to be of n x n size, inherits from

List2D, and therefore has a dependency to

influence.extender.list.multilist

Importing:

from influence.list import ragged

#or

from influence.list.ragged import RaggedList

Initializing:

r = RaggedList(rows=1, cols=1)

#creates a ragged list starting with rows rows and cols cols

#defaults to one for both if no arguments are given

Methods:

r.print()

#prints the ragged list

r.in_bounds(r_index, c_index)

#returns true if r_index and c_index are in bounds of the list

#returns false otherwise

r.set(r_index, c_index, item)

#sets value at r_index and c_index to item if in bounds

#else extends the ragged list so r_index and c_index are in bounds

r.get(r_index, c_index)

#returns value at r_index and c_index if in bounds

#else returns None

item in r

#returns true if item is in r, else returns false

r.index(item)

#returns the indices of item if in r

#else returns [-1]

r.__len__() / len(r)

#returns the length of r

AsList Class

Used to turn strings into lists, duplicate class found in string

subpackage

Importing:

from influence.list import aslist

#or

from influence.list.aslist import AsList

Methods:

AsList.character_list(string)

#returns string as a list of characters

AsList.word_list(string)

#returns string as a list with each word

#a word is found when a space is reached in the string

#spaces are not included in the list

AsList.word_list_with_spaces(string)

#same as AsList.word_list(string) except spaces are part of the list

AsList Class

Used to turn strings into lists, duplicate class found in list

subpackage

Importing:

from influence.string import aslist

#or

from influence.string.aslist import AsList

Methods:

AsList.character_list(string)

#returns string as a list of characters

AsList.word_list(string)

#returns string as a list with each word

#a word is found when a space is reached in the string

#spaces are not included in the list

AsList.word_list_with_spaces(string)

#same as AsList.word_list(string) except spaces are part of the list

Subtract Class

Allows for subtracting of strings, but does not change the input string,

instead returns a new string

Importing:

from influence.string import subtract

#or

from influence.string.subtract import Subtract

Methods:

Subtract.subtract(initial, remove)

#removes the first instance of remove from initial

#returns a new string

#remove can be multiple letters, but must be a string

Subtract.subtract_all(initial, remove)

#removes all instances of remove from initial

#returns a new string

#remove can be multiple letters, but must be a string

Const Class

---

Gives the user access to constants in math

Importing:

from influence.math import const

#or

from influence.math.const import MathConstants

Fields:

MathConstants.pi #returns the value of pi

MathConstants.e #returns the value of e

MathConstants.tau #returns the value of tau

MathConstants.phi #returns the value of phi

Stats Class

Allows for statistics with int or float datasets

Importing:

from influence.math import stats

#or

from influence.math.stats import Stats

Methods:

Stats.min(dataset)

#returns the lowest value in dataset

Stats.max(dataset)

#returns the highest value in dataset

Stats.range(dataset)

#returns the range of the dataset (max - min)

Stats.mean(dataset)

#returns the mean of the dataset

Stats.variance(dataset)

#returns the variance of the dataset

Stats.standard_deviation(dataset)

#returns the standard deviation of the dataset

Stats.median(dataset)

#returns the median of the dataset

Stats.mode(dataset)

#returns the mode of the dataset as a list

Cos Class

Does permutations and combinations equations, inherits from Stats, and

therefore has a dependency to influence.extender.math.stats

Importing:

from influence.math import cos

#or

from influence.math.cos import Combinatorics

Methods:

Combinatorics.factorial(num)

#returns the factorial of num

Combinatorics.P(n, r)

#returns the permutations equation (n! / (n-r)!)

Combinatorics.C(n, r)

#returns the combinations equation (n! / [(n-r)! * r!])

Frac Class

Represents a fraction

Importing:

from influence.math import frac

#or

from influence.math.frac import Fraction

Initializing:

f = Fraction(num, denom)

#initializes a fraction to numerator num and denominator denom

Methods:

f.simplify()

#simplifies this fraction, if possible

f.__float__() / float(f)

#returns the float value of the fraction

f.__int__() / int(f)

#returns the int value of the fractions

f.__str__() / str(f)

#returns the fraction as a string

f.to_mixed_number(self)

#returns f as a mixed number

Compare:

f1 = Fraction(1, 2)

f2 = Fraction(3, 4)

#fraction allows for

f1 < f2

f1 <= f2

f1 == f2

f1 > f2

f1 >= f2

MixedNum Class

Represents a mixed number

Importing:

from influence.math import mixednum

#or

from influence.math.mixednum import MixedNumber

Initializing:

m = MixedNumber(coeff, num, denom)

#creates a mixed number with a coefficient coeff, numerator num

#and denominator denom

Methods:

m.simplify()

#simplifies this mixed number, if possible

m.__float__() / float(m)

#returns the float value of the mixed number

m.__int__() / int(m)

#returns the int value of the mixed number

m.__str__() / str(m)

#returns the mixed number as a str

m.to_fraction()

#returns the mixed number as a new improper fraction

Compare:

m1 = MixedNumber(1, 2, 3)

m2 = MixedNumber(4, 5, 6)

#fraction allows for

m1 < m2

m1 <= m2

m1 == m2

m1 > m2

m1 >= m2

Grapher Subpackage

Allows for graphing equations

Importing:

from influence.math.grapher import Equation

from influence.math.grapher import GraphingError

from influence.math.grapher import Grapher

Equation Class:

Represents an equation

Initializing:

e = Equation(eq)

#eq cannot be inferred

#ie 4x+3 needs to be 4*x+3

#ie 4x^2+2 needs to be 4*(x**2)+3

GraphingError Class:

GraphingError.HostileAttackError is thrown when a hostile attack is

detected with eval GraphingError.InstanceError is thrown when graphing,

the parameter is not an instance of Equation

Grapher Class:

Grapher.graph(eq)

#graphs eq, if and only if isinstance(eq, Equation) returns True

Agrapher Subpackage

Asynchronous graphing is currently a WIP but are still able to be used

Importing:

from influence.math.asyncgrapher import Equation

from influence.math.asyncgrapher import GraphingError

from influence.math.asyncgrapher import Grapher

Agrapher works in the same exact way except Grapher.graph(eq,

timetoclose=None), can have a given timeout

Array Class

Makes an array. An array is like a list, except it has a definite,

unchangeable size, but elements can be changed inside of it (unlike a

tuple)

Importing:

from influence.array import arrays

#or

from influence.array.arrays import Array

Initializing:

arr = Array(capacity)

#initializes the array to its definite length

Methods:

arr[index]

#gets the value at index

arr[start:stop:step]

#returns a list from an array from a slice of start, stop, and step

#raises IndexError if index out of bounds

arr[index] = item

#sets the value at index to item

#raises IndexError if index out of bounds

arr.__iter__() / iter(arr)

#returns an iterator for the array

iterator.__next__() / next(iterator)

#gets the next element from the iterator

arr.print()

#prints the array

item in arr

#returns true if item is in arr, false otherwise

arr.index(item)

#returns the index of item if in arr

#returns -1 if not found

arr.__len__() / len(arr)

#returns the length of arr

not arr

#returns True if arr has a capacity of 0

arr.__str__() / str(arr)

#returns arr as a str

arr1 + arr2

arr1 += arr2

#adds the arrays together

Array2D Class

Creates a 2D Array, inherits from Array, and therefore has a dependency

to influence.upgrader.array.arrays

Importing:

from influence.array import multiarray

#or

from influence.array.multiarray import Array2D

Initializing:

arr = Array2D(r, c)

#creates a 2D array to a fixed amount of rows (r) and columns (c)

Methods:

arr[r_index][c_index]

#returns the value at r_index and c_index

#raises IndexError if index out of bounds

arr[r_index][c_index] = item

#sets value at r_index and c_index to item

#raises IndexError if index out of bounds

arr.print()

#prints the 2D array

item in arr

#returns true if item is in arr, false otherwise

arr.index(item)

#returns the indices of item in arr, if found

#returns [-1] otherwise

arr.__len__() / len(arr)

#returns length of arr

StringBuffer Class

Makes strings mutable, like in java

Importing:

from influence.string import stringbuffer

#or

from influence.string.stringbuffer import StringBuffer

Initializing:

s = StringBuffer(str='')

#initializes a string buffer to str, empty if none entered

Methods:

s.__len__() / len(s)

#returns the length of s

obj in s

#returns true if obj is in s, false otherwise

s.__iter__() / iter(s)

#returns an iterator for s

s.__next__() / next(s)

#gets next letter in s

s.__str__() / str(s)

#gets s as a normal string

s[index]

#gets letter at index

s[start:stop:step]

#gets letters starting at start, up to but discluding stop, incrementing by step

s[index] = item

#sets letter at index to item

s.append(append)

#appends append to s

s.index(obj)

#returns the index of obj in s

s.insert(index, obj)

#inserts obj at index

s.replace(start, stop, obj)

#replaces the chars from stop to stop (discluding stop) with obj

del s[index]

#deletes the char at index

s1 + s2

s1 += s2

#adds stringbuffers together

Stack Class

Represents a stack of items, top being the newest, and bottom being the

oldest

Importing:

from influence.list import stack

#or

from influence.list.stack import Stack

Initializing:

s = Stack()

#creates an empty stack

Methods:

s.push(obj)

#puts an item to the top of the stack

s.pop()

#removes the top item in the stack

#raises stack.EmptyStackError if stack is empty

s.peek()

#gets the top item in the stack without removing it

#returns None if stack is empty

s.empty()

#returns True if s is empty

obj in s

#returns True if obj is in s, False otherwise

s.index(obj)

#returns the index of obj, -1 if not found

s.__len__() / len(s)

#returns the length of s

s.__str__() / str(s)

#returns s as a str

InsertableDict Class

A dict which can insert items at a certain index

Importing:

from influence.dict import idict

#or

from influence.dict.idict import InsertableDict

Initializing:

i = InsertableDict()

#creates an empty insertable dict

Methods:

i[key]

#gets the value from i of key key

i[key] = item

#sets the value at key to item

#or creates a new key and value if key not in i

i.__len__() / len(i)

#gets the length of i

i.get(key)

#like i[key] but returns None if key is not in i

i.append(key, item)

#appends key and item to the end of the i

i.keys()

#returns the keys of i

i.values()

#returns the values of i

i.__str__() / str(i)

#returns i as a str

i.remove(key)

#removes the key and value of key

i.pop()

#removes the last element in i

i.__iter__() / iter(i)

#returns an iter object for i

i.__next__() / next(i)

#returns the next element in i

i.insert(index, key, value)

#inserts key and value at index (index starts at 0)

TreeSet Class

A normal set (where you can’t have duplicate items), except all items

are automatically sorted upon adding

Importing:

from influence.set import treeset

#or

from influence.set.treeset import TreeSet

Initializing:

ts = TreeSet(t)

#creates a treeset that takes in values of type t

Methods:

ts.add(item)

#adds item to ts and sorts the set

#raises ValueError if item is not of instance t

#returns True if added, False if item already in set

obj in ts

#returns True if obj is in ts, False otherwise

ts.remove(item)

#removes item from ts

#raises an error if item not in ts

ts.discard(item)

#removes item from ts if found

#does not raise an error if not found

ts.__len__() / len(ts)

#returns the length of ts

ts.__str__() / str(ts)

#returns ts as a str

ts.__iter__() / iter(ts)

#returns an iterator for ts

ts.__next__() / next(ts)

#returns the next element in ts

ts.pop()

#removes the last element in ts

TreeMap Class

A normal dictionary, except items are automatically sorted by key from

least to greatest

Importing:

from influence.dict import treemap

#or

from influence.dict.treemap import TreeMap

Initializing:

tm = TreeMap()

#initializes an empty treemap

Methods:

tm.add(key, value)

#adds key and value to tm and sorts the dict

obj in tm

#returns True if obj is in tm.keys() or tm.values(), False otherwise

tm.keys()

#returns the keys in tm

tm.values()

#returns the values in tm

tm.__str__() / str(tm)

#returns tm as a str

tm.remove(key)

#removes the key and value of key from tm

tm.pop()

#removes the last element key and value from tm

tm.__len__() / len(tm)

#returns the length of tm

tm.__iter__() / iter(tm)

#returns an iterator for tm

tm.__next__() / next(tm)

#returns the next element in tm

StemLeaf Class

Creates a stem and leaf plot

Importing:

from influence.math import stemandleaf

#or

from influence.math.stemandleaf import StemLeaf

Initializing:

sl = StemLeaf()

#initializes an empty stem and leaf plot

Methods:

sl.plot(stem, leaf)

#adds the stem and leaf to plot

sl.remove(stem, leaf)

#removes the leaf from the given stem if found

#raises stemandleaf.NoStemError if stem not found

#raises stemandleaf.NoLeafError if leaf not found in stem

sl.__str__() / str(sl)

#returns sl as a str

sl.empty()

#returns True if sl is empty, False otherwise

License

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MIT License

Copyright (c) 2020 RandomKiddo

Permission is hereby granted, free of charge, to any person obtaining a

copy of this software and associated documentation files (the

“Software”), to deal in the Software without restriction, including

without limitation the rights to use, copy, modify, merge, publish,

distribute, sublicense, and/or sell copies of the Software, and to

permit persons to whom the Software is furnished to do so, subject to

the following conditions:

The above copyright notice and this permission notice shall be included

in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS

OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF

MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY

CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,

TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE

SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.