count_rasm

count_rasm(text, system=None)

counts the occerences of each letter (As system defines) in sura.

Args

  • text: [str], a list of strings , each inner list is ayah .

  • system: Optional, [[char]], revise Alphabetical Systems, if system is not passed, the normal alphabet is applied.

Returns

(N * P) ndarray (Matrix A): N is the number of verses, P is the alphabet (as defined in system).

A[i][j] is the number of the letter j in the verse i.

Example

newSystem = [[q.beh, q.teh, q.theh], [q.jeem, q.hah, q.khah]]
q.count_rasm(q.quran.get_sura(110), newSystem)

>>>[[1 2 1 0 0 0 1 0 4 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 3 0 1 1 1 0 0]
[1 2 0 0 2 0 0 0 5 0 2 0 1 0 1 0 0 0 0 0 0 0 2 0 0 4 0 3 1 3 1 3]
[6 2 0 0 0 0 1 0 4 0 1 0 2 0 2 0 0 0 0 0 0 1 2 0 2 0 1 2 2 2 0 0]]

search_string_with_tashkeel

search_string_with_tashkeel(string, key)

Args

  • string: str, sentence to search by key.

  • key: str, taskeel pattern.

Assumption

Searches tashkeel that is exciplitly included in string.

Returns

  • find: list of pairs where x and y are the start and end index of the matched.

  • nod-found: []

Example

string = 'صِفْ ذَاْ ثَنَاْ كَمْ جَاْدَ شَخْصٌ'
q.search_string_with_tashkeel(string, 'َْ')

>>> [(3, 5), (7, 9), (10, 12), (13, 15), (17, 19)]

frequency_of_character

frequency_of_character(characters, verse=None, chapterNum=0, verseNum=0, with_tashkeel=False)

counts the number of characters in a specific verse or sura or even the entrire Quran ,

Note

If you don't pass verse and chapterNum he will get all Quran

Args

  • verse: str, this verse that you need to count it and default is None. chapterNum, int, chapter number is a number of 'sura' that will count it , and default is 0.

  • verseNum: int, verse number in sura.

  • chracters: [], list of characters that you want to count them.

  • with_tashkeel: Bool, to check if you want to search with tashkeel.

Returns

{dic} : {str : int} a dictionary and keys is a characters and value is count of every chracter.

Example

q.frequency_of_character(['أ',"ب","تُ"],verseNum=2,with_tashkeel=False)
#that will count the vers number **2** in all swar
>>> {'أ': 101, 'ب': 133, 'تُ': 0}

q.frequency_of_character(['أ',"ب","تُ"],chapterNum=1,verseNum=2,with_tashkeel=False)
#that will count the vers number **2** in chapter **1**
>>> {'أ': 0, 'ب': 1, 'تُ': 0}

q.frequency_of_character(['أ',"ب","تُ"],chapterNum=1,verseNum=2,with_tashkeel=False)
#that will count in **all Quran**
>>> {'أ': 8900, 'ب': 11491, 'تُ': 2149}

frequency_sura_level

frequency_sura_level(suraNumber)

Computes the frequency dictionary for a sura

Args

  • suraNumber: 1 <= Int <= 114.

  • Return:

  • [aya_frequency_dictionary]: the key of aya_frequency_dictionary is a unique word in aya and the corresponding value is its frequency. A list of frequency dictionaries for each verse of Sura.

Note

  • frequency dictionary is a python dict, which carries word frequencies for an aya.
  • Its key is (str) word, its value is (int) word frequency

Example

q.frequency_sura_level(suraNumber=1)

>>> [{بسم': 1, 'الله': 1, 'الرحمن': 1, 'الرحيم': 1'},
{الحمد': 1, 'لله': 1, 'رب': 1, 'العلمين': 1'},
{الرحمن': 1, 'الرحيم': 1'},
{ملك': 1, 'يوم': 1, 'الدين': 1'},
{إياك': 1, 'نعبد': 1, 'وإياك': 1, 'نستعين': 1'},
{اهدنا': 1, 'الصرط': 1, 'المستقيم': 1'},
{عليهم': 2',
 صرط': 1',
 الذين': 1',
 أنعمت': 1',
 غير': 1',
 المغضوب': 1',
 ولا': 1',
 الضالين': 1'}]

frequency_quran_level

frequency_quran_level()

Compute the words frequences of the Quran.

Returns

  • [sura_level_frequency_dict]: Revise the output of frequency_sura_level.
In [19]: len(quran_words_frequences)
Out[19]: 114

# Al Fati-ha
In [20]: len(quran_words_frequences[0])
Out[20]: 7

sort_dictionary_by_similarity

sort_dictionary_by_similarity(frequency_dictionary, threshold=0.8)

this function using to cluster words using similarity and sort every bunch of word by most common and sort bunches descending in same time

Args

  • frequency_dictionary: dict, frequency dictionary to be sorted.

Returns

dict : {str: int} sorted dictionary

Example

frequency_dic = q.generate_frequency_dictionary(114)
q.sort_dictionary_by_similarity(frequency_dic)
# this dictionary is sorted using similarity 0.8
>>> {'أعوذ': 1, 'إذا': 2, 'العقد': 1, 'الفلق': 1, 'النفثت': 1, 'برب': 1, 'حاسد': 1, 'حسد': 1, 'خلق': 1, 'شر': 4, 'غاسق': 1, 'فى': 1, 'قل': 1, 'ما': 1, 'من': 1, 'وقب': 1, 'ومن': 3}

check_sura_with_frequency

check_sura_with_frequency(sura_num, freq_dec)

this function check if frequency dictionary of specific sura is compatible with original sura in shapes count

Args

suraNumber (int): sura number

Returns

  • Boolean: True :- if compatible Flase :- if not

Example

frequency_dic = q.generate_frequency_dictionary(114)
q.check_sura_with_frequency(114, frequency_dic)
>>> True

search_sequence

search_sequence(sequancesList, verse=None, chapterNum=0, verseNum=0, mode=3)

take list of sequances and return matched sequance, it search in verse ot chapter or All Quran ,

it return for every match : 1 - matched sequance 2 - chapter number of occurrence 3 - token number if word and 0 if sentence

Note : - if found verse != None it will use it en search .

  • if no verse and found chapterNum and verseNum it will

  • use this verse and use it to search.

  • if no verse and no verseNum and found chapterNum it will

  • search in chapter.

  • if no verse and no chapterNum and no verseNum it will search in All Quran.

it has many modes:

  • search with decorated sequance (with tashkeel), and return matched sequance with decorates (with tashkil).

  • search without decorated sequance (without tashkeel), and return matched sequance without decorates (without tashkil).

  • search without decorated sequance (without tashkeel), and return matched sequance with decorates (with tashkil).

Args

  • chapterNum: int, number of chapter where function search.

  • verseNum: int, number of verse wher function search.

  • sequancesList: [], a list of sequances that you want to match them.

  • mode: int, this mode that you need to use and default mode 3.

Returns

  • dict: key is sequances and value is a list of matched_sequance and their positions.

Example

# search in chapter = 1 only using mode 3 (default)
q.search_sequence(sequancesList=['ملك يوم الدين'],chapterNum=1)
#it will return
#{'sequance-1' : [ (matched_sequance , position , vers_num , chapter_num) , (....) ],
# 'sequance-2' : [ (matched_sequance , position , vers_num , chapter_num) , (....) ] }
# Note : position == 0 if sequance is a sentence and == word position if sequance is a word
>>> {'ملك يوم الدين': [('مَلِكِ يَوْمِ الدِّينِ', 0, 4, 1)]}

# search in all Quran using mode 3 (default)
q.search_sequence(sequancesList=['ملك يوم'])
>>> {'ملك يوم': [('مَلِكِ يَوْمِ', 0, 4, 1),  ('الْمُلْكُ يَوْمَ', 0, 73, 6),  ('الْمُلْكُ يَوْمَئِذٍ', 0, 56, 22),  ('الْمُلْكُ يَوْمَئِذٍ', 0, 26, 25)]}