cereja 1.2.6

Cereja is a bundle of useful functions that I don't want to rewrite.

Cereja 🍒

Cereja is a bundle of useful functions that I don't want to rewrite.

How many times have you needed to rewrite that function or base class? Well, I thought then of joining all my lines of code, bit by bit, in one place.

Not well structured yet :( ... But you can help me !!!

Getting Started DEV

Do not be shy \0/ ... Clone the repository and submit a function or module you made or use some function you liked.

See CONTRIBUTING 💻

Prerequisites

• Python 3.6+
• Pip3

Installing

Install Cereja Package

python3 -m pip install --user cereja

or for all users

python3 -m pip install cereja

Note: If you are using Windows, you do not need to use python3, but make sure your python path settings are correct.

Cereja Example usage

See some of the Cereja tools

Filetools

Filetools has several functions, have the converters for end of line (CRLF, LF and CR), you can convert CRLF to LF or CR and vice versa

Txt File Manipulation Example - most files

import cereja as cj

data = ['first line', 'second line', 'third line']
file = cj.File('test.txt', data) # ram only, not yet saved
print(file) # FileBase<test.txt>
print(file.data) # ['first line', 'second line', 'third line']

# iterable
for line in file:
    pass

# indexable
file[0] # 'first line'
file[:3] # ['first line', 'second line', 'third line']

# Insert Data
file.insert('other line')
file.insert('other line2')
print(file.data) # ['other line2', 'other line', 'first line', 'second line', 'third line']
# it is allowed to use index assignment
file[0] = 'other line'
# can use file.append
file.append('end line')

# Data Recovery
file.undo() # You selected amendment 3
print(file.data) # ['other line2', 'other line', 'first line', 'second line', 'third line']
file.redo() # You selected amendment 4
print(file.data) # ['other line', 'other line2', 'other line', 'first line', 'second line', 'third line']

# Save Data
file.save()

File Manipulation Example - .json

import cereja as cj

data = {'key': 'value', 'key2': 'value2', 'key3': 'value3'}
file = cj.File('test.json', data) # ram only, not yet saved
print(file) # JsonFile<test.json>
print(file.data) # {'key': 'value', 'key2': 'value2', 'key3': 'value3'}

# Iterable
for key, value in file.items(): # only .json data, use .items(), .values() and .keys()
    pass

file['key'] # 'value'


# Insert Data
file['key4'] = 'value4'
print(file.data) # {'key': 'value', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4'}

# Data Recovery
file.undo() # You selected amendment 1
print(file.data) # {'key': 'value', 'key2': 'value2', 'key3': 'value3'}
file.redo() # You selected amendment 2
print(file.data) # {'key': 'value', 'key2': 'value2', 'key3': 'value3', 'key4': 'value4'}

# Save Data
file.save()

File Manipulation Example - .csv File

import cereja as cj

file = cj.File('test.csv', fieldnames=['col1','col2','col3']) # ram only, not yet saved
print(file) # CsvFile<test.csv>
file.add_row([1,2,3])
print(file.lines) # [[1, 2, 3]]
file.add_row([1,2], fill_with=0)
print(file.lines) # [[1, 2, 3], [1, 2, 0]]

# convert to dict
file.to_dict() # {'col1': [1, 1], 'col2': [2, 2], 'col3': [3, 0]}

# or get generation row by row with col
print(list(file.data)) # [{'col1': 1, 'col2': 2, 'col3': 3}, {'col1': 1, 'col2': 2, 'col3': 0}]

# Iterable
for row in file:
    pass

# indexing col values
print(file['col1']) # [1, 1]
# or use index and get a row
print(file[0]) # [1, 2, 3]

# Save Data
file.save()

# Reading
file = cj.CsvFile.load('test.csv') # have a fun! lol
dir(file) # see all methods and attr

Reading Other Files

import cereja as cj

file = cj.File.load('path_to_file.ext')
# now you have a file instance, have fun!
# you can insert lines, remove and save \0/
file.save(exist_ok=True) # or change path file.save(on_new_path='new_path.ext', exist_ok=True)

Convert between CRLF, LF and CR

python3 -m pip install cereja

python3 -m cereja --crlf_to_lf "/dir_or_file_path"

Corpus

Great training and test separator.

Create from list data

import cereja as cj

X = ['how are you?', 'my name is Joab', 'I like coffee', 'how are you joab?', 'how', 'we are the world']
Y = ['como você está?', 'meu nome é Joab', 'Eu gosto de café', 'Como você está joab?', 'como', 'Nós somos o mundo']

corpus = cj.Corpus(source_data=X, target_data=Y, source_name='en', target_name='pt')
print(corpus) # Corpus(examples: 6 - source_vocab_size: 13 - target_vocab_size:15)
print(corpus.source) # LanguageData(examples: 6 - vocab_size: 13)
print(corpus.target) # LanguageData(examples: 6 - vocab_size: 15)

corpus.source.phrases_freq
# Counter({'how are you': 1, 'my name is joab': 1, 'i like coffee': 1, 'how are you joab': 1, 'how': 1, 'we are the world': 1})

corpus.source.word_freq
# Counter({'how': 3, 'are': 3, 'you': 2, 'joab': 2, 'my': 1, 'name': 1, 'is': 1, 'i': 1, 'like': 1, 'coffee': 1, 'we': 1, 'the': 1, 'world': 1})

corpus.target.phrases_freq
# Counter({'como você está': 1, 'meu nome é joab': 1, 'eu gosto de café': 1, 'como você está joab': 1, 'como': 1, 'nós somos o mundo': 1})

corpus.target.words_freq
# Counter({'como': 3, 'você': 2, 'está': 2, 'joab': 2, 'meu': 1, 'nome': 1, 'é': 1, 'eu': 1, 'gosto': 1, 'de': 1, 'café': 1, 'nós': 1, 'somos': 1, 'o': 1, 'mundo': 1})

# split_data function guarantees test data without data identical to training
# and only with vocabulary that exists in training
train, test = corpus.split_data() # default percent of training is 80%

Read from .csv

import cereja as cj

corpus = cj.Corpus.load_corpus_from_csv('path_to_file.csv', src_col_name='x_data', trg_col_name='y_data', source_name='en', target_name='pt')
# now you have a Corpus instance, have fun! (:

Progress

import cereja as cj
import time

def process_data(i: int):
    # simulates some processing 
    time.sleep(cj.rand_n()/max(abs(i), 1))

my_iterable = range(1, 500)
my_progress = cj.Progress("My Progress")

for i in my_progress(my_iterable):
    process_data(i)

Custom Display

import cereja as cj
import time

progress = cj.Progress("My Progress")
print(progress)

print(progress[0])
print(progress[1])
print(progress[2])

class MyCustomState(cj.StateBase):
    def display(self, current_value, max_value, *args, **kwargs):
        return f'{current_value} -> {max_value}'
    def done(self, *args, **kwargs):
        return f'FINISHED'

progress[0] = MyCustomState

for i in progress(range(1, 500)):
    time.sleep(1/i)

With statement

import cereja as cj
import time

with cj.Progress("My Progress") as prog:
    time.sleep(5)
    for i in prog(range(1, 500)):
        time.sleep(1/i)

Utils

import cereja as cj

# Arraytools
data = [[1,2,3],[3,3,3]]
cj.is_iterable(data) # True
cj.is_sequence(data) # True
cj.is_numeric_sequence(data) # True
cj.is_empty(data) # False
cj.get_shape(data) # (2, 3)

data = cj.flatten(data) # [1, 2, 3, 3, 3, 3]
cj.prod(data) # 162
cj.sub(data) # -13
cj.div(data) # 0.006172839506172839

cj.rand_n(0.0, 2.0, n=3) # [0.3001196087729699, 0.639679494102923, 1.060200897124107]
cj.rand_n(1,10) # 5.086403830031244
cj.array_randn((3, 3, 3)) # [[[0.015077210355770374, 0.014298110484612511, 0.030410666810216064], [0.029319083335697604, 0.0072365209507707666, 0.010677361074992], [0.010576754075922935, 0.04146379877648334, 0.02188348813336284]], [[0.0451851551098092, 0.037074906805326824, 0.0032484586475421007], [0.025633380630695347, 0.010312669541918484, 0.0373624007621097], [0.047923908102496145, 0.0027939333359724224, 0.05976224377251878]], [[0.046869510719106486, 0.008325638358172866, 0.0038702998343255893], [0.06475268683502387, 0.0035638592537234623, 0.06551037943638163], [0.043317416824708604, 0.06579372884523939, 0.2477564291871006]]]
cj.group_items_in_batches(items=[1,2,3,4], items_per_batch=3, fill=0) # [[1, 2, 3], [4, 0, 0]]
cj.remove_duplicate_items(['hi', 'hi', 'ih']) # ['hi', 'ih'] 
cj.get_cols([['line1_col1','line1_col2'],['line2_col1','line2_col2']]) # [['line1_col1', 'line2_col1'], ['line1_col2', 'line2_col2']]
cj.dotproduct([1,2], [1,2]) # 5


a = cj.array_gen((3,3), 1) # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
b = cj.array_gen((3,3), 1) # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
cj.dot(a, b) # [[3, 3, 3], [3, 3, 3], [3, 3, 3]]
cj.theta_angle((2,2), (0, -2)) # 135.0

See Usage - Jupyter Notebook

License

This project is licensed under the MIT License - see the LICENSE file for details