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

Project description

Cereja 🍒

Python package PyPI version Downloads MIT LICENSE Issues Get start on Colab

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

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

See CONTRIBUTING 💻

Setup

Installing

Install Cereja Package

python3 -m pip install --user cereja

or for all users

python3 -m pip install cereja

Note: If you're using Windows, you don't need use python3, but make sure your python path settings are correct.

Cereja Example usage

See some of the Cereja tools

Filetools

Have a fun

from cereja import FileIO
# load File
file = FileIO.load('test.json') # return FileIO.json object (has interface for .txt, .csv, .json and more)
print(dir(file))

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.mathtools
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]
cereja.mathtools.prod(data) # 162
cereja.mathtools.sub(data) # -13
cereja.mathtools.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']]
cereja.mathtools.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]]
cereja.mathtools.dot(a, b) # [[3, 3, 3], [3, 3, 3], [3, 3, 3]]
cereja.mathtools.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

Project details


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