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 !!!
Don't be shy \0/ ... Clone the repository and submit a function or module you made or use some function you liked.
See CONTRIBUTING 💻
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.
See some of the Cereja tools
Filetools has several functions, have the converters for end of line (CRLF, LF and CR), you can convert CRLF to LF or CR, or contrariwise.
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()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()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 attrimport 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)python3 -m pip install cereja
python3 -m cereja --crlf_to_lf "/dir_or_file_path"Great training and test separator.
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%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! (: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)
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)
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)
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)) # 135.0This project is licensed under the MIT License - see the LICENSE file for details