%global _empty_manifest_terminate_build 0
Name: python-cereja
Version: 1.8.4
Release: 1
Summary: Cereja is a bundle of useful functions that I don't want to rewrite.
License: Copyright (c) 2019 The Cereja Project 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.
URL: https://github.com/cereja-project/cereja
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/27/f3/5f875370bc2b13cad7241fe66ca26e0d4956e4155354eeb8616a305c6fa7/cereja-1.8.4.tar.gz
BuildArch: noarch
%description
# Cereja 🍒
[](https://badge.fury.io/py/cereja)
[](https://pepy.tech/project/cereja)
[](LICENSE)
[](https://github.com/jlsneto/cereja/issues/new/choose)
[](https://colab.research.google.com/github/jlsneto/cereja/blob/master/docs/cereja_example.ipynb)
*Cereja was written only with the Standard Python Library, and it was a great way to improve knowledge in the Language
also to avoid the rewriting of code.*
## 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](CONTRIBUTING.md) 💻
## Setup
* [Python 3.6+](https://www.python.org/downloads/ "Download python")
* [Pip3](https://pip.pypa.io "Download Pip")
## Install
```
pip install --user cereja
```
or for all users
```
pip install cereja
```
## Cereja Example usage
See some of the Cereja tools
To access the *Cereja's* tools you need to import it `import cereja as cj`.
### 📝 [FileIO](docs/file.md)
#### Create new files
```python
import cereja as cj
file_json = cj.FileIO.create('./json_new_file.json', data={'k': 'v', 'k2': 'v2'})
file_txt = cj.FileIO.create('./txt_new_file.txt', ['line1', 'line2', 'line3'])
file_json.save()
file_txt.save()
print(file_json.exists)
# True
print(file_txt.exists)
# True
# see what you can do .txt file
print(cj.can_do(file_txt))
# see what you can do .json file
print(cj.can_do(file_json))
```
#### Load and edit files
```python
import cereja as cj
file_json = cj.FileIO.load('./json_new_file.json')
print(file_json.data)
# {'k': 'v', 'k2': 'v2'}
file_json.add(key='new_key', value='value')
print(file_json.data)
# {'k': 'v', 'k2': 'v2', 'new_key': 'value'}
file_txt = cj.FileIO.load('./txt_new_file.txt')
print(file_txt.data)
# ['line1', 'line2', 'line3']
file_txt.add('line4')
print(file_txt.data)
# ['line1', 'line2', 'line3', 'line4']
file_txt.save(exist_ok=True) # Override
file_json.save(exist_ok=True) # Override
```
### 📍 Path
```python
import cereja as cj
file_path = cj.Path('/my/path/file.ext')
print(cj.can_do(file_path))
# ['change_current_dir', 'cp', 'created_at', 'exists', 'get_current_dir', 'is_dir', 'is_file', 'is_hidden', 'is_link', 'join', 'last_access', 'list_dir', 'list_files', 'mv', 'name', 'parent', 'parent_name', 'parts', 'path', 'rm', 'root', 'rsplit', 'sep', 'split', 'stem', 'suffix', 'updated_at', 'uri']
```
### 🆗 HTTP Requests
```python
import cereja as cj
# Change url, headers and data values.
url = 'localhost:8000/example'
headers = {'Authorization': 'TOKEN'} # optional
data = {'q': 'test'} # optional
response = cj.request.post(url, data=data, headers=headers)
if response.code == 200:
data = response.data
# have a fun!
```
### ⏳ [Progress](docs/display.md)
```python
import cereja as cj
import time
my_iterable = ['Cereja', 'is', 'very', 'easy']
for i in cj.Progress.prog(my_iterable):
print(f"current: {i}")
time.sleep(2)
# Output on terminal ...
# 🍒 Sys[out] » current: Cereja
# 🍒 Sys[out] » current: is
# 🍒 Cereja Progress » [▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▱▱▱▱▱▱▱▱▱▱▱▱▱▱] - 50.00% - 🕢 00:00:02 estimated
```
### 🧠 [Data Preparation](docs/ml.md)
📊 **Freq**
```python
import cereja as cj
freq = cj.Freq([1, 2, 3, 3, 10, 10, 4, 4, 4, 4])
# Output -> Freq({1: 1, 2: 1, 3: 2, 10: 2, 4: 4})
freq.most_common(2)
# Output -> {4: 4, 3: 2}
freq.least_freq(2)
# Output -> {2: 1, 1: 1}
freq.probability
# Output -> OrderedDict([(4, 0.4), (3, 0.2), (10, 0.2), (1, 0.1), (2, 0.1)])
freq.sample(min_freq=1, max_freq=2)
# Output -> {3: 2, 10: 2, 1: 1, 2: 1}
# Save json file.
freq.to_json('./freq.json')
```
🧹 **Text Preprocess**
```python
import cereja as cj
text = "Oi tudo bem?? meu nome é joab!"
text = cj.preprocess.remove_extra_chars(text)
print(text)
# Output -> 'Oi tudo bem? meu nome é joab!'
text = cj.preprocess.separate(text, sep=['?', '!'])
# Output -> 'Oi tudo bem ? meu nome é joab !'
text = cj.preprocess.accent_remove(text)
# Output -> 'Oi tudo bem ? meu nome e joab !'
# and more ..
# You can use class Preprocessor ...
preprocessor = cj.Preprocessor(stop_words=(),
punctuation='!?,.', to_lower=True, is_remove_punctuation=False,
is_remove_stop_words=False,
is_remove_accent=True)
print(preprocessor.preprocess(text))
# Output -> 'oi tudo bem ? meu nome e joab !'
print(preprocessor.preprocess(text, is_destructive=True))
# Output -> 'oi tudo bem meu nome e joab'
```
🔣 **Tokenizer**
```python
import cereja as cj
text = ['oi tudo bem meu nome é joab']
tokenizer = cj.Tokenizer(text, use_unk=True)
# tokens 0 to 9 is UNK
# hash_ used to replace UNK
token_sequence, hash_ = tokenizer.encode('meu nome é Neymar Júnior')
# Output -> [([10, 12, 11, 0, 1], 'eeb755960ce70c')]
decoded_sequence = tokenizer.decode(token_sequence, hash_=hash_)
# Output -> 'meu nome é Neymar Júnior'
```
⏸ **Corpus**
Great training and test separator.
```python
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%
```
### 🔢 Array
```python
import cereja as cj
cj.array.is_empty(data) # False
cj.array.get_shape(data) # (2, 3)
data = cj.array.flatten(data) # [1, 2, 3, 3, 3, 3]
cj.array.prod(data) # 162
cj.array.sub(data) # -13
cj.array.div(data) # 0.006172839506172839
cj.array.rand_n(0.0, 2.0, n=3) # [0.3001196087729699, 0.639679494102923, 1.060200897124107]
cj.array.rand_n(1, 10) # 5.086403830031244
cj.array.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.array.group_items_in_batches(items=[1, 2, 3, 4], items_per_batch=3, fill=0) # [[1, 2, 3], [4, 0, 0]]
cj.array.remove_duplicate_items(['hi', 'hi', 'ih']) # ['hi', 'ih']
cj.array.get_cols([['line1_col1', 'line1_col2'],
['line2_col1', 'line2_col2']]) # [['line1_col1', 'line2_col1'], ['line1_col2', 'line2_col2']]
cj.array.dotproduct([1, 2], [1, 2]) # 5
a = cj.array.array_gen((3, 3), 1) # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
b = cj.array.array_gen((3, 3), 1) # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
cj.array.dot(a, b) # [[3, 3, 3], [3, 3, 3], [3, 3, 3]]
cj.mathtools.theta_angle((2, 2), (0, -2)) # 135.0
```
### 🧰 Utils
```python
import cereja as cj
data = {"key1": 'value1', "key2": 'value2', "key3": 'value3', "key4": 'value4'}
cj.utils.chunk(list(range(10)), batch_size=3)
# [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
cj.utils.chunk(list(range(10)), batch_size=3, fill_with=0, is_random=True)
# [[9, 7, 8], [0, 3, 2], [4, 1, 5], [6, 0, 0]]
# Invert Dict
cj.utils.invert_dict(data)
# Output -> {'value1': 'key1', 'value2': 'key2', 'value3': 'key3', 'value4': 'key4'}
# Get sample of large data
cj.utils.sample(data, k=2, is_random=True)
# Output -> {'key1': 'value1', 'key4': 'value4'}
cj.utils.fill([1, 2, 3, 4], max_size=20, with_=0)
# Output -> [1, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
cj.utils.rescale_values([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], granularity=4)
# Output -> [1, 3, 5, 7]
cj.utils.import_string('cereja.file._io.FileIO')
# Output ->
cj.utils.list_methods(cj.Path)
# Output -> ['change_current_dir', 'cp', 'get_current_dir', 'join', 'list_dir', 'list_files', 'mv', 'rm', 'rsplit', 'split']
cj.utils.string_to_literal('[1,2,3,4]')
# Output -> [1, 2, 3, 4]
cj.utils.time_format(3600)
# Output -> '01:00:00'
cj.utils.truncate("Cereja is fun.", k=3)
# Output -> 'Cer...'
data = [[1, 2, 3], [3, 3, 3]]
cj.utils.is_iterable(data) # True
cj.utils.is_sequence(data) # True
cj.utils.is_numeric_sequence(data) # True
```
[See Usage - Jupyter Notebook](./docs/cereja_example.ipynb)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
%package -n python3-cereja
Summary: Cereja is a bundle of useful functions that I don't want to rewrite.
Provides: python-cereja
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-cereja
# Cereja 🍒
[](https://badge.fury.io/py/cereja)
[](https://pepy.tech/project/cereja)
[](LICENSE)
[](https://github.com/jlsneto/cereja/issues/new/choose)
[](https://colab.research.google.com/github/jlsneto/cereja/blob/master/docs/cereja_example.ipynb)
*Cereja was written only with the Standard Python Library, and it was a great way to improve knowledge in the Language
also to avoid the rewriting of code.*
## 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](CONTRIBUTING.md) 💻
## Setup
* [Python 3.6+](https://www.python.org/downloads/ "Download python")
* [Pip3](https://pip.pypa.io "Download Pip")
## Install
```
pip install --user cereja
```
or for all users
```
pip install cereja
```
## Cereja Example usage
See some of the Cereja tools
To access the *Cereja's* tools you need to import it `import cereja as cj`.
### 📝 [FileIO](docs/file.md)
#### Create new files
```python
import cereja as cj
file_json = cj.FileIO.create('./json_new_file.json', data={'k': 'v', 'k2': 'v2'})
file_txt = cj.FileIO.create('./txt_new_file.txt', ['line1', 'line2', 'line3'])
file_json.save()
file_txt.save()
print(file_json.exists)
# True
print(file_txt.exists)
# True
# see what you can do .txt file
print(cj.can_do(file_txt))
# see what you can do .json file
print(cj.can_do(file_json))
```
#### Load and edit files
```python
import cereja as cj
file_json = cj.FileIO.load('./json_new_file.json')
print(file_json.data)
# {'k': 'v', 'k2': 'v2'}
file_json.add(key='new_key', value='value')
print(file_json.data)
# {'k': 'v', 'k2': 'v2', 'new_key': 'value'}
file_txt = cj.FileIO.load('./txt_new_file.txt')
print(file_txt.data)
# ['line1', 'line2', 'line3']
file_txt.add('line4')
print(file_txt.data)
# ['line1', 'line2', 'line3', 'line4']
file_txt.save(exist_ok=True) # Override
file_json.save(exist_ok=True) # Override
```
### 📍 Path
```python
import cereja as cj
file_path = cj.Path('/my/path/file.ext')
print(cj.can_do(file_path))
# ['change_current_dir', 'cp', 'created_at', 'exists', 'get_current_dir', 'is_dir', 'is_file', 'is_hidden', 'is_link', 'join', 'last_access', 'list_dir', 'list_files', 'mv', 'name', 'parent', 'parent_name', 'parts', 'path', 'rm', 'root', 'rsplit', 'sep', 'split', 'stem', 'suffix', 'updated_at', 'uri']
```
### 🆗 HTTP Requests
```python
import cereja as cj
# Change url, headers and data values.
url = 'localhost:8000/example'
headers = {'Authorization': 'TOKEN'} # optional
data = {'q': 'test'} # optional
response = cj.request.post(url, data=data, headers=headers)
if response.code == 200:
data = response.data
# have a fun!
```
### ⏳ [Progress](docs/display.md)
```python
import cereja as cj
import time
my_iterable = ['Cereja', 'is', 'very', 'easy']
for i in cj.Progress.prog(my_iterable):
print(f"current: {i}")
time.sleep(2)
# Output on terminal ...
# 🍒 Sys[out] » current: Cereja
# 🍒 Sys[out] » current: is
# 🍒 Cereja Progress » [▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▱▱▱▱▱▱▱▱▱▱▱▱▱▱] - 50.00% - 🕢 00:00:02 estimated
```
### 🧠 [Data Preparation](docs/ml.md)
📊 **Freq**
```python
import cereja as cj
freq = cj.Freq([1, 2, 3, 3, 10, 10, 4, 4, 4, 4])
# Output -> Freq({1: 1, 2: 1, 3: 2, 10: 2, 4: 4})
freq.most_common(2)
# Output -> {4: 4, 3: 2}
freq.least_freq(2)
# Output -> {2: 1, 1: 1}
freq.probability
# Output -> OrderedDict([(4, 0.4), (3, 0.2), (10, 0.2), (1, 0.1), (2, 0.1)])
freq.sample(min_freq=1, max_freq=2)
# Output -> {3: 2, 10: 2, 1: 1, 2: 1}
# Save json file.
freq.to_json('./freq.json')
```
🧹 **Text Preprocess**
```python
import cereja as cj
text = "Oi tudo bem?? meu nome é joab!"
text = cj.preprocess.remove_extra_chars(text)
print(text)
# Output -> 'Oi tudo bem? meu nome é joab!'
text = cj.preprocess.separate(text, sep=['?', '!'])
# Output -> 'Oi tudo bem ? meu nome é joab !'
text = cj.preprocess.accent_remove(text)
# Output -> 'Oi tudo bem ? meu nome e joab !'
# and more ..
# You can use class Preprocessor ...
preprocessor = cj.Preprocessor(stop_words=(),
punctuation='!?,.', to_lower=True, is_remove_punctuation=False,
is_remove_stop_words=False,
is_remove_accent=True)
print(preprocessor.preprocess(text))
# Output -> 'oi tudo bem ? meu nome e joab !'
print(preprocessor.preprocess(text, is_destructive=True))
# Output -> 'oi tudo bem meu nome e joab'
```
🔣 **Tokenizer**
```python
import cereja as cj
text = ['oi tudo bem meu nome é joab']
tokenizer = cj.Tokenizer(text, use_unk=True)
# tokens 0 to 9 is UNK
# hash_ used to replace UNK
token_sequence, hash_ = tokenizer.encode('meu nome é Neymar Júnior')
# Output -> [([10, 12, 11, 0, 1], 'eeb755960ce70c')]
decoded_sequence = tokenizer.decode(token_sequence, hash_=hash_)
# Output -> 'meu nome é Neymar Júnior'
```
⏸ **Corpus**
Great training and test separator.
```python
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%
```
### 🔢 Array
```python
import cereja as cj
cj.array.is_empty(data) # False
cj.array.get_shape(data) # (2, 3)
data = cj.array.flatten(data) # [1, 2, 3, 3, 3, 3]
cj.array.prod(data) # 162
cj.array.sub(data) # -13
cj.array.div(data) # 0.006172839506172839
cj.array.rand_n(0.0, 2.0, n=3) # [0.3001196087729699, 0.639679494102923, 1.060200897124107]
cj.array.rand_n(1, 10) # 5.086403830031244
cj.array.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.array.group_items_in_batches(items=[1, 2, 3, 4], items_per_batch=3, fill=0) # [[1, 2, 3], [4, 0, 0]]
cj.array.remove_duplicate_items(['hi', 'hi', 'ih']) # ['hi', 'ih']
cj.array.get_cols([['line1_col1', 'line1_col2'],
['line2_col1', 'line2_col2']]) # [['line1_col1', 'line2_col1'], ['line1_col2', 'line2_col2']]
cj.array.dotproduct([1, 2], [1, 2]) # 5
a = cj.array.array_gen((3, 3), 1) # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
b = cj.array.array_gen((3, 3), 1) # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
cj.array.dot(a, b) # [[3, 3, 3], [3, 3, 3], [3, 3, 3]]
cj.mathtools.theta_angle((2, 2), (0, -2)) # 135.0
```
### 🧰 Utils
```python
import cereja as cj
data = {"key1": 'value1', "key2": 'value2', "key3": 'value3', "key4": 'value4'}
cj.utils.chunk(list(range(10)), batch_size=3)
# [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
cj.utils.chunk(list(range(10)), batch_size=3, fill_with=0, is_random=True)
# [[9, 7, 8], [0, 3, 2], [4, 1, 5], [6, 0, 0]]
# Invert Dict
cj.utils.invert_dict(data)
# Output -> {'value1': 'key1', 'value2': 'key2', 'value3': 'key3', 'value4': 'key4'}
# Get sample of large data
cj.utils.sample(data, k=2, is_random=True)
# Output -> {'key1': 'value1', 'key4': 'value4'}
cj.utils.fill([1, 2, 3, 4], max_size=20, with_=0)
# Output -> [1, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
cj.utils.rescale_values([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], granularity=4)
# Output -> [1, 3, 5, 7]
cj.utils.import_string('cereja.file._io.FileIO')
# Output ->
cj.utils.list_methods(cj.Path)
# Output -> ['change_current_dir', 'cp', 'get_current_dir', 'join', 'list_dir', 'list_files', 'mv', 'rm', 'rsplit', 'split']
cj.utils.string_to_literal('[1,2,3,4]')
# Output -> [1, 2, 3, 4]
cj.utils.time_format(3600)
# Output -> '01:00:00'
cj.utils.truncate("Cereja is fun.", k=3)
# Output -> 'Cer...'
data = [[1, 2, 3], [3, 3, 3]]
cj.utils.is_iterable(data) # True
cj.utils.is_sequence(data) # True
cj.utils.is_numeric_sequence(data) # True
```
[See Usage - Jupyter Notebook](./docs/cereja_example.ipynb)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
%package help
Summary: Development documents and examples for cereja
Provides: python3-cereja-doc
%description help
# Cereja 🍒
[](https://badge.fury.io/py/cereja)
[](https://pepy.tech/project/cereja)
[](LICENSE)
[](https://github.com/jlsneto/cereja/issues/new/choose)
[](https://colab.research.google.com/github/jlsneto/cereja/blob/master/docs/cereja_example.ipynb)
*Cereja was written only with the Standard Python Library, and it was a great way to improve knowledge in the Language
also to avoid the rewriting of code.*
## 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](CONTRIBUTING.md) 💻
## Setup
* [Python 3.6+](https://www.python.org/downloads/ "Download python")
* [Pip3](https://pip.pypa.io "Download Pip")
## Install
```
pip install --user cereja
```
or for all users
```
pip install cereja
```
## Cereja Example usage
See some of the Cereja tools
To access the *Cereja's* tools you need to import it `import cereja as cj`.
### 📝 [FileIO](docs/file.md)
#### Create new files
```python
import cereja as cj
file_json = cj.FileIO.create('./json_new_file.json', data={'k': 'v', 'k2': 'v2'})
file_txt = cj.FileIO.create('./txt_new_file.txt', ['line1', 'line2', 'line3'])
file_json.save()
file_txt.save()
print(file_json.exists)
# True
print(file_txt.exists)
# True
# see what you can do .txt file
print(cj.can_do(file_txt))
# see what you can do .json file
print(cj.can_do(file_json))
```
#### Load and edit files
```python
import cereja as cj
file_json = cj.FileIO.load('./json_new_file.json')
print(file_json.data)
# {'k': 'v', 'k2': 'v2'}
file_json.add(key='new_key', value='value')
print(file_json.data)
# {'k': 'v', 'k2': 'v2', 'new_key': 'value'}
file_txt = cj.FileIO.load('./txt_new_file.txt')
print(file_txt.data)
# ['line1', 'line2', 'line3']
file_txt.add('line4')
print(file_txt.data)
# ['line1', 'line2', 'line3', 'line4']
file_txt.save(exist_ok=True) # Override
file_json.save(exist_ok=True) # Override
```
### 📍 Path
```python
import cereja as cj
file_path = cj.Path('/my/path/file.ext')
print(cj.can_do(file_path))
# ['change_current_dir', 'cp', 'created_at', 'exists', 'get_current_dir', 'is_dir', 'is_file', 'is_hidden', 'is_link', 'join', 'last_access', 'list_dir', 'list_files', 'mv', 'name', 'parent', 'parent_name', 'parts', 'path', 'rm', 'root', 'rsplit', 'sep', 'split', 'stem', 'suffix', 'updated_at', 'uri']
```
### 🆗 HTTP Requests
```python
import cereja as cj
# Change url, headers and data values.
url = 'localhost:8000/example'
headers = {'Authorization': 'TOKEN'} # optional
data = {'q': 'test'} # optional
response = cj.request.post(url, data=data, headers=headers)
if response.code == 200:
data = response.data
# have a fun!
```
### ⏳ [Progress](docs/display.md)
```python
import cereja as cj
import time
my_iterable = ['Cereja', 'is', 'very', 'easy']
for i in cj.Progress.prog(my_iterable):
print(f"current: {i}")
time.sleep(2)
# Output on terminal ...
# 🍒 Sys[out] » current: Cereja
# 🍒 Sys[out] » current: is
# 🍒 Cereja Progress » [▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▱▱▱▱▱▱▱▱▱▱▱▱▱▱] - 50.00% - 🕢 00:00:02 estimated
```
### 🧠 [Data Preparation](docs/ml.md)
📊 **Freq**
```python
import cereja as cj
freq = cj.Freq([1, 2, 3, 3, 10, 10, 4, 4, 4, 4])
# Output -> Freq({1: 1, 2: 1, 3: 2, 10: 2, 4: 4})
freq.most_common(2)
# Output -> {4: 4, 3: 2}
freq.least_freq(2)
# Output -> {2: 1, 1: 1}
freq.probability
# Output -> OrderedDict([(4, 0.4), (3, 0.2), (10, 0.2), (1, 0.1), (2, 0.1)])
freq.sample(min_freq=1, max_freq=2)
# Output -> {3: 2, 10: 2, 1: 1, 2: 1}
# Save json file.
freq.to_json('./freq.json')
```
🧹 **Text Preprocess**
```python
import cereja as cj
text = "Oi tudo bem?? meu nome é joab!"
text = cj.preprocess.remove_extra_chars(text)
print(text)
# Output -> 'Oi tudo bem? meu nome é joab!'
text = cj.preprocess.separate(text, sep=['?', '!'])
# Output -> 'Oi tudo bem ? meu nome é joab !'
text = cj.preprocess.accent_remove(text)
# Output -> 'Oi tudo bem ? meu nome e joab !'
# and more ..
# You can use class Preprocessor ...
preprocessor = cj.Preprocessor(stop_words=(),
punctuation='!?,.', to_lower=True, is_remove_punctuation=False,
is_remove_stop_words=False,
is_remove_accent=True)
print(preprocessor.preprocess(text))
# Output -> 'oi tudo bem ? meu nome e joab !'
print(preprocessor.preprocess(text, is_destructive=True))
# Output -> 'oi tudo bem meu nome e joab'
```
🔣 **Tokenizer**
```python
import cereja as cj
text = ['oi tudo bem meu nome é joab']
tokenizer = cj.Tokenizer(text, use_unk=True)
# tokens 0 to 9 is UNK
# hash_ used to replace UNK
token_sequence, hash_ = tokenizer.encode('meu nome é Neymar Júnior')
# Output -> [([10, 12, 11, 0, 1], 'eeb755960ce70c')]
decoded_sequence = tokenizer.decode(token_sequence, hash_=hash_)
# Output -> 'meu nome é Neymar Júnior'
```
⏸ **Corpus**
Great training and test separator.
```python
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%
```
### 🔢 Array
```python
import cereja as cj
cj.array.is_empty(data) # False
cj.array.get_shape(data) # (2, 3)
data = cj.array.flatten(data) # [1, 2, 3, 3, 3, 3]
cj.array.prod(data) # 162
cj.array.sub(data) # -13
cj.array.div(data) # 0.006172839506172839
cj.array.rand_n(0.0, 2.0, n=3) # [0.3001196087729699, 0.639679494102923, 1.060200897124107]
cj.array.rand_n(1, 10) # 5.086403830031244
cj.array.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.array.group_items_in_batches(items=[1, 2, 3, 4], items_per_batch=3, fill=0) # [[1, 2, 3], [4, 0, 0]]
cj.array.remove_duplicate_items(['hi', 'hi', 'ih']) # ['hi', 'ih']
cj.array.get_cols([['line1_col1', 'line1_col2'],
['line2_col1', 'line2_col2']]) # [['line1_col1', 'line2_col1'], ['line1_col2', 'line2_col2']]
cj.array.dotproduct([1, 2], [1, 2]) # 5
a = cj.array.array_gen((3, 3), 1) # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
b = cj.array.array_gen((3, 3), 1) # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
cj.array.dot(a, b) # [[3, 3, 3], [3, 3, 3], [3, 3, 3]]
cj.mathtools.theta_angle((2, 2), (0, -2)) # 135.0
```
### 🧰 Utils
```python
import cereja as cj
data = {"key1": 'value1', "key2": 'value2', "key3": 'value3', "key4": 'value4'}
cj.utils.chunk(list(range(10)), batch_size=3)
# [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
cj.utils.chunk(list(range(10)), batch_size=3, fill_with=0, is_random=True)
# [[9, 7, 8], [0, 3, 2], [4, 1, 5], [6, 0, 0]]
# Invert Dict
cj.utils.invert_dict(data)
# Output -> {'value1': 'key1', 'value2': 'key2', 'value3': 'key3', 'value4': 'key4'}
# Get sample of large data
cj.utils.sample(data, k=2, is_random=True)
# Output -> {'key1': 'value1', 'key4': 'value4'}
cj.utils.fill([1, 2, 3, 4], max_size=20, with_=0)
# Output -> [1, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
cj.utils.rescale_values([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], granularity=4)
# Output -> [1, 3, 5, 7]
cj.utils.import_string('cereja.file._io.FileIO')
# Output ->
cj.utils.list_methods(cj.Path)
# Output -> ['change_current_dir', 'cp', 'get_current_dir', 'join', 'list_dir', 'list_files', 'mv', 'rm', 'rsplit', 'split']
cj.utils.string_to_literal('[1,2,3,4]')
# Output -> [1, 2, 3, 4]
cj.utils.time_format(3600)
# Output -> '01:00:00'
cj.utils.truncate("Cereja is fun.", k=3)
# Output -> 'Cer...'
data = [[1, 2, 3], [3, 3, 3]]
cj.utils.is_iterable(data) # True
cj.utils.is_sequence(data) # True
cj.utils.is_numeric_sequence(data) # True
```
[See Usage - Jupyter Notebook](./docs/cereja_example.ipynb)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
%prep
%autosetup -n cereja-1.8.4
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-cereja -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 1.8.4-1
- Package Spec generated