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@@ -0,0 +1 @@ +/cereja-1.8.4.tar.gz diff --git a/python-cereja.spec b/python-cereja.spec new file mode 100644 index 0000000..b409862 --- /dev/null +++ b/python-cereja.spec @@ -0,0 +1,1095 @@ +%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) + +<div align="center"> + <img src="https://i.ibb.co/Fw8SSfd/cereja-logo.png" height="300" width="300" alt="CEREJA"> +</div> + +*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 -> <class 'cereja.file._io.FileIO'> + +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) + +<div align="center"> + <img src="https://i.ibb.co/Fw8SSfd/cereja-logo.png" height="300" width="300" alt="CEREJA"> +</div> + +*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 -> <class 'cereja.file._io.FileIO'> + +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) + +<div align="center"> + <img src="https://i.ibb.co/Fw8SSfd/cereja-logo.png" height="300" width="300" alt="CEREJA"> +</div> + +*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 -> <class 'cereja.file._io.FileIO'> + +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 <Python_Bot@openeuler.org> - 1.8.4-1 +- Package Spec generated @@ -0,0 +1 @@ +610ced0e8c08e647d54958463e03d11a cereja-1.8.4.tar.gz |