semvec/pypi-packages huggingface dataset:
bio-check 1.4.0
Simulation Verification Service and API
Requires Python: >=3.9

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/19uxh93pZvhCGXkC7a15SmAx4oH4MV7OJ#scrollTo=j_mN-EE3vanZ)
![GitHub CI](https://github.com/biosimulators/bio-check/actions/workflows/ci.yaml/badge.svg)
![GitHub CD](https://github.com/biosimulators/bio-check/actions/workflows/cd.yaml/badge.svg)
# BioCheck: A Biological Simulation Verification Service
#### __This service utilizes separate containers for REST API management, job processing, and datastorage with MongoDB, ensuring scalable and robust performance.__

## Getting Started:

### **HIGH-LEVEL `bio_check` API:**

The primary method of user-facing interaction for this service is done through the use of a high-level "notebook" api called `bio_check`. 
A convenient notebook demonstrating the functionality of this service is hosted on Google Colab and can be accessed by clicking the above "Open In Colab" badge.

[View the template notebook as an image](demos/verification_api_demo.md)


Installation of this tooling can be performed using PyPI as such:

```bash
pip install bio-check
```

**PLEASE NOTE: You must have `>=python3.9` in order to use the high-level api.**

### **FOR DEVELOPERS:**

This application (`bio_check`) uses a microservices architecture which presents the following libraries:

- `api`: This library handles all requests including saving uploaded files, pending job creation, fetching results, and contains the user-facing endpoints.
- `storage`: This library handles MongoDB configs as well as bucket-like storages for uploaded files.
- `worker`: This library handles all job processing tasks for verification services such as job status adjustment, job retrieval, and comparison execution.

The installation process is outlined as follows:

1. `git clone https://github.com/biosimulators/bio-check.git`
2. `cd bio-check/bio_check`
3. `touch .env`
4. Enter the following fields into the `.env` file: 
        
        MONGO_URI=<uri of your mongo instance. In this case we use the standard mongodb image with the app name bio-check>
        GOOGLE_APPLICATION_CREDENTIALS=<path to your gcloud credentials .json file. Contact us for access>
        BUCKET_NAME=bio-check-requests-1  # name of the bucket used in this app

5. **Ensure that your IP address has been authorized in the `bio-check` cluster within Mongo Atlas.**


### Notes:
- This application currently uses MongoDB as the database store in which jobs are read/written. Database access is given to both the `api` and `worker` libraries. Such database access is 
executed/implemented with the use of a `Supervisor` singleton.


### **Note (05/22/2024):**
The only package source that is currently supported by this tooling is `PyPI`. The support of other potential 
package sources such as `conda`, `brew`, `apt`, and more is currently under development.
pytkas 1.0.0
PYthon ToolKits and AutomationS (PYTKAS) library containing my classes & UDFs I use during various Data Science and Software Engineering projects
Requires Python: >=3.8

# pytka
My own **PY**thon **T**ool**K**its and **A**utomations library  built over time with the purpose to reuse anytime

💡**HINT**: The package contains `pytka_demo.ipynb` file
that aims to showcase all of the available functions. 
It's recommended to use it within **Google Colab** since it
provides better Markdown support and allows for easier navigation

# 📋 All available functions & classes 

#### *Use doctrings to understand what the function/class is about and how to use it*

## 📋 dataframes
1. 🌡️ optimize_dataframe()
2. 🚮 remove_dataframes()
3. 😀 dummify_dataframe()
4. 🗑️ remove_column_if_present()
5. 📰 text_input_to_numericals() 
6. 🧮 df_memory_usage()

## 💡 eda
1. AutoEDA() class

## 🎁 features

1. ☀️ extend_features_with_similarities_and_distances(),
2. 📐 calculate_cosine_similarity(), 
3. 📏 calculate_distances()
4. ⚖️ imbalanced_resampling()
5. 🪱 filter_outliers()
6. 🚄 quick_pca()

## 🏆 kaggle

1. 🔗 create_download_link()
2. ✅ validate_kaggle_submission() 

## ⏱️ logging

1. 🖥️ list_devices()
2. 🤖 mlflow_experiment()
3. 🦶 step_time_calculation()

## 🦍 modelling

1. 🏹 UltimateClassifier() class
2. 😾 train_catboost()

## 🧠 neural_nets

1. 🪠 sparse_softmax()

## 🛠️ nlp

1. 🔠 string_to_lowercase_word_list()
2. 👅 calculate_english_word_ratio()
3. ⛓️‍💥 avg_word_length()
4. ⛓️ avg_sentence_word_length() 
5. 🟦 tfidf_vectorize_sequences()
6. 👻 describe_tfidf_vectorizer()


## 🐟 optuna

1. 🦇 optuna_parallel_coordinates()

## 🚜 others

1. 🎁 create_zip()

## 🎨 plots

1. 🛜 make_html_filename()
2. 📊 calculate_boxplot_stats()

# ⏭️ Upcoming

1. Unittests 
2. CircleCI pipelines