semvec/pypi-packages huggingface dataset:
strongmind-deployment-beta 1.1.159
Deployment tools for Strongmind
Requires Python: >=3.9

# StrongMind Deployment

## What?
Deployment scripts for StrongMind, using Pulumi.

## Development
Write pytest tests for functionality you need. When you need to use this with real AWS resources in a development environment, you'll need to follow these steps.

* Log your shell into the AWS account you're test deploying to (If you don't already have access keys set up, you can find how to do this in the [Amazon SSO portal](https://strongmind.awsapps.com/start#/), under "Programmatic Access")
* Find the relevant container image you're deploying [from ECR](https://us-west-2.console.aws.amazon.com/ecr/repositories?region=us-west-2). (Choose the relevant project container, then the image tag, and then copy the URI field).
* Find the Rails Master Key for that project. Usually this is kept in the [github actions secrets](https://github.com/StrongMind/frozen-desserts/settings/secrets/actions) for that project.
* Get the cloudflare API token and pulumi state passwords from bitwarden if you have access, or get this from devops otherwise.
* Use these to construct an environment in your preferred fashion with the following keys
  * CONTAINER_IMAGE
  * RAILS_MASTER_KEY
  * CLOUDFLARE_API_TOKEN
  * PULUMI_CONFIG_PASSPHRASE
* In the project that you are testing with, in the infrastructure directory, there will be a requirements.txt file. In order to use your development code, rather than the published version of this library, you will need to change the line that says "strongmind_deployment" to `-e /path/to/this/directory`, using the directory that this README is located in.
* Reinstall the requirements in your pulumi infrastructure directory. Usually this looks like 
```shell
source venv/bin/activate
pip install -r requirements.txt
```

You can now use pulumi commands like `pulumi preview` and `pulumi up` to make changes.

We usually use the [frozen-desserts](https://github.com/StrongMind/frozen-desserts) application to do simple tests of a non-production application.
TOPSIS-Sort-B 1.0.4
Topsis-Sort-B package
Keywords: python, topsis, topsis-sort-b

# Topsis Sort B - LIB PYPI

 The TOPSIS-Sort-B is an enhanced variation of the TOPSIS-Sort method, designed to address classification and sorting problems in multiple criteria decision-making. In this method, boundary profiles are employed to determine categorization classes and to sort alternatives based on the proximity of their proximity coefficients to the established profiles.

## Tecnologias usads
| Tecnologias | Versão  | Install                               |
|-------------|---------|---------------------------------------|
| Python      | 3.12.1  | `pip install python==3.12.1`          |
| Numpy       | 1.26.4  | `pip install numpy==1.26.4`           |
| Pandas      | 2.2.1   | `pip install pandas==2.2.1`           |


## Installtion
1. Install the required dependencies by running the following command: pip `pip install topsisSortLib`
## How to Run the Application
1. After installing the package, import the library.
2. from topsisSortLib import topsis_b_sort_profile_classification

## How to Use
1. To utilize the topsis_b_sort_profile_classification function, follow these steps:
2. Import pandas: Begin by importing the pandas library as pd.
  # How to Use

To utilize the `topsis_b_sort_profile_classification` function, follow these steps:

1. **Import pandas**: Begin by importing the pandas library.

    ```python
    import pandas as pd
    ```

2. **Load CSV File**: Load your CSV file into a pandas DataFrame using `pd.read_csv()`.

    ```python
    df = pd.read_csv('your_file.csv')
    ```

3. **Clean Data**: Clean the DataFrame by converting all non-numeric values to numeric using `pd.to_numeric()` and filling any missing values with zero.

    ```python
    df = df.apply(pd.to_numeric, errors='coerce').fillna(0)
    ```

4. **Call Function**: Pass the cleaned DataFrame along with other necessary arguments into the `topsis_b_sort_profile_classification` function.

    ```python
    result = topsis_b_sort_profile_classification(
        decision_matrix=df,
        domain_matrix=your_domain_matrix,
        dominant_profiles=your_dominant_profiles,
        weights=your_weights
    )
    ```

Make sure to replace `your_file.csv`, `your_domain_matrix`, `your_dominant_profiles`, and `your_weights` with the appropriate variables or data structures.

  `
## References

- Silva, D. F. L., & Filho, A. T. A. (2020). Sorting with TOPSIS through boundary and characteristic profiles. Journal Name, Volume(1), 141.
- GeeksforGeeks.TOPSIS method for Multiple-Criteria Decision Making (MCDM). Retrieved from [[URL](https://www.geeksforgeeks.org/topsis-method-for-multiple-criteria-decision-making-mcdm/)]

## Deploy
- Aplicação
- library [[URL](https://pypi.org/project/topsisSortLib/)]
aero-data-ai 0.0.1
The official project path management library published by team Aero Data AI @InvoLead.
Requires Python: >=3

Hey!
Team Aero Data AI this side,
The sole purpose of this library is to help you increase the shareability of your codebase. It simplifies managing the root path of your project directory across all your Python files, allowing you to effortlessly access files without using absolute path of your system in project's code files on your system. Below are the some functions you can use:

> set_project_path(project_name:str, project_directory_path:str)
Use this function to set the path of your project directory.
    The function "get_project_path" can be used later to import the complete path of your project directory in any file.
    Args: 
    project_name : Name of your project.
    project_directory_path : Path to your project's root directory

    Returns: Nothing to return ehh!

> get_project_path(project_name:str)
Fetch the path of a project.
    Args:
    - project_name (str): Name of the project.
    Returns:
    - str or None: Path of the project if found, None otherwise.

> remove_project_path(project_name:str)
Removes the given project_name and project_path
    Args: 
    project_name : Name of your project that you wish to remove.
    Returns: Nothing to return ehh!

> show_project_paths()
Shows the list of all the projects and projects paths added till date.
CrypticPhenoImpute 0.1.6
Imputes cryptic phenotypes analyzed in Blair et al. into arbitrary clinical datasets.

# CrypticPhenoImpute

This software imputes the cryptic phenotypes inferred in Blair et al. 2020 into arbitrary ICD10-CM/ICD10-UKBB datasets. It is for replication purposes only, and it will not perform de novo model inference. Note, model files are not distributed with the package and must be downloaded on the fly from GitHub. Therefore, imputation may take several minutes as files are downloaded prior to utilization.

# Installation

pip install CrypticPhenoImpute

# Use

This software is intended to be used from the command line. Please type 'python -m CrypticPhenoImpute -h' into the command line following installation for instructions.