semvec/pypi-packages huggingface dataset:
json-diff-cli 1.0.2
To display json differences on CLI
License: MIT
Requires Python: >=3.8
Keywords: json, diff

Generates differences between two json files on the console in a colored and columnar manner.

## Motivation

To find difference between two long json files on the cli itself, instead of downloading files and copy pasting on some website to find the difference. Helpful when comparing production env data with the admin dashboard data during the debugging process.

## Installation

``pip install json-diff-cli``

## Quickstart

* if a key appears in first file but not in second file OR the key appears in second file but not in first file,
color of the key changes to red

* if a value of a specific key is different in both the files, 
color of that key-value pair changes to green:

Combination of above 2 example

[![Example](https://raw.githubusercontent.com/Shivani-20/json-diff-cli/main/visuals/jdif_cli.png)]

## Caveats

```python

>>> jdif
usage: jdif [-h] jsonFilePath1 jsonFilePath2
jdif: error: the following arguments are required: jsonFilePath1, jsonFilePath2
Arguments absent: please specify file paths as shown in the example below:
jdif example_1.json example_2.json

>>> jdif /../ /.../
Unable to read file in '\..', please verify the path and filename alongwith the extension

>>> jdif a1.json b1.json 
Invalid json in 'b1.json'

>>> jdif a1.json b1.json
Empty json in 'b1.json'

>>> jdif a1.json b1.json
JSON is same in both the files
```
**order** of the keys might change within an object because we are first processing values then modified keys:

suppose if j1.json has:
```json
{
    "key1": true,
    "key3": "abcd"
}
```
j2.json:
```json
{
    "key4": false,
    "key3": ""
}
```
``` python
>>> jdif j1.json j2.json
```

[![Example](https://raw.githubusercontent.com/Shivani-20/json-diff-cli/main/visuals/order.png)]


## Features

* Takes care of unicode, windows1252 characters, so the strings could be in any language or represent a symbol
* Takes care of long descriptive values or keys
* Takes care of empty valued strings
* Instead of dumping into some other file, it displays the differences on the console itself
iddn 0.2.4
Multi-omics dependency network analysis under two conditions
License: MIT
Requires Python: >=3.9

# iDDN

We developed an efficient and accurate multi-omics differential network analysis tool 
– integrative Differential Dependency Networks (iDDN).
iDDN is capable of jointly learning sparse common and rewired network structures, 
which is especially useful for genomics, proteomics, and other biomedical studies.
This repository provides the source code and examples of using iDDN.


![iDDN overview](https://github.com/cbil-vt/iDDN/blob/main/src/iddn_data/iddn_overview.png?raw=true)

## Installation
### Option 1: install from PyPI
iDDN can then be installed with the following command.
```bash
pip install iddn
```
<!-- ```bash
pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple iddn
``` -->

If you meet any issue, one way is to install iDDN into a new Conda environment. 
To create and activate an environment named `iddn`, run this:
```bash
conda create -n iddn python=3.11
conda activate iddn
```
Then run `pip install iddn`.

### Option 2: install the development version

Alternatively, you can clone the repository, or just download or unzip it. Then we can install DDN 3.0.
```bash
$ pip install ./
```
Or you may want to install it in development mode.
```bash
$ pip install -e ./
```

## Usage

This toy example applies iDDN on a synthetic data. More details can be found in the first tutorial.
```python
from iddn import tools  # Run iDDN algorithm and process the output
from iddn_data import load_data  # Load example data and images
example = load_data.load_example("example.npz")
dat1 = example["dat1"]
dat2 = example["dat2"]
dep_mat = example["dep_mat"]
result = tools.iddn_basic_pipeline(dat1, dat2, dep_mat, lambda1=0.15, lambda2=0.05)
```

For more details and examples, check the [documentation](https://iddn.readthedocs.io/en/latest/), 
which includes four tutorials and the API reference.
The tutorials can also be found in the `docs/notebooks` folder.

For details about the code to run simulations and make figures in the paper,
check out the [repository here](https://github.com/cbil-vt/iddn_experiments).

## Tests

To run tests, go to the folder of DDN3 source code, then run `pytest`.
```bash
pytest tests
```
It will compare output of DDN with reference values. It tests iDDN with two acceleration strategies.

## Contributing

Please report bugs in the issues or email Yizhi Wang (yzwang@vt.edu).
If you are interested in adding features or fixing bug, feel free to contact us.

## License

The `iddn` package is licensed under the terms of the MIT license.

## Citations

[1] Yizhi Wang, Yi Fu, et al. "iDDN: Determining trans-omics 
network structure and rewiring with integrative differential dependency networks".
ddn3 1.0.4
Dependency network analysis under two conditions
License: MIT
Requires Python: >=3.9

# DDN 3.0
We developed an efficient and accurate differential network analysis tool – Differential Dependency Networks (DDN).
DDN is capable of jointly learning sparse common and rewired network structures, which is especially useful for genomics, proteomics, and other biomedical studies.
DDN 3.0 significantly improves the speed of previous versions of DDN and is available as a Python package.
This repository provides the source code and examples of using DDN.

## Installation
### Option 1: install into a new Conda environment using pip
One way is to install DDN into a new Conda environment. To create and activate an environment named `ddn`, run this:
```bash
conda create -n ddn python=3.11
conda activate ddn
```
Python 3.12 may have some issues with Numba in Windows.

DDN 3.0 can then be installed with the following command.
```bash
pip install ddn3
```
<!-- ```bash
pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple ddn
``` -->

### Option 2: install into an existing Conda environment
If you want to install DDN into an existing Conda environment, it is suggested to install dependencies from Conda first.

First, we need to install some common dependencies.
```bash
$ conda install -c conda-forge numpy scipy numba networkx matplotlib jupyter scipy pandas scikit-learn
```

Then run
```bash
pip install ddn3
```

Alternatively, you can clone the repository (https://github.com/cbil-vt/DDN3), or just download and unzip it. Then we can install DDN 3.0.
```bash
$ pip install ./
```
Or you may want to install it in development mode.
```bash
$ pip install -e ./
```

## Usage

This toy example generates two random datasets and estimates two networks, one for each dataset.
```python
import numpy as np
from ddn import ddn
dat1 = np.random.randn(1000, 10)
dat2 = np.random.randn(1000, 10)
networks = ddn.ddn(dat1, dat2, lambda1=0.3, lambda2=0.1)
```

For more details and examples, check the [documentation](https://ddn-30.readthedocs.io/en/latest/), which includes three tutorials and the API reference.
The tutorials can also be found in the `docs/notebooks` folder.

## Tests

To run tests, go to the folder of DDN3 source code, then run `pytest`.
```bash
pytest iddn_data
```
It will compare the output of DDN with reference values. It tests DDN with various acceleration strategies.

## Experiments
The simulation-related code can be found at https://github.com/cbil-vt/DDN3_simulation.
The results of the simulation and the figures are archived here: https://zenodo.org/records/10543473.

## Contributing

Please report bugs in the issues. 
You may also email us directly: Yizhi Wang (yzwang@vt.edu), Yingzhou Lu (lyz66@vt.edu), or Yue Wang (yuewang@vt.edu).
If you are interested in adding features or fixing bugs, feel free to contact us.

## License

The `ddn3` package is licensed under the terms of the MIT license.

## Citations

[1] DDN3.0: Determining significant rewiring of biological network structure with differential dependency networks.