# aprint
A Python print package for those who don't care the actual elements of array-like data (e.g. `numpy.array`, `torch.tensor`) within a complex data structure.
## Background
By default, `print` will display the actual elements in array-like data, such as `numpy.array` or `torch.tensor`, which is not very friendly for those who don't care about the actual elements.
Inspired by the leading scientific computing language [Julia](https://julialang.org/), this package will print the **type and shape** of the data structure, as well as the **type and shape** of the elements within the data structure.
## Installation
```
pip install git+https://github.com/huangyuxi/aprint.git
```
## Usage
```python
>>> import numpy as np
>>> import torch
>>> from aprint import aprint
>>> aprint(np.array([1, 2, 3]))
int64[3] numpy.ndarray
>>> aprint({'a': torch.zeros(16, 32).cuda(), 2: [np.array([1, 2]), (torch.zeros(2).to_sparse(), torch.zeros(2,3).to_sparse_csr(), torch.zeros(2,3,4).to_sparse_csc()), {'5', 6, 7.}]}, indent_str='| ')
{ # dict with len=2
| 'a': float32[16×32] torch.Tensor at 'cuda:0' with grad=False,
| 2: [ # list with len=3
| | int64[2] numpy.ndarray,
| | ( # tuple with len=3
| | | float32[2] torch.Tensor at 'cpu' with layout='COO', grad=False,
| | | float32[2×3] torch.Tensor at 'cpu' with layout='CSR', grad=False,
| | | float32[2×3×4] torch.Tensor at 'cpu' with layout='CSC', grad=False,
| | ),
| | { # set with len=3
| | | '5',
| | | 6,
| | | 7.0,
| | },
| ],
}
>>> aprint([1, [2, [3, [4, [5]]]]], max_depth=3, indent_str="⋮ ") # default max_depth=5
[ # list with len=2
⋮ 1,
⋮ [ # list with len=2
⋮ ⋮ 2,
⋮ ⋮ [ # list with len=2
⋮ ⋮ ⋮ 3,
⋮ ⋮ ⋮ [ # list with len=2 ... ],
⋮ ⋮ ],
⋮ ],
]
```
## Future Work
- [ ] support more types
- [ ] colorful output
- [ ] glance of the data
## License
MIT License
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"description": "# aprint\n\nA Python print package for those who don't care the actual elements of array-like data (e.g. `numpy.array`, `torch.tensor`) within a complex data structure.\n\n\n## Background\n\nBy default, `print` will display the actual elements in array-like data, such as `numpy.array` or `torch.tensor`, which is not very friendly for those who don't care about the actual elements.\nInspired by the leading scientific computing language [Julia](https://julialang.org/), this package will print the **type and shape** of the data structure, as well as the **type and shape** of the elements within the data structure.\n\n\n## Installation\n\n```\npip install git+https://github.com/huangyuxi/aprint.git\n```\n\n## Usage\n\n```python\n>>> import numpy as np\n>>> import torch\n>>> from aprint import aprint\n\n>>> aprint(np.array([1, 2, 3]))\nint64[3] numpy.ndarray\n\n>>> aprint({'a': torch.zeros(16, 32).cuda(), 2: [np.array([1, 2]), (torch.zeros(2).to_sparse(), torch.zeros(2,3).to_sparse_csr(), torch.zeros(2,3,4).to_sparse_csc()), {'5', 6, 7.}]}, indent_str='| ')\n{ # dict with len=2\n| 'a': float32[16\u00d732] torch.Tensor at 'cuda:0' with grad=False,\n| 2: [ # list with len=3\n| | int64[2] numpy.ndarray,\n| | ( # tuple with len=3\n| | | float32[2] torch.Tensor at 'cpu' with layout='COO', grad=False,\n| | | float32[2\u00d73] torch.Tensor at 'cpu' with layout='CSR', grad=False,\n| | | float32[2\u00d73\u00d74] torch.Tensor at 'cpu' with layout='CSC', grad=False,\n| | ),\n| | { # set with len=3\n| | | '5',\n| | | 6,\n| | | 7.0,\n| | },\n| ],\n}\n\n>>> aprint([1, [2, [3, [4, [5]]]]], max_depth=3, indent_str=\"\u22ee \") # default max_depth=5\n[ # list with len=2\n\u22ee 1,\n\u22ee [ # list with len=2\n\u22ee \u22ee 2,\n\u22ee \u22ee [ # list with len=2\n\u22ee \u22ee \u22ee 3,\n\u22ee \u22ee \u22ee [ # list with len=2 ... ],\n\u22ee \u22ee ],\n\u22ee ],\n]\n```\n\n## Future Work\n\n- [ ] support more types\n- [ ] colorful output\n- [ ] glance of the data\n\n\n## License\n\nMIT License\n",
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