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https://github.com/xl0/lovely-numpy
NumPy arrays, ready for human consumption
https://github.com/xl0/lovely-numpy
deep-learning library numpy statistics visualization
Last synced: 5 days ago
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NumPy arrays, ready for human consumption
- Host: GitHub
- URL: https://github.com/xl0/lovely-numpy
- Owner: xl0
- License: mit
- Created: 2022-11-17T12:32:39.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-07-20T19:40:58.000Z (6 months ago)
- Last Synced: 2024-12-28T12:12:47.258Z (12 days ago)
- Topics: deep-learning, library, numpy, statistics, visualization
- Language: Jupyter Notebook
- Homepage: https://xl0.github.io/lovely-numpy
- Size: 22.5 MB
- Stars: 65
- Watchers: 4
- Forks: 4
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
💟 Lovely NumPy
================
## [Read full docs](https://xl0.github.io/lovely-numpy)
### More lovely stuff
##### Working with numbers
- [PyTorch](https://pytorch.org/): ❤️ [Lovely
Tensors](https://github.com/xl0/lovely-tensors)
- [JAX](https://jax.readthedocs.io/): 💘 [Lovely
`JAX`](https://github.com/xl0/lovely-jax)
- [TinyGrad](https://github.com/tinygrad/tinygrad): 🫀 [Lovely
Grad](https://github.com/xl0/lovely-grad)
##### Proompting
- Log prompts with 💌 [Lovely
Prompts](https://github.com/xl0/lovely-prompts)
- Better LangChain: 😎 [Proompter](https://github.com/xl0/proompter)
##### Community
- [Discord](https://discord.gg/qBaqauUWXP)
## Install
``` sh
pip install lovely-numpy
```
or
``` sh
conda install -c conda-forge lovely-numpy
```
## How to use
How often do you find yourself debugging NumPy code? You dump your
variable to the cell output, and see this:
``` python
numbers
```
array([[[-0.3541, -0.1975, -0.6715],
[-0.3369, -0.1975, -0.9853],
...,
[-0.4739, -0.3725, -0.689 ],
[ 2.2489, 2.4111, 2.396 ]],
[[-0.4054, -0.25 , -0.7238],
[-0.4226, -0.2325, -1.0724],
...,
[-0.8507, -0.6702, -1.0201],
[ 2.1633, 2.3585, 2.3263]],
...,
[[-0.8507, -0.3901, -1.1944],
[-0.7822, -0.2325, -1.4559],
...,
[-1.5014, -1.2304, -1.4733],
[ 2.1804, 2.4111, 2.4308]],
[[-0.8335, -0.4076, -1.2293],
[-0.8164, -0.285 , -1.5256],
...,
[-1.5528, -1.2829, -1.5256],
[ 2.1119, 2.341 , 2.3611]]], dtype=float32)
Was it really useful for you, as a human, to see all these numbers?
What is the shape? The size?
What are the statistics?
Are any of the values `nan` or `inf`?
Is it an image of a man holding a tench?
``` python
from lovely_numpy import lo
```
## Lo and behold!
``` python
lo(numbers)
```
array[196, 196, 3] f32 n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
Better, eh?
``` python
lo(numbers[1,:6,1]) # Still shows values if there are not too many.
```
array[6] f32 x∈[-0.408, -0.232] μ=-0.340 σ=0.075 [-0.250, -0.232, -0.338, -0.408, -0.408, -0.408]
``` python
spicy = numbers[0,:12,0].copy()
spicy[0] *= 10000
spicy[1] /= 10000
spicy[2] = float('inf')
spicy[3] = float('-inf')
spicy[4] = float('nan')
spicy = spicy.reshape((2,6))
lo(spicy) # Spicy stuff
```
array[2, 6] f32 n=12 x∈[-3.541e+03, -3.369e-05] μ=-393.776 σ=1.113e+03 +Inf! -Inf! NaN!
``` python
lo(np.zeros((10, 10))) # A zero array - make it obvious
```
array[10, 10] n=100 all_zeros
``` python
lo(spicy, verbose=True)
```
array[2, 6] f32 n=12 x∈[-3.541e+03, -3.369e-05] μ=-393.776 σ=1.113e+03 +Inf! -Inf! NaN!
array([[-3540.5432, -0. , ..., nan, -0.4054],
[ -0.4226, -0.4911, ..., -0.5424, -0.5082]],
dtype=float32)
## Going `.deeper`
``` python
lo(numbers.transpose(2,1,0)).deeper
```
array[3, 196, 196] f32 n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
array[196, 196] f32 n=38416 x∈[-2.118, 2.249] μ=-0.324 σ=1.036
array[196, 196] f32 n=38416 x∈[-1.966, 2.429] μ=-0.274 σ=0.973
array[196, 196] f32 n=38416 x∈[-1.804, 2.640] μ=-0.567 σ=1.178
``` python
# You can go deeper if you need to
lo(numbers[:3,:4]).deeper(2)
```
array[3, 4, 3] f32 n=36 x∈[-1.125, -0.197] μ=-0.563 σ=0.280
array[4, 3] f32 n=12 x∈[-0.985, -0.197] μ=-0.487 σ=0.259
array[3] f32 x∈[-0.672, -0.197] μ=-0.408 σ=0.197 [-0.354, -0.197, -0.672]
array[3] f32 x∈[-0.985, -0.197] μ=-0.507 σ=0.343 [-0.337, -0.197, -0.985]
array[3] f32 x∈[-0.881, -0.303] μ=-0.530 σ=0.252 [-0.405, -0.303, -0.881]
array[3] f32 x∈[-0.776, -0.303] μ=-0.506 σ=0.199 [-0.440, -0.303, -0.776]
array[4, 3] f32 n=12 x∈[-1.072, -0.232] μ=-0.571 σ=0.281
array[3] f32 x∈[-0.724, -0.250] μ=-0.460 σ=0.197 [-0.405, -0.250, -0.724]
array[3] f32 x∈[-1.072, -0.232] μ=-0.576 σ=0.360 [-0.423, -0.232, -1.072]
array[3] f32 x∈[-0.968, -0.338] μ=-0.599 σ=0.268 [-0.491, -0.338, -0.968]
array[3] f32 x∈[-0.968, -0.408] μ=-0.651 σ=0.235 [-0.577, -0.408, -0.968]
array[4, 3] f32 n=12 x∈[-1.125, -0.285] μ=-0.631 σ=0.280
array[3] f32 x∈[-0.828, -0.303] μ=-0.535 σ=0.219 [-0.474, -0.303, -0.828]
array[3] f32 x∈[-1.125, -0.285] μ=-0.628 σ=0.360 [-0.474, -0.285, -1.125]
array[3] f32 x∈[-1.020, -0.390] μ=-0.651 σ=0.268 [-0.542, -0.390, -1.020]
array[3] f32 x∈[-1.003, -0.478] μ=-0.708 σ=0.219 [-0.645, -0.478, -1.003]
## Now in `.rgb` color
The important queston - is it our man?
``` python
lo(numbers).rgb
```
*Maaaaybe?* Looks like someone normalized him.
``` python
in_stats = ( (0.485, 0.456, 0.406), # mean
(0.229, 0.224, 0.225) ) # std
# numbers.rgb(in_stats, cl=True) # For channel-last input format
lo(numbers).rgb(denorm=in_stats)
```
It’s indeed our hero, the Tenchman!
## See the `.chans`
``` python
# .chans will map values betwen [-1,1] to colors.
# Make our values fit into that range to avoid clipping.
mean = np.array(in_stats[0])
std = np.array(in_stats[1])
numbers_01 = (numbers*std + mean).clip(0,1)
lo(numbers_01)
```
array[196, 196, 3] n=115248 (0.9Mb) x∈[0., 1.000] μ=0.361 σ=0.248
``` python
lo(numbers_01).chans
```
## Grouping
``` python
# Make 8 images with progressively higher brightness and stack them 2x2x2.
eight_images = (np.stack([numbers]*8) + np.linspace(-2, 2, 8)[:,None,None,None])
eight_images = (eight_images
*np.array(in_stats[1])
+np.array(in_stats[0])
).clip(0,1).reshape(2,2,2,196,196,3)
lo(eight_images)
```
array[2, 2, 2, 196, 196, 3] n=921984 (7.0Mb) x∈[0., 1.000] μ=0.382 σ=0.319
``` python
lo(eight_images).rgb
```
## Histogram
``` python
lo(numbers+3).plt
```
``` python
lo(numbers+3).plt(center="mean", max_s=1000)
```
``` python
lo(numbers+3).plt(center="range")
```
## Options \| [Docs](03d_utils.config.html)
``` python
from lovely_numpy import set_config, config, lovely
```
``` python
set_config(precision=5, sci_mode=True, color=False)
lo(np.array([1.,2,np.nan]))
```
array[3] μ=1.50000e+00 σ=5.00000e-01 NaN! [1.00000e+00, 2.00000e+00, nan]
``` python
set_config(precision=None, sci_mode=None, color=None) # None -> Reset to defaults
lo(np.array([1.,2,np.nan]))
```
array[3] μ=1.500 σ=0.500 NaN! [1.000, 2.000, nan]
``` python
# Or with config context manager.
with config(sci_mode=True):
print(lo(np.array([1,2,3])))
print(lo(np.array([1,2,3])))
```
array[3] i64 x∈[1, 3] μ=2.000e+00 σ=8.165e-01 [1, 2, 3]
array[3] i64 x∈[1, 3] μ=2.000 σ=0.816 [1, 2, 3]
## Without Lo
``` python
from lovely_numpy import rgb, chans, plot
```
``` python
lovely(numbers) # Returns `str`, that's why you see ''.
# Note: lo(x) returns a wrapper object with a `__repr__` and other methods.
```
'array[196, 196, 3] f32 n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073'
``` python
rgb(numbers, denorm=in_stats)
```
``` python
chans(numbers*0.3+0.5)
```
``` python
plot(numbers)
```
## Matplotlib integration \| [Docs](matplotlib.html)
``` python
lo(numbers).rgb(in_stats).fig # matplotlib figure
```
``` python
lo(numbers).plt.fig.savefig('pretty.svg') # Save it
```
``` python
!file pretty.svg; rm pretty.svg
```
pretty.svg: SVG Scalable Vector Graphics image
``` python
fig = plt.figure(figsize=(8,3))
fig.set_constrained_layout(True)
gs = fig.add_gridspec(2,2)
ax1 = fig.add_subplot(gs[0, :])
ax2 = fig.add_subplot(gs[1, 0])
ax3 = fig.add_subplot(gs[1,1:])
ax2.set_axis_off()
ax3.set_axis_off()
lo(numbers_01).plt(ax=ax1)
lo(numbers_01).rgb(ax=ax2)
lo(numbers_01).chans(ax=ax3);
```
