👁️ Lo and behold!

---

Lo

def Lo(
    x:Union, # Your data
    plain:bool=False, # Show as plain text - values only
    verbose:NoneType=None, # Verbose - show values too
    depth:int=0, # Expand up to `depth`
    color:Optional=None, # Use ANSI colors
):

Lo and behold! What a lovely numpy.ndarray!

---

lo

def lo(
    x:Union, # Your data
    plain:bool=False, # Show as plain text - values only
    verbose:Optional=None, # Verbose - show values too
    depth:int=0, # Expand up to `depth`
    color:Optional=None, # Use ANSI colors
):

Examples

t = np.array([[1,2,3], [4,5,6]])
t

array([[1, 2, 3],
       [4, 5, 6]])

lo(t).v # Verbose

array[2, 3] i64 n=6 x∈[1, 6] μ=3.500 σ=1.708 [[1, 2, 3], [4, 5, 6]]
array([[1, 2, 3],
       [4, 5, 6]])

lo(t).p # Plain

array([[1, 2, 3],
       [4, 5, 6]])

lo(t).deeper

array[2, 3] i64 n=6 x∈[1, 6] μ=3.500 σ=1.708 [[1, 2, 3], [4, 5, 6]]
  array[3] i64 x∈[1, 3] μ=2.000 σ=0.816 [1, 2, 3]
  array[3] i64 x∈[4, 6] μ=5.000 σ=0.816 [4, 5, 6]

lo(t[None]).deeper(2) # We need to go deeper

array[1, 2, 3] i64 n=6 x∈[1, 6] μ=3.500 σ=1.708 [[[1, 2, 3], [4, 5, 6]]]
  array[2, 3] i64 n=6 x∈[1, 6] μ=3.500 σ=1.708 [[1, 2, 3], [4, 5, 6]]
    array[3] i64 x∈[1, 3] μ=2.000 σ=0.816 [1, 2, 3]
    array[3] i64 x∈[4, 6] μ=5.000 σ=0.816 [4, 5, 6]

in_stats = ( (0.485, 0.456, 0.406),     # mean
             (0.229, 0.224, 0.225) )    # std
image = np.load("mysteryman.npy").transpose(1,2,0)
lo(image)

array[196, 196, 3] f32 n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073

spicy = image.flatten()[:12].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)

array[2, 6] f32 n=12 x∈[-3.541e+03, -1.975e-05] μ=-393.848 σ=1.113e+03 +Inf! -Inf! NaN!

# image = np.zeros((196,196,3))
# image[:75,::2,:] = 1
# image[75::2,:,:] = 1

lo(image).rgb #.fig.savefig("output.png", metadata={"Software": None})

lo(image).rgb(scale=2, denorm=in_stats)

lo(image*0.3+0.5)

array[196, 196, 3] f32 n=115248 (0.4Mb) x∈[-0.135, 1.292] μ=0.384 σ=0.322

x = np.random.randn(100000)+3
lo(x).plt(center="mean")