🧾 View as a summary

Pretty printing

---

lovely

def lovely(
    x:Union, # The data you want to explore
    plain:bool=False, # Plain old way
    verbose:bool=False, # Both summaty and plain
    depth:int=0, # Show deeper summary, up to `depth`
    lvl:int=0, # Indentation level
    color:Optional=None, # Override `get_config().color`
    show_histogram:Optional=None, # Show histogram
)->str: # The summary

Pretty-print the stats of a numpy array or scalar

Examples

nasties = randoms[:12].copy()

nasties[0] *= 10000
nasties[1] /= 10000
nasties[3] = float('inf')
nasties[4] = float('-inf')
nasties[5] = float('nan')
nasties = nasties.reshape((2,6))

print(lovely(nasties))

array[2, 6] n=12 x∈[-0.151, 1.764e+04] μ=1.960e+03 σ=5.544e+03 +Inf! -Inf! NaN!

print(lovely(randoms[0]))
print(lovely(randoms[:2]))
print(lovely(randoms[:6].reshape(2, 3))) # More than 2 elements -> show statistics
print(lovely(randoms[:11])) # More than 10 -> don't show values

1.764
array[2] μ=1.082 σ=0.682 [1.764, 0.400]
array[2, 3] n=6 x∈[-0.977, 2.241] μ=1.046 σ=1.090 [[1.764, 0.400, 0.979], [2.241, 1.868, -0.977]]
array[11] x∈[-0.977, 2.241] μ=0.684 σ=0.938

Do we have any floating point nasties? Are the values all zeros?

# Statistics and range are calculated on good values only, if there are at lest 3 of them.
print(lovely(nasties))

array[2, 6] n=12 x∈[-0.151, 1.764e+04] μ=1.960e+03 σ=5.544e+03 +Inf! -Inf! NaN!

print(lovely(nasties, color=False))

array[2, 6] n=12 x∈[-0.151, 1.764e+04] μ=1.960e+03 σ=5.544e+03 +Inf! -Inf! NaN!

print(lovely(np.array([float("nan")]*11)))

array[11] NaN!

print(lovely(np.zeros(12, dtype=np.float16)))
print(lovely(np.array([], dtype=int)))

array[12] f16 all_zeros
array[0] i64 empty

str(lovely(np.array([], dtype=int)))

'array[0] i64 \x1b[38;2;127;127;127mempty\x1b[0m'

np.set_printoptions(precision=3)
print(lovely(nasties, verbose=True))

array[2, 6] n=12 x∈[-0.151, 1.764e+04] μ=1.960e+03 σ=5.544e+03 +Inf! -Inf! NaN!
array([[ 1.764e+04,  4.002e-05,  9.787e-01,        inf,       -inf,
               nan],
       [ 9.501e-01, -1.514e-01, -1.032e-01,  4.106e-01,  1.440e-01,
         1.454e+00]])

print(lovely(nasties, plain=True))

array([[ 1.764e+04,  4.002e-05,  9.787e-01,        inf,       -inf,
               nan],
       [ 9.501e-01, -1.514e-01, -1.032e-01,  4.106e-01,  1.440e-01,
         1.454e+00]])

image = np.load("mysteryman.npy")
image[1,100,100] = float('nan')

print(lovely(image, depth=1))

array[3, 196, 196] f32 n=115248 (0.4Mb) x∈[-2.118 |▂▅█▇▂▁▁▂▂▁| 2.640] μ=-0.388 σ=1.073 NaN!
  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 NaN!
  array[196, 196] f32 n=38416 x∈[-1.804 |▃█▃▁▁▁▁▁▂▁| 2.640] μ=-0.567 σ=1.178

# We don't really supposed complex numbers yet
c = np.random.randn(2) + 1j*np.random.randn(2)
print(lovely(c))

array([ 1.883-1.27j , -1.348+0.969j])

# Other weirs stuff

w = np.array(["a", "b", "c"])
print(lovely(w))

z = np.array([{}, {"a": 1}, {"b": 2, "c": 3}])
print(lovely(z))

array(['a', 'b', 'c'], dtype='<U1')
array([{}, {'a': 1}, {'b': 2, 'c': 3}], dtype=object)

i = np.array([1, 2, 3])
test_eq(str(lovely(i)), "array[3] i64 x∈[1, 3] μ=2.000 σ=0.816 [1, 2, 3]")

i = np.array([True, False, True])
test_eq(str(lovely(i)), 'array[3] bool x∈[False, True] μ=0.667 σ=0.471 [True, False, True]')