lovely-numpy
  1. ๐Ÿ”Ž Array Representations
  2. ๐Ÿ“Š View as a histogram
  • ๐Ÿ’Ÿ Lovely NumPy
  • ๐Ÿ”Ž Array Representations
    • ๐Ÿงพ View as a summary
    • ๐Ÿ–Œ๏ธ View as RGB images
    • ๐Ÿ“Š View as a histogram
    • ๐Ÿ“บ View channels
  • ๐Ÿ–ผ๏ธ Image utils
    • ๐ŸŽจ color mapping
    • ๐Ÿ”ฒ Pad and frame
    • ๐Ÿ Image grid
  • โœจ Misc
    • ๐Ÿ‘๏ธ Lo and behold!
    • ๐ŸŽญ Matplotlib integration
    • ๐Ÿค” Config

On this page

  • plot
  1. ๐Ÿ”Ž Array Representations
  2. ๐Ÿ“Š View as a histogram

๐Ÿ“Š View as a histogram


source

plot

 plot (x:numpy.ndarray, center:str='zero', max_s:int=10000, plt0:Any=True,
       ax:Optional[matplotlib.axes._axes.Axes]=None, ddof:int=0)
Type Default Details
x ndarray Your data
center str zero Center plot on zero, mean, or range
max_s int 10000 Draw up to this many samples. =0 to draw all
plt0 Any True Take zero values into account
ax Optional None Optionally, supply your own matplotlib axes.
ddof int 0 Apply bias correction to std
Returns PlotProxy
plot(np.array([]))

np.random.seed(1)
x = np.random.randn(100000)+3
plot(x)

plot(x, center="range")

plot(x-3, center="mean")

plot(np.minimum(x-3, 0))

plot(np.maximum(x-3, 0), plt0=0)

# Very large outliers - don't print all sigmas

x2 = x.copy()
x2[0] = 1000
plot(x2, center="range")

fig, (ax1,ax2) = plt.subplots(2, figsize=(6, 4))
fig.tight_layout()
plot(x, ax=ax1)
plot(np.zeros(100), ax=ax2);