I have a set of RGB values in an array rgb_array of the form
[255.000, 56,026, 0.000]
[246.100, 60,000, 0.000]
...
>>> print(rbg_array)
1000, 3
that I'd like to plot similarly to the color gradient shown above.
How can I best use matpotlib's imshow to achieve this?
Supposing your array has N rows where each row contains 3 floats between 0 and 255, you can create an image as follows. First convert it to a numpy array of integers, and reshape it to (1, N, 3). This will make it a 1xN image. Then, display the image using imshow. You need to set an extent to get the x and y axes as in your example, or just set them to [0, 1, 0, 1]. Also the aspect ratio needs to be controlled, as otherwise the pixels would be considered "square".
import numpy as np
import matplotlib.pyplot as plt
rgb_array = [[255.000, 56.026 + (255 - 56.026) * i / 400, 255 * i / 400] for i in range(400)]
rgb_array += [[255 - 255 * i / 600, 255 - 255 * i / 600, 255] for i in range(600)]
img = np.array(rgb_array, dtype=int).reshape((1, len(rgb_array), 3))
plt.imshow(img, extent=[0, 16000, 0, 1], aspect='auto')
plt.show()
Don't use this method - @JohanC provides a much superior solution of creating an image rather than making a bar-graph.
I'm not so good on Matplotlib, but came up with this. There may be more efficient methods, so someone correct me please if this is the wrong approach.
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
NSAMPLES = 100
# Synthesize R, G, B and A channels with dummy data
# The thing to note is that the samples are REAL and in range [0..1]
r = np.linspace(0,1,NSAMPLES).astype(np.float)
g = 1.0 - r
b = np.full(NSAMPLES,0.5,np.float)
a = np.full(NSAMPLES,1,np.float)
# Merge into a single array, 4 deep
RGBA = np.dstack((r,g,b,a))
# Plot
height, width = 40, 1
plt.bar(np.arange(NSAMPLES), height, width, color=rgba.reshape(-1,4))
plt.title("Some Funky Barplot")
plt.show()
The array RGBA looks like this:
array([[[0. , 1. , 0.5 , 1. ],
[0.01010101, 0.98989899, 0.5 , 1. ],
[0.02020202, 0.97979798, 0.5 , 1. ],
[0.03030303, 0.96969697, 0.5 , 1. ],
[0.04040404, 0.95959596, 0.5 , 1. ],
[0.05050505, 0.94949495, 0.5 , 1. ],
[0.06060606, 0.93939394, 0.5 , 1. ],
[0.07070707, 0.92929293, 0.5 , 1. ],
[0.08080808, 0.91919192, 0.5 , 1. ],
[0.09090909, 0.90909091, 0.5 , 1. ],
[0.1010101 , 0.8989899 , 0.5 , 1. ],
[0.11111111, 0.88888889, 0.5 , 1. ],
[0.12121212, 0.87878788, 0.5 , 1. ],
[0.13131313, 0.86868687, 0.5 , 1. ],
[0.14141414, 0.85858586, 0.5 , 1. ],
[0.15151515, 0.84848485, 0.5 , 1. ],
[0.16161616, 0.83838384, 0.5 , 1. ],
[0.17171717, 0.82828283, 0.5 , 1. ],
[0.18181818, 0.81818182, 0.5 , 1. ],
[0.19191919, 0.80808081, 0.5 , 1. ],
[0.2020202 , 0.7979798 , 0.5 , 1. ],
[0.21212121, 0.78787879, 0.5 , 1. ],
[0.22222222, 0.77777778, 0.5 , 1. ],
[0.23232323, 0.76767677, 0.5 , 1. ],
[0.24242424, 0.75757576, 0.5 , 1. ],
[0.25252525, 0.74747475, 0.5 , 1. ],
[0.26262626, 0.73737374, 0.5 , 1. ],
[0.27272727, 0.72727273, 0.5 , 1. ],
[0.28282828, 0.71717172, 0.5 , 1. ],
[0.29292929, 0.70707071, 0.5 , 1. ],
[0.3030303 , 0.6969697 , 0.5 , 1. ],
[0.31313131, 0.68686869, 0.5 , 1. ],
[0.32323232, 0.67676768, 0.5 , 1. ],
[0.33333333, 0.66666667, 0.5 , 1. ],
[0.34343434, 0.65656566, 0.5 , 1. ],
[0.35353535, 0.64646465, 0.5 , 1. ],
[0.36363636, 0.63636364, 0.5 , 1. ],
[0.37373737, 0.62626263, 0.5 , 1. ],
[0.38383838, 0.61616162, 0.5 , 1. ],
[0.39393939, 0.60606061, 0.5 , 1. ],
[0.4040404 , 0.5959596 , 0.5 , 1. ],
[0.41414141, 0.58585859, 0.5 , 1. ],
[0.42424242, 0.57575758, 0.5 , 1. ],
[0.43434343, 0.56565657, 0.5 , 1. ],
[0.44444444, 0.55555556, 0.5 , 1. ],
[0.45454545, 0.54545455, 0.5 , 1. ],
[0.46464646, 0.53535354, 0.5 , 1. ],
[0.47474747, 0.52525253, 0.5 , 1. ],
[0.48484848, 0.51515152, 0.5 , 1. ],
[0.49494949, 0.50505051, 0.5 , 1. ],
[0.50505051, 0.49494949, 0.5 , 1. ],
[0.51515152, 0.48484848, 0.5 , 1. ],
[0.52525253, 0.47474747, 0.5 , 1. ],
[0.53535354, 0.46464646, 0.5 , 1. ],
[0.54545455, 0.45454545, 0.5 , 1. ],
[0.55555556, 0.44444444, 0.5 , 1. ],
[0.56565657, 0.43434343, 0.5 , 1. ],
[0.57575758, 0.42424242, 0.5 , 1. ],
[0.58585859, 0.41414141, 0.5 , 1. ],
[0.5959596 , 0.4040404 , 0.5 , 1. ],
[0.60606061, 0.39393939, 0.5 , 1. ],
[0.61616162, 0.38383838, 0.5 , 1. ],
[0.62626263, 0.37373737, 0.5 , 1. ],
[0.63636364, 0.36363636, 0.5 , 1. ],
[0.64646465, 0.35353535, 0.5 , 1. ],
[0.65656566, 0.34343434, 0.5 , 1. ],
[0.66666667, 0.33333333, 0.5 , 1. ],
[0.67676768, 0.32323232, 0.5 , 1. ],
[0.68686869, 0.31313131, 0.5 , 1. ],
[0.6969697 , 0.3030303 , 0.5 , 1. ],
[0.70707071, 0.29292929, 0.5 , 1. ],
[0.71717172, 0.28282828, 0.5 , 1. ],
[0.72727273, 0.27272727, 0.5 , 1. ],
[0.73737374, 0.26262626, 0.5 , 1. ],
[0.74747475, 0.25252525, 0.5 , 1. ],
[0.75757576, 0.24242424, 0.5 , 1. ],
[0.76767677, 0.23232323, 0.5 , 1. ],
[0.77777778, 0.22222222, 0.5 , 1. ],
[0.78787879, 0.21212121, 0.5 , 1. ],
[0.7979798 , 0.2020202 , 0.5 , 1. ],
[0.80808081, 0.19191919, 0.5 , 1. ],
[0.81818182, 0.18181818, 0.5 , 1. ],
[0.82828283, 0.17171717, 0.5 , 1. ],
[0.83838384, 0.16161616, 0.5 , 1. ],
[0.84848485, 0.15151515, 0.5 , 1. ],
[0.85858586, 0.14141414, 0.5 , 1. ],
[0.86868687, 0.13131313, 0.5 , 1. ],
[0.87878788, 0.12121212, 0.5 , 1. ],
[0.88888889, 0.11111111, 0.5 , 1. ],
[0.8989899 , 0.1010101 , 0.5 , 1. ],
[0.90909091, 0.09090909, 0.5 , 1. ],
[0.91919192, 0.08080808, 0.5 , 1. ],
[0.92929293, 0.07070707, 0.5 , 1. ],
[0.93939394, 0.06060606, 0.5 , 1. ],
[0.94949495, 0.05050505, 0.5 , 1. ],
[0.95959596, 0.04040404, 0.5 , 1. ],
[0.96969697, 0.03030303, 0.5 , 1. ],
[0.97979798, 0.02020202, 0.5 , 1. ],
[0.98989899, 0.01010101, 0.5 , 1. ],
[1. , 0. , 0.5 , 1. ]]])
