77 lines
2.7 KiB
Python
77 lines
2.7 KiB
Python
# Karoo Iris Plot
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# by Kai Staats, MSc UCT / AIMS and Arun Kumar, PhD
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# version 0.9.1.6
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import sys
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import numpy as np
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import matplotlib.pyplot as mpl
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np.set_printoptions(linewidth = 320) # set the terminal to print 320 characters before line-wrapping in order to view Trees
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'''
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This is a functional, even if rudimentary script designed to help you visualise your 2D or 3D data against a function
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generated by Karoo GP. The challenge comes with solving complex equations for a single variable such that you have a
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plot-able function. If the algebra required is beyond your skills (or you forgot what you learned in high school),
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tools such as Matlab may be of some assistance. If you desire to normalise your data in advance of using this script,
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the Karoo GP normalisation script included in the karoo_gp/toos/ directory is very easy to use.
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By default, this script plots a Karoo GP derived function against a scatter plot of one of the Iris datasets
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included with this package: karoo_gp/files/Iris_dataset/data_IRIS_virginica-vs-setosa_3-col_PLOT.csv
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If you are new to plotting, https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ for a good plotting tutorial
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provides a good, visual tutorial, as do many, many other web and video based guides.
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'''
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### USER INTERACTION ###
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if len(sys.argv) == 1:
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filename = '../files/Iris_dataset/data_IRIS_virginica-vs-setosa_3-col_PLOT.csv'
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print '\n\t\033[31mYou have not assigned an input file, therefore "IRIS_virginica-vs-setosa_3-col_PLOT" will be used.\033[0;0m'
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elif len(sys.argv) > 2: print '\n\t\033[31mERROR! You have assigned too many command line arguments. Try again ...\033[0;0m'; sys.exit()
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else: filename = sys.argv[1]
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### LOAD THE DATA and PREPARE AN EMPTY ARRAY ###
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print '\n\t\033[36mLoading dataset:', filename, '\033[0;0m\n'
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data = np.loadtxt(filename, delimiter=',', dtype = str)
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data_a, data_b, data_c = [], [], []
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tmp = data[:,0]
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for n in range(len(tmp)):
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data_a.append(float(tmp[n]))
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tmp = data[:,1]
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for n in range(len(tmp)):
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data_b.append(float(tmp[n]))
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tmp = data[:,2]
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for n in range(len(tmp)):
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data_c.append(float(tmp[n]))
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### PREP THE FUNCTION ###
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b = np.arange(2, 4, 0.25) # plot from n to m in steps o
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c = np.arange(2, 4, 0.25) # plot from n to m in steps o
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b, c = np.meshgrid(b, c)
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# -b*c + c**2 + c - 1 # Karoo GP derived function
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# -a/c - b**2 + c**2 # Karoo GP derived function
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# -a - b + c**2 # Karoo GP derived function becomes a = -b + c**2
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a = -b + c**2
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### PLOT THE FUNCTION and DATA###
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fig = mpl.figure()
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ax = fig.add_subplot(111, projection = '3d')
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ax.scatter(data_a, data_b, data_c, c = 'r', marker = 'o') # 3D data
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ax.plot_wireframe(a,b,c) # 3D function
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ax.set_xlabel('a')
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ax.set_ylabel('b')
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ax.set_zlabel('c')
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mpl.show()
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