I have two columns(rcs,range) in a dataframe.
| rcs | range |
|---|---|
| -40 | 12.9 |
| -35 | 14.9 |
| -30 | 22.9 |
| -25 | 35.44 |
| -20 | 43.48 |
| -15 | 62.4 |
| -10 | 92.4 |
| -5 | 132.99 |
| 0 | 182.6 |
| 5 | 252.99 |
I want to plot a curve with equation rcs = range^4
I tried the following 1.as a polynomial curve fitting
def curve_gen(x,a,b,c,d,e): #for polynomial
return (a*(x**4))+(b*(x**3))+(c*(x**2))+(d*x)+e
y = df['rcs'].values
x = df['range'].values
pop,_ = curve_fit(curve_gen,x,y)
a,b,c,d,e = pop
pl.scatter(y,x)
pl.plot(curve_gen(x,a,b,c,d,e),x,color = 'red')
pl.show()
in the above plot,the curve is not a smooth curve and is not starting from -40
2.power law curve fit
def power_law(x,a):
return a*np.power(x,4)
y = df['rcs'].values
x = df['range'].values
pop,_ = curve_fit(power_law,x,y)
a = pop
pl.scatter(y,x)
pl.plot(power_law(x,a),x,color = 'red')
pl.show()
this one giving me a wrong plot.the red line is not passing through the blue points
how to solve the above issue using curve_fit?and plot
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Answer
You can use numpy.polyfit and numpy.poly1d:
# initial data plt.plot(df['rcs'], df['range'], marker='o', ls='', label='data') # fit fit = np.polyfit(df['rcs'], df['range'], deg=4) # array([1.87062937e-05, 4.24655012e-03, 3.34652273e-01, 1.20759569e+01, # 1.83604091e+02]) # get X range X = np.arange(df['rcs'].min(), df['rcs'].max()+1) # plot mapped fit onto X plt.plot(X, np.poly1d(fit)(X), label='fit')
output:
