I'm new to Python and have any programming background..
I'm trying to plot 2 data sets of y for the same x data set, linear regress it using scipy and get the R^2 value. This is how i've gotten so far:
import matplotlib
import matplotlib.pyplot as pl
from scipy import stats
#first order
'''sin(Δθ)'''
y1 = [-0.040422445,-0.056402365,-0.060758191]
#second order
'''sin(Δθ)'''
y2 = [-0.083967708, -0.107420964, -0.117248521]
''''λ, theo (nm)'''
x= [404.66, 546.07, 579.06]
pl.title('Angular displacements vs. Theoretical wavelength')
pl.xlabel('theoretical λ (in nm)')
pl.y1label('sin(Δθ) of 1st order images')
pl.y2label('sin(Δθ) of 2nd order images')
plot1 = pl.plot(x, y1, 'r')
plot2 = pl.plot(x, y2, 'b')
pl.legend([plot1, plot2], ('1st order images', '2nd order images'), 'best', numpoints=1)
slope1, intercept1, r_value1, p_value1, std_err1 = stats.linregress(x,y1)
slope2, intercept2, r_value2, p_value2, std_err2 = stats.linregress(x,y2)
print "r-squared:", r_value1**2
print "r-squared:", r_value2**2
pl.show()
There are multiple errors in this code.
You cannot simply type greek letters in plot labels and titles, here is how you can do it:
pl.xlabel(r'theoretical $\lambda$ (in nm)')
y1label
and y2label
are not objects of the pl
module
In Python, # blah blah
is different from '''blah blah'''
. The first one is a comment, the second one is an expression. You can assign the second one to a variable (a = '''blah blah'''
) but you cannot assign the first one to a variable: a = # blah blah
yields a SyntaxError.
Here is a code that should work:
import matplotlib
import matplotlib.pyplot as pl
from scipy import stats
y1 = [-0.040422445,-0.056402365,-0.060758191]
y2 = [-0.083967708, -0.107420964, -0.117248521]
x= [404.66, 546.07, 579.06]
pl.title('Angular displacements vs. Theoretical wavelength')
pl.xlabel(r'theoretical $\lambda$ (in nm)')
pl.ylabel(r'sin($\Delta\theta$)')
y1label = '1st order images'
y2label = '2nd order images'
plot1 = pl.plot(x, y1, 'r', label=y1label)
plot2 = pl.plot(x, y2, 'b', label=y2label)
pl.legend()
slope1, intercept1, r_value1, p_value1, std_err1 = stats.linregress(x,y1)
slope2, intercept2, r_value2, p_value2, std_err2 = stats.linregress(x,y2)
print "r-squared:", r_value1**2
print "r-squared:", r_value2**2
pl.show()