Note
Click here to download the full example code
WIF Corrrelation Plot wif_corrplot¶
Corrrelation Plot by Flash Field Between Two Variables
wif_corrplot is created to further investigate relationship between two variables. Beside the basic scatterplot, regression fitting line and R_squared annotation are both included.
from wfmap.data import load_data
from wfmap import wif_corrplot
import scipy.stats.distributions as dist
data = load_data().query('80<MR<180')
norm = dist.norm_gen()
data['Fit'] = data['MR'] + \
norm.rvs(data['MR'].median(), data['MR'].std(), size=len(data['MR']))
fig = wif_corrplot(data, 'MR', 'Fit')
Linear regression is applied by default, while polynomial fit is also supported, modify the fit_deg to see the outcome.
data['Fit2'] = (data['MR']-data['MR'].median())**2 + data['MR'] * \
norm.rvs(data['MR'].median(), data['MR'].std(), size=len(data['MR']))
fig2 = wif_corrplot(data, 'MR', 'Fit2', fit_deg=2)
Total running time of the script: ( 0 minutes 1.378 seconds)
Download Python source code: plot_8_wif_corrplot.py