statsmodels.api and scipy.stats not producing proper fit

I'm trying to plot a line of best fit through two sets of data with scipy.stats and statsmodels.api.

import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm
from scipy import stats

# toy data
y1 =  np.array([1,2,3,4,5])
x1 =  np.array([2,4,6,8,10])
y2 = np.array([1,3.0,5.0,7.0,9.0])
x2 = np.array([1,2.9,5.3,7.4,8.9])

#  should produce straight lines through each data set
plt.scatter(x1, y1, label = 'LRIS')
plt.scatter(x2, y2, label = 'PFCam')
for x, y in zip([x1, x2], [y1, y2]):
    model = sm.OLS(y, sm.add_constant(x))
    results = model.fit()
    params = stats.linregress(x, y)
    plt.plot(params[0]*x + params[1])

plt.xlabel('log Integration time, t [s]')
plt.ylabel('V [mag]')
plt.legend()
plt.show()

produces

I don't understand what's going on to produce the lines of 'best' fit like this.

You wanted to plot X vs Y:

    plt.plot(x, x * params.slope + params.intercept)

LGTM.