def test_sg_filter(self):
x=np.linspace(0,10*np.pi,1001)
y=np.sin(x)+np.random.normal(size=1001,scale=0.05)
d=Data(x,y,column_headers=["Time","Signal"],setas="xy")
d.SG_Filter(order=1,result=True)
d.setas="x.y"
d.y=d.y-np.cos(x)
self.assertAlmostEqual(d.y[5:-5].mean(), 0,places=2,msg="Failed to differentiate correctly")
"""Smoothing Data methods example."""
from Stoner import Data
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(9, 6))
d = Data("Noisy_Data.txt", setas="xy")
d.fig = fig
d.plot(color='grey')
# Filter with Savitsky-Golay filter, linear over 7 ppoints
d.SG_Filter(result=True, points=11, header="S-G Filtered")
d.setas = "x.y"
d.plot(lw=2, label="SG Filter")
d.setas = "xy"
#Filter with cubic splines
d.spline(replace=2, order=3, smoothing=4, header="Spline")
d.setas = "x.y"
d.plot(lw=2, label="Spline")
d.setas = "xy"
# Rebin data
d.smooth("hamming", size=0.2, result=True, replace=False, header="Smoothed")
d.setas = "x...y"
d.plot(lw=2, label="Smoooth", color="green")
d.setas = "xy"
d2 = d.bin(bins=100, mode="lin")
d2.fig = d.fig
d2.plot(lw=2, label="Re-binned", color="blue")
d2.xlim = (3.5, 6.5)
d2.ylim = (-0.2, 0.4)
