"""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)
"""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)
"""Re-binning data example."""
from Stoner import Data
from Stoner.plot.utils import errorfill
d = Data("Noisy_Data.txt", setas="xy")
d.template.fig_height = 6
d.template.fig_width = 8
d.figure(figsize=(6, 8))
d.subplot(411)
e = d.bin(bins=0.05, mode="lin")
f = d.bin(bins=0.25, mode="lin")
g = d.bin(bins=0.05, mode="log")
h = d.bin(bins=50, mode="log")
for i, (binned, label) in enumerate(
zip([e, f, g, h], ["0.05 Linear", "0.25 Linear", "0.05 Log", "50 log"])
):
binned.subplot(411 + i)
d.plot()
binned.fig = d.fig
binned.plot(plotter=errorfill, label=label)
d.xlim = (1, 6)
d.ylim(-0.1, 0.4)
d.title = "Bin demo" if i == 0 else ""
d.tight_layout()
