"""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)
"""Scale data to stitch it together."""
from Stoner import Data
from Stoner.Util import format_error
import matplotlib.pyplot as plt
# Load and plot two sets of data
s1 = Data("Stitch-scan1.txt", setas="xy")
s2 = Data("Stitch-scan2.txt", setas="xy")
s1.plot(label="Set 1")
s2.fig = s1.fig
s2.plot(label="Set 2")
# Stitch scan 2 onto scan 1
s2.stitch(s1)
s2.plot(label="Stictched")
s2.title = "Stictching Example"
# Tidy up the plot by adding annotation fo the stirching co-efficients
labels = ["A", "B", "C"]
txt = []
lead = r"$x'\rightarrow x+A$" + "\n" + r"$y'=\rightarrow By+C$" + "\n"
for l, v, e in zip(labels, s2["Stitching Coefficients"],
s2["Stitching Coeffient Errors"]):
txt.append(format_error(v, e, latex=True, prefix=l + "="))
plt.text(0.7, 0.65, lead + "\n".join(txt), fontdict={"size": "x-small"})
plt.draw()
"""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)
"""Scale data to stitch it together."""
from Stoner import Data
from Stoner.Util import format_error
import matplotlib.pyplot as plt
# Load and plot two sets of data
s1 = Data("Stitch-scan1.txt", setas="xy")
s2 = Data("Stitch-scan2.txt", setas="xy")
s1.plot(label="Set 1")
s2.fig = s1.fig
s2.plot(label="Set 2")
# Stitch scan 2 onto scan 1
s2.stitch(s1)
s2.plot(label="Stictched")
s2.title = "Stictching Example"
# Tidy up the plot by adding annotation fo the stirching co-efficients
labels = ["A", "B", "C"]
txt = []
lead = r"$x'\rightarrow x+A$" + "\n" + r"$y'=\rightarrow By+C$" + "\n"
for l, v, e in zip(
labels, s2["Stitching Coefficients"], s2["Stitching Coeffient Errors"]
):
txt.append(format_error(v, e, latex=True, prefix=l + "="))
plt.text(0.7, 0.65, lead + "\n".join(txt), fontdict={"size": "x-small"})
plt.draw()
