"""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()