Python Data.normalise方法代码示例

# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import normalise [as 别名]
"""Example customising a plot using default style."""
from Stoner import Data, __home__
from Stoner.plot.formats import DefaultPlotStyle
import os.path as path
from cycler import cycler

filename = path.realpath(path.join(__home__, "..", "doc", "samples", "sample.txt"))

d = Data(filename, setas="xyy", template=DefaultPlotStyle)
d.normalise(2, scale=(0.0, 10.0))  # Just to keep the plot sane!

# Change the color and line style cycles
d.template["axes.prop_cycle"] = cycler(color=["Blue", "Purple"]) + cycler(
    linestyle=["-", "--"]
)

# Can also access as an attribute
d.template.template_lines__linewidth = 2.0

# Set the default figure size
d.template["figure.figsize"] = (6, 8)
d.template["figure.autolayout"] = True
# Make figure (before using subplot method) and select first subplot
d.figure()
d.subplot(211)
# Pkot with our customised defaults
d.plot()
d.grid(True, color="green", linestyle="-.")
d.title = "Customised Plot settings"
# Reset the template to defaults and switch to next subplot
d.template.clear()

# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import normalise [as 别名]
data=Data(filename)  #Use FALSE to get a dialog box to the file containing Tc data

#Define my working x and y axes
#Split one file into a folder of two files by the iterator column
fldr=data.split(iterator)

result=Data()
for data in fldr: #For each iteration ramp in the Tc data
    row=[data.mean(iterator)]
    data.figure(figsize=(8,4))
    for i,r_col in enumerate(r_cols):
        data.setas(x=t_col,y=r_col)
        data.del_rows(isnan(data.y))

        #Normalise data on y axis between +/- 1
        data.normalise(base=(-1.,1.), replace=True)

        #Swap x and y axes around so that R is x and T is y
        data=~data

        #Curve fit a straight line, using only the central 90% of the resistance transition
        data.curve_fit(linear,bounds=lambda x,r:-threshold<x<threshold,result=True,p0=[7.0,0.0]) #result=True to record fit into metadata

        #Plot the results
        data.setas[-1]="y"
        data.subplot(1,len(r_cols),i+1)
        data.plot(fmt=["k.","r-"])
        data.annotate_fit(linear,x=-1.,y=7.3c,fontsize="small")
        data.title="Ramp {}".format(data[iterator][0])
        row.extend([data["linear:intercept"],data["linear:intercept err"]])
    data.tight_layout()

# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import normalise [as 别名]
#################### Important - if using multiprocessing on Windows, this  block must be ######
#################### Inside a if __name__=="__main__": test. ###################################
###############################################################################################

if __name__ == "__main__":
    # Load data
    d = Data(join(__home__, "..", "sample-data", "FMR-data.txt"))
    # Rename columns and reset plot labels
    d.rename("multi[1]:y", "Field").rename("multi[0]:y", "Frequency").rename(
        "Absorption::X", "FMR"
    )
    d.labels = None

    # Deine x and y columns and normalise to a big number
    d.setas(x="Field", y="FMR")
    d.normalise(base=(-1e6, 1e6))
    fldr = d.split(field_sign, "Frequency")

    # Split the data file into separate files by frequencies and sign of field
    fldr = PlotFolder(fldr)  # Convert to a PlotFolder
    fldr.template = template  # Set my custom plot template
    for f in fldr[-1]:  # Invert the negative field side
        f.x = -f.x[::-1]
        f.y = -f.y[::-1]

    resfldr = PlotFolder()  # Somewhere to keep the results from +ve and -ve fields

    for s in fldr.groups:  # Fit each FMR spectra
        subfldr = fldr[s]
        subfldr.metadata["Field Sign"] = s