PlotMethodsMixin

class Stoner.folders.mixins.PlotMethodsMixin

Bases: object

A Mixin for Stoner.folders.core.baseFolder with extra methods for plotting lots of files.

Example

# -*- coding: utf-8 -*-
"""Demonstrate PlotFolder Class."""
from os.path import join

from numpy import where
from scipy.constants import mu_0
from scipy.stats import gmean

from Stoner import Data, __home__
from Stoner.analysis.fitting.models.magnetism import FMR_Power, Inverse_Kittel
from Stoner.analysis.fitting.models.generic import Linear
from Stoner.plot.formats import DefaultPlotStyle, TexEngFormatter
from Stoner.folders import PlotFolder

# Customise a plot template
template = DefaultPlotStyle()
template.xformatter = TexEngFormatter()
template.xformatter.unit = "T"
template.yformatter = TexEngFormatter


def field_sign(r):
    """Return string key for sign of field.."""
    pos = r["Field"] >= 0
    return where(pos, "pos", "neg")


def extra(i, j, d):
    """Customise each individual plot."""
    d.axvline(x=d["cut"], ls="--")
    d.title = r"$\nu={:.1f}\,$GHz".format(d.mean("Frequency") / 1e9)
    d.xlabel = r"Field $\mu_0H\,$"
    d.ylabel = "Abs. (arb)"
    d.plt_legend(loc=3)
    d.annotate_fit(FMR_Power, mode="eng", fontdict={"size": 8}, x=0.05, y=0.25)


def do_fit(f):
    """Fit just one set of data."""
    f.template = template
    f["cut"] = f.threshold(0.75e5, rising=False, falling=True)
    f["Frequency"] = (f // "Frequency").mean()
    f.lmfit(
        FMR_Power, result=True, header="Fit", bounds=lambda x, r: x < f["cut"]
    )
    return f


###############################################################################################
#################### 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["neg"]:  # 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

        print("s={}".format(s))
        (do_fit @ subfldr.each)()
        result = subfldr.metadata.slice("Frequency", FMR_Power, output="Data")
        result.metadata.update(
            {x: subfldr[0][x] for x in subfldr.metadata.common_keys}
        )

        # Now plot all the fits
        subfldr.setas[12] = "y"
        subfldr.plots_per_page = 6  # Plot results
        subfldr.plot(
            figsize=(8, 8), fmt=["k.", "r-"], markersize=1, extra=extra
        )

        # Work with the overall results
        try:
            result.setas(
                y="H_res$", e="H_res err", x="Frequency"
            )
            result.y = result.y / mu_0  # Convert to A/m
            result.e = result.e / mu_0

            resfldr += result  # Stash the results
        except KeyError:
            continue

    # Merge the two field signs into a single file, taking care of the error columns too
    result = resfldr[0].clone
    for c in [0, 1, 3, 5, 7]:
        result.data[:, c] = (resfldr[1][:, c] + resfldr[0][:, c]) / 2.0
    for c in [2, 4, 6, 8]:
        result.data[:, c] = gmean((resfldr[0][:, c], resfldr[1][:, c]), axis=0)

    # Doing the Kittel fit with an orthogonal distance regression as we have x errors not y errors
    p0 = [2, 200e3, 10e3]  # Some sensible guesses
    result.lmfit(
        Inverse_Kittel, p0=p0, result=True, header="Kittel Fit", output="report"
    )
    result.setas[-1] = "y"

    # result.template.yformatter = TexEngFormatter
    # result.template.xformatter = TexEngFormatter
    result.labels = None
    result.figure(figsize=(6, 8))
    result.subplot(211)
    result.plot(fmt=["r.", "b-"])
    result.annotate_fit(Inverse_Kittel, x=7e9, y=1e5, fontdict={"size": 8})
    result.ylabel = "$H_{res} \\mathrm{(Am^{-1})}$"
    result.title = "Inverse Kittel Fit"

    # Get alpha
    result.subplot(212)
    result.setas(y="Delta_H", e="Delta_H err", x="Freq")
    result.y /= mu_0
    result.e /= mu_0
    result.lmfit(Linear, result=True, header="Width", output="report")
    result.setas[-1] = "y"
    result.plot(fmt=["r.", "b-"])
    result.annotate_fit(Linear, x=5.5e9, y=2.8e3, fontdict={"size": 8})

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Methods Summary

figure(*args, **kargs)	Pass through for matplotlib.pyplot.figure() but alos takes a note of the arguments for later.
plot(*args, **kargs)	Call the plot method for each metadataObject, but switching to a subplot each time.

Methods Documentation

figure(*args, **kargs)

Pass through for matplotlib.pyplot.figure() but alos takes a note of the arguments for later.

plot(*args, **kargs)

Call the plot method for each metadataObject, but switching to a subplot each time.

Parameters

• args – Positional arguments to pass through to the Stoner.plot.PlotMixin.plot() call.

• kargs – Keyword arguments to pass through to the Stoner.plot.PlotMixin.plot() call.

Keyword Arguments

• extra (callable(i,j,d)) – A callable that can carry out additional processing per plot after the plot is done

• figsize (tuple(x,y)) – Size of the figure to create

• dpi (float) – dots per inch on the figure

• edgecolor,facecolor (matplotlib colour) – figure edge and frame colours.

• frameon (bool) – Turns figure frames on or off

• FigureClass (class) – Passed to matplotlib figure call.

• plots_per_page (int) – maximum number of plots per figure.

• tight_layout (dict or False) – If not False, arguments to pass to a call of matplotlib.pyplot.tight_layout(). Defaults to {}

Returns

A list of matplotlib.pyplot.Axes instances.

Notes

If the underlying type of the Stoner.Core.metadataObject instances in the PlotFolder lacks a plot method, then the instances are converted to Stoner.Core.Data.

Each plot is generated as sub-plot on a page. The number of rows and columns of subplots is computed from the aspect ratio of the figure and the number of files in the PlotFolder.