Arranging Multiple DCCT Plots

Visualize a simulated inversion recovery dataset utilizing the benefits of the DCCT plotting style.

We can image data in both the phase cycling domain, as well as the coherence transfer domain. Artifacts can clearly be discerned from signal in the coherence transfer domain as well as visualizing the inversion of phase using the domain colored plotting style.

----------  logging output to /home/jmfranck/pyspecdata.0.log  ----------
Warning!!! FT clear startpoints is obsolete! use ft_new_startpoint instead
1: Data

from pylab import rcParams
import pyspecdata as psd
from numpy.random import seed
import sympy as s
from collections import OrderedDict
from matplotlib.gridspec import GridSpec

seed(2021)
rcParams["image.aspect"] = "auto"  # needed for sphinx gallery
# sphinx_gallery_thumbnail_number = 1
psd.init_logging(level="debug")

with psd.figlist_var() as fl:
    # provide the symbols that we use for the fake data:
    t2, td, vd, ph1, ph2 = s.symbols("t2 td vd ph1 ph2")
    echo_time = 5e-3
    data = psd.fake_data(
        # Give the functional form of the fake data.
        # This is an inversion recovery with
        # T₁ of 0.2
        # T₂* broadening of 50 Hz
        # (echo maximum at echo_time)
        # amplitude of 21
        # resonance offset 100 Hz
        21
        * (1 - 2 * s.exp(-vd / 0.2))
        * s.exp(+1j * 2 * s.pi * 100 * (t2) - abs(t2) * 50 * s.pi),
        # next we give our dimensions with outer loops first, as they
        # would be acquired on the spectrometer
        # (ordering does matter, because fake_data applies a
        # time-dependent resonance variation -- see fake_data doc.)
        OrderedDict([
            ("vd", psd.nddata(psd.r_[0:1:40j], "vd")),
            ("ph1", psd.nddata(psd.r_[0, 2] / 4.0, "ph1")),
            ("ph2", psd.nddata(psd.r_[0:4] / 4.0, "ph2")),
            ("t2", psd.nddata(psd.r_[0:0.2:256j] - echo_time, "t2")),
        ]),
        {"ph1": 0, "ph2": 1},
    )
    # reorder into a format more suitable for plotting
    data.reorder(["ph1", "ph2", "vd", "t2"])
    fig = fl.next("Data")  # Make figure object to place the DCCT
    gs = GridSpec(1, 2, figure=fig, left=0.05, right=0.95)
    psd.DCCT(
        data,
        fig,
        title="Time Domain",
        bbox=gs[0, 0],
    )
    data.ft("t2")
    psd.DCCT(
        data,
        fig,
        title="Frequency Domain",
        bbox=gs[0, 1],
    )