Guassian Sims

Gaussian k-sample simulations are found in hyppo.tools. Here, we visualize what these simulations look like. We use these Gaussian simulations when comparing our algorithms against multivariate analysis of variance (MANOVA).

Out:

/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1582: UserWarning: Trying to register the cmap 'rocket' which already exists.
  mpl_cm.register_cmap(_name, _cmap)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1583: UserWarning: Trying to register the cmap 'rocket_r' which already exists.
  mpl_cm.register_cmap(_name + "_r", _cmap_r)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1582: UserWarning: Trying to register the cmap 'mako' which already exists.
  mpl_cm.register_cmap(_name, _cmap)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1583: UserWarning: Trying to register the cmap 'mako_r' which already exists.
  mpl_cm.register_cmap(_name + "_r", _cmap_r)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1582: UserWarning: Trying to register the cmap 'icefire' which already exists.
  mpl_cm.register_cmap(_name, _cmap)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1583: UserWarning: Trying to register the cmap 'icefire_r' which already exists.
  mpl_cm.register_cmap(_name + "_r", _cmap_r)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1582: UserWarning: Trying to register the cmap 'vlag' which already exists.
  mpl_cm.register_cmap(_name, _cmap)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1583: UserWarning: Trying to register the cmap 'vlag_r' which already exists.
  mpl_cm.register_cmap(_name + "_r", _cmap_r)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1582: UserWarning: Trying to register the cmap 'flare' which already exists.
  mpl_cm.register_cmap(_name, _cmap)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1583: UserWarning: Trying to register the cmap 'flare_r' which already exists.
  mpl_cm.register_cmap(_name + "_r", _cmap_r)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1582: UserWarning: Trying to register the cmap 'crest' which already exists.
  mpl_cm.register_cmap(_name, _cmap)
/opt/buildhome/python3.7/lib/python3.7/site-packages/seaborn/cm.py:1583: UserWarning: Trying to register the cmap 'crest_r' which already exists.
  mpl_cm.register_cmap(_name + "_r", _cmap_r)

import matplotlib.pyplot as plt
import seaborn as sns
from hyppo.tools import gaussian_3samp

# make plots look pretty
sns.set(color_codes=True, style="white", context="talk", font_scale=2)
PALETTE = sns.color_palette("Greys", n_colors=9)
sns.set_palette(PALETTE[2::2])

# constants
N = 500
CASES = [1, 2, 3, 4, 5]


# make a function to plot the guassian simulations
def plot_gaussian_sims():
    """Plot simulations"""
    fig, ax = plt.subplots(nrows=1, ncols=5, figsize=(28, 6))

    sim_titles = [
        "None Different",
        "One Different",
        "All Different",
        "One Not Gaussian",
        "None Gaussian",
    ]

    # plt.suptitle("Gaussian Simulations", y=0.93, va="baseline")

    for i, col in enumerate(ax):
        sim_title = sim_titles[i]

        # rotated k-sample simulation
        sims = gaussian_3samp(N, epsilon=4, weight=0.9, case=CASES[i])

        # plot the nose and noise-free sims
        for index in range(len(sims)):
            col.scatter(
                sims[index][:, 0],
                sims[index][:, 1],
                label="Sample {}".format(index + 1),
            )

        # make the plot look pretty
        col.set_title("{}".format(sim_title))
        col.set_xticks([])
        col.set_yticks([])
        col.set_xlim(-5, 5)
        if CASES[i] not in [2, 4]:
            col.set_ylim(-5, 5)
        sns.despine(left=True, bottom=True, right=True)

    leg = plt.legend(
        bbox_to_anchor=(0.5, 0.17),
        bbox_transform=plt.gcf().transFigure,
        ncol=3,
        loc="upper center",
    )
    leg.get_frame().set_linewidth(0.0)
    for legobj in leg.legendHandles:
        legobj.set_linewidth(5.0)
    plt.subplots_adjust(hspace=0.75)


# run the created function for the guassian simulations
plot_gaussian_sims()