# Guassian SimsΒΆ

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).

```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 Gaussian 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 Gaussian simulations
plot_gaussian_sims()
```

Total running time of the script: ( 0 minutes 0.245 seconds)

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