Visualize distribution of velocities

The goal of this example is to show the distribution of velocities used to train the model and compare it with the distribution of the velocities while running the center out reach task.

Set data source

Retrieve the data from AWS S3

from urllib.parse import urljoin

import pooch

DOWNLOAD_BASE_URL = "https://neural-data-simulator.s3.amazonaws.com/sample_data/v1/"

MONKEY_DATA_PATH = pooch.retrieve(
    url=urljoin(DOWNLOAD_BASE_URL, "monkey_velocities.npz"),
    known_hash="md5:b1ef650106210d65da2a3075e7767a21",
)
HUMAN_DATA_PATH = pooch.retrieve(
    url=urljoin(DOWNLOAD_BASE_URL, "human_velocities.npz"),
    known_hash="md5:b2391f7759845f5256f651dc4e16fb6e",
)

Load data

Load the data to be plotted.

from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import numpy as np

monkey_file = np.load(MONKEY_DATA_PATH)
human_file = np.load(HUMAN_DATA_PATH)

monkey_data = monkey_file["arr_0"]
human_data = human_file["data"]

Scale monkey data

Monkey data has to be scaled in the same way it was done when training the model.

from sklearn.preprocessing import StandardScaler

VELOCITY_LIMIT = 20

scaler = StandardScaler()
scaler.fit(monkey_data)

monkey_data = scaler.transform(monkey_data)
monkey_data = np.clip(monkey_data, -VELOCITY_LIMIT, VELOCITY_LIMIT)

Plot data

plt.rcParams.update({"font.size": 14})

figure(figsize=(20, 5), dpi=180)
plt.hist(human_data[:, 0], color="black", bins=20, label="velocity x", alpha=0.5)
plt.hist(human_data[:, 1], color="red", bins=20, label="velocity y", alpha=0.5)

plt.legend()
plt.title("Human Velocities")
plt.show()

figure(figsize=(20, 5), dpi=180)
plt.hist(monkey_data[:, 0], color="black", bins=20, label="velocity x", alpha=0.5)
plt.hist(monkey_data[:, 1], color="red", bins=20, label="velocity y", alpha=0.5)

plt.legend()
plt.title("Monkey Velocities")
plt.show()

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