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playground.py
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# Author: Michał Bednarek PUT Poznan
# Comment: Helper script for validating data created from the simulation
import pickle
import matplotlib.pyplot as plt
import numpy as np
# path = "./data/dataset/final_ds/real/real_train.pickle"
# paths = ["./data/dataset/final_ds/real/real_train.pickle",
# "./data/dataset/final_ds/real/real_val.pickle",
# "./data/dataset/final_ds/real/real_test.pickle"]
#
# paths = ["./data/dataset/final_ds/sim/sim_train.pickle",
# "./data/dataset/final_ds/sim/sim_val.pickle"]
# paths = ["./data/dataset/40_10_60/",
# "./data/dataset/final_ds/real/real_val.pickle",
# "./data/dataset/final_ds/real/real_test.pickle"]
# path = "data/dataset/40_10_60/real_dataset_train.pickle"
real = "./data/experiments/real_200_300/train_050.pickle"
train = "./data/experiments/sim_all/train.pickle"
def noised_modality(data, noise_mag: float = 0.2):
noise = np.random.uniform(-noise_mag, noise_mag, size=data.shape)
data += noise
return data
def compute_magnitude(samples):
return np.sqrt(samples[:, :, 0] ** 2 + samples[:, :, 1] ** 2 + samples[:, :, 2] ** 2)
def playground():
# labels, samples = list(), list()
# for path in paths:
# with open(path, "rb") as fp:
# ds = pickle.load(fp)
# labels.append(ds["stiffness"])
# samples.append(ds["data"])
#
# labels = np.concatenate([*labels], axis=0)
# samples = np.concatenate([*samples], axis=0)
#
# values = np.unique(labels)
# train_dataset_x, train_dataset_y = list(), list()
# val_dataset_x, val_dataset_y = list(), list()
# test_dataset_x, test_dataset_y = list(), list()
#
# for i, val in enumerate(values):
# arr = np.where(labels == val, 1, 0)
# idx = np.argwhere(arr == 1).flatten()
#
# idx_train, idx_val, idx_test = idx[:30], idx[30:40], idx[40:100]
#
# # samples split
# x_train, y_train = samples[idx_train, :, :], labels[idx_train]
# x_train[..., 0] *= -1.0
# x_train[..., 2] *= -1.0
# train_dataset_x.append(x_train)
# train_dataset_y.append(y_train)
#
# x_val, y_val = samples[idx_val, :, :], labels[idx_val]
# x_val[..., 0] *= -1.0
# x_val[..., 2] *= -1.0
# val_dataset_x.append(x_val)
# val_dataset_y.append(y_val)
#
# x_test, y_test = samples[idx_test, :, :], labels[idx_test]
# x_test[..., 0] *= -1.0
# x_test[..., 2] *= -1.0
# test_dataset_x.append(x_test)
# test_dataset_y.append(y_test)
#
# print("Val: {}, num_samples: {}".format(val, arr.sum()))
#
# train_dataset_x = np.vstack(train_dataset_x)
# train_dataset_y = np.vstack(train_dataset_y).flatten()
#
# val_dataset_x = np.vstack(val_dataset_x)
# val_dataset_y = np.vstack(val_dataset_y).flatten()
#
# test_dataset_x = np.vstack(test_dataset_x)
# test_dataset_y = np.vstack(test_dataset_y).flatten()
#
# file = open('data/dataset/40_10_60/real_dataset_train.pickle', 'wb')
# pickle.dump({
# "data": train_dataset_x,
# "stiffness": train_dataset_y
# }, file)
# file.close()
#
# file = open('data/dataset/40_10_60/real_dataset_val.pickle', 'wb')
# pickle.dump({
# "data": val_dataset_x,
# "stiffness": val_dataset_y
# }, file)
# file.close()
#
# file = open('data/dataset/40_10_60/real_dataset_test.pickle', 'wb')
# pickle.dump({
# "data": test_dataset_x,
# "stiffness": test_dataset_y
# }, file)
# file.close()
with open(train, "rb") as fp:
sim_data = pickle.load(fp)
with open(real, "rb") as fp:
data_real = pickle.load(fp)
data = {"data": np.concatenate([sim_data["data"], data_real["data"]], 0),
"stiffness": np.concatenate([sim_data["data"], data_real["data"]], 0)}
acc1, acc2 = list(), list()
w1, w2 = list(), list()
m = np.mean(data["data"], axis=(0, 1), keepdims=True)
s = np.std(data["data"], axis=(0, 1), keepdims=True)
# data_real["data"] = (data_real["data"] - m) / s
for sampl, stif in zip(data_real["data"], data_real["stiffness"]):
acc1 = np.sqrt(sampl[:, 0] ** 2 + sampl[:, 1] ** 2)
acc2 = np.sqrt(sampl[:, 3] ** 2 + sampl[:, 4] ** 2)
w1 = np.sqrt(sampl[:, 6] ** 2 + sampl[:, 7] ** 2)
w2 = np.sqrt(sampl[:, 9] ** 2 + sampl[:, 10] ** 2)
mag = [acc1, acc2, w1, w2]
# acc
for i, signal in enumerate(mag):
plt.subplot(4, 1, i + 1)
plt.plot(signal, 'r')
plt.show()
input(stif)
#
# signal = np.stack([acc1, acc2], -1)
# file = open('data/dataset/val_acc_only_sim.pickle', 'wb')
# pickle.dump({
# "data": signal,
# "stiffness": data["stiffness"]
# }, file)
# file.close()
if __name__ == '__main__':
playground()