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move sliced dataset testing script

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This commit is contained in:
Joseph Hopfmüller
2024-12-02 18:50:13 +01:00
parent 1dcefecf59
commit aa2e7a4cb4
2 changed files with 88 additions and 51 deletions
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88
src/single-core-regen/sliced_dataset_test.py Normal file
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# move into dir single-core-regen before running
from util.datasets import FiberRegenerationDataset
from torch.utils.data import DataLoader
from matplotlib import pyplot as plt
import numpy as np
# def eye_dataset(dataset, no_symbols=None, offset=False, show=True):
# if no_symbols is None:
# no_symbols = len(dataset)
# _, axs = plt.subplots(2,2, sharex=True, sharey=True)
# xaxis = np.linspace(0,dataset.symbols_per_slice,dataset.samples_per_slice)
# roll = dataset.samples_per_symbol//2 if offset else 0
# for E_out, E_in in dataset[roll:dataset.samples_per_symbol*no_symbols+roll:dataset.samples_per_symbol]:
# E_in_x, E_in_y, E_out_x, E_out_y = E_in[0], E_in[1], E_out[0], E_out[1]
# axs[0,0].plot(xaxis, np.abs( E_in_x.numpy())**2, alpha=0.05, color='C0')
# axs[1,0].plot(xaxis, np.abs( E_in_y.numpy())**2, alpha=0.05, color='C0')
# axs[0,1].plot(xaxis, np.abs(E_out_x.numpy())**2, alpha=0.05, color='C0')
# axs[1,1].plot(xaxis, np.abs(E_out_y.numpy())**2, alpha=0.05, color='C0')
# if show:
# plt.show()
# # def plt_dataloader(dataloader, show=True):
# # _, axs = plt.subplots(2,2, sharex=True, sharey=True)
# # E_outs, E_ins = next(iter(dataloader))
# # for i, (E_out, E_in) in enumerate(zip(E_outs, E_ins)):
# # xaxis = np.linspace(dataset.symbols_per_slice*i,dataset.symbols_per_slice+dataset.symbols_per_slice*i,dataset.samples_per_slice)
# # E_in_x, E_in_y, E_out_x, E_out_y = E_in[0], E_in[1], E_out[0], E_out[1]
# # axs[0,0].plot(xaxis, np.abs(E_in_x.numpy())**2)
# # axs[1,0].plot(xaxis, np.abs(E_in_y.numpy())**2)
# # axs[0,1].plot(xaxis, np.abs(E_out_x.numpy())**2)
# # axs[1,1].plot(xaxis, np.abs(E_out_y.numpy())**2)
# # if show:
# # plt.show()
if __name__ == "__main__":
dataset = FiberRegenerationDataset("data/20241115-175517-128-16384-10000-0-0-17-0-PAM4-0.ini", symbols=13, drop_first=100, output_dim=26, num_symbols=100)
loader = DataLoader(dataset, batch_size=10, shuffle=True)
x = []
y_fiber_in = []
y_fiber_out = []
for i, batch in enumerate(loader):
# if i > 128:
# break
fiber_in, fiber_out, timestamp = batch
fiber_out = fiber_out.reshape(fiber_out.shape[0], -1, 2)
fiber_out = fiber_out[:,fiber_out.shape[1]//2, :]
# input_data = input_data.reshape(-1,2)
# target = target.reshape(-1,2).squeeze()
# timestamp = timestamp.reshape(-1,1).squeeze()
x.append(timestamp.detach().numpy())
y_fiber_in.append(fiber_in.abs().square().detach().numpy())
y_fiber_out.append(fiber_out.abs().square().detach().numpy())
x = np.concat(x)
y_fiber_in = np.concat(y_fiber_in)
y_fiber_out = np.concat(y_fiber_out)
# order = np.argsort(x)
# x = x[order]
# y = y[order]
fig, axs = plt.subplots(2,2, sharex=True, sharey=True)
axs[0,0].scatter((x/dataset.samples_per_symbol)%2, y_fiber_in[:,0], s=1, alpha=0.1)
axs[1,0].scatter((x/dataset.samples_per_symbol)%2, y_fiber_in[:,1], s=1, alpha=0.1)
axs[0,1].scatter((x/dataset.samples_per_symbol)%2, y_fiber_out[:,0], s=1, alpha=0.1)
axs[1,1].scatter((x/dataset.samples_per_symbol)%2, y_fiber_out[:,1], s=1, alpha=0.1)
plt.show()
# eye_dataset(dataset, 1000, offset=True, show=False)
# train_loader = DataLoader(dataset, batch_size=10, shuffle=False)
# plt_dataloader(train_loader, show=False)
# plt.show()

51
src/single-core-regen/testing/sliced_dataset_test.py
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# move into dir single-core-regen before running
from util.dataset import SlicedDataset
from torch.utils.data import DataLoader
from matplotlib import pyplot as plt
import numpy as np
def eye_dataset(dataset, no_symbols=None, offset=False, show=True):
if no_symbols is None:
no_symbols = len(dataset)
_, axs = plt.subplots(2,2, sharex=True, sharey=True)
xaxis = np.linspace(0,dataset.symbols_per_slice,dataset.samples_per_slice)
roll = dataset.samples_per_symbol//2 if offset else 0
for E_out, E_in in dataset[roll:dataset.samples_per_symbol*no_symbols+roll:dataset.samples_per_symbol]:
E_in_x, E_in_y, E_out_x, E_out_y = E_in[0], E_in[1], E_out[0], E_out[1]
axs[0,0].plot(xaxis, np.abs( E_in_x.numpy())**2, alpha=0.05, color='C0')
axs[1,0].plot(xaxis, np.abs( E_in_y.numpy())**2, alpha=0.05, color='C0')
axs[0,1].plot(xaxis, np.abs(E_out_x.numpy())**2, alpha=0.05, color='C0')
axs[1,1].plot(xaxis, np.abs(E_out_y.numpy())**2, alpha=0.05, color='C0')
if show:
plt.show()
# def plt_dataloader(dataloader, show=True):
# _, axs = plt.subplots(2,2, sharex=True, sharey=True)
# E_outs, E_ins = next(iter(dataloader))
# for i, (E_out, E_in) in enumerate(zip(E_outs, E_ins)):
# xaxis = np.linspace(dataset.symbols_per_slice*i,dataset.symbols_per_slice+dataset.symbols_per_slice*i,dataset.samples_per_slice)
# E_in_x, E_in_y, E_out_x, E_out_y = E_in[0], E_in[1], E_out[0], E_out[1]
# axs[0,0].plot(xaxis, np.abs(E_in_x.numpy())**2)
# axs[1,0].plot(xaxis, np.abs(E_in_y.numpy())**2)
# axs[0,1].plot(xaxis, np.abs(E_out_x.numpy())**2)
# axs[1,1].plot(xaxis, np.abs(E_out_y.numpy())**2)
# if show:
# plt.show()
if __name__ == "__main__":
dataset = SlicedDataset("data/20241115-175517-128-16384-10000-0-0-17-0-PAM4-0.ini", symbols=1, drop_first=100)
print(dataset[0][0].shape)
eye_dataset(dataset, 1000, offset=True, show=False)
train_loader = DataLoader(dataset, batch_size=10, shuffle=False)
# plt_dataloader(train_loader, show=False)
plt.show()