def plotPerformance(self, results: EDASDataset, title, **kwargs):
plt.title(title)
print("Plotting: " + ",".join(list(results.ids)))
valLoss: np.ndarray = results.getArray("val_loss").xr.values
trainlLoss: np.ndarray = results.getArray("loss").xr.values
x = range(valLoss.shape[0])
plt.plot(x, valLoss, "r-", label="Validation Loss")
plt.plot(x, trainlLoss, "b--", label="Training Loss")
plt.legend()
plt.show()
def plotPrediction(self, results: EDASDataset, title, **kwargs):
plt.title(title)
print("Plotting: " + ",".join(list(results.ids)))
prediction: np.ndarray = results.getArray("prediction").xr.values
target: np.ndarray = results.getArray("target").xr.values
x = range(prediction.shape[0])
plt.plot(x, prediction, "r-", label="prediction")
plt.plot(x, target, "b--", label="target")
plt.legend()
plt.show()
