To help combine statistics and machine learning with multiparameter persistence, we would like to map signed barcodes to a Banach space or Hilbert space. We use iteratively refined triangulations to define a Schauder basis of compactly supported Lipschitz functionals. We prove that evaluation of these functionals embeds signed barcodes into sequence space via a map which is both linear, and Lipschitz with respect to the 1-Wasserstein distance. I will illustrate these results with examples for one-parameter persistence, two-parameter persistence, and the variant called mixup barcodes.