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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.23126 |
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| _version_ | 1866912300625035264 |
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| author | PASTA Collaboration Rosignoli, L. Della Croce, A. Leitinger, E. Leuzzi, L. Papini, G. Traina, A. Sartori, S. Borghi, N. Ceccarelli, E. |
| author_facet | PASTA Collaboration Rosignoli, L. Della Croce, A. Leitinger, E. Leuzzi, L. Papini, G. Traina, A. Sartori, S. Borghi, N. Ceccarelli, E. |
| contents | In the big data era of Astrophysics, the improvement of visualization techniques can greatly enhance the ability to identify and interpret key features in complex datasets. This aspect of data analysis will become even more relevant in the near future, with the expected growth of data volumes. With our studies, we aim to drive progress in this field and inspire further research. We present the second release of pastamarkers, a Python-based matplotlib package that we initially presented last year. In this new release we focus on big data visualization and update the content of our first release. We find that analyzing complex problems and mining large data sets becomes significantly more intuitive and engaging when using the familiar and appetizing colors of pasta sauces instead of traditional colormaps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | pastamarkers 2: pasta sauce colormaps for your flavorful results PASTA Collaboration Rosignoli, L. Della Croce, A. Leitinger, E. Leuzzi, L. Papini, G. Traina, A. Sartori, S. Borghi, N. Ceccarelli, E. Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology In the big data era of Astrophysics, the improvement of visualization techniques can greatly enhance the ability to identify and interpret key features in complex datasets. This aspect of data analysis will become even more relevant in the near future, with the expected growth of data volumes. With our studies, we aim to drive progress in this field and inspire further research. We present the second release of pastamarkers, a Python-based matplotlib package that we initially presented last year. In this new release we focus on big data visualization and update the content of our first release. We find that analyzing complex problems and mining large data sets becomes significantly more intuitive and engaging when using the familiar and appetizing colors of pasta sauces instead of traditional colormaps. |
| title | pastamarkers 2: pasta sauce colormaps for your flavorful results |
| topic | Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2503.23126 |