{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:RSEHEECEM2C6CXMEDQQT5PEUUS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e0f8af14f4ce963368739e95b1c83edab57d723cda72a8520b9d910a032c5445","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.OT","submitted_at":"2017-01-28T13:37:56Z","title_canon_sha256":"6b80d5dd27b264e3cdcc59908028a40d1402161887a0ef90a1f275c94d1af14a"},"schema_version":"1.0","source":{"id":"1701.08290","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.08290","created_at":"2026-05-18T00:51:54Z"},{"alias_kind":"arxiv_version","alias_value":"1701.08290v1","created_at":"2026-05-18T00:51:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.08290","created_at":"2026-05-18T00:51:54Z"},{"alias_kind":"pith_short_12","alias_value":"RSEHEECEM2C6","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"RSEHEECEM2C6CXME","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"RSEHEECE","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:823bc8de2106260d7de4a0552e07a6c0c9cffed0e19257034a6263dbe1668c71","target":"graph","created_at":"2026-05-18T00:51:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Data visualizations can reveal trends and patterns that are not otherwise obvious from the raw data or summary statistics. While visualizing low-dimensional data is relatively straightforward (for example, plotting the change in a variable over time as (x,y) coordinates on a graph), it is not always obvious how to visualize high-dimensional datasets in a similarly intuitive way. Here we present HypeTools, a Python toolbox for visualizing and manipulating large, high-dimensional datasets. Our primary approach is to use dimensionality reduction techniques (Pearson, 1901; Tipping & Bishop, 1999) ","authors_text":"Andrew C. Heusser, Jeremy R. Manning, Kirsten Ziman, Lucy L. W. Owen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.OT","submitted_at":"2017-01-28T13:37:56Z","title":"HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.08290","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0bdcff07fec07cff14167b1e9cf23fc1d5a87d7a5e8571826d089a848e0a6eb9","target":"record","created_at":"2026-05-18T00:51:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e0f8af14f4ce963368739e95b1c83edab57d723cda72a8520b9d910a032c5445","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.OT","submitted_at":"2017-01-28T13:37:56Z","title_canon_sha256":"6b80d5dd27b264e3cdcc59908028a40d1402161887a0ef90a1f275c94d1af14a"},"schema_version":"1.0","source":{"id":"1701.08290","kind":"arxiv","version":1}},"canonical_sha256":"8c887210446685e15d841c213ebc94a4bf5b15aa9492a97695fc706f4e77cab3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8c887210446685e15d841c213ebc94a4bf5b15aa9492a97695fc706f4e77cab3","first_computed_at":"2026-05-18T00:51:54.653817Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:51:54.653817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QzbsvR/nHPCy6dzWJ+ntQ1Eich2kadEKhjEnOZgZRSvwv0A0zGXkks0PJP2EslCrcJzbwo/tUz7XajGNmZkkAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:51:54.654553Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.08290","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0bdcff07fec07cff14167b1e9cf23fc1d5a87d7a5e8571826d089a848e0a6eb9","sha256:823bc8de2106260d7de4a0552e07a6c0c9cffed0e19257034a6263dbe1668c71"],"state_sha256":"406f3dc5ef504ee676ec72e15b57a34cfae3094d1bd7b3096ecb79d876657c2f"}