metacube 0.1.0

Interactive 3D cube visualization for multi-dimensional datasets

metacube (Python)

Interactive 3D cube visualization for tabular data. Provide a CSV and a config YAML — metacube transforms it and produces a self-contained HTML file or serves it locally.

Try it in the browser without installing →

Installation

pip install metacube

Quick Start

# Copy bundled example files
metacube examples --dest my_data/

# Explore interactively (no files written)
metacube dev my_data/census_tissue_general_counts.csv \
  --config my_data/census_tissue_general.yaml

# Export a standalone HTML file
metacube export my_data/census_tissue_general_counts.csv \
  --config my_data/census_tissue_general.yaml \
  --output viz.html

CLI Reference

metacube dev — transform and serve in one step

metacube dev <CSV_FILE> --config <CONFIG_YAML> [--port 8000] [--no-browser]

Transforms the CSV in memory and serves a local visualization. Nothing is written to disk. Press Ctrl+C to stop.

metacube dev data.csv --config config.yaml
metacube dev data.csv --config config.yaml --port 9000 --no-browser

---

metacube export — transform and export in one step

metacube export <CSV_FILE> --config <CONFIG_YAML> [--output viz.html]

Produces a fully self-contained HTML file that works offline (no server needed). Default output name: <csv_stem>.html.

metacube export data.csv --config config.yaml
metacube export data.csv --config config.yaml --output my_viz.html

---

metacube transform — CSV → JSON

metacube transform <CSV_FILE> --config <CONFIG_YAML> [--output data.json]

Converts a CSV + config to an intermediate CubeData JSON. Use this when you want to cache the transformed data and serve or build from it later.

metacube transform data.csv --config config.yaml            # writes data.json
metacube transform data.csv --config config.yaml -o out.json
metacube transform data.csv --config config.yaml | jq .config  # pipe to jq

---

metacube build — JSON → HTML

metacube build <DATA_JSON> [--output cube.html]

Injects a pre-transformed CubeData JSON into the visualization template.

metacube build data.json
metacube build data.json --output my_viz.html

---

metacube serve — JSON → browser

metacube serve <DATA_JSON> [--port 8000] [--no-browser]

Serves a pre-transformed JSON locally.

metacube serve data.json
metacube serve data.json --port 9000

---

metacube examples — copy bundled example files

metacube examples [--dest ./] [--list]

Copies bundled example CSV and config YAML files to a local directory.

metacube examples                    # copy to current directory
metacube examples --dest my_data/    # copy to my_data/
metacube examples --list             # print file names only

---

Python API

from metacube import csv_to_cube_data, build_html, serve

# Transform
data = csv_to_cube_data("data.csv", "config.yaml")

# Export standalone HTML
build_html(data, "viz.html")

# Serve locally
serve(data, port=8000, open_browser=True)

---

Step-by-step Workflow

# 1. Transform CSV → JSON (once, reuse for multiple outputs)
metacube transform dataset.csv --config config.yaml --output data.json

# 2a. Explore in browser
metacube serve data.json

# 2b. Or export for sharing
metacube build data.json --output shareable.html

---

Config YAML Reference

Full reference with all modes and examples: docs/config.md

The config YAML maps CSV columns to visualization axes and enables optional features.

Minimal config

title: "My Dataset"

axes:
  x:
    label: "Species"
    column: "species"
  y:
    label: "Assay"
    column: "assay"
  z:
    label: "Tissue"
    column: "tissue"

size_colour: "cell_count"

Full config with all options

title: "My Dataset"

axes:
  x:
    label: "Organism"
    column: "organism"
    max_labels: 5          # keep only top N values by count (optional)
    colors:                # per-value hex colors (optional)
      "Human": "#3b82f6"
      "Mouse": "#10b981"
    group_separator: " - " # group axis labels by prefix (optional)
  y:
    label: "Assay"
    column: "assay"
    max_labels: 20
  z:
    label: "Tissue"
    column: "tissue"
    max_labels: 30

size_colour: "cell_count"     # numeric column for color gradient (omit for flat blue)
datasets_column: "dataset_id" # column of dataset IDs, shown in hover tooltip (optional)

# ── Drilldown: click a cell to see more detail ────────────────────────────────

drilldown:
  # Option A: treemap popup (floating panel, stays alongside the cube)
  type: "treemap"
  category_column: "dataset_title"    # outer grouping in the treemap
  subcategory_column: "cell_type"     # inner tiles in the treemap
  count_column: "cell_count"          # column to sum for tile size

  # Option B: zoom into a finer-grained inner cube
  # type: "zoom"
  # axes:
  #   z:                              # redefine one or more axes
  #     label: "Organ"
  #     column: "organ"
  #     max_labels: 30
  # size_colour: "cell_count"
  # treemap:                          # optional: also show treemap panel in zoom view
  #   category_column: "dataset_title"
  #   subcategory_column: "cell_type"
  #   count_column: "cell_count"

# ── Slice: 2D matrix for the selected cell ───────────────────────────────────

slice:
  fixed_axis: "x"            # "x", "y", or "z" — the axis held fixed
  count_column: "cell_count" # column to aggregate (optional)

# ── Info panel: structured text on click ─────────────────────────────────────

info:
  columns:
    title:  "study_title"
    author: "first_author"
    year:   "year"
    doi:    "doi"
    url:    "url"

# ── Axis ordering ─────────────────────────────────────────────────────────────

axis_order: "frequency"  # "frequency" (default) or "cluster"
                         # frequency: labels ordered by descending total count
                         #            (same rank as max_labels — most common first)
                         # cluster:   after max_labels selection, labels are reordered
                         #            by average-linkage cosine clustering on their
                         #            co-occurrence profiles across the other two axes.
                         #            Groups categories with similar occupancy patterns.

Config options summary

Field	Required	Description
title	no	Visualization title shown in the header
axes.{x,y,z}.column	yes	CSV column name for each axis
axes.{x,y,z}.label	no	Display label (defaults to column name)
axes.{x,y,z}.max_labels	no	Keep only top N values by count
axes.{x,y,z}.colors	no	Map of value → hex color
axes.{x,y,z}.group_separator	no	String to split labels into groups
size_colour	no	Numeric column for colour gradient (omit for flat blue)
datasets_column	no	Column of dataset/study IDs, shown in hover tooltip
drilldown	no	Drilldown config (treemap or zoom)
slice	no	2D matrix slice in the details panel
info	no	Structured entries shown as center modal on cell click
info_box	no	Structured entries shown inline in the side panel on cell click
axis_order	no	"frequency" (default) or "cluster" — see below

axis_order: "frequency" (default): labels are presented in descending order of total count, matching the max_labels selection rank.

axis_order: "cluster": after max_labels selection, each axis is reordered by average-linkage hierarchical clustering on co-occurrence profiles. Each label becomes a row vector of its counts across all combinations of the other two axes (mode-k tensor unfolding); rows are L2-normalised and pairwise cosine distances feed scipy.cluster.hierarchy.linkage(method="average"). This groups categories with similar occupancy patterns together, making large-scale structure easier to read. This uses scipy, which is installed as a dependency of metacube. The R package uses stats::hclust and needs no additional dependencies.

---

Complete Example

Given a CSV data.csv:

organism,assay,tissue,study,cell_type,count
Human,scRNA-seq,blood,Study A,T cell,5000
Human,scRNA-seq,blood,Study A,B cell,2000
Human,scRNA-seq,liver,Study B,hepatocyte,8000
Mouse,scRNA-seq,blood,Study C,T cell,3000

And config.yaml:

title: "Single-cell Atlas"

axes:
  x:
    label: "Organism"
    column: "organism"
  y:
    label: "Assay"
    column: "assay"
  z:
    label: "Tissue"
    column: "tissue"

size_colour: "count"

drilldown:
  type: "treemap"
  category_column: "study"
  subcategory_column: "cell_type"
  count_column: "count"

slice:
  fixed_axis: "x"
  count_column: "count"

metacube export data.csv --config config.yaml --output atlas.html
# Open atlas.html in any browser — no server needed

License

MIT — see LICENSE.