Ecosystem — StatsLibX & SynaptIX

v0.3.0

ViewX is the visualization engine of the GhostAnalyst data ecosystem. It receives report payloads from StatsLibX (statistics) and SynaptIX (machine learning) through one shared contract, and every library also works standalone.

The Three Libraries

Library	Role	Install
statslibx	Statistics, data handling, simulations	pip install statslibx
synaptix	ML models, evaluation and tuning	pip install synaptix
viewx	Dashboards, EDA, PDF, slides	pip install viewx

Data flows: StatsLibX produces analysis results and DataFrames (pandas or polars) that feed SynaptIX models; both produce report payloads that ViewX renders.

The Payload Contract

from_report_payload() renders any dict with this shape into a Dashboard, PDF report or presentation:

payload.py

python

from viewx import from_report_payload

payload = {
    "title": "Analysis Report",           # required
    "sections": ["Overview", "Details"],  # section order
    "tables": [{                          # required: at least one table
        "section": "Overview",
        "title": "Summary",
        "data": {"metric": ["mae", "r2"], "value": [1.2, 0.94]},
    }],
    "valueboxes": [{"label": "Rows", "value": 150}],
    "figures": [plotly_figure],           # optional: Plotly figures
    "metadata": {"source": "statslibx"},
}

from_report_payload(payload, target="html", filename="report.html")
# targets: "html" (Dashboard) | "report" (PDF) | "presentation" (Slides)

From StatsLibX

from_statslibx.py

python

from statslibx import DescriptiveStats, load_iris

df = load_iris()
summary = DescriptiveStats(df).summary()

# Option 1 — direct method on the result (pip install statslibx[viewx])
summary.to_html("iris.html", theme="dark_enterprise", include_figures=True, data=df)

# Option 2 — manual pipeline with the payload contract
from statslibx import to_report_data
from viewx import from_report_payload

payload = to_report_data(summary, include_figures=True, data=df)
from_report_payload(payload, target="presentation", filename="iris_slides.html")

Without ViewX installed, StatsLibX analysis works normally; export methods raise a clear error with the install instruction.

From SynaptIX

from_synaptix.py

python

import synaptix as sx

df = sx.load_dataset("iris")
X, y = df.drop(columns="species"), df["species"]
model = sx.supervised.RandomForestClassifier().fit(X, y)
evaluation = model.evaluate(X, y)

# Direct export (pip install synaptix[viewx])
evaluation.to_html("evaluacion.html", theme="dark_enterprise")

# Or via the same payload contract
payload = evaluation.to_report_data(include_figures=True)
from_report_payload(payload, target="html", filename="evaluacion.html")

Full Ecosystem Flow

ecosystem.py

python

# pip install statslibx[viewx] synaptix[viewx,stats] viewx
from statslibx import DescriptiveStats, load_iris
import synaptix as sx
from viewx import from_report_payload

df = load_iris()

# 1. Statistics and data quality (StatsLibX)
DescriptiveStats(df).summary()

# 2. Model + evaluation + statistical diagnostics (SynaptIX + StatsLibX)
X, y = df.drop(columns="species"), df["species"]
model = sx.supervised.RandomForestClassifier().fit(X, y)
evaluation = model.evaluate(X, y)
model.diagnostics(X, y)   # inferential tests via statslibx

# 3. Everything into one dashboard (ViewX)
payload = evaluation.to_report_data(include_figures=True)
from_report_payload(payload, target="html", filename="analisis.html")

The three dataset loaders share the same signature (load_dataset(name, backend=..., return_X_y=...)), and polars DataFrames produced by StatsLibX feed SynaptIX models directly.