ENGINE:
DATA SCIENCE & VISUALIZATIONS
STREAM PARSER:
__CHART__ NATIVE SVG/PNG
PACKAGES:
PANDAS, NUMPY, SCIPY, MATPLOTLIB
2.0X ANALYTICS TIER
< ENGINE DIRECTORY
Interactive Lab
REST API Snippets
PYTHON ANALYTICS PIPELINE
Loss Curve (Chart)
Pandas Aggregate
import numpy as np import io import base64 # Simulating Neural Network Loss Convergence epochs = np.arange(1, 21) loss = 1.0 / (epochs ** 0.5) + np.random.normal(0, 0.02, 20) print(f"[INFO] Processed {len(epochs)} Training Epochs") print(f"[INFO] Final Loss: {loss[-1]:.4f} | Min Loss: {np.min(loss):.4f}") # Native __CHART__ SVG Stream Parser Output svg_data = '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 500 200" width="100%" height="200"><rect width="500" height="200" fill="%23040814" rx="8"/><polyline fill="none" stroke="%2376b900" stroke-width="3" points="20,160 80,120 140,140 200,60 260,80 320,30 380,50 440,20 480,45"/><circle cx="320" cy="30" r="5" fill="%2300f0ff"/><circle cx="440" cy="20" r="5" fill="%23a855f7"/><text x="30" y="35" fill="%2394a3b8" font-size="12" font-family="monospace">Loss Convergence Curve (Epochs vs MSE)</text></svg>' print(f"__CHART__:data:image/svg+xml;utf8,{svg_data}")
RUN & RENDER CHART
INLINE RENDERED VISUALIZATION
SVG STREAM
Execute code to generate live vectorized charts and analytical matrices.
STDOUT LOGS:
[IDLE] Waiting for execution...