Tensor Logic

Documentation

Introduction

The TensorLogic API provides programmatic access to our Temporal Structural Engine (TSE). It allows developers to generate high-precision chronological matrices based on astronomical coordinates and True Solar Time (TST) correction algorithms.

Our architecture treats time not as a linear sequence, but as a cyclical set of multi-dimensional variables. This API is designed for productivity applications, scheduling algorithms, and behavioral analysis platforms.

$BASE_URL is assigned upon account activation

Authentication

Authenticate your account by including your secret key in API requests. You can manage your API keys in the Dashboard.

HTTP Headers
Authorization: Bearer tl_live_8a92b3c...Content-Type: application/json

Construct Matrix

POST
/matrix/construct

Generates the foundational 4-column coordinate system (Year, Month, Day, Hour) for a specific temporal instance. Automatically applies Longitude correction for True Solar Time.

Request Body

JSON Payload
{
  "timestamp_utc": "2024-05-12T14:30:00Z",
  "geo_coordinates": {
    "latitude": 39.9042,
    "longitude": 116.4074
  },
  "options": {
    "solar_correction": true,
    "precision_mode": "strict"
  }
}

Response (200 OK)

JSON Response
{
  "id": "mtx_8829102",
  "meta": {
    "calc_duration_ms": 14,
    "solar_term_offset": 4.23,
    "version": "v2.1.0"
  },
  "data": {
    "columns": {
      "year": { "stem_id": "S_01", "vector_weight": 0.85, "polarity": 1 },
      "month": { "stem_id": "S_06", "vector_weight": 0.42, "polarity": 0 },
      "day": { "stem_id": "S_09", "vector_weight": 0.91, "polarity": 1 },
      "hour": { "stem_id": "S_04", "vector_weight": 0.33, "polarity": 0 }
    },
    "attributes": {
      "dominant_vector": "V_05_EPSILON",
      "missing_vector": "V_04_DELTA",
      "structure_pattern": "PATTERN_72_A"
    }
  }
}

Cycle Projection

POST
/analytics/projection

Projects the "Base Matrix" against future time streams to calculate efficiency coefficients and resistance levels. Used for identifying "Flow State" windows.

Request Body

JSON Payload
{
  "matrix_id": "mtx_8829102",
  "target_period": {
    "start": "2025-01-01",
    "end": "2025-12-31"
  },
  "granularity": "month"
}

Response (200 OK)

JSON Response
{
  "projection_id": "proj_9921",
  "timeline": [
    {
      "label": "2025-02",
      "cycle_code": "S2_B6",
      "efficiency_score": 88.5,
      "resistance_factor": 0.12,
      "status": "OPTIMAL_FLOW",
      "tags": ["collaborative", "high_output"]
    },
    {
      "label": "2025-03",
      "cycle_code": "S3_B7",
      "efficiency_score": 42.0,
      "resistance_factor": 0.89,
      "status": "HIGH_FRICTION"
    }
  ]
}

Data Models

Vector Types (Base Elements)

Our engine categorizes temporal energy into 5 distinct vector types based on interaction logic.

Vector IDCode NameDescription
V_01ALPHAExpansive, Upward Growth
V_02BETARadiant, High Volatility
V_03GAMMAStabilizing, Neutral Ground
V_04DELTAContractive, Structured
V_05EPSILONFluid, Adaptive Flow

Polarity

1
Logic High
Active State / Positive Charge
0
Logic Low
Passive State / Negative Charge