API Documentation - Elneuro | REST API Reference

Overview

The Elneuro API provides programmatic access to our analytics platform, enabling you to integrate statistical analysis, machine learning, and quality control capabilities directly into your applications and workflows.

API Access Requirements

API access is available for Pro and Enterprise subscribers. Free tier users can explore the API documentation but cannot make authenticated requests.

Base URL

https://api.elneuro.com/v1

Request Format

All requests use HTTPS
Request bodies should be JSON with Content-Type: application/json
Responses are JSON formatted
Use UTF-8 encoding for all strings

Authentication

The Elneuro API uses JWT (JSON Web Tokens) for authentication. Include your token in the Authorization header of all requests.

Obtaining an API Token

Log in to your Elneuro account
Navigate to Account Settings > API Access
Click "Generate API Key"
Copy and securely store your token

Using Your Token

# Include in request headers
Authorization: Bearer YOUR_API_TOKEN

# Example with curl
curl -H "Authorization: Bearer eyJhbGciOiJIUzI1NiIs..." \
     https://api.elneuro.com/v1/data/datasets
            

Security Best Practices

Never commit API tokens to version control
Use environment variables to store tokens
Rotate tokens periodically
Use separate tokens for development and production

Rate Limits

API requests are rate-limited based on your subscription tier:

Tier	Requests/Minute	Requests/Day
Pro	60	10,000
Enterprise	300	100,000

Rate limit information is included in response headers:

X-RateLimit-Limit: 60
X-RateLimit-Remaining: 58
X-RateLimit-Reset: 1640000000
            

Error Handling

The API uses standard HTTP status codes and returns detailed error information:

Code	Description
200	Success
201	Created
400	Bad Request - Invalid parameters
401	Unauthorized - Invalid or missing token
403	Forbidden - Insufficient permissions
404	Not Found - Resource doesn't exist
429	Too Many Requests - Rate limit exceeded
500	Internal Server Error

Error Response Format

{
    "error": {
        "code": "invalid_parameter",
        "message": "The 'column' parameter must be a valid column name",
        "details": {
            "parameter": "column",
            "value": "nonexistent_column"
        }
    }
}
            

Data Management

POST /data/upload

Upload a dataset for analysis. Supports CSV and Excel formats.

Request Body (multipart/form-data)

Parameter	Type	Description
file required	file	CSV or Excel file (max 50MB)
name	string	Dataset name (auto-generated if not provided)

Response

{
    "dataset_id": "ds_abc123xyz",
    "name": "sales_data_2024",
    "rows": 1500,
    "columns": 12,
    "column_types": {
        "date": "datetime",
        "revenue": "numeric",
        "region": "categorical"
    },
    "created_at": "2024-01-15T10:30:00Z"
}
                    

GET /data/datasets

List all datasets associated with your account.

Response

{
    "datasets": [
        {
            "dataset_id": "ds_abc123xyz",
            "name": "sales_data_2024",
            "rows": 1500,
            "columns": 12,
            "created_at": "2024-01-15T10:30:00Z"
        }
    ],
    "total": 1
}
                    

Statistical Analysis

POST /analysis/descriptive

Compute descriptive statistics for specified columns.

Request Body

{
    "dataset_id": "ds_abc123xyz",
    "columns": ["revenue", "quantity"],
    "statistics": ["mean", "std", "min", "max", "percentiles"]
}
                

Response

{
    "results": {
        "revenue": {
            "mean": 15234.56,
            "std": 4521.23,
            "min": 500.00,
            "max": 45000.00,
            "percentiles": {
                "25": 12000.00,
                "50": 14500.00,
                "75": 18000.00
            }
        }
    }
}
                    

POST /analysis/hypothesis-test

Perform hypothesis tests (t-test, ANOVA, chi-square, etc.).

Request Body

{
    "dataset_id": "ds_abc123xyz",
    "test_type": "two_sample_t",
    "column": "revenue",
    "group_column": "region",
    "alpha": 0.05
}
                

Machine Learning

POST /ml/train

Train a machine learning model on your dataset.

Request Body

{
    "dataset_id": "ds_abc123xyz",
    "target": "revenue",
    "features": ["quantity", "discount", "region"],
    "model_type": "random_forest",
    "task": "regression",
    "test_size": 0.2
}
                

Supported Model Types

linear_regression, ridge, lasso
logistic_regression
decision_tree, random_forest, gradient_boosting
svm, knn
neural_network

POST /ml/predict

Generate predictions using a trained model.

Request Body

{
    "model_id": "model_xyz789",
    "data": [
        {"quantity": 100, "discount": 0.1, "region": "North"},
        {"quantity": 250, "discount": 0.15, "region": "South"}
    ]
}
                

Control Charts (SPC)

POST /spc/control-chart

Generate a control chart with calculated limits.

Request Body

{
    "dataset_id": "ds_abc123xyz",
    "column": "measurement",
    "chart_type": "xbar_r",
    "subgroup_size": 5,
    "spec_limits": {
        "lsl": 9.5,
        "usl": 10.5
    }
}
                

Chart Types

xbar_r - X-bar and R chart
xbar_s - X-bar and S chart
i_mr - Individuals and Moving Range
p_chart - Proportion chart
np_chart - Count chart
c_chart - Defects chart
u_chart - Defects per unit
cusum - CUSUM chart
ewma - EWMA chart

Python SDK

The official Python SDK provides a convenient interface to the Elneuro API.

Installation

pip install elneuro

Quick Start

import elneuro

# Initialize client
client = elneuro.Client(api_key="YOUR_API_KEY")

# Upload data
dataset = client.data.upload("sales_data.csv")

# Run descriptive statistics
stats = client.analysis.descriptive(
    dataset_id=dataset.id,
    columns=["revenue", "quantity"]
)

# Train a model
model = client.ml.train(
    dataset_id=dataset.id,
    target="revenue",
    features=["quantity", "discount"],
    model_type="random_forest"
)

# Make predictions
predictions = model.predict([
    {"quantity": 100, "discount": 0.1}
])
            

Integrations

Database Connections

Connect directly to your data sources:

PostgreSQL, MySQL, SQL Server
Snowflake, BigQuery, Redshift
MongoDB, Elasticsearch

BI Tool Integration

Embed Elneuro analytics in your dashboards:

Tableau, Power BI
Looker, Metabase
Custom iframe embedding

Webhooks

Configure webhooks to receive notifications when analyses complete or thresholds are breached.

Webhook Payload

{
    "event": "analysis.complete",
    "timestamp": "2024-01-15T10:35:00Z",
    "data": {
        "analysis_id": "an_xyz789",
        "type": "control_chart",
        "status": "completed",
        "alerts": [
            {
                "type": "out_of_control",
                "point": 45,
                "value": 10.8,
                "rule": "above_ucl"
            }
        ]
    }
}
            