// adi_hybrid_neuromorphic_internet4.0_system.cpp
// This program integrates components to create a hybrid system
// that leverages traditional HPC and neuromorphic computing.
// The primary goal is to demonstrate a workflow where a numerical task (gradient calculation)
// is handled by the HPC layer, and the resulting features are processed by a simulated
// Spiking Neural Network (SNN) for pattern recognition.
// Shouts out to the ASEF worldwide, let's reduce our pickaxe junking for Internet 4.0 so Natural Resource Scientists have less worries. How we do this? My initial first post which has been a point of interest to me allows classical computers to do this work though at a greater efficiency through distributed throughput than some packaging deal between assumed stock valuation sentiment of buyers and sellers. Lets hope our family estuaries are forward leveraged ahead of debenture capitalist cycles to debt risk indentitude of our common civilization.
// How do you do this? Call your insurance company, bank options management cycler to your home and of course expect your representative or constitutional governance bodies to respect the case of sovereignty or sovietry to provision our collective future saliently.
// --- Necessary Headers ---
#include <iostream>
#include <vector>
#include <string>
#include <map>
#include <memory>
#include <cmath>
#include <numeric>
#include <algorithm>
#include <stdexcept>
#include <thread>
#include <mutex>
#include <random>
#include <execution>
#include <omp.h>
#include <bit>
#include <cstring>
// For SIMD JSON parsing (assumes simdjson is available)
#include "simdjson.h"
using namespace simdjson;
// For HTTP server (assumes cpp-httplib is available)
#define CPPHTTPLIB_OPENSSL_SUPPORT
#include "httplib.h"
// For CUDA GPU support (assumes CUDA Toolkit and Thrust are installed)
#include <cuda_runtime.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/transform.h>
#include <thrust/adjacent_difference.h>
// --- Common Constants for Workflow Operations ---
// These constants define the types of operations that can be
// included in a workflow sent from a client.
const int OPERATION_CALCULATE_GRADIENT_1D = 2;
const int OPERATION_WORKFLOW = 6;
const int OPERATION_NEUROMORPHIC_PREDICT = 7;
// --- Helper Functions for CUDA and CPU support detection ---
// These functions check for the presence of the necessary hardware and
// runtime libraries.
bool has_cuda_support() {
int device_count = 0;
cudaError_t err = cudaGetDeviceCount(&device_count);
return err == cudaSuccess && device_count > 0;
}
// --- Neuromorphic Component: Spiking Neural Network ---
// Leaky Integrate-and-Fire (LIF) Neuron Model
// This class simulates a single LIF neuron. Its membrane potential
// integrates input current over time and "leaks" back to a resting potential.
// It fires a "spike" and resets when its potential exceeds a threshold.
class LIFNeuron {
public:
LIFNeuron(double tau_m = 20.0, double v_rest = -65.0, double v_reset = -65.0, double v_thresh = -50.0)
: tau_m(tau_m), v_rest(v_rest), v_reset(v_reset), v_thresh(v_thresh), membrane_potential(v_rest) {}
// Updates the neuron's state and returns true if it spiked.
bool update(double input_current, double dt) {
double dv = (-(membrane_potential - v_rest) + input_current) / tau_m;
membrane_potential += dv * dt;
if (membrane_potential >= v_thresh) {
membrane_potential = v_reset;
return true; // Spike
}
return false; // No spike
}
private:
double tau_m, v_rest, v_reset, v_thresh, membrane_potential;
};
// A simple Spiking Neural Network with two layers.
// This network processes an input vector by propagating spikes and
// counting the total spikes in the output layer.
class SpikingNetwork {
public:
SpikingNetwork(int input_size, int hidden_size, int output_size)
: input_size(input_size), hidden_size(hidden_size), output_size(output_size) {
// Initialize neurons
hidden_layer.resize(hidden_size);
output_layer.resize(output_size);
// Initialize random weights
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(0.0, 1.0);
input_to_hidden_weights.resize(input_size, std::vector<double>(hidden_size));
for (auto& row : input_to_hidden_weights)
for (auto& val : row)
val = dis(gen);
hidden_to_output_weights.resize(hidden_size, std::vector<double>(output_size));
for (auto& row : hidden_to_output_weights)
for (auto& val : row)
val = dis(gen);
}
// Processes the input vector over a simulated period of time.
std::vector<int> predict(const std::vector<double>& input_vector, int num_timesteps = 100, double dt = 1.0) {
// Ensure input size matches the network's expected input.
if (input_vector.size() != input_size) {
throw std::runtime_error("Input vector size mismatch.");
}
std::vector<int> output_spike_counts(output_size, 0);
// Simulation loop over time steps
for (int t = 0; t < num_timesteps; ++t) {
std::vector<double> hidden_currents(hidden_size, 0.0);
// Calculate currents for the hidden layer
for (int i = 0; i < input_size; ++i) {
for (int j = 0; j < hidden_size; ++j) {
hidden_currents[j] += input_vector[i] * input_to_hidden_weights[i][j];
}
}
std::vector<bool> hidden_spikes(hidden_size, false);
std::vector<double> output_currents(output_size, 0.0);
// Update hidden neurons and propagate spikes
for (int j = 0; j < hidden_size; ++j) {
if (hidden_layer[j].update(hidden_currents[j], dt)) {
hidden_spikes[j] = true;
}
}
// Calculate currents for the output layer based on hidden spikes
for (int j = 0; j < hidden_size; ++j) {
if (hidden_spikes[j]) {
for (int k = 0; k < output_size; ++k) {
output_currents[k] += hidden_to_output_weights[j][k];
}
}
}
// Update output neurons and accumulate spike counts
for (int k = 0; k < output_size; ++k) {
if (output_layer[k].update(output_currents[k], dt)) {
output_spike_counts[k]++;
}
}
}
return output_spike_counts;
}
private:
int input_size, hidden_size, output_size;
std::vector<LIFNeuron> hidden_layer;
std::vector<LIFNeuron> output_layer;
std::vector<std::vector<double>> input_to_hidden_weights;
std::vector<std::vector<double>> hidden_to_output_weights;
};
// --- Thrust Functor for Gradient Calculation ---
// This functor is used to compute the difference between adjacent elements on the GPU.
// It is specifically designed to work with Thrust's parallel algorithms.
struct gradient_functor {
template <typename T>
__host__ __device__ T operator()(const T& x, const T& y) const {
return y - x;
}
};
// --- Core Workflow Logic ---
std::string handle_workflow(const std::string& request_body) {
ondemand::parser parser;
padded_string json_data = padded_string::load(request_body);
ondemand::document doc = parser.iterate(json_data);
std::map<std::string, std::vector<double>> intermediate_results;
std::string response_str;
try {
for (auto element : doc["workflow"].get_array()) {
std::string op_type = std::string(element["operation_type"].get_string());
std::string output_id = std::string(element["output_id"].get_string());
std::vector<double> input_data_vec;
// Determine input source (direct data or intermediate result)
std::string input_type = std::string(element["input_data"]["type"].get_string());
if (input_type == "direct") {
for (auto val : element["input_data"]["data"].get_array()) {
input_data_vec.push_back(val.get_double());
}
} else if (input_type == "reference") {
std::string ref_id = std::string(element["input_data"]["reference_id"].get_string());
if (intermediate_results.count(ref_id)) {
input_data_vec = intermediate_results[ref_id];
} else {
throw std::runtime_error("Reference ID not found: " + ref_id);
}
}
// --- Execute Operation ---
if (op_type == "CALCULATE_GRADIENT_1D") {
std::vector<double> gradient;
if (input_data_vec.size() > 1) {
// Use GPU via Thrust if available, otherwise fall back to CPU
if (has_cuda_support()) {
std::cout << "Calculating gradient on GPU with Thrust." << std::endl;
thrust::host_vector<double> h_input = input_data_vec;
thrust::device_vector<double> d_input = h_input;
thrust::device_vector<double> d_gradient(d_input.size() - 1);
thrust::adjacent_difference(
d_input.begin(), d_input.end(), d_gradient.begin(), gradient_functor()
);
gradient = d_gradient; // Copy back to host
} else {
std::cout << "Calculating gradient on CPU with OpenMP." << std::endl;
gradient.resize(input_data_vec.size() - 1);
#pragma omp parallel for
for (size_t i = 0; i < input_data_vec.size() - 1; ++i) {
gradient[i] = input_data_vec[i+1] - input_data_vec[i];
}
}
}
intermediate_results[output_id] = gradient;
} else if (op_type == "NEUROMORPHIC_PREDICT") {
std::cout << "Processing data with the simulated SNN." << std::endl;
SpikingNetwork snn(input_data_vec.size(), 10, 5); // Example network
std::vector<int> spike_counts = snn.predict(input_data_vec);
// Build a JSON response with the spike counts
std::string spikes_json = "{ \"spike_counts\": [";
for (size_t i = 0; i < spike_counts.size(); ++i) {
spikes_json += std::to_string(spike_counts[i]);
if (i < spike_counts.size() - 1) {
spikes_json += ", ";
}
}
spikes_json += "] }";
response_str = spikes_json;
} else {
throw std::runtime_error("Unknown operation type: " + op_type);
}
}
} catch (const std::exception& e) {
return std::string("Error: ") + e.what();
}
return response_str;
}
// --- Server and Client Logic ---
void start_server() {
httplib::Server svr;
svr.Post("/workflow", [&](const httplib::Request& req, httplib::Response& res) {
try {
std::string response_str = handle_workflow(req.body);
res.set_content(response_str, "application/json");
res.status = 200;
} catch (const std::exception& e) {
res.set_content(e.what(), "text/plain");
res.status = 500;
}
});
std::cout << "Server listening on localhost:8080" << std::endl;
svr.listen("0.0.0.0", 8080);
}
void start_client() {
std::cout << "Client started. Sending workflow to server." << std::endl;
httplib::Client cli("localhost", 8080);
// This JSON defines a two-step workflow:
// 1. Calculate the gradient of a sample time-series signal.
// 2. Use that gradient as input for the neuromorphic SNN.
std::string workflow_json = R"({
"workflow": [
{
"operation_type": "CALCULATE_GRADIENT_1D",
"input_data": {
"type": "direct",
"data": [10.0, 11.5, 13.0, 12.0, 10.5, 9.0, 8.5]
},
"output_id": "gradient_result"
},
{
"operation_type": "NEUROMORPHIC_PREDICT",
"input_data": {
"type": "reference",
"reference_id": "gradient_result"
},
"output_id": "neuromorphic_result"
}
]
})";
if (auto res = cli.Post("/workflow", workflow_json, "application/json")) {
if (res->status == 200) {
std::cout << "Server response: " << res->body << std::endl;
} else {
std::cerr << "Server error: " << httplib::to_string(res.error()) << " Status: " << res->status << std::endl;
}
} else {
std::cerr << "Client error: " << httplib::to_string(res.error()) << std::endl;
}
}
int main() {
std::thread server_thread(start_server);
std::this_thread::sleep_for(std::chrono::seconds(1));
std::thread client_thread(start_client);
server_thread.join();
client_thread.join();
return 0;
}
-- VHDL Architecture for a Conceptual Neuromorphic Core with Optical Routing
This improved VHDL code introduces a new ONoC_Interface component. This component acts as the interface between the internal electrical signals of your neuromorphic core and the external, high-speed optical network. By using an optical link, data transfer between the processor and other components can happen at a much higher bandwidth, which is crucial for large-scale, distributed systems. The original design's input_data_bus and spike_counts_bus are now internal to the main architecture, receiving their data from or sending it to the new ONoC_Interface component. This decouples the processing logic from the I/O bottleneck, allowing the system to operate much more efficiently.
-- This architecture extends the original design by introducing a new
-- optical interconnect layer. This layer replaces the traditional
-- input and output data buses with high-throughput optical channels,
-- managed by an on-chip Optical Network-on-Chip (ONoC).
library ieee;
use ieee.std_logic_1164.all;
use ieee.numeric_std.all;
-- A conceptual package for optical-related signals.
-- In a real design, this would abstract the physical layer.
package optical_types is
-- A conceptual type for a wide, high-speed optical channel.
-- Assuming a 128-bit wide data path for high throughput.
type optical_channel is record
data : std_logic_vector(127 downto 0);
valid : std_logic;
ready : std_logic;
end record;
end package optical_types;
use work.optical_types.all;
-- Entity Declaration (top-level interface for the hardware block)
entity NeuromorphicProcessor_Optical is
port (
clk : in std_logic; -- System clock
reset : in std_logic; -- Reset signal
-- Optical Input Interface (from an external optical switch)
optical_in_channel : in optical_channel;
-- Optical Output Interface (to an external optical switch)
optical_out_channel : out optical_channel
);
end entity NeuromorphicProcessor_Optical;
-- Architecture Definition
architecture Behavioral of NeuromorphicProcessor_Optical is
-- Internal signals for the network state.
-- This includes the data plane and control plane signals for the ONoC.
signal internal_data_bus : std_logic_vector(63 downto 0);
signal internal_data_valid : std_logic := '0';
signal internal_data_ready : std_logic := '0';
signal internal_spike_counts_bus : std_logic_vector(63 downto 0);
signal internal_spike_counts_valid : std_logic := '0';
-- Component for a single Leaky Integrate-and-Fire (LIF) neuron.
component LIFNeuron is
port (
clk, reset : in std_logic;
input_current : in std_logic_vector(63 downto 0);
spike_out : out std_logic;
membrane_potential : out std_logic_vector(63 downto 0)
);
end component;
-- Component for the Optical Network-on-Chip (ONoC) Interface.
-- This module manages the optical-to-electrical and electrical-to-optical
-- conversion and routing. It's the key element for throughput improvement.
component ONoC_Interface is
port (
clk, reset : in std_logic;
-- Optical Link
optical_in : in optical_channel;
optical_out: out optical_channel;
-- Electrical Link to the Neuromorphic Core
electrical_in_bus : in std_logic_vector(63 downto 0);
electrical_in_valid : in std_logic;
electrical_in_ready : out std_logic;
electrical_out_bus : out std_logic_vector(63 downto 0);
electrical_out_valid : out std_logic;
electrical_out_ready : in std_logic
);
end component;
begin
-- Instantiate the ONoC Interface to handle data movement.
ONoC_Inst : ONoC_Interface
port map (
clk => clk,
reset => reset,
optical_in => optical_in_channel,
optical_out => optical_out_channel,
electrical_in_bus => internal_spike_counts_bus,
electrical_in_valid => internal_spike_counts_valid,
electrical_in_ready => open, -- Ready is internal to the ONoC_Interface here
electrical_out_bus => internal_data_bus,
electrical_out_valid=> internal_data_valid,
electrical_out_ready=> internal_data_ready
);
-- Hidden Layer Instantiation: (Original logic remains the same)
-- For each neuron in the hidden layer, instantiate the LIFNeuron component.
-- The input current to each hidden neuron is a weighted sum of the input vector values.
-- Output Layer Instantiation: (Original logic remains the same)
-- The output neurons receive spikes from the hidden layer.
-- The output of this layer is a counter that accumulates spikes over time.
-- The I/O logic for the neuromorphic core now talks to the ONoC interface.
-- The ONoC provides the input data and handles the output data transfer,
-- allowing the core to operate at its maximum internal clock speed without
-- being bottlenecked by slower electrical I/O.
process (clk, reset)
begin
if reset = '1' then
-- Reset all internal registers and counters
internal_data_ready <= '0';
elsif rising_edge(clk) then
-- FSM logic to handle input data from the ONoC interface.
if internal_data_valid = '1' then
-- Load data from internal_data_bus
-- The ONoC_Interface handles the handshaking with the external system.
-- This allows for immediate data availability on the internal bus.
-- Signal that the neuromorphic core has received the data
internal_data_ready <= '1';
else
-- Once data is processed, de-assert ready
internal_data_ready <= '0';
end if;
-- FSM logic to run the simulation and output results to the ONoC interface.
-- When results are ready, assert internal_spike_counts_valid and put data on the bus.
-- The ONoC_Interface will then take this data and route it out optically.
end if;
end process;
end architecture Behavioral;
# adi_hybrid_neuromorphic_suite.py
# This script is a Python translation and refit of the logic from the provided
# C and C++ files. It creates a hybrid computing system with a Flask web server,
# a JSON-based workflow engine, and a neuromorphic Spiking Neural Network (SNN).
# It leverages NumPy for high-performance CPU computation and can optionally use
# CuPy for GPU acceleration if a compatible NVIDIA GPU and CUDA are available.
import numpy as np
import json
from flask import Flask, request, jsonify
import requests
import threading
import time
# --- GPU Support Check ---
# This section checks for the availability of CuPy, which is used for GPU acceleration.
# If CuPy is not found, the application will gracefully fall back to using NumPy on the CPU.
try:
import cupy as cp
CUPY_AVAILABLE = True
print("CuPy found. GPU acceleration is enabled.")
except ImportError:
CUPY_AVAILABLE = False
print("CuPy not found. Using NumPy for CPU computation.")
# --- Neuromorphic Component: Spiking Neural Network (SNN) ---
class LIFNeuron:
"""
A Python implementation of the Leaky Integrate-and-Fire (LIF) neuron model.
This class simulates the behavior of a single biological neuron.
"""
def __init__(self, tau_m=20.0, v_rest=-65.0, v_reset=-65.0, v_thresh=-50.0):
self.tau_m = tau_m # Membrane time constant
self.v_rest = v_rest # Resting potential
self.v_reset = v_reset # Reset potential after a spike
self.v_thresh = v_thresh # Firing threshold
self.membrane_potential = v_rest
def update(self, input_current, dt=1.0):
"""
Updates the neuron's membrane potential based on input current.
Returns True if the neuron fires a spike, otherwise False.
"""
dv = (-(self.membrane_potential - self.v_rest) + input_current) / self.tau_m
self.membrane_potential += dv * dt
if self.membrane_potential >= self.v_thresh:
self.membrane_potential = self.v_reset
return True # Spike occurred
return False # No spike
class SpikingNetwork:
"""
A simple two-layer Spiking Neural Network (SNN).
This network processes input data by simulating the firing of interconnected LIF neurons.
"""
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# Initialize neurons for hidden and output layers
self.hidden_layer = [LIFNeuron() for _ in range(hidden_size)]
self.output_layer = [LIFNeuron() for _ in range(output_size)]
# Initialize weights with random values using NumPy for efficiency
self.input_to_hidden_weights = np.random.rand(input_size, hidden_size)
self.hidden_to_output_weights = np.random.rand(hidden_size, output_size)
def predict(self, input_vector, num_timesteps=100, dt=1.0):
"""
Processes an input vector over a series of time steps and returns the
total number of spikes from the output neurons.
"""
if len(input_vector) != self.input_size:
raise ValueError("Input vector size does not match network input size.")
output_spike_counts = np.zeros(self.output_size, dtype=int)
# The core simulation loop
for _ in range(num_timesteps):
# Calculate input currents for the hidden layer using matrix multiplication
hidden_currents = np.dot(input_vector, self.input_to_hidden_weights)
# Update hidden neurons and record spikes
hidden_spikes = np.array([neuron.update(current, dt) for neuron, current in zip(self.hidden_layer, hidden_currents)])
# Calculate input currents for the output layer if any hidden neurons spiked
if np.any(hidden_spikes):
output_currents = np.dot(hidden_spikes, self.hidden_to_output_weights)
else:
output_currents = np.zeros(self.output_size)
# Update output neurons and count spikes
for k, current in enumerate(output_currents):
if self.output_layer[k].update(current, dt):
output_spike_counts[k] += 1
return output_spike_counts.tolist()
# --- High-Performance Computing Functions ---
def calculate_gradient_1d(data):
"""
Calculates the 1D gradient (difference between adjacent elements) of an array.
It automatically uses CuPy for GPU acceleration if available, otherwise NumPy.
"""
if not isinstance(data, (np.ndarray, list)):
raise TypeError("Input data must be a list or NumPy array.")
if CUPY_AVAILABLE:
# Use GPU
print("Calculating gradient on GPU with CuPy.")
gpu_array = cp.asarray(data)
gradient = cp.diff(gpu_array)
return cp.asnumpy(gradient).tolist() # Return result as a standard Python list
else:
# Use CPU
print("Calculating gradient on CPU with NumPy.")
cpu_array = np.asarray(data)
gradient = np.diff(cpu_array)
return gradient.tolist()
# --- Core Workflow and Server Logic ---
app = Flask(__name__)
intermediate_results = {}
@app.route('/workflow', methods=['POST'])
def handle_workflow_request():
"""
Handles incoming JSON workflow requests. This function parses the workflow,
executes the specified operations in sequence, and returns the final result.
"""
try:
workflow = request.json['workflow']
final_result = None
for step in workflow:
op_type = step['operation_type']
input_data_def = step['input_data']
output_id = step['output_id']
# Resolve input data (either direct or from a previous step)
if input_data_def['type'] == 'direct':
input_data = input_data_def['data']
elif input_data_def['type'] == 'reference':
ref_id = input_data_def['reference_id']
if ref_id in intermediate_results:
input_data = intermediate_results[ref_id]
else:
return jsonify({"error": f"Reference ID not found: {ref_id}"}), 400
# Execute the requested operation
if op_type == 'CALCULATE_GRADIENT_1D':
result = calculate_gradient_1d(input_data)
intermediate_results[output_id] = result
final_result = result
elif op_type == 'NEUROMORPHIC_PREDICT':
# Initialize the SNN with the correct input size
snn = SpikingNetwork(input_size=len(input_data), hidden_size=20, output_size=5)
result = snn.predict(input_data)
intermediate_results[output_id] = result
final_result = {"spike_counts": result}
else:
return jsonify({"error": f"Unknown operation type: {op_type}"}), 400
return jsonify(final_result)
except Exception as e:
return jsonify({"error": str(e)}), 500
def start_server():
"""Starts the Flask web server."""
# Running in debug mode is not recommended for production
app.run(host='0.0.0.0', port=8080, debug=False)
def start_client():
"""
A simple client to demonstrate sending a workflow request to the server.
"""
print("\nClient started. Sending workflow to the server...")
# This JSON defines the same two-step workflow as the C++ example:
# 1. Calculate the gradient of a time-series signal.
# 2. Feed the gradient into the SNN for prediction.
workflow_json = {
"workflow": [
{
"operation_type": "CALCULATE_GRADIENT_1D",
"input_data": {
"type": "direct",
"data": [10.0, 11.5, 13.0, 12.0, 10.5, 9.0, 8.5]
},
"output_id": "gradient_result"
},
{
"operation_type": "NEUROMORPHIC_PREDICT",
"input_data": {
"type": "reference",
"reference_id": "gradient_result"
},
"output_id": "neuromorphic_result"
}
]
}
try:
response = requests.post('http://localhost:8080/workflow', json=workflow_json)
response.raise_for_status() # Raise an exception for bad status codes
print("Server Response:")
print(response.json())
except requests.exceptions.RequestException as e:
print(f"Client Error: Could not connect to the server. {e}")
if __name__ == '__main__':
# Start the server in a separate thread so the client can run
server_thread = threading.Thread(target=start_server)
server_thread.daemon = True
server_thread.start()
# Give the server a moment to start up
time.sleep(2)
# Run the client
start_client()
# Keep the main thread alive to allow the server to run
# In a real application, you might have a more robust shutdown mechanism
server_thread.join()
# --- Extended Use Case System and Examples ---
#
# The following sections are commented-out conceptual examples of how this
# hybrid computing suite could be extended for other applications.
#
# ==============================================================================
# --- USE CASE 1: REAL-TIME ANOMALY DETECTION IN FINANCIAL DATA ---
# ==============================================================================
#
# CONCEPT:
# A financial institution wants to detect anomalous trading patterns in real-time.
# High-frequency trading data (e.g., stock prices, volumes) streams into the system.
# The system must preprocess this data and use the neuromorphic SNN to spot
# patterns that deviate from the norm, potentially indicating market manipulation
# or a system glitch.
#
# WORKFLOW:
# 1. Data Ingestion: A separate process (e.g., a Kafka consumer) receives raw trade data.
# 2. Preprocessing (HPC): The raw data is batched into time windows (e.g., 1-second intervals).
# The `CALCULATE_GRADIENT_1D` operation is used on the price data to determine the
# rate of change (velocity) and acceleration, which are key features. This happens on the GPU.
# 3. Anomaly Detection (Neuromorphic): The calculated gradients (features) are fed into
# a pre-trained Spiking Neural Network. The SNN is trained to recognize "normal"
# market behavior. If an input pattern results in an unusual spike count from the
# output neurons (e.g., a neuron designated for "high volatility" fires excessively),
# an alert is triggered.
#
#
# @app.route('/financial_anomaly', methods=['POST'])
# def handle_financial_data():
# """
# A conceptual endpoint for handling a stream of financial data.
# """
# trade_data = request.json.get('trades', [])
# if not trade_data:
# return jsonify({"error": "No trade data provided"}), 400
#
# # In a real system, this would be a more complex workflow submission
# # to the existing '/workflow' endpoint.
# prices = [trade['price'] for trade in trade_data]
#
# # 1. Preprocessing: Calculate price velocity
# price_velocity = calculate_gradient_1d(prices)
#
# # 2. Neuromorphic Prediction
# # Assume an SNN is trained for this specific task
# snn_input_size = len(price_velocity)
# anomaly_snn = SpikingNetwork(input_size=snn_input_size, hidden_size=50, output_size=3)
# # Output neurons could represent: [normal, moderate_volatility, high_anomaly]
# spike_counts = anomaly_snn.predict(price_velocity)
#
# # 3. Decision Logic
# if spike_counts[2] > 10: # Threshold for the "high_anomaly" neuron
# alert_message = f"High anomaly detected! Spike count: {spike_counts[2]}"
# print(alert_message)
# return jsonify({"status": "ALERT", "message": alert_message})
# else:
# return jsonify({"status": "OK", "spike_counts": spike_counts})
#
# ==============================================================================
# --- USE CASE 2: GAME ENGINE NETWORK THROUGHPUT & WEBCASTING ---
# ==============================================================================
#
# CONCEPT:
# This server can be extended to act as a simple backend for a multiplayer game
# or a live webcasting service.
#
# --- Part A: Game Engine State Synchronization ---
#
# A simple game where players control positions. The server receives updates
# from clients and broadcasts the new game state to all connected clients.
# The SNN could be used here for bot AI, predicting player movement.
#
# from flask_socketio import SocketIO, emit
#
# # This would require installing flask_socketio: pip install flask-socketio
# socketio = SocketIO(app)
# game_state = {'players': {}} # Store player positions
#
# @socketio.on('connect')
# def handle_connect():
# print('Client connected')
#
# @socketio.on('disconnect')
# def handle_disconnect():
# print('Client disconnected')
#
# @socketio.on('player_update')
# def handle_player_update(data):
# """
# Receives a position update from a player and broadcasts it.
# 'data' would be a JSON like: {'player_id': 'some_id', 'position': [x, y, z]}
# """
# player_id = data.get('player_id')
# position = data.get('position')
# if player_id and position:
# game_state['players'][player_id] = position
# # Broadcast the new state to all clients
# emit('game_state_update', game_state, broadcast=True)
#
# --- Part B: Simple Webcasting and HTML Form Posting ---
#
# The server can serve a simple HTML page and handle form submissions.
# It can also use WebSockets to push live updates to the web page.
#
# from flask import render_template_string
#
# HTML_TEMPLATE = """
# <!DOCTYPE html>
# <html>
# <head>
# <title>Hybrid Compute Interface</title>
# <script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.0.1/socket.io.js"></script>
# </head>
# <body>
# <h1>Post a Message</h1>
# <form action="/post_message" method="post">
# <input type="text" name="message" placeholder="Enter message">
# <button type="submit">Post</button>
# </form>
# <h2>Live Webcast:</h2>
# <div id="webcast"></div>
#
# <script>
# var socket = io.connect('http://' + document.domain + ':' + location.port);
# socket.on('new_message', function(data) {
# var p = document.createElement('p');
# p.innerHTML = data.message;
# document.getElementById('webcast').appendChild(p);
# });
# </script>
# </body>
# </html>
# """
#
# @app.route('/')
# def index():
# """Serves the main HTML page."""
# return render_template_string(HTML_TEMPLATE)
#
# @app.route('/post_message', methods=['POST'])
# def post_message():
# """Handles form submission and webcasts the message."""
# message = request.form.get('message', 'empty message')
# # Use the socketio instance from Part A to broadcast
# socketio.emit('new_message', {'message': message})
# return 'Message posted and broadcasted!'
#
#
# To run the full example with WebSockets, you would need to modify the
# main execution block:
#
# if __name__ == '__main__':
# # The server would be started with socketio.run() instead of app.run()
# # socketio.run(app, host='0.0.0.0', port=8080)
# pass
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