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{
  "scenarios": 1000,
  "steps": 12,
  "variables": [
    {
      "name": "capital",
      "init": 1000.0,
      "formula": "capital[t-1] * (1 + taux)"
    }
  ]
}

{"formula": "capital[t-1] * 1.05"}
{"formula": "capital[t] - capital[0]"}
{"formula": "sum(primes_payees[0:t+1])"}

X-API-Key: be_live_XXXXXXXX...

X-Team-Id: 550e8400-e29b-41d4-a716-446655440000

curl -X POST "https://api.subspacecomputing.com/project" \
  -H "X-API-Key: be_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "scenarios": 1,
    "steps": 12,
    "variables": [
      {
        "name": "capital",
        "init": 1000.0,
        "formula": "capital[t-1] * 1.05"
      }
    ]
  }'

curl -X POST "https://api.subspacecomputing.com/simulate" \
  -H "X-API-Key: be_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "scenarios": 1000,
    "steps": 12,
    "variables": [
      {
        "name": "taux",
        "dist": "uniform",
        "params": {"min": 0.03, "max": 0.07},
        "per": "scenario"
      },
      {
        "name": "capital",
        "init": 1000.0,
        "formula": "capital[t-1] * (1 + taux)"
      }
    ]
  }'

curl -X POST "https://api.subspacecomputing.com/project/batch" \
  -H "X-API-Key: be_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "batch_params": [
      {"entity_id": "emp_001", "age": 45, "salary": 60000},
      {"entity_id": "emp_002", "age": 50, "salary": 80000}
    ],
    "template": {
      "scenarios": 1,
      "steps": "65 - batch_params.age",
      "variables": [...]
    },
    "aggregations": [...]
  }'

curl -X POST "https://api.subspacecomputing.com/projection-runs/{run_id}/replay/0" \
  -H "X-API-Key: be_live_..." \
  -H "Content-Type: application/json"

curl -X GET "https://api.subspacecomputing.com/run-artifacts/{artifact_id}" \
  -H "X-API-Key: be_live_..."

curl -X POST "https://api.subspacecomputing.com/validate" \
  -H "Content-Type: application/json" \
  -d '{
    "scenarios": 100,
    "steps": 12,
    "variables": [...]
  }'

{
  "valid": true,
  "errors": [],
  "warnings": [],
  "estimated_scenarios": 3,
  "estimated_duration_ms": 0
}

pip install subspacecomputing

from subspacecomputing import BSCE

# Initialiser le client (production) — team_id optionnel (X-Team-Id)
client = BSCE(api_key="be_live_...", team_id="uuid-equipe-optionnel")

# Changer le contexte équipe après coup
client.set_team_id("autre-uuid-equipe")

spec = {
    "scenarios": 1,  # DOIT être 1
    "steps": 12,
    "variables": [
        {
            "name": "capital",
            "init": 1000.0,
            "formula": "capital[t-1] * 1.05"
        }
    ]
}

result = client.project(spec)
print(result['final_values']['capital'])
# Récupérer la run (status, result_summary, etc.)
if result.get('run_id'):
    run = client.get_run(result['run_id'])

spec = {
    "scenarios": 1000,
    "steps": 12,
    "variables": [
        {
            "name": "taux",
            "dist": "uniform",
            "params": {"min": 0.03, "max": 0.07},
            "per": "scenario"
        },
        {
            "name": "capital",
            "init": 1000.0,
            "formula": "capital[t-1] * (1 + taux)"
        }
    ]
}

result = client.simulate(spec)
print(f"Moyenne: {result['last_mean']['capital']}")
print(f"Médiane: {result['statistics']['capital']['median']}")
# Récupérer la run après simulate (status, result_summary, artefact_id, etc.)
if result.get('run_id'):
    run = client.get_run(result['run_id'])

result = client.simulate(spec)  # avec meta.store_seeds_for_replay: true
if result.get('run_id'):
    run = client.get_run(result['run_id'])
    # Rejouer le scénario 0
    replay = client.replay_run(result['run_id'], scenario_id=0)
    print("Valeurs finales rejouées:", replay['final_values'][0])

template = {
    "scenarios": 1,
    "steps": "65 - batch_params.age",
    "variables": [
        {
            "name": "capital_retraite",
            "init": 0.0,
            "formula": "capital_retraite[t-1] * 1.05 + batch_params.salary * 0.10"
        }
    ]
}

batch_params = [
    {"entity_id": "emp_001", "age": 45, "salary": 60000},
    {"entity_id": "emp_002", "age": 50, "salary": 80000}
]

result = client.project_batch(
    template=template,
    batch_params=batch_params
)

for entity in result['entities']:
    print(f"{entity['_entity_id']}: {entity['final_values'][0]['capital_retraite']}")

validation = client.validate(spec)
if validation.get('valid'):
    print("✅ SP Model valide")
    if validation.get('estimated_scenarios'):
        print(f"   Scénarios estimés: {validation['estimated_scenarios']}")
    if validation.get('warnings'):
        print(f"   Avertissements: {validation['warnings']}")
else:
    print(f"❌ Erreurs: {validation['errors']}")

data = client.get_run_artifact("uuid-artifact")

npm install @beausoft/subspace

import { BSCE } from '@beausoft/subspace';

// new BSCE(apiKey, baseUrl?, teamId?)
const client = new BSCE('be_live_...', undefined, 'uuid-equipe-optionnel');
// ou : new BSCE('be_live_...', 'https://api.subspacecomputing.com', 'uuid-equipe');

client.setTeamId('autre-uuid-equipe');

const spec = {
  scenarios: 1,  // DOIT être 1
  steps: 12,
  variables: [
    {
      name: 'capital',
      init: 1000.0,
      formula: 'capital[t-1] * 1.05'
    }
  ]
};

const result = await client.project(spec);
console.log(result.final_values.capital);
// Récupérer la run (status, result_summary, etc.)
if (result.run_id) {
  const run = await client.getRun(result.run_id);
}

const spec = {
  scenarios: 1000,
  steps: 12,
  variables: [
    {
      name: 'taux',
      dist: 'uniform',
      params: { min: 0.03, max: 0.07 },
      per: 'scenario'
    },
    {
      name: 'capital',
      init: 1000.0,
      formula: 'capital[t-1] * (1 + taux)'
    }
  ]
};

const result = await client.simulate(spec);
console.log(`Moyenne: ${result.last_mean.capital}`);
console.log(`Médiane: ${result.statistics.capital.median}`);
// Récupérer la run après simulate (status, result_summary, artefact_id, etc.)
if (result.run_id) {
  const run = await client.getRun(result.run_id);
}

const template = {
  scenarios: 1,
  steps: '65 - batch_params.age',
  variables: [
    {
      name: 'age_actuel',
      init: 'batch_params.age'
    },
    {
      name: 'salaire',
      init: 'batch_params.salary',
      formula: 'salaire[t-1] * 1.03'
    },
    {
      name: 'capital_retraite',
      init: 0.0,
      formula: 'capital_retraite[t-1] * 1.05 + salaire[t] * 0.10'
    }
  ]
};

const batchParams = [
  { entity_id: 'emp_001', age: 45, salary: 60000 },
  { entity_id: 'emp_002', age: 50, salary: 80000 }
];

const aggregations = [
  { name: 'capital_total', formula: 'sum(capital_retraite[t_final])' }
];

const result = await client.projectBatch(template, batchParams, aggregations);

result.entities.forEach(entity => {
  console.log(`${entity._entity_id}: ${entity.final_values[0].capital_retraite}`);
});