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Usage Patterns

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Basic Client Session

from dalysdk import DalyClient

with DalyClient(workspace_api_key="wk_...", user_api_key="uk_...") as client:
    locations = client.locations.list()
    print("locations:", len(locations))

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File-Based Library Uploads

with DalyClient(workspace_api_key="wk_...", user_api_key="uk_...") as client:
    module = client.modules.create_from_file("/path/module.pan")
    inverter = client.inverters.create_from_file("/path/inverter.ond")

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Queue Energy Model and Poll Task

import time
from dalysdk import DalyClient

payload = {
    "locationId": 1,
    "blocks": [
        {
            "inverterId": 1,
            "moduleId": 1,
            "array": {"stringCount": 10, "stringLength": 28},
            "layout": {"arrayType": "fixed", "gcr": 0.4, "azimuth": 180, "axisTilt": 0, "tilt": 25},
            "shading": {"farShading": False, "nearShading": "unlimited", "electricalImpact": False},
            "losses": {
                "moduleQuality": 0.0,
                "mismatch": 0.02,
                "dcohmic": 0.01,
                "lid": 0.01,
                "stringVoltageMismatch": 0.01,
                "invAuxLoss": True,
                "uC": 25,
                "uV": 1.2,
            },
            "acSystem": {
                "numberOfInverters": 1,
                "losses": {"acohmicLoss": 0.01, "transformer": {"ironLoss": 0.005, "copperLoss": 0.005}},
            },
            "albedo": {"jan": 0.2, "feb": 0.2, "mar": 0.2, "apr": 0.2, "may": 0.2, "jun": 0.2, "jul": 0.2, "aug": 0.2, "sep": 0.2, "oct": 0.2, "nov": 0.2, "dec": 0.2},
        }
    ],
}

with DalyClient(workspace_api_key="wk_...", user_api_key="uk_...") as client:
    queued = client.energy_models.create(payload, async_mode=True, timeout=120)
    task_id = queued["taskId"]
    model_id = queued["energyModelId"]

    while True:
        task = client.tasks.get(task_id)
        status = task.get("status")
        if status in {"completed", "failed", "cancelled"}:
            break
        time.sleep(2)

    if status == "completed":
        result = client.energy_models.get(model_id)
        print(result.keys())

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Edit and Rerun a Saved Imported Template

from copy import deepcopy

from dalysdk import DalyClient

with DalyClient(workspace_api_key="wk_...", user_api_key="uk_...") as client:
    imported = client.projects.import_from_files(
        file_paths=["/path/MyProject.PRJ", "/path/Variant.VC"]
    )
    energy_model_id = imported["energyModels"][0]["energyModelId"]

    saved = client.energy_models.get_with_inputs(energy_model_id)
    edited_inputs = deepcopy(saved["inputs"])
    edited_inputs["output"] = {"name": "Imported Variant - Edited"}

    client.energy_models.update(energy_model_id, edited_inputs)
    queued = client.energy_models.run_saved(energy_model_id, async_mode=True)

    print(queued["energyModelId"])  # same row ID
    print(queued["taskDetails"]["taskId"])
run_saved() reruns the currently saved JSON in place. It does not create a new energy-model row.

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Batch Energy Model Runs

Use BatchRunner to submit many runs and wait for all to complete without writing your own poll loop. See Batch Runner for the full API reference. 
from dalysdk import DalyClient, BatchRunner

with DalyClient(workspace_api_key="wk_...", user_api_key="uk_...") as client:
    batch = BatchRunner(client)

    # Submit individually or in bulk
    batch.submit("Site A - Tracker", epm_input_a)
    batch.submit_many([
        ("Site B - Fixed", epm_input_b),
        ("Site C - Bifacial", epm_input_c),
    ])

    # Block until all finish (1-hour timeout, poll every 10s)
    all_done = batch.wait(timeout=3600, poll_interval=10)

    # Inspect results
    print(batch.summary())
    for result in batch.results():
        print(result["name"], result["data"]["summary"])

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Workflow Inspection

with DalyClient(workspace_api_key="wk_...", user_api_key="uk_...") as client:
    wf = client.workflows.get(123)
    print(wf["workflow"]["status"])
    print(len(wf["tasks"]))