New generation of generative design software for thermal & flow topology optimization for optimal heat sink design
Retrieves the result of a specific simulation from ColdStream. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Starts a new simulation in ColdStream with specified parameters and submits the created case. See the documentation
Updates an existing project with new parameters or data in ColdStream. See the documentation
The Diabatix ColdStream API provides automated thermal analysis capabilities, allowing users to streamline the cooling design process for various components and devices. With this API, you can automate the design of thermal systems, optimize existing cooling solutions, and simulate different scenarios to find the most effective thermal management strategy. In Pipedream, you can leverage this API to build automated workflows that integrate thermal analysis into your engineering cycles, ensuring your designs meet the necessary thermal specifications before physical prototypes are ever built.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
diabatix_coldstream: {
type: "app",
app: "diabatix_coldstream",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://identity.coldstream.diabatix.com/users/me`,
headers: {
Authorization: `Bearer ${this.diabatix_coldstream.$auth.access_token}`,
"accept": `application/json`,
},
})
},
})
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt
file or running pip
.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}