{"task": {"agent_timeout": 1800, "task": "789", "verifier_timeout": 1800, "instruction": "# 789: DS-1000 Task\n\n## Prompt\nProblem:\nI\u2019m trying to solve a simple ODE to visualise the temporal response, which works well for constant input conditions using the new solve_ivp integration API in SciPy. For example:\ndef dN1_dt_simple(t, N1):\n    return -100 * N1\nsol = solve_ivp(fun=dN1_dt_simple, t_span=[0, 100e-3], y0=[N0,])\nHowever, I wonder is it possible to plot the response to a time-varying input? For instance, rather than having y0 fixed at N0, can I find the response to a simple sinusoid? Specifically, I want to add `t-sin(t) if 0 < t < 2pi else 2pi` to original y. The result I want is values of solution at time points.\nIs there a compatible way to pass time-varying input conditions into the API?\nA:\n<code>\nimport scipy.integrate\nimport numpy as np\nN0 = 1\ntime_span = [0, 10]\n</code>\nsolve this question with example variable `sol` and set `result = sol.y`\nBEGIN SOLUTION\n<code>\n\n## What to do\n- Edit `solution/solution.py` so the code passes the DS-1000 tests.\n- Do not access the internet or install new packages; required libraries are preinstalled in the Docker image.\n- Run tests locally via `bash tests/test.sh`.\n\n## Notes\n- Keep the variable names/signatures implied by the prompt/code_context.\n- The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`).\n", "memory": "", "runnable": false, "difficulty": "", "language": "", "cpus": "", "instruction_truncated": false, "category": "", "compose": false, "has_solution": true, "oracle": null, "docker_image": "ds1000:latest", "taskset": "ds1000", "tags": []}, "runs": []}