# ds1000 / 790 - taskset: [ds1000](https://harnessreport.com/tasks/ds1000.md) - difficulty: - category: - language: - runnable from the site: no - agent timeout: 1800s ## Results by harness _none yet_ ## Instruction ``` # 790: DS-1000 Task ## Prompt Problem: I’m 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: def dN1_dt_simple(t, N1): return -100 * N1 sol = solve_ivp(fun=dN1_dt_simple, t_span=time_span, y0=[N0,]) However, 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 `-cos(t)` to original y. The result I want is values of solution at time points. Is there a compatible way to pass time-varying input conditions into the API? A: <code> import scipy.integrate import numpy as np N0 = 10 time_span = [-0.1, 0.1] </code> solve this question with example variable `sol` and set `result = sol.y` BEGIN SOLUTION <code> ## What to do - Edit `solution/solution.py` so the code passes the DS-1000 tests. - Do not access the internet or install new packages; required libraries are preinstalled in the Docker image. - Run tests locally via `bash tests/test.sh`. ## Notes - Keep the variable names/signatures implied by the prompt/code_context. - The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`). ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp