# ds1000 / 507 - 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 ``` # 507: DS-1000 Task ## Prompt Problem: I want to process a gray image in the form of np.array. *EDIT: chose a slightly more complex example to clarify Suppose im = np.array([ [0,0,0,0,0,0] [0,0,1,1,1,0] [0,1,1,0,1,0] [0,0,0,1,1,0] [0,0,0,0,0,0]]) I'm trying to create this: [ [0,1,1,1], [1,1,0,1], [0,0,1,1] ] That is, to remove the peripheral zeros(black pixels) that fill an entire row/column. I can brute force this with loops, but intuitively I feel like numpy has a better means of doing this. A: <code> import numpy as np im = np.array([[0,0,0,0,0,0], [0,0,1,1,1,0], [0,1,1,0,1,0], [0,0,0,1,1,0], [0,0,0,0,0,0]]) </code> result = ... # put solution in this variable 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