# ds1000 / 374 - 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 ``` # 374: DS-1000 Task ## Prompt Problem: What is the equivalent of R's ecdf(x)(x) function in Python, in either numpy or scipy? Is ecdf(x)(x) basically the same as: import numpy as np def ecdf(x): # normalize X to sum to 1 x = x / np.sum(x) return np.cumsum(x) or is something else required? What I want to do is to apply the generated ECDF function to an eval array to gets corresponding values for elements in it. A: <code> import numpy as np grades = np.array((93.5,93,60.8,94.5,82,87.5,91.5,99.5,86,93.5,92.5,78,76,69,94.5, 89.5,92.8,78,65.5,98,98.5,92.3,95.5,76,91,95,61)) eval = np.array([88, 87, 62]) </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