# ds1000 / 397

- 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

```
# 397: DS-1000 Task

## Prompt
Problem:
I could not find a built-in function in Python to generate a log uniform distribution given a min and max value (the R equivalent is here), something like: loguni[n, min, max, base] that returns n log uniformly distributed in the range min and max.
The closest I found though was numpy.random.uniform.
That is, given range of x, I want to get samples of given size (n) that suit log-uniform distribution. 
Any help would be appreciated!
A:
<code>
import numpy as np
def f(min=1, max=np.e, n=10000):
    # return the solution in this function
    # result = f(min=1, max=np.e, n=10000)
    ### BEGIN SOLUTION

## 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`).
```
---
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