forked from Advanced_Python/advanced-python-homework-2023
114 lines
3.5 KiB
Markdown
114 lines
3.5 KiB
Markdown
# Interpreters
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## Results
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<img src="result.png" alt="drawing" width="650"/>
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<!--  -->
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### Observations
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- Functions w/o Numpy in PyPy run **much faster**. JIT compilation optimizes the function's code and runs compiled versions of them $\Rightarrow$ improvement in time
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- Entire file evaluation for PyPy takes **a bit longer**. This may be a drawback of JIT optimization
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- [PyPy with Numpy is slow](https://stackoverflow.com/questions/42536308/why-is-pypy-slower-for-adding-numpy-arrays). Reason:
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- Numpy is written in C
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- PyPy's JIT compilation is not compatible with C
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- Extra conversion is required (takes time)
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- Numpy functions don't give a good improvement in perfomance. Possibly because the code uses classic Python loops instead of numpy vectorisation.
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## Tested interpreters
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- CPython 3.9
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- CPython 3.11
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- PyPy 3.9 latest
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- Pyodide (did not test full file because it only runs in a browser)
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- Xeus (again no full file)
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## Failed to test
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- PyPy 3.9 v5.7
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- couldn't install numpy `Ignoring ensurepip failure: pip 9.0.1 requires SSL/TLS`
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- error when testing with types:
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```python
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invalid syntax --> image: list[list[int]] = [[0 for i in range(qpoints)] for j in range(ppoints)]
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```
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- Jython (dependencies issues)
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## Testing Code
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- Tested functions code is in `./test_funcs` package
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- File for testing function times: `test_func_only.py`
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- Files for entire evaluation testing: `test_full_{func}.py`
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- Bash script: `test_full.sh`
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- Analysis file: `analyse.ipynb`
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### Realisations of mandelbrot functions tested:
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```python
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def linspace(start, stop, n):
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if n == 1:
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yield stop
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return
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h = (stop - start) / (n - 1)
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for i in range(n):
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yield start + h * i
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def mandelbrot_with_types(
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pmin: float = -2.5,
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pmax: float = 1.5,
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qmin: float = -2,
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qmax: float = 2,
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ppoints: int = 200,
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qpoints: int = 200,
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max_iterations: int = 300,
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infinity_border: float = 100) -> list[list[int]]:
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image: list[list[int]] = [[0 for i in range(qpoints)] for j in range(ppoints)]
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for ip, p in enumerate(linspace(pmin, pmax, ppoints)):
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for iq, q in enumerate(linspace(qmin, qmax, qpoints)):
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c: complex = p + 1j * q
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z: complex = 0
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for k in range(max_iterations):
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z = z ** 2 + c
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if abs(z) > infinity_border:
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image[ip][iq] = 1
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break
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return image
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def mandelbrot_no_types(
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pmin=-2.5,
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pmax=1.5,
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qmin=-2,
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qmax=2,
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ppoints=200,
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qpoints=200,
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max_iterations=300,
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infinity_border=100):
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image = [[0 for i in range(qpoints)] for j in range(ppoints)]
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for ip, p in enumerate(linspace(pmin, pmax, ppoints)):
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for iq, q in enumerate(linspace(qmin, qmax, qpoints)):
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c = p + 1j * q
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z = 0
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for k in range(max_iterations):
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z = z ** 2 + c
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if abs(z) > infinity_border:
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image[ip][iq] = 1
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break
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return image
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def mandelbrot_np(
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pmin=-2.5,
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pmax=1.5,
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qmin=-2,
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qmax=2,
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ppoints=200,
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qpoints=200,
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max_iterations=300,
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infinity_border=100):
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image = np.zeros((ppoints, qpoints))
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for ip, p in enumerate(np.linspace(pmin, pmax, ppoints)):
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for iq, q in enumerate(np.linspace(qmin, qmax, qpoints)):
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c = p + 1j * q
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z = 0
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for k in range(max_iterations):
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z = z ** 2 + c
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if abs(z) > infinity_border:
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image[ip, iq] = 1
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break
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return image
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```
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