# Interpreters

## Results
<img src="result.png" alt="drawing" width="650"/>
<!-- ![result.png](result.png) -->

### Observations
- 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
- Entire file evaluation for PyPy takes **a bit longer**. This may be a drawback of JIT optimization
- [PyPy with Numpy is slow](https://stackoverflow.com/questions/42536308/why-is-pypy-slower-for-adding-numpy-arrays). Reason:
  - Numpy is written in C
  - PyPy's JIT compilation is not compatible with C
  - Extra conversion is required (takes time)
- Numpy functions don't give a good improvement in perfomance. Possibly because the code uses classic Python loops instead of numpy vectorisation.

## Tested interpreters
- CPython 3.9
- CPython 3.11
- PyPy 3.9 latest
- Pyodide (did not test full file because it only runs in a browser)
- Xeus (again no full file)
## Failed to test
- PyPy 3.9 v5.7
  - couldn't install numpy `Ignoring ensurepip failure: pip 9.0.1 requires SSL/TLS`
  - error when testing with types:
```python
invalid syntax --> image: list[list[int]] = [[0 for i in range(qpoints)] for j in range(ppoints)]
```
- Jython (dependencies issues)

## Testing Code
- Tested functions code is in `./test_funcs` package
- File for testing function times: `test_func_only.py`
- Files for entire evaluation testing: `test_full_{func}.py`
- Bash script: `test_full.sh`
- Analysis file: `analyse.ipynb`

### Realisations of mandelbrot functions tested:
```python
def linspace(start, stop, n):
    if n == 1:
        yield stop
        return
    h = (stop - start) / (n - 1)
    for i in range(n):
        yield start + h * i

def mandelbrot_with_types(
    pmin: float = -2.5,
    pmax: float = 1.5,
    qmin: float = -2,
    qmax: float = 2,
    ppoints: int = 200,
    qpoints: int = 200,
    max_iterations: int = 300,
    infinity_border: float = 100) -> list[list[int]]:

    image: list[list[int]] = [[0 for i in range(qpoints)] for j in range(ppoints)]
    for ip, p in enumerate(linspace(pmin, pmax, ppoints)):
        for iq, q in enumerate(linspace(qmin, qmax, qpoints)):
            c: complex = p + 1j * q
            z: complex  = 0
            for k in range(max_iterations):
                z = z ** 2 + c
                if abs(z) > infinity_border:
                    image[ip][iq] = 1
                    break
    return image

def mandelbrot_no_types(
    pmin=-2.5,
    pmax=1.5,
    qmin=-2,
    qmax=2,
    ppoints=200,
    qpoints=200,
    max_iterations=300,
    infinity_border=100):
    image = [[0 for i in range(qpoints)] for j in range(ppoints)]
    for ip, p in enumerate(linspace(pmin, pmax, ppoints)):
        for iq, q in enumerate(linspace(qmin, qmax, qpoints)):
            c = p + 1j * q
            z = 0
            for k in range(max_iterations):
                z = z ** 2 + c
                if abs(z) > infinity_border:
                    image[ip][iq] = 1
                    break
    return image

def mandelbrot_np(
    pmin=-2.5,
    pmax=1.5,
    qmin=-2,
    qmax=2,
    ppoints=200,
    qpoints=200,
    max_iterations=300,
    infinity_border=100):
    image = np.zeros((ppoints, qpoints))

    for ip, p in enumerate(np.linspace(pmin, pmax, ppoints)):
        for iq, q in enumerate(np.linspace(qmin, qmax, qpoints)):
            c = p + 1j * q
            z = 0
            for k in range(max_iterations):
                z = z ** 2 + c
                if abs(z) > infinity_border:

                    image[ip, iq] = 1
                    break
    return image
```