advanced-python-homework-2023/interpreters/README.md

Interpreters

Results

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. 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:

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:

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