49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
import timeit
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from collections import deque, defaultdict
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import numpy as np
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def measure_execution_time(circuit, state, repeat=100, **kwargs):
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results = deque()
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for _ in range(repeat):
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s = state.deepcopy()
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start = timeit.default_timer()
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result = circuit * s
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stop = timeit.default_timer()
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results.append(stop - start)
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return min(results)
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def measure_all(input_circuits_and_states, **kwargs):
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results = defaultdict(deque)
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N = len(input_circuits_and_states)
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for n, (key, circuit, state) in enumerate(input_circuits_and_states):
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print(f"running tests... {int(n/N * 100)} %", end="\r", flush=True)
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results[key].append(measure_execution_time(circuit, state, **kwargs))
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print("running tests... done ")
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return results
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def execution_statistics(results, scale=1, **kwargs):
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Ns = deque()
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avgs = deque()
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std_devs = deque()
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for k,v in sorted(results.items(), key=lambda x: x[0]):
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result = np.array(v, dtype=np.double)
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if(scale is not None):
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result /= scale[k]
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N = len(result)
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avg = np.average(result)
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std_dev = np.std(result) / np.sqrt(N)
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Ns.append(N)
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avgs.append(avg)
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std_devs.append(std_dev)
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Ns = np.array(Ns).astype(np.int64)
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avgs = np.array(avgs).astype(np.double)
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std_devs = np.array(std_devs).astype(np.double)
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return Ns, avgs, std_devs
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