begin memory and speed profiling

profiling/graphene.py

+# %% 
+import numpy as np
+from codes.model import Model
+from codes import kwant_examples
+from codes.kwant_helper import utils
+from pyinstrument import Profiler
+import timeit
+import memray
+
+# %%
+graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()
+
+params = {'U' : 0.5, 'V' : 1.1}
+filling = 2
+nK = 300
+
+int_model = utils.builder2tb_model(int_builder, params)
+tb_model = utils.builder2tb_model(graphene_builder)
+guess = utils.generate_guess(frozenset(int_model), len(list(tb_model.values())[0]))
+
+model = Model(tb_model, int_model, filling)
+def scf_loop():
+    model.mfieldFFT(guess, nK=nK)
+
+# %% Memory profile
+with memray.Tracker("memoryProfile.bin"):
+    scf_loop()
+
+# %% Time profiler
+profiler = Profiler()
+
+profiler.start()
+scf_loop()
+profiler.stop()
+profiler.write_html(path='timeProfile.html')
+
+# %% 
+number = 1
+
+timeSCF = timeit.timeit(scf_loop, number=number)/number
+
+H = np.random.rand(nK, nK)
+H += H.T.conj()
+timeDiag = timeit.timeit(lambda: np.linalg.eigh(H), number=number)/number
+
+print(f"Single SCF loop takes {timeSCF} whereas a single diagonalization of a corresponding system takes {timeDiag}")
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