formatted with black

benchmarks/plot.py

@@ -9,94 +9,94 @@
 
 
 def load(folder_name):
-	filepaths = []
+    filepaths = []
 
-	for libname in liblist:
-		path = f"./{folder_name}/{libname}/.benchmarks/*/*.json"
-		flist = glob.glob(path)
-		flist = [fname.replace("\\", "/") for fname in flist]
-		# pick latest one
-		flist.sort(key=lambda x: int(x.split("/")[-1].split("_")[0]), reverse=True)
-		if len(flist) > 0:
-			filepaths.append((libname, flist[0]))
+    for libname in liblist:
+        path = f"./{folder_name}/{libname}/.benchmarks/*/*.json"
+        flist = glob.glob(path)
+        flist = [fname.replace("\\", "/") for fname in flist]
+        # pick latest one
+        flist.sort(key=lambda x: int(x.split("/")[-1].split("_")[0]), reverse=True)
+        if len(flist) > 0:
+            filepaths.append((libname, flist[0]))
 
-	dat = defaultdict(dict)
-	for filepath in filepaths:
-		data = json.load(open(filepath[1]))
+    dat = defaultdict(dict)
+    for filepath in filepaths:
+        data = json.load(open(filepath[1]))
 
-		def fetch_normal(libname, dat, data):
-			items = data["benchmarks"]
-			for item in items:
-				name = item["group"]
-				nqubits = int(item["param"])
-				stats = item["stats"]
-				key = name
-				dat[key][nqubits] = float(stats["min"])
+        def fetch_normal(libname, dat, data):
+            items = data["benchmarks"]
+            for item in items:
+                name = item["group"]
+                nqubits = int(item["param"])
+                stats = item["stats"]
+                key = name
+                dat[key][nqubits] = float(stats["min"])
 
-		fetch_normal(filepath[0], dat, data)
+        fetch_normal(filepath[0], dat, data)
 
-	return dat
+    return dat
 
 
 def filter_fun(x, name):
-	if name == "qiskit":
-		return (name in x) and ("qulacs" not in x)
-	elif name == "qulacs":
-		return (name in x) and ("qiskit" not in x)
+    if name == "qiskit":
+        return (name in x) and ("qulacs" not in x)
+    elif name == "qulacs":
+        return (name in x) and ("qiskit" not in x)
 
-	return name in x
+    return name in x
 
 
 def plot(dat, folder):
-	test_classes = ["statevector", "estimator", "sampler", "gradient"]
-	cmap = plt.get_cmap("tab10")
-	cnt = 0
-
-	fig, axs = plt.subplots(2, 2, figsize=(10, 10))
-	if len(test_classes) == 1:
-		axs = [axs]
-
-	expts = list(dat.keys())
-
-	for name in liblist:
-		cid = liblist.index(name)
-		tests = list(filter(lambda x: filter_fun(x, name), expts))
-		for i, test in enumerate(test_classes):
-			sub_tests = list(filter(lambda x: test in x, tests))
-			for sub_test in sub_tests:
-				stats = dat[sub_test]
-				fil = np.array(list(stats.items())).T
-				legend = (
-					"qulacs_exp_val" if sub_test == "qulacs_estimator" else sub_test
-				)
-				# axs[i].plot(fil[0], fil[1], ".-", label=legend, c=cmap(cid))
-				axs[i // 2][i % 2].plot(fil[0], fil[1], ".-", label=legend)
-
-	for i in range(2):
-		axs[i][0].set_ylabel("Time [sec]", fontsize=15)
-		axs[-1][i].set_xlabel("# of qubits", fontsize=15)
-
-	for ax, test in zip(axs.flatten(), test_classes):
-		ax.legend()
-		ax.set_xticks([5, 10, 15, 20])
-		ax.set_yscale("log")
-		ax.set_title(test)
-		ax.tick_params(axis="x", labelsize=12)
-		ax.tick_params(axis="y", labelsize=12)
-
-		ax.grid(which="major", color="black", linestyle="-", alpha=0.3)
-		ax.grid(which="minor", color="black", linestyle="-", alpha=0.1)
-
-	# plt.xticks(fontsize=15)
-	# plt.yticks(fontsize=15)
-	plt.tight_layout()
-	plt.legend(fontsize=10)
-	plt.savefig(f"./plots/fig_compare_{folder}.png")
-	plt.clf()
+    test_classes = ["statevector", "estimator", "sampler", "gradient"]
+    cmap = plt.get_cmap("tab10")
+    cnt = 0
+
+    fig, axs = plt.subplots(2, 2, figsize=(10, 10))
+    if len(test_classes) == 1:
+        axs = [axs]
+
+    expts = list(dat.keys())
+
+    for name in liblist:
+        cid = liblist.index(name)
+        tests = list(filter(lambda x: filter_fun(x, name), expts))
+        for i, test in enumerate(test_classes):
+            sub_tests = list(filter(lambda x: test in x, tests))
+            for sub_test in sub_tests:
+                stats = dat[sub_test]
+                fil = np.array(list(stats.items())).T
+                legend = (
+                    "qulacs_exp_val" if sub_test == "qulacs_estimator" else sub_test
+                )
+                # axs[i].plot(fil[0], fil[1], ".-", label=legend, c=cmap(cid))
+                axs[i // 2][i % 2].plot(fil[0], fil[1], ".-", label=legend)
+
+    for i in range(2):
+        axs[i][0].set_ylabel("Time [sec]", fontsize=15)
+        axs[-1][i].set_xlabel("# of qubits", fontsize=15)
+
+    for ax, test in zip(axs.flatten(), test_classes):
+        ax.legend()
+        ax.set_xticks([5, 10, 15, 20])
+        ax.set_yscale("log")
+        ax.set_title(test)
+        ax.tick_params(axis="x", labelsize=12)
+        ax.tick_params(axis="y", labelsize=12)
+
+        ax.grid(which="major", color="black", linestyle="-", alpha=0.3)
+        ax.grid(which="minor", color="black", linestyle="-", alpha=0.1)
+
+    # plt.xticks(fontsize=15)
+    # plt.yticks(fontsize=15)
+    plt.tight_layout()
+    plt.legend(fontsize=10)
+    plt.savefig(f"./plots/fig_compare_{folder}.png")
+    plt.clf()
 
 
 if __name__ == "__main__":
-	# for folder in ["cpu", "gpu"]:
-	for folder in ["cpu"]:
-		dat = load(folder)
-		plot(dat, folder)
+    # for folder in ["cpu", "gpu"]:
+    for folder in ["cpu"]:
+        dat = load(folder)
+        plot(dat, folder)