fix Windows line endings in one .py

Author: ulysses4ever

benchmarks/scripts/criterion-drop-in-replacement/criterionmethodology.py

@@ -1,81 +1,81 @@
-#!/usr/bin/env python3
-import numpy as np
-from sys import argv
-import subprocess
-from time import time
-import math
-
-from matplotlib import pyplot as plt
-
-MAKE_PLOT = False
-
-def linear_regression_with_std(x, y):
-    x = np.array(x)
-    y = np.array(y)
-    x_mean = np.mean(x)
-    y_mean = np.mean(y)
-    numerator = np.sum((x - x_mean) * (y - y_mean))
-    denominator = np.sum((x - x_mean) ** 2)
-    slope = numerator / denominator
-    intercept = y_mean - slope * x_mean
-    y_pred = slope * x + intercept
-    residuals = y - y_pred
-    std_dev = np.std(residuals)
-    return slope, intercept, std_dev
-
-def do_bench(cliargs, iters):
-    print([cliargs[1], str(iters)] + cliargs[2:])
-    out = str(subprocess.check_output([cliargs[1], str(iters)] + cliargs[2:]))
-    s1 = out[out.find("SELFTIMED")+11:]
-    s2 = float(s1[:s1.find("\n")-4])
-    selftimed = s2
-
-    b1 = out[out.find("BATCHTIME")+11:]
-    b2 = float(b1[:b1.find("SELFTIMED")-2])
-    batchtime = b2
-
-    print(f"ITERS: {iters}, BATCHTIME: {batchtime}, SELFTIMED: {selftimed}")
-    return batchtime
-
-def converge(cliargs):
-    xs = []
-    ys = []
-    iters = 1
-    t = time()
-    while len(xs) == 0:
-        st = do_bench(cliargs, iters)
-        if st * iters < 0.65:
-            iters *= 2
-            continue
-        xs.append(iters)
-        ys.append(st)
-    for _ in range(2):
-        if time() - t < 3.5:
-            iters = int(math.trunc(float(iters) * 1.2) + 1)
-        else:
-            iters += 1 + iters // 20
-        st = do_bench(cliargs, iters)
-        xs.append(iters)
-        ys.append(st)
-    while time() - t < 3.5:
-        if time() - t < 3.5:
-            iters = int(math.trunc(float(iters) * 1.2) + 1)
-        else:
-            iters += 1 + iters // 20
-        st = do_bench(cliargs, iters)
-        xs.append(iters)
-        ys.append(st)
-    m, b, sigma = linear_regression_with_std(xs, ys)
-    print(f"Slope (Mean): {m}, Intercept (Overhead): {b}, Stdev: {sigma}")
-    p, lnc, lngsd = linear_regression_with_std([math.log(x) for x in xs], [math.log(y) for y in ys])
-    c, gsd = math.exp(lnc), math.exp(lngsd)
-    print(f"Power (Distortion): {p}, Factor (Geomean) {c}, GeoStdev {gsd}")
-    if MAKE_PLOT:
-        plt.plot(xs, ys, 'rx')
-        plt.plot([xs[0], xs[-1]], [m*xs[0]+b, m*xs[-1]+b], color="blue")
-        plt.plot(xs, [c*x**p for x in xs], color="green")
-        plt.savefig("plot.png")
-    return m, sigma, c, gsd
-
-if __name__ == "__main__":
-    print(converge(argv))
+#!/usr/bin/env python
+import numpy as np
+from sys import argv
+import subprocess
+from time import time
+import math
+
+from matplotlib import pyplot as plt
+
+MAKE_PLOT = False
+
+def linear_regression_with_std(x, y):
+    x = np.array(x)
+    y = np.array(y)
+    x_mean = np.mean(x)
+    y_mean = np.mean(y)
+    numerator = np.sum((x - x_mean) * (y - y_mean))
+    denominator = np.sum((x - x_mean) ** 2)
+    slope = numerator / denominator
+    intercept = y_mean - slope * x_mean
+    y_pred = slope * x + intercept
+    residuals = y - y_pred
+    std_dev = np.std(residuals)
+    return slope, intercept, std_dev
+
+def do_bench(cliargs, iters):
+    print([cliargs[1], str(iters)] + cliargs[2:])
+    out = str(subprocess.check_output([cliargs[1], str(iters)] + cliargs[2:]))
+    s1 = out[out.find("SELFTIMED")+11:]
+    s2 = float(s1[:s1.find("\n")-4])
+    selftimed = s2
+
+    b1 = out[out.find("BATCHTIME")+11:]
+    b2 = float(b1[:b1.find("SELFTIMED")-2])
+    batchtime = b2
+
+    print(f"ITERS: {iters}, BATCHTIME: {batchtime}, SELFTIMED: {selftimed}")
+    return batchtime
+
+def converge(cliargs):
+    xs = []
+    ys = []
+    iters = 1
+    t = time()
+    while len(xs) == 0:
+        st = do_bench(cliargs, iters)
+        if st * iters < 0.65:
+            iters *= 2
+            continue
+        xs.append(iters)
+        ys.append(st)
+    for _ in range(2):
+        if time() - t < 3.5:
+            iters = int(math.trunc(float(iters) * 1.2) + 1)
+        else:
+            iters += 1 + iters // 20
+        st = do_bench(cliargs, iters)
+        xs.append(iters)
+        ys.append(st)
+    while time() - t < 3.5:
+        if time() - t < 3.5:
+            iters = int(math.trunc(float(iters) * 1.2) + 1)
+        else:
+            iters += 1 + iters // 20
+        st = do_bench(cliargs, iters)
+        xs.append(iters)
+        ys.append(st)
+    m, b, sigma = linear_regression_with_std(xs, ys)
+    print(f"Slope (Mean): {m}, Intercept (Overhead): {b}, Stdev: {sigma}")
+    p, lnc, lngsd = linear_regression_with_std([math.log(x) for x in xs], [math.log(y) for y in ys])
+    c, gsd = math.exp(lnc), math.exp(lngsd)
+    print(f"Power (Distortion): {p}, Factor (Geomean) {c}, GeoStdev {gsd}")
+    if MAKE_PLOT:
+        plt.plot(xs, ys, 'rx')
+        plt.plot([xs[0], xs[-1]], [m*xs[0]+b, m*xs[-1]+b], color="blue")
+        plt.plot(xs, [c*x**p for x in xs], color="green")
+        plt.savefig("plot.png")
+    return m, sigma, c, gsd
+
+if __name__ == "__main__":
+    print(converge(argv))