1 files changed, 0 insertions, 111 deletions

diff --git a/util/utils.py b/util/utils.py
deleted file mode 100644
index 014c4c6..0000000
--- a/util/utils.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import hashlib
-import numpy as np
-from matplotlib import ticker
-from math import sqrt, log
-import ec
-from asn1crypto.core import Sequence
-
-def hw(i):
-    res = 0
-    while i:
-        res += 1
-        i &= i - 1
-    return res
-
-
-def moving_average(a, n) :
-    ret = np.cumsum(a, dtype=float)
-    ret[n:] = ret[n:] - ret[:-n]
-    return ret[n - 1:] / n
-
-
-def time_scale(data, orig_unit, target_unit, scaling_factor):
-    if orig_unit == "instr":
-        return orig_unit
-    units = {
-        "milli": ("ms", 1000000),
-        "micro": (r"$\mu s$", 1000),
-        "nano":  ("ns", 1)
-    }
-    upper = units[orig_unit][1] * scaling_factor
-    lower = units[target_unit][1]
-    if upper > lower:
-        data *= upper // lower
-    elif lower > upper:
-        np.floor_divide(data, lower // upper, data)
-    return (r"$\frac{1}{" + str(scaling_factor) + "}$" if scaling_factor != 1 else "") + units[target_unit][0]
-
-def recompute_nonces(data, curve_name, hash_algo):
-    try:
-        curve = ec.get_curve(curve_name)
-    except:
-        curve = ec.load_curve(curve_name)
-    verified = False
-    for elem in data:
-        if elem["nonce"] is not None:
-            continue
-        if elem["index"] % (len(data)//10) == 0:
-            print(".", end="")
-        if hash_algo is None:
-            hm = int.from_bytes(elem["data"], byteorder="big") % curve.group.n
-        else:
-            h = hashlib.new(hash_algo, elem["data"])
-            hm = int(h.hexdigest(), 16)
-            if h.digest_size * 8 > curve.group.n.bit_length():
-                hm >> h.digest_size * 8 - curve.group.n.bit_length()
-            hm = hm % curve.group.n
-        r, s = Sequence.load(elem["signature"]).native.values()
-        r = ec.Mod(r, curve.group.n)
-        s = ec.Mod(s, curve.group.n)
-        rx = r * elem["priv"]
-        hmrx = hm + rx
-        nonce = s.inverse() * hmrx
-        if not verified:
-            res = int(nonce) * curve.g
-            if int(res.x) % curve.group.n != int(r):
-                print("Nonce recomputation couldnt verify!")
-                raise ValueError
-            else:
-                print("Nonce recomputation works")
-                verified = True
-        elem["nonce"] = int(nonce)
-
-def hist_size_func(choice):
-    if choice == "sqrt":
-        return lambda n, xmin, xmax, var, xlower, xupper: int(sqrt(n)) + 1
-    elif choice == "sturges":
-        return lambda n, xmin, xmax, var, xlower, xupper: int(log(n, 2)) + 1
-    elif choice == "rice":
-        return lambda n, xmin, xmax, var, xlower, xupper: int(2 * n**(1/3))
-    elif choice == "scott":
-        return lambda n, xmin, xmax, var, xlower, xupper: (xmax - xmin) // int((3.5 * sqrt(var)) / (n**(1/3)))
-    elif choice == "fd":
-        return lambda n, xmin, xmax, var, xlower, xupper: (xmax - xmin) // int(2 * (xupper - xlower) / (n**(1/3)))
-    else:
-        return lambda n, xmin, xmax, var, xlower, xupper: hist_size
-
-
-def plot_hist(axes, data, xlabel=None, log=False, avg=True, median=True, bins=None, **kwargs):
-    time_max = max(data)
-    time_min = min(data)
-    time_avg = np.average(data)
-    time_median = np.median(data)
-    if bins is None:
-        bins = time_max - time_min + 1
-    hist = axes.hist(data, bins=bins, log=log, **kwargs)
-    if avg:
-        axes.axvline(x=time_avg, alpha=0.7, linestyle="dotted", color="blue", label="avg = {}".format(time_avg))
-    if median:
-        axes.axvline(x=time_median, alpha=0.7, linestyle="dotted", color="green", label="median = {}".format(time_median))
-    axes.set_ylabel("count" + ("\n(log)" if log else ""))
-    axes.set_xlabel("time" if xlabel is None else xlabel)
-    axes.xaxis.set_major_locator(ticker.MaxNLocator())
-    if avg or median:
-        axes.legend(loc="best")
-    return hist
-
-
-def miller_correction(entropy, samples, bins):
-    return entropy + (bins - 1)/(2*samples)
-
-