diff options
| -rw-r--r-- | epare/visualize.ipynb | 112 |
1 files changed, 5 insertions, 107 deletions
diff --git a/epare/visualize.ipynb b/epare/visualize.ipynb index 5b61ca1..709e566 100644 --- a/epare/visualize.ipynb +++ b/epare/visualize.ipynb @@ -77,41 +77,6 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "2df8cd8c-9528-4755-83b5-10ecabaead54", - "metadata": {}, - "outputs": [], - "source": [ - "def divides_any(l: int, small_scalars: set[int]) -> bool:\n", - " for s in small_scalars:\n", - " if s%l==0:\n", - " return True\n", - " return False\n", - "\n", - "def process_small_scalars(scalar_results: MultResults, divisors: set[int]) -> ProbMap:\n", - " result = {}\n", - " for divisor in divisors:\n", - " count = 0\n", - " for smult in scalar_results.multiplications:\n", - " if divides_any(divisor, smult):\n", - " count += 1\n", - " result[divisor] = count / scalar_results.samples\n", - " return ProbMap(result, scalar_results.samples)\n", - "\n", - "def load_chunk(fname: str, divisors: set[int]) -> dict[MultIdent, ProbMap]:\n", - " with open(fname, \"rb\") as f:\n", - " multiples = pickle.load(f)\n", - " res = {}\n", - " for mult, results in multiples.items():\n", - " res[mult] = process_small_scalars(results, divisors)\n", - " return res\n", - "\n", - "def conf_interval(p: float, samples: int, alpha: float = 0.05) -> tuple[float, float]:\n", - " return proportion_confint(round(p*samples), samples, alpha, method=\"wilson\")" - ] - }, - { "cell_type": "markdown", "id": "2596562f-8a6a-4a25-ae82-a6b9562d8a40", "metadata": {}, @@ -127,39 +92,9 @@ "metadata": {}, "outputs": [], "source": [ - "def powers_of(k, max_power=20):\n", - " return [k**i for i in range(1, max_power)]\n", - "\n", - "def prod_combine(one, other):\n", - " return [a * b for a, b in itertools.product(one, other)]\n", - "\n", - "small_primes = [3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199]\n", - "medium_primes = [211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397]\n", - "large_primes = [401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997]\n", - "all_integers = list(range(1, 400))\n", - "all_even = list(range(2, 400, 2))\n", - "all_odd = list(range(1, 400, 2))\n", - "all_primes = small_primes + medium_primes + large_primes\n", - "\n", - "divisor_map = {\n", - " \"small_primes\": small_primes,\n", - " \"medium_primes\": medium_primes,\n", - " \"large_primes\": large_primes,\n", - " \"all_primes\": all_primes,\n", - " \"all_integers\": all_integers,\n", - " \"all_even\": all_even,\n", - " \"all_odd\": all_odd,\n", - " \"powers_of_2\": powers_of(2),\n", - " \"powers_of_2_large\": powers_of(2, 256),\n", - " \"powers_of_2_large_3\": [i * 3 for i in powers_of(2, 256)],\n", - " \"powers_of_2_large_p1\": [i + 1 for i in powers_of(2, 256)],\n", - " \"powers_of_2_large_m1\": [i - 1 for i in powers_of(2, 256)],\n", - " \"powers_of_2_large_pmautobus\": sorted(set([i + j for i in powers_of(2, 256) for j in range(-5,5) if i+j > 0])),\n", - " \"powers_of_3\": powers_of(3),\n", - "}\n", - "divisor_map[\"all\"] = list(sorted(set().union(*[v for v in divisor_map.values()])))\n", + "from common import divisor_map\n", "for d, ds in divisor_map.items():\n", - " print(d, len(ds))" + " print(f\"{d:<27}\", ds[:3], \"...\", ds[-1:])" ] }, { @@ -175,18 +110,11 @@ }, { "cell_type": "markdown", - "id": "5b427252-a3ff-4a55-940c-3c8659caa799", - "metadata": {}, - "source": [ - "Select the mults you want to compute the prob-maps for here as well as a set of divisors. It is good to set `all` here, compute the prob-maps for all the divisors, save them and they continue with visualizing them on subsets of divisors." - ] - }, - { - "cell_type": "markdown", "id": "8b008248-a0aa-41fa-933c-f325f8eec31b", "metadata": {}, "source": [ - "## Configuration" + "## Configuration\n", + "Select the mults you want to compute the prob-maps for here as well as a set of divisors. It is good to set `all` here, compute the prob-maps for all the divisors, save them and they continue with visualizing them on subsets of divisors." ] }, { @@ -198,7 +126,7 @@ "source": [ "selected_mults = all_mults\n", "divisor_name = \"all\"\n", - "kind = \"necessary\"\n", + "kind = \"precomp+necessary\"\n", "showci = False\n", "selected_divisors = divisor_map[divisor_name]" ] @@ -206,36 +134,6 @@ { "cell_type": "code", "execution_count": null, - "id": "4d2b0f1a-b50a-4548-b63e-c6507e95503d", - "metadata": {}, - "outputs": [], - "source": [ - "# This cell computes the probmaps from the multiplication result chunks. Only do this on small amount of chunks.\n", - "# distributions_mults = {}\n", - "# files = sorted(glob.glob(f\"multiples_{bits}_{kind}_chunk*.pickle\"))\n", - "# with TaskExecutor(max_workers=num_workers) as pool:\n", - "# for fname in files:\n", - "# pool.submit_task(fname,\n", - "# load_chunk,\n", - "# fname, selected_divisors)\n", - "# for fname, future in tqdm(pool.as_completed(), leave=False, total=len(pool.tasks), smoothing=0):\n", - "# if error := future.exception():\n", - "# print(f\"Error {fname}, {error}\")\n", - "# continue\n", - "# new_distrs = future.result()\n", - "# for mult, prob_map in new_distrs.items():\n", - "# if mult in distributions_mults:\n", - "# distributions_mults[mult].merge(prob_map)\n", - "# else:\n", - "# distributions_mults[mult] = prob_map\n", - "# Save\n", - "# with open(f\"{divisor_name}_{kind}_distrs.pickle\", \"wb\") as f:\n", - "# pickle.dump(distributions_mults, f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, "id": "19d986ab-5fe7-4dd6-b5b5-4e75307217d6", "metadata": {}, "outputs": [], |