Trying to understand rust divergence
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import scipy\n",
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"from glob import glob\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def read_xy(path: str):\n",
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" df = pd.read_csv(path)\n",
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" df['N'] = df.index + 1\n",
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" df['r'] = (df.x ** 2 + df.y ** 2) ** 0.5\n",
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" df['cr'] = df.r.cummax()\n",
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" df['fd'] = np.log(df.N) / np.log(df.cr)\n",
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"\n",
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" return df\n",
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"\n",
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"\n",
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"def convergent_tail_index(series, tol):\n",
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" diffs = np.abs(np.ediff1d(series))\n",
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" for i in range(0, len(diffs)):\n",
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" if np.max(diffs[i:]) <= tol:\n",
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" return i\n",
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"\n",
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" # No convergence found\n",
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" return None\n",
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"\n",
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"\n",
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"def mean_of_tail(series, tol=0.05):\n",
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" tail_index = convergent_tail_index(series, tol)\n",
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" return np.mean(series[tail_index:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/joshuacoles/Library/Caches/pypoetry/virtualenvs/data-analysis-B4Au_hWl-py3.10/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
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" result = getattr(ufunc, method)(*inputs, **kwargs)\n",
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"/Users/joshuacoles/Library/Caches/pypoetry/virtualenvs/data-analysis-B4Au_hWl-py3.10/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
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" result = getattr(ufunc, method)(*inputs, **kwargs)\n",
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"/Users/joshuacoles/Library/Caches/pypoetry/virtualenvs/data-analysis-B4Au_hWl-py3.10/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
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" result = getattr(ufunc, method)(*inputs, **kwargs)\n",
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"/Users/joshuacoles/Library/Caches/pypoetry/virtualenvs/data-analysis-B4Au_hWl-py3.10/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
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" result = getattr(ufunc, method)(*inputs, **kwargs)\n"
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]
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}
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],
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"source": [
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"a = read_xy(\"/Users/joshuacoles/Developer/checkouts/jc3091/CompB DLA/c-codebase/a.csv\")\n",
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"b = read_xy(\"/Users/joshuacoles/Developer/checkouts/jc3091/CompB DLA/c-codebase/b.csv\")\n",
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"c = read_xy(\"/Users/joshuacoles/Developer/checkouts/jc3091/CompB DLA/c-codebase/c.csv\")\n",
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"d = read_xy(\"/Users/joshuacoles/Developer/checkouts/jc3091/CompB DLA/c-codebase/d.csv\")"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"outputs": [
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{
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"data": {
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"text/plain": "1.7271570214996996"
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mean_of_tail(a.fd)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"outputs": [
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{
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"data": {
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"text/plain": "1.7271570214996996"
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mean_of_tail(b.fd)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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"cell_type": "code",
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"execution_count": 18,
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"outputs": [
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{
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"data": {
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"text/plain": "1.7634427392486482"
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mean_of_tail(c.fd)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"outputs": [
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{
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"data": {
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"text/plain": "1.7634427392486482"
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mean_of_tail(d.fd)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/joshuacoles/Library/Caches/pypoetry/virtualenvs/data-analysis-B4Au_hWl-py3.10/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
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" result = getattr(ufunc, method)(*inputs, **kwargs)\n"
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]
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},
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{
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"data": {
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"text/plain": "20769"
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},
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"execution_count": 31,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"convergent_tail_index(read_xy(\"../fucking_massive.csv\").fd, 0.001)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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+29244
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#!/usr/bin/env bash
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# NOTE: Requires GNU parallel
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MODEL="models/rust-sp"
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#MODEL=echo
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mkdir -p data/rust-sp
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PROBABILITIES=$@
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for i in $PROBABILITIES; do
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mkdir data/rust-sp/"$i"
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done
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MAX_PARTICLES=2000
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parallel "$MODEL" '{1}' $MAX_PARTICLES '{2}' 'data/rust-sp/{2}/{1}.csv' ::: {1..100} ::: $PROBABILITIES
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#!/usr/bin/env bash
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# NOTE: Requires GNU parallel
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# USAGE: MAX_PARTICLES=... sp-harness.bash <model> <run_id> <probabilities...>
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# Run ID
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MODEL="$1"
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ID="$2"
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shift
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shift
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ROOT="data/$ID"
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mkdir -p "$ROOT"
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PROBABILITIES=$@
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for i in $PROBABILITIES; do
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mkdir -p "$ROOT/$i"
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done
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MAX_PARTICLES="${MAX_PARTICLES:=2000}"
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parallel "'$MODEL'" '{1}' $MAX_PARTICLES '{2}' "$ROOT/{2}/{1}.csv" ::: {1..100} ::: $PROBABILITIES
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