From cd62fa859087307ed0ce6d2adad99eb1c471f809 Mon Sep 17 00:00:00 2001 From: Alan Lujan Date: Sat, 3 Feb 2024 15:39:22 -0500 Subject: [PATCH] cycles = 0 --- ...cal Buffer Stock on Portfolio Models.ipynb | 3108 +++++++++-------- macro/dashboard_default.ipynb | 165 +- macro/roots.csv | 730 ++++ 3 files changed, 2531 insertions(+), 1472 deletions(-) create mode 100644 macro/roots.csv diff --git a/macro/Numerical Buffer Stock on Portfolio Models.ipynb b/macro/Numerical Buffer Stock on Portfolio Models.ipynb index 6c967c3..48842f2 100644 --- a/macro/Numerical Buffer Stock on Portfolio Models.ipynb +++ b/macro/Numerical Buffer Stock on Portfolio Models.ipynb @@ -38,12 +38,12 @@ { "data": { "text/plain": [ - "{'cycles': 1,\n", + "{'cycles': 0,\n", " 'CRRA': 5.0,\n", - " 'Rfree': 1.03,\n", + " 'Rfree': 1.0,\n", " 'DiscFac': 0.9,\n", - " 'LivPrb': [0.98],\n", - " 'PermGroFac': [1.01],\n", + " 'LivPrb': [1.0],\n", + " 'PermGroFac': [1.0],\n", " 'BoroCnstArt': 0.0,\n", " 'MaxKinks': 400,\n", " 'AgentCount': 10000,\n", @@ -74,7 +74,7 @@ " 'CubicBool': False,\n", " 'neutral_measure': False,\n", " 'NewbornTransShk': False,\n", - " 'RiskyAvg': 1.08,\n", + " 'RiskyAvg': 1.05,\n", " 'RiskyStd': 0.2,\n", " 'RiskyCount': 5,\n", " 'ShareCount': 25,\n", @@ -88,6 +88,13 @@ } ], "source": [ + "init_portfolio\n", + "init_portfolio[\"cycles\"] = 0 # NEED THIS FOR INFINITE HORIZON\n", + "init_portfolio[\"PermGroFac\"] = [1.0] # no drift in perm income\n", + "# risk free return, set to 1 to focus on equity premium\n", + "init_portfolio[\"Rfree\"] = 1.0\n", + "init_portfolio[\"RiskyAvg\"] = 1.05 # eq_prem is RiskyAvg - Rfree = 0.05\n", + "init_portfolio[\"LivPrb\"] = [1.0] # no death\n", "init_portfolio" ] }, @@ -97,10 +104,10 @@ "metadata": {}, "outputs": [], "source": [ - "def interp_func(x,y):\n", + "def interp_func(x, y):\n", " def func(z):\n", " return np.interp(z, x, y)\n", - " \n", + "\n", " return func" ] }, @@ -110,7 +117,7 @@ "metadata": {}, "outputs": [], "source": [ - "at = SequentialPortfolioConsumerType(PermGroFac=[1.0], UnempPrb=0.00)\n", + "at = SequentialPortfolioConsumerType(**init_portfolio)\n", "at.track_vars += [\"aNrm\", \"cNrm\", \"mNrm\", \"Risky\", \"Share\", \"aLvl\", \"pLvl\"]\n", "at.solve()\n", "\n", @@ -132,14 +139,12 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -155,14 +160,13 @@ "source": [ "dividend_growth_rate = 1.000203\n", "dividend_std = 0.011983\n", + "dividend_shock_std = dividend_std / math.sqrt(dividend_growth_rate)\n", "\n", "\n", "def price_dividend_ratio_random_walk(DiscFac, CRRA, days_per_quarter=90):\n", " # Assuming DiscFac in argument in quarterly\n", " DiscFac_daily = DiscFac ** (1.0 / days_per_quarter)\n", "\n", - " dividend_shock_std = dividend_std / math.sqrt(dividend_growth_rate)\n", - "\n", " subjective_return = (\n", " dividend_growth_rate ** (1 - CRRA)\n", " * DiscFac_daily\n", @@ -180,39 +184,37 @@ "outputs": [], "source": [ "def compute_target_wealth(\n", - " CRRA=6.0,\n", + " CRRA=5.0,\n", " DiscFac=0.9,\n", - " RiskyAvg=1.08,\n", + " RiskyAvg=1.05,\n", " RiskyStd=0.20,\n", - " PermShkStd=[0.0],\n", - " PermGroFac=[1.0001],\n", - " UnempPrb=0.00\n", + " PermShkStd=[0.1],\n", + " TranShkStd=[0.1],\n", "):\n", - " agent_parameters = {}\n", + " agent_parameters = init_portfolio.copy() # COPY DEFAULT DICTIONARY\n", "\n", + " # Replace only exploratory parameters\n", " agent_parameters[\"CRRA\"] = CRRA\n", " agent_parameters[\"DiscFac\"] = DiscFac\n", " agent_parameters[\"RiskyAvg\"] = RiskyAvg\n", " agent_parameters[\"RiskyStd\"] = RiskyStd\n", " agent_parameters[\"PermShkStd\"] = PermShkStd\n", - " agent_parameters[\"PermGroFac\"] = PermGroFac\n", - " agent_parameters[\"UnempPrb\"] = UnempPrb\n", - " \n", + " agent_parameters[\"TranShkStd\"] = TranShkStd\n", + "\n", " agent = SequentialPortfolioConsumerType(**agent_parameters)\n", - " #pprint(agent.parameters)\n", - " \n", + " # pprint(agent.parameters)\n", + "\n", " linear_roots, log_linear_roots, cubic_spline_roots = [], [], []\n", - " \n", + "\n", " try:\n", " agent.solve()\n", " solved = True\n", " except Exception as e:\n", " solved = False\n", - " \n", + "\n", " return solved, linear_roots, log_linear_roots, cubic_spline_roots\n", - " \n", "\n", - " ## subjective return\n", + " # subjective return\n", " srle1 = price_dividend_ratio_random_walk(DiscFac, CRRA)\n", " print(\"subjective_return < 1?: \" + str(srle1))\n", "\n", @@ -244,31 +246,35 @@ " share * agent.parameters[\"RiskyAvg\"]\n", " + (1 - share) * agent.parameters[\"Rfree\"]\n", " )\n", - " + 1\n", + " + 1 # assuming average income is 1\n", " )\n", "\n", " return mNrm_next\n", "\n", - " mNrm = np.linspace(0, 5, 1000)\n", + " mNrm = cFunc.x_list\n", "\n", " # plt.plot(mNrm, cFunc(mNrm), label=\"c\")\n", "\n", " plt.plot(mNrm, mNrm - expected_m_next(mNrm), label=\"m - E[m']\")\n", "\n", - " linear_roots = fsolve(interp_func(mNrm, mNrm - expected_m_next(mNrm)), [mNrm[0]])\n", - " log_linear_roots = np.log(fsolve(interp_func(mNrm, mNrm - expected_m_next(mNrm)), [mNrm[0]]))\n", - " cubic_spline_roots = CubicSpline(mNrm, mNrm - expected_m_next(mNrm)).roots()\n", + " linear_roots = fsolve(interp_func(\n", + " mNrm, mNrm - expected_m_next(mNrm)), [mNrm[0]])\n", + " # log_linear_roots = np.log(\n", + " # fsolve(interp_func(mNrm, mNrm - expected_m_next(mNrm)), [mNrm[0]])\n", + " # )\n", + " # cubic_spline_roots = CubicSpline(\n", + " # mNrm, mNrm - expected_m_next(mNrm)).roots()\n", " print(f\"m - E[m] linear interp roots: {linear_roots}\")\n", - " print(f\"m - E[m] log roots: {log_linear_roots}\")\n", - " print(f\"m - E[m] CubicSpine roots: {cubic_spline_roots}\")\n", + " # print(f\"m - E[m] log roots: {log_linear_roots}\")\n", + " # print(f\"m - E[m] CubicSpine roots: {cubic_spline_roots}\")\n", "\n", " plt.plot(mNrm, np.zeros_like(mNrm), label=\"0\")\n", "\n", " # plt.plot(mNrm, (mNrm - cFunc(mNrm)) * ShareFunc(mNrm), label =\"wealth-into-market\" )\n", "\n", " plt.legend()\n", - " \n", - " return solved, linear_roots, log_linear_roots, cubic_spline_roots" + "\n", + " return solved, linear_roots # , log_linear_roots, cubic_spline_roots" ] }, { @@ -280,20 +286,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "subjective_return: 0.9999677237554393\n", + "subjective_return: 0.9994530880363419\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02372074]\n", - "m - E[m] log roots: [0.02344378]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02372043e+00]\n" + "m - E[m] linear interp roots: [16.35182267]\n" ] }, { "data": { "text/plain": [ - "(True,\n", - " array([1.02372074]),\n", - " array([0.02344378]),\n", - " array([-1.93921018e+05, 1.02372043e+00]))" + "(True, array([16.35182267]))" ] }, "execution_count": 9, @@ -302,14 +303,12 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -323,13 +322,12 @@ "metadata": {}, "outputs": [], "source": [ - "CRRA_grid = [2.0, 4.0, 6.0]\n", - "DiscFac_grid = [0.9, 0.95, 0.975]\n", - "RiskyAvg_grid = [1.08]\n", - "RiskyStd_grid = [0.20]\n", - "PermShkStd_grid =[0.0, 0.01, 0.1]\n", - "PermGroFac_grid =[1.0, 1.001, 1.1]\n", - "UnempPrb_grid= [0.00, 0.001, 0.1]" + "CRRA_grid = [4.0, 5.0, 6.0]\n", + "DiscFac_grid = [0.85, 0.9, 0.95]\n", + "RiskyAvg_grid = [1.04, 1.05, 1.06]\n", + "RiskyStd_grid = [0.1, 0.2, 0.3]\n", + "PermShkStd_grid = [0.0, 0.1, 0.2]\n", + "TranShkStd_grid = [0.0, 0.1, 0.2]" ] }, { @@ -341,1318 +339,1611 @@ "name": "stdout", "output_type": "stream", "text": [ - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 9.99995150e-01 2.91333916e+04]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00004270e+00 1.62662898e+04]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.17413511]\n", - "m - E[m] log roots: [0.1605318]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.17413511e+00 1.01660764e+01]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 9.99988779e-01 5.84869876e+04]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00003161e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.17355179]\n", - "m - E[m] log roots: [0.16003487]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.17355178e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 1.78762596e+04]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.11862012]\n", - "m - E[m] log roots: [0.11209589]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.11862012e+00 4.05575359e+01]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 9.99996803e-01 1.19597849e+04]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00004424e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.17419872]\n", - "m - E[m] log roots: [0.16058597]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.17419872e+00 9.07007108e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 9.99988332e-01 1.74182855e+04]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00003317e+00 9.17626091e+04]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.17361548]\n", - "m - E[m] log roots: [0.16008914]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.17361547e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.11868828]\n", - "m - E[m] log roots: [0.11215682]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.11868828e+00 4.07985775e+01]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.00362905]\n", - "m - E[m] log roots: [0.00362248]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00369664e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.00761696]\n", - "m - E[m] log roots: [0.0075881]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00761598e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.18042259]\n", - "m - E[m] log roots: [0.1658725]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.18042260e+00 5.95739218e+00]\n" + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.45546364]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/sb/projects/ufm/SHARKFin/sharkfin-env/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:178: RuntimeWarning: The iteration is not making good progress, as measured by the \n", + "C:\\Users\\alujan\\AppData\\Local\\Temp\\ipykernel_50284\\2921339691.py:75: RuntimeWarning: The iteration is not making good progress, as measured by the \n", " improvement from the last ten iterations.\n", - " warnings.warn(msg, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.00264135]\n", - "m - E[m] log roots: [0.00263787]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00261179e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.00665043]\n", - "m - E[m] log roots: [0.00662841]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00664539e+00 1.73304278e+04]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.17984737]\n", - "m - E[m] log roots: [0.16538508]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.17984737e+00 6.04092358e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.75374932e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.63607492e+00]\n", - "subjective_return: 0.9987706573085862\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.12534177]\n", - "m - E[m] log roots: [0.11808678]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.12534175e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.01716162]\n", - "m - E[m] log roots: [0.01701603]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.01716158e+00 1.67319762e+02]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02103644]\n", - "m - E[m] log roots: [0.02081823]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02103644e+00 1.05127344e+02]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.19530091]\n", - "m - E[m] log roots: [0.17839796]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.19530105e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.01614351]\n", - "m - E[m] log roots: [0.01601459]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.01614350e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02004026]\n", - "m - E[m] log roots: [0.0198421]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02004024e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.19469567]\n", - "m - E[m] log roots: [0.17789149]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.19469528e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.13817791]\n", - "m - E[m] log roots: [0.12942866]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.13817838e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.01730178]\n", - "m - E[m] log roots: [0.01715381]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.01730174e+00 1.52754962e+02]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02117297]\n", - "m - E[m] log roots: [0.02095194]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02117299e+00 1.01307029e+02]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.19536566]\n", - "m - E[m] log roots: [0.17845213]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.19536582e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.01628396]\n", - "m - E[m] log roots: [0.0161528]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.01628393e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02017708]\n", - "m - E[m] log roots: [0.01997622]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02017705e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.1947605]\n", - "m - E[m] log roots: [0.17794575]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.19476019e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.13824736]\n", - "m - E[m] log roots: [0.12948967]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.13824778e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03087267]\n", - "m - E[m] log roots: [0.03040569]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03087266e+00 8.35615977e+02]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03443262]\n", - "m - E[m] log roots: [0.03385308]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03443259e+00]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.20170487]\n", - "m - E[m] log roots: [0.18374128]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.20170487e+00 9.71583688e+03]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02987914]\n", - "m - E[m] log roots: [0.02944146]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02987905e+00 1.39941289e+02]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03345738]\n", - "m - E[m] log roots: [0.03290986]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03345732e+00 1.69858101e+02]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.20110761]\n", - "m - E[m] log roots: [0.18324414]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.20110761e+00 6.98501767e+03]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 1.16158154e+04]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 1.82608571e+04]\n", - "subjective_return: 0.9993708459504581\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.14503316]\n", - "m - E[m] log roots: [0.1354336]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.14503316e+00 4.67161807e+04]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03073866]\n", - "m - E[m] log roots: [0.03027569]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03073865e+00]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03441444]\n", - "m - E[m] log roots: [0.03383551]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03441442e+00]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.20595188]\n", - "m - E[m] log roots: [0.18726919]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.20595188e+00 2.42914467e+04]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02971641]\n", - "m - E[m] log roots: [0.02928343]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02971630e+00]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.0334112]\n", - "m - E[m] log roots: [0.03286518]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03341114e+00 2.57859864e+04]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.20534424]\n", - "m - E[m] log roots: [0.1867652]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.20534424e+00]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.82349460e+04]\n" + " linear_roots = fsolve(interp_func(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.14803975]\n", - "m - E[m] log roots: [0.13805592]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.14803974e+00]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03087744]\n", - "m - E[m] log roots: [0.03041032]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03087744e+00]\n", - "subjective_return: 0.9996593225093474\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03455007]\n", - "m - E[m] log roots: [0.03396662]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03455005e+00 9.55352121e+03]\n", - "subjective_return: 0.9996593225093474\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.47342658]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.53280956]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.6439013]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.67277071]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.76246]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.45006899]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.47001103]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.53029739]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.44751436]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.46613654]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.52597777]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.63090921]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.6580887]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.74391061]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [9.14748909]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [9.15733445]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [9.1865383]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.42663658]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.44406771]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.49790499]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.59197261]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.61595679]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.69136674]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [7.7060012]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [7.71621026]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [7.74705479]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.47824613]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.49761168]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.56071297]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.70002843]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.732868]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.83492894]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.87209926]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.89507599]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.96433263]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.46973089]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.48977533]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.55297424]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.68349388]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.71408939]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.81036036]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [11.13064058]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [11.13986094]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [11.16701548]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.45683014]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.2060163]\n", - "m - E[m] log roots: [0.18732262]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.20601631e+00 1.21825539e+04]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.47542731]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02985547]\n", - "m - E[m] log roots: [0.02941847]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02985538e+00]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.53385549]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03354711]\n", - "m - E[m] log roots: [0.03299668]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03354705e+00]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.656871]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.20540875]\n", - "m - E[m] log roots: [0.18681872]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.20540875e+00]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.68591695]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.77762108]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 3.91162483e+04]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [8.35819473]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.14810876]\n", - "m - E[m] log roots: [0.13811603]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.14810875e+00]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [8.36808475]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04430826]\n", - "m - E[m] log roots: [0.04335471]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04430826e+00]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [8.39809191]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04767805]\n", - "m - E[m] log roots: [0.04657633]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04767823e+00 1.31382345e+04]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.50307263]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.21232474]\n", - "m - E[m] log roots: [0.19253979]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.21232477e+00]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.52385507]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04331367]\n", - "m - E[m] log roots: [0.04240187]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04331369e+00 1.71108312e+04]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.59123123]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04670172]\n", - "m - E[m] log roots: [0.045644]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04670177e+00 2.57963294e+04]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.77298456]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.21172506]\n", - "m - E[m] log roots: [0.19204501]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.21172507e+00]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.810582]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 2.34487447e+04]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [1.92824781]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 1.36947913e+04]\n", - "subjective_return: 0.9996593225093474\n", + "m - E[m] linear interp roots: [9.40585609]\n", + "subjective_return: 0.9984478188518532\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.15485289]\n", - "m - E[m] log roots: [0.14397297]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.15485295e+00 3.68629313e+01]\n", + "m - E[m] linear interp roots: [9.43226526]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [9.5116408]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.49426572]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.51517329]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.58275098]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.74817667]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.78323166]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.89283056]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [14.6239391]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [14.63206989]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [14.65655876]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.48166583]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.5032223]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.57008753]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.71297475]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.74507137]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.84692859]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [9.27948484]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [9.28904595]\n", + "subjective_return: 0.9984478188518532\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [9.3181485]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.01104671]\n", - "m - E[m] log roots: [0.01098614]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.01104666e+00]\n", + "m - E[m] linear interp roots: [1.6025199]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03140094]\n", - "m - E[m] log roots: [0.03091801]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03140074e+00]\n", + "m - E[m] linear interp roots: [1.62924544]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.31376538]\n", - "m - E[m] log roots: [0.27289735]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.31376125e+00 2.67661081e+05]\n", + "m - E[m] linear interp roots: [1.71399924]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.01004705]\n", - "m - E[m] log roots: [0.00999692]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.01004940e+00]\n", + "m - E[m] linear interp roots: [3.35104346]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03053423]\n", - "m - E[m] log roots: [0.03007734]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03053430e+00]\n", + "m - E[m] linear interp roots: [3.39935717]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.31334945]\n", - "m - E[m] log roots: [0.27258071]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.31334561e+00 3.33166462e+05]\n", + "m - E[m] linear interp roots: [3.54771673]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 1.81484975e+04]\n", + "m - E[m] linear interp roots: [25.54210597]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 3.01250928e+04]\n", + "m - E[m] linear interp roots: [25.57117474]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.27330008]\n", - "m - E[m] log roots: [0.24161202]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.27330011e+00]\n", + "m - E[m] linear interp roots: [25.65934913]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.01127744]\n", - "m - E[m] log roots: [0.01121432]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.01127715e+00]\n", + "m - E[m] linear interp roots: [1.59180116]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03159594]\n", - "m - E[m] log roots: [0.03110706]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03159567e+00]\n", + "m - E[m] linear interp roots: [1.6174534]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.31382257]\n", - "m - E[m] log roots: [0.27294088]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.31381843e+00]\n", + "m - E[m] linear interp roots: [1.70044676]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.01027826]\n", - "m - E[m] log roots: [0.01022579]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.01027986e+00]\n", + "m - E[m] linear interp roots: [2.72315881]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03072959]\n", - "m - E[m] log roots: [0.03026689]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03072967e+00]\n", + "m - E[m] linear interp roots: [2.78151741]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.31340656]\n", - "m - E[m] log roots: [0.27262419]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.31340265e+00 1.55231815e+05]\n", + "m - E[m] linear interp roots: [2.95987856]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 2.00600465e+04]\n", + "m - E[m] linear interp roots: [21.31911694]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 4.09263073e+04]\n", + "m - E[m] linear interp roots: [21.32600784]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.27336448]\n", - "m - E[m] log roots: [0.2416626]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.27336451e+00]\n", + "m - E[m] linear interp roots: [21.34678931]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03318408]\n", - "m - E[m] log roots: [0.03264538]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03318402e+00]\n", + "m - E[m] linear interp roots: [1.55773863]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.0501537]\n", - "m - E[m] log roots: [0.04893654]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.05015362e+00]\n", + "m - E[m] linear interp roots: [1.58084023]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.31947275]\n", - "m - E[m] log roots: [0.27723222]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.31947262e+00 1.74058728e+06]\n", + "m - E[m] linear interp roots: [1.65410876]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03222338]\n", - "m - E[m] log roots: [0.03171509]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03222330e+00 7.65567709e+01]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "m - E[m] linear interp roots: [2.37585634]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.43931444]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.61531893]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [16.78194041]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [16.7894865]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [16.81234939]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.64626424]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.67658765]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.7726416]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [4.4295765]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [4.48295229]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [4.64503325]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [30.73814587]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [30.77977944]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [30.90584187]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.63492808]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.66365172]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.75665567]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.5453265]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.61268344]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.82090831]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [28.78992269]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [28.79578849]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [28.81308989]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.61457903]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.64284172]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.73031784]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.83688942]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.91657704]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.16865787]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [18.75248559]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [18.75973003]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [18.78166779]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.6988222]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.73398306]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.84569964]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.88914995]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.95591279]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.15699816]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [42.62286804]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [42.69554469]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [42.91438488]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.68624107]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.71914076]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.82516739]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.10383097]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.20431086]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.52047829]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.82892398]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.83262185]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.95322006]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.66219759]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.69359882]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.79323157]\n", + "subjective_return: 0.9990821279602972\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.82108337]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04932169]\n", - "m - E[m] log roots: [0.04814394]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04932318e+00 1.57241256e+02]\n", + "m - E[m] linear interp roots: [3.95441452]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.31905689]\n", - "m - E[m] log roots: [0.27691701]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.31905676e+00]\n", + "m - E[m] linear interp roots: [4.40755269]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 1.68096837e+05]\n", + "m - E[m] linear interp roots: [21.7494748]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000001e+00 2.35085754e+05]\n", + "m - E[m] linear interp roots: [21.75651963]\n", "subjective_return: 0.9990821279602972\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.27959226]\n", - "m - E[m] log roots: [0.24654148]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.27959245e+00 1.45542230e+01]\n", + "m - E[m] linear interp roots: [21.77711076]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02482593]\n", - "m - E[m] log roots: [0.02452277]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02482545e+00]\n", + "m - E[m] linear interp roots: [2.09726335]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04366344]\n", - "m - E[m] log roots: [0.04273706]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04366353e+00]\n", + "m - E[m] linear interp roots: [2.16754047]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32403423]\n", - "m - E[m] log roots: [0.28068331]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32403431e+00]\n", + "m - E[m] linear interp roots: [2.39092065]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02382521]\n", - "m - E[m] log roots: [0.02354582]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02382494e+00]\n", + "m - E[m] linear interp roots: [17.84516952]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04279529]\n", - "m - E[m] log roots: [0.04190488]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04279533e+00]\n", + "m - E[m] linear interp roots: [17.84755066]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32361094]\n", - "m - E[m] log roots: [0.28036357]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32361103e+00]\n", + "m - E[m] linear interp roots: [17.85532359]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", + "m - E[m] linear interp roots: [15.65446795]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 4.26036100e+06]\n", + "m - E[m] linear interp roots: [15.65848745]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.28326979]\n", - "m - E[m] log roots: [0.24941135]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.28327054e+00]\n", + "m - E[m] linear interp roots: [15.67064501]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02505357]\n", - "m - E[m] log roots: [0.02474488]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02505310e+00]\n", + "m - E[m] linear interp roots: [2.02326706]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04385616]\n", - "m - E[m] log roots: [0.0429217]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04385626e+00]\n", + "m - E[m] linear interp roots: [2.08262043]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32409193]\n", - "m - E[m] log roots: [0.28072689]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32409202e+00]\n", + "m - E[m] linear interp roots: [2.27019048]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02405333]\n", - "m - E[m] log roots: [0.0237686]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02405300e+00]\n", + "m - E[m] linear interp roots: [4.29253179]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04298838]\n", - "m - E[m] log roots: [0.04209003]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04298843e+00]\n", + "m - E[m] linear interp roots: [4.29475201]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32366871]\n", - "m - E[m] log roots: [0.28040721]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32366879e+00]\n", + "m - E[m] linear interp roots: [4.39580919]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", + "m - E[m] linear interp roots: [1.1875]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", + "m - E[m] linear interp roots: [1.21875]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.28333298]\n", - "m - E[m] log roots: [0.24946058]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.28333373e+00]\n", + "m - E[m] linear interp roots: [1.25]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.0467566]\n", - "m - E[m] log roots: [0.04569643]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04675664e+00]\n", + "m - E[m] linear interp roots: [1.93699806]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.06253651]\n", - "m - E[m] log roots: [0.06065898]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.06253649e+00]\n", + "m - E[m] linear interp roots: [1.98314284]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32973337]\n", - "m - E[m] log roots: [0.28497845]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32973346e+00]\n", + "m - E[m] linear interp roots: [2.12652103]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.0457948]\n", - "m - E[m] log roots: [0.04477717]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04579479e+00]\n", + "m - E[m] linear interp roots: [2.31842145]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\alujan\\AppData\\Local\\Temp\\ipykernel_50284\\2921339691.py:75: RuntimeWarning: The iteration is not making good progress, as measured by the \n", + " improvement from the last five Jacobian evaluations.\n", + " linear_roots = fsolve(interp_func(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.06167439]\n", - "m - E[m] log roots: [0.05984728]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.06167440e+00]\n", + "m - E[m] linear interp roots: [2.36738668]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32931616]\n", - "m - E[m] log roots: [0.28466465]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32931626e+00 7.41234170e+00]\n", + "m - E[m] linear interp roots: [2.40558546]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00]\n", + "m - E[m] linear interp roots: [0.421875]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 9.99999415e-01 6.12219371e+05]\n", + "m - E[m] linear interp roots: [0.44140625]\n", "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.28947888]\n", - "m - E[m] log roots: [0.25423817]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.28947898e+00 6.64672197e+03]\n", - "subjective_return: 0.9999710702948802\n", + "m - E[m] linear interp roots: [0.46875]\n", + "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03154824]\n", - "m - E[m] log roots: [0.03106082]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03154822e+00 8.57635068e+05]\n", - "subjective_return: 0.9999710702948802\n", + "m - E[m] linear interp roots: [2.67348296]\n", + "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04962192]\n", - "m - E[m] log roots: [0.04843002]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04962257e+00 4.30336007e+05]\n", - "subjective_return: 0.9999710702948802\n", + "m - E[m] linear interp roots: [2.81608284]\n", + "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32902172]\n", - "m - E[m] log roots: [0.28444312]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32902183e+00]\n", - "subjective_return: 0.9999710702948802\n", + "m - E[m] linear interp roots: [3.26882057]\n", + "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03054192]\n", - "m - E[m] log roots: [0.0300848]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03054190e+00]\n", - "subjective_return: 0.9999710702948802\n", + "m - E[m] linear interp roots: [9.93904762]\n", + "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04875307]\n", - "m - E[m] log roots: [0.04760191]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04875463e+00 4.27318159e+05]\n", - "subjective_return: 0.9999710702948802\n", + "m - E[m] linear interp roots: [9.94163691]\n", + "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32859784]\n", - "m - E[m] log roots: [0.28412413]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32859794e+00]\n", - "subjective_return: 0.9999710702948802\n", + "m - E[m] linear interp roots: [10.15316709]\n", + "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.31105212e+00]\n", - "subjective_return: 0.9999710702948802\n", + "m - E[m] linear interp roots: [9.99323564]\n", + "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.12260363e+00]\n", - "subjective_return: 0.9999710702948802\n", + "m - E[m] linear interp roots: [9.99741616]\n", + "subjective_return: 0.9996825037734139\n", "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.28809175]\n", - "m - E[m] log roots: [0.25316186]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.28809192e+00]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.0317756]\n", - "m - E[m] log roots: [0.0312812]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03177556e+00 5.62569092e+05]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04981354]\n", - "m - E[m] log roots: [0.04861257]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04981385e+00 3.38149864e+05]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32907931]\n", - "m - E[m] log roots: [0.28448645]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32907941e+00]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.03076974]\n", - "m - E[m] log roots: [0.03030585]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03076974e+00 4.50652333e+05]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.04894505]\n", - "m - E[m] log roots: [0.04778495]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04894674e+00 7.68852916e+05]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.32865549]\n", - "m - E[m] log roots: [0.28416752]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.32865559e+00]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.28698069e+00]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.10880962e+00]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.28815459]\n", - "m - E[m] log roots: [0.25321064]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.28815476e+00]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.05335911]\n", - "m - E[m] log roots: [0.05198421]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.05335785e+00 3.46268258e+05]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.06859081]\n", - "m - E[m] log roots: [0.06634078]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.06859081e+00 4.24589839e+06]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.33470992]\n", - "m - E[m] log roots: [0.28871398]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.33471000e+00 4.55510421e+01]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.05239326]\n", - "m - E[m] log roots: [0.05106687]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.05239218e+00 3.54730306e+05]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.06772788]\n", - "m - E[m] log roots: [0.06553292]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.06772786e+00 4.43851448e+05]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.33429205]\n", - "m - E[m] log roots: [0.28840085]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.33429215e+00 2.01505147e+01]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 9.99999997e-01]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000148e+00]\n", - "subjective_return: 0.9999710702948802\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.29428017]\n", - "m - E[m] log roots: [0.25795469]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.29428029e+00]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02381916]\n", - "m - E[m] log roots: [0.02353991]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02381881e+00]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.08589507]\n", - "m - E[m] log roots: [0.0824046]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.08589503e+00 8.88674513e+03]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.40202054]\n", - "m - E[m] log roots: [0.33791444]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40202057e+00 3.12485279e+01]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02283495]\n", - "m - E[m] log roots: [0.02257814]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02283497e+00]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.08524904]\n", - "m - E[m] log roots: [0.08180949]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.08524876e+00 3.32779332e+04]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.40167468]\n", - "m - E[m] log roots: [0.33766772]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40167472e+00 2.97269817e+01]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 3.53118987e+06]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02938909]\n", - "m - E[m] log roots: [0.02896551]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02939034e+00]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.36793175]\n", - "m - E[m] log roots: [0.31329993]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.36793187e+00 9.63604836e+04]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02413275]\n", - "m - E[m] log roots: [0.02384616]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02413228e+00 8.06272595e+04]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.08608119]\n", - "m - E[m] log roots: [0.08257598]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.08608119e+00 8.70746666e+03]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.40207402]\n", - "m - E[m] log roots: [0.33795258]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40207405e+00 3.15495983e+01]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02314919]\n", - "m - E[m] log roots: [0.02288531]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02314911e+00]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.08543538]\n", - "m - E[m] log roots: [0.08198118]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.08543518e+00 2.36673628e+04]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.40172824]\n", - "m - E[m] log roots: [0.33770593]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40172828e+00 2.89247397e+01]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 4.63003148e+05]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.02956756]\n", - "m - E[m] log roots: [0.02913887]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.02956931e+00]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.3679923]\n", - "m - E[m] log roots: [0.31334419]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.36799243e+00 5.00927917e+06]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.05340754]\n", - "m - E[m] log roots: [0.05203018]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.05340581e+00 2.07907991e+05]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.10376671]\n", - "m - E[m] log roots: [0.09872861]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.10376477e+00 5.47870595e+04]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.40729881]\n", - "m - E[m] log roots: [0.34167213]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40729850e+00]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.0524699]\n", - "m - E[m] log roots: [0.05113969]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.05246837e+00]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.10314069]\n", - "m - E[m] log roots: [0.09816128]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.10314126e+00 1.14720571e+05]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.40695562]\n", - "m - E[m] log roots: [0.34142823]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40695526e+00]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 9.99999999e-01 3.39276643e+06]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.0463153]\n", - "m - E[m] log roots: [0.04527475]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.04631524e+00 8.38080987e+05]\n", - "subjective_return: 0.9999677237554393\n", - "subjective_return < 1?: True\n", - "m - E[m] linear interp roots: [1.37387081]\n", - "m - E[m] log roots: [0.31763217]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.37386913e+00 1.49274039e+05]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03301132]\n", - "m - E[m] log roots: [0.03247815]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03301124e+00 1.80749051e+06]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.09257573]\n", - "m - E[m] log roots: [0.08853797]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.09257573e+00 6.53490808e+05]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.40895091]\n", - "m - E[m] log roots: [0.34284539]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40895145e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03201942]\n", - "m - E[m] log roots: [0.03151749]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03201934e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.09192769]\n", - "m - E[m] log roots: [0.08794466]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.09192769e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.40860041]\n", - "m - E[m] log roots: [0.34259659]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40860080e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.51044022e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03487366]\n", - "m - E[m] log roots: [0.03427935]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03487367e+00 6.16134979e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.37472456]\n", - "m - E[m] log roots: [0.31825339]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.37472373e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03332438]\n", - "m - E[m] log roots: [0.03278116]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03332431e+00 2.39802478e+05]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.09276125]\n", - "m - E[m] log roots: [0.08870775]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.09276125e+00 2.22396166e+05]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.40900481]\n", - "m - E[m] log roots: [0.34288365]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40900534e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03233312]\n", - "m - E[m] log roots: [0.0318214]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03233302e+00 1.40603505e+05]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.09211342]\n", - "m - E[m] log roots: [0.08811474]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.09211342e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.40865438]\n", - "m - E[m] log roots: [0.34263491]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.40865481e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.47075662e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03505109]\n", - "m - E[m] log roots: [0.03445079]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03505110e+00 6.14514154e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.37478539]\n", - "m - E[m] log roots: [0.31829764]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.37478461e+00 2.11012152e+05]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.06244932]\n", - "m - E[m] log roots: [0.06057693]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.06244933e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.11069841]\n", - "m - E[m] log roots: [0.10498901]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.11069881e+00 6.87315113e+05]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.41425421]\n", - "m - E[m] log roots: [0.34660233]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.41425426e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.06150776]\n", - "m - E[m] log roots: [0.05969031]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.06150776e+00 1.76002593e+05]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.1100254]\n", - "m - E[m] log roots: [0.1043829]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.11002673e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.41391026]\n", - "m - E[m] log roots: [0.3463591]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.41391032e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 9.99999949e-01 7.33978956e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.05171847]\n", - "m - E[m] log roots: [0.05042546]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.05171193e+00 6.54670615e+00]\n", - "subjective_return: 1.000568631747324\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.38068483]\n", - "m - E[m] log roots: [0.32257963]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.38068486e+00 5.63176790e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.0374705]\n", - "m - E[m] log roots: [0.03678553]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03747049e+00 1.37249830e+06]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.09580915]\n", - "m - E[m] log roots: [0.09149304]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.09580915e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.41232323]\n", - "m - E[m] log roots: [0.34523603]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.41232327e+00 1.25041390e+04]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03647822]\n", - "m - E[m] log roots: [0.03582864]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03647822e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.09516013]\n", - "m - E[m] log roots: [0.09090059]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.09516014e+00 2.31107830e+05]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.41197241]\n", - "m - E[m] log roots: [0.3449876]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.41197242e+00 4.60046260e+04]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.53106983e+00]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03752783]\n", - "m - E[m] log roots: [0.03684079]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03752783e+00 6.80885379e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.37803373]\n", - "m - E[m] log roots: [0.32065765]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.37803366e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03778225]\n", - "m - E[m] log roots: [0.03708598]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03778225e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.09599438]\n", - "m - E[m] log roots: [0.09166206]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.09599438e+00 1.25024814e+05]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.41237704]\n", - "m - E[m] log roots: [0.34527413]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.41237708e+00 1.45777175e+04]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03679061]\n", - "m - E[m] log roots: [0.03612999]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03679061e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.09534557]\n", - "m - E[m] log roots: [0.0910699]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.09534558e+00 1.43801010e+07]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.41202629]\n", - "m - E[m] log roots: [0.34502575]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.41202631e+00 2.29183058e+04]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000000e+00 6.54907325e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.03770476]\n", - "m - E[m] log roots: [0.03701131]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.03770476e+00 6.84621848e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.3780944]\n", - "m - E[m] log roots: [0.32070168]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.37809434e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.06682701]\n", - "m - E[m] log roots: [0.06468883]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.06682700e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.11413567]\n", - "m - E[m] log roots: [0.10807892]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.11413586e+00 8.11476701e+05]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.41761855]\n", - "m - E[m] log roots: [0.34897839]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.41761858e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.06588505]\n", - "m - E[m] log roots: [0.06380548]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.06588505e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.11346156]\n", - "m - E[m] log roots: [0.10747369]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.11346163e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.41727424]\n", - "m - E[m] log roots: [0.34873548]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.41727426e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.]\n", - "m - E[m] log roots: [0.]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.00000021e+00 1.10548493e+01]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.0545031]\n", - "m - E[m] log roots: [0.05306966]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.05449793e+00]\n", - "subjective_return: 1.0008574540568687\n", - "subjective_return < 1?: False\n", - "m - E[m] linear interp roots: [1.38398038]\n", - "m - E[m] log roots: [0.32496368]\n", - "m - E[m] CubicSpine roots: [-1.93921018e+05 1.38398043e+00 6.65338607e+00]\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "rows = []\n", - "\n", - "for CRRA in CRRA_grid:\n", - " for DiscFac in DiscFac_grid:\n", - " for RiskyAvg in RiskyAvg_grid:\n", - " for RiskyStd in RiskyStd_grid:\n", - " for PermShkStd in PermShkStd_grid:\n", - " for PermGroFac in PermGroFac_grid:\n", - " for UnempPrb in UnempPrb_grid:\n", - " s, lr, llr, csr = compute_target_wealth(\n", - " CRRA=CRRA,\n", - " DiscFac=DiscFac,\n", - " RiskyAvg=RiskyAvg,\n", - " RiskyStd=RiskyStd,\n", - " PermShkStd=[PermShkStd],\n", - " PermGroFac=[PermGroFac],\n", - " UnempPrb=UnempPrb\n", - " )\n", - " \n", - " rows.append({\n", - " 'CRRA' : CRRA,\n", - " 'DiscFac' : DiscFac,\n", - " 'RiskyAvg' : RiskyAvg,\n", - " 'RiskyStd' : RiskyStd,\n", - " 'PermShkStd' : PermShkStd,\n", - " 'PermGroFac' : PermGroFac,\n", - " 'UnempPrb' : UnempPrb,\n", - " 'solved' : s,\n", - " 'linear root' : lr,\n", - " })\n", - "\n" - ] + "m - E[m] linear interp roots: [10.01010761]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.34626775]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.43570675]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.71962671]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.0331113]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.1058627]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.11187982]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.65625]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.6875]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.734375]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.13054526]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.19591279]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.40751573]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.88750582]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.92495928]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.92861151]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.296875]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.296875]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.29492188]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [4.59755062]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [4.7141945]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.16195437]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.16655224]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.30215365]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.31115125]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [7.18957802]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [7.31895251]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [7.39473013]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.60097201]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.86696004]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.136393]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.35293051]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.35557342]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.45792807]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.546875]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.5390625]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.578125]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.50974598]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.6120869]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.95318264]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.68935314]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.68990048]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.72543356]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.33984375]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.3359375]\n", + "subjective_return: 0.9996825037734139\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [0.3359375]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.5974784]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.61926444]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.68970309]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.14597778]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.19647394]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.3513279]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [16.82602439]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [16.8520166]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [16.92992823]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.59894192]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.62064151]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.6906813]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.08113221]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.12987665]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.28041277]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [14.5069089]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [14.51539211]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [14.54120636]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.55739367]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.57652256]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.63728217]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.95218211]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.9916099]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.11133855]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [12.63447763]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [12.64350004]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [12.67086756]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.62137015]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.64551483]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.72241329]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.36639123]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.42344676]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.59669642]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.54931354]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.58115422]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.67637576]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.62380474]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.64765129]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.7236535]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.24837447]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.30486831]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.47955498]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.05487034]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.06269462]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.08673455]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.58468906]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.60561556]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.67249891]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.13566976]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.18060852]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.3136559]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [13.48630394]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [13.49512335]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [13.5217355]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.64817513]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.67418149]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.75869996]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.67938478]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.74238784]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.93190862]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [18.68506137]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [18.72422886]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [18.84223929]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.6514608]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.67710191]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.76002495]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.48684485]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.55347362]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.75923038]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [21.46491365]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [21.47192654]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [21.49300985]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.62389869]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.64779459]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.72287075]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.35800454]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.42771559]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.65117447]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [14.67994231]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [14.68844794]\n", + "subjective_return: 0.9988185434083661\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [14.71421336]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.74966531]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.78383066]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.89348254]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.87459844]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.93631533]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [9.12445754]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [114.03268253]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [114.23284112]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [114.84374789]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.75221787]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.78492065]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.88978528]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [10.43256363]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [10.452484]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [10.51315589]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [29.98807534]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [29.99440463]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [30.01351402]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.68680324]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.71464276]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.80284961]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.85602694]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.8764657]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.93834413]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [24.50498917]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [24.51204396]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [24.53328974]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.8019063]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.84098225]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.96591816]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [11.31758221]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [11.40866271]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [11.6868707]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [72.26623977]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [71.9925259]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [71.74128707]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.80259994]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.83982951]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.95931565]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [16.33449581]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [16.35182267]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [16.40494195]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.91991453]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.9248315]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.01002101]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.7466135]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.77828837]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.87909644]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.44783141]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.46719593]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [8.52549446]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [26.91811037]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [26.92479809]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [26.94501562]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.86685864]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.91204548]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.05646683]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.13618082]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.34486549]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.98123152]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [34.00428753]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [34.16613054]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [34.79416042]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.8640661]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.90681497]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.04562287]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.12360995]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.12889113]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [6.14419057]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.01680164]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.02185932]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.07623487]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.84226725]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.88057268]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [2.0007769]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [10.85708494]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [10.875047]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [10.92894159]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [30.52959748]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [30.535736]\n", + "subjective_return: 0.9994530880363419\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [30.55439547]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.30657024]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.39295151]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.6702255]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [9.55022069]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [9.55245254]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [9.77834103]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [6.96875]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [6.96875]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [7.1328125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.24064868]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.31529426]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.55336989]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.3863327]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.36469542]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.43274982]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.23828125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.23828125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.23828125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.06393853]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.11709362]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.28092073]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.0625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.0625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.0859375]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [60.84547782]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [60.84918214]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [60.86040184]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.94593149]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [3.12745861]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [3.72897359]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [6.64707766]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [6.71212638]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [6.81408107]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [5.90963272]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [5.91532804]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [5.93257618]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.60473414]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.71982948]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [3.10186097]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.32279618]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.32487974]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.36265415]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.265625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.265625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.265625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.41422399]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.49393689]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.71592522]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.0625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.0625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.046875]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.2265625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.2265625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.2265625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [4.49892319]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [4.59653125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [5.03786898]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [4.91000096]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [4.89725439]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [5.02371148]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [5.08534422]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [5.09118582]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [5.12448933]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [4.40526097]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [4.95612734]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [5.18989929]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.35908285]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.35953493]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.39641489]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.2890625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.2890625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.2890625]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [2.91693921]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [3.09131826]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [3.84806126]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.078125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.078125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [1.078125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.23828125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.23828125]\n", + "subjective_return: 1.0000536867695282\n", + "subjective_return < 1?: False\n", + "m - E[m] linear interp roots: [0.23828125]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.73086062]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.75910926]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.84876111]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [4.36883996]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [4.42293624]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [4.58624857]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [39.44373939]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [39.49050781]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [39.63070099]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.73838652]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.76649]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.85486565]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [4.93676449]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.03486966]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [5.23108393]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [19.98580431]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [19.99394405]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [20.01832969]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.67382723]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.69753438]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [1.77271171]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.77383872]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.80442839]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [3.89801733]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.71559511]\n", + "subjective_return: 0.9993328523894356\n", + "subjective_return < 1?: True\n", + "m - E[m] linear interp roots: [17.72407286]\n" + ] + } + ], + "source": [ + "rows = []\n", + "\n", + "for CRRA in CRRA_grid:\n", + " for DiscFac in DiscFac_grid:\n", + " for RiskyAvg in RiskyAvg_grid:\n", + " for RiskyStd in RiskyStd_grid:\n", + " for PermShkStd in PermShkStd_grid:\n", + " for TranShkStd in TranShkStd_grid:\n", + " # s, lr, llr, csr = compute_target_wealth(\n", + " s, lr = compute_target_wealth(\n", + " CRRA=CRRA,\n", + " DiscFac=DiscFac,\n", + " RiskyAvg=RiskyAvg,\n", + " RiskyStd=RiskyStd,\n", + " PermShkStd=[PermShkStd],\n", + " TranShkStd=[TranShkStd],\n", + " )\n", + "\n", + " rows.append(\n", + " {\n", + " \"CRRA\": CRRA,\n", + " \"DiscFac\": DiscFac,\n", + " \"RiskyAvg\": RiskyAvg,\n", + " \"RiskyStd\": RiskyStd,\n", + " \"PermShkStd\": PermShkStd,\n", + " \"TranShkStd\": TranShkStd,\n", + " \"solved\": s,\n", + " \"linear root\": lr,\n", + " }\n", + " )" + ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1661,26 +1952,18 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "df.to_csv('roots.csv')" + "df.to_csv(\"roots.csv\")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_23404/1134722465.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", - " df.corr()\n" - ] - }, { "data": { "text/html": [ @@ -1707,88 +1990,77 @@ " RiskyAvg\n", " RiskyStd\n", " PermShkStd\n", - " PermGroFac\n", - " UnempPrb\n", + " TranShkStd\n", " solved\n", + " linear root\n", " \n", " \n", " \n", " \n", " CRRA\n", " 1.000000e+00\n", - " 2.770408e-16\n", - " NaN\n", - " NaN\n", - " 2.861956e-16\n", - " 2.809359e-15\n", - " -2.027987e-16\n", + " -1.039977e-15\n", + " 6.727943e-14\n", + " -3.706454e-16\n", + " 7.881213e-17\n", + " 3.726443e-16\n", " NaN\n", + " 0.144583\n", " \n", " \n", " DiscFac\n", - " 2.770408e-16\n", + " -1.039977e-15\n", " 1.000000e+00\n", + " 3.404796e-14\n", + " -5.343377e-16\n", + " 1.674044e-16\n", + " 5.889493e-17\n", " NaN\n", - " NaN\n", - " 3.996653e-16\n", - " -1.989004e-16\n", - " -8.541733e-17\n", - " NaN\n", + " -0.124368\n", " \n", " \n", " RiskyAvg\n", + " 6.727943e-14\n", + " 3.404796e-14\n", + " 1.000000e+00\n", + " 2.515974e-15\n", + " 5.648559e-16\n", + " 2.146096e-16\n", " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " -0.068249\n", " \n", " \n", " RiskyStd\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " PermShkStd\n", - " 2.861956e-16\n", - " 3.996653e-16\n", - " NaN\n", - " NaN\n", + " -3.706454e-16\n", + " -5.343377e-16\n", + " 2.515974e-15\n", " 1.000000e+00\n", - " 2.453659e-16\n", - " -1.015307e-17\n", + " -7.138780e-19\n", + " -2.498573e-18\n", " NaN\n", + " -0.092178\n", " \n", " \n", - " PermGroFac\n", - " 2.809359e-15\n", - " -1.989004e-16\n", - " NaN\n", - " NaN\n", - " 2.453659e-16\n", + " PermShkStd\n", + " 7.881213e-17\n", + " 1.674044e-16\n", + " 5.648559e-16\n", + " -7.138780e-19\n", " 1.000000e+00\n", - " -2.757886e-17\n", + " 1.070817e-17\n", " NaN\n", + " 0.559969\n", " \n", " \n", - " UnempPrb\n", - " -2.027987e-16\n", - " -8.541733e-17\n", - " NaN\n", - " NaN\n", - " -1.015307e-17\n", - " -2.757886e-17\n", + " TranShkStd\n", + " 3.726443e-16\n", + " 5.889493e-17\n", + " 2.146096e-16\n", + " -2.498573e-18\n", + " 1.070817e-17\n", " 1.000000e+00\n", " NaN\n", + " 0.009777\n", " \n", " \n", " solved\n", @@ -1801,30 +2073,41 @@ " NaN\n", " NaN\n", " \n", + " \n", + " linear root\n", + " 1.445829e-01\n", + " -1.243680e-01\n", + " -6.824900e-02\n", + " -9.217838e-02\n", + " 5.599694e-01\n", + " 9.776556e-03\n", + " NaN\n", + " 1.000000\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " CRRA DiscFac RiskyAvg RiskyStd PermShkStd \\\n", - "CRRA 1.000000e+00 2.770408e-16 NaN NaN 2.861956e-16 \n", - "DiscFac 2.770408e-16 1.000000e+00 NaN NaN 3.996653e-16 \n", - "RiskyAvg NaN NaN NaN NaN NaN \n", - "RiskyStd NaN NaN NaN NaN NaN \n", - "PermShkStd 2.861956e-16 3.996653e-16 NaN NaN 1.000000e+00 \n", - "PermGroFac 2.809359e-15 -1.989004e-16 NaN NaN 2.453659e-16 \n", - "UnempPrb -2.027987e-16 -8.541733e-17 NaN NaN -1.015307e-17 \n", - "solved NaN NaN NaN NaN NaN \n", + " CRRA DiscFac RiskyAvg RiskyStd \\\n", + "CRRA 1.000000e+00 -1.039977e-15 6.727943e-14 -3.706454e-16 \n", + "DiscFac -1.039977e-15 1.000000e+00 3.404796e-14 -5.343377e-16 \n", + "RiskyAvg 6.727943e-14 3.404796e-14 1.000000e+00 2.515974e-15 \n", + "RiskyStd -3.706454e-16 -5.343377e-16 2.515974e-15 1.000000e+00 \n", + "PermShkStd 7.881213e-17 1.674044e-16 5.648559e-16 -7.138780e-19 \n", + "TranShkStd 3.726443e-16 5.889493e-17 2.146096e-16 -2.498573e-18 \n", + "solved NaN NaN NaN NaN \n", + "linear root 1.445829e-01 -1.243680e-01 -6.824900e-02 -9.217838e-02 \n", "\n", - " PermGroFac UnempPrb solved \n", - "CRRA 2.809359e-15 -2.027987e-16 NaN \n", - "DiscFac -1.989004e-16 -8.541733e-17 NaN \n", - "RiskyAvg NaN NaN NaN \n", - "RiskyStd NaN NaN NaN \n", - "PermShkStd 2.453659e-16 -1.015307e-17 NaN \n", - "PermGroFac 1.000000e+00 -2.757886e-17 NaN \n", - "UnempPrb -2.757886e-17 1.000000e+00 NaN \n", - "solved NaN NaN NaN " + " PermShkStd TranShkStd solved linear root \n", + "CRRA 7.881213e-17 3.726443e-16 NaN 0.144583 \n", + "DiscFac 1.674044e-16 5.889493e-17 NaN -0.124368 \n", + "RiskyAvg 5.648559e-16 2.146096e-16 NaN -0.068249 \n", + "RiskyStd -7.138780e-19 -2.498573e-18 NaN -0.092178 \n", + "PermShkStd 1.000000e+00 1.070817e-17 NaN 0.559969 \n", + "TranShkStd 1.070817e-17 1.000000e+00 NaN 0.009777 \n", + "solved NaN NaN NaN NaN \n", + "linear root 5.599694e-01 9.776556e-03 NaN 1.000000 " ] }, "execution_count": 14, @@ -1840,34 +2123,45 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "list(df[~df['solved']]['PermGroFac'])" + "list(df[~df[\"solved\"]][\"PermShkStd\"])" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "243" + "729" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df['solved'].sum()" + "df[\"solved\"].sum()" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1896,8 +2190,7 @@ " RiskyAvg\n", " RiskyStd\n", " PermShkStd\n", - " PermGroFac\n", - " UnempPrb\n", + " TranShkStd\n", " solved\n", " linear root\n", " \n", @@ -1905,63 +2198,58 @@ " \n", " \n", " 0\n", - " 2.0\n", - " 0.900\n", - " 1.08\n", - " 0.2\n", + " 4.0\n", + " 0.85\n", + " 1.04\n", + " 0.1\n", + " 0.0\n", " 0.0\n", - " 1.000\n", - " 0.000\n", " True\n", - " [1.0]\n", + " [1.4554636365277989]\n", " \n", " \n", " 1\n", - " 2.0\n", - " 0.900\n", - " 1.08\n", - " 0.2\n", + " 4.0\n", + " 0.85\n", + " 1.04\n", + " 0.1\n", " 0.0\n", - " 1.000\n", - " 0.001\n", + " 0.1\n", " True\n", - " [1.0]\n", + " [1.4735069461412893]\n", " \n", " \n", " 2\n", - " 2.0\n", - " 0.900\n", - " 1.08\n", - " 0.2\n", + " 4.0\n", + " 0.85\n", + " 1.04\n", + " 0.1\n", " 0.0\n", - " 1.000\n", - " 0.100\n", + " 0.2\n", " True\n", - " [1.1741351124225394]\n", + " [1.5328095557259418]\n", " \n", " \n", " 3\n", - " 2.0\n", - " 0.900\n", - " 1.08\n", - " 0.2\n", + " 4.0\n", + " 0.85\n", + " 1.04\n", + " 0.1\n", + " 0.1\n", " 0.0\n", - " 1.001\n", - " 0.000\n", " True\n", - " [1.0]\n", + " [1.643931473520318]\n", " \n", " \n", " 4\n", - " 2.0\n", - " 0.900\n", - " 1.08\n", - " 0.2\n", - " 0.0\n", - " 1.001\n", - " 0.001\n", + " 4.0\n", + " 0.85\n", + " 1.04\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " True\n", - " [1.0]\n", + " [1.672770714359117]\n", " \n", " \n", " ...\n", @@ -1973,104 +2261,98 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " 238\n", + " 724\n", " 6.0\n", - " 0.975\n", - " 1.08\n", - " 0.2\n", + " 0.95\n", + " 1.06\n", + " 0.3\n", + " 0.1\n", " 0.1\n", - " 1.001\n", - " 0.001\n", " True\n", - " [1.113461564174906]\n", + " [0.7783203125016975]\n", " \n", " \n", - " 239\n", + " 725\n", " 6.0\n", - " 0.975\n", - " 1.08\n", - " 0.2\n", + " 0.95\n", + " 1.06\n", + " 0.3\n", " 0.1\n", - " 1.001\n", - " 0.100\n", + " 0.2\n", " True\n", - " [1.4172742386048491]\n", + " [0.7783203125016975]\n", " \n", " \n", - " 240\n", + " 726\n", " 6.0\n", - " 0.975\n", - " 1.08\n", + " 0.95\n", + " 1.06\n", + " 0.3\n", " 0.2\n", - " 0.1\n", - " 1.100\n", - " 0.000\n", + " 0.0\n", " True\n", - " [1.0]\n", + " [0.2011718750004388]\n", " \n", " \n", - " 241\n", + " 727\n", " 6.0\n", - " 0.975\n", - " 1.08\n", + " 0.95\n", + " 1.06\n", + " 0.3\n", " 0.2\n", " 0.1\n", - " 1.100\n", - " 0.001\n", " True\n", - " [1.0545030980564383]\n", + " [0.2011718750004388]\n", " \n", " \n", - " 242\n", + " 728\n", " 6.0\n", - " 0.975\n", - " 1.08\n", + " 0.95\n", + " 1.06\n", + " 0.3\n", + " 0.2\n", " 0.2\n", - " 0.1\n", - " 1.100\n", - " 0.100\n", " True\n", - " [1.3839803793791963]\n", + " [0.2011718750004388]\n", " \n", " \n", "\n", - "

243 rows × 9 columns

\n", + "

729 rows × 8 columns

\n", "" ], "text/plain": [ - " CRRA DiscFac RiskyAvg RiskyStd PermShkStd PermGroFac UnempPrb \\\n", - "0 2.0 0.900 1.08 0.2 0.0 1.000 0.000 \n", - "1 2.0 0.900 1.08 0.2 0.0 1.000 0.001 \n", - "2 2.0 0.900 1.08 0.2 0.0 1.000 0.100 \n", - "3 2.0 0.900 1.08 0.2 0.0 1.001 0.000 \n", - "4 2.0 0.900 1.08 0.2 0.0 1.001 0.001 \n", - ".. ... ... ... ... ... ... ... \n", - "238 6.0 0.975 1.08 0.2 0.1 1.001 0.001 \n", - "239 6.0 0.975 1.08 0.2 0.1 1.001 0.100 \n", - "240 6.0 0.975 1.08 0.2 0.1 1.100 0.000 \n", - "241 6.0 0.975 1.08 0.2 0.1 1.100 0.001 \n", - "242 6.0 0.975 1.08 0.2 0.1 1.100 0.100 \n", + " CRRA DiscFac RiskyAvg RiskyStd PermShkStd TranShkStd solved \\\n", + "0 4.0 0.85 1.04 0.1 0.0 0.0 True \n", + "1 4.0 0.85 1.04 0.1 0.0 0.1 True \n", + "2 4.0 0.85 1.04 0.1 0.0 0.2 True \n", + "3 4.0 0.85 1.04 0.1 0.1 0.0 True \n", + "4 4.0 0.85 1.04 0.1 0.1 0.1 True \n", + ".. ... ... ... ... ... ... ... \n", + "724 6.0 0.95 1.06 0.3 0.1 0.1 True \n", + "725 6.0 0.95 1.06 0.3 0.1 0.2 True \n", + "726 6.0 0.95 1.06 0.3 0.2 0.0 True \n", + "727 6.0 0.95 1.06 0.3 0.2 0.1 True \n", + "728 6.0 0.95 1.06 0.3 0.2 0.2 True \n", "\n", - " solved linear root \n", - "0 True [1.0] \n", - "1 True [1.0] \n", - "2 True [1.1741351124225394] \n", - "3 True [1.0] \n", - "4 True [1.0] \n", - ".. ... ... \n", - "238 True [1.113461564174906] \n", - "239 True [1.4172742386048491] \n", - "240 True [1.0] \n", - "241 True [1.0545030980564383] \n", - "242 True [1.3839803793791963] \n", + " linear root \n", + "0 [1.4554636365277989] \n", + "1 [1.4735069461412893] \n", + "2 [1.5328095557259418] \n", + "3 [1.643931473520318] \n", + "4 [1.672770714359117] \n", + ".. ... \n", + "724 [0.7783203125016975] \n", + "725 [0.7783203125016975] \n", + "726 [0.2011718750004388] \n", + "727 [0.2011718750004388] \n", + "728 [0.2011718750004388] \n", "\n", - "[243 rows x 9 columns]" + "[729 rows x 8 columns]" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2092,9 +2374,9 @@ "formats": "ipynb,py:percent" }, "kernelspec": { - "display_name": "sharkfin", + "display_name": "sharkfin-dev", "language": "python", - "name": "sharkfin" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2106,7 +2388,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/macro/dashboard_default.ipynb b/macro/dashboard_default.ipynb index 94e9d66..2c5e965 100644 --- a/macro/dashboard_default.ipynb +++ b/macro/dashboard_default.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,10 +35,10 @@ "metadata": {}, "outputs": [], "source": [ - "def interp_func(x,y):\n", + "def interp_func(x, y):\n", " def func(z):\n", " return np.interp(z, x, y)\n", - " \n", + "\n", " return func" ] }, @@ -50,12 +50,12 @@ { "data": { "text/plain": [ - "{'cycles': 1,\n", + "{'cycles': 0,\n", " 'CRRA': 5.0,\n", - " 'Rfree': 1.03,\n", + " 'Rfree': 1.0,\n", " 'DiscFac': 0.9,\n", - " 'LivPrb': [0.98],\n", - " 'PermGroFac': [1.01],\n", + " 'LivPrb': [1.0],\n", + " 'PermGroFac': [1.0],\n", " 'BoroCnstArt': 0.0,\n", " 'MaxKinks': 400,\n", " 'AgentCount': 10000,\n", @@ -86,7 +86,7 @@ " 'CubicBool': False,\n", " 'neutral_measure': False,\n", " 'NewbornTransShk': False,\n", - " 'RiskyAvg': 1.08,\n", + " 'RiskyAvg': 1.05,\n", " 'RiskyStd': 0.2,\n", " 'RiskyCount': 5,\n", " 'ShareCount': 25,\n", @@ -100,7 +100,15 @@ } ], "source": [ - "init_portfolio" + "init_portfolio\n", + "init_portfolio\n", + "init_portfolio[\"cycles\"] = 0 # NEED THIS FOR INFINITE HORIZON\n", + "init_portfolio[\"PermGroFac\"] = [1.0] # no drift in perm income\n", + "# risk free return, set to 1 to focus on equity premium\n", + "init_portfolio[\"Rfree\"] = 1.0\n", + "init_portfolio[\"RiskyAvg\"] = 1.05 # eq_prem is RiskyAvg - Rfree = 0.05\n", + "init_portfolio[\"LivPrb\"] = [1.0] # no death\n", + "init_portfolio\n" ] }, { @@ -109,7 +117,7 @@ "metadata": {}, "outputs": [], "source": [ - "at = SequentialPortfolioConsumerType(PermGroFac=[1.0])\n", + "at = SequentialPortfolioConsumerType(**init_portfolio)\n", "at.track_vars += [\"aNrm\", \"cNrm\", \"mNrm\", \"Risky\", \"Share\", \"aLvl\", \"pLvl\"]\n", "at.solve()\n", "\n", @@ -131,14 +139,12 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -174,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": { "jupyter": { "outputs_hidden": false @@ -185,8 +191,8 @@ }, "outputs": [], "source": [ - "def portfolio_choice(CRRA=6.0, DiscFac=0.9, RiskyAvg=1.08, RiskyStd=0.20):\n", - " agent_parameters = {}\n", + "def portfolio_choice(CRRA=5.0, DiscFac=0.9, RiskyAvg=1.05, RiskyStd=0.20):\n", + " agent_parameters = init_portfolio.copy()\n", "\n", " agent_parameters[\"CRRA\"] = CRRA\n", "\n", @@ -195,13 +201,13 @@ " agent_parameters[\"RiskyAvg\"] = RiskyAvg\n", "\n", " agent_parameters[\"RiskyStd\"] = RiskyStd\n", - " \n", + "\n", " print(agent_parameters)\n", "\n", " print(\"Solving...\")\n", "\n", " agent = SequentialPortfolioConsumerType(**agent_parameters)\n", - " \n", + "\n", " pprint(agent.parameters)\n", "\n", " agent.solve()\n", @@ -212,9 +218,9 @@ "\n", " print(\"subjective_return < 1?: \" + str(srle1))\n", "\n", - " plot_funcs(agent.solution[0].ShareFuncAdj, 0, 5)\n", + " plot_funcs(agent.solution[0].ShareFuncAdj, 0, 20)\n", "\n", - " plot_funcs(agent.solution[0].cFuncAdj, 0, 5)\n", + " plot_funcs(agent.solution[0].cFuncAdj, 0, 20)\n", "\n", " cFunc = agent.solution[0].cFuncAdj\n", "\n", @@ -254,15 +260,18 @@ "\n", " return mNrm_next\n", "\n", - " mNrm = np.linspace(0, 5, 1000)\n", + " mNrm = np.linspace(0, 20, 1000)\n", "\n", " # plt.plot(mNrm, cFunc(mNrm), label=\"c\")\n", "\n", " plt.plot(mNrm, mNrm - expected_m_next(mNrm), label=\"m - E[m']\")\n", "\n", - " print(f\"m - E[m] linear interp roots: {fsolve(interp_func(mNrm, mNrm - expected_m_next(mNrm)), [mNrm[0]])}\")\n", - " print(f\"m - E[m] log roots: {np.log(fsolve(interp_func(mNrm, mNrm - expected_m_next(mNrm)), [mNrm[0]]))}\")\n", - " print(f\"m - E[m] CubicSpine roots: {CubicSpline(mNrm, mNrm - expected_m_next(mNrm)).roots()}\")\n", + " print(\n", + " f\"m - E[m] linear interp roots: {fsolve(interp_func(mNrm, mNrm - expected_m_next(mNrm)), [mNrm[0]])}\")\n", + " print(\n", + " f\"m - E[m] log roots: {np.log(fsolve(interp_func(mNrm, mNrm - expected_m_next(mNrm)), [mNrm[0]]))}\")\n", + " print(\n", + " f\"m - E[m] CubicSpine roots: {CubicSpline(mNrm, mNrm - expected_m_next(mNrm)).roots()}\")\n", "\n", " plt.plot(mNrm, np.zeros_like(mNrm), label=\"0\")\n", "\n", @@ -273,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": { "jupyter": { "outputs_hidden": false @@ -286,12 +295,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "595f77184752466d8c8ed79c7f25cd22", + "model_id": "c7f95a1dfc114d669490a55f5e46c4ed", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(IntSlider(value=6, description='CRRA', max=10, min=2), FloatSlider(value=0.9, descriptio…" + "interactive(children=(IntSlider(value=5, description='CRRA', max=10, min=2), FloatSlider(value=0.9, descriptio…" ] }, "metadata": {}, @@ -300,10 +309,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -314,7 +323,7 @@ " CRRA=(2, 10, 1),\n", " DiscFac=(0.5, 0.99, 0.02),\n", " RiskyAvg=(1.0, 1.1, 0.01),\n", - " RiskyStd=(0.01, 0.3, 0.05),\n", + " RiskyStd=(0.00, 0.3, 0.05),\n", " continuous_update=False,\n", ")" ] @@ -351,57 +360,95 @@ "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'interp1d' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [10]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m xr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(x[\u001b[38;5;241m0\u001b[39m], x[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \u001b[38;5;241m100\u001b[39m)\n\u001b[1;32m 6\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(x,y)\n\u001b[0;32m----> 7\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(xr, \u001b[43minterp1d\u001b[49m(x, y)(xr))\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m#plt.plot(xr, UnivariateSpline(x, y)(xr))\u001b[39;00m\n\u001b[1;32m 9\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(xr, CubicSpline(x, y)(xr))\n", - "\u001b[0;31mNameError\u001b[0m: name 'interp1d' is not defined" - ] + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "x = [0,1,2,3,4]\n", - "y = [2,1,0,-1,-2]\n", + "x = [0, 1, 2, 3, 4]\n", + "y = [2, 1, 0, -1, -2]\n", "\n", "xr = np.linspace(x[0], x[-1], 100)\n", "\n", - "plt.scatter(x,y)\n", - "plt.plot(xr, interp1d(x, y)(xr))\n", - "#plt.plot(xr, UnivariateSpline(x, y)(xr))\n", + "plt.scatter(x, y)\n", + "plt.plot(xr, interp_func(x, y)(xr))\n", + "# plt.plot(xr, UnivariateSpline(x, y)(xr))\n", "plt.plot(xr, CubicSpline(x, y)(xr))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "CubicSpline(x, y).roots()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2. , 1.95959596, 1.91919192, 1.87878788, 1.83838384,\n", + " 1.7979798 , 1.75757576, 1.71717172, 1.67676768, 1.63636364,\n", + " 1.5959596 , 1.55555556, 1.51515152, 1.47474747, 1.43434343,\n", + " 1.39393939, 1.35353535, 1.31313131, 1.27272727, 1.23232323,\n", + " 1.19191919, 1.15151515, 1.11111111, 1.07070707, 1.03030303,\n", + " 0.98989899, 0.94949495, 0.90909091, 0.86868687, 0.82828283,\n", + " 0.78787879, 0.74747475, 0.70707071, 0.66666667, 0.62626263,\n", + " 0.58585859, 0.54545455, 0.50505051, 0.46464646, 0.42424242,\n", + " 0.38383838, 0.34343434, 0.3030303 , 0.26262626, 0.22222222,\n", + " 0.18181818, 0.14141414, 0.1010101 , 0.06060606, 0.02020202,\n", + " -0.02020202, -0.06060606, -0.1010101 , -0.14141414, -0.18181818,\n", + " -0.22222222, -0.26262626, -0.3030303 , -0.34343434, -0.38383838,\n", + " -0.42424242, -0.46464646, -0.50505051, -0.54545455, -0.58585859,\n", + " -0.62626263, -0.66666667, -0.70707071, -0.74747475, -0.78787879,\n", + " -0.82828283, -0.86868687, -0.90909091, -0.94949495, -0.98989899,\n", + " -1.03030303, -1.07070707, -1.11111111, -1.15151515, -1.19191919,\n", + " -1.23232323, -1.27272727, -1.31313131, -1.35353535, -1.39393939,\n", + " -1.43434343, -1.47474747, -1.51515152, -1.55555556, -1.5959596 ,\n", + " -1.63636364, -1.67676768, -1.71717172, -1.75757576, -1.7979798 ,\n", + " -1.83838384, -1.87878788, -1.91919192, -1.95959596, -2. ])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "interp_func(x,y)(xr)" + "interp_func(x, y)(xr)" ] }, { @@ -417,9 +464,9 @@ "formats": "ipynb,py:percent" }, "kernelspec": { - "display_name": "sharkfin", + "display_name": "sharkfin-dev", "language": "python", - "name": "sharkfin" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -431,7 +478,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/macro/roots.csv b/macro/roots.csv new file mode 100644 index 0000000..c517243 --- /dev/null +++ b/macro/roots.csv @@ -0,0 +1,730 @@ +,CRRA,DiscFac,RiskyAvg,RiskyStd,PermShkStd,TranShkStd,solved,linear root +0,4.0,0.85,1.04,0.1,0.0,0.0,True,[1.45546364] +1,4.0,0.85,1.04,0.1,0.0,0.1,True,[1.47350695] +2,4.0,0.85,1.04,0.1,0.0,0.2,True,[1.53280956] +3,4.0,0.85,1.04,0.1,0.1,0.0,True,[1.64393147] +4,4.0,0.85,1.04,0.1,0.1,0.1,True,[1.67277071] +5,4.0,0.85,1.04,0.1,0.1,0.2,True,[1.76246488] +6,4.0,0.85,1.04,0.1,0.2,0.0,True,[8.45006899] +7,4.0,0.85,1.04,0.1,0.2,0.1,True,[8.47001103] +8,4.0,0.85,1.04,0.1,0.2,0.2,True,[8.53029825] +9,4.0,0.85,1.04,0.2,0.0,0.0,True,[1.44751436] +10,4.0,0.85,1.04,0.2,0.0,0.1,True,[1.46624453] +11,4.0,0.85,1.04,0.2,0.0,0.2,True,[1.52597777] +12,4.0,0.85,1.04,0.2,0.1,0.0,True,[1.63090921] +13,4.0,0.85,1.04,0.2,0.1,0.1,True,[1.65818046] +14,4.0,0.85,1.04,0.2,0.1,0.2,True,[1.74391061] +15,4.0,0.85,1.04,0.2,0.2,0.0,True,[9.14781274] +16,4.0,0.85,1.04,0.2,0.2,0.1,True,[9.1576716] +17,4.0,0.85,1.04,0.2,0.2,0.2,True,[9.18688509] +18,4.0,0.85,1.04,0.3,0.0,0.0,True,[1.42674276] +19,4.0,0.85,1.04,0.3,0.0,0.1,True,[1.44407596] +20,4.0,0.85,1.04,0.3,0.0,0.2,True,[1.4979844] +21,4.0,0.85,1.04,0.3,0.1,0.0,True,[1.59197587] +22,4.0,0.85,1.04,0.3,0.1,0.1,True,[1.61606668] +23,4.0,0.85,1.04,0.3,0.1,0.2,True,[1.69149566] +24,4.0,0.85,1.04,0.3,0.2,0.0,True,[7.70614578] +25,4.0,0.85,1.04,0.3,0.2,0.1,True,[7.71635087] +26,4.0,0.85,1.04,0.3,0.2,0.2,True,[7.74716515] +27,4.0,0.85,1.05,0.1,0.0,0.0,True,[1.47830329] +28,4.0,0.85,1.05,0.1,0.0,0.1,True,[1.49761168] +29,4.0,0.85,1.05,0.1,0.0,0.2,True,[1.56071297] +30,4.0,0.85,1.05,0.1,0.1,0.0,True,[1.70008552] +31,4.0,0.85,1.05,0.1,0.1,0.1,True,[1.732868] +32,4.0,0.85,1.05,0.1,0.1,0.2,True,[1.83498803] +33,4.0,0.85,1.05,0.1,0.2,0.0,True,[8.87209926] +34,4.0,0.85,1.05,0.1,0.2,0.1,True,[8.89507599] +35,4.0,0.85,1.05,0.1,0.2,0.2,True,[8.96433263] +36,4.0,0.85,1.05,0.2,0.0,0.0,True,[1.46984392] +37,4.0,0.85,1.05,0.2,0.0,0.1,True,[1.48977533] +38,4.0,0.85,1.05,0.2,0.0,0.2,True,[1.55300071] +39,4.0,0.85,1.05,0.2,0.1,0.0,True,[1.68349388] +40,4.0,0.85,1.05,0.2,0.1,0.1,True,[1.71408939] +41,4.0,0.85,1.05,0.2,0.1,0.2,True,[1.81036036] +42,4.0,0.85,1.05,0.2,0.2,0.0,True,[11.13119667] +43,4.0,0.85,1.05,0.2,0.2,0.1,True,[11.14044302] +44,4.0,0.85,1.05,0.2,0.2,0.2,True,[11.16764328] +45,4.0,0.85,1.05,0.3,0.0,0.0,True,[1.45682176] +46,4.0,0.85,1.05,0.3,0.0,0.1,True,[1.47555612] +47,4.0,0.85,1.05,0.3,0.0,0.2,True,[1.53386673] +48,4.0,0.85,1.05,0.3,0.1,0.0,True,[1.65689022] +49,4.0,0.85,1.05,0.3,0.1,0.1,True,[1.68591695] +50,4.0,0.85,1.05,0.3,0.1,0.2,True,[1.77762108] +51,4.0,0.85,1.05,0.3,0.2,0.0,True,[8.35837875] +52,4.0,0.85,1.05,0.3,0.2,0.1,True,[8.36824397] +53,4.0,0.85,1.05,0.3,0.2,0.2,True,[8.39813592] +54,4.0,0.85,1.06,0.1,0.0,0.0,True,[1.5030856] +55,4.0,0.85,1.06,0.1,0.0,0.1,True,[1.52385507] +56,4.0,0.85,1.06,0.1,0.0,0.2,True,[1.59135733] +57,4.0,0.85,1.06,0.1,0.1,0.0,True,[1.77298456] +58,4.0,0.85,1.06,0.1,0.1,0.1,True,[1.810582] +59,4.0,0.85,1.06,0.1,0.1,0.2,True,[1.92831536] +60,4.0,0.85,1.06,0.1,0.2,0.0,True,[9.40585609] +61,4.0,0.85,1.06,0.1,0.2,0.1,True,[9.43226526] +62,4.0,0.85,1.06,0.1,0.2,0.2,True,[9.5116408] +63,4.0,0.85,1.06,0.2,0.0,0.0,True,[1.49426572] +64,4.0,0.85,1.06,0.2,0.0,0.1,True,[1.51532246] +65,4.0,0.85,1.06,0.2,0.0,0.2,True,[1.58277977] +66,4.0,0.85,1.06,0.2,0.1,0.0,True,[1.74841728] +67,4.0,0.85,1.06,0.2,0.1,0.1,True,[1.78326775] +68,4.0,0.85,1.06,0.2,0.1,0.2,True,[1.89283056] +69,4.0,0.85,1.06,0.2,0.2,0.0,True,[14.62505751] +70,4.0,0.85,1.06,0.2,0.2,0.1,True,[14.63316596] +71,4.0,0.85,1.06,0.2,0.2,0.2,True,[14.65754309] +72,4.0,0.85,1.06,0.3,0.0,0.0,True,[1.48166583] +73,4.0,0.85,1.06,0.3,0.0,0.1,True,[1.50323677] +74,4.0,0.85,1.06,0.3,0.0,0.2,True,[1.57008753] +75,4.0,0.85,1.06,0.3,0.1,0.0,True,[1.71307507] +76,4.0,0.85,1.06,0.3,0.1,0.1,True,[1.74517714] +77,4.0,0.85,1.06,0.3,0.1,0.2,True,[1.84692859] +78,4.0,0.85,1.06,0.3,0.2,0.0,True,[9.27957183] +79,4.0,0.85,1.06,0.3,0.2,0.1,True,[9.28907262] +80,4.0,0.85,1.06,0.3,0.2,0.2,True,[9.31830238] +81,4.0,0.9,1.04,0.1,0.0,0.0,True,[1.60252638] +82,4.0,0.9,1.04,0.1,0.0,0.1,True,[1.62924544] +83,4.0,0.9,1.04,0.1,0.0,0.2,True,[1.71399924] +84,4.0,0.9,1.04,0.1,0.1,0.0,True,[3.3510729] +85,4.0,0.9,1.04,0.1,0.1,0.1,True,[3.39935717] +86,4.0,0.9,1.04,0.1,0.1,0.2,True,[3.54771673] +87,4.0,0.9,1.04,0.1,0.2,0.0,True,[23.46394985] +88,4.0,0.9,1.04,0.1,0.2,0.1,True,[23.48350913] +89,4.0,0.9,1.04,0.1,0.2,0.2,True,[23.54311774] +90,4.0,0.9,1.04,0.2,0.0,0.0,True,[1.59180116] +91,4.0,0.9,1.04,0.2,0.0,0.1,True,[1.61757601] +92,4.0,0.9,1.04,0.2,0.0,0.2,True,[1.70047552] +93,4.0,0.9,1.04,0.2,0.1,0.0,True,[2.72315881] +94,4.0,0.9,1.04,0.2,0.1,0.1,True,[2.78155024] +95,4.0,0.9,1.04,0.2,0.1,0.2,True,[2.95987856] +96,4.0,0.9,1.04,0.2,0.2,0.0,True,[21.09218609] +97,4.0,0.9,1.04,0.2,0.2,0.1,True,[21.09856458] +98,4.0,0.9,1.04,0.2,0.2,0.2,True,[21.11783981] +99,4.0,0.9,1.04,0.3,0.0,0.0,True,[1.5577473] +100,4.0,0.9,1.04,0.3,0.0,0.1,True,[1.58086002] +101,4.0,0.9,1.04,0.3,0.0,0.2,True,[1.65420482] +102,4.0,0.9,1.04,0.3,0.1,0.0,True,[2.37585634] +103,4.0,0.9,1.04,0.3,0.1,0.1,True,[2.43931444] +104,4.0,0.9,1.04,0.3,0.1,0.2,True,[2.61539164] +105,4.0,0.9,1.04,0.3,0.2,0.0,True,[16.78209886] +106,4.0,0.9,1.04,0.3,0.2,0.1,True,[16.78965769] +107,4.0,0.9,1.04,0.3,0.2,0.2,True,[16.81255434] +108,4.0,0.9,1.05,0.1,0.0,0.0,True,[1.64626424] +109,4.0,0.9,1.05,0.1,0.0,0.1,True,[1.67658765] +110,4.0,0.9,1.05,0.1,0.0,0.2,True,[1.7726416] +111,4.0,0.9,1.05,0.1,0.1,0.0,True,[4.4295765] +112,4.0,0.9,1.05,0.1,0.1,0.1,True,[4.48296338] +113,4.0,0.9,1.05,0.1,0.1,0.2,True,[4.64503325] +114,4.0,0.9,1.05,0.1,0.2,0.0,True,[26.22473748] +115,4.0,0.9,1.05,0.1,0.2,0.1,True,[26.24703695] +116,4.0,0.9,1.05,0.1,0.2,0.2,True,[26.3149318] +117,4.0,0.9,1.05,0.2,0.0,0.0,True,[1.63492808] +118,4.0,0.9,1.05,0.2,0.0,0.1,True,[1.66365172] +119,4.0,0.9,1.05,0.2,0.0,0.2,True,[1.75665567] +120,4.0,0.9,1.05,0.2,0.1,0.0,True,[3.5453265] +121,4.0,0.9,1.05,0.2,0.1,0.1,True,[3.61268344] +122,4.0,0.9,1.05,0.2,0.1,0.2,True,[3.82090831] +123,4.0,0.9,1.05,0.2,0.2,0.0,True,[27.95328998] +124,4.0,0.9,1.05,0.2,0.2,0.1,True,[27.95839477] +125,4.0,0.9,1.05,0.2,0.2,0.2,True,[27.972904] +126,4.0,0.9,1.05,0.3,0.0,0.0,True,[1.61457903] +127,4.0,0.9,1.05,0.3,0.0,0.1,True,[1.64284172] +128,4.0,0.9,1.05,0.3,0.0,0.2,True,[1.73032189] +129,4.0,0.9,1.05,0.3,0.1,0.0,True,[2.83688942] +130,4.0,0.9,1.05,0.3,0.1,0.1,True,[2.91657704] +131,4.0,0.9,1.05,0.3,0.1,0.2,True,[3.16865787] +132,4.0,0.9,1.05,0.3,0.2,0.0,True,[18.75283618] +133,4.0,0.9,1.05,0.3,0.2,0.1,True,[18.76006394] +134,4.0,0.9,1.05,0.3,0.2,0.2,True,[18.78194306] +135,4.0,0.9,1.06,0.1,0.0,0.0,True,[1.6988222] +136,4.0,0.9,1.06,0.1,0.0,0.1,True,[1.73398306] +137,4.0,0.9,1.06,0.1,0.0,0.2,True,[1.84569964] +138,4.0,0.9,1.06,0.1,0.1,0.0,True,[5.88914995] +139,4.0,0.9,1.06,0.1,0.1,0.1,True,[5.95591279] +140,4.0,0.9,1.06,0.1,0.1,0.2,True,[6.15699816] +141,4.0,0.9,1.06,0.1,0.2,0.0,True,[30.36221334] +142,4.0,0.9,1.06,0.1,0.2,0.1,True,[30.38582963] +143,4.0,0.9,1.06,0.1,0.2,0.2,True,[30.45754168] +144,4.0,0.9,1.06,0.2,0.0,0.0,True,[1.68624107] +145,4.0,0.9,1.06,0.2,0.0,0.1,True,[1.71916189] +146,4.0,0.9,1.06,0.2,0.0,0.2,True,[1.8252134] +147,4.0,0.9,1.06,0.2,0.1,0.0,True,[5.10383097] +148,4.0,0.9,1.06,0.2,0.1,0.1,True,[5.2043237] +149,4.0,0.9,1.06,0.2,0.1,0.2,True,[5.52047829] +150,4.0,0.9,1.06,0.2,0.2,0.0,True,[5.82892519] +151,4.0,0.9,1.06,0.2,0.2,0.1,True,[5.83262292] +152,4.0,0.9,1.06,0.2,0.2,0.2,True,[5.95322069] +153,4.0,0.9,1.06,0.3,0.0,0.0,True,[1.66221671] +154,4.0,0.9,1.06,0.3,0.0,0.1,True,[1.69359882] +155,4.0,0.9,1.06,0.3,0.0,0.2,True,[1.79339011] +156,4.0,0.9,1.06,0.3,0.1,0.0,True,[3.82108337] +157,4.0,0.9,1.06,0.3,0.1,0.1,True,[3.95441452] +158,4.0,0.9,1.06,0.3,0.1,0.2,True,[4.40771629] +159,4.0,0.9,1.06,0.3,0.2,0.0,True,[21.57420734] +160,4.0,0.9,1.06,0.3,0.2,0.1,True,[21.58080089] +161,4.0,0.9,1.06,0.3,0.2,0.2,True,[21.52117541] +162,4.0,0.95,1.04,0.1,0.0,0.0,True,[2.09745385] +163,4.0,0.95,1.04,0.1,0.0,0.1,True,[2.16754047] +164,4.0,0.95,1.04,0.1,0.0,0.2,True,[2.39092065] +165,4.0,0.95,1.04,0.1,0.1,0.0,True,[31.40766976] +166,4.0,0.95,1.04,0.1,0.1,0.1,True,[31.41005066] +167,4.0,0.95,1.04,0.1,0.1,0.2,True,[31.41782359] +168,4.0,0.95,1.04,0.1,0.2,0.0,True,[15.65446795] +169,4.0,0.95,1.04,0.1,0.2,0.1,True,[15.65848745] +170,4.0,0.95,1.04,0.1,0.2,0.2,True,[15.67064501] +171,4.0,0.95,1.04,0.2,0.0,0.0,True,[2.02326706] +172,4.0,0.95,1.04,0.2,0.0,0.1,True,[2.08262699] +173,4.0,0.95,1.04,0.2,0.0,0.2,True,[2.27019048] +174,4.0,0.95,1.04,0.2,0.1,0.0,True,[4.29253179] +175,4.0,0.95,1.04,0.2,0.1,0.1,True,[4.29475201] +176,4.0,0.95,1.04,0.2,0.1,0.2,True,[4.38018419] +177,4.0,0.95,1.04,0.2,0.2,0.0,True,[1.15625] +178,4.0,0.95,1.04,0.2,0.2,0.1,True,[1.21875] +179,4.0,0.95,1.04,0.2,0.2,0.2,True,[1.25] +180,4.0,0.95,1.04,0.3,0.0,0.0,True,[1.93698643] +181,4.0,0.95,1.04,0.3,0.0,0.1,True,[1.98314038] +182,4.0,0.95,1.04,0.3,0.0,0.2,True,[2.12652495] +183,4.0,0.95,1.04,0.3,0.1,0.0,True,[2.33404645] +184,4.0,0.95,1.04,0.3,0.1,0.1,True,[2.36738668] +185,4.0,0.95,1.04,0.3,0.1,0.2,True,[2.40561451] +186,4.0,0.95,1.04,0.3,0.2,0.0,True,[0.421875] +187,4.0,0.95,1.04,0.3,0.2,0.1,True,[0.44140625] +188,4.0,0.95,1.04,0.3,0.2,0.2,True,[0.47265625] +189,4.0,0.95,1.05,0.1,0.0,0.0,True,[2.67370733] +190,4.0,0.95,1.05,0.1,0.0,0.1,True,[2.81608284] +191,4.0,0.95,1.05,0.1,0.0,0.2,True,[3.26882057] +192,4.0,0.95,1.05,0.1,0.1,0.0,True,[9.93905665] +193,4.0,0.95,1.05,0.1,0.1,0.1,True,[9.9416414] +194,4.0,0.95,1.05,0.1,0.1,0.2,True,[10.15316709] +195,4.0,0.95,1.05,0.1,0.2,0.0,True,[10.11823564] +196,4.0,0.95,1.05,0.1,0.2,0.1,True,[9.99741616] +197,4.0,0.95,1.05,0.1,0.2,0.2,True,[10.01010761] +198,4.0,0.95,1.05,0.2,0.0,0.0,True,[2.34626775] +199,4.0,0.95,1.05,0.2,0.0,0.1,True,[2.43581518] +200,4.0,0.95,1.05,0.2,0.0,0.2,True,[2.71962671] +201,4.0,0.95,1.05,0.2,0.1,0.0,True,[3.0487363] +202,4.0,0.95,1.05,0.2,0.1,0.1,True,[3.1058627] +203,4.0,0.95,1.05,0.2,0.1,0.2,True,[3.12945794] +204,4.0,0.95,1.05,0.2,0.2,0.0,True,[0.65625] +205,4.0,0.95,1.05,0.2,0.2,0.1,True,[0.6875] +206,4.0,0.95,1.05,0.2,0.2,0.2,True,[0.703125] +207,4.0,0.95,1.05,0.3,0.0,0.0,True,[2.13054526] +208,4.0,0.95,1.05,0.3,0.0,0.1,True,[2.19591279] +209,4.0,0.95,1.05,0.3,0.0,0.2,True,[2.40751573] +210,4.0,0.95,1.05,0.3,0.1,0.0,True,[1.88750582] +211,4.0,0.95,1.05,0.3,0.1,0.1,True,[1.92105303] +212,4.0,0.95,1.05,0.3,0.1,0.2,True,[1.94228339] +213,4.0,0.95,1.05,0.3,0.2,0.0,True,[0.30078125] +214,4.0,0.95,1.05,0.3,0.2,0.1,True,[0.30078125] +215,4.0,0.95,1.05,0.3,0.2,0.2,True,[0.30078125] +216,4.0,0.95,1.06,0.1,0.0,0.0,True,[4.6127251] +217,4.0,0.95,1.06,0.1,0.0,0.1,True,[4.7141945] +218,4.0,0.95,1.06,0.1,0.0,0.2,True,[5.16282828] +219,4.0,0.95,1.06,0.1,0.1,0.0,True,[6.1743808] +220,4.0,0.95,1.06,0.1,0.1,0.1,True,[6.29435233] +221,4.0,0.95,1.06,0.1,0.1,0.2,True,[6.34240125] +222,4.0,0.95,1.06,0.1,0.2,0.0,True,[7.18957907] +223,4.0,0.95,1.06,0.1,0.2,0.1,True,[7.31895351] +224,4.0,0.95,1.06,0.1,0.2,0.2,True,[7.39473098] +225,4.0,0.95,1.06,0.2,0.0,0.0,True,[3.60097201] +226,4.0,0.95,1.06,0.2,0.0,0.1,True,[3.86696004] +227,4.0,0.95,1.06,0.2,0.0,0.2,True,[5.136393] +228,4.0,0.95,1.06,0.2,0.1,0.0,True,[2.3529353] +229,4.0,0.95,1.06,0.2,0.1,0.1,True,[2.38682387] +230,4.0,0.95,1.06,0.2,0.1,0.2,True,[2.45792807] +231,4.0,0.95,1.06,0.2,0.2,0.0,True,[0.53125] +232,4.0,0.95,1.06,0.2,0.2,0.1,True,[0.5390625] +233,4.0,0.95,1.06,0.2,0.2,0.2,True,[0.578125] +234,4.0,0.95,1.06,0.3,0.0,0.0,True,[2.50974598] +235,4.0,0.95,1.06,0.3,0.0,0.1,True,[2.6120869] +236,4.0,0.95,1.06,0.3,0.0,0.2,True,[2.95341963] +237,4.0,0.95,1.06,0.3,0.1,0.0,True,[1.67958752] +238,4.0,0.95,1.06,0.3,0.1,0.1,True,[1.6840411] +239,4.0,0.95,1.06,0.3,0.1,0.2,True,[1.74105856] +240,4.0,0.95,1.06,0.3,0.2,0.0,True,[0.34082031] +241,4.0,0.95,1.06,0.3,0.2,0.1,True,[0.34082031] +242,4.0,0.95,1.06,0.3,0.2,0.2,True,[0.34082031] +243,5.0,0.85,1.04,0.1,0.0,0.0,True,[1.5974784] +244,5.0,0.85,1.04,0.1,0.0,0.1,True,[1.6193291] +245,5.0,0.85,1.04,0.1,0.0,0.2,True,[1.68982843] +246,5.0,0.85,1.04,0.1,0.1,0.0,True,[2.1461289] +247,5.0,0.85,1.04,0.1,0.1,0.1,True,[2.19662132] +248,5.0,0.85,1.04,0.1,0.1,0.2,True,[2.35138041] +249,5.0,0.85,1.04,0.1,0.2,0.0,True,[16.82602439] +250,5.0,0.85,1.04,0.1,0.2,0.1,True,[16.8520166] +251,5.0,0.85,1.04,0.1,0.2,0.2,True,[16.92992823] +252,5.0,0.85,1.04,0.2,0.0,0.0,True,[1.59894192] +253,5.0,0.85,1.04,0.2,0.0,0.1,True,[1.62066774] +254,5.0,0.85,1.04,0.2,0.0,0.2,True,[1.69081142] +255,5.0,0.85,1.04,0.2,0.1,0.0,True,[2.08113221] +256,5.0,0.85,1.04,0.2,0.1,0.1,True,[2.12987665] +257,5.0,0.85,1.04,0.2,0.1,0.2,True,[2.28041277] +258,5.0,0.85,1.04,0.2,0.2,0.0,True,[14.50724053] +259,5.0,0.85,1.04,0.2,0.2,0.1,True,[14.51571605] +260,5.0,0.85,1.04,0.2,0.2,0.2,True,[14.54149288] +261,5.0,0.85,1.04,0.3,0.0,0.0,True,[1.55739586] +262,5.0,0.85,1.04,0.3,0.0,0.1,True,[1.57654381] +263,5.0,0.85,1.04,0.3,0.0,0.2,True,[1.63728236] +264,5.0,0.85,1.04,0.3,0.1,0.0,True,[1.95217813] +265,5.0,0.85,1.04,0.3,0.1,0.1,True,[1.99174579] +266,5.0,0.85,1.04,0.3,0.1,0.2,True,[2.11133978] +267,5.0,0.85,1.04,0.3,0.2,0.0,True,[12.63448428] +268,5.0,0.85,1.04,0.3,0.2,0.1,True,[12.64352245] +269,5.0,0.85,1.04,0.3,0.2,0.2,True,[12.67093242] +270,5.0,0.85,1.05,0.1,0.0,0.0,True,[1.62138158] +271,5.0,0.85,1.05,0.1,0.0,0.1,True,[1.64551483] +272,5.0,0.85,1.05,0.1,0.0,0.2,True,[1.72241329] +273,5.0,0.85,1.05,0.1,0.1,0.0,True,[2.36647394] +274,5.0,0.85,1.05,0.1,0.1,0.1,True,[2.42347305] +275,5.0,0.85,1.05,0.1,0.1,0.2,True,[2.59669642] +276,5.0,0.85,1.05,0.1,0.2,0.0,True,[17.54931354] +277,5.0,0.85,1.05,0.1,0.2,0.1,True,[17.58115422] +278,5.0,0.85,1.05,0.1,0.2,0.2,True,[17.67637576] +279,5.0,0.85,1.05,0.2,0.0,0.0,True,[1.62380474] +280,5.0,0.85,1.05,0.2,0.0,0.1,True,[1.64765129] +281,5.0,0.85,1.05,0.2,0.0,0.2,True,[1.7236535] +282,5.0,0.85,1.05,0.2,0.1,0.0,True,[2.24837447] +283,5.0,0.85,1.05,0.2,0.1,0.1,True,[2.30489641] +284,5.0,0.85,1.05,0.2,0.1,0.2,True,[2.47955498] +285,5.0,0.85,1.05,0.2,0.2,0.0,True,[17.05529625] +286,5.0,0.85,1.05,0.2,0.2,0.1,True,[17.06314755] +287,5.0,0.85,1.05,0.2,0.2,0.2,True,[17.08725893] +288,5.0,0.85,1.05,0.3,0.0,0.0,True,[1.58468986] +289,5.0,0.85,1.05,0.3,0.0,0.1,True,[1.60568905] +290,5.0,0.85,1.05,0.3,0.0,0.2,True,[1.67249797] +291,5.0,0.85,1.05,0.3,0.1,0.0,True,[2.13567383] +292,5.0,0.85,1.05,0.3,0.1,0.1,True,[2.18062473] +293,5.0,0.85,1.05,0.3,0.1,0.2,True,[2.31375172] +294,5.0,0.85,1.05,0.3,0.2,0.0,True,[13.48651628] +295,5.0,0.85,1.05,0.3,0.2,0.1,True,[13.49532743] +296,5.0,0.85,1.05,0.3,0.2,0.2,True,[13.52190364] +297,5.0,0.85,1.06,0.1,0.0,0.0,True,[1.64817513] +298,5.0,0.85,1.06,0.1,0.0,0.1,True,[1.67424445] +299,5.0,0.85,1.06,0.1,0.0,0.2,True,[1.75873767] +300,5.0,0.85,1.06,0.1,0.1,0.0,True,[2.67938478] +301,5.0,0.85,1.06,0.1,0.1,0.1,True,[2.74238784] +302,5.0,0.85,1.06,0.1,0.1,0.2,True,[2.93190862] +303,5.0,0.85,1.06,0.1,0.2,0.0,True,[18.68506137] +304,5.0,0.85,1.06,0.1,0.2,0.1,True,[18.72422886] +305,5.0,0.85,1.06,0.1,0.2,0.2,True,[18.84223929] +306,5.0,0.85,1.06,0.2,0.0,0.0,True,[1.6514608] +307,5.0,0.85,1.06,0.2,0.0,0.1,True,[1.67712829] +308,5.0,0.85,1.06,0.2,0.0,0.2,True,[1.76004396] +309,5.0,0.85,1.06,0.2,0.1,0.0,True,[2.48685136] +310,5.0,0.85,1.06,0.2,0.1,0.1,True,[2.55349905] +311,5.0,0.85,1.06,0.2,0.1,0.2,True,[2.75927065] +312,5.0,0.85,1.06,0.2,0.2,0.0,True,[21.2063468] +313,5.0,0.85,1.06,0.2,0.2,0.1,True,[21.2127125] +314,5.0,0.85,1.06,0.2,0.2,0.2,True,[21.1270724] +315,5.0,0.85,1.06,0.3,0.0,0.0,True,[1.62389838] +316,5.0,0.85,1.06,0.3,0.0,0.1,True,[1.64779471] +317,5.0,0.85,1.06,0.3,0.0,0.2,True,[1.72286829] +318,5.0,0.85,1.06,0.3,0.1,0.0,True,[2.35800454] +319,5.0,0.85,1.06,0.3,0.1,0.1,True,[2.42771559] +320,5.0,0.85,1.06,0.3,0.1,0.2,True,[2.65120075] +321,5.0,0.85,1.06,0.3,0.2,0.0,True,[14.68005213] +322,5.0,0.85,1.06,0.3,0.2,0.1,True,[14.68858733] +323,5.0,0.85,1.06,0.3,0.2,0.2,True,[14.71443114] +324,5.0,0.9,1.04,0.1,0.0,0.0,True,[1.74966531] +325,5.0,0.9,1.04,0.1,0.0,0.1,True,[1.78383066] +326,5.0,0.9,1.04,0.1,0.0,0.2,True,[1.89348254] +327,5.0,0.9,1.04,0.1,0.1,0.0,True,[8.87459844] +328,5.0,0.9,1.04,0.1,0.1,0.1,True,[8.93631533] +329,5.0,0.9,1.04,0.1,0.1,0.2,True,[9.12445754] +330,5.0,0.9,1.04,0.1,0.2,0.0,True,[20.19171473] +331,5.0,0.9,1.04,0.1,0.2,0.1,True,[20.26345174] +332,5.0,0.9,1.04,0.1,0.2,0.2,True,[20.48120258] +333,5.0,0.9,1.04,0.2,0.0,0.0,True,[1.75221787] +334,5.0,0.9,1.04,0.2,0.0,0.1,True,[1.78492065] +335,5.0,0.9,1.04,0.2,0.0,0.2,True,[1.88978528] +336,5.0,0.9,1.04,0.2,0.1,0.0,True,[10.43327819] +337,5.0,0.9,1.04,0.2,0.1,0.1,True,[10.45293936] +338,5.0,0.9,1.04,0.2,0.1,0.2,True,[10.51368861] +339,5.0,0.9,1.04,0.2,0.2,0.0,True,[29.1437567] +340,5.0,0.9,1.04,0.2,0.2,0.1,True,[29.14880442] +341,5.0,0.9,1.04,0.2,0.2,0.2,True,[29.16462915] +342,5.0,0.9,1.04,0.3,0.0,0.0,True,[1.68681932] +343,5.0,0.9,1.04,0.3,0.0,0.1,True,[1.71463618] +344,5.0,0.9,1.04,0.3,0.0,0.2,True,[1.80288066] +345,5.0,0.9,1.04,0.3,0.1,0.0,True,[6.85632983] +346,5.0,0.9,1.04,0.3,0.1,0.1,True,[6.87688971] +347,5.0,0.9,1.04,0.3,0.1,0.2,True,[6.93871987] +348,5.0,0.9,1.04,0.3,0.2,0.0,True,[23.64092312] +349,5.0,0.9,1.04,0.3,0.2,0.1,True,[23.65050463] +350,5.0,0.9,1.04,0.3,0.2,0.2,True,[23.67888648] +351,5.0,0.9,1.05,0.1,0.0,0.0,True,[1.8019063] +352,5.0,0.9,1.05,0.1,0.0,0.1,True,[1.84098846] +353,5.0,0.9,1.05,0.1,0.0,0.2,True,[1.96591816] +354,5.0,0.9,1.05,0.1,0.1,0.0,True,[11.31758221] +355,5.0,0.9,1.05,0.1,0.1,0.1,True,[11.4086852] +356,5.0,0.9,1.05,0.1,0.1,0.2,True,[11.6868707] +357,5.0,0.9,1.05,0.1,0.2,0.0,True,[36.92282005] +358,5.0,0.9,1.05,0.1,0.2,0.1,True,[37.0781689] +359,5.0,0.9,1.05,0.1,0.2,0.2,True,[37.54966499] +360,5.0,0.9,1.05,0.2,0.0,0.0,True,[1.80259994] +361,5.0,0.9,1.05,0.2,0.0,0.1,True,[1.83984299] +362,5.0,0.9,1.05,0.2,0.0,0.2,True,[1.95931565] +363,5.0,0.9,1.05,0.2,0.1,0.0,True,[16.33585274] +364,5.0,0.9,1.05,0.2,0.1,0.1,True,[16.35286707] +365,5.0,0.9,1.05,0.2,0.1,0.2,True,[16.40515207] +366,5.0,0.9,1.05,0.2,0.2,0.0,True,[2.91991453] +367,5.0,0.9,1.05,0.2,0.2,0.1,True,[2.9248315] +368,5.0,0.9,1.05,0.2,0.2,0.2,True,[3.00220851] +369,5.0,0.9,1.05,0.3,0.0,0.0,True,[1.74659984] +370,5.0,0.9,1.05,0.3,0.0,0.1,True,[1.77839359] +371,5.0,0.9,1.05,0.3,0.0,0.2,True,[1.87908889] +372,5.0,0.9,1.05,0.3,0.1,0.0,True,[8.44808642] +373,5.0,0.9,1.05,0.3,0.1,0.1,True,[8.46727185] +374,5.0,0.9,1.05,0.3,0.1,0.2,True,[8.52617445] +375,5.0,0.9,1.05,0.3,0.2,0.0,True,[25.72896037] +376,5.0,0.9,1.05,0.3,0.2,0.1,True,[25.738485] +377,5.0,0.9,1.05,0.3,0.2,0.2,True,[25.76799175] +378,5.0,0.9,1.06,0.1,0.0,0.0,True,[1.86687547] +379,5.0,0.9,1.06,0.1,0.0,0.1,True,[1.91204548] +380,5.0,0.9,1.06,0.1,0.0,0.2,True,[2.05646683] +381,5.0,0.9,1.06,0.1,0.1,0.0,True,[17.13618082] +382,5.0,0.9,1.06,0.1,0.1,0.1,True,[17.34486549] +383,5.0,0.9,1.06,0.1,0.1,0.2,True,[17.98123152] +384,5.0,0.9,1.06,0.1,0.2,0.0,True,[34.13795724] +385,5.0,0.9,1.06,0.1,0.2,0.1,True,[34.3004525] +386,5.0,0.9,1.06,0.1,0.2,0.2,True,[34.79419656] +387,5.0,0.9,1.06,0.2,0.0,0.0,True,[1.86407025] +388,5.0,0.9,1.06,0.2,0.0,0.1,True,[1.90681497] +389,5.0,0.9,1.06,0.2,0.0,0.2,True,[2.04562287] +390,5.0,0.9,1.06,0.2,0.1,0.0,True,[6.1271789] +391,5.0,0.9,1.06,0.2,0.1,0.1,True,[6.12810201] +392,5.0,0.9,1.06,0.2,0.1,0.2,True,[6.14360163] +393,5.0,0.9,1.06,0.2,0.2,0.0,True,[2.0168028] +394,5.0,0.9,1.06,0.2,0.2,0.1,True,[2.02186038] +395,5.0,0.9,1.06,0.2,0.2,0.2,True,[2.07623563] +396,5.0,0.9,1.06,0.3,0.0,0.0,True,[1.84224839] +397,5.0,0.9,1.06,0.3,0.0,0.1,True,[1.88061136] +398,5.0,0.9,1.06,0.3,0.0,0.2,True,[2.00077148] +399,5.0,0.9,1.06,0.3,0.1,0.0,True,[10.85834869] +400,5.0,0.9,1.06,0.3,0.1,0.1,True,[10.87629633] +401,5.0,0.9,1.06,0.3,0.1,0.2,True,[10.92981909] +402,5.0,0.9,1.06,0.3,0.2,0.0,True,[29.64183415] +403,5.0,0.9,1.06,0.3,0.2,0.1,True,[29.64730939] +404,5.0,0.9,1.06,0.3,0.2,0.2,True,[29.66334297] +405,5.0,0.95,1.04,0.1,0.0,0.0,True,[2.30663676] +406,5.0,0.95,1.04,0.1,0.0,0.1,True,[2.39295151] +407,5.0,0.95,1.04,0.1,0.0,0.2,True,[2.6702255] +408,5.0,0.95,1.04,0.1,0.1,0.0,True,[9.55023611] +409,5.0,0.95,1.04,0.1,0.1,0.1,True,[9.55246679] +410,5.0,0.95,1.04,0.1,0.1,0.2,True,[9.77835134] +411,5.0,0.95,1.04,0.1,0.2,0.0,True,[6.96875] +412,5.0,0.95,1.04,0.1,0.2,0.1,True,[6.96875] +413,5.0,0.95,1.04,0.1,0.2,0.2,True,[7.1328125] +414,5.0,0.95,1.04,0.2,0.0,0.0,True,[2.24064868] +415,5.0,0.95,1.04,0.2,0.0,0.1,True,[2.31530642] +416,5.0,0.95,1.04,0.2,0.0,0.2,True,[2.55336989] +417,5.0,0.95,1.04,0.2,0.1,0.0,True,[1.3707077] +418,5.0,0.95,1.04,0.2,0.1,0.1,True,[1.36469542] +419,5.0,0.95,1.04,0.2,0.1,0.2,True,[1.43274982] +420,5.0,0.95,1.04,0.2,0.2,0.0,True,[0.2421875] +421,5.0,0.95,1.04,0.2,0.2,0.1,True,[0.2421875] +422,5.0,0.95,1.04,0.2,0.2,0.2,True,[0.2421875] +423,5.0,0.95,1.04,0.3,0.0,0.0,True,[2.06391576] +424,5.0,0.95,1.04,0.3,0.0,0.1,True,[2.11708027] +425,5.0,0.95,1.04,0.3,0.0,0.2,True,[2.28091579] +426,5.0,0.95,1.04,0.3,0.1,0.0,True,[1.0625] +427,5.0,0.95,1.04,0.3,0.1,0.1,True,[1.0625] +428,5.0,0.95,1.04,0.3,0.1,0.2,True,[1.0859375] +429,5.0,0.95,1.04,0.3,0.2,0.0,True,[33.5] +430,5.0,0.95,1.04,0.3,0.2,0.1,True,[33.5] +431,5.0,0.95,1.04,0.3,0.2,0.2,True,[33.5] +432,5.0,0.95,1.05,0.1,0.0,0.0,True,[2.94593149] +433,5.0,0.95,1.05,0.1,0.0,0.1,True,[3.12745861] +434,5.0,0.95,1.05,0.1,0.0,0.2,True,[3.72897359] +435,5.0,0.95,1.05,0.1,0.1,0.0,True,[6.64707766] +436,5.0,0.95,1.05,0.1,0.1,0.1,True,[6.71212638] +437,5.0,0.95,1.05,0.1,0.1,0.2,True,[6.81408107] +438,5.0,0.95,1.05,0.1,0.2,0.0,True,[5.90963349] +439,5.0,0.95,1.05,0.1,0.2,0.1,True,[5.91532875] +440,5.0,0.95,1.05,0.1,0.2,0.2,True,[5.93257667] +441,5.0,0.95,1.05,0.2,0.0,0.0,True,[2.60483556] +442,5.0,0.95,1.05,0.2,0.0,0.1,True,[2.71982948] +443,5.0,0.95,1.05,0.2,0.0,0.2,True,[3.10186097] +444,5.0,0.95,1.05,0.2,0.1,0.0,True,[1.32280929] +445,5.0,0.95,1.05,0.2,0.1,0.1,True,[1.32489988] +446,5.0,0.95,1.05,0.2,0.1,0.2,True,[1.3626719] +447,5.0,0.95,1.05,0.2,0.2,0.0,True,[0.265625] +448,5.0,0.95,1.05,0.2,0.2,0.1,True,[0.265625] +449,5.0,0.95,1.05,0.2,0.2,0.2,True,[0.265625] +450,5.0,0.95,1.05,0.3,0.0,0.0,True,[2.41422399] +451,5.0,0.95,1.05,0.3,0.0,0.1,True,[2.49401425] +452,5.0,0.95,1.05,0.3,0.0,0.2,True,[2.71595126] +453,5.0,0.95,1.05,0.3,0.1,0.0,True,[1.0625] +454,5.0,0.95,1.05,0.3,0.1,0.1,True,[1.0625] +455,5.0,0.95,1.05,0.3,0.1,0.2,True,[1.046875] +456,5.0,0.95,1.05,0.3,0.2,0.0,True,[0.2421875] +457,5.0,0.95,1.05,0.3,0.2,0.1,True,[0.2421875] +458,5.0,0.95,1.05,0.3,0.2,0.2,True,[0.2421875] +459,5.0,0.95,1.06,0.1,0.0,0.0,True,[4.49818289] +460,5.0,0.95,1.06,0.1,0.0,0.1,True,[4.61514524] +461,5.0,0.95,1.06,0.1,0.0,0.2,True,[5.04520227] +462,5.0,0.95,1.06,0.1,0.1,0.0,True,[4.9100115] +463,5.0,0.95,1.06,0.1,0.1,0.1,True,[4.89726257] +464,5.0,0.95,1.06,0.1,0.1,0.2,True,[5.03152423] +465,5.0,0.95,1.06,0.1,0.2,0.0,True,[5.0853446] +466,5.0,0.95,1.06,0.1,0.2,0.1,True,[5.0911861] +467,5.0,0.95,1.06,0.1,0.2,0.2,True,[5.12448933] +468,5.0,0.95,1.06,0.2,0.0,0.0,True,[4.40543649] +469,5.0,0.95,1.06,0.2,0.0,0.1,True,[4.96505435] +470,5.0,0.95,1.06,0.2,0.0,0.2,True,[5.18989929] +471,5.0,0.95,1.06,0.2,0.1,0.0,True,[1.35909233] +472,5.0,0.95,1.06,0.2,0.1,0.1,True,[1.36149622] +473,5.0,0.95,1.06,0.2,0.1,0.2,True,[1.38274694] +474,5.0,0.95,1.06,0.2,0.2,0.0,True,[0.30078125] +475,5.0,0.95,1.06,0.2,0.2,0.1,True,[0.30078125] +476,5.0,0.95,1.06,0.2,0.2,0.2,True,[0.30078125] +477,5.0,0.95,1.06,0.3,0.0,0.0,True,[2.91693921] +478,5.0,0.95,1.06,0.3,0.0,0.1,True,[3.09156413] +479,5.0,0.95,1.06,0.3,0.0,0.2,True,[3.84806126] +480,5.0,0.95,1.06,0.3,0.1,0.0,True,[1.078125] +481,5.0,0.95,1.06,0.3,0.1,0.1,True,[1.078125] +482,5.0,0.95,1.06,0.3,0.1,0.2,True,[1.078125] +483,5.0,0.95,1.06,0.3,0.2,0.0,True,[0.2421875] +484,5.0,0.95,1.06,0.3,0.2,0.1,True,[0.2421875] +485,5.0,0.95,1.06,0.3,0.2,0.2,True,[0.2421875] +486,6.0,0.85,1.04,0.1,0.0,0.0,True,[1.73101745] +487,6.0,0.85,1.04,0.1,0.0,0.1,True,[1.75910926] +488,6.0,0.85,1.04,0.1,0.0,0.2,True,[1.84876111] +489,6.0,0.85,1.04,0.1,0.1,0.0,True,[4.36883996] +490,6.0,0.85,1.04,0.1,0.1,0.1,True,[4.42293624] +491,6.0,0.85,1.04,0.1,0.1,0.2,True,[4.58624857] +492,6.0,0.85,1.04,0.1,0.2,0.0,True,[33.20003642] +493,6.0,0.85,1.04,0.1,0.2,0.1,True,[33.26588871] +494,6.0,0.85,1.04,0.1,0.2,0.2,True,[33.46507787] +495,6.0,0.85,1.04,0.2,0.0,0.0,True,[1.7384602] +496,6.0,0.85,1.04,0.2,0.0,0.1,True,[1.76652814] +497,6.0,0.85,1.04,0.2,0.0,0.2,True,[1.85486201] +498,6.0,0.85,1.04,0.2,0.1,0.0,True,[4.93676449] +499,6.0,0.85,1.04,0.2,0.1,0.1,True,[5.03486966] +500,6.0,0.85,1.04,0.2,0.1,0.2,True,[5.2309686] +501,6.0,0.85,1.04,0.2,0.2,0.0,True,[19.98613165] +502,6.0,0.85,1.04,0.2,0.2,0.1,True,[19.99426576] +503,6.0,0.85,1.04,0.2,0.2,0.2,True,[20.01861737] +504,6.0,0.85,1.04,0.3,0.0,0.0,True,[1.67387621] +505,6.0,0.85,1.04,0.3,0.0,0.1,True,[1.69752375] +506,6.0,0.85,1.04,0.3,0.0,0.2,True,[1.77270748] +507,6.0,0.85,1.04,0.3,0.1,0.0,True,[3.77405547] +508,6.0,0.85,1.04,0.3,0.1,0.1,True,[3.804572] +509,6.0,0.85,1.04,0.3,0.1,0.2,True,[3.8980996] +510,6.0,0.85,1.04,0.3,0.2,0.0,True,[17.71571334] +511,6.0,0.85,1.04,0.3,0.2,0.1,True,[17.72418641] +512,6.0,0.85,1.04,0.3,0.2,0.2,True,[17.74982489] +513,6.0,0.85,1.05,0.1,0.0,0.0,True,[1.76224464] +514,6.0,0.85,1.05,0.1,0.0,0.1,True,[1.79245792] +515,6.0,0.85,1.05,0.1,0.0,0.2,True,[1.88979315] +516,6.0,0.85,1.05,0.1,0.1,0.0,True,[4.8869197] +517,6.0,0.85,1.05,0.1,0.1,0.1,True,[4.95047449] +518,6.0,0.85,1.05,0.1,0.1,0.2,True,[5.14169189] +519,6.0,0.85,1.05,0.1,0.2,0.0,True,[33.5913216] +520,6.0,0.85,1.05,0.1,0.2,0.1,True,[33.67877224] +521,6.0,0.85,1.05,0.1,0.2,0.2,True,[33.94233146] +522,6.0,0.85,1.05,0.2,0.0,0.0,True,[1.7765265] +523,6.0,0.85,1.05,0.2,0.0,0.1,True,[1.80683184] +524,6.0,0.85,1.05,0.2,0.0,0.2,True,[1.90372693] +525,6.0,0.85,1.05,0.2,0.1,0.0,True,[6.68943325] +526,6.0,0.85,1.05,0.2,0.1,0.1,True,[6.85965271] +527,6.0,0.85,1.05,0.2,0.1,0.2,True,[7.00596017] +528,6.0,0.85,1.05,0.2,0.2,0.0,True,[22.74242331] +529,6.0,0.85,1.05,0.2,0.2,0.1,True,[22.74941782] +530,6.0,0.85,1.05,0.2,0.2,0.2,True,[22.77026759] +531,6.0,0.85,1.05,0.3,0.0,0.0,True,[1.70376035] +532,6.0,0.85,1.05,0.3,0.0,0.1,True,[1.72919561] +533,6.0,0.85,1.05,0.3,0.0,0.2,True,[1.81028989] +534,6.0,0.85,1.05,0.3,0.1,0.0,True,[4.39458293] +535,6.0,0.85,1.05,0.3,0.1,0.1,True,[4.42298275] +536,6.0,0.85,1.05,0.3,0.1,0.2,True,[4.5109092] +537,6.0,0.85,1.05,0.3,0.2,0.0,True,[18.74607708] +538,6.0,0.85,1.05,0.3,0.2,0.1,True,[18.75450574] +539,6.0,0.85,1.05,0.3,0.2,0.2,True,[18.77963956] +540,6.0,0.85,1.06,0.1,0.0,0.0,True,[1.79666988] +541,6.0,0.85,1.06,0.1,0.0,0.1,True,[1.82954] +542,6.0,0.85,1.06,0.1,0.0,0.2,True,[1.93534293] +543,6.0,0.85,1.06,0.1,0.1,0.0,True,[5.49803407] +544,6.0,0.85,1.06,0.1,0.1,0.1,True,[5.5737149] +545,6.0,0.85,1.06,0.1,0.1,0.2,True,[5.80204001] +546,6.0,0.85,1.06,0.1,0.2,0.0,True,[32.65713216] +547,6.0,0.85,1.06,0.1,0.2,0.1,True,[32.85147265] +548,6.0,0.85,1.06,0.1,0.2,0.2,True,[33.44166637] +549,6.0,0.85,1.06,0.2,0.0,0.0,True,[1.81112726] +550,6.0,0.85,1.06,0.2,0.0,0.1,True,[1.84415048] +551,6.0,0.85,1.06,0.2,0.0,0.2,True,[1.9508372] +552,6.0,0.85,1.06,0.2,0.1,0.0,True,[9.97781234] +553,6.0,0.85,1.06,0.2,0.1,0.1,True,[10.00650137] +554,6.0,0.85,1.06,0.2,0.1,0.2,True,[10.09027253] +555,6.0,0.85,1.06,0.2,0.2,0.0,True,[27.65094304] +556,6.0,0.85,1.06,0.2,0.2,0.1,True,[27.65669402] +557,6.0,0.85,1.06,0.2,0.2,0.2,True,[27.67413681] +558,6.0,0.85,1.06,0.3,0.0,0.0,True,[1.7450875] +559,6.0,0.85,1.06,0.3,0.0,0.1,True,[1.77352295] +560,6.0,0.85,1.06,0.3,0.0,0.2,True,[1.86298825] +561,6.0,0.85,1.06,0.3,0.1,0.0,True,[5.25731049] +562,6.0,0.85,1.06,0.3,0.1,0.1,True,[5.28441483] +563,6.0,0.85,1.06,0.3,0.1,0.2,True,[5.36548016] +564,6.0,0.85,1.06,0.3,0.2,0.0,True,[20.17770627] +565,6.0,0.85,1.06,0.3,0.2,0.1,True,[20.18584062] +566,6.0,0.85,1.06,0.3,0.2,0.2,True,[20.21048517] +567,6.0,0.9,1.04,0.1,0.0,0.0,True,[1.91160349] +568,6.0,0.9,1.04,0.1,0.0,0.1,True,[1.95197341] +569,6.0,0.9,1.04,0.1,0.0,0.2,True,[2.08144719] +570,6.0,0.9,1.04,0.1,0.1,0.0,True,[24.16169476] +571,6.0,0.9,1.04,0.1,0.1,0.1,True,[24.23478399] +572,6.0,0.9,1.04,0.1,0.1,0.2,True,[24.45219375] +573,6.0,0.9,1.04,0.1,0.2,0.0,True,[26.97946631] +574,6.0,0.9,1.04,0.1,0.2,0.1,True,[27.07545354] +575,6.0,0.9,1.04,0.1,0.2,0.2,True,[27.3662244] +576,6.0,0.9,1.04,0.2,0.0,0.0,True,[1.92511525] +577,6.0,0.9,1.04,0.2,0.0,0.1,True,[1.96565294] +578,6.0,0.9,1.04,0.2,0.0,0.2,True,[2.09680469] +579,6.0,0.9,1.04,0.2,0.1,0.0,True,[18.38079063] +580,6.0,0.9,1.04,0.2,0.1,0.1,True,[18.39601611] +581,6.0,0.9,1.04,0.2,0.1,0.2,True,[18.4418488] +582,6.0,0.9,1.04,0.2,0.2,0.0,True,[33.5] +583,6.0,0.9,1.04,0.2,0.2,0.1,True,[33.5] +584,6.0,0.9,1.04,0.2,0.2,0.2,True,[33.5] +585,6.0,0.9,1.04,0.3,0.0,0.0,True,[1.81782565] +586,6.0,0.9,1.04,0.3,0.0,0.1,True,[1.85022611] +587,6.0,0.9,1.04,0.3,0.0,0.2,True,[1.95313851] +588,6.0,0.9,1.04,0.3,0.1,0.0,True,[13.58336276] +589,6.0,0.9,1.04,0.3,0.1,0.1,True,[13.60051019] +590,6.0,0.9,1.04,0.3,0.1,0.2,True,[13.65224179] +591,6.0,0.9,1.04,0.3,0.2,0.0,True,[30.62736442] +592,6.0,0.9,1.04,0.3,0.2,0.1,True,[30.63124594] +593,6.0,0.9,1.04,0.3,0.2,0.2,True,[30.64277411] +594,6.0,0.9,1.05,0.1,0.0,0.0,True,[1.97290895] +595,6.0,0.9,1.05,0.1,0.0,0.1,True,[2.01886722] +596,6.0,0.9,1.05,0.1,0.0,0.2,True,[2.16731837] +597,6.0,0.9,1.05,0.1,0.1,0.0,True,[30.37416977] +598,6.0,0.9,1.05,0.1,0.1,0.1,True,[30.72437728] +599,6.0,0.9,1.05,0.1,0.1,0.2,True,[31.409957] +600,6.0,0.9,1.05,0.1,0.2,0.0,True,[23.63522363] +601,6.0,0.9,1.05,0.1,0.2,0.1,True,[23.73320834] +602,6.0,0.9,1.05,0.1,0.2,0.2,True,[24.03064922] +603,6.0,0.9,1.05,0.2,0.0,0.0,True,[1.9880708] +604,6.0,0.9,1.05,0.2,0.0,0.1,True,[2.0343179] +605,6.0,0.9,1.05,0.2,0.0,0.2,True,[2.18469361] +606,6.0,0.9,1.05,0.2,0.1,0.0,True,[3.61981428] +607,6.0,0.9,1.05,0.2,0.1,0.1,True,[3.65425497] +608,6.0,0.9,1.05,0.2,0.1,0.2,True,[3.72726303] +609,6.0,0.9,1.05,0.2,0.2,0.0,True,[1.09375] +610,6.0,0.9,1.05,0.2,0.2,0.1,True,[1.1171875] +611,6.0,0.9,1.05,0.2,0.2,0.2,True,[1.15625] +612,6.0,0.9,1.05,0.3,0.0,0.0,True,[1.87808643] +613,6.0,0.9,1.05,0.3,0.0,0.1,True,[1.91459986] +614,6.0,0.9,1.05,0.3,0.0,0.2,True,[2.02965354] +615,6.0,0.9,1.05,0.3,0.1,0.0,True,[1.97927761] +616,6.0,0.9,1.05,0.3,0.1,0.1,True,[1.98220634] +617,6.0,0.9,1.05,0.3,0.1,0.2,True,[15.74785149] +618,6.0,0.9,1.05,0.3,0.2,0.0,True,[32.] +619,6.0,0.9,1.05,0.3,0.2,0.1,True,[32.] +620,6.0,0.9,1.05,0.3,0.2,0.2,True,[32.] +621,6.0,0.9,1.06,0.1,0.0,0.0,True,[2.04721171] +622,6.0,0.9,1.06,0.1,0.0,0.1,True,[2.10065455] +623,6.0,0.9,1.06,0.1,0.0,0.2,True,[2.27547816] +624,6.0,0.9,1.06,0.1,0.1,0.0,True,[16.57548448] +625,6.0,0.9,1.06,0.1,0.1,0.1,True,[16.57805516] +626,6.0,0.9,1.06,0.1,0.1,0.2,True,[16.92353905] +627,6.0,0.9,1.06,0.1,0.2,0.0,True,[14.0394372] +628,6.0,0.9,1.06,0.1,0.2,0.1,True,[14.07711904] +629,6.0,0.9,1.06,0.1,0.2,0.2,True,[14.10143121] +630,6.0,0.9,1.06,0.2,0.0,0.0,True,[2.06420891] +631,6.0,0.9,1.06,0.2,0.0,0.1,True,[2.118197] +632,6.0,0.9,1.06,0.2,0.0,0.2,True,[2.29435034] +633,6.0,0.9,1.06,0.2,0.1,0.0,True,[3.00359143] +634,6.0,0.9,1.06,0.2,0.1,0.1,True,[3.00707738] +635,6.0,0.9,1.06,0.2,0.1,0.2,True,[3.08884882] +636,6.0,0.9,1.06,0.2,0.2,0.0,True,[0.6875] +637,6.0,0.9,1.06,0.2,0.2,0.1,True,[0.71875] +638,6.0,0.9,1.06,0.2,0.2,0.2,True,[0.8125] +639,6.0,0.9,1.06,0.3,0.0,0.0,True,[1.97158534] +640,6.0,0.9,1.06,0.3,0.0,0.1,True,[2.0147291] +641,6.0,0.9,1.06,0.3,0.0,0.2,True,[2.14982894] +642,6.0,0.9,1.06,0.3,0.1,0.0,True,[1.79743887] +643,6.0,0.9,1.06,0.3,0.1,0.1,True,[1.80250372] +644,6.0,0.9,1.06,0.3,0.1,0.2,True,[1.84596816] +645,6.0,0.9,1.06,0.3,0.2,0.0,True,[33.5] +646,6.0,0.9,1.06,0.3,0.2,0.1,True,[33.5] +647,6.0,0.9,1.06,0.3,0.2,0.2,True,[33.5] +648,6.0,0.95,1.04,0.1,0.0,0.0,True,[2.52324988] +649,6.0,0.95,1.04,0.1,0.0,0.1,True,[2.62895307] +650,6.0,0.95,1.04,0.1,0.0,0.2,True,[2.96742351] +651,6.0,0.95,1.04,0.1,0.1,0.0,True,[5.52254201] +652,6.0,0.95,1.04,0.1,0.1,0.1,True,[5.52506399] +653,6.0,0.95,1.04,0.1,0.1,0.2,True,[5.64219676] +654,6.0,0.95,1.04,0.1,0.2,0.0,True,[3.34375] +655,6.0,0.95,1.04,0.1,0.2,0.1,True,[3.375] +656,6.0,0.95,1.04,0.1,0.2,0.2,True,[3.421875] +657,6.0,0.95,1.04,0.2,0.0,0.0,True,[2.49058909] +658,6.0,0.95,1.04,0.2,0.0,0.1,True,[2.58321111] +659,6.0,0.95,1.04,0.2,0.0,0.2,True,[2.89005428] +660,6.0,0.95,1.04,0.2,0.1,0.0,True,[0.86132813] +661,6.0,0.95,1.04,0.2,0.1,0.1,True,[0.86230469] +662,6.0,0.95,1.04,0.2,0.1,0.2,True,[0.86035156] +663,6.0,0.95,1.04,0.2,0.2,0.0,True,[0.203125] +664,6.0,0.95,1.04,0.2,0.2,0.1,True,[0.203125] +665,6.0,0.95,1.04,0.2,0.2,0.2,True,[0.203125] +666,6.0,0.95,1.04,0.3,0.0,0.0,True,[2.19482751] +667,6.0,0.95,1.04,0.3,0.0,0.1,True,[2.25480731] +668,6.0,0.95,1.04,0.3,0.0,0.2,True,[2.44159182] +669,6.0,0.95,1.04,0.3,0.1,0.0,True,[0.7265625] +670,6.0,0.95,1.04,0.3,0.1,0.1,True,[0.7265625] +671,6.0,0.95,1.04,0.3,0.1,0.2,True,[0.71875] +672,6.0,0.95,1.04,0.3,0.2,0.0,True,[32.] +673,6.0,0.95,1.04,0.3,0.2,0.1,True,[32.] +674,6.0,0.95,1.04,0.3,0.2,0.2,True,[32.] +675,6.0,0.95,1.05,0.1,0.0,0.0,True,[3.26092073] +676,6.0,0.95,1.05,0.1,0.0,0.1,True,[3.49576876] +677,6.0,0.95,1.05,0.1,0.0,0.2,True,[4.36275348] +678,6.0,0.95,1.05,0.1,0.1,0.0,True,[4.5192404] +679,6.0,0.95,1.05,0.1,0.1,0.1,True,[4.58454525] +680,6.0,0.95,1.05,0.1,0.1,0.2,True,[4.6558802] +681,6.0,0.95,1.05,0.1,0.2,0.0,True,[3.5] +682,6.0,0.95,1.05,0.1,0.2,0.1,True,[3.5] +683,6.0,0.95,1.05,0.1,0.2,0.2,True,[3.59375] +684,6.0,0.95,1.05,0.2,0.0,0.0,True,[2.93813864] +685,6.0,0.95,1.05,0.2,0.0,0.1,True,[3.09781196] +686,6.0,0.95,1.05,0.2,0.0,0.2,True,[3.6699257] +687,6.0,0.95,1.05,0.2,0.1,0.0,True,[0.9375] +688,6.0,0.95,1.05,0.2,0.1,0.1,True,[0.9375] +689,6.0,0.95,1.05,0.2,0.1,0.2,True,[0.9375] +690,6.0,0.95,1.05,0.2,0.2,0.0,True,[0.2265625] +691,6.0,0.95,1.05,0.2,0.2,0.1,True,[0.2265625] +692,6.0,0.95,1.05,0.2,0.2,0.2,True,[0.2265625] +693,6.0,0.95,1.05,0.3,0.0,0.0,True,[2.51489968] +694,6.0,0.95,1.05,0.3,0.0,0.1,True,[2.59617931] +695,6.0,0.95,1.05,0.3,0.0,0.2,True,[2.84307673] +696,6.0,0.95,1.05,0.3,0.1,0.0,True,[0.73828125] +697,6.0,0.95,1.05,0.3,0.1,0.1,True,[0.73828125] +698,6.0,0.95,1.05,0.3,0.1,0.2,True,[0.74023438] +699,6.0,0.95,1.05,0.3,0.2,0.0,True,[33.5] +700,6.0,0.95,1.05,0.3,0.2,0.1,True,[33.5] +701,6.0,0.95,1.05,0.3,0.2,0.2,True,[33.5] +702,6.0,0.95,1.06,0.1,0.0,0.0,True,[4.4776553] +703,6.0,0.95,1.06,0.1,0.0,0.1,True,[4.59066656] +704,6.0,0.95,1.06,0.1,0.0,0.2,True,[4.92437857] +705,6.0,0.95,1.06,0.1,0.1,0.0,True,[3.79131276] +706,6.0,0.95,1.06,0.1,0.1,0.1,True,[3.79443548] +707,6.0,0.95,1.06,0.1,0.1,0.2,True,[3.92928207] +708,6.0,0.95,1.06,0.1,0.2,0.0,True,[3.47097015] +709,6.0,0.95,1.06,0.1,0.2,0.1,True,[3.54101627] +710,6.0,0.95,1.06,0.1,0.2,0.2,True,[3.56392127] +711,6.0,0.95,1.06,0.2,0.0,0.0,True,[4.60130415] +712,6.0,0.95,1.06,0.2,0.0,0.1,True,[4.61553089] +713,6.0,0.95,1.06,0.2,0.0,0.2,True,[4.8247] +714,6.0,0.95,1.06,0.2,0.1,0.0,True,[1.015625] +715,6.0,0.95,1.06,0.2,0.1,0.1,True,[1.015625] +716,6.0,0.95,1.06,0.2,0.1,0.2,True,[1.015625] +717,6.0,0.95,1.06,0.2,0.2,0.0,True,[0.2421875] +718,6.0,0.95,1.06,0.2,0.2,0.1,True,[0.2421875] +719,6.0,0.95,1.06,0.2,0.2,0.2,True,[0.2421875] +720,6.0,0.95,1.06,0.3,0.0,0.0,True,[3.47770162] +721,6.0,0.95,1.06,0.3,0.0,0.1,True,[3.60362262] +722,6.0,0.95,1.06,0.3,0.0,0.2,True,[3.974112] +723,6.0,0.95,1.06,0.3,0.1,0.0,True,[0.77832031] +724,6.0,0.95,1.06,0.3,0.1,0.1,True,[0.77832031] +725,6.0,0.95,1.06,0.3,0.1,0.2,True,[0.77832031] +726,6.0,0.95,1.06,0.3,0.2,0.0,True,[0.20117188] +727,6.0,0.95,1.06,0.3,0.2,0.1,True,[0.20117188] +728,6.0,0.95,1.06,0.3,0.2,0.2,True,[0.20117188]