ruff format the example notebook

econ-ark · Nov 13, 2023 · 62e8d5f · 62e8d5f
1 parent 1a33be6
commit 62e8d5f
Showing 1 changed file with 44 additions and 31 deletions.
diff --git a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb
@@ -36,12 +36,12 @@
     "    \"AgentCount\": 3,  # Number of agents of this type\n",
     "    \"T_sim\": 120,  # Number of periods to simulate\n",
     "    \"aNrmInitMean\": -6.0,  # Mean of log initial assets\n",
-    "    \"aNrmInitStd\": 0, #1.0,  # Standard deviation of log initial assets\n",
+    "    \"aNrmInitStd\": 0,  # 1.0,  # Standard deviation of log initial assets\n",
     "    \"pLvlInitMean\": 0.0,  # Mean of log initial permanent income\n",
     "    \"pLvlInitStd\": 0.0,  # Standard deviation of log initial permanent income\n",
     "    \"PermGroFacAgg\": 1.0,  # Aggregate permanent income growth factor\n",
     "    \"T_age\": None,  # Age after which simulated agents are automatically killed,\n",
-    "    \"LivPrb\": [0.98]\n",
+    "    \"LivPrb\": [0.98],\n",
     "}\n",
     "\n",
     "PFexample.assign_parameters(**SimulationParams)"
@@ -594,7 +594,7 @@
     }
    ],
    "source": [
-    "PFexample.track_vars = [\"who_dies\",\"mNrm\",\"pLvl\",\"aNrm\"]\n",
+    "PFexample.track_vars = [\"who_dies\", \"mNrm\", \"pLvl\", \"aNrm\"]\n",
     "PFexample.make_shock_history()\n",
     "\n",
     "PFexample.initialize_sim()\n",
@@ -681,7 +681,12 @@
     }
    ],
    "source": [
-    "plt.plot(np.log(np.mean(PFexample.history[\"mNrm\"], axis=1) - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))))\n",
+    "plt.plot(\n",
+    "    np.log(\n",
+    "        np.mean(PFexample.history[\"mNrm\"], axis=1)\n",
+    "        - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))\n",
+    "    )\n",
+    ")\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.show()"
@@ -705,7 +710,7 @@
     }
    ],
    "source": [
-    "PFexample.newborn_init_history['pLvl'][1,PFexample.history[\"who_dies\"][1,:] > 0].shape"
+    "PFexample.newborn_init_history[\"pLvl\"][1, PFexample.history[\"who_dies\"][1, :] > 0].shape"
    ]
   },
   {
@@ -726,7 +731,7 @@
     }
    ],
    "source": [
-    "PFexample.newborn_init_history['aNrm'][2, PFexample.history[\"who_dies\"][2,:] > 0].shape"
+    "PFexample.newborn_init_history[\"aNrm\"][2, PFexample.history[\"who_dies\"][2, :] > 0].shape"
    ]
   },
   {
@@ -791,19 +796,17 @@
    "outputs": [],
    "source": [
     "pfn_simulator = AgentTypeMonteCarloSimulator(\n",
-    "    pfn.model['parameters'],\n",
-    "    pfn.model['shocks'],\n",
-    "    pfn.model['dynamics'],\n",
-    "    {\n",
-    "        'c_nrm' : lambda m_nrm: PFexample.solution[0].cFunc(m_nrm)\n",
-    "    },\n",
-    "    { # initial states\n",
-    "        'a_nrm' : Lognormal(-6, 0),\n",
+    "    pfn.model[\"parameters\"],\n",
+    "    pfn.model[\"shocks\"],\n",
+    "    pfn.model[\"dynamics\"],\n",
+    "    {\"c_nrm\": lambda m_nrm: PFexample.solution[0].cFunc(m_nrm)},\n",
+    "    {  # initial states\n",
+    "        \"a_nrm\": Lognormal(-6, 0),\n",
     "        #'live' : 1,\n",
-    "        'p' : 1.0\n",
+    "        \"p\": 1.0,\n",
     "    },\n",
-    "    agent_count = 3,\n",
-    "    T_sim = 120\n",
+    "    agent_count=3,\n",
+    "    T_sim=120,\n",
     ")"
    ]
   },
@@ -815,9 +818,9 @@
    "outputs": [],
    "source": [
     "pfn_simulator.read_shocks = True\n",
-    "#pfn_simulator.shock_history['live'] = 1 - np.roll(PFexample.history[\"who_dies\"], -1)\n",
+    "# pfn_simulator.shock_history['live'] = 1 - np.roll(PFexample.history[\"who_dies\"], -1)\n",
     "\n",
-    "pfn_simulator.shock_history['live'] = 1 - PFexample.history[\"who_dies\"]"
+    "pfn_simulator.shock_history[\"live\"] = 1 - PFexample.history[\"who_dies\"]"
    ]
   },
   {
@@ -827,9 +830,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pfn_simulator.newborn_init_history['a_nrm'] = PFexample.newborn_init_history['aNrm']\n",
-    "pfn_simulator.newborn_init_history['p'] = PFexample.newborn_init_history['pLvl']\n",
-    "#pfn_simulator.newborn_init_history['live'] = PFexample.newborn_init_history['pLvl']"
+    "pfn_simulator.newborn_init_history[\"a_nrm\"] = PFexample.newborn_init_history[\"aNrm\"]\n",
+    "pfn_simulator.newborn_init_history[\"p\"] = PFexample.newborn_init_history[\"pLvl\"]\n",
+    "# pfn_simulator.newborn_init_history['live'] = PFexample.newborn_init_history['pLvl']"
    ]
   },
   {
@@ -1689,7 +1692,7 @@
     }
    ],
    "source": [
-    "#pf_simulator.track_vars = [\"mNrm\"]\n",
+    "# pf_simulator.track_vars = [\"mNrm\"]\n",
     "pfn_simulator.initialize_sim()\n",
     "pfn_simulator.simulate(sim_periods=120)"
    ]
@@ -1714,7 +1717,7 @@
     }
    ],
    "source": [
-    "plt.plot(np.mean(pfn_simulator.history['m_nrm'], axis=1))\n",
+    "plt.plot(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.show()"
@@ -1748,7 +1751,12 @@
     }
    ],
    "source": [
-    "plt.plot(np.log(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1) - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))))\n",
+    "plt.plot(\n",
+    "    np.log(\n",
+    "        np.mean(pfn_simulator.history[\"m_nrm\"], axis=1)\n",
+    "        - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))\n",
+    "    )\n",
+    ")\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.show()"
@@ -1784,7 +1792,7 @@
     }
    ],
    "source": [
-    "plt.plot(pfn_simulator.history['live'].sum(axis=1))"
+    "plt.plot(pfn_simulator.history[\"live\"].sum(axis=1))"
    ]
   },
   {
@@ -1807,8 +1815,11 @@
     }
    ],
    "source": [
-    "plt.plot(np.mean(pfn_simulator.history['a_nrm'], axis=1), label = 'Generic monte carlo')\n",
-    "plt.plot(np.mean(PFexample.history[\"aNrm\"], axis=1), label = 'HARK 0.13 PerfForesightConsumerType')\n",
+    "plt.plot(np.mean(pfn_simulator.history[\"a_nrm\"], axis=1), label=\"Generic monte carlo\")\n",
+    "plt.plot(\n",
+    "    np.mean(PFexample.history[\"aNrm\"], axis=1),\n",
+    "    label=\"HARK 0.13 PerfForesightConsumerType\",\n",
+    ")\n",
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Mean normalized market resources\")\n",
     "plt.legend()\n",
@@ -1840,7 +1851,9 @@
     }
    ],
    "source": [
-    "np.mean(pfn_simulator.history['a_nrm'], axis=1) - np.mean(PFexample.history[\"aNrm\"], axis=1)"
+    "np.mean(pfn_simulator.history[\"a_nrm\"], axis=1) - np.mean(\n",
+    "    PFexample.history[\"aNrm\"], axis=1\n",
+    ")"
    ]
   },
   {
@@ -1862,7 +1875,7 @@
     }
    ],
    "source": [
-    "np.where(pfn_simulator.history['live'] < 1)"
+    "np.where(pfn_simulator.history[\"live\"] < 1)"
    ]
   },
   {
@@ -1884,7 +1897,7 @@
     }
    ],
    "source": [
-    "np.where(1 - PFexample.history['who_dies'] < 1)"
+    "np.where(1 - PFexample.history[\"who_dies\"] < 1)"
    ]
   },
   {