clean up notebooks

econ-ark · Jul 11, 2024 · 31baea5 · 31baea5
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diff --git a/examples/Calibration/Income_calibrations.ipynb b/examples/Calibration/Income_calibrations.ipynb
@@ -7,22 +7,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "Created on Sun Jan  3 10:50:02 2021\n",
+    "\"\"\"Created on Sun Jan  3 10:50:02 2021\n",
     "\n",
     "@author: Mateo\n",
     "\"\"\"\n",
     "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
     "from HARK.Calibration.Income.IncomeTools import (\n",
-    "    parse_income_spec,\n",
-    "    find_profile,\n",
     "    Cagetti_income,\n",
     "    CGM_income,\n",
+    "    find_profile,\n",
+    "    parse_income_spec,\n",
     ")\n",
     "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "# What year to use as the base monetary year?\n",
     "# (pick 1992 as it is used by both of the papers we are comparing)\n",
     "adjust_infl_to = 1992"
@@ -58,19 +57,22 @@
     "    label = spec[0]\n",
     "\n",
     "    params = parse_income_spec(\n",
-    "        age_min=age_min, age_max=age_max, adjust_infl_to=adjust_infl_to, **spec[1]\n",
+    "        age_min=age_min,\n",
+    "        age_max=age_max,\n",
+    "        adjust_infl_to=adjust_infl_to,\n",
+    "        **spec[1],\n",
     "    )\n",
     "    MeanY = find_profile(params[\"PermGroFac\"], params[\"P0\"])\n",
     "\n",
     "    plt.plot(ages, MeanY, label=label)\n",
     "\n",
     "plt.title(\n",
     "    \"Mean paths of permanent income calibrations in\\n\"\n",
-    "    + \"Cocco, Gomes & Maenhout (2005)\"\n",
+    "    + \"Cocco, Gomes & Maenhout (2005)\",\n",
     ")\n",
     "plt.xlabel(\"Age\")\n",
     "plt.ylabel(\n",
-    "    \"Mean Permanent Income,\\n\" + \"Thousands of {} U.S. dollars\".format(adjust_infl_to)\n",
+    "    \"Mean Permanent Income,\\n\" + f\"Thousands of {adjust_infl_to} U.S. dollars\",\n",
     ")\n",
     "plt.legend()\n",
     "plt.show()"
@@ -122,7 +124,7 @@
     "plt.title(\"Mean paths of permanent income calibrations in\\n\" + \"Cagetti (2003)\")\n",
     "plt.xlabel(\"Age\")\n",
     "plt.ylabel(\n",
-    "    \"Mean Permanent Income,\\n\" + \"Thousands of {} U.S. dollars\".format(adjust_infl_to)\n",
+    "    \"Mean Permanent Income,\\n\" + f\"Thousands of {adjust_infl_to} U.S. dollars\",\n",
     ")\n",
     "plt.legend()\n",
     "plt.show()"

diff --git a/examples/Calibration/Life_Cycle_example.ipynb b/examples/Calibration/Life_Cycle_example.ipynb
@@ -7,24 +7,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from HARK.ConsumptionSaving.ConsIndShockModel import (\n",
-    "    IndShockConsumerType,\n",
-    "    init_lifecycle,\n",
-    ")\n",
+    "from copy import copy\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
     "\n",
     "from HARK.Calibration.Income.IncomeTools import (\n",
+    "    CGM_income,\n",
     "    parse_income_spec,\n",
     "    parse_time_params,\n",
-    "    CGM_income,\n",
     ")\n",
-    "\n",
     "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n",
     "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n",
     "    income_wealth_dists_from_scf,\n",
     ")\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "from copy import copy\n",
+    "from HARK.ConsumptionSaving.ConsIndShockModel import (\n",
+    "    IndShockConsumerType,\n",
+    "    init_lifecycle,\n",
+    ")\n",
     "from HARK.utilities import plot_funcs"
    ]
   },
@@ -54,13 +54,20 @@
     "\n",
     "# Initial distribution of wealth and permanent income\n",
     "dist_params = income_wealth_dists_from_scf(\n",
-    "    base_year=adjust_infl_to, age=birth_age, education=education, wave=1995\n",
+    "    base_year=adjust_infl_to,\n",
+    "    age=birth_age,\n",
+    "    education=education,\n",
+    "    wave=1995,\n",
     ")\n",
     "\n",
     "# We need survival probabilities only up to death_age-1, because survival\n",
     "# probability at death_age is 1.\n",
     "liv_prb = parse_ssa_life_table(\n",
-    "    female=True, cross_sec=True, year=2004, min_age=birth_age, max_age=death_age - 1\n",
+    "    female=True,\n",
+    "    cross_sec=True,\n",
+    "    year=2004,\n",
+    "    min_age=birth_age,\n",
+    "    max_age=death_age - 1,\n",
     ")\n",
     "\n",
     "# Parameters related to the number of periods implied by the calibration\n",
@@ -258,7 +265,7 @@
     "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n",
     "plt.legend()\n",
     "plt.xlabel(\"Age\")\n",
-    "plt.ylabel(\"Thousands of {} USD\".format(adjust_infl_to))\n",
+    "plt.ylabel(f\"Thousands of {adjust_infl_to} USD\")\n",
     "plt.title(\"Variable Medians Conditional on Survival\")\n",
     "plt.grid()"
    ]

diff --git a/examples/Calibration/SCF_distributions.ipynb b/examples/Calibration/SCF_distributions.ipynb
@@ -7,18 +7,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "Created on Mon Jan 18 13:57:50 2021\n",
+    "\"\"\"Created on Mon Jan 18 13:57:50 2021\n",
     "\n",
     "@author: Mateo\n",
     "\"\"\"\n",
     "\n",
+    "from itertools import product, starmap\n",
+    "\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
     "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n",
     "    income_wealth_dists_from_scf,\n",
     ")\n",
-    "import seaborn as sns\n",
-    "from itertools import product, starmap\n",
-    "import pandas as pd\n",
     "\n",
     "# List the education levels and years\n",
     "educ_lvls = [\"NoHS\", \"HS\", \"College\"]\n",
@@ -41,7 +42,7 @@
     "base_year, age, education, year = list(zip(*params))\n",
     "\n",
     "frame = pd.DataFrame(\n",
-    "    {\"base_year\": base_year, \"age\": age, \"education\": education, \"wave\": year}\n",
+    "    {\"base_year\": base_year, \"age\": age, \"education\": education, \"wave\": year},\n",
     ")\n",
     "\n",
     "results = list(starmap(income_wealth_dists_from_scf, params))\n",

diff --git a/examples/Calibration/Sabelhaus_Song_var_profiles.ipynb b/examples/Calibration/Sabelhaus_Song_var_profiles.ipynb
@@ -7,8 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "Created on Thu Jan 14 16:44:09 2021\n",
+    "\"\"\"Created on Thu Jan 14 16:44:09 2021\n",
     "\n",
     "@author: Mateo\n",
     "\n",
@@ -22,9 +21,10 @@
     "\"\"\"\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "from HARK.Calibration.Income.IncomeTools import sabelhaus_song_var_profile\n",
     "import numpy as np\n",
     "\n",
+    "from HARK.Calibration.Income.IncomeTools import sabelhaus_song_var_profile\n",
+    "\n",
     "# Set up ages and cohorts at which we will get the variances\n",
     "age_min = 27\n",
     "age_max = 54\n",
@@ -84,7 +84,7 @@
     "    plt.plot(\n",
     "        variances[i][\"Age\"],\n",
     "        np.power(variances[i][\"TranShkStd\"], 2),\n",
-    "        label=\"Tran. {} cohort\".format(coh_label),\n",
+    "        label=f\"Tran. {coh_label} cohort\",\n",
     "    )\n",
     "\n",
     "plt.legend()\n",
@@ -96,7 +96,7 @@
     "    plt.plot(\n",
     "        variances[i][\"Age\"],\n",
     "        np.power(variances[i][\"PermShkStd\"], 2),\n",
-    "        label=\"Perm. {} cohort\".format(coh_label),\n",
+    "        label=f\"Perm. {coh_label} cohort\",\n",
     "    )\n",
     "\n",
     "plt.legend()"

diff --git a/examples/Calibration/US_SSA_life_tables.ipynb b/examples/Calibration/US_SSA_life_tables.ipynb
diff --git a/examples/ConsBequestModel/example_TerminalBequest.ipynb b/examples/ConsBequestModel/example_TerminalBequest.ipynb
@@ -18,14 +18,14 @@
     "    parse_income_spec,\n",
     "    parse_time_params,\n",
     ")\n",
-    "from HARK.ConsumptionSaving.ConsBequestModel import (\n",
-    "    BequestWarmGlowConsumerType,\n",
-    "    init_warm_glow_terminal_only,\n",
-    ")\n",
     "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n",
     "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n",
     "    income_wealth_dists_from_scf,\n",
     ")\n",
+    "from HARK.ConsumptionSaving.ConsBequestModel import (\n",
+    "    BequestWarmGlowConsumerType,\n",
+    "    init_warm_glow_terminal_only,\n",
+    ")\n",
     "from HARK.utilities import plot_funcs"
    ]
   },

diff --git a/examples/ConsBequestModel/example_TerminalBequestPort.ipynb b/examples/ConsBequestModel/example_TerminalBequestPort.ipynb
@@ -18,14 +18,14 @@
     "    parse_income_spec,\n",
     "    parse_time_params,\n",
     ")\n",
-    "from HARK.ConsumptionSaving.ConsBequestModel import (\n",
-    "    BequestWarmGlowPortfolioType,\n",
-    "    init_portfolio_bequest,\n",
-    ")\n",
     "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n",
     "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n",
     "    income_wealth_dists_from_scf,\n",
     ")\n",
+    "from HARK.ConsumptionSaving.ConsBequestModel import (\n",
+    "    BequestWarmGlowPortfolioType,\n",
+    "    init_portfolio_bequest,\n",
+    ")\n",
     "from HARK.utilities import plot_funcs"
    ]
   },

diff --git a/examples/ConsBequestModel/example_WarmGlowBequest.ipynb b/examples/ConsBequestModel/example_WarmGlowBequest.ipynb
@@ -18,14 +18,14 @@
     "    parse_income_spec,\n",
     "    parse_time_params,\n",
     ")\n",
-    "from HARK.ConsumptionSaving.ConsBequestModel import (\n",
-    "    BequestWarmGlowConsumerType,\n",
-    "    init_warm_glow,\n",
-    ")\n",
     "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n",
     "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n",
     "    income_wealth_dists_from_scf,\n",
     ")\n",
+    "from HARK.ConsumptionSaving.ConsBequestModel import (\n",
+    "    BequestWarmGlowConsumerType,\n",
+    "    init_warm_glow,\n",
+    ")\n",
     "from HARK.utilities import plot_funcs"
    ]
   },

diff --git a/examples/ConsBequestModel/example_WarmGlowBequestPort.ipynb b/examples/ConsBequestModel/example_WarmGlowBequestPort.ipynb
@@ -18,14 +18,14 @@
     "    parse_income_spec,\n",
     "    parse_time_params,\n",
     ")\n",
-    "from HARK.ConsumptionSaving.ConsBequestModel import (\n",
-    "    BequestWarmGlowPortfolioType,\n",
-    "    init_portfolio_bequest,\n",
-    ")\n",
     "from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table\n",
     "from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (\n",
     "    income_wealth_dists_from_scf,\n",
     ")\n",
+    "from HARK.ConsumptionSaving.ConsBequestModel import (\n",
+    "    BequestWarmGlowPortfolioType,\n",
+    "    init_portfolio_bequest,\n",
+    ")\n",
     "from HARK.utilities import plot_funcs"
    ]
   },

diff --git a/examples/ConsBequestModel/example_WealthPortfolio.ipynb b/examples/ConsBequestModel/example_WealthPortfolio.ipynb
@@ -20,7 +20,9 @@
     "cycles = 0\n",
     "port_agent = PortfolioConsumerType(cycles=cycles)\n",
     "wealth_agent = WealthPortfolioConsumerType(\n",
-    "    cycles=cycles, WealthShare=0.5, WealthShift=0.0\n",
+    "    cycles=cycles,\n",
+    "    WealthShare=0.5,\n",
+    "    WealthShift=0.0,\n",
     ")"
    ]
   },
@@ -152,7 +154,9 @@
    ],
    "source": [
     "plot_funcs(\n",
-    "    [wealth_agent.solution[0].ShareFuncAdj, port_agent.solution[0].ShareFuncAdj], 0, 100\n",
+    "    [wealth_agent.solution[0].ShareFuncAdj, port_agent.solution[0].ShareFuncAdj],\n",
+    "    0,\n",
+    "    100,\n",
     ")"
    ]
   },

diff --git a/examples/ConsIndShockModel/Finite Cyclical Test.ipynb b/examples/ConsIndShockModel/Finite Cyclical Test.ipynb
@@ -10,7 +10,7 @@
     "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
     "from HARK.utilities import plot_funcs\n",
     "\n",
-    "mystr = lambda number: \"{:.4f}\".format(number)"
+    "mystr = lambda number: f\"{number:.4f}\""
    ]
   },
   {

diff --git a/examples/ConsIndShockModel/IndShockConsumerType.ipynb b/examples/ConsIndShockModel/IndShockConsumerType.ipynb
@@ -20,12 +20,13 @@
    "outputs": [],
    "source": [
     "# Initial imports and notebook setup, click arrow to show\n",
-    "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
-    "from HARK.utilities import plot_funcs_der, plot_funcs\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "mystr = lambda number: \"{:.4f}\".format(number)"
+    "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
+    "from HARK.utilities import plot_funcs, plot_funcs_der\n",
+    "\n",
+    "mystr = lambda number: f\"{number:.4f}\""
    ]
   },
   {
@@ -318,7 +319,9 @@
     "plot_funcs(IndShockExample.solution[0].cFunc, IndShockExample.solution[0].mNrmMin, 5)\n",
     "print(\"Marginal propensity to consume for an idiosyncratic shocks consumer type:\")\n",
     "plot_funcs_der(\n",
-    "    IndShockExample.solution[0].cFunc, IndShockExample.solution[0].mNrmMin, 5\n",
+    "    IndShockExample.solution[0].cFunc,\n",
+    "    IndShockExample.solution[0].mNrmMin,\n",
+    "    5,\n",
     ")"
    ]
   },

diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb
@@ -27,9 +27,9 @@
     "import numpy as np\n",
     "\n",
     "from HARK.ConsumptionSaving.ConsIndShockModel import KinkedRconsumerType\n",
-    "from HARK.utilities import plot_funcs_der, plot_funcs\n",
+    "from HARK.utilities import plot_funcs, plot_funcs_der\n",
     "\n",
-    "mystr = lambda number: \"{:.4f}\".format(number)"
+    "mystr = lambda number: f\"{number:.4f}\""
    ]
   },
   {

diff --git a/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb b/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb
@@ -31,7 +31,7 @@
     "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType\n",
     "from HARK.utilities import plot_funcs\n",
     "\n",
-    "mystr = lambda number: \"{:.4f}\".format(number)"
+    "mystr = lambda number: f\"{number:.4f}\""
    ]
   },
   {

diff --git a/examples/ConsNewKeynesianModel/Jacobian_Example.ipynb b/examples/ConsNewKeynesianModel/Jacobian_Example.ipynb
@@ -26,12 +26,13 @@
    },
    "outputs": [],
    "source": [
+    "import time\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "from HARK.ConsumptionSaving.ConsNewKeynesianModel import (\n",
     "    NewKeynesianConsumerType,\n",
-    ")\n",
-    "\n",
-    "import time\n",
-    "import matplotlib.pyplot as plt"
+    ")"
    ]
   },
   {