Merge pull request #974 from sbenthall/v1.0.0-cleanup

PEP8 updates to examples for v1.0.0

examples/ConsIndShockModel/Finite Cyclical Test.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Initial imports and notebook setup, click arrow to show\n",
     "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n",
-    "from HARK.utilities import plotFuncsDer, plotFuncs\n",
+    "from HARK.utilities import plot_funcs_der, plot_funcs\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "mystr = lambda number : \"{:.4f}\".format(number)"
@@ -74,7 +74,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "unpackcFunc is deprecated and it will soon be removed, please use unpack('cFunc') instead.\n"
+      "unpack_cFunc is deprecated and it will soon be removed, please use unpack('cFunc') instead.\n"
      ]
     },
     {
@@ -102,10 +102,10 @@
     "CyclicalExample.cycles = 0 # Make this consumer type have an infinite horizon\n",
     "CyclicalExample.solve()\n",
     "\n",
-    "CyclicalExample.unpackcFunc()\n",
+    "CyclicalExample.unpack_cFunc()\n",
     "print('Quarterly consumption functions:')\n",
     "mMin = min([X.mNrmMin for X in CyclicalExample.solution])\n",
-    "plotFuncs(CyclicalExample.cFunc,mMin,5)"
+    "plot_funcs(CyclicalExample.cFunc,mMin,5)"
    ]
   },
   {
@@ -144,7 +144,7 @@
     "FiniteCyclicalExample.unpack('cFunc')\n",
     "print('Quarterly consumption functions:')\n",
     "mMin = min([X.mNrmMin for X in FiniteCyclicalExample.solution])\n",
-    "plotFuncs(FiniteCyclicalExample.cFunc,mMin,5)"
+    "plot_funcs(FiniteCyclicalExample.cFunc,mMin,5)"
    ]
   },
   {

examples/ConsumptionSaving/example_ConsAggShockModel.ipynb

@@ -73,10 +73,10 @@
     "\n",
     "    # Make a Cobb-Douglas economy for the agents\n",
     "    EconomyExample = CobbDouglasEconomy(agents=[AggShockExample])\n",
-    "    EconomyExample.makeAggShkHist()  # Simulate a history of aggregate shocks\n",
+    "    EconomyExample.make_AggShkHist()  # Simulate a history of aggregate shocks\n",
     "\n",
     "    # Have the consumers inherit relevant objects from the economy\n",
-    "    AggShockExample.getEconomyData(EconomyExample)"
+    "    AggShockExample.get_economy_data(EconomyExample)"
    ]
   },
   {
@@ -136,13 +136,13 @@
     },
     {
      "ename": "NameError",
-     "evalue": "name 'plotFuncs' is not defined",
+     "evalue": "name 'plot_funcs' is not defined",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-5-8f9086566e0b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Aggregate savings as a function of aggregate market resources:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mplotFuncs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEconomyExample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAFunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mEconomyExample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkSS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     print(\n\u001b[1;32m     18\u001b[0m         \u001b[0;34m\"Consumption function at each aggregate market resources gridpoint (in general equilibrium):\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'plotFuncs' is not defined"
+      "\u001b[0;32m<ipython-input-5-8f9086566e0b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Aggregate savings as a function of aggregate market resources:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mplot_funcs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEconomyExample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAFunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mEconomyExample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkSS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     print(\n\u001b[1;32m     18\u001b[0m         \u001b[0;34m\"Consumption function at each aggregate market resources gridpoint (in general equilibrium):\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'plot_funcs' is not defined"
      ]
     }
    ],
@@ -199,8 +199,8 @@
     "    # Make a Cobb-Douglas economy for the agents\n",
     "    MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample])\n",
     "    MrkvEconomyExample.DampingFac = 0.2  # Turn down damping\n",
-    "    MrkvEconomyExample.makeAggShkHist()  # Simulate a history of aggregate shocks\n",
-    "    AggShockMrkvExample.getEconomyData(\n",
+    "    MrkvEconomyExample.make_AggShkHist()  # Simulate a history of aggregate shocks\n",
+    "    AggShockMrkvExample.get_economy_data(\n",
     "        MrkvEconomyExample\n",
     "    )  # Have the consumers inherit relevant objects from the economy"
    ]
@@ -309,8 +309,8 @@
     "        )\n",
     "    ]\n",
     "    KSeconomy.PermGroFacAgg = [1.0, 1.0]\n",
-    "    KSexampleType.getEconomyData(KSeconomy)\n",
-    "    KSeconomy.makeAggShkHist()\n",
+    "    KSexampleType.get_economy_data(KSeconomy)\n",
+    "    KSeconomy.make_AggShkHist()\n",
     "\n",
     "    # Solve the K-S model\n",
     "    t_start = process_time()\n",
@@ -366,8 +366,8 @@
     "    PolyStateEconomy.intercept_prev = StateCount * [0.0]\n",
     "    PolyStateEconomy.update()\n",
     "    PolyStateEconomy.makeAggShkDstn()\n",
-    "    PolyStateEconomy.makeAggShkHist()  # Simulate a history of aggregate shocks\n",
-    "    PolyStateExample.getEconomyData(\n",
+    "    PolyStateEconomy.make_AggShkHist()  # Simulate a history of aggregate shocks\n",
+    "    PolyStateExample.get_economy_data(\n",
     "        PolyStateEconomy\n",
     "    )  # Have the consumers inherit relevant objects from the economy\n",
     "\n",

examples/ConsumptionSaving/example_ConsAggShockModel.py

@@ -36,10 +36,10 @@ def mystr(number):
 
     # Make a Cobb-Douglas economy for the agents
     EconomyExample = CobbDouglasEconomy(agents=[AggShockExample])
-    EconomyExample.makeAggShkHist()  # Simulate a history of aggregate shocks
+    EconomyExample.make_AggShkHist()  # Simulate a history of aggregate shocks
 
     # Have the consumers inherit relevant objects from the economy
-    AggShockExample.getEconomyData(EconomyExample)
+    AggShockExample.get_economy_data(EconomyExample)
 
 if solve_agg_shocks_micro:
     # Solve the microeconomic model for the aggregate shocks example type (and display results)
@@ -103,8 +103,8 @@ def mystr(number):
     # Make a Cobb-Douglas economy for the agents
     MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample])
     MrkvEconomyExample.DampingFac = 0.2  # Turn down damping
-    MrkvEconomyExample.makeAggShkHist()  # Simulate a history of aggregate shocks
-    AggShockMrkvExample.getEconomyData(
+    MrkvEconomyExample.make_AggShkHist()  # Simulate a history of aggregate shocks
+    AggShockMrkvExample.get_economy_data(
         MrkvEconomyExample
     )  # Have the consumers inherit relevant objects from the economy
 
@@ -192,8 +192,8 @@ def mystr(number):
         )
     ]
     KSeconomy.PermGroFacAgg = [1.0, 1.0]
-    KSexampleType.getEconomyData(KSeconomy)
-    KSeconomy.makeAggShkHist()
+    KSexampleType.get_economy_data(KSeconomy)
+    KSeconomy.make_AggShkHist()
 
     # Solve the K-S model
     t_start = process_time()
@@ -242,8 +242,8 @@ def mystr(number):
     PolyStateEconomy.intercept_prev = StateCount * [0.0]
     PolyStateEconomy.update()
     PolyStateEconomy.makeAggShkDstn()
-    PolyStateEconomy.makeAggShkHist()  # Simulate a history of aggregate shocks
-    PolyStateExample.getEconomyData(
+    PolyStateEconomy.make_AggShkHist()  # Simulate a history of aggregate shocks
+    PolyStateExample.get_economy_data(
         PolyStateEconomy
     )  # Have the consumers inherit relevant objects from the economy
 

examples/ConsumptionSaving/example_ConsGenIncProcessModel.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from HARK.utilities import plotFuncs\n",
+    "from HARK.utilities import plot_funcs\n",
     "from time import process_time\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -224,7 +224,7 @@
     "    \"Consumption function for normalized problem (without explicit permanent income):\"\n",
     ")\n",
     "mNrmMin = NormalizedExample.solution[0].mNrmMin\n",
-    "plotFuncs(NormalizedExample.solution[0].cFunc, mNrmMin, mNrmMin + 20)"
+    "plot_funcs(NormalizedExample.solution[0].cFunc, mNrmMin, mNrmMin + 20)"
    ]
   },
   {

examples/ConsumptionSaving/example_ConsGenIncProcessModel.py

@@ -1,5 +1,5 @@
 # %%
-from HARK.utilities import plotFuncs
+from HARK.utilities import plot_funcs
 from time import process_time
 import matplotlib.pyplot as plt
 import numpy as np
@@ -95,7 +95,7 @@ def mystr(number):
     "Consumption function for normalized problem (without explicit permanent income):"
 )
 mNrmMin = NormalizedExample.solution[0].mNrmMin
-plotFuncs(NormalizedExample.solution[0].cFunc, mNrmMin, mNrmMin + 20)
+plot_funcs(NormalizedExample.solution[0].cFunc, mNrmMin, mNrmMin + 20)
 
 # %% [markdown]
 # The "explicit permanent income" solution deviates from the solution to the normalized problem because

examples/ConsumptionSaving/example_ConsIndShock.ipynb

@@ -13,7 +13,7 @@
     "    init_lifecycle,\n",
     "    init_cyclical\n",
     ")\n",
-    "from HARK.utilities import plotFuncsDer, plotFuncs\n",
+    "from HARK.utilities import plot_funcs_der, plot_funcs\n",
     "from time import time"
    ]
   },
@@ -103,7 +103,7 @@
     "# Plot the perfect foresight consumption function\n",
     "print(\"Perfect foresight consumption function:\")\n",
     "mMin = PFexample.solution[0].mNrmMin\n",
-    "plotFuncs(PFexample.cFunc[0], mMin, mMin + 10)"
+    "plot_funcs(PFexample.cFunc[0], mMin, mMin + 10)"
    ]
   },
   {
@@ -218,9 +218,9 @@
    "source": [
     "# Plot the consumption function and MPC for the infinite horizon consumer\n",
     "print(\"Concave consumption function:\")\n",
-    "plotFuncs(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)\n",
+    "plot_funcs(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)\n",
     "print(\"Marginal consumption function:\")\n",
-    "plotFuncsDer(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)"
+    "plot_funcs_der(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)"
    ]
   },
   {
@@ -252,7 +252,7 @@
     "# Compare the consumption functions for the perfect foresight and idiosyncratic\n",
     "# shock types.  Risky income cFunc asymptotically approaches perfect foresight cFunc.\n",
     "print(\"Consumption functions for perfect foresight vs idiosyncratic shocks:\")\n",
-    "plotFuncs(\n",
+    "plot_funcs(\n",
     "    [PFexample.cFunc[0], IndShockExample.cFunc[0]],\n",
     "    IndShockExample.solution[0].mNrmMin,\n",
     "    100,\n",
@@ -268,7 +268,7 @@
     "# Compare the value functions for the two types\n",
     "if IndShockExample.vFuncBool:\n",
     "    print(\"Value functions for perfect foresight vs idiosyncratic shocks:\")\n",
-    "    plotFuncs(\n",
+    "    plot_funcs(\n",
     "        [PFexample.solution[0].vFunc, IndShockExample.solution[0].vFunc],\n",
     "        IndShockExample.solution[0].mNrmMin + 0.5,\n",
     "        10,\n",
@@ -355,7 +355,7 @@
     "mMin = min(\n",
     "    [LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]\n",
     ")\n",
-    "plotFuncs(LifecycleExample.cFunc[: LifecycleExample.T_retire], mMin, 5)"
+    "plot_funcs(LifecycleExample.cFunc[: LifecycleExample.T_retire], mMin, 5)"
    ]
   },
   {
@@ -386,7 +386,7 @@
    "source": [
     "# Plot the consumption functions during retirement\n",
     "print(\"Consumption functions while retired:\")\n",
-    "plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire :], 0, 5)"
+    "plot_funcs(LifecycleExample.cFunc[LifecycleExample.T_retire :], 0, 5)"
    ]
   },
   {
@@ -474,7 +474,7 @@
     "# Plot the consumption functions for the cyclical consumer type\n",
     "print(\"Quarterly consumption functions:\")\n",
     "mMin = min([X.mNrmMin for X in CyclicalExample.solution])\n",
-    "plotFuncs(CyclicalExample.cFunc, mMin, 5)"
+    "plot_funcs(CyclicalExample.cFunc, mMin, 5)"
    ]
   },
   {
@@ -514,7 +514,7 @@
     "print(\"Solving a kinky consumer took \" + mystr(end_time - start_time) + \" seconds.\")\n",
     "KinkyExample.unpack('cFunc')\n",
     "print(\"Kinky consumption function:\")\n",
-    "plotFuncs(KinkyExample.cFunc[0], KinkyExample.solution[0].mNrmMin, 5)"
+    "plot_funcs(KinkyExample.cFunc[0], KinkyExample.solution[0].mNrmMin, 5)"
    ]
   },
   {

examples/ConsumptionSaving/example_ConsIndShock.py

@@ -5,7 +5,7 @@
     init_lifecycle,
     init_cyclical
 )
-from HARK.utilities import plotFuncsDer, plotFuncs
+from HARK.utilities import plot_funcs_der, plot_funcs
 from time import time
 
 mystr = lambda number: "{:.4f}".format(number)
@@ -31,7 +31,7 @@
 # Plot the perfect foresight consumption function
 print("Perfect foresight consumption function:")
 mMin = PFexample.solution[0].mNrmMin
-plotFuncs(PFexample.cFunc[0], mMin, mMin + 10)
+plot_funcs(PFexample.cFunc[0], mMin, mMin + 10)
 
 if do_simulation:
     PFexample.T_sim = 120  # Set number of simulation periods
@@ -55,14 +55,14 @@
 
 # Plot the consumption function and MPC for the infinite horizon consumer
 print("Concave consumption function:")
-plotFuncs(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)
+plot_funcs(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)
 print("Marginal consumption function:")
-plotFuncsDer(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)
+plot_funcs_der(IndShockExample.cFunc[0], IndShockExample.solution[0].mNrmMin, 5)
 
 # Compare the consumption functions for the perfect foresight and idiosyncratic
 # shock types.  Risky income cFunc asymptotically approaches perfect foresight cFunc.
 print("Consumption functions for perfect foresight vs idiosyncratic shocks:")
-plotFuncs(
+plot_funcs(
     [PFexample.cFunc[0], IndShockExample.cFunc[0]],
     IndShockExample.solution[0].mNrmMin,
     100,
@@ -71,7 +71,7 @@
 # Compare the value functions for the two types
 if IndShockExample.vFuncBool:
     print("Value functions for perfect foresight vs idiosyncratic shocks:")
-    plotFuncs(
+    plot_funcs(
         [PFexample.solution[0].vFunc, IndShockExample.solution[0].vFunc],
         IndShockExample.solution[0].mNrmMin + 0.5,
         10,
@@ -102,11 +102,11 @@
 mMin = min(
     [LifecycleExample.solution[t].mNrmMin for t in range(LifecycleExample.T_cycle)]
 )
-plotFuncs(LifecycleExample.cFunc[: LifecycleExample.T_retire], mMin, 5)
+plot_funcs(LifecycleExample.cFunc[: LifecycleExample.T_retire], mMin, 5)
 
 # Plot the consumption functions during retirement
 print("Consumption functions while retired:")
-plotFuncs(LifecycleExample.cFunc[LifecycleExample.T_retire :], 0, 5)
+plot_funcs(LifecycleExample.cFunc[LifecycleExample.T_retire :], 0, 5)
 
 # Simulate some data; results stored in mNrmNow_hist, cNrmNow_hist, pLvlNow_hist, and t_age_hist
 if do_simulation:
@@ -129,7 +129,7 @@
 # Plot the consumption functions for the cyclical consumer type
 print("Quarterly consumption functions:")
 mMin = min([X.mNrmMin for X in CyclicalExample.solution])
-plotFuncs(CyclicalExample.cFunc, mMin, 5)
+plot_funcs(CyclicalExample.cFunc, mMin, 5)
 
 # Simulate some data; results stored in cHist, mHist, bHist, aHist, MPChist, and pHist
 if do_simulation:
@@ -148,7 +148,7 @@
 print("Solving a kinky consumer took " + mystr(end_time - start_time) + " seconds.")
 KinkyExample.unpack('cFunc')
 print("Kinky consumption function:")
-plotFuncs(KinkyExample.cFunc[0], KinkyExample.solution[0].mNrmMin, 5)
+plot_funcs(KinkyExample.cFunc[0], KinkyExample.solution[0].mNrmMin, 5)
 
 if do_simulation:
     KinkyExample.T_sim = 120

examples/ConsumptionSaving/example_ConsMarkovModel.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 # %%
-from HARK.utilities import plotFuncs
+from HARK.utilities import plot_funcs
 from time import process_time
 from copy import deepcopy, copy
 import numpy as np
@@ -132,7 +132,7 @@
 ]
 
 # %% [markdown]
-# Note that $\texttt{MarkovConsumerType}$ currently has no method to automatically construct a valid IncomeDstn - $\texttt{IncomeDstn}$ is manually constructed in each case. Writing a method to supersede $\texttt{IndShockConsumerType.updateIncomeProcess}$ for the “Markov model” would be a welcome contribution!
+# Note that $\texttt{MarkovConsumerType}$ currently has no method to automatically construct a valid IncomeDstn - $\texttt{IncomeDstn}$ is manually constructed in each case. Writing a method to supersede $\texttt{IndShockConsumerType.update_income_process}$ for the “Markov model” would be a welcome contribution!
 
 # %%
 # Interest factor, permanent growth rates, and survival probabilities are constant arrays
@@ -153,10 +153,10 @@
     + " seconds."
 )
 print("Consumption functions for each discrete state:")
-plotFuncs(SerialUnemploymentExample.solution[0].cFunc, 0, 50)
+plot_funcs(SerialUnemploymentExample.solution[0].cFunc, 0, 50)
 if SerialUnemploymentExample.vFuncBool:
     print("Value functions for each discrete state:")
-    plotFuncs(SerialUnemploymentExample.solution[0].vFunc, 5, 50)
+    plot_funcs(SerialUnemploymentExample.solution[0].vFunc, 5, 50)
 
 # %%
 # Simulate some data; results stored in cHist, mNrmNow_hist, cNrmNow_hist, and MrkvNow_hist
@@ -241,7 +241,7 @@
 )
 print("Consumption functions for each discrete state:")
 mNrmMin = np.min([ImmunityExample.solution[0].mNrmMin[j] for j in range(StateCount)])
-plotFuncs(ImmunityExample.solution[0].cFunc, mNrmMin, 10)
+plot_funcs(ImmunityExample.solution[0].cFunc, mNrmMin, 10)
 
 # %% [markdown]
 # ### 3. Serial permanent income growth
@@ -305,7 +305,7 @@
     + " seconds."
 )
 print("Consumption functions for each discrete state:")
-plotFuncs(SerialGroExample.solution[0].cFunc, 0, 10)
+plot_funcs(SerialGroExample.solution[0].cFunc, 0, 10)
 
 # %%
 # Make a consumer with serially correlated interest factors
@@ -328,4 +328,4 @@
     + " seconds."
 )
 print("Consumption functions for each discrete state:")
-plotFuncs(SerialRExample.solution[0].cFunc, 0, 10)
+plot_funcs(SerialRExample.solution[0].cFunc, 0, 10)