fix examples

HARK/ConsumptionSaving/ConsLaborModel.py

@@ -510,7 +510,7 @@ def getControls(self):
             )  # Assign labor supply
         self.controls['cNrm'] = cNrmNow
         self.MPCnow = MPCnow
-        self.controls['LbrNow'] = LbrNow
+        self.controls['Lbr'] = LbrNow
 
     def getPostStates(self):
         """
@@ -530,7 +530,7 @@ def getPostStates(self):
             these = t == self.t_cycle
             mNrmNow[these] = (
                 self.state_now['bNrm'][these]
-                + self.controls['LbrNow'][these] * self.shocks['TranShk'][these]
+                + self.controls['Lbr'][these] * self.shocks['TranShk'][these]
             )  # mNrm = bNrm + yNrm
             aNrmNow[these] = mNrmNow[these] - self.controls['cNrm'][these]  # aNrm = mNrm - cNrm
         self.state_now['mNrm'] = mNrmNow

examples/ConsIndShockModel/KinkedRconsumerType.py

@@ -9,9 +9,9 @@
 #       format_version: '1.2'
 #       jupytext_version: 1.2.4
 #   kernelspec:
-#     display_name: Python 3
+#     display_name: econ-ark-3.8
 #     language: python
-#     name: python3
+#     name: econ-ark-3.8
 # ---
 
 # %% [markdown]
@@ -214,7 +214,7 @@
 # Now let's plot the distribution of (normalized) assets $a_t$ for the current population, after simulating for $500$ periods; this should be fairly close to the long run distribution:
 
 # %%
-plt.plot(np.sort(KinkyExample.state_now['aNrmNow']), np.linspace(0.0, 1.0, KinkyExample.AgentCount))
+plt.plot(np.sort(KinkyExample.state_now['aNrm']), np.linspace(0.0, 1.0, KinkyExample.AgentCount))
 plt.xlabel("End-of-period assets")
 plt.ylabel("Cumulative distribution")
 plt.ylim(-0.01, 1.01)

examples/ConsIndShockModel/PerfForesightConsumerType.py

@@ -9,9 +9,9 @@
 #       format_version: '1.2'
 #       jupytext_version: 1.2.4
 #   kernelspec:
-#     display_name: Python 3.7 econ-ark
+#     display_name: econ-ark-3.8
 #     language: python
-#     name: econ-ark
+#     name: econ-ark-3.8
 # ---
 
 # %% [markdown]
@@ -285,10 +285,12 @@
 # %% pycharm= {"name": "#%%\n"}
 PFexample.initializeSim()
 PFexample.simulate(80)
-PFexample.state_prev['aNrmNow'] += -5.0  # Adjust all simulated consumers' assets downward by 5
+PFexample.state_prev['aNrm'] += -5.0  # Adjust all simulated consumers' assets downward by 5
 PFexample.simulate(40)
 
 plt.plot(np.mean(PFexample.history['mNrm'], axis=1))
 plt.xlabel("Time")
 plt.ylabel("Mean normalized market resources")
 plt.show()
+
+# %%

examples/ConsumptionSaving/example_ConsAggShockModel.py

@@ -97,7 +97,7 @@ def mystr(number):
 if solve_markov_micro or solve_markov_market or solve_krusell_smith:
     # Make a Markov aggregate shocks consumer type
     AggShockMrkvExample = AggShockMarkovConsumerType()
-    AggShockMrkvExample.IncomeDstn[0] = 2 * [AggShockMrkvExample.IncomeDstn[0]]
+    AggShockMrkvExample.IncShkDstn[0] = 2 * [AggShockMrkvExample.IncShkDstn[0]]
     AggShockMrkvExample.cycles = 0
 
     # Make a Cobb-Douglas economy for the agents
@@ -167,7 +167,7 @@ def mystr(number):
     # Make a Krusell-Smith agent type
     # NOTE: These agents aren't exactly like KS, as they don't have serially correlated unemployment
     KSexampleType = deepcopy(AggShockMrkvExample)
-    KSexampleType.IncomeDstn[0] = [
+    KSexampleType.IncShkDstn[0] = [
         DiscreteDistribution(
             np.array([0.96, 0.04]),
             [ np.array([1.0, 1.0]), np.array([1.0 / 0.96, 0.0])]
@@ -228,7 +228,7 @@ def mystr(number):
     PolyStateExample = AggShockMarkovConsumerType()
     PolyStateExample.MrkvArray = PolyMrkvArray
     PolyStateExample.PermGroFacAgg = PermGroFacAgg
-    PolyStateExample.IncomeDstn[0] = StateCount * [PolyStateExample.IncomeDstn[0]]
+    PolyStateExample.IncShkDstn[0] = StateCount * [PolyStateExample.IncShkDstn[0]]
     PolyStateExample.cycles = 0
 
     # Make a Cobb-Douglas economy for the agents
@@ -263,3 +263,5 @@ def mystr(number):
         + str(t_end - t_start)
         + " seconds."
     )
+
+

examples/ConsumptionSaving/example_ConsLaborModel.py

@@ -10,9 +10,9 @@
 #       format_version: '1.4'
 #       jupytext_version: 1.2.4
 #   kernelspec:
-#     display_name: Python 3
+#     display_name: econ-ark-3.8
 #     language: python
-#     name: python3
+#     name: econ-ark-3.8
 #   language_info:
 #     codemirror_mode:
 #       name: ipython
@@ -22,7 +22,7 @@
 #     name: python
 #     nbconvert_exporter: python
 #     pygments_lexer: ipython3
-#     version: 3.6.9
+#     version: 3.8.7
 # ---
 
 from HARK.ConsumptionSaving.ConsLaborModel import (
@@ -129,7 +129,7 @@
 if do_simulation:
     t_start = process_time()
     LaborIntMargExample.T_sim = 120  # Set number of simulation periods
-    LaborIntMargExample.track_vars = ["bNrmNow", 'cNrm']
+    LaborIntMargExample.track_vars = ["bNrm", 'cNrm']
     LaborIntMargExample.initializeSim()
     LaborIntMargExample.simulate()
     t_end = process_time()
@@ -146,7 +146,7 @@
     N = LaborIntMargExample.AgentCount
     CDF = np.linspace(0.0, 1, N)
 
-    plt.plot(np.sort(LaborIntMargExample.cNrmNow), CDF)
+    plt.plot(np.sort(LaborIntMargExample.controls['cNrm']), CDF)
     plt.xlabel(
         "Consumption cNrm in " + str(LaborIntMargExample.T_sim) + "th simulated period"
     )
@@ -155,7 +155,7 @@
     plt.ylim(0.0, 1.0)
     plt.show()
 
-    plt.plot(np.sort(LaborIntMargExample.LbrNow), CDF)
+    plt.plot(np.sort(LaborIntMargExample.controls['Lbr']), CDF)
     plt.xlabel(
         "Labor supply Lbr in " + str(LaborIntMargExample.T_sim) + "th simulated period"
     )
@@ -164,7 +164,7 @@
     plt.ylim(0.0, 1.0)
     plt.show()
 
-    plt.plot(np.sort(LaborIntMargExample.state_now['aNrmNow']), CDF)
+    plt.plot(np.sort(LaborIntMargExample.state_now['aNrm']), CDF)
     plt.xlabel(
         "End-of-period assets aNrm in "
         + str(LaborIntMargExample.T_sim)