Updated files to run for sphinx-docs. Added absolute path calls to ma…

…ny files.

.gitignore

@@ -54,6 +54,7 @@ coverage.xml
 
 # Sphinx documentation
 docs/_build/
+docs/generated/
 
 # PyBuilder
 target/

ConsumptionSavingModel/ConsIndShockModel.py

@@ -13,7 +13,10 @@
 See HARK documentation for mathematical descriptions of the models being solved.
 '''
 import sys 
-sys.path.insert(0,'../')
+import os
+sys.path.insert(0, os.path.abspath('../'))
+sys.path.insert(0, os.path.abspath('./'))
+#sys.path.insert(0,'../')
 
 from copy import copy, deepcopy
 import numpy as np
@@ -2368,4 +2371,4 @@ def constructAssetsGrid(parameters):
         KinkyExample.initializeSim()
         KinkyExample.simConsHistory()
 
-
+

FashionVictim/FashionVictimModel.py

@@ -6,7 +6,10 @@
 '''
 
 import sys 
-sys.path.insert(0,'../')
+#sys.path.insert(0,'../')
+import os
+sys.path.insert(0, os.path.abspath('../'))
+
 from HARKcore import AgentType, Solution, NullFunc
 from HARKinterpolation import LinearInterp
 from HARKutilities import approxUniform, plotFuncs
@@ -477,4 +480,4 @@ def calcFashionEvoFunc(pPop):
 
     TestMarket.solve()
     plt.plot(TestMarket.pPop_hist)
-    plt.show()
+    plt.show()

SolvingMicroDSOPs/SetupSCFdata.py

@@ -6,7 +6,10 @@
 # Import the HARK library.  The assumption is that this code is in a folder
 # contained in the HARK folder. 
 import sys 
-sys.path.insert(0,'../')
+#sys.path.insert(0,'../')
+import os
+sys.path.insert(0, os.path.abspath('../'))
+
 
 # The following libraries are part of the standard python distribution
 import numpy as np                   # Numerical Python
@@ -17,10 +20,10 @@
 from HARKutilities import warnings
 
 # Set the path to the empirical data:
-scf_data_path = './'
+scf_data_path = data_location = os.path.dirname(os.path.abspath(__file__))  # os.path.abspath('./')   #'./'
 
 # Open the file handle and create a reader object and a csv header
-infile = open(scf_data_path + 'SCFdata.csv', 'rb')  
+infile = open(scf_data_path + '/SCFdata.csv', 'rb')  
 csv_reader = csv.reader(infile)
 data_csv_header = csv_reader.next()
 
@@ -62,4 +65,4 @@
     print("This module is imported by StructEstimation, providing data for")
     print("the example estimation.  Please see that module if you want more")
     print("interesting output.")
-
+

SolvingMicroDSOPs/StructEstimation.py

@@ -11,8 +11,12 @@
 # Import the HARK library.  The assumption is that this code is in a folder
 # contained in the HARK folder. 
 import sys 
-sys.path.insert(0,'../')
-sys.path.insert(0,'../ConsumptionSavingModel')
+#sys.path.insert(0,'../')
+#sys.path.insert(0,'../ConsumptionSavingModel')
+import os
+sys.path.insert(0, os.path.abspath('../'))
+sys.path.insert(0, os.path.abspath('../ConsumptionSavingModel'))
+
 
 import EstimationParameters as Params           # Parameters for the consumer type and the estimation
 import ConsIndShockModel as Model               # The consumption-saving micro model

cstwMPC/SetupParamsCSTW.py

@@ -4,6 +4,7 @@
 import numpy as np
 import csv
 from copy import copy, deepcopy
+import os
 
 # Choose percentiles of the data to match and which estimation to run
 do_lifecycle = False          # Use lifecycle model if True, perpetual youth if False
@@ -59,7 +60,8 @@
 PermShkStd = np.ndarray.tolist(PermShkStd)
 
 # Import survival probabilities from SSA data
-f = open('USactuarial.txt','r')
+data_location = os.path.dirname(os.path.abspath(__file__))
+f = open(data_location + '/' + 'USactuarial.txt','r')
 actuarial_reader = csv.reader(f,delimiter='\t')
 raw_actuarial = list(actuarial_reader)
 base_death_probs = []
@@ -68,7 +70,7 @@
 f.close
 
 # Import adjustments for education and apply them to the base mortality rates
-f = open('EducMortAdj.txt','r')
+f = open(data_location + '/' + 'EducMortAdj.txt','r')
 adjustment_reader = csv.reader(f,delimiter=' ')
 raw_adjustments = list(adjustment_reader)
 d_death_probs = []
@@ -184,7 +186,7 @@
 intercept_prev = 0.0               # Initial intercept of kNextFunc (aggregate shocks model)
 
 # Import the SCF wealth data
-f = open(SCF_data_file,'r')
+f = open(data_location + '/' + SCF_data_file,'r')
 SCF_reader = csv.reader(f,delimiter='\t')
 SCF_raw = list(SCF_reader)
 SCF_wealth = np.zeros(len(SCF_raw)) + np.nan
@@ -291,4 +293,4 @@
     print("Sorry, SetupParamsCSTW doesn't actually do anything on its own.")
     print("This module is imported by cstwMPC, providing data and calibrated")
     print("parameters for the various estimations.  Please see that module if")
-    print("you want more interesting output.")
+    print("you want more interesting output.")

cstwMPC/cstwMPC.py

@@ -9,8 +9,14 @@
 # Import the HARK library.  The assumption is that this code is in a folder
 # contained in the HARK folder. Also import ConsumptionSavingModel
 import sys 
-sys.path.insert(0,'../')
-sys.path.insert(0,'../ConsumptionSavingModel')
+import os
+#sys.path.insert(0,'../')
+#sys.path.insert(0,'../ConsumptionSavingModel')
+
+sys.path.insert(0, os.path.abspath('../'))
+sys.path.insert(0, os.path.abspath('../ConsumptionSavingModel'))
+
+
 
 import numpy as np
 from copy import deepcopy
@@ -857,4 +863,4 @@ def betaDistObjective(nabla):
         agg_shock_weights = np.concatenate((np.zeros(200),np.ones(Params.sim_periods_agg_shocks-200)))
         agg_shock_weights = agg_shock_weights/np.sum(agg_shock_weights)
         makeCSTWstats(beta_agg,nabla_agg,agg_shocks_type_list,agg_shock_weights)
-
+