Merge pull request #999 from MridulS/futures

clean up future imports

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -4,10 +4,6 @@
 basic solver.  Also includes a subclass of Market called CobbDouglas economy,
 used for solving "macroeconomic" models with aggregate shocks.
 """
-from __future__ import division, print_function
-from __future__ import absolute_import
-from builtins import str
-from builtins import range
 import numpy as np
 import scipy.stats as stats
 from HARK.interpolation import (

HARK/ConsumptionSaving/ConsGenIncProcessModel.py

@@ -4,10 +4,6 @@
 ConsIndShockModel by explicitly tracking persistent income as a state variable,
 and allows (log) persistent income to follow an AR1 process rather than random walk.
 """
-from __future__ import division, print_function
-from __future__ import absolute_import
-from builtins import str
-from builtins import range
 from copy import deepcopy
 import numpy as np
 from HARK import AgentType, MetricObject, make_one_period_oo_solver

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -12,10 +12,6 @@
 See NARK https://HARK.githhub.io/Documentation/NARK for information on variable naming conventions.
 See HARK documentation for mathematical descriptions of the models being solved.
 """
-
-from builtins import str
-from builtins import range
-from builtins import object
 from copy import copy, deepcopy
 import numpy as np
 from scipy.optimize import newton

HARK/ConsumptionSaving/ConsMarkovModel.py

@@ -4,9 +4,6 @@
 include a Markov state; the interest factor, permanent growth factor, and income
 distribution can vary with the discrete state.
 """
-from __future__ import division, print_function
-from __future__ import absolute_import
-from builtins import range
 from copy import deepcopy
 import numpy as np
 from HARK import AgentType

HARK/ConsumptionSaving/ConsMedModel.py

@@ -1,10 +1,6 @@
 """
 Consumption-saving models that also include medical spending.
 """
-from __future__ import division, print_function
-from __future__ import absolute_import
-from builtins import str
-from builtins import range
 import numpy as np
 from scipy.optimize import brentq
 from HARK import  AgentType, MetricObject, make_one_period_oo_solver

HARK/ConsumptionSaving/ConsPrefShockModel.py

@@ -6,10 +6,6 @@
 2) A combination of (1) and ConsKinkedR, demonstrating how to construct a new model
    by inheriting from multiple classes.
 """
-from __future__ import division, print_function
-from __future__ import absolute_import
-from builtins import str
-from builtins import range
 import numpy as np
 from HARK import make_one_period_oo_solver
 from HARK.distribution import MeanOneLogNormal

HARK/ConsumptionSaving/ConsRepAgentModel.py

@@ -4,10 +4,6 @@
 take a heterogeneous agents approach.  In RA models, all attributes are either
 time invariant or exist on a short cycle; models must be infinite horizon.
 """
-from __future__ import division, print_function
-from __future__ import absolute_import
-from builtins import str
-from builtins import range
 import numpy as np
 from HARK.interpolation import LinearInterp, MargValueFuncCRRA
 from HARK.distribution import (MarkovProcess, Uniform)

HARK/ConsumptionSaving/TractableBufferStockModel.py

@@ -20,9 +20,6 @@
 Despite the non-standard solution method, the iterative process can be embedded
 in the HARK framework, as shown below.
 """
-from __future__ import division, print_function
-from __future__ import absolute_import
-from builtins import str
 import numpy as np
 
 # Import the HARK library.

HARK/core.py

@@ -6,12 +6,6 @@
 model adds an additional layer, endogenizing some of the inputs to the micro
 problem by finding a general equilibrium dynamic rule.
 """
-from __future__ import print_function, division
-from __future__ import absolute_import
-
-from builtins import str
-from builtins import range
-from builtins import object
 import sys
 import os
 from distutils.dir_util import copy_tree

HARK/interpolation.py

@@ -6,9 +6,6 @@
 convergence.  The interpolator classes currently in this module inherit their
 distance method from MetricObject.
 """
-from __future__ import division, print_function
-from __future__ import absolute_import
-from builtins import range
 import numpy as np
 from .core import MetricObject
 from copy import deepcopy

HARK/simulation.py

@@ -2,6 +2,5 @@
 Currently empty. Will be used for future simulation handling code.
 """
 
-from __future__ import division
 import warnings  # A library for runtime warnings
 import numpy as np  # Numerical Python

HARK/tests/test_HARKutilities.py

@@ -1,9 +1,6 @@
 """
 This file implements unit tests to check HARK/utilities.py
 """
-from __future__ import print_function, division
-from __future__ import absolute_import
-
 import HARK.utilities
 
 # Bring in modules we need

HARK/tests/test_discrete.py

@@ -1,9 +1,6 @@
 """
 This file implements unit tests to check discrete choice functions
 """
-from __future__ import print_function, division
-from __future__ import absolute_import
-
 from HARK import interpolation
 
 # Bring in modules we need

HARK/utilities.py

@@ -3,12 +3,6 @@
 continuous distributions with discrete ones, utility functions (and their
 derivatives), manipulation of discrete distributions, and basic plotting tools.
 """
-
-from __future__ import division  # Import Python 3.x division function
-from __future__ import print_function
-from builtins import str
-from builtins import range
-from builtins import object
 import functools
 
 import numpy as np  # Python's numeric library, abbreviated "np"

examples/Journeys/Journey_1_param.py

@@ -1,7 +1,6 @@
 '''
 Set if parameters for the first journey
 '''
-from __future__ import division, print_function
 from copy import copy
 import numpy as np
 

examples/Journeys/Quickstart_tutorial/Jounery_1_param.py

@@ -1,7 +1,6 @@
 '''
 Set if parameters for the first journey
 '''
-from __future__ import division, print_function
 from copy import copy
 import numpy as np
 

examples/LifecycleModel/EstimationParameters.py

@@ -2,8 +2,6 @@
 Specifies the full set of calibrated values required to estimate the SolvingMicroDSOPs
 model.  The empirical data is stored in a separate csv file and is loaded in SetupSCFdata.
 '''
-from __future__ import print_function
-
 # ---------------------------------------------------------------------------------
 # - Define all of the model parameters for SolvingMicroDSOPs and ConsumerExamples -
 # ---------------------------------------------------------------------------------