Merge branch 'master' into i1105

Documentation/CHANGELOG.md

@@ -41,6 +41,7 @@ Release Date: TBD
 * Adds `HARK.distribution.expected` alias for `DiscreteDistribution.expected`. [#1156](https://github.com/econ-ark/HARK/pull/1156)
 * Renames attributes in `DiscreteDistribution`: `X` to `atoms` and `pmf` to `pmv`. [#1164](https://github.com/econ-ark/HARK/pull/1164), [#1051](https://github.com/econ-ark/HARK/pull/1151), [#1159](https://github.com/econ-ark/HARK/pull/1159). 
 * Remove or replace automated tests that depend on brittle simulation results. [#1148](https://github.com/econ-ark/HARK/pull/1148)
+* Updates asset grid constructor from `ConsIndShockModel.py` to allow for linearly-spaced grids when `aXtraNestFac == -1`. [#1172](https://github.com/econ-ark/HARK/pull/1172)
 
 ### 0.12.0
 

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -32,13 +32,13 @@
 from HARK.datasets.SCF.WealthIncomeDist.SCFDistTools import income_wealth_dists_from_scf
 from HARK.distribution import (
     DiscreteDistribution,
-    expected,
     IndexDistribution,
     Lognormal,
     MeanOneLogNormal,
     Uniform,
     add_discrete_outcome_constant_mean,
     combine_indep_dstns,
+    expected,
 )
 from HARK.interpolation import CubicHermiteInterp as CubicInterp
 from HARK.interpolation import (
@@ -57,7 +57,7 @@
     CRRAutilityP_inv,
     CRRAutilityP_invP,
     CRRAutilityPP,
-    make_grid_exp_mult,
+    construct_assets_grid,
 )
 from scipy.optimize import newton
 
@@ -3178,69 +3178,6 @@ def apply_flat_income_tax(
     return IncShkDstn_new
 
 
-# =======================================================
-# ================ Other useful functions ===============
-# =======================================================
-
-
-def construct_assets_grid(parameters):
-    """
-    Constructs the base grid of post-decision states, representing end-of-period
-    assets above the absolute minimum.
-
-    All parameters are passed as attributes of the single input parameters.  The
-    input can be an instance of a ConsumerType, or a custom Parameters class.
-
-    Parameters
-    ----------
-    aXtraMin:                  float
-        Minimum value for the a-grid
-    aXtraMax:                  float
-        Maximum value for the a-grid
-    aXtraCount:                 int
-        Size of the a-grid
-    aXtraExtra:                [float]
-        Extra values for the a-grid.
-    exp_nest:               int
-        Level of nesting for the exponentially spaced grid
-
-    Returns
-    -------
-    aXtraGrid:     np.ndarray
-        Base array of values for the post-decision-state grid.
-    """
-    # Unpack the parameters
-    aXtraMin = parameters.aXtraMin
-    aXtraMax = parameters.aXtraMax
-    aXtraCount = parameters.aXtraCount
-    aXtraExtra = parameters.aXtraExtra
-    grid_type = "exp_mult"
-    exp_nest = parameters.aXtraNestFac
-
-    # Set up post decision state grid:
-    aXtraGrid = None
-    if grid_type == "linear":
-        aXtraGrid = np.linspace(aXtraMin, aXtraMax, aXtraCount)
-    elif grid_type == "exp_mult":
-        aXtraGrid = make_grid_exp_mult(
-            ming=aXtraMin, maxg=aXtraMax, ng=aXtraCount, timestonest=exp_nest
-        )
-    else:
-        raise Exception(
-            "grid_type not recognized in __init__."
-            + "Please ensure grid_type is 'linear' or 'exp_mult'"
-        )
-
-    # Add in additional points for the grid:
-    for a in aXtraExtra:
-        if a is not None:
-            if a not in aXtraGrid:
-                j = aXtraGrid.searchsorted(a)
-                aXtraGrid = np.insert(aXtraGrid, j, a)
-
-    return aXtraGrid
-
-
 # Make a dictionary to specify a lifecycle consumer with a finite horizon
 
 # Main calibration characteristics

HARK/tests/test_HARKutilities.py

@@ -1,10 +1,11 @@
 """
 This file implements unit tests to check HARK/utilities.py
 """
-import HARK.utilities
-
 # Bring in modules we need
 import unittest
+from types import SimpleNamespace
+
+import HARK.utilities
 import numpy as np
 
 
@@ -64,3 +65,22 @@ def test_CRRAutilityPPPP(self):
         self.derivative_func_comparison(
             HARK.utilities.CRRAutilityPPPP, HARK.utilities.CRRAutilityPPP
         )
+
+    def test_asset_grid(self):
+        # test linear asset grid
+
+        params = {
+            "aXtraMin": 0.0,
+            "aXtraMax": 1.0,
+            "aXtraCount": 5,
+            "aXtraExtra": [None],
+            "aXtraNestFac": -1,
+        }
+
+        params = SimpleNamespace(**params)
+
+        aXtraGrid = HARK.utilities.construct_assets_grid(params)
+
+        test = np.unique(np.diff(aXtraGrid).round(decimals=3))
+
+        self.assertEqual(test.size, 1)

HARK/utilities.py

@@ -6,6 +6,7 @@
 import cProfile
 import functools
 import pstats
+import warnings
 
 import numpy as np  # Python's numeric library, abbreviated "np"
 
@@ -16,17 +17,16 @@
 #     exception_type, value, traceback = sys.exc_info()
 #     raise ImportError('HARK must be used in a graphical environment.', exception_type, value, traceback)
 from scipy.interpolate import interp1d
-import warnings
 
 
 def memoize(obj):
     """
-   A decorator to (potentially) make functions more efficient.
+    A decorator to (potentially) make functions more efficient.
 
-   With this decorator, functions will "remember" if they have been evaluated with given inputs
-   before.  If they have, they will "remember" the outputs that have already been calculated
-   for those inputs, rather than calculating them again.
-   """
+    With this decorator, functions will "remember" if they have been evaluated with given inputs
+    before.  If they have, they will "remember" the outputs that have already been calculated
+    for those inputs, rather than calculating them again.
+    """
     cache = obj._cache = {}
 
     @functools.wraps(obj)
@@ -143,8 +143,8 @@ def CRRAutility(c, gam):
 
 def uFunc_CRRA_stone_geary(c, CRRA, stone_geary):
     """
-    Evaluates Stone-Geary version of a constant relative risk aversion (CRRA) 
-    utility of consumption c wiht given risk aversion parameter CRRA and 
+    Evaluates Stone-Geary version of a constant relative risk aversion (CRRA)
+    utility of consumption c wiht given risk aversion parameter CRRA and
     Stone-Geary intercept parameter stone_geary
 
     Parameters
@@ -154,7 +154,7 @@ def uFunc_CRRA_stone_geary(c, CRRA, stone_geary):
     CRRA : float
         Relative risk aversion
     stone_geary : float
-        Intercept in Stone-Geary utility 
+        Intercept in Stone-Geary utility
     Returns
     -------
     (unnamed) : float
@@ -163,7 +163,7 @@ def uFunc_CRRA_stone_geary(c, CRRA, stone_geary):
     Tests
     -----
     Test a value which should pass:
-    >>> c, CRRA, stone_geary = 1.0, 2.0, 0.0    
+    >>> c, CRRA, stone_geary = 1.0, 2.0, 0.0
     >>> utility(c=c, CRRA=CRRA, stone_geary=stone_geary )
     -1.0
     """
@@ -175,8 +175,8 @@ def uFunc_CRRA_stone_geary(c, CRRA, stone_geary):
 
 def uPFunc_CRRA_stone_geary(c, CRRA, stone_geary):
     """
-    Marginal utility of Stone-Geary version of a constant relative risk aversion (CRRA) 
-    utility of consumption c wiht given risk aversion parameter CRRA and 
+    Marginal utility of Stone-Geary version of a constant relative risk aversion (CRRA)
+    utility of consumption c wiht given risk aversion parameter CRRA and
     Stone-Geary intercept parameter stone_geary
 
     Parameters
@@ -186,7 +186,7 @@ def uPFunc_CRRA_stone_geary(c, CRRA, stone_geary):
     CRRA : float
         Relative risk aversion
     stone_geary : float
-        Intercept in Stone-Geary utility 
+        Intercept in Stone-Geary utility
     Returns
     -------
     (unnamed) : float
@@ -198,7 +198,7 @@ def uPFunc_CRRA_stone_geary(c, CRRA, stone_geary):
 
 def uPPFunc_CRRA_stone_geary(c, CRRA, stone_geary):
     """
-    Marginal marginal utility of Stone-Geary version of a CRRA utilty function 
+    Marginal marginal utility of Stone-Geary version of a CRRA utilty function
     with risk aversion parameter CRRA and Stone-Geary intercept parameter stone_geary
 
     Parameters
@@ -208,7 +208,7 @@ def uPPFunc_CRRA_stone_geary(c, CRRA, stone_geary):
     CRRA : float
         Relative risk aversion
     stone_geary : float
-        Intercept in Stone-Geary utility 
+        Intercept in Stone-Geary utility
     Returns
     -------
     (unnamed) : float
@@ -239,7 +239,7 @@ def CRRAutilityP(c, gam):
     if gam == 1:
         return 1 / c
 
-    return c ** -gam
+    return c**-gam
 
 
 def CRRAutilityPP(c, gam):
@@ -468,7 +468,7 @@ def CARAutilityPPP(c, alpha):
     (unnamed): float
         Marginal marginal marginal utility
     """
-    return alpha ** 2.0 * np.exp(-alpha * c)
+    return alpha**2.0 * np.exp(-alpha * c)
 
 
 def CARAutility_inv(u, alpha):
@@ -531,6 +531,67 @@ def CARAutility_invP(u, alpha):
     return 1.0 / (alpha * (1.0 - u))
 
 
+# =======================================================
+# ================ Other useful functions ===============
+# =======================================================
+
+
+def construct_assets_grid(parameters):
+    """
+    Constructs the base grid of post-decision states, representing end-of-period
+    assets above the absolute minimum.
+
+    All parameters are passed as attributes of the single input parameters.  The
+    input can be an instance of a ConsumerType, or a custom Parameters class.
+
+    Parameters
+    ----------
+    aXtraMin:                  float
+        Minimum value for the a-grid
+    aXtraMax:                  float
+        Maximum value for the a-grid
+    aXtraCount:                 int
+        Size of the a-grid
+    aXtraExtra:                [float]
+        Extra values for the a-grid.
+    exp_nest:               int
+        Level of nesting for the exponentially spaced grid.
+        If -1, the grid is linearly spaced.
+
+    Returns
+    -------
+    aXtraGrid:     np.ndarray
+        Base array of values for the post-decision-state grid.
+    """
+    # Unpack the parameters
+    aXtraMin = parameters.aXtraMin
+    aXtraMax = parameters.aXtraMax
+    aXtraCount = parameters.aXtraCount
+    aXtraExtra = parameters.aXtraExtra
+    exp_nest = parameters.aXtraNestFac
+
+    # Set up post decision state grid:
+    if exp_nest == -1:
+        aXtraGrid = np.linspace(aXtraMin, aXtraMax, aXtraCount)
+    elif exp_nest >= 0:
+        aXtraGrid = make_grid_exp_mult(
+            ming=aXtraMin, maxg=aXtraMax, ng=aXtraCount, timestonest=exp_nest
+        )
+    else:
+        raise ValueError(
+            "aXtraNestFac not recognized in __init__."
+            + "Please ensure aXtraNestFac is either -1 or a positive integer."
+        )
+
+    # Add in additional points for the grid:
+    for a in aXtraExtra:
+        if a is not None and a not in aXtraGrid:
+            j = aXtraGrid.searchsorted(a)
+            aXtraGrid = np.insert(aXtraGrid, j, a)
+
+    return aXtraGrid
+
+
 # ==============================================================================
 # ============== Functions for generating state space grids  ===================
 # ==============================================================================
@@ -916,7 +977,7 @@ def plot_funcs_der(functions, bottom, top, N=1000, legend_kwds=None):
 
 
 def determine_platform():
-    """ Untility function to return the platform currenlty in use.
+    """Untility function to return the platform currenlty in use.
 
     Returns
     ---------
@@ -942,7 +1003,7 @@ def determine_platform():
 
 
 def test_latex_installation(pf):
-    """ Test to check if latex is installed on the machine.
+    """Test to check if latex is installed on the machine.
 
     Parameters
     -----------
@@ -988,7 +1049,7 @@ def test_latex_installation(pf):
 
 
 def in_ipynb():
-    """ If the ipython process contains 'terminal' assume not in a notebook.
+    """If the ipython process contains 'terminal' assume not in a notebook.
 
     Returns
     --------
@@ -1005,7 +1066,7 @@ def in_ipynb():
 
 
 def setup_latex_env_notebook(pf, latexExists):
-    """ This is needed for use of the latex_envs notebook extension
+    """This is needed for use of the latex_envs notebook extension
     which allows the use of environments in Markdown.
 
     Parameters
@@ -1014,6 +1075,7 @@ def setup_latex_env_notebook(pf, latexExists):
         output of determine_platform()
     """
     import os
+
     import matplotlib.pyplot as plt
 
     plt.rc("font", family="serif")
@@ -1047,7 +1109,7 @@ def setup_latex_env_notebook(pf, latexExists):
 
 
 def make_figs(figure_name, saveFigs, drawFigs, target_dir="Figures"):
-    """ Utility function to save figure in multiple formats and display the image.
+    """Utility function to save figure in multiple formats and display the image.
 
     Parameters
     ----------
@@ -1091,7 +1153,7 @@ def make_figs(figure_name, saveFigs, drawFigs, target_dir="Figures"):
 
 
 def find_gui():
-    """ Quick fix to check if matplotlib is running in a GUI environment.
+    """Quick fix to check if matplotlib is running in a GUI environment.
 
     Returns
     -------