distribution to distributions

Documentation/overview/ARKitecture.md

@@ -29,11 +29,10 @@ from HARK.rewards import CRRAutility
 CRRAutility(1, 2)
 ```
 
-Python modules in HARK can generally be categorized into two types: tools and models. **Tool modules** contain functions and classes with general purpose tools that have no inherent ''economic content'', but that can be used in many economic models as building blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include functions for data analysis (e.g. calculating Lorenz shares from data, or constructing a non-parametric kernel regression), functions to create and manipulate discrete approximations to continuous distributions, or classes for constructing interpolated approximations to non-parametric functions. The most commonly used tool modules reside in HARK's root directory and have names like **_HARK.distribution_** and **_HARK.interpolation_**.
+Python modules in HARK can generally be categorized into two types: tools and models. **Tool modules** contain functions and classes with general purpose tools that have no inherent ''economic content'', but that can be used in many economic models as building blocks or utilities; they could plausibly be useful in non-economic settings. Tools might include functions for data analysis (e.g. calculating Lorenz shares from data, or constructing a non-parametric kernel regression), functions to create and manipulate discrete approximations to continuous distributions, or classes for constructing interpolated approximations to non-parametric functions. The most commonly used tool modules reside in HARK's root directory and have names like **_HARK.distributions_** and **_HARK.interpolation_**.
 
 **Model modules** specify particular economic models, including classes to represent agents in the model (and the ''market structure'' in which they interact) and functions for solving the ''one period problem'' of those models. For example, **ConsIndShockModel.py** concerns consumption-saving models in which agents have CRRA utility over consumption and face idiosyncratic shocks to permanent and transitory income. The module includes classes for representing ''types'' of consumers, along with functions for solving (several flavors of) the one period consumption-saving problem. Model modules generally have **_Model_** in their name, and the classes for representing agents all have **_Type_** at the end of their name (as instances represent a collection of ex ante homogeneous agents who share common model and parameters-- a "type"). For example, **_HARK.ConsumptionSaving.ConsIndShockModel_** includes the class **_IndShockConsumerType_**.
 
-
 ## HARK
 
 After you [installed](https://docs.econ-ark.org/Documentation/guides/installation.html) or [cloned the repository of](https://github.com/econ-ark/HARK) HARK, you can explore the content of it. In the subfolder HARK, you can find a range of [general purpose tools](#general-purpose-tools), as well as the next subfolder ConsumptionSaving which has [AgentType subclasses](#agenttype-class) and [Market subclasses](#market-class).
@@ -59,9 +58,9 @@ Macroeconomic models in HARK use the **_Market_** class to represent a market (o
 
 The **_HARK.utilities_** module contains a variety of general purpose tools, including some data manipulation tools (e.g. for calculating an average of data conditional on being within a percentile range of different data), basic kernel regression tools, convenience functions for retrieving information about functions, and basic plotting tools using **_matplotlib.pyplot_**. See [here](https://docs.econ-ark.org/Documentation/reference/tools/utilities.html) for further documentation.
 
-#### HARK.distribution
+#### HARK.distributions
 
-The **_HARK.distribution_** module includes classes for representing continuous distributions in a relatively consistent framework. Critically for numeric purposes, it also has methods and functions for constructing discrete approximations to those distributions (e.g. **_approx\_lognormal()_** to approximate a log-normal distribution) as well as manipulating these representations (e.g. appending one outcome to an existing distribution, or combining independent univariate distributions into one multivariate distribution). As a convention in HARK, continuous distributions are approximated as finite discrete distributions when solving models. This both simplifies solution methods (reducing numeric integrals to simple dot products) and allows users to easily test whether their chosen degree of discretization yields a sufficient approximation to the full distribution. See [here](https://docs.econ-ark.org/Documentation/reference/tools/distribution.html) for further documentation.
+The **_HARK.distributions_** module includes classes for representing continuous distributions in a relatively consistent framework. Critically for numeric purposes, it also has methods and functions for constructing discrete approximations to those distributions (e.g. **_approx\_lognormal()_** to approximate a log-normal distribution) as well as manipulating these representations (e.g. appending one outcome to an existing distribution, or combining independent univariate distributions into one multivariate distribution). As a convention in HARK, continuous distributions are approximated as finite discrete distributions when solving models. This both simplifies solution methods (reducing numeric integrals to simple dot products) and allows users to easily test whether their chosen degree of discretization yields a sufficient approximation to the full distribution. See [here](https://docs.econ-ark.org/Documentation/reference/tools/distribution.html) for further documentation.
 
 #### HARK.interpolation
 
@@ -107,7 +106,6 @@ A discrete time model in our framework is characterized by a sequence of ''perio
 
 An instance of **_AgentType_** also has the attributes named in **_time_vary_** and **_time_inv_**, and may have other attributes that are not included in either (e.g. values not used in the model solution, but instead to construct objects used in the solution). Note that **_time_vary_** may include attributes that are never used by a function in **_solveOnePeriod_**. Most saliently, the attribute **_solution_** is time-varying but is not used to solve individual periods.
 
-
 #### A Universal Solver
 
 When an instance of **_AgentType_** invokes its **_solve_** method, the solution to the agent's problem is stored in the attribute **_solution_**. The solution is computed by recursively solving the sequence of periods defined by the variables listed in **_time_vary_** and **_time_inv_** using the functions in **_solve\_one_period_**. The time-varying inputs are updated each period, including the successive period's solution as **_solution_next_**; the same values of time invariant inputs in **_time_inv_** are passed to the solver in every period. The first call to **_solve\_one\_period_** uses **_solution_terminal_** as **_solution_next_**. In an infinite horizon problem (**_cycles_=0**), the sequence of periods is solved until the solutions of successive cycles have a ''distance'' of less than **_tolerance_**. Usually, the "sequence" of periods in such models is just *one* period long.

Documentation/reference/tools/distribution.rst

@@ -1,7 +1,7 @@
 Distribution
 -------------
 
-.. automodule:: HARK.distribution
+.. automodule:: HARK.distributions
    :members:
    :undoc-members:
    :show-inheritance:

HARK/Calibration/Assets/AssetProcesses.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 from scipy.optimize import minimize_scalar
-from HARK.distribution import (
+from HARK.distributions import (
     combine_indep_dstns,
     DiscreteDistributionLabeled,
     IndexDistribution,

HARK/Calibration/Income/IncomeProcesses.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 from HARK.metric import MetricObject
-from HARK.distribution import (
+from HARK.distributions import (
     add_discrete_outcome_constant_mean,
     combine_indep_dstns,
     DiscreteDistribution,

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -22,7 +22,7 @@
     init_idiosyncratic_shocks,
 )
 from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
-from HARK.distribution import (
+from HARK.distributions import (
     MarkovProcess,
     MeanOneLogNormal,
     Uniform,

HARK/ConsumptionSaving/ConsBequestModel.py

@@ -36,7 +36,7 @@
     make_portfolio_solution_terminal,
 )
 from HARK.ConsumptionSaving.ConsRiskyAssetModel import make_simple_ShareGrid
-from HARK.distribution import expected
+from HARK.distributions import expected
 from HARK.interpolation import (
     BilinearInterp,
     ConstantFunction,

HARK/ConsumptionSaving/ConsGenIncProcessModel.py

@@ -23,7 +23,7 @@
     ConsumerSolution,
     IndShockConsumerType,
 )
-from HARK.distribution import Lognormal, expected
+from HARK.distributions import Lognormal, expected
 from HARK.interpolation import (
     BilinearInterp,
     ConstantFunction,

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -30,7 +30,7 @@
 from HARK.Calibration.SCF.WealthIncomeDist.SCFDistTools import (
     income_wealth_dists_from_scf,
 )
-from HARK.distribution import (
+from HARK.distributions import (
     Lognormal,
     MeanOneLogNormal,
     Uniform,

HARK/ConsumptionSaving/ConsLabeledModel.py

@@ -22,7 +22,7 @@
 )
 from HARK.ConsumptionSaving.LegacyOOsolvers import ConsIndShockSetup
 from HARK.core import make_one_period_oo_solver
-from HARK.distribution import DiscreteDistributionLabeled
+from HARK.distributions import DiscreteDistributionLabeled
 from HARK.metric import MetricObject
 from HARK.rewards import UtilityFuncCRRA
 

HARK/ConsumptionSaving/ConsMarkovModel.py

@@ -19,7 +19,7 @@
     PerfForesightConsumerType,
     make_basic_CRRA_solution_terminal,
 )
-from HARK.distribution import MarkovProcess, Uniform, expected
+from HARK.distributions import MarkovProcess, Uniform, expected
 from HARK.interpolation import (
     CubicInterp,
     LinearInterp,

HARK/ConsumptionSaving/ConsMedModel.py

@@ -21,7 +21,7 @@
     VariableLowerBoundFunc2D,
 )
 from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution
-from HARK.distribution import Lognormal, add_discrete_outcome_constant_mean, expected
+from HARK.distributions import Lognormal, add_discrete_outcome_constant_mean, expected
 from HARK.interpolation import (
     BilinearInterp,
     BilinearInterpOnInterp1D,

HARK/ConsumptionSaving/ConsPortfolioFrameModel.py

@@ -13,7 +13,7 @@
     PortfolioConsumerType,
     init_portfolio,
 )
-from HARK.distribution import (
+from HARK.distributions import (
     Bernoulli,
     IndexDistribution,
     Lognormal,

HARK/ConsumptionSaving/ConsPortfolioModel.py

@@ -26,7 +26,7 @@
     RiskyAssetConsumerType,
     make_simple_ShareGrid,
 )
-from HARK.distribution import expected
+from HARK.distributions import expected
 from HARK.interpolation import (
     BilinearInterp,
     ConstantFunction,

HARK/ConsumptionSaving/ConsPrefShockModel.py

@@ -22,7 +22,7 @@
     get_PermShkDstn_from_IncShkDstn,
     get_TranShkDstn_from_IncShkDstn,
 )
-from HARK.distribution import MeanOneLogNormal, expected
+from HARK.distributions import MeanOneLogNormal, expected
 from HARK.interpolation import (
     CubicInterp,
     LinearInterp,

HARK/ConsumptionSaving/ConsRepAgentModel.py

@@ -17,7 +17,7 @@
     make_basic_CRRA_solution_terminal,
 )
 from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
-from HARK.distribution import MarkovProcess
+from HARK.distributions import MarkovProcess
 from HARK.interpolation import LinearInterp, MargValueFuncCRRA
 from HARK.utilities import make_assets_grid
 

HARK/ConsumptionSaving/ConsRiskyAssetModel.py

@@ -22,7 +22,7 @@
     get_PermShkDstn_from_IncShkDstn,
     get_TranShkDstn_from_IncShkDstn,
 )
-from HARK.distribution import (
+from HARK.distributions import (
     Bernoulli,
     expected,
     IndexDistribution,

HARK/ConsumptionSaving/ConsRiskyContribModel.py

@@ -45,7 +45,7 @@
     init_risky_asset,
     IndShockRiskyAssetConsumerType_constructor_default,
 )
-from HARK.distribution import calc_expectation
+from HARK.distributions import calc_expectation
 from HARK.interpolation import BilinearInterp  # 2D interpolator
 from HARK.interpolation import (
     ConstantFunction,  # Interpolator-like class that returns constant value

HARK/ConsumptionSaving/ConsWealthPortfolioModel.py

@@ -6,7 +6,7 @@
     PortfolioSolution,
     make_portfolio_solution_terminal,
 )
-from HARK.distribution import expected
+from HARK.distributions import expected
 from HARK.interpolation import (
     BilinearInterp,
     CubicInterp,

HARK/ConsumptionSaving/LegacyOOsolvers.py

@@ -10,7 +10,7 @@
 from dataclasses import dataclass
 import numpy as np
 from HARK import NullFunc
-from HARK.distribution import expected, calc_expectation, DiscreteDistribution
+from HARK.distributions import expected, calc_expectation, DiscreteDistribution
 from HARK.interpolation import (
     BilinearInterp,
     BilinearInterpOnInterp1D,

HARK/ConsumptionSaving/TractableBufferStockModel.py

@@ -27,7 +27,7 @@
 from scipy.optimize import brentq, newton
 
 from HARK import AgentType, NullFunc
-from HARK.distribution import Bernoulli, Lognormal
+from HARK.distributions import Bernoulli, Lognormal
 from HARK.interpolation import CubicInterp
 
 # Import the HARK library.

HARK/ConsumptionSaving/tests/test_ConsAggShockModel.py

@@ -11,7 +11,7 @@
     KrusellSmithEconomy,
     KrusellSmithType,
 )
-from HARK.distribution import Uniform
+from HARK.distributions import Uniform
 from HARK.tests import HARK_PRECISION
 
 

HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py

@@ -7,7 +7,7 @@
     MarkovConsumerType,
     init_indshk_markov,
 )
-from HARK.distribution import (
+from HARK.distributions import (
     DiscreteDistribution,
     DiscreteDistributionLabeled,
     MeanOneLogNormal,

HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py

@@ -8,7 +8,7 @@
     SmallOpenEconomy,
     init_cobb_douglas,
 )
-from HARK.distribution import Uniform
+from HARK.distributions import Uniform
 
 
 class testSmallOpenEconomy(unittest.TestCase):

HARK/ConsumptionSaving/tests/test_modelcomparisons.py

@@ -19,7 +19,7 @@
 )
 from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
 from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType
-from HARK.distribution import DiscreteDistributionLabeled
+from HARK.distributions import DiscreteDistributionLabeled
 
 
 class Compare_PerfectForesight_and_Infinite(unittest.TestCase):

HARK/core.py

@@ -20,7 +20,7 @@
 
 import numpy as np
 import pandas as pd
-from HARK.distribution import (
+from HARK.distributions import (
     Distribution,
     IndexDistribution,
     TimeVaryingDiscreteDistribution,

HARK/distributions/__init__.py

@@ -25,8 +25,6 @@
 ]
 
 from HARK.distributions.base import (
-    DiscreteDistribution,
-    DiscreteDistributionLabeled,
     Distribution,
     IndexDistribution,
     MarkovProcess,
@@ -39,7 +37,11 @@
     Uniform,
     Weibull,
 )
-from HARK.distributions.discrete import Bernoulli
+from HARK.distributions.discrete import (
+    Bernoulli,
+    DiscreteDistribution,
+    DiscreteDistributionLabeled,
+)
 from HARK.distributions.multivariate import MVLogNormal, MVNormal
 from HARK.distributions.utils import (
     add_discrete_outcome_constant_mean,

HARK/frame.py

@@ -7,7 +7,7 @@
 import numpy as np
 
 from HARK import AgentType, Model
-from HARK.distribution import Distribution
+from HARK.distributions import Distribution
 
 
 class Frame:

HARK/model.py

@@ -4,7 +4,7 @@
 
 from dataclasses import dataclass, field, replace
 from copy import copy, deepcopy
-from HARK.distribution import (
+from HARK.distributions import (
     Distribution,
     DiscreteDistributionLabeled,
     combine_indep_dstns,

HARK/models/consumer.py

@@ -1,4 +1,4 @@
-from HARK.distribution import Bernoulli, Lognormal, MeanOneLogNormal
+from HARK.distributions import Bernoulli, Lognormal, MeanOneLogNormal
 from HARK.model import Control, DBlock, RBlock
 
 """

HARK/models/perfect_foresight.py

@@ -1,4 +1,4 @@
-from HARK.distribution import Bernoulli
+from HARK.distributions import Bernoulli
 from HARK.model import Control, DBlock
 
 # This way of distributing parameters across the scope is clunky

HARK/models/perfect_foresight_normalized.py

@@ -1,4 +1,4 @@
-from HARK.distribution import Bernoulli
+from HARK.distributions import Bernoulli
 from HARK.model import Control, DBlock
 
 # This way of distributing parameters across the scope is clunky

HARK/models/test_models.py

@@ -1,4 +1,4 @@
-from HARK.distribution import Lognormal
+from HARK.distributions import Lognormal
 import HARK.models.consumer as cons
 import HARK.models.perfect_foresight as pfm
 import HARK.models.perfect_foresight_normalized as pfnm

HARK/parser.py

@@ -1,4 +1,4 @@
-from HARK.distribution import Bernoulli, Lognormal, MeanOneLogNormal
+from HARK.distributions import Bernoulli, Lognormal, MeanOneLogNormal
 from sympy.utilities.lambdify import lambdify
 from sympy.parsing.sympy_parser import parse_expr
 import yaml

HARK/simulation/monte_carlo.py

@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from HARK.distribution import (
+from HARK.distributions import (
     Distribution,
     IndexDistribution,
     TimeVaryingDiscreteDistribution,

HARK/simulation/test_monte_carlo.py

@@ -4,7 +4,7 @@
 
 import unittest
 
-from HARK.distribution import Bernoulli, IndexDistribution, MeanOneLogNormal
+from HARK.distributions import Bernoulli, IndexDistribution, MeanOneLogNormal
 from HARK.model import Aggregate, Control, DBlock
 from HARK.simulation.monte_carlo import *
 

HARK/tests/test_approxDstns.py

@@ -7,7 +7,7 @@
 import numpy as np
 
 # Bring in modules we need
-import HARK.distribution as distribution
+import HARK.distributions as distribution
 
 
 class testsForDCEGM(unittest.TestCase):

HARK/tests/test_core.py

@@ -12,7 +12,7 @@
     init_idiosyncratic_shocks,
 )
 from HARK.core import AgentPopulation, AgentType, Parameters, distribute_params
-from HARK.distribution import Uniform
+from HARK.distributions import Uniform
 from HARK.metric import MetricObject, distance_metric
 
 

HARK/tests/test_distribution.py

@@ -3,7 +3,7 @@
 import numpy as np
 import xarray as xr
 
-from HARK.distribution import (
+from HARK.distributions import (
     Bernoulli,
     DiscreteDistribution,
     DiscreteDistributionLabeled,

HARK/tests/test_model.py

@@ -1,6 +1,6 @@
 import unittest
 
-from HARK.distribution import Bernoulli, DiscreteDistribution
+from HARK.distributions import Bernoulli, DiscreteDistribution
 import HARK.model as model
 from HARK.model import Control
 import HARK.models.consumer as cons