Merge pull request #1396 from MridulS/upgrade_ruff

Simplify pre-commit usage

.pre-commit-config.yaml

@@ -2,49 +2,17 @@ exclude: Documentation/example_notebooks/
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.4
+    rev: v0.3.2
     hooks:
       - id: ruff
-        types_or: [jupyter]
+        types_or: [ python, pyi, jupyter ]
+        args:
+          - --fix
       - id: ruff-format
-        args: [--check]
-        types_or: [jupyter]
-
-  - repo: https://github.com/psf/black
-    rev: 23.7.0
-    hooks:
-      - id: black
-        exclude: ^examples/
-
-  - repo: https://github.com/asottile/pyupgrade
-    rev: v3.10.1
-    hooks:
-      - id: pyupgrade
-        args: ["--py38-plus"]
-        exclude: ^examples/
-
-  - repo: https://github.com/asottile/blacken-docs
-    rev: 1.15.0
-    hooks:
-      - id: blacken-docs
-        exclude: ^examples/
-
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-        name: isort (python)
-        args: ["--profile", "black", "--filter-files", "--skip", "__init__.py"]
-        exclude: ^examples/
-
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.1
-    hooks:
-      - id: prettier
-        exclude: ^examples/
+        types_or: [ python, pyi, jupyter ]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -4,6 +4,7 @@
 basic solver.  Also includes a subclass of Market called CobbDouglas economy,
 used for solving "macroeconomic" models with aggregate shocks.
 """
+
 from copy import deepcopy
 
 import numpy as np

HARK/ConsumptionSaving/ConsGenIncProcessModel.py

@@ -4,6 +4,7 @@
 ConsIndShockModel by explicitly tracking persistent income as a state variable,
 and allows (log) persistent income to follow an AR1 process rather than random walk.
 """
+
 import numpy as np
 
 from HARK import AgentType, make_one_period_oo_solver

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -2568,9 +2568,7 @@ def calc_transition_matrix(self, shk_dstn=None):
             if not hasattr(shk_dstn, "pmv"):
                 shk_dstn = self.IncShkDstn
 
-            self.cPol_Grid = (
-                []
-            )  # List of consumption policy grids for each period in T_cycle
+            self.cPol_Grid = []  # List of consumption policy grids for each period in T_cycle
             self.aPol_Grid = []  # List of asset policy grids for each period in T_cycle
             self.tran_matrix = []  # List of transition matrices
 
@@ -2951,9 +2949,7 @@ def J_from_F(F):
         else:
             peturbed_list = [getattr(self, shk_param) + dx] + (
                 params["T_cycle"] - 1
-            ) * [
-                getattr(self, shk_param)
-            ]  # Sequence of interest rates the agent
+            ) * [getattr(self, shk_param)]  # Sequence of interest rates the agent
 
         setattr(ZerothColAgent, shk_param, peturbed_list)  # Set attribute to agent
 

HARK/ConsumptionSaving/ConsLabeledModel.py

@@ -897,7 +897,9 @@ class ConsRiskyAssetLabeledSolver(ConsIndShockLabeledSolver):
     """
 
     solution_next: ConsumerSolutionLabeled  # solution to next period's problem
-    ShockDstn: DiscreteDistributionLabeled  #  distribution of shocks to income and returns
+    ShockDstn: (
+        DiscreteDistributionLabeled  #  distribution of shocks to income and returns
+    )
     LivPrb: float  # survival probability
     DiscFac: float  # intertemporal discount factor
     CRRA: float  # coefficient of relative risk aversion

HARK/ConsumptionSaving/ConsLaborModel.py

@@ -8,6 +8,7 @@
 productivity shocks.  Agents choose their quantities of labor and consumption after
 observing both of these shocks, so the transitory shock is a state variable.
 """
+
 import sys
 from copy import copy
 
@@ -343,7 +344,6 @@ def uPinv(X):
 
 
 class LaborIntMargConsumerType(IndShockConsumerType):
-
     """
     A class representing agents who make a decision each period about how much
     to consume vs save and how much labor to supply (as a fraction of their time).
@@ -737,13 +737,13 @@ def plot_LbrFunc(self, t, bMin=None, bMax=None, ShkSet=None):
 init_labor_intensive["LbrCostCoeffs"] = [-1.0]
 init_labor_intensive["WageRte"] = [1.0]
 init_labor_intensive["IncUnemp"] = 0.0
-init_labor_intensive[
-    "TranShkCount"
-] = 15  # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks
+init_labor_intensive["TranShkCount"] = (
+    15  # Crank up permanent shock count - Number of points in discrete approximation to transitory income shocks
+)
 init_labor_intensive["PermShkCount"] = 16  # Crank up permanent shock count
-init_labor_intensive[
-    "aXtraCount"
-] = 200  # May be important to have a larger number of gridpoints (than 48 initially)
+init_labor_intensive["aXtraCount"] = (
+    200  # May be important to have a larger number of gridpoints (than 48 initially)
+)
 init_labor_intensive["aXtraMax"] = 80.0
 init_labor_intensive["BoroCnstArt"] = None
 
@@ -791,6 +791,6 @@ def plot_LbrFunc(self, t, bMin=None, bMax=None, ShkSet=None):
 init_labor_lifecycle["LbrCostCoeffs"] = np.array([-2.0, 0.4])
 init_labor_lifecycle["T_cycle"] = 10
 # init_labor_lifecycle['T_retire']   = 7 # IndexError at line 774 in interpolation.py.
-init_labor_lifecycle[
-    "T_age"
-] = 11  # Make sure that old people die at terminal age and don't turn into newborns!
+init_labor_lifecycle["T_age"] = (
+    11  # Make sure that old people die at terminal age and don't turn into newborns!
+)

HARK/ConsumptionSaving/ConsMarkovModel.py

@@ -470,9 +470,7 @@ def calc_EndOfPrdvP(self):
                     np.logical_and(self.possible_transitions[:, j], which_states)
                 ):  # only consider a future state if one of the relevant states could transition to it
                     EndOfPrdvP_all[j, :] = self.EndOfPrdvPfunc_list[j](aGrid)
-                    if (
-                        self.CubicBool
-                    ):  # Add conditional end-of-period (marginal) marginal value to the arrays
+                    if self.CubicBool:  # Add conditional end-of-period (marginal) marginal value to the arrays
                         EndOfPrdvPP_all[j, :] = self.EndOfPrdvPfunc_list[j].derivativeX(
                             aGrid
                         )
@@ -606,9 +604,7 @@ def make_solution(self, cNrm, mNrm):
             solution_cond = ConsumerSolution(
                 cFunc=cFuncNow, vPfunc=vPfuncNow, mNrmMin=self.mNrmMinNow
             )
-            if (
-                self.CubicBool
-            ):  # Add the state-conditional marginal marginal value function (if desired)
+            if self.CubicBool:  # Add the state-conditional marginal marginal value function (if desired)
                 solution_cond = self.add_vPPfunc(solution_cond)
 
             # Add the current-state-conditional solution to the overall period solution

HARK/ConsumptionSaving/ConsMedModel.py

@@ -1,6 +1,7 @@
 """
 Consumption-saving models that also include medical spending.
 """
+
 from copy import deepcopy
 
 import numpy as np

HARK/ConsumptionSaving/ConsPortfolioFrameModel.py

@@ -161,7 +161,7 @@ def birth_pLvlNow(self, N):
                     {
                         "mean": init_portfolio["RiskyAvg"],
                         "std": init_portfolio["RiskyStd"],
-                    }
+                    },
                     # seed=self.RNG.integers(0, 2 ** 31 - 1) : TODO: Seed logic
                 ).discretize(init_portfolio["RiskyCount"], method="equiprobable"),
                 aggregate=True,

HARK/ConsumptionSaving/ConsPortfolioModel.py

@@ -3,6 +3,7 @@
 agents who must allocate their resources among consumption, saving in a risk-free
 asset (with a low return), and saving in a risky asset (with higher average return).
 """
+
 from copy import deepcopy
 
 import numpy as np

HARK/ConsumptionSaving/ConsPrefShockModel.py

@@ -6,6 +6,7 @@
 2) A combination of (1) and ConsKinkedR, demonstrating how to construct a new model
    by inheriting from multiple classes.
 """
+
 import numpy as np
 
 from HARK import make_one_period_oo_solver

HARK/ConsumptionSaving/ConsRepAgentModel.py

@@ -4,6 +4,7 @@
 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.
 """
+
 import numpy as np
 
 from HARK.ConsumptionSaving.ConsIndShockModel import (
@@ -12,7 +13,7 @@
     init_idiosyncratic_shocks,
 )
 from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
-from HARK.distribution import MarkovProcess, Uniform
+from HARK.distribution import MarkovProcess
 from HARK.interpolation import LinearInterp, MargValueFuncCRRA
 
 __all__ = ["RepAgentConsumerType", "RepAgentMarkovConsumerType"]

HARK/ConsumptionSaving/ConsRiskyAssetModel.py

@@ -5,6 +5,7 @@
 simulation methods. It is meant as a container of methods for dealing with
 risky assets that will be useful to models what will inherit from it.
 """
+
 from dataclasses import dataclass
 
 import numpy as np

HARK/ConsumptionSaving/ConsRiskyContribModel.py

@@ -23,6 +23,7 @@
    }
 
 """
+
 from copy import deepcopy
 
 import numpy as np
@@ -425,9 +426,9 @@ def sim_one_period(self):
         # Advance time for all agents
         self.t_age = self.t_age + 1  # Age all consumers by one period
         self.t_cycle = self.t_cycle + 1  # Age all consumers within their cycle
-        self.t_cycle[
-            self.t_cycle == self.T_cycle
-        ] = 0  # Resetting to zero for those who have reached the end
+        self.t_cycle[self.t_cycle == self.T_cycle] = (
+            0  # Resetting to zero for those who have reached the end
+        )
 
     def get_states_Reb(self):
         """
@@ -513,7 +514,7 @@ def get_states_Sha(self):
 
         # Post-states are assets after rebalancing
 
-        if not "tau" in self.time_vary:
+        if "tau" not in self.time_vary:
             mNrmTilde, nNrmTilde = rebalance_assets(
                 self.controls["dfrac"],
                 self.state_now["mNrm"],

HARK/ConsumptionSaving/TractableBufferStockModel.py

@@ -20,6 +20,7 @@
 Despite the non-standard solution method, the iterative process can be embedded
 in the HARK framework, as shown below.
 """
+
 from copy import copy
 
 import numpy as np
@@ -621,9 +622,9 @@ def sim_birth(self, which_agents):
         self.shocks["eStateNow"][which_agents] = 1.0
         # How many periods since each agent was born
         self.t_age[which_agents] = 0
-        self.t_cycle[
-            which_agents
-        ] = 0  # Which period of the cycle each agent is currently in
+        self.t_cycle[which_agents] = (
+            0  # Which period of the cycle each agent is currently in
+        )
         return None
 
     def sim_death(self):

HARK/ConsumptionSaving/tests/test_ConsAggShockModel.py

@@ -23,7 +23,10 @@ def setUp(self):
 
         # Make agents heterogeneous in their discount factor
         self.agents = distribute_params(
-            agent, "DiscFac", 3, Uniform(bot=0.90, top=0.94)  # Impatient agents
+            agent,
+            "DiscFac",
+            3,
+            Uniform(bot=0.90, top=0.94),  # Impatient agents
         )
 
         # Make an economy with those agents living in it

HARK/ConsumptionSaving/tests/test_ConsMarkovModel.py

@@ -56,9 +56,9 @@ def setUp(self):
 
         init_serial_unemployment = copy(init_idiosyncratic_shocks)
         init_serial_unemployment["MrkvArray"] = [MrkvArray]
-        init_serial_unemployment[
-            "UnempPrb"
-        ] = 0.0  # to make income distribution when employed
+        init_serial_unemployment["UnempPrb"] = (
+            0.0  # to make income distribution when employed
+        )
         init_serial_unemployment["global_markov"] = False
         self.model = MarkovConsumerType(**init_serial_unemployment)
         self.model.cycles = 0

HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py

@@ -169,9 +169,9 @@ def test_GICRawFails(self):
         GICRaw_fail_dictionary = dict(self.base_params)
         GICRaw_fail_dictionary["Rfree"] = 1.08
         GICRaw_fail_dictionary["PermGroFac"] = [1.00]
-        GICRaw_fail_dictionary[
-            "cycles"
-        ] = 0  # cycles=0 makes this an infinite horizon consumer
+        GICRaw_fail_dictionary["cycles"] = (
+            0  # cycles=0 makes this an infinite horizon consumer
+        )
 
         GICRawFailExample = IndShockConsumerType(**GICRaw_fail_dictionary)
 
@@ -896,9 +896,7 @@ def test_calc_tran_matrix(self):
         asset = example1.aPol_Grid  # Normalized Asset Policy Grid
 
         example1.calc_ergodic_dist()
-        vecDstn = (
-            example1.vec_erg_dstn
-        )  # Distribution of market resources and permanent income as a vector (m*p)x1 vector where
+        vecDstn = example1.vec_erg_dstn  # Distribution of market resources and permanent income as a vector (m*p)x1 vector where
 
         # Compute Aggregate Consumption and Aggregate Assets
         gridc = np.zeros((len(c), len(p)))

HARK/ConsumptionSaving/tests/test_IndShockConsumerTypeFast.py

@@ -127,9 +127,9 @@ def test_GICRawFails(self):
         GICRaw_fail_dictionary = dict(self.base_params)
         GICRaw_fail_dictionary["Rfree"] = 1.08
         GICRaw_fail_dictionary["PermGroFac"] = [1.00]
-        GICRaw_fail_dictionary[
-            "cycles"
-        ] = 0  # cycles=0 makes this an infinite horizon consumer
+        GICRaw_fail_dictionary["cycles"] = (
+            0  # cycles=0 makes this an infinite horizon consumer
+        )
 
         GICRawFailExample = IndShockConsumerTypeFast(**GICRaw_fail_dictionary)
 

HARK/ConsumptionSaving/tests/test_SmallOpenEconomy.py

@@ -1,7 +1,6 @@
 import copy
 import unittest
 
-import numpy as np
 
 from HARK import distribute_params
 from HARK.ConsumptionSaving.ConsAggShockModel import (
@@ -20,7 +19,10 @@ def test_small_open(self):
 
         # Make agents heterogeneous in their discount factor
         agents = distribute_params(
-            agent, "DiscFac", 3, Uniform(bot=0.90, top=0.94)  # Impatient agents
+            agent,
+            "DiscFac",
+            3,
+            Uniform(bot=0.90, top=0.94),  # Impatient agents
         )
 
         # Make an economy with those agents living in it

HARK/ConsumptionSaving/tests/test_modelInits.py

@@ -2,7 +2,6 @@
 This file tests whether HARK's models are initialized correctly.
 """
 
-
 # Bring in modules we need
 import unittest
 from copy import copy
@@ -18,7 +17,6 @@
     init_lifecycle,
 )
 from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
-from HARK.utilities import plot_funcs, plot_funcs_der
 
 
 class testInitialization(unittest.TestCase):
@@ -92,9 +90,9 @@ def test_MarkovConsumerType(self):
             # Make a consumer with serially correlated unemployment, subject to boom and bust cycles
             init_serial_unemployment = copy(init_idiosyncratic_shocks)
             init_serial_unemployment["MrkvArray"] = [MrkvArray]
-            init_serial_unemployment[
-                "UnempPrb"
-            ] = 0.0  # to make income distribution when employed
+            init_serial_unemployment["UnempPrb"] = (
+                0.0  # to make income distribution when employed
+            )
             init_serial_unemployment["global_markov"] = False
             SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment)
         except: