Merge pull request #1390 from econ-ark/AdjustRepr

Change repr to describe()

Documentation/CHANGELOG.md

@@ -8,17 +8,30 @@ For more information on HARK, see [our Github organization](https://github.com/e
 
 ## Changes
 
+### 0.14.1 (IN DEVELOPMENT)
+
+Release date: ???
+
+#### Major Changes
+
+none
+
+#### Minor Changes
+
+- Fixes a bug in make_figs arising from the metadata argument being incompatible with jpg. [#1386](https://github.com/econ-ark/HARK/pull/1386)
+- Reverts behavior of the repr method of the Model class, so that long strings aren't generated. Full description is available with describe(). [#1390](https://github.com/econ-ark/HARK/pull/1390)
+
 ### 0.14.0
 
 Release Date: February 12, 2024
 
-### Major Changes
+#### Major Changes
 
 - Adds `HARK.core.AgentPopulation` class to represent a population of agents with ex-ante heterogeneous parametrizations as distributions. [#1237](https://github.com/econ-ark/HARK/pull/1237)
 - Adds `HARK.core.Parameters` class to represent a collection of time varying and time invariant parameters in a model. [#1240](https://github.com/econ-ark/HARK/pull/1240)
 - Adds `HARK.simulation.monte_carlo` module for generic Monte Carlo simulation functions using Python model configurations. [1296](https://github.com/econ-ark/HARK/pull/1296)
 
-### Minor Changes
+#### Minor Changes
 
 - Adds option `sim_common_Rrisky` to control whether risky-asset models draw common or idiosyncratic returns in simulation. [#1250](https://github.com/econ-ark/HARK/pull/1250),[#1253](https://github.com/econ-ark/HARK/pull/1253)
 - Addresses [#1255](https://github.com/econ-ark/HARK/issues/1255). Makes age-varying stochastic returns possible and draws from their discretized version. [#1262](https://github.com/econ-ark/HARK/pull/1262)
@@ -35,7 +48,7 @@ Release Date: February 12, 2024
 
 Release Date: February 16, 2023
 
-### Major Changes
+#### Major Changes
 
 - Updates the DCEGM tools to address the flaws identified in [issue #1062](https://github.com/econ-ark/HARK/issues/1062). PR: [1100](https://github.com/econ-ark/HARK/pull/1100).
 - Updates `IndexDstn`, introducing the option to use an existing RNG instead of creating a new one, and creating and storing all the conditional distributions at initialization. [1104](https://github.com/econ-ark/HARK/pull/1104)
@@ -62,7 +75,7 @@ Release Date: February 16, 2023
 - Reorganizes `HARK.distribution`. All distributions now inherit all features from `scipy.stats`. New `ContinuousFrozenDistribution` and `DiscreteFrozenDistribution` to use `scipy.stats` distributions not yet implemented in HARK. New `Distribution.discretize(N, method = "***")` replaces `Distribution.approx(N)`. New `DiscreteDistribution.limit` attribute describes continuous origin and discretization method. [#1197](https://github.com/econ-ark/HARK/pull/1197).
 - Creates new class of _labeled_ models under `ConsLabeledModel` that use xarray for more expressive modeling of underlying mathematical and economics variables. [#1177](https://github.com/econ-ark/HARK/pull/1177)
 
-### Minor Changes
+#### Minor Changes
 
 - Updates the lognormal-income-process constructor from `ConsIndShockModel.py` to use `IndexDistribution`. [#1024](https://github.com/econ-ark/HARK/pull/1024), [#1115](https://github.com/econ-ark/HARK/pull/1115)
 - Allows for age-varying unemployment probabilities and replacement incomes with the lognormal income process constructor. [#1112](https://github.com/econ-ark/HARK/pull/1112)

HARK/core.py

@@ -323,7 +323,7 @@ def __str__(self):
         s += ">"
         return s
 
-    def __repr__(self):
+    def describe(self):
         return self.__str__()
 
 

HARK/tests/test_core.py

@@ -109,8 +109,8 @@ def test_solve(self):
         self.assertEqual(len(self.agent.solution), 4)
         self.assertTrue(isinstance(self.agent.solution[0], MetricObject))
 
-    def test___repr__(self):
-        self.assertTrue("Parameters" in self.agent.__repr__())
+    def test_describe(self):
+        self.assertTrue("Parameters" in self.agent.describe())
 
     def test___eq__(self):
         agent2 = AgentType(cycles=1)