Prepare retreat (#162)

Co-authored-by: Sebastian Gsell <sebastian.gsell93@gmail.com>
Co-authored-by: Annica Gehlen <39128048+amageh@users.noreply.github.com>

Author: 3 people

.pre-commit-config.yaml

@@ -54,10 +54,10 @@ repos:
         name: isort
         args:
           - --profile=black
-  - repo: https://github.com/asottile/setup-cfg-fmt
-    rev: v2.8.0
-    hooks:
-      - id: setup-cfg-fmt
+#  - repo: https://github.com/asottile/setup-cfg-fmt
+#    rev: v2.8.0
+#    hooks:
+#      - id: setup-cfg-fmt
   - repo: https://github.com/psf/black
     rev: 25.1.0
     hooks:
@@ -106,7 +106,7 @@ repos:
       - id: codespell
         additional_dependencies:
           - tomli
-        exclude: tests/sandbox/|docs/source/Tutorials/interface_plots.ipynb
+        exclude: tests/sandbox/|docs/source/background/interface_plots.ipynb
 # -   repo: https://github.com/mgedmin/check-manifest
 #     rev: "0.49"
 #     hooks:

setup.cfg _setup.cfgsetup.cfg renamed to _setup.cfg

@@ -21,3 +21,5 @@ ignore:
   - src/toy_models/*
   - src/dcegm/likelihood.py
   - src/dcegm/wealth_correction.py
+  - src/dcegm/cli.py
+  - src/dcegm/interfaces/interface.py

setup.py _setup.pysetup.py renamed to _setup.py

@@ -264,7 +264,7 @@
     "    \"period\": 37,  # Periods start at 0, the last period number T=44 has index 43 so period T-5 has index 38\n",
     "    \"lagged_choice\": 0,  # choice in the previous period d_{t-1}, zero meaning working\n",
     "    \"choice\": 0,  # choice in the current period d_t, zero meaning working\n",
-    "    \"dummy_exog\": 0,  # dummy exogenous state variable can be ignored for now\n",
+    "    \"dummy_stochastic\": 0,  # dummy exogenous state variable can be ignored for now\n",
     "}\n",
     "\n",
     "# get the index of the state choice pair in the endogenous grid\n",
@@ -346,7 +346,7 @@
     "    state_choice_dict = {\n",
     "        \"period\": 37,\n",
     "        \"lagged_choice\": 0,\n",
-    "        \"dummy_exog\": 0,\n",
+    "        \"dummy_stochastic\": 0,\n",
     "        \"choice\": 0,\n",
     "    }\n",
     "    value, policy, endog_grid = solve_func(params)\n",
@@ -420,7 +420,7 @@
     "    state_choice_dict = {\n",
     "        \"period\": 37,\n",
     "        \"lagged_choice\": 0,\n",
-    "        \"dummy_exog\": 0,\n",
+    "        \"dummy_stochastic\": 0,\n",
     "        \"choice\": 0,\n",
     "    }\n",
     "    value, policy, endog_grid = solve_func(params)\n",
@@ -585,7 +585,7 @@
     "                state_choice_dict = {\n",
     "                    \"period\": test_period,\n",
     "                    \"lagged_choice\": 0,\n",
-    "                    \"dummy_exog\": 0,\n",
+    "                    \"dummy_stochastic\": 0,\n",
     "                    \"choice\": 0,\n",
     "                }\n",
     "                value, policy, endog_grid = solve_func(params)\n",

codecov.yml

@@ -0,0 +1,10 @@
+.. _limitations:
+
+Limitations
+===========
+
+`dc-egm` is a versatile Python package capable of solving and simulating a large class of economic models flexibly and efficiently..
+
+However, there are limitations to what kind of features can be implemented and how complex models can become to be feasible to solve.
+
+Below we discuss what kind of models `dc-egm` is designed for and importantly what limitations to be aware of when implementing a model.

docs/source/_static/images/annica.jpeg

@@ -0,0 +1,6 @@
+.. _literature:
+
+Literature
+==========
+
+Below you find related literature to provide a background of the `dc-egm` algorithm and the related class of economic models.