Add parameters for qualified business income and update to taxcalc 3.0

cs-config/cs_config/functions.py

@@ -4,7 +4,6 @@
 import paramtools
 import pandas as pd
 import inspect
-from .helpers import convert_defaults, convert_adj
 from .outputs import credit_plot, rate_plot, liability_plot
 from .constants import MetaParameters
 from bokeh.models import ColumnDataSource
@@ -20,12 +19,21 @@
 with open(os.path.join(TCDIR, "policy_current_law.json"), "r") as f:
     pcl = json.loads(f.read())
 
-RES = convert_defaults(pcl)
 
+def fix_checkbox(params):
+    """
+    Replace param_checkbox with param-indexed.
+    """
+    pol_params = {}
+    # drop checkbox parameters.
+    for param, data in params.items():
+        if param.endswith("checkbox"):
+            base_param = param.split("_checkbox")[0]
+            pol_params[f"{base_param}-indexed"] = data
+        else:
+            pol_params[param] = data
 
-class TCParams(paramtools.Parameters):
-    defaults = RES
-
+    return pol_params
 
 def get_version():
     version = taxcrunch.__version__
@@ -40,12 +48,15 @@ def get_inputs(meta_params_dict):
     metaparams.adjust(meta_params_dict)
 
     params = CruncherParams()
-    policy_params = TCParams()
+    policy_params = Policy()
 
     policy_params.set_state(
         year=metaparams.year.tolist())
 
     filtered_pol_params = OrderedDict()
+    policy_params._schema["operators"].update(
+        {"label_to_extend": None, "uses_extend_func": False, "array_first": False}
+    )
     for k, v in policy_params.dump().items():
         if k =="schema" or v.get("section_1", False):
             filtered_pol_params[k] = v
@@ -73,6 +84,10 @@ def get_inputs(meta_params_dict):
         "otheritem",
         "childcare",
         "mortgage",
+        "businc",
+        "sstb",
+        "w2paid",
+        "qualprop",
         "mtr_options",
         "schema"
     ]
@@ -93,14 +108,10 @@ def validate_inputs(meta_params_dict, adjustment, errors_warnings):
     params.adjust(adjustment["Tax Information"], raise_errors=False)
     errors_warnings["Tax Information"]["errors"].update(params.errors)
 
-    pol_params = {}
-    # drop checkbox parameters.
-    for param, data in list(adjustment["Policy"].items()):
-        if not param.endswith("checkbox"):
-            pol_params[param] = data
+    policy_adj = fix_checkbox(adjustment["Policy"])
 
-    policy_params = TCParams()
-    policy_params.adjust(pol_params, raise_errors=False)
+    policy_params = Policy()
+    policy_params.adjust(policy_adj, raise_errors=False, ignore_warnings=True)
     errors_warnings["Policy"]["errors"].update(policy_params.errors)
 
     return {"errors_warnings": errors_warnings}
@@ -110,17 +121,17 @@ def run_model(meta_params_dict, adjustment):
     meta_params = MetaParameters()
     meta_params.adjust(meta_params_dict)
 
-    policy_mods = convert_adj(adjustment["Policy"], meta_params.year.tolist())
-
     adjustment["Tax Information"]["year"] = meta_params.year
     params = CruncherParams()
     params.adjust(adjustment["Tax Information"], raise_errors=False)
     newvals = params.specification()
 
+    policy_mods = fix_checkbox(adjustment["Policy"])
+
     crunch = Cruncher(inputs=newvals, custom_reform=policy_mods)
 
     # make dataset for bokeh plots
-    ivar = crunch.ivar
+    ivar = crunch.batch_ivar
     _, mtr_opt, _ = crunch.taxsim_inputs()
     df = pd.concat([ivar] * 5000, ignore_index=True)
     increments = pd.DataFrame(list(range(0, 500000, 100)))
@@ -156,6 +167,9 @@ def run_model(meta_params_dict, adjustment):
     elif mtr_opt == 'Mortgage':
         span = int(ivar[23])
         df[23] = increments
+    elif mtr_opt == 'Business Income':
+        span = int(ivar[24])
+        df[24] = increments
 
     b = Batch(df)
     df_base = b.create_table()

cs-config/cs_config/tests/test_functions.py

@@ -1,6 +1,6 @@
 from cs_kit import CoreTestFunctions
 
-from cs_config import functions, helpers
+from cs_config import functions
 
 
 class TestFunctions1(CoreTestFunctions):
@@ -10,31 +10,4 @@ class TestFunctions1(CoreTestFunctions):
     bad_adjustment = {"Tax Information": {"mstat": [{"value": 2}]}, "Policy":{"STD": -1}}
     ok_adjustment = {"Tax Information": {"mstat": [{"value": "Joint"}]}, "Policy":{}}
     get_inputs = functions.get_inputs
-    get_version = functions.get_version
-
-
-def test_convert_adj():
-    adj = {
-        "STD": [
-            {"MARS": "single", "year": "2019", "value": 0},
-            {"MARS": "mjoint", "year": 2019, "value": 1}
-        ],
-        "EITC_c": [{"EIC": "0kids", "year": "2019", "value": 1000.0}],
-        "BEN_ssi_repeal": [
-            {"year": 2019, "value": True}
-        ]
-    }
-
-    res = helpers.convert_adj(adj, 2019)
-
-    assert res == {
-        "STD": {
-            2019: [0, 1, 12200.0, 18350.0, 24400.0]
-        },
-        "EITC_c": {
-            2019: [1000.0, 3526.0, 5828.0, 6557.0]
-        },
-        "BEN_ssi_repeal": {
-            2019: True
-        }
-    }
+    get_version = functions.get_version

docs/INPUT_INSTRUCTIONS.md

@@ -5,7 +5,7 @@ To analyze multiple tax filers, the user must specify a csv file with the filers
 
 1. RECID (ID for tax filer)
 2. year
-3. mstat (1 - single; 2 - joint (married))
+3. mstat (1 - single; 2 - married)
 4. page (age of primary taxpayer)
 5. sage (age of spouse)
 6. depx (number of dependents)
@@ -27,9 +27,13 @@ To analyze multiple tax filers, the user must specify a csv file with the filers
 22. otheritem (other itemized deductions subject to SALT cap, e.g. state and local taxes)
 23. childcare (child care expenses)
 24. mortgage (itemized deductions not subject to SALT cap, e.g., charitable contributions and home mortgage interest)
+25. businc (business income)
+26. sstb (1 - business income from professional services business; 0 - business income not from professional pervices business)
+27. w2paid (W-2 wages paid by business)
+28. qualprop (filer's share of qualified property)
 
 For example, a 50-year old single filer with 3 dependents under 13 who makes $50,000 per year could be represented by the following:
 
 ```
-1,2019,1,50,0,3,3,3,3,50000,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+1,2019,1,50,0,3,3,3,3,50000,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
 ```