Taxcalc tmd

.github/workflows/check_jupyterbook.yml

@@ -21,7 +21,7 @@ jobs:
       - name: Build # Build Jupyter Book
         shell: bash -l {0}
         run: |
-          pip install jupyter-book
+          pip install "jupyter-book<2.0.0"
           pip install -e .
           python -m ipykernel install --user --name=taxbrain-dev
           cd docs

.github/workflows/deploy_jupyterbook.yml

@@ -24,7 +24,7 @@ jobs:
       - name: Build # Build Jupyter Book
         shell: bash -l {0}
         run: |
-          pip install jupyter-book
+          pip install "jupyter-book<2.0.0"
           pip install -e .
           python -m ipykernel install --user --name=taxbrain-dev
           cd docs

cs-config/cs_config/functions.py

@@ -154,7 +154,7 @@ def run_model(meta_params_dict, adjustment):
         sampling_seed = 180
         full_sample = pd.read_csv(input_path)
         data_start_year = taxcalc.Records.CPSCSV_YEAR
-        weights = taxcalc.Records.CPS_WEIGHTS_FILENAME
+        weights = os.path.join(taxcalc.Records.CODE_PATH, "cps_weights.csv.gz")
     else:
         raise ValueError(
             f"Data source '{meta_params.data_source}' is not supported."

cs-config/cs_config/helpers.py

@@ -306,7 +306,7 @@ def arbitrary_defaultdict():
             )
             # format table
             for col in tbl.columns:
-                tbl.update(tbl[col].apply("${:,.2f}".format))
+                tbl[col] = tbl[col].apply("${:,.2f}".format)
 
             title = RESULTS_TABLE_TITLES[id]
             tags = RESULTS_TABLE_TAGS[id]

environment.yml

@@ -3,7 +3,7 @@ channels:
 - conda-forge
 dependencies:
 - python>=3.6.5
-- taxcalc>=3.0.0
+- taxcalc>=6.0.0
 - behresp>=0.11.0
 - pandas>=0.23
 - numpy>=1.14

taxbrain/report.py

@@ -92,9 +92,9 @@ def format_table(df, int_cols, float_cols, float_perc=2):
             table of output
         """
         for col in int_cols:
-            df.update(df[col].astype(int).apply("{:,}".format))
+            df[col] = df[col].astype(int).apply("{:,}".format)
         for col in float_cols:
-            df.update(
+            df[col] = (
                 df[col]
                 .astype(float)
                 .apply("{:,.{}}".format, args=(float_perc,))
@@ -124,14 +124,14 @@ def export_plot(plot, graph):
         full_filename = Path(output_path, filename)
         plot.savefig(full_filename, dpi=1200, bbox_inches="tight")
 
-        return str(full_filename)
+        return filename
 
     if not tb.has_run:
         tb.run()
     if not name:
         name = f"Policy Report-{date()}"
     if not outdir:
-        outdir = name.replace(" ", "_")
+        outdir = name.replace(" ", "-").replace(",", "")
     if author:
         author = f"Report Prepared by {author.title()}"
     # create directory to hold report contents
@@ -189,9 +189,10 @@ def export_plot(plot, graph):
     # create differences table
     if verbose:
         print("Creating differences table")
-    diff_table = tb.differences_table(
-        tb.start_year, "standard_income_bins", "combined"
-    ).fillna(0)
+    with pd.option_context("future.no_silent_downcasting", True):
+        diff_table = tb.differences_table(
+            tb.start_year, "standard_income_bins", "combined"
+        ).fillna(0)
     diff_table.index = DIFF_TABLE_ROW_NAMES
 
     decile_diff_table = tb.differences_table(
@@ -306,7 +307,7 @@ def export_plot(plot, graph):
     report_md = write_text(template_path, **text_args)
 
     # write PDF, markdown files
-    filename = name.replace(" ", "-")
+    filename = name.replace(" ", "-").replace(",", "")
     pdf_path = Path(output_path, f"{filename}.pdf")
     md_path = Path(output_path, f"{filename}.md")
     md_path.write_text(report_md)

taxbrain/taxbrain.py

@@ -15,6 +15,7 @@
 from taxbrain.corporate_incidence import distribute as dist_corp
 from typing import Union
 from paramtools import ValidationError
+from pathlib import Path
 
 
 class TaxBrain:
@@ -30,6 +31,11 @@ class TaxBrain:
         "Behavioral-Responses": behresp.__version__,
     }
 
+    # add expected TMD filenames as constants
+    TMD_DATA_FILE = "tmd.csv.gz"
+    TMD_WEIGHTS_FILE = "tmd_weights.csv.gz"
+    TMD_GROWFACTORS_FILE = "tmd_growfactors.csv"
+
     def __init__(
         self,
         start_year: int,
@@ -684,14 +690,20 @@ def _make_calculators(self):
         if self.microdata == "CPS":
             records = tc.Records.cps_constructor(data=None, gfactors=gf_base)
         elif self.microdata == "PUF":
-            records = tc.Records(
+            records = tc.Records.puf_constructor(
+                data="puf.csv",
                 gfactors=gf_base,
-                weights=tc.Records.PUF_WEIGHTS_FILENAME,
+                # weights=tc.Records.PUF_WEIGHTS_FILENAME,
             )
         elif self.microdata == "TMD":
+            gf_base = tc.GrowFactors(self.TMD_GROWFACTORS_FILE)
+            if self.params["growdiff_baseline"]:
+                gd_base.apply_to(gf_base)
+
             records = tc.Records.tmd_constructor(
-                "tmd.csv",
-                gfactors=gf_base,
+                data_path=Path(self.TMD_DATA_FILE),
+                weights_path=Path(self.TMD_WEIGHTS_FILE),
+                growfactors=gf_base,
             )
         elif isinstance(self.microdata, dict):
             if self.microdata["growfactors"] is None:
@@ -735,14 +747,20 @@ def _make_calculators(self):
         if self.microdata == "CPS":
             records = tc.Records.cps_constructor(data=None, gfactors=gf_reform)
         elif self.microdata == "PUF":
-            records = tc.Records(
+            records = tc.Records.puf_constructor(
+                data="puf.csv",
                 gfactors=gf_reform,
-                weights=tc.Records.PUF_WEIGHTS_FILENAME,
+                # weights=tc.Records.PUF_WEIGHTS_FILENAME,
             )
         elif self.microdata == "TMD":
+            gf_reform = tc.GrowFactors(self.TMD_GROWFACTORS_FILE)
+            if self.params["growdiff_response"]:
+                gd_reform.apply_to(gf_reform)
+
             records = tc.Records.tmd_constructor(
-                "tmd.csv",
-                gfactors=gf_reform,
+                data_path=Path(self.TMD_DATA_FILE),
+                weights_path=Path(self.TMD_WEIGHTS_FILE),
+                growfactors=gf_reform,
             )
         elif isinstance(self.microdata, dict):
             if self.microdata["growfactors"] is None:
@@ -803,15 +821,20 @@ def _make_stacked_objects(self):
         if self.microdata == "CPS":
             records = tc.Records.cps_constructor(data=None, gfactors=gf_base)
         elif self.microdata == "PUF":
-            records = tc.Records(
-                "puf.csv",
+            records = tc.Records.puf_constructor(
+                data="puf.csv",
                 gfactors=gf_base,
-                weights=tc.Records.PUF_WEIGHTS_FILENAME,
+                # weights=tc.Records.PUF_WEIGHTS_FILENAME,
             )
         elif self.microdata == "TMD":
+            gf_base = tc.GrowFactors(self.TMD_GROWFACTORS_FILE)
+            if self.params["growdiff_baseline"]:
+                gd_base.apply_to(gf_base)
+
             records = tc.Records.tmd_constructor(
-                "tmd.csv",
-                gfactors=gf_base,
+                data_path=Path(self.TMD_DATA_FILE),
+                weights_path=Path(self.TMD_WEIGHTS_FILE),
+                growfactors=gf_base,
             )
         elif isinstance(self.microdata, dict):
             if self.microdata["growfactors"] is None:
@@ -857,15 +880,20 @@ def _make_stacked_objects(self):
                 data=None, gfactors=gf_reform
             )
         elif self.microdata == "PUF":
-            reform_records = tc.Records(
-                "puf.csv",
+            reform_records = tc.Records.puf_constructor(
+                data="puf.csv",
                 gfactors=gf_reform,
-                weights=tc.Records.PUF_WEIGHTS_FILENAME,
+                # weights=tc.Records.PUF_WEIGHTS_FILENAME,
             )
         elif self.microdata == "TMD":
-            records = tc.Records.tmd_constructor(
-                "tmd.csv",
-                gfactors=gf_reform,
+            gf_reform = tc.GrowFactors(self.TMD_GROWFACTORS_FILE)
+            if self.params["growdiff_response"]:
+                gd_reform.apply_to(gf_reform)
+
+            reform_records = tc.Records.tmd_constructor(
+                data_path=Path(self.TMD_DATA_FILE),
+                weights_path=Path(self.TMD_WEIGHTS_FILE),
+                growfactors=gf_reform,
             )
         elif isinstance(self.microdata, dict):
             if self.microdata["growfactors"] is None:

taxbrain/tests/conftest.py

@@ -12,8 +12,8 @@ def reform_json_str():
     reform = """
         {
             "policy": {
-                "SS_thd50": {"2019": [50000, 100000, 50000, 50000, 50000]},
-                "SS_thd85": {"2019": [50000, 100000, 50000, 50000, 50000]},
+                "SS_thd1": {"2019": [50000, 100000, 50000, 50000, 50000]},
+                "SS_thd2": {"2019": [50000, 100000, 50000, 50000, 50000]},
                 "SS_Earnings_thd": {"2019": 400000},
                 "FICA_ss_trt_employee": {"2020": 0.0625,
                                 "2021": 0.063,

taxbrain/tests/test_brain.py

@@ -139,6 +139,7 @@ def test_stacked_run_corporate():
 
 
 def test_weighted_totals(tb_static):
+    tb_static.run()
     table = tb_static.weighted_totals("combined")
     assert isinstance(table, pd.DataFrame)
     # table.to_csv("expected_weighted_table.csv")
@@ -165,6 +166,7 @@ def test_weighted_totals(tb_static):
 
 
 def test_multi_var_table(tb_dynamic):
+    tb_dynamic.run()
     with pytest.raises(ValueError):
         tb_dynamic.multi_var_table(["iitax"], "calc")
     with pytest.raises(TypeError):
@@ -176,6 +178,7 @@ def test_multi_var_table(tb_dynamic):
 
 
 def test_differences_table(tb_dynamic):
+    tb_dynamic.run()
     table = tb_dynamic.differences_table(2018, "weighted_deciles", "combined")
     assert isinstance(table, pd.DataFrame)
 

taxbrain/tests/test_corporate_incidence.py

@@ -307,22 +307,22 @@ def test_validation():
     # expected shares if full burden on shareholders:
     expected = pd.DataFrame(
         data=[
-            0.000031,
-            0.0,
-            0.001008,
-            0.003991,
-            0.003974,
-            0.008655,
-            0.013243,
-            0.020796,
-            0.027376,
-            0.052368,
-            0.077858,
-            0.790699,
-            1,
-            0.074559,
-            0.122593,
-            0.593546,
+            3.12755650e-05,
+            0.00000000e00,
+            9.41971262e-04,
+            3.66821040e-03,
+            3.90232598e-03,
+            8.00940431e-03,
+            1.28149782e-02,
+            2.03518571e-02,
+            2.64449904e-02,
+            5.06202883e-02,
+            7.93136449e-02,
+            7.93901054e-01,
+            1.00000000e00,
+            7.38510850e-02,
+            1.25578069e-01,
+            5.94471900e-01,
         ],
         index=[
             "0-10n",

taxbrain/tests/test_utils.py

@@ -3,16 +3,19 @@
 
 
 def test_distribution_plot(tb_static):
+    tb_static.run()
     fig = taxbrain.distribution_plot(tb_static, 2019)
 
 
 def test_differences_plot(tb_static):
+    tb_static.run()
     fig = taxbrain.differences_plot(tb_static, "combined")
     with pytest.raises(AssertionError):
         taxbrain.differences_plot(tb_static, "wages")
 
 
 def test_volcano_plot(tb_static):
+    tb_static.run()
     fig = taxbrain.volcano_plot(tb_static, 2019)
     with pytest.raises(ValueError):
         taxbrain.volcano_plot(tb_static, 2019, min_y=-10000)
@@ -27,10 +30,12 @@ def test_volcano_plot(tb_static):
 
 
 def test_lorenz_curve(tb_static):
+    tb_static.run()
     fig = taxbrain.lorenz_curve(tb_static, 2019)
 
 
 def test_revenue_plot(tb_static):
+    tb_static.run()
     fig = taxbrain.revenue_plot(tb_static)
     with pytest.raises(ValueError):
         taxbrain.revenue_plot(tb_static, tax_vars=["income", "combined"])