Rename spm_unit_net_income to spm_resources

changelog_entry.yaml

@@ -0,0 +1,5 @@
+- bump: minor
+  changes:
+    added:
+    - Code coverage for SPM Unit.
+    - Renamed spm_unit_net_income to spm_resources

docs/gov/irs/credits/cdcc.ipynb

@@ -162613,7 +162613,7 @@
                 "\n",
                 "class ignore_reported(Reform):\n",
                 "    def apply(self):\n",
-                "        self.neutralize_variable(\"spm_unit_net_income_reported\")\n",
+                "        self.neutralize_variable(\"spm_resources_reported\")\n",
                 "\n",
                 "\n",
                 "class neutralize_cdcc(Reform):\n",
@@ -162625,8 +162625,8 @@
                 "sim_no_cdcc = Microsimulation((ignore_reported, neutralize_cdcc), year=2020)\n",
                 "\n",
                 "(\n",
-                "    sim.calc(\"spm_unit_net_income\", period=2022).sum()\n",
-                "    - sim_no_cdcc.calc(\"spm_unit_net_income\", period=2022).sum()\n",
+                "    sim.calc(\"spm_resources\", period=2022).sum()\n",
+                "    - sim_no_cdcc.calc(\"spm_resources\", period=2022).sum()\n",
                 ") / 1e6"
             ]
         }

docs/gov/irs/credits/clean-vehicle.ipynb

@@ -85340,7 +85340,7 @@
     "    return pd.DataFrame(\n",
     "        dict(\n",
     "            employment_income=sim.calc(\"employment_income\")[0],\n",
-    "            spm_unit_net_income=sim.calc(\"spm_unit_net_income\")[0],\n",
+    "            spm_resources=sim.calc(\"spm_resources\")[0],\n",
     "            credit=sim.calc(\"new_clean_vehicle_credit\")[0],\n",
     "            adults=adults,\n",
     "            children=children,\n",
@@ -85362,7 +85362,7 @@
     "wide = df.pivot(\n",
     "    index=[\"employment_income\", \"adults\", \"children\"],\n",
     "    columns=\"purchased_qualifying_new_clean_vehicle\",\n",
-    "    values=\"spm_unit_net_income\",\n",
+    "    values=\"spm_resources\",\n",
     ").reset_index()\n",
     "wide[\"benefit\"] = wide[True] - wide[False]\n",
     "\n",
@@ -170708,7 +170708,7 @@
     "    return pd.DataFrame(\n",
     "        dict(\n",
     "            employment_income=sim.calc(\"employment_income\")[0],\n",
-    "            spm_unit_net_income=sim.calc(\"spm_unit_net_income\")[0],\n",
+    "            spm_resources=sim.calc(\"spm_resources\")[0],\n",
     "            credit=sim.calc(\"used_clean_vehicle_credit\")[0],\n",
     "            adults=adults,\n",
     "            children=children,\n",
@@ -170734,7 +170734,7 @@
     "wide = df.pivot(\n",
     "    index=[\"employment_income\", \"adults\", \"children\"],\n",
     "    columns=\"purchased_qualifying_used_clean_vehicle\",\n",
-    "    values=\"spm_unit_net_income\",\n",
+    "    values=\"spm_resources\",\n",
     ").reset_index()\n",
     "\n",
     "wide[\"benefit\"] = wide[True] - wide[False]\n",

docs/gov/states/de/tax-benefit.ipynb

@@ -25334,14 +25334,14 @@
                 "    sim.add_household(name=\"household\", members=members, state_code=\"DE\")\n",
                 "    sim.vary(\"employment_income\", max=100_000, step=100)\n",
                 "    employment_income = sim.calc(\"employment_income\")[0]\n",
-                "    spm_unit_net_income = sim.calc(\"spm_unit_net_income\")[0].round()\n",
+                "    spm_resources = sim.calc(\"spm_resources\")[0].round()\n",
                 "    mtr = 1 - sim.deriv(\n",
-                "        \"spm_unit_net_income\", \"employment_income\", wrt_target=\"head\"\n",
+                "        \"spm_resources\", \"employment_income\", wrt_target=\"head\"\n",
                 "    )\n",
                 "    return pd.DataFrame(\n",
                 "        dict(\n",
                 "            employment_income=employment_income,\n",
-                "            spm_unit_net_income=spm_unit_net_income,\n",
+                "            spm_resources=spm_resources,\n",
                 "            mtr=mtr,\n",
                 "            adults=adults,\n",
                 "            children=str(children),\n",
@@ -25359,7 +25359,7 @@
                 "\n",
                 "LABELS = dict(\n",
                 "    employment_income=\"Employment income\",\n",
-                "    spm_unit_net_income=\"Net income\",\n",
+                "    spm_resources=\"Net income\",\n",
                 "    mtr=\"Marginal tax rate\",\n",
                 "    adults=\"Adults\",\n",
                 "    children=\"Children\",\n",
@@ -25370,7 +25370,7 @@
                 "fig = px.line(\n",
                 "    df,\n",
                 "    \"employment_income\",\n",
-                "    \"spm_unit_net_income\",\n",
+                "    \"spm_resources\",\n",
                 "    color=\"children\",\n",
                 "    animation_frame=\"adults\",\n",
                 "    labels=LABELS,\n",
@@ -53643,22 +53643,22 @@
                 "\n",
                 "sim.vary(\"employment_income\", max=100_000, step=100)\n",
                 "employment_income = sim.calc(\"employment_income\")[0]\n",
-                "spm_unit_net_income = sim.calc(\"spm_unit_net_income\")[0].round()\n",
+                "spm_resources = sim.calc(\"spm_resources\")[0].round()\n",
                 "mtr = 1 - sim.deriv(\n",
-                "    \"spm_unit_net_income\", \"employment_income\", wrt_target=\"head\"\n",
+                "    \"spm_resources\", \"employment_income\", wrt_target=\"head\"\n",
                 ")\n",
                 "df = pd.DataFrame(\n",
                 "    dict(\n",
                 "        employment_income=employment_income,\n",
-                "        spm_unit_net_income=spm_unit_net_income,\n",
+                "        spm_resources=spm_resources,\n",
                 "        mtr=mtr,\n",
                 "    )\n",
                 ")\n",
                 "\n",
                 "fig = px.line(\n",
                 "    df,\n",
                 "    \"employment_income\",\n",
-                "    \"spm_unit_net_income\",\n",
+                "    \"spm_resources\",\n",
                 "    labels=LABELS,\n",
                 "    title=\"Net income for a single person aged 65 in Delaware\",\n",
                 ")\n",

docs/gov/states/ma/tax-benefit.ipynb

@@ -25349,14 +25349,14 @@
                 "    sim.add_household(name=\"household\", members=members, state_code=\"MA\")\n",
                 "    sim.vary(\"employment_income\", max=100_000, step=100)\n",
                 "    employment_income = sim.calc(\"employment_income\")[0]\n",
-                "    spm_unit_net_income = sim.calc(\"spm_unit_net_income\")[0].round()\n",
+                "    spm_resources = sim.calc(\"spm_resources\")[0].round()\n",
                 "    mtr = 1 - sim.deriv(\n",
-                "        \"spm_unit_net_income\", \"employment_income\", wrt_target=\"head\"\n",
+                "        \"spm_resources\", \"employment_income\", wrt_target=\"head\"\n",
                 "    )\n",
                 "    return pd.DataFrame(\n",
                 "        dict(\n",
                 "            employment_income=employment_income,\n",
-                "            spm_unit_net_income=spm_unit_net_income,\n",
+                "            spm_resources=spm_resources,\n",
                 "            mtr=mtr,\n",
                 "            adults=adults,\n",
                 "            children=str(children),\n",
@@ -25374,7 +25374,7 @@
                 "\n",
                 "LABELS = dict(\n",
                 "    employment_income=\"Employment income\",\n",
-                "    spm_unit_net_income=\"Net income\",\n",
+                "    spm_resources=\"Net income\",\n",
                 "    mtr=\"Marginal tax rate\",\n",
                 "    adults=\"Adults\",\n",
                 "    children=\"Children\",\n",
@@ -25385,7 +25385,7 @@
                 "fig = px.line(\n",
                 "    df,\n",
                 "    \"employment_income\",\n",
-                "    \"spm_unit_net_income\",\n",
+                "    \"spm_resources\",\n",
                 "    color=\"children\",\n",
                 "    animation_frame=\"adults\",\n",
                 "    labels=LABELS,\n",
@@ -53658,22 +53658,22 @@
                 "\n",
                 "sim.vary(\"employment_income\", max=100_000, step=100)\n",
                 "employment_income = sim.calc(\"employment_income\")[0]\n",
-                "spm_unit_net_income = sim.calc(\"spm_unit_net_income\")[0].round()\n",
+                "spm_resources = sim.calc(\"spm_resources\")[0].round()\n",
                 "mtr = 1 - sim.deriv(\n",
-                "    \"spm_unit_net_income\", \"employment_income\", wrt_target=\"head\"\n",
+                "    \"spm_resources\", \"employment_income\", wrt_target=\"head\"\n",
                 ")\n",
                 "df = pd.DataFrame(\n",
                 "    dict(\n",
                 "        employment_income=employment_income,\n",
-                "        spm_unit_net_income=spm_unit_net_income,\n",
+                "        spm_resources=spm_resources,\n",
                 "        mtr=mtr,\n",
                 "    )\n",
                 ")\n",
                 "\n",
                 "fig = px.line(\n",
                 "    df,\n",
                 "    \"employment_income\",\n",
-                "    \"spm_unit_net_income\",\n",
+                "    \"spm_resources\",\n",
                 "    labels=LABELS,\n",
                 "    title=\"Net income for a single person aged 65 in Massachusetts\",\n",
                 ")\n",

docs/gov/states/mo/tax-benefit.ipynb

@@ -25332,14 +25332,14 @@
                 "    sim.add_household(name=\"household\", members=members, state_code=\"MO\")\n",
                 "    sim.vary(\"employment_income\", max=100_000, step=100)\n",
                 "    employment_income = sim.calc(\"employment_income\")[0]\n",
-                "    spm_unit_net_income = sim.calc(\"spm_unit_net_income\")[0].round()\n",
+                "    spm_resources = sim.calc(\"spm_resources\")[0].round()\n",
                 "    mtr = 1 - sim.deriv(\n",
-                "        \"spm_unit_net_income\", \"employment_income\", wrt_target=\"head\"\n",
+                "        \"spm_resources\", \"employment_income\", wrt_target=\"head\"\n",
                 "    )\n",
                 "    return pd.DataFrame(\n",
                 "        dict(\n",
                 "            employment_income=employment_income,\n",
-                "            spm_unit_net_income=spm_unit_net_income,\n",
+                "            spm_resources=spm_resources,\n",
                 "            mtr=mtr,\n",
                 "            adults=adults,\n",
                 "            children=str(children),\n",
@@ -25357,7 +25357,7 @@
                 "\n",
                 "LABELS = dict(\n",
                 "    employment_income=\"Employment income\",\n",
-                "    spm_unit_net_income=\"Net income\",\n",
+                "    spm_resources=\"Net income\",\n",
                 "    mtr=\"Marginal tax rate\",\n",
                 "    adults=\"Adults\",\n",
                 "    children=\"Children\",\n",
@@ -25368,7 +25368,7 @@
                 "fig = px.line(\n",
                 "    df,\n",
                 "    \"employment_income\",\n",
-                "    \"spm_unit_net_income\",\n",
+                "    \"spm_resources\",\n",
                 "    color=\"children\",\n",
                 "    animation_frame=\"adults\",\n",
                 "    labels=LABELS,\n",
@@ -53620,22 +53620,22 @@
                 "\n",
                 "sim.vary(\"employment_income\", max=100_000, step=100)\n",
                 "employment_income = sim.calc(\"employment_income\")[0]\n",
-                "spm_unit_net_income = sim.calc(\"spm_unit_net_income\")[0].round()\n",
+                "spm_resources = sim.calc(\"spm_resources\")[0].round()\n",
                 "mtr = 1 - sim.deriv(\n",
-                "    \"spm_unit_net_income\", \"employment_income\", wrt_target=\"head\"\n",
+                "    \"spm_resources\", \"employment_income\", wrt_target=\"head\"\n",
                 ")\n",
                 "df = pd.DataFrame(\n",
                 "    dict(\n",
                 "        employment_income=employment_income,\n",
-                "        spm_unit_net_income=spm_unit_net_income,\n",
+                "        spm_resources=spm_resources,\n",
                 "        mtr=mtr,\n",
                 "    )\n",
                 ")\n",
                 "\n",
                 "fig = px.line(\n",
                 "    df,\n",
                 "    \"employment_income\",\n",
-                "    \"spm_unit_net_income\",\n",
+                "    \"spm_resources\",\n",
                 "    labels=LABELS,\n",
                 "    title=\"Net income for a single person aged 65 in Missouri\",\n",
                 ")\n",

docs/gov/states/ny/cdcc.ipynb

@@ -9962,7 +9962,7 @@
     "\n",
     "    class ignore_reported(Reform):\n",
     "        def apply(self):\n",
-    "            self.neutralize_variable(\"spm_unit_net_income_reported\")\n",
+    "            self.neutralize_variable(\"spm_resources_reported\")\n",
     "\n",
     "    class neutralize(Reform):\n",
     "        def apply(self):\n",
@@ -9974,10 +9974,10 @@
     "    )\n",
     "    program_value = sim.calc(variable, period=policy_year).sum()\n",
     "    baseline_net_income = sim.calc(\n",
-    "        \"spm_unit_net_income\", period=policy_year\n",
+    "        \"spm_resources\", period=policy_year\n",
     "    ).sum()\n",
     "    neutralized_net_income = sim_neutralized.calc(\n",
-    "        \"spm_unit_net_income\", period=policy_year\n",
+    "        \"spm_resources\", period=policy_year\n",
     "    ).sum()\n",
     "    budgetary_impact = neutralized_net_income - baseline_net_income\n",
     "    return round(program_value / 1e6), round(budgetary_impact / 1e6)\n",

docs/gov/states/ny/ctc.ipynb

@@ -49446,7 +49446,7 @@
     "\n",
     "    class ignore_reported(Reform):\n",
     "        def apply(self):\n",
-    "            self.neutralize_variable(\"spm_unit_net_income_reported\")\n",
+    "            self.neutralize_variable(\"spm_resources_reported\")\n",
     "\n",
     "    class neutralize(Reform):\n",
     "        def apply(self):\n",
@@ -49458,10 +49458,10 @@
     "    )\n",
     "    program_value = sim.calc(variable, period=policy_year).sum()\n",
     "    baseline_net_income = sim.calc(\n",
-    "        \"spm_unit_net_income\", period=policy_year\n",
+    "        \"spm_resources\", period=policy_year\n",
     "    ).sum()\n",
     "    neutralized_net_income = sim_neutralized.calc(\n",
-    "        \"spm_unit_net_income\", period=policy_year\n",
+    "        \"spm_resources\", period=policy_year\n",
     "    ).sum()\n",
     "    budgetary_impact = neutralized_net_income - baseline_net_income\n",
     "    return round(program_value / 1e6), round(budgetary_impact / 1e6)\n",

docs/gov/states/ny/eitc.ipynb

@@ -31519,11 +31519,11 @@
      "text": [
       "/Users/maxghenis/opt/anaconda3/envs/py37/lib/python3.7/site-packages/policyengine_core/holders/holder.py:169: Warning:\n",
       "\n",
-      "You cannot set a value for the variable spm_unit_net_income_reported, as it has been neutralized. The value you provided ([28327. 76811. 15350. ... 17282. 78821. 53152.]) will be ignored.\n",
+      "You cannot set a value for the variable spm_resources_reported, as it has been neutralized. The value you provided ([28327. 76811. 15350. ... 17282. 78821. 53152.]) will be ignored.\n",
       "\n",
       "/Users/maxghenis/opt/anaconda3/envs/py37/lib/python3.7/site-packages/policyengine_core/holders/holder.py:169: Warning:\n",
       "\n",
-      "You cannot set a value for the variable spm_unit_net_income_reported, as it has been neutralized. The value you provided ([28327. 76811. 15350. ... 17282. 78821. 53152.]) will be ignored.\n",
+      "You cannot set a value for the variable spm_resources_reported, as it has been neutralized. The value you provided ([28327. 76811. 15350. ... 17282. 78821. 53152.]) will be ignored.\n",
       "\n"
      ]
     },
@@ -31559,7 +31559,7 @@
     "\n",
     "    class ignore_reported(Reform):\n",
     "        def apply(self):\n",
-    "            self.neutralize_variable(\"spm_unit_net_income_reported\")\n",
+    "            self.neutralize_variable(\"spm_resources_reported\")\n",
     "\n",
     "    class neutralize(Reform):\n",
     "        def apply(self):\n",
@@ -31571,10 +31571,10 @@
     "    )\n",
     "    program_value = sim.calc(variable, period=policy_year).sum()\n",
     "    baseline_net_income = sim.calc(\n",
-    "        \"spm_unit_net_income\", period=policy_year\n",
+    "        \"spm_resources\", period=policy_year\n",
     "    ).sum()\n",
     "    neutralized_net_income = sim_neutralized.calc(\n",
-    "        \"spm_unit_net_income\", period=policy_year\n",
+    "        \"spm_resources\", period=policy_year\n",
     "    ).sum()\n",
     "    budgetary_impact = neutralized_net_income - baseline_net_income\n",
     "    return round(program_value / 1e6), round(budgetary_impact / 1e6)\n",

docs/gov/states/ny/tax-benefit.ipynb

@@ -25466,14 +25466,14 @@
                 "    sim.add_household(name=\"household\", members=members, state_code=\"NY\")\n",
                 "    sim.vary(\"employment_income\", max=100_000, step=100)\n",
                 "    employment_income = sim.calc(\"employment_income\")[0]\n",
-                "    spm_unit_net_income = sim.calc(\"spm_unit_net_income\")[0].round()\n",
+                "    spm_resources = sim.calc(\"spm_resources\")[0].round()\n",
                 "    mtr = 1 - sim.deriv(\n",
-                "        \"spm_unit_net_income\", \"employment_income\", wrt_target=\"head\"\n",
+                "        \"spm_resources\", \"employment_income\", wrt_target=\"head\"\n",
                 "    )\n",
                 "    return pd.DataFrame(\n",
                 "        dict(\n",
                 "            employment_income=employment_income,\n",
-                "            spm_unit_net_income=spm_unit_net_income,\n",
+                "            spm_resources=spm_resources,\n",
                 "            mtr=mtr,\n",
                 "            adults=adults,\n",
                 "            children=str(children),\n",
@@ -25491,7 +25491,7 @@
                 "\n",
                 "LABELS = dict(\n",
                 "    employment_income=\"Employment income\",\n",
-                "    spm_unit_net_income=\"Net income\",\n",
+                "    spm_resources=\"Net income\",\n",
                 "    mtr=\"Marginal tax rate\",\n",
                 "    adults=\"Adults\",\n",
                 "    children=\"Children\",\n",
@@ -25502,7 +25502,7 @@
                 "fig = px.line(\n",
                 "    df,\n",
                 "    \"employment_income\",\n",
-                "    \"spm_unit_net_income\",\n",
+                "    \"spm_resources\",\n",
                 "    color=\"children\",\n",
                 "    animation_frame=\"adults\",\n",
                 "    labels=LABELS,\n",

policyengine_us/data/datasets/acs/acs.py

@@ -109,7 +109,7 @@ def add_person_variables(acs: h5py.File, person: DataFrame) -> None:
 
 
 def add_spm_variables(acs: h5py.File, spm_unit: DataFrame) -> None:
-    acs["spm_unit_net_income_reported"] = spm_unit.SPM_RESOURCES
+    acs["spm_resources_reported"] = spm_unit.SPM_RESOURCES
     acs["spm_unit_spm_threshold"] = spm_unit.SPM_POVTHRESHOLD
 
 

policyengine_us/tests/policy/baseline/household/cliff_gap.yaml

@@ -7,7 +7,7 @@
     taxable_interest_income: 100_000
     state_fips: 48  # TX
   output:
-    spm_unit_net_income: 151_539.00
+    spm_resources: 151_539.00
     household_net_income: 151_539.00
 
 - name: household_net_income test TX-netinc-alt
@@ -19,7 +19,7 @@
     taxable_interest_income: 100_000
     state_fips: 48  # TX
   output:
-    spm_unit_net_income: 152_670.00
+    spm_resources: 152_670.00
     household_net_income: 152_670.00
 
 - name: cliff_gap test TX-cliff_gap