diff --git a/.gitignore b/.gitignore
index ebbab06..c2b68b7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,4 @@
!eitc.csv
!spm_threshold_agi.csv
**/_build
+!population_by_state.csv
diff --git a/Makefile b/Makefile
index 9bf8088..da9c5a7 100644
--- a/Makefile
+++ b/Makefile
@@ -31,6 +31,8 @@ docker:
documentation:
jb clean docs && jb build docs
+ python docs/add_plotly_to_book.py docs
+
data:
python policyengine_us_data/datasets/acs/acs.py
diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index e69de29..11e0c33 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -0,0 +1,5 @@
+- bump: minor
+ changes:
+ added:
+ - Metric comparisons by dataset to the documentation.
+ - Calibration of state populations.
diff --git a/docs/add_plotly_to_book.py b/docs/add_plotly_to_book.py
new file mode 100644
index 0000000..822e77a
--- /dev/null
+++ b/docs/add_plotly_to_book.py
@@ -0,0 +1,27 @@
+import argparse
+from pathlib import Path
+
+# This command-line tools enables Plotly charts to show in the HTML files for the Jupyter Book documentation.
+
+parser = argparse.ArgumentParser()
+parser.add_argument("book_path", help="Path to the Jupyter Book.")
+
+args = parser.parse_args()
+
+# Find every HTML file in the Jupyter Book. Then, add a script tag to the start of the tag in each file, with the contents:
+#
+
+book_folder = Path(args.book_path)
+
+for html_file in book_folder.glob("**/*.html"):
+ with open(html_file, "r") as f:
+ html = f.read()
+
+ # Add the script tag to the start of the tag.
+ html = html.replace(
+ "",
+ '',
+ )
+
+ with open(html_file, "w") as f:
+ f.write(html)
diff --git a/docs/results.ipynb b/docs/results.ipynb
index acb152e..f65669e 100644
--- a/docs/results.ipynb
+++ b/docs/results.ipynb
@@ -310,7 +310,7 @@
},
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": 94,
"metadata": {},
"outputs": [
{
@@ -7270,7 +7270,8 @@
" title=\"Weight\",\n",
" type=\"log\",\n",
" ),\n",
- ")"
+ ")\n",
+ "fig"
]
}
],
diff --git a/policyengine_us_data/datasets/acs/acs.py b/policyengine_us_data/datasets/acs/acs.py
index 43f0c4d..0ecd3ee 100644
--- a/policyengine_us_data/datasets/acs/acs.py
+++ b/policyengine_us_data/datasets/acs/acs.py
@@ -111,7 +111,7 @@ class ACS_2022(ACS):
time_period = 2022
file_path = STORAGE_FOLDER / "acs_2022.h5"
census_acs = CensusACS_2022
- url = "release://PolicyEngine/policyengine-us-data/1.11.0/acs_2022.h5"
+ url = "release://PolicyEngine/policyengine-us-data/1.13.0/acs_2022.h5"
if __name__ == "__main__":
diff --git a/policyengine_us_data/datasets/cps/cps.py b/policyengine_us_data/datasets/cps/cps.py
index cbcd756..ce51aef 100644
--- a/policyengine_us_data/datasets/cps/cps.py
+++ b/policyengine_us_data/datasets/cps/cps.py
@@ -648,7 +648,7 @@ class CPS_2024(CPS):
label = "CPS 2024 (2022-based)"
file_path = STORAGE_FOLDER / "cps_2024.h5"
time_period = 2024
- url = "release://policyengine/policyengine-us-data/1.11.0/cps_2024.h5"
+ url = "release://policyengine/policyengine-us-data/1.13.0/cps_2024.h5"
class PooledCPS(Dataset):
@@ -707,7 +707,7 @@ class Pooled_3_Year_CPS_2023(PooledCPS):
CPS_2023,
]
time_period = 2023
- url = "release://PolicyEngine/policyengine-us-data/1.11.0/pooled_3_year_cps_2023.h5"
+ url = "release://PolicyEngine/policyengine-us-data/1.13.0/pooled_3_year_cps_2023.h5"
if __name__ == "__main__":
diff --git a/policyengine_us_data/datasets/cps/enhanced_cps.py b/policyengine_us_data/datasets/cps/enhanced_cps.py
index da28b34..b9171c3 100644
--- a/policyengine_us_data/datasets/cps/enhanced_cps.py
+++ b/policyengine_us_data/datasets/cps/enhanced_cps.py
@@ -189,7 +189,7 @@ class EnhancedCPS_2024(EnhancedCPS):
name = "enhanced_cps_2024"
label = "Enhanced CPS 2024"
file_path = STORAGE_FOLDER / "enhanced_cps_2024.h5"
- url = "release://policyengine/policyengine-us-data/1.11.0/enhanced_cps_2024.h5"
+ url = "release://policyengine/policyengine-us-data/1.13.0/enhanced_cps_2024.h5"
if __name__ == "__main__":
diff --git a/policyengine_us_data/storage/population_by_state.csv b/policyengine_us_data/storage/population_by_state.csv
new file mode 100644
index 0000000..0931747
--- /dev/null
+++ b/policyengine_us_data/storage/population_by_state.csv
@@ -0,0 +1,53 @@
+state,population,population_under_5
+CA,38965193.00,2104120.00
+TX,30503301.00,1921708.00
+FL,22610726.00,1130536.00
+NY,19571216.00,1037274.00
+PA,12961683.00,661046.00
+IL,12549689.00,665134.00
+OH,11785935.00,660012.00
+GA,11029227.00,639695.00
+NC,10835491.00,606787.00
+MI,10037261.00,531975.00
+NJ,9290841.00,520287.00
+VA,8715698.00,488079.00
+WA,7812880.00,421896.00
+AZ,7431344.00,393861.00
+TN,7126489.00,413336.00
+MA,7001399.00,343069.00
+IN,6862199.00,404870.00
+MO,6196156.00,353181.00
+MD,6180253.00,352274.00
+WI,5910955.00,313281.00
+CO,5877610.00,311513.00
+MN,5737915.00,327061.00
+SC,5373555.00,290172.00
+AL,5108468.00,291183.00
+LA,4573749.00,278999.00
+KY,4526154.00,262517.00
+OR,4233358.00,203201.00
+OK,4053824.00,243229.00
+CT,3617176.00,180859.00
+UT,3417734.00,232406.00
+IA,3207004.00,186006.00
+PR,3205691.00,96171.00
+NV,3194176.00,172486.00
+AR,3067732.00,180996.00
+KS,2940546.00,176433.00
+MS,2939690.00,173442.00
+NM,2114371.00,107833.00
+NE,1978379.00,124638.00
+ID,1964726.00,113954.00
+WV,1770071.00,86733.00
+HI,1435138.00,77497.00
+NH,1402054.00,63092.00
+ME,1395722.00,61412.00
+MT,1132812.00,57773.00
+RI,1095962.00,52606.00
+DE,1031890.00,54690.00
+SD,919318.00,57917.00
+ND,783926.00,49387.00
+AK,733406.00,46205.00
+DC,678972.00,38701.00
+VT,647464.00,27193.00
+WY,584057.00,30955.00
\ No newline at end of file
diff --git a/policyengine_us_data/storage/uprating_factors.csv b/policyengine_us_data/storage/uprating_factors.csv
index 3269bdc..897e3b1 100644
--- a/policyengine_us_data/storage/uprating_factors.csv
+++ b/policyengine_us_data/storage/uprating_factors.csv
@@ -3,6 +3,7 @@ alimony_expense,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.
alimony_income,1.0,1.255,1.322,1.357,1.446,1.504,1.535,1.567,1.576,1.595,1.622,1.655,1.689,1.723,1.779
american_opportunity_credit,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718
amt_foreign_tax_credit,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718
+capital_gains_before_response,1.0,1.824,1.11,1.195,1.244,1.195,1.14,1.122,1.126,1.145,1.173,1.206,1.243,1.283,1.326
casualty_loss,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718
cdcc_relevant_expenses,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718
charitable_cash_donations,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718
diff --git a/policyengine_us_data/storage/uprating_growth_factors.csv b/policyengine_us_data/storage/uprating_growth_factors.csv
index d020204..380f22d 100644
--- a/policyengine_us_data/storage/uprating_growth_factors.csv
+++ b/policyengine_us_data/storage/uprating_growth_factors.csv
@@ -3,6 +3,7 @@ alimony_expense,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.
alimony_income,0,0.255,0.053,0.026,0.066,0.04,0.021,0.021,0.006,0.012,0.017,0.02,0.021,0.02,0.033
american_opportunity_credit,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033
amt_foreign_tax_credit,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033
+capital_gains_before_response,0,0.824,-0.391,0.077,0.041,-0.039,-0.046,-0.016,0.004,0.017,0.024,0.028,0.031,0.032,0.034
casualty_loss,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033
cdcc_relevant_expenses,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033
charitable_cash_donations,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033
diff --git a/policyengine_us_data/utils/loss.py b/policyengine_us_data/utils/loss.py
index 4669f89..a01b16a 100644
--- a/policyengine_us_data/utils/loss.py
+++ b/policyengine_us_data/utils/loss.py
@@ -322,6 +322,24 @@ def build_loss_matrix(dataset: type, time_period):
)
targets_array.append(row["count"])
+ # Population by state and population under 5 by state
+
+ state_population = pd.read_csv(STORAGE_FOLDER / "population_by_state.csv")
+
+ for _, row in state_population.iterrows():
+ in_state = sim.calculate("state_code", map_to="person") == row["state"]
+ label = f"census/population_by_state/{row['state']}"
+ loss_matrix[label] = sim.map_result(in_state, "person", "household")
+ targets_array.append(row["population"])
+
+ under_5 = sim.calculate("age").values < 5
+ in_state_under_5 = in_state * under_5
+ label = f"census/population_under_5_by_state/{row['state']}"
+ loss_matrix[label] = sim.map_result(
+ in_state_under_5, "person", "household"
+ )
+ targets_array.append(row["population_under_5"])
+
if any(loss_matrix.isna().sum() > 0):
raise ValueError("Some targets are missing from the loss matrix")