Clean up

.github/workflows/build_and_test.yml

@@ -1,4 +1,4 @@
-name: Build and test [Python 3.10, 3.11]
+name: Build and test [Python 3.10]
 
 on: [push, pull_request]
 
@@ -7,7 +7,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10"]
 
     steps:
       - name: Checkout

.github/workflows/deploy_docs.yml

@@ -18,7 +18,7 @@ jobs:
         with:
           activate-environment: iot-dev
           environment-file: environment.yml
-          python-version: 3.11
+          python-version: "3.10"
           auto-activate-base: false
 
       - name: Build # Build Jupyter Book

.github/workflows/docs_check.yml

@@ -15,7 +15,7 @@ jobs:
         with:
           activate-environment: iot-dev
           environment-file: environment.yml
-          python-version: 3.11
+          python-version: "3.10"
           auto-activate-base: false
 
       - name: Build # Build Jupyter Book

environment.yml

@@ -2,8 +2,8 @@ name: iot-dev
 channels:
 - conda-forge
 dependencies:
-- python
-- "taxcalc>=4.0.0"
+- python<3.11
+- "taxcalc==4.4.0"
 - pandas
 - numpy
 - scipy
@@ -15,7 +15,7 @@ dependencies:
 - pylint
 - coverage
 - pip:
-  - jupyter-book>=0.9.1
+  - jupyter-book<2.0.0
   - black
   - plotly-express
   - kaleido

iot/inverse_optimal_tax.py

@@ -288,9 +288,7 @@ def mills_ratio(t):
                 # tails, which can zero out the fitted density and
                 # poison downstream calculations (theta_z, g_z) with
                 # infs/NaNs.
-                return np.sqrt(np.pi / 2) * scipy.special.erfcx(
-                    t / np.sqrt(2)
-                )
+                return np.sqrt(np.pi / 2) * scipy.special.erfcx(t / np.sqrt(2))
 
             def pln_pdf(y, mu, sigma, alpha):
                 x1 = alpha * sigma - (np.log(y) - mu) / sigma
@@ -336,11 +334,9 @@ def fit_pln(data, weights, initial_guess):
 
             def pln_cdf(y, mu, sigma, alpha):
                 x1 = alpha * sigma - (np.log(y) - mu) / sigma
-                CDF = (
-                    st.norm.cdf((np.log(y) - mu) / sigma)
-                    - st.norm.pdf((np.log(y) - mu) / sigma)
-                    * mills_ratio(x1)
-                )
+                CDF = st.norm.cdf((np.log(y) - mu) / sigma) - st.norm.pdf(
+                    (np.log(y) - mu) / sigma
+                ) * mills_ratio(x1)
                 return CDF
 
             def pln_dpdf(y, mu, sigma, alpha):
@@ -480,14 +476,10 @@ def find_eti(iot, g_z=None, eti_0=0.25, boundary="z0"):
         tail_integral = np.append(rev_cumsum, 0.0)
 
         eti_beliefs = (
-            ((1 - iot.mtr) / iot.mtr)
-            * (1 / (iot.z * iot.f))
-            * tail_integral
+            ((1 - iot.mtr) / iot.mtr) * (1 / (iot.z * iot.f)) * tail_integral
         )
     else:
-        raise ValueError(
-            f"boundary must be 'z0' or 'inf', got '{boundary}'"
-        )
+        raise ValueError(f"boundary must be 'z0' or 'inf', got '{boundary}'")
 
     return eti_beliefs