[Missing Data pymc-devs#461] removed extra # comments

Signed-off-by: Nathaniel <[email protected]>
NathanielF · Feb 3, 2023 · 411b2be · 411b2be
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commit 411b2be
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diff --git a/examples/case_studies/Missing_Data_Imputation.ipynb b/examples/case_studies/Missing_Data_Imputation.ipynb
@@ -857,7 +857,7 @@
     "\n",
     "\n",
     "sensitivity = {}\n",
-    "n_missing = np.linspace(30, 100, 5)  ## Change or alter the range as desired\n",
+    "n_missing = np.linspace(30, 100, 5)  # Change or alter the range as desired\n",
     "bootstrap_iterations = 100  # change to large number running a real analysis in this case\n",
     "for n in n_missing:\n",
     "    sensitivity[int(n)] = {}\n",
@@ -8629,27 +8629,27 @@
     "\n",
     "with pm.Model(coords=coords) as hierarchical_model:\n",
     "\n",
-    "    ## Priors\n",
+    "    # Priors\n",
     "    company_beta_lmx = pm.Normal(\"company_beta_lmx\", 0, 1)\n",
     "    company_beta_male = pm.Normal(\"company_beta_male\", 0, 1)\n",
     "    company_alpha = pm.Normal(\"company_alpha\", 20, 2)\n",
     "    team_alpha = pm.Normal(\"team_alpha\", 0, 1, dims=\"team\")\n",
     "    team_beta_lmx = pm.Normal(\"team_beta_lmx\", 0, 1, dims=\"team\")\n",
     "    sigma = pm.HalfNormal(\"sigma\", 4, dims=\"employee\")\n",
     "\n",
-    "    ## Imputed Predictors\n",
+    "    # Imputed Predictors\n",
     "    mu_lmx = pm.Normal(\"mu_lmx\", 10, 5)\n",
     "    sigma_lmx = pm.HalfNormal(\"sigma_lmx\", 5)\n",
     "    lmx_pred = pm.Normal(\"lmx_pred\", mu_lmx, sigma_lmx, observed=df_employee[\"lmx\"].values)\n",
     "\n",
-    "    ## Combining Levels\n",
+    "    # Combining Levels\n",
     "    alpha_global = pm.Deterministic(\"alpha_global\", company_alpha + team_alpha[team_idx])\n",
     "    beta_global_lmx = pm.Deterministic(\n",
     "        \"beta_global_lmx\", company_beta_lmx + team_beta_lmx[team_idx]\n",
     "    )\n",
     "    beta_global_male = pm.Deterministic(\"beta_global_male\", company_beta_male)\n",
     "\n",
-    "    ## Likelihood\n",
+    "    # Likelihood\n",
     "    mu = pm.Deterministic(\n",
     "        \"mu\",\n",
     "        alpha_global + beta_global_lmx * lmx_pred + beta_global_male * df_employee[\"male\"].values,\n",

diff --git a/examples/case_studies/Missing_Data_Imputation.myst.md b/examples/case_studies/Missing_Data_Imputation.myst.md
@@ -255,7 +255,7 @@ data_200.reset_index(inplace=True, drop=True)
 
 
 sensitivity = {}
-n_missing = np.linspace(30, 100, 5)  ## Change or alter the range as desired
+n_missing = np.linspace(30, 100, 5)  # Change or alter the range as desired
 bootstrap_iterations = 100  # change to large number running a real analysis in this case
 for n in n_missing:
     sensitivity[int(n)] = {}
@@ -708,27 +708,27 @@ coords = {"team": teams, "employee": np.arange(len(df_employee))}
 
 with pm.Model(coords=coords) as hierarchical_model:
 
-    ## Priors
+    # Priors
     company_beta_lmx = pm.Normal("company_beta_lmx", 0, 1)
     company_beta_male = pm.Normal("company_beta_male", 0, 1)
     company_alpha = pm.Normal("company_alpha", 20, 2)
     team_alpha = pm.Normal("team_alpha", 0, 1, dims="team")
     team_beta_lmx = pm.Normal("team_beta_lmx", 0, 1, dims="team")
     sigma = pm.HalfNormal("sigma", 4, dims="employee")
 
-    ## Imputed Predictors
+    # Imputed Predictors
     mu_lmx = pm.Normal("mu_lmx", 10, 5)
     sigma_lmx = pm.HalfNormal("sigma_lmx", 5)
     lmx_pred = pm.Normal("lmx_pred", mu_lmx, sigma_lmx, observed=df_employee["lmx"].values)
 
-    ## Combining Levels
+    # Combining Levels
     alpha_global = pm.Deterministic("alpha_global", company_alpha + team_alpha[team_idx])
     beta_global_lmx = pm.Deterministic(
         "beta_global_lmx", company_beta_lmx + team_beta_lmx[team_idx]
     )
     beta_global_male = pm.Deterministic("beta_global_male", company_beta_male)
 
-    ## Likelihood
+    # Likelihood
     mu = pm.Deterministic(
         "mu",
         alpha_global + beta_global_lmx * lmx_pred + beta_global_male * df_employee["male"].values,