create simulation function and run simulations on all 4 univariate tr…

…ansforms in simulations.ipynb

src/distrx/transforms.py

@@ -41,28 +41,28 @@ def __init__(self, transform: str) -> None:
         self.transform = transform
 
     def __call__(
-        self, mu: npt.ArrayLike, sigma: npt.ArrayLike, n: npt.ArrayLike
+        self, mu: npt.ArrayLike, sigma: npt.ArrayLike
     ) -> Tuple[np.ndarray, np.ndarray]:
         match self.transform:
             case "log":
-                return self.log_trans(mu, sigma, n)
+                return self.log_trans(mu, sigma)
             case "logit":
-                return self.logit_trans(mu, sigma, n)
+                return self.logit_trans(mu, sigma)
             case "exp":
-                return self.exp_trans(mu, sigma, n)
+                return self.exp_trans(mu, sigma)
             case "expit":
-                return self.expit_trans(mu, sigma, n)
+                return self.expit_trans(mu, sigma)
             case _:
                 raise ValueError(f"Invalid transform '{self.transform}'.")
 
     def log_trans(
-        self, mu: npt.ArrayLike, sigma: npt.ArrayLike, n: npt.ArrayLike
+        self, mu: npt.ArrayLike, sigma: npt.ArrayLike
     ) -> Tuple[np.ndarray, np.ndarray]:
         """Performs delta method on data under log transform
 
         .. math::
 
-            \\log(mu), \\frac{\\sigma}{\\mu} \\cdot \\frac{1}{\\sqrt{n}}
+            \\log(mu), \\frac{\\sigma}{\\mu}
 
         Parameters
         ----------
@@ -77,16 +77,16 @@ def log_trans(
             Transformed mean and standard error
         """
         log = c2fun_dict["log"]
-        return log(mu), sigma * log(mu, order=1) / np.sqrt(n)
+        return log(mu), sigma * log(mu, order=1)
 
     def logit_trans(
-        self, mu: npt.ArrayLike, sigma: npt.ArrayLike, n: npt.ArrayLike
+        self, mu: npt.ArrayLike, sigma: npt.ArrayLike
     ) -> Tuple[np.ndarray, np.ndarray]:
         """Performs delta method on data under logit transform
 
         .. math::
 
-            \\log(\\frac{\\mu}{1 - \\mu}), \\frac{\\sigma}{\\mu \\cdot (1 - \\mu)} \\cdot \\frac{1}{\\sqrt{n}}
+            \\log(\\frac{\\mu}{1 - \\mu}), \\frac{\\sigma}{\\mu \\cdot (1 - \\mu)}
 
         Parameters
         ----------
@@ -101,16 +101,16 @@ def logit_trans(
             Transformed mean and standard error
         """
         logit = c2fun_dict["logit"]
-        return logit(mu), sigma * logit(mu, order=1) / np.sqrt(n)
+        return logit(mu), sigma * logit(mu, order=1)
 
     def exp_trans(
-        self, mu: npt.ArrayLike, sigma: npt.ArrayLike, n: npt.ArrayLike
+        self, mu: npt.ArrayLike, sigma: npt.ArrayLike
     ) -> Tuple[np.ndarray, np.ndarray]:
         """Performs delta method on data under exponential transform
 
         .. math::
 
-            \\exp(\\mu), \\sigma \\cdot \\exp(\\mu) \\cdot \\frac{1}{\\sqrt{n}}
+            \\exp(\\mu), \\sigma \\cdot \\exp(\\mu)
 
         Parameters
         ----------
@@ -125,16 +125,16 @@ def exp_trans(
             Transformed mean and standard error
         """
         exp = c2fun_dict["exp"]
-        return exp(mu), sigma * exp(mu, order=1) / np.sqrt(n)
+        return exp(mu), sigma * exp(mu, order=1)
 
     def expit_trans(
-        self, mu: npt.ArrayLike, sigma: npt.ArrayLike, n: npt.ArrayLike
+        self, mu: npt.ArrayLike, sigma: npt.ArrayLike
     ) -> Tuple[np.ndarray, np.ndarray]:
         """Performs delta method on data under expit transform
 
         .. math::
 
-            \\frac{1}{1 + \\exp(-\\mu)}, \\sigma \\cdot \\frac{\\exp(\\mu)}{(1 + \\exp(\\mu))^2} \\cdot \\frac{1}{\\sqrt{n}}
+            \\frac{1}{1 + \\exp(-\\mu)}, \\sigma \\cdot \\frac{\\exp(\\mu)}{(1 + \\exp(\\mu))^2}
 
         Parameters
         ----------
@@ -149,7 +149,7 @@ def expit_trans(
             Transformed mean and standard error
         """
         expit = c2fun_dict["expit"]
-        return expit(mu), sigma * expit(mu, order=1) / np.sqrt(n)
+        return expit(mu), sigma * expit(mu, order=1)
 
 
 class FirstOrderBivariate:
@@ -226,7 +226,6 @@ def percentage_change_trans(
 def transform_univariate(
     mu: npt.ArrayLike,
     sigma: npt.ArrayLike,
-    n: npt.ArrayLike,
     transform: str,
     method: str = "delta",
 ) -> Tuple[np.ndarray, np.ndarray]:
@@ -258,11 +257,11 @@ def transform_univariate(
     """
 
     mu, sigma = np.array(mu), np.array(sigma)
-    _check_input(mu, sigma, n)
+    _check_input(mu, sigma)
     match method:
         case "delta":
             transformer = FirstOrder(transform)
-            return transformer(mu, sigma, n)
+            return transformer(mu, sigma)
         case _:
             raise ValueError(f"Invalid method '{method}'.")
 
@@ -364,9 +363,7 @@ def transform_bivariate(
 #     return delta_hat, np.sqrt(sigma_trans)
 
 
-def _check_input(
-    mu: npt.ArrayLike, sigma: npt.ArrayLike, n: npt.ArrayLike
-) -> None:
+def _check_input(mu: npt.ArrayLike, sigma: npt.ArrayLike) -> None:
     """Run checks on input data.
 
     Parameters
@@ -382,7 +379,7 @@ def _check_input(
 
     """
     # _check_lengths_match(mu, sigma)
-    _check_sigma_n_positive(sigma, n)
+    _check_sigma_positive(sigma)
 
 
 def _check_lengths_match(mu: npt.ArrayLike, sigma: npt.ArrayLike) -> None:
@@ -400,7 +397,7 @@ def _check_lengths_match(mu: npt.ArrayLike, sigma: npt.ArrayLike) -> None:
         raise ValueError("Lengths of mu and sigma don't match.")
 
 
-def _check_sigma_n_positive(sigma: npt.ArrayLike, n: npt.ArrayLike) -> None:
+def _check_sigma_positive(sigma: npt.ArrayLike) -> None:
     """Check that `sigma` is positive.
 
     Parameters
@@ -413,7 +410,3 @@ def _check_sigma_n_positive(sigma: npt.ArrayLike, n: npt.ArrayLike) -> None:
         warnings.warn("Sigma vector contains zeros.")
     if np.any(sigma < 0.0):
         raise ValueError("Sigma values must be positive.")
-    if np.any(n == 0.0):
-        warnings.warn("Sigma vector contains zeros.")
-    if np.any(n < 0.0):
-        raise ValueError("Sigma values must be positive.")