review

Author: dweindl

doc/example/distributions.ipynb

@@ -141,7 +141,7 @@
    "cell_type": "markdown",
    "id": "dab4b2d1e0f312d8",
    "metadata": {},
-   "source": "If a parameter scale is specified (`parameterScale=lin|log|log10`) and the chosen distribution is not a `parameterScale*`-type distribution, then the distribution parameters are taken as is, i.e., the `parameterScale` is not applied to the distribution parameters. In the context of PEtab prior distributions, `parameterScale` will only be used for the start point sampling for optimization, where the sample will be transformed accordingly. This is demonstrated below. The left plot always shows the prior distribution for unscaled parameter values, and the right plot shows the prior distribution for scaled parameter values. Note that in the objective function, the prior is always on the unscaled parameters.\n"
+   "source": "If a parameter scale is specified (`parameterScale=lin|log|log10`), the distribution parameters are used as is without applying the `parameterScale` to them. The exception are the `parameterScale*`-type distributions, as explained below. In the context of PEtab prior distributions, `parameterScale` will only be used for the start point sampling for optimization, where the sample will be transformed accordingly. This is demonstrated below. The left plot always shows the prior distribution for unscaled parameter values, and the right plot shows the prior distribution for scaled parameter values. Note that in the objective function, the prior is always on the unscaled parameters.\n"
   },
   {
    "cell_type": "code",

petab/v1/distributions.py

@@ -27,7 +27,7 @@ class Distribution(abc.ABC):
     :param log: If ``True``, the distribution is transformed to its
         corresponding log distribution (e.g., Normal -> LogNormal).
         If a float, the distribution is transformed to its corresponding
-        log distribution with the given base (e.g., Normal -> Log10Normal).
+        log distribution with the given log-base (e.g., Normal -> Log10Normal).
         If ``False``, no transformation is applied.
     :param trunc: The truncation points (lower, upper) of the distribution
         or ``None`` if the distribution is not truncated.
@@ -99,7 +99,7 @@ def _exp(self, x: np.ndarray | float) -> np.ndarray | float:
     def _log(self, x: np.ndarray | float) -> np.ndarray | float:
         """Apply the log transformation if enabled.
 
-        Compute the log of x with the specified base if a log transformation
+        Compute the log of `x` with the specified base if a log transformation
         is applied to the distribution. Otherwise, return the input.
 
         :param x: The value to transform.
@@ -279,11 +279,10 @@ class Normal(Distribution):
         are the truncation limits of the transformed distribution.
     :param log: If ``True``, the distribution is transformed to a log-normal
         distribution. If a float, the distribution is transformed to a
-        log-normal distribution with the given base.
+        log-normal distribution with the given log-base.
         If ``False``, no transformation is applied.
         If a transformation is applied, the location and scale parameters
-        and the truncation limits are the location, scale and truncation limits
-        of the underlying normal distribution.
+        are the location and scale of the underlying normal distribution.
     """
 
     def __init__(
@@ -332,7 +331,7 @@ class Uniform(Distribution):
     :param high: The upper bound of the distribution.
     :param log: If ``True``, the distribution is transformed to a log-uniform
         distribution. If a float, the distribution is transformed to a
-        log-uniform distribution with the given base.
+        log-uniform distribution with the given log-base.
         If ``False``, no transformation is applied.
         If a transformation is applied, the lower and upper bounds are the
         lower and upper bounds of the underlying uniform distribution.
@@ -376,11 +375,10 @@ class Laplace(Distribution):
         are the truncation limits of the transformed distribution.
     :param log: If ``True``, the distribution is transformed to a log-Laplace
         distribution. If a float, the distribution is transformed to a
-        log-Laplace distribution with the given base.
+        log-Laplace distribution with the given log-base.
         If ``False``, no transformation is applied.
         If a transformation is applied, the location and scale parameters
-        and the truncation limits are the location, scale and truncation limits
-        of the underlying Laplace distribution.
+        are the location and scale of the underlying Laplace distribution.
     """
 
     def __init__(