Merge branch 'develop' into object_model

Author: dweindl

.pre-commit-config.yaml

@@ -2,7 +2,7 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.4.0
+  rev: v5.0.0
   hooks:
   - id: check-added-large-files
   - id: check-merge-conflict
@@ -12,7 +12,7 @@ repos:
   - id: trailing-whitespace
 - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
-  rev: v0.1.11
+  rev: v0.9.10
   hooks:
     # Run the linter.
     - id: ruff

doc/example/distributions.ipynb

@@ -1,8 +1,9 @@
 {
  "cells": [
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "372289411a2aa7b3",
+   "metadata": {},
    "source": [
     "# Prior distributions in PEtab\n",
     "\n",
@@ -18,14 +19,16 @@
     "* *Initialization priors* can be used as a hint for the optimization algorithm. They will not enter the objective function. They are specified in the `initializationPriorType` and `initializationPriorParameters` columns of the parameter table.\n",
     "\n",
     "\n"
-   ],
-   "id": "372289411a2aa7b3"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
    "metadata": {
     "collapsed": true
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -45,143 +48,156 @@
     "    sample = prior.sample(10000)\n",
     "\n",
     "    # pdf\n",
-    "    xmin = min(sample.min(), prior.lb_scaled if prior.bounds is not None else sample.min())\n",
-    "    xmax = max(sample.max(), prior.ub_scaled if prior.bounds is not None else sample.max())\n",
+    "    xmin = min(\n",
+    "        sample.min(),\n",
+    "        prior.lb_scaled if prior.bounds is not None else sample.min(),\n",
+    "    )\n",
+    "    xmax = max(\n",
+    "        sample.max(),\n",
+    "        prior.ub_scaled if prior.bounds is not None else sample.max(),\n",
+    "    )\n",
     "    x = np.linspace(xmin, xmax, 500)\n",
     "    y = prior.pdf(x)\n",
-    "    ax.plot(x, y, color='red', label='pdf')\n",
+    "    ax.plot(x, y, color=\"red\", label=\"pdf\")\n",
     "\n",
-    "    sns.histplot(sample, stat='density', ax=ax, label=\"sample\")\n",
+    "    sns.histplot(sample, stat=\"density\", ax=ax, label=\"sample\")\n",
     "\n",
     "    # bounds\n",
     "    if prior.bounds is not None:\n",
     "        for bound in (prior.lb_scaled, prior.ub_scaled):\n",
     "            if bound is not None and np.isfinite(bound):\n",
-    "                ax.axvline(bound, color='black', linestyle='--', label='bound')\n",
+    "                ax.axvline(bound, color=\"black\", linestyle=\"--\", label=\"bound\")\n",
     "\n",
     "    ax.set_title(str(prior))\n",
-    "    ax.set_xlabel('Parameter value on the parameter scale')\n",
+    "    ax.set_xlabel(\"Parameter value on the parameter scale\")\n",
     "    ax.grid(False)\n",
     "    handles, labels = ax.get_legend_handles_labels()\n",
-    "    unique_labels = dict(zip(labels, handles))\n",
+    "    unique_labels = dict(zip(labels, handles, strict=False))\n",
     "    ax.legend(unique_labels.values(), unique_labels.keys())\n",
     "    plt.show()"
-   ],
-   "id": "initial_id",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "The basic distributions are the uniform, normal, Laplace, log-normal, and log-laplace distributions:\n",
-   "id": "db36a4a93622ccb8"
+   "id": "db36a4a93622ccb8",
+   "metadata": {},
+   "source": "The basic distributions are the uniform, normal, Laplace, log-normal, and log-laplace distributions:\n"
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "4f09e50a3db06d9f",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plot(Prior(UNIFORM, (0, 1)))\n",
     "plot(Prior(NORMAL, (0, 1)))\n",
     "plot(Prior(LAPLACE, (0, 1)))\n",
     "plot(Prior(LOG_NORMAL, (0, 1)))\n",
     "plot(Prior(LOG_LAPLACE, (1, 0.5)))"
-   ],
-   "id": "4f09e50a3db06d9f",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "If a parameter scale is specified (`parameterScale=lin|log|log10` not a `parameterScale*`-type distribution), the sample is transformed accordingly (but not the distribution parameters):\n",
-   "id": "dab4b2d1e0f312d8"
+   "id": "dab4b2d1e0f312d8",
+   "metadata": {},
+   "source": "If a parameter scale is specified (`parameterScale=lin|log|log10` not a `parameterScale*`-type distribution), the sample is transformed accordingly (but not the distribution parameters):\n"
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "f6192c226f179ef9",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plot(Prior(NORMAL, (10, 2), transformation=LIN))\n",
     "plot(Prior(NORMAL, (10, 2), transformation=LOG))\n",
     "\n",
-    "# Note that the log-normal distribution is different from a log-transformed normal distribution:\n",
+    "# Note that the log-normal distribution is different\n",
+    "#  from a log-transformed normal distribution:\n",
     "plot(Prior(LOG_NORMAL, (10, 2), transformation=LIN))"
-   ],
-   "id": "f6192c226f179ef9",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "On the log-transformed parameter scale, `Log*` and `parameterScale*` distributions are equivalent:",
-   "id": "4281ed48859e6431"
+   "id": "4281ed48859e6431",
+   "metadata": {},
+   "source": "On the log-transformed parameter scale, `Log*` and `parameterScale*` distributions are equivalent:"
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "34c95268e8921070",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plot(Prior(LOG_NORMAL, (10, 2), transformation=LOG))\n",
     "plot(Prior(PARAMETER_SCALE_NORMAL, (10, 2)))"
-   ],
-   "id": "34c95268e8921070",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "Prior distributions can also be defined on the parameter scale by using the types `parameterScaleUniform`, `parameterScaleNormal` or `parameterScaleLaplace`. In these cases, 1) the distribution parameter are interpreted on the transformed parameter scale, and 2) a sample from the given distribution is used directly, without applying any transformation according to `parameterScale` (this implies, that for `parameterScale=lin`, there is no difference between `parameterScaleUniform` and `uniform`):",
-   "id": "263c9fd31156a4d5"
+   "id": "263c9fd31156a4d5",
+   "metadata": {},
+   "source": "Prior distributions can also be defined on the parameter scale by using the types `parameterScaleUniform`, `parameterScaleNormal` or `parameterScaleLaplace`. In these cases, 1) the distribution parameter are interpreted on the transformed parameter scale, and 2) a sample from the given distribution is used directly, without applying any transformation according to `parameterScale` (this implies, that for `parameterScale=lin`, there is no difference between `parameterScaleUniform` and `uniform`):"
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "5ca940bc24312fc6",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plot(Prior(UNIFORM, (0.01, 2), transformation=LOG10))\n",
     "plot(Prior(PARAMETER_SCALE_UNIFORM, (0.01, 2), transformation=LOG10))\n",
     "\n",
     "plot(Prior(UNIFORM, (0.01, 2), transformation=LIN))\n",
-    "plot(Prior(PARAMETER_SCALE_UNIFORM, (0.01, 2), transformation=LIN))\n"
-   ],
-   "id": "5ca940bc24312fc6",
-   "outputs": [],
-   "execution_count": null
+    "plot(Prior(PARAMETER_SCALE_UNIFORM, (0.01, 2), transformation=LIN))"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "To prevent the sampled parameters from exceeding the bounds, the sampled parameters are clipped to the bounds. The bounds are defined in the parameter table. Note that the current implementation does not support sampling from a truncated distribution. Instead, the samples are clipped to the bounds. This may introduce unwanted bias, and thus, should only be used with caution (i.e., the bounds should be chosen wide enough):",
-   "id": "b1a8b17d765db826"
+   "id": "b1a8b17d765db826",
+   "metadata": {},
+   "source": "To prevent the sampled parameters from exceeding the bounds, the sampled parameters are clipped to the bounds. The bounds are defined in the parameter table. Note that the current implementation does not support sampling from a truncated distribution. Instead, the samples are clipped to the bounds. This may introduce unwanted bias, and thus, should only be used with caution (i.e., the bounds should be chosen wide enough):"
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "source": [
-    "plot(Prior(NORMAL, (0, 1), bounds=(-4, 4)))  # negligible clipping-bias at 4 sigma\n",
-    "plot(Prior(UNIFORM, (0, 1), bounds=(0.1, 0.9)))  # significant clipping-bias"
-   ],
+   "execution_count": null,
    "id": "4ac42b1eed759bdd",
+   "metadata": {},
    "outputs": [],
-   "execution_count": null
+   "source": [
+    "plot(\n",
+    "    Prior(NORMAL, (0, 1), bounds=(-4, 4))\n",
+    ")  # negligible clipping-bias at 4 sigma\n",
+    "plot(Prior(UNIFORM, (0, 1), bounds=(0.1, 0.9)))  # significant clipping-bias"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "Further distribution examples:",
-   "id": "45ffce1341483f24"
+   "id": "45ffce1341483f24",
+   "metadata": {},
+   "source": "Further distribution examples:"
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "581e1ac431860419",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plot(Prior(NORMAL, (10, 1), bounds=(6, 14), transformation=\"log10\"))\n",
-    "plot(Prior(PARAMETER_SCALE_NORMAL, (10, 1), bounds=(10**6, 10**14), transformation=\"log10\"))\n",
+    "plot(\n",
+    "    Prior(\n",
+    "        PARAMETER_SCALE_NORMAL,\n",
+    "        (10, 1),\n",
+    "        bounds=(10**6, 10**14),\n",
+    "        transformation=\"log10\",\n",
+    "    )\n",
+    ")\n",
     "plot(Prior(LAPLACE, (10, 2), bounds=(6, 14)))"
-   ],
-   "id": "581e1ac431860419",
-   "outputs": [],
-   "execution_count": null
+   ]
   }
  ],
  "metadata": {

petab/__init__.py

@@ -8,6 +8,7 @@
         PEtab should use for operations that can be performed in parallel.
         By default, all operations are performed sequentially.
 """
+
 import importlib
 import sys
 from functools import partial

petab/petablint.py

@@ -161,7 +161,7 @@ def main():
             validate(args.yaml_file_name)
         except SchemaValidationError as e:
             logger.error(
-                "Provided YAML file does not adhere to PEtab " f"schema: {e}"
+                f"Provided YAML file does not adhere to PEtab schema: {e}"
             )
             sys.exit(1)
 
@@ -205,9 +205,7 @@ def main():
     if args.parameter_file_name:
         logger.debug(f"\tParameter table: {args.parameter_file_name}")
     if args.visualization_file_name:
-        logger.debug(
-            "\tVisualization table: " f"{args.visualization_file_name}"
-        )
+        logger.debug(f"\tVisualization table: {args.visualization_file_name}")
 
     try:
         problem = petab.Problem.from_files(

petab/v1/C.py

@@ -2,6 +2,7 @@
 """
 This file contains constant definitions.
 """
+
 import math as _math
 import sys
 

petab/v1/composite_problem.py

@@ -1,4 +1,5 @@
 """PEtab problems consisting of multiple models"""
+
 import os
 
 import pandas as pd

petab/v1/core.py

@@ -1,4 +1,5 @@
 """PEtab core functions (or functions that don't fit anywhere else)"""
+
 import logging
 import os
 import re

petab/v1/distributions.py

@@ -1,4 +1,5 @@
 """Probability distributions used by PEtab."""
+
 from __future__ import annotations
 
 import abc

petab/v1/format_version.py

@@ -1,2 +1,3 @@
 """PEtab file format version"""
+
 __format_version__ = 1

petab/v1/lint.py

@@ -556,7 +556,7 @@ def check_parameter_bounds(parameter_df: pd.DataFrame) -> None:
             ] in [LOG, LOG10]:
                 raise AssertionError(
                     f"Bounds for {row[PARAMETER_SCALE]} scaled parameter "
-                    f"{ row.name} must be positive."
+                    f"{row.name} must be positive."
                 )
             if (
                 row.get(PARAMETER_SCALE, LIN) in [LOG, LOG10]
@@ -586,8 +586,7 @@ def assert_parameter_prior_type_is_valid(parameter_df: pd.DataFrame) -> None:
         for _, row in parameter_df.iterrows():
             if row[col] not in PRIOR_TYPES and not core.is_empty(row[col]):
                 raise AssertionError(
-                    f"{col} must be one of {PRIOR_TYPES} but is "
-                    f"'{row[col]}'."
+                    f"{col} must be one of {PRIOR_TYPES} but is '{row[col]}'."
                 )
 
 
@@ -945,7 +944,7 @@ def lint_problem(problem: "petab.Problem") -> bool:
             for obs_id in problem.observable_df.index:
                 if problem.model.has_entity_with_id(obs_id):
                     logger.error(
-                        f"Observable ID {obs_id} shadows model " "entity."
+                        f"Observable ID {obs_id} shadows model entity."
                     )
                     errors_occurred = True
     else:
@@ -1002,8 +1001,7 @@ def lint_problem(problem: "petab.Problem") -> bool:
         or problem.observable_df is None
     ):
         logger.warning(
-            "Not all files of the PEtab problem definition could "
-            "be checked."
+            "Not all files of the PEtab problem definition could be checked."
         )
     else:
         logger.info("PEtab format check completed successfully.")
@@ -1213,7 +1211,7 @@ def check_ids(ids: Iterable[str], kind: str = "") -> None:
         offset = 2
         error_output = "\n".join(
             [
-                f"Line {index+offset}: "
+                f"Line {index + offset}: "
                 + ("Missing ID" if pd.isna(_id) else _id)
                 for index, _id in invalids
             ]