Fix pre-commit configuration (#2171)

Relevant changes are only in .pre-commit-config.yaml

* run black on notebooks via pre-commit
* set proper line width (pyproject.toml is not used if we are running things from the repo root)
* compatible line width for black + isort
* fix json error in binder/overview.ipynb
* re-blacken everything

Author: dweindl

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ repos:
   hooks:
     - id: isort
       name: isort (python)
-      args: ["--profile", "black", "--filter-files"]
+      args: ["--profile", "black", "--filter-files", "--line-length", "79"]
 - repo: https://github.com/pre-commit/pre-commit-hooks
   rev: v4.4.0
   hooks:
@@ -17,12 +17,14 @@ repos:
   - id: end-of-file-fixer
   - id: trailing-whitespace
 - repo: https://github.com/psf/black
-  rev: 23.3.0
+  rev: 23.7.0
   hooks:
-    - id: black
+    - id: black-jupyter
       # It is recommended to specify the latest version of Python
       # supported by your project here, or alternatively use
       # pre-commit's default_language_version, see
       # https://pre-commit.com/#top_level-default_language_version
       language_version: python3.11
+      args: ["--line-length", "79"]
+
 exclude: '^(ThirdParty|models)/'

binder/overview.ipynb

@@ -34,7 +34,8 @@
     "\n",
     "* [Interfacing JAX](../python/examples/example_jax/ExampleJax.ipynb)\n",
     "\n",
-    "  Provides guidance on how to combine AMICI with differential programming frameworks such as JAX.\n"
+    "  Provides guidance on how to combine AMICI with differential programming frameworks such as JAX.\n",
+    "\n",
     "* [Efficient spline interpolation](../python/examples/example_splines/ExampleSplines.ipynb)\n",
     "\n",
     "  Shows how to add annotated spline formulas to existing SBML models in order to speed up AMICI's model import.\n",

documentation/ExampleJax.ipynb

@@ -46,10 +46,10 @@
      "output_type": "stream",
      "text": [
       "Cloning into 'tmp/benchmark-models'...\n",
-      "remote: Enumerating objects: 336, done.\u001B[K\n",
-      "remote: Counting objects: 100% (336/336), done.\u001B[K\n",
-      "remote: Compressing objects: 100% (285/285), done.\u001B[K\n",
-      "remote: Total 336 (delta 88), reused 216 (delta 39), pack-reused 0\u001B[K\n",
+      "remote: Enumerating objects: 336, done.\u001b[K\n",
+      "remote: Counting objects: 100% (336/336), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (285/285), done.\u001b[K\n",
+      "remote: Total 336 (delta 88), reused 216 (delta 39), pack-reused 0\u001b[K\n",
       "Receiving objects: 100% (336/336), 2.11 MiB | 7.48 MiB/s, done.\n",
       "Resolving deltas: 100% (88/88), done.\n"
      ]
@@ -58,7 +58,8 @@
    "source": [
     "!git clone --depth 1 https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git tmp/benchmark-models || (cd tmp/benchmark-models && git pull)\n",
     "from pathlib import Path\n",
-    "folder_base = Path('.') / \"tmp\" / \"benchmark-models\" / \"Benchmark-Models\""
+    "\n",
+    "folder_base = Path(\".\") / \"tmp\" / \"benchmark-models\" / \"Benchmark-Models\""
    ]
   },
   {
@@ -77,6 +78,7 @@
    "outputs": [],
    "source": [
     "import petab\n",
+    "\n",
     "model_name = \"Boehm_JProteomeRes2014\"\n",
     "yaml_file = folder_base / model_name / (model_name + \".yaml\")\n",
     "petab_problem = petab.Problem.from_yaml(yaml_file)"
@@ -570,6 +572,7 @@
    ],
    "source": [
     "from amici.petab_import import import_petab_problem\n",
+    "\n",
     "amici_model = import_petab_problem(petab_problem, force_compile=True)"
    ]
   },
@@ -606,14 +609,16 @@
    "source": [
     "from amici.petab_objective import simulate_petab\n",
     "import amici\n",
+    "\n",
     "amici_solver = amici_model.getSolver()\n",
     "amici_solver.setSensitivityOrder(amici.SensitivityOrder.first)\n",
     "\n",
+    "\n",
     "def amici_hcb_base(parameters: jnp.array):\n",
     "    return simulate_petab(\n",
-    "        petab_problem, \n",
-    "        amici_model, \n",
-    "        problem_parameters=dict(zip(petab_problem.x_free_ids, parameters)), \n",
+    "        petab_problem,\n",
+    "        amici_model,\n",
+    "        problem_parameters=dict(zip(petab_problem.x_free_ids, parameters)),\n",
     "        scaled_parameters=True,\n",
     "        solver=amici_solver,\n",
     "    )"
@@ -635,13 +640,14 @@
    "outputs": [],
    "source": [
     "def amici_hcb_llh(parameters: jnp.array):\n",
-    "    return amici_hcb_base(parameters)['llh']\n",
+    "    return amici_hcb_base(parameters)[\"llh\"]\n",
+    "\n",
     "\n",
     "def amici_hcb_sllh(parameters: jnp.array):\n",
-    "    sllh = amici_hcb_base(parameters)['sllh']\n",
-    "    return jnp.asarray(tuple(\n",
-    "        sllh[par_id] for par_id in petab_problem.x_free_ids\n",
-    "    ))"
+    "    sllh = amici_hcb_base(parameters)[\"sllh\"]\n",
+    "    return jnp.asarray(\n",
+    "        tuple(sllh[par_id] for par_id in petab_problem.x_free_ids)\n",
+    "    )"
    ]
   },
   {
@@ -663,6 +669,8 @@
     "from jax import custom_jvp\n",
     "\n",
     "import numpy as np\n",
+    "\n",
+    "\n",
     "@custom_jvp\n",
     "def jax_objective(parameters: jnp.array):\n",
     "    return hcb.call(\n",
@@ -695,7 +703,9 @@
     "    sllh = hcb.call(\n",
     "        amici_hcb_sllh,\n",
     "        parameters,\n",
-    "        result_shape=jax.ShapeDtypeStruct((petab_problem.parameter_df.estimate.sum(),), np.float64),\n",
+    "        result_shape=jax.ShapeDtypeStruct(\n",
+    "            (petab_problem.parameter_df.estimate.sum(),), np.float64\n",
+    "        ),\n",
     "    )\n",
     "    return llh, sllh.dot(x_dot)"
    ]
@@ -717,19 +727,25 @@
    "source": [
     "from jax import value_and_grad\n",
     "\n",
-    "parameter_scales = petab_problem.parameter_df.loc[petab_problem.x_free_ids, petab.PARAMETER_SCALE].values\n",
+    "parameter_scales = petab_problem.parameter_df.loc[\n",
+    "    petab_problem.x_free_ids, petab.PARAMETER_SCALE\n",
+    "].values\n",
+    "\n",
     "\n",
     "@jax.jit\n",
     "@value_and_grad\n",
     "def jax_objective_with_parameter_transform(parameters: jnp.array):\n",
-    "    par_scaled = jnp.asarray(tuple(\n",
-    "        value if scale == petab.LIN\n",
-    "        else jnp.log(value) if scale == petab.LOG\n",
-    "        else jnp.log10(value)\n",
-    "        for value, scale in zip(parameters, parameter_scales)\n",
-    "    ))\n",
-    "    return jax_objective(par_scaled)\n",
-    "    "
+    "    par_scaled = jnp.asarray(\n",
+    "        tuple(\n",
+    "            value\n",
+    "            if scale == petab.LIN\n",
+    "            else jnp.log(value)\n",
+    "            if scale == petab.LOG\n",
+    "            else jnp.log10(value)\n",
+    "            for value, scale in zip(parameters, parameter_scales)\n",
+    "        )\n",
+    "    )\n",
+    "    return jax_objective(par_scaled)"
    ]
   },
   {
@@ -755,7 +771,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llh_jax, sllh_jax = jax_objective_with_parameter_transform(petab_problem.x_nominal_free)"
+    "llh_jax, sllh_jax = jax_objective_with_parameter_transform(\n",
+    "    petab_problem.x_nominal_free\n",
+    ")"
    ]
   },
   {
@@ -777,7 +795,9 @@
     "# TODO remove me as soon as sllh in simulate_petab is fixed\n",
     "sllh = {\n",
     "    name: value / (np.log(10) * par_value)\n",
-    "    for (name, value), par_value in zip(r['sllh'].items(), petab_problem.x_nominal_free)\n",
+    "    for (name, value), par_value in zip(\n",
+    "        r[\"sllh\"].items(), petab_problem.x_nominal_free\n",
+    "    )\n",
     "}"
    ]
   },
@@ -802,7 +822,8 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "pd.Series(dict(amici=r['llh'], jax=float(llh_jax)))"
+    "\n",
+    "pd.Series(dict(amici=r[\"llh\"], jax=float(llh_jax)))"
    ]
   },
   {
@@ -905,7 +926,9 @@
     }
    ],
    "source": [
-    "pd.DataFrame(index=sllh.keys(), data=dict(amici=sllh.values(), jax=np.asarray(sllh_jax)))"
+    "pd.DataFrame(\n",
+    "    index=sllh.keys(), data=dict(amici=sllh.values(), jax=np.asarray(sllh_jax))\n",
+    ")"
    ]
   },
   {
@@ -925,7 +948,9 @@
    "outputs": [],
    "source": [
     "jax.config.update(\"jax_enable_x64\", True)\n",
-    "llh_jax, sllh_jax = jax_objective_with_parameter_transform(petab_problem.x_nominal_free)"
+    "llh_jax, sllh_jax = jax_objective_with_parameter_transform(\n",
+    "    petab_problem.x_nominal_free\n",
+    ")"
    ]
   },
   {
@@ -956,7 +981,7 @@
     }
    ],
    "source": [
-    "pd.Series(dict(amici=r['llh'], jax=float(llh_jax)))"
+    "pd.Series(dict(amici=r[\"llh\"], jax=float(llh_jax)))"
    ]
   },
   {
@@ -1059,7 +1084,9 @@
     }
    ],
    "source": [
-    "pd.DataFrame(index=sllh.keys(), data=dict(amici=sllh.values(), jax=np.asarray(sllh_jax)))"
+    "pd.DataFrame(\n",
+    "    index=sllh.keys(), data=dict(amici=sllh.values(), jax=np.asarray(sllh_jax))\n",
+    ")"
    ]
   }
  ],

documentation/GettingStarted.ipynb

@@ -26,7 +26,8 @@
    "outputs": [],
    "source": [
     "import amici\n",
-    "sbml_importer = amici.SbmlImporter('model_steadystate_scaled.xml')"
+    "\n",
+    "sbml_importer = amici.SbmlImporter(\"model_steadystate_scaled.xml\")"
    ]
   },
   {
@@ -42,8 +43,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_name = 'model_steadystate'\n",
-    "model_dir = 'model_dir'\n",
+    "model_name = \"model_steadystate\"\n",
+    "model_dir = \"model_dir\"\n",
     "sbml_importer.sbml2amici(model_name, model_dir)"
    ]
   },
@@ -82,7 +83,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.setParameterByName('p1',1e-3)"
+    "model.setParameterByName(\"p1\", 1e-3)"
    ]
   },
   {
@@ -122,7 +123,7 @@
    "outputs": [],
    "source": [
     "# set timepoints\n",
-    "model.setTimepoints([0,1])\n",
+    "model.setTimepoints([0, 1])\n",
     "rdata = amici.runAmiciSimulation(model, solver)"
    ]
   },

documentation/conf.py

@@ -52,7 +52,9 @@ def my_exhale_generate_doxygen(doxygen_input):
     DomainDirectiveFactory as breathe_DomainDirectiveFactory,
 )
 
-old_breathe_DomainDirectiveFactory_create = breathe_DomainDirectiveFactory.create
+old_breathe_DomainDirectiveFactory_create = (
+    breathe_DomainDirectiveFactory.create
+)
 
 
 def my_breathe_DomainDirectiveFactory_create(domain: str, args):
@@ -67,7 +69,9 @@ def my_breathe_DomainDirectiveFactory_create(domain: str, args):
     return cls(domain + ":" + name, *args[1:])
 
 
-breathe_DomainDirectiveFactory.create = my_breathe_DomainDirectiveFactory_create
+breathe_DomainDirectiveFactory.create = (
+    my_breathe_DomainDirectiveFactory_create
+)
 
 
 # END Monkeypatch breathe
@@ -102,7 +106,9 @@ def install_doxygen():
     subprocess.run(cmd, shell=True, check=True)
     assert os.path.islink(os.path.join(some_dir_on_path, "doxygen"))
     # verify it's available
-    res = subprocess.run(["doxygen", "--version"], check=False, capture_output=True)
+    res = subprocess.run(
+        ["doxygen", "--version"], check=False, capture_output=True
+    )
     print(res.stdout.decode(), res.stderr.decode())
     assert version in res.stdout.decode()
 
@@ -176,7 +182,10 @@ def install_doxygen():
 
 intersphinx_mapping = {
     "pysb": ("https://pysb.readthedocs.io/en/stable/", None),
-    "petab": ("https://petab.readthedocs.io/projects/libpetab-python/en/latest/", None),
+    "petab": (
+        "https://petab.readthedocs.io/projects/libpetab-python/en/latest/",
+        None,
+    ),
     "pandas": ("https://pandas.pydata.org/docs/", None),
     "numpy": ("https://numpy.org/devdocs/", None),
     "sympy": ("https://docs.sympy.org/latest/", None),
@@ -291,7 +300,9 @@ def install_doxygen():
     "verboseBuild": True,
 }
 
-mtocpp_filter = os.path.join(amici_dir, "matlab", "mtoc", "config", "mtocpp_filter.sh")
+mtocpp_filter = os.path.join(
+    amici_dir, "matlab", "mtoc", "config", "mtocpp_filter.sh"
+)
 exhale_projects_args = {
     "AMICI_CPP": {
         "exhaleDoxygenStdin": "\n".join(
@@ -504,10 +515,14 @@ def process_docstring(app, what, name, obj, options, lines):
         for old, new in typemaps.items():
             lines[i] = lines[i].replace(old, new)
         lines[i] = re.sub(
-            r"amici::(Model|Solver|ExpData) ", r":class:`amici\.amici\.\1\`", lines[i]
+            r"amici::(Model|Solver|ExpData) ",
+            r":class:`amici\.amici\.\1\`",
+            lines[i],
         )
         lines[i] = re.sub(
-            r"amici::(runAmiciSimulation[s]?)", r":func:`amici\.amici\.\1`", lines[i]
+            r"amici::(runAmiciSimulation[s]?)",
+            r":func:`amici\.amici\.\1`",
+            lines[i],
         )
 
 

documentation/recreate_reference_list.py

@@ -42,7 +42,10 @@ def get_sub_bibliography(year, by_year, bibfile):
 
     entries = ",".join(["@" + x for x in by_year[year]])
     stdin_input = (
-        "---\n" f"bibliography: {bibfile}\n" f'nocite: "{entries}"\n...\n' f"# {year}"
+        "---\n"
+        f"bibliography: {bibfile}\n"
+        f'nocite: "{entries}"\n...\n'
+        f"# {year}"
     )
 
     out = subprocess.run(
@@ -67,7 +70,8 @@ def main():
     with open(outfile, "w") as f:
         f.write("# References\n\n")
         f.write(
-            "List of publications using AMICI. " f"Total number is {num_total}.\n\n"
+            "List of publications using AMICI. "
+            f"Total number is {num_total}.\n\n"
         )
         f.write(
             "If you applied AMICI in your work and your publication is "