Repository: yaml2sbml-dev/yaml2sbml (language: python)

Issue:
Add logo license
This is necessary so that people can use the logo in presentations etc.. I suggest a creative commons license, as in https://amici.readthedocs.io/en/latest/LICENSE.html#amici-logo


Discussion / hints:

Fix patch:
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 565d3f6..9da5ccd 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,13 +1,26 @@
 name: CI
 
-on: [pull_request]
+# trigger
+on:
+  pull_request:
+  push:
+    branches:
+    - master
 
 jobs:
   base:
-    runs-on: ubuntu-latest
     strategy:
       matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+        include:
+        - os: ubuntu-latest
+          path: ~/.cache/pip
+        - os: macos-latest
+          path: ~/Library/Caches/pip
+        - os: windows-latest
+          path: ~\AppData\Local\pip\Cache
         python-version: [3.6, 3.9]
+    runs-on: ${{ matrix.os }}
 
     steps:
     - name: Check out repository
@@ -19,10 +32,10 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Cache
-      uses: actions/cache@v1
+      uses: actions/cache@v2
       with:
-        path: ~/.cache/pip
-        key: ${{ runner.os }}-${{ matrix.python-version }}-ci-base
+        path: ${{ matrix.path }}
+        key: ci-base-${{ runner.os }}-${{ matrix.python-version }}
 
     - name: Install tox
       run: pip install tox
@@ -55,17 +68,14 @@ jobs:
       uses: actions/cache@v1
       with:
         path: ~/.cache/pip
-        key: ${{ runner.os }}-${{ matrix.python-version }}-ci-notebooks
+        key: ci-notebooks-${{ runner.os }}-${{ matrix.python-version }}
 
-    - name: Install apt dependencies
-      run: |
-        .github/workflows/install_deps.sh amici
-
-    - name: Install tox
-      run: pip install tox
+    - name: Install dependencies
+      run: .github/workflows/install_deps.sh amici
 
     - name: Test notebooks
       run: tox -e notebooks
+      timeout-minutes: 60
 
   doc:
     runs-on: ubuntu-latest
@@ -86,14 +96,10 @@ jobs:
       uses: actions/cache@v1
       with:
         path: ~/.cache/pip
-        key: ${{ runner.os }}-${{ matrix.python-version }}-ci-doc
-
-    - name: Install apt dependencies
-      run: |
-        .github/workflows/install_deps.sh doc
+        key: ci-doc-${{ runner.os }}-${{ matrix.python-version }}
 
-    - name: Install tox
-      run: pip install tox
+    - name: Install dependencies
+      run: .github/workflows/install_deps.sh doc
 
     - name: Test documentation build
       run: tox -e doc
diff --git a/.github/workflows/install_deps.sh b/.github/workflows/install_deps.sh
index 93b16cb..60bcc4f 100755
--- a/.github/workflows/install_deps.sh
+++ b/.github/workflows/install_deps.sh
@@ -1,12 +1,18 @@
 #!/bin/sh
 
+# Install tox
+pip install tox
+
+# Update apt package lists
+sudo apt-get update
+
 # iterate over optional dependencies
 for par in "$@"; do
   case $par in
     amici)
       # for amici
       sudo apt-get install \
-        swig3.0 libatlas-base-dev libhdf5-serial-dev
+        swig libatlas-base-dev libhdf5-serial-dev
     ;;
 
     doc)
diff --git a/.gitignore b/.gitignore
index 09b9f02..4422703 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,4 @@
-__pycache__/
+**/__pycache__/
 *.egg-info/
 
 # PyCharm
@@ -12,44 +12,44 @@ yaml2sbml/tests/test_yaml2sbml/sbml_test.xml
 
 ### Files generated when running the notebooks (AMICI + PEtab files)
 
-## Lotka Volterra Python
-./doc/examples/Lotka_Volterra/Lotka_Volterra_python/amici_models
-./doc/examples/Lotka_Volterra/Lotka_Volterra_python/amici_models/*
+**/amici_models/
+**/AMICI_models/
 
-./doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_AMICI
-./doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_AMICI/*
+## FSP examples
 
-./doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_basic.xml
+/doc/examples/Finite_State_Projection/gene_expression.xml
 
-./doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_PEtab/*
-!./doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_PEtab/measurement_table.tsv
+## Format Features
 
-## Lotka Volterra CLI
-./doc/examples/Lotka_Volterra/Lotka_Volterra_CLI/Lotka_Volterra_PEtab/*
-./doc/examples/Lotka_Volterra/Lotka_Volterra_CLI/Lotka_Volterra_PEtab.yml
-./doc/examples/Lotka_Volterra/Lotka_Volterra_CLI/Lotka_Volterra_PEtab/*.xml
+/doc/examples/Format_Features/functions_in_rhs.xml
+/doc/examples/Format_Features/step_function.xml
+/doc/examples/Format_Features/time_dependent_rhs.xml
 
-## Lotka Volterra Model Editor
-./doc/examples/Lotka_Volterra/Lotka_Volterra_Model_Editor/*.yaml
-./doc/examples/Lotka_Volterra/Lotka_Volterra_Model_Editor/*.xml
-./doc/examples/Lotka_Volterra/Lotka_Volterra_Model_Editor/Lotka_Volterra_PEtab
+## Lotka Volterra
 
-## Sorensen example
-./doc/examples/Sorensen/*.xml
-./doc/examples/Sorensen/amici_models
+/doc/examples/Lotka_Volterra/Lotka_Volterra_CLI/Lotka_Volterra_PEtab.yml
+/doc/examples/Lotka_Volterra/Lotka_Volterra_CLI/Lotka_Volterra_PEtab/
+/doc/examples/Lotka_Volterra/Lotka_Volterra_CLI/Lotka_Volterra_basic.xml
 
-## FSP examples
-./doc/examples/Finite_State_Projection/amici_models
-./doc/examples/Finite_State_Projection/*.xml
+/doc/examples/Lotka_Volterra/Lotka_Volterra_Model_Editor/Lotka_Volterra_PEtab.yaml
+/doc/examples/Lotka_Volterra/Lotka_Volterra_Model_Editor/Lotka_Volterra_PEtab/
+/doc/examples/Lotka_Volterra/Lotka_Volterra_Model_Editor/Lotka_Volterra_basic.xml
+/doc/examples/Lotka_Volterra/Lotka_Volterra_Model_Editor/Lotka_Volterra_basic.yaml
 
-## Format Features
+/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_AMICI/
+/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_PEtab/Lotka_Volterra_with_observables.xml
+/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_PEtab/experimental_conditions_Lotka_Volterra_with_observables.tsv
+/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_PEtab/observables_Lotka_Volterra_with_observables.tsv
+/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_PEtab/parameters_Lotka_Volterra_with_observables.tsv
+/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_PEtab/problem.yml
+/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra_basic.xml
 
-./doc/examples/Format_Features/__pycache__
-./doc/examples/Format_Features/AMICI_models
+## Sorensen
 
-./doc/examples/Format_Features/*.xml
+/doc/examples/Sorensen/Fig71_Sorensen1985.xml
 
 ### JupyterNotebooks ###
+
 # gitignore template for Jupyter Notebooks
 # website: http://jupyter.org/
 
@@ -91,21 +91,9 @@ Network Trash Folder
 Temporary Items
 .apdisk
 
-# Gradle and Maven with auto-import
-# When using Gradle or Maven with auto-import, you should exclude module files,
-# since they will be recreated, and may cause churn.  Uncomment if using
-# auto-import.
-# .idea/artifacts
-# .idea/compiler.xml
-# .idea/jarRepositories.xml
-# .idea/modules.xml
-# .idea/*.iml
-# .idea/modules
-# *.iml
-# *.ipr
-
-# CMake
-cmake-build-*/
+# Build
+/build/
+/dist/
 
 # File-based project format
 *.iws
diff --git a/README.md b/README.md
index 36dd194..13ed3d7 100644
--- a/README.md
+++ b/README.md
@@ -4,6 +4,7 @@
 [![codecov](https://codecov.io/gh/yaml2sbml-dev/yaml2sbml/branch/master/graph/badge.svg)](https://codecov.io/gh/yaml2sbml-dev/yaml2sbml)
 [![Documentation Status](https://readthedocs.org/projects/yaml2sbml/badge/?version=latest)](https://yaml2sbml.readthedocs.io/en/latest/?badge=latest)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/632acdc8d4ef4f50bf69892b8862fd24)](https://www.codacy.com/gh/yaml2sbml-dev/yaml2sbml/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=yaml2sbml-dev/yaml2sbml&amp;utm_campaign=Badge_Grade)
+[![PyPI](https://badge.fury.io/py/yaml2sbml.svg)](https://badge.fury.io/py/yaml2sbml)
 
 ## Table of contents
 
@@ -22,7 +23,7 @@
 
 `yaml2sbml` is a small package in Python to convert an ODE model specified in a YAML file into 
 [**SBML**](http://www.sbml.org/) for ODE simulation and into 
-[**PEtab**](https://github.com/martamatos/yaml2sbml) for parameter fitting. `yaml2sbml` offers:
+[**PEtab**](https://github.com/PEtab-dev/PEtab/) for parameter fitting. `yaml2sbml` offers:
 
 
 * a translator of ODE models specified in YAML into SBML/PEtab via a Python and a command-line interface;
diff --git a/doc/examples/Finite_State_Projection/Finite_State_Projection.ipynb b/doc/examples/Finite_State_Projection/Finite_State_Projection.ipynb
index 1c569fe..bcb107a 100644
--- a/doc/examples/Finite_State_Projection/Finite_State_Projection.ipynb
+++ b/doc/examples/Finite_State_Projection/Finite_State_Projection.ipynb
@@ -79,7 +79,9 @@
     "    \n",
     "    model.add_ode(state_id = f\"x_{r}_{p}\", \n",
     "                  right_hand_side=rhs,\n",
-    "                  initial_value=poisson.pmf(r, lambda_r)*poisson.pmf(p, lambda_p))\n",
+    "                  #convert from np.float to float\n",
+    "                  initial_value=float(poisson.pmf(r, lambda_r)*poisson.pmf(p, lambda_p))\n",
+    "                 )\n",
     "\n",
     "model.write_to_sbml('gene_expression.xml', overwrite=True)"
    ]
diff --git a/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra.ipynb b/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra.ipynb
index 0e51406..6b4b4c7 100644
--- a/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra.ipynb
+++ b/doc/examples/Lotka_Volterra/Lotka_Volterra_python/Lotka_Volterra.ipynb
@@ -67,20 +67,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'Lotka_Volterra_basic.yml'",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-9f1df3c2ade9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0myaml_file_basic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Lotka_Volterra_basic.yml'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0myaml2sbml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_yaml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myaml_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myaml_file_basic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Dokumente/PhD/Code/GitHub/yaml2sbml/yaml2sbml/yaml2sbml/yaml_validation.py\u001b[0m in \u001b[0;36mvalidate_yaml\u001b[0;34m(yaml_dir)\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myaml_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf_in\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m             \u001b[0myaml_contents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf_in\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0myaml_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myaml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myaml_contents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Lotka_Volterra_basic.yml'"
-     ],
-     "output_type": "error"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "YAML file is valid ✅\n"
+     ]
     }
    ],
    "source": [
@@ -327,7 +322,17 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff95ff93d30>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1332x144 with 1 Axes>"
       ]
diff --git a/doc/examples/Sorensen/yaml2sbml_Sorensen.ipynb b/doc/examples/Sorensen/yaml2sbml_Sorensen.ipynb
index d9426a3..0799be7 100644
--- a/doc/examples/Sorensen/yaml2sbml_Sorensen.ipynb
+++ b/doc/examples/Sorensen/yaml2sbml_Sorensen.ipynb
@@ -190,6 +190,9 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.1"
+  },
+  "execution": {
+   "timeout": 3600
   }
  },
  "nbformat": 4,
diff --git a/doc/format_specification.rst b/doc/format_specification.rst
index a3ab99b..b6fd8b1 100644
--- a/doc/format_specification.rst
+++ b/doc/format_specification.rst
@@ -67,7 +67,7 @@ odes
 
 Define **ODEs** (and states). An ODE consists of a `stateId` (string), a `rightHandSide` (string, encoding a mathematical expression), and an `initial value`. Initial values can be either numerical values or parameter Ids.
 
-For a more detailed description of the parsing of mathematical expressions ( for  `rightHandSide`) we refer to the [corresponding section](#parsing-of-mathematical-equations) of this documentation.
+For a more detailed description of the parsing of mathematical expressions ( for  `rightHandSide`) we refer to the :ref:`corresponding section<Parsing of mathematical equations>` of this documentation.
 
 
 
@@ -130,7 +130,7 @@ observables \[optional\]
 Define **observables**. Observables are not part of the SBML standard. If the SBML is generated via the `yaml2sbml.yaml2sbml` command and the `observables_as_assignments` flag is set to `True`, observables are represented as assignments to parameters of the form observable_<observable_id>.
 If the SBML is created via `yaml2sbml.yaml2petab`, observables are represented in the PEtab observables table. The entries are written to the corresponding columns of the PEtab observable table. According to the PEtab standard, an observable table can take the following entries:  `observableId, observableName, observableFormula, observableTransformation, noiseFormula, noiseDistribution`.
 
-For a detailed discussion see the `corresponding part of the PEtab documentation <https://github.com/PEtab-dev/PEtab/blob/master/doc/documentation_data_format.rst#observables-table>`_.
+For a detailed discussion see the corresponding part of the PEtab documentation <https://github.com/PEtab-dev/PEtab/blob/master/doc/documentation_data_format.rst#observables-table>`_.
 
 
 
@@ -157,7 +157,7 @@ The "trivial condition table" (if only one setup exists) is generated by:
         - conditionId: condition1
 
 
-For a detailed discussion see the `corresponding part of the PEtab documentation <https://github.com/PEtab-dev/PEtab/blob/master/doc/documentation_data_format.rst#condition-table>`_.
+For a detailed discussion see the corresponding part of the `PEtab documentation <https://github.com/PEtab-dev/PEtab/blob/master/doc/documentation_data_format.rst#condition-table>`_.
 
 
 
diff --git a/doc/index.rst b/doc/index.rst
index 74eff03..4dce1fc 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -13,6 +13,9 @@ yaml2sbml
 .. image:: https://app.codacy.com/project/badge/Grade/632acdc8d4ef4f50bf69892b8862fd24
    :target: https://www.codacy.com/gh/yaml2sbml-dev/yaml2sbml/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=yaml2sbml-dev/yaml2sbml&amp;utm_campaign=Badge_Grade
    :alt: Code quality
+.. image:: https://badge.fury.io/py/yaml2sbml.svg
+   :target: https://badge.fury.io/py/yaml2sbml
+   :alt: PyPI
 
 :Release: |version|
 :Source code: https://github.com/yaml2sbml-dev/yaml2sbml
@@ -20,7 +23,6 @@ yaml2sbml
 .. image:: logo/logo_yaml2sbml_long.png
    :alt: yaml2sbml logo
    :align: center
-   :scale: 30 %
 
 **yaml2sbml** allows the user to convert a system of ODEs specified in a YAML_
 file to SBML_.
@@ -44,6 +46,8 @@ converted to PEtab_.
 
    release_notes
    license
+   log
+   logo
    contact
 
 .. toctree::
diff --git a/doc/logo.rst b/doc/logo.rst
new file mode 100644
index 0000000..50dbca0
--- /dev/null
+++ b/doc/logo.rst
@@ -0,0 +1,15 @@
+Logo
+====
+
+.. image:: logo/logo_yaml2sbml_long.png
+   :alt: yaml2sbml logo
+   :align: center
+
+yaml2sbml's logo can be found in multiple variants in the
+`doc/logo <https://github.com/yaml2sbml-dev/yaml2sbml/tree/main/doc/logo>`_
+folder, in svg and png format.
+It is made available under a Creative Commons CC0 1.0 Universal (CC0 1.0)
+license, with the terms given in `doc/logo/LICENSE.md <https://github.com/yaml2sbml-dev/yaml2sbml/blob/master/doc/logo/LICENSE.md>`_.
+We encourage to use it e.g. in presentations and posters.
+
+We thank Elba Raimúndez for her contributions to the logo design.
diff --git a/doc/logo/LICENSE.md b/doc/logo/LICENSE.md
new file mode 100644
index 0000000..ccdb359
--- /dev/null
+++ b/doc/logo/LICENSE.md
@@ -0,0 +1,121 @@
+Creative Commons Legal Code
+
+ CC0 1.0 Universal
+
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diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index 3c51ecb..9f5c475 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -5,17 +5,25 @@ Release Notes
 0.2 series
 ..........
 
+0.2.2 (2021-03-19)
+------------------
+
+* Add checks in SBML conversion, e.g. to catch invalid identifiers and equations (# 118).
+* Add tests for MacOS and Windows (#115).
+* Fix pydocstyle and swig (#120).
+* Add a logo license. (#116)
+* Smaller fixes in notebooks (#113, #117).
 
 0.2.1 (2021-02-22)
 ------------------
 
-* New option to write observables as assignments in `yaml2sbml`.
-* Set SBML file name as model id in the SBML.
-* Clarify docs and warnings, e.g. if formula starts with a minus.
-* Add issue template.
-* Restructure README.
-* Testing via tox.
-* Replace `requirements.txt` and `setup.py` by `setup.cfg`.
+* New option to write observables as assignments in `yaml2sbml` (#105).
+* Set SBML file name as model id in the SBML (#105).
+* Clarify docs and warnings, e.g. if formula starts with a minus (#109).
+* Add issue template (#96).
+* Restructure README (#55, #103, #104).
+* Testing via tox (#88, #94, #95).
+* Replace `requirements.txt` and `setup.py` by `setup.cfg` (#87).
 
 0.2.0 (2021-02-03)
 ------------------
diff --git a/setup.cfg b/setup.cfg
index cd461cc..5916790 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -33,10 +33,10 @@ classifiers =
     License :: OSI Approved :: MIT License
     Operating System :: OS Independent
     Programming Language :: Python
-    Programming Language :: Python :: 3.6
-    Programming Language :: Python :: 3.7
-    Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.8
+    Programming Language :: Python :: 3.7
+    Programming Language :: Python :: 3.6
 keywords = YAML, SBML, PEtab, ODE
 
 [options]
@@ -55,20 +55,19 @@ packages = find:
 
 [options.extras_require]
 examples =
-    numpy >= 1.19.4
-    scipy >= 1.6.0
+    amici >= 0.11.14
+    matplotlib >= 3.1.0
     notebook >= 6.1.4
-    # TODO Unfix when AMICI piecewise has been solved
-    amici == 0.11.10
+    numpy >= 1.19.4
     pypesto >= 0.2.3
-    matplotlib >= 3.1.0
+    scipy >= 1.6.0
 doc =
-    sphinx >= 3.4.3
-    nbsphinx >= 0.8.0
+    ipython >= 7.18.1
     nbconvert >= 6.0.7
+    nbsphinx >= 0.8.0
+    sphinx >= 3.4.3
     sphinx-rtd-theme >= 0.5.1
     sphinx_autodoc_typehints >= 1.11.1
-    ipython >= 7.18.1
 
 [options.entry_points]
 console_scripts =
diff --git a/tox.ini b/tox.ini
index 127a7c8..0541ab1 100644
--- a/tox.ini
+++ b/tox.ini
@@ -20,10 +20,10 @@ envlist =
 [testenv]
 # Dependencies
 deps =
-    coverage
-    pytest
-    pytest-cov
-    pytest-console-scripts
+    coverage >= 5.5
+    pytest >= 6.2.2
+    pytest-cov >= 2.11.1
+    pytest-console-scripts >= 1.1.0
 
 # Sub-environments
 #  inherit settings defined in the base
@@ -32,8 +32,7 @@ deps =
 # No installation of yaml2sbml required
 skip_install = true
 deps =
-    pygments
-    pyroma
+    pyroma >= 3.1
 commands = pyroma --min=10 .
 description =
     Run pyroma to check the package friendliness
@@ -41,20 +40,21 @@ description =
 [testenv:flake8]
 skip_install = true
 deps =
-    flake8
-    flake8-bandit
-    flake8-bugbear
-    flake8-colors
-    flake8-commas
-    flake8-comprehensions
-    flake8-print
-    flake8-use-fstring
-    pep8-naming
-    pydocstyle
+    flake8 >= 3.8.3
+    flake8-bandit >= 2.1.2
+    flake8-bugbear >= 20.1.4
+    flake8-colors >= 0.1.6
+    flake8-commas >= 2.0.0
+    flake8-comprehensions >= 3.2.3
+    flake8-print >= 3.1.4
+    flake8-use-fstring >= 1.1
+    pep8-naming >= 0.11.1
+    pydocstyle >= 5.1.1
 commands =
     flake8 yaml2sbml tests setup.py
+    pydocstyle yaml2sbml tests setup.py
 description =
-    Run flake8 with various plugins
+    Run flake8 with various plugins, and pydocstyle
 
 [testenv:unittests]
 commands =
@@ -67,7 +67,7 @@ description =
 
 [testenv:notebooks]
 deps =
-    nbmake
+    nbmake >= 0.4
 # Extras required from yaml2sbml
 extras = examples
 commands =
diff --git a/yaml2sbml/YamlModel.py b/yaml2sbml/YamlModel.py
index 37fa606..f68630b 100644
--- a/yaml2sbml/YamlModel.py
+++ b/yaml2sbml/YamlModel.py
@@ -181,16 +181,20 @@ def _get_reduced_model_dict(self) -> dict:
 
     # functionalities regarding the time
     def is_set_time(self):
+        """Check whether there is a time variable."""
         return 'variable' in self._yaml_model['time'].keys()
 
     def set_time(self,
                  time_variable: str):
+        """Set time variable."""
         self._yaml_model['time'] = {'variable': time_variable}
 
     def delete_time(self):
+        """Delete time variable."""
         self._yaml_model['time'] = {}
 
     def get_time(self):
+        """Get time variable."""
         if self.is_set_time():
             return self._yaml_model['time']['variable']
         else:
diff --git a/yaml2sbml/version.py b/yaml2sbml/version.py
index fc79d63..3693240 100644
--- a/yaml2sbml/version.py
+++ b/yaml2sbml/version.py
@@ -1,1 +1,3 @@
-__version__ = '0.2.1'
+"""yaml2sbml version."""
+
+__version__ = '0.2.2'
diff --git a/yaml2sbml/yaml2sbml.py b/yaml2sbml/yaml2sbml.py
index 9c5b283..1e007c1 100644
--- a/yaml2sbml/yaml2sbml.py
+++ b/yaml2sbml/yaml2sbml.py
@@ -224,8 +224,16 @@ def _create_time(model: sbml.Model, time_var: str):
         model: the SBML model to which the species will be added.
         time_var: str, the time variable
     """
+    if time_var == 'time':
+        return
+
     time_parameter = model.createParameter()
-    time_parameter.setId(time_var)
+
+    if time_parameter.setId(time_var) != sbml.LIBSBML_OPERATION_SUCCESS:
+
+        raise RuntimeError(f'Unable to generate time parameter with id '
+                           f'{time_var}. Invalid SBML identifier.')
+
     time_parameter.setName(time_var)
     time_parameter.setConstant(False)
 
@@ -266,7 +274,11 @@ def _create_parameter(model: sbml.Model, parameter_id: str, value: str = None):
         value: the parameter value, if value is None, no parameter is set.
     """
     k = model.createParameter()
-    k.setId(parameter_id)
+
+    if k.setId(parameter_id) != sbml.LIBSBML_OPERATION_SUCCESS:
+        raise RuntimeError(f'Unable to generate parameter with id '
+                           f'{parameter_id}. Invalid SBML identifier.')
+
     k.setName(parameter_id)
     k.setConstant(True)
 
@@ -306,14 +318,26 @@ def _create_assignment(model: sbml.Model, assignment_id: str, formula: str):
         formula: str: contains the equation for the assignment rule
     """
     assignment_parameter = model.createParameter()
-    assignment_parameter.setId(assignment_id)
+
+    if assignment_parameter.setId(assignment_id):
+        raise RuntimeError(f'Unable to generate assignment with id '
+                           f'{assignment_id}. Invalid SBML identifier.')
+
     assignment_parameter.setName(assignment_id)
     assignment_parameter.setConstant(False)
     assignment_parameter.setUnits('dimensionless')
 
     assignment_rule = model.createAssignmentRule()
     assignment_rule.setVariable(assignment_id)
-    assignment_rule.setMath(sbml.parseL3Formula(formula))
+
+    sbml_formula = sbml.parseL3Formula(formula)
+
+    if sbml_formula is not None:
+        assignment_rule.setMath(sbml.parseL3Formula(formula))
+    else:
+        raise RuntimeError(f'Unable to generate assignment for formula '
+                           f'{formula}, libsbml can not parse the given '
+                           f'expression.')
 
 
 def _read_functions_block(model: sbml.Model, functions_list: list):
@@ -351,9 +375,20 @@ def _create_function(model: sbml.Model,
         formula: the formula of the function
     """
     f = model.createFunctionDefinition()
-    f.setId(function_id)
+
+    if f.setId(function_id) != sbml.LIBSBML_OPERATION_SUCCESS:
+        raise RuntimeError(f'Unable to generate function with id '
+                           f'{function_id}. Invalid SBML identifier.')
+
     math = sbml.parseL3Formula('lambda(' + arguments + ', ' + formula + ')')
-    f.setMath(math)
+
+    if math is not None:
+        f.setMath(math)
+    else:
+        raise RuntimeError(f'Unable to generate assignment for funtion '
+                           f'{function_id}, libsbml can not parse the given '
+                           f'function expression, given by '
+                           f'lambda({arguments} , {formula}).')
 
 
 def _read_odes_block(model: sbml.Model, odes_list: list):
@@ -391,7 +426,9 @@ def _create_species(model: sbml.Model, species_id: str, initial_amount: str):
         s: the SBML species
     """
     s = model.createSpecies()
-    s.setId(species_id)
+    if s.setId(species_id) != sbml.LIBSBML_OPERATION_SUCCESS:
+        raise RuntimeError(f'Unable to generate species with id '
+                           f'{species_id}. Invalid SBML identifier.')
 
     try:
         s.setInitialAmount(float(initial_amount))
@@ -411,7 +448,7 @@ def _create_species(model: sbml.Model, species_id: str, initial_amount: str):
     return s
 
 
-def _create_rate_rule(model: sbml.Model, species: str, formula: str):
+def _create_rate_rule(model: sbml.Model, species_id: str, formula: str):
     """
     Create an SBML rateRule for a species and add it to the SBML model.
 
@@ -419,13 +456,17 @@ def _create_rate_rule(model: sbml.Model, species: str, formula: str):
 
     Arguments:
         model: SBML model to which the rate rule will be added.
-        species: the species name of the ODE
+        species_id: the id of the state of the ODE
         formula: the right-hand-side of the ODE
     """
     r = model.createRateRule()
-    r.setId('d/dt_' + species)
-    r.setVariable(species)
+    r.setId('d_dt_' + species_id)
+    r.setVariable(species_id)
     math_ast = sbml.parseL3Formula(formula)
+    if math_ast is None:
+        raise RuntimeError(f'Unable to generate the rate rule for the state '
+                           f'{species_id}, libsbml can not parse the right-'
+                           f'hand side, given by {formula}).')
     r.setMath(math_ast)
 
 


Test patch:
diff --git a/tests/__init__.py b/tests/__init__.py
index e69de29..232940a 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -0,0 +1,1 @@
+"""yaml2sbml tests."""
diff --git a/tests/test_yaml2sbml.py b/tests/test_yaml2sbml.py
index 6348a8c..4312cc9 100644
--- a/tests/test_yaml2sbml.py
+++ b/tests/test_yaml2sbml.py
@@ -1,7 +1,7 @@
 import os
 import unittest
 
-from yaml2sbml.yaml2sbml import yaml2sbml
+from yaml2sbml.yaml2sbml import yaml2sbml, _parse_yaml
 
 
 class TestYaml2SBML(unittest.TestCase):
@@ -76,6 +76,26 @@ def test_yaml_import_observables(self):
 
         os.remove(sbml_test_dir)
 
+    def test_catch_invalid_sbml_identifier(self):
+        """
+        Check for invalid SBML identifiers.
+        """
+        yaml_dir = os.path.join(self.test_folder,
+                                'ode_input_invalid_SBML_identifier.yaml')
+        with self.assertRaises(RuntimeError):
+            _parse_yaml(yaml_dir,
+                        'Test_Model')
+
+    def test_catch_invalid_math(self):
+        """
+        Check for strings that parseL3Formula can not parse.
+        """
+        yaml_dir = os.path.join(self.test_folder,
+                                'ode_input_invalid_formula.yaml')
+        with self.assertRaises(RuntimeError):
+            _parse_yaml(yaml_dir,
+                        'Test_Model')
+
 
 if __name__ == '__main__':
     suite = unittest.TestSuite()
diff --git a/tests/test_yaml2sbml/ode_input_invalid_SBML_identifier.yaml b/tests/test_yaml2sbml/ode_input_invalid_SBML_identifier.yaml
new file mode 100644
index 0000000..e0f8259
--- /dev/null
+++ b/tests/test_yaml2sbml/ode_input_invalid_SBML_identifier.yaml
@@ -0,0 +1,14 @@
+time:
+    variable: t
+
+odes:
+- stateId: 1_x
+  rightHandSide: k_1 * x + t
+  initialValue: 0
+
+parameters:
+- parameterId: k_1
+  nominalValue: 1
+
+
+
diff --git a/tests/test_yaml2sbml/ode_input_invalid_formula.yaml b/tests/test_yaml2sbml/ode_input_invalid_formula.yaml
new file mode 100644
index 0000000..ff3252b
--- /dev/null
+++ b/tests/test_yaml2sbml/ode_input_invalid_formula.yaml
@@ -0,0 +1,11 @@
+time:
+    variable: t
+
+odes:
+- stateId: x
+  rightHandSide: np.exp(k_1 * x + t)
+  initialValue: 0
+
+parameters:
+- parameterId: k_1
+  nominalValue: 1
diff --git a/tests/test_yaml2sbml/ode_input_invalid_time_1.yaml b/tests/test_yaml2sbml/ode_input_invalid_time_1.yaml
index 08acc01..f51d63e 100644
--- a/tests/test_yaml2sbml/ode_input_invalid_time_1.yaml
+++ b/tests/test_yaml2sbml/ode_input_invalid_time_1.yaml
@@ -1,9 +1,11 @@
 time:
   - t
+
 odes:
-- initialValue: 0
+- stateId: x
   rightHandSide: k_1 * x + t
-  stateId: x
+  initialValue: 0
+
 parameters:
-- nominalValue: 1
-  parameterId: k_1
+- parameterId: k_1
+  nominalValue: 1
diff --git a/tests/test_yaml2sbml/ode_input_invalid_time_2.yaml b/tests/test_yaml2sbml/ode_input_invalid_time_2.yaml
index dedcf75..6c2ec60 100644
--- a/tests/test_yaml2sbml/ode_input_invalid_time_2.yaml
+++ b/tests/test_yaml2sbml/ode_input_invalid_time_2.yaml
@@ -1,9 +1,11 @@
 time:
   - variable: t
+
 odes:
-- initialValue: 0
+- stateId: x
   rightHandSide: k_1 * x + t
-  stateId: x
+  initialValue: 0
+
 parameters:
-- nominalValue: 1
-  parameterId: k_1
+- parameterId: k_1
+  nominalValue: 1