fix import issue

Author: MaHaWo

docs/notebooks/plot.ipynb

@@ -15,25 +15,19 @@
    },
    "outputs": [],
    "source": [
-    "!pip install -q sme_contrib\n",
-    "import sme\n",
-    "import sme_contrib.plot as smeplot\n",
-    "from matplotlib import pyplot as plt\n",
+    "# !pip install -q sme_contrib\n",
     "import pyvista as pv\n",
     "from pyvista import examples\n",
+    "\n",
+    "pv.set_jupyter_backend(\"static\")\n",
+    "\n",
+    "import sme\n",
+    "import sme_contrib.plot as smeplot\n",
     "import numpy as np\n",
     "import tempfile\n",
     "from IPython.display import HTML\n",
-    "from IPython.display import Video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pv.set_jupyter_backend(\"static\")"
+    "from IPython.display import Video\n",
+    "from matplotlib import pyplot as plt"
    ]
   },
   {
@@ -217,6 +211,15 @@
     "- `concentrations3D` and `concentrationsAnimate3D`: These high-level API directly uses `sme.SimulationResult` objects as data input, but only plots concentrations by default. These are wrappers around the low-level functions that provide default plotting functions for each pane and handle the data preparation for each pane automatically."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**README:**  Using `import pyvista as pv` in both a notebook and a module imported in the notebook will cause a premature initialization of `pyvista` when the imported module is initialized and in turn a configuration conflict that will prevent the notebook from working properly. You thus have to make sure that the `pyvista` module is only imported in one of the two. Since notebooks often have specific requirements for how the plots can be shown (statically or interactively) it is best to only import the needed objects from pyvista when developing a module and only importing the package-level module (`import pyvista...`) within notebooks that use this module. \n",
+    "\n",
+    "If a complete import of pyvista in a module is unavoidable, make sure to only import the called functions in the notebook and not the entire module, to prevent pyvista's `__init__` from running prematurely."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -227,8 +230,7 @@
     "model = sme.open_sbml_file(model_file)\n",
     "results = model.simulate(500, 10)\n",
     "\n",
-    "species = list(results[0].species_concentration.keys())\n",
-    "species"
+    "species = list(results[0].species_concentration.keys())"
    ]
   },
   {
@@ -249,7 +251,15 @@
     "    }\n",
     "\n",
     "\n",
-    "datasets = exampledata()\n",
+    "datasets = exampledata()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "tempdir = tempfile.TemporaryDirectory()"
    ]
   },
@@ -322,7 +332,9 @@
     "        \"brain\": plot_brain,\n",
     "    },\n",
     "    linked_views=False,\n",
-    ")"
+    ")\n",
+    "\n",
+    "facetgrid.show()"
    ]
   },
   {
@@ -393,6 +405,15 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(vidpath, embed=True, width=800, height=600)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -413,12 +434,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "facetgrid = smeplot.concentrations3D(\n",
+    "smeplot.concentrations3D(\n",
     "    simulation_result=results[10],\n",
     "    species=[\"A_nucl\"],\n",
     "    cmap=\"tab10\",\n",
     "    show_cmap=True,\n",
-    ")"
+    ").show()"
    ]
   },
   {
@@ -442,6 +463,15 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(vidpath, embed=True, width=800, height=600)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -457,7 +487,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": ".venv39",
+   "display_name": ".venv313",
    "language": "python",
    "name": "python3"
   },
@@ -471,7 +501,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.21"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,

src/sme_contrib/plot.py

@@ -4,14 +4,13 @@
 from matplotlib import pyplot as plt
 from matplotlib.colors import LinearSegmentedColormap as lscmap
 from matplotlib import animation
-import pyvista as pv
+from pyvista import Plotter, LookupTable
 from typing import Any, Callable, Union
 import sme
 from pathlib import Path
 
 from .pyvista_utils import (
     find_layout,
-    make_discrete_colormap,
 )
 
 
@@ -135,29 +134,29 @@ def facet_grid_3D(
     data: dict[str, np.ndarray],
     plotfuncs: dict[str, Callable],
     show_cmap: bool = False,
-    cmap: Union[str, np.ndarray, pv.LookupTable] = "viridis",
+    cmap: Union[str, np.ndarray, LookupTable] = "viridis",
     portrait: bool = False,
     linked_views: bool = True,
     plotter_kwargs: Union[dict, None] = None,
     plotfuncs_kwargs: Union[dict[str, dict[str, Any]], None] = None,
-) -> pv.Plotter:
+) -> Plotter:
     """
     Create a 3D facet plot using PyVista.
 
     This follows the seaborn.FacetGrid concept. This function creates a grid of subplots where each subplot is filled by a function in the plotfuncs argument. The keys for plotfuncs and data must be the same, such that plotfuncs can be unambiguously mapped over the data dictionary. Do not attempt to plot 2D images and 3D images into the same facet grid, as this will create odd artifacts and may not work as expected.
 
     Args:
         data : (dict[str, np.ndarray]) A dictionary where keys are labels and values are numpy arrays containing the data to be plotted.
-        plotfuncs : (dict[str, Callable]) A dictionary where keys are labels and values are functions with signature ``f(label:str, data:np.ndarray | pyvista.ImageData | pyvista.UniformGrid, plotter:pv.Plotter, panel:tuple[int, int], show_cmap:bool=show_cmap, cmap=cmap, **plotfuncs_kwargs )`` -> None
+        plotfuncs : (dict[str, Callable]) A dictionary where keys are labels and values are functions with signature ``f(label:str, data:np.ndarray | pyvista.ImageData | pyvista.UniformGrid, plotter:Plotter, panel:tuple[int, int], show_cmap:bool=show_cmap, cmap=cmap, **plotfuncs_kwargs )`` -> None
         show_cmap : bool, optional Whether to show the color map. Default is False.
-        cmap : (str | np.ndarray | pv.LookupTable), optional The color map to use. Default is "viridis".
+        cmap : (str | np.ndarray | LookupTable), optional The color map to use. Default is "viridis".
         portrait : (bool), optional Whether to use a portrait layout. Default is False.
         linked_views : (bool), optional Whether to link the views of the subplots. Default is True.
         plotter_kwargs : (dict, optional) Additional keyword arguments to pass to the PyVista Plotter.
         plotfuncs_kwargs : (dict[str, dict[str, Any]]), optional Additional keyword arguments to pass to each plotting function.
 
     Returns:
-        pv.Plotter The PyVista Plotter object with the created facet plot.
+        Plotter The PyVista Plotter object with the created facet plot.
     """
     if data.keys() != plotfuncs.keys():
         raise ValueError(
@@ -166,7 +165,7 @@ def facet_grid_3D(
 
     layout = find_layout(len(data), portrait=portrait)
 
-    plotter = pv.Plotter(
+    plotter = Plotter(
         shape=layout, **(plotter_kwargs if plotter_kwargs is not None else {})
     )
 
@@ -200,7 +199,7 @@ def facet_grid_animate_3D(
     data: list[dict[str, np.ndarray]],
     plotfuncs: dict[str, Callable],
     show_cmap: bool = False,
-    cmap: Union[str, np.ndarray, pv.LookupTable] = "viridis",
+    cmap: Union[str, np.ndarray, LookupTable] = "viridis",
     portrait: bool = False,
     linked_views: bool = True,
     titles: Union[list[dict[str, str]], None] = None,
@@ -217,7 +216,7 @@ def facet_grid_animate_3D(
         data : (list[dict[str, np.ndarray]]) A list of dictionaries containing the data for each timestep.
         plotfuncs : (dict[str, Callable]) A dictionary of plotting functions keyed by data label. The keys for plotfuncs and data must be the same.
         show_cmap : (bool), optional Whether to show the color map (default is False).
-        cmap : (str | np.ndarray | pv.LookupTable, optional) The colormap to use (default is "viridis").
+        cmap : (str | np.ndarray | LookupTable, optional) The colormap to use (default is "viridis").
         portrait : (bool), optional Whether to use portrait layout (default is False).
         linked_views : (bool), optional Whether to link the views of the subplots (default is True).
         titles : (list[dict[str, str]]), optional A list of dictionaries containing titles for each subplot (default is an empty list).
@@ -265,7 +264,7 @@ def create_frame(
     # preparations
     layout = find_layout(len(plotfuncs), portrait=portrait)
 
-    plotter = pv.Plotter(
+    plotter = Plotter(
         shape=layout, **plotter_kwargs if plotter_kwargs is not None else {}
     )
 
@@ -290,19 +289,19 @@ def create_frame(
 def concentrations3D(
     simulation_result: sme.SimulationResult,
     species: list[str],
-    cmap: Union[str, np.ndarray, pv.LookupTable] = "viridis",
+    cmap: Union[str, np.ndarray, LookupTable] = "viridis",
     show_cmap: bool = False,
     plotter_kwargs: Union[None, dict[str, Any]] = None,
     plotfunc_kwargs: Union[None, dict[str, Any]] = None,
-) -> pv.Plotter:
+) -> Plotter:
     """Plot a 3D facet grid of species concentrations.
     This function creates a 3D facet grid of species concentrations. Each panel will be a 3D plot of the concentration of a single species.
     This function is a wrapper around the facet_grid_3D function.
 
     Args:
         simulation_result (sme.SimulationResult): a single simulation result object, i.e., a single recorded frame of the simulations
         species (list[str]): A list of species strings
-        cmap (str | np.ndarray | pv.LookupTable, optional): Name of a matplotlib colorbar. Defaults to "viridis".
+        cmap (str | np.ndarray | LookupTable, optional): Name of a matplotlib colorbar. Defaults to "viridis".
         show_cmap (bool, optional): Whether or not to show the colorbar on the plot. Defaults to False.
         plotter_kwargs (dict[str, Any], optional): Additional keyword arguments for the used pyVista.Plotter. Defaults to None.
         plotfunc_kwargs (dict[str, Any], optional): Additional keyword arguments passed to plotter.add_mesh. Defaults to None.
@@ -311,7 +310,7 @@ def concentrations3D(
         ValueError: if a given species is not found in the simulation result
 
     Returns:
-        pv.Plotter: pyvista.Plotter object the data has been plotted into
+        Plotter: pyvista.Plotter object the data has been plotted into
     """
     # turn the simulation result into numpy ndarray
     datadict = {}
@@ -328,10 +327,10 @@ def concentrations3D(
     def plotfunc(
         label: str,
         data: np.ndarray,
-        plotter: pv.Plotter,
+        plotter: Plotter,
         panel: tuple[int, int],
         show_cmap: bool,
-        cmap: Union[str, np.ndarray, pv.LookupTable],
+        cmap: Union[str, np.ndarray, LookupTable],
         **kwargs: dict[str, Any],
     ):
         # create a pyvista grid
@@ -369,7 +368,7 @@ def concentrationsAnimate3D(
     simulation_results: sme.SimulationResultList,
     species: list[str],
     show_cmap: bool = False,
-    cmap: Union[str, np.ndarray, pv.LookupTable] = "viridis",
+    cmap: Union[str, np.ndarray, LookupTable] = "viridis",
     portrait: bool = False,
     titles: Union[list[dict[str, str]], None] = None,
     linked_views: bool = True,
@@ -386,7 +385,7 @@ def concentrationsAnimate3D(
         simulation_results (sme.SimulationResultList): a list of `SimulationResult` objects, i.e., a list of recorded frames of the simulations
         species (list[str]): list of species to plot
         show_cmap (bool, optional): Whether to show the colorbar on theplots or not. Defaults to False.
-        cmap (Union[str, np.ndarray, pv.LookupTable], optional): name of matplotlib colormap or custom colormap that maps scalar values to rbp. Defaults to "viridis".
+        cmap (Union[str, np.ndarray, LookupTable], optional): name of matplotlib colormap or custom colormap that maps scalar values to rbp. Defaults to "viridis".
         portrait (bool, optional): Whether to use the smaller or larger number of plots as rows. Defaults to False.
         titles (Union[list[dict[str, str]], None], optional): Titles of the different plots if not just the species name is desired. Defaults to None.
         linked_views (bool, optional): link the view cameras. Defaults to True.