Merge branch 'develop' into doc_edata

python/sdist/amici/plotting.py

@@ -6,6 +6,7 @@
 from typing import Iterable, Optional, Sequence, Union
 
 import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
 import seaborn as sns
 from matplotlib.axes import Axes
@@ -16,42 +17,55 @@
 
 def plot_state_trajectories(
     rdata: ReturnDataView,
-    state_indices: Optional[Iterable[int]] = None,
+    state_indices: Optional[Sequence[int]] = None,
     ax: Optional[Axes] = None,
     model: Model = None,
     prefer_names: bool = True,
+    marker=None,
 ) -> None:
     """
-    Plot state trajectories
+    Plot state trajectories.
 
     :param rdata:
         AMICI simulation results as returned by
-        :func:`amici.amici.runAmiciSimulation`
-
+        :func:`amici.amici.runAmiciSimulation`.
     :param state_indices:
-        Indices of states for which trajectories are to be plotted
-
+        Indices of state variables for which trajectories are to be plotted.
     :param ax:
-        matplotlib Axes instance to plot into
-
+        :class:`matplotlib.pyplot.Axes` instance to plot into.
     :param model:
-        amici model instance
-
+        The model *rdata* was generated from.
     :param prefer_names:
         Whether state names should be preferred over IDs, if available.
+    :param marker:
+        Point marker for plotting (see
+        `matplotlib documentation <https://matplotlib.org/stable/api/markers_api.html>`_).
     """
     if not ax:
         fig, ax = plt.subplots()
     if not state_indices:
         state_indices = range(rdata["x"].shape[1])
-    for ix in state_indices:
-        if model is None:
-            label = f"$x_{{{ix}}}$"
-        elif prefer_names and model.getStateNames()[ix]:
-            label = model.getStateNames()[ix]
-        else:
-            label = model.getStateIds()[ix]
-        ax.plot(rdata["t"], rdata["x"][:, ix], label=label)
+
+    if marker is None:
+        # Show marker if only one time point is available,
+        #  otherwise nothing will be shown
+        marker = "o" if len(rdata.t) == 1 else None
+
+    if model is None and rdata.ptr.state_ids is None:
+        labels = [f"$x_{{{ix}}}$" for ix in state_indices]
+    elif model is not None and prefer_names:
+        labels = np.asarray(model.getStateNames())[list(state_indices)]
+        labels = [
+            l if l else model.getStateIds()[ix]
+            for ix, l in enumerate(labels)
+        ]
+    elif model is not None:
+        labels = np.asarray(model.getStateIds())[list(state_indices)]
+    else:
+        labels = np.asarray(rdata.ptr.state_ids)[list(state_indices)]
+
+    for ix, label in zip(state_indices, labels):
+        ax.plot(rdata["t"], rdata["x"][:, ix], marker=marker, label=label)
         ax.set_xlabel("$t$")
         ax.set_ylabel("$x(t)$")
         ax.legend()
@@ -64,38 +78,54 @@ def plot_observable_trajectories(
     ax: Optional[Axes] = None,
     model: Model = None,
     prefer_names: bool = True,
+    marker=None,
 ) -> None:
     """
-    Plot observable trajectories
+    Plot observable trajectories.
 
     :param rdata:
         AMICI simulation results as returned by
-        :func:`amici.amici.runAmiciSimulation`
-
+        :func:`amici.amici.runAmiciSimulation`.
     :param observable_indices:
-        Indices of observables for which trajectories are to be plotted
-
+        Indices of observables for which trajectories are to be plotted.
     :param ax:
-        matplotlib Axes instance to plot into
-
+        :class:`matplotlib.pyplot.Axes` instance to plot into.
     :param model:
-        amici model instance
-
+        The model *rdata* was generated from.
     :param prefer_names:
-        Whether observables names should be preferred over IDs, if available.
+        Whether observable names should be preferred over IDs, if available.
+    :param marker:
+        Point marker for plotting (see
+        `matplotlib documentation <https://matplotlib.org/stable/api/markers_api.html>`_).
+
     """
     if not ax:
         fig, ax = plt.subplots()
     if not observable_indices:
         observable_indices = range(rdata["y"].shape[1])
-    for iy in observable_indices:
-        if model is None:
-            label = f"$y_{{{iy}}}$"
-        elif prefer_names and model.getObservableNames()[iy]:
-            label = model.getObservableNames()[iy]
-        else:
-            label = model.getObservableIds()[iy]
-        ax.plot(rdata["t"], rdata["y"][:, iy], label=label)
+
+    if marker is None:
+        # Show marker if only one time point is available,
+        #  otherwise nothing will be shown
+        marker = "o" if len(rdata.t) == 1 else None
+
+    if model is None and rdata.ptr.observable_ids is None:
+        labels = [f"$y_{{{iy}}}$" for iy in observable_indices]
+    elif model is not None and prefer_names:
+        labels = np.asarray(model.getObservableNames())[
+            list(observable_indices)
+        ]
+        labels = [
+            l if l else model.getObservableIds()[ix]
+            for ix, l in enumerate(labels)
+        ]
+    elif model is not None:
+        labels = np.asarray(model.getObservableIds())[list(observable_indices)]
+    else:
+        labels = np.asarray(rdata.ptr.observable_ids)[list(observable_indices)]
+
+    for iy, label in zip(observable_indices, labels):
+        ax.plot(rdata["t"], rdata["y"][:, iy], marker=marker, label=label)
         ax.set_xlabel("$t$")
         ax.set_ylabel("$y(t)$")
         ax.legend()
@@ -106,8 +136,8 @@ def plot_jacobian(rdata: ReturnDataView):
     """Plot Jacobian as heatmap."""
     df = pd.DataFrame(
         data=rdata.J,
-        index=rdata._swigptr.state_ids_solver,
-        columns=rdata._swigptr.state_ids_solver,
+        index=rdata.ptr.state_ids_solver,
+        columns=rdata.ptr.state_ids_solver,
     )
     sns.heatmap(df, center=0.0)
     plt.title("Jacobian")
@@ -124,10 +154,10 @@ def plot_expressions(
     """Plot the given expressions evaluated on the given simulation outputs.
 
     :param exprs:
-        A symbolic expression, e.g. a sympy expression or a string that can be sympified.
-        Can include state variable, expression, and observable IDs, depending on whether
-        the respective data is available in the simulation results.
-        Parameters are not yet supported.
+        A symbolic expression, e.g., a sympy expression or a string that can be
+        sympified. It Can include state variable, expression, and
+        observable IDs, depending on whether the respective data is available
+        in the simulation results. Parameters are not yet supported.
     :param rdata:
         The simulation results.
     """

python/sdist/setup.cfg

@@ -52,7 +52,10 @@ test =
     pytest-rerunfailures
     coverage
     shyaml
-    antimony
+    antimony>=2.13
+    # see https://github.com/sys-bio/antimony/issues/92
+    #  unsupported x86_64 / x86_64h
+    antimony!=2.14; platform_system=='Darwin' and platform_machine in 'x86_64h'
 vis =
     matplotlib
     seaborn