try to make tests work by removing plotly from env

devtools/conda-envs/test_env.yaml

@@ -19,7 +19,6 @@ dependencies:
   - pandas
   - pytables
   - matplotlib
-  - plotly
 
 
     # Pip-only installs

reeds/function_libs/visualization/sampling_plots.py

@@ -1,11 +1,7 @@
-from typing import Union, List
+from typing import List
 
 import numpy as np
 from matplotlib import pyplot as plt
-from matplotlib.colors import Colormap, to_rgba
-
-import plotly.graph_objects as go
-from plotly.colors import convert_to_RGB_255
 
 from reeds.function_libs.visualization import plots_style as ps
 from reeds.function_libs.visualization.utils import nice_s_vals
@@ -380,76 +376,4 @@ def plot_stateOccurence_matrix(data: dict,
 
     if (not out_dir is None):
         fig.savefig(out_dir + '/sampling_maxContrib_matrix.png', bbox_inches='tight')
-        plt.close()
-
-def plot_state_transitions(state_transitions: np.ndarray, title: str = None, colors: Union[List[str], Colormap] =  ps.qualitative_tab_map, out_path: str = None):
-    """
-    Make a Sankey plot showing the flows between states.
-     
-    Parameters
-    ----------
-    state_transitions : np.ndarray
-        num_states * num_states 2D array containing the number of transitions between states
-    title: str, optional
-        printed title of the plot
-    colors: Union[List[str], Colormap], optional
-        if you don't like the default colors
-    out_path: str, optional
-        path to save the image to. if none, the image is returned as a plotly figure
-    Returns
-    -------
-    None or fig
-        plotly figure if if was not saved
-    """
-    num_states = len(state_transitions)
-
-    if isinstance(colors, Colormap):
-        colors = [colors(i) for i in np.linspace(0, 1, num_states)]
-    elif len(colors) < num_states:
-        raise Exception("Insufficient colors to plot all states")
-
-    def v_distribute(total_transitions):
-        # Vertically distribute nodes in plot based on total number of transitions per state
-        box_sizes = total_transitions / total_transitions.sum()
-        box_vplace = [np.sum(box_sizes[:i]) + box_sizes[i]/2 for i in range(len(box_sizes))]
-        return box_vplace
-
-    y_placements = v_distribute(np.sum(state_transitions, axis=1)) + v_distribute(np.sum(state_transitions, axis=0))
-
-    # Convert colors to plotly format and make them transparent
-    rgba_colors = []
-    for color in colors:
-        rgba = to_rgba(color)
-        rgba_plotly = convert_to_RGB_255(rgba[:-1])
-        # Add opacity
-        rgba_plotly = rgba_plotly + (0.8,)
-        # Make string
-        rgba_colors.append("rgba" + str(rgba_plotly))
-
-    # Indices 0..n-1 are the source and n..2n-1 are the target.
-    fig = go.Figure(data=[go.Sankey(
-        node = dict(
-          pad = 5,
-          thickness = 20,
-          line = dict(color = "black", width = 2),
-          label = [f"state {i+1}" for i in range(num_states)]*2,
-          color = rgba_colors[:num_states]*2,
-          x = [0.1]*num_states + [1]*num_states,
-          y = y_placements
-        ),
-        link = dict(
-          arrowlen = 30,
-          source = np.array([[i]*num_states for i in range(num_states)]).flatten(),
-          target = np.array([[i for i in range(num_states, 2*num_states)] for _ in range(num_states)]).flatten(),
-          value = state_transitions.flatten(),
-          color = np.array([[c]*num_states for c in rgba_colors[:num_states]]).flatten()
-      ),
-        arrangement="fixed",
-    )])
-    fig.update_layout(title_text=title, font_size=20, title_x=0.5, height=max(600, num_states*100))
-
-    if out_path:
-        fig.write_image(out_path)
-        return None
-    else:
-        return fig
+        plt.close()

reeds/function_libs/visualization/state_transitions_plots.py

@@ -0,0 +1,81 @@
+from typing import Union, List
+import numpy as np
+
+from matplotlib.colors import Colormap, to_rgba
+import plotly.graph_objects as go
+from plotly.colors import convert_to_RGB_255
+
+from reeds.function_libs.visualization import plots_style as ps
+
+
+def plot_state_transitions(state_transitions: np.ndarray, title: str = None, colors: Union[List[str], Colormap] =  ps.qualitative_tab_map, out_path: str = None):
+    """
+    Make a Sankey plot showing the flows between states.
+     
+    Parameters
+    ----------
+    state_transitions : np.ndarray
+        num_states * num_states 2D array containing the number of transitions between states
+    title: str, optional
+        printed title of the plot
+    colors: Union[List[str], Colormap], optional
+        if you don't like the default colors
+    out_path: str, optional
+        path to save the image to. if none, the image is returned as a plotly figure
+    Returns
+    -------
+    None or fig
+        plotly figure if if was not saved
+    """
+    num_states = len(state_transitions)
+
+    if isinstance(colors, Colormap):
+        colors = [colors(i) for i in np.linspace(0, 1, num_states)]
+    elif len(colors) < num_states:
+        raise Exception("Insufficient colors to plot all states")
+
+    def v_distribute(total_transitions):
+        # Vertically distribute nodes in plot based on total number of transitions per state
+        box_sizes = total_transitions / total_transitions.sum()
+        box_vplace = [np.sum(box_sizes[:i]) + box_sizes[i]/2 for i in range(len(box_sizes))]
+        return box_vplace
+
+    y_placements = v_distribute(np.sum(state_transitions, axis=1)) + v_distribute(np.sum(state_transitions, axis=0))
+
+    # Convert colors to plotly format and make them transparent
+    rgba_colors = []
+    for color in colors:
+        rgba = to_rgba(color)
+        rgba_plotly = convert_to_RGB_255(rgba[:-1])
+        # Add opacity
+        rgba_plotly = rgba_plotly + (0.8,)
+        # Make string
+        rgba_colors.append("rgba" + str(rgba_plotly))
+
+    # Indices 0..n-1 are the source and n..2n-1 are the target.
+    fig = go.Figure(data=[go.Sankey(
+        node = dict(
+          pad = 5,
+          thickness = 20,
+          line = dict(color = "black", width = 2),
+          label = [f"state {i+1}" for i in range(num_states)]*2,
+          color = rgba_colors[:num_states]*2,
+          x = [0.1]*num_states + [1]*num_states,
+          y = y_placements
+        ),
+        link = dict(
+          arrowlen = 30,
+          source = np.array([[i]*num_states for i in range(num_states)]).flatten(),
+          target = np.array([[i for i in range(num_states, 2*num_states)] for _ in range(num_states)]).flatten(),
+          value = state_transitions.flatten(),
+          color = np.array([[c]*num_states for c in rgba_colors[:num_states]]).flatten()
+      ),
+        arrangement="fixed",
+    )])
+    fig.update_layout(title_text=title, font_size=20, title_x=0.5, height=max(600, num_states*100))
+
+    if out_path:
+        fig.write_image(out_path)
+        return None
+    else:
+        return fig