Merge pull request #55 from haesleinhuepf/markersize

Make markersize in plots configurable

docs/scatterplot.ipynb

@@ -230,7 +230,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33e97c513c2446b887d8063b0860293d",
+       "model_id": "90f8e79f06174f038601ab84bbba8f64",
        "version_major": 2,
        "version_minor": 0
       },
@@ -323,7 +323,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1bc5c7a22b944f82859f5e643fed62cb",
+       "model_id": "98ab4d4f5a1f41329a2d7d039cf57d52",
        "version_major": 2,
        "version_minor": 0
       },
@@ -386,10 +386,59 @@
     "df[\"selection\"]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8c418529-5596-415a-ac1f-7485da7790e0",
+   "metadata": {},
+   "source": [
+    "## Custom widgets\n",
+    "You can also build such a side-by-side view with  two plots yourself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d8f85ddd-160e-44f7-a5e3-eb42e032f093",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6364820096204b41969d4d12eeabd489",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HBox(children=(VBox(children=(VBox(children=(HBox(children=(Label(value='Axes '), Dropdown(layo…"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import HBox\n",
+    "\n",
+    "# Program functions that are executed when the selection is changed.\n",
+    "def update2(e=None):\n",
+    "    widget2.update()\n",
+    "\n",
+    "def update1(e=None):\n",
+    "    widget1.update()\n",
+    "    \n",
+    "widget1 = stackview.scatterplot(df, column_x=\"centroid-0\", column_y=\"centroid-1\", selection_changed_callback=update2, markersize=50)\n",
+    "widget2 = stackview.scatterplot(df, column_x=\"area\", column_y=\"aspect_ratio\", selection_changed_callback=update1)\n",
+    "\n",
+    "# Arrange the widgets side by side using HBox\n",
+    "HBox([widget1, widget2])\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "405b6843-28dd-40dd-a22d-925c0b34040b",
+   "id": "e54890a5-e89c-4861-bf5b-8aa1c5f7f697",
    "metadata": {},
    "outputs": [],
    "source": []

stackview/_clusterplot.py

@@ -1,5 +1,5 @@
 def clusterplot(df, labels, column_x:str="x", column_y:str="y", column_selection:str="selection", image=None,
-                     zoom_factor:float=1, zoom_spline_order:int=0, figsize=(4, 4), alpha:float=0.5):
+                     zoom_factor:float=1, zoom_spline_order:int=0, figsize=(4, 4), alpha:float=0.5, markersize=4):
     """
     Visualizes a scatter plot of columns in a given dataframe next to a segmented image.
 
@@ -28,7 +28,9 @@ def clusterplot(df, labels, column_x:str="x", column_y:str="y", column_selection
         The size of the scatter plot figure
     alpha: float, optional
         sets the transparency of the curtain
-
+    markersize: int
+        The size of the markers
+        
     Returns
     -------
     An ipywidgets widget
@@ -66,7 +68,7 @@ def update(selection, label_image, selected_image, widget):
                                          widget=image_display)
 
     scatterplot = scatterplot(df, column_x, column_y, column_selection, figsize=figsize,
-                                        selection_changed_callback=update_selection)
+                                        selection_changed_callback=update_selection, markersize=markersize)
 
     return grid([[
         image_display,

stackview/_scatterplot.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-def scatterplot(df, column_x: str = "x", column_y: str = "y", column_selection: str = "selection", figsize=(4, 4), selection_changed_callback=None):
+def scatterplot(df, column_x: str = "x", column_y: str = "y", column_selection: str = "selection", figsize=(4, 4), selection_changed_callback=None, markersize:int=4):
     """
     Create a scatterplot of a pandas dataframe while interactively choosing the columns and using a lasso tool for selecting data points
 
@@ -18,6 +18,8 @@ def scatterplot(df, column_x: str = "x", column_y: str = "y", column_selection:
         The size of the scatter plot figure
     selection_changed_callback: function
         The function to call when the selection changes
+    markersize: int
+        The size of the markers
 
     Returns
     -------
@@ -27,7 +29,7 @@ def scatterplot(df, column_x: str = "x", column_y: str = "y", column_selection:
     from ipywidgets import VBox, HBox, Layout
     from ._utilities import _no_resize
 
-    plotter = ScatterPlotter(df, column_x, column_y, column_selection, figsize, selection_changed_callback=selection_changed_callback)
+    plotter = ScatterPlotter(df, column_x, column_y, column_selection, figsize, selection_changed_callback=selection_changed_callback, markersize=markersize)
     small_layout = Layout(width='auto', padding='0px', margin='0px', align_items='center', justify_content='center')
 
     import ipywidgets
@@ -56,11 +58,13 @@ def on_change(event):
         plotter.widget
     ]))
 
+    result.update = plotter.update
+
     return result
 
 
 class ScatterPlotter():
-    def __init__(self, df, column_x, column_y, column_selection, figsize, selection_changed_callback):
+    def __init__(self, df, column_x, column_y, column_selection, figsize, selection_changed_callback, markersize):
         """
         An interactive scatter plotter for pandas dataframes.
         Use `.widget` on this object to get access to the graphical user interface.
@@ -79,6 +83,8 @@ def __init__(self, df, column_x, column_y, column_selection, figsize, selection_
             The size of the figure
         selection_changed_callback: function
             The function to call when the selection changes
+        markersize: int
+            The size of the markers
         """
         import matplotlib.pyplot as plt
         from matplotlib._pylab_helpers import Gcf
@@ -94,10 +100,11 @@ def __init__(self, df, column_x, column_y, column_selection, figsize, selection_
         self.set_data(df, column_x, column_y)
         self.selection_column = column_selection
         self.selection_changed_callback = selection_changed_callback
+        self.markersize = markersize
 
         # create figure
         self.fig = plt.figure(figsize=figsize)
-        self.fig.tight_layout(pad=0, h_pad=0, w_pad=0)
+        #self.fig.tight_layout(pad=0, h_pad=0, w_pad=0)
         plt.subplots_adjust(left=0.15, right=1, top=1, bottom=0.1)
 
         self.ax = None
@@ -139,7 +146,7 @@ def set_selection(self, selection):
     def update(self):
         self.fig.clf()
         self.ax = self.fig.gca()
-        self.plotted_points = self.ax.scatter(self.data[0], self.data[1])
+        self.plotted_points = self.ax.scatter(self.data[0], self.data[1], s=self.markersize)
         self.ax.set_xlabel(self.column_x)
         self.ax.set_ylabel(self.column_y)
         self.selector = Selector(self.fig, self.ax, self.plotted_points, callback=self.set_selection)
@@ -160,7 +167,7 @@ def __init__(self, parent, ax, collection, callback):
         self.num_points = len(self.offsets)
         self.collection = collection
 
-        self.lasso = LassoSelector(ax, onselect=self.on_select)
+        self.lasso = LassoSelector(ax, onselect=self.on_select, props=dict(color='magenta'))
         self.selected_indices = []
         self.callback = callback