Contribute examples to etsdemo

chaco/__init__.py

@@ -9,3 +9,7 @@
     __version__ = "not-built"
 
 __requires__ = ["traits", "traitsui", "pyface", "numpy", "enable"]
+
+__extras_require__ = {
+    'examples': ['encore', 'scipy', 'pandas']
+}

chaco/examples/_etsdemo_info.py

@@ -0,0 +1,34 @@
+# (C) Copyright 2005-2021 Enthought, Inc., Austin, TX
+# All rights reserved.
+#
+# This software is provided without warranty under the terms of the BSD
+# license included in LICENSE.txt and may be redistributed only under
+# the conditions described in the aforementioned license. The license
+# is also available online at http://www.enthought.com/licenses/BSD.txt
+#
+# Thanks for using Enthought open source!
+
+""" This module provides functions to be advertised in the distribution
+entry points.
+"""
+
+import pkg_resources
+
+
+def info(request):
+    """ Return a configuration for contributing examples to the
+    Demo application.
+    Parameters
+    ----------
+    request : dict
+        Information provided by the demo application.
+        Currently this is a placeholder.
+    Returns
+    -------
+    response : dict
+    """
+    return {
+        "version": 1,
+        "name": "Chaco Examples",
+        "root": pkg_resources.resource_filename("chaco.examples", "demo"),
+    }

examples/demo/advanced/data_cube.py → chaco/examples/demo/advanced/data_cube.py

@@ -6,13 +6,6 @@
 
 import warnings
 
-# Outstanding TODOs:
-#  - need to add line inspectors to side and bottom plots, and synchronize
-#    with center plot
-#  - need to set the various image plots to use the same colormap instance,
-#    and that colormap's range needs to be set to min/max of the entire cube
-#  - refactor create_window() so there is less code duplication
-#  - try to eliminate the use of model.xs, ys, zs in favor of bounds tuples
 from numpy import amin, amax, zeros, fromfile, transpose, uint8
 
 # Standard library imports
@@ -192,7 +185,7 @@ def normal_mouse_wheel(self, event):
             self.wheel_cb(self, event.mouse_wheel)
 
 
-class PlotFrame(DemoFrame):
+class Demo(DemoFrame):
 
     # These are the indices into the cube that each of the image plot views
     # will show; the default values are non-zero just to make it a little
@@ -466,6 +459,6 @@ def cleanup_data():
 if __name__ == "__main__":
     # Save demo so that it doesn't get garbage collected when run within
     # existing event loop (i.e. from ipython).
-    demo = demo_main(PlotFrame, size=(800, 700), title="Cube analyzer")
+    demo = demo_main(Demo, size=(800, 700), title="Cube analyzer")
     if run_cleanup:
         cleanup_data()

examples/demo/advanced/scalar_image_function_inspector.py → chaco/examples/demo/advanced/scalar_image_function_inspector.py

@@ -454,8 +454,8 @@ class TimerController(HasTraits):
     # The plot view which will be affected by timed animation
     view = Instance(PlotUI)
 
-    # The ModelView instance that contains the animation options:
-    model_view = Instance("ModelView")
+    # The DemoModelView instance that contains the animation options:
+    model_view = Instance("DemoModelView")
 
     # Whether the view is animated:
     animated = DelegatesTo("model_view")
@@ -556,7 +556,7 @@ def randomize(new_direction=1, color_change=False):
             metadata["selections"] = x, y
 
 
-class ModelView(HasTraits):
+class DemoModelView(HasTraits):
 
     model = Instance(Model)
     view = Instance(PlotUI)
@@ -614,19 +614,15 @@ def _start_timer(self):
 
     def edit_traits(self, *args, **kws):
         self._start_timer()
-        return super(ModelView, self).edit_traits(*args, **kws)
+        return super(DemoModelView, self).edit_traits(*args, **kws)
 
     def configure_traits(self, *args, **kws):
         self._start_timer()
-        return super(ModelView, self).configure_traits(*args, **kws)
+        return super(DemoModelView, self).configure_traits(*args, **kws)
 
 
-def show_plot(**kwargs):
-    model = Model(**kwargs)
-    view = PlotUI(**kwargs)
-    modelview = ModelView(model=model, view=view)
-    modelview.configure_traits()
+demo = DemoModelView(model=Model(), view=PlotUI())
 
 
 if __name__ == "__main__":
-    show_plot()
+    demo.configure_traits()

examples/demo/basic/horizon_plot.py → chaco/examples/demo/basic/horizon_plot.py

@@ -186,11 +186,9 @@ class Demo(HasTraits):
     )
 
 
-demo = Demo()
+filled, horizon = _create_plot_components()
+demo = Demo(horizon=horizon, filled=filled)
 
-if __name__ == "__main__":
 
-    filled, horizon = _create_plot_components()
-    demo.horizon = horizon
-    demo.filled = filled
+if __name__ == "__main__":
     demo.configure_traits()

examples/demo/basic/minor_ticks_axis.py → chaco/examples/demo/basic/minor_ticks_axis.py

@@ -57,6 +57,8 @@ def _plot_default(self):
         return plot
 
 
+demo = MinorTickDemo()
+
+
 if __name__ == "__main__":
-    demo = MinorTickDemo()
     demo.configure_traits()

examples/demo/basic/scatter_inspector2.py → chaco/examples/demo/basic/scatter_inspector2.py

@@ -54,42 +54,40 @@ def show_data(data_idx):
         return show_data
 
 
-if __name__ == "__main__":
+def _create_plot_component():
+    # Create a fake dataset from which 2 dimensions will be displayed in a
+    # scatter plot:
+    x = np.random.uniform(0.0, 10.0, 50)
+    y = np.random.uniform(0.0, 5.0, 50)
+    data = pd.DataFrame(
+        {"x": x, "y": y, "dataset": np.random.choice(list("abcdefg"), 50)}
+    )
+    plot_data = ArrayPlotData(x=x, y=y)
+    plot = Plot(plot_data)
+    scatter = plot.plot(("x", "y"), type="scatter")[0]
+
+    # Attach the inspector and its overlays
+    inspector = DataframeScatterInspector(component=scatter, data=data)
+    scatter.tools.append(inspector)
+
+    text_overlay = DataframeScatterOverlay(
+        component=plot,
+        inspector=inspector,
+        bgcolor="black",
+        alpha=0.6,
+        text_color="white",
+        border_color="none",
+    )
+    plot.overlays.append(text_overlay)
 
-    def _create_plot_component():
-        # Create a fake dataset from which 2 dimensions will be displayed in a
-        # scatter plot:
-        x = np.random.uniform(0.0, 10.0, 50)
-        y = np.random.uniform(0.0, 5.0, 50)
-        data = pd.DataFrame(
-            {"x": x, "y": y, "dataset": np.random.choice(list("abcdefg"), 50)}
-        )
-        plot_data = ArrayPlotData(x=x, y=y)
-        plot = Plot(plot_data)
-        scatter = plot.plot(("x", "y"), type="scatter")[0]
-
-        # Attach the inspector and its overlays
-        inspector = DataframeScatterInspector(component=scatter, data=data)
-        scatter.tools.append(inspector)
-
-        text_overlay = DataframeScatterOverlay(
-            component=plot,
-            inspector=inspector,
-            bgcolor="black",
-            alpha=0.6,
-            text_color="white",
-            border_color="none",
-        )
-        plot.overlays.append(text_overlay)
-
-        # Optional: add an overlay on the point to confirm what is hovered over
-        # Note that this overlay magically knows about hovered points by
-        # listening to renderer events rather than inspector events:
-        point_overlay = ScatterInspectorOverlay(
-            component=scatter, hover_color="red", hover_marker_size=6
-        )
-        scatter.overlays.append(point_overlay)
-        return plot
+    # Optional: add an overlay on the point to confirm what is hovered over
+    # Note that this overlay magically knows about hovered points by
+    # listening to renderer events rather than inspector events:
+    point_overlay = ScatterInspectorOverlay(
+        component=scatter, hover_color="red", hover_marker_size=6
+    )
+    scatter.overlays.append(point_overlay)
+    return plot
 
 
 # =============================================================================

examples/demo/chaco_trait_editor.py → chaco/examples/demo/chaco_trait_editor.py

@@ -3,15 +3,15 @@
 interval.
 """
 
-
+from traits.api import HasTraits
 from traits.etsconfig.api import ETSConfig
 
 if ETSConfig.toolkit == "wx":
     from traitsui.wx.editor import Editor
 else:
     from traitsui.qt4.editor import Editor
 
-from traitsui.api import EditorFactory
+from traitsui.api import EditorFactory, Item, View
 
 from enable.api import ColorTrait, Window
 
@@ -173,18 +173,15 @@ def update_editor(self):
 IntervalEditor = IntervalEditorFactory
 
 
-# --- Demonstration ---
-
-if __name__ == "__main__":
-    from traits.api import HasTraits
-    from traitsui.api import View, Item
-
-    class IntervalTest(HasTraits):
+class IntervalTest(HasTraits):
         interval = Interval(low=0, high=1)
 
         traits_view = View(
             Item("interval", editor=IntervalEditor()), resizable=True
         )
 
-    it = IntervalTest()
-    it.configure_traits()
+
+demo = IntervalTest()
+
+if __name__ == "__main__":
+    demo.configure_traits()

examples/demo/financial/stock_prices.py → chaco/examples/demo/financial/stock_prices.py

@@ -60,7 +60,7 @@ def create_dates(numpoints, units="days"):
     return dates
 
 
-class PlotFrame(DemoFrame):
+class Demo(DemoFrame):
     def _create_price_plots(self, times, prices, mini_height=75):
         """Creates the two plots of prices and returns them.  One of the
         plots can be zoomed and panned, and the other plot (smaller) always
@@ -221,5 +221,5 @@ def _create_component(self):
     # Save demo so that it doesn't get garbage collected when run within
     # existing event loop (i.e. from ipython).
     demo = demo_main(
-        PlotFrame, size=(800, 600), title="Stock price and volume"
+        Demo, size=(800, 600), title="Stock price and volume"
     )

examples/demo/hyetograph.py → chaco/examples/demo/hyetograph.py

@@ -1,6 +1,7 @@
 from chaco.api import ArrayPlotData, Plot
 from enable.api import ComponentEditor
 from traits.api import (
+    Bool,
     HasTraits,
     Instance,
     Int,
@@ -43,6 +44,12 @@ class Hyetograph(HasTraits):
 
     nrcs_plot = Instance(Plot)
 
+    initialized = Bool(False)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.initialized = True
+
     def _intensity_plot_default(self):
         intensity_plot = Plot(ArrayPlotData(x=self.timeline, y=self.intensity))
         intensity_plot.x_axis.title = "Time (hr)"
@@ -104,7 +111,7 @@ def calculate_runoff(self):
                 vr[i] = 0
         self.nrcs = vr
 
-    @observe('duration, year_storm, county, curve_number')
+    @observe('duration, year_storm, county, curve_number, initialized')
     def _perform_calculations(self, event=None):
         self.calculate_intensity()
         self.calculate_runoff()
@@ -126,10 +133,6 @@ def _update_polt_type(self, event):
         self.intensity_plot.invalidate_and_redraw()
         self.nrcs_plot.invalidate_and_redraw()
 
-    def start(self):
-        self._perform_calculations()
-        self.configure_traits()
-
     traits_view = View(
         Item('plot_type'),
         Item("intensity_plot", editor=ComponentEditor()),
@@ -144,6 +147,8 @@ def start(self):
     )
 
 
+popup = Hyetograph()
+
+
 if __name__ == "__main__":
-    hyetograph = Hyetograph()
-    hyetograph.start()
+    popup.configure_traits()

examples/demo/image_plot_origin_and_orientation.py → chaco/examples/demo/image_plot_origin_and_orientation.py

@@ -3,10 +3,11 @@
 
 The origin parameter sets a plot's default origin to the specified corner
 of the plot window. These positions has the following behavior:
-    * 'left' : index increases left to right
-    * 'right' : index increases right to left
-    * 'top' : index increases top to bottom
-    * 'bottom' : index increases bottom to top
+
+* 'left' : index increases left to right
+* 'right' : index increases right to left
+* 'top' : index increases top to bottom
+* 'bottom' : index increases bottom to top
 
 The orientation parameter switches the x- and y-axes. Alternatively, you can
 think of this as a transpose about the origin.

examples/demo/multi_line_plot_demo.py → chaco/examples/demo/multi_line_plot_demo.py

@@ -138,23 +138,25 @@ def _offset_changed(self, off):
         self.multi_line_plot_renderer._amplitude_changed()
 
 
+# Sample rate.
+fs = 500
+# Total time.
+T = 5.0
+num_samples = fs * T
+t = np.arange(num_samples) / fs
+
+channels = np.arange(12)
+# Frequencies of the sine functions in each channel.
+freqs = 3 * (channels[:, None] + 1)
+y = np.sin(freqs * t)
+
+# Create an instance of DataModel.  This is the data to
+# be plotted with a MultiLinePlot.
+data = DataModel(x_index=t, y_index=channels, data=y)
+
+# Create the demo class, and show it.
+demo = MultiLinePlotDemo(model=data)
+
+
 if __name__ == "__main__":
-    # Sample rate.
-    fs = 500
-    # Total time.
-    T = 5.0
-    num_samples = fs * T
-    t = np.arange(num_samples) / fs
-
-    channels = np.arange(12)
-    # Frequencies of the sine functions in each channel.
-    freqs = 3 * (channels[:, None] + 1)
-    y = np.sin(freqs * t)
-
-    # Create an instance of DataModel.  This is the data to
-    # be plotted with a MultiLinePlot.
-    data = DataModel(x_index=t, y_index=channels, data=y)
-
-    # Create the demo class, and show it.
-    demo = MultiLinePlotDemo(model=data)
     demo.configure_traits()