sphinx gallery

examples/desktop/line_collection/line_stack_3d.py

@@ -6,7 +6,7 @@
 """
 
 # test_example = false
-# sphinx_gallery_pygfx_docs = 'hidden'
+# sphinx_gallery_pygfx_docs = 'animate'
 
 import numpy as np
 import fastplotlib as fpl
@@ -97,8 +97,10 @@ def animate_colors(subplot):
 
 figure[0, 0].camera.set_state(camera_state)
 
-figure.canvas.set_logical_size(500, 500)
+figure.canvas.set_logical_size(700, 560)
 
+# NOTE: `if __name__ == "__main__"` is NOT how to use fastplotlib interactively
+# please see our docs for using fastplotlib interactively in ipython and jupyter
 if __name__ == "__main__":
     print(__doc__)
     fpl.run()

examples/desktop/misc/simple_event.py

@@ -9,56 +9,43 @@
 # sphinx_gallery_pygfx_docs = 'screenshot'
 
 import fastplotlib as fpl
-import numpy as np
+import imageio.v3 as iio
 
-# generate some date
-# linspace, create 100 evenly spaced x values from -10 to 10
-xs = np.linspace(-10, 10, 100)
-# sine wave
-ys = np.sin(xs)
-sine = np.column_stack([xs, ys])
-
-# cosine wave
-ys = np.cos(xs) + 5
-cosine = np.column_stack([xs, ys])
-
-# sinc function
-a = 0.5
-ys = np.sinc(xs) * 3 + 8
-sinc = np.column_stack([xs, ys])
+data = iio.imread("imageio:camera.png")
 
 # Create a figure
 figure = fpl.Figure()
 
-# we will add all the lines to the same subplot
-subplot = figure[0, 0]
-
 # plot sine wave, use a single color
-sine_graphic = subplot.add_line(data=sine, thickness=5, colors="magenta")
-
-# you can also use colormaps for lines!
-cosine_graphic = subplot.add_line(data=cosine, thickness=12, cmap="autumn")
-
-# or a list of colors for each datapoint
-colors = ["r"] * 25 + ["purple"] * 25 + ["y"] * 25 + ["b"] * 25
-sinc_graphic = subplot.add_line(data=sinc, thickness=5, colors=colors)
+image_graphic = figure[0,0].add_image(data=data)
 
 # show the plot
 figure.show()
 
-subplot.auto_scale(maintain_aspect=True)
-
 
+# define callback function to print the event data
 def callback_func(event_data):
-    print(event_data)
+    print(event_data.info)
 
 
 # Will print event data when the color changes
-cosine_graphic.colors.add_event_handler(callback_func)
+image_graphic.add_event_handler(callback_func, "cmap")
+
+image_graphic.cmap = "viridis"
+
+
+# adding a click event
+@image_graphic.add_event_handler("click")
+def click_event(event_data):
+    # get the click location in screen coordinates
+    xy = (event_data.x, event_data.y)
+
+    # map the screen coordinates to world coordinates
+    xy = figure[0,0].map_screen_to_world(xy)[:-1]
+
+    # print the click location
+    print(xy)
 
-# more complex indexing of colors
-# from point 15 - 30, set every 3rd point as "cyan"
-cosine_graphic.colors[15:50:3] = "cyan"
 
 figure.canvas.set_logical_size(700, 560)
 

examples/desktop/scatter/scatter.py

@@ -5,7 +5,7 @@
 Example showing scatter plot.
 """
 
-# test_example = true
+# test_example = false
 # sphinx_gallery_pygfx_docs = 'screenshot'
 
 import fastplotlib as fpl

examples/desktop/scatter/scatter_cmap.py

@@ -5,7 +5,7 @@
 Example showing cmap change for scatter plot.
 """
 
-# test_example = true
+# test_example = false
 # sphinx_gallery_pygfx_docs = 'screenshot'
 
 import fastplotlib as fpl

examples/desktop/scatter/scatter_cmap_iris.py

@@ -0,0 +1,44 @@
+"""
+Iris Scatter Colormap
+=====================
+
+Example showing cmap change for scatter plot.
+"""
+
+# test_example = true
+# sphinx_gallery_pygfx_docs = 'hidden'
+
+import fastplotlib as fpl
+import numpy as np
+from pathlib import Path
+from sklearn.cluster import AgglomerativeClustering
+
+
+figure = fpl.Figure()
+
+data_path = Path(__file__).parent.parent.joinpath("data", "iris.npy")
+data = np.load(data_path)
+
+agg = AgglomerativeClustering(n_clusters=3)
+agg.fit_predict(data)
+
+scatter_graphic = figure[0, 0].add_scatter(
+    data=data[:, :-1],  # use only xy data
+    sizes=15,
+    alpha=0.7,
+    cmap="Set1",
+    cmap_transform=agg.labels_  # use the labels as a transform to map colors from the colormap
+)
+
+figure.show()
+
+figure.canvas.set_logical_size(800, 800)
+
+figure[0, 0].auto_scale()
+
+scatter_graphic.cmap = "tab10"
+
+
+if __name__ == "__main__":
+    print(__doc__)
+    fpl.run()

examples/desktop/scatter/scatter_colorslice.py

@@ -5,7 +5,7 @@
 Example showing color slice for scatter plot.
 """
 
-# test_example = true
+# test_example = false
 # sphinx_gallery_pygfx_docs = 'screenshot'
 
 import fastplotlib as fpl

examples/desktop/scatter/scatter_colorslice_iris.py

@@ -0,0 +1,44 @@
+"""
+Iris Scatter Plot Color Slicing
+===============================
+
+Example showing color slice for scatter plot.
+"""
+
+# test_example = true
+# sphinx_gallery_pygfx_docs = 'hidden'
+
+import fastplotlib as fpl
+import numpy as np
+from pathlib import Path
+
+
+figure = fpl.Figure()
+
+data_path = Path(__file__).parent.parent.joinpath("data", "iris.npy")
+data = np.load(data_path)
+
+n_points = 50
+colors = ["yellow"] * n_points + ["cyan"] * n_points + ["magenta"] * n_points
+
+scatter_graphic = figure[0, 0].add_scatter(
+    data=data[:, :-1],
+    sizes=6,
+    alpha=0.7,
+    colors=colors  # use colors from the list of strings
+)
+
+figure.show()
+
+figure.canvas.set_logical_size(800, 800)
+
+figure[0, 0].auto_scale()
+
+scatter_graphic.colors[0:75] = "red"
+scatter_graphic.colors[75:150] = "white"
+scatter_graphic.colors[::2] = "blue"
+
+
+if __name__ == "__main__":
+    print(__doc__)
+    fpl.run()

examples/desktop/scatter/scatter_dataslice.py

@@ -5,7 +5,7 @@
 Example showing data slice for scatter plot.
 """
 
-# test_example = true
+# test_example = false
 # sphinx_gallery_pygfx_docs = 'screenshot'
 
 import fastplotlib as fpl

examples/desktop/scatter/scatter_dataslice_iris.py

@@ -0,0 +1,42 @@
+"""
+Iris Scatter Plot Data Slicing
+==============================
+
+Example showing data slice for scatter plot.
+"""
+
+# test_example = true
+# sphinx_gallery_pygfx_docs = 'hidden'
+
+import fastplotlib as fpl
+import numpy as np
+from pathlib import Path
+
+
+figure = fpl.Figure()
+
+data_path = Path(__file__).parent.parent.joinpath("data", "iris.npy")
+data = np.load(data_path)
+
+n_points = 50
+colors = ["yellow"] * n_points + ["cyan"] * n_points + ["magenta"] * n_points
+
+scatter_graphic = figure[0, 0].add_scatter(data=data[:, :-1], sizes=6, alpha=0.7, colors=colors)
+
+figure.show()
+
+figure.canvas.set_logical_size(800, 800)
+
+figure[0, 0].auto_scale()
+
+scatter_graphic.data[0] = np.array([[5, 3, 1.5]])
+scatter_graphic.data[1] = np.array([[4.3, 3.2, 1.3]])
+scatter_graphic.data[2] = np.array([[5.2, 2.7, 1.7]])
+
+scatter_graphic.data[10:15] = scatter_graphic.data[0:5] + np.array([1, 1, 1])
+scatter_graphic.data[50:100:2] = scatter_graphic.data[100:150:2] + np.array([1, 1, 0])
+
+
+if __name__ == "__main__":
+    print(__doc__)
+    fpl.run()

examples/desktop/scatter/scatter_iris.py

@@ -5,8 +5,8 @@
 Example showing scatter plot using sklearn iris dataset.
 """
 
-# test_example = false
-# sphinx_gallery_pygfx_docs = 'screenshot'
+# test_example = true
+# sphinx_gallery_pygfx_docs = 'hidden'
 
 import fastplotlib as fpl
 import numpy as np
@@ -17,7 +17,7 @@
 
 current_file = Path(sys.argv[0]).resolve()
 
-data_path = Path(current_file.parent.parent.joinpath("data", "iris.npy"))
+data_path = Path(__file__).parent.parent.joinpath("data", "iris.npy")
 data = np.load(data_path)
 
 n_points = 50