fastplotlib · kushalkolar · Jul 21, 2025 · Jul 21, 2025 · Jul 21, 2025 · Jul 21, 2025
@@ -23,16 +23,16 @@
 figure = fpl.Figure(size=(700, 560))
 
 # add a line
-line_graphic = figure[0, 0].add_line(data)
+line = figure[0, 0].add_line(data)
 
 # add a scatter, share the line graphic buffer!
-scatter_graphic = figure[0, 0].add_scatter(data=line_graphic.data, sizes=25, colors="r")
+scatter = figure[0, 0].add_scatter(data=line.data, sizes=25, colors="r")
 
 is_moving = False
 vertex_index = None
 
 
-@scatter_graphic.add_event_handler("pointer_down")
+@scatter.add_event_handler("pointer_down")
 def start_drag(ev: pygfx.PointerEvent):
     global is_moving
     global vertex_index
@@ -42,7 +42,7 @@ def start_drag(ev: pygfx.PointerEvent):
 
     is_moving = True
     vertex_index = ev.pick_info["vertex_index"]
-    scatter_graphic.colors[vertex_index] = "cyan"
+    scatter.colors[vertex_index] = "cyan"
 
 
 @figure.renderer.add_event_handler("pointer_move")
@@ -63,13 +63,13 @@ def move_point(ev):
     if pos is None:
         # end movement
         is_moving = False
-        scatter_graphic.colors[vertex_index] = "r"  # reset color
+        scatter.colors[vertex_index] = "r"  # reset color
         vertex_index = None
         return
 
     # change scatter data
     # since we are sharing the buffer, the line data will also change
-    scatter_graphic.data[vertex_index, :-1] = pos[:-1]
+    scatter.data[vertex_index, :-1] = pos[:-1]
 
     # re-enable controller
     figure[0, 0].controller.enabled = True
@@ -83,7 +83,7 @@ def end_drag(ev: pygfx.PointerEvent):
     # end movement
     if is_moving:
         # reset color
-        scatter_graphic.colors[vertex_index] = "r"
+        scatter.colors[vertex_index] = "r"
 
     is_moving = False
     vertex_index = None

@@ -18,14 +18,14 @@
 figure = fpl.Figure(size=(700, 560))
 
 # create image graphic
-image_graphic = figure[0, 0].add_image(data=data)
+image = figure[0, 0].add_image(data=data)
 
 # show the plot
 figure.show()
 
 
 # adding a click event, we can also use decorators to add event handlers
-@image_graphic.add_event_handler("click")
+@image.add_event_handler("click")
 def click_event(ev: pygfx.PointerEvent):
     # get the click location in screen coordinates
     xy = (ev.x, ev.y)

@@ -16,18 +16,18 @@
 xs = np.linspace(0, 4 * np.pi, 100)
 # sine wave
 ys = np.sin(xs)
-sine = np.column_stack([xs, ys])
+sine_data = np.column_stack([xs, ys])
 
 # cosine wave
 ys = np.cos(xs)
-cosine = np.column_stack([xs, ys])
+cosine_data = np.column_stack([xs, ys])
 
 # create line graphics
-sine_graphic = figure[0, 0].add_line(data=sine)
-cosine_graphic = figure[0, 0].add_line(data=cosine, offset=(0, 4, 0))
+sine = figure[0, 0].add_line(data=sine_data)
+cosine = figure[0, 0].add_line(data=cosine_data, offset=(0, 4, 0))
 
 # make a list of the line graphics for convenience
-lines = [sine_graphic, cosine_graphic]
+lines = [sine, cosine]
 
 
 def change_thickness(ev: fpl.GraphicFeatureEvent):
@@ -66,11 +66,11 @@ def change_data(ev: fpl.GraphicFeatureEvent):
 # set the y-value of the middle 40 points of the sine graphic to 1
 # after the sine_graphic sets its data, the event handlers will be called
 # and therefore the cosine graphic will also set its data using the event data
-sine_graphic.data[30:70, 1] = np.ones(40)
+sine.data[30:70, 1] = np.ones(40)
 
 # set the thickness of the cosine graphic, this will trigger an event
 # that causes the sine graphic's thickness to also be set from this value
-cosine_graphic.thickness = 10
+cosine.thickness = 10
 
 # NOTE: fpl.loop.run() should not be used for interactive sessions
 # See the "JupyterLab and IPython" section in the user guide

@@ -84,7 +84,7 @@ def make_circle(center, radius: float, n_points: int = 75) -> np.ndarray:
 xs = np.linspace(-10, 10, 100)
 # sine wave
 ys = np.sin(xs)
-sine = np.dstack([xs, ys])[0]
+sine = np.column_stack([xs, ys])
 
 # make 10 identical waves
 sine_waves = 10 * [sine]

@@ -61,55 +61,55 @@ def make_circle(center, radius: float, p, q, n_points: int) -> np.ndarray:
 
 # create sine and cosine data
 xs = np.linspace(0, 2 * np.pi, 360)
-sine = np.sin(xs * P)
-cosine = np.cos(xs * Q)
+sine_data = np.sin(xs * P)
+cosine_data = np.cos(xs * Q)
 
 # circle data
 circle_data = make_circle(center=(0, 0), p=P, q=Q, radius=1, n_points=360)
 
 # make the circle line graphic, set the cmap transform using the sine function
-circle_graphic = figure["circle"].add_line(
-    circle_data, thickness=4, cmap="bwr", cmap_transform=sine
+circle = figure["circle"].add_line(
+    circle_data, thickness=4, cmap="bwr", cmap_transform=sine_data
 )
 
 # line to show the circle radius
 # use it to indicate the current position of the sine and cosine selctors (below)
 radius_data = np.array([[0, 0, 0], [*circle_data[0], 0]])
-circle_radius_graphic = figure["circle"].add_line(
+circle_radius = figure["circle"].add_line(
     radius_data, thickness=6, colors="magenta"
 )
 
 # sine line graphic, cmap transform set from the sine function
-sine_graphic = figure["sin"].add_line(
-    sine, thickness=10, cmap="bwr", cmap_transform=sine
+sine = figure["sin"].add_line(
+    sine_data, thickness=10, cmap="bwr", cmap_transform=sine_data
 )
 
 # cosine line graphic, cmap transform set from the sine function
 # illustrates the sine function values on the cosine graphic
-cosine_graphic = figure["cos"].add_line(
-    cosine, thickness=10, cmap="bwr", cmap_transform=sine
+cosine = figure["cos"].add_line(
+    cosine_data, thickness=10, cmap="bwr", cmap_transform=sine_data
 )
 
 # add linear selectors to the sine and cosine line graphics
-sine_selector = sine_graphic.add_linear_selector()
-cosine_selector = cosine_graphic.add_linear_selector()
+sine_selector = sine.add_linear_selector()
+cosine_selector = cosine.add_linear_selector()
 
 
 def set_circle_cmap(ev):
     # sets the cmap transforms
 
     cmap_transform = ev.graphic.data[:, 1]  # y-val data of the sine or cosine graphic
-    for g in [sine_graphic, cosine_graphic]:
+    for g in [sine, cosine]:
         g.cmap.transform = cmap_transform
 
     # set circle cmap transform
-    circle_graphic.cmap.transform = cmap_transform
+    circle.cmap.transform = cmap_transform
 
 # when the sine or cosine graphic is clicked, the cmap_transform
 # of the sine, cosine and circle line graphics are all set from
 # the y-values of the clicked line
-sine_graphic.add_event_handler(set_circle_cmap, "click")
-cosine_graphic.add_event_handler(set_circle_cmap, "click")
+sine.add_event_handler(set_circle_cmap, "click")
+cosine.add_event_handler(set_circle_cmap, "click")
 
 
 def set_x_val(ev):
@@ -120,7 +120,7 @@ def set_x_val(ev):
     sine_selector.selection = value
     cosine_selector.selection = value
 
-    circle_radius_graphic.data[1, :-1] = circle_data[index]
+    circle_radius.data[1, :-1] = circle_data[index]
 
 # add same event handler to both graphics
 sine_selector.add_event_handler(set_x_val, "selection")
@@ -138,19 +138,19 @@ def __init__(self, figure, size, location, title):
         self._q = 1
 
     def _set_data(self):
-        global sine_graphic, cosine_graphic, circle_graphic, circle_radius_graphic, circle_data
+        global sine, cosine, circle, circle_radius, circle_data
 
         # make new data
-        sine = np.sin(xs * self._p)
-        cosine = np.cos(xs * self._q)
+        sine_data = np.sin(xs * self._p)
+        cosine_data = np.cos(xs * self._q)
         circle_data = make_circle(center=(0, 0), p=self._p, q=self._q, radius=1, n_points=360)
 
 
         # set the graphics
-        sine_graphic.data[:, 1] = sine
-        cosine_graphic.data[:, 1] = cosine
-        circle_graphic.data[:, :2] = circle_data
-        circle_radius_graphic.data[1, :-1] = circle_data[sine_selector.get_selected_index()]
+        sine.data[:, 1] = sine_data
+        cosine.data[:, 1] = cosine_data
+        circle.data[:, :2] = circle_data
+        circle_radius.data[1, :-1] = circle_data[sine_selector.get_selected_index()]
 
     def update(self):
         flag_set_data = False

@@ -21,7 +21,7 @@
 data = np.vstack([sine * i for i in range(2_300)])
 
 # plot the image data
-img = figure[0, 0].add_image(data=data, name="heatmap")
+image = figure[0, 0].add_image(data=data, name="heatmap")
 del data
 
 figure.show()

@@ -8,19 +8,20 @@
 # test_example = true
 # sphinx_gallery_pygfx_docs = 'screenshot'
 
-import fastplotlib as fpl
 import imageio.v3 as iio
 
+import fastplotlib as fpl
+
 im = iio.imread("imageio:camera.png")
 
 figure = fpl.Figure(size=(700, 560))
 
 # plot the image data
-image_graphic = figure[0, 0].add_image(data=im, name="random-image")
+image = figure[0, 0].add_image(data=im, name="random-image")
 
 figure.show()
 
-image_graphic.cmap = "viridis"
+image.cmap = "viridis"
 
 # NOTE: fpl.loop.run() should not be used for interactive sessions
 # See the "JupyterLab and IPython" section in the user guide

@@ -16,7 +16,7 @@
 figure = fpl.Figure(size=(700, 560))
 
 # plot the image data
-image_graphic = figure[0, 0].add_image(data=im, name="iio astronaut")
+image = figure[0, 0].add_image(data=im, name="iio astronaut")
 
 figure.show()
 

@@ -16,12 +16,12 @@
 figure = fpl.Figure(size=(700, 560))
 
 # plot the image data
-image_graphic = figure[0, 0].add_image(data=im, name="iio astronaut")
+image = figure[0, 0].add_image(data=im, name="iio astronaut")
 
 figure.show()
 
-image_graphic.vmin = 0.5
-image_graphic.vmax = 0.75
+image.vmin = 0.5
+image.vmax = 0.75
 
 # NOTE: fpl.loop.run() should not be used for interactive sessions
 # See the "JupyterLab and IPython" section in the user guide

@@ -16,7 +16,7 @@
 data = iio.imread("imageio:camera.png")
 
 # plot the image data
-image_graphic = figure[0, 0].add_image(data=data, name="iio camera")
+image = figure[0, 0].add_image(data=data, name="iio camera")
 
 figure.show()
 

@@ -18,7 +18,7 @@
     [[0, 1, 2],
      [3, 4, 5]]
 )
-image_graphic = figure[0, 0].add_image(data)
+image = figure[0, 0].add_image(data)
 
 figure.show()
 

@@ -16,12 +16,12 @@
 data = iio.imread("imageio:astronaut.png")
 
 # plot the image data
-image_graphic = figure[0, 0].add_image(data=data, name="iio astronaut")
+image = figure[0, 0].add_image(data=data, name="iio astronaut")
 
 figure.show()
 
-image_graphic.vmin = 0.5
-image_graphic.vmax = 0.75
+image.vmin = 0.5
+image.vmax = 0.75
 
 # NOTE: fpl.loop.run() should not be used for interactive sessions
 # See the "JupyterLab and IPython" section in the user guide

@@ -16,25 +16,25 @@
 xs = np.linspace(-10, 10, 100)
 # sine wave
 ys = np.sin(xs)
-sine = np.dstack([xs, ys])[0]
+sine_data = np.column_stack([xs, ys])
 
 # cosine wave
 ys = np.cos(xs) + 5
-cosine = np.dstack([xs, ys])[0]
+cosine_data = np.column_stack([xs, ys])
 
 # sinc function
 a = 0.5
 ys = np.sinc(xs) * 3 + 8
-sinc = np.dstack([xs, ys])[0]
+sinc_data = np.column_stack([xs, ys])
 
-sine_graphic = figure[0, 0].add_line(data=sine, thickness=5, colors="magenta")
+sine = figure[0, 0].add_line(data=sine_data, thickness=5, colors="magenta")
 
 # you can also use colormaps for lines!
-cosine_graphic = figure[0, 0].add_line(data=cosine, thickness=12, cmap="autumn")
+cosine = figure[0, 0].add_line(data=cosine_data, thickness=12, cmap="autumn")
 
 # or a list of colors for each datapoint
 colors = ["r"] * 25 + ["purple"] * 25 + ["y"] * 25 + ["b"] * 25
-sinc_graphic = figure[0, 0].add_line(data=sinc, thickness=5, colors=colors)
+sinc = figure[0, 0].add_line(data=sinc_data, thickness=5, colors=colors)
 
 figure[0, 0].axes.grids.xy.visible = True
 figure.show()

@@ -16,24 +16,24 @@
 xs = np.linspace(-10, 10, 100)
 # sine wave
 ys = np.sin(xs)
-sine = np.dstack([xs, ys])[0]
+sine_data = np.column_stack([xs, ys])
 
 # cosine wave
 ys = np.cos(xs) - 5
-cosine = np.dstack([xs, ys])[0]
+cosine_data = np.column_stack([xs, ys])
 
 # cmap_transform from an array, so the colors on the sine line will be based on the sine y-values
-sine_graphic = figure[0, 0].add_line(
-    data=sine,
+sine = figure[0, 0].add_line(
+    data=sine_data,
     thickness=10,
     cmap="plasma",
-    cmap_transform=sine[:, 1]
+    cmap_transform=sine_data[:, 1]
 )
 
 # qualitative colormaps, useful for cluster labels or other types of categorical labels
 labels = [0] * 25 + [5] * 10 + [1] * 35 + [2] * 30
-cosine_graphic = figure[0, 0].add_line(
-    data=cosine,
+cosine = figure[0, 0].add_line(
+    data=cosine_data,
     thickness=10,
     cmap="tab10",
     cmap_transform=labels