fastplotlib · kushalkolar · Jun 21, 2023 · Jun 17, 2023 · Jun 17, 2023 · Jun 17, 2023
diff --git a/examples/line/line.py b/examples/line/line.py
@@ -9,13 +9,8 @@
 from fastplotlib import Plot
 import numpy as np
 
-from wgpu.gui.offscreen import WgpuCanvas
-from pygfx import WgpuRenderer
 
-canvas = WgpuCanvas()
-renderer = WgpuRenderer(canvas)
-
-plot = Plot(canvas=canvas, renderer=renderer)
+plot = Plot()
 
 xs = np.linspace(-10, 10, 100)
 # sine wave

diff --git a/examples/line/line_cmap.py b/examples/line/line_cmap.py
@@ -0,0 +1,46 @@
+"""
+Line Plot
+============
+Example showing cosine, sine, sinc lines.
+"""
+
+# test_example = true
+
+import fastplotlib as fpl
+import numpy as np
+
+
+plot = fpl.Plot()
+
+xs = np.linspace(-10, 10, 100)
+# sine wave
+ys = np.sin(xs)
+sine = np.dstack([xs, ys])[0]
+
+# cosine wave
+ys = np.cos(xs) - 5
+cosine = np.dstack([xs, ys])[0]
+
+# cmap_values from an array, so the colors on the sine line will be based on the sine y-values
+sine_graphic = plot.add_line(
+    data=sine,
+    thickness=10,
+    cmap="plasma",
+    cmap_values=sine[:, 1]
+)
+
+# qualitative colormaps, useful for cluster labels or other types of categorical labels
+cmap_values = [0] * 25 + [5] * 10 + [1] * 35 + [2] * 30
+cosine_graphic = plot.add_line(
+    data=cosine,
+    thickness=10,
+    cmap="tab10",
+    cmap_values=cmap_values
+)
+
+plot.show()
+
+plot.canvas.set_logical_size(800, 800)
+
+if __name__ == "__main__":
+    fpl.run()
diff --git a/examples/line/line_colorslice.py b/examples/line/line_colorslice.py
@@ -9,13 +9,8 @@
 from fastplotlib import Plot
 import numpy as np
 
-from wgpu.gui.offscreen import WgpuCanvas
-from pygfx import WgpuRenderer
 
-canvas = WgpuCanvas()
-renderer = WgpuRenderer(canvas)
-
-plot = Plot(canvas=canvas, renderer=renderer)
+plot = Plot()
 
 xs = np.linspace(-10, 10, 100)
 # sine wave

diff --git a/examples/line/line_dataslice.py b/examples/line/line_dataslice.py
@@ -9,13 +9,8 @@
 from fastplotlib import Plot
 import numpy as np
 
-from wgpu.gui.offscreen import WgpuCanvas
-from pygfx import WgpuRenderer
 
-canvas = WgpuCanvas()
-renderer = WgpuRenderer(canvas)
-
-plot = Plot(canvas=canvas, renderer=renderer)
+plot = Plot()
 
 xs = np.linspace(-10, 10, 100)
 # sine wave

diff --git a/examples/line/line_present_scaling.py b/examples/line/line_present_scaling.py
@@ -9,13 +9,8 @@
 from fastplotlib import Plot
 import numpy as np
 
-from wgpu.gui.offscreen import WgpuCanvas
-from pygfx import WgpuRenderer
 
-canvas = WgpuCanvas()
-renderer = WgpuRenderer(canvas)
-
-plot = Plot(canvas=canvas, renderer=renderer)
+plot = Plot()
 
 xs = np.linspace(-10, 10, 100)
 # sine wave

diff --git a/examples/line_collection/__init__.py b/examples/line_collection/__init__.py
diff --git a/examples/line_collection/line_collection.py b/examples/line_collection/line_collection.py
@@ -0,0 +1,39 @@
+"""
+Line Plot
+============
+Example showing how to plot line collections
+"""
+
+# test_example = true
+
+from itertools import product
+import numpy as np
+import fastplotlib as fpl
+
+
+def make_circle(center, radius: float, n_points: int = 75) -> np.ndarray:
+    theta = np.linspace(0, 2 * np.pi, n_points)
+    xs = radius * np.sin(theta)
+    ys = radius * np.cos(theta)
+
+    return np.column_stack([xs, ys]) + center
+
+
+spatial_dims = (100, 100)
+
+circles = list()
+for center in product(range(0, spatial_dims[0], 15), range(0, spatial_dims[1], 15)):
+    circles.append(make_circle(center, 5, n_points=75))
+
+pos_xy = np.vstack(circles)
+
+plot = fpl.Plot()
+
+plot.add_line_collection(circles, cmap="jet", thickness=5)
+
+plot.show()
+
+plot.canvas.set_logical_size(800, 800)
+
+if __name__ == "__main__":
+    fpl.run()
diff --git a/examples/line_collection/line_collection_cmap_values.py b/examples/line_collection/line_collection_cmap_values.py
@@ -0,0 +1,50 @@
+"""
+Line Plot
+============
+Example showing how to plot line collections
+"""
+
+# test_example = true
+
+from itertools import product
+import numpy as np
+import fastplotlib as fpl
+
+
+def make_circle(center, radius: float, n_points: int = 75) -> np.ndarray:
+    theta = np.linspace(0, 2 * np.pi, n_points)
+    xs = radius * np.sin(theta)
+    ys = radius * np.cos(theta)
+
+    return np.column_stack([xs, ys]) + center
+
+
+spatial_dims = (50, 50)
+
+circles = list()
+for center in product(range(0, spatial_dims[0], 15), range(0, spatial_dims[1], 15)):
+    circles.append(make_circle(center, 5, n_points=75))
+
+pos_xy = np.vstack(circles)
+
+# this makes 16 circles, so we can create 16 cmap values, so it will use these values to set the
+# color of the line based by using the cmap as a LUT with the corresponding cmap_value
+
+# highest values, lowest values, mid-high values, mid values
+cmap_values = [10] * 4 + [0] * 4 + [7] * 4 + [5] * 4
+
+plot = fpl.Plot()
+
+plot.add_line_collection(
+    circles,
+    cmap="bwr",
+    cmap_values=cmap_values,
+    thickness=10
+)
+
+plot.show()
+
+plot.canvas.set_logical_size(800, 800)
+
+if __name__ == "__main__":
+    fpl.run()
diff --git a/examples/line_collection/line_collection_cmap_values_qualitative.py b/examples/line_collection/line_collection_cmap_values_qualitative.py
@@ -0,0 +1,56 @@
+"""
+Line Plot
+============
+Example showing how to plot line collections
+"""
+
+# test_example = true
+
+from itertools import product
+import numpy as np
+import fastplotlib as fpl
+
+
+def make_circle(center, radius: float, n_points: int = 75) -> np.ndarray:
+    theta = np.linspace(0, 2 * np.pi, n_points)
+    xs = radius * np.sin(theta)
+    ys = radius * np.cos(theta)
+
+    return np.column_stack([xs, ys]) + center
+
+
+spatial_dims = (50, 50)
+
+circles = list()
+for center in product(range(0, spatial_dims[0], 15), range(0, spatial_dims[1], 15)):
+    circles.append(make_circle(center, 5, n_points=75))
+
+pos_xy = np.vstack(circles)
+
+# this makes 16 circles, so we can create 16 cmap values, so it will use these values to set the
+# color of the line based by using the cmap as a LUT with the corresponding cmap_value
+
+# qualitative colormap used for mapping 16 cmap values for each line
+# for example, these could be cluster labels
+cmap_values = [
+    0, 1, 1, 2,
+    0, 0, 1, 1,
+    2, 2, 3, 3,
+    1, 1, 1, 5
+]
+
+plot = fpl.Plot()
+
+plot.add_line_collection(
+    circles,
+    cmap="tab10",
+    cmap_values=cmap_values,
+    thickness=10
+)
+
+plot.show()
+
+plot.canvas.set_logical_size(800, 800)
+
+if __name__ == "__main__":
+    fpl.run()
diff --git a/examples/line_collection/line_collection_colors.py b/examples/line_collection/line_collection_colors.py
@@ -0,0 +1,43 @@
+"""
+Line Plot
+============
+Example showing how to plot line collections
+"""
+
+# test_example = true
+
+from itertools import product
+import numpy as np
+import fastplotlib as fpl
+
+
+def make_circle(center, radius: float, n_points: int = 75) -> np.ndarray:
+    theta = np.linspace(0, 2 * np.pi, n_points)
+    xs = radius * np.sin(theta)
+    ys = radius * np.cos(theta)
+
+    return np.column_stack([xs, ys]) + center
+
+
+spatial_dims = (50, 50)
+
+circles = list()
+for center in product(range(0, spatial_dims[0], 15), range(0, spatial_dims[1], 15)):
+    circles.append(make_circle(center, 5, n_points=75))
+
+pos_xy = np.vstack(circles)
+
+# set line collection colors manually
+# this will produce 16 circles so we will define 16 colors
+colors = ["blue"] * 4 + ["red"] * 4 + ["yellow"] * 4 + ["w"] * 4
+
+plot = fpl.Plot()
+
+plot.add_line_collection(circles, colors=colors, thickness=10)
+
+plot.show()
+
+plot.canvas.set_logical_size(800, 800)
+
+if __name__ == "__main__":
+    fpl.run()
diff --git a/examples/line_collection/line_stack.py b/examples/line_collection/line_stack.py
@@ -0,0 +1,30 @@
+"""
+Line Plot
+============
+Example showing how to plot line collections
+"""
+
+# test_example = true
+
+import numpy as np
+import fastplotlib as fpl
+
+
+xs = np.linspace(0, 100, 1000)
+# sine wave
+ys = np.sin(xs) * 20
+
+# make 25 lines
+data = np.vstack([ys] * 25)
+
+plot = fpl.Plot()
+
+# line stack takes all the same arguments as line collection and behaves similarly
+plot.add_line_stack(data, cmap="jet")
+
+plot.show(maintain_aspect=False)
+
+plot.canvas.set_logical_size(900, 600)
+
+if __name__ == "__main__":
+    fpl.run()
diff --git a/examples/scatter/scatter_cmap.py b/examples/scatter/scatter_cmap.py
@@ -6,35 +6,41 @@
 
 # test_example = true
 
-from fastplotlib import Plot
+from fastplotlib import Plot, run
 import numpy as np
 from pathlib import Path
+from sklearn.cluster import AgglomerativeClustering
 
-from wgpu.gui.offscreen import WgpuCanvas
-from pygfx import WgpuRenderer
 
-canvas = WgpuCanvas()
-renderer = WgpuRenderer(canvas)
-
-plot = Plot(canvas=canvas, renderer=renderer)
+plot = Plot()
 
 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 = plot.add_scatter(data=data[:, :-1], sizes=6, alpha=0.7, colors=colors)
+agg = AgglomerativeClustering(n_clusters=3)
+
+agg.fit_predict(data)
+
+
+scatter_graphic = plot.add_scatter(
+    data=data[:, :-1],
+    sizes=15,
+    alpha=0.7,
+    cmap="Set1",
+    cmap_values=agg.labels_
+)
 
 plot.show()
 
 plot.canvas.set_logical_size(800, 800)
 
 plot.auto_scale()
 
-scatter_graphic.cmap = "viridis"
+scatter_graphic.cmap = "tab10"
 
-img = np.asarray(plot.renderer.target.draw())
+# img = np.asarray(plot.renderer.target.draw())
 
 if __name__ == "__main__":
     print(__doc__)
+    run()
diff --git a/examples/screenshots/line_cmap.png b/examples/screenshots/line_cmap.png
diff --git a/examples/screenshots/line_collection.png b/examples/screenshots/line_collection.png
diff --git a/examples/screenshots/line_collection_cmap_values.png b/examples/screenshots/line_collection_cmap_values.png
diff --git a/examples/screenshots/line_collection_cmap_values_qualitative.png b/examples/screenshots/line_collection_cmap_values_qualitative.png