Generated documentation for docs//pulls/309

jenkins-neurorse-plenoptic-PR-309-2

docs/pulls/309/_downloads/1529dad3c6f78dd530d43172bfef8f56/conceptual_intro.py

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+import json
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+# set the style
+plt.style.use("fivethirtyeight")
+plt.rcParams["axes.facecolor"] = "white"
+plt.rcParams["figure.facecolor"] = "white"
+plt.rcParams["axes.edgecolor"] = "white"
+plt.rcParams["savefig.facecolor"] = "white"
+plt.rcParams["savefig.edgecolor"] = "white"
+plt.rcParams["savefig.bbox"] = "tight"
+
+with open("conceptual_intro_data.json") as f:
+    data = json.load(f)
+    # 2-deg fundamentals (energy linear, 5nm stepsize) from Stockman & Sharpe 2000,
+    # downloaded from http://www.cvrl.org/cones.htm
+    CONES = data["CONES"]
+    # from math tools homework, interpolated to have same number of entries as
+    # CONES
+    PRIMARIES = np.array(data["PRIMARIES"])
+
+# for matrix multiplication
+CONES_MATRIX = np.stack([CONES["S"], CONES["M"], CONES["L"]])
+CONES_MATRIX[np.isnan(CONES_MATRIX)] = 0
+
+RANDOM_LIGHT = np.random.random(len(PRIMARIES))
+
+
+def cones():
+    _, axes = plt.subplots(1, 2, figsize=(10, 5))
+    axes[0].plot(CONES["nm"], CONES["S"], c="b")
+    axes[0].plot(CONES["nm"], CONES["M"], c="g")
+    axes[0].plot(CONES["nm"], CONES["L"], c="r")
+    axes[0].set(
+        xlabel="Wavelength (nm)",
+        ylabel="Cone sensitivity (arbitrary units)",
+        title="Cone sensitivity curves",
+    )
+    random_light_weights = CONES_MATRIX @ RANDOM_LIGHT
+    x = np.arange(len(random_light_weights))
+    axes[1].bar(x, random_light_weights, width=0.4, color=["b", "g", "r"])
+    axes[1].set(
+        xlabel="Cone class",
+        ylabel="Cone response (arbitrary units)",
+        xticks=x,
+        xticklabels=["S", "M", "L"],
+        title="Cone responses to random light",
+    )
+
+
+def primaries():
+    _, axes = plt.subplots(1, 2, figsize=(10, 5))
+    axes[0].plot(CONES["nm"], RANDOM_LIGHT)
+    for p, c in zip(PRIMARIES.T, ["g", "b", "r"]):
+        axes[1].plot(CONES["nm"], p, c=c)
+    axes[0].set(
+        xlabel="Wavelength (nm)",
+        ylabel="Energy (arbitrary units)",
+        title="Random light",
+    )
+    axes[1].set(xlabel="Wavelength (nm)", title="Primaries")
+
+
+def matched_light():
+    _, axes = plt.subplots(1, 2, figsize=(10, 5))
+    cones_to_primaries = np.linalg.inv(CONES_MATRIX @ PRIMARIES)
+    matched_light = PRIMARIES @ cones_to_primaries @ CONES_MATRIX @ RANDOM_LIGHT
+    axes[0].plot(CONES["nm"], RANDOM_LIGHT, label="Random light")
+    axes[0].plot(CONES["nm"], matched_light, "--", label="Matched light")
+    axes[0].set(
+        xlabel="Wavelength (nm)",
+        ylabel="Energy (arbitrary units)",
+        title="Random and matched light",
+    )
+    axes[0].legend(loc="upper left")
+    random_light_weights = CONES_MATRIX @ RANDOM_LIGHT
+    matched_light_weights = CONES_MATRIX @ matched_light
+    x = np.arange(len(random_light_weights))
+    # from https://matplotlib.org/stable/gallery/lines_bars_and_markers/barchart.html
+    width = 0.4
+    styles = [
+        {
+            "color": ["b", "g", "r"],
+            "edgecolor": ["b", "g", "r"],
+            "linewidth": plt.rcParams["lines.linewidth"],
+        },
+        {
+            "color": "w",
+            "edgecolor": ["b", "g", "r"],
+            "linestyle": "--",
+            "linewidth": plt.rcParams["lines.linewidth"],
+        },
+    ]
+
+    labels = ["Random light", "Matched light"]
+    weights = [random_light_weights, matched_light_weights]
+
+    for multiplier, (name, wts, sty) in enumerate(zip(labels, weights, styles)):
+        offset = width * multiplier
+        axes[1].bar(x + offset, wts, label=name, width=width, **sty)
+
+    axes[1].set(
+        xlabel="Cone class",
+        ylabel="Cone response (arbitrary units)",
+        xticks=x + width / 2,
+        xticklabels=["S", "M", "L"],
+        title="Cone responses",
+    )