new figs, figure script for lin regression dummy data

regression/lecture/lin_regress.py

@@ -0,0 +1,91 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from IPython import embed
+
+def create_data():
+    m = 0.75
+    n=  -30
+    x = np.arange(0.,101., 2.5)
+    y = m * x + n;
+    noise = np.random.randn(len(x))*15
+    y += noise
+    return x, y, m, n
+
+    
+def plot_data(x, y):
+    plt.xkcd()
+    plt.scatter(x, y, marker='o', color='dodgerblue', s=40)
+    plt.xlabel("Input x")
+    plt.ylabel("Output y")
+    plt.xlim([-2.5, 102.5])
+    ax = plt.gca()
+    ax.spines["right"].set_visible(False)
+    ax.spines["top"].set_visible(False)
+    ax.yaxis.set_ticks_position('left')
+    ax.xaxis.set_ticks_position('bottom')
+    ax.xaxis.linewidth=1.5
+    ax.yaxis.linewidth=1.5
+    ax.tick_params(direction="out", width=1.25)
+    ax.tick_params(direction="out", width=1.25)
+    fig = plt.gcf()
+    fig.set_facecolor("white")
+    fig.set_size_inches(3., 3.)
+    fig.savefig("figures/lin_regress.pdf")
+    plt.close()
+    
+
+def plot_data_slopes(x, y, m, n):
+    plt.xkcd()
+    plt.scatter(x, y, marker='o', color='dodgerblue', s=40)
+    for i in np.linspace(m/4, m*1.5, 5):
+        plt.plot(x, i*x+n, color="r", lw=2)
+
+    plt.xlabel("Input x")
+    plt.ylabel("Output y")
+    plt.xlim([-2.5, 102.5])
+    ax = plt.gca()
+    ax.spines["right"].set_visible(False)
+    ax.spines["top"].set_visible(False)
+    ax.yaxis.set_ticks_position('left')
+    ax.xaxis.set_ticks_position('bottom')
+    ax.xaxis.linewidth=1.5
+    ax.yaxis.linewidth=1.5
+    ax.tick_params(direction="out", width=1.25)
+    ax.tick_params(direction="out", width=1.25)
+    fig = plt.gcf()
+    fig.set_facecolor("white")
+    fig.set_size_inches(3., 3.)
+    fig.savefig("figures/lin_regress_slope.pdf")
+    plt.close()
+
+
+def plot_data_intercepts(x, y, m, n):
+    plt.xkcd()
+    plt.scatter(x, y, marker='o', color='dodgerblue', s=40)
+    for i in np.linspace(n-n/2, n+n/2, 5):
+        plt.plot(x, m * x + i, color="r", lw=2)
+
+    plt.xlabel("Input x")
+    plt.ylabel("Output y")
+    plt.xlim([-2.5, 102.5])
+    ax = plt.gca()
+    ax.spines["right"].set_visible(False)
+    ax.spines["top"].set_visible(False)
+    ax.yaxis.set_ticks_position('left')
+    ax.xaxis.set_ticks_position('bottom')
+    ax.xaxis.linewidth=1.5
+    ax.yaxis.linewidth=1.5
+    ax.tick_params(direction="out", width=1.25)
+    ax.tick_params(direction="out", width=1.25)
+    fig = plt.gcf()
+    fig.set_facecolor("white")
+    fig.set_size_inches(3., 3.)
+    fig.savefig("figures/lin_regress_intercept.pdf")
+    plt.close()
+
+
+if __name__ == "__main__":
+    x, y, m, n = create_data()
+    plot_data(x,y)
+    plot_data_slopes(x,y,m,n)
+    plot_data_intercepts(x,y,m,n)