Separate out figure gen into different python files

notebooks/rmax-n.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import scipy
+
+from notebooks.lib import read_load
+
+
+def linear(x, a, b):
+    return x * a + b
+
+
+alpha = read_load("../data/alpha")
+meaned_by_N = alpha.groupby('N').agg({'fd': ['mean', 'std']}) \
+    .reset_index() \
+    .replace([np.inf, -np.inf], np.nan)
+
+without_prefix = alpha[alpha.N > 50]
+
+p, pcov = scipy.optimize.curve_fit(linear, np.log(without_prefix.cr), np.log(without_prefix.N))
+linear_extent = np.linspace(0, np.max(np.log(alpha.cr)))
+
+plt.scatter(np.log(alpha.cr), np.log(alpha.N), s=1, marker='.', color="tab:blue")
+plt.plot(linear_extent, linear(linear_extent, *p), color="tab:red")
+
+plt.xlabel("$\\log r_{max}$")
+plt.ylabel("$\\log N$")
+
+plt.savefig('../figures/rmax-n.svg')
+plt.savefig('../figures/rmax-n.png')
+plt.show()

notebooks/sp-fd.py

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+import numpy as np
+from matplotlib import pyplot as plt
+
+from notebooks.lib import read_sp
+
+c_sp = read_sp("../data/stick-probability")
+by_run = c_sp.groupby(['probability', 'N'])
+
+by_probability = by_run.agg(
+    overall_fd=('fd', lambda fd: np.mean(fd[-100:])),
+    overall_fd_std=('fd', 'std')
+).reset_index().groupby('probability')
+
+ggg = by_probability.agg(
+    fd=('overall_fd', 'mean'),
+    # TODO Check stats
+    fd_std=('overall_fd_std', lambda std: np.sqrt(np.mean(np.square(std))))
+)
+
+plt.fill_between(ggg.index, ggg.fd - ggg.fd_std, ggg.fd + ggg.fd_std, alpha=0.2, label=f"Standard error band")
+plt.plot(ggg.index, ggg.fd, color='tab:blue', label='fd mean, seeds = 100')
+plt.plot(ggg.index, ggg.fd)
+
+plt.xlabel("$p_{stick}$")
+plt.ylabel("$fd$")
+plt.legend()
+
+plt.savefig('../figures/sp-fd.svg')
+plt.savefig('../figures/sp-fd.png')
+plt.show()