add reproducible plotting section

docs/learning/walkers_plot/environment.yml

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+name: recode
+
+channels:
+  - defaults
+  - conda-forge
+
+dependencies:
+  - python=3.9
+  - pytest=7.1
+  - pytest-cov=3.0
+  - ipykernel=6.9
+  - numpy=1.21
+  - scipy=1.7
+  - matplotlib=3.5
+  - numba=0.55
+  - pre-commit
+  - pip=21.2
+  - pip:
+      - --editable . #install MCFF from the local repository using pip and do it in editable mode

docs/learning/walkers_plot/generate_montecarlo_walkers.py

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+#!/usr/bin/env python3
+# The above lets us run this script by just typing ./generate_montecarlo_walkers.py at the command line
+"""
+This script generates the data for the monte carlo walkers plot Fig. 2 in the paper {link}
+To regenerate the plot:
+
+$ conda env create -p ./env -f environment.yml # generate the environment in a local env folder
+$ conda active ./env # activate it
+$ python generate_montecarlo_walkers.py # creates data.pickle
+$ python plot_montecarlo_walkers.py # creates plot.pdf
+
+Last tested and working with MCFF commit hash 63523481e89ae8c8f74a900ae43b035e3312f9c8
+"""
+import numpy as np
+import pickle
+from datetime import datetime, timezone
+
+import MCFF
+from MCFF.mcmc import mcmc_generator
+
+import subprocess
+from pathlib import Path
+
+
+def get_module_git_hash(module):
+    "Get the commit hash of a module installed from a git repo with pip install -e ."
+    cwd = Path(module.__file__).parent
+    return (
+        subprocess.run(
+            ["git", "rev-parse", "HEAD"], check=True, capture_output=True, cwd=cwd
+        )
+        .stdout.decode()
+        .strip()
+    )
+
+
+seed = [
+    2937053738,
+    1783364611,
+    3145507090,
+]  # generated once with rng.integers(2**63, size = 3) and then saved
+
+np.random.seed(
+    seed
+)  # This makes our random numbers reproducable when the notebook is rerun in order
+
+### The measurement we will make ###
+def average_color(state):
+    return np.mean(state)
+
+
+### Simulation Inputs ###
+N = 20  # Use an NxN system
+Ts = [10, 4.5, 3]  # What temperatures to use
+steps = 200  # How many times to sample the state
+stepsize = N**2  # How many individual monte carlo flips to do in between each sample
+N_repeats = 10  # How many times to repeat each run at fixed temperature
+initial_state = np.ones(shape=(N, N))  # the intial state to use
+flips = (
+    np.arange(steps) * stepsize
+)  # Use this to plot the data in terms of individual flip attemps
+inputs = dict(
+    N=N,
+    Ts=Ts,
+    steps=steps,
+    stepsize=stepsize,
+    N_repeats=10,
+    initial_state=initial_state,
+    flips=flips,
+)
+
+### Simulation Code ###
+average_color_data = np.array(
+    [
+        [
+            [
+                average_color(s)
+                for s in mcmc_generator(initial_state, steps, stepsize=stepsize, T=T)
+            ]
+            for _ in range(N_repeats)
+        ]
+        for T in Ts
+    ]
+)
+
+data = dict(
+    MCFF_commit_hash=get_module_git_hash(MCFF),
+    date=datetime.now(timezone.utc),
+    inputs=inputs,
+    average_color_data=average_color_data,
+)
+
+# save the data to data.pickle
+with open("./data.pickle", "wb") as f:
+    pickle.dump(data, f)

docs/learning/walkers_plot/plot_montecarlo_walkers.py

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+#!/usr/bin/env python3
+"""
+This script plots the monte carlo walkers plot Fig. 2 in the paper {link}
+To regenerate the plot:
+
+$ conda env create -p ./env -f environment.yml # generate the environment in a local env folder
+$ conda active ./env # activate it
+$ python generate_montecarlo_walkers.py # creates data.pickle
+$ python plot_montecarlo_walkers.py # creates plot.pdf
+
+Last tested and working with MCFF commit hash 63523481e89ae8c8f74a900ae43b035e3312f9c8
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from numba import jit
+import pickle
+
+# This loads some custom styles for matplotlib
+import json, matplotlib
+
+with open("../assets/matplotlibrc.json") as f:
+    matplotlib.rcParams.update(json.load(f))
+
+from itertools import count
+
+with open("./data.pickle", "rb") as f:
+    data = pickle.load(f)
+
+# splat the keys and values back into the global namespace,
+# beware that this could overwrite previously defined variables like 'count' by accident
+globals().update(**data)
+globals().update(**data["inputs"])
+
+
+fig, axes = plt.subplots(
+    figsize=(15, 7),
+    nrows=3,
+    ncols=2,
+    sharey="row",
+    sharex="col",
+    gridspec_kw=dict(hspace=0, wspace=0, width_ratios=(4, 1)),
+)
+
+for i, ax, hist_ax in zip(count(), axes[:, 0], axes[:, 1]):
+    c = average_color_data[i]
+    indiv_line, *_ = ax.plot(flips, c.T, alpha=0.4, color="k", linewidth=0.9)
+    (mean_line,) = ax.plot(flips, np.mean(c, axis=0))
+    hist_ax.hist(c.flatten(), orientation="horizontal", label=f"T = {Ts[i]}")
+
+axes[-1, 0].set(xlabel=f"Monte Carlo Flip Attempts")
+axes[-1, 1].set(xlabel="Probability Density")
+axes[1, 0].set(ylabel=r"Average Color $\langle c \rangle$")
+axes[-1, 0].legend([mean_line, indiv_line], ["Mean", "Individual walker"])
+for ax in axes[:, 1]:
+    ax.legend(loc=4)
+
+fig.savefig("./plot.pdf")