<jupyter_start><jupyter_text>Plotting<jupyter_code>plt_lim = [1, 10]
plt_rng = np.arange(plt_lim[0], plt_lim[1] + 1)
len(linestyles)
font = {'fontname':'CMU Bright'}

plt.rc('axes', titlesize=25)
plt.rc('axes', labelsize=25)
#plt.rc('text', usetex=True)
plt.rc('text', usetex=False)

plt.figure(figsize=(10, 10))

plt.subplot(2, 2, 1)
for i, (method, name, color, ls, ms) in enumerate(zip(methods, names, colors, linestyles, markers)):
    i_err, v_err, _ = errors[method]
    if i%2==0:
        me = 2
    else:
        me=(1,2)
    plt.plot(plt_rng, [i_err[thr] / (n_i * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name, marker=ms, markersize=10, markevery=me, markeredgewidth=2)
plt.title('Illumination',**font)
plt.xlabel('threshold [px]',**font)
plt.xlim(plt_lim)
plt.xticks(plt_rng)
plt.ylabel('MMA',**font)
plt.ylim([0, 1])
#plt.gca().axes.set_yticklabels([])
plt.grid()
plt.tick_params(axis='both', which='major', labelsize=15)

plt.subplot(2, 2, 2)
for i, (method, name, color, ls, ms) in enumerate(zip(methods, names, colors, linestyles, markers)):
    i_err, v_err, _ = errors[method]
    if i%2==0:
        me = 2
    else:
        me=(1,2)
    plt.plot(plt_rng, [v_err[thr] / (n_v * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name, marker=ms, markersize=10, markevery=me, markeredgewidth=2)
    
plt.title('Viewpoint',**font)
plt.xlabel('threshold [px]',**font)
plt.xlim(plt_lim)
plt.xticks(plt_rng)
#plt.ylabel('MMA',**font)
plt.ylim([0, 1])
plt.gca().axes.set_yticklabels([])
plt.grid()
plt.tick_params(axis='both', which='major', labelsize=15)

plt.subplot(2, 2, 3)
for i, (method, name, color, ls, ms) in enumerate(zip(methods, names, colors, linestyles, markers)):
    i_err, v_err, _ = errors[method]
    if i%2==0:
        me = 2
    else:
        me=(1,2)
    plt.plot(plt_rng, [(i_err[thr] + v_err[thr]) / ((n_i + n_v) * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name, marker=ms, markersize=10, markevery=me, markeredgewidth=2)
plt.title('Overall',**font)
plt.xlabel('threshold [px]',**font)
plt.xlim(plt_lim)
plt.xticks(plt_rng)
#plt.ylabel('MMA',**font)
plt.ylim([0, 1])
plt.gca().set_yticklabels("")
plt.grid()
plt.tick_params(axis='both', which='major', labelsize=15)

plt.subplot(2, 2, 4)
for i, (method, name, color, ls, ms) in enumerate(zip(methods, names, colors, linestyles, markers)):
    i_err, v_err, _ = errors[method]
    plt.plot([], [], color=color, ls=ls, linewidth=3, label=name, marker=ms, markersize=10, markevery=me, markeredgewidth=2)
plt.axis('off')
plt.legend(loc='center',prop={'size': 15, 'family':'CMU Bright'})

plt.subplots_adjust(hspace=0.4,wspace=0.1)

plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2006651884700665, 'fraction_numerical': 0.028824833702882482, 'mean_word_length': 5.507211538461538, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '415519', 'license': 'MIT', 'n_tokens_mistral': 1262, 'n_tokens_neox': 1149, 'n_words': 220, 'path': 'eval/eval_hpatches_generate_plot.ipynb', 'repo_name': 'wookiekim/sparse-ncnet', 'size': '2706'}