<jupyter_start><jupyter_text>Plot Fig. S4<jupyter_code>dec = 2
plt.figure(figsize=(2,2))
colors= []
for ds in d:
    df_sim_2 = np.convolve(ds['val']['df']*1e6/(2*np.pi), np.ones(16)/16)
    df_python_filt = np.convolve(ds['val']['df_python'], np.ones(16)/16.0, 'valid',)
    df_python_filt2 = np.convolve(df_python_filt, np.ones(16)/16.0, 'full')
    
    lines = plt.plot(ds['val']['t'][::3], df_sim_2[::3], '.', markersize=4, label="$V_t(t_0) = ${} V".format(ds['V0_frac']*10))[0]
    plt.plot(ds['val']['t'][15:], df_python_filt2, '-', color=lines.get_color(),
             linewidth=1.5)
    
    colors.append(lines.get_color())

plt.xlim(-50, 150)
plt.ylim(-30, -15)
plt.xlabel("Time $t$ [μs]")
plt.ylabel("Freq. shift $\Delta f$ [Hz]")


# plt.legend()
plt.savefig('figs/5c.pdf', bbox_inches='tight')
fig, axes = plt.subplots(nrows=1, ncols=4, sharey=True,
                         sharex=True, figsize=(4.75, 1.85))
for (ds, ax, color) in zip(d, axes, colors):
    teq0 = ds['val']['t'][15:]==0
    df_python_filt = np.convolve(ds['val']['df_python'], np.ones(16)/16.0, 'valid',)
    df_python_filt2 = np.convolve(df_python_filt, np.ones(16)/16.0, 'full')
    df0 = df_python_filt2[teq0]
    print(df0)
    df_sim_2 = np.convolve(ds['val']['df']*1e6/(2*np.pi), np.ones(16)/16)
    
    lines = ax.plot(ds['val']['t'][::3], df_sim_2[::3] - df0, '.', markersize=4, color=color)[0]
    ax.plot(ds['val']['t'][15:], df_python_filt2-df0, '-', color=lines.get_color(),
             linewidth=1.5, label="$V_t(t_0) = ${} V".format(ds['V0_frac']*10))
    
    ax.set_xlabel("$t$ [μs]")
    
ax.set_xlim(-50, 200)
ax.set_ylim(-12.6, 1)
axes[0].set_ylabel("Freq. shift $\Delta f$ [Hz]")

fig.savefig('figs/5d.pdf', bbox_inches='tight', transparent=True)<jupyter_output>[-17.41419447]
[-19.96131767]
[-22.68223594]
[-25.57694756]<jupyter_text>Print Experimental Parameters<jupyter_code>{key: val for key, val in d[0].items() if key != 'val'}
{key: val for key, val in d[0]['val']['sim'].items()}<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2422145328719723, 'fraction_numerical': 0.07464162135442412, 'mean_word_length': 4.411764705882353, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '157860', 'license': 'MIT', 'n_tokens_mistral': 983, 'n_tokens_neox': 906, 'n_words': 126, 'path': '5-plot.ipynb', 'repo_name': 'ryanpdwyer/light-dependent-impedance-spectra', 'size': '2023'}