<jupyter_start><jupyter_text>Inzidenz Geglättet<jupyter_code>def plot_lk_inz_smooth(*lk_namen, smooth=7):
    fig = go.Figure()
    for lk in lk_namen:
        lk_df = prepare_lk(lk)

        fig.add_trace(go.Scatter(x=lk_df.index,
                                 y=lk_df['inz'].rolling(smooth).mean(),
                                 mode='lines',
                                 name=lk,
                                 visible="legendonly",
                                 line=dict(width=2)))

    fig.update_layout(
        width=1000,
        height=500,
        title=f'Inzidenz geglättet ({smooth} Tage Fenster)'
    )
    fig.show()


plot_lk_inz_smooth('SK München', 'LK München', 'Berlin', 'SK Flensburg', 'LK Tirschenreuth', 'LK Regensburg',
                   'LK Miesbach', smooth=7)<jupyter_output><empty_output><jupyter_text>Änderung der Inzidenz<jupyter_code>def plot_lk_inz_change(*lk_namen, smooth=7):
    fig = go.Figure()
    for lk in lk_namen:
        lk_df = prepare_lk(lk)

        fig.add_trace(go.Scatter(x=lk_df.index,
                                 y=lk_df['inz_dif'].rolling(smooth).mean(),
                                 mode='lines',
                                 name=lk,
                                 line=dict(width=2)))

        fig.add_trace(go.Scatter(x=lk_df.index,
                                 y=lk_df['inz_dif'],
                                 mode='markers',
                                 name=lk,
                                 marker=dict(
                                     color='grey',
                                     size=2,
                                 )))

    fig.update_layout(
        width=1000,
        height=500,
        yaxis_range=[-11,11],
        title=f'Änderung der Inzidenz Geglättet ({smooth} Tage Fenster)'
    )
    fig.show()


plot_lk_inz_change('SK München', 'Berlin', 'SK Flensburg', 'LK Tirschenreuth', 'LK Regensburg', 'LK Miesbach',
                   smooth=21)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11333000499251124, 'fraction_numerical': 0.012980529206190713, 'mean_word_length': 1.4, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 16, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '560705', 'license': 'MIT', 'n_tokens_mistral': 675, 'n_tokens_neox': 624, 'n_words': 89, 'path': 'inf.ipynb', 'repo_name': 'lor-enz/covid-science', 'size': '2003'}