<jupyter_start><jupyter_text>Setup<jupyter_code>from specific import *<jupyter_output><empty_output><jupyter_text>Get shifted data<jupyter_code>(
    endog_data,
    exog_data,
    master_mask,
    filled_datasets,
    masked_datasets,
    land_mask,
) = get_offset_data()<jupyter_output><empty_output><jupyter_text>Mapping<jupyter_code>plt.hist(endog_data.values, bins=1000)
plt.xscale("log")
plt.yscale("log")
import scipy.stats

scipy.stats.percentileofscore(endog_data.values, 0.2)
combined = exog_data.copy()
combined["GFED4 BA"] = endog_data.values
with figure_saver("high_dry_days_low_fapar", sub_directory="map_plots"):
    mpl.rc("figure", figsize=(11, 4))
    constrained_map_plot(
        {"Dry Day Period": (3.7e1, None), "FAPAR 3NN": (None, 0.22)},
        combined,
        master_mask,
        plot_variable="GFED4 BA",
        coastline_kwargs={"linewidth": 0.4},
        boundaries=[0.01, 0.1, 0.2, 0.4],
        cmap="inferno",
    )
with figure_saver("high_dry_days_high_fapar", sub_directory="map_plots"):
    mpl.rc("figure", figsize=(11, 4))
    constrained_map_plot(
        {"Dry Day Period": (3.7e1, None), "FAPAR 3NN": (0.41, None)},
        combined,
        master_mask,
        plot_variable="GFED4 BA",
        coastline_kwargs={"linewidth": 0.4},
        boundaries=[0.01, 0.1, 0.2, 0.4],
        cmap="inferno",
    )<jupyter_output><empty_output><jupyter_text>Correlation Plot<jupyter_code>exog_data.columns
with figure_saver("corr_plot"):
    corr_plot(
        shorten_columns(exog_data[sort_features(exog_data.columns)]),
        fig_kwargs={"figsize": (8, 5)},
    )<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14819350887936314, 'fraction_numerical': 0.032455603184323334, 'mean_word_length': 4.305194805194805, 'pattern_counts': {'":': 7, '<': 17, '<?xml version=': 0, '>': 17, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '97468', 'license': 'MIT', 'n_tokens_mistral': 667, 'n_tokens_neox': 633, 'n_words': 89, 'path': 'analyses/seasonality_paper_st/best_top_15/variable_diagnostics.ipynb', 'repo_name': 'akuhnregnier/fuel-build-up', 'size': '1633'}