PSE_2 = (
    VNP46A2.pivot_table(values=[VAR], index="date", columns=["NAME_2"])
    .resample("W-SUN", label="right")
    .mean()
)
PSE_1 = (
    VNP46A2.pivot_table(values=[VAR], index="date", columns=["NAME_1"])
    .resample("W-SUN", label="right")
    .mean()
)

PSE_2

p = figure(
    title="Palestine: Monthly Nighttime Lights (2022-2023)",
    width=800,
    height=600,
    x_axis_label="Date",
    x_axis_type="datetime",
    y_axis_label=r"Radiance [nW $$cm^{-2}$$ $$sr^{-1}$$]",
    tools="pan,wheel_zoom,box_zoom,reset,save,box_select",
)
p.add_layout(
    Title(
        text=f"Monthly NTL Radiance Average (VNP46A2 and VNP46A3) for each second-level administrative division",
        text_font_size="12pt",
        text_font_style="italic",
    ),
    "above",
)
p.add_layout(
    Title(
        text=f"Data Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.",
        text_font_size="10pt",
        text_font_style="italic",
    ),
    "below",
)
p.add_layout(Legend(), "right")

p.add_tools(
    HoverTool(
        tooltips=[
            ("Month", "@x{%B %Y}"),
            ("Radiance", "@y{0.00}"),
        ],
        formatters={"@x": "datetime"},
    )
)
renderers = []
for column, color in zip(PSE_2_MS.columns, cc.b_glasbey_category10):
    try:
        r = p.line(
            PSE_2_MS.index,
            PSE_2_MS[column],
            legend_label=column[1],
            line_color=color,
            line_width=2,
        )
        r.visible = False
        renderers.append(r)
    except:
        pass
renderers[-1].visible = True

for column, color in zip(VNP46A3.columns, cc.b_cyclic_grey_15_85_c0):
    r = p.line(
        VNP46A3.index,
        VNP46A3[column],
        legend_label=column[1],
        line_color=color,
        line_width=2,
    )
    r.visible = False
    renderers.append(r)
renderers[-1].visible = True

p.legend.location = "bottom_left"
p.legend.click_policy = "hide"
p.title.text_font_size = "16pt"

output_notebook()
show(p)

PERCENTCHANGE_2 = 100 * (
    PSE_2 / PSE_2[(PSE_2.index >= "2022-01-01") & (PSE_2.index < "2023-01-01")].mean()
    - 1
)
PERCENTCHANGE_1 = 100 * (
    PSE_1 / PSE_1[(PSE_1.index >= "2022-01-01") & (PSE_2.index < "2023-01-01")].mean()
    - 1
)
pd.set_option("display.max_rows", None)
PERCENTCHANGE_2[PERCENTCHANGE_2.index >= "2023-10-01"].style.map(
    lambda x: "background-color: #DF4661" if x < -50 else "background-color: white"
)

show(create_plot(PERCENTCHANGE_2))

WOW_2 = 100 * (PSE_2.pct_change(1, axis="rows", fill_method=None))