How to use the altair.Scale function in altair

def visualize(display_df):
        viridis = ['#440154', '#472c7a', '#3b518b', '#2c718e', '#21908d', '#27ad81', '#5cc863', '#aadc32', '#fde725']
        import altair as alt
        color_scale = alt.Scale(
            domain=(display_df.dropna().trending.min(),
                    0,
                    display_df.dropna().trending.max()),
            range=[viridis[0], viridis[len(viridis) // 2], viridis[-1]]
        )

        return alt.Chart(display_df).mark_circle().encode(
            alt.X('variable'),
            alt.Y('term'),
            size='frequency',
            color=alt.Color('trending:Q', scale=color_scale),
        )

longitude2="lon2:Q"
).transform_lookup(
    lookup="origin", 
    from_=lookup_data
).transform_lookup(
    lookup="destination", 
    from_=lookup_data, 
    as_=["state", "lat2", "lon2"]
).transform_filter(
    select_city
)

points = alt.Chart(flights_airport).mark_circle().encode(
    latitude="latitude:Q",
    longitude="longitude:Q",
    size=alt.Size("routes:Q", scale=alt.Scale(range=[0, 1000]), legend=None),
    order=alt.Order("routes:Q", sort="descending"),
    tooltip=["origin:N", "routes:Q"]
).transform_aggregate(
    routes="count()", 
    groupby=["origin"]
).transform_lookup(
    lookup="origin", 
    from_=lookup_data
).transform_filter(
    (alt.datum.state != "PR") & (alt.datum.state != "VI")
).add_selection(
    select_city
)

(background + connections + points).configure_view(stroke=None)

.mark_bar()
        .transform_window(
            sort=[alt.SortField(field=default_rank_col, order="ascending")],
            # We don't use an alt.WindowFieldDef here because python gets
            # confused when you use "as" as an actual argument name. So we just
            # use this syntax.
            window=[{"op": "row_number", "as": "qurro_x"}],
        )
        .encode(
            # type="ordinal" needed on the scale here to make bars adjacent;
            # see https://stackoverflow.com/a/55544817/10730311.
            x=alt.X(
                "qurro_x",
                title="Feature Rankings",
                type="ordinal",
                scale=alt.Scale(paddingOuter=1, paddingInner=0, rangeStep=1),
                axis=alt.Axis(ticks=False, labelAngle=0),
            ),
            y=alt.Y(default_rank_col, type="quantitative"),
            color=alt.Color(
                "qurro_classification",
                title="Log-Ratio Classification",
                scale=alt.Scale(
                    domain=["None", "Numerator", "Denominator", "Both"],
                    range=["#e0e0e0", "#f00", "#00f", "#949"],
                ),
            ),
            tooltip=[
                alt.Tooltip(
                    field="qurro_x",
                    title="Current Ranking",
                    type="quantitative",

scale=alt.Scale(domain=[min_y, max_y],
                                    nice=False, padding=5)),
            size=alt.condition(nearest, alt.value(80), alt.value(30)),
            color=alt.Color('kind:N', legend=alt.Legend(title='Legend'))
        ).add_selection(
            nearest
        ).properties(
            height=size,
            width=size
        ).transform_calculate(
            neg_z_imag='-datum.z_imag'
        ).interactive()

        bode = alt.Chart(df).mark_circle().encode(
            alt.X('f:Q', axis=alt.Axis(title="f [Hz]"),
                  scale=alt.Scale(type='log', nice=False)),
            size=alt.condition(nearest, alt.value(80), alt.value(30)),
            color='kind:N'
        ).add_selection(
            nearest
        ).properties(
            width=size,
            height=size/2 - 25
        ).transform_calculate(
            mag="sqrt(pow(datum.z_real,2) + pow(datum.z_imag,2))",
            neg_phase="-(180/PI)*atan(datum.z_imag/datum.z_real)"
        ).interactive()

        bode_mag = bode.encode(y=alt.Y('mag:Q',
                                       axis=alt.Axis(title="|Z| [Ω]")))
        bode_phs = bode.encode(y=alt.Y('neg_phase:Q',
                                       axis=alt.Axis(title="-ϕ [°]")))

source = data.population.url

slider = alt.binding_range(min=1850, max=2000, step=10)
select_year = alt.selection_single(name='year', fields=['year'], bind=slider)

base = alt.Chart(source).add_selection(
    select_year
).transform_filter(
    select_year
).transform_calculate(
    gender=alt.expr.if_(alt.datum.sex == 1, 'Male', 'Female')
)


color_scale = alt.Scale(domain=['Male', 'Female'],
                        range=['#1f77b4', '#e377c2'])

left = base.transform_filter(
    alt.datum.gender == 'Female'
).encode(
    y=alt.Y('age:O', axis=None),
    x=alt.X('sum(people):Q',
            title='population',
            sort=alt.SortOrder('descending')),
    color=alt.Color('gender:N', scale=color_scale, legend=None)
).mark_bar().properties(title='Female')

middle = base.encode(
    y=alt.Y('age:O', axis=None),
    text=alt.Text('age:Q'),
).mark_text().properties(width=20)

find the plot command

    >>> e = {'encoding': 'x', 'field': 'petalWidth', 'scale': 'log'}
    >>> r = _get_plot_command(e)
    >>> assert r.to_dict() == {'field': 'petalWidth', 'scale': {'type': 'log'}}
    """
    d = {k: v for k, v in e.items()}
    if 'field' not in e:
        return

    encoding = d.pop('encoding')
    column = d.pop('field')

    scale = {}
    if any([key in d for key in ['scale', 'zero']]):
        scale = {'scale': altair.Scale(type=d.pop('scale', None),
                                       zero=d.pop('zero', None))}

    d.update(scale)
    return getattr(altair, encoding.capitalize())(column, **d)

)
        .encode(
            # type="ordinal" needed on the scale here to make bars adjacent;
            # see https://stackoverflow.com/a/55544817/10730311.
            x=alt.X(
                "qurro_x",
                title="Feature Rankings",
                type="ordinal",
                scale=alt.Scale(paddingOuter=1, paddingInner=0, rangeStep=1),
                axis=alt.Axis(ticks=False, labelAngle=0),
            ),
            y=alt.Y(default_rank_col, type="quantitative"),
            color=alt.Color(
                "qurro_classification",
                title="Log-Ratio Classification",
                scale=alt.Scale(
                    domain=["None", "Numerator", "Denominator", "Both"],
                    range=["#e0e0e0", "#f00", "#00f", "#949"],
                ),
            ),
            tooltip=[
                alt.Tooltip(
                    field="qurro_x",
                    title="Current Ranking",
                    type="quantitative",
                ),
                alt.Tooltip(
                    field="qurro_classification",
                    title="Log-Ratio Classification",
                    type="nominal",
                ),
                "Feature ID",

annotation = [None] * len(spec.mz)
        color = [colors[None]] * len(spec.mz)
    calc_mz = [a.calc_mz if a is not None else None for a in annotation]
    fragment = [str(a) if a is not None else None for a in annotation]
    spec_df = pd.DataFrame({'exp_mz': spec.mz, 'intensity': intensity,
                            'calc_mz': calc_mz, 'fragment': fragment,
                            'color': color})

    x_axis = altair.X('exp_mz', axis=altair.Axis(title='m/z',
                                                 titleFontStyle='italic',
                                                 grid=grid),
                      scale=altair.Scale(nice=True, padding=5, zero=False))
    y_axis = altair.Y('intensity',
                      axis=altair.Axis(title='Intensity', format='%',
                                       grid=grid),
                      scale=altair.Scale(nice=True, padding=5))
    color = altair.Color('color', scale=None, legend=None)
    tooltip_not_annotated = [altair.Tooltip('exp_mz', format='.4f',
                                            title='Experimental m/z'),
                             altair.Tooltip('intensity', format='.0%',
                                            title='Intensity')]
    tooltip_annotated = [altair.Tooltip('fragment', title='Fragment'),
                         altair.Tooltip('exp_mz', format='.4f',
                                        title='Experimental m/z'),
                         altair.Tooltip('calc_mz', format='.4f',
                                        title='Theoretical m/z'),
                         altair.Tooltip('intensity', format='.0%',
                                        title='Intensity')]
    # Unannotated peaks.
    spec_plot = (altair.Chart(spec_df[spec_df['fragment'].isna()])
                 .mark_rule(size=2).encode(x=x_axis, y=y_axis, color=color,
                                           tooltip=tooltip_not_annotated))

def person_class_bar():
    dat_ori = getuserdb()
    dat = dat_ori.loc[dat_ori.this_user == True,:].copy()
    dat['mnth_yr'] = dat['workshop_start'].dt.to_period('M').astype(str)
    dat = dat.melt(
        id_vars=['mnth_yr', 'workshop_category'],
        value_vars=['workshop_hours', 'class_size'])
    chart = alt.Chart(dat).mark_bar().encode(
        column='variable',
        x=alt.X("sum(value)"),
        y=alt.Y('mnth_yr'),
        color=alt.Color(
            'workshop_category',
            scale=alt.Scale(range=['#7dbbd2cc', '#bbc6cbe6', '#6eb0ea', '#d1d8e2', '#1a1d21', '#8f9fb3' ]),legend=None),
            tooltip=['workshop_category', 'sum(value)']
        ).configure_axis(
            grid=False
        ).properties(
            width=250
        )
        
    return chart.to_json()

x=alt.X(
                "x",
                type="ordinal",
                axis=alt.Axis(
                    title="Gene",
                    ticks=False,
                    labels=False,
                    orient=alt.AxisOrient("top"),
                ),
            ),
            y=alt.Y(
                "y",
                type="ordinal",
                axis=alt.Axis(title="Gene", ticks=False, labels=False),
            ),
            color=alt.Color("z:Q", scale=alt.Scale(scheme="viridis", domain=[-1, 1])),
        )
        .properties(width=250, height=250)
    )