python - 如何在 Python/Plotly 等值线图中放大 map ？

Question

我正在使用 Plotly 制作等值线图。但是，地理 map 看起来很小。

like that:

我的代码在这里:

fig = px.choropleth(df,
                    geojson=geojson,
                    locations="Capitalize",
                    featureidkey="properties.name",
                    color="Scale",
                    hover_name='Capitalize',
                    hover_data=['Quantity'],
                    animation_frame="Period",
                    projection="mercator",)

fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(title_text = 'Product A',
                  autosize=True,
                  hovermode='closest',
                  title_x = 0.5,
                  margin={"r":0,"t":100,"l":0,"b":0},
                  geo=dict(showframe = False, showcoastlines = False))

宽度和高度属性不完全符合我的要求。我只想放大 map 。我怎样才能巧妙地做到这一点？

Answer 1

我遇到了同样的问题。我找到了这个 related issue ，它说:

Plotly tries to take all the available space without changing the image ratio. If you have a very wide div there will be a lot of empty space to left and right due but it will be filled from the top to the bottom.

我增加了布局的高度。我从这里开始:

fig.update_layout(margin=dict(l=0, r=0, b=0, t=0),
                  width=1500)

为此:

fig.update_layout(margin=dict(l=0, r=0, b=0, t=0),
                  width=1500, 
                  height=800)

这极大地改善了我的图像大小。

这仍然在我的 map 周围留下了相当大的空白。我通过计算边界框而不是使用 fitbounds 改进了这一点。我使用了辅助模块 turfpy 来计算几何体的边界框，使用 pip install turfpy 安装。

from turfpy.measurement import bbox
from functools import reduce

def compute_bbox(gj):
    gj_bbox_list = list(
        map(lambda f: bbox(f['geometry']), gj['features']))
    gj_bbox = reduce(
        lambda b1, b2: [min(b1[0], b2[0]), min(b1[1], b2[1]),
                        max(b1[2], b2[2]), max(b1[3], b2[3])],
        gj_bbox_list)
    return gj_bbox

gj_bbox = compute_bbox(gj)

fig = px.choropleth(fdf,
                    geojson=gj,
                    locations=locationcol,
                    color=datacol,
                    color_continuous_scale="Viridis",
                    range_color=(0, max_value),
                    featureidkey=key,
                    scope='europe',
                    hover_data=[namecol, 'LAD11NM']
                    )
fig.update_geos(
    # fitbounds="locations",
    center_lon=(gj_bbox[0]+gj_bbox[2])/2.0,
    center_lat=(gj_bbox[1]+gj_bbox[3])/2.0,
    lonaxis_range=[gj_bbox[0], gj_bbox[2]],
    lataxis_range=[gj_bbox[1], gj_bbox[3]],
    visible=False,
)