将 fiona 结构展平为散景字典

Question

我有一个 fiona 数据结构 http://toblerity.org/fiona/manual.html

我试着把它弄平，这样我就可以在散景中绘制它。

的缩短版本及其邮政编码边界形状文件

下面是一条记录，例如来自 fiona 的邮政编码 43452，缩短版本

{'geometry': {'coordinates': [[(-83.674464, 41.331119),
                               (-83.67444499999999,41.331123999999996),
                               (-83.67215, 41.331634)
]],
              'type': 'Polygon'},
 'id': '1',
 'properties': OrderedDict([('ZCTA5CE10', '43452'),
                            ('GEOID10', '43452'),
                            ('CLASSFP10', 'B5'),
                            ('MTFCC10', 'G6350'),
                            ('FUNCSTAT10', 'S'),
                            ('ALAND10', 121783676),
                            ('AWATER10', 13437379),
                            ('INTPTLAT10', '+41.5157923'),
                            ('INTPTLON10', '-082.9809454')]),
 'type': 'Feature'}

https://docs.bokeh.org/en/latest/docs/gallery/texas.html

我看例子来自Bokeh example 我需要这样的结构

    {
'zipcode':[43452] , 'rate':[1]
,'x':[[83.674464,-83.67444499999999,-83.67215]]
,'y':[[41.331119,41.331123999999996,41.331634]]
}

因为这是一条记录，如果我想做两个邮政编码，它会是这样的

    {
'zipcode':[43452,41111] , 'rate':[1,2]
,'x':[[83.674464,-83.67444499999999,-83.67215],[66.6,-77.7,88.9]]
,'y':[[41.331119,41.331123999999996,41.331634],[66.2,-77.2,88.3]]
}

我想我可以一次做一个循环和一个记录，然后将每一行追加到字典结构中。看起来很难看，我想有更简单的方法吗？

我用来压平的代码...丑..

xx=[[*x] for x in zip(*[(1,2),(3,4),(5,6)])]
kk=0
for feat in c:
    kk=kk+1
    #pprint.pprint(feat)   
    if (kk>1):
        break
    #xx=[[*x] for x in zip(*feat['geometry']['coordinates'][0][1])]   
    #pprint.pprint(feat['geometry']['coordinates'][0])
    tt=feat['geometry']['coordinates'][0]
    xx=[[*x] for x in zip(*tt)] 
    x_cor=xx[0]
    y_cor=xx[1]
    print (x_cor)
    print(y_cor)
    #print(xx)

Answer 1

绘制地理数据时使用 Bokeh 的 GeoJSONDataSource 更容易。下面的示例显示了如何使用 Fiona and/or GeoPandas 读取和转换 shapefile，您可以使用其中任何一个。并用 Bokeh 绘制结果。

进口

import geopandas as gpd
import fiona
import json

from bokeh.io import show, output_notebook
from bokeh.models import GeoJSONDataSource, LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure

输入数据

shapefile = 'cb_2016_us_county_500k.shp' # from census.gov

正在使用 Geopandas 加载数据

# read all counties
counties = gpd.read_file(shapefile)

# filter only Texas
texas = counties.query('STATEFP=="48"')

# Export to geojson
geojson = texas.to_json()

或与菲奥娜一起阅读

# open shapefile
with fiona.open(r'D:\Data\US_counties\cb_2016_us_county_500k.shp') as f:

    # filter Texas
    texas = list(filter(lambda x: x['properties']['STATEFP'] == "48",  f.values()))

    # export to geojson
    geojson = json.dumps({'type': 'FeatureCollection', 'features': texas})

使用散景绘图

color_column = 'ALAND'

geo_source = GeoJSONDataSource(geojson=geojson)

p = figure(title="Texas")

p.patches('xs', 'ys', fill_alpha=0.7, 
          fill_color={'field': color_column, 'transform': LogColorMapper(palette=palette)}, 
          line_color='black', line_width=0.5, source=geo_source)

show(p)

将 fiona 结构展平为散景字典

flatten a fiona structure to dictionary for bokeh

python

pandas

bokeh

进口

输入数据

正在使用 Geopandas 加载数据

或与菲奥娜一起阅读

使用散景绘图