from bokeh.plotting import figure, show
from bokeh.tile_providers import CARTODBPOSITRON, get_provider
from bokeh.io import output_notebook
output_notebook()
tile_provider = get_provider(CARTODBPOSITRON)
p = figure(x_range=(-2000000, 6000000), y_range=(-1000000, 7000000),
           x_axis_type="mercator", y_axis_type="mercator")
p.add_tile(tile_provider)
show(p)

from bokeh.plotting import curdoc, figure
from bokeh.models import GeoJSONDataSource
from bokeh.io import show
# 读入中国地图数据并传给GeoJSONDataSource
with open("china.json", encoding="utf8") as f:
    geo_source = GeoJSONDataSource(geojson=f.read())
# 设置一张画布
p = figure(width=500, height=500)
# 使用patches函数以及geo_source绘制地图
p.patches(xs='xs', ys='ys', source=geo_source)
show(p)

with open("china.json", encoding="utf8") as f:
    data = json.loads(f.read())
# 判断是不是  北京地区数据
def isBeijing(district):
    if 'beijing' in district['properties']['woe-name'].lower():
        return True
    return False
# data['features'] = list(filter(isInLondon, data['features']))
# 过滤数据
# 为每一个地区增加一个color属性
for i in range(len(data['features'])):
    data['features'][i]['properties']['color'] = ['red', 'blue', 'yellow', 'orange', 'gray', 'purple'][i % 6]
    data['features'][i]['properties']['number'] = random.randint(0, 20_000)
geo_source = GeoJSONDataSource(geojson=json.dumps(data))
p = figure(width=500, height=500, tooltips="@name, number: @number")
p.patches(xs='xs', ys='ys', fill_alpha=0.7,
        line_color='white',
        line_width=0.5,
        color="color",   # 增加颜色属性，这里的"color"对应每个地区的color属性
        source=geo_source)
p.axis.axis_label = None
p.axis.visible = False
p.grid.grid_line_color = None
show(p)

import pandas as pd
import geopandas
import matplotlib.pyplot as plt
%matplotlib inline
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.plot()
plt.show()

china_nine = geopandas.read_file(r"geojson/九段线GS（2019）1719号.geojson")
china = geopandas.read_file('china-new.json')
fig, ax = plt.subplots(figsize=(12, 8),dpi=80)
ax = china.plot(ax=ax, column='number')
ax = china_nine.plot(ax=ax)
plt.show()

!pip install geopandas==0.3.0
!pip install pyshp==1.2.10
!pip install shapely==1.6.3

import plotly.graph_objects as go
fig = go.Figure(go.Scattermapbox(
    mode = "markers+lines",
    lon = [10, 20, 30],
    lat = [10, 20,30],
    marker = {'size': 10}))
fig.add_trace(go.Scattermapbox(
    mode = "markers+lines",
    lon = [-50, -60,40],
    lat = [30, 10, -20],
    marker = {'size': 10}))
fig.update_layout(
    margin ={'l':0,'t':0,'b':0,'r':0},
    mapbox = {
        'center': {'lon': 113.65000, 'lat': 34.76667},
        'style': "stamen-terrain",
        'center': {'lon': -20, 'lat': -20},
        'zoom': 1})
fig.show()

import pandas as pd
import plotly.express as px
import numpy as np
import json
with open(r"china_province.geojson", encoding='utf8') as f:
    provinces_map = json.load(f)
df = pd.read_csv(r'data.csv')
df.确诊 = df.确诊.map(np.log)
fig = px.choropleth_mapbox(
    df,
    geojson=provinces_map,
    color='确诊',
    locations="地区",
    featureidkey="properties.NL_NAME_1",
    mapbox_style="carto-darkmatter",
    color_continuous_scale='viridis',
    center={"lat": 37.110573, "lon": 106.493924},
    zoom=3,
)
fig.show()

%matplotlib inline
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
plt.show()

import datetime
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from cartopy.feature.nightshade import Nightshade
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
date = datetime.datetime(2021, 12, 2, 21)
ax.set_title(f'Night time shading for {date}')
ax.stock_img()
ax.add_feature(Nightshade(date, alpha=0.2))
plt.show()

import cartopy.io.shapereader as shpreader
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import cartopy.io.shapereader as shapereader
import matplotlib.ticker as mticker
#从文件中加载中国区域shp
shpfile = shapereader.Reader(r'ne_10m_admin_0_countries_chn\ne_10m_admin_0_countries_chn.shp')
# 设置 figure 大小
fig = plt.figure(figsize=[8, 5.5])
# 设置投影方式并绘制主图
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=180))
ax.add_geometries(
        shpfile.geometries(),
        ccrs.PlateCarree())
ax.set_extent([70, 140, 0, 55],crs=ccrs.PlateCarree())
plt.show()

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import shapely.geometry as sgeom
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14) 
def sample_data():
#    lons = [110, 115, 120, 122, 124 ]
    lons = [124, 122, 120, 115, 110 ]
    lats = [33, 32, 28, 30, 28 ]
    return lons, lats
#ax = plt.axes([0, 0, 1, 1], projection=ccrs.LambertConformal())
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([70, 140, 0, 55],crs=ccrs.Geodetic())
shapename = 'admin_1_states_provinces'
states_shp = shpreader.natural_earth(resolution='10m', category='cultural', name=shapename)
lons, lats = sample_data()
# to get the effect of having just the states without a map "background"
# turn off the outline and background patches
ax.background_patch.set_visible(False)
ax.outline_patch.set_visible(False)
plt.title(u'China Province Level', fontproperties=font)
# turn the lons and lats into a shapely LineString
track = sgeom.LineString(zip(lons, lats))
track_buffer = track.buffer(1)
for state in shpreader.Reader(states_shp).geometries():
    # pick a default color for the land with a black outline,
    # this will change if the storm intersects with our track
    facecolor = [0.9375, 0.9375, 0.859375]
    edgecolor = 'black'
    if state.intersects(track):
        facecolor = 'red'
    elif state.intersects(track_buffer):
        facecolor = '#FF7E00'
    ax.add_geometries([state], ccrs.PlateCarree(),
                      facecolor=facecolor, edgecolor=edgecolor)
# make two proxy artists to add to a legend
direct_hit = mpatches.Rectangle((0, 0), 1, 1, facecolor="red")
within_2_deg = mpatches.Rectangle((0, 0), 1, 1, facecolor="#FF7E00")
labels = [u'省份level1',
          '省份level2']
plt.legend([direct_hit, within_2_deg], labels,
           loc='lower left', bbox_to_anchor=(0.025, -0.1), fancybox=True, prop=font)
ax.figure.set_size_inches(14, 9)
plt.show()

import folium
# define the world map
world_map = folium.Map()
# display world map
world_map

# 绘制边界
import json
df = pd.read_csv(r'plotly-choropleth-mapbox-demo-master/data.csv')
# read china border 
with open(r"plotly-choropleth-mapbox-demo-master/china_province.geojson", encoding='utf8') as f:
    china = json.load(f)
chn_map = folium.Map(location=[40, 100], zoom_start=4)
folium.Choropleth(
    geo_data=china,
    name="choropleth",
    data=df,
    columns=["地区", "确诊"],
    key_on="properties.NL_NAME_1",
    fill_color="YlGn",
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name="新冠确诊",
).add_to(chn_map)
folium.LayerControl().add_to(chn_map)
chn_map

from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
c = (
    Map()
    .add("测试数据", [list(z) for z in zip(Faker.country, Faker.values())], "world")
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Map-世界地图"),
        visualmap_opts=opts.VisualMapOpts(max_=200),
    )
)
c.render_notebook()

c = (
    Map()
    .add("测试数据", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Map-VisualMap（中国）"),
        visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True),
    )
)
c.render_notebook()