如何放大地球地图上的X轴？

发布于 2025-01-27 18:01:58 字数 950 浏览 2 评论 0原文

因此，我想扩展此图像，您可以看到它在X轴上看起来很奇怪，因此我在与Geopandas和所有这些内容一起工作时真的很新：

    from importlib.resources import path
import geopandas as gpd
import matplotlib.pyplot as plt
import pathlib
import pandas as pd
import json
import numpy as np
from os import environ
import shapely.geometry
def suppress_qt_warnings():
    environ["QT_DEVICE_PIXEL_RATIO"] = "0"
    environ["QT_AUTO_SCREEN_SCALE_FACTOR"] = "1"
    environ["QT_SCREEN_SCALE_FACTORS"] = "1"
    environ["QT_SCALE_FACTOR"] = "1"

if __name__ == "__main__":
    suppress_qt_warnings()

#f = pathlib.Path() / "GEOJSONDESCARGAS" /"mapa_base_Limites_Municipales_IGN_2021.json"
x= gpd.read_file("mapa_base_Limites_Municipales_IGN_2021.json")

x.plot(cmap="jet",edgecolor= "black", column="municipio")
#plt.xlim(100.0, 5500000.0)
#plt.figure(figsize=(1,1),dpi=80)
plt.style.use("seaborn")
#plt.xticks(rotation=45)

原文

So i want to expand this image, you can see it looks weird on the x axis so Im really new on working with geopandas and all this stuff:

    from importlib.resources import path
import geopandas as gpd
import matplotlib.pyplot as plt
import pathlib
import pandas as pd
import json
import numpy as np
from os import environ
import shapely.geometry
def suppress_qt_warnings():
    environ["QT_DEVICE_PIXEL_RATIO"] = "0"
    environ["QT_AUTO_SCREEN_SCALE_FACTOR"] = "1"
    environ["QT_SCREEN_SCALE_FACTORS"] = "1"
    environ["QT_SCALE_FACTOR"] = "1"

if __name__ == "__main__":
    suppress_qt_warnings()

#f = pathlib.Path() / "GEOJSONDESCARGAS" /"mapa_base_Limites_Municipales_IGN_2021.json"
x= gpd.read_file("mapa_base_Limites_Municipales_IGN_2021.json")

x.plot(cmap="jet",edgecolor= "black", column="municipio")
#plt.xlim(100.0, 5500000.0)
#plt.figure(figsize=(1,1),dpi=80)
plt.style.use("seaborn")
#plt.xticks(rotation=45)

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初懵 2025-02-03 18:01:58

您尚未提供指向您使用过的 geojson 。已经用 geojson 代替了Pamplona的面积限制。添加一个链接，指向 geojson 可以质疑
这确实是按预期的绘制的，并且与您的代码相适当尺寸
，您的 geojson 是不同的crs（这是从轴中的值的比例来看）

from importlib.resources import path
import geopandas as gpd
import matplotlib.pyplot as plt
import pathlib
import pandas as pd
import json
import numpy as np
from os import environ
import shapely.geometry
def suppress_qt_warnings():
    environ["QT_DEVICE_PIXEL_RATIO"] = "0"
    environ["QT_AUTO_SCREEN_SCALE_FACTOR"] = "1"
    environ["QT_SCREEN_SCALE_FACTORS"] = "1"
    environ["QT_SCALE_FACTOR"] = "1"

if __name__ == "__main__":
    suppress_qt_warnings()

#f = pathlib.Path() / "GEOJSONDESCARGAS" /"mapa_base_Limites_Municipales_IGN_2021.json"
# x= gpd.read_file("mapa_base_Limites_Municipales_IGN_2021.json")
x = gpd.read_file("https://raw.githubusercontent.com/montera34/airbnbnavarra/master/data/output/limites/barrios-pamplona-wgs84.geojson")
x = x.rename(columns={"BARRIO":"municipio"})

x.plot(cmap="jet",edgecolor= "black", column="municipio")
#plt.xlim(100.0, 5500000.0)
#plt.figure(figsize=(1,1),dpi=80)
plt.style.use("seaborn")
#plt.xticks(rotation=45)

you have not provided a link to the geojson you have used. Have replaced it with geojson that is area limits in Pamplona. Add a link to where the geojson can be found to question
this does plot as expected, with appropriate scaling of axes with you code
clearly your geojson is a different CRS (this is obvious from scale of values in axes)

from importlib.resources import path
import geopandas as gpd
import matplotlib.pyplot as plt
import pathlib
import pandas as pd
import json
import numpy as np
from os import environ
import shapely.geometry
def suppress_qt_warnings():
    environ["QT_DEVICE_PIXEL_RATIO"] = "0"
    environ["QT_AUTO_SCREEN_SCALE_FACTOR"] = "1"
    environ["QT_SCREEN_SCALE_FACTORS"] = "1"
    environ["QT_SCALE_FACTOR"] = "1"

if __name__ == "__main__":
    suppress_qt_warnings()

#f = pathlib.Path() / "GEOJSONDESCARGAS" /"mapa_base_Limites_Municipales_IGN_2021.json"
# x= gpd.read_file("mapa_base_Limites_Municipales_IGN_2021.json")
x = gpd.read_file("https://raw.githubusercontent.com/montera34/airbnbnavarra/master/data/output/limites/barrios-pamplona-wgs84.geojson")
x = x.rename(columns={"BARRIO":"municipio"})

x.plot(cmap="jet",edgecolor= "black", column="municipio")
#plt.xlim(100.0, 5500000.0)
#plt.figure(figsize=(1,1),dpi=80)
plt.style.use("seaborn")
#plt.xticks(rotation=45)

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