Python中怎么绘制矢量数据

这期内容当中小编将会给大家带来有关Python中怎么绘制矢量数据，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。

1 矢量数据绘制

学习目标：

• 为多个矢量数据集绘制地图，并根据属性进行配色

• 自定义地图图例

创建自定义地图

在本节中，将学习如何自定义地图符号和用于在Python中表示矢量数据的颜色和符号，使用geopandas和matplotlib进行地图绘制

首先导入需要使用到的包:

import osimport matplotlib.pyplot as pltimport numpy as npfrom shapely.geometry import boximport geopandas as gpdimport earthpy as et

# 下载数据# data = et.data.get_data('spatial-vector-lidar')os.chdir(os.path.join(et.io.HOME, 'learning','python_data_plot'))

# 导入数据sjer_roads_path="data/california/madera-county-roads/tl_2013_06039_roads.shp"sjer_roads = gpd.read_file(sjer_roads_path)print(type(sjer_roads['RTTYP']))print(sjer_roads['RTTYP'].unique())

['M' None 'S' 'C']

缺失值替换

可以看出道路类型中有一些缺失的值，由于需要绘制所有的道路类型，甚至那些设置为None的道路类型，下面将RTTYP属性None为Unknown

sjer_roads['RTTYP'].replace(np.nan,"Unknown",inplace=True)# sjer_roads.loc[sjer_roads['RTTYP'].isnull(), 'RTTYP'] = 'Unknown'print(sjer_roads['RTTYP'].unique())

['M' 'Unknown' 'S' 'C']

如果使用geopandas.Plot()绘制数据，当设置了column =参数后，则geopandas将为线条自动选择颜色，可以使用legend = True参数添加图例

fig, ax = plt.subplots(figsize=(14,6))sjer_roads.plot(column='RTTYP',                categorical=True,                legend=True,                ax=ax               )# 调整图例位置leg = ax.get_legend()leg.set_bbox_to_anchor((1.15,0.5))# 隐藏边框ax.set_axis_off()plt.show()

根据属性指定颜色

为了按属性值绘制一个矢量图层，这样每条道路图层就会根据它各自的属性值来着色，所以图例也代表了同样的符号，需要三个步骤：

1. 创建一个将特定颜色与特定属性值关联的字典

2. 循环遍历并将该颜色应用于每个属性值

3. 最后，在绘图中添加一个label参数，以便调用ax.legend()生成最终的图例

下面，先创建一个字典来定义您希望使用哪种颜色绘制每种道路类型：

# Create a dictionary where you assign each attribute value to a particular colorroadPalette = {'M': 'blue',               'S': 'green',               'C': 'purple',               'Unknown': 'grey'}roadPalette

{'M': 'blue', 'S': 'green', 'C': 'purple', 'Unknown': 'grey'}

接下来，循环遍历每个属性值，并使用字典中指定的颜色用该属性值绘制线条

fig, ax = plt.subplots(figsize=(10,10))# 根据道路类型分组进行绘制for ctype,data in sjer_roads.groupby('RTTYP'):        color = roadPalette[ctype]        data.plot(color=color,              ax=ax,              label=ctype             )ax.legend(bbox_to_anchor=(1.0, .5), prop={'size': 12})ax.set(title='Madera County Roads')ax.set_axis_off()plt.show()

调整线条宽度

可以通过linewidth =属性对线条宽度进行设置，

fig, ax = plt.subplots(figsize=(10, 10))# Loop through each group (unique attribute value) in the roads layer and assign it a colorfor ctype, data in sjer_roads.groupby('RTTYP'):    color = roadPalette[ctype]    data.plot(color=color,              ax=ax,              label=ctype,              linewidth=4)  # Make all lines thicker# Add title and legend to plotax.legend()ax.set(title='Madera County Roads')ax.set_axis_off()plt.show()

根据属性调整线条宽度

与着色相同，先创建线条宽度与类型的映射关系，然后分组进行循环绘制

# Create dictionary to map each attribute value to a line widthlineWidths = {'M': 1, 'S': 1, 'C': 4, 'Unknown': .5}# Plot data adjusting the linewidth attributefig, ax = plt.subplots(figsize=(10, 10))ax.set_axis_off()for ctype, data in sjer_roads.groupby('RTTYP'):    color = roadPalette[ctype]        data.plot(color=color,              ax=ax,              label=ctype,                            # Assign each group to a line width using the dictionary created above              linewidth=lineWidths[ctype])ax.legend()ax.set(title='Madera County \n Line width varies by TYPE Attribute Value')plt.show()

自定义绘制图例

在上面的实验中，使用label=True显示图例，ax.legend()的loc=参数可以对图例位置进行调整，ax.legend()的常用参数有：

• loc=(how-far-right,how-far-above)

• fontsize=，设置图例字体大小

• frameon=，是否显示图例边框

lineWidths = {'M': 1, 'S': 2, 'C': 1.5, 'Unknown': 3}fig, ax = plt.subplots(figsize=(10, 10))# Loop through each attribute value and assign each # with the correct color & width specified in the dictionaryfor ctype, data in sjer_roads.groupby('RTTYP'):    color = roadPalette[ctype]    label = ctype        data.plot(color=color,              ax=ax,              linewidth=lineWidths[ctype],              label=label)ax.set(title='Madera County \n Line width varies by TYPE Attribute Value')# Place legend in the lower right hand corner of the plotax.legend(loc='lower right',          fontsize=15,          frameon=True)ax.set_axis_off()plt.show()

观察当将图例frameon属性设置为False并调整线宽时会发生什么情况，注意loc = ()参数被赋予一个元组，它定义了图例相对于绘图区域的x和y的位置

lineWidths = {'M': 1, 'S': 2, 'C': 1.5, 'Unknown': 3}fig, ax = plt.subplots(figsize=(10, 10))for ctype, data in sjer_roads.groupby('RTTYP'):    color = roadPalette[ctype]    label = ctype    data.plot(color=color,              ax=ax,              linewidth=lineWidths[ctype],              label=label)ax.set(title='Madera County \n Line width varies by TYPE Attribute Value')ax.legend(loc=(1, 0.5),          fontsize=15,          frameon=False,          title="LEGEND")ax.set_axis_off()plt.show()

同时对线宽和颜色进行调整

roadPalette = {'M': 'grey', 'S': "blue",               'C': "magenta", 'Unknown': "lightgrey"}lineWidths = {'M': 1, 'S': 2, 'C': 1.5, 'Unknown': 3}fig, ax = plt.subplots(figsize=(10, 10))for ctype, data in sjer_roads.groupby('RTTYP'):    color = roadPalette[ctype]    label = ctype        data.plot(color=color,              ax=ax,              linewidth=lineWidths[ctype],              label=label)    ax.set(title='Madera County Roads \n Pretty Colors')ax.legend(loc='lower right',          fontsize=20,          frameon=False)ax.set_axis_off()plt.show()

向地图中添加点图层

接下来，向地图添加另一个图层，看看如何创建一个更复杂的地图，添加SJER_plot_centroids shapefile，并同时表示两个图层的图例

该点图层包含三种类型：grass,soil,trees

# 导入点图层sjer_plots_path ="data/california/neon-sjer-site/vector_data/SJER_plot_centroids.shp"sjer_plots = gpd.read_file(sjer_plots_path)sjer_plots.head(5)

就像上面所做的一样，创建一个字典来指定与每个图形类型相关联的颜色

pointsPalette = {'trees': 'chartreuse',                 'grass': 'darkgreen', 'soil': 'burlywood'}lineWidths = {'M': .5, 'S': 2, 'C': 2, 'Unknown': .5}fig, ax = plt.subplots(figsize=(10, 10))for ctype, data in sjer_plots.groupby('plot_type'):    color = pointsPalette[ctype]    label = ctype    data.plot(color=color,              ax=ax,              label=label,              markersize=100)    ax.set(title='Study area plot locations\n by plot type (grass, soil and trees)')ax.legend(fontsize=20,          frameon=True,          loc=(1, .1),          title="LEGEND")ax.set_axis_off()plt.show()

将点图层叠加到道路图层

接下来，在道路图层上叠加绘制点数据，然后创建一个包含线和点的自定义图例

注意: 在这个例子中，两个图层的投影信息必须匹配

# Reproject the data# 数据投影sjer_roads_utm = sjer_roads.to_crs(sjer_plots.crs)

fig, ax = plt.subplots(figsize=(10, 10))# 点图层绘制for ctype, data in sjer_plots.groupby('plot_type'):    color = pointsPalette[ctype]    label = ctype # label参数对于图例的生成很重要    data.plot(color=color,              ax=ax,              label=label,              markersize=100)# 道路图层绘制    for ctype, data in sjer_roads_utm.groupby('RTTYP'):    color = roadPalette[ctype]    label = ctype        data.plot(color=color,              ax=ax,              linewidth=lineWidths[ctype],              label=label)    ax.set(title='Study area plot locations\n by plot type (grass, soil and trees)')ax.legend(fontsize=15,          frameon=False,          loc=('lower right'),          title="LEGEND")ax.set_axis_off()plt.show()

上述就是小编为大家分享的Python中怎么绘制矢量数据了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注行业资讯频道。