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This map drawing tool is amazing, I recommend it~~

2022-08-01 02:35:00 【The Way of Python Data】

来源：DataCharm

Previously, the editor introduced a number of articles about the drawing of spatial visualization charts in the public account,Liked by many students,But there are also many students who say noPython的版本,Today, the editor recommends an awesome academic research map visualization tool-Python-pygmt库,顾名思义,pygmtIt is based on the powerful drawing functionGMT软件,且使用pygmt前必须安装GMT(Generic Mapping Tools)软件.GMT具体安装步骤可参考：GMT中文手册^[1].需要注意的是,现阶段pygmt还不能完全支持GMTAll chart types that can be drawn,Subsequent updates will continue to improve.This tweet is mostly rightPython-pygmt的一个介绍,主要内容如下：

Python-pygmt安装
Python-pygmt 示例绘制

Python-pygmt安装

The reason why it is specially introduced herepygmt库安装,Because the library is best to useAnaconda进行安装,And need to be done separatelypygmt运行环境的搭建.pygmtThe recommended installation script statement on the official website is as follows(默认GMT已安装)：

conda create --name pygmt --channel conda-forge pygmt

Activate the one you just built using the following statementpygmt虚拟环境：

conda activate pygmt

「注意」：Here I recommend using itPythonWhen doing space charting,It is best to build the virtual environment separately,Avoid dependency library version conflicts.

Python-pygmt 示例绘制

This part of the editor mainly introducespygmtThe main types of charts that can be drawn at this stage,主要内容如下：

Shorelines(海岸线)

fig = pygmt.Figure()
fig.basemap(region="g", projection="W15c", frame=True)
fig.coast(shorelines=True)
fig.show()

Shorelines(海岸线)

data points(气泡图)

fig = pygmt.Figure()
fig.basemap(region=region, projection="M15c", frame=True)
fig.coast(land="black", water="skyblue")
pygmt.makecpt(cmap="viridis", series=[data.depth_km.min(), data.depth_km.max()])
fig.plot(
    x=data.longitude,
    y=data.latitude,
    size=0.02 * 2**data.magnitude,
    color=data.depth_km,
    cmap=True,
    style="cc",
    pen="black",
)
fig.colorbar(frame='af+l"Depth (km)"')
fig.show()

data points(气泡图)

map with contour lines

fig = pygmt.Figure()
fig.grdimage(
    grid=grid,
    cmap="haxby",
    projection="M10c",
    frame=True,
)
fig.grdcontour(
    annotation=1000,
    interval=250,
    grid=grid,
    limit=[-4000, -2000],
)
fig.show()

map with contour lines

Roads

import geopandas as gpd
import pygmt

# Read shapefile data using geopandas
gdf = gpd.read_file(
    "http://www2.census.gov/geo/tiger/TIGER2015/PRISECROADS/tl_2015_15_prisecroads.zip"
)
# The dataset contains different road types listed in the RTTYP column,
# here we select the following ones to plot:
roads_common = gdf[gdf.RTTYP == "M"]  # Common name roads
roads_state = gdf[gdf.RTTYP == "S"]  # State recognized roads
roads_interstate = gdf[gdf.RTTYP == "I"]  # Interstate roads

fig = pygmt.Figure()

# Define target region around O'ahu (Hawai'i)
region = [-158.3, -157.6, 21.2, 21.75]  # xmin, xmax, ymin, ymax

title = r"Main roads of O\047ahu (Hawai\047i)"  # \047 is octal code for '
fig.basemap(region=region, projection="M12c", frame=["af", f'WSne+t"{title}"'])
fig.coast(land="gray", water="dodgerblue4", shorelines="1p,black")

# Plot the individual road types with different pen settings and assign labels
# which are displayed in the legend
fig.plot(data=roads_common, pen="5p,dodgerblue", label="CommonName")
fig.plot(data=roads_state, pen="2p,gold", label="StateRecognized")
fig.plot(data=roads_interstate, pen="2p,red", label="Interstate")

# Add legend
fig.legend()

fig.show()

Roads

Blockmean

import pygmt

# Load sample data
data = pygmt.datasets.load_sample_data(name="japan_quakes")
# Select only needed columns
data = data[["longitude", "latitude", "depth_km"]]

# Set the region for the plot
region = [130, 152.5, 32.5, 52.5]
# Define spacing in x and y direction (150 by 150 minute blocks)
spacing = "150m"

fig = pygmt.Figure()

# Calculate mean depth in km from all events within 150x150 minute
# bins using blockmean
df = pygmt.blockmean(data=data, region=region, spacing=spacing)
# convert to grid
grd = pygmt.xyz2grd(data=df, region=region, spacing=spacing)

fig.grdimage(
    grid=grd,
    region=region,
    frame=["af", '+t"Mean earthquake depth inside each block"'],
    cmap="batlow",
)
# plot slightly transparent landmasses on top
fig.coast(land="darkgray", transparency=40)
# plot original data points
fig.plot(
    x=data.longitude, y=data.latitude, style="c0.3c", color="white", pen="1p,black"
)
fig.colorbar(frame=["x+lkm"])

fig.shift_origin(xshift="w+5c")

# Calculate number of total locations within 150x150 minute bins via
# blockmean's summary parameter
df = pygmt.blockmean(data=data, region=region, spacing=spacing, summary="n")
grd = pygmt.xyz2grd(data=df, region=region, spacing=spacing)

fig.grdimage(
    grid=grd,
    region=region,
    frame=["af", '+t"Number of points inside each block"'],
    cmap="batlow",
)
fig.coast(land="darkgray", transparency=40)
fig.plot(
    x=data.longitude, y=data.latitude, style="c0.3c", color="white", pen="1p,black"
)
fig.colorbar(frame=["x+lcount"])

fig.show()

Blockmean

The above is a brief introduction to thePython-pygmtThe library is commonly used in scientific research mapping types,More other chart types,大家可参考：Python-pygmt官网例子^[2]

总结

Today's tweet,小编介绍了PythonA powerful mapping tool in the language-pygmt,The library is powerful based on drawingGMT工具,与之相比,Drawing scripts are simpler and easier to understand,Interested friends can study hard,The editor will also include the library drawing with a map type~~