Blockmean

The pygmt.blockmean method allows to calculate different quantities inside blocks/bins whose dimensions are defined via the spacing parameter. The following example shows how to calculate averages of given values inside each block as well as how to report the number of points inside each bin.

Out:

<IPython.core.display.Image object>

import pygmt

# Load sample data
data = pygmt.datasets.load_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, region=region, spacing=spacing)
# convert to grid
grd = pygmt.xyz2grd(df, region=region, spacing=spacing)

fig.grdimage(
    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, region=region, spacing=spacing, summary="n")
grd = pygmt.xyz2grd(df, region=region, spacing=spacing)

fig.grdimage(
    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()