[英]Datashader integration for polygons in plotly mapbox
I'm using plotly's Scattermapbox to overlay a map with a shaded image of polygons created by datashader's shade function (based on https://plotly.com/python/datashader/ ), but the projections do not seem to align, see picture below.我正在使用 plotly 的 Scattermapbox 将 map 与由数据着色器的阴影 function 创建的多边形阴影图像叠加(基于https://plotly.com/python/datashader/ ),但投影似乎没有对齐,请参见下图. Any suggestions how I can overcome this problem using plotly's Scattermapbox and datashader?
有什么建议可以使用 plotly 的 Scattermapbox 和数据着色器来解决这个问题吗?
Reproducible example:可重现的例子:
import geopandas as gpd
import plotly.graph_objects as go
import spatialpandas as spd
import datashader as ds
from colorcet import fire
import datashader.transfer_functions as tf
# load data
world = gpd.read_file(
gpd.datasets.get_path('naturalearth_lowres')
)
# world = world.to_crs(epsg=3857)
# create spatialpandas DataFrame
df_world = spd.GeoDataFrame(world)
# create datashader canvas and aggregate
cvs = ds.Canvas(plot_width=1000, plot_height=1000)
agg = cvs.polygons(df_world, geometry='geometry', agg=ds.mean('pop_est'))
# create shaded image
tf.shade(agg, cmap=fire)
# create shaded image and convert to Python image
img = tf.shade(agg, cmap=fire)[::-1].to_pil()
coords_lat, coords_lon = agg.coords["y"].values, agg.coords["x"].values
# Corners of the image, which need to be passed to mapbox
coordinates = [
[coords_lon[0], coords_lat[0]],
[coords_lon[-1], coords_lat[0]],
[coords_lon[-1], coords_lat[-1]],
[coords_lon[0], coords_lat[-1]],
]
fig = go.Figure(go.Scattermapbox())
fig.update_layout(
mapbox_style="open-street-map",
mapbox_layers=[
{
"sourcetype": "image",
"source": img,
"coordinates": coordinates,
}
]
)
fig.show()
I read that Scattermapbox only supports Mercator projection which I found confusing as the examples in plotly's documentation seem to be in long/lat format, but I tried converting the coordinates of the GeoDataFrame to epsg 3857, see我读到 Scattermapbox 只支持 Mercator 投影,我发现它令人困惑,因为 plotly 文档中的示例似乎是 long/lat 格式,但我尝试将 GeoDataFrame 的坐标转换为 epsg 3857,请参阅
# world = world.to_crs(epsg=3857)
The results is that the shaded image becomes invisible.结果是阴影图像变得不可见。 Any help would be highly appreciated.
任何帮助将不胜感激。
Have you tried with epsg:4326?你试过 epsg:4326 了吗? In my case, I use this one and the geometries are placed correctly.
在我的例子中,我使用了这个并且几何图形被正确放置。
On the other hand, with geopandas to convert the geometry column of the dataframe you have to use the parameter "inplace=True".另一方面,使用 geopandas 转换 dataframe 的几何列时,您必须使用参数“inplace=True”。
We have discovered the solution to this issue: Below is each step / function code and description:我们已经找到了解决这个问题的方法:下面是每个步骤/function 代码和说明:
Imports for reference:导入参考:
import datashader as ds
import datashader.transfer_functions as tf
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import rasterio
import shapely.geometry
import xarray as xr
_helper_add_pseudomercator_optimized
: Creates array from the meshgrid with the proper mercator coordinates from the original raster with epsg:4326
. _helper_add_pseudomercator_optimized
:使用epsg:4326
的原始栅格中的适当墨卡托坐标从 meshgrid 创建数组。
def _helper_add_pseudomercator_optimized(raster):
"""Adds mercator coordinates epsg:3857 from a raster with epsg:4326.
Originally defined as `add_psuedomercator_adam_manuel_optimized`
Args:
raster: xr.DataArray: `xr.DataArray` to transform coordinates
Returns:
`xr.DataArray` with coordinates (x, y) transformed from epsg:4326 to epsg:3857
"""
# Transformer that converts coordinates from epsg 4326 to 3857
gcs_to_3857 = Transformer.from_crs(4326, 3857, always_xy=True)
x_vals = list(raster.x.values.squeeze()) # x values from the raster dimension x
y_vals = list(raster.y.values.squeeze()) # x values from the raster dimension x
# Allows transformation of non-square coordinates
y_dummy_vals = [raster.y.values[0] for v in raster.x.values] # dummy values
x_dummy_vals = [raster.x.values[0] for v in raster.y.values] # dummy values
x, _ = gcs_to_3857.transform(x_vals, y_dummy_vals) # Obtain x output here only
_, y = gcs_to_3857.transform(x_dummy_vals, y_vals) # Obtain y output here only\
# Create meshgrid with the x and y mercator converted coordinates
lon, lat = np.meshgrid(x, y)
# Add meshgrid to raster -> raster now has mercator coordinates for every point
raster["x_mercator"] = xr.DataArray(lon, dims=("y", "x"))
raster["y_mercator"] = xr.DataArray(lat, dims=("y", "x"))
return raster
def _helper_affine_transform(raster):
"""Create new affine from a raster. Used to get new affine from the transformed affine.
Args:
raster: xr.DataArray: `xr.DataArray` to get the original affine and then transform
Returns:
New affine transform for a coarsened array
"""
res = (raster.x[-1].values - raster.x[0].values) / raster.x.shape[0]
scale = Affine.scale(res, -res)
transform = (
Affine.translation(raster.x[0].values - res / 2, raster.y[0].values - res / 2)
* scale
)
return transform
def _helper_to_datashader_quadmesh(raster, y="lat", x="lon"):
"""Create lower level quadmesh with data based on flood raster. Map Flooding
to lower level map.
Args:
raster: xr.DataArray: `xr.DataArray` raster of flooded regions
Returns:
`datashader.Canvas` based on quadmesh from original flood raster
"""
cvs = ds.Canvas(plot_height=5000, plot_width=5000)
z = xr.DataArray(
raster.values.squeeze(),
dims=["y", "x"],
coords={
"Qy": (["y", "x"], raster[y].values),
"Qx": (["y", "x"], raster[x].values),
},
name="z",
)
return cvs.quadmesh(z, x="Qx", y="Qy")
def _helper_img_coordinates(raster):
"""Get coordinates of the corners of the baseline raster.
Args:
raster: xr.DataArray: `xr.DataArray` to get corner coordinates from
Returns:
coordinates of where to plot the flooded raster on the map
"""
coords_lat, coords_lon = (raster.y.values, raster.x.values)
if len(coords_lat.shape) > 1:
coords_lat = coords_lat[:, 0]
coords_lon = coords_lon[0, :]
coordinates = [
[coords_lon[0], coords_lat[0]],
[coords_lon[-1], coords_lat[0]],
[coords_lon[-1], coords_lat[-1]],
[coords_lon[0], coords_lat[-1]],
]
return coordinates
All operations together for the below sequence:以下顺序的所有操作一起:
# Add mercator coordinates to the raster
raster = _helper_add_pseudomercator_optimized(raster)
# Create quadmesh from the burned raster
agg_mesh = _helper_to_datashader_quadmesh(raster, x="x_mercator", y="y_mercator")
# Don't plot values where the flooding is zero
agg_mesh = agg_mesh.where(agg_mesh < 0)
# Convert to datashader shade
im = tf.shade(agg_mesh, Theme.color_scale)
# Convert to image
img = im.to_pil()
# Get coordinates to plot raster on map
coordinates = _helper_img_coordinates(baseline_raster)
Then this image produced by datashader can be added to a plotly plot using the plotly objects layer, and providing this layer to the figure然后可以使用 plotly 对象层将数据着色器生成的图像添加到 plotly plot,并将该层提供给图形
layer = go.layout.mapbox.Layer(
below="water",
coordinates=coordinates,
sourcetype="image",
source=img,
)
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