[GIS] Get coordinates from Folium’s feature LatLngPopup in Python

foliumpython

I am looking for a way to get coordinates from a marker/popup in Folium.
I want to use the coordinates for further calculations.
I know, that Folium is more for visualizing data and maps than getting information back.

I found an interesting post where the coordinates were sent back via href (https://gis.stackexchange.com/a/353035/168662), but this does not work for me. I am displaying a folium map in a PyQt5 Window.

Here is a simple code to display the coordinates when clicking on a map with Jupyter Notebook:

import folium


m = folium.Map()
popup1 = folium.LatLngPopup()

m.add_child(popup1)
m

To get the coordinates, I want something like:

print("Latitude of Popup: ", popup1.getLat() )

Best Answer

Look at this site. I think this may interest you: https://github.com/python-visualization/folium/blob/master/examples/plugin-MousePosition.ipynb

import folium
from folium.plugins import MousePosition


m = folium.Map()

MousePosition().add_to(m)

m

m = folium.Map()

formatter = "function(num) {return L.Util.formatNum(num, 3) + ' º ';};"

MousePosition(
    position="topright",
    separator=" | ",
    empty_string="NaN",
    lng_first=True,
    num_digits=20,
    prefix="Coordinates:",
    lat_formatter=formatter,
    lng_formatter=formatter,
).add_to(m)

m

Related Solutions

[GIS] How to get X Y coordinates and cell value of each pixel in a raster using Python

Following @Dango's idea I created and tested (on small rasters with the same extent and cell size) the following code:

import arcpy, numpy

inRaster = r"C:\tmp\RastersArray.gdb\InRaster"
inRaster2 = r"C:\tmp\RastersArray.gdb\InRaster2"

##Get properties of the input raster
inRasterDesc = arcpy.Describe(inRaster)

#coordinates of the lower left corner
rasXmin = inRasterDesc.Extent.Xmin
rasYmin = inRasterDesc.Extent.Ymin

# Cell size, raster size
rasMeanCellHeight = inRasterDesc.MeanCellHeight
rasMeanCellWidth = inRasterDesc.MeanCellWidth
rasHeight = inRasterDesc.Height
rasWidth = inRasterDesc.Width

##Calculate coordinates basing on raster properties
#create numpy array of coordinates of cell centroids
def rasCentrX(rasHeight, rasWidth):
    coordX = rasXmin + (0.5*rasMeanCellWidth + rasWidth)
    return coordX
inRasterCoordX = numpy.fromfunction(rasCentrX, (rasHeight,rasWidth)) #numpy array of X coord

def rasCentrY(rasHeight, rasWidth):
    coordY = rasYmin + (0.5*rasMeanCellHeight + rasHeight)
    return coordY
inRasterCoordY = numpy.fromfunction(rasCentrY, (rasHeight,rasWidth)) #numpy array of Y coord

#combine arrays of coordinates (although array for Y is before X, dstack produces [X, Y] pairs)
inRasterCoordinates = numpy.dstack((inRasterCoordY,inRasterCoordX))


##Raster conversion to NumPy Array
#create NumPy array from input rasters 
inRasterArrayTopLeft = arcpy.RasterToNumPyArray(inRaster)
inRasterArrayTopLeft2 = arcpy.RasterToNumPyArray(inRaster2)

#flip array upside down - then lower left corner cells has the same index as cells in coordinates array
inRasterArray = numpy.flipud(inRasterArrayTopLeft)
inRasterArray2 = numpy.flipud(inRasterArrayTopLeft2)


# combine coordinates and value
inRasterFullArray = numpy.dstack((inRasterCoordinates, inRasterArray.T))

#add values from second raster
rasterValuesArray = numpy.dstack((inRasterFullArray, inRasterArray2.T))

Based on @hmfly code, you can have access to desired values:

(height, width, dim )=rasterValuesArray.shape
for row in range(0,height):
    for col in range(0,width):
        #now you have access to single array of values for one cell location

Unfortunately there's one 'but' - the code is right for NumPy arrays which can be handled by system memory. For my system (8GB), the largest array was about 9000,9000.

As my experience doesn't let me provide more help, you can consider some suggestions about dealing wiht large arrays: https://stackoverflow.com/questions/1053928/python-numpy-very-large-matrices

arcpy.RasterToNumPyArray method allows to specify the subset of raster converted to NumPy array (ArcGIS10 help page) what can be useful when chunking large dataset into submatrices.