File:Most active tlp sites on the moon 4b.png

# NASA "CGI Moon Kit"
# https://svs.gsfc.nasa.gov/cgi-bin/details.cgi?aid=4720

Image map: lroc_color_poles_4k.tif Displacement: ./indata/ldem_4.tif

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3. TLP sites on the moon
   1. Python3 basemap, imageio
4. TLP data from
5. 1. ChatGPT
7. Moon imagemap and dem from:
8. 1. https://svs.gsfc.nasa.gov/4720/

import matplotlib.pyplot as plt

import cartopy.crs as ccrs import imageio import numpy as np

from mpl_toolkits.basemap import shiftgrid from matplotlib.colors import LightSource

def acquire_image_rgb(iname1): img0 = imageio.imread(iname1) shp0=np.shape(img0) print(shp0) width=shp0[1] height=shp0[0] print(width, height) lats = np.linspace(-np.pi / 2, np.pi / 2, height) lons = np.linspace(0, 2 * np.pi, width) img1=img0[:,:,0] #plt.imshow(img1) #plt.show() #quit(-1) return(img1, lons, lats)

def acquire_image_gray(iname1): img0 = imageio.imread(iname1) shp0=np.shape(img0) print(shp0) width=shp0[1] height=shp0[0] print(width, height) lats = np.linspace(-np.pi / 2, np.pi / 2, height) lons = np.linspace(0, 2 * np.pi, width) img1=img0 #plt.imshow(img1) #plt.show() #quit(-1) return(img1, lons, lats)

def sample_data(shape=(4000, 2000)):

   """Returns ``lons``, ``lats`` and ``data`` of some fake data."""
   nlats, nlons = shape
   lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)
   lons = np.linspace(0, 2 * np.pi, nlons)
   lons, lats = np.meshgrid(lons, lats)
   wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)
   mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)

   lats = np.rad2deg(lats)
   lons = np.rad2deg(lons)
   data = wave + mean

   return lons, lats, data

imagename1="./indata/lroc_color_poles_4k.tif" imagename2="./indata/ldem_16.tif"

1. imagename1="./indata/testmap.jpg"

1. imagename2="./indata/testdem.jpg"

data1, lons, lats=acquire_image_rgb(imagename1)

data2, lons, lats=acquire_image_gray(imagename2)

1. data2, lons, lats=acquire_image_gray(imagename2)

ls = LightSource(azdeg=0,altdeg=65)

1. shade data, creating an rgb array.

data3= ls.shade(data2,plt.cm.gray)

1. ax = plt.axes(projection=ccrs.Mollweide())

ax = plt.axes(projection=ccrs.Orthographic())

1. names=["Aristarchus"]
2. lox=[-47.4]
3. lay=[23.7]
4. events=[100]

names=['Aristarchus', 'Plato', 'Kepler', 'Tycho', 'Alphonsus', 'Grimaldi', 'Copernicus','Gassendi', 'Eratosthenes', 'Posidonius', 'Theophilus', 'Langrenus', 'Proclus','Atlas', 'Aristoteles', 'Eudoxus', 'Hyginus N', 'Römer', 'Messier', 'Pico'] lox= [-47.4, -9.3, -38.0, -11.2, -3.2, -68.3, -20.0, -40.0, -11.3, 29.9, 26.4, 60.9,46.8, 44.4, 17.4, 16.3, 6.3, 36.4, 47.7, -8.8]

lay=[23.7, 51.6, 8.1, -43.3, -13.6, -5.2, 9.7, -17.5, 14.5, 31.8, -11.4, -8.9, 16.1,46.7, 50.2, 44.3, 7.8, 25.3, -2.3, 45.6]

events=[63, 55, 37, 34, 33, 27, 23, 20, 16, 14, 14, 13, 12, 12, 11, 11, 10, 10, 9, 9]

1. lons, lats, data = sample_data()
   1. ax.contourf(lons, lats, data,transform=ccrs.PlateCarree(),cmap='Spectral')

ax.imshow(data1, transform=ccrs.PlateCarree(),cmap='gray') ax.imshow(data3, transform=ccrs.PlateCarree(),cmap='gray', alpha=0.5)

for n in range(0, len(names)): #ax.scatter(lox[n], lay[n], transform=ccrs.PlateCarree(), color="red", alpha=0.2, s=events[n]*4) #ax.scatter(lox[n], lay[n], transform=ccrs.PlateCarree(), color="orange", alpha=0.4, s=events[n]*3) ax.scatter(lox[n], lay[n], transform=ccrs.PlateCarree(), color="yellow", alpha=0.6, s=events[n]*2)

1. ax.coastlines()

ax.set_global()

plt.show()