File:Reddish tlp sites on the moon 1.png

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Captions

Captions

Reddish tlp sites on the Moon

Summary

[edit]
Description
English: Reddish TLP sites on the Moon.
Date
Source Own work
Author Merikanto

Source of TLP data for this TLP map of moon is

English: *Description: This map displays an approximate distribution of transient lunar phenomena. It is based on a monochrome map by Barbara Middlehurst and Patrick Moore that was published in the book, On the Moon, 2001, Cassel & Co., ISBN 0304354694. Red dots indicate TLP that appeared to the observer as a reddish cloud. Yellow dots are all other events. 

   Size: 281 × 284 pixels.
   Source:' This is a modified version of a photo obtained from the NASA Planetary Photojournal. It was modified by RJHall.

Map data for this map comes from 

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

Image map: lroc_color_poles_8k.tif Displacement: ./indata/ldem_16.tif

Python3 source code to draw map is

                                                  1. 3
  3. TLP sites on the Moon
    1. Python3 basemap, imageio
    3. 17.06.2024 0000.0003a2
  6. TLP data from
    1. ChatGPT
  9. Moon imagemap and dem from:
    1. https://svs.gsfc.nasa.gov/4720/

import matplotlib.pyplot as plt

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

  1. 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


    1. main program


imagename1="../../alunardata1/lroc_color_poles_8k.tif"

  1. imagename1="../../alunardata1/lroc_color_poles_2k_g.tif"


imagename2="../../alunardata1/ldem_16.tif"

  1. imagename1="./indata/testmap.jpg"
  2. 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())
  1. ax = plt.axes(projection=ccrs.PlateCarree())

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


names0=['Aristarchus', 'Plato', 'Kepler', 'Tycho', 'Alphonsus', 'Grimaldi', 'Copernicus','Gassendi', 'Eratosthenes', 'Posidonius', 'Theophilus', 'Langrenus', 'Proclus','Atlas', 'Aristoteles', 'Eudoxus', 'Hyginus N', 'Römer', 'Messier', 'Pico'] lox0= [-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]

lay0=[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]

events0=[63, 55, 37, 34, 33, 27, 23, 20, 16, 14, 14, 13, 12, 12, 11, 11, 10, 10, 9, 9]



    1. patrick moore reddish tlp data

lox3=[-47.55608205 ,-20.1 , -9.3 , -39.84734885 ,-68., 

-67.3    ,    -12.72 ,        4.3     ,    -0.29160565 , -3.8,
  9.    ,      18.86 ,       29.11589291  ,35.1   ,      39.1,
 45.7   ,     -32.56592573 ,-39.80200336 ,-40.61822217, -47.85082774,
 28.20647868]

lay3=[ 23.65900912 , 9.5 , 51.6 , -17.46934981 ,-5.5, 

 32.  ,        26.4      ,   43.45    ,    67.04458682, -13.0,
 14.21   ,     44.9    ,     30.66851108 , 50.9    ,     50.8,
 21.2  ,        3.1977772  ,-15.42880278 ,-14.65792946  ,25.8809381,
 32.64549853]
    1. names only partally ok !!!

names3=["Aristachus", "Copernicus", "Plato", "Gassendi", "Grimaldi", "p1", "p2", "mons1", "p3", "Alphonsus", "p4", "p5" ,"p5", "p6"]

    1. patrick moore: other than red tlp data

lox2=[-11.20951883 , 32.75868201 ,-12.19801255 , 51.7 , 25.68106695, 

  0.21746862 , 17.8126569  , 46.3208159  , 28.96286611 , 27.7,
 17.41725941 , 23.95       , 44.5        , 54.32       , 61.97855649,
 61.6        , 61.425       ,68.73985356 , 57.98504184 , 57.86642259,
 31.23640167 , 31.45387029 , 26.3334728  , 24.51464435 , 23.40753138,
 17.06140167 , 17.28875523 , 13.81914226 , 60.71328452 , 73.28692469,
 11.09089958 , 10.89320084 ,  7.88817992 ,  4.2        , 27.5,
-55.57       , -4.35       , -1.7        , -1.99675732 , -8.5208159,
 -3.9539749  , -5.91119247 ,-13.38420502 ,-48.28       ,-66.81229079,
-74.2        ,-33.35177824 ,-29.36       ,-29.25941423 ,-20.9165272,
-25.54267782, -23.92      , -23.16040795 ,-33.39131799 ,-42.37672594,
-48.86124477 ,-27.82609833 , 41.36846234 , 26.89691423 , 49.19733264,
 40.26134937 ,  6.09900628 , -0.85998954 , -1.09722803 , -2.75789749,
 -9.        , -14.54074268,  -7.71025105,  21.98410042]

lay2=[-43.3751046 , -0.35585774 ,-61.08891213 ,-55.2 , -67.09895397, 

-33.01569038 ,-33.25292887 , -3.51903766  , 1.97698745 ,-14.3,
-10.68561715 ,-13.4        , 11.9        , 12.3        , 10.99205021,
 13.45      , 12.09916318 , 15.42050209 , 23.76338912 , 22.49811715,
 17.6248431  , 21.47996862 , 17.23933054 , 15.14372385 , 12.57364017,
 16.21129707  , 2.83697699 , 18.1289749  , -6.40543933 ,-26.80794979,
  4.89304393  , 2.2834205  ,  9.08425732 ,  5.02       , 33.6,
-44.35       ,  2.91       , -9.6        , -5.53556485 ,-22.41903766,
-21.58870293 ,-16.62646444 ,-29.93158996, -20.95      ,-16.86370293,
 -2.55      , 41.44754184 , -9.22       , 15.49958159 , 25.74037657,
 27.71736402 , 33.94       , 40.46893305 , 41.4376569  , 49.9584728,
 55.25679916 , 64.35094142 , 59.4084728  , 66.84194561 , 53.39843096,
 48.96997908 , 36.98943515 , 40.46893305 , 45.96495816 , 47.50700837,
 46.        ,  48.77228033 , 15.75658996 , 16.98232218]


    1. names nok

names2=["Tycho", "y1", "y2", "y3", "y4", "y5", "y6", "f1","m1", "m2", "m3", "m4", "m5", "m6", "m7", "m8", "m9", "m10", "m11", "m12", "m13", "m14", "m15", "m16", "m17", "ma1", "ma2", "ma3", "fecr1", "edge1", "ka1", "ka2", "ka3", "ka4", "ka5", "sch1", "big1", "big2", "ka6", "ka7", "ka8", "ka9", "big3", "ka10", "edge2", "edge3", "ma3", "ma4", "ma5", "ma6", "ma7", "ma8", "sinus1", "big4", "edge4", "edge5", "v1", "v2", "big5", "v3", "q1", "piko1", "piko2", "piko3", "piko4", "piko5"]



  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(lox3)): ax.scatter(lox3[n], lay3[n], transform=ccrs.PlateCarree(), color="red", alpha=0.4, s=240)

  1. for n in range(0, len(lox2)):
  2. ax.scatter(lox2[n], lay2[n], transform=ccrs.PlateCarree(), color="yellow", alpha=0.2, s=120)
  1. ax.scatter(lox2[n], lay2[n], transform=ccrs.PlateCarree(), color="yellow", alpha=0.4, s=40)
  2. ax.scatter(lox0[n], lay0[n], transform=ccrs.PlateCarree(), color="green", alpha=0.4, s=40)
  3. ax.scatter(lox1[n], lay1[n], transform=ccrs.PlateCarree(), color="red", alpha=0.4, s=60)
  4. #ax.scatter(lox[n], lay[n], transform=ccrs.PlateCarree(), color="orange", alpha=0.4, s=events[n]*3)
  5. ax.scatter(lox[n], lay[n], transform=ccrs.PlateCarree(), color="yellow", alpha=0.6, s=events[n]*2)

ax.set_global()

plt.show()


Licensing

[edit]
I, the copyright holder of this work, hereby publish it under the following license:
Creative Commons CC-Zero This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication.
The person who associated a work with this deed has dedicated the work to the public domain by waiving all of their rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

File history

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Date/TimeThumbnailDimensionsUserComment
current10:41, 17 June 2024Thumbnail for version as of 10:41, 17 June 2024845 × 817 (468 KB)Merikanto (talk | contribs)Update of coordinates
11:21, 9 June 2024Thumbnail for version as of 11:21, 9 June 2024808 × 803 (468 KB)Merikanto (talk | contribs)Update
11:19, 9 June 2024Thumbnail for version as of 11:19, 9 June 20241,680 × 963 (532 KB)Merikanto (talk | contribs)Uploaded own work with UploadWizard

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