File:Reddish and other tlp sites on the moon 1.png

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Captions

Captions

Reddish and other TLP sites on the Moon

Summary

[edit]
Description
English: Reddish and other 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 0000.0003a
  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)

for n in range(0, len(lox2)): 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:
w:en:Creative Commons
attribution
This file is licensed under the Creative Commons Attribution 4.0 International license.
You are free:
  • to share – to copy, distribute and transmit the work
  • to remix – to adapt the work
Under the following conditions:
  • attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

File history

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Date/TimeThumbnailDimensionsUserComment
current10:36, 17 June 2024Thumbnail for version as of 10:36, 17 June 2024834 × 817 (481 KB)Merikanto (talk | contribs)Attempt to correct coordinates
12:32, 16 June 2024Thumbnail for version as of 12:32, 16 June 2024848 × 793 (442 KB)Merikanto (talk | contribs)Uploaded own work with UploadWizard

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