Difference between revisions of "File:Parallel Mesh Generation Around the world.png"

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= Additional Assets =
 
Script to generate it :
 
Script to generate it :
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<div class="toccolours mw-collapsible mw-collapsed" style="width:600px; overflow:auto;">
 
<syntaxhighlight lang="python" >
 
<syntaxhighlight lang="python" >
 
import cartopy.crs as ccrs
 
import cartopy.crs as ccrs
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plt.show()
 
plt.show()
 
</syntaxhighlight>
 
</syntaxhighlight>
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</div>
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Sample data:
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<div class="toccolours mw-collapsible mw-collapsed" style="width:600px; overflow:auto;">
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<syntaxhighlight lang="python" >
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places = dict()
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# Paris,Saclay INRIA
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lat = 48.73668
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lon = 2.180034
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x = lon -25
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y = lat -5
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places['INRIA'] = [lon,lat,x,y]
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#Swansea
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lat = 51.6214
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lon = -3.9436
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x = lon - 35
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y = lat +2
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places['Swansea'] = [lon,lat,x,y]
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# London Imperial
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lat = 51.5074
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lon = -0.1278
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x = lon -40
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y = lat + 8
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places['Imperial'] = [ lon,lat,x,y]
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# Belgium, Université catholique de Louvain
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lat = 50.85045
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lon = 4.34878
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x = lon -45
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y = lat -3
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places['UCLouvain'] = [lon,lat,x,y]
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#Fairfax
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lat = 38.8321946
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lon = -77.308036
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x = lon +7
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y = lat -3
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places['GMU'] = [lon,lat,x,y]
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#Troy
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lat = 42.7302
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lon = -73.6788
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x = lon +15
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y = lat -5
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places['RPI'] = [lon,lat,x,y]
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# NASA LaRC
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lon = -76.385486
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lat = 37.096157
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x = lon +15
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y = lat -8
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places['LaRC'] = [lon,lat,x,y]
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# ODU
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lat= 36.8466667
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lon = -76.2855556
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x = lon +15
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y = lat -15
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places['ODU'] = [lon,lat,x,y]
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# St. Louis
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lat = 38.627003
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lon = -90.199402
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x = lon -15
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y = lat +5
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places['Boeing'] = [lon,lat,x,y]
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# UTexas , Austin
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lat = 30.2672
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lon = -97.7431
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x = lon - 25
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y = lat - 15
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places['UTexas'] = [lon,lat,x,y]
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# Alburquerque
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lat = 35.106766
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lon =-106.629181
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x = lon -15
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y = lat +5
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places['Sandia'] = [lon,lat,x,y]
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# British Columbia , vancouver
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lat = 49.2827
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lon = -123.1207
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x= lon +10
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y = lat +2
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places['UBC'] = [ lon,lat,x,y]
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# Santiago Chile
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lon = -70.6666667
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lat =  -33.45
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x = lon -30
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y = lat
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places['UChile'] = [lon,lat,x,y]
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</syntaxhighlight>
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</div>

Latest revision as of 21:42, 10 September 2018

Additional Assets

Script to generate it : 

import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
fig=plt.figure(figsize=[15,10])

ax = plt.axes(projection=ccrs.Robinson())
ax.add_feature(cfeature.LAND,facecolor='lightgrey')
#ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.BORDERS)

# make the map global rather than have it zoom in to
# the extents of any plotted data
#ax.set_global()

#ax.set_extent([-160,160,-90,90])


#https://stackoverflow.com/a/25421922
transform = ccrs.PlateCarree()._as_mpl_transform(ax)
for key, value in places.items():
    print(key)
    lon,lat,x,y = value
    # add marker
    plt.plot(lon,lat, marker='D',color='red',markersize=3,transform=ccrs.Geodetic())
    # add text
    plt.annotate(key,xy=(lon,lat), xytext=(x,y),arrowprops=dict(arrowstyle="->",color='red'),xycoords=transform)

# Save the plot by calling plt.savefig() BEFORE plt.show()
#plt.savefig('map.svg')
plt.savefig('map.png',dpi=200,bbox_inches='tight')

plt.show()

Sample data: 

places = dict()

# Paris,Saclay INRIA
lat = 48.73668
lon = 2.180034
x = lon -25
y = lat -5
places['INRIA'] = [lon,lat,x,y]

#Swansea
lat = 51.6214 
lon = -3.9436
x = lon - 35
y = lat +2
places['Swansea'] = [lon,lat,x,y]

# London Imperial
lat = 51.5074
lon = -0.1278
x = lon -40
y = lat + 8
places['Imperial'] = [ lon,lat,x,y]

# Belgium, Université catholique de Louvain
lat = 50.85045
lon = 4.34878
x = lon -45
y = lat -3
places['UCLouvain'] = [lon,lat,x,y]

#Fairfax 
lat = 38.8321946
lon = -77.308036
x = lon +7
y = lat -3
places['GMU'] = [lon,lat,x,y]

#Troy 
lat = 42.7302
lon = -73.6788
x = lon +15
y = lat -5
places['RPI'] = [lon,lat,x,y]

# NASA LaRC
lon = -76.385486
lat = 37.096157
x = lon +15
y = lat -8
places['LaRC'] = [lon,lat,x,y]

# ODU
lat= 36.8466667
lon = -76.2855556
x = lon +15
y = lat -15
places['ODU'] = [lon,lat,x,y]

# St. Louis
lat = 38.627003
lon = -90.199402
x = lon -15
y = lat +5
places['Boeing'] = [lon,lat,x,y]

# UTexas , Austin 
lat = 30.2672
lon = -97.7431
x = lon - 25
y = lat - 15
places['UTexas'] = [lon,lat,x,y]

# Alburquerque
lat = 35.106766
lon =-106.629181
x = lon -15
y = lat +5
places['Sandia'] = [lon,lat,x,y]

# British Columbia , vancouver 
lat = 49.2827
lon = -123.1207
x= lon +10
y = lat +2
places['UBC'] = [ lon,lat,x,y]

# Santiago Chile
lon = -70.6666667
lat =  -33.45
x = lon -30
y = lat 
places['UChile'] = [lon,lat,x,y]

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Date/TimeThumbnailDimensionsUserComment
current21:41, 10 September 2018Thumbnail for version as of 21:41, 10 September 20182,372 × 1,226 (542 KB)Ctsolakis (talk | contribs)
18:21, 6 September 2018Thumbnail for version as of 18:21, 6 September 20181,794 × 1,557 (319 KB)Ctsolakis (talk | contribs)
18:06, 6 September 2018Thumbnail for version as of 18:06, 6 September 20181,794 × 1,581 (323 KB)Ctsolakis (talk | contribs)
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