def plotEarthMeridiansTriangles(verbose=True, save=False, show=True, dpi=300, resolution='c'):
'''Draw Dymax Triangles, All countries, and Meridians'''
lonlat_islands, dymax_islands = getIslands(resolution)
n = 1000
plt.figure(figsize=(20,12))
plt.title('Dymaxion Map Projection')
### Dymaxion Latitude Meridians
lons = np.linspace(-180,180,n)
latgrid = np.linspace(-85,85,35)
points = []
start = time.time()
for lat in latgrid:
for lon in lons:
points += [convert.lonlat2dymax(lon,lat)]
if verbose: print(':: mapped {:d} points to dymax projection @ {:.1f} pts/sec [{:.1f} secs total]'.format(len(points),len(points)/(time.time()-start),time.time()-start))
points = np.array(points)
plt.plot(points[:,0],points[:,1],',',c='k',alpha=.3)#,'.',lw=0)#,c=range(n))
### Dymaxion Longitude Meridians
lats = np.linspace(-85,85,n)
longrid = np.linspace(-180,175,72)
points = []
start = time.time()
for lon in longrid:
for lat in lats:
points += [convert.lonlat2dymax(lon,lat)]
if verbose: print(':: mapped {:d} points to dymax projection @ {:.1f} pts/sec [{:.1f} secs total]'.format(len(points),len(points)/(time.time()-start),time.time()-start))
points = np.array(points)
plt.plot(points[:,0],points[:,1],',',c='k',alpha=.3)#,'.',lw=0)#,c=range(n))
### Dymaxion Face Tiles
for jdx in range(constants.facecount):
if jdx == 8 or jdx == 15: continue
points = convert.face2dymax(jdx,push=.95)
xcenter,ycenter = convert.dymax_centers[jdx]
plt.text(xcenter,ycenter,str(jdx),size='x-large')
plt.plot(points[:,0],points[:,1],lw=5,alpha=.7)
### Draw Landmasses
patches = []
for island in dymax_islands:
polygon = Polygon(np.array(island))#, closed=False, fill=False)
patches.append(polygon)
p = PatchCollection(patches, alpha=.3, linewidths=1.,facecolors=None)
colors = 100*np.random.random(len(patches))
p.set_array(np.array(colors))
plt.gca().add_collection(p)
if verbose: print(':: plotted',len(patches),'coastlines')
plt.gca().set_xlim([0,5.5])
plt.gca().set_ylim([0,2.6])
plt.gca().set_aspect('equal')
if save: plt.savefig('dymax_earthmeridianstriangles.png',bbox_inches='tight',dpi=dpi,transparent=True,pad_inches=0)
if show:
plt.tight_layout()
plt.show()
else: plt.close()
def plotEarthSubTriangles(verbose=True, save=False, show=True, dpi=300, resolution='c'):
'''Each Icosahedron Face has six sub-triangles that are splitting on.'''
lonlat_islands, dymax_islands = getIslands(resolution)
plt.figure(figsize=(20,12))
xs, ys = [],[]
lcds = []
for i in range(10000):
lon = np.random.random()*360-180
lat = np.random.random()*180-90
x, y, lcd = convert.lonlat2dymax(lon,lat, getlcd=True)
xs += [x]
ys += [y]
lcds += [lcd]
### Draw LCD Triangle Indices
colorlist = 'rgcmyg'
for val in range(10000):
plt.text(xs[val],ys[val],str(lcds[val]),color=colorlist[lcds[val]],ha='center',va='center',size='small',alpha=.5)
### Draw Large Fuller Triangles
for jdx in range(constants.facecount):
if jdx == 8 or jdx == 15: continue
points = convert.face2dymax(jdx, push=.95)
xcenter,ycenter = convert.dymax_centers[jdx]
plt.plot(points[:,0],points[:,1],lw=5,alpha=.5)
### Draw Fuller LCD Sub-Triangles
for jdx in range(constants.facecount):
if jdx == 8 or jdx == 15: continue
points = convert.face2dymax(jdx, push=.999, atomic=True)
xcenter, ycenter = convert.dymax_centers[jdx]
plt.text(xcenter,ycenter,str(jdx),size='xx-large',ha='center',va='center')
plt.plot(points[:,0],points[:,1],lw=1,alpha=1,color='k',ls='dotted')
### Draw Landmasses
patches = []
for island in dymax_islands:
polygon = Polygon(np.array(island), closed=True, fill=True)
patches.append(polygon)
colors = 100*np.random.random(len(patches))
p = PatchCollection(patches, cmap=plt.cm.jet, alpha=.5,linewidths=0.)
p.set_array(np.array(colors))
plt.gca().add_collection(p)
if verbose: print(':: plotted',len(patches),'coastlines')
plt.gca().set_xlim([0,5.5])
plt.gca().set_ylim([0,2.6])
plt.gca().set_aspect('equal')
if save: plt.savefig('dymax_earthsubtriangles.png',bbox_inches='tight',dpi=dpi,transparent=True,pad_inches=0)
if show:
plt.tight_layout()
plt.show()
else: plt.close()
def getIslands(verbose=True, resolution="c"):
"""
Get coastlines from GSHHS:
Global Self-consistent Hierarchical High-resolution Shorelines
Returns outlines in (Lon,Lat) Geodesic and (X,Y) Dymax format.
Valid resolutions: c (crude), l (low), i (intermediate), h (high), f (full)
"""
### Load Coastlines
binfile = open(data_directory + "gshhs_" + resolution + ".dat", "rb")
data = np.fromfile(binfile, "<f4")
data = data.reshape(len(data) / 2, 2)
start = time.time()
dymaxdata = np.zeros_like(data)
for idx, row in enumerate(data):
dymaxdata[idx] = convert.lonlat2dymax(row[0], row[1])
if verbose:
print(
":: mapped {:d} points to dymax projection @ {:.1f} pts/sec [{:.1f} secs total]".format(
len(dymaxdata), len(dymaxdata) / (time.time() - start), time.time() - start
)
)
### Load Metadata
with open(data_directory + "gshhsmeta_" + resolution + ".dat", "r") as derp:
places = derp.read()
places = places.split("\n")
lonlat_islands = []
dymax_islands = []
# 1, area, numpoints, limit_south, limit_north, startbyte, numbytes, id-(E/W crosses dateline east or west)
for place in places:
if len(place) < 2:
continue
column = place.split()
# print(column)
if float(column[1]) < 500 and resolution == "c":
continue # eliminate tiny area islands
start_idx = int(column[5]) // 8
stop_idx = start_idx + int(column[6]) // 8
lonlat_islands += [data[start_idx:stop_idx]]
dymax_islands += [dymaxdata[start_idx:stop_idx]]
if verbose:
print(":: computed", len(places), "coastlines")
return lonlat_islands, dymax_islands
def getIslands(verbose=True, resolution='c'):
'''
Get coastlines from GSHHS:
Global Self-consistent Hierarchical High-resolution Shorelines
Returns outlines in (Lon,Lat) Geodesic and (X,Y) Dymax format.
Valid resolutions: c (crude), l (low), i (intermediate), h (high), f (full)
'''
### Load Coastlines
binfile = open(data_directory+'gshhs_'+resolution+'.dat','rb')
data = np.fromfile(binfile,'<f4')
data = data.reshape(len(data)/2,2)
start = time.time()
dymaxdata = np.zeros_like(data)
for idx, row in enumerate(data):
dymaxdata[idx] = convert.lonlat2dymax(row[0],row[1])
if verbose: print(':: mapped {:d} points to dymax projection @ {:.1f} pts/sec [{:.1f} secs total]'.format(len(dymaxdata),len(dymaxdata)/(time.time()-start),time.time()-start))
### Load Metadata
with open(data_directory+'gshhsmeta_'+resolution+'.dat','r') as derp:
places = derp.read()
places = places.split('\n')
lonlat_islands = []
dymax_islands = []
#1, area, numpoints, limit_south, limit_north, startbyte, numbytes, id-(E/W crosses dateline east or west)
for place in places:
if len(place) < 2: continue
column = place.split()
#print(column)
if float(column[1]) < 500 and resolution == 'c': continue # eliminate tiny area islands
start_idx = int(column[5])//8
stop_idx = start_idx + int(column[6])//8
lonlat_islands += [data[start_idx:stop_idx]]
dymax_islands += [dymaxdata[start_idx:stop_idx]]
if verbose: print(':: computed',len(places),'coastlines')
return lonlat_islands, dymax_islands
def convertRectImage2DymaxImage(inFilename, outFilename, verbose=True, scale = 300, speedup = 1, save=False, show=True):
'''
Convert rectilinear image to dymax projection image.
scale is number of pixels per dymax xy unit.
How to calculate output scale:
width = 160 # in cm
resolution = 30 # in px/cm
scale = (width * resolution) / 5.5
__OR__
final_size_in_pixels = (scale * 5.5, scale * 2.6)
speedup gives a sparse preview of the output image and is specified as a
time divisor. On an intel Q6600 14 million points take about 6 minutes.
A speedup value of 10 reduces compute time to
'''
start = time.time()
im = Image.open(inFilename) #Can be many different formats. #15 vertical and horizontal pixels per degree
pix = im.load()
if verbose: print(':: input image resolution =',im.size) # Get the width and hight of the image for iterating over
### LongLat2Dymax returns x = (0,5.5) and y=(0,2.6)
dymax_xsize, dymax_ysize = int(5.5*scale),int(2.6*scale)
dymaximg = Image.new( 'RGBA', (dymax_xsize,dymax_ysize), (255,0,0,0)) # create a new transparent
if verbose: print(':: output image resolution =',(dymax_xsize,dymax_ysize)) # Get the width and hight of the image for iterating over
### X and Y are indexed from topleft to bottom right
if verbose: print(':: sweeping over Longitudes:')
xsize,ysize = im.size
for i, lon in enumerate(np.linspace(-180,180,xsize/speedup,endpoint=True)):
i*= speedup
if i % 20 == 0:
print('{:+07.2f} '.format(lon),end='')
stdout.flush() # I would add flush=True to print, but thats only in python3.3+
for j, lat in enumerate(np.linspace(90,-90,ysize/speedup,endpoint=True)):
j*= speedup
newx,newy = convert.lonlat2dymax(lon,lat)
newx = int(newx*scale)-1
newy = int(newy*scale)
try:dymaximg.putpixel((newx, newy), pix[i,j])
# Sometimes a point won't map to an edge properly
except IndexError: print('{{{:d},{:d}}}'.format(newx,newy), end='')
if verbose: print()
dymaximg = ImageOps.flip(dymaximg) #it's upside down, who cares why
'''
### Convert white pixels to transparent (there must be a better way)
print('converting')
dymaximg.convert('RGBA')
pixdata = dymaximg.load()
for y in range(dymaximg.size[1]):
for x in range(dymaximg.size[0]):
if pixdata[x, y] == (255, 255, 255, 255):
pixdata[x, y] = (255, 255, 255, 0)
'''
numpoints = im.size[0] * im.size[1] // speedup
if verbose: print(':: mapped {:d} points to dymax projection @ {:.1f} pts/sec [{:.1f} secs total]'.format(numpoints,numpoints/(time.time()-start),time.time()-start))
plt.figure(figsize=(20,12),frameon=False)
plt.gca().axis('off')
if save: dymaximg.save(outFilename, format='PNG')
if show:
plt.tight_layout()
plt.imshow(dymaximg)
plt.show()
else: plt.close()
