komrade update!
quadrismegistus
4 years ago
parent
2af4df2537
commit
0aced94204
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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# Code from
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# https://github.com/snorfalorpagus/ascii-world-map
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import json
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from functools import partial
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from shutil import get_terminal_size
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from shapely.geometry import shape, Point
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from shapely import ops
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import pyproj,math,os
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import rtree
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import warnings
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warnings.filterwarnings(action='ignore')
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# read the data into a list of shapely geometries
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with open("data/world-countries2.json") as f:
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data = json.load(f)
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def print_map(countries=[]):
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geoms_all = [
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shape(feature["geometry"])
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for feature in data["features"]
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]
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geoms = [
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shape(feature["geometry"])
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for feature in data["features"]
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if not countries or feature.get('properties',{}).get('name',None) in countries
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]
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# transform the geometries into web mercator
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wgs84 = pyproj.Proj(init="EPSG:4326")
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webmerc = pyproj.Proj(proj="webmerc")
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t = partial(pyproj.transform, wgs84, webmerc)
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geoms = [ops.transform(t, geom) for geom in geoms]
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geoms_all = [ops.transform(t, geom) for geom in geoms_all]
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# create a spatial index of the geometries
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def gen(geoms):
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for n, geom in enumerate(geoms):
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yield n, geom.bounds, geom
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index = rtree.index.Index(gen(geoms))
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index_all = rtree.index.Index(gen(geoms_all))
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# get the window size
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size = get_terminal_size(fallback=(80, 24))
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columns = size.columns
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lines = size.lines - 1 - 3 # allow for prompt at bottom
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# calculate the projected extent and pixel size
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# xmin, ymin = t(-180, -85)
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# xmax, ymax = t(180, 85)
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xmin, ymin = t(-180, -62)
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xmax, ymax = t(180, 79)
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pixel_width = (xmax - xmin) / columns
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pixel_height = (ymax - ymin) / lines
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land = "*"
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water = " "
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highlight='█'
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# stringl=[]
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# os.system('cls' if os.name == 'nt' else 'clear')
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for line in range(lines):
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for col in range(columns):
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# get the projected x, y of the pixel centroid
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x = xmin + (col + 0.5) * pixel_width
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y = ymax - (line + 0.5) * pixel_height
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# check for a collision
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objects = [n.object for n in index.intersection((x, y, x, y), objects=True)]
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value = False
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done=False
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for geom in objects:
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value = geom.intersects(Point(x, y))
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if value:
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print(highlight,end="")
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# stringl+=[highlight]
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done=True
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break
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if not done:
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objects = [n.object for n in index_all.intersection((x, y, x, y), objects=True)]
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for geom in objects:
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value = geom.intersects(Point(x, y))
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if value:
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break
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print(land if value else water, end="")
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# stringl+=[land if value else water]
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print("")
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# stringl+=['\n']
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# string = ''.join(stringl)
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# print(string)
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places = {
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'Cambridge':(52.205338,0.121817),
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'Sydney':(-33.868820,151.209290),
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'New York':(40.712776,-74.005974),
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'Hong Kong':(22.278300,114.174700),
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'Cape Town':(33.9249, 18.4241),
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'San Francisco':(-12.125640,-77.018967),
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}
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places_utm = {
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'Honolulu':(618431.58,2357505.97),
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'Tokyo':(394946.08,3946063.75),
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'Ushuaia':(544808.23,3927028.51),
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'Reykjavik':(459698.38,7111571.73)
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}
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def print_map_simple(places):
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size = get_terminal_size(fallback=(80, 24))
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columns = size.columns
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lines = size.lines - 1 - 3 # allow for prompt at bottom
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# calculate the projected extent and pixel size
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# xmin, ymin = (-180, -85)
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# xmax, ymax = (180, 85)
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# pixel_width = (xmax - xmin) / columns
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# pixel_height = (ymax - ymin) / lines
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long_min,long_max = -180,180
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# lat_min,lat_max = -85,85
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lat_min,lat_max = -75,80
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# utm_easting_min = 166640
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# utm_easting_max = 833360
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# utm_northing_min = 1110400
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# utm_northing_max = 9334080
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utm_easting_min = places_utm['Honolulu'][0]
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utm_easting_max = places_utm['Tokyo'][0]
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utm_northing_min = places_utm['Ushuaia'][1]
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utm_northing_max = places_utm['Reykjavik'][1]
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# import pyproj as proj
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# setup your projections
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# crs_wgs = proj.Proj(init='epsg:4326') # assuming you're using WGS84 geographic
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# crs_bng = proj.Proj(init='epsg:27700') # use a locally appropriate projected CRS
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import utm
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normed = {}
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for place,(lat,long) in places.items():
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# wgs84 = pyproj.Proj(init="EPSG:4326")
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# webmerc = pyproj.Proj(proj="webmerc")
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# x, y = proj.transform(wgs84, webmerc, long, lat)
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longx = (long - long_min) / (long_max - long_min)
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laty = (lat - lat_min) / (lat_max - lat_min)
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utm_easting,utm_northing,utm_zone_num,utm_zone_letter = utm.from_latlon(lat,long)
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utmx = (utm_easting - utm_easting_min) / (utm_easting_max - utm_easting_min)
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utmy = (utm_northing - utm_northing_min) / (utm_northing_max - utm_northing_min)
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# norm = ( int(longx*columns), int(laty*lines) )
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norm = ( int(utmx*columns), int(utmy*lines) )
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# print(place,(utm_easting,utm_northing),(utmx,utmy),norm)
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norm = (norm[0], lines - norm[1])
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normed[norm] = place
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p_i=None
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place_now=None
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for line in range(lines):
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for col in range(columns):
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if (col,line) in normed:
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print('*',end="")
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place=normed[(col,line)]
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place_now=place
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p_i=0
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elif p_i is not None:
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try:
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print(place_now[p_i],end="")
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p_i+=1
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except IndexError:
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place_now=None
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p_i=None
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else:
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print(" ",end="")
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print()
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# print_map(['Brazil','Netherlands','Thailand'])
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# print_map_simple(places)
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@ -0,0 +1,258 @@
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# Code from
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# https://github.com/snorfalorpagus/ascii-world-map
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import json
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from functools import partial
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from shutil import get_terminal_size
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from shapely.geometry import shape, Point
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from shapely import ops
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import pyproj,math,os
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import rtree
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import curses,random,time
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import warnings
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warnings.filterwarnings(action='ignore')
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# read the data into a list of shapely geometries
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with open(os.path.join(os.path.dirname(__file__),"data/world-countries2.json")) as f:
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data = json.load(f)
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def print_map(countries=[]):
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geoms_all = [
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shape(feature["geometry"])
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for feature in data["features"]
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]
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geoms = [
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shape(feature["geometry"])
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for feature in data["features"]
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if not countries or feature.get('properties',{}).get('name',None) in countries
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]
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# transform the geometries into web mercator
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wgs84 = pyproj.Proj(init="EPSG:4326")
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webmerc = pyproj.Proj(proj="webmerc")
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t = partial(pyproj.transform, wgs84, webmerc)
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geoms = [ops.transform(t, geom) for geom in geoms]
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geoms_all = [ops.transform(t, geom) for geom in geoms_all]
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# create a spatial index of the geometries
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def gen(geoms):
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for n, geom in enumerate(geoms):
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yield n, geom.bounds, geom
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index = rtree.index.Index(gen(geoms))
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index_all = rtree.index.Index(gen(geoms_all))
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# get the window size
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size = get_terminal_size(fallback=(80, 24))
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columns = size.columns
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lines = size.lines - 1 - 3 # allow for prompt at bottom
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# calculate the projected extent and pixel size
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# xmin, ymin = t(-180, -85)
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# xmax, ymax = t(180, 85)
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xmin, ymin = t(-180, -62)
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xmax, ymax = t(180, 79)
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pixel_width = (xmax - xmin) / columns
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pixel_height = (ymax - ymin) / lines
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land = "*"
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water = " "
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highlight='█'
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# stringl=[]
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# os.system('cls' if os.name == 'nt' else 'clear')
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for line in range(lines):
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for col in range(columns):
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# get the projected x, y of the pixel centroid
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x = xmin + (col + 0.5) * pixel_width
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y = ymax - (line + 0.5) * pixel_height
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# check for a collision
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objects = [n.object for n in index.intersection((x, y, x, y), objects=True)]
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value = False
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done=False
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for geom in objects:
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value = geom.intersects(Point(x, y))
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if value:
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print(highlight,end="")
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# stringl+=[highlight]
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done=True
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break
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if not done:
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objects = [n.object for n in index_all.intersection((x, y, x, y), objects=True)]
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for geom in objects:
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value = geom.intersects(Point(x, y))
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if value:
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break
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print(land if value else water, end="")
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# stringl+=[land if value else water]
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print("")
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# stringl+=['\n']
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# string = ''.join(stringl)
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# print(string)
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places = {
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'Cambridge':(52.205338,0.121817),
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'Sydney':(-33.868820,151.209290),
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'New York':(40.712776,-74.005974),
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'Hong Kong':(22.278300,114.174700),
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'Cape Town':(-33.9249, 18.4241),
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'San Francisco':(37.774929,-122.419418),
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'Honolulu':(21.306944,-157.858337),
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'Tokyo':(35.689487,139.691711),
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'Ushuaia':(-54.801910,-68.302948),
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'Reykjavik':(64.126518,-21.817438)
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}
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places_utm = {
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'Honolulu':(618431.58,2357505.97),
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'Tokyo':(394946.08,3946063.75),
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'Ushuaia':(544808.23,3927028.51),
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'Reykjavik':(459698.38,7111571.73)
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}
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def print_map_simple(places):
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size = get_terminal_size(fallback=(80, 24))
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columns = size.columns
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lines = size.lines - 1 - 3 # allow for prompt at bottom
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# calculate the projected extent and pixel size
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# xmin, ymin = (-180, -85)
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# xmax, ymax = (180, 85)
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# pixel_width = (xmax - xmin) / columns
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# pixel_height = (ymax - ymin) / lines
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long_min,long_max = -180,180
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# lat_min,lat_max = -85,85
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lat_min,lat_max = -75,80
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# utm_easting_min = 166640
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# utm_easting_max = 833360
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# utm_northing_min = 1110400
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# utm_northing_max = 9334080
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utm_easting_min = places_utm['Honolulu'][0]
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utm_easting_max = places_utm['Tokyo'][0]
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utm_northing_min = places_utm['Ushuaia'][1]
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utm_northing_max = places_utm['Reykjavik'][1]
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# import pyproj as proj
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# setup your projections
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# crs_wgs = proj.Proj(init='epsg:4326') # assuming you're using WGS84 geographic
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# crs_bng = proj.Proj(init='epsg:27700') # use a locally appropriate projected CRS
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import utm
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normed = {}
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for place,(lat,long) in places.items():
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# wgs84 = pyproj.Proj(init="EPSG:4326")
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# webmerc = pyproj.Proj(proj="webmerc")
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# x, y = proj.transform(wgs84, webmerc, long, lat)
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longx = (long - long_min) / (long_max - long_min)
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laty = (lat - lat_min) / (lat_max - lat_min)
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utm_easting,utm_northing,utm_zone_num,utm_zone_letter = utm.from_latlon(lat,long)
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utmx = (utm_easting - utm_easting_min) / (utm_easting_max - utm_easting_min)
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utmy = (utm_northing - utm_northing_min) / (utm_northing_max - utm_northing_min)
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# norm = ( int(longx*columns), int(laty*lines) )
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norm = ( int(utmx*columns), int(utmy*lines) )
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# print(place,(utm_easting,utm_northing),(utmx,utmy),norm)
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norm = (norm[0], lines - norm[1])
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normed[norm] = place
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p_i=None
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place_now=None
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for line in range(lines):
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for col in range(columns):
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if (col,line) in normed:
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print('*',end="")
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place=normed[(col,line)]
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place_now=place
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p_i=0
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elif p_i is not None:
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try:
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print(place_now[p_i],end="")
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p_i+=1
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except IndexError:
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place_now=None
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p_i=None
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else:
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print(" ",end="")
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print()
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# print_map(['Brazil','Netherlands','Thailand'])
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# print_map_simple(places)
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def print_map(places):
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curses.wrapper(run_print_map)
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def run_print_map(stdscr):
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curses.use_default_colors()
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stdscr.addstr(0,0,'helloooooo')
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stdscr.refresh()
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rows, cols = stdscr.getmaxyx()
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print(rows,cols)
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rows = rows-10
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cols = cols - 10
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df = do_print_map(places,rows,cols)
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for df_i,df_row in df.iterrows():
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#try:
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stdscr.addstr(df_row.y_win,df_row.x_win,'x '+df_row.place)
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#except curses.error:
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# pass
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stdscr.getch()
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def do_print_map(places,rows,cols):
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normed = []
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for place,(lat,long) in places.items():
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wgs84 = pyproj.Proj(init="EPSG:4326")
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webmerc = pyproj.Proj(proj="webmerc")
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x, y = pyproj.transform(wgs84, webmerc, long, lat)
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norm = {'place':place,'lat':lat,'long':long,'x':x,'y':y}
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normed.append(norm)
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import pandas as pd
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df=pd.DataFrame(normed)
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def do_norm(x,xcol): return (x - xcol.min()) / (xcol.max() - xcol.min())
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df['x_norm'] = [do_norm(x,df['x']) for x in df['x']]
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df['y_norm'] = [do_norm(y,df['y']) for y in df['y']]
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df['x_win'] = [int(x*cols) for x in df['x_norm']]
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df['y_win'] = [rows - int(y*rows) for y in df['y_norm']]
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return df
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if __name__ == '__main__':
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# do_print_map(places,60,30)
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print_map(places)
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