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Comrad/komrade/cli/worldmap_curses.py

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# Code from
# https://github.com/snorfalorpagus/ascii-world-map
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import os,sys; sys.path.append(os.path.abspath(os.path.join(os.path.abspath(os.path.join(os.path.dirname(__file__),'..')),'..')))
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import json
from functools import partial
from shutil import get_terminal_size
from shapely.geometry import shape, Point
from shapely import ops
import pyproj,math,os
import rtree
import curses,random,time
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from komrade.utils import Logger
import pandas as pd
import numpy as np
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import warnings
warnings.filterwarnings(action='ignore')
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PLACE_MARKER='@'
BASEMAP_MARKER='_'
PATH_MARKER='+'
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# # read the data into a list of shapely geometries
# with open(os.path.join(os.path.dirname(__file__),"data/world-countries2.json")) as f:
# data = json.load(f)
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default_places = {
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'Cambridge':(52.205338,0.121817),
'Sydney':(-33.868820,151.209290),
'New York':(40.712776,-74.005974),
'Hong Kong':(22.278300,114.174700),
'Cape Town':(-33.9249, 18.4241),
'San Francisco':(37.774929,-122.419418),
'Honolulu':(21.306944,-157.858337),
'Tokyo':(35.689487,139.691711),
'Ushuaia':(-54.801910,-68.302948),
'Reykjavik':(64.126518,-21.817438)
}
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# print_map(['Brazil','Netherlands','Thailand'])
# print_map_simple(places)
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class Map(Logger):
def __init__(self,stdscr):
self.stdscr=stdscr
self.base_df=None
self.last_coords=None
self.stdscr.clear()
@property
def width(self):
return get_terminal_size().columns - 1
# from komrade.constants import CLI_WIDTH
# return CLI_WIDTH
@property
def height(self):
return get_terminal_size().lines - 1
# from komrade.constants import CLI_HEIGHT
# return CLI_HEIGHT
def precompute_basemap(self,countries=[]):
data_fn=os.path.join(
os.path.dirname(__file__),
"data/world-countries.json"
)
with open(data_fn) as f:
data = json.load(f)
geoms = [
shape(feature["geometry"])
for feature in data["features"]
]
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# transform the geometries into web mercator
wgs84 = pyproj.Proj(init="EPSG:4326")
webmerc = pyproj.Proj(proj="webmerc")
t = partial(pyproj.transform, wgs84, webmerc)
geoms = [ops.transform(t, geom) for geom in geoms]
# create a spatial index of the geometries
def gen(geoms):
for n, geom in enumerate(geoms):
yield n, geom.bounds, geom
index = rtree.index.Index(gen(geoms))
# get the window size
columns = self.width
lines = self.height # allow for prompt at bottom
# calculate the projected extent and pixel size
# xmin, ymin = t(-180, -85)
# xmax, ymax = t(180, 85)
xmin, ymin = t(-170, -55)
xmax, ymax = t(165, 75)
pixel_width = (xmax - xmin) / columns
pixel_height = (ymax - ymin) / lines
land = "*"
water = " "
# stringl=[]
# os.system('cls' if os.name == 'nt' else 'clear')
ld=[]
for line in range(lines):
for col in range(columns):
# get the projected x, y of the pixel centroid
x = xmin + (col + 0.5) * pixel_width
y = ymax - (line + 0.5) * pixel_height
# check for a collision
# self.log((col,line), (x,y),'???')
objects = [n.object for n in index.intersection((x, y, x, y), objects=True)]
value=None
for geom in objects:
value = geom.intersects(Point(x, y))
if value:
d={'x':x,'y':y} #,'col':col,'row':line}
ld+=[d]
break
self.stdscr.addstr(line,col,land if value else water)
self.stdscr.refresh()
# print(land if value else water, end="")
# print("")
# stringl+=['\n']
df=pd.DataFrame(ld)
# self.log(df,'!!')
df['x_norm']=self.do_norm(df['x'])
df['y_norm']=self.do_norm(df['y'])
df.to_csv(os.path.join(os.path.dirname(data_fn),'basemap.csv'),index=False)
# string = ''.join(stringl)
# print(string)
def do_norm(self,xcol):
# self.log('<--',xcol)
minn=xcol.min()
maxx=xcol.max()
xcol=pd.Series([x + minn for x in xcol])
minn=xcol.min()
maxx=xcol.max()
res = [(x - minn) / (maxx - minn) for x in xcol]
# self.log('-->',res)
return res
def add_base_map(self):
# x,y coords
self.base_df=df=pd.read_csv(os.path.join(os.path.dirname(__file__),'data/basemap.csv'))
# self.log(df)
# convert to screen,coords
coords = {
(
int(x*self.width),
int(y*self.height)
)
for x,y in zip(df.x_norm,df.y_norm)
}
# self.log(coords)
# stop
for row in range(self.width):
for line in range(self.height):
if (row,line) in coords:
self.stdscr.addstr(self.height - line,row,BASEMAP_MARKER)
self.stdscr.refresh()
# self.stdscr.getch()
def run_print_map(self,places=[],labels=False,msg=[],offset_y=0):
if msg:
for i,x in enumerate(msg):
x='--> '+x if i else x
self.stdscr.addstr(i,0,x)
self.stdscr.refresh()
self.msg=msg
if not places: return
df = self.do_print_map(places)
self.log(df,'!?!?!?')
coords = {
(
int(x*self.width),
int(y*self.height)
)
for x,y in zip(df.x_norm,df.y_norm)
}
self.log('coords:',coords)
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for x,y in coords:
# lines?
self.log('xy:',x,y,self.last_coords)
if self.last_coords:
lx,ly=self.last_coords
went_north = bool(ly-y)
went_east = bool(lx-x)
self.log(f'{lx} -> {x} (x); {ly} -> {y} (y)')
self.log(f'went east? {went_east}; went north? {went_north}')
path_x = list(range(lx if lx<x else x, (lx if lx>x else x)+1))
path_y = list(range(ly if ly<y else y, (ly if ly>y else y)+1))
if lx>x: path_x.reverse()
if ly>y: path_y.reverse()
self.log('path_x:',path_x)
self.log('path_y:',path_y)
minlen=min(len(path_x), len(path_y))
hops_x = slice(path_x,minlen)
hops_y = slice(path_y,minlen)
lcoord_x=None
lcoord_y=None
for hop_x,hop_y in zip(hops_x,hops_y):
self.log('hop_x',hop_x)
self.log('hop_y',hop_y)
hopmaxlen=max([len(hop_x),len(hop_y)])
hopcoords=[]
for hi in range(hopmaxlen):
hx=hop_x[hi] if hi<len(hop_x) else hop_x[-1]
hy=hop_y[hi] if hi<len(hop_y) else hop_y[-1]
hopcoords+=[(hx,hy)]
for xx,yy in hopcoords:
ycoord=self.height - yy - offset_y
xcoord=xx
self.log('!?',xcoord,ycoord,self.stdscr.instr(ycoord, xcoord,1).decode(),PLACE_MARKER)
if self.stdscr.instr(ycoord, xcoord, 1).decode() != PLACE_MARKER:
self.stdscr.addstr(ycoord,xcoord,PATH_MARKER)
self.stdscr.refresh()
time.sleep(0.01)
lcoord_x=xx
lcoord_y=yy
self.last_coords=(x,y)
self.stdscr.addstr(self.height - y - offset_y,x,PLACE_MARKER)
self.stdscr.refresh()
time.sleep(.1)
def endwin(self):
# time.sleep(1)
curses.endwin()
def do_print_map(self,places):
normed = []
import utm
for place,(lat,long) in places:# .items():
wgs84 = pyproj.Proj(init="EPSG:4326")
webmerc = pyproj.Proj(proj="webmerc")
x, y = pyproj.transform(wgs84, webmerc, long, lat)
# x,y,_,_ = utm.from_latlon(lat,long)
norm = {'place':place,'lat':lat,'long':long,'x':x,'y':y}
normed.append(norm)
self.log('norm:',norm)
import pandas as pd
df=pd.DataFrame(normed)#.dropna()
df=df.append(self.base_df).fillna('') # add basemap!
df=df[['place','x','y']]
self.log(df,'with basemap')
df=df[~df.isin([np.nan, np.inf, -np.inf]).any(1)]
# self.log('normed',df)
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df['x_norm'] = self.do_norm(df.x)
df['y_norm'] = self.do_norm(df.y)
# self.log('NORMED\n',df)
self.log('nas dropped',df)
return df[df.place!='']
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def make_map():
return curses.wrapper(make_map_curses)
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def make_map_curses(stdscr):
curses.use_default_colors()
map = Map(stdscr)
return map
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def slice(l,num_slices=None,slice_length=None,runts=True,random=False):
"""
Returns a new list of n evenly-sized segments of the original list
"""
if random:
import random
random.shuffle(l)
if not num_slices and not slice_length: return l
if not slice_length: slice_length=int(len(l)/num_slices)
newlist=[l[i:i+slice_length] for i in range(0, len(l), slice_length)]
if runts: return newlist
return [lx for lx in newlist if len(lx)==slice_length]
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if __name__ == '__main__':
try:
map=make_map()
map.precompute_basemap()
# map.add_base_map()
except KeyboardInterrupt:
map.endwin()
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