2
0
mirror of https://github.com/ComradCollective/Comrad synced 2024-11-16 00:12:48 +00:00
Comrad/komrade/cli/worldmap_curses.py
quadrismegistus 3cea558b50 tor mapping!
2020-09-20 13:43:34 +01:00

318 lines
9.7 KiB
Python

# Code from
# https://github.com/snorfalorpagus/ascii-world-map
import os,sys; sys.path.append(os.path.abspath(os.path.join(os.path.abspath(os.path.join(os.path.dirname(__file__),'..')),'..')))
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
from komrade.utils import Logger
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings(action='ignore')
PLACE_MARKER='@'
BASEMAP_MARKER='_'
PATH_MARKER='+'
# # 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)
default_places = {
'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)
}
# print_map(['Brazil','Netherlands','Thailand'])
# print_map_simple(places)
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"]
]
# 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)
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)
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!='']
def make_map():
return curses.wrapper(make_map_curses)
def make_map_curses(stdscr):
curses.use_default_colors()
map = Map(stdscr)
return map
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]
if __name__ == '__main__':
try:
map=make_map()
map.precompute_basemap()
# map.add_base_map()
except KeyboardInterrupt:
map.endwin()