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@ -1,6 +1,6 @@
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# Code from
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# 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
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from functools import partial
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@ -10,101 +10,23 @@ 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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from komrade.utils import Logger
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import pandas as pd
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import numpy as np
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import warnings
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warnings.filterwarnings(action='ignore')
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PLACE_MARKER='@'
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BASEMAP_MARKER='_'
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PATH_MARKER='+'
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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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# # 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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# 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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default_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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@ -118,141 +40,278 @@ places = {
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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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# print_map(['Brazil','Netherlands','Thailand'])
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# print_map_simple(places)
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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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class Map(Logger):
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def __init__(self,stdscr):
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self.stdscr=stdscr
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self.base_df=None
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self.last_coords=None
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self.stdscr.clear()
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@property
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def width(self):
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return get_terminal_size().columns - 1
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# from komrade.constants import CLI_WIDTH
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# return CLI_WIDTH
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@property
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def height(self):
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return get_terminal_size().lines - 1
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# from komrade.constants import CLI_HEIGHT
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# return CLI_HEIGHT
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def precompute_basemap(self,countries=[]):
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data_fn=os.path.join(
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os.path.dirname(__file__),
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"data/world-countries.json"
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)
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with open(data_fn) as f:
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data = json.load(f)
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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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]
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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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# 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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# 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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# get the window size
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columns = self.width
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lines = self.height # 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(-170, -55)
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xmax, ymax = t(165, 75)
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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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# stringl=[]
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# os.system('cls' if os.name == 'nt' else 'clear')
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ld=[]
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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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# self.log((col,line), (x,y),'???')
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objects = [n.object for n in index.intersection((x, y, x, y), objects=True)]
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value=None
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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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d={'x':x,'y':y} #,'col':col,'row':line}
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ld+=[d]
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break
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self.stdscr.addstr(line,col,land if value else water)
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self.stdscr.refresh()
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# print(land if value else water, end="")
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# print("")
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# stringl+=['\n']
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df=pd.DataFrame(ld)
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# self.log(df,'!!')
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df['x_norm']=self.do_norm(df['x'])
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df['y_norm']=self.do_norm(df['y'])
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df.to_csv(os.path.join(os.path.dirname(data_fn),'basemap.csv'),index=False)
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# string = ''.join(stringl)
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# print(string)
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def do_norm(self,xcol):
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# self.log('<--',xcol)
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minn=xcol.min()
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maxx=xcol.max()
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xcol=pd.Series([x + minn for x in xcol])
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minn=xcol.min()
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maxx=xcol.max()
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res = [(x - minn) / (maxx - minn) for x in xcol]
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# self.log('-->',res)
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return res
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def add_base_map(self):
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# x,y coords
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self.base_df=df=pd.read_csv(os.path.join(os.path.dirname(__file__),'data/basemap.csv'))
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# self.log(df)
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# convert to screen,coords
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coords = {
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(
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int(x*self.width),
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int(y*self.height)
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)
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for x,y in zip(df.x_norm,df.y_norm)
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}
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# self.log(coords)
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# stop
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for row in range(self.width):
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for line in range(self.height):
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if (row,line) in coords:
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self.stdscr.addstr(self.height - line,row,BASEMAP_MARKER)
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self.stdscr.refresh()
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# self.stdscr.getch()
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def run_print_map(self,places=[],labels=False,msg=[],offset_y=0):
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if msg:
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for i,x in enumerate(msg):
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x='--> '+x if i else x
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self.stdscr.addstr(i,0,x)
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self.stdscr.refresh()
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self.msg=msg
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if not places: return
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df = self.do_print_map(places)
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self.log(df,'!?!?!?')
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coords = {
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(
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int(x*self.width),
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int(y*self.height)
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)
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for x,y in zip(df.x_norm,df.y_norm)
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}
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self.log('coords:',coords)
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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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for x,y in coords:
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# lines?
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self.log('xy:',x,y,self.last_coords)
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if self.last_coords:
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lx,ly=self.last_coords
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went_north = bool(ly-y)
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went_east = bool(lx-x)
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self.log(f'{lx} -> {x} (x); {ly} -> {y} (y)')
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self.log(f'went east? {went_east}; went north? {went_north}')
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path_x = list(range(lx if lx<x else x, (lx if lx>x else x)+1))
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path_y = list(range(ly if ly<y else y, (ly if ly>y else y)+1))
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if lx>x: path_x.reverse()
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if ly>y: path_y.reverse()
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self.log('path_x:',path_x)
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self.log('path_y:',path_y)
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minlen=min(len(path_x), len(path_y))
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hops_x = slice(path_x,minlen)
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hops_y = slice(path_y,minlen)
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lcoord_x=None
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lcoord_y=None
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for hop_x,hop_y in zip(hops_x,hops_y):
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self.log('hop_x',hop_x)
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self.log('hop_y',hop_y)
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hopmaxlen=max([len(hop_x),len(hop_y)])
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hopcoords=[]
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for hi in range(hopmaxlen):
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hx=hop_x[hi] if hi<len(hop_x) else hop_x[-1]
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hy=hop_y[hi] if hi<len(hop_y) else hop_y[-1]
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hopcoords+=[(hx,hy)]
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for xx,yy in hopcoords:
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ycoord=self.height - yy - offset_y
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xcoord=xx
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self.log('!?',xcoord,ycoord,self.stdscr.instr(ycoord, xcoord,1).decode(),PLACE_MARKER)
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if self.stdscr.instr(ycoord, xcoord, 1).decode() != PLACE_MARKER:
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self.stdscr.addstr(ycoord,xcoord,PATH_MARKER)
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self.stdscr.refresh()
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time.sleep(0.01)
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lcoord_x=xx
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lcoord_y=yy
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self.last_coords=(x,y)
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self.stdscr.addstr(self.height - y - offset_y,x,PLACE_MARKER)
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self.stdscr.refresh()
|
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time.sleep(.1)
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def endwin(self):
|
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# time.sleep(1)
|
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curses.endwin()
|
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def do_print_map(self,places):
|
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|
|
normed = []
|
|
|
|
|
import utm
|
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|
|
|
for place,(lat,long) in places:# .items():
|
|
|
|
|
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)
|
|
|
|
|
# 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)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
normed[norm] = place
|
|
|
|
|
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!='']
|
|
|
|
|
|
|
|
|
|
p_i=None
|
|
|
|
|
place_now=None
|
|
|
|
|
for line in range(lines):
|
|
|
|
|
for col in range(columns):
|
|
|
|
|
if (col,line) in normed:
|
|
|
|
|
print('*',end="")
|
|
|
|
|
place=normed[(col,line)]
|
|
|
|
|
place_now=place
|
|
|
|
|
p_i=0
|
|
|
|
|
elif p_i is not None:
|
|
|
|
|
try:
|
|
|
|
|
print(place_now[p_i],end="")
|
|
|
|
|
p_i+=1
|
|
|
|
|
except IndexError:
|
|
|
|
|
place_now=None
|
|
|
|
|
p_i=None
|
|
|
|
|
else:
|
|
|
|
|
print(" ",end="")
|
|
|
|
|
print()
|
|
|
|
|
|
|
|
|
|
def make_map():
|
|
|
|
|
return curses.wrapper(make_map_curses)
|
|
|
|
|
|
|
|
|
|
def make_map_curses(stdscr):
|
|
|
|
|
curses.use_default_colors()
|
|
|
|
|
map = Map(stdscr)
|
|
|
|
|
return map
|
|
|
|
|
|
|
|
|
|
# print_map(['Brazil','Netherlands','Thailand'])
|
|
|
|
|
# print_map_simple(places)
|
|
|
|
|
|
|
|
|
|
def print_map(places):
|
|
|
|
|
curses.wrapper(run_print_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]
|
|
|
|
|
|
|
|
|
|
def run_print_map(stdscr):
|
|
|
|
|
curses.use_default_colors()
|
|
|
|
|
stdscr.addstr(0,0,'helloooooo')
|
|
|
|
|
stdscr.refresh()
|
|
|
|
|
|
|
|
|
|
rows, cols = stdscr.getmaxyx()
|
|
|
|
|
print(rows,cols)
|
|
|
|
|
rows = rows-10
|
|
|
|
|
cols = cols - 10
|
|
|
|
|
|
|
|
|
|
df = do_print_map(places,rows,cols)
|
|
|
|
|
for df_i,df_row in df.iterrows():
|
|
|
|
|
#try:
|
|
|
|
|
stdscr.addstr(df_row.y_win,df_row.x_win,'x '+df_row.place)
|
|
|
|
|
#except curses.error:
|
|
|
|
|
# pass
|
|
|
|
|
stdscr.getch()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def do_print_map(places,rows,cols):
|
|
|
|
|
normed = []
|
|
|
|
|
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)
|
|
|
|
|
norm = {'place':place,'lat':lat,'long':long,'x':x,'y':y}
|
|
|
|
|
normed.append(norm)
|
|
|
|
|
|
|
|
|
|
import pandas as pd
|
|
|
|
|
df=pd.DataFrame(normed)
|
|
|
|
|
def do_norm(x,xcol): return (x - xcol.min()) / (xcol.max() - xcol.min())
|
|
|
|
|
df['x_norm'] = [do_norm(x,df['x']) for x in df['x']]
|
|
|
|
|
df['y_norm'] = [do_norm(y,df['y']) for y in df['y']]
|
|
|
|
|
df['x_win'] = [int(x*cols) for x in df['x_norm']]
|
|
|
|
|
df['y_win'] = [rows - int(y*rows) for y in df['y_norm']]
|
|
|
|
|
|
|
|
|
|
return df
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
try:
|
|
|
|
|
map=make_map()
|
|
|
|
|
map.precompute_basemap()
|
|
|
|
|
# map.add_base_map()
|
|
|
|
|
except KeyboardInterrupt:
|
|
|
|
|
map.endwin()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
|
|
|
|
|
# do_print_map(places,60,30)
|
|
|
|
|
print_map(places)
|
|
|
|
|
|
quadrismegistus
4 years ago
