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"""
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+ import copy
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import math
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- import numpy as np
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+
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import matplotlib .pyplot as plt
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- import copy
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- from scipy .stats import norm
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+ import numpy as np
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from scipy .ndimage import gaussian_filter
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+ from scipy .stats import norm
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# Parameters
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EXTEND_AREA = 10.0 # [m] grid map extention length
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show_animation = True
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- class grid_map () :
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+ class GridMap :
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def __init__ (self ):
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self .data = None
@@ -117,7 +118,7 @@ def motion_model(x, u):
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def draw_heatmap (data , mx , my ):
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maxp = max ([max (igmap ) for igmap in data ])
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- plt .pcolor (mx , my , data , vmax = maxp , cmap = plt .cm .Blues )
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+ plt .pcolor (mx , my , data , vmax = maxp , cmap = plt .cm .get_cmap ( " Blues" ) )
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plt .axis ("equal" )
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@@ -157,8 +158,7 @@ def normalize_probability(gmap):
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def init_gmap (xyreso , minx , miny , maxx , maxy ):
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-
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- gmap = grid_map ()
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+ gmap = GridMap ()
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gmap .xyreso = xyreso
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gmap .minx = minx
@@ -168,7 +168,7 @@ def init_gmap(xyreso, minx, miny, maxx, maxy):
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gmap .xw = int (round ((gmap .maxx - gmap .minx ) / gmap .xyreso ))
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gmap .yw = int (round ((gmap .maxy - gmap .miny ) / gmap .xyreso ))
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- gmap .data = [[1.0 for i in range (gmap .yw )] for i in range (gmap .xw )]
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+ gmap .data = [[1.0 for _ in range (gmap .yw )] for _ in range (gmap .xw )]
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gmap = normalize_probability (gmap )
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return gmap
@@ -183,7 +183,7 @@ def map_shift(gmap, xshift, yshift):
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nix = ix + xshift
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niy = iy + yshift
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- if nix >= 0 and nix < gmap .xw and niy >= 0 and niy < gmap .yw :
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+ if 0 <= nix < gmap .xw and 0 <= niy < gmap .yw :
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gmap .data [ix + xshift ][iy + yshift ] = tgmap [ix ][iy ]
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return gmap
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