def __init__(self, x, y, ax, msize=1, area=None, **kwargs):
marker_size = 50
# change markers if area is defined.
# solution taken from here:
# https://stackoverflow.com/questions/52303660/iterating-markers-in-plots/52303895#52303895
# create array for linewidth corresponding to intensity
if area is not None:
area_sub = (area - min(area))
area_norm = area_sub / max(area_sub)
marker_linewidth = 2 + 13 * (1 - area_norm)
else:
# create frame like marker
marker_linewidth = 5 * np.ones_like(y)
# create all paths
frames = []
for marker_lw in marker_linewidth:
marker_rest = marker_size - 2 * marker_lw
markerstring = (f'm 0,0 v 0 {marker_size} h {marker_size} '
f'v -{marker_size} '
f'z m {marker_lw},{marker_lw} '
f'h {marker_rest} v {marker_rest} '
f'h -{marker_rest} z')
frame = parse_path(markerstring)
frames.append(frame)
self.n = len(x)
self.ax = ax
self.ax.figure.canvas.draw()
self.size_data = msize
self.size = msize
self.sc = self.mscatter(x, y, s=self.size, m=frames, **kwargs)
self._resize()
self.cid = ax.figure.canvas.mpl_connect('draw_event', self._resize)
def get_baseline_end(self,
glyph_type,
position,
rotation=0.0,
user_parameters=None):
glyph = self.glyphs_library[glyph_type]
merged_parameters = glyph['defaults'].copy()
if user_parameters is not None:
# Collate parameters (user parameters take priority)
for key in user_parameters.keys():
merged_parameters[key] = user_parameters[key]
baseline_path = None
for path in glyph['paths']:
if path['class'] == 'baseline':
svg_text = self.__eval_svg_data(path['d'], merged_parameters)
baseline_path = svgpath2mpl.parse_path(svg_text)
break
if baseline_path is not None:
# Draw glyph to the axis with correct styling parameters
baseline_y = glyph['defaults']['baseline_y']
y_flipped_path = self.__flip_position_rotate_glyph(
baseline_path, baseline_y, position, rotation)
return (y_flipped_path.vertices[1, 0], y_flipped_path.vertices[1,
1])
else:
return None
def __init__(self, angle=None, path_string=None):
self.angle = angle or []
self.path_string = path_string or """m 66.893258,227.10128 h 5.37899 v 0.91881 h 1.65571 l 1e-5,-3.8513 3.68556,-1e-5 v -1.43933
l -2.23863,10e-6 v -2.73937 l 5.379,-1e-5 v 2.73938 h -2.23862 v 1.43933 h 3.68556 v 8.60486 l -3.68556,1e-5 v 1.43158
h 2.23862 v 2.73989 h -5.37899 l -1e-5,-2.73989 h 2.23863 v -1.43159 h -3.68556 v -3.8513 h -1.65573 l 1e-5,0.91881 h -5.379 z"""
self.path = parse_path( self.path_string )
self.path.vertices -= self.path.vertices.mean( axis=0 )
self.marker = mpl.markers.MarkerStyle( marker=self.path )
self.marker._transform = self.marker.get_transform().rotate_deg(angle)
def get_rawpaths_to_draw(self, glyph, merged_params):
new_paths_to_draw = []
for path in glyph['paths']:
if path['type'] not in ['baseline']:
svg_text = self.eval_svg_data(path['d'], merged_params)
new_paths_to_draw.append(svg2mpl.parse_path(svg_text))
for circle in glyph['circles']:
new_paths_to_draw.append(self.get_circle_path(circle))
return new_paths_to_draw
def load_svg(path):
"""
"""
paths, attributes = svg2paths(path)
result = ddd.group2()
for k, v in enumerate(attributes):
#print(v) # v['d'] # print d-string of k-th path in SVG
# Ex svgpath = 'M10 10 C 20 20, 40 20, 50 10Z'
mpl_path = parse_path(v['d'])
'''
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(200, 200))
ax = fig.add_subplot(111)
ax.axis([0, 0, 200, 200])
collection = matplotlib.collections.PathCollection([mpl_path])
collection.set_transform(ax.transData)
#patch = matplotlib.patches.PathPatch(mpl_path, facecolor="red", lw=2)
ax.add_artist(collection)
#ax.add_patch(patch)
ax.set_xlim([0, 200])
ax.set_ylim([200, 0])
plt.show()
'''
coords = mpl_path.to_polygons(closed_only=True)
item = ddd.polygon(coords[0]).clean() #.convex_hull()
for c in coords[1:]:
ng = ddd.polygon(c).clean() #.convex_hull()
#ng.show()
#print (ng.geom.is_valid)
#if not ng.geom.is_valid: continue
if item.contains(ng):
item = item.subtract(ng)
else:
item = item.union(ng)
#result = ddd.group([ddd.polygon(c) for c in coords], empty=2)
result.append(item)
#result = result.scale([1.0 / (48 * 64), -1.0 / (48 * 64)])
#result = result.simplify(0.005) #
#result.show()
result = result.union().scale([1, -1]).clean(0)
xmin, ymin, xmax, ymax = result.bounds()
result = result.translate([0, -(ymin + ymax)])
#result = ddd.align.anchor(result, ddd.ANCHOR_CENTER)
return result
def test_parse_path():
path = parse_path(d)
patch = mpl.patches.PathPatch(path,
facecolor=fill,
edgecolor=stroke,
linewidth=stroke_width)
fig = plt.figure(figsize=(12, 5.25))
ax = fig.add_subplot(111)
ax.add_patch(patch)
ax.set_aspect(1)
ax.set_xlim([0, 1200])
ax.set_ylim([0, 400])
def open(self):
# read the sveg file
doc = minidom.parse(self._filename)
path_strings = [
path.getAttribute('d') for path in doc.getElementsByTagName('path')
]
doc.unlink()
# the path_strings will now be an array of strings containing coords
self._paths = []
for entry in path_strings:
path = svgpath2mpl.parse_path(entry)
self._paths.append(path)
def parse_svg_into_mpl(svg_path):
mpl_path = parse_path(svg_path)
def center(mpl_path):
mpl_path.vertices -= mpl_path.vertices.mean(axis=0)
return mpl_path
def upsidedown(mpl_path):
mpl_path.vertices[:, 1] *= -1
return mpl_path
return upsidedown(center(mpl_path))
def get_glyph_patch(path_data, color, x, y, dx, dy, **kwargs):
kwargs.setdefault('facecolor', color)
kwargs.setdefault('edgecolor', 'none')
path = parse_path(path_data)
# normalize and flip upside down
path.vertices[:, 0] -= path.vertices[:, 0].min()
path.vertices[:, 1] -= path.vertices[:, 1].min()
path.vertices[:, 0] /= path.vertices[:, 0].max()
path.vertices[:, 1] /= path.vertices[:, 1].max()
path.vertices[:, 1] = 1 - path.vertices[:, 1]
# scale then translate
path.vertices *= [dx, dy]
path.vertices += [x, y]
return PathPatch(path, **kwargs)
def add_fish(ax, offset=(0, 0), scale=1):
"""Plot the siluhette of a fish."""
path_fish = "m0 0c-13.119 71.131-12.078 130.72-12.078 138.78-5.372 8.506-3.932 18.626-3.264 23.963-6.671 1.112-2.891 4.002-2.891 5.114s-2.224 8.005.445 9.116c-.223 3.113.222 0 0 1.557-.223 1.556-3.558 3.558-2.891 8.227.667 4.67 3.558 10.228 6.226 9.784 2.224 4.892 5.559 4.669 7.56 4.447 2.001-.223 8.672-.445 10.228-6.004 5.115-1.556 5.562-4.002 5.559-6.67-.003-3.341.223-8.45-3.113-12.008 3.336-4.224.667-13.786-3.335-13.786 1.59-8.161-2.446-13.786-3.558-20.679-2.223-34.909-.298-102.74 1.112-141.84"
path = parse_path(path_fish)
min_p = np.min(path.vertices, 0)
path.vertices -= min_p
f = np.abs(path.vertices[:, 1]).max() * scale
path.vertices[:, 0] = path.vertices[:, 0] / f
path.vertices[:, 1] = path.vertices[:, 1] / f
path.vertices += np.array(offset)
collection = collections.PathCollection([path],
linewidths=0,
facecolors=["#909090"])
ax.add_artist(collection)
def test_parse_path():
d = "M300,200 h-150 a150,150 0 1,0 150,-150 z"
fill = "red"
stroke = "blue"
stroke_width = 5
path = parse_path(d)
patch = mpl.patches.PathPatch(path,
facecolor=fill,
edgecolor=stroke,
linewidth=stroke_width)
fig = plt.figure(figsize=(12, 5.25))
ax = fig.add_subplot(111)
ax.add_patch(patch)
ax.set_aspect(1)
ax.set_xlim([0, 1200])
ax.set_ylim([0, 400])
def assign_nested_coords(
slices: List[Slice], svg_file: str, bread_box: BoundingBox
) -> List[Slice]:
Y_MAX = 210
L_x = int(bread_box.width)
L_y = int(bread_box.height)
L_y_mm = _NEST_L * L_y / L_x / _MM_IN_COORD_UNITS
reverse_scaler = _MM_IN_COORD_UNITS * L_x / _NEST_L
tree = etree.parse(svg_file)
root = tree.getroot()
path_elems = root.findall(".//{http://www.w3.org/2000/svg}path")
paths = [parse_path(elem.attrib["d"]) for elem in path_elems]
# Rotation matrix to rotate to bread box angle
rotation_mat = np.array(
[
[np.cos(bread_box.angle), -np.sin(bread_box.angle)],
[np.sin(bread_box.angle), np.cos(bread_box.angle)],
]
)
for idx, (slice_, path) in enumerate(zip(slices, paths)):
# Translate to origin
vertices = np.array(path.vertices)
vertices[:, 1] -= Y_MAX - L_y_mm
# Scale to original image size
scaled = vertices * reverse_scaler
# Rotate polygons so that they're back at the bread box angle
# dot product is 2 x 2 * 2 x num_points -> 2 x num_points
# Transpose that, and we're back to num_points x 2
scaled = np.dot(rotation_mat, scaled.T).T
# Translate to bread
# scaled[:, 0] += bread.box[0]
# scaled[:, 1] += bread.box[1]
scaled[:, 0] += bread_box.reference[0]
scaled[:, 1] += bread_box.reference[1]
slice_.nested_coords = scaled
return slices
def open(self):
# read the svg file
if os.path.exists(self._filename):
doc = minidom.parse(self._filename)
else:
# see if we can find a built in version
local_content = pkgutil.get_data('frc1678.limeplotter.loader', 'svgs/' + self._filename)
doc = minidom.parseString(local_content)
path_strings = [path.getAttribute('d') for path
in doc.getElementsByTagName('path')]
doc.unlink()
# the path_strings will now be an array of strings containing coords
self._paths = []
for entry in path_strings:
path = svgpath2mpl.parse_path(entry)
self._paths.append(path)
def draw_glyph(self,
ax,
glyph_type,
position,
rotation=0.0,
user_parameters=None,
user_style=None):
glyph = self.glyphs_library[glyph_type]
merged_parameters = glyph['defaults'].copy()
if user_parameters is not None:
# Collate parameters (user parameters take priority)
for key in user_parameters.keys():
merged_parameters[key] = user_parameters[key]
paths_to_draw = []
for path in glyph['paths']:
if path['class'] not in ['baseline', 'bounding-box']:
merged_style = path['style']
if path['id'] is not None and user_style is not None and path[
'id'] in user_style.keys():
# Merge the styling dictionaries (user takes preference)
merged_style = user_style[path['id']].copy()
for k in path['style'].keys():
if k not in merged_style.keys():
merged_style[k] = path['style'][k]
svg_text = self.__eval_svg_data(path['d'], merged_parameters)
paths_to_draw.append(
[svgpath2mpl.parse_path(svg_text), merged_style])
# Draw glyph to the axis with correct styling parameters
baseline_y = glyph['defaults']['baseline_y']
all_y_flipped_paths = []
for path in paths_to_draw:
y_flipped_path = self.__flip_position_rotate_glyph(
path[0], baseline_y, position, rotation)
all_y_flipped_paths.append([y_flipped_path])
patch = patches.PathPatch(y_flipped_path, **path[1])
if ax is not None:
ax.add_patch(patch)
return self.__bounds_from_paths_to_draw(
all_y_flipped_paths), self.get_baseline_end(
glyph_type,
position,
rotation=rotation,
user_parameters=user_parameters)
def get_hurricane_marker():
hurricane = parse_path(
"""M 188.79857,492.55018 L 180.09663,484.17671 L 188.57725,474.97463
C 212.44187,449.07984 230.37031,423.34927 246.04359,392.5
C 254.14781,376.5487 265.0005,350.78866 265.0005,347.50373
C 265.0005,346.53236 263.21255,347.40666 259.7505,350.07094
C 251.67361,356.28665 233.85001,364.64767 222.5005,367.54485
C 204.24051,372.20605 178.92084,371.97166 159.45635,366.96123
C 147.77122,363.95331 130.93184,355.3283 122.0005,347.77659
C 95.11018,325.04006 81.65749,291.36529 81.74139,247
C 81.78993,221.33674 85.91479,197.1747 94.55247,171.95714
C 111.06665,123.74428 136.98179,82.210848 180.29075,34.54693
L 185.6999,28.59386 L 189.6002,31.718323
C 191.74536,33.436777 195.9159,37.308373 198.86805,40.32187
L 204.23561,45.800955 L 193.66355,57.483549
C 168.69038,85.080007 151.53704,109.91644 136.8182,139.79028
C 130.67851,152.2516 118.91503,180.17836 119.52809,180.83739
C 119.70071,181.02295 122.91512,178.62979 126.67122,175.51926
C 144.84799,160.46658 171.06913,152.9127 200.0005,154.39429
C 227.96505,155.82638 249.78837,164.40176 267.15103,180.78081
C 291.49094,203.74185 302.41509,234.21538 302.36063,279
C 302.33536,299.77768 300.97355,312.12979 296.41891,332.89349
C 286.70405,377.18157 262.85893,424.36347 228.55502,467.17452
C 219.26505,478.76833 199.25099,501.02345 198.17004,500.96183
C 197.80179,500.94084 193.58463,497.15559 188.79857,492.55018
z M 212.92994,343.99452 C 242.28307,336.85605 266.31414,312.68729
273.9846,282.59004 C 276.76052,271.6979 276.75301,253.72727 273.96762,242
C 266.78666,211.76606 241.98871,187.12253 211.5005,179.92186
C 203.8953,178.12567 200.40831,177.86988 189.0005,178.27134
C 173.93019,178.80168 167.30498,180.26871 156.08925,185.55888
C 132.8924,196.50023 116.23621,216.81521 109.90648,241.88639
C 108.09535,249.06004 107.84969,252.38603 108.2077,264.88639
C 108.58615,278.10034 108.93262,280.39476 111.82513,288.842
C 113.58452,293.98009 116.23139,300.28009 117.70707,302.842
C 137.50495,337.21285 174.70639,353.29022 212.92994,343.99452 z""")
return hurricane
def main():
popu = input("Enter a number for the initial population: ")
if popu != '':
INITIAL_POPULATION = int(popu)
food = input("Enter a number for the food amount: ")
if food != '':
FOOD_PER_STEP = int(food)
carrot = parse_path(
"""m 1.258156,32.01557 c -2.03400005,2.607 -1.15300005,5.051 -0.45800005,5.904 1.52000005,2.286 3.61800005,3.695 6.23600005,4.188 0.207,0.039 0.418,0.052 0.627,0.078 -0.27,0.227 -0.529,0.455 -0.773,0.685 -1.757,1.663 -2.6,3.44 -2.505,5.282 0.146,2.819 2.484,4.646 2.584,4.723 l 0.047,0.034 c 4.119,2.797 8.743,-0.588 8.938,-0.734 l 0.176,-0.168 c 0.072,-0.088 0.141,-0.191 0.212,-0.282 0.452,2.319 1.705,4.163 3.73,5.479 0.475,0.38 3.506,2.62 7.043,0.823 0.438,-0.223 0.834,-0.511 1.208,-0.827 1.036,0.293 2.188,0.381 3.33,-0.023 1.552,-0.55 2.568,-1.871 2.718,-3.535 0.16,-1.769 -0.732,-3.482 -2.271,-4.365 -1.409,-0.808 -3.097,-2.304 -4.665,-5.047 0.933,0.064 1.835,-0.035 2.687,-0.342 5.081,-1.825 25.176999,-31.267 28.741999,-40.8279992 0.3,-0.806 0.144,-1.649 -0.409,-2.20300001 -0.367,-0.367 -0.862,-0.56 -1.39,-0.56 -0.267,0 -0.542,0.049 -0.813,0.15 C 46.623156,4.0375708 17.061156,24.03357 15.266156,29.03257 c -0.245,0.683 -0.361,1.399 -0.37,2.136 -2.742,-1.343 -4.192,-2.65 -4.948,-3.688 -1.102,-1.512 -2.879,-2.446 -4.651,-2.446 -0.019,0 -0.038,0 -0.057,0 -1.477,0.017 -2.788,0.695 -3.598,1.86 -1.20400005,1.734 -0.77700005,3.606 -0.135,4.83 -0.085,0.098 -0.173,0.193 -0.249,0.291 z m 1.16,4.732 c -0.114,-0.149 -1.049,-1.483 0.3,-3.347 0.042,0.05 0.086,0.095 0.129,0.144 0.073,0.084 0.146,0.165 0.22,0.247 0.189,0.211 0.379,0.414 0.572,0.609 0.059,0.06 0.118,0.12 0.177,0.178 3.13,3.066 6.698,4.031 9.936,4.031 1.148,0 2.255,-0.121 3.286,-0.31 -0.111,0.05 -0.226,0.1 -0.339,0.149 -0.157,0.069 -0.314,0.138 -0.475,0.206 -0.173,0.073 -0.348,0.144 -0.526,0.214 -0.148,0.059 -0.3,0.116 -0.451,0.173 -0.278,0.104 -0.56,0.205 -0.847,0.301 -0.179,0.06 -0.358,0.12 -0.539,0.175 -0.175,0.054 -0.353,0.105 -0.53,0.155 -0.197,0.055 -0.395,0.106 -0.594,0.155 -0.195,0.048 -0.391,0.095 -0.588,0.137 -0.276,0.059 -0.553,0.111 -0.831,0.155 -0.15,0.024 -0.3,0.044 -0.45,0.063 -1.174,0.152 -2.349,0.167 -3.466,-0.042 -2.096,-0.395 -3.711,-1.484 -4.984,-3.393 z m 12.247,13.89 c -0.582,0.397 -3.809,2.431 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-1.027,1.585 -1.558,2.245 z m 11.506,5.625 c -2.549,1.269 -4.77,-0.552 -4.86,-0.627 -0.035,-0.03 -0.071,-0.057 -0.109,-0.082 -1.609,-1.033 -2.56,-2.422 -2.906,-4.245 -0.148,-0.778 -0.17,-1.594 -0.109,-2.424 l 0.007,-0.09 c 0.13,-1.575 0.579,-3.198 1.149,-4.707 -0.041,0.959 0.005,1.947 0.167,2.941 0.005,0.031 0.012,0.061 0.017,0.091 0.037,0.215 0.078,0.43 0.126,0.646 0.029,0.132 0.063,0.264 0.097,0.395 0.028,0.108 0.056,0.217 0.088,0.325 0.051,0.179 0.106,0.357 0.166,0.535 0.016,0.048 0.034,0.097 0.051,0.145 0.49,1.399 1.266,2.769 2.419,4.049 0.032,0.035 0.064,0.071 0.096,0.106 0.165,0.179 0.334,0.356 0.514,0.53 0.087,0.085 0.18,0.169 0.271,0.253 0.139,0.128 0.275,0.257 0.423,0.382 0.269,0.231 0.55,0.459 0.849,0.682 0.002,0.002 0.009,0.008 0.012,0.01 0.08,0.07 0.256,0.215 0.507,0.403 0.002,10e-4 0.003,0.003 0.005,0.004 0.256,0.191 0.589,0.42 0.985,0.655 0.013,0.009 0.023,0.016 0.035,0.023 z m 4.933,-5.25 c 0.864,0.495 1.364,1.457 1.275,2.45 -0.038,0.415 -0.249,1.424 -1.394,1.829 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)
carrot.vertices -= carrot.vertices.mean(axis=0)
smiley = parse_path(
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)
smiley.vertices -= smiley.vertices.mean(axis=0)
game = Game(FOOD_PER_STEP, INITIAL_POPULATION)
print("Initial state ")
print("Population: ", len(game.rabbits))
i = 1
pop = len(game.rabbits)
fig, axs = plt.subplots(2, 2)
fig.suptitle('Natural Selection Simulation')
its = np.array([0])
speeds = np.array([1.0])
sizes = np.array([1.0])
pops = np.array([INITIAL_POPULATION])
xc = np.array([r.posx for r in game.rabbits])
yc = np.array([r.posy for r in game.rabbits])
ss = np.array([r.size for r in game.rabbits])
xp = np.array([r.posx for r in game.foods])
yp = np.array([r.posy for r in game.foods])
axs[0][0].title.set_text("\n\nDistribution")
axs[0][0].scatter(xc, yc, s=ss * 200, color='black', marker=smiley)
axs[0][0].scatter(xp, yp, s=50.0, marker=carrot, color='orange')
axs[0][0].set_facecolor('mediumseagreen')
axs[0][0].set_xlim([0, GAME_X])
axs[0][0].set_ylim([0, GAME_Y])
axs[0][1].plot(its, speeds, 'b')
axs[0][1].set_xlim([0, 100])
axs[0][1].set_xlabel('Iterations')
axs[0][1].set_ylabel('Avg. speed')
axs[0][1].set_ylim([0, 3])
axs[1][0].plot(its, sizes, 'g')
axs[1][0].set_xlim([0, 100])
axs[1][0].set_ylim([0, 5])
axs[1][0].set_xlabel('Iterations')
axs[1][0].set_ylabel('Avg. size')
axs[1][1].plot(its, pops, 'r')
axs[1][1].set_xlim([0, 100])
axs[1][1].set_ylim([0, 500])
axs[1][1].set_xlabel('Iterations')
axs[1][1].set_ylabel('Population')
plt.draw()
while True > 0:
if len(game.rabbits) == 0:
plt.pause(0.1)
continue
plt.pause(0.1)
game.movement()
game.clean()
game.reproduce()
game.clean()
print("Iteration #", i)
sum_speed = 0
sum_size = 0
sum_energy = 0
for r in game.rabbits:
sum_speed += r.speed
sum_size += r.size
sum_energy += r.energy_level
its = np.append(its, i)
speeds = np.append(speeds, sum_speed / pop)
sizes = np.append(sizes, sum_size / pop)
pops = np.append(pops, pop)
xc = np.array([r.posx for r in game.rabbits])
yc = np.array([r.posy for r in game.rabbits])
ss = np.array([r.size for r in game.rabbits])
xp = np.array([r.posx for r in game.foods])
yp = np.array([r.posy for r in game.foods])
print(
" Population: {} Avg. speed: {:.4f} Avg. size: {:.4f} Avg. energy: {:.4f}"
.format(pop, sum_speed / pop, sum_size / pop, sum_energy / pop))
print(" SD. speed: {:.4f} SD. sizes: {:.4f}".format(
np.std(speeds), np.std(sizes)))
axs[0][0].clear()
axs[0][0].scatter(xc, yc, s=ss * 200.0, color='black', marker=smiley)
axs[0][0].scatter(xp, yp, s=50.0, marker=carrot, color='orange')
axs[0][0].set_xticklabels([])
axs[0][0].set_yticklabels([])
axs[0][1].plot(its, speeds, 'b')
axs[0][1].set_xlim([0, max(NUM_ITERATIONS, i + 10)])
axs[0][1].set_ylim([0, max(3, max(speeds) + 1)])
axs[1][0].plot(its, sizes, 'g')
axs[1][0].set_xlim([0, max(NUM_ITERATIONS, i + 10)])
axs[1][0].set_ylim([0, max(3, max(sizes) + 1)])
axs[1][1].plot(its, pops, 'r')
axs[1][1].set_xlim([0, max(NUM_ITERATIONS, i + 10)])
axs[1][1].set_ylim([0, max(10, max(pops) + 100)])
plt.draw()
if i == 1:
time.sleep(1)
i += 1
pop = len(game.rabbits)
"id": [utils.get_id(i) for i in p],
"labels": l
}
print([utils.get_id(i) for i in p])
# Editor Interface Plot
ng = pd.read_csv('Database/NGC.csv')
ms = pd.read_csv('Database/messier_objects.csv')
cb = pd.read_csv('Database/constellation_borders.csv')
ty1 = pd.read_csv('Database/tycho-1.csv')
ty2 = pd.read_csv('Database/tycho-2.csv')
ga = parse_path(
"""M 490.60742,303.18917 A 276.31408,119.52378 28.9 0 1 190.94051,274.29027 276.31408,119.52378 28.9 0 1 6.8010582,36.113705 276.31408,119.52378 28.9 0 1 306.46799,65.012613 276.31408,119.52378 28.9 0 1 490.60742,303.18917 Z"""
)
ga.vertices -= ga.vertices.mean(axis=0)
# red
cl = parse_path(
"""M 541.64941,265.49102 A 270.8247,265.49102 0 0 1 270.82471,530.98205 270.8247,265.49102 0 0 1 0,265.49102 270.8247,265.49102 0 0 1 270.82471,0 270.8247,265.49102 0 0 1 541.64941,265.49102 Z"""
)
cl.vertices -= cl.vertices.mean(axis=0)
# yellow
pn2 = parse_path(
"""m 0,326.75709 v 18.09653 h 671.61069 v -18.09653 z m 326.7571,344.85359 h 18.0965 V 0 h -18.0965 z"""
)
pn2.vertices -= pn2.vertices.mean(axis=0)
# black
def char(ch):
def tuple_to_imag(t):
return t[0] + t[1] * 1j
from freetype import Face
#face = Face('/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf')
face = Face(ddd.DATA_DIR + '/fonts/OpenSansEmoji.ttf')
face.set_char_size(48 * 64)
face.load_char(ch)
#kerning = face.get_kerning(ch, 'x') # or from previous, actually?
#print(kerning)
outline = face.glyph.outline
y = [t[1] for t in outline.points]
# flip the points
outline_points = [(p[0], max(y) - p[1]) for p in outline.points]
start, end = 0, 0
paths = []
for i in range(len(outline.contours)):
end = outline.contours[i]
points = outline_points[start:end + 1]
points.append(points[0])
tags = outline.tags[start:end + 1]
tags.append(tags[0])
segments = [
[
points[0],
],
]
for j in range(1, len(points)):
segments[-1].append(points[j])
if tags[j] and j < (len(points) - 1):
segments.append([
points[j],
])
for segment in segments:
if len(segment) == 2:
paths.append(
Line(start=tuple_to_imag(segment[0]),
end=tuple_to_imag(segment[1])))
elif len(segment) == 3:
paths.append(
QuadraticBezier(start=tuple_to_imag(segment[0]),
control=tuple_to_imag(segment[1]),
end=tuple_to_imag(segment[2])))
elif len(segment) == 4:
C = ((segment[1][0] + segment[2][0]) / 2.0,
(segment[1][1] + segment[2][1]) / 2.0)
paths.append(
QuadraticBezier(start=tuple_to_imag(segment[0]),
control=tuple_to_imag(segment[1]),
end=tuple_to_imag(C)))
paths.append(
QuadraticBezier(start=tuple_to_imag(C),
control=tuple_to_imag(segment[2]),
end=tuple_to_imag(segment[3])))
start = end + 1
path = Path(*paths)
#wsvg(path, filename="/tmp/test.svg")
path_d = path.d()
# https://gis.stackexchange.com/questions/301605/how-to-create-shape-in-shapely-from-an-svg-path-element
# This page also has info about SVG reading!
from svgpath2mpl import parse_path
#svgpath = 'M10 10 C 20 20, 40 20, 50 10Z'
mpl_path = parse_path(path_d)
coords = mpl_path.to_polygons()
# Add or subtract
char_2d = ddd.polygon(coords[0])
for c in coords[1:]:
ng = ddd.polygon(c)
#print (ng.geom.is_valid)
if not ng.geom.is_valid: continue
if char_2d.contains(ng):
char_2d = char_2d.subtract(ng)
else:
char_2d = char_2d.union(ng)
#result = ddd.group([ddd.polygon(c) for c in coords], empty=2)
result = char_2d
result = result.scale([1.0 / (48 * 64), -1.0 / (48 * 64)])
result = result.simplify(0.005) #
return result
def visualize2D(tick, obs, params, robots, robot1, centroid, traj_global):
"""
Visualization: transition to sub pub is needed
"""
draw_map(obs)
# draw_gradient(robots[1].U) if params.num_robots > 1 \
# else draw_gradient(robots[0].U)
smiley = parse_path(
"""M458 2420 c-215 -38 -368 -257 -329 -469 34 -182 175 -314 354 -329 l57 -4 0 45 0 44 -42 7 c-101 16 -187 79 -236 171 -37 69 -38 187 -4 257 30 60 90 120 150 150 70 34 188 33 258 -4 89 -47 153 -136 169 -235 l7 -43 50 0 51 0 -6 59 c-13 147 -124 285 -268 334 -60 20 -152 28 -211 17z M1940 2417 c-172 -39 -302 -181 -317 -347 l-6 -60 51 0 50 0 12 52 c14 70 49 126 110 181 118 106 284 100 399 -14 64 -64 86 -120 86 -214 0 -67 -5 -88 -27 -130 -49 -92 -135 -155 -236 -171 l-42 -7 0 -49 0 -50 58 4 c115 8 242 91 306 200 36 61 59 177 51 248 -30 244 -260 410 -495 357z M506 2038 c-9 -12 -16 -41 -16 -64 0 -39 11 -56 158 -240 87 -110 161 -205 166 -212 5 -9 10 -382 6 -494 0 -3 -74 -97 -165 -208 l-165 -202 0 -52 c0 -68 18 -86 86 -86 40 0 55 5 80 28 17 15 112 89 211 166 l180 138 239 0 239 -1 209 -165 c203 -162 210 -166 256 -166 60 0 80 20 80 81 0 43 -8 55 -170 264 l-170 220 0 230 c0 202 2 233 18 257 9 15 86 108 170 208 l152 180 0 54 c0 65 -19 86 -76 86 -36 0 -58 -15 -234 -151 -107 -83 -205 -158 -217 -166 -19 -12 -67 -15 -260 -15 l-238 1 -209 165 -209 166 -53 0 c-43 0 -56 -4 -68 -22z M415 926 c-199 -63 -321 -258 -286 -457 31 -179 161 -309 340 -340 75 -14 171 1 248 37 116 55 209 188 220 314 l6 60 -49 0 -49 0 -17 -70 c-20 -84 -62 -147 -123 -188 -154 -102 -363 -44 -446 124 -35 72 -34 189 3 259 49 92 135 155 236 171 l42 7 0 48 0 49 -42 -1 c-24 0 -61 -6 -83 -13z M2020 882 l0 -50 43 -7 c99 -16 188 -80 235 -169 22 -43 27 -64 27 -131 0 -98 -23 -155 -90 -219 -177 -172 -471 -67 -511 183 l-7 41 -50 0 -50 0 6 -60 c11 -126 102 -257 218 -314 251 -123 542 26 590 303 39 221 -132 448 -351 468 l-60 6 0 -51z"""
)
smiley.vertices -= smiley.vertices.mean(axis=0)
# for robot in robots: plt.plot(
# robot.sp[0], robot.sp[1], '^', color='blue',
# markersize=10, zorder=15) # robots poses
for target_i in range(0, 4):
if target_i == params.victim_index_1:
plt.plot(robots[target_i].sp[0],
robots[target_i].sp[1],
marker=smiley,
color='orange',
markersize=10,
zorder=15) # targetted robots poses
# print("params.victim_index_1 in visualize2D: " +
# str(params.victim_index_1))
elif target_i == params.victim_index_2:
plt.plot(robots[target_i].sp[0],
robots[target_i].sp[1],
marker=smiley,
color='purple',
markersize=10,
zorder=15) # targetted robots poses
# elif target_i == params.victim_index_2:
# elif target_i == params.victim_index_3:
else:
plt.plot(robots[target_i].sp[0],
robots[target_i].sp[1],
marker=smiley,
color='blue',
markersize=10,
zorder=15) # robots poses
plt.plot(obs[-3][0][0],
obs[-3][0][1],
marker=smiley,
color='red',
markersize=10,
zorder=15) # attacker drone poses
plt.plot(obs[-2][0][0],
obs[-2][0][1],
marker=smiley,
color='cyan',
markersize=10,
zorder=15) # attacker drone poses
robots_poses = []
for robot in robots:
robots_poses.append(robot.sp)
robots_poses.sort(
key=lambda p: atan2(p[1] - centroid[1], p[0] - centroid[0]))
plt.gca().add_patch(Polygon(robots_poses, color='yellow'))
plt.plot(centroid[0],
centroid[1],
'*',
color='b',
markersize=10,
label='Centroid position')
plt.plot(robot1.route[:, 0],
robot1.route[:, 1],
linewidth=2,
color='green',
label="Leader's path",
zorder=10)
# for robot in robots[1:]:
# plt.plot(
# robot.route[:, 0], robot.route[:, 1], '--', linewidth=2,
# color='green', zorder=10)
# if params.mode_replay is False:
# plt.plot(
# P[:, 0], P[:, 1], linewidth=3, color='orange',
# label='Global planner path')
# if params.mode_replay is False:
# plt.plot(
# traj_global[sp_ind, 0], traj_global[sp_ind, 1], 'ro',
# color='blue', markersize=7, label='Global planner setpoint')
# plt.plot(
# xy_start[-1][0], xy_start[-1][1], 'bo', color='red', markersize=20,
# label='start')
# plt.plot(
# xy_goal[0], xy_goal[1], 'bo', color='green', markersize=20,
# label='goal')
time = tick
plt.text(-2.2,
2.2,
'Time = ' + str(time),
bbox=dict(facecolor='red', alpha=0.2))
plt.text(-2.2,
1.8,
'l = r, f1 = bl, f2 = br, f3 = or',
bbox=dict(facecolor='blue', alpha=0.2))
def get_bounding_box(path):
minx, miny, maxx, maxy = Polygon(parse_path(path).to_polygons()[0]).bounds
# As the coordinates of the polygon will be rounded, the bounding box is stretched to the next integer-coordinates bounding box
minx, miny, maxx, maxy = floor(minx), floor(miny), ceil(maxx), ceil(maxy)
return BoundingBox(minx, miny, maxx - minx, maxy - miny)
def plot_vehicle_routes_wrapper(vehicle_routes, place_coords, item_coords,
colors, starts, ends, zoom_out):
# Plotting of the routes in matplotlib.
figsize = (10, 8)
fig = plt.figure(figsize=figsize)
graph.set_plot_font()
ax = fig.add_subplot(111)
icon_path = "iconmonstr-location-1.svg"
doc = minidom.parse(icon_path) # parseString also exists
path_strings = [
path.getAttribute('d') for path in doc.getElementsByTagName('path')
]
smiley = parse_path(
"""m 739.01202,391.98936 c 13,26 13,57 9,85 -6,27 -18,52 -35,68 -21,20 -50,23 -77,18 -15,-4 -28,-12 -39,-23 -18,-17 -30,-40 -36,-67 -4,-20 -4,-41 0,-60 l 6,-21 z m -302,-1 c 2,3 6,20 7,29 5,28 1,57 -11,83 -15,30 -41,52 -72,60 -29,7 -57,0 -82,-15 -26,-17 -45,-49 -50,-82 -2,-12 -2,-33 0,-45 1,-10 5,-26 8,-30 z M 487.15488,66.132209 c 121,21 194,115.000001 212,233.000001 l 0,8 25,1 1,18 -481,0 c -6,-13 -10,-27 -13,-41 -13,-94 38,-146 114,-193.000001 45,-23 93,-29 142,-26 z m -47,18 c -52,6 -98,28.000001 -138,62.000001 -28,25 -46,56 -51,87 -4,20 -1,57 5,70 l 423,1 c 2,-56 -39,-118 -74,-157 -31,-34 -72,-54.000001 -116,-63.000001 -11,-2 -38,-2 -49,0 z m 138,324.000001 c -5,6 -6,40 -2,58 3,16 4,16 10,10 14,-14 38,-14 52,0 15,18 12,41 -6,55 -3,3 -5,5 -5,6 1,4 22,8 34,7 42,-4 57.6,-40 66.2,-77 3,-17 1,-53 -4,-59 l -145.2,0 z m -331,-1 c -4,5 -5,34 -4,50 2,14 6,24 8,24 1,0 3,-2 6,-5 17,-17 47,-13 58,9 7,16 4,31 -8,43 -4,4 -7,8 -7,9 0,0 4,2 8,3 51,17 105,-20 115,-80 3,-15 0,-43 -3,-53 z m 61,-266 c 0,0 46,-40 105,-53.000001 66,-15 114,7 114,7 0,0 -14,76.000001 -93,95.000001 -76,18 -126,-49 -126,-49 z"""
)
smiley = parse_path(path_strings[0])
smiley.vertices -= smiley.vertices.mean(axis=0)
marker = smiley
marker = marker.transformed(mpl.transforms.Affine2D().rotate_deg(180))
markerface = ['y.', 'g.', 'b.']
for i, item in enumerate(item_coords):
clat, clon = zip(*[c for c in item_coords[item]])
# markerface[i % len(colors)]
ax.plot(clon, clat, '.', color=colors[i], markersize=10)
# Plot all the nodes as black dots.
clat, clon = zip(*[c for c in place_coords])
ax.plot(clon,
clat,
'm.',
color=colors[len(colors) - 1],
marker=marker,
markersize=10)
# ax.plot(clon, clat, 'b.', markersize=20)
# ax.plot(clon, clat, 'b.', markersize=20)
# plot the routes as arrows
ax.legend(["T", "C", "S", "M", "P"])
if vehicle_routes is not None:
plot_vehicle_routes(vehicle_routes, ax, place_coords, starts, ends,
True)
xlim = ax.get_xlim()
ylim = ax.get_ylim()
# example of how to zoomout by a factor of 0.1
factor = zoom_out
new_xlim = (xlim[0] + xlim[1])/2 + np.array((-0.5, 0.5)) * \
(xlim[1] - xlim[0]) * (1 + factor)
ax.set_xlim(new_xlim)
new_ylim = (ylim[0] + ylim[1])/2 + np.array((-0.5, 0.5)) * \
(ylim[1] - ylim[0]) * (1 + factor)
ax.set_ylim(new_ylim)
plt.grid(zorder=0)
graph.set_disp("DMS fill", "longitude", "latitude")
plt.show()
return fig
from svgpath2mpl import parse_path
import matplotlib.pyplot as plt
import numpy as np
# Use Inkscape to edit SVG,
# Path -> Combine to convert multiple paths into a single path
# Use Path -> Object to path to convert objects to SVG path
smiley = parse_path("""m 739.01202,391.98936 c 13,26 13,57 9,85 -6,27 -18,52 -35,68 -21,20 -50,23 -77,18 -15,-4 -28,-12 -39,-23 -18,-17 -30,-40 -36,-67 -4,-20 -4,-41 0,-60 l 6,-21 z m -302,-1 c 2,3 6,20 7,29 5,28 1,57 -11,83 -15,30 -41,52 -72,60 -29,7 -57,0 -82,-15 -26,-17 -45,-49 -50,-82 -2,-12 -2,-33 0,-45 1,-10 5,-26 8,-30 z M 487.15488,66.132209 c 121,21 194,115.000001 212,233.000001 l 0,8 25,1 1,18 -481,0 c -6,-13 -10,-27 -13,-41 -13,-94 38,-146 114,-193.000001 45,-23 93,-29 142,-26 z m -47,18 c -52,6 -98,28.000001 -138,62.000001 -28,25 -46,56 -51,87 -4,20 -1,57 5,70 l 423,1 c 2,-56 -39,-118 -74,-157 -31,-34 -72,-54.000001 -116,-63.000001 -11,-2 -38,-2 -49,0 z m 138,324.000001 c -5,6 -6,40 -2,58 3,16 4,16 10,10 14,-14 38,-14 52,0 15,18 12,41 -6,55 -3,3 -5,5 -5,6 1,4 22,8 34,7 42,-4 57.6,-40 66.2,-77 3,-17 1,-53 -4,-59 l -145.2,0 z m -331,-1 c -4,5 -5,34 -4,50 2,14 6,24 8,24 1,0 3,-2 6,-5 17,-17 47,-13 58,9 7,16 4,31 -8,43 -4,4 -7,8 -7,9 0,0 4,2 8,3 51,17 105,-20 115,-80 3,-15 0,-43 -3,-53 z m 61,-266 c 0,0 46,-40 105,-53.000001 66,-15 114,7 114,7 0,0 -14,76.000001 -93,95.000001 -76,18 -126,-49 -126,-49 z""")
smiley.vertices -= smiley.vertices.mean(axis=0)
x = np.linspace(-3, 3, 20)
plt.plot(x, np.sin(x), marker=smiley, markersize=20, color='c')
plt.show()
#%%
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import numpy as np, os, re
import matplotlib.pyplot as plt
os.chdir('C:/corina/states')
xmin, xmax = -3, 3
ymin, ymax = -3, 3
Ef = [-3.77, -2.99]
def plot_shell(date=dt.date(2017, 12, 31),
pace_d=0.0,
joe_d=0.0,
holly_d=0.0,
plot_file='output/jolly_miles_0.png'):
"""
Plot a rowing shell by importing an svg and turning it into a matplotlib path.
"""
with open('shell.svg', 'r') as svg_file:
svg_tree = etree.parse(svg_file)
svg_root = svg_tree.getroot()
svg_width = int(svg_root.attrib['width'][:-2])
svg_height = int(svg_root.attrib['height'][:-2])
svg_aspect_ratio = svg_height / svg_width
pace_boat = []
for svg_elem in svg_tree.iter():
if svg_elem.tag.split('}')[1] == 'path':
pace_boat.append(parse_path(svg_elem.get('d')))
holly_boat = pace_boat.copy()
joe_boat = pace_boat.copy()
boat_width = 250.0
scale_factor = boat_width / svg_width
boat_height = svg_height * scale_factor
# Get course in miles
plot_width = 1009.0 + boat_width * 2.0
plot_height = svg_aspect_ratio * plot_width
lane_height = plot_height / 3.0
lane_padding = (lane_height - boat_height) / 2.0
joe_y = lane_padding
holly_y = lane_height + lane_padding
pace_y = lane_height * 2.0 + lane_padding
fig, ax = plt.subplots(1, 1, figsize=(15, 5))
tf = ax.transData # Note: the plot transform should be the last transform!
ax.plot([0, 0], [0, plot_height], '-', color='seagreen', lw=5, zorder=1)
ax.plot([1009, 1009], [0, plot_height],
'-',
color='darkslategrey',
lw=5,
zorder=1)
lane_buoys_x = np.linspace(-boat_width, plot_width - boat_width, 37)
lane_buoys_y = np.ones(lane_buoys_x.shape)
ax.scatter(lane_buoys_x,
lane_buoys_y * 0.0,
25,
marker='o',
color='orange',
zorder=2)
ax.scatter(lane_buoys_x,
lane_buoys_y * lane_height,
25,
marker='o',
color='orange',
zorder=2)
ax.scatter(lane_buoys_x,
lane_buoys_y * lane_height * 2.0,
25,
marker='o',
color='orange',
zorder=2)
ax.scatter(lane_buoys_x,
lane_buoys_y * lane_height * 3.0,
25,
marker='o',
color='orange',
zorder=2)
for part in pace_boat:
part_patch = patches.PathPatch(part,
lw=2,
color='darkslategrey',
zorder=3)
ti = transforms.Affine2D().scale(scale_factor)
ti += transforms.Affine2D().translate(-boat_width + pace_d, pace_y)
part_patch.set_transform(ti + tf)
ax.add_patch(part_patch)
for part in holly_boat:
part_patch = patches.PathPatch(part, lw=2, color='darkcyan', zorder=3)
ti = transforms.Affine2D().scale(scale_factor)
ti += transforms.Affine2D().translate(-boat_width + holly_d, holly_y)
part_patch.set_transform(ti + tf)
ax.add_patch(part_patch)
for part in joe_boat:
part_patch = patches.PathPatch(part, lw=2, color='crimson', zorder=3)
ti = transforms.Affine2D().scale(scale_factor)
ti += transforms.Affine2D().translate(-boat_width + joe_d, joe_y)
part_patch.set_transform(ti + tf)
ax.add_patch(part_patch)
ax.set_xlim(-boat_width, plot_width - boat_width)
ax.set_ylim(-10, plot_height + 10)
plot_x_ticks = np.append(np.arange(0, 1000, 50), 1009)
ax.set_xticks(plot_x_ticks)
ax.tick_params(axis='y', which='both', left='off', labelleft='off')
ax.set_title(f'{(holly_d+joe_d):.1f} Jolly miles for 2018 so far')
ax.set_title(f'{date}', loc='right')
fig.tight_layout()
plt.savefig(plot_file)
plt.close()
import pandas as pd
import mplcursors
import tkinter.messagebox as mb
import re
from tkinter import simpledialog
from scipy import spatial
import ast
# #
ng = pd.read_csv('NGC.csv', low_memory=False)
ms = pd.read_csv('messier_objects.csv', low_memory=False)
cb = pd.read_csv('constellation_borders.csv', low_memory=False)
ty = pd.read_csv('tycho-1.csv', low_memory=False)
ell = parse_path(
"""M 490.60742,303.18917 A 276.31408,119.52378 28.9 0 1 190.94051,274.29027 276.31408,119.52378 28.9 0 1 6.8010582,36.113705 276.31408,119.52378 28.9 0 1 306.46799,65.012613 276.31408,119.52378 28.9 0 1 490.60742,303.18917 Z""")
ell.vertices -= ell.vertices.mean(axis=0)
# #
mag = 6 # limiting the magnitude for Tycho-1 stars;
mag_ty = ty[(ty['V'] <= mag)] # Tycho
dup_ng = ng.copy(deep=True) # NGC
dup_ms = ms.copy(deep=True) # Messier
dup_ty = mag_ty.copy(deep=True) # Tycho
dup_cb = cb.copy(deep=True) # Constellation borders
dup_ng['RAJ2000'] -= 360 # NGC transform
dup_ms['RAJ2000'] -= 360 # Messier transform
dup_ty['RAJ2000'] -= 360 # Tycho transform
dup_cb['RAJ2000'] -= 360 # Constellation borders transform
video_dir = os.path.join(result_dir, 'videos')
# create dirs
os.makedirs(result_dir, exist_ok=True)
os.makedirs(train_dir, exist_ok=True)
os.makedirs(test_dir, exist_ok=True)
os.makedirs(video_dir, exist_ok=True)
return result_dir, train_dir, test_dir, video_dir
# custom symbols for rendering based on SVG paths
# base station icon from noun project: https://thenounproject.com/search/?q=base+station&i=1286474
station_path = "M31.5,19c0-4.1-3.4-7.5-7.5-7.5s-7.5,3.4-7.5,7.5c0,2.9,1.6,5.4,4,6.7l0.9-1.8c-1.7-0.9-2.9-2.8-2.9-4.9 " \
"c0-3,2.5-5.5,5.5-5.5s5.5,2.5,5.5,5.5c0,2.1-1.2,3.9-2.9,4.9l0.9,1.8C29.9,24.4,31.5,21.9,31.5,19z " \
"M37,19c0-7.2-5.8-13-13-13s-13,5.8-13,13c0,5.1,2.9,9.4,7.1,11.6l0.9-1.8c-3.6-1.8-6-5.5-6-9.8 " \
"c0-6.1,4.9-11,11-11s11,4.9,11,11c0,4.3-2.5,8-6,9.8l0.9,1.8C34.1,28.4,37,24.1,37,19z " \
"M42,19c0-9.9-8.1-18-18-18S6,9.1,6,19c0,7,4.1,13.1,10,16.1l0.9-1.8C11.6,30.7,8,25.2,8,19 " \
"c0-8.8,7.2-16,16-16s16,7.2,16,16c0,6.2-3.6,11.7-8.8,14.3l0.9,1.8C37.9,32.1,42,26,42,19z " \
"M24,22c-1.7,0-3-1.3-3-3s1.3-3,3-3s3,1.3,3,3S25.7,22,24,22z M24,18c-0.6,0-1,0.4-1,1s0.4,1,1,1 " \
"s1-0.4,1-1S24.6,18,24,18z " \
"M34.2,44.1L24,23.1l-10.2,21c-0.3,0.6-0.3,1.3,0.1,1.9c0.4,0.6,1,0.9,1.7,0.9h16.8c0.7,0,1.3-0.4,1.7-0.9 "\
"C34.5,45.5,34.5,44.7,34.2,44.1z M25,29.8l2.2,4.4L25,36V29.8z M23,36l-2.2-1.8l2.2-4.4V36z " \
"M22.5,38.2l-6,5.1l3.5-7.3L22.5,38.2z " \
"M23,40.4V45h-5.5L23,40.4z M25,40.3l5.5,4.7H25V40.3z M25.6,38.2l2.5-2.1l3.5,7.2L25.6,38.2z"
station_symbol = svgpath2mpl.parse_path(station_path)
station_symbol.vertices -= station_symbol.vertices.mean(axis=0)
# rotate (otherwise up side down): https://stackoverflow.com/a/48231144/2745116
station_symbol = station_symbol.transformed(
matplotlib.transforms.Affine2D().rotate_deg(180))
def char(self, ch):
def tuple_to_imag(t):
return t[0] + t[1] * 1j
#face = Face('/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf')
face = Face(ddd.DATA_DIR + '/fonts/OpenSansEmoji.ttf')
face.set_char_size(self.char_size)
face.load_char(ch)
#kerning = face.get_kerning(ch, 'x') # or from previous, actually?
#print(kerning)
outline = face.glyph.outline
y = [t[1] for t in outline.points]
# flip the points
outline_points = [(p[0], max(y) - p[1]) for p in outline.points]
start, end = 0, 0
paths = []
for i in range(len(outline.contours)):
end = outline.contours[i]
points = outline_points[start:end + 1]
points.append(points[0])
tags = outline.tags[start:end + 1]
tags.append(tags[0])
segments = [
[
points[0],
],
]
for j in range(1, len(points)):
segments[-1].append(points[j])
if tags[j] and j < (len(points) - 1):
segments.append([
points[j],
])
for segment in segments:
if len(segment) == 2:
paths.append(
Line(start=tuple_to_imag(segment[0]),
end=tuple_to_imag(segment[1])))
elif len(segment) == 3:
paths.append(
QuadraticBezier(start=tuple_to_imag(segment[0]),
control=tuple_to_imag(segment[1]),
end=tuple_to_imag(segment[2])))
elif len(segment) == 4:
paths.append(
CubicBezier(start=tuple_to_imag(segment[0]),
control1=tuple_to_imag(segment[1]),
control2=tuple_to_imag(segment[2]),
end=tuple_to_imag(segment[3])))
#C = ((segment[1][0] + segment[2][0]) / 2.0,
# (segment[1][1] + segment[2][1]) / 2.0)
#paths.append(QuadraticBezier(start=tuple_to_imag(segment[0]),
# control=tuple_to_imag(segment[1]),
# end=tuple_to_imag(C)))
#paths.append(QuadraticBezier(start=tuple_to_imag(C),
# control=tuple_to_imag(segment[2]),
# end=tuple_to_imag(segment[3])))
start = end + 1
path = Path(*paths)
#wsvg(path, filename="/tmp/test.svg")
path_d = path.d()
# https://gis.stackexchange.com/questions/301605/how-to-create-shape-in-shapely-from-an-svg-path-element
# This page also has info about SVG reading!
#svgpath = 'M10 10 C 20 20, 40 20, 50 10Z'
mpl_path = parse_path(path_d)
coords = mpl_path.to_polygons(closed_only=True)
item = None
for c in coords: # coords[1:]:
if len(c) < 3: continue
ng = ddd.polygon(c) #.clean(eps=char_size / 100) #.convex_hull()
#ng.show()
if item is None:
item = ng
elif item.contains(ng):
item = item.subtract(ng)
else:
item = item.union(ng)
item = item.clean(
eps=self.char_size /
200) # Note that this is effectively limiting resolution
#result = ddd.group([ddd.polygon(c) for c in coords], empty=2)
result = item
result = result.scale([1.0 / self.char_size, -1.0 / self.char_size])
result = result.simplify(
0.005) # Note that this is effectively limiting resolution
return (result, face)
def path_to_polygon(path):
return Polygon(parse_path(path).to_polygons()[0])
