def draw_bar_chart(dictionary, title, x_label, y_label):
bar_chart = plt.bar(range(len(dictionary)), dictionary.values(), align='center',
color=cm.OrRd(np.linspace(.4, .8, 10)),
zorder=3)
value_list = list(dictionary.values())
high = max(value_list)
plt.ylim([0, high + 10])
plt.title(title)
plt.ylabel(y_label)
plt.xlabel(x_label)
ax = plt.gca()
ax.grid()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
for tick in ax.xaxis.get_major_ticks() + ax.yaxis.get_major_ticks():
tick.label.set_fontsize(8)
plt.tick_params(top=False, bottom=False, left=False, right=False, labelleft=True, labelbottom=True)
plt.xticks(range(len(dictionary)), list(dictionary.keys()), rotation=90)
plt.tight_layout()
# plt.subplots_adjust(left=0.1, right=0.95, top=0.9, bottom=0.25)
auto_label(bar_chart)
plt.savefig(ml_utils.get_dir_path() + title + '.png')
plt.clf()
def wscmap():
# This function returns the colormap and bins for the PM spatial plots
# this is designed to have a vmin =0 and vmax = 140
# return cmap,bins
colors1 = cm.viridis(linspace(0, 1, 128))
colors2 = cm.OrRd(linspace(.2, 1, 128))
colors = vstack((colors1, colors2))
return mcolors.LinearSegmentedColormap.from_list('wscmap', colors), arange(
0, 40.2, .2)
def graph_color(data, maximum, svg_file):
tree = ET.parse(svg_file)
root = tree.getroot()
gselect = r'{http://www.w3.org/2000/svg}g'
paths = []
for p in root.iter(gselect):
paths.append(p)
for path in paths:
try:
scale = data[path.attrib['id']] / maximum
s = clrs.rgb2hex(cm.OrRd(scale))
path.attrib['style'] = 'fill:' + s
except KeyError:
continue
tree.write('out1.svg')
def get_cmaps_biasCNN():
"""
Create color maps
"""
nsteps = 8
colors_all_1 = np.moveaxis(
np.expand_dims(cm.Greys(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all_2 = np.moveaxis(
np.expand_dims(cm.GnBu(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all_3 = np.moveaxis(
np.expand_dims(cm.YlGn(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all_4 = np.moveaxis(
np.expand_dims(cm.OrRd(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all = np.concatenate((colors_all_1[np.arange(2, nsteps, 1), :, :],
colors_all_2[np.arange(2, nsteps, 1), :, :],
colors_all_3[np.arange(2, nsteps, 1), :, :],
colors_all_4[np.arange(2, nsteps, 1), :, :],
colors_all_2[np.arange(2, nsteps, 1), :, :]),
axis=1)
int_inds = [3, 3, 3, 3]
colors_main = np.asarray(
[colors_all[int_inds[ii], ii, :] for ii in range(np.size(int_inds))])
colors_main = np.concatenate((colors_main, colors_all[5, 1:2, :]), axis=0)
# plot the color map
#plt.figure();plt.imshow(np.expand_dims(colors_main,axis=0))
colors_sf = np.moveaxis(
np.expand_dims(cm.GnBu(np.linspace(0, 1, 8)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_sf = colors_sf[np.arange(2, 8, 1), :, :]
return colors_main, colors_sf
def draw_hoods(boroughs, dataset):
if dataset != "pickups" and dataset != "drops":
raise Exception(
"Error: the two datasets are 'pickups' and "
"'drops', not ", str(dataset))
datafile = 'neighborhoods/nyc-pediacities-neighborhoods-v3-polygon.geojson'
hoodlist = parse_neighborhood_file(datafile)
xlim = len(boroughs) == 5
f1 = 15 if len(boroughs) == 5 else 10
fig = pyplot.figure(1, figsize=(f1, 10))
myplot = fig.add_subplot(111)
if xlim:
myplot.set_xlim(-74.35, -73.6)
myplot.set_ylim(40.45, 40.95)
hood_count = load_precomputed_count(boroughs)
maxmin = load_precomputed_maxmin(boroughs)
norm = colors.LogNorm(maxmin["min"][dataset]["count"],
maxmin["max"][dataset]["count"])
for n in hoodlist.keys():
# keep it to requested boroughs
if hoodlist[n]["boroughCode"] not in boroughs:
continue
count = hood_count[n][dataset]
color = colors.rgb2hex(cm.OrRd(norm(count)))
if hoodlist[n]["location"]["type"] == "MultiPolygon":
for c in hoodlist[n]["location"]["coordinates"]:
patch = PolygonPatch({
"type": "Polygon",
"coordinates": c
},
fc=color,
ec='#000000',
alpha=1.0,
zorder=2)
myplot.add_patch(patch)
else:
patch = PolygonPatch(hoodlist[n]["location"],
fc=color,
ec="#000000",
alpha=1.0,
zorder=2)
myplot.add_patch(patch)
if not xlim:
myplot.relim()
myplot.autoscale_view()
pyplot.xlabel("longitude")
pyplot.ylabel("latitude")
filename = ''.join(str(b) for b in boroughs)
pyplot.savefig(filename + dataset + '.png', bbox_inches='tight', dpi=100)
def draw_hoods3D(boroughs, dataset):
if dataset != "pickups" and dataset != "drops":
raise Exception(
"Error: the two datasets are 'pickups' "
"and 'drops', not ", str(dataset))
datafile = 'neighborhoods/nyc-pediacities-neighborhoods-v3-polygon.geojson'
hoodlist = parse_neighborhood_file(datafile)
manhattan = len(boroughs) == 1 and boroughs[0] == 1
fig = pyplot.figure(1, figsize=(7, 6))
ax = fig.gca(projection='3d')
ax.set_zlabel("# of rides")
ax.set_xlabel("longitude")
ax.set_ylabel("latitude")
if manhattan:
ax.set_xlim3d(-74.07, -73.85)
ax.set_ylim3d(40.65, 40.90)
else:
ax.set_xlim3d(-74.35, -73.6)
ax.set_ylim3d(40.45, 40.95)
hood_count = load_precomputed_count(boroughs)
maxmin = load_precomputed_maxmin(boroughs)
norm = colors.LogNorm(maxmin["min"][dataset]["count"],
maxmin["max"][dataset]["count"])
for n in hoodlist.keys():
# keep it to requested boroughs
if hoodlist[n]["boroughCode"] not in boroughs:
continue
count = hood_count[n][dataset]
if norm(count) < 0:
count = 0
rgba = cm.OrRd(norm(count))
color = colors.rgb2hex(rgba)
tint = colors.rgb2hex(
(rgba[0] * 0.9, rgba[1] * 0.9, rgba[2] * 0.9, rgba[3] * 0.9))
if hoodlist[n]["location"]["type"] == "MultiPolygon":
for c in hoodlist[n]["location"]["coordinates"]:
patch = PolygonPatch({
"type": "Polygon",
"coordinates": c
},
fc=color,
ec=tint,
alpha=1.0,
zorder=2)
ax.add_patch(patch)
art3d.pathpatch_2d_to_3d(patch, z=norm(count), zdir='z')
num_coords = len(c[0])
verts = []
for i in range(num_coords):
P = c[0][i % num_coords]
Q = c[0][(i + 1) % num_coords]
x = [P[0], Q[0], Q[0], P[0]]
y = [P[1], Q[1], Q[1], P[1]]
z = [0, 0, norm(count), norm(count)]
verts.append(zip(x, y, z))
coll = Poly3DCollection(verts)
coll.set_facecolors(color)
coll.set_edgecolors(tint)
ax.add_collection3d(coll)
else:
patch = PolygonPatch(hoodlist[n]["location"],
fc=color,
ec=tint,
alpha=1.0,
zorder=2)
ax.add_patch(patch)
art3d.pathpatch_2d_to_3d(patch, z=norm(count), zdir='z')
num_coords = len(hoodlist[n]["location"]["coordinates"][0])
verts = []
for i in range(num_coords):
P = hoodlist[n]["location"]["coordinates"][0][i % num_coords]
Q = hoodlist[n]["location"]["coordinates"][0][(i + 1) %
num_coords]
x = [P[0], Q[0], Q[0], P[0]]
y = [P[1], Q[1], Q[1], P[1]]
z = [0, 0, norm(count), norm(count)]
verts.append(zip(x, y, z))
coll = Poly3DCollection(verts)
coll.set_facecolors(color)
coll.set_edgecolors(tint)
ax.add_collection3d(coll)
filename = ''.join(str(b) for b in boroughs)
pyplot.savefig(filename + dataset + '.png', bbox_inches='tight', dpi=100)