def matched_turn_num(gen_cand_gps, real_cand_gps, dist=20):
num = 0
for x0, y0, x1, y1 in gen_cand_gps:
for _x0, _y0, _x1, _y1 in real_cand_gps:
if gis.distance(x0, y0, _x0, _y0) < dist and gis.distance(x1, y1, _x1, _y1) < dist:
num += 1
return num
def matched_turn_num(gen_cand_gps, real_cand_gps, dist=20):
num = 0
for x0, y0, x1, y1 in gen_cand_gps:
for _x0, _y0, _x1, _y1 in real_cand_gps:
if gis.distance(x0, y0, _x0, _y0) < dist and gis.distance(
x1, y1, _x1, _y1) < dist:
num += 1
return num
def test_distance(self):
# Hamburg
lat1 = math.radians(53.550556)
lon1 = math.radians(9.993333)
# München
lat2 = math.radians(48.137222)
lon2 = math.radians(11.575556)
# Setze Erdradius von 6373km vorraus:
self.assertEqual(round(distance(lat1, lon1, lat2, lon2)), 612251)
def heuristic(a, b):
lat1 = math.radians(graph.node[a]["lat"])
lon1 = math.radians(graph.node[a]["lon"])
lat2 = math.radians(graph.node[b]["lat"])
lon2 = math.radians(graph.node[b]["lon"])
return gis.distance(lat1, lon1, lat2, lon2)
print('{0}/{1} nodes processed'.format(i, n))
print('Delete {0} orphaned nodes'.format(len(orphaned)))
graph.remove_nodes_from(orphaned)
print('Calculate offset')
points = [node[1]['pos'] for node in graph.nodes(data=True)]
min_x = min(points, key=lambda p: p[0])[0]
min_y = min(points, key=lambda p: p[1])[1]
for node in graph.nodes_iter():
pos = (graph.node[node]['pos'][0] - min_x, graph.node[node]['pos'][1] - min_y)
graph.node[node]['pos'] = pos
print('Translated data by ({0}, {1})'.format(-min_x, -min_y))
print('Calculate edge weights')
n = graph.number_of_edges()
i = 0
for edge in graph.edges():
lat1 = math.radians(graph.node[edge[0]]['lat'])
lon1 = math.radians(graph.node[edge[0]]['lon'])
lat2 = math.radians(graph.node[edge[1]]['lat'])
lon2 = math.radians(graph.node[edge[1]]['lon'])
graph[edge[0]][edge[1]]['weight'] = distance(lat1, lon1, lat2, lon2)
i += 1
print('{0}/{1} edges processed'.format(i, n), end='\r')
print('{0}/{1} edges processed'.format(i, n))
print('Write {0}'.format(output_file))
write_gpickle(graph, output_file)
stop = timeit.default_timer()
print('Program ran in {0} seconds'.format(stop - start))
def main(ex_name,
data,
side,
k,
ratio,
topic_model,
percent,
width,
alpha,
beta,
sel_cand_method,
maxiter,
cands_num,
fig_width,
max_value,
combine_dist,
_log,
_run,
inc=0,
true_pass=False,
cut_length=0):
if data == 'chicago':
data_file = "../Data/Chicago/chicago.pickle"
axis = gis.chc_utm_axis
map_file = "../Data/Chicago/min_map_df.csv"
elif data == 'minsh_2000':
data_file = "../Data/Shanghai/minsh_2000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_4000':
data_file = "../Data/Shanghai/minsh_4000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_6000':
data_file = "../Data/Shanghai/minsh_6000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_8000':
data_file = "../Data/Shanghai/minsh_8000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_10000':
data_file = "../Data/Shanghai/minsh_10000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_sparse_1000':
data_file = "../Data/Shanghai/minsh_sparse_1000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_sparse_5000':
data_file = "../Data/Shanghai/minsh_sparse_5000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_sparse_10000':
data_file = "../Data/Shanghai/minsh_sparse_10000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_20000':
data_file = "../Data/Shanghai/maxsh_20000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_40000':
data_file = "../Data/Shanghai/maxsh_40000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_60000':
data_file = "../Data/Shanghai/maxsh_60000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_80000':
data_file = "../Data/Shanghai/maxsh_80000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_100000':
data_file = "../Data/Shanghai/maxsh_100000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
global ex
#ex.add_artifact('origin_gen_map.png')
#return {}
map_df = pd.read_csv(map_file)
map_df = gis.scale_map_by_axis(map_df, axis)
map_df.index = map_df.rid
_run.info['ex_name'] = ex_name
_log.info(
'topic_model: %s \tdata_file: %s \tside: %d\tk: %d\tpercent: %.4f\twidth: %d\talpha: %.2f\tbeta: %.2f'
% (topic_model, data_file, side, k, percent, width, alpha, beta))
trajs = pd.read_pickle(data_file)
result = {}
_run.info['time'] = {}
_run.info['side_length'] = gis.distance(axis[0],
axis[2],
axis[0],
axis[2] + side,
utm=True)
total_time = 0
t0 = time.time()
mat = trajmap.Sag(trajs, side, inc=inc, cut_length=cut_length)
t1 = time.time()
total_time += t1 - t0
_run.info['time']['1_Make_matrix'] = t1 - t0
_run.info['matrix_shape'] = (mat.width, mat.height)
delta_time = None
io_time = 0
t0 = time.time()
if topic_model == 'pLSA':
trajmap.get_P(mat)
mat.G = nx.DiGraph()
mat.candidates = set()
mat.pLSA(k, maxiter)
trajmap.iteration(mat, width, k, percent, max_value, true_pass)
elif topic_model == 'LDA':
trajmap.get_P(mat)
mat.G = nx.DiGraph()
mat.candidates = set()
io_time = mat.LDA(k, maxiter, alpha, beta)
trajmap.iteration(mat, width, k, percent, max_value, true_pass)
elif topic_model == 'pLSA_cut':
trajmap.get_P(mat)
#mat.sag = mat.sag.toarray()
trajmap.generate_map(mat, width, k, maxiter, percent, max_value)
t1 = time.time()
total_time += t1 - t0 - io_time
_run.info['time']['Generate_Map'] = t1 - t0
_run.info['candidates_num'] = len(mat.candidates)
_run.info['candidates'] = list(mat.candidates)
'''
result['candidates_error'] = gis.candidates_error(mat, map_df)
plot.map_df(map_df, ca, axis=axis)
plt.axis(axis)
plt.savefig('candidates.png')
ex.add_artifact('candidates.png')
'''
_M = mat.G.copy()
'''
t0 = time.time()
gps = {}
M, gps = trajmap.smooth_M(mat, mat.G, gps, _dist=combine_dist)
t1 = time.time()
total_time += t1-t0
_run.info['time']['4_Smooth_map'] = t1-t0
'''
_run.info['total_time'] = total_time
gen_map_df = gis.convert_nx2map_df(mat, _M, {})
result['origin'] = {}
if axis[0] == gis.maxsh_utm_axis[0]:
result['origin'] = gis.evaluate_map(gen_map_df, map_df,
gis.minsh_utm_axis)
else:
result['origin'] = gis.evaluate_map(gen_map_df, map_df, axis)
result['origin']['length'] = gis.map_length(gen_map_df)
result['time'] = total_time
plt.figure(figsize=(fig_width,
fig_width * (axis[3] - axis[2]) / (axis[1] - axis[0])))
ca = plt.gca()
plot.map_df(map_df, ca=ca, axis=axis, color='b')
plot.map_df(gen_map_df, ca=ca, axis=axis, color='r', linewidth=1.5)
plot.dots(mat, mat.candidates, ca=ca, gps=True, color='r')
plt.axis(axis)
plt.savefig('origin_gen_map.png')
ex.add_artifact('origin_gen_map.png')
csv_name = '../Data/csv/%s_%s_%d_%d_%d_%f_%d_%f.csv' % (
data, topic_model, side, k, width, max_value, maxiter, percent)
gen_map_df.to_csv(csv_name)
ex.add_artifact(csv_name)
M = mat.G.copy()
#if data == 'minsh_6000' and ex_name == 'visualization':
if ex_name == 'visualization':
result['smoothed'] = {}
for _dist in [1.5, 2, 3]:
_M, gps = trajmap.smooth_M(mat, M.to_directed(), {}, _dist)
gen_map_df = gis.convert_nx2map_df(mat, _M, gps)
result['smoothed'][_dist] = gis.evaluate_map(
gen_map_df, map_df, axis)
#result['smoothed']['length'] = gis.map_length(gen_map_df)
plt.figure(figsize=(fig_width, fig_width * (axis[3] - axis[2]) /
(axis[1] - axis[0])))
ca = plt.gca()
plot.map_df(map_df, ca=ca, axis=axis, color='b')
plot.map_df(gen_map_df, ca=ca, axis=axis, color='r', linewidth=2)
plt.axis(axis)
plt.savefig('gen_map.png')
ex.add_artifact('gen_map.png')
return result
def main(ex_name, data, side, k, ratio, topic_model, percent, width, alpha, beta, sel_cand_method, maxiter, cands_num, fig_width, max_value, combine_dist,_log, _run, inc=0, true_pass=False, cut_length=0):
if data == 'chicago':
data_file = "../Data/Chicago/chicago.pickle"
axis = gis.chc_utm_axis
map_file = "../Data/Chicago/min_map_df.csv"
elif data == 'minsh_2000':
data_file = "../Data/Shanghai/minsh_2000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_4000':
data_file = "../Data/Shanghai/minsh_4000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_6000':
data_file = "../Data/Shanghai/minsh_6000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_8000':
data_file = "../Data/Shanghai/minsh_8000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_10000':
data_file = "../Data/Shanghai/minsh_10000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_sparse_1000':
data_file = "../Data/Shanghai/minsh_sparse_1000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_sparse_5000':
data_file = "../Data/Shanghai/minsh_sparse_5000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'minsh_sparse_10000':
data_file = "../Data/Shanghai/minsh_sparse_10000.pickle"
axis = gis.minsh_utm_axis
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_20000':
data_file = "../Data/Shanghai/maxsh_20000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_40000':
data_file = "../Data/Shanghai/maxsh_40000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_60000':
data_file = "../Data/Shanghai/maxsh_60000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_80000':
data_file = "../Data/Shanghai/maxsh_80000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
elif data == 'maxsh_100000':
data_file = "../Data/Shanghai/maxsh_100000.pickle"
axis = gis.maxsh_utm_axis
#map_file = "../Data/Shanghai/sh_map_df.csv"
map_file = "../Data/Shanghai/matched_sh_map_df.csv"
global ex
#ex.add_artifact('origin_gen_map.png')
#return {}
map_df = pd.read_csv(map_file)
map_df = gis.scale_map_by_axis(map_df, axis)
map_df.index = map_df.rid
_run.info['ex_name'] = ex_name
_log.info('topic_model: %s \tdata_file: %s \tside: %d\tk: %d\tpercent: %.4f\twidth: %d\talpha: %.2f\tbeta: %.2f' % (topic_model, data_file, side, k, percent, width, alpha, beta))
trajs = pd.read_pickle(data_file)
result = {}
_run.info['time'] = {}
_run.info['side_length'] = gis.distance(axis[0], axis[2], axis[0], axis[2]+side, utm=True)
total_time = 0
t0 = time.time()
mat = trajmap.Sag(trajs, side, inc=inc, cut_length=cut_length)
t1 = time.time()
total_time += t1-t0
_run.info['time']['1_Make_matrix'] = t1 - t0
_run.info['matrix_shape'] = (mat.width, mat.height)
delta_time = None
io_time = 0
t0 = time.time()
if topic_model == 'pLSA':
trajmap.get_P(mat)
mat.G = nx.DiGraph()
mat.candidates = set()
mat.pLSA(k, maxiter)
trajmap.iteration(mat, width, k, percent, max_value, true_pass)
elif topic_model == 'LDA':
trajmap.get_P(mat)
mat.G = nx.DiGraph()
mat.candidates = set()
io_time = mat.LDA(k, maxiter, alpha, beta)
trajmap.iteration(mat, width, k, percent, max_value, true_pass)
elif topic_model == 'pLSA_cut':
trajmap.get_P(mat)
#mat.sag = mat.sag.toarray()
trajmap.generate_map(mat, width, k, maxiter, percent, max_value)
t1 = time.time()
total_time += t1-t0-io_time
_run.info['time']['Generate_Map'] = t1 - t0
_run.info['candidates_num'] = len(mat.candidates)
_run.info['candidates'] = list(mat.candidates)
'''
result['candidates_error'] = gis.candidates_error(mat, map_df)
plot.map_df(map_df, ca, axis=axis)
plt.axis(axis)
plt.savefig('candidates.png')
ex.add_artifact('candidates.png')
'''
_M = mat.G.copy()
'''
t0 = time.time()
gps = {}
M, gps = trajmap.smooth_M(mat, mat.G, gps, _dist=combine_dist)
t1 = time.time()
total_time += t1-t0
_run.info['time']['4_Smooth_map'] = t1-t0
'''
_run.info['total_time'] = total_time
gen_map_df = gis.convert_nx2map_df(mat, _M, {})
result['origin'] = {}
if axis[0] == gis.maxsh_utm_axis[0]:
result['origin'] = gis.evaluate_map(gen_map_df, map_df, gis.minsh_utm_axis)
else:
result['origin'] = gis.evaluate_map(gen_map_df, map_df, axis)
result['origin']['length'] = gis.map_length(gen_map_df)
result['time'] = total_time
plt.figure(figsize=(fig_width, fig_width*(axis[3]-axis[2])/(axis[1]-axis[0])))
ca = plt.gca()
plot.map_df(map_df, ca=ca, axis=axis, color='b')
plot.map_df(gen_map_df, ca=ca, axis=axis, color='r', linewidth=1.5)
plot.dots(mat, mat.candidates, ca=ca, gps=True, color='r')
plt.axis(axis)
plt.savefig('origin_gen_map.png')
ex.add_artifact('origin_gen_map.png')
csv_name = '../Data/csv/%s_%s_%d_%d_%d_%f_%d_%f.csv'%(data, topic_model, side, k, width, max_value, maxiter, percent)
gen_map_df.to_csv(csv_name)
ex.add_artifact(csv_name)
M = mat.G.copy()
#if data == 'minsh_6000' and ex_name == 'visualization':
if ex_name == 'visualization':
result['smoothed'] = {}
for _dist in [1.5, 2, 3]:
_M, gps = trajmap.smooth_M(mat, M.to_directed(), {}, _dist)
gen_map_df = gis.convert_nx2map_df(mat, _M, gps)
result['smoothed'][_dist] = gis.evaluate_map(gen_map_df, map_df, axis)
#result['smoothed']['length'] = gis.map_length(gen_map_df)
plt.figure(figsize=(fig_width, fig_width*(axis[3]-axis[2])/(axis[1]-axis[0])))
ca = plt.gca()
plot.map_df(map_df, ca=ca, axis=axis, color='b')
plot.map_df(gen_map_df, ca=ca, axis=axis, color='r', linewidth=2)
plt.axis(axis)
plt.savefig('gen_map.png')
ex.add_artifact('gen_map.png')
return result
