import geopandas as gpd
import scipy as sp
import matplotlib.pyplot as plt
sys.path.append(os.environ['LAV_DIR'] + '/src/')
baseDir = os.environ['LAV_DIR']
import geomadi.lib_graph as gra
import geomadi.geo_octree as g_o
import geomadi.train_viz as t_v
import shapely as sh
from shapely.geometry.polygon import Polygon
metric = "speed"
if (len(sys.argv) > 1): metric = sys.argv[1]
projDir = baseDir + "raw/gps/" + metric + "/"
dL = os.listdir(projDir)
gO = g_o.octree(BoundBox=[5.866, 47.2704, 15.0377, 55.0574])
for d in dL:
print(d)
den = pd.read_csv(projDir + d)
if 'dens' not in locals(): dens = den
else:
dens = dens.merge(den, on="octree", how="outer")
dens = dens.replace(float('nan'), 0.)
tLx = [x for x in dens.columns if bool(re.search("_x", x))]
tLy = [x for x in dens.columns if bool(re.search("_y", x))]
dens.loc[:, tLx] = dens[tLx].values + dens[tLy].values
for i in tLy:
del dens[i]
dens.columns = ['octree'] + [x.split("_")[0] for x in tLx]
norm = 1. / (df['n'].values)
df.loc[:, tLx] = np.multiply(tX + tY, norm[:, np.newaxis])
for i in tLy + ['n_x', 'n_y']:
del df[i]
for i in tLx:
df.rename(columns={i: i.split("_")[0]}, inplace=True)
return df
if False:
#library testing
print("x | y | dx | dy")
import geomadi.geo_octree as g_o
import importlib
importlib.reload(g_o)
gO = g_o.octree(BoundBox=[5.866, 47.2704, 15.0377, 55.0574], padding=0.1)
g2 = gO.encode(14.989551, 48.218262, 10)
print(gO.decode(gO.encode(14.989551, 48.218262, 10)))
print("de center %s" % (gO.encode(10.28826401, 51.13341344, 15)))
if False:
#choose algebra base
l = np.array([1, 2, 4, 8])
comb = [i - j for i in l for j in l]
base, count = np.unique(comb, return_counts=True)
print(base, count)
print(len(count) / sum(count))
print(sum(l))
plt.bar(range(len(comb)), sorted(comb))
plt.show()
l2 = [-1, 0, 1]