def curveFromBin(binValues, pb, delta=1):
'''
binValues: 经排序的数值列表
'''
N = len(binValues)
binCumulateCount = np.array(range(1, N + 1))
li = Interpolate(binCumulateCount, binValues, delta)
cnt = pb * N / 100
bin = li.evalArray(cnt, sorted=True)
return CumulativeCurve(bin, cnt)
def main1():
random.seed(0)
np.random.seed(0)
ll = Localization(grid_len=10)
# ll.init_data()
inter = Interpolate(ll.sensors, ll.means, ll.stds) #''' args... '''
sensors_inter, means_inter, stds_inter = inter.idw_inter(factor=4)
ll_inter = Localization(grid_len=40)
ll_inter.init_data_direct(ll.covariance, sensors_inter, means_inter,
stds_inter)
def input(self):
'''
'''
fig, ax = plt.subplots(1)
self.filename = self.filelist[self.date]
self.data = Interpolate(filename=self.filename, bounds=c.BOUNDS)
xyz = self.data.xyz
ax.scatter(x=xyz[:, 0], y=xyz[:, 1], c=xyz[:, 2], cmap='viridis')
title = self._set_title(fn=self.filename)
self._format_plt(fig=fig, ax=ax, title=title)
plt.close('all')
return fig
def __init__(self, module_2):
super(Module1, self).__init__()
self.layer1 = nn.Sequential(
nn.MaxPool2d(kernel_size=2, stride=2), # 2x
Inception(128, 32, [(3, 32, 32), (5, 32, 32), (7, 32, 32)]),
Inception(128, 32, [(3, 32, 32), (5, 32, 32), (7, 32, 32)]),
module_2,
Inception(128, 32, [(3, 64, 32), (5, 64, 32), (7, 64, 32)]),
Inception(128, 16, [(3, 32, 16), (7, 32, 16), (11, 32, 16)]),
Interpolate(scale_factor=2, mode='nearest'))
self.layer2 = nn.Sequential(
Inception(128, 16, [(3, 64, 16), (7, 64, 16), (11, 64, 16)]))
def __init__(self):
super(Module4, self).__init__()
self.layer1 = nn.Sequential(
Inception(256, 64, [(3, 32, 64), (5, 32, 64), (7, 32, 64)]),
Inception(256, 64, [(3, 32, 64), (5, 32, 64), (7, 32, 64)]))
self.layer2 = nn.Sequential(
nn.AvgPool2d(kernel_size=2, stride=2),
Inception(256, 64, [(3, 32, 64), (5, 32, 64), (7, 32, 64)]),
Inception(256, 64, [(3, 32, 64), (5, 32, 64), (7, 32, 64)]),
Inception(256, 64, [(3, 32, 64), (5, 32, 64), (7, 32, 64)]),
Interpolate(scale_factor=2,
mode='nearest') # Up to 8x, 256 channel
)
def __init__(self, module_3):
super(Module2, self).__init__()
self.layer1 = nn.Sequential(
nn.MaxPool2d(kernel_size=2, stride=2), # 4x
Inception(128, 32, [(3,32,32), (5,32,32), (7,32,32)]),
Inception(128, 64, [(3,32,64), (5,32,64), (7,32,64)]),
module_3,
Inception(256, 64, [(3,32,64), (5,32,64), (7,32,64)]),
Inception(256, 32, [(3,32,32), (5,32,32), (7,32,32)]),
Interpolate(scale_factor=2, mode='nearest') # up to 2x, output is 128 channel
)
self.layer2 = nn.Sequential(
Inception(128, 32, [(3,32,32), (5,32,32), (7,32,32)]),
Inception(128, 32, [(3,64,32), (7,64,32), (11,64,32)])
)
def curveFromBinCount(binCounts, pb, delta=1):
'''
binCounts: 元素为(bin，count)元组的列表，可以是多次累积，bin可以无序
'''
from collections import Counter
c0 = Counter()
for bin, cnt in binCounts:
c0[bin] += cnt
c1 = sorted(c0.items(), key=lambda item: item[0])
clen = len(c1)
if c1[0][1] == 0:
b = np.zeros(clen)
binCumulateCount = np.zeros(clen)
b[0] = c1[0][0]
binCumulateCount[0] = 0
for i in range(1, clen - 1):
b[i] = b[i - 1] + 2 * (c1[i][0] - b[i - 1])
binCumulateCount[i] = binCumulateCount[i - 1] + c1[i][1]
b[-1] = c1[-1][0]
binCumulateCount[-1] = binCumulateCount[-2]
else:
b = np.zeros(clen + 1)
binCumulateCount = np.zeros(clen + 1)
b[0] = c1[0][0] - delta
binCumulateCount[0] = 0
for i in range(clen):
b[i + 1] = c1[i][0]
binCumulateCount[i + 1] = binCumulateCount[i] + c1[i][1]
N = binCumulateCount[-1]
li = Interpolate(binCumulateCount, b, 0)
cnt = pb * N / 100
bin = li.evalArray(cnt, sorted=True)
return CumulativeCurve(bin, cnt)
def __init__(self, module_4):
super(Module3, self).__init__()
self.layer1 = nn.Sequential(
Inception(256, 64, [(3, 32, 64), (5, 32, 64), (7, 32, 64)]),
Inception(256, 64, [(3, 64, 64), (7, 64, 64), (11, 64, 64)]))
self.layer2 = nn.Sequential(
nn.AvgPool2d(kernel_size=2, stride=2), # 8x
Inception(256, 64, [(3, 32, 64), (5, 32, 64), (7, 32, 64)]),
Inception(256, 64, [(3, 32, 64), (5, 32, 64), (7, 32, 64)]),
module_4, # down 16x then up to 8x
Inception(256, 64, [(3, 32, 64), (5, 32, 64), (7, 32, 64)]),
Inception(256, 64, [(3, 64, 64), (7, 64, 64), (11, 64, 64)]),
Interpolate(scale_factor=2,
mode='nearest') # up to 4x. 256 channel
)
def merge(curves, pb):
"""
curves: 元素为(bins，counts)元组的列表
"""
binList = []
for c in curves:
binList.append(c[0])
bs = np.unique(np.ravel(np.array(binList)))
bsLen = len(bs)
binCounts = np.zeros(bsLen)
for cbin, cCount in curves:
li = Interpolate(cbin, cCount, 0)
cs = li.evalArray(bs, sorted=True)
binCounts = binCounts + cs
newTotal = binCounts[-1]
li = Interpolate(binCounts * 100 / newTotal, bs, 0)
newbin = li.evalArray(pb, sorted=True)
return CumulativeCurve(newbin, pb * newTotal / 100)
def cumulativeCount(self, b):
if not self._interpolateFromBin:
self._interpolateFromBin = Interpolate(self._bin, self._cnt, 0)
return self._interpolateFromBin.eval(b)
def p(self, r):
if self._interpolateFromPct is None:
self._interpolateFromPct = Interpolate(self._pb, self._bin, 0)
return self._interpolateFromPct.innerEval(r)
class CumulativeCurve:
"""
累积概率曲线定义为小于等于bin值的概率
最大值对应100
最小值对应1/Count，略大于0，因此概率0对应的bin值取为min-delta
pb: 累积概率曲线的概率分档点，缺省为
range(0,101,1),[0.01,0.1,0.5,99.5,99.9,99.99],[0.27,4.55,95.45,99.73]
delta: 原始数据的min和累积概率为0时分档bin间的差，缺省为
0
"""
pb = np.hstack((range(0, 101, 1), [0.01, 0.1, 0.5, 99.5, 99.9,
99.99], [0.27, 4.55, 95.45, 99.73]))
pb.sort()
def __init__(self, bin, cnt):
self._bin, self._cnt = bin, cnt
self._N = cnt[-1]
self._pb = cnt * 100 / self._N
self._interpolateFromPct = None
self._interpolateFromBin = None
self._sum = None
self._sum2 = None
def __hash__(self):
return hash((self._bin.tobytes(), self._cnt.tobytes()))
def __repr__(self):
return "bin: {}\ncnt: {}\n".format(self._bin, self._cnt)
@staticmethod
def almostEqual(a, b):
if a == b:
return True
if a.__class__ != b.__class__:
return False
if a._N != b._N:
return False
if ((max(abs(a._bin - b._bin)) < 1E-5)
and (max(abs(a._cnt - b._cnt)) < 1E-5)):
return True
return False
@staticmethod
def importCSV(filename):
"""
文件格式
bin pb cnt
13518.0 , 0.0, 0.0
15635.97, 0.01, 7.705
......
"""
bin, cnt = np.loadtxt(filename,
delimiter=',',
usecols=(0, 2),
unpack=True)
return CumulativeCurve(bin, cnt)
def exportCSV(self, filename):
with open(filename, 'wt') as f:
writer = csv.writer(f)
for i in range(len(self._pb)):
writer.writerow((self._bin[i], self._pb[i], self._cnt[i]))
def getCurve(self):
return self._bin, self._cnt
@property
def max(self):
return self._bin[-1]
@property
def min(self):
'''比实际的最小值小delta'''
return self._bin[0]
@property
def count(self):
return self._N
def p(self, r):
if self._interpolateFromPct is None:
self._interpolateFromPct = Interpolate(self._pb, self._bin, 0)
return self._interpolateFromPct.innerEval(r)
@property
def median(self):
return self.p(50)
def cumulativeCount(self, b):
if not self._interpolateFromBin:
self._interpolateFromBin = Interpolate(self._bin, self._cnt, 0)
return self._interpolateFromBin.eval(b)
def cumulativePercentage(self, b):
if not self._interpolateFromBin:
self._interpolateFromBin = Interpolate(self._bin, self._cnt, 0)
return self._interpolateFromBin.eval(b) / self._N
def rangeCount(self, a, b):
return self.cumulativeCount(b) - self.cumulativeCount(a)
def binCount(self):
cntlen = len(self._cnt)
v = []
v.append((self._bin[0], 0))
for i in range(1, cntlen):
v.append(((self._bin[i] + self._bin[i - 1]) / 2,
self._cnt[i] - self._cnt[i - 1]))
v.append((self._bin[-1], 0))
return v
@property
def sum(self):
if self._sum is not None:
return self._sum
s = 0
for b, c in self.binCount():
s += b * c
self._sum = s
return s
@property
def sum2(self):
if self._sum2 is not None:
return self._sum2
s2 = 0
for b, c in self.binCount():
s2 += b * b * c
self._sum2 = s2
return s2
@property
def avg(self):
return self.sum / self._N
@property
def std(self):
s = self.sum
s2 = self.sum2
n = self._N
assert (n > 1)
return math.sqrt((s2 - s * s / n) / (n - 1))
def sample(n=1):
assert (n > 0)
if n == 1:
return self.p(np.random.rand())
else:
return [self.p(np.random.rand()) for i in range(n)]
@staticmethod
def curveFromBin(binValues, pb, delta=1):
'''
binValues: 经排序的数值列表
'''
N = len(binValues)
binCumulateCount = np.array(range(1, N + 1))
li = Interpolate(binCumulateCount, binValues, delta)
cnt = pb * N / 100
bin = li.evalArray(cnt, sorted=True)
return CumulativeCurve(bin, cnt)
@staticmethod
def curveFromBinCount(binCounts, pb, delta=1):
'''
binCounts: 元素为(bin，count)元组的列表，可以是多次累积，bin可以无序
'''
from collections import Counter
c0 = Counter()
for bin, cnt in binCounts:
c0[bin] += cnt
c1 = sorted(c0.items(), key=lambda item: item[0])
clen = len(c1)
if c1[0][1] == 0:
b = np.zeros(clen)
binCumulateCount = np.zeros(clen)
b[0] = c1[0][0]
binCumulateCount[0] = 0
for i in range(1, clen - 1):
b[i] = b[i - 1] + 2 * (c1[i][0] - b[i - 1])
binCumulateCount[i] = binCumulateCount[i - 1] + c1[i][1]
b[-1] = c1[-1][0]
binCumulateCount[-1] = binCumulateCount[-2]
else:
b = np.zeros(clen + 1)
binCumulateCount = np.zeros(clen + 1)
b[0] = c1[0][0] - delta
binCumulateCount[0] = 0
for i in range(clen):
b[i + 1] = c1[i][0]
binCumulateCount[i + 1] = binCumulateCount[i] + c1[i][1]
N = binCumulateCount[-1]
li = Interpolate(binCumulateCount, b, 0)
cnt = pb * N / 100
bin = li.evalArray(cnt, sorted=True)
return CumulativeCurve(bin, cnt)
@staticmethod
def merge(curves, pb):
"""
curves: 元素为(bins，counts)元组的列表
"""
binList = []
for c in curves:
binList.append(c[0])
bs = np.unique(np.ravel(np.array(binList)))
bsLen = len(bs)
binCounts = np.zeros(bsLen)
for cbin, cCount in curves:
li = Interpolate(cbin, cCount, 0)
cs = li.evalArray(bs, sorted=True)
binCounts = binCounts + cs
newTotal = binCounts[-1]
li = Interpolate(binCounts * 100 / newTotal, bs, 0)
newbin = li.evalArray(pb, sorted=True)
return CumulativeCurve(newbin, pb * newTotal / 100)
def cumulativePercentage(self, b):
if not self._interpolateFromBin:
self._interpolateFromBin = Interpolate(self._bin, self._cnt, 0)
return self._interpolateFromBin.eval(b) / self._N
class Interact(param.Parameterized):
'''
'''
def __init__(self):
super(Interact, self).__init__()
self.filelist = os.listdir(name + '/data')
self.filelist.sort()
self.dates = [dt.strptime(fn[:8], '%Y%m%d') for fn in self.filelist]
self.dict = {
d.strftime('%Y-%m-%d'): i
for i, d in enumerate(self.dates)
}
nums = [i for i in range(74)] #update this
date = param.Selector(default=70, objects=nums)
res = param.Integer(30, bounds=(10, 300), step=10)
sigma = param.Number(1.0, bounds=(0.0, 3.0))
@staticmethod
def _format_plt(fig,
ax,
title,
m=0.02,
bgc='#292929',
axc='#eee',
lblc='#fff'):
ax.margins(m)
ax.set_aspect(aspect=1)
ax.set_xlabel('Easting')
ax.set_ylabel('Northing')
ax.set_title(title, color=axc, loc='left', pad=20)
ax.xaxis.label.set_color(lblc)
ax.yaxis.label.set_color(lblc)
ax.tick_params(axis='x', colors=lblc)
ax.tick_params(axis='y', colors=lblc)
ax.spines['bottom'].set_color(axc)
ax.spines['top'].set_color(axc)
ax.spines['right'].set_color(axc)
ax.spines['left'].set_color(axc)
ax.set_facecolor(bgc)
fig.patch.set_facecolor(bgc)
@staticmethod
def _set_title(fn, opt='scatter'):
'''
'''
date = fn[:4] + '-' + fn[4:6] + '-' + fn[6:8]
if opt == 'scatter':
return date + ': LiDAR Pointcloud'
if opt == 'grid':
return date + ': Interpolation'
@param.depends('date')
def input(self):
'''
'''
fig, ax = plt.subplots(1)
self.filename = self.filelist[self.date]
self.data = Interpolate(filename=self.filename, bounds=c.BOUNDS)
xyz = self.data.xyz
ax.scatter(x=xyz[:, 0], y=xyz[:, 1], c=xyz[:, 2], cmap='viridis')
title = self._set_title(fn=self.filename)
self._format_plt(fig=fig, ax=ax, title=title)
plt.close('all')
return fig
@param.depends('res', 'sigma')
def output(self):
'''
'''
fig, ax = plt.subplots(1)
grid = self.data.interpolate_grid(xyz=self.data.xyz, res=self.res)
self.array = ndimage.gaussian_filter(grid, sigma=self.sigma)
ax.imshow(self.array, origin='lower')
title = self._set_title(fn=self.data.filename, opt='grid')
self._format_plt(fig=fig, ax=ax, title=title)
plt.close('all')
return fig
def export(self):
'''
'''
outfile = TemporaryFile()
np.save(outfile, self.array)
_ = outfile.seek(0)
res = str(self.res)
res = res if len(res) == 3 else '0' + res
sigma = str(self.sigma).replace(".", "")
sigma = sigma if len(sigma) == 2 else sigma + '0'
params = f'R{res}S{sigma}'
name = f'{self.filename[:8]}_NN{params}.npy'
return pn.widgets.FileDownload(file=outfile, filename=name)