def Merge(self, prec=epsilon):
"""
Merge merges consecutive ramp(s) if they have the same acceleration
"""
if not self.isEmpty:
if Abs((Abs(mp.log10(prec)) - (Abs(mp.floor(mp.log10(prec)))))) < Abs((Abs(mp.log10(prec)) - (Abs(mp.ceil(mp.log10(prec)))))):
precexp = mp.floor(mp.log10(prec))
else:
precexp = mp.ceil(mp.log10(prec))
aCur = self.ramps[0].a
nmerged = 0 # the number of merged ramps
for i in xrange(1, len(self.ramps)):
j = i - nmerged
if (Abs(self.ramps[j].a) > 1):
if Abs((Abs(mp.log10(Abs(self.ramps[j].a))) - (Abs(mp.floor(mp.log10(Abs(self.ramps[j].a))))))) < Abs((Abs(mp.log10(Abs(self.ramps[j].a))) - (Abs(mp.ceil(mp.log10(Abs(self.ramps[j].a))))))):
threshold = 10**(precexp + mp.floor(mp.log10(Abs(self.ramps[j].a))) + 1)
else:
threshold = 10**(precexp + mp.ceil(mp.log10(Abs(self.ramps[j].a))) + 1)
else:
threshold = 10**(precexp)
if Abs(Sub(self.ramps[j].a, aCur)) < threshold:
# merge ramps
redundantRamp = self.ramps.pop(j)
newDur = Add(self.ramps[j - 1].duration, redundantRamp.duration)
self.ramps[j - 1].UpdateDuration(newDur)
# merge switchpointsList
self.switchpointsList.pop(j)
nmerged += 1
else:
aCur = self.ramps[j].a
def v(sigma, t, a0, ksumcache):
if (sigma >= 0):
return 1 + 0.4 * mp.power(9, sigma) / a0 + 0.346 * mp.power(
2, 3 * sigma / 2.0) / (a0**2)
if (sigma < 0):
K = int(mp.floor(-1 * sigma) + 3)
return 1 + mp.power(0.9, mp.ceil(-1 * sigma)) * ksumcache[K]
def compress(self, time_series: List[int]) -> int:
if time_series is None or len(time_series) == 0:
return 0
one_letter_probability = mp.mpf(1.0) / (self._alphabet_max_symbol -
self._alphabet_min_symbol + 1)
series_probability = mp.power(one_letter_probability, len(time_series))
return int(mp.ceil(-mp.log(series_probability, 2)))
def v(sigma, s, t):
T0 = s.imag
T0dash = T0 + mp.pi() * t / 8.0
a0 = mp.sqrt(T0dash / (2 * mp.pi()))
if (sigma >= 0):
return 1 + 0.4 * mp.power(9, sigma) / a0 + 0.346 * mp.power(
2, 3 * sigma / 2.0) / (a0**2)
if (sigma < 0):
K = int(mp.floor(-1 * sigma) + 3)
ksum = 0.0
for k in range(1, K + 2):
ksum += mp.power(1.1 / a0, k) * mp.gamma(mp.mpf(k) / 2.0)
return 1 + mp.power(0.9, mp.ceil(-1 * sigma)) * ksum
b = [root for root in roots if mp.im(root) == mp.mpf(0)][0]
bottom = b + mp.power(b, 2) / (wx + b)
print(
"Print dimensions: {wx!s} x {wy!s}\n"
"Resulting border: {b!s}\n"
"Resulting bottom border: {bottom!s}"
.format(wx=wx, wy=wy, b=b, bottom=bottom)
)
v = dict(
mat_l=0 - b,
mat_t=b,
mat_w=wx + 2 * b,
mat_h=-1 * (wy + b + bottom),
)
v['grid_l'] = 0 - mp.ceil(b)
v['grid_t'] = mp.ceil(b)
v['grid_w'] = mp.ceil(v['mat_w'])
v['grid_h'] = mp.ceil(v['mat_h'] - 1)
print(r"""
\begin{{tikzpicture}}
\draw[help lines,very thin,color=blue!50!green!50,step=1] ({grid_l!s}, {grid_t!s}) grid +({grid_w!s}, {grid_h!s});
% Mat
\draw[fill=black,opacity=0.5] ({mat_l!s},{mat_t!s}) rectangle +({mat_w!s},{mat_h!s});
% Window
\draw[fill=white] (0,0) rectangle +(9,-6);
\end{{tikzpicture}}
""".format(**v))
# Compute roots and weights for different values of x
# Note that there may be some "duplication" if the end of one interval
# does not exactly match the beginning of the next (which may occur with frange)
rtswts = {}
for x in xlist:
if (x in rtswts):
continue
roots, weights = rysroots(n, x)
rtswts[x] = [roots, weights]
print('x = {0}'.format(figs(x)))
for i in range(len(roots)):
print('{0}: {1} {2}'.format(i+1, figs(mp.sqrt(roots[i])), figs(weights[i], exp=True)))
xlist = xlists[n-1]
# Now proceed with the fitting
l_min = int(mp.ceil((fit_degree+1)*fine_step/dx[n-1]))+1
nx = len(xlist)
num = []
coeff = []
center = []
ini = 0
print()
print('{0} points'.format(nx))
l_prev = 0
while (ini < nx-1):
# TODO: bad luck if there are not enough points left to fit
if (ini+l_min > nx):
raise
# l is the number of data points to fit (minimum is l_min)
# ini is the initial point to include in the fit, so the range is ini to ini+l-1 (ini:ini+l for python)
l_max = nx-ini
def segregate(n):
n = int(mp.floor(mp.sqrt(n)) + 1)
n_d = int(mp.ceil(mp.fdiv(n, pro_cnt)))
n_list = [[x, x + n_d] for x in range(1, n, n_d)]
n_list[0][0], n_list[-1][1] = 2, n
return n_list, len(n_list)