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In these patients, the assessment of eyes, speech and motor responses, needed for the Glasgow Coma Scale, is somewhat interfered with, because of the presence of the primary neurological deficit produced by the primary CNS pathology. In such instances, the best eye, speech and limb response which can be achieved (in either of the two eyes, or in any of the four limbs) is the one which is used for the Coma Scale assessment. Despite this interference the Coma Scale, charted at intervals as shown in Fig. 11.3, provides a very valuable guide to an unconscious patient's progress

The remaining two methods, rollback() and rollback max(), rollback a symbol or the maximum of two symbols from time T to time t The actual calculation of the conditional expecations is carried out by the rollback component of the model stored in the controller..

Uzsoky and Solymar (1956) introduce a tolerance factor T that is the ratio of Q to D: T = Q/D. It has also been used by Lo et al. (1966), but called the sensitivity factor S. This factor is important as it tells whether Q is increasing faster than D as the SDR increases. They show that for end re arrays, the uniform case has T 1 over a wide range of spacings; the Hansen Woodyard has T 0.3 over a wide range of spacings; the amplitude-optimized case gives a rapidly rising T for spacings below about 0.2 ; the fully optimized case T rapidly rises for spacings below about 0.45 . Thus the Q is rising fast (roughly exponentially) while the directivity is rising slowly. A different approach was taken by Newman et al. (1978); they de ned a sensitivity factor, which was the sum of element current magnitudes squared divided by the magnitude of the sum of currents. Also de ned was a safety factor, and of course errors in terms of rms values of amplitude, phase, and position errors. For sidelobe delity in the 20, 30, and 40 dB region, the sensitivity factor should be 103, 104, and 105. Using a safety factor of one, the total allowable error (1 ) was roughly 3%, 1%, and 0.3%. Gilbert and Morgan (1955) maximized directivity subject to a xed background pattern (see also Section 2.4.3).

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The ppf.date time.year fraction module provides functionality to compute year fractions:

Compute a new estimate of position, ^ X Xu Dx; Results are shown in Fig. 2.7: Correction equations DXbest DYbest 1 DR1 DR2 ; 2 sin y 1 DR1 DR2 ; 2 cos y 2:23

The Hull White Model >>> r = ppf.model.hull white.requestor() >>> s = ppf.model.hull white.state("eur", 11, 3.5) >>> sx = s.fill(0.25, r, env) >>> f = fill(2.0) >>> PtT = f.numeraire rebased bond(0.25, 1.5, "eur", env, r, sx) >>> for i in range(11): print PtT[i] 1.02531512052 1.02531512052 1.02531512052 1.02531512052 1.02531512052 1.02531512052 1.02531512052 1.02531512052 1.02531512052 1.02531512052 1.02531512052 >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> from ppf.math.interpolation import loglinear times = [0.0, 0.5, 1.0, 1.5, 2.0] factors = [math.exp(-0.05*t) for t in times] c = ppf.market.curve(times, factors, loglinear) expiries = [0.1, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0] tenors = [0, 90] values = numpy.zeros((8, 2)) surf = ppf.market.surface(expiries, tenors, values) from ppf.date time import * pd = date(2008, 01, 01) env = ppf.market.environment(pd) key = "zc.disc.eur" env.add curve(key, c) key = "ve.term.eur.hw" env.add surface(key, surf) key = "cv.mr.eur.hw" env.add constant(key, 0.0) rd = date(2008, 07, 01) libor obs = ppf.core.libor rate(None, 0, 0, rd, "eur",\ rd, shift(rd+months(6), modified following),\ basis act 360, ppf.core.fixing(False)) >>> r = ppf.model.hull white.requestor() >>> s = ppf.model.hull white.state("eur", 11, 3.5) >>> sx = s.fill(0.25, r, env) >>> f = fill(2.0) >>> libortT = f.libor(0.25, libor obs, env, r, sx) >>> for i in range(11): print libortT[i] 0.0499418283138 0.0499418283138 0.0499418283138 0.0499418283138 0.0499418283138