Section 08 - Performance Analysis - A - 3X Higher EFPA uncertainty
[1]:
from AMAT.planet import Planet
from AMAT.vehicle import Vehicle
[2]:
import numpy as np
import matplotlib.pyplot as plt
[3]:
planet = Planet('URANUS')
planet.loadAtmosphereModel('../../../atmdata/Uranus/uranus-gram-avg.dat', 0 , 1 ,2, 3, heightInKmFlag=True)
planet.h_skip = 1000.0E3
planet.h_low = 120e3
planet.h_trap = 100e3
[45]:
vehicle=Vehicle('Titania', 3200.0, 146 , 0.24, np.pi*4.5**2.0, 0.0, 1.125, planet)
vehicle.setInitialState(1000.0,-15.22,75.55,29.2877,88.687,-11.7 ,0.0,0.0)
vehicle.setSolverParams(1E-6)
# Set the guidance parameters described in the paper.
# See the function description for parameter details.
# Set max roll rate constraint to 30 deg/s
vehicle.setMaxRollRate(30.0)
# Set Ghdot = 83
# Set Gq = 9.0
# Set v_switch_kms = 16.0 :: this parameter is not really used, and should just be set to low value
# Set low_Alt_km = 120
# Set numPoints_lowAlt = 101
# Set hdot_threshold = -500 m/s
vehicle.setEquilibriumGlideParams(83.0, 9.0, 16.0, 120.0, 101, -500.0)
# Set target orbit parameters
# periapsis = 4000.0 km
# apoapsis = 550,000 km :: set slightly above target (500e3) to account for bias in targeting algorithm
# apoapsis tolerance = 10 km
vehicle.setTargetOrbitParams(4000.0, 550.0E3, 10.0E3)
[46]:
# Set path to atmfiles with randomly perturbed atmosphere files.
atmfiles = ['../../../atmdata/Uranus/perturbed/G-01.txt',
'../../../atmdata/Uranus/perturbed/G-02.txt',
'../../../atmdata/Uranus/perturbed/G-03.txt',
'../../../atmdata/Uranus/perturbed/G-04.txt',
'../../../atmdata/Uranus/perturbed/G-05.txt']
[47]:
vehicle.setupMonteCarloSimulation(1201, 200, atmfiles, 0, 1, 2, 3, 4, True, \
-11.70, 0.30, 0.24, 0.013, 0.5, 0.1, 2400.0)
Small run N = 200
[48]:
vehicle.runMonteCarlo2(200, '../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA')
RUN #: 1, SAMPLE #: 34, EFPA: -11.45, SIGMA: -0.29, LD: 0.23, APO : 506673.46
RUN #: 2, SAMPLE #: 71, EFPA: -11.59, SIGMA: 0.21, LD: 0.25, APO : 556624.79
RUN #: 3, SAMPLE #: 176, EFPA: -12.08, SIGMA: 0.82, LD: 0.23, APO : 187112.93
RUN #: 4, SAMPLE #: 175, EFPA: -11.89, SIGMA: 0.60, LD: 0.25, APO : 569524.02
RUN #: 5, SAMPLE #: 77, EFPA: -11.50, SIGMA: 0.66, LD: 0.25, APO : 553550.53
RUN #: 6, SAMPLE #: 105, EFPA: -11.78, SIGMA: 0.18, LD: 0.25, APO : 547685.80
RUN #: 7, SAMPLE #: 104, EFPA: -11.58, SIGMA: -1.88, LD: 0.25, APO : 517967.97
RUN #: 8, SAMPLE #: 123, EFPA: -11.89, SIGMA: -0.90, LD: 0.22, APO : 595645.42
RUN #: 9, SAMPLE #: 7, EFPA: -11.93, SIGMA: -1.39, LD: 0.24, APO : 521503.83
RUN #: 10, SAMPLE #: 12, EFPA: -12.17, SIGMA: -1.06, LD: 0.24, APO : 96651.14
RUN #: 11, SAMPLE #: 88, EFPA: -12.12, SIGMA: -0.82, LD: 0.25, APO : 323986.00
RUN #: 12, SAMPLE #: 162, EFPA: -11.48, SIGMA: -1.17, LD: 0.26, APO : 460122.79
RUN #: 13, SAMPLE #: 127, EFPA: -11.54, SIGMA: -1.80, LD: 0.22, APO : 505367.37
RUN #: 14, SAMPLE #: 181, EFPA: -11.51, SIGMA: 0.84, LD: 0.22, APO : 493673.51
RUN #: 15, SAMPLE #: 5, EFPA: -11.36, SIGMA: -0.95, LD: 0.23, APO : 566881.26
RUN #: 16, SAMPLE #: 48, EFPA: -11.76, SIGMA: -0.14, LD: 0.26, APO : 570096.60
RUN #: 17, SAMPLE #: 25, EFPA: -11.74, SIGMA: -0.22, LD: 0.25, APO : 523592.41
RUN #: 18, SAMPLE #: 96, EFPA: -11.38, SIGMA: -0.06, LD: 0.23, APO : 475309.84
RUN #: 19, SAMPLE #: 172, EFPA: -11.70, SIGMA: 0.48, LD: 0.25, APO : 580760.70
RUN #: 20, SAMPLE #: 1, EFPA: -11.72, SIGMA: -0.95, LD: 0.24, APO : 526449.26
RUN #: 21, SAMPLE #: 162, EFPA: -12.40, SIGMA: -0.94, LD: 0.26, APO : 68892.80
RUN #: 22, SAMPLE #: 30, EFPA: -11.97, SIGMA: -0.13, LD: 0.23, APO : 524454.64
RUN #: 23, SAMPLE #: 117, EFPA: -11.54, SIGMA: 0.71, LD: 0.24, APO : 555666.99
RUN #: 24, SAMPLE #: 3, EFPA: -11.87, SIGMA: 0.55, LD: 0.22, APO : 528042.58
RUN #: 25, SAMPLE #: 105, EFPA: -11.41, SIGMA: -0.32, LD: 0.23, APO : 539394.10
RUN #: 26, SAMPLE #: 162, EFPA: -11.43, SIGMA: -0.16, LD: 0.24, APO : 540115.78
RUN #: 27, SAMPLE #: 62, EFPA: -11.72, SIGMA: 0.73, LD: 0.23, APO : 518484.73
RUN #: 28, SAMPLE #: 165, EFPA: -11.39, SIGMA: 1.47, LD: 0.22, APO : 554028.22
RUN #: 29, SAMPLE #: 54, EFPA: -11.64, SIGMA: 0.25, LD: 0.24, APO : 509392.68
RUN #: 30, SAMPLE #: 24, EFPA: -11.83, SIGMA: -0.95, LD: 0.25, APO : 513312.77
RUN #: 31, SAMPLE #: 187, EFPA: -12.30, SIGMA: 0.09, LD: 0.26, APO : 60522.60
RUN #: 32, SAMPLE #: 19, EFPA: -11.94, SIGMA: 0.51, LD: 0.24, APO : 543214.54
RUN #: 33, SAMPLE #: 126, EFPA: -11.88, SIGMA: 1.19, LD: 0.25, APO : 485612.84
RUN #: 34, SAMPLE #: 145, EFPA: -11.69, SIGMA: -1.08, LD: 0.24, APO : 560316.43
RUN #: 35, SAMPLE #: 189, EFPA: -11.61, SIGMA: -0.89, LD: 0.26, APO : 483568.68
RUN #: 36, SAMPLE #: 61, EFPA: -11.54, SIGMA: 0.03, LD: 0.23, APO : 487032.59
RUN #: 37, SAMPLE #: 198, EFPA: -11.53, SIGMA: -0.34, LD: 0.25, APO : 531104.99
RUN #: 38, SAMPLE #: 22, EFPA: -11.84, SIGMA: -0.46, LD: 0.23, APO : 509258.99
RUN #: 39, SAMPLE #: 188, EFPA: -10.93, SIGMA: 0.65, LD: 0.25, APO : -205176.79
RUN #: 40, SAMPLE #: 156, EFPA: -11.22, SIGMA: -1.50, LD: 0.24, APO : 523678.71
RUN #: 41, SAMPLE #: 178, EFPA: -11.49, SIGMA: -0.63, LD: 0.25, APO : 495353.57
RUN #: 42, SAMPLE #: 96, EFPA: -12.05, SIGMA: -0.43, LD: 0.24, APO : 426406.27
RUN #: 43, SAMPLE #: 43, EFPA: -11.53, SIGMA: -1.98, LD: 0.24, APO : 526804.77
RUN #: 44, SAMPLE #: 99, EFPA: -11.69, SIGMA: -1.13, LD: 0.24, APO : 490100.12
RUN #: 45, SAMPLE #: 117, EFPA: -11.91, SIGMA: 0.04, LD: 0.26, APO : 577475.03
RUN #: 46, SAMPLE #: 159, EFPA: -11.34, SIGMA: -0.51, LD: 0.26, APO : 498341.98
RUN #: 47, SAMPLE #: 174, EFPA: -12.11, SIGMA: -0.48, LD: 0.23, APO : 626376.51
RUN #: 48, SAMPLE #: 97, EFPA: -11.71, SIGMA: -0.27, LD: 0.25, APO : 488054.59
RUN #: 49, SAMPLE #: 82, EFPA: -12.30, SIGMA: 0.47, LD: 0.24, APO : 66963.08
RUN #: 50, SAMPLE #: 151, EFPA: -11.44, SIGMA: 0.83, LD: 0.27, APO : 525777.52
RUN #: 51, SAMPLE #: 7, EFPA: -11.87, SIGMA: -0.92, LD: 0.27, APO : 495743.58
RUN #: 52, SAMPLE #: 56, EFPA: -11.92, SIGMA: 0.03, LD: 0.25, APO : 561528.54
RUN #: 53, SAMPLE #: 80, EFPA: -11.34, SIGMA: 0.77, LD: 0.24, APO : 570107.95
RUN #: 54, SAMPLE #: 143, EFPA: -12.24, SIGMA: -0.65, LD: 0.24, APO : 139057.25
RUN #: 55, SAMPLE #: 35, EFPA: -11.48, SIGMA: -0.01, LD: 0.24, APO : 505004.92
RUN #: 56, SAMPLE #: 159, EFPA: -11.98, SIGMA: -0.03, LD: 0.25, APO : 529227.96
RUN #: 57, SAMPLE #: 99, EFPA: -11.27, SIGMA: 2.17, LD: 0.26, APO : 527247.87
RUN #: 58, SAMPLE #: 189, EFPA: -12.05, SIGMA: 0.70, LD: 0.24, APO : 407725.38
RUN #: 59, SAMPLE #: 68, EFPA: -12.05, SIGMA: 0.37, LD: 0.23, APO : 376170.65
RUN #: 60, SAMPLE #: 84, EFPA: -12.39, SIGMA: -1.43, LD: 0.26, APO : 57998.27
RUN #: 61, SAMPLE #: 185, EFPA: -11.64, SIGMA: 0.98, LD: 0.24, APO : 531870.29
RUN #: 62, SAMPLE #: 49, EFPA: -11.83, SIGMA: 1.98, LD: 0.23, APO : 538968.12
RUN #: 63, SAMPLE #: 137, EFPA: -11.92, SIGMA: 0.71, LD: 0.24, APO : 551332.65
RUN #: 64, SAMPLE #: 60, EFPA: -11.52, SIGMA: 0.71, LD: 0.24, APO : 550290.89
RUN #: 65, SAMPLE #: 12, EFPA: -11.96, SIGMA: 0.62, LD: 0.24, APO : 562556.88
RUN #: 66, SAMPLE #: 159, EFPA: -11.84, SIGMA: 0.64, LD: 0.25, APO : 568030.72
RUN #: 67, SAMPLE #: 163, EFPA: -11.22, SIGMA: 0.84, LD: 0.26, APO : 566145.91
RUN #: 68, SAMPLE #: 132, EFPA: -11.46, SIGMA: 0.86, LD: 0.23, APO : 532194.47
RUN #: 69, SAMPLE #: 4, EFPA: -12.34, SIGMA: 0.14, LD: 0.26, APO : 64686.18
RUN #: 70, SAMPLE #: 167, EFPA: -11.99, SIGMA: 0.14, LD: 0.23, APO : 612798.55
RUN #: 71, SAMPLE #: 136, EFPA: -11.77, SIGMA: -0.73, LD: 0.24, APO : 521548.01
RUN #: 72, SAMPLE #: 64, EFPA: -11.99, SIGMA: 1.46, LD: 0.23, APO : 459115.22
RUN #: 73, SAMPLE #: 72, EFPA: -11.08, SIGMA: 0.30, LD: 0.25, APO : -287422.20
RUN #: 74, SAMPLE #: 114, EFPA: -12.25, SIGMA: 0.36, LD: 0.25, APO : 94272.19
RUN #: 75, SAMPLE #: 141, EFPA: -12.03, SIGMA: -0.73, LD: 0.24, APO : 569616.70
RUN #: 76, SAMPLE #: 27, EFPA: -11.55, SIGMA: 0.26, LD: 0.23, APO : 533095.62
RUN #: 77, SAMPLE #: 130, EFPA: -11.69, SIGMA: 1.82, LD: 0.24, APO : 520846.97
RUN #: 78, SAMPLE #: 36, EFPA: -11.75, SIGMA: -0.90, LD: 0.24, APO : 512972.74
RUN #: 79, SAMPLE #: 127, EFPA: -11.63, SIGMA: -0.05, LD: 0.21, APO : 518688.77
RUN #: 80, SAMPLE #: 22, EFPA: -11.58, SIGMA: -0.42, LD: 0.23, APO : 547364.21
RUN #: 81, SAMPLE #: 15, EFPA: -11.60, SIGMA: 0.59, LD: 0.25, APO : 595504.14
RUN #: 82, SAMPLE #: 52, EFPA: -12.00, SIGMA: -0.33, LD: 0.23, APO : 597994.50
RUN #: 83, SAMPLE #: 117, EFPA: -11.15, SIGMA: 0.67, LD: 0.25, APO : 510244.25
RUN #: 84, SAMPLE #: 131, EFPA: -11.67, SIGMA: -0.96, LD: 0.24, APO : 540417.75
RUN #: 85, SAMPLE #: 188, EFPA: -12.22, SIGMA: 0.71, LD: 0.27, APO : 100155.29
RUN #: 86, SAMPLE #: 103, EFPA: -11.48, SIGMA: 0.24, LD: 0.23, APO : 514826.19
RUN #: 87, SAMPLE #: 106, EFPA: -11.77, SIGMA: 0.14, LD: 0.25, APO : 481187.97
RUN #: 88, SAMPLE #: 32, EFPA: -11.54, SIGMA: -0.99, LD: 0.25, APO : 564528.65
RUN #: 89, SAMPLE #: 47, EFPA: -11.84, SIGMA: 1.42, LD: 0.26, APO : 509859.79
RUN #: 90, SAMPLE #: 102, EFPA: -11.60, SIGMA: 2.24, LD: 0.24, APO : 511849.64
RUN #: 91, SAMPLE #: 132, EFPA: -11.98, SIGMA: -0.01, LD: 0.25, APO : 611874.01
RUN #: 92, SAMPLE #: 136, EFPA: -11.38, SIGMA: 0.98, LD: 0.24, APO : 553651.84
RUN #: 93, SAMPLE #: 70, EFPA: -12.14, SIGMA: -0.09, LD: 0.25, APO : 196730.11
RUN #: 94, SAMPLE #: 133, EFPA: -11.73, SIGMA: 0.90, LD: 0.24, APO : 535511.49
RUN #: 95, SAMPLE #: 150, EFPA: -12.25, SIGMA: 0.62, LD: 0.25, APO : 94625.31
RUN #: 96, SAMPLE #: 140, EFPA: -11.56, SIGMA: 0.03, LD: 0.23, APO : 534041.02
RUN #: 97, SAMPLE #: 196, EFPA: -12.13, SIGMA: -0.54, LD: 0.25, APO : 162698.74
RUN #: 98, SAMPLE #: 102, EFPA: -11.53, SIGMA: 0.90, LD: 0.27, APO : 496461.04
RUN #: 99, SAMPLE #: 183, EFPA: -11.64, SIGMA: 1.19, LD: 0.26, APO : 573322.78
RUN #: 100, SAMPLE #: 152, EFPA: -11.82, SIGMA: -1.07, LD: 0.23, APO : 561307.00
RUN #: 101, SAMPLE #: 191, EFPA: -12.31, SIGMA: 0.68, LD: 0.25, APO : 83272.41
RUN #: 102, SAMPLE #: 90, EFPA: -11.91, SIGMA: -1.04, LD: 0.23, APO : 504916.71
RUN #: 103, SAMPLE #: 93, EFPA: -11.57, SIGMA: -2.50, LD: 0.21, APO : 466742.75
RUN #: 104, SAMPLE #: 194, EFPA: -12.08, SIGMA: -0.03, LD: 0.24, APO : 313081.80
RUN #: 105, SAMPLE #: 112, EFPA: -11.75, SIGMA: -0.66, LD: 0.26, APO : 510612.42
RUN #: 106, SAMPLE #: 11, EFPA: -11.59, SIGMA: 0.22, LD: 0.24, APO : 523786.76
RUN #: 107, SAMPLE #: 107, EFPA: -11.43, SIGMA: 0.82, LD: 0.24, APO : 512275.22
RUN #: 108, SAMPLE #: 161, EFPA: -11.62, SIGMA: -0.83, LD: 0.23, APO : 523899.91
RUN #: 109, SAMPLE #: 65, EFPA: -12.07, SIGMA: -0.26, LD: 0.25, APO : 255210.84
RUN #: 110, SAMPLE #: 99, EFPA: -11.28, SIGMA: 0.07, LD: 0.26, APO : 531682.23
RUN #: 111, SAMPLE #: 174, EFPA: -12.20, SIGMA: -1.17, LD: 0.23, APO : 132191.14
RUN #: 112, SAMPLE #: 194, EFPA: -11.84, SIGMA: -1.73, LD: 0.21, APO : 601430.74
RUN #: 113, SAMPLE #: 48, EFPA: -11.96, SIGMA: 3.12, LD: 0.24, APO : 244265.00
RUN #: 114, SAMPLE #: 46, EFPA: -12.09, SIGMA: 0.02, LD: 0.25, APO : 195492.86
RUN #: 115, SAMPLE #: 141, EFPA: -11.51, SIGMA: 0.11, LD: 0.25, APO : 535022.74
RUN #: 116, SAMPLE #: 115, EFPA: -11.73, SIGMA: 1.89, LD: 0.25, APO : 495789.22
RUN #: 117, SAMPLE #: 32, EFPA: -10.80, SIGMA: 1.14, LD: 0.23, APO : -98828.45
RUN #: 118, SAMPLE #: 85, EFPA: -11.77, SIGMA: -1.30, LD: 0.25, APO : 511076.81
RUN #: 119, SAMPLE #: 12, EFPA: -11.52, SIGMA: -0.10, LD: 0.23, APO : 525842.13
RUN #: 120, SAMPLE #: 102, EFPA: -11.54, SIGMA: -0.53, LD: 0.24, APO : 496323.94
RUN #: 121, SAMPLE #: 187, EFPA: -11.92, SIGMA: 0.80, LD: 0.24, APO : 518752.40
RUN #: 122, SAMPLE #: 88, EFPA: -11.06, SIGMA: -1.33, LD: 0.24, APO : 483112.54
RUN #: 123, SAMPLE #: 187, EFPA: -11.55, SIGMA: 0.42, LD: 0.25, APO : 521946.94
RUN #: 124, SAMPLE #: 154, EFPA: -11.44, SIGMA: 0.25, LD: 0.22, APO : 545445.85
RUN #: 125, SAMPLE #: 74, EFPA: -12.19, SIGMA: -0.74, LD: 0.25, APO : 132032.22
RUN #: 126, SAMPLE #: 194, EFPA: -12.13, SIGMA: -0.11, LD: 0.24, APO : 219507.13
RUN #: 127, SAMPLE #: 80, EFPA: -12.11, SIGMA: 0.59, LD: 0.26, APO : 232618.23
RUN #: 128, SAMPLE #: 182, EFPA: -11.25, SIGMA: -0.45, LD: 0.22, APO : 535774.95
RUN #: 129, SAMPLE #: 102, EFPA: -11.56, SIGMA: 0.63, LD: 0.24, APO : 481855.46
RUN #: 130, SAMPLE #: 143, EFPA: -11.49, SIGMA: 1.99, LD: 0.23, APO : 490552.70
RUN #: 131, SAMPLE #: 94, EFPA: -10.96, SIGMA: 0.69, LD: 0.25, APO : -121939.67
RUN #: 132, SAMPLE #: 158, EFPA: -11.46, SIGMA: -0.88, LD: 0.24, APO : 482192.20
RUN #: 133, SAMPLE #: 121, EFPA: -11.89, SIGMA: -0.58, LD: 0.22, APO : 488963.26
RUN #: 134, SAMPLE #: 7, EFPA: -11.45, SIGMA: -2.02, LD: 0.25, APO : 532583.58
RUN #: 135, SAMPLE #: 32, EFPA: -11.82, SIGMA: 0.47, LD: 0.24, APO : 490108.25
RUN #: 136, SAMPLE #: 57, EFPA: -11.13, SIGMA: 0.80, LD: 0.24, APO : 508518.14
RUN #: 137, SAMPLE #: 142, EFPA: -11.45, SIGMA: 1.30, LD: 0.22, APO : 497438.64
RUN #: 138, SAMPLE #: 2, EFPA: -11.42, SIGMA: 0.40, LD: 0.24, APO : 514263.47
RUN #: 139, SAMPLE #: 166, EFPA: -11.58, SIGMA: -0.12, LD: 0.26, APO : 501760.01
RUN #: 140, SAMPLE #: 63, EFPA: -11.57, SIGMA: -0.74, LD: 0.24, APO : 552279.86
RUN #: 141, SAMPLE #: 103, EFPA: -11.63, SIGMA: -0.59, LD: 0.24, APO : 462930.31
RUN #: 142, SAMPLE #: 200, EFPA: -11.55, SIGMA: -0.80, LD: 0.23, APO : 520793.05
RUN #: 143, SAMPLE #: 104, EFPA: -11.66, SIGMA: 1.57, LD: 0.25, APO : 504137.98
RUN #: 144, SAMPLE #: 82, EFPA: -11.31, SIGMA: 0.09, LD: 0.25, APO : 484523.34
RUN #: 145, SAMPLE #: 43, EFPA: -11.97, SIGMA: 0.74, LD: 0.23, APO : 562126.82
RUN #: 146, SAMPLE #: 40, EFPA: -11.97, SIGMA: 0.05, LD: 0.25, APO : 598041.44
RUN #: 147, SAMPLE #: 85, EFPA: -11.63, SIGMA: 1.03, LD: 0.23, APO : 502024.29
RUN #: 148, SAMPLE #: 75, EFPA: -11.50, SIGMA: 0.34, LD: 0.26, APO : 475190.44
RUN #: 149, SAMPLE #: 145, EFPA: -11.88, SIGMA: 0.50, LD: 0.24, APO : 563575.46
RUN #: 150, SAMPLE #: 164, EFPA: -12.02, SIGMA: 0.77, LD: 0.25, APO : 571904.08
RUN #: 151, SAMPLE #: 68, EFPA: -11.76, SIGMA: 0.42, LD: 0.24, APO : 513048.45
RUN #: 152, SAMPLE #: 53, EFPA: -11.60, SIGMA: -1.04, LD: 0.26, APO : 530146.52
RUN #: 153, SAMPLE #: 162, EFPA: -12.18, SIGMA: -1.08, LD: 0.26, APO : 237159.38
RUN #: 154, SAMPLE #: 28, EFPA: -11.91, SIGMA: 0.83, LD: 0.24, APO : 558258.11
RUN #: 155, SAMPLE #: 125, EFPA: -11.51, SIGMA: 0.81, LD: 0.22, APO : 485188.63
RUN #: 156, SAMPLE #: 151, EFPA: -11.83, SIGMA: -0.22, LD: 0.24, APO : 520208.92
RUN #: 157, SAMPLE #: 188, EFPA: -11.78, SIGMA: 0.87, LD: 0.26, APO : 519159.11
RUN #: 158, SAMPLE #: 28, EFPA: -11.67, SIGMA: -1.41, LD: 0.24, APO : 499355.17
RUN #: 159, SAMPLE #: 123, EFPA: -11.83, SIGMA: 0.15, LD: 0.25, APO : 506560.75
RUN #: 160, SAMPLE #: 179, EFPA: -11.95, SIGMA: -0.99, LD: 0.26, APO : 497962.99
RUN #: 161, SAMPLE #: 186, EFPA: -11.29, SIGMA: 0.27, LD: 0.21, APO : 547391.74
RUN #: 162, SAMPLE #: 99, EFPA: -11.67, SIGMA: 0.61, LD: 0.23, APO : 533212.78
RUN #: 163, SAMPLE #: 169, EFPA: -11.51, SIGMA: -1.60, LD: 0.25, APO : 514248.79
RUN #: 164, SAMPLE #: 27, EFPA: -11.63, SIGMA: -1.63, LD: 0.23, APO : 522330.35
RUN #: 165, SAMPLE #: 193, EFPA: -11.72, SIGMA: 1.01, LD: 0.23, APO : 573926.87
RUN #: 166, SAMPLE #: 107, EFPA: -12.23, SIGMA: 1.61, LD: 0.25, APO : 91304.29
RUN #: 167, SAMPLE #: 16, EFPA: -11.94, SIGMA: 1.24, LD: 0.23, APO : 538570.35
RUN #: 168, SAMPLE #: 126, EFPA: -11.98, SIGMA: -0.30, LD: 0.26, APO : 585785.21
RUN #: 169, SAMPLE #: 32, EFPA: -11.45, SIGMA: -0.06, LD: 0.26, APO : 517495.31
RUN #: 170, SAMPLE #: 55, EFPA: -11.79, SIGMA: 0.17, LD: 0.23, APO : 528475.24
RUN #: 171, SAMPLE #: 90, EFPA: -11.71, SIGMA: -1.64, LD: 0.22, APO : 566690.73
RUN #: 172, SAMPLE #: 3, EFPA: -12.19, SIGMA: 0.86, LD: 0.25, APO : 134939.05
RUN #: 173, SAMPLE #: 127, EFPA: -11.72, SIGMA: -1.54, LD: 0.25, APO : 548083.53
RUN #: 174, SAMPLE #: 196, EFPA: -11.90, SIGMA: 0.30, LD: 0.22, APO : 533782.16
RUN #: 175, SAMPLE #: 151, EFPA: -11.25, SIGMA: -1.23, LD: 0.26, APO : 549103.34
RUN #: 176, SAMPLE #: 53, EFPA: -11.27, SIGMA: -0.80, LD: 0.26, APO : 510315.42
RUN #: 177, SAMPLE #: 134, EFPA: -12.11, SIGMA: -0.53, LD: 0.25, APO : 218600.35
RUN #: 178, SAMPLE #: 45, EFPA: -11.52, SIGMA: 0.86, LD: 0.25, APO : 507244.97
RUN #: 179, SAMPLE #: 36, EFPA: -11.89, SIGMA: -1.06, LD: 0.25, APO : 516131.23
RUN #: 180, SAMPLE #: 16, EFPA: -11.39, SIGMA: 0.43, LD: 0.21, APO : 538555.96
RUN #: 181, SAMPLE #: 25, EFPA: -11.50, SIGMA: 1.77, LD: 0.25, APO : 502387.15
RUN #: 182, SAMPLE #: 153, EFPA: -11.68, SIGMA: 0.44, LD: 0.25, APO : 535382.93
RUN #: 183, SAMPLE #: 77, EFPA: -11.64, SIGMA: 0.70, LD: 0.24, APO : 517909.23
RUN #: 184, SAMPLE #: 58, EFPA: -11.66, SIGMA: -0.06, LD: 0.23, APO : 517697.47
RUN #: 185, SAMPLE #: 79, EFPA: -11.60, SIGMA: -0.31, LD: 0.22, APO : 514688.57
RUN #: 186, SAMPLE #: 12, EFPA: -11.31, SIGMA: 0.34, LD: 0.23, APO : 574256.15
RUN #: 187, SAMPLE #: 127, EFPA: -11.96, SIGMA: -0.28, LD: 0.24, APO : 529303.89
RUN #: 188, SAMPLE #: 53, EFPA: -12.08, SIGMA: 1.61, LD: 0.22, APO : 206067.92
RUN #: 189, SAMPLE #: 29, EFPA: -11.62, SIGMA: -0.17, LD: 0.24, APO : 541876.60
RUN #: 190, SAMPLE #: 54, EFPA: -12.03, SIGMA: 0.01, LD: 0.23, APO : 305478.73
RUN #: 191, SAMPLE #: 83, EFPA: -11.60, SIGMA: 0.92, LD: 0.24, APO : 547886.69
RUN #: 192, SAMPLE #: 108, EFPA: -11.58, SIGMA: 1.05, LD: 0.22, APO : 582551.95
RUN #: 193, SAMPLE #: 102, EFPA: -11.71, SIGMA: 0.31, LD: 0.25, APO : 486528.59
RUN #: 194, SAMPLE #: 81, EFPA: -11.54, SIGMA: -1.28, LD: 0.23, APO : 511513.94
RUN #: 195, SAMPLE #: 54, EFPA: -11.66, SIGMA: 1.51, LD: 0.24, APO : 516495.86
RUN #: 196, SAMPLE #: 69, EFPA: -11.41, SIGMA: -0.20, LD: 0.24, APO : 483598.91
RUN #: 197, SAMPLE #: 54, EFPA: -12.02, SIGMA: -0.22, LD: 0.24, APO : 381775.46
RUN #: 198, SAMPLE #: 107, EFPA: -11.78, SIGMA: 0.18, LD: 0.26, APO : 518064.83
RUN #: 199, SAMPLE #: 111, EFPA: -11.54, SIGMA: 0.91, LD: 0.24, APO : 536557.12
RUN #: 200, SAMPLE #: 10, EFPA: -11.74, SIGMA: 2.13, LD: 0.23, APO : 512372.21
[4]:
p1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/terminal_periapsis_arr.txt')
x1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/terminal_apoapsis_arr.txt')
y1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/acc_net_g_max_arr.txt')
z1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/q_stag_max_arr.txt')
h1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/heatload_max_arr.txt')
u1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/periapsis_raise_DV_arr.txt')
v1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/apoapsis_raise_DV_arr.txt')
[6]:
fig, axes = plt.subplots(1, 2, figsize = (6.5, 3.25))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].hist(x1/1000.0, bins=10, color='xkcd:periwinkle')
axes[0].set_xlabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[0].set_ylabel('Number of cases', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].plot(p1, x1/1000.0, 'bo', markersize=5, mfc='None')
axes[1].set_xlabel('Periapsis altitude, km',fontsize=12)
axes[1].set_ylabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.show()
Remove cases which did not capture
[7]:
del_index1 = np.where(x1 < 0)
del_index1
[7]:
(array([ 38, 72, 116, 130]),)
These four cases failed to capture. Delete them from the results for plotting.
[8]:
p1 = np.delete(p1, del_index1 )
x1 = np.delete(x1, del_index1 )
y1 = np.delete(y1, del_index1 )
z1 = np.delete(z1, del_index1 )
h1 = np.delete(h1, del_index1 )
u1 = np.delete(u1, del_index1 )
v1 = np.delete(v1, del_index1 )
[9]:
fig, axes = plt.subplots(1, 2, figsize = (6.5, 3.25))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].hist(x1/1000.0, bins=20, color='xkcd:periwinkle')
axes[0].set_xlabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[0].set_ylabel('Number of cases', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].plot(p1, x1/1000.0, 'bo', markersize=5, mfc='None')
axes[1].set_xlabel('Periapsis altitude, km',fontsize=12)
axes[1].set_ylabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.show()
[13]:
print("--------------------------------------------------------------------------------------------------------")
print("Parameter | Min. 0.13-percentile Mean 99.87-percentile Max")
print("--------------------------------------------------------------------------------------------------------")
print("Apoapsis altitude, km | " + str(round(np.min(x1))) + " " + str(round(np.percentile(x1, 0.13))) +\
" " + str(round(np.average(x1))) +
" " + str(round(np.percentile(x1, 99.87)))+\
" " + str(round(np.max(x1))))
print("Periapsis altitude, km | " + str(round(np.min(p1))) + " " + str(round(np.percentile(p1, 0.13))) +\
" " + str(round(np.average(p1))) +
" " + str(round(np.percentile(p1, 99.87)))+\
" " + str(round(np.max(p1))))
print("Peak deceleration, g | " + str(round(np.min(y1), 2)) + " " + str(round(np.percentile(y1, 0.13), 2)) +\
" " + str(round(np.average(y1), 2)) +
" " + str(round(np.percentile(y1, 99.87), 2))+\
" " + str(round(np.max(y1), 2)))
print("Peak heat rate, W/cm2 | " + str(round(np.min(z1))) + " " + str(round(np.percentile(z1, 0.13))) +\
" " + str(round(np.average(z1))) +
" " + str(round(np.percentile(z1, 99.87)))+\
" " + str(round(np.max(z1))))
print("Peak heat load, kJ/cm2 | " + str(round(np.min(h1))) + " " + str(round(np.percentile(h1, 0.13))) +\
" " + str(round(np.average(h1))) +
" " + str(round(np.percentile(h1, 99.87)))+\
" " + str(round(np.max(h1))))
print("Periapsis raise dV, m/s| " + str(round(np.min(u1), 2)) + " " + str(round(np.percentile(u1, 0.13), 2)) +\
" " + str(round(np.average(u1), 2)) +
" " + str(round(np.percentile(u1, 99.87), 2))+\
" " + str(round(np.max(u1), 2)))
print("Apoapsis corr. dV, m/s | " + str(round(np.min(v1), 2)) + " " + str(round(np.percentile(v1, 0.13), 2)) +\
" " + str(round(np.average(v1), 2)) +
" " + str(round(np.percentile(v1, 99.87), 2))+\
" " + str(round(np.max(v1), 2)))
print("--------------------------------------------------------------------------------------------------------")
--------------------------------------------------------------------------------------------------------
Parameter | Min. 0.13-percentile Mean 99.87-percentile Max
--------------------------------------------------------------------------------------------------------
Apoapsis altitude, km | 57998 58638 471497 622934 626377
Periapsis altitude, km | 36 38 179 235 241
Peak deceleration, g | 5.45 5.55 8.57 12.78 12.94
Peak heat rate, W/cm2 | 1482 1485 1766 2017 2022
Peak heat load, kJ/cm2 | 201 201 219 254 254
Periapsis raise dV, m/s| 56.76 56.98 85.12 315.61 317.38
Apoapsis corr. dV, m/s | -55.49 -53.23 187.45 2276.22 2292.84
--------------------------------------------------------------------------------------------------------
[11]:
fig, axes = plt.subplots(1, 2, figsize = (6.75, 3.5))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].axvline(x=550.0, linewidth=1.5, color='k', linestyle='dashed')
axes[0].hist(x1/1000.0, bins=20, color='xkcd:periwinkle')
axes[0].set_xlabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[0].set_ylabel('Number of cases', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].plot(p1, x1/1000.0, 'bo', markersize=5, mfc='None')
axes[1].axhline(y=550.0, linewidth=1.5, color='k', linestyle='dashed')
axes[1].set_xlabel('Periapsis altitude, km',fontsize=12)
axes[1].set_ylabel('Apoapsis altitude x '+r'$10^3$'+', km', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/periapsis-vs-apoapsis-XA.png', dpi= 300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/periapsis-vs-apoapsis-XA.pdf', dpi=300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/periapsis-vs-apoapsis-XA.eps', dpi=300,bbox_inches='tight')
plt.show()
[14]:
fig, axes = plt.subplots(1, 2, figsize = (6.75, 3.5))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].plot(y1, z1, 'ro', markersize=5, mfc='None')
axes[0].axhline(y=np.percentile(z1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[0].axvline(x=np.percentile(y1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[0].set_xlabel('Peak deceleration, Earth g', fontsize=12)
axes[0].set_ylabel('Peak stagnation-point heat rate, '+r'$W/cm^2$', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].plot(z1, h1, 'go', markersize=5, mfc='None')
axes[1].axhline(y=np.percentile(h1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[1].axvline(x=np.percentile(z1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[1].set_xlabel('Peak stagnation-point heat rate, '+r'$W/cm^2$', fontsize=12)
axes[1].set_ylabel('Peak stagnation-point heat load, '+r'$kJ/cm^2$', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/heat-rate-vs-g-load-XA.png', dpi= 300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/heat-rate-vs-g-load-XA.pdf', dpi=300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/heat-rate-vs-g-load-XA.eps', dpi=300,bbox_inches='tight')
plt.show()
[59]:
fig, axes = plt.subplots(1, 2, figsize = (6.75, 3.5))
plt.rc('font',family='Times New Roman')
params = {'mathtext.default': 'regular' }
plt.rcParams.update(params)
fig.tight_layout()
plt.subplots_adjust(wspace=0.30)
axes[0].hist(u1, bins=30, color='xkcd:periwinkle')
axes[0].axvline(x=np.percentile(u1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[0].set_xlabel('Periapse raise '+r'$\Delta V$'+', m/s', fontsize=12)
axes[0].set_ylabel('Number of cases', fontsize=12)
axes[0].tick_params(direction='in')
axes[0].yaxis.set_ticks_position('both')
axes[0].xaxis.set_ticks_position('both')
axes[0].tick_params(axis='x',labelsize=12)
axes[0].tick_params(axis='y',labelsize=12)
axes[1].hist(v1, bins=30, color='xkcd:orange')
axes[1].axvline(x=np.percentile(v1, 99.87), linewidth=1.5, color='k', linestyle='dotted')
axes[1].set_xlabel('Apoapsis correction '+r'$\Delta V$'+', m/s', fontsize=12)
axes[1].set_ylabel('Number of cases', fontsize=12)
axes[1].tick_params(direction='in')
axes[1].yaxis.set_ticks_position('both')
axes[1].xaxis.set_ticks_position('both')
axes[1].tick_params(axis='x',labelsize=12)
axes[1].tick_params(axis='y',labelsize=12)
for ax in axes:
ax.xaxis.set_tick_params(direction='in', which='both')
ax.yaxis.set_tick_params(direction='in', which='both')
ax.xaxis.set_tick_params(width=1, length=8)
ax.yaxis.set_tick_params(width=1, length=8)
ax.xaxis.set_tick_params(width=1, length=6, which='minor')
ax.yaxis.set_tick_params(width=1, length=6, which='minor')
ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)
ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_linewidth(2)
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/prm-arm-maneuvers-XA.png', dpi= 300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/prm-arm-maneuvers-XA.pdf', dpi=300,bbox_inches='tight')
plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/prm-arm-maneuvers-XA.eps', dpi=300,bbox_inches='tight')
plt.show()
[15]:
def percent_between(a, lower, upper, closed_left=True, closed_right=True):
"""
Finds the percentage of values between a range for a numpy array
Parameters
----------
a: np.ndarray
numpy array to calculate percentage
lower: int, float
lower bound
upper: int, float
upper bound
closed_left:
closed left bound ( > vs >= )
closed_right:
closed right bound ( < vs <= )
"""
l = np.greater if not closed_left else np.greater_equal
r = np.less if not closed_right else np.less_equal
c = l(a, lower) & r(a, upper)
return np.count_nonzero(c) / float(a.size)
[20]:
p1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/terminal_periapsis_arr.txt')
x1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/terminal_apoapsis_arr.txt')
y1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/acc_net_g_max_arr.txt')
z1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/q_stag_max_arr.txt')
h1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/heatload_max_arr.txt')
u1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/periapsis_raise_DV_arr.txt')
v1 = np.loadtxt('../../../data/acta-astronautica/uranus-orbiter-probe/MCBXA/apoapsis_raise_DV_arr.txt')
[26]:
percent_between(x1, -1e9, 0)
[26]:
0.02
[21]:
percent_between(x1, 500e3, 600e3)
[21]:
0.635
[22]:
percent_between(x1, 450e3, 650e3)
[22]:
0.815
[23]:
percent_between(x1, 350e3, 750e3)
[23]:
0.835
[29]:
percent_between(x1, 0, 7500e3)
[29]:
0.98
[31]:
1 - percent_between(x1, 350e3, 750e3) - percent_between(x1, -1e9, 0)
[31]:
0.14500000000000005
[34]:
0.145 + 0.835 +0.02
[34]:
1.0
[ ]: