import numpy as np
"""
Contains the data to generate the plots in certain insets.
"""
[docs]def sherbData():
# FOR THE STP INSET BOX/WHISKE
# From Thompson et al. 2012 WAF
sherb_ytexts = ['2.5', '2', '1.5', '1', '0.5', '0', ' ']
sherb_xtexts = ['3SIG', '3SIGTOR', '3NULL', 'ESIG', 'ESIGTOR', 'ENULL']
sherb = [[0.25, 0.8, 1.02, 1.25, 1.95], #3SIG
[0.6, 1.0, 1.2, 1.4, 1.8], #3SIGTOR
[0.1, 0.6, 0.8, 1.05, 1.6], #3NULL
[0.0, 0.6, 1.0, 1.35, 2.3], #ESIG
[0.2, 1.0, 1.25, 1.55, 2.0], # ESIGTOR
[0.0, 0.4, 0.75, 1.0, 1.95]] # ENULL
sherb = np.array(sherb)
stp_inset_data = {}
stp_inset_data['sherb_ytexts'] = sherb_ytexts
stp_inset_data['sherb_xtexts'] = sherb_xtexts
stp_inset_data['sherb'] = sherb
return stp_inset_data
[docs]def shipData():
# FOR THE SHIP INSET BOX/WHISKER
# Developed interally by Ryan Jewell (SPC)
ship_ytexts = ['5', '4', '3', '2', '1', '0', ' ']
ship_xtexts = ['<= 1.5\"', '>= 2.5\"']
ship_dist = [[0.2, 0.3, 0.2, 0.9, 1.2],
[1.1, 1.4, 0.8, 2.8, 4.0]] # <= 1.5"
ship_dist = np.array(ship_dist)
ship_inset_data = {}
ship_inset_data['ship_xtexts'] = ship_xtexts
ship_inset_data['ship_ytexts'] = ship_ytexts
ship_inset_data['ship_dist'] = ship_dist
return ship_inset_data
[docs]def stpData():
# FOR THE STP INSET BOX/WHISKER
# From Thompson et al. 2012 WAF
stp_ytexts = ['11', '10', '9', '8', '7', '6', '5', '4', '3', '2', '1', '0']
stp_xtexts = ['EF4+', 'EF3', 'EF2', 'EF1', 'EF0', 'NONTOR']
ef = [[1.2, 2.6, 5.3, 8.3, 11.0], #ef4
[0.2, 1.0, 2.4, 4.5, 8.4], #ef3
[0.0, 0.6, 1.7, 3.7, 5.6], #ef2
[0.0, 0.3, 1.2, 2.6, 4.5], #ef1
[0.0, 0.1, 0.8, 2.0, 3.7], # ef-0
[0.0, 0.0, 0.2, 0.7, 1.7]] #nontor
ef = np.array(ef)
stp_inset_data = {}
stp_inset_data['stp_ytexts'] = stp_ytexts
stp_inset_data['stp_xtexts'] = stp_xtexts
stp_inset_data['ef'] = ef
return stp_inset_data
[docs]def condSTPData():
# For the CONDITIONAL STP VS EF INSET (LINE PLOT)
# Provided by Bryan Smith (SPC) and Rich Thompson (SPC)
ef1plus = np.asarray([15.3, 28.0, 36.1, 47.8, 52.3, 54.4, 60.3, 61.6, 61.2, 73.2, 73.4])
ef2plus = np.asarray([2.7, 6.6, 6.5, 13.8, 14.9, 17.5, 21.8, 31.5, 33.3, 46.2, 53.1])
ef3plus = np.asarray([0, 1.2, 1.2, 2.7, 2.7, 4.1, 7.3, 10.0, 13.2, 25.8, 39.1])
ef4plus = np.asarray([0, 0, 0, 0, 0.2, .1, 1.5, 2.3, 6.2, 9.7, 17.2])
xtexts = ['0', '.01-.49', '.5-.99', '1-1.99', '2-2.99', '3-3.99', '4-5.99', '6-7.99', '8-9.99', '10-11.99', '>12']
xticks = ['0', '.01-.5', '.5-1', '1-2', '2-3', '3-4', '4-6', '6-8', '8-10', '10-12', '>12']
ytexts = [' ', '0', '10', '20', '30', '40', '50', '60', '70'][::-1]
condSTP_inset_data = {}
condSTP_inset_data['xtexts'] = xtexts
condSTP_inset_data['ytexts'] = ytexts
condSTP_inset_data['xticks'] = xticks
condSTP_inset_data['EF1+'] = ef1plus
condSTP_inset_data['EF2+'] = ef2plus
condSTP_inset_data['EF3+'] = ef3plus
condSTP_inset_data['EF4+'] = ef4plus
return condSTP_inset_data
[docs]def vrotData():
# For the VROT plot
# Provided by Bryan Smith (SPC) and Rich Thompson (SPC)
xtexts = ['0-9.9', '10-19.9', '20-29.9', '30-39.9', '40-49.9', '50-59.9', '60-69.9', '70-79.9', '80-89.9', '90-99.9', '100-109.9']
ytexts = [' ', '0', '10', '20', '30', '40', '50', '60', '70'][::-1]
xpts = np.arange(5, 115, 10)
ef01 = [100.0, 98.6, 95.3, 91.0, 80.2, 61.9, 42.1, 29.1, 16.3, 5.6, 0.0]
ef23 = [0.0, 1.0, 4.7, 9.0, 19.3, 36.5, 51.1, 62.8, 65.1, 50.0, 25.0]
ef45 = [0.0, 0.0, 0.0, 0.0, 0.5, 1.6, 6.8, 8.1, 18.6, 44.4, 75.0]
vrot_inset_data = {}
vrot_inset_data['xpts'] = xpts
vrot_inset_data['xtexts'] = xtexts
vrot_inset_data['ytexts'] = ytexts
vrot_inset_data['EF0-EF1'] = ef01
vrot_inset_data['EF2-EF3'] = ef23
vrot_inset_data['EF4-EF5'] = ef45
return vrot_inset_data