def driveSelector(DRIVE, TYPE):
# Split Drive #############################################################
if DRIVE == 'SD':
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(
["WA", "H", "R", "B", "C", "WB"], gsd.SD_ECO)
yRange = 12500 * 2
colors = COLEN
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(["H*", "O-", "Total"],
gsd.SD_HLT)
yRange = 12500
colors = COLHN
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(["C*", "O-", "Total"],
gsd.SD_TRS)
yRange = 12500
colors = COLTN
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(["O-", "W*", "Total"],
gsd.SD_WLD)
yRange = 12500
colors = COLWN
# Return values ###########################################################
return {'gDict': aggD, 'yRange': yRange, 'colors': colors}
def driveSelector(DRIVE, HEALTH, pathRoot):
if DRIVE == 1:
(pathExt, pathO) = ("SplitDrive/2019_10_12_GARBAGE/", 'SD')
if HEALTH is True:
aggregationDictionary = monet.generateAggregationDictionary(
["H", "Other", "Total"],
[[4, 5, 6, 11, 14, 15, 16, 21, 24, 25, 26],
[
0, 1, 2, 3, 7, 8, 9, 10, 12, 13, 17, 18, 19, 20, 22, 23,
27, 28, 29
],
[
4, 5, 6, 11, 14, 15, 16, 21, 24, 25, 26, 0, 1, 2, 3, 7, 8,
9, 10, 12, 13, 17, 18, 19, 20, 22, 23, 27, 28, 29
]])
else:
aggregationDictionary = monet.generateAggregationDictionary(
["H", "B", "R", "W", "C", "Total"],
[[1, 4, 4, 5, 6, 11, 14, 14, 15, 16, 21, 24, 24, 25, 26],
[3, 6, 8, 9, 9, 13, 16, 18, 19, 19, 23, 26, 28, 29, 29],
[2, 5, 7, 7, 8, 12, 15, 17, 17, 18, 22, 25, 27, 27, 28],
[
0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6,
7, 7, 8, 8, 9, 9, 10, 10, 10, 11, 11, 12, 12, 13, 13, 14,
15, 16, 17, 18, 19, 20, 20, 21, 22, 23
],
[
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 20, 21, 21,
22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, 28, 28,
29, 29
],
[
1, 4, 4, 5, 6, 11, 14, 14, 15, 16, 21, 24, 24, 25, 26, 3,
6, 8, 9, 9, 13, 16, 18, 19, 19, 23, 26, 28, 29, 29, 2, 5,
7, 7, 8, 12, 15, 17, 17, 18, 22, 25, 27, 27, 28, 0, 0, 0,
0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8,
8, 9, 9, 10, 10, 10, 11, 11, 12, 12, 13, 13, 14, 15, 16,
17, 18, 19, 20, 20, 21, 22, 23, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24,
25, 25, 26, 26, 27, 27, 28, 28, 29, 29
]])
if HEALTH is True:
prepend = "H"
else:
prepend = "E"
if pathRoot != '':
pathsRoot = monet.listDirectoriesWithPathWithinAPath(pathRoot +
pathExt)
else:
pathsRoot = ''
return [pathsRoot, aggregationDictionary, prepend, pathO]
def driveParameters(TYPE, popSize):
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(*ASD_ECO)
yRange = popSize
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(*ASD_HLT)
yRange = popSize / 2
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(*ASD_TRS)
yRange = popSize / 2
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(*ASD_WLD)
yRange = popSize
return (aggD, yRange, 'autosomal')
def driveParameters(TYPE, popSize):
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(*YXS_ECO)
yRange = popSize
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(*YXS_HLT)
yRange = popSize / 2
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(*YXS_TRS)
yRange = popSize / 2
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(*YXS_WLD)
yRange = popSize
return (aggD, yRange, 'yLinked')
def driveParameters(TYPE, popSize):
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(*CRS_ECO)
yRange = popSize
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(*CRS_HLT)
yRange = popSize / 4
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(*CRS_TRS)
yRange = popSize
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(*CRS_WLD)
yRange = popSize
return (aggD, yRange, 'SplitDrive-YG')
def driveParameters(TYPE, popSize):
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(*FMS_ECO)
yRange = popSize * 4
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(*FMS_HLT)
yRange = popSize
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(*FMS_TRS)
yRange = popSize / 2
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(*FMS_WLD)
yRange = popSize / 2
return (aggD, yRange, 'ifegenia_5')
def driveParameters(TYPE, popSize):
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(*LDR_ECO)
yRange = popSize*2
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(*LDR_HLT)
yRange = popSize/2
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(*LDR_TRS)
yRange = popSize/2
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(*LDR_WLD)
yRange = popSize/2
return (aggD, yRange, 'LDR')
def driveSelector(DRIVE):
if DRIVE == 1:
id = "CLEAVR"
aggregationDictionary = monet.generateAggregationDictionary(
["W1", "W2", "H1", "B1", "B2", "R1"],
[[0, 6, 12, 0, 1, 2, 6, 7, 8, 12, 13, 14],
[0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[1, 3, 7, 9, 13, 15, 3, 4, 9, 10, 15, 16],
[2, 4, 5, 8, 10, 11, 14, 16, 17, 5, 11, 17],
[
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 12, 13, 14, 15,
16, 17
], []])
elif DRIVE == 2:
id = "CLEAVRX"
aggregationDictionary = monet.generateAggregationDictionary(
["W1", "W2", "H1", "B1", "B2", "R1"],
[[0, 6, 12, 18, 21, 24, 0, 1, 2, 6, 7, 8, 12, 13, 14, 18, 21, 24],
[
0, 1, 2, 3, 4, 5, 18, 19, 20, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 18, 19, 20, 21, 22, 23
],
[1, 3, 7, 9, 13, 15, 19, 22, 25, 3, 4, 9, 10, 15, 16, 19, 22, 25],
[
2, 4, 5, 8, 10, 11, 14, 16, 17, 20, 23, 26, 5, 11, 17, 20, 23,
26
],
[
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 21, 22, 23, 24,
25, 26, 12, 13, 14, 15, 16, 17, 24, 25, 26
], []])
elif DRIVE == 3:
id = "CRISPR"
aggregationDictionary = monet.generateAggregationDictionary(
["W1", "W2", "H1", "B1", "B2", "R1"],
[[0, 1, 2, 3, 0], [], [4, 5, 6, 1, 4], [9, 3, 6, 8, 9], [],
[7, 8, 2, 5, 7]])
elif DRIVE == 4:
id = "CRISPRX"
aggregationDictionary = monet.generateAggregationDictionary(
["W1", "W2", "H1", "B1", "B2", "R1"],
[[0, 1, 2, 3, 0, 4], [], [4, 5, 6, 1, 4, 8], [9, 3, 6, 8, 9, 13],
[], [7, 8, 2, 5, 7, 11]])
return [id, aggregationDictionary]
def driveSelectorVideo(DRIVE, HEALTH, pathRoot):
if DRIVE == 1:
(pathExt, pathO) = ("SplitDrive/2019_10_12_GARBAGE/", 'SD')
if HEALTH is True:
aggregationDictionary = monet.generateAggregationDictionary(
["H", "Other"], [[4, 5, 6, 11, 14, 15, 16, 21, 24, 25, 26],
[
0, 1, 2, 3, 7, 8, 9, 10, 12, 13, 17, 18,
19, 20, 22, 23, 27, 28, 29
]])
colors = ["#f20060A0", "#b0a9fc00"]
else:
aggregationDictionary = monet.generateAggregationDictionary(
["H", "B", "R", "W", "C"],
[[1, 4, 4, 5, 6, 11, 14, 14, 15, 16, 21, 24, 24, 25, 26],
[3, 6, 8, 9, 9, 13, 16, 18, 19, 19, 23, 26, 28, 29, 29],
[2, 5, 7, 7, 8, 12, 15, 17, 17, 18, 22, 25, 27, 27, 28],
[
0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6,
7, 7, 8, 8, 9, 9, 10, 10, 10, 11, 11, 12, 12, 13, 13, 14,
15, 16, 17, 18, 19, 20, 20, 21, 22, 23
],
[
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 20, 21, 21,
22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 27, 28, 28,
29, 29
]])
colors = [
"#09044620", "#f2006020", "#c6d8ff20", "#7692ff20",
"#29339b20", "#7fff3a20"
]
if HEALTH is True:
prepend = "H"
else:
prepend = "E"
if pathRoot != '':
pathsRoot = monet.listDirectoriesWithPathWithinAPath(pathRoot +
pathExt)
else:
pathsRoot = ''
return [pathsRoot, aggregationDictionary, colors, pathO]
import numpy as np
#import temporaryFunctionsDefinitions as tempFun
import plotly
import plotly.graph_objs as go
import plotly.offline as offline
offline.init_notebook_mode(connected=True)
###############################################################################
# Factorial Experiment Example
###############################################################################
dataType = float
filename = "2018_10_10_ANALYZED"
path = "/Users/sanchez.hmsc/Desktop/" + filename + "/"
wildsList = [1, 1, 2, 2, 3, 3, 4, 5, 6, 1, 1, 2, 4, 4, 5, 7, 7, 8]
homingList = [4, 5, 6, 7, 7, 8, 8, 9, 9, 2, 3, 3, 5, 6, 6, 8, 9, 9]
aggregationDictionary = monet.generateAggregationDictionary(
["W", "H"], [[x - 1 for x in wildsList], [x - 1 for x in homingList]])
ratiosDictionary = {"numerator": [1], "denominator": [0, 1]}
###############################################################################
# Export Individual CSVs for Factorial Slots
###############################################################################
# Running in single-thread
# experimentFolders=fac.listDirectoriesInPath(path)
# for folder in experimentFolders:
# fac.loadFolderAndWriteFactorialCSV(
# folder,
# path,
# aggregationDictionary,
# ratiosDictionary
# )
import MoNeT_MGDrivE as monet
import numpy as np
# -----------------------------------------------------------------------------
# Loading Data
# -----------------------------------------------------------------------------
# Define the experiment's path, aggregation dictionary, and read filenames
type = float
experimentString = "0001"
path = "/Users/Biyonka/Desktop/Output/1clustering_analysis/drive_output/eta20001_iter1/ANALYZED/"
aggregationDictionary = monet.generateAggregationDictionary(
["W", "H", "R", "B"],
[[0, 0, 1, 2, 3], [1, 4, 4, 5, 6], [2, 5, 7, 7, 8], [3, 6, 8, 9, 9]])
filenames = monet.readExperimentFilenames(path + experimentString)
# To analyze a single node ...................................................
# Load a single node (auxiliary function just for demonstration)
nodeIndex = 0
nodeData = monet.loadNodeData(filenames.get("male")[nodeIndex],
filenames.get("female")[nodeIndex],
dataType=float)
# To analyze the sum of the whole landscape ..................................
# Sum landscape into one array ("in place" memory-wise)
landscapeSumData = monet.sumLandscapePopulationsFromFiles(filenames,
male=True,
female=True,
dataType=float)
# Aggregate genotypes (node or landscape) ....................................
aggData = monet.aggregateGenotypesInNode(landscapeSumData,
aggregationDictionary)
def driveParameters(TYPE, popSize=360000):
if TYPE == 'HUM':
aggD = monet.generateAggregationDictionary(*HUM_HLT)
yRange = popSize
return (aggD, yRange, 'HUM')
INT_DICTIONARY = monet.generateAggregationDictionary(
["R1","R2,""R1+R2", "All"],
[
[
13, 13, 14, 14, 18, 18, 19, 19, 32, 32, 33, 33, 37, 37, 38, 38, 50,
50, 51, 51, 55, 55, 56, 56, 64, 64, 65, 65, 66, 66, 67, 68, 69, 69,
70, 70, 71, 71, 72, 73, 80, 80, 81, 81, 82, 82, 83, 84, 85, 85, 86,
86, 87, 87, 88, 89, 98, 98, 99, 99, 103, 103, 104, 104, 112, 112,
113, 113, 117, 117, 118, 118, 125, 125, 126, 126, 130, 130, 131, 131,
134, 134, 135, 135, 136, 136, 137, 138, 139, 139, 140, 140, 141, 141,
142, 143, 145, 145, 146, 146, 147, 147, 148, 149, 150, 150, 151, 151,
152, 152, 153, 154, 158, 159, 163, 164, 167, 168, 172, 173, 175, 176,
180, 181, 184, 185, 186, 190, 191, 192, 198, 199, 202, 203, 205, 206,
223, 223, 224, 224, 228, 228, 229, 229
],
[
0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5,
5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10,
11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 14, 14, 15, 15, 15, 15, 16,
16, 16, 16, 17, 17, 17, 17, 18, 18, 19, 19, 20, 20, 20, 20, 21, 21,
21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 24, 24, 24, 24, 25, 25, 25,
25, 26, 26, 26, 26, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29,
30, 30, 30, 30, 31, 31, 31, 31, 32, 32, 33, 33, 34, 34, 34, 34, 35,
35, 35, 35, 36, 36, 36, 36, 37, 37, 38, 38, 39, 39, 39, 39, 40, 40,
40, 40, 41, 41, 41, 41, 42, 42, 42, 42, 43, 43, 43, 43, 44, 44, 44,
44, 45, 45, 45, 45, 46, 46, 46, 46, 47, 47, 47, 47, 48, 48, 48, 48,
49, 49, 49, 49, 50, 50, 51, 51, 52, 52, 52, 52, 53, 53, 53, 53, 54,
54, 54, 54, 55, 55, 56, 56, 57, 57, 57, 57, 58, 58, 58, 58, 59, 59,
59, 59, 60, 60, 60, 60, 61, 61, 61, 61, 62, 62, 62, 62, 63, 63, 63,
63, 64, 64, 65, 65, 66, 66, 67, 68, 69, 69, 70, 70, 71, 71, 72, 73,
74, 74, 74, 74, 75, 75, 75, 75, 76, 76, 76, 76, 77, 77, 77, 77, 78,
78, 78, 78, 79, 79, 79, 79, 80, 80, 81, 81, 82, 82, 83, 84, 85, 85,
86, 86, 87, 87, 88, 89, 90, 90, 90, 90, 91, 91, 91, 91, 92, 92, 92,
92, 93, 93, 93, 93, 94, 94, 94, 94, 95, 95, 95, 95, 96, 96, 96, 96,
97, 97, 97, 97, 98, 98, 99, 99, 100, 100, 100, 100, 101, 101, 101,
101, 102, 102, 102, 102, 103, 103, 104, 104, 105, 105, 105, 105, 106,
106, 106, 106, 107, 107, 107, 107, 108, 108, 108, 108, 109, 109, 109,
109, 110, 110, 110, 110, 111, 111, 111, 111, 112, 112, 113, 113, 114,
114, 114, 114, 115, 115, 115, 115, 116, 116, 116, 116, 117, 117, 118,
118, 119, 119, 119, 119, 120, 120, 120, 120, 121, 121, 121, 121, 122,
122, 122, 122, 123, 123, 123, 123, 124, 124, 124, 124, 125, 125, 126,
126, 127, 127, 127, 127, 128, 128, 128, 128, 129, 129, 129, 129, 130,
130, 131, 131, 132, 132, 132, 132, 133, 133, 133, 133, 134, 134, 135,
135, 136, 136, 137, 138, 139, 139, 140, 140, 141, 141, 142, 143, 144,
144, 144, 144, 145, 145, 146, 146, 147, 147, 148, 149, 150, 150, 151,
151, 152, 152, 153, 154, 155, 155, 155, 155, 156, 156, 156, 156, 157,
157, 157, 157, 158, 159, 160, 160, 160, 160, 161, 161, 161, 161, 162,
162, 162, 162, 163, 164, 165, 165, 165, 165, 166, 166, 166, 166, 167,
168, 169, 169, 169, 169, 170, 170, 170, 170, 171, 171, 171, 171, 172,
173, 174, 174, 174, 174, 175, 176, 177, 177, 177, 177, 178, 178, 178,
178, 179, 179, 179, 179, 180, 181, 184, 185, 186, 190, 191, 192, 195,
195, 195, 195, 196, 196, 196, 196, 197, 197, 197, 197, 198, 199, 200,
200, 200, 200, 201, 201, 201, 201, 202, 203, 204, 204, 204, 204, 205,
206, 210, 210, 210, 210, 211, 211, 211, 211, 212, 212, 212, 212, 213,
213, 213, 213, 214, 214, 214, 214, 215, 215, 215, 215, 216, 216, 216,
216, 217, 217, 217, 217, 218, 218, 218, 218, 219, 219, 219, 219, 220,
220, 220, 220, 221, 221, 221, 221, 222, 222, 222, 222, 223, 223, 224,
224, 225, 225, 225, 225, 226, 226, 226, 226, 227, 227, 227, 227, 228,
228, 229, 229
]
]
)
]
COLEO = [i[:-2]+'FF' for i in COLEN]
COLEM = monet.generateAlphaColorMapFromColorArray(COLEO)
###############################################################################
# Gene-Dictionaries
###############################################################################
# Linked Drive Replacement ----------------------------------------------------
# 0: WW, 1: WH, 2: WR, 3: WB, 4: HH,
# 5: HR, 6: HB, 7: RR, 8: RB, 9: BB
drive_LDR = {
'id': 'Replacement', 'folder': 'LDR', 'loc': 1,
'HLT': {
'gDict': monet.generateAggregationDictionary(
["H", "Other", "Total"],
[
[1, 4, 5, 6],
[0, 2, 3, 7, 8, 9],
[1, 4, 5, 6, 0, 2, 3, 7, 8, 9]
]
),
'color': [COLHN, COLHO, COLHM]
},
'ECO': {
'gDict': monet.generateAggregationDictionary(
["H", "B", "R", "W", "Total"],
[
[1, 4, 4, 5, 6],
[3, 6, 8, 9, 9],
[2, 5, 7, 7, 8],
[0, 0, 1, 2, 3],
[
0, 0, 1, 1, 2, 2, 3, 3, 4, 4,
############################################################################### # MCR Construct ############################################################################### # ----------------------------------------------------------------------------- # Data Handling # ----------------------------------------------------------------------------- # Define the experiment's path, aggregation dictionary, and read filenames type = float experimentString = "E_095_075_006_015" path = "/Users/sanchez.hmsc/odrive/[email protected]/GMLandscape/ParserDataset/" aggregationDictionary = monet.generateAggregationDictionary( ["W", "H", "R", "B"], [ [0, 0, 1, 2, 3], # Wild [1, 4, 4, 5, 6], # Homing [2, 5, 7, 7, 8], # Resistant [3, 6, 8, 9, 9] # Broken ]) filenames = monet.readExperimentFilenames(path + experimentString) # To analyze a single node ................................................... # Load a single node (auxiliary function just for demonstration) # nodeIndex = 0 # nodeData = monet.loadNodeData( # filenames.get("male")[nodeIndex], # filenames.get("female")[nodeIndex], # dataType=float # ) # To analyze the sum of the whole landscape ..................................
return drive_yShred
###############################################################################
# CRISPR
###############################################################################
drive_CRISPR = {
'id':
'CRISPR',
'folder':
'CRISPR',
'loc':
1,
'HLT':
monet.generateAggregationDictionary(
["H", "Other", "Total"],
[[1, 4, 5, 6], [0, 2, 3, 7, 8, 9], [1, 4, 5, 6, 0, 2, 3, 7, 8, 9]]),
'ECO':
monet.generateAggregationDictionary(
["H", "B", "R", "W", "Total"],
[[1, 4, 4, 5, 6], [3, 6, 8, 9, 9], [2, 5, 7, 7, 8], [0, 0, 1, 2, 3],
[1, 4, 4, 5, 6, 3, 6, 8, 9, 9, 2, 5, 7, 7, 8, 0, 0, 1, 2, 3]])
}
drive_CRISPRI = {
'id':
'CRISPRI',
'folder':
'CRISPRIdeal',
'loc':
1,
'HLT':
def driveSelector(DRIVE, pathRoot):
if DRIVE == 1:
pathExt = "CRISPR/"
aggregationDictionary = monet.generateAggregationDictionary(
["W", "H", "R", "B", "G"],
[
[0, 0, 1, 2, 3],
[1, 4, 4, 5, 6],
[2, 5, 7, 7, 8],
[3, 6, 8, 9, 9],
[]
]
)
yRange = 11000
if DRIVE == 2:
pathExt = "CRISPRX/"
aggregationDictionary = monet.generateAggregationDictionary(
["W", "H", "R", "B", "Y"],
[
[0, 0, 1, 2, 3, 4, 4, 8, 11, 13],
[1, 5, 5, 6, 7, 8],
[2, 6, 9, 9, 10, 11],
[3, 7, 10, 12, 12, 13],
[]
]
)
yRange = 11000
if DRIVE == 3:
pathExt = "tGD/"
aggregationDictionary = monet.generateAggregationDictionary(
["WA+WB", "H", "RA", "RB", "G"],
[
[
0, 5, 10, 15, 90, 95, 100, 155, 160, 195, 0, 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 24, 29, 34, 42, 47, 52,
59, 64, 69, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
101, 102, 103, 104, 109, 114, 122, 127, 134, 139, 145, 150, 155, 156,
157, 158, 159, 160, 161, 162, 163, 164, 169, 177, 184, 190, 195, 196,
197, 198, 199,
0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 27, 29, 32, 34, 37, 39, 40, 41,
42, 43, 45, 47, 48, 50, 52, 53, 55, 57, 58, 59, 64, 69, 74, 75, 76,
77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28,
30, 31, 33, 35, 36, 38, 39, 40, 41, 44, 46, 49, 51, 54, 56, 57, 58,
60, 61, 62, 63, 65, 66, 67, 68, 70, 71, 72, 73, 74, 79, 84, 89
],
[
1, 6, 11, 16, 20, 22, 24, 25, 27, 29, 30, 32, 34, 35, 37, 60, 65, 70,
91, 96, 101, 105, 107, 109, 110, 112, 114, 115, 117, 135, 140, 156,
161, 165, 167, 169, 170, 172, 185, 196, 200, 202, 20, 21, 23, 25, 26,
28, 30, 31, 33, 35, 36, 38, 43, 48, 53, 76, 81, 86, 105, 106, 108,
110, 111, 113, 115, 116, 118, 123, 128, 146, 151, 165, 166, 168, 170,
171, 173, 178, 191, 200, 201, 203, 2, 7, 12, 17, 21, 26, 31, 36, 39,
40, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 61, 66, 71, 92,
97, 102, 106, 111, 116, 119, 120, 122, 123, 124, 125, 127, 128, 129,
130, 136, 141, 157, 162, 166, 171, 174, 175, 177, 178, 179, 180, 186,
197, 201, 204, 205, 39, 41, 44, 46, 49, 51, 54, 56, 77, 82, 87, 119,
121, 124, 126, 129, 131, 147, 152, 174, 176, 179, 181, 192, 204, 206
],
[
3, 8, 13, 18, 57, 59, 62, 64, 67, 69, 72, 93, 98, 103, 132, 134, 137,
139, 142, 158, 163, 182, 184, 187, 198, 207, 22, 27, 32, 37, 40, 45,
50, 55, 57, 58, 60, 61, 62, 63, 65, 66, 67, 68, 70, 71, 72, 73, 78,
83, 88, 107, 112, 117, 120, 125, 130, 132, 133, 135, 136, 137, 138,
140, 141, 142, 143, 148, 153, 167, 172, 175, 180, 182, 183, 185, 186,
187, 188, 193, 202, 205, 207, 208, 4, 9, 14, 19, 23, 28, 33, 38, 41,
46, 51, 56, 58, 63, 68, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 94, 99, 104, 108, 113, 118, 121, 126, 131,
133, 138, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,
159, 164, 168, 173, 176, 181, 183, 188, 189, 190, 191, 192, 193, 194,
199, 203, 206, 208, 209, 74, 79, 84, 89, 144, 149, 154, 189, 194, 209
],
[
10, 11, 12, 13, 14, 30, 31, 33, 49, 51, 65, 66, 67, 68, 84, 96, 97,
98, 99, 111, 113, 126, 135, 136, 138, 155, 156, 157, 158, 159, 165,
166, 167, 168, 169, 172, 174, 175, 176, 177, 178, 180, 182, 183, 184,
189, 190, 191, 192, 193, 29, 32, 47, 48, 50, 64, 80, 81, 82, 83, 95,
109, 110, 112, 122, 123, 124, 125, 134, 137, 145, 146, 147, 148, 149,
155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
170, 171, 173, 174, 175, 176, 179, 181, 182, 183, 185, 186, 187, 188,
189, 194, 15, 16, 17, 18, 19, 35, 36, 38, 54, 56, 70, 71, 72, 73, 89,
100, 101, 102, 103, 104, 115, 116, 118, 129, 131, 140, 141, 142, 143,
154, 160, 161, 162, 163, 164, 170, 171, 173, 179, 181, 185, 186, 187,
188, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,
207, 208, 209, 34, 37, 52, 53, 55, 69, 85, 86, 87, 88, 114, 117, 127,
128, 130, 139, 150, 151, 152, 153, 169, 172, 177, 178, 180, 184, 190,
191, 192, 193, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
206, 207, 208, 209
],
[
5, 6, 7, 8, 9, 25, 26, 28, 44, 46, 60, 61, 62, 63, 79, 90, 91, 92,
93, 94, 95, 105, 106, 107, 108, 109, 110, 112, 114, 117, 119, 120,
121, 122, 123, 124, 125, 127, 128, 130, 132, 133, 134, 137, 139, 144,
145, 146, 147, 148, 149, 150, 151, 152, 153, 24, 27, 42, 43, 45, 59,
75, 76, 77, 78, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102,
103, 104, 105, 106, 107, 108, 111, 113, 115, 116, 118, 119, 120, 121,
126, 129, 131, 132, 133, 135, 136, 138, 140, 141, 142, 143, 144, 154
]
]
)
yRange = 11000
if DRIVE == 4:
pathExt = "tGDX/"
aggregationDictionary = monet.generateAggregationDictionary(
["WA+WB", "H", "RA", "RB", "G"],
[
[
0, 5, 10, 15, 90, 95, 100, 155, 160, 195, 210, 215, 220, 225, 0, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 24,
29, 34, 42, 47, 52, 59, 64, 69, 75, 80, 85, 90, 91, 92, 93, 94, 95,
96, 97, 98, 99, 100, 101, 102, 103, 104, 109, 114, 122, 127, 134,
139, 145, 150, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 169,
177, 184, 190, 195, 196, 197, 198, 199, 210, 211, 212, 213, 214, 215,
216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 27, 29, 32, 34, 37, 39, 40, 41,
42, 43, 45, 47, 48, 50, 52, 53, 55, 57, 58, 59, 64, 69, 74, 75, 76,
77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 210, 211, 212, 213, 214, 0,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 25, 26, 28, 30, 31, 33, 35, 36, 38, 39, 40, 41, 44,
46, 49, 51, 54, 56, 57, 58, 60, 61, 62, 63, 65, 66, 67, 68, 70, 71,
72, 73, 74, 79, 84, 89, 210, 211, 212, 213, 214, 215, 216, 217, 218,
219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229
],
[
1, 6, 11, 16, 20, 22, 24, 25, 27, 29, 30, 32, 34, 35, 37, 60, 65, 70,
91, 96, 101, 105, 107, 109, 110, 112, 114, 115, 117, 135, 140, 156,
161, 165, 167, 169, 170, 172, 185, 196, 200, 202, 211, 216, 221, 226,
20, 21, 23, 25, 26, 28, 30, 31, 33, 35, 36, 38, 43, 48, 53, 76, 81,
86, 105, 106, 108, 110, 111, 113, 115, 116, 118, 123, 128, 146, 151,
165, 166, 168, 170, 171, 173, 178, 191, 200, 201, 203, 2, 7, 12, 17,
21, 26, 31, 36, 39, 40, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54,
55, 61, 66, 71, 92, 97, 102, 106, 111, 116, 119, 120, 122, 123, 124,
125, 127, 128, 129, 130, 136, 141, 157, 162, 166, 171, 174, 175, 177,
178, 179, 180, 186, 197, 201, 204, 205, 212, 217, 222, 227, 39, 41,
44, 46, 49, 51, 54, 56, 77, 82, 87, 119, 121, 124, 126, 129, 131,
147, 152, 174, 176, 179, 181, 192, 204, 206
],
[
3, 8, 13, 18, 57, 59, 62, 64, 67, 69, 72, 93, 98, 103, 132, 134, 137,
139, 142, 158, 163, 182, 184, 187, 198, 207, 213, 218, 223, 228, 22,
27, 32, 37, 40, 45, 50, 55, 57, 58, 60, 61, 62, 63, 65, 66, 67, 68,
70, 71, 72, 73, 78, 83, 88, 107, 112, 117, 120, 125, 130, 132, 133,
135, 136, 137, 138, 140, 141, 142, 143, 148, 153, 167, 172, 175, 180,
182, 183, 185, 186, 187, 188, 193, 202, 205, 207, 208, 4, 9, 14, 19,
23, 28, 33, 38, 41, 46, 51, 56, 58, 63, 68, 73, 74, 75, 76, 77, 78,
79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 94, 99, 104, 108, 113,
118, 121, 126, 131, 133, 138, 143, 144, 145, 146, 147, 148, 149, 150,
151, 152, 153, 154, 159, 164, 168, 173, 176, 181, 183, 188, 189, 190,
191, 192, 193, 194, 199, 203, 206, 208, 209, 214, 219, 224, 229, 74,
79, 84, 89, 144, 149, 154, 189, 194, 209
],
[
10, 11, 12, 13, 14, 30, 31, 33, 49, 51, 65, 66, 67, 68, 84, 96, 97,
98, 99, 111, 113, 126, 135, 136, 138, 155, 156, 157, 158, 159, 165,
166, 167, 168, 169, 172, 174, 175, 176, 177, 178, 180, 182, 183, 184,
189, 190, 191, 192, 193, 220, 221, 222, 223, 224, 29, 32, 47, 48, 50,
64, 80, 81, 82, 83, 95, 109, 110, 112, 122, 123, 124, 125, 134, 137,
145, 146, 147, 148, 149, 155, 156, 157, 158, 159, 160, 161, 162, 163,
164, 165, 166, 167, 168, 170, 171, 173, 174, 175, 176, 179, 181, 182,
183, 185, 186, 187, 188, 189, 194, 15, 16, 17, 18, 19, 35, 36, 38,
54, 56, 70, 71, 72, 73, 89, 100, 101, 102, 103, 104, 115, 116, 118,
129, 131, 140, 141, 142, 143, 154, 160, 161, 162, 163, 164, 170, 171,
173, 179, 181, 185, 186, 187, 188, 194, 195, 196, 197, 198, 199, 200,
201, 202, 203, 204, 205, 206, 207, 208, 209, 225, 226, 227, 228, 229,
34, 37, 52, 53, 55, 69, 85, 86, 87, 88, 114, 117, 127, 128, 130, 139,
150, 151, 152, 153, 169, 172, 177, 178, 180, 184, 190, 191, 192, 193,
195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,
209
],
[
5, 6, 7, 8, 9, 25, 26, 28, 44, 46, 60, 61, 62, 63, 79, 90, 91, 92,
93, 94, 95, 105, 106, 107, 108, 109, 110, 112, 114, 117, 119, 120,
121, 122, 123, 124, 125, 127, 128, 130, 132, 133, 134, 137, 139, 144,
145, 146, 147, 148, 149, 150, 151, 152, 153, 215, 216, 217, 218, 219,
24, 27, 42, 43, 45, 59, 75, 76, 77, 78, 90, 91, 92, 93, 94, 96, 97,
98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 111, 113, 115,
116, 118, 119, 120, 121, 126, 129, 131, 132, 133, 135, 136, 138, 140,
141, 142, 143, 144, 154
]
]
)
yRange = 11000
if DRIVE == 5:
pathExt = "tGDCross/"
aggregationDictionary = monet.generateAggregationDictionary(
["WA+WB", "H", "RA", "RB", "G"],
[
[
0, 5, 10, 15, 90, 95, 100, 155, 160, 195, 0, 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 24, 29, 34, 42, 47, 52,
59, 64, 69, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
101, 102, 103, 104, 109, 114, 122, 127, 134, 139, 145, 150, 155, 156,
157, 158, 159, 160, 161, 162, 163, 164, 169, 177, 184, 190, 195, 196,
197, 198, 199,
0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 27, 29, 32, 34, 37, 39, 40, 41,
42, 43, 45, 47, 48, 50, 52, 53, 55, 57, 58, 59, 64, 69, 74, 75, 76,
77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28,
30, 31, 33, 35, 36, 38, 39, 40, 41, 44, 46, 49, 51, 54, 56, 57, 58,
60, 61, 62, 63, 65, 66, 67, 68, 70, 71, 72, 73, 74, 79, 84, 89
],
[
1, 6, 11, 16, 20, 22, 24, 25, 27, 29, 30, 32, 34, 35, 37, 60, 65, 70,
91, 96, 101, 105, 107, 109, 110, 112, 114, 115, 117, 135, 140, 156,
161, 165, 167, 169, 170, 172, 185, 196, 200, 202, 20, 21, 23, 25, 26,
28, 30, 31, 33, 35, 36, 38, 43, 48, 53, 76, 81, 86, 105, 106, 108,
110, 111, 113, 115, 116, 118, 123, 128, 146, 151, 165, 166, 168, 170,
171, 173, 178, 191, 200, 201, 203, 2, 7, 12, 17, 21, 26, 31, 36, 39,
40, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 61, 66, 71, 92,
97, 102, 106, 111, 116, 119, 120, 122, 123, 124, 125, 127, 128, 129,
130, 136, 141, 157, 162, 166, 171, 174, 175, 177, 178, 179, 180, 186,
197, 201, 204, 205, 39, 41, 44, 46, 49, 51, 54, 56, 77, 82, 87, 119,
121, 124, 126, 129, 131, 147, 152, 174, 176, 179, 181, 192, 204, 206
],
[
3, 8, 13, 18, 57, 59, 62, 64, 67, 69, 72, 93, 98, 103, 132, 134, 137,
139, 142, 158, 163, 182, 184, 187, 198, 207, 22, 27, 32, 37, 40, 45,
50, 55, 57, 58, 60, 61, 62, 63, 65, 66, 67, 68, 70, 71, 72, 73, 78,
83, 88, 107, 112, 117, 120, 125, 130, 132, 133, 135, 136, 137, 138,
140, 141, 142, 143, 148, 153, 167, 172, 175, 180, 182, 183, 185, 186,
187, 188, 193, 202, 205, 207, 208, 4, 9, 14, 19, 23, 28, 33, 38, 41,
46, 51, 56, 58, 63, 68, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 94, 99, 104, 108, 113, 118, 121, 126, 131,
133, 138, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,
159, 164, 168, 173, 176, 181, 183, 188, 189, 190, 191, 192, 193, 194,
199, 203, 206, 208, 209, 74, 79, 84, 89, 144, 149, 154, 189, 194, 209
],
[
10, 11, 12, 13, 14, 30, 31, 33, 49, 51, 65, 66, 67, 68, 84, 96, 97,
98, 99, 111, 113, 126, 135, 136, 138, 155, 156, 157, 158, 159, 165,
166, 167, 168, 169, 172, 174, 175, 176, 177, 178, 180, 182, 183, 184,
189, 190, 191, 192, 193, 29, 32, 47, 48, 50, 64, 80, 81, 82, 83, 95,
109, 110, 112, 122, 123, 124, 125, 134, 137, 145, 146, 147, 148, 149,
155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
170, 171, 173, 174, 175, 176, 179, 181, 182, 183, 185, 186, 187, 188,
189, 194, 15, 16, 17, 18, 19, 35, 36, 38, 54, 56, 70, 71, 72, 73, 89,
100, 101, 102, 103, 104, 115, 116, 118, 129, 131, 140, 141, 142, 143,
154, 160, 161, 162, 163, 164, 170, 171, 173, 179, 181, 185, 186, 187,
188, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,
207, 208, 209, 34, 37, 52, 53, 55, 69, 85, 86, 87, 88, 114, 117, 127,
128, 130, 139, 150, 151, 152, 153, 169, 172, 177, 178, 180, 184, 190,
191, 192, 193, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
206, 207, 208, 209
],
[
5, 6, 7, 8, 9, 25, 26, 28, 44, 46, 60, 61, 62, 63, 79, 90, 91, 92,
93, 94, 95, 105, 106, 107, 108, 109, 110, 112, 114, 117, 119, 120,
121, 122, 123, 124, 125, 127, 128, 130, 132, 133, 134, 137, 139, 144,
145, 146, 147, 148, 149, 150, 151, 152, 153, 24, 27, 42, 43, 45, 59,
75, 76, 77, 78, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102,
103, 104, 105, 106, 107, 108, 111, 113, 115, 116, 118, 119, 120, 121,
126, 129, 131, 132, 133, 135, 136, 138, 140, 141, 142, 143, 144, 154
]
]
)
yRange = 11000
if DRIVE == 6:
pathExt = "tGDXCross/"
aggregationDictionary = monet.generateAggregationDictionary(
["WA+WB", "H", "RA", "RB", "G"],
[
[
0, 5, 10, 15, 90, 95, 100, 155, 160, 195, 210, 215, 220, 225, 0, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 24,
29, 34, 42, 47, 52, 59, 64, 69, 75, 80, 85, 90, 91, 92, 93, 94, 95,
96, 97, 98, 99, 100, 101, 102, 103, 104, 109, 114, 122, 127, 134,
139, 145, 150, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 169,
177, 184, 190, 195, 196, 197, 198, 199, 210, 211, 212, 213, 214, 215,
216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 27, 29, 32, 34, 37, 39, 40, 41,
42, 43, 45, 47, 48, 50, 52, 53, 55, 57, 58, 59, 64, 69, 74, 75, 76,
77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 210, 211, 212, 213, 214, 0,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 25, 26, 28, 30, 31, 33, 35, 36, 38, 39, 40, 41, 44,
46, 49, 51, 54, 56, 57, 58, 60, 61, 62, 63, 65, 66, 67, 68, 70, 71,
72, 73, 74, 79, 84, 89, 210, 211, 212, 213, 214, 215, 216, 217, 218,
219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229
],
[
1, 6, 11, 16, 20, 22, 24, 25, 27, 29, 30, 32, 34, 35, 37, 60, 65, 70,
91, 96, 101, 105, 107, 109, 110, 112, 114, 115, 117, 135, 140, 156,
161, 165, 167, 169, 170, 172, 185, 196, 200, 202, 211, 216, 221, 226,
20, 21, 23, 25, 26, 28, 30, 31, 33, 35, 36, 38, 43, 48, 53, 76, 81,
86, 105, 106, 108, 110, 111, 113, 115, 116, 118, 123, 128, 146, 151,
165, 166, 168, 170, 171, 173, 178, 191, 200, 201, 203, 2, 7, 12, 17,
21, 26, 31, 36, 39, 40, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54,
55, 61, 66, 71, 92, 97, 102, 106, 111, 116, 119, 120, 122, 123, 124,
125, 127, 128, 129, 130, 136, 141, 157, 162, 166, 171, 174, 175, 177,
178, 179, 180, 186, 197, 201, 204, 205, 212, 217, 222, 227, 39, 41,
44, 46, 49, 51, 54, 56, 77, 82, 87, 119, 121, 124, 126, 129, 131,
147, 152, 174, 176, 179, 181, 192, 204, 206
],
[
3, 8, 13, 18, 57, 59, 62, 64, 67, 69, 72, 93, 98, 103, 132, 134, 137,
139, 142, 158, 163, 182, 184, 187, 198, 207, 213, 218, 223, 228, 22,
27, 32, 37, 40, 45, 50, 55, 57, 58, 60, 61, 62, 63, 65, 66, 67, 68,
70, 71, 72, 73, 78, 83, 88, 107, 112, 117, 120, 125, 130, 132, 133,
135, 136, 137, 138, 140, 141, 142, 143, 148, 153, 167, 172, 175, 180,
182, 183, 185, 186, 187, 188, 193, 202, 205, 207, 208, 4, 9, 14, 19,
23, 28, 33, 38, 41, 46, 51, 56, 58, 63, 68, 73, 74, 75, 76, 77, 78,
79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 94, 99, 104, 108, 113,
118, 121, 126, 131, 133, 138, 143, 144, 145, 146, 147, 148, 149, 150,
151, 152, 153, 154, 159, 164, 168, 173, 176, 181, 183, 188, 189, 190,
191, 192, 193, 194, 199, 203, 206, 208, 209, 214, 219, 224, 229, 74,
79, 84, 89, 144, 149, 154, 189, 194, 209
],
[
10, 11, 12, 13, 14, 30, 31, 33, 49, 51, 65, 66, 67, 68, 84, 96, 97,
98, 99, 111, 113, 126, 135, 136, 138, 155, 156, 157, 158, 159, 165,
166, 167, 168, 169, 172, 174, 175, 176, 177, 178, 180, 182, 183, 184,
189, 190, 191, 192, 193, 220, 221, 222, 223, 224, 29, 32, 47, 48, 50,
64, 80, 81, 82, 83, 95, 109, 110, 112, 122, 123, 124, 125, 134, 137,
145, 146, 147, 148, 149, 155, 156, 157, 158, 159, 160, 161, 162, 163,
164, 165, 166, 167, 168, 170, 171, 173, 174, 175, 176, 179, 181, 182,
183, 185, 186, 187, 188, 189, 194, 15, 16, 17, 18, 19, 35, 36, 38,
54, 56, 70, 71, 72, 73, 89, 100, 101, 102, 103, 104, 115, 116, 118,
129, 131, 140, 141, 142, 143, 154, 160, 161, 162, 163, 164, 170, 171,
173, 179, 181, 185, 186, 187, 188, 194, 195, 196, 197, 198, 199, 200,
201, 202, 203, 204, 205, 206, 207, 208, 209, 225, 226, 227, 228, 229,
34, 37, 52, 53, 55, 69, 85, 86, 87, 88, 114, 117, 127, 128, 130, 139,
150, 151, 152, 153, 169, 172, 177, 178, 180, 184, 190, 191, 192, 193,
195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,
209
],
[
5, 6, 7, 8, 9, 25, 26, 28, 44, 46, 60, 61, 62, 63, 79, 90, 91, 92,
93, 94, 95, 105, 106, 107, 108, 109, 110, 112, 114, 117, 119, 120,
121, 122, 123, 124, 125, 127, 128, 130, 132, 133, 134, 137, 139, 144,
145, 146, 147, 148, 149, 150, 151, 152, 153, 215, 216, 217, 218, 219,
24, 27, 42, 43, 45, 59, 75, 76, 77, 78, 90, 91, 92, 93, 94, 96, 97,
98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 111, 113, 115,
116, 118, 119, 120, 121, 126, 129, 131, 132, 133, 135, 136, 138, 140,
141, 142, 143, 144, 154
]
]
)
yRange = 11000
return [pathExt, aggregationDictionary, yRange]
(SUM, AGG, SPA, REP, SRP) = (True, False, False, True, False)
(PT_ROT, PT_PRE, PT_OUT, PT_IMG, PT_MTR) = aux.selectPath(USR)
(expDirsMean, expDirsTrac) = aux.getExpPaths(PT_ROT)
###############################################################################
# Genotypes
###############################################################################
# Health ----------------------------------------------------------------------
genes = ('WW', 'WH', 'WR', 'WB', 'HH', 'HR', 'HB', 'RR', 'RB', 'BB')
locs = {
'H': (('H', (0, 1)), ),
'O': (('W', (0, 1)), ('R', (0, 1)), ('B', (0, 1)))
}
hset = set(aux.aggregateGeneAppearances(genes, locs['H']))
oset = set(aux.aggregateGeneAppearances(genes, locs['O']))
healthSet = (hset, oset - hset, oset | hset)
HLT = monet.generateAggregationDictionary(["H", "W", "Total"],
[list(i) for i in healthSet])
# Ecology ---------------------------------------------------------------------
locsE = {
'H': (('H', (0, 1)), ),
'W': (('W', (0, 1)), ),
'R': (('R', (0, 1)), ),
'B': (('B', (0, 1)), )
}
hlst = aux.aggregateGeneAppearances(genes, locsE['H'])
wlst = aux.aggregateGeneAppearances(genes, locsE['W'])
rlst = aux.aggregateGeneAppearances(genes, locsE['R'])
blst = aux.aggregateGeneAppearances(genes, locsE['B'])
ECO = monet.generateAggregationDictionary(["H", "W", "R", "B"],
[hlst, wlst, rlst, blst])
###############################################################################
# Load folders
def driveSelector(DRIVE, TYPE):
# Linked Drive -###########################################################
# aggD = 0
# yRange = 0
# colors = 0
if DRIVE == 'linkedDrive':
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(
["W", "H", "R", "B"], gld.LD_ECO
)
yRange = 11000
colors = COLEN
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(
["H*", "O-", "Total"], gld.LD_HLT
)
yRange = 11000/2
colors = COLHN
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(
["H*", "O-", "Total"], gld.LD_HLT
)
yRange = 11000/2
colors = COLTN
# Split Drive #############################################################
if DRIVE == 'splitDrive':
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(
["WA", "H", "R", "B", "C", "WB"], gsd.SD_ECO
)
yRange = 11000
colors = COLEN
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(
["H*", "O-", "Total"], gsd.SD_HLT
)
yRange = 11000/2
colors = COLHN
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(
["C*", "O-", "Total"], gsd.SD_TRS
)
yRange = 11000/2
colors = COLTN
# tGD Drive ###############################################################
if DRIVE == 'tGD':
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(
["WA", "H", "RA", "RB", "G", "WB"], gtgd.TGD_ECO
)
yRange = 11000
colors = COLEN
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(
["H*", "O-", "Total"], gtgd.TGD_HLT
)
yRange = 11000/2
colors = COLHN
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(
["G*", "O-", "Total"], gtgd.TGD_TRS
)
yRange = 11000/2
colors = COLTN
# Return values ###########################################################
return {'gDict': aggD, 'yRange': yRange, 'colors': colors}
##############################################################################
# Notes
##############################################################################
STACK = False
TRACES = True
FACTORIAL = False
nameExp = ""
pathRoot = "/Volumes/marshallShare/pgSIT/"
pathExperiments = "CRISPR_SIT/"
pathPlots = pathRoot + "images/"
##############################################################################
RELEASE_DAY = 25
colors = ["#f20060", "#29339b", "#c6d8ff", "#7fff3a", "#7692ff", "#29339b"]
cmaps = monet.generateAlphaColorMapFromColorArray(colors)
aggregationDictionary = monet.generateAggregationDictionary(
["W", "R"], [[0, 0, 1], [1, 2, 2]])
styleS = {
"width": 0,
"alpha": .9,
"dpi": 1024,
"legend": False,
"aspect": .00175,
"colors": colors,
"xRange": [0, 600],
"yRange": [0, 8 * 11000]
}
styleT = {
"width": .075,
"alpha": .1,
"dpi": 1024,
"legend": False,
def driveParameters(TYPE, popSize=750):
if TYPE == 'INC':
aggD = monet.generateAggregationDictionary(*HUM_INC)
yRange = popSize
return (aggD, yRange, 'INC')
# dependent variables for styling
##############################################################################
if EXP == 0:
pathRoot = '/Volumes/marshallShare/ThresholdResub/factorialSweep/GV/Wolbachia_1_05/ANALYZED/'
style['yRange'] = [0, 125000]
(probeRatios, expHead) = ([.49, .66, .68, .79, .81, .9], 'E_04_05_0010_')
else:
pathRoot = '/Volumes/marshallShare/ThresholdResub/factorialSweep/YK/Wolbachia_1_05/ANALYZED/'
style['yRange'] = [0, 40000]
(probeRatios, expHead) = ([.76, .88, .98], 'E_02_05_0010_')
style['aspect'] = .2 * (style['xRange'][1] / style['yRange'][1])
##############################################################################
# Drive variables for aggregation and steady-state calculations
##############################################################################
firstRelease = 20
aggregationDictionary = monet.generateAggregationDictionary(['W', 'H'],
[[1], [0]])
##############################################################################
# Loops through experiment folders
##############################################################################
for i in range(0, 1050, 50):
experimentString = expHead + str(i).rjust(4, '0')
expsPath = pathRoot + experimentString + '/'
print(expsPath)
###########################################################################
# Stacked population plots (sums the whole landscape into a single
# population count over time)
###########################################################################
if STACK:
# Parses the paths of all CSV files starting with 'F_' and/or 'M_'
filenames = monet.readExperimentFilenames(expsPath)
# Loads all the files provided and sums them into one array of dims:
def driveSelector(DRIVE, TYPE):
# Linked Drive -###########################################################
if DRIVE == 'linkedDrive':
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(["W", "H", "R", "B"],
gld.LD_ECO)
yRange = 11000 * 2
colors = COLEN
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(["H*", "O-", "Total"],
gld.LD_HLT)
yRange = 11000 / 2
colors = COLHN
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(["H*", "O-", "Total"],
gld.LD_TRS)
yRange = 11000
colors = COLTN
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(["O-", "W*", "Total"],
gld.LD_WLD)
yRange = 11000
colors = COLWN
elif TYPE == 'CST':
aggD = monet.generateAggregationDictionary(["O-", "W*", "Total"],
gld.LD_CST)
yRange = 11000
colors = COLCN
# Split Drive #############################################################
if DRIVE == 'splitDrive':
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(
["WA", "H", "R", "B", "C", "WB"], gsd.SD_ECO)
yRange = 11000 * 2
colors = COLEN
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(["H*", "O-", "Total"],
gsd.SD_HLT)
yRange = 11000 / 2
colors = COLHN
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(["C*", "O-", "Total"],
gsd.SD_TRS)
yRange = 11000
colors = COLTN
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(["O-", "W*", "Total"],
gsd.SD_WLD)
yRange = 11000
colors = COLWN
elif TYPE == 'CST':
aggD = monet.generateAggregationDictionary(["O-", "W*", "Total"],
gsd.SD_CST)
yRange = 11000
colors = COLCN
# tGD Drive ###############################################################
if DRIVE == 'tGD':
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(
["WA", "H", "RA", "RB", "G", "WB"], gtgd.TGD_ECO)
yRange = 11000 * 2
colors = COLEN
elif TYPE == 'CAS':
aggD = monet.generateAggregationDictionary(
["W", "H", "R", "B", "Total"], gtgd.TGD_CAS)
yRange = 11000
colors = [
'#8338ec1A', '#45d40c1A', '#1888e31A', '#bc10971A', '#0C48871A'
]
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(["H*", "O-", "Total"],
gtgd.TGD_HLT)
yRange = 11000 / 2
colors = COLHN
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(["G*", "O-", "Total"],
gtgd.TGD_TRS)
yRange = 11000
colors = COLTN
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(["O-", "W*", "Total"],
gtgd.TGD_WLD)
yRange = 11000
colors = COLWN
elif TYPE == 'CST':
aggD = monet.generateAggregationDictionary(["C", "G", "Total"],
gtgd.TGD_CST)
yRange = 11000
colors = COLCN
# ClvR Drive ###############################################################
if DRIVE == 'ClvR':
if TYPE == 'ECO':
aggD = monet.generateAggregationDictionary(
["WA", "H", "RA", "RB", "G", "WB"], clv.TGD_ECO)
yRange = 11000 * 2
colors = COLEN
elif TYPE == 'HLT':
aggD = monet.generateAggregationDictionary(["H*", "O-", "Total"],
clv.TGD_HLT)
yRange = 11000 / 2
colors = COLHN
elif TYPE == 'TRS':
aggD = monet.generateAggregationDictionary(["G*", "O-", "Total"],
clv.TGD_TRS)
yRange = 11000
colors = COLTN
elif TYPE == 'WLD':
aggD = monet.generateAggregationDictionary(["O-", "W*", "Total"],
clv.TGD_WLD)
yRange = 11000
colors = COLWN
elif TYPE == 'CST':
aggD = monet.generateAggregationDictionary(["C", "G", "Total"],
clv.TGD_CST)
yRange = 11000
colors = COLCN
# Return values ###########################################################
return {'gDict': aggD, 'yRange': yRange, 'colors': colors}
