def loadSummedMeanResponse(aFiles, GDICT, MALE, FEMALE):
print(aux.CBLU, end='\r')
# print('\t Loading mean response...'+aux.PADC, end='\r')
landData = monet.loadLandscapeData(aFiles, male=MALE, female=FEMALE)
# print('\t Aggregating mean response...'+aux.PADC, end='\r')
aggDataM = monet.aggregateGenotypesInLandscape(landData, GDICT)
aggDataM['landscape'] = sum(aggDataM['landscape'])
print(aux.CEND, end='\r')
return aggDataM
def calculateGeneSpatiotemporals(filenames):
landscapeData = monet.loadLandscapeData(filenames, dataType=float)
genotypes = landscapeData["genotypes"]
aggregationDictionary = monet.autoGenerateGenotypesDictionary(
["W", "H", "R", "B"],
genotypes
)
aggregatedNodesData = monet.aggregateGenotypesInLandscape(
landscapeData,
aggregationDictionary
)
geneSpatiotemporals = monet.getGenotypeArraysFromLandscape(
aggregatedNodesData
)
return geneSpatiotemporals
# Heatmap population plots (maintains the spatial information, so that the
# spread of the gene drive can be analyzed geographically)
###########################################################################
if HEAT:
# Parses the paths of all CSV files starting with 'F_' and/or 'M_'
filenames = monet.readExperimentFilenames(pathRoot + experimentString +
'/')
# Loads all the files provided without summing them
landscapeData = monet.loadLandscapeData(filenames,
male=maleToggle,
female=femaleToggle,
dataType=float)
# Groups the genotypes into "bins" provided by the
# "aggregationDictionary" by summing the counts in each one of the
# columns.
aggregatedNodesData = monet.aggregateGenotypesInLandscape(
landscapeData, aggregationDictionary)
# Reshapes the data to take the form:
# [nodeNUM, [originalGenotypes, time, [counts]]
geneSpatiotemporals = monet.getGenotypeArraysFromLandscape(
aggregatedNodesData)
# Plotting-related instructions
overlay = monet.plotGenotypeOverlayFromLandscape(
geneSpatiotemporals,
style={
"aspect": 30 * style['aspect'],
"cmap": cmaps
},
vmax=
27.5 #monet.maxAlleleInLandscape(geneSpatiotemporals["geneLandscape"])
)
monet.quickSaveFigure(overlay,
gradient = np.linspace(0, 1, len(expDirsMean))
for (i, pathMean) in enumerate(expDirsMean):
# for (i, pathMean) in enumerate(expDirsMean):
print('* Analyzing {}: ({}/{})'.format(rid,
str(i + 1).zfill(2),
str(expNum)),
end='\r')
expName = pathMean.split('/')[-1]
dirsMean = pathMean
expOutImgPath = expOutRootPath
# Mean response -------------------------------------------------------
filenames = monet.readExperimentFilenames(pathMean)
landscapeData = monet.loadLandscapeData(filenames,
male=True,
female=True)
aggregatedNodesData = monet.aggregateGenotypesInLandscape(
landscapeData, DRIVE)
trace = aggregatedNodesData['landscape'][NOI].T[GOI] / SSPOP
# Plot
ax.plot([i / 7 for i in range(len(trace))],
trace,
color=cm(gradient[i]),
alpha=.95,
lw=.6)
ax.set_xlim(0, xRange)
ax.set_ylim(0, yRange)
ax.set_aspect(xRange)
ax.grid(True, alpha=.5, lw=.2, ls='--')
fig.savefig('{}S_{}_{}.pdf'.format(PATH_IMG, rid, spltNm[-1]),
bbox_inches='tight',
pad_inches=0.1,
transparent=True)
# Experiment Selector ---------------------------------------------------------
expOutRootPath = PATH_IMG # + driveID
expOutExpPath = expOutRootPath + '/' + fldrName
monet.makeFolder(expOutRootPath)
monet.makeFolder(expOutExpPath)
# Create output folder --------------------------------------------------------
(dirTraces, dirMean) = (dirsTraces[0], dirsMean[0])
expOutSetPath = expOutExpPath + '/' + EXP + '/'
monet.makeFolder(expOutSetPath)
# Mean response ---------------------------------------------------------------
print(aux.PADL)
print('* Loading mean response data...')
# Load mean response and aggregate --------------------------------------------
filenames = monet.readExperimentFilenames(dirMean)
landscapeData = monet.loadLandscapeData(filenames, male=MALE, female=FEMALE)
aggregatedNodesData = monet.aggregateGenotypesInLandscape(
landscapeData, drvPars.get('HLT'))
# Get the max range for each node ---------------------------------------------
maxPops = []
for i in aggregatedNodesData["landscape"]:
maxPops.append(i[burn][-1] * 1)
# Get the crosses through thresholds ------------------------------------------
(chngDays, prtcDays) = ([], [])
for j in range(len(aggregatedNodesData['landscape'])):
nodePop = aggregatedNodesData['landscape'][j]
thrsBool = monet.comparePopToThresholds(nodePop,
gIx, [0, 1],
thresholds,
refPop=maxPops[j])
chngDays.append(monet.getConditionChangeDays(thrsBool))
prtcDays.append(monet.countConditionDays(thrsBool))
###############################################################################
