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-rwxr-xr-xwqflask/basicStatistics/BasicStatisticsFunctions.py207
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diff --git a/wqflask/basicStatistics/BasicStatisticsFunctions.py b/wqflask/basicStatistics/BasicStatisticsFunctions.py
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+from __future__ import print_function
+
+#import string
+from math import *
+#import piddle as pid
+#import os
+import traceback
+
+from pprint import pformat as pf
+
+from corestats import Stats
+
+import reaper
+from htmlgen import HTMLgen2 as HT
+
+#from utility import Plot
+from utility import webqtlUtil
+from base import webqtlConfig
+from dbFunction import webqtlDatabaseFunction
+
+def basicStatsTable(vals, trait_type=None, cellid=None, heritability=None):
+ print("basicStatsTable called - len of vals", len(vals))
+ st = {} # This is the dictionary where we'll put everything for the template
+ valsOnly = []
+ dataXZ = vals[:]
+ for i in range(len(dataXZ)):
+ valsOnly.append(dataXZ[i][1])
+
+ (st['traitmean'],
+ st['traitmedian'],
+ st['traitvar'],
+ st['traitstdev'],
+ st['traitsem'],
+ st['N']) = reaper.anova(valsOnly) #ZS: Should convert this from reaper to R in the future
+
+ #tbl = HT.TableLite(cellpadding=20, cellspacing=0)
+ #dataXZ = vals[:]
+ dataXZ = sorted(vals, webqtlUtil.cmpOrder)
+
+ print("data for stats is:", pf(dataXZ))
+ for num, item in enumerate(dataXZ):
+ print(" %i - %s" % (num, item))
+ print(" length:", len(dataXZ))
+
+ st['min'] = dataXZ[0][1]
+ st['max'] = dataXZ[-1][1]
+
+ numbers = [x[1] for x in dataXZ]
+ stats = Stats(numbers)
+
+ at75 = stats.percentile(75)
+ at25 = stats.percentile(25)
+ print("should get a stack")
+ traceback.print_stack()
+ print("Interquartile:", at75 - at25)
+
+ #tbl.append(HT.TR(HT.TD("Statistic",align="left", Class="fs14 fwb ffl b1 cw cbrb", width = 180),
+ # HT.TD("Value", align="right", Class="fs14 fwb ffl b1 cw cbrb", width = 60)))
+ #tbl.append(HT.TR(HT.TD("N of Samples",align="left", Class="fs13 b1 cbw c222"),
+ # HT.TD(N,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+ #tbl.append(HT.TR(HT.TD("Mean",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ # HT.TD("%2.3f" % traitmean,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+ #tbl.append(HT.TR(HT.TD("Median",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ # HT.TD("%2.3f" % traitmedian,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+ ##tbl.append(HT.TR(HT.TD("Variance",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ ## HT.TD("%2.3f" % traitvar,nowrap="yes",align="left", Class="fs13 b1 cbw c222")))
+ #tbl.append(HT.TR(HT.TD("Standard Error (SE)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ # HT.TD("%2.3f" % traitsem,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+ #tbl.append(HT.TR(HT.TD("Standard Deviation (SD)", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ # HT.TD("%2.3f" % traitstdev,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+ #tbl.append(HT.TR(HT.TD("Minimum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ # HT.TD("%s" % dataXZ[0][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+ #tbl.append(HT.TR(HT.TD("Maximum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ # HT.TD("%s" % dataXZ[-1][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+
+
+
+ if (trait_type != None and trait_type == 'ProbeSet'):
+ #tbl.append(HT.TR(HT.TD("Range (log2)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ # HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+ #tbl.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ # HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+ #tbl.append(HT.TR(HT.TD(HT.Span(HT.Href(url="/glossary.html#Interquartile", target="_blank", text="Interquartile Range", Class="non_bold")), align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+ # HT.TD("%2.2f" % pow(2.0,(dataXZ[int((N-1)*3.0/4.0)][1]-dataXZ[int((N-1)/4.0)][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+ st['range_log2'] = dataXZ[-1][1]-dataXZ[0][1]
+ st['range_fold'] = pow(2.0, (dataXZ[-1][1]-dataXZ[0][1]))
+ st['interquartile'] = pow(2.0, (dataXZ[int((st['N']-1)*3.0/4.0)][1]-dataXZ[int((st['N']-1)/4.0)][1]))
+
+ #XZ, 04/01/2009: don't try to get H2 value for probe.
+ if not cellid:
+ if heritability:
+ # This field needs to still be put into the Jinja2 template
+ st['heritability'] = heritability
+ #tbl.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("%s" % heritability, nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+
+ # Lei Yan
+ # 2008/12/19
+
+ return st
+
+def plotNormalProbability(vals=None, RISet='', title=None, showstrains=0, specialStrains=[None], size=(750,500)):
+
+ dataXZ = vals[:]
+ dataXZ.sort(webqtlUtil.cmpOrder)
+ dataLabel = []
+ dataX = map(lambda X: X[1], dataXZ)
+
+ showLabel = showstrains
+ if len(dataXZ) > 50:
+ showLabel = 0
+ for item in dataXZ:
+ strainName = webqtlUtil.genShortStrainName(RISet=RISet, input_strainName=item[0])
+ dataLabel.append(strainName)
+
+ dataY=Plot.U(len(dataX))
+ dataZ=map(Plot.inverseCumul,dataY)
+ c = pid.PILCanvas(size=(750,500))
+ Plot.plotXY(c, dataZ, dataX, dataLabel = dataLabel, XLabel='Expected Z score', connectdot=0, YLabel='Trait value', title=title, specialCases=specialStrains, showLabel = showLabel)
+
+ filename= webqtlUtil.genRandStr("nP_")
+ c.save(webqtlConfig.IMGDIR+filename, format='gif')
+
+ img=HT.Image('/image/'+filename+'.gif',border=0)
+
+ return img
+
+def plotBoxPlot(vals):
+
+ valsOnly = []
+ dataXZ = vals[:]
+ for i in range(len(dataXZ)):
+ valsOnly.append(dataXZ[i][1])
+
+ plotHeight = 320
+ plotWidth = 220
+ xLeftOffset = 60
+ xRightOffset = 40
+ yTopOffset = 40
+ yBottomOffset = 60
+
+ canvasHeight = plotHeight + yTopOffset + yBottomOffset
+ canvasWidth = plotWidth + xLeftOffset + xRightOffset
+ canvas = pid.PILCanvas(size=(canvasWidth,canvasHeight))
+ XXX = [('', valsOnly[:])]
+
+ Plot.plotBoxPlot(canvas, XXX, offset=(xLeftOffset, xRightOffset, yTopOffset, yBottomOffset), XLabel= "Trait")
+ filename= webqtlUtil.genRandStr("Box_")
+ canvas.save(webqtlConfig.IMGDIR+filename, format='gif')
+ img=HT.Image('/image/'+filename+'.gif',border=0)
+
+ plotLink = HT.Span("More about ", HT.Href(text="Box Plots", url="http://davidmlane.com/hyperstat/A37797.html", target="_blank", Class="fs13"))
+
+ return img, plotLink
+
+def plotBarGraph(identification='', RISet='', vals=None, type="name"):
+
+ this_identification = "unnamed trait"
+ if identification:
+ this_identification = identification
+
+ if type=="rank":
+ dataXZ = vals[:]
+ dataXZ.sort(webqtlUtil.cmpOrder)
+ title='%s' % this_identification
+ else:
+ dataXZ = vals[:]
+ title='%s' % this_identification
+
+ tvals = []
+ tnames = []
+ tvars = []
+ for i in range(len(dataXZ)):
+ tvals.append(dataXZ[i][1])
+ tnames.append(webqtlUtil.genShortStrainName(RISet=RISet, input_strainName=dataXZ[i][0]))
+ tvars.append(dataXZ[i][2])
+ nnStrain = len(tnames)
+
+ sLabel = 1
+
+ ###determine bar width and space width
+ if nnStrain < 20:
+ sw = 4
+ elif nnStrain < 40:
+ sw = 3
+ else:
+ sw = 2
+
+ ### 700 is the default plot width minus Xoffsets for 40 strains
+ defaultWidth = 650
+ if nnStrain > 40:
+ defaultWidth += (nnStrain-40)*10
+ defaultOffset = 100
+ bw = int(0.5+(defaultWidth - (nnStrain-1.0)*sw)/nnStrain)
+ if bw < 10:
+ bw = 10
+
+ plotWidth = (nnStrain-1)*sw + nnStrain*bw + defaultOffset
+ plotHeight = 500
+ #print [plotWidth, plotHeight, bw, sw, nnStrain]
+ c = pid.PILCanvas(size=(plotWidth,plotHeight))
+ Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title=title, sLabel = sLabel, barSpace = sw)
+
+ filename= webqtlUtil.genRandStr("Bar_")
+ c.save(webqtlConfig.IMGDIR+filename, format='gif')
+ img=HT.Image('/image/'+filename+'.gif',border=0)
+
+ return img