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-rw-r--r--wqflask/basicStatistics/BasicStatisticsFunctions.py207
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diff --git a/wqflask/basicStatistics/BasicStatisticsFunctions.py b/wqflask/basicStatistics/BasicStatisticsFunctions.py
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index 1e5646a1..00000000
--- a/wqflask/basicStatistics/BasicStatisticsFunctions.py
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@@ -1,207 +0,0 @@
-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 db 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.GENERATED_IMAGE_DIR+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.GENERATED_IMAGE_DIR+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.GENERATED_IMAGE_DIR+filename, format='gif')
- img=HT.Image('/image/'+filename+'.gif',border=0)
-
- return img
generated by cgit v1.2.3 (git 2.44.0) at 2024-11-21 22:30:09 +0000