#import string
from math import *
#import piddle as pid
#import os

#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):

	valsOnly = []
	dataXZ = vals[:]
	for i in range(len(dataXZ)):
		valsOnly.append(dataXZ[i][1])

	traitmean, traitmedian, traitvar, traitstdev, traitsem, 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.sort(webqtlUtil.cmpOrder)
	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'):
			#IRQuest = HT.Href(text="Interquartile Range", url=webqtlConfig.glossaryfile +"#Interquartile",target="_blank", Class="fs14")
			#IRQuest.append(HT.BR())
			#IRQuest.append(" (fold difference)")
			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"))

			#XZ, 04/01/2009: don't try to get H2 value for probe.
			if cellid:
				pass
			else:
				if 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")))
				else:
					pass
			# Lei Yan
			# 2008/12/19

	return tbl

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