def getQuantBetaData(request):
samples = Sample.objects.all()
samples.query = pickle.loads(request.session['selected_samples'])
selected = samples.values_list('sampleid')
qs1 = Sample.objects.all().filter(sampleid__in=selected)
if request.is_ajax():
allJson = request.GET["all"]
all = simplejson.loads(allJson)
button = int(all["button"])
taxaLevel = int(all["taxa"])
distance = int(all["distance"])
norm = int(all["normalize"])
PC1 = all["PC1"]
metaString = all["meta"]
metaDict = simplejson.JSONDecoder(object_pairs_hook=multidict).decode(metaString)
metaDF = quantBetaMetaDF(qs1, metaDict)
myList = metaDF['sampleid'].tolist()
mySet = list(set(myList))
taxaDF = taxaProfileDF(mySet)
factor = 'none'
if norm == 1:
factor = 'none'
elif norm == 2:
factor = 'min'
elif norm == 3:
factor = '10th percentile'
elif norm == 4:
factor = '25th percentile'
elif norm == 5:
factor = 'median'
normDF = normalizeBeta(taxaDF, taxaLevel, mySet, factor)
finalDF = metaDF.merge(normDF, on='sampleid', how='outer')
pd.set_option('display.max_rows', finalDF.shape[0], 'display.max_columns', finalDF.shape[1], 'display.width', 1000)
fieldList = []
for key in metaDict:
fieldList.append(metaDict[key])
matrixDF = pd.DataFrame()
if button == 1:
matrixDF = finalDF.pivot(index='taxaid', columns='sampleid', values='count')
elif button == 2:
matrixDF = finalDF.pivot(index='taxaid', columns='sampleid', values='rel_abund')
elif button == 3:
matrixDF = finalDF.pivot(index='taxaid', columns='sampleid', values='rich')
elif button == 4:
matrixDF = finalDF.pivot(index='taxaid', columns='sampleid', values='diversity')
datamtx = asarray(matrixDF[mySet].T)
numrows, numcols = shape(datamtx)
dists = zeros((numrows, numrows))
if distance == 1:
dist = pdist(datamtx, 'braycurtis')
dists = squareform(dist)
elif distance == 2:
dist = pdist(datamtx, 'canberra')
dists = squareform(dist)
elif distance == 3:
dist = pdist(datamtx, 'dice')
dists = squareform(dist)
elif distance == 4:
dist = pdist(datamtx, 'euclidean')
dists = squareform(dist)
elif distance == 5:
dist = pdist(datamtx, 'jaccard')
dists = squareform(dist)
eigvals, coordinates, proportion_explained = PCoA(dists)
numaxes = len(eigvals)
axesList = []
for i in range(numaxes):
j = i + 1
axesList.append('PC' + str(j))
valsDF = pd.DataFrame(eigvals, columns=['EigenVals'], index=axesList)
propDF = pd.DataFrame(proportion_explained, columns=['Variance Explained (R2)'], index=axesList)
eigenDF = valsDF.join(propDF)
metaDF.set_index('sampleid', drop=True, inplace=True)
pcoaDF = pd.DataFrame(coordinates, columns=axesList, index=mySet)
resultDF = metaDF.join(pcoaDF)
pd.set_option('display.max_rows', resultDF.shape[0], 'display.max_columns', resultDF.shape[1], 'display.width', 1000)
finalDict = {}
seriesList = []
xAxisDict = {}
yAxisDict = {}
dataList = resultDF[[PC1, fieldList[0]]].values.tolist()
seriesDict = {}
seriesDict['type'] = 'scatter'
seriesDict['name'] = fieldList
seriesDict['data'] = dataList
seriesList.append(seriesDict)
x = resultDF[PC1].values.tolist()
y = resultDF[fieldList[0]].values.tolist()
if max(x) == min(x):
stop = 0
else:
stop = 1
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
p_value = "%0.3f" % p_value
r_square = r_value * r_value
r_square = "%0.4f" % r_square
min_y = slope*min(x) + intercept
max_y = slope*max(x) + intercept
slope = "%.3E" % slope
intercept = "%.3E" % intercept
regrList = []
regrList.append([min(x), min_y])
regrList.append([max(x), max_y])
if stop == 0:
regDict = {}
elif stop == 1:
regrDict = {}
regrDict['type'] = 'line'
regrDict['name'] = 'R2: ' + str(r_square) + '; p-value: ' + str(p_value) + '<br>' + '(y = ' + str(slope) + 'x' + ' + ' + str(intercept) + ')'
regrDict['data'] = regrList
seriesList.append(regrDict)
xTitle = {}
xTitle['text'] = PC1
xAxisDict['title'] = xTitle
yTitle = {}
yTitle['text'] = fieldList[0]
yAxisDict['title'] = yTitle
finalDict['series'] = seriesList
finalDict['xAxis'] = xAxisDict
finalDict['yAxis'] = yAxisDict
result = ""
result = result + '===============================================\n'
if taxaLevel == 1:
result = result + 'Taxa level: Kingdom' + '\n'
elif taxaLevel == 2:
result = result + 'Taxa level: Phyla' + '\n'
elif taxaLevel == 3:
result = result + 'Taxa level: Class' + '\n'
elif taxaLevel == 4:
result = result + 'Taxa level: Order' + '\n'
elif taxaLevel == 5:
result = result + 'Taxa level: Family' + '\n'
elif taxaLevel == 6:
result = result + 'Taxa level: Genus' + '\n'
elif taxaLevel == 7:
result = result + 'Taxa level: Species' + '\n'
if button == 1:
result = result + 'Dependent Variable: Sequence Reads' + '\n'
elif button == 2:
result = result + 'Dependent Variable: Relative Abundance' + '\n'
elif button == 3:
result = result + 'Dependent Variable: Species Richness' + '\n'
elif button == 4:
result = result + 'Dependent Variable: Shannon Diversity' + '\n'
result = result + 'Independent Variable: ' + str(fieldList[0]) + '\n'
if distance == 1:
result = result + 'Distance score: Bray-Curtis' + '\n'
elif distance == 2:
result = result + 'Distance score: Canberra' + '\n'
elif distance == 3:
result = result + 'Distance score: Dice' + '\n'
elif distance == 4:
result = result + 'Distance score: Euclidean' + '\n'
elif distance == 5:
result = result + 'Distance score: Jaccard' + '\n'
result = result + '===============================================\n'
result = result + str(eigenDF) + '\n'
result = result + '===============================================\n'
result = result + '\n\n\n\n'
finalDict['text'] = result
resultDF.reset_index(drop=True, inplace=True)
res_table = resultDF.to_html(classes="table display")
res_table = res_table.replace('border="1"', 'border="0"')
finalDict['res_table'] = str(res_table)
nameList = list(metaDF['sample_name'])
distsDF = pd.DataFrame(dists, columns=nameList, index=nameList)
dist_table = distsDF.to_html(classes="table display")
dist_table = dist_table.replace('border="1"', 'border="0"')
finalDict['dist_table'] = str(dist_table)
res = simplejson.dumps(finalDict)
return HttpResponse(res, content_type='application/json')
def getQuantBetaData(request):
samples = Sample.objects.all()
samples.query = pickle.loads(request.session['selected_samples'])
selected = samples.values_list('sampleid')
qs1 = Sample.objects.all().filter(sampleid__in=selected)
if request.is_ajax():
allJson = request.GET["all"]
all = simplejson.loads(allJson)
button = int(all["button"])
taxaLevel = int(all["taxa"])
distance = int(all["distance"])
norm = int(all["normalize"])
PC1 = all["PC1"]
metaString = all["meta"]
metaDict = simplejson.JSONDecoder(
object_pairs_hook=multidict).decode(metaString)
metaDF = quantBetaMetaDF(qs1, metaDict)
myList = metaDF['sampleid'].tolist()
mySet = list(set(myList))
taxaDF = taxaProfileDF(mySet)
factor = 'none'
if norm == 1:
factor = 'none'
elif norm == 2:
factor = 'min'
elif norm == 3:
factor = '10th percentile'
elif norm == 4:
factor = '25th percentile'
elif norm == 5:
factor = 'median'
normDF = normalizeBeta(taxaDF, taxaLevel, mySet, factor)
finalDF = metaDF.merge(normDF, on='sampleid', how='outer')
pd.set_option('display.max_rows', finalDF.shape[0],
'display.max_columns', finalDF.shape[1], 'display.width',
1000)
fieldList = []
for key in metaDict:
fieldList.append(metaDict[key])
matrixDF = pd.DataFrame()
if button == 1:
matrixDF = finalDF.pivot(index='taxaid',
columns='sampleid',
values='count')
elif button == 2:
matrixDF = finalDF.pivot(index='taxaid',
columns='sampleid',
values='rel_abund')
elif button == 3:
matrixDF = finalDF.pivot(index='taxaid',
columns='sampleid',
values='rich')
elif button == 4:
matrixDF = finalDF.pivot(index='taxaid',
columns='sampleid',
values='diversity')
datamtx = asarray(matrixDF[mySet].T)
numrows, numcols = shape(datamtx)
dists = zeros((numrows, numrows))
if distance == 1:
dist = pdist(datamtx, 'braycurtis')
dists = squareform(dist)
elif distance == 2:
dist = pdist(datamtx, 'canberra')
dists = squareform(dist)
elif distance == 3:
dist = pdist(datamtx, 'dice')
dists = squareform(dist)
elif distance == 4:
dist = pdist(datamtx, 'euclidean')
dists = squareform(dist)
elif distance == 5:
dist = pdist(datamtx, 'jaccard')
dists = squareform(dist)
eigvals, coordinates, proportion_explained = PCoA(dists)
numaxes = len(eigvals)
axesList = []
for i in range(numaxes):
j = i + 1
axesList.append('PC' + str(j))
valsDF = pd.DataFrame(eigvals, columns=['EigenVals'], index=axesList)
propDF = pd.DataFrame(proportion_explained,
columns=['Variance Explained (R2)'],
index=axesList)
eigenDF = valsDF.join(propDF)
metaDF.set_index('sampleid', drop=True, inplace=True)
pcoaDF = pd.DataFrame(coordinates, columns=axesList, index=mySet)
resultDF = metaDF.join(pcoaDF)
pd.set_option('display.max_rows', resultDF.shape[0],
'display.max_columns', resultDF.shape[1],
'display.width', 1000)
finalDict = {}
seriesList = []
xAxisDict = {}
yAxisDict = {}
dataList = resultDF[[PC1, fieldList[0]]].values.tolist()
seriesDict = {}
seriesDict['type'] = 'scatter'
seriesDict['name'] = fieldList
seriesDict['data'] = dataList
seriesList.append(seriesDict)
x = resultDF[PC1].values.tolist()
y = resultDF[fieldList[0]].values.tolist()
if max(x) == min(x):
stop = 0
else:
stop = 1
slope, intercept, r_value, p_value, std_err = stats.linregress(
x, y)
p_value = "%0.3f" % p_value
r_square = r_value * r_value
r_square = "%0.4f" % r_square
min_y = slope * min(x) + intercept
max_y = slope * max(x) + intercept
slope = "%.3E" % slope
intercept = "%.3E" % intercept
regrList = []
regrList.append([min(x), min_y])
regrList.append([max(x), max_y])
if stop == 0:
regDict = {}
elif stop == 1:
regrDict = {}
regrDict['type'] = 'line'
regrDict['name'] = 'R2: ' + str(
r_square) + '; p-value: ' + str(
p_value) + '<br>' + '(y = ' + str(
slope) + 'x' + ' + ' + str(intercept) + ')'
regrDict['data'] = regrList
seriesList.append(regrDict)
xTitle = {}
xTitle['text'] = PC1
xAxisDict['title'] = xTitle
yTitle = {}
yTitle['text'] = fieldList[0]
yAxisDict['title'] = yTitle
finalDict['series'] = seriesList
finalDict['xAxis'] = xAxisDict
finalDict['yAxis'] = yAxisDict
result = ""
result = result + '===============================================\n'
if taxaLevel == 1:
result = result + 'Taxa level: Kingdom' + '\n'
elif taxaLevel == 2:
result = result + 'Taxa level: Phyla' + '\n'
elif taxaLevel == 3:
result = result + 'Taxa level: Class' + '\n'
elif taxaLevel == 4:
result = result + 'Taxa level: Order' + '\n'
elif taxaLevel == 5:
result = result + 'Taxa level: Family' + '\n'
elif taxaLevel == 6:
result = result + 'Taxa level: Genus' + '\n'
elif taxaLevel == 7:
result = result + 'Taxa level: Species' + '\n'
if button == 1:
result = result + 'Dependent Variable: Sequence Reads' + '\n'
elif button == 2:
result = result + 'Dependent Variable: Relative Abundance' + '\n'
elif button == 3:
result = result + 'Dependent Variable: Species Richness' + '\n'
elif button == 4:
result = result + 'Dependent Variable: Shannon Diversity' + '\n'
result = result + 'Independent Variable: ' + str(fieldList[0]) + '\n'
if distance == 1:
result = result + 'Distance score: Bray-Curtis' + '\n'
elif distance == 2:
result = result + 'Distance score: Canberra' + '\n'
elif distance == 3:
result = result + 'Distance score: Dice' + '\n'
elif distance == 4:
result = result + 'Distance score: Euclidean' + '\n'
elif distance == 5:
result = result + 'Distance score: Jaccard' + '\n'
result = result + '===============================================\n'
result = result + str(eigenDF) + '\n'
result = result + '===============================================\n'
result = result + '\n\n\n\n'
finalDict['text'] = result
resultDF.reset_index(drop=True, inplace=True)
res_table = resultDF.to_html(classes="table display")
res_table = res_table.replace('border="1"', 'border="0"')
finalDict['res_table'] = str(res_table)
nameList = list(metaDF['sample_name'])
distsDF = pd.DataFrame(dists, columns=nameList, index=nameList)
dist_table = distsDF.to_html(classes="table display")
dist_table = dist_table.replace('border="1"', 'border="0"')
finalDict['dist_table'] = str(dist_table)
res = simplejson.dumps(finalDict)
return HttpResponse(res, content_type='application/json')
def getCatBetaData(request):
samples = Sample.objects.all()
samples.query = pickle.loads(request.session['selected_samples'])
selected = samples.values_list('sampleid')
qs1 = Sample.objects.all().filter(sampleid__in=selected)
if request.is_ajax():
allJson = request.GET["all"]
all = simplejson.loads(allJson)
button = int(all["button"])
taxaLevel = int(all["taxa"])
distance = int(all["distance"])
norm = int(all["normalize"])
PC1 = all["PC1"]
PC2 = all["PC2"]
metaString = all["meta"]
metaDict = simplejson.JSONDecoder(object_pairs_hook=multidict).decode(metaString)
metaDF = catBetaMetaDF(qs1, metaDict)
myList = metaDF['sampleid'].tolist()
mySet = list(set(myList))
taxaDF = taxaProfileDF(mySet)
factor = 'none'
if norm == 1:
factor = 'none'
elif norm == 2:
factor = 'min'
elif norm == 3:
factor = '10th percentile'
elif norm == 4:
factor = '25th percentile'
elif norm == 5:
factor = 'median'
normDF = normalizeBeta(taxaDF, taxaLevel, mySet, factor)
finalDF = metaDF.merge(normDF, on='sampleid', how='outer')
pd.set_option('display.max_rows', finalDF.shape[0], 'display.max_columns', finalDF.shape[1], 'display.width', 1000)
fieldList = []
for key in metaDict:
fieldList.append(key)
matrixDF = pd.DataFrame()
if button == 1:
matrixDF = finalDF.pivot(index='taxaid', columns='sampleid', values='count')
elif button == 2:
matrixDF = finalDF.pivot(index='taxaid', columns='sampleid', values='rel_abund')
elif button == 3:
matrixDF = finalDF.pivot(index='taxaid', columns='sampleid', values='rich')
elif button == 4:
matrixDF = finalDF.pivot(index='taxaid', columns='sampleid', values='diversity')
datamtx = asarray(matrixDF[mySet].T)
numrows, numcols = shape(datamtx)
dists = np.zeros((numrows, numrows))
if distance == 1:
dist = pdist(datamtx, 'braycurtis')
dists = squareform(dist)
elif distance == 2:
dist = pdist(datamtx, 'canberra')
dists = squareform(dist)
elif distance == 3:
dist = pdist(datamtx, 'dice')
dists = squareform(dist)
elif distance == 4:
dist = pdist(datamtx, 'euclidean')
dists = squareform(dist)
elif distance == 5:
dist = pdist(datamtx, 'jaccard')
dists = squareform(dist)
eigvals, coordinates, proportion_explained = PCoA(dists)
numaxes = len(eigvals)
axesList = []
for i in range(numaxes):
j = i + 1
axesList.append('PC' + str(j))
valsDF = pd.DataFrame(eigvals, columns=['EigenVals'], index=axesList)
propDF = pd.DataFrame(proportion_explained, columns=['Variance Explained (R2)'], index=axesList)
eigenDF = valsDF.join(propDF)
metaDF.set_index('sampleid', drop=True, inplace=True)
pcoaDF = pd.DataFrame(coordinates, columns=axesList, index=mySet)
resultDF = metaDF.join(pcoaDF)
pd.set_option('display.max_rows', resultDF.shape[0], 'display.max_columns', resultDF.shape[1], 'display.width', 1000)
### create trtList that merges all categorical values
groupList = metaDF[fieldList].values.tolist()
trtList = []
for i in groupList:
trtList.append(':'.join(i))
### check to see if all samples are the same size
sizeList = []
grouped = resultDF.groupby(fieldList)
for name, group in grouped:
(row, col) = shape(group)
sizeList.append(row)
setSize = len(set(sizeList))
if setSize == 1:
try:
bigf, p = permanova_oneway(dists, trtList, 200)
except:
bigf = float('nan')
p = float('nan')
else:
bigf = float('nan')
p = float('nan')
finalDict = {}
seriesList = []
xAxisDict = {}
yAxisDict = {}
grouped = resultDF.groupby(fieldList)
for name, group in grouped:
dataList = group[[PC1, PC2]].values.tolist()
if isinstance(name, unicode):
trt = name
else:
trt = ' & '.join(list(name))
seriesDict = {}
seriesDict['name'] = trt
seriesDict['data'] = dataList
seriesList.append(seriesDict)
xTitle = {}
xTitle['text'] = PC1
xAxisDict['title'] = xTitle
yTitle = {}
yTitle['text'] = PC2
yAxisDict['title'] = yTitle
finalDict['series'] = seriesList
finalDict['xAxis'] = xAxisDict
finalDict['yAxis'] = yAxisDict
result = ""
result = result + '===============================================\n'
if taxaLevel == 1:
result = result + 'Taxa level: Kingdom' + '\n'
elif taxaLevel == 2:
result = result + 'Taxa level: Phyla' + '\n'
elif taxaLevel == 3:
result = result + 'Taxa level: Class' + '\n'
elif taxaLevel == 4:
result = result + 'Taxa level: Order' + '\n'
elif taxaLevel == 5:
result = result + 'Taxa level: Family' + '\n'
elif taxaLevel == 6:
result = result + 'Taxa level: Genus' + '\n'
elif taxaLevel == 7:
result = result + 'Taxa level: Species' + '\n'
if button == 1:
result = result + 'Dependent Variable: Sequence Reads' + '\n'
elif button == 2:
result = result + 'Dependent Variable: Relative Abundance' + '\n'
elif button == 3:
result = result + 'Dependent Variable: Species Richness' + '\n'
elif button == 4:
result = result + 'Dependent Variable: Shannon Diversity' + '\n'
indVar = ' x '.join(fieldList)
result = result + 'Independent Variable: ' + str(indVar) + '\n'
if distance == 1:
result = result + 'Distance score: Bray-Curtis' + '\n'
elif distance == 2:
result = result + 'Distance score: Canberra' + '\n'
elif distance == 3:
result = result + 'Distance score: Dice' + '\n'
elif distance == 4:
result = result + 'Distance score: Euclidean' + '\n'
elif distance == 5:
result = result + 'Distance score: Jaccard' + '\n'
if math.isnan(bigf):
result = result + '===============================================\n'
result = result + 'perMANOVA cannot be performed...' + '\n'
result = result + 'The current version requires all treatments to be of equal sample size.' + '\n'
else:
result = result + '===============================================\n'
result = result + 'perMANOVA results' + '\n'
result = result + 'f-value: ' + str(bigf) + '\n'
result = result + 'p-value: ' + str(p) + '\n'
result = result + '===============================================\n'
result = result + str(eigenDF) + '\n'
result = result + '===============================================\n'
result = result + '\n\n\n\n'
finalDict['text'] = result
resultDF.reset_index(drop=True, inplace=True)
res_table = resultDF.to_html(classes="table display")
res_table = res_table.replace('border="1"', 'border="0"')
finalDict['res_table'] = str(res_table)
nameList = list(metaDF['sample_name'])
distsDF = pd.DataFrame(dists, columns=nameList, index=nameList)
dist_table = distsDF.to_html(classes="table display")
dist_table = dist_table.replace('border="1"', 'border="0"')
finalDict['dist_table'] = str(dist_table)
res = simplejson.dumps(finalDict)
return HttpResponse(res, content_type='application/json')
def getCatBetaData(request):
samples = Sample.objects.all()
samples.query = pickle.loads(request.session['selected_samples'])
selected = samples.values_list('sampleid')
qs1 = Sample.objects.all().filter(sampleid__in=selected)
if request.is_ajax():
allJson = request.GET["all"]
all = simplejson.loads(allJson)
button = int(all["button"])
taxaLevel = int(all["taxa"])
distance = int(all["distance"])
norm = int(all["normalize"])
PC1 = all["PC1"]
PC2 = all["PC2"]
metaString = all["meta"]
metaDict = simplejson.JSONDecoder(
object_pairs_hook=multidict).decode(metaString)
metaDF = catBetaMetaDF(qs1, metaDict)
myList = metaDF['sampleid'].tolist()
mySet = list(set(myList))
taxaDF = taxaProfileDF(mySet)
factor = 'none'
if norm == 1:
factor = 'none'
elif norm == 2:
factor = 'min'
elif norm == 3:
factor = '10th percentile'
elif norm == 4:
factor = '25th percentile'
elif norm == 5:
factor = 'median'
normDF = normalizeBeta(taxaDF, taxaLevel, mySet, factor)
finalDF = metaDF.merge(normDF, on='sampleid', how='outer')
pd.set_option('display.max_rows', finalDF.shape[0],
'display.max_columns', finalDF.shape[1], 'display.width',
1000)
fieldList = []
for key in metaDict:
fieldList.append(key)
matrixDF = pd.DataFrame()
if button == 1:
matrixDF = finalDF.pivot(index='taxaid',
columns='sampleid',
values='count')
elif button == 2:
matrixDF = finalDF.pivot(index='taxaid',
columns='sampleid',
values='rel_abund')
elif button == 3:
matrixDF = finalDF.pivot(index='taxaid',
columns='sampleid',
values='rich')
elif button == 4:
matrixDF = finalDF.pivot(index='taxaid',
columns='sampleid',
values='diversity')
datamtx = asarray(matrixDF[mySet].T)
numrows, numcols = shape(datamtx)
dists = np.zeros((numrows, numrows))
if distance == 1:
dist = pdist(datamtx, 'braycurtis')
dists = squareform(dist)
elif distance == 2:
dist = pdist(datamtx, 'canberra')
dists = squareform(dist)
elif distance == 3:
dist = pdist(datamtx, 'dice')
dists = squareform(dist)
elif distance == 4:
dist = pdist(datamtx, 'euclidean')
dists = squareform(dist)
elif distance == 5:
dist = pdist(datamtx, 'jaccard')
dists = squareform(dist)
eigvals, coordinates, proportion_explained = PCoA(dists)
numaxes = len(eigvals)
axesList = []
for i in range(numaxes):
j = i + 1
axesList.append('PC' + str(j))
valsDF = pd.DataFrame(eigvals, columns=['EigenVals'], index=axesList)
propDF = pd.DataFrame(proportion_explained,
columns=['Variance Explained (R2)'],
index=axesList)
eigenDF = valsDF.join(propDF)
metaDF.set_index('sampleid', drop=True, inplace=True)
pcoaDF = pd.DataFrame(coordinates, columns=axesList, index=mySet)
resultDF = metaDF.join(pcoaDF)
pd.set_option('display.max_rows', resultDF.shape[0],
'display.max_columns', resultDF.shape[1],
'display.width', 1000)
### create trtList that merges all categorical values
groupList = metaDF[fieldList].values.tolist()
trtList = []
for i in groupList:
trtList.append(':'.join(i))
### check to see if all samples are the same size
sizeList = []
grouped = resultDF.groupby(fieldList)
for name, group in grouped:
(row, col) = shape(group)
sizeList.append(row)
setSize = len(set(sizeList))
if setSize == 1:
try:
bigf, p = permanova_oneway(dists, trtList, 200)
except:
bigf = float('nan')
p = float('nan')
else:
bigf = float('nan')
p = float('nan')
finalDict = {}
seriesList = []
xAxisDict = {}
yAxisDict = {}
grouped = resultDF.groupby(fieldList)
for name, group in grouped:
dataList = group[[PC1, PC2]].values.tolist()
if isinstance(name, unicode):
trt = name
else:
trt = ' & '.join(list(name))
seriesDict = {}
seriesDict['name'] = trt
seriesDict['data'] = dataList
seriesList.append(seriesDict)
xTitle = {}
xTitle['text'] = PC1
xAxisDict['title'] = xTitle
yTitle = {}
yTitle['text'] = PC2
yAxisDict['title'] = yTitle
finalDict['series'] = seriesList
finalDict['xAxis'] = xAxisDict
finalDict['yAxis'] = yAxisDict
result = ""
result = result + '===============================================\n'
if taxaLevel == 1:
result = result + 'Taxa level: Kingdom' + '\n'
elif taxaLevel == 2:
result = result + 'Taxa level: Phyla' + '\n'
elif taxaLevel == 3:
result = result + 'Taxa level: Class' + '\n'
elif taxaLevel == 4:
result = result + 'Taxa level: Order' + '\n'
elif taxaLevel == 5:
result = result + 'Taxa level: Family' + '\n'
elif taxaLevel == 6:
result = result + 'Taxa level: Genus' + '\n'
elif taxaLevel == 7:
result = result + 'Taxa level: Species' + '\n'
if button == 1:
result = result + 'Dependent Variable: Sequence Reads' + '\n'
elif button == 2:
result = result + 'Dependent Variable: Relative Abundance' + '\n'
elif button == 3:
result = result + 'Dependent Variable: Species Richness' + '\n'
elif button == 4:
result = result + 'Dependent Variable: Shannon Diversity' + '\n'
indVar = ' x '.join(fieldList)
result = result + 'Independent Variable: ' + str(indVar) + '\n'
if distance == 1:
result = result + 'Distance score: Bray-Curtis' + '\n'
elif distance == 2:
result = result + 'Distance score: Canberra' + '\n'
elif distance == 3:
result = result + 'Distance score: Dice' + '\n'
elif distance == 4:
result = result + 'Distance score: Euclidean' + '\n'
elif distance == 5:
result = result + 'Distance score: Jaccard' + '\n'
if math.isnan(bigf):
result = result + '===============================================\n'
result = result + 'perMANOVA cannot be performed...' + '\n'
result = result + 'The current version requires all treatments to be of equal sample size.' + '\n'
else:
result = result + '===============================================\n'
result = result + 'perMANOVA results' + '\n'
result = result + 'f-value: ' + str(bigf) + '\n'
result = result + 'p-value: ' + str(p) + '\n'
result = result + '===============================================\n'
result = result + str(eigenDF) + '\n'
result = result + '===============================================\n'
result = result + '\n\n\n\n'
finalDict['text'] = result
resultDF.reset_index(drop=True, inplace=True)
res_table = resultDF.to_html(classes="table display")
res_table = res_table.replace('border="1"', 'border="0"')
finalDict['res_table'] = str(res_table)
nameList = list(metaDF['sample_name'])
distsDF = pd.DataFrame(dists, columns=nameList, index=nameList)
dist_table = distsDF.to_html(classes="table display")
dist_table = dist_table.replace('border="1"', 'border="0"')
finalDict['dist_table'] = str(dist_table)
res = simplejson.dumps(finalDict)
return HttpResponse(res, content_type='application/json')