def pruneReferenceTree_Nmicrobiol201648(taxa):
treefmt = None
with open(nmicrobiol201648_s6_PATHd8, "r") as f:
#with open(itol_newick, "r") as f:
treefmt = f.read()
# Strip internal support, and parse the resulting string
tree = PhyloTree(stripTreeInternalSupport(treefmt))
#ts.show_leaf_name = True
#ts.layout_fn = nodeLayoutWithTaxonomicNames
unmatched = []
matched = []
#xxx = set([1287680, 115713, 203267, 188937, 4781, 187420, 243230, 130081, 227882, 228908, 227377, 224308, 5693, 345663, 208964, 224325, 1116230, 243273, 213585, 64091, 45670, 1069680, 1397361, 280699, 1047168, 284811, 284812, 46234, 418459, 214684, 262768, 243365, 273063, 511145, 176299, 272557, 272558, 402612, 283166, 223926, 163003, 559292, 1041607, 1183438, 2769, 122586, 273116, 593117, 192222, 1574623, 243159, 160490, 212717, 272623, 272631, 272632, 63737, 272634, 1341181, 1125630, 99287, 27923, 400667, 269084, 257314, 96563, 300852, 4927, 381764, 242507, 65357, 104782, 336722, 190304, 882, 347515, 353152, 83332, 93061, 194439, 1223560, 267671, 196164, 1245935, 449447, 420778, 195522, 556484, 5061, 391623, 70601, 85962, 272844, 259536, 272633, 220668, 169963, 295405, 237561, 407035, 997884, 1432061, 1010810, 562, 1010800])
labelToTaxId = readTranslationMap()
numProcessed = 0
numMatched = 0
# Annotate tree leaves
for node in tree.traverse():
if node.is_leaf():
#items = node.name.split("_") # "items" (words) are separated by '_'
#for i,x in enumerate(items):
# if x.startswith("Submit") or x.startswith("submit"):
# print("Removing submission note on node: %s" % items)
# items = items[:i]
# break
matchingName = None
matchingTaxId = None
#print("---------------------------")
#print(n)
# Check if the label has a mapping in the id-conversion table
matchingTaxId = labelToTaxId.get(node.name)
#if not matchingTaxId is None:
# matchingName = ncbiTaxa.get_taxid_translator((matchingTaxId,))[matchingTaxId]
# Did we find a match for this leaf?
#if not matchingName is None: # match found
if not matchingTaxId is None:
node.label = node.name
node.name = str(matchingTaxId)
#node.matchingName
#print("<%s>" % node.name)
#lineageItems = [x for x in items[:speciesStartItem] if not x in taxonItemsToIgnore]
lineageItems = ncbiTaxa.get_lineage(matchingTaxId)
# TODO - Fix lineageItems ?
node.add_features(taxId = matchingTaxId, lineageItems = lineageItems)
#node.add_features(taxId = matchingTaxId)
#if matchingTaxId != speciesLevelTaxon:
# node.add_features(speciesLevelTaxon=speciesLevelTaxon)
matched.append("%s [%d]" % (node.label, matchingTaxId))
#print("--"*20)
#print(matchingName)
#print(items[:speciesStartItem])
numMatched += 1
else: # no match found
unmatched.append(node.name)
node.name = "n/a"
numProcessed += 1
if (rl()):
print("(processed %d matched %d)" % (numProcessed, numMatched))
#if(numProcessed>1000):
# break
# Save unmatched names to file (for examination)
with open("unmatched_names.txt", "w") as f:
f.writelines(["%s\n" % x for x in sorted(unmatched)])
# Save unmatched names to file (for examination)
with open("matched_names.txt", "w") as f:
f.writelines(["%s\n" % x for x in sorted(matched)])
print("//"*30)
print("//"*30)
print("//"*30)
print("//"*30)
outer = {}
# Try to annotate non-leaf nodes with common taxonomic group
for node in tree.traverse(strategy='postorder'): # children first
if node.is_leaf():
continue
a = []
for c in node.children:
try:
a.append( c.lineageItems )
except AttributeError:
pass
print(">>" * 20)
print(len(a))
print(a)
if a:
out = None
if len(a)==2:
out = []
#if a[0][-1]=="Anabaena" or a[1][-1]=="Anabaena":
# print(">"*50)
# print(a)
# print(">"*50)
for u,v in zip(a[0], a[1]):
if u==v:
out.append(u)
else:
break
elif len(a)==1:
#if a[0][-1]=="Anabaena":
# print(">"*50)
# print(a)
# print(">"*50)
out = a[0]
else:
assert(False)
if out:
#print("out = %s" % out)
print("//"*20)
print(out[-1])
node.add_features(lineageItems = out, testK = out[-1])
outer[out[-1]] = id(node)
print(">>> %s" % out[-1])
if out[-1]==2:
print("- - "*5)
print(a)
print("- - "*5)
# #if out[-1] == "Nostocaceae":
# #print(node.name)
# #print(a)
# #pass
#else:
# print("*-"*20)
# print(node.name)
# print(a)
for node in tree.traverse(strategy='postorder'): # children first
if node.is_leaf():
continue
try:
l = node.testK
if not l is None:
if outer[l]==id(node):
node.add_features(testL = l)
except AttributeError as e:
pass
print("//"*30)
print("//"*30)
print("//"*30)
print("//"*30)
# Now we have our annotated reference phylogenetic tree
# Get our target list of species to appear on the final tree
#taxa = getSpeciesToInclude()
allNames = ncbiTaxa.get_taxid_translator(taxa)
#taxa.append(1906157)
#taxa.append(251221)
#print(ncbiTaxa.get_rank(taxa))
for x in (45157,4896,44056):
print(x)
print(tree.search_nodes(taxId=x))
f = set()
fnodes = []
notf = set()
for x in taxa:
print("=="*20)
print("Searching for %d" % x)
found = tree.search_nodes(taxId=x)
if found:
f.add(x)
fnodes.append(found[0])
print("Exact match found")
else:
containingSpeciesLevelTaxon = getContainingSpeciesLevelTaxon(x)
if x != containingSpeciesLevelTaxon:
found = tree.search_nodes(taxId=containingSpeciesLevelTaxon)
if not found:
found = tree.search_nodes(speciesLevelTaxon=containingSpeciesLevelTaxon)
# TODO - CONTINUE HERE
#if not found:
# found = tree.search_nodes(
if found:
f.add(x)
fnodes.append(found[0])
print("Found")
else:
print("Not found at all...")
#elif ncbiTaxa.get_rank([x])[x] == 'no rank':
# parent = ncbiTaxa.get_lineage(x)[-2]
# found = tree.search_nodes(taxId=parent)
# if found:
# f.add(x)
# fnodes.append(found[0])
if x not in f:
#print("--"*50)
#print("TaxId not found: %d" % x)
#print("Name: %s" % allNames[x])
containingSpeciesLevelTaxon = getContainingSpeciesLevelTaxon(x)
#print("Species TaxId: %d %s" % (containingSpeciesLevelTaxon, "" if x==containingSpeciesLevelTaxon else "***"))
lineage = ncbiTaxa.get_lineage(x)
#print("Lineage: %s" % lineage)
names = ncbiTaxa.get_taxid_translator(lineage)
#print(names)
for y in reversed(lineage):
name = names[y]
res = bool(tree.search_nodes(testK = name))
#print("%s: %s" % (name, res))
notf.add(x)
print("Found (%d): %s" % (len(f), f))
print(ncbiTaxa.get_rank(list(f)))
print("Couldn't find (%d): %s" % (len(notf), notf))
print(list(ncbiTaxa.get_taxid_translator(list(notf)).values()))
print(len(fnodes))
tree2 = tree.copy()
print("Before pruning: %d" % len(tree2))
if fnodes:
#tree2.prune(fnodes, preserve_branch_length=True)
pruneTree(tree2, fnodes)
print("After pruning: %d" % len(tree2))
return (tree, tree2)
def nodeLayoutFunc(node):
taxid = int(node.name)
if taxid in taxidsToKeep:
taxGroupName = ncbiTaxa.get_taxid_translator(
[taxid]
)[taxid] # There has to be an easier way to look up names...
row = None
rangeRows = None
print(len(ranges))
if (len(ranges) == 1):
row = df[(df['ExplanatoryVar'] == var)
& (df['TaxGroup'] == taxid) &
(df['Range'] == ranges[0])]
assert (len(row) == len(ranges))
elif len(ranges) > 1:
row = df[(df['ExplanatoryVar'] == var)
& (df['TaxGroup'] == taxid) &
(df['Range'] == 0)]
assert (len(row) == 1)
rangeRows = df[(df['ExplanatoryVar'] == var)
& (df['TaxGroup'] == taxid) &
(df['Range'].isin(set(ranges)))]
else:
assert (False)
overallPval = float(row['Pvalue'].values[0])
name = TextFace("%s" % taxGroupName,
fsize=baseFontSize * 2.5)
name.tight_text = True
name.margin_left = 20
name.margin_right = 0
name.margin_top = 40
name.margin_bottom = 12
faces.add_face_to_node(name, node, column=0)
#print(rangeRows)
# For each range to be included in this plot, add a bar
for rangeId in ranges:
#print("rangeId = %s" % (rangeId))
rowForThisRange = None
if len(ranges) == 1:
rowForThisRange = row
else:
rowForThisRange = rangeRows[rangeRows['Range'] ==
rangeId]
assert (len(rowForThisRange) == 1)
# Extract p-value and "effect-size" (signed R^2)
effectSize = float(
rowForThisRange['EffectSize'].values[0])
pval = float(rowForThisRange['Pvalue'].values[0])
# Set bar-graph color and significance markers
barColor = ""
significanceMarker = ""
if (pval < significanceLevel):
significanceMarker = " %s" % unichr(0x2731)
if effectSize < 0:
barColor = "#1133ff"
else:
barColor = "#ff3311"
else: # not significant
if effectSize < 0:
barColor = "#b0b0f0"
else:
barColor = "#ccb090"
# Add the minus sign if needed
signChar = ""
if effectSize < 0:
signChar = unichr(
0x2212
) # minus sign (more legible than a hypen...)
v = RectFace(width=abs(effectSize) * barScale,
height=baseFontSize * 3.5,
fgcolor=barColor,
bgcolor=barColor,
label={
"text":
"%s%.2g %s" %
(signChar, abs(effectSize),
significanceMarker),
"fontsize":
baseFontSize * 1.8,
"color":
"black"
})
#v.rotation = -90
v.margin_top = 1
v.margin_left = 30
v.margin_right = 8
v.margin_bottom = 12
faces.add_face_to_node(v, node, column=0)
details = TextFace(
"N=%d" % row['NumSpecies'], fsize=baseFontSize *
1.5) #, fsize=baseFontSize) #, fstyle="italic")
details.background.color = "#dfdfdf"
details.margin_left = 6
details.margin_right = 20
#details.margin_top=5
#details.margin_bottom=0
faces.add_face_to_node(details, node, column=1)
nstyle = NodeStyle()
nstyle["size"] = 0
node.set_style(nstyle)
def speciesByPhylaTable():
allPhyla = parseReport() # get all existing phyla
domainCounts = Counter()
phylaCounts = Counter()
skippedCounts = Counter()
#classesByPhyla = {} # Disable tallying by class, since these are not used for many taxons
ordersByPhyla = {}
familiesByPhyla = {}
genusesByPhyla = {}
phylaDf = pd.DataFrame({
'Domain': pd.Categorical([]), # Bacteria, Eukaryota, Archaea
'Phylum': pd.Categorical([]), # Phylum name (string)
'TaxId': pd.Series([], dtype='int'), # Phylum TaxId
'ParentTaxId': pd.Series([], dtype='int'), # Parent TaxId
'NumSpecies': pd.Series([],
dtype='int'), # Species count for this phyla
# 'NumClasses': pd.Series([], dtype='int'), # Species count for this phyla
'NumOrders': pd.Series([], dtype='int'), # Orders count for this phyla
'NumFamilies': pd.Series([],
dtype='int'), # Families count for this phyla
'NumGenuses': pd.Series([],
dtype='int'), # Genuses count for this phyla
'RowType': pd.Categorical([])
}) # Phylum count or total
for group, phyla in allPhyla.items():
for phylum, record in phyla.items():
# Add item for each phylum
taxId = record['taxId']
phylaDf = phylaDf.append(
pd.DataFrame({
'Domain':
pd.Categorical([group]),
'Phylum':
pd.Categorical([phylum]),
'TaxId':
pd.Series([taxId], dtype='int'),
'ParentTaxId':
pd.Series([record['parentTaxId']], dtype='int'),
'NumSpecies':
pd.Series([0], dtype='int'),
# 'NumClasses': pd.Series([0], dtype='int'),
'NumOrders':
pd.Series([0], dtype='int'),
'NumFamilies':
pd.Series([0], dtype='int'),
'NumGenuses':
pd.Series([0], dtype='int'),
'RowType':
pd.Categorical(['Phylum'])
}))
#classesByPhyla[record['taxId']] = set()
ordersByPhyla[record['taxId']] = set()
familiesByPhyla[record['taxId']] = set()
genusesByPhyla[record['taxId']] = set()
# Create "special" items
pid = 1
for group in allPhyla.keys():
# Add "Unknown phylum" tally for each domain
phylaDf = phylaDf.append(
pd.DataFrame({
'Domain': pd.Categorical([group]),
'Phylum': pd.Categorical(['[Unknown]']),
'TaxId': pd.Series([pid], dtype='int'),
'ParentTaxId': pd.Series([0], dtype='int'),
'NumSpecies': pd.Series([0], dtype='int'),
# 'NumClasses': pd.Series([0], dtype='int'),
'NumOrders': pd.Series([0], dtype='int'),
'NumFamilies': pd.Series([0], dtype='int'),
'NumGenuses': pd.Series([0], dtype='int'),
'RowType': pd.Categorical(['Total'])
}))
pid += 1
# Add totals tally for each domain
phylaDf = phylaDf.append(
pd.DataFrame({
'Domain': pd.Categorical([group]),
'Phylum': pd.Categorical(['[Total]']),
'TaxId': pd.Series([pid], dtype='int'),
'ParentTaxId': pd.Series([0], dtype='int'),
'NumSpecies': pd.Series([0], dtype='int'),
# 'NumClasses': pd.Series([0], dtype='int'),
'NumOrders': pd.Series([0], dtype='int'),
'NumFamilies': pd.Series([0], dtype='int'),
'NumGenuses': pd.Series([0], dtype='int'),
'RowType': pd.Categorical(['Total'])
}))
pid += 1
# Add overally totals items
phylaDf = phylaDf.append(
pd.DataFrame({
'Domain': pd.Categorical(['[All]']),
'Phylum': pd.Categorical(['[Total]']),
'TaxId': pd.Series([pid], dtype='int'),
'ParentTaxId': pd.Series([0], dtype='int'),
'NumSpecies': pd.Series([0], dtype='int'),
# 'NumClasses': pd.Series([0], dtype='int'),
'NumOrders': pd.Series([0], dtype='int'),
'NumFamilies': pd.Series([0], dtype='int'),
'NumGenuses': pd.Series([0], dtype='int'),
'RowType': pd.Categorical(['Total'])
}))
phylaDf.set_index('TaxId', inplace=True)
skippedSpecies = []
# Count the number of species under each phylum
for taxId in allSpeciesSource():
if taxId in speciesToExclude: continue
lineage = ncbiTaxa.get_lineage(taxId)
names = ncbiTaxa.get_taxid_translator(lineage)
ranks = ncbiTaxa.get_rank(lineage)
# Determine kingdom/domain
kingdomTaxId = [
t for t, rank in ranks.items() if rank == 'superkingdom'
]
if not kingdomTaxId:
kingdomTaxId = [
t for t, rank in ranks.items() if rank == 'kingdom'
]
domain = names[kingdomTaxId[0]]
domainCounts.update([domain])
# Determine phylum
phylumTaxId = [t for t, rank in ranks.items() if rank == 'phylum']
if not phylumTaxId:
skippedSpecies.append(taxId)
skippedCounts.update([domain])
print("Skipping %d: (%s) missing phylum" % (taxId, names[taxId]))
#print(formatLineage(lineage, names))
continue # This table is structured by phylum; information will be missing for any species missing a phylum; it will be included in the "species missing phylum" ([Unknown]) row.
else:
phylumTaxId = phylumTaxId[0]
if phylumTaxId:
phylaCounts.update(
[phylumTaxId]) # tally this species under the specified phylum
#classTaxId = [t for t,rank in ranks.items() if rank=='class']
#if classTaxId:
# classesByPhyla[phylumTaxId].add( classTaxId[0] )
orderTaxId = [t for t, rank in ranks.items() if rank == 'order']
if orderTaxId:
ordersByPhyla[phylumTaxId].add(orderTaxId[0])
familyTaxId = [t for t, rank in ranks.items() if rank == 'family']
if familyTaxId:
familiesByPhyla[phylumTaxId].add(familyTaxId[0])
genusTaxId = [t for t, rank in ranks.items() if rank == 'genus']
if genusTaxId:
genusesByPhyla[phylumTaxId].add(genusTaxId[0])
assert (sum(skippedCounts.values()) == len(skippedSpecies))
# Update the phyla counts
for phylaTaxId, counts in phylaCounts.items():
#phylaDf.loc[phylaTaxId, 'NumClasses'] = len(classesByPhyla[phylaTaxId])
phylaDf.loc[phylaTaxId, 'NumOrders'] = len(ordersByPhyla[phylaTaxId])
phylaDf.loc[phylaTaxId,
'NumFamilies'] = len(familiesByPhyla[phylaTaxId])
phylaDf.loc[phylaTaxId, 'NumGenuses'] = len(genusesByPhyla[phylaTaxId])
phylaDf.loc[phylaTaxId, 'NumSpecies'] = counts
# Update the "Unknown phyla" count for each domain
for group, countMissing in skippedCounts.items():
#print('-'*20)
#print("%s - %d missing" % (group, countMissing))
dummyTaxIdForBasketGroup = phylaDf[(phylaDf.Domain == group) & (
phylaDf.Phylum == '[Unknown]')].index[0]
phylaDf.loc[dummyTaxIdForBasketGroup, 'NumSpecies'] = countMissing
# Update the total for each domain
for group, totalCount in domainCounts.items():
dummyTaxIdForBasketGroup = phylaDf[
(phylaDf.Domain == group) & (phylaDf.Phylum == '[Total]')].index[0]
phylaDf.loc[dummyTaxIdForBasketGroup, 'NumSpecies'] = totalCount
# Update the overall total count
dummyTaxIdForBasketGroup = phylaDf[(phylaDf.Domain == "[All]") &
(phylaDf.Phylum == '[Total]')].index[0]
phylaDf.loc[dummyTaxIdForBasketGroup,
'NumSpecies'] = sum(domainCounts.values())
phylaDf.loc[dummyTaxIdForBasketGroup,
'NumOrders'] = sum([len(x) for x in ordersByPhyla.values()])
phylaDf.loc[dummyTaxIdForBasketGroup, 'NumFamilies'] = sum(
[len(x) for x in familiesByPhyla.values()])
phylaDf.loc[dummyTaxIdForBasketGroup,
'NumGenuses'] = sum([len(x) for x in genusesByPhyla.values()])
# Prepare and save the final table
phylaReportDf = phylaDf[phylaDf['NumSpecies'] > 0] # remove "empty" items
phylaReportDf = phylaReportDf.sort_values(
by=['Domain', 'RowType', 'Phylum']) # sort rows
print(phylaReportDf)
phylaReportDf.to_html('phyla_report.html',
columns=[
'Phylum', 'NumOrders', 'NumFamilies',
'NumGenuses', 'NumSpecies', 'Domain'
])
phylaReportDf.to_excel('phyla_report.xlsx', sheet_name='Phyla Summary')
with open("phyla_report.rst", "w") as f:
f.write(
phylaReportDf.drop([
'RowType', 'NumFamilies', 'NumGenuses', 'NumOrders',
'ParentTaxId'
],
axis=1).pipe(tabulate,
headers='keys',
tablefmt='rst'))
# Prepare the "Missing phyla" report
missingPhylaReportDf = phylaDf[phylaDf['NumSpecies'] == 0]
missingPhylaReportDf = missingPhylaReportDf.sort_values(
by=['Domain', 'RowType', 'Phylum']) # sort rows
missingPhylaReportDf.to_html('phyla_report_missing.html',
columns=['Phylum', 'NumSpecies', 'Domain'])
missingPhylaReportDf.to_excel('phyla_report_missing.xlsx',
sheet_name='Missing Phyla Summary')
# print counts
print(domainCounts)
#print(phylaCounts)
# Display "skipped items" warning
if (skippedSpecies):
print("=" * 50)
print("Warning: Skipped %d species" % len(skippedSpecies))
print(skippedCounts)
print("=" * 50)
},
index=pd.Index([], name='tax_id', dtype='int'))
# Add kingdom data to the data-frame
for k, v in taxidToKingdom.items():
df.loc[k, 'kingdom'] = v
df.loc[k, 'full.name'] = getSpeciesName(k)
df.loc[k, 'short.name'] = shortNames[k]
assert (df.loc[k, 'kingdom'] == v)
# Get list of large taxonomic groups (based on the lineages of all species)
majorGroups = getMajorTaxonomicGroups(taxidToLineage)
# Add a binary membership column for each major group
for groupTaxId, _ in majorGroups:
groupName = ncbiTaxa.get_taxid_translator([groupTaxId])[groupTaxId]
groupName = "Member_%s_%d" % (groupName.replace(" ", "_").replace(
"/", "_").replace("-", "_"), groupTaxId)
groupDf = pd.DataFrame({groupName: pd.Series(dtype='bool')},
index=pd.Index(df.index.values,
name='tax_id',
dtype='int'))
for taxId, lineage in taxidToLineage.items():
isMember = int(groupTaxId in lineage)
groupDf.loc[taxId, groupName] = isMember
df = pd.merge(df, groupDf, how='inner', left_index=True, right_index=True
) # Add the new column (is there an easier way to do this?)
def speciesStatisticsAndValidityReport(args):
import _distributed
speciesDf = pd.DataFrame({
'TaxId': pd.Series([], dtype='int'), # Species TaxId
'Species': pd.Series([], dtype='str'), # Species binomial name
'Nickname': pd.Series([], dtype='str'),
'Domain': pd.Categorical([]), # Bacteria, Eukaryota, Archaea
'Phylum': pd.Categorical([]), # Phylum name (string)
'NumCDSs': pd.Series([], dtype='int'), # CDS count for this species
'NumCDSsInProfile':
pd.Series([], dtype='int'
), # Num seqs with 20 shuffled profiles for this species
'AnnotatedNumCDSs': pd.Series([], dtype='int'), #
'CDSDifference': pd.Series([], dtype='float'), #
'NumNativeSeqs': pd.Series([], dtype='int'), #
'GCContentInCDS': pd.Series([], dtype='float'), #
'AnnotatedGCContent': pd.Series([], dtype='float'), #
'RowType': pd.Categorical([]), # Species count or total
'Warnings': pd.Series([], dtype='str'), #
'CDSWarnings': pd.Series([], dtype='int'), #
'CDSWarnings_': pd.Series([], dtype='str'), #
'FirstAA': pd.Series([], dtype='str'), #
'LastAA': pd.Series([], dtype='str') #
})
scheduler = _distributed.open()
results = {}
delayedCalls_native = []
shuffledCounts = {}
delayedCalls_shuffledProfiles = []
for taxId in allSpeciesSource():
if taxId in speciesToExclude:
continue # always exclude species from the blacklist
if args.taxid and taxId not in args.taxid:
continue # if a whitelist is specified, skip other species
warnings = []
## DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ##
#if randint(0, 20) > 0:
# continue
## DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ### DEBUG ONLY ##
cdsCountInRedis = countSpeciesCDS(taxId)
#cdsCountProfiles = countx(taxId, (310, 10, "begin", 0), 102, 11)
annotatedProteinCount = getSpeciesProperty(taxId, 'protein-count')[0]
annotatedGCContent = getSpeciesProperty(taxId, 'gc-content')[0]
proteinDifference = None
if not annotatedProteinCount is None:
proteinDifference = (1.0 - float(cdsCountInRedis) /
float(annotatedProteinCount)) * 100.0
if abs(proteinDifference) > 9.9:
warnings.append("CDS_count")
else:
warnings.append("No_CDS_count")
# Determine phylum
lineage = ncbiTaxa.get_lineage(taxId)
names = ncbiTaxa.get_taxid_translator(lineage)
ranks = ncbiTaxa.get_rank(lineage)
# Determine kingdom/domain
domain = ""
kingdomTaxId = [
t for t, rank in ranks.items() if rank == 'superkingdom'
]
if not kingdomTaxId:
kingdomTaxId = [
t for t, rank in ranks.items() if rank == 'kingdom'
]
domain = names[kingdomTaxId[0]]
phylumName = ""
# Determine phylum
phylumTaxId = [t for t, rank in ranks.items() if rank == 'phylum']
if phylumTaxId:
phylumName = names[phylumTaxId[0]]
speciesDf = speciesDf.append(
pd.DataFrame({
'TaxId':
pd.Series([taxId], dtype='int'), # Species TaxId
'Species':
pd.Series([getSpeciesName(taxId)], dtype='str'),
'Nickname':
pd.Series([shortNames[taxId]], dtype='str'),
'Domain':
pd.Categorical([domain]), # Bacteria, Eukaryota, Archaea
'Phylum':
pd.Categorical([phylumName]), # Phylum name (string)
'NumCDSs':
pd.Series([cdsCountInRedis],
dtype='int'), # CDS count for this species
'NumCDSsInProfile':
pd.Series([0],
dtype='int'), # Num seqs with 20 shuffled profiles
'AnnotatedNumCDSs':
pd.Series([
0
if annotatedProteinCount is None else annotatedProteinCount
],
dtype='int'), #
'CDSDifference':
pd.Series([proteinDifference], dtype='float'), #
'NumNativeSeqs':
pd.Series([0], dtype='int'), #
'GCContentInCDS':
pd.Series([0.0], dtype='float'), #
'AnnotatedGCContent':
pd.Series([annotatedGCContent], dtype='float'), #
'RowType':
pd.Categorical(["species"]), # Species count or total
'Warnings':
pd.Series([", ".join(warnings)], dtype='str'), #
'CDSWarnings':
pd.Series([0], dtype='int'),
'CDSWarnings_':
pd.Series([""], dtype='str'),
'FirstAA':
pd.Series([""], dtype='str'),
'LastAA':
pd.Series([""], dtype='str'),
'Source':
pd.Series([""], dtype='str')
}))
fractionSize = 1000 # How many sequences (roughly) to process in each task
numFractions = cdsCountInRedis / fractionSize
if numFractions == 0: numFractions = 1
for i in range(numFractions):
# DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #
#if i%100!=5: continue
# DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #### DEBUG ONLY #
call = dask.delayed(calcNativeSequencesStatistics)(taxId, i,
numFractions)
delayedCalls_native.append(call)
call = dask.delayed(countShuffledProfiles)(taxId,
(310, 10, "begin", 0), 102,
11)
delayedCalls_shuffledProfiles.append(call)
speciesDf.set_index('TaxId', inplace=True)
print("Starting {} calls...".format(
len(delayedCalls_native) + len(delayedCalls_shuffledProfiles)))
futures = scheduler.compute(
delayedCalls_native + delayedCalls_shuffledProfiles
) # submit all delayed calculations; obtain futures immediately
try:
_distributed.progress(futures) # wait for all calculations to complete
except Exception as e:
print(E)
print("\n")
print("Waiting for all tasks to complete...")
_distributed.wait(futures)
results = {}
errorsCount = 0
for f in futures:
try:
ret = scheduler.gather(f)
if (len(ret) == 9):
(taxId, fraction, cdsCount, gcCounts, totalCounts, cdsWarnings,
warnings, firstAA, lastAA) = ret
current = None
if taxId in results:
current = results[taxId]
else:
current = (0, 0, 0, 0, Counter(), Counter(), Counter())
current = (current[0] + cdsCount, current[1] + gcCounts,
current[2] + totalCounts, current[3] + cdsWarnings,
current[4] + warnings, current[5] + firstAA,
current[6] + lastAA)
results[taxId] = current
elif (len(ret) == 2):
(taxId, numShuffledSeqs) = ret
shuffledCounts[taxId] = numShuffledSeqs
else:
assert (False)
except Exception as e:
print(e)
errorsCount += 1
for taxId, result in results.items():
(numNativeSeqs, gcCounts, totalCounts, cdsWarnings, warnings, firstAA,
lastAA) = result
speciesDf.at[taxId, 'NumNativeSeqs'] = numNativeSeqs
speciesDf.at[taxId, 'GCContentInCDS'] = round(
float(gcCounts) / float(totalCounts) * 100.0, 1)
speciesDf.at[taxId, 'CDSWarnings'] = cdsWarnings
speciesDf.at[taxId, 'CDSWarnings_'] = summarizeCounter(warnings)
speciesDf.at[taxId, 'FirstAA'] = summarizeCounter(firstAA)
speciesDf.at[taxId, 'LastAA'] = summarizeCounter(lastAA)
#if numNativeSeqs < species.at[taxId, 'NumCDSs']:
# pass
for taxId, result in shuffledCounts.items():
speciesDf.at[taxId, 'NumCDSsInProfile'] = result
speciesDf = speciesDf.sort_values(by=['Domain', 'Species']) # sort rows
speciesDf.to_html('species_report.html',
float_format='{0:.1f}'.format,
columns=[
'Species', 'Nickname', 'NumCDSs', 'NumCDSsInProfile',
'AnnotatedNumCDSs', 'CDSDifference', 'NumNativeSeqs',
'GCContentInCDS', 'AnnotatedGCContent', 'Phylum',
'Domain', 'Warnings', 'CDSWarnings', 'CDSWarnings_',
'FirstAA', 'LastAA'
])
with open("species_report_simple.rst", "w") as f:
f.write(
speciesDf.drop([
'RowType', 'Warnings', 'CDSWarnings', 'CDSWarnings_',
'FirstAA', 'LastAA', 'CDSDifference'
],
axis=1).pipe(tabulate,
headers='keys',
tablefmt='rst'))
speciesDf.to_html('species_report_simple.html',
float_format='{0:.1f}'.format,
columns=[
'Species', 'Nickname', 'NumCDSs', 'NumCDSsInProfile',
'AnnotatedNumCDSs', 'CDSDifference', 'NumNativeSeqs',
'GCContentInCDS', 'AnnotatedGCContent', 'Phylum',
'Domain'
])
speciesDf.to_excel('species_report.xlsx', sheet_name='Species summary')