def download_nexson_from_phylografter(paths, download_db, lock_policy):
DOMAIN = os.environ.get('PHYLOGRAFTER_DOMAIN_PREF')
if DOMAIN is None:
DOMAIN = 'http://www.reelab.net/phylografter'
headers = {
'accept-encoding': 'gzip',
'content-type': 'application/json',
'accept': 'application/json',
}
nexson = paths['nexson']
lockfile = nexson + '.lock'
was_locked, owns_lock = lock_policy.wait_for_lock(lockfile)
try:
if not owns_lock:
return False
study = os.path.split(nexson)[-1]
if VERBOSE:
sys.stderr.write('Downloading %s...\n' % study)
SUBMIT_URI = DOMAIN + '/study/export_gzipNexSON.json/' + study
resp = requests.get(SUBMIT_URI, headers=headers, allow_redirects=True)
resp.raise_for_status()
try:
uncompressed = gzip.GzipFile(mode='rb',
fileobj=StringIO(
resp.content)).read()
results = uncompressed
except:
raise
if isinstance(results, unicode) or isinstance(results, str):
er = json.loads(results)
else:
raise RuntimeError('Non gzipped response, but not a string is:',
results)
should_write = False
if not os.path.exists(study):
should_write = True
else:
prev_content = json.load(open(study, 'rU'))
if prev_content != er:
should_write = True
if should_write:
store_state_JSON(er, study)
if download_db is not None:
try:
download_db['studies'].remove(int(study))
except:
warn('%s not in %s' % (study, paths['nexson_state_db']))
pass
else:
store_state_JSON(download_db, paths['nexson_state_db'])
finally:
lock_policy.remove_lock()
return True
def download_nexson_from_phylografter(paths, download_db, lock_policy):
DOMAIN = os.environ.get('PHYLOGRAFTER_DOMAIN_PREF')
if DOMAIN is None:
DOMAIN = 'http://www.reelab.net/phylografter'
headers = {
'accept-encoding' : 'gzip',
'content-type' : 'application/json',
'accept' : 'application/json',
}
nexson = paths['nexson']
lockfile = nexson + '.lock'
was_locked, owns_lock = lock_policy.wait_for_lock(lockfile)
try:
if not owns_lock:
return False
study = os.path.split(nexson)[-1]
if VERBOSE:
sys.stderr.write('Downloading %s...\n' % study)
SUBMIT_URI = DOMAIN + '/study/export_gzipNexSON.json/' + study
resp = requests.get(SUBMIT_URI,
headers=headers,
allow_redirects=True)
resp.raise_for_status()
try:
uncompressed = gzip.GzipFile(mode='rb', fileobj=StringIO(resp.content)).read()
results = uncompressed
except:
raise
if isinstance(results, unicode) or isinstance(results, str):
er = json.loads(results)
else:
raise RuntimeError('Non gzipped response, but not a string is:', results)
should_write = False
if not os.path.exists(study):
should_write = True
else:
prev_content = json.load(open(study, 'rU'))
if prev_content != er:
should_write = True
if should_write:
store_state_JSON(er, study)
if download_db is not None:
try:
download_db['studies'].remove(int(study))
except:
warn('%s not in %s' % (study, paths['nexson_state_db']))
pass
else:
store_state_JSON(download_db, paths['nexson_state_db'])
finally:
lock_policy.remove_lock()
return True
def target_is_dirty(src_path_list, dest_path_list, trigger=None):
if bool(trigger):
return True
for p in src_path_list:
if not os.path.exists(p):
warn('Source path "%s" does not exist' % p)
return True
for dest_path in dest_path_list:
if not os.path.exists(dest_path):
return True
smt = max([os.path.getmtime(i) for i in src_path_list])
dmt = min([os.path.getmtime(i) for i in dest_path_list])
return (smt >= dmt)
def target_is_dirty(src_path_list, dest_path_list, trigger=None):
if bool(trigger):
return True
for p in src_path_list:
if not os.path.exists(p):
warn('Source path "%s" does not exist' % p)
return True
for dest_path in dest_path_list:
if not os.path.exists(dest_path):
return True
smt = max([os.path.getmtime(i) for i in src_path_list])
dmt = min([os.path.getmtime(i) for i in dest_path_list])
return (smt >= dmt)
def _process_tree_log_info(curr_tree, tree_list, tree_id2tree, tree_notes,
nexson_obj):
TREE_INFO_LABELS = (
'tree info',
'ingested tree',
'property added',
)
TREE_ID_BEARING_LABELS = ('checking for uniqueness of ott ids',
'name fixing on tree',
'checking if tree could be added to graph',
'null or duplicate names. skipping tree')
for el in tree_notes:
try:
label = el['label'].lower()
except:
_process_tree_part_log_info(curr_tree, el, nexson_obj)
else:
if label in TREE_INFO_LABELS:
for k, v in el.items():
if k != 'label':
if k == 'ot:tag':
curr_tree.setdefault(k, []).append(v)
else:
if k in curr_tree:
warn('Overwriting "%s" -> "%s" with value "%s"'
% (k, curr_tree[k], v))
curr_tree[k] = v
elif label in TREE_ID_BEARING_LABELS: # check index in case a tree has been omitted...
tid = el['tree id']
if tid != curr_tree['id']:
debug('%s swapping curr_tree to tree with id %s' %
(str(el), tid))
curr_tree = tree_id2tree[tid]
if label == 'null or duplicate names. skipping tree':
curr_tree['can be ingested'] = False
elif label == 'all nodes have ottolids':
curr_tree['had_ott_ids_before_tnrs'] = True
elif label == 'postpruning newick':
curr_tree['postpruning newick'] = el['tree']
elif label not in TO_IGNORE_LABELS:
warn('Ignored label "%s" in tree' % label)
def _process_tree_log_info(curr_tree, tree_list, tree_id2tree, tree_notes, nexson_obj):
TREE_INFO_LABELS = ('tree info',
'ingested tree',
'property added',
)
TREE_ID_BEARING_LABELS = ('checking for uniqueness of ott ids',
'name fixing on tree',
'checking if tree could be added to graph',
'null or duplicate names. skipping tree'
)
for el in tree_notes:
try:
label = el['label'].lower()
except:
_process_tree_part_log_info(curr_tree, el, nexson_obj)
else:
if label in TREE_INFO_LABELS:
for k, v in el.items():
if k != 'label':
if k == 'ot:tag':
curr_tree.setdefault(k, []).append(v)
else:
if k in curr_tree:
warn('Overwriting "%s" -> "%s" with value "%s"' %(k, curr_tree[k], v))
curr_tree[k] = v
elif label in TREE_ID_BEARING_LABELS: # check index in case a tree has been omitted...
tid = el['tree id']
if tid != curr_tree['id']:
debug('%s swapping curr_tree to tree with id %s' % (str(el), tid))
curr_tree = tree_id2tree[tid]
if label == 'null or duplicate names. skipping tree':
curr_tree['can be ingested'] = False
elif label == 'all nodes have ottolids':
curr_tree['had_ott_ids_before_tnrs'] = True
elif label == 'postpruning newick':
curr_tree['postpruning newick'] = el['tree']
elif label not in TO_IGNORE_LABELS:
warn('Ignored label "%s" in tree' % label)
def write_status_obj_as_html(status_obj, output):
'''
Writes the status html
Takes:
`status_obj` a dict parsed from "status JSON"
`output` a file-like obj
'''
study_info = status_obj['study_info']
tree_list = status_obj['tree_list']
phylografter_study_id = study_info['phylografter_study_id']
phylografter_study_link = study_info['phylografter_study_link']
output.write('''<html>
<head>
<title>Snaphsot of treemachine status for phylografter study %s</title>
</head>''' % phylografter_study_id)
output.write('''<body>
<h2>Study Info</h2>
<p><a href="%s">%s</a></p>
<table border="1">
''' % (phylografter_study_link, phylografter_study_link))
kl = study_info.keys()
kl.sort()
for k in kl:
v = study_info[k]
output.write(' <tr><td>' + str(k) + '</td><td>' +
proc_val_for_html(v) + '</td>\n')
output.write(''' </table>
''')
##############################3
# Write tree info
##############################3
to_ignore_keys = [
'tree_index',
]
detail_keys = [
'number of external nodes',
'number edges',
'number nodes',
'ot:tag', # [tag, tag]
]
verbose_keys = [
'postpruning newick', # newick string
'subtrees', # dict. Keys newick -> dict with name and node keys
'by_ott' # {ottID -> {'taxonomy', }}
]
valuable_tree_keys = [
'id', 'ot:branchLengthMode', 'ingroup', 'ot:inGroupClade',
'ot:focalClade'
]
special_keys = [
'status',
'had_ott_ids_before_tnrs', # bool
'deprecated', # bool
'reused_tip_labels', #
'pruned_dup', # = [name, name, ...]
'null_ottol_ids_in_final_mapping', # ['original name']
'null ott id for node', # ['name']
'overlapping_tip_taxa',
'importable_into_treemachine',
'phylografter_tree_link',
'ott id missing',
'tnrs resolved ottolid',
'pruned unmapped',
]
keys_to_lists = ['ot:tag']
all_known_tree_keys = special_keys + verbose_keys + valuable_tree_keys + detail_keys + to_ignore_keys
num_importable = study_info['num_trees_importable']
for tree in tree_list:
tid = tree['id']
phylografter_tree_link = tree['phylografter_tree_link']
output.write('''<h3>Tree %s</h3>
<p><a href="%s">%s</a></p>
''' % (tid, phylografter_tree_link, phylografter_tree_link))
# STATUS
output.write('''<p><b>Status: </b>%s</p>
''' % (tree['status']['text']))
r = tree['status']['reasons']
if r:
output.write('''<table border="1">
''')
for row in r:
rc = row['rc']
if rc == 'USER':
_display_user_status_code(output, row)
elif rc == 'DUPLICATE':
_display_duplicate_taxon_status_code(output, row)
elif rc == 'OVERLAPPING':
_display_overlapping_taxon_status_code(output, row)
elif rc in ['PRUNING', 'PRUNING_UNMAPPED']:
_display_pruning_taxon_status_code(output, row)
elif rc == 'TNRS':
_display_tnrs_taxon_status_code(output, row)
else:
assert rc == 'NULLID'
_display_null_taxon_status_code(output, row)
output.write(''' </table>
''')
# STATUS
output.write('''<p><b>Info</b></p>
''')
output.write('''<table border="1">\n ''')
kl = tree.keys()
for k in kl:
if k not in all_known_tree_keys:
warn('Unexpected key in tree: ' + k)
for k in keys_to_lists:
tl = tree.get(k)
if tl is not None:
tree[k] = '"%s"' % '", "'.join(tl)
for k in valuable_tree_keys + detail_keys:
try:
v = tree[k]
output.write(' <tr><td>%s</td><td>%s</td>\n' %
(k, proc_val_for_html(v)))
except:
output.write(' <tr><td>%s</td><td>%s</td>\n' %
(k, warn_html("Missing!")))
output.write(''' </table>
<hr/>
''')
if num_importable != 1:
output.write('<p>' + warn_html(str(num_importable) + ' trees') +
' importable from this study!</p>\n')
else:
output.write('<p>1 tree importable from this study.</p>\n')
output.write('''</body>
</head>
''')
def process_treemachine_log_info(log_obj, nexson_obj, default_study_id):
'''Returns study_info_dict, list_of_tree_info objects
'''
nexson_obj.incoming_ottid_to_otu = get_ott_id_to_taxon_map(
nexson_obj.otu_list)
study = {}
tree_list = []
context_el = None
tree_id2tree = {}
for el in log_obj:
try:
label = el['label'].lower()
if label == 'otus':
_add_unique(el, study, 'number', 'num_otus')
context_el = study
elif label == 'study tagged as deprecated. ignore.':
study['Deprecated'] = 'Study tagged as deprecated.'
elif label == 'processing tree':
tree_list.append({})
context_el = tree_list[-1]
context_el['tree_index'] = len(tree_list) - 1
_add_unique(el, context_el, '@id', 'id')
tree_id2tree[context_el['id']] = context_el
elif label == 'tree tagged as deprecated. ignoring.':
tid = el['@id']
tree_id2tree[tid]['deprecated'] = True
elif label not in TO_IGNORE_LABELS:
warn('Ignored label "%s"' % label)
except:
_process_tree_log_info(context_el, tree_list, tree_id2tree, el,
nexson_obj)
# move study info to study dict
STUDY_INFO_PREFIXES = ['ot:study', 'ot:dataDeposit', 'ot:curatorName']
for tree in tree_list:
for k in tree.keys():
is_study_info = False
for p in STUDY_INFO_PREFIXES:
if k.startswith(p):
is_study_info = True
if is_study_info:
if k in study:
assert study[k] == tree[k]
else:
study[k] = tree[k]
del tree[k]
# process NexSON and tm out and add some helpful fields to the dicts
importable_count = 0
phylografter_domain = 'http://www.reelab.net/phylografter/'
for tree in tree_list:
i = diagnose_tree_status(tree, nexson_obj)
tree['importable_into_treemachine'] = 'true' if i else 'false'
if i:
importable_count += 1
tid = tree['id']
tree[
'phylografter_tree_link'] = phylografter_domain + 'stree/svgView/' + tid
study['num_trees_importable'] = importable_count
study['phylografter_study_id'] = study.get('ot:studyId', default_study_id)
study[
'phylografter_study_link'] = phylografter_domain + 'study/view/' + study[
'phylografter_study_id']
return {'study_info': study, 'tree_list': tree_list}
def _process_tree_part_log_info(curr_tree, part_info, nexson_obj):
prev_subtree = None
for el in part_info:
if isinstance(el, list):
dup_dict = {}
overlap_dict = {}
for sub in el:
label = sub['label'].lower()
if label == 'matched anc':
assert prev_subtree is not None
prev_subtree['name'] = sub.get('name')
prev_subtree['node'] = sub.get('node')
elif label == 'duplicate':
_update_keys(dup_dict, sub, ['name', 'OTT ID', 'nexsonid'])
elif label == 'overlapping retained':
_update_keys(overlap_dict.setdefault('retained', {}), sub,
['name', 'nexsonid'])
elif label == 'overlapping pruned':
_update_keys(overlap_dict.setdefault('pruned', {}), sub,
['name', 'nexsonid'])
elif label not in TO_IGNORE_LABELS:
warn('Ignored label "%s" in tree' % label)
if len(dup_dict) > 0:
_add_otu_from_node_nexsonid(dup_dict, dup_dict.get('nexsonid'),
nexson_obj)
curr_tree['reused_tip_labels'].append(dup_dict)
if len(overlap_dict) > 0:
r = overlap_dict['retained']
_add_otu_from_node_nexsonid(r, r.get('nexsonid'), nexson_obj)
p = overlap_dict['pruned']
_add_otu_from_node_nexsonid(p, p.get('nexsonid'), nexson_obj)
curr_tree['overlapping_tip_taxa'].append(overlap_dict)
else:
label = el['label'].lower()
if label == 'taxon mapping':
by_ott = curr_tree.setdefault('by_ott', {})
ott_id = el['OTT ID']
by_ott[el['OTT ID']] = {'taxonomy': el['taxonomy']}
elif label == 'subtree':
sd = {
} # Change the following to the rvalue to retain the subtrees... => curr_tree.setdefault('subtrees', {})
n = el['newick']
prev_subtree = {}
sd[n] = prev_subtree
elif label == 'ott id reused in tree':
curr_tree.setdefault('reused_tip_labels', [])
elif label == 'overlapping tips':
curr_tree.setdefault('overlapping_tip_taxa', [])
elif label == 'pruning dups and overlapping':
_add_otu_from_node_nexsonid(el, el.get('nexsonid'), nexson_obj)
curr_tree.setdefault('pruned_dup', []).append(el)
elif label == 'pruning unmapped':
_add_otu_from_node_nexsonid(el, el.get('nexsonid'), nexson_obj)
curr_tree.setdefault('pruned unmapped', []).append(el)
elif label == 'error ottolid indexed to a null node!':
curr_tree.setdefault('null_ottol_ids_in_final_mapping',
[]).append(el)
elif label == 'null ott id for node':
curr_tree.setdefault('null ott id for node', []).append(el)
elif label == 'ott id missing':
curr_tree.setdefault('ott id missing', []).append({
'name':
el['name'],
'nexsonid':
el['nexsonid']
})
elif label == 'tnrs resolved ottolid':
nid = el['nexsonid']
searched_on = el['searched on']
ott_id = el['OTT ID']
name = el['name']
c = nexson_obj.__dict__.get('corrected', {})
node = nexson_obj.node_for_nexsonid(nid)
nexsontree = nexson_obj.tree_nexsonid_to_tree[curr_tree['id']]
oid2node = nexsontree.ottid_to_node_list
nl = oid2node[node.otu.ott_id]
assert node in nl
nl.remove(node)
new_otu_list = oid2node.get(ott_id)
if new_otu_list is None or len(new_otu_list) == 0:
o = OTU({'@id': None, '@label': name, 'meta': None})
o.original_label = searched_on
o.ott_id = ott_id
nexson_obj.otu_list.append(o)
oid2node[ott_id] = [node]
else:
node.otu = new_otu_list[0].otu
new_otu_list.append(node)
curr_tree.setdefault('tnrs resolved ottolid', []).append({
'searched on':
searched_on,
'OTT ID':
ott_id,
'name':
name,
'nexsonid':
nid
})
elif label not in TO_IGNORE_LABELS:
warn('Ignored label "%s" in part' % label)
def write_status_obj_as_html(status_obj, output):
'''
Writes the status html
Takes:
`status_obj` a dict parsed from "status JSON"
`output` a file-like obj
'''
study_info= status_obj['study_info']
tree_list = status_obj['tree_list']
phylografter_study_id = study_info['phylografter_study_id']
phylografter_study_link = study_info['phylografter_study_link']
output.write('''<html>
<head>
<title>Snaphsot of treemachine status for phylografter study %s</title>
</head>''' % phylografter_study_id)
output.write('''<body>
<h2>Study Info</h2>
<p><a href="%s">%s</a></p>
<table border="1">
''' % (phylografter_study_link, phylografter_study_link))
kl = study_info.keys()
kl.sort()
for k in kl:
v = study_info[k]
output.write(' <tr><td>' + str(k) + '</td><td>' + proc_val_for_html(v) + '</td>\n')
output.write(''' </table>
''')
##############################3
# Write tree info
##############################3
to_ignore_keys =[ 'tree_index', ]
detail_keys = ['number of external nodes',
'number edges',
'number nodes',
'ot:tag', # [tag, tag]
]
verbose_keys = ['postpruning newick', # newick string
'subtrees', # dict. Keys newick -> dict with name and node keys
'by_ott' # {ottID -> {'taxonomy', }}
]
valuable_tree_keys = ['id',
'ot:branchLengthMode',
'ingroup',
'ot:inGroupClade',
'ot:focalClade']
special_keys = ['status',
'had_ott_ids_before_tnrs', # bool
'deprecated', # bool
'reused_tip_labels', #
'pruned_dup', # = [name, name, ...]
'null_ottol_ids_in_final_mapping', # ['original name']
'null ott id for node', # ['name']
'overlapping_tip_taxa',
'importable_into_treemachine',
'phylografter_tree_link',
'ott id missing',
'tnrs resolved ottolid',
'pruned unmapped',
]
keys_to_lists = ['ot:tag']
all_known_tree_keys = special_keys + verbose_keys + valuable_tree_keys + detail_keys + to_ignore_keys
num_importable = study_info['num_trees_importable']
for tree in tree_list:
tid = tree['id']
phylografter_tree_link = tree['phylografter_tree_link']
output.write('''<h3>Tree %s</h3>
<p><a href="%s">%s</a></p>
''' % (tid, phylografter_tree_link, phylografter_tree_link))
# STATUS
output.write('''<p><b>Status: </b>%s</p>
''' % (tree['status']['text']))
r = tree['status']['reasons']
if r:
output.write('''<table border="1">
''' )
for row in r:
rc = row['rc']
if rc == 'USER':
_display_user_status_code(output, row)
elif rc == 'DUPLICATE':
_display_duplicate_taxon_status_code(output, row)
elif rc == 'OVERLAPPING':
_display_overlapping_taxon_status_code(output, row)
elif rc in ['PRUNING', 'PRUNING_UNMAPPED']:
_display_pruning_taxon_status_code(output, row)
elif rc == 'TNRS':
_display_tnrs_taxon_status_code(output, row)
else:
assert rc == 'NULLID'
_display_null_taxon_status_code(output, row)
output.write(''' </table>
''')
# STATUS
output.write('''<p><b>Info</b></p>
''')
output.write('''<table border="1">\n ''')
kl = tree.keys()
for k in kl:
if k not in all_known_tree_keys:
warn('Unexpected key in tree: ' + k)
for k in keys_to_lists:
tl = tree.get(k)
if tl is not None:
tree[k] = '"%s"' % '", "'.join(tl)
for k in valuable_tree_keys + detail_keys:
try:
v = tree[k]
output.write(' <tr><td>%s</td><td>%s</td>\n' % (k, proc_val_for_html(v)))
except:
output.write(' <tr><td>%s</td><td>%s</td>\n' % (k, warn_html("Missing!")))
output.write(''' </table>
<hr/>
''')
if num_importable != 1:
output.write('<p>' + warn_html(str(num_importable) + ' trees') + ' importable from this study!</p>\n')
else:
output.write('<p>1 tree importable from this study.</p>\n')
output.write('''</body>
</head>
''')
def process_treemachine_log_info(log_obj, nexson_obj, default_study_id):
'''Returns study_info_dict, list_of_tree_info objects
'''
nexson_obj.incoming_ottid_to_otu = get_ott_id_to_taxon_map(nexson_obj.otu_list)
study = {}
tree_list = []
context_el = None
tree_id2tree = {}
for el in log_obj:
try:
label = el['label'].lower()
if label == 'otus':
_add_unique(el, study, 'number', 'num_otus')
context_el = study
elif label == 'study tagged as deprecated. ignore.':
study['Deprecated'] = 'Study tagged as deprecated.'
elif label == 'processing tree':
tree_list.append({})
context_el = tree_list[-1]
context_el['tree_index'] = len(tree_list) - 1
_add_unique(el, context_el, '@id', 'id')
tree_id2tree[context_el['id']] = context_el
elif label =='tree tagged as deprecated. ignoring.':
tid = el['@id']
tree_id2tree[tid]['deprecated'] = True
elif label not in TO_IGNORE_LABELS:
warn('Ignored label "%s"' % label)
except:
_process_tree_log_info(context_el, tree_list, tree_id2tree, el, nexson_obj)
# move study info to study dict
STUDY_INFO_PREFIXES = ['ot:study', 'ot:dataDeposit', 'ot:curatorName']
for tree in tree_list:
for k in tree.keys():
is_study_info = False
for p in STUDY_INFO_PREFIXES:
if k.startswith(p):
is_study_info = True
if is_study_info:
if k in study:
assert study[k] == tree[k]
else:
study[k] = tree[k]
del tree[k]
# process NexSON and tm out and add some helpful fields to the dicts
importable_count =0
phylografter_domain = 'http://www.reelab.net/phylografter/'
for tree in tree_list:
i = diagnose_tree_status(tree, nexson_obj)
tree['importable_into_treemachine'] = 'true' if i else 'false'
if i:
importable_count += 1
tid = tree['id']
tree['phylografter_tree_link'] = phylografter_domain + 'stree/svgView/' + tid
study['num_trees_importable'] = importable_count
study['phylografter_study_id'] = study.get('ot:studyId', default_study_id)
study['phylografter_study_link'] = phylografter_domain + 'study/view/' + study['phylografter_study_id']
return {'study_info': study,
'tree_list': tree_list}
def _process_tree_part_log_info(curr_tree, part_info, nexson_obj):
prev_subtree = None
for el in part_info:
if isinstance(el, list):
dup_dict = {}
overlap_dict = {}
for sub in el:
label = sub['label'].lower()
if label == 'matched anc':
assert prev_subtree is not None
prev_subtree['name'] = sub.get('name')
prev_subtree['node'] = sub.get('node')
elif label == 'duplicate':
_update_keys(dup_dict, sub, ['name', 'OTT ID', 'nexsonid'])
elif label == 'overlapping retained':
_update_keys(overlap_dict.setdefault('retained', {}), sub, ['name', 'nexsonid'])
elif label == 'overlapping pruned':
_update_keys(overlap_dict.setdefault('pruned', {}), sub, ['name', 'nexsonid'])
elif label not in TO_IGNORE_LABELS:
warn('Ignored label "%s" in tree' % label)
if len(dup_dict) > 0:
_add_otu_from_node_nexsonid(dup_dict, dup_dict.get('nexsonid'), nexson_obj)
curr_tree['reused_tip_labels'].append(dup_dict)
if len(overlap_dict) > 0:
r = overlap_dict['retained']
_add_otu_from_node_nexsonid(r, r.get('nexsonid'), nexson_obj)
p = overlap_dict['pruned']
_add_otu_from_node_nexsonid(p, p.get('nexsonid'), nexson_obj)
curr_tree['overlapping_tip_taxa'].append(overlap_dict)
else:
label = el['label'].lower()
if label == 'taxon mapping':
by_ott = curr_tree.setdefault('by_ott', {})
ott_id = el['OTT ID']
by_ott[el['OTT ID']] = {'taxonomy': el['taxonomy']}
elif label == 'subtree':
sd = {} # Change the following to the rvalue to retain the subtrees... => curr_tree.setdefault('subtrees', {})
n = el['newick']
prev_subtree = {}
sd[n] = prev_subtree
elif label == 'ott id reused in tree':
curr_tree.setdefault('reused_tip_labels', [])
elif label == 'overlapping tips':
curr_tree.setdefault('overlapping_tip_taxa', [])
elif label == 'pruning dups and overlapping':
_add_otu_from_node_nexsonid(el, el.get('nexsonid'), nexson_obj)
curr_tree.setdefault('pruned_dup', []).append(el)
elif label == 'pruning unmapped':
_add_otu_from_node_nexsonid(el, el.get('nexsonid'), nexson_obj)
curr_tree.setdefault('pruned unmapped', []).append(el)
elif label == 'error ottolid indexed to a null node!':
curr_tree.setdefault('null_ottol_ids_in_final_mapping', []).append(el)
elif label == 'null ott id for node':
curr_tree.setdefault('null ott id for node', []).append(el)
elif label == 'ott id missing':
curr_tree.setdefault('ott id missing', []).append({'name': el['name'], 'nexsonid': el['nexsonid']})
elif label == 'tnrs resolved ottolid':
nid = el['nexsonid']
searched_on = el['searched on']
ott_id = el['OTT ID']
name = el['name']
c = nexson_obj.__dict__.get('corrected', {})
node = nexson_obj.node_for_nexsonid(nid)
nexsontree = nexson_obj.tree_nexsonid_to_tree[curr_tree['id']]
oid2node = nexsontree.ottid_to_node_list
nl = oid2node[node.otu.ott_id]
assert node in nl
nl.remove(node)
new_otu_list = oid2node.get(ott_id)
if new_otu_list is None or len(new_otu_list) == 0:
o = OTU({'@id': None, '@label':name, 'meta':None})
o.original_label = searched_on
o.ott_id = ott_id
nexson_obj.otu_list.append(o)
oid2node[ott_id] = [node]
else:
node.otu = new_otu_list[0].otu
new_otu_list.append(node)
curr_tree.setdefault('tnrs resolved ottolid', []).append({'searched on': searched_on,
'OTT ID': ott_id,
'name': name,
'nexsonid': nid})
elif label not in TO_IGNORE_LABELS:
warn('Ignored label "%s" in part' % label)