def delete_sequences_from_experiment(experiment_id_list):
global db
global username
global temp_user_info
[db,connection_data] = connect_to_ig_database()
try:
username = appsoma_api.environment_get_username()
temp_user_info = defaultdict(str,db.users.find_one({'user':username}))
except:
pass
if not type(experiment_id_list) is list:
experiment_id_list = [convert_to_objectid(experiment_id_list)]
else:
experiment_id_list = [convert_to_objectid(exp) for exp in experiment_id_list]
if temp_user_info['administrator']!=True:
exps_to_delete =[result['_id'] for result in db.exps.find({'OWNERS_OF_EXPERIMENT':temp_user_info['user'],'_id':{'$in':experiment_id_list}},{'_id':1})]
else:
exps_to_delete = experiment_id_list
if exps_to_delete == []:
raise Exception("User {0} does not have access to the listed experiments: {1}".format(temp_user_info['user'],str(experiment_id_list)))
operation_result = db.seqs.remove({expIdentifier:{'$in':exps_to_delete}})
db.exps.update({'_id':{'$in':exps_to_delete}},{'$set':{'SEQ_COUNT':0},'$unset':{'ANALYSES_SETTINGS':"",'ANALYSIS_SCHEMA':"",'ANALYSES_COUNT':""}},multi=True)
num_deleted = operation_result['n']
print "Deleted {0} sequences".format(str(num_deleted))
connection_data.close()
def QueryAllDocInfoByID(self,
id_list=[],
extra_filters={},
gene_functionality_filter={'$nin': []}):
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in': id_list}
if type(gene_functionality_filter) != 'dict':
raise Exception('Gene functionality filter must be a dict')
self.cursor = self.db.aggregate([{
'$match': extra_filters
}, {
'$unwind': "$GERMLINE_GENES"
}, {
'$match': {
"GERMLINE_GENES.FUNCTIONALITY": gene_functionality_filter
}
}])
self.results = [r.pop('GERMLINE_GENES')
for r in self.cursor] #self.cursor['result']
#for i,r in enumerate(self.results):
# self.results[i].pop('GERMLINE_GENES')
return self
def GetMotifForProgram(self,id_list=[],query={},project={'Locus':1,'Lmotif':1,'Rmotif':1,'Ltrim':1,'Rtrim':1}):
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
query['_id'] = {'$in':id_list}
cursor = self.db.find(query,project)
#return results as tuple whose indices are as follows: ['Locus','Lmotif','Rmotif','Ltrim','Rtrim']]
return self.formatMotif([(x['Locus'],x['Lmotif'],x['Rmotif'],x['Ltrim'],x['Rtrim']) for x in cursor])
def QueryGenesByID(self,id_list=[],extra_filters={},gene_functionality_filter={'$nin':[]},include_in_group = ["CHAIN","SPECIES","LOCUS"]):
if '$in' in gene_functionality_filter and gene_functionality_filter['$in']==[]:
gene_functionality_filter = {'$nin':[]}
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in':id_list}
include_in_group = {field:'$'+field for field in include_in_group}
if type(gene_functionality_filter) != dict:
raise Exception('Gene functionality filter must be a dict')
#self.cursor = self.db.find({'_id':{'$in':id_list}})
self.cursor = self.db.aggregate([
{'$match':extra_filters}, #search for documents which match variables passed in this dictionary
{'$unwind':"$GERMLINE_GENES"}, #unwind results such that each element is now a unique germline gene (germline genes function was originally list of all genes in set)
{'$match':{"GERMLINE_GENES.FUNCTIONALITY": gene_functionality_filter } }, #filter genes by their productivity
{'$sort':{"GERMLINE_GENES.ALLELE_NAME":1}}, #sort results by allele name
{'$group':{'_id':"$GENETYPE",'genes':{'$push':"$GERMLINE_GENES"},'additional_info':{'$push':include_in_group } } } #and finally group together all genes by their gene type (V,D,AND J)
])
#self.results = {subset['_id']: [dict(gene_dict.items()+subset['additional_info'][row].items()) for row,gene_dict in enumerate(subset['genes'])] for subset in self.cursor['result']} #summarize teh results as a dictionary where each key corresponds to genetype(V,D,J,ETC) and each value is a list of dictionaries showing document results, it merges results from "genes" and "additional_info" dictionaires into one
self.results = {subset['_id']: [dict(gene_dict.items()+subset['additional_info'][row].items()) for row,gene_dict in enumerate(subset['genes'])] for subset in self.cursor} #summarize teh results as a dictionary where each key corresponds to genetype(V,D,J,ETC) and each value is a list of dictionaries showing document results, it merges results from "genes" and "additional_info" dictionaires into one
return self
def QueryGenesByID(self,
id_list=[],
extra_filters={},
gene_functionality_filter={'$nin': []},
include_in_group=["CHAIN", "SPECIES", "LOCUS"]):
if '$in' in gene_functionality_filter and gene_functionality_filter[
'$in'] == []:
gene_functionality_filter = {'$nin': []}
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in': id_list}
include_in_group = {field: '$' + field for field in include_in_group}
if type(gene_functionality_filter) != dict:
raise Exception('Gene functionality filter must be a dict')
#self.cursor = self.db.find({'_id':{'$in':id_list}})
self.cursor = self.db.aggregate([
{
'$match': extra_filters
}, #search for documents which match variables passed in this dictionary
{
'$unwind': "$GERMLINE_GENES"
}, #unwind results such that each element is now a unique germline gene (germline genes function was originally list of all genes in set)
{
'$match': {
"GERMLINE_GENES.FUNCTIONALITY": gene_functionality_filter
}
}, #filter genes by their productivity
{
'$sort': {
"GERMLINE_GENES.ALLELE_NAME": 1
}
}, #sort results by allele name
{
'$group': {
'_id': "$GENETYPE",
'genes': {
'$push': "$GERMLINE_GENES"
},
'additional_info': {
'$push': include_in_group
}
}
} #and finally group together all genes by their gene type (V,D,AND J)
])
#self.results = {subset['_id']: [dict(gene_dict.items()+subset['additional_info'][row].items()) for row,gene_dict in enumerate(subset['genes'])] for subset in self.cursor['result']} #summarize teh results as a dictionary where each key corresponds to genetype(V,D,J,ETC) and each value is a list of dictionaries showing document results, it merges results from "genes" and "additional_info" dictionaires into one
self.results = {
subset['_id']: [
dict(gene_dict.items() +
subset['additional_info'][row].items())
for row, gene_dict in enumerate(subset['genes'])
]
for subset in self.cursor
} #summarize teh results as a dictionary where each key corresponds to genetype(V,D,J,ETC) and each value is a list of dictionaries showing document results, it merges results from "genes" and "additional_info" dictionaires into one
return self
def GetAllDocInfo(self,id_list=[],extra_filters = {}):#return top-level results of all documents but leave out information regarding genes within a document
if id_list != []:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in': id_list}
self.cursor = self.db.find(extra_filters,{'GERMLINE_GENES':0})
self.results = []
for i in self.cursor:
self.results.append(i)
return self.results
def QueryDistinctValsByID(self,
id_list=[],
extra_filters={},
distinct_fields=[]):
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in': id_list}
results = {
field: self.db.find(extra_filters).distinct(field)
for field in distinct_fields
}
return results
def GetAllDocInfo(
self,
id_list=[],
extra_filters={}
): #return top-level results of all documents but leave out information regarding genes within a document
if id_list != []:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in': id_list}
self.cursor = self.db.find(extra_filters, {'GERMLINE_GENES': 0})
self.results = []
for i in self.cursor:
self.results.append(i)
return self.results
def QueryAllDocsByID(self,id_list=[],extra_filters={},gene_functionality_filter={'$nin':[]}):
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in':id_list}
if type(gene_functionality_filter) != 'dict':
raise Exception('Gene functionality filter must be a dict')
self.cursor = self.db.aggregate([
{'$match':extra_filters},
{'$unwind':"$GERMLINE_GENES"},
{'$match': {"GERMLINE_GENES.FUNCTIONALITY": gene_functionality_filter} },
{'$sort':{"GERMLINE_GENES.ALLELE_NAME":1}}
])
return self
def GetMotifForProgram(self,
id_list=[],
query={},
project={
'Locus': 1,
'Lmotif': 1,
'Rmotif': 1,
'Ltrim': 1,
'Rtrim': 1
}):
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
query['_id'] = {'$in': id_list}
cursor = self.db.find(query, project)
#return results as tuple whose indices are as follows: ['Locus','Lmotif','Rmotif','Ltrim','Rtrim']]
return self.formatMotif([(x['Locus'], x['Lmotif'], x['Rmotif'],
x['Ltrim'], x['Rtrim']) for x in cursor])
def QueryAllDocInfoByID(self,id_list=[],extra_filters={},gene_functionality_filter={'$nin':[]}):
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in':id_list}
if type(gene_functionality_filter) != 'dict':
raise Exception('Gene functionality filter must be a dict')
self.cursor = self.db.aggregate([
{'$match':extra_filters},
{'$unwind':"$GERMLINE_GENES"},
{'$match': {"GERMLINE_GENES.FUNCTIONALITY": gene_functionality_filter} }
])
self.results = [r.pop('GERMLINE_GENES') for r in self.cursor] #self.cursor['result']
#for i,r in enumerate(self.results):
# self.results[i].pop('GERMLINE_GENES')
return self
def QueryAllDocsByID(self,
id_list=[],
extra_filters={},
gene_functionality_filter={'$nin': []}):
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in': id_list}
if type(gene_functionality_filter) != 'dict':
raise Exception('Gene functionality filter must be a dict')
self.cursor = self.db.aggregate([{
'$match': extra_filters
}, {
'$unwind': "$GERMLINE_GENES"
}, {
'$match': {
"GERMLINE_GENES.FUNCTIONALITY": gene_functionality_filter
}
}, {
'$sort': {
"GERMLINE_GENES.ALLELE_NAME": 1
}
}])
return self
def QueryDistinctValsByID(self, id_list=[], extra_filters={}, distinct_fields=[]):
if id_list:
id_list = [convert_to_objectid(id) for id in id_list]
extra_filters['_id'] = {'$in':id_list}
results = {field: self.db.find(extra_filters).distinct(field) for field in distinct_fields}
return results
def benni_update_analyses(analysis_data, update_replace, user_info=None):
dateupdated = time.strftime('%D')
#get list of allowed experiments
allowed_exps =[result['_id'] for result in db.exps.find({'OWNERS_OF_EXPERIMENT':temp_user_info['user']},{'_id':1})]
if not isinstance(analysis_data, list):
analysis_data = [analysis_data]
experiment_upsert_counts = defaultdict(Counter)
#store schema for all experiments currently being updated
#for each experiment/key in variable store:
#sets of the following: each annotated field from database for that experiment
#for each field/key, store: analysis name which contains it and the datatype for that field
#example: current_schema_by_exp = {'EXP':{'VREGION.VGENES.ANALYSES':['IMGT','IGBLAST'],
#'VREGION.VGENES.DATATYPES':[list_of_string]}}
current_exp_data = {}
current_schema_by_exp = {}
time_counter = 0
time_counter2 = 0
schema_timer=0
dict_timer=0
modify_timer=0
command_timer=0
datatype_timer=0
issues = 0
updates = []
#out_updates = []
tc = time.time()
for analysis in analysis_data:
# Get seq and exp ids, and check that they already exist in the database.
if idIdentifier not in analysis.keys():
#no seq identifier found
print "USER DID NOT PASS SEQID"
issues+=1
continue
analysis_list = analysis.pop('ANALYSIS', None)
if not analysis_list:
#user did not pass in 'ANALYSIS' as key
print "USER DID NOT PASS ANALYSIS KEY"
issues+=1
continue
sequence_obj_id = convert_to_objectid(analysis[idIdentifier])
#query to ensure that the sequence object id exists
#'_id' of RAW nucleotide sequences will ALWAYS be equal to the sequence_obj_id.
#in addition, RAW nucleotide sequences will ALWAYS have an analysis_name corresponding to seqRawData variable in global variables script
if temp_user_info['administrator']:
#if administrator then dont need to search by exp_id
seq_query = db.seqs.find({'_id': sequence_obj_id}, {expIdentifier: 1}).limit(1).hint('_id_').next()
else:
#for non-administrators, only allow updates on experiments where write access was granted
seq_query = db.seqs.find({'_id': sequence_obj_id,expIdentifier:{'$in':allowed_exps}}, {expIdentifier: 1}).limit(1).hint('_id_').next()
if seq_query:
#sequence_ID found and user allowed to edit sequence
experiment_obj_id = seq_query[expIdentifier]
else:
#sequence_ID not found or do not have WRITE PERMISSION to this sequence
print(sequence_obj_id)
issues+=1
print("SEQ_ID_NOT_FOUND!!")
continue
#raise Exception("User {0} does not have write access to the following experiment: {1}".format(temp_user_info['user'],str(experiment_obj_id)))
#this is the first time this experiment has been updated
if str(experiment_obj_id) not in current_schema_by_exp:
new_experiment = db.exps.find_one({'_id': experiment_obj_id})
#get current schema for experiment
current_analyses_command_list = new_experiment['ANALYSES_SETTINGS'] if 'ANALYSES_SETTINGS' in new_experiment else [] #get current analysis commands
current_schema_by_exp[str(experiment_obj_id)] = {
'schema': defaultdict(set,{field:set(value) for field,value in useful.flatten_dictionary(new_experiment['ANALYSIS_SCHEMA']).iteritems()} if 'ANALYSIS_SCHEMA' in new_experiment else {}),
'analyses_commands': current_analyses_command_list,
'analyses_commands_dict':defaultdict(lambda:-1,{command:index for index,command in enumerate(current_analyses_command_list)}),#get current anlayses commands as a dict
'analyses_commands_dict_len':len(current_analyses_command_list),#get length of analyses list
'analyses_commands_initial_dict_len':len(current_analyses_command_list)#store initial len for later
}
#first go through each of the analyses and group them by ANALYSIS_NAME and RECOMBINATION_TYPE
grouped_analysis_list = []
analysis_command_list = []
combos = {}
for each_analysis in analysis_list:
# Check necessary fields are present
if not set(each_analysis.keys()) >= set(['ANALYSIS_NAME', 'RECOMBINATION_TYPE', 'DATA']): # these required fields should probably be defined in schema
missing_fields = set(['ANALYSIS_NAME', 'RECOMBINATION_TYPE', 'DATA']) - set(each_analysis.keys())
issues+=1
print("Error code 0")
continue
#raise Exception('The analysis data is missing these fields required by the database schema: '.format(', '.join(missing_fields)))
if each_analysis['ANALYSIS_NAME'] == seqRawData:
#this is not allowed. no user can update an analysis type using the analysis type that corresponds to the raw sequence
issues+=1
print("Error code 1")
continue
modified_data = {}
#modify the keys and values for each analysis
for key,value in each_analysis['DATA'].iteritems():
key = key.upper().replace(' ','_')
if value!=None:
#defaultdict in the variable (see immunogrep_database_schema.schema_fields_and_data_types) ensures that any key not accoutned for in varaible is given a default
#value for to_db and to_file settings
value = schema_fields_and_data_types[key]['to_db'](value)# if key in schema_fields_and_data_types else schema.default_fields_data_types(value)
modified_data[key] = value
analysis_command = modified_data.pop('COMMAND',None)
#now use the analysis_name and recombination_type to group together results for the same doucment together
if each_analysis['ANALYSIS_NAME']+'.'+each_analysis['RECOMBINATION_TYPE'] in combos:
index_pos = combos[each_analysis['ANALYSIS_NAME']+'.'+each_analysis['RECOMBINATION_TYPE']]
grouped_analysis_list[index_pos]['DATA'].update(modified_data)
if analysis_command:
grouped_analysis_list[index_pos]['SETTINGS'].append(analysis_command)
else:
each_analysis['DATA'] = modified_data
combos[each_analysis['ANALYSIS_NAME']+'.'+each_analysis['RECOMBINATION_TYPE']] = len(grouped_analysis_list)
grouped_analysis_list.append(each_analysis)
if analysis_command:
grouped_analysis_list[-1]['SETTINGS'] = [analysis_command]
else:
grouped_analysis_list[-1]['SETTINGS'] = []
for ind,each_analysis in enumerate(grouped_analysis_list):
query_dict = {idIdentifier: sequence_obj_id,expIdentifier: experiment_obj_id, 'ANALYSIS_NAME': each_analysis['ANALYSIS_NAME'], 'RECOMBINATION_TYPE': each_analysis['RECOMBINATION_TYPE']}
#this function in database schema module defines a few finalized treatments of the data
#for example, it adds CDR lengths to the data and creates a sepreate fields called query_data_fields for improved queries of certain fields such as V-GENES
modified_data = each_analysis['DATA']
[modified_data,query_data_fields] = schema.AddedSeqCollectionFields(modified_data)
#update EXPERIMENT SCHEMA with nonempty fields.
for field,value in modified_data.iteritems():# useful.removeEmptyVals(modified_data).iteritems():
# #go through each schema/annotated field
# #add analysis type to the current schema for that field
if value or value==False:
current_schema_by_exp[str(experiment_obj_id)]['schema'][field+'.ANALYSES'].add(each_analysis['ANALYSIS_NAME'])
# #add the datatype for the value of this field for that current schema
current_schema_by_exp[str(experiment_obj_id)]['schema'][field+'.DATATYPE'].add(return_data_types(value))
analysis_command = each_analysis.pop('SETTINGS',None)
if analysis_command:
analysis_number_command = []
for each_command in analysis_command:
index_position = current_schema_by_exp[str(experiment_obj_id)]['analyses_commands_dict'][each_command]
if index_position==-1: #command/string not found yet
#append to list of commands
current_schema_by_exp[str(experiment_obj_id)]['analyses_commands'].append(each_command)
index_position = current_schema_by_exp[str(experiment_obj_id)]['analyses_commands_dict_len']
current_schema_by_exp[str(experiment_obj_id)]['analyses_commands_dict'][each_command] = index_position #sotre new index position
current_schema_by_exp[str(experiment_obj_id)]['analyses_commands_dict_len']+=1#add to total length of list
analysis_number_command.append(index_position)
analysis_number_command = list(set(analysis_number_command))
else:
analysis_number_command = []
if update_replace: #do not use set command. instead replace everything under 'DATA' and 'QUERY_DATA'
modified_data = dict(DotAccessible(modified_data)) #remove NONE VALUES, EMPTY LISTS, EMPTY STRINGS
update_query = {'DATE_UPDATED':dateupdated,'DATA':modified_data}
if analysis_number_command:
update_query['SETTINGS'] = analysis_number_command# list(set(analysis_number_command))
#these fields contain special treatment of cretain fields for improved queries.
#for example we transform VGENES, such that both genes and alleles are stored as a list
#SEE AddedSeqCollectionFelds function in database schema for details
query_data_fields = dict(DotAccessible(query_data_fields))
update_query = useful.removeEmptyVals(update_query)
if query_data_fields:
#response = db.seqs.update(query_dict, {'$set':update_query}, upsert=True)
update_query['QUERY_DATA'] = query_data_fields
updates.append(pymongo.UpdateOne(query_dict,{'$set':update_query}, upsert=True))
else:
#response = db.seqs.update(query_dict, {'$set':update_query}, upsert=True)
updates.append(pymongo.UpdateOne(query_dict,{'$set':update_query,'$unset':{'QUERY_DATA':""}}, upsert=True))
#out_updates.append(query_dict)
#out_updates.append(update_query)
else: #use set command and therefore only change individual fields
modified_data = {'DATA.'+field:value for field,value in modified_data.iteritems()}
modified_data['DATE_UPDATED'] = dateupdated
add_commands = {'SETTINGS':{'$each':list(set(analysis_number_command))}}
#these fields contain special treatment of cretain fields for improved queries.
#for example we transform VGENES, such that both genes and alleles are stored as a list
#SEE AddedSeqCollectionFelds function in database schema for details
for query_field,value in query_data_fields.iteritems():
modified_data['QUERY_DATA.'+query_field] = value
[set_non_empty_fields,unset_empty_fields] = useful.divideEmptyAndNonEmptyVals(modified_data) #go through dictionary and seperate the fields based on non-empty fields and empty-fields
if unset_empty_fields: #we need to remove empty fields
updates.append(pymongo.UpdateOne(query_dict, {'$set': set_non_empty_fields,'$unset':unset_empty_fields,'$addToSet':add_commands}, upsert=True))
#out_updates.append(query_dict)
#out_updates.append(set_non_empty_fields)
#out_updates.append(unset_empty_fields)
#out_updates.append(add_commands)
#response = db.seqs.update(query_dict, {'$set': set_non_empty_fields,'$unset':unset_empty_fields}, upsert=True)
else: #no need to remove empty fields
#response = db.seqs.update(query_dict, {'$set': set_non_empty_fields}, upsert=True)
updates.append(pymongo.UpdateOne(query_dict, {'$set': set_non_empty_fields,'$addToSet':add_commands}, upsert=True))
#out_updates.append(query_dict)
#out_updates.append(set_non_empty_fields)
#out_updates.append(add_commands)
#keep track of all analysis types and recombination types added in this experiment
experiment_upsert_counts[str(experiment_obj_id)][each_analysis['ANALYSIS_NAME']+'.'+each_analysis['RECOMBINATION_TYPE']]+=1
# Check if the update was an insert, and if so, add 1 to the count dictionary for that analysis type
#if (response['n'] == 1) and not response['updatedExisting']:
# experiment_upsert_counts[str(experiment_obj_id)][each_analysis['ANALYSIS_NAME']+'.'+each_analysis['RECOMBINATION_TYPE']] += 1
print('and inserting')
ttest=time.time()-tc
#with open('scratch/testingdbstuff.txt','w') as fout:
# for line in out_updates:
# fout.write(bson_dumps(line,indent=4)+'\n')
db.seqs.bulk_write(updates,ordered=False)
# dbtime=time.time()-ttest
# #print 'total: '+str(time.time()-tc)
print 'dbtime: '+str(ttest)
## print 'querytime: '+str(time_counter)
## print 'settingup_updates: '+str(time_counter2)
## print 'dict timer: '+str(dict_timer)
## print 'modify_timer: '+str(modify_timer)
## print 'command timer: '+str(command_timer)
## print 'datatype_timer: '+str(datatype_timer)
## print 'schema_timer: '+str(schema_timer)
print issues
#print experiment_upsert_counts
# Update counts and experiment schema in the affected experiments
for experiment_obj_id_str, update_settings in current_schema_by_exp.iteritems():
exp_schema = update_settings['schema']
#for each experiment, figure out the number of documents with a specfic recombination type and analysis name
analyses_counter = {}
count_these_analysis_types = experiment_upsert_counts[experiment_obj_id_str]# in experiment_upsert_counts.iteritems():
for analysis,v in count_these_analysis_types.iteritems():
analysis_recomb = analysis.split('.')
analyses_counter[analysis] = db.seqs.find({expIdentifier:ObjectId(experiment_obj_id_str),'ANALYSIS_NAME':analysis_recomb[0],'RECOMBINATION_TYPE':analysis_recomb[1]}).count()
set_fields = {'ANALYSIS_SCHEMA.'+field:list(value) for field,value in exp_schema.iteritems()} #add the schema information
if update_settings['analyses_commands_initial_dict_len']!=update_settings['analyses_commands_dict_len']: #check to see if we need to add new analyses commands
set_fields['ANALYSES_SETTINGS'] = update_settings['analyses_commands']
update_command = {}
if analyses_counter:
#update_command['$inc'] = analyses_counter # do we need to update analyses counts
for all_analyses,counts in analyses_counter.iteritems():
set_fields['ANALYSES_COUNT.'+all_analyses] = counts
if set_fields:
update_command['$set'] = set_fields
if update_command:#do we need to update analyses
db.exps.update({'_id':ObjectId(experiment_obj_id_str)},update_command)
def benni_insert_sequences(seq_data, experiment_obj_id, user_info=None):
"""
Input: a JSON string of sequence info, or list of JSON documents for sequences, and the Object ID of the experiment the sequence belongs to.
(This experiment Object ID should be checked before the function is called.)
Output: ?
Insert single record into sequence database
"""
experiment_obj_id = convert_to_objectid(experiment_obj_id)
if temp_user_info['administrator']:
experiment = db.exps.find_one({'_id': experiment_obj_id})
if not(experiment):
raise Exception("Can't find experiment with Object ID: {}".format(str(experiment_obj_id)))
else:
experiment = db.exps.find_one({'_id': experiment_obj_id,'OWNERS_OF_EXPERIMENT':temp_user_info['user']})
if not(experiment):
raise Exception("Either the experiment does not exist or the User {0} does not have write access to the following experiment: {1}".format(temp_user_info['user'],str(experiment_obj_id)))
#if we have already stored a schema for this experiment, then store the previous schema in this varaible
#we use flatten_dictionary function to conver the dictionary into a flat dicionatry of DOT notation (subdocuments are seperated by '.')
#convert current_schema from lists into sets
current_schema = {field:set(value) for field,value in useful.flatten_dictionary(experiment['ANALYSIS_SCHEMA']).iteritems()} if 'ANALYSIS_SCHEMA' in experiment else {}
current_schema = defaultdict(set,current_schema)
#if we have already stored a list of settings for the experiment from previous sequences then store these analyses into our variable, if not then use empty list
current_analyses_commands_list = experiment['ANALYSES_SETTINGS'] if 'ANALYSES_SETTINGS' in experiment else []
current_analyses_commands_dict = defaultdict(lambda:-1,{command:index for index,command in enumerate(current_analyses_commands_list)})
#if we have already stored a list of filenames for sequences in expermient from previous sequences then store these filenames into our variable, if not then use empty list
current_filenames_list = experiment['FILENAMES'] if 'FILENAMES' in experiment else []
current_filenames_dict = defaultdict(lambda:-1,{fn:index for index,fn in enumerate(current_filenames_list)})
command_list_len = len(current_analyses_commands_list)
initial_command_list_len = command_list_len
initial_filenames_list_len = len(current_filenames_list)
dateupdated = time.strftime('%D')
# add exp obj_id to seq data, insert sequence, update exp count
if not isinstance(seq_data, list):
seq_data = [seq_data]
seq_analysis_insert_list = []
seq_analysis_counter = Counter()
#print seq_data
#t = time.time()
for seq in seq_data: # casting to list in case seq_data is a single dictionary
##the following fields are added to raw seq doc by server
seq_doc = {}
seq_doc[expIdentifier] = experiment_obj_id #currently expIdentifier = 'EXP_ID'
seq_key = ObjectId() #for sequences we want _id and SEQ_KEY to be identical, so we generate them ahead of insertion
seq_doc['_id'] = seq_key
seq_doc[idIdentifier] = seq_key #currently idIdentifier = 'SEQ_ID'
seq_doc['ANALYSIS_NAME'] = seqRawData #currently this string is '@SEQ'
seq_doc['DATE_UPDATED'] = dateupdated
##
if not set(seq.keys()) >= set(schema.Seqs_Collection()['required_fields'].keys()) - set([expIdentifier, idIdentifier, 'ANALYSIS_NAME']):
missing_fields = set(schema.Seqs_Collection()['required_fields'].keys()) - set(seq_data.keys())
#### DO WE WANT TO RAISE AN EXCEPTION OR WRITE TO A LOG FILE??
# raise Exception('The experiment data is missing these fields required by the database schema: '.format(', '.join(missing_fields)))
print 'The sequence data is missing these fields required by the database schema: '.format(', '.join(missing_fields))
continue#do nothing for now
analysis_list = seq.pop('ANALYSIS', None)
mod_seq_data = {}
for key,value in seq.iteritems():
key = key.upper().replace(' ','_')
value = schema_fields_and_data_types[key]['to_db'](value)# if key in schema_fields_and_data_types else schema.default_fields_data_types(value)
mod_seq_data[key] = value
seq_doc['DATA'] =mod_seq_data #(append all user defined data about the sequence to DATA subdocument)
seq_analysis_insert_list.append(useful.removeEmptyVals(seq_doc))
if analysis_list:
seq_analysis_insert_list.extend(generate_analyses_list(analysis_list, seq_key, experiment_obj_id,dateupdated))
seq_analysis_counter = Counter()
#print 'step1: '+str(time.time()-t)
#go through each possible analysis
#t = time.time()
for i,analysis in enumerate(seq_analysis_insert_list):
if analysis['ANALYSIS_NAME']!=seqRawData:
#if there is a setting for how this sequence was edited, then remove it from DATA field
#Next, check if setting currently exists in our list of analyses_settings
#if not append to list
analysis_command = analysis['DATA'].pop('COMMAND',None)
#this is not rawd ata, it is annotated data
seq_analysis_counter['ANALYSES_COUNT.'+analysis['ANALYSIS_NAME']+'.'+analysis['RECOMBINATION_TYPE']]+=1
flat_data = analysis['DATA'] #useful.flatten_dictionary(analysis['DATA'])
if analysis_command:
analysis_command_num = []
for each_analysis_command in analysis_command:
index_position = current_analyses_commands_dict[each_analysis_command]
if index_position==-1: #command/string not found yet
#append to list of commands
current_analyses_commands_list.append(each_analysis_command)
index_position = command_list_len
current_analyses_commands_dict[each_analysis_command] = index_position #sotre new index position
command_list_len+=1#add to total length of list
analysis_command_num.append(index_position)
analysis['SETTINGS'] = list(set(analysis_command_num))
else:
analysis_command = None
for field,value in flat_data.iteritems():
#go through each schema/annotated field
#add analysis type to the current schema for that field
current_schema[field+'.ANALYSES'].add(analysis['ANALYSIS_NAME'])
#add the datatype for the value of this field for that current schema
current_schema[field+'.DATATYPE'].add(return_data_types(value))
else:
#for raw data, just increase sequence count
seq_analysis_counter['SEQ_COUNT']+=1
if 'FILENAME' in analysis['DATA']:
if analysis['DATA']['FILENAME'] not in current_filenames_dict:
current_filenames_dict[analysis['DATA']['FILENAME']] = len(current_filenames_list)
current_filenames_list.append(analysis['DATA']['FILENAME'])
analysis['DATA']['FILENAME'] = current_filenames_dict[analysis['DATA']['FILENAME']]
#fields under DATA and QUERY_DATA are currently in dot notation
#before inserting to database, make sure to unflatten the fields under DATA and QUERY_DATA using DotAccessible
if 'QUERY_DATA' in analysis:
seq_analysis_insert_list[i]['QUERY_DATA'] = dict(DotAccessible(analysis['QUERY_DATA']))
if 'DATA' in analysis:
seq_analysis_insert_list[i]['DATA'] = dict(DotAccessible(analysis['DATA']))
#with open('scratch/testingdbstuff.txt','w') as fout:
# for line in seq_analysis_insert_list:
# fout.write(bson_dumps(line,indent=4)+'\n')
#print 'step 2: '+str(time.time()-t)
#r1 = time.time()
db.seqs.insert_many(seq_analysis_insert_list,ordered=False)
#print 'totaltime: '+str(time.time()-r1)
update_command = {'$inc':seq_analysis_counter}
set_fields = {'ANALYSIS_SCHEMA.'+field:list(value) for field,value in current_schema.iteritems()} #add any fields detected in current schema
if command_list_len != initial_command_list_len: #add any fields detected in analyses
set_fields['ANALYSES_SETTINGS'] = current_analyses_commands_list
if initial_filenames_list_len!=len(current_filenames_list):#add new filenames to the experiment metadata
set_fields['FILENAMES'] = current_filenames_list
if set_fields: #there are fields that need to be modified in document
update_command['$set'] = set_fields
db.exps.update({'_id':experiment_obj_id},update_command)
def delete_analyses_from_experiment(experiment_id_list,analysis_types=[],recombination_types=[]):
global db
global username
global temp_user_info
[db,connection_data] = connect_to_ig_database()
try:
username = appsoma_api.environment_get_username()
temp_user_info = defaultdict(str,db.users.find_one({'user':username}))
except:
pass
if not type(experiment_id_list) is list:
experiment_id_list = [convert_to_objectid(experiment_id_list)]
else:
experiment_id_list = [convert_to_objectid(exp) for exp in experiment_id_list]
if temp_user_info['administrator']!=True:
exps_to_delete =[result['_id'] for result in db.exps.find({'OWNERS_OF_EXPERIMENT':temp_user_info['user'],'_id':{'$in':experiment_id_list}},{'_id':1})]
else:
exps_to_delete = experiment_id_list
if exps_to_delete == []:
raise Exception("User {0} does not have access to the listed experiments: {1}".format(temp_user_info['user'],str(experiment_id_list)))
if not analysis_types:
analysis_query = {'$ne':seqRawData}
else:
if not type(analysis_types) is list:
analysis_types = [analysis_types]
#user can never delete 'raw data' sequences. use previous function to do that
analysis_types = [a for a in analysis_types if analysis_types != seqRawData]
analysis_query = {'$in':analysis_types}
remove_these_analyses_counts = { 'ANALYSES_COUNT.'+analyses_deleted:"" for analyses_deleted in analysis_types}
if recombination_types:
if not type(recombination_types) is list:
recombination_types = [recombination_types]
operation_result = db.seqs.remove({expIdentifier:{'$in':exps_to_delete},'ANALYSIS_NAME':analysis_query,'RECOMBINATION_TYPE':{'$in':recombination_types}})
else:
operation_result = db.seqs.remove({expIdentifier:{'$in':exps_to_delete},'ANALYSIS_NAME':analysis_query})
#update counts for each of the analysis types
exp_metadata = db.exps.find({'_id':{'$in':exps_to_delete}},{'ANALYSES_COUNT':1,'_id':1}) #get the analyses counts BEFORE the update
for exp_results in exp_metadata:
updated_analyses_counts = {}
exp_id = exp_results['_id']
#for each experiment in the query, go through each possible analysis
if 'ANALYSES_COUNT' in exp_results:
for each_analysis_type in exp_results['ANALYSES_COUNT']:
#RE-COUNT the number of sequences with this analysis type after
num_counts = db.seqs.find({expIdentifier:exp_id,'ANALYSIS_NAME':each_analysis_type}).count()
if num_counts>0:
updated_analyses_counts[each_analysis_type] = num_counts
if updated_analyses_counts:
db.exps.update({'_id':exp_id},{'$set':{'ANALYSES_COUNT':updated_analyses_counts}})
else:
db.exps.update({'_id':exp_id},{'$unset':{'ANALYSES_COUNT':""}})
num_deleted = operation_result['n']
print "Deleted {0} sequences".format(str(num_deleted))
connection_data.close()
