def upload():
if request.method == 'POST':
#Checks if username is correct
user_name = request.form.get('input_user')
if user_name != '':
return render_template('invalid_username.html')
#Checks if password is correct
password = request.form.get('input_password')
if password != '':
return render_template('invalid_password.html')
# Retrieve data submitted through the 'Contribute' form
submission = request.form.get('new_submission_name')
now = datetime.datetime.now().date()
notebook = request.form.get("notebook_link")
int_type = request.form.get('input_interaction')
organism = request.form.get('input_species')
resource = request.form.get('input_resource')
file_type = request.form.get('input_type')
file = request.files['input_file']
###### Processing 'file' and uploading data to server ###############
#wrap FileStorage object in a TextIOWrapper
#So file can later be used
f = io.TextIOWrapper(file)
# Submit data on submissions before processing the data
#Must be done prior to processing the data so that the
#submissions foreign key can be integrated into the
# interactions table inserted when the file is processed
submissions = pd.DataFrame(
columns=['submission_name', 'submission_type_fk', 'resource_fk'])
submissions = submissions.append([{
'submission_name': submission,
'submission_type_fk': int(int_type),
'resource_fk': int(resource),
'date_contributed': str(now),
'processing_script': str(notebook),
'file_type_fk': int(file_type)
}])
#Insert submissions into the table
insert_s = "INSERT INTO submissions (submission_name, submission_type_fk, resource_fk, date_contributed, processing_script, file_type_fk) VALUES" + ', '.join(
[
'("{submission_name}", {submission_type_fk}, {resource_fk}, "{date_contributed}", "{processing_script}", {file_type_fk})'
.format(**rowData)
for index, rowData in submissions.iterrows()
])
sub = engine.execute(insert_s)
#Retrieve id of last insert into a database table (i.e. submission foreign key)
submission_fk = sub.lastrowid
print(submission_fk)
#Process a gmt file if file type = 1
if file_type == '1':
gmt = f
gmt_data = [x.strip().split('\t') for x in gmt.readlines()]
sig_version = Scripts.GMT_to_SIG(gmt_data,
secure_filename(file.filename))
interactions = Scripts.SIG_to_Genes(sig_version, submission_fk,
organism, engine)
insert_gene = "INSERT INTO interactions (source_gene_fk, target_gene_fk, submission_fk) VALUES" + ', '.join(
[
'({source_gene_fk}, {target_gene_fk}, {submission_fk})'.
format(**rowData)
for index, rowData in interactions.iterrows()
])
engine.execute(insert_gene)
##### Calculate Website Statistics for 'Submissions' Page for GMT File ################
stat_df = sig_version.drop([1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12],
axis=1)
stat_df.columns = ['ProteinA', 'ProteinB']
# First stat: Number of Total Interactions
interaction_num = len(stat_df)
# Second stat: Number of Sources/Hubs
hub_terms = len(stat_df.ProteinA.unique())
print(hub_terms)
# Third stat: Number of Targets
target_terms = len(stat_df.ProteinB.unique())
print(target_terms)
#Fourth stat: Total Number of Unique Terms
if stat_df['ProteinA'].str.contains('_').all():
stat_df['ProteinA'] = [
x.split('_')[:-1] for x in stat_df.ProteinA
]
stat_df['ProteinA'] = [
'_'.join(x) for x in stat_df['ProteinA']
]
if stat_df['ProteinA'].str.contains('-').all():
stat_df['ProteinA'] = [
x.split('-')[:-1] for x in stat_df.ProteinA
]
stat_df['ProteinA'] = [
'-'.join(x) for x in stat_df['ProteinA']
]
else:
pass
if stat_df['ProteinB'].str.contains(',').all():
stat_df['ProteinB'] = [
x.split(',')[:-1] for x in stat_df.ProteinB
]
stat_df['ProteinB'] = [
','.join(x) for x in stat_df['ProteinB']
]
else:
pass
unique_terms = len(
pd.concat([stat_df.ProteinA, stat_df.ProteinB],
axis=0).unique())
print(unique_terms)
#Fifth stat: Avg. Interactions per Term
stat_df.set_index('ProteinA', inplace=True)
stat_df = stat_df.groupby('ProteinA').agg(lambda x: tuple(x))
stat_df['targets'] = [
int(len(lst))
for source, lst in stat_df['ProteinB'].iteritems()
]
avg_term = stat_df.targets.mean(axis=0)
print(round(avg_term))
insert_stat = "INSERT INTO statistics (interaction_num, hub_terms, target_terms, unique_terms, avg_term, submission_fk) VALUES" + '(%d' % interaction_num + ', %d' % hub_terms + ', %d,' % target_terms + '%d,' % unique_terms + '%d,' % avg_term + ' %d)' % submission_fk
engine.execute(insert_stat)
#Process a sig file if the file type = 2
if file_type == '2':
sig = pd.read_table(f, header=None)
if len(sig.columns) == 1:
sig = pd.read_table(
f,
header=None,
names=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
sep=' ')
interactions = Scripts.SIG_to_Genes(sig, submission_fk, organism,
engine)
insert_gene = "INSERT INTO interactions (source_gene_fk, target_gene_fk, submission_fk) VALUES" + ', '.join(
[
'({source_gene_fk}, {target_gene_fk}, {submission_fk})'.
format(**rowData)
for index, rowData in interactions.iterrows()
])
engine.execute(insert_gene)
##### Calculate Website Statistics for 'Submissions' Page for GMT File ################
stat_df = sig.drop([1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12], axis=1)
stat_df.columns = ['ProteinA', 'ProteinB']
# First stat: Number of Total Interactions
interaction_num = len(stat_df)
# Second stat: Number of Sources/Hubs
hub_terms = len(stat_df.ProteinA.unique())
print(hub_terms)
# Third stat: Number of Targets
target_terms = len(stat_df.ProteinB.unique())
print(target_terms)
#Fourth stat: Total Number of Unique Terms
if stat_df['ProteinA'].str.contains('_').all():
stat_df['ProteinA'] = [
x.split('_')[:-1] for x in stat_df.ProteinA
]
stat_df['ProteinA'] = [
'_'.join(x) for x in stat_df['ProteinA']
]
if stat_df['ProteinA'].str.contains('-').all():
stat_df['ProteinA'] = [
x.split('-')[:-1] for x in stat_df.ProteinA
]
stat_df['ProteinA'] = [
'-'.join(x) for x in stat_df['ProteinA']
]
else:
pass
if stat_df['ProteinB'].str.contains(',').all():
stat_df['ProteinB'] = [
x.split(',')[:-1] for x in stat_df.ProteinB
]
stat_df['ProteinB'] = [
','.join(x) for x in stat_df['ProteinB']
]
else:
pass
unique_terms = len(
pd.concat([stat_df.ProteinA, stat_df.ProteinB],
axis=0).unique())
print(unique_terms)
#Fifth stat: Avg. Interactions per Term
stat_df.set_index('ProteinA', inplace=True)
stat_df = stat_df.groupby('ProteinA').agg(lambda x: tuple(x))
stat_df['targets'] = [
int(len(lst))
for source, lst in stat_df['ProteinB'].iteritems()
]
avg_term = stat_df.targets.mean(axis=0)
print(round(avg_term))
insert_stat = "INSERT INTO statistics (interaction_num, hub_terms, target_terms, unique_terms, avg_term, submission_fk) VALUES" + '(%d' % interaction_num + ', %d' % hub_terms + ', %d,' % target_terms + '%d,' % unique_terms + '%d,' % avg_term + ' %d)' % submission_fk
engine.execute(insert_stat)
#Return to 'Contribute' page after making the submission
return redirect(url_for('Contribute'))
def upload():
if request.method == 'POST':
#this works!
#Check if username is correct!
user_name = request.form.get('input_user')
if user_name != '':
return "Invalid username, please try again"
#this works!
#Check if password is correct!
password = request.form.get('input_password')
if password != '':
return "Invalid password, please try again"
#this works!
submission = request.form.get('new_submission_name')
#this works!
date = request.form.get('date')
print(date)
#this works!
notebook = request.form.get("notebook_link")
print(notebook)
#this works!
int_type = request.form.get('input_interaction')
#this works!
organism = request.form.get('input_species')
#this works!
resource = request.form.get('input_resource')
#this works!
file_type = request.form.get('input_type')
#this works!
file = request.files['input_file']
print(file)
###### Processing 'file' and uploading data to server ###############
#this works! (for now)
f = io.TextIOWrapper(file)
print(f)
#this works!
submissions = pd.DataFrame(columns = ['submission_name', 'submission_type_fk', 'resource_fk'])
submissions = submissions.append([{
'submission_name': submission,
'submission_type_fk': int(int_type),
'resource_fk': int(resource)
}])
print(submissions)
#When uncommented, this works! UNCOMMENT FOR FINAL TRIAL
insert_s = "INSERT INTO submissions (submission_name, submission_type_fk, resource_fk) VALUES"+ ', '.join(['("{submission_name}", {submission_type_fk}, {resource_fk})'.format(**rowData) for index, rowData in submissions.iterrows()])
sub = engine.execute(insert_s)
submission_fk = sub.lastrowid
print(submission_fk)
#add other 'if' statements to send file to right python processor based on int_type
#also at some point once this whole process is over we also want to save these files
#somewhere and convert them to their alternate format --> I know procedure to save locally
#but is this what we really want? --> Won't be automatic uploads elsewhere
#this works!
if file_type == '1':
gmt = f
gmt_data = [x.strip().split('\t') for x in gmt.readlines()]
sig_version = Scripts.GMT_to_SIG(gmt_data, secure_filename(file.filename))
print(sig_version.head())
interactions = Scripts.SIG_to_Genes(sig_version, submission_fk, organism, engine)
insert_gene = "INSERT INTO interactions (source_gene_fk, target_gene_fk, submission_fk) VALUES"+ ', '.join(['({source_gene_fk}, {target_gene_fk}, {submission_fk})'.format(**rowData) for index, rowData in interactions.iterrows()])
engine.execute(insert_gene)
#this works!
if file_type == '2':
sig = pd.read_table(f, header = None)
if 5 not in sig.columns:
sig = pd.read_table(f, header = None,
names = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], sep = ' ')
print(sig.head())
interactions = Scripts.SIG_to_Genes(sig, submission_fk, organism, engine)
insert_gene = "INSERT INTO interactions (source_gene_fk, target_gene_fk, submission_fk) VALUES"+ ', '.join(['({source_gene_fk}, {target_gene_fk}, {submission_fk})'.format(**rowData) for index, rowData in interactions.iterrows()])
engine.execute(insert_gene)
return redirect(url_for('Contribute'))
