def FF_plot(batch_id):
L = Layout(batch_id)
FF = FetalFraction(batch_id)
FF.format_case_dict()
FF.format_contol_dict()
FF.form_prediction_interval()
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
DC = DataClasifyer()
DC.get_QC_warnings(sample_db)
return render_template('batch_page/tab_FF.html',
## Warnings Table
seq_warnings = DC.QC_warnings,
## Header
batch_name = batch.batch_name,
batch_id = batch_id,
seq_date = batch.date,
## Plotts
nr_contol_samples = FF.nr_contol_samples,
predict = FF.perdiction,
cases = FF.samples,
control = FF.control,
case_size = L.case_size,
case_line = L.case_line,
many_colors = L.many_colors_dict,
sample_list = FF.sample_list
)
def NCV18_plot(batch_id):
#getting thresholds and layout
DC = DataClasifyer()
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
DC.get_QC_warnings(sample_db)
L = Layout(batch_id)
# Getting and formating sample and NCV data for the control samples in the plot
control_normal, control_abnormal = BDF.control_NCV18()
# Getting and formating sample and NCV data for the samples in the batch
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
NCV_db = NCV.query.filter(NCV.batch_id == batch_id).all()
TA = TrisAbnormality(batch_id, NCV_db)
case_data = TA.make_case_data(control_normal, '18')
tris_chrom_abn = TA.make_tris_chrom_abn(control_abnormal, '18')
return render_template('batch_page/tab_NCV18.html',
## Warnings Table
seq_warnings = DC.QC_warnings,
## Header
batch_name = batch.batch_name,
seq_date = batch.date,
batch_id = batch_id,
## Plotts
chrom = '18',
case_data = case_data,
tris_chrom_abn = tris_chrom_abn,
case_size = L.case_size,
case_line = L.case_line,
abn_size = L.abn_size,
abn_line = L.abn_line,
abn_symbol = L.abn_symbol,
ncv_abn_colors = L.ncv_abn_colors,
tris_thresholds = DC.tris_thresholds)
def coverage_plot(batch_id):
L = Layout(batch_id)
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
DC = DataClasifyer(NCV_db)
DC.handle_NCV()
DC.get_QC_warnings(sample_db)
CP = CoveragePlot(batch_id)
CP.make_cov_plot_data()
return render_template('batch_page/tab_coverage.html',
## Header
batch_name = batch.batch_name,
seq_date = batch.date,
## Warnings Table
seq_warnings = DC.QC_warnings,
samp_cov_db = CP.coverage_plot,
cov_colors = L.cov_colors,
case_size = L.case_size,
case_line = L.case_line,
## Buttons
batch_id = batch_id,
sample_ids = ','.join(sample.sample_ID for sample in NCV_db),
sample_list = CP.sample_list)
def covX_covY(batch_id):
L = Layout(batch_id)
CC = CovXCovY(batch_id)
CC.format_case_dict()
CC.format_contol_dict()
CC.format_pos_contol()
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
DC = DataClasifyer()
DC.get_QC_warnings(sample_db)
return render_template('batch_page/tab_covX_covY.html',
## Warnings Table
seq_warnings = DC.QC_warnings,
## Header
batch_name = batch.batch_name,
batch_id = batch_id,
seq_date = batch.date,
## Plotts
nr_contol_samples = CC.nr_contol_samples,
cases = CC.samples,
control = CC.control,
pos_contol = CC.pos_contol,
case_size = L.case_size,
case_line = L.case_line,
ncv_abn_colors = L.ncv_abn_colors,
abn_size = L.abn_size,
abn_line = L.abn_line,
abn_symbol = L.abn_symbol,
many_colors = L.many_colors_dict,
sample_list = CC.sample_list
)
def sample_page( sample_id):
sample = Sample.query.filter_by(sample_ID = sample_id).first()
NCV_dat = NCV.query.filter_by(sample_ID = sample_id)
batch_id = sample.batch_id
batch = Batch.query.filter_by(batch_id = batch_id).first()
DC = DataClasifyer(NCV_dat)
DC.handle_NCV()
chrom_abnorm = ['T13','T18', 'T21', 'X0', 'XXX','XXY','XYY']
db_entries = {c:sample.__dict__['status_'+c].replace('\r\n', '').strip() for c in chrom_abnorm }
db_entries_change = {c:sample.__dict__['status_change_'+c] for c in chrom_abnorm}
return render_template('sample_page/sample_page.html',
## Header & Info Box
current_user = current_user,
NCV_dat = NCV_dat.first(),
sample = sample,
batch_id = batch_id,
batch_name = batch.batch_name,
batch = batch,
NCV_sex = DC.NCV_sex[sample_id],
NCV_warn = DC.NCV_classified[sample_id],
## Status Table
sample_id = sample_id,
chrom_abnorm = chrom_abnorm,
db_entries = db_entries,
db_entries_change = db_entries_change)
def sample_page(sample_id):
sample = Sample.query.filter_by(sample_ID=sample_id).first()
batch_id = sample.batch_id
batch = Batch.query.filter_by(batch_id=batch_id).first()
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
DC = DataClasifyer()
DC.handle_NCV(NCV_db)
NCV_dat = NCV.query.filter_by(sample_ID=sample_id).first()
chrom_abnorm = ['T13', 'T18', 'T21', 'X0', 'XXX', 'XXY', 'XYY']
db_entries = {
c: sample.__dict__['status_' + c].replace('\r\n', '').strip()
for c in chrom_abnorm
}
db_entries_change = {
c: sample.__dict__['status_change_' + c]
for c in chrom_abnorm
}
db_entries_comments = {
c: sample.__dict__['comment_' + c]
for c in chrom_abnorm
}
PP = PlottPage(batch_id)
PP.make_NCV_stat()
PP.make_chrom_abn()
sample_state_dict = PP.sample_state_dict
for state in sample_state_dict:
sample_state_dict[state]['T_13'] = Sample.query.filter_by(
status_T13=state)
sample_state_dict[state]['T_18'] = Sample.query.filter_by(
status_T18=state)
sample_state_dict[state]['T_21'] = Sample.query.filter_by(
status_T21=state)
return render_template(
'sample_page.html',
NCV_dat=NCV_dat,
tris_abn=PP.tris_abn,
sex_chrom_abn=PP.sex_chrom_abn,
abn_status_list=[
'Verified', 'False Positive', 'Probable', 'Suspected'
],
sex_abn_colors=PP.sex_abn_colors,
sample=sample,
NCV_db=NCV.query.filter_by(sample_ID=sample_id).first(),
batch_id=batch_id,
batch_name=batch.batch_name,
batch=batch,
nr_validation_samps=PP.nr_validation_samps,
sample_id=sample_id,
chrom_abnorm=chrom_abnorm,
db_entries=db_entries,
db_entries_comments=db_entries_comments,
db_entries_change=db_entries_change,
NCV_stat=PP.NCV_stat,
NCV_131821=['NCV_13', 'NCV_18', 'NCV_21'],
state_dict=sample_state_dict,
sex_tresholds=DC.sex_tresholds,
tris_thresholds=DC.tris_thresholds,
NCV_sex=DC.NCV_sex[sample_id],
NCV_warn=DC.NCV_classified[sample_id])
def samples():
ncv_columns = ['include', 'comment','sample_name']
sample_columns = ['T13','T18', 'T21', 'X0', 'XXX','XXY','XYY']
filter_dict = dict.fromkeys(ncv_columns+sample_columns+['batch'],'')
# making filters
ncv_filters = []
sample_filters = []
batch_filter = []
if not request.args.get('clear_filters'):
batch = request.args.get('batch')
if batch:
batch_filter.append(Batch.batch_name.contains(str(batch)))
filter_dict['batch'] = batch
for column in ncv_columns:
NCV_filter = request.args.get(column)
if NCV_filter:
filter_dict[column] = NCV_filter
if column in ['include']:
ncv_filters.append(NCV.__dict__[column] == NCV_filter)
else:
ncv_filters.append(NCV.__dict__[column].contains(str(NCV_filter)))
for column in sample_columns:
NCV_filter = request.args.get(column)
if NCV_filter:
filter_dict[column] = NCV_filter
sample_filters.append(Sample.__dict__['status_'+ column] == NCV_filter)
# filtering
NCV_db = NCV.query
if ncv_filters:
NCV_db = NCV_db.filter(and_(* ncv_filters))
if sample_filters:
NCV_db = NCV_db.join(Sample).filter(and_(* sample_filters))
if batch_filter:
NCV_db = NCV_db.join(Batch).filter(and_(* batch_filter))
if not (ncv_filters or sample_filters or batch_filter):
# show first 50
NCV_db = NCV.query.all()[0:50]
else:
NCV_db = NCV_db.all()
# get clasifications
DC = DataClasifyer(NCV_db)
DC.handle_NCV()
return render_template('samples.html',
abn_status_list = ['Other','False Positive','Suspected', 'Probable', 'Verified','Failed'],
chrom_abnorm = sample_columns,
nr_included_samps = NCV.query.filter(NCV.include).count(),
NCV_db = NCV_db,
NCV_sex = DC.NCV_sex,
NCV_warnings = DC.NCV_classified,
filter_dict = filter_dict
)
def sample_tris_plot( sample_id):
L = Layout()
sample = Sample.query.filter_by(sample_ID = sample_id).first()
NCV_dat = NCV.query.filter_by(sample_ID = sample_id)
batch_id = sample.batch_id
batch = Batch.query.filter_by(batch_id = batch_id).first()
batch_stat = BatchStat.query.filter(BatchStat.batch_id == batch_id).first()
DC = DataClasifyer(NCV_dat)
DC.handle_NCV()
# Getting and formating sample and NCV data for the control samples in the plot
TA = TrisAbnormality(batch_id, NCV_dat)
control_normal, control_abnormal_13 = BDF.control_NCV13()
case_data13 = TA.make_case_data(control_normal, '13')
control_normal, control_abnormal_18 = BDF.control_NCV18()
case_data18 = TA.make_case_data(control_normal,'18')
control_normal, control_abnormal_21 = BDF.control_NCV21()
case_data21 = TA.make_case_data(control_normal,'21')
TA.make_tris_abn_sample_page(control_abnormal_13 + control_abnormal_18 + control_abnormal_21)
case_data = {'NCV_13':case_data13,
'NCV_18':case_data18,
'NCV_21':case_data21}
return render_template('sample_page/sample_tris_plot.html',
## Header & Info Box
NCV_dat = NCV_dat.first(),
sample = sample,
sample_id = sample_id,
batch_id = batch_id,
batch_name = batch.batch_name,
batch = batch,
NCV_sex = DC.NCV_sex[sample_id],
NCV_warn = DC.NCV_classified[sample_id],
## Plots
batch_stat = batch_stat,
tris_abn = TA.tris_abn,
case_size = L.case_size,
abn_size = L.abn_size,
abn_symbol = L.abn_symbol,
abn_line = L.abn_line,
abn_status_list = ['Other','False Positive','Suspected', 'Probable', 'Verified','False Negative'],
ncv_abn_colors = L.ncv_abn_colors,
case_data = case_data,
tris_thresholds = DC.tris_thresholds)
def batch():
NCV_db = NCV.query
DH = DataHandler()
sample_db = Sample.query
DC = DataClasifyer()
DC.handle_NCV(NCV_db)
DC.get_QC_warnings(sample_db)
return render_template('start_page.html',
batches = Batch.query,
nr_included_samps = DH.nr_included_samps,
samples = Sample.query,
NCV_db = NCV.query,
NCV_sex = DC.NCV_sex,
NCV_warnings = DC.NCV_classified)
def samples():
# import ipdb; ipdb.set_trace()
NCV_db = NCV.query
sample_db = Sample.query
DC = DataClasifyer()
DC.handle_NCV(NCV_db)
DC.get_manually_classified(sample_db)
return render_template('samples.html',
nr_included_samps = NCV.query.filter(NCV.include).count(),
NCV_db = sample_db,
NCV_sex = DC.NCV_sex,
sample_names = DC.sample_names,
NCV_man_class = DC.man_class,
NCV_warnings = DC.NCV_classified,
NCV_comment = DC.NCV_comment,
batch_info = DC.batch
)
def sample(batch_id):
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
DC = DataClasifyer()
DC.handle_NCV(NCV_db)
DC.get_QC_warnings(sample_db)
DC.get_manually_classified(sample_db)
PP = PlottPage(batch_id)
PP.make_NCV_stat()
PP.make_chrom_abn()
PP.make_cov_plot_data()
return render_template('batch_page.html',
NCV_samples = NCV.query.filter(NCV.batch_id == batch_id),
batch_name = batch.batch_name,
man_class = DC.man_class,
NCV_stat = PP.NCV_stat,
NCV_sex = DC.NCV_sex,
seq_date = batch.date,
nr_validation_samps = PP.nr_validation_samps,
samp_range = range(len(PP.NCV_stat['NCV_X']['NCV_cases'])),
tris_chrom_abn = PP.tris_chrom_abn,
sex_chrom_abn = PP.sex_chrom_abn,
abn_status_list = ['Verified','False Positive', 'Probable', 'Suspected'],
many_colors = PP.many_colors,
sex_abn_colors = PP.sex_abn_colors,
sex_tresholds = DC.sex_tresholds,
tris_thresholds = DC.tris_thresholds,
seq_warnings = DC.QC_warnings,
warnings = DC.NCV_classified,
NCV_rounded = DC.NCV_data,
samp_cov_db = PP.coverage_plot,
sample_ids = ','.join(sample.sample_ID for sample in NCV_db))
def sample_xy_plot( sample_id):
L = Layout()
sample = Sample.query.filter_by(sample_ID = sample_id).first()
NCV_dat = NCV.query.filter_by(sample_ID = sample_id)
batch_id = sample.batch_id
batch = Batch.query.filter_by(batch_id = batch_id).first()
DC = DataClasifyer(NCV_dat)
DC.handle_NCV()
DC.make_sex_tresholds(BDF.NCV_passed_X)
chrom_abnorm = ['T13','T18', 'T21', 'X0', 'XXX','XXY','XYY']
db_entries = {c:sample.__dict__['status_'+c].replace('\r\n', '').strip() for c in chrom_abnorm }
db_entries_change = {c:sample.__dict__['status_change_'+c] for c in chrom_abnorm}
# Getting and formating sample and NCV data for the control samples in the plot
control_normal_X, control_normal_Y, control_normal_XY_names = BDF.control_NCVXY()
SA = SexAbnormality(batch_id, NCV_dat)
SA.make_case_data('NCV_X', control_normal_X)
SA.make_case_data('NCV_Y', control_normal_Y)
SA.make_sex_chrom_abn()
return render_template('sample_page/sample_xy_plot.html',
## Header & Info Box
NCV_dat = NCV_dat.first(),
sample_name = sample.sample_name,
sample = sample,
sample_id = sample_id,
batch_id = batch_id,
batch_name = batch.batch_name,
batch_date = batch.date,
NCV_sex = DC.NCV_sex[sample_id],
NCV_warn = DC.NCV_classified[sample_id],
## Plots
sex_chrom_abn = SA.sex_chrom_abn,
case_size = L.case_size,
abn_size = L.abn_size,
abn_symbol = L.abn_symbol,
abn_line = L.abn_line,
abn_status_list = ['Other','False Positive','Suspected', 'Probable', 'Verified'],
ncv_abn_colors = L.ncv_abn_colors,
case_data = SA.case_data,
sex_tresholds = DC.sex_tresholds)
def sample_page( sample_id):
sample = Sample.query.filter_by(sample_ID = sample_id).first()
batch_id = sample.batch_id
batch = Batch.query.filter_by(batch_id = batch_id).first()
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
DC = DataClasifyer()
DC.handle_NCV(NCV_db)
NCV_dat = NCV.query.filter_by(sample_ID = sample_id).first()
chrom_abnorm = ['T13','T18', 'T21', 'X0', 'XXX','XXY','XYY']
db_entries = {c:sample.__dict__['status_'+c].replace('\r\n', '').strip() for c in chrom_abnorm }
db_entries_change = {c:sample.__dict__['status_change_'+c] for c in chrom_abnorm}
db_entries_comments = {c : sample.__dict__['comment_'+c] for c in chrom_abnorm}
PP = PlottPage(batch_id)
PP.make_NCV_stat()
PP.make_chrom_abn()
sample_state_dict = PP.sample_state_dict
for state in sample_state_dict:
sample_state_dict[state]['T_13'] = Sample.query.filter_by(status_T13 = state)
sample_state_dict[state]['T_18'] = Sample.query.filter_by(status_T18 = state)
sample_state_dict[state]['T_21'] = Sample.query.filter_by(status_T21 = state)
return render_template('sample_page.html',
NCV_dat = NCV_dat,
tris_abn = PP.tris_abn,
sex_chrom_abn = PP.sex_chrom_abn,
abn_status_list = ['Verified','False Positive', 'Probable', 'Suspected'],
sex_abn_colors = PP.sex_abn_colors,
sample = sample,
NCV_db = NCV.query.filter_by(sample_ID = sample_id).first(),
batch_id = batch_id,
batch_name = batch.batch_name,
batch = batch,
nr_validation_samps = PP.nr_validation_samps,
sample_id = sample_id,
chrom_abnorm = chrom_abnorm,
db_entries = db_entries,
db_entries_comments = db_entries_comments,
db_entries_change = db_entries_change,
NCV_stat = PP.NCV_stat,
NCV_131821 = ['NCV_13', 'NCV_18', 'NCV_21'],
state_dict = sample_state_dict,
sex_tresholds = DC.sex_tresholds,
tris_thresholds = DC.tris_thresholds,
NCV_sex = DC.NCV_sex[sample_id],
NCV_warn = DC.NCV_classified[sample_id])
def sample_page( sample_id):
sample = Sample.query.filter_by(sample_ID = sample_id).first()
batch_id = sample.batch_id
batch = Batch.query.filter_by(batch_id = batch_id).first()
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
DC = DataClasifyer(NCV_db)
DC.handle_NCV()
DC.make_sex_tresholds(BDF.NCV_passed_X)
NCV_dat = NCV.query.filter_by(sample_ID = sample_id).first()
chrom_abnorm = ['T13','T18', 'T21', 'X0', 'XXX','XXY','XYY']
db_entries = {c:sample.__dict__['status_'+c].replace('\r\n', '').strip() for c in chrom_abnorm }
db_entries_change = {c:sample.__dict__['status_change_'+c] for c in chrom_abnorm}
PP = PlottPage(batch_id, BDF)
PP.make_NCV_stat()
PP.make_chrom_abn()
return render_template('sample_page.html',
## Header & Info Box
NCV_dat = NCV_dat,
sample = sample,
batch_id = batch_id,
batch_name = batch.batch_name,
batch = batch,
NCV_sex = DC.NCV_sex[sample_id],
NCV_warn = DC.NCV_classified[sample_id],
## Comment
NCV_db = NCV.query.filter_by(sample_ID = sample_id).first(),
## NCV Table
sample_id = sample_id,
chrom_abnorm = chrom_abnorm,
db_entries = db_entries,
db_entries_change = db_entries_change,
## Plots
tris_abn = PP.tris_abn,
sex_chrom_abn = PP.sex_chrom_abn,
case_size = PP.case_size,
abn_size = PP.abn_size,
abn_status_list = ['Other','False Positive','Suspected', 'Probable', 'Verified'],
ncv_abn_colors = PP.ncv_abn_colors,
NCV_stat = PP.NCV_stat,
NCV_131821 = ['NCV_13', 'NCV_18', 'NCV_21'],
sex_tresholds = DC.sex_tresholds,
tris_thresholds = DC.tris_thresholds,
tris_chrom_abn = PP.tris_chrom_abn)
def sample(batch_id):
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
DC = DataClasifyer(NCV_db)
DC.handle_NCV()
DC.make_sex_tresholds(BDF.NCV_passed_X)
DC.get_QC_warnings(sample_db)
DC.get_manually_classified(sample_db)
PP = PlottPage(batch_id, BDF)
PP.make_NCV_stat()
PP.make_chrom_abn()
PP.make_cov_plot_data()
return render_template('batch_page.html',
## Header
batch_name = batch.batch_name,
seq_date = batch.date,
## Warnings Table
seq_warnings = DC.QC_warnings,
## NCV Table
NCV_samples = NCV.query.filter(NCV.batch_id == batch_id),
man_class = DC.man_class_merged,
NCV_sex = DC.NCV_sex,
warnings = DC.NCV_classified,
NCV_rounded = DC.NCV_data,
## Plotts
NCV_stat = PP.NCV_stat,
case_size = PP.case_size,
abn_size = PP.abn_size,
samp_range = range(len(PP.NCV_stat['NCV_X']['NCV_cases'])),
tris_chrom_abn = PP.tris_chrom_abn,
sex_chrom_abn = PP.sex_chrom_abn,
abn_status_list = ['Other','False Positive','Suspected', 'Probable', 'Verified'],
cov_colors = PP.cov_colors,
many_colors = PP.many_colors,
ncv_abn_colors = PP.ncv_abn_colors,
sex_tresholds = DC.sex_tresholds,
tris_thresholds = DC.tris_thresholds,
## Coverage
bool_warns = filter(None, DC.NCV_classified.values()),
samp_cov_db = PP.coverage_plot,
## Buttons
batch_id = batch_id,
sample_ids = ','.join(sample.sample_ID for sample in NCV_db))
def batch():
NCV_db = NCV.query
DH = DataHandler()
sample_db = Sample.query
DC = DataClasifyer()
DC.handle_NCV(NCV_db)
DC.get_QC_warnings(sample_db)
return render_template('start_page.html',
batches=Batch.query,
nr_included_samps=DH.nr_included_samps,
samples=Sample.query,
NCV_db=NCV.query,
NCV_sex=DC.NCV_sex,
NCV_warnings=DC.NCV_classified)
def NCVXY_plot(batch_id):
L = Layout(batch_id)
# Getting and formating sample and NCV data for the samples in the batch
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
DC = DataClasifyer(NCV_db)
DC.handle_NCV()
DC.make_sex_tresholds(BDF.NCV_passed_X)
DC.get_QC_warnings(sample_db)
# Getting and formating sample and NCV data for the control samples in the plot
control_normal_X, control_normal_Y, control_normal_XY_names = BDF.control_NCVXY()
SA = SexAbnormality(batch_id, NCV_db)
SA.make_case_data('NCV_X', control_normal_X)
SA.make_case_data('NCV_Y', control_normal_Y)
SA.make_sex_chrom_abn()
return render_template('batch_page/tab_NCVXY.html',
## Header
batch_name = batch.batch_name,
seq_date = batch.date,
## Warnings Table
seq_warnings = DC.QC_warnings,
## Plotts
NCV_pass_names = control_normal_XY_names,
case_data = SA.case_data,
case_size = L.case_size,
case_line = L.case_line,
abn_size = L.abn_size,
abn_line = L.abn_line,
abn_symbol = L.abn_symbol,
samp_range = range(len(SA.case_data['NCV_X']['samples'])),
sex_chrom_abn = SA.sex_chrom_abn,
abn_status_list = ['Other','False Positive','Suspected', 'Probable', 'Verified','False Negative'],
many_colors = L.many_colors_dict,
sex_tresholds = DC.sex_tresholds,
ncv_abn_colors = L.ncv_abn_colors,
## Buttons
batch_id = batch_id,
sample_ids = ','.join(sample.sample_ID for sample in NCV_db),
sample_list = SA.sample_list)
def sample(batch_id):
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
DC = DataClasifyer()
DC.handle_NCV(NCV_db)
DC.get_QC_warnings(sample_db)
DC.get_manually_classified(sample_db)
PP = PlottPage(batch_id)
PP.make_NCV_stat()
PP.make_chrom_abn()
PP.make_cov_plot_data()
return render_template(
'batch_page.html',
NCV_samples=NCV.query.filter(NCV.batch_id == batch_id),
batch_name=batch.batch_name,
man_class=DC.man_class,
NCV_stat=PP.NCV_stat,
NCV_sex=DC.NCV_sex,
seq_date=batch.date,
nr_validation_samps=PP.nr_validation_samps,
samp_range=range(len(PP.NCV_stat['NCV_X']['NCV_cases'])),
tris_chrom_abn=PP.tris_chrom_abn,
sex_chrom_abn=PP.sex_chrom_abn,
abn_status_list=[
'Verified', 'False Positive', 'Probable', 'Suspected'
],
many_colors=PP.many_colors,
sex_abn_colors=PP.sex_abn_colors,
sex_tresholds=DC.sex_tresholds,
tris_thresholds=DC.tris_thresholds,
seq_warnings=DC.QC_warnings,
warnings=DC.NCV_classified,
NCV_rounded=DC.NCV_data,
samp_cov_db=PP.coverage_plot,
sample_ids=','.join(sample.sample_ID for sample in NCV_db))
def sample(batch_id):
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
DC = DataClasifyer(NCV_db)
DC.handle_NCV()
DC.get_QC_warnings(sample_db)
DC.get_manually_classified(sample_db)
return render_template('batch_page/batch_page.html',
current_user = current_user,
## Header
batch_name = batch.batch_name,
seq_date = batch.date,
## Warnings Table
seq_warnings = DC.QC_warnings,
## NCV Table
NCV_samples = NCV.query.filter(NCV.batch_id == batch_id),
man_class = DC.man_class_merged,
NCV_sex = DC.NCV_sex,
warnings = DC.NCV_classified,
batch_table_data = DC.NCV_data,
## Buttons
batch_id = batch_id,
sample_ids = ','.join(sample.sample_ID for sample in NCV_db))
def report(batch_id, coverage):
L = Layout(batch_id)
NCV_db = NCV.query.filter(NCV.batch_id == batch_id)
sample_db = Sample.query.filter(Sample.batch_id == batch_id)
batch = Batch.query.filter(Batch.batch_id == batch_id).first()
DC = DataClasifyer(NCV_db)
DC.handle_NCV()
DC.make_sex_tresholds(BDF.NCV_passed_X)
DC.get_QC_warnings(sample_db)
DC.get_manually_classified(sample_db)
CP = CoveragePlot(batch_id)
CP.make_cov_plot_data()
control_normal_X, control_normal_Y, control_normal_XY_names = BDF.control_NCVXY()
SA = SexAbnormality(batch_id, NCV_db)
SA.make_case_data('NCV_X', control_normal_X)
SA.make_case_data('NCV_Y', control_normal_Y)
SA.make_sex_chrom_abn()
TA = TrisAbnormality(batch_id, NCV_db)
control_normal, control_abnormal = BDF.control_NCV13()
case_data13 = TA.make_case_data(control_normal, '13')
tris_chrom_abn13 = TA.make_tris_chrom_abn(control_abnormal, '13')
control_normal, control_abnormal = BDF.control_NCV18()
case_data18 = TA.make_case_data(control_normal,'18')
tris_chrom_abn18 = TA.make_tris_chrom_abn(control_abnormal, '18')
control_normal, control_abnormal = BDF.control_NCV21()
case_data21 = TA.make_case_data(control_normal, '21')
tris_chrom_abn21 = TA.make_tris_chrom_abn(control_abnormal, '21')
tris_case_data = {'NCV_13':case_data13,
'NCV_18':case_data18,
'NCV_21':case_data21}
tris_chrom_abn = {'13':tris_chrom_abn13,
'18':tris_chrom_abn18,
'21':tris_chrom_abn21}
CC = CovXCovY(batch_id)
CC.format_case_dict()
CC.format_contol_dict()
CC.format_pos_contol()
ST = Statistics()
ST.get_20_latest()
return render_template('batch_page/report_page.html',
## Header
batch_name = batch.batch_name,
## Warnings Table
seq_warnings = DC.QC_warnings,
## NCV Table
NCV_samples = NCV.query.filter(NCV.batch_id == batch_id),
man_class = DC.man_class_merged,
NCV_sex = DC.NCV_sex,
warnings = DC.NCV_classified,
batch_table_data = DC.NCV_data,
## Plotts
batch_names = ST.batch_names,
thresholds = ST.thresholds,
batch_ids = ST.batch_ids,
case_size = L.case_size,
abn_size = L.abn_size,
abn_symbol = L.abn_symbol,
case_line = L.case_line,
abn_line = L.abn_line,
cov_colors = L.cov_colors,
many_colors = L.many_colors_dict,
samp_range = range(len(SA.case_data['NCV_X']['samples'])),
tris_chrom_abn = tris_chrom_abn,
tris_case_data = tris_case_data,
case_data = SA.case_data, ## i xy-plot
sex_chrom_abn = SA.sex_chrom_abn,
abn_status_list = ['Other','False Positive','Suspected', 'Probable', 'Verified'],
ncv_abn_colors = L.ncv_abn_colors,
sex_tresholds = DC.sex_tresholds,
tris_thresholds = DC.tris_thresholds,
NCV_pass_names = control_normal_XY_names,
## Coverage
coverage = coverage,
samp_cov_db = CP.coverage_plot,
nr_contol_samples = CC.nr_contol_samples,
cases = CC.samples,
control = CC.control,
pos_contol = CC.pos_contol,
sample_list = SA.sample_list)