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 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(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 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 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 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)
