Skip to main content

LIMS/lab classes for clinicedc/edc projects

Project description

pypi actions codecov downloads

edc-lab

Add to settings:

INSTALLED_APPS = [
    ...
    'edc_lab.apps.AppConfig',
    ...
]

Configuration

Create aliquot types:

# aliquot types
wb = AliquotType(name='whole_blood', alpha_code='WB', numeric_code='02')
bc = AliquotType(name='buffy_coat', alpha_code='BC', numeric_code='16')
pl = AliquotType(name='plasma', alpha_code='PL', numeric_code='32')

Add possible derivatives to an aliquot type:

# in this case, plasma and buffy coat are possible derivatives
wb.add_derivatives(pl, bc)

Set up a processing profile:

viral_load = ProcessingProfile(
    name='viral_load', aliquot_type=wb)
process_bc = Process(aliquot_type=bc, aliquot_count=4)
process_pl = Process(aliquot_type=pl, aliquot_count=2)
viral_load.add_processes(process_bc, process_pl)

Create a``panel`` that uses the processing profile:

panel = RequisitionPanel(
    name='Viral Load',
    processing_profile=viral_load)

Add the panel (and others) to a lab profile:

lab_profile = LabProfile(
    name='lab_profile',
    requisition_model='edc_lab.subjectrequisition')
lab_profile.add_panel(panel)

Register the lab_profile with the site global:

site_labs.register(lab_profile)

Usage

Create a requisition model instance:

requisition = SubjectRequisition.objects.create(
    subject_visit=self.subject_visit,
    panel_name=self.panel.name,
    is_drawn=YES)

Pass the requisition to Specimen

specimen = Specimen(requisition=requisition)

Process:

specimen.process()

Aliquots have been created according to the configured processing profile:

>>> specimen.primary_aliquot.identifier
'99900GV63F00000201'

>>> for aliquot in specimen.aliquots.order_by('count'):
       print(aliquot.aliquot_identifier)
'99900GV63F00000201'
'99900GV63F02013202'
'99900GV63F02013203'
'99900GV63F02011604'
'99900GV63F02011605'
'99900GV63F02011606'
'99900GV63F02011607'

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

edc-lab-0.3.30.tar.gz (75.5 kB view details)

Uploaded Source

Built Distribution

edc_lab-0.3.30-py3-none-any.whl (119.5 kB view details)

Uploaded Python 3

File details

Details for the file edc-lab-0.3.30.tar.gz.

File metadata

  • Download URL: edc-lab-0.3.30.tar.gz
  • Upload date:
  • Size: 75.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.9

File hashes

Hashes for edc-lab-0.3.30.tar.gz
Algorithm Hash digest
SHA256 a444fbb22b76df8271785581395bfaf1615033641f35f26f704104873e7e471a
MD5 606ee35337afad6e1ffa8053ffec0cc9
BLAKE2b-256 a675af5dfc8eff5bdc3925beb828304d92814eb4f0800070b1093baca568225e

See more details on using hashes here.

File details

Details for the file edc_lab-0.3.30-py3-none-any.whl.

File metadata

  • Download URL: edc_lab-0.3.30-py3-none-any.whl
  • Upload date:
  • Size: 119.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.9

File hashes

Hashes for edc_lab-0.3.30-py3-none-any.whl
Algorithm Hash digest
SHA256 88267cb8d5f0e22146e886b6f7caba1e326ffb4b038d52177d0b9513fc885166
MD5 664cee9b72ea1cd45d15ee9748b610f4
BLAKE2b-256 9ba245ecd1d51e2388c425bfcbb88b501279896d453931c05b6cc7f188556019

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page