Skip to main content

Contains the classes used for generix data analysis

Project description

phd

Notebook is used to do the actual data processing Module contains all necessary functions

getter (ie all functions strating with 'get') is used to get data. The getter will either generate the data or retrieve it from the cache if it exists

the cache

REMI: QUESTIONS FOR JASMINE JAS: i think this convention is ok - discuss

  • I'm wondering what to do with figure naming because potentially many figure for the grouping will we be built. Im currently using an automatic naming convention {"histogram": 'f"{experiment}for{compound}in{region}"', "correlogram": 'f"{experiment}{correlogram_type}{buildCorrelogramFilenmae(tocorrelate, columns)}"', "head_twitch_histogram": 'f"head_twitch_histogram{experiment}for{to_plot}"',} but maybe the user would want to pick the name themself? probably better for them to remember what is what in the case of multiple stats choices
TO USE:

add csv with columns : mouse_id , group_id , COMPOUND_REGION... or BEHAVIOR_TIME (e.g. HT_20) to input folder

fill info in cell 1 of notebook (compound_ratio_mapping, ect)

perform outlier selection for experiment (including ratios chiosen in first cell)

generate quantitative histograms and aggregated stats table functions : REMI?

generate correlograms (use case for all three in functions) : REMI? clasical_corellogram : getAndPlotSingleCorrelogram(filename, experiment='agonist_antagonist', correlogram_type='compound',
to_correlate='GLU', p_value_threshold=0.05, n_minimum=5, from_scratch= True)

square_correlogram       :      getAndPlotSingleCorrelogram(filename, experiment='agonist_antagonist', correlogram_type='compound',
                                                            to_correlate='GLU-GABA', p_value_threshold=0.05, n_minimum=5, from_scratch= True)


bar_corellogram
                                                #see whatsapp image 3/5/23
    within BR       /       within compound

Project details


Download files

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

Source Distribution

cyberlabrat-0.1.4.tar.gz (35.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

cyberlabrat-0.1.4-py3-none-any.whl (39.0 kB view details)

Uploaded Python 3

File details

Details for the file cyberlabrat-0.1.4.tar.gz.

File metadata

  • Download URL: cyberlabrat-0.1.4.tar.gz
  • Upload date:
  • Size: 35.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.12

File hashes

Hashes for cyberlabrat-0.1.4.tar.gz
Algorithm Hash digest
SHA256 56b6b8f5a12a2186e5979f1689b37a57c95226087360735df25519de5720f930
MD5 8b5da5fe2ad78a366fdf5881cd0a92bd
BLAKE2b-256 e996088efc7e3e71986b04bf317dd43665948eacd972e619788fac3e7f231cef

See more details on using hashes here.

File details

Details for the file cyberlabrat-0.1.4-py3-none-any.whl.

File metadata

  • Download URL: cyberlabrat-0.1.4-py3-none-any.whl
  • Upload date:
  • Size: 39.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.12

File hashes

Hashes for cyberlabrat-0.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 e577164cda8b80b7ae94f473778fcf34f455603bad2837002529726e8580e1b0
MD5 66f3d4120f1df46bb9f3f3ee9f31770e
BLAKE2b-256 dd4fac30162abefd40b8aeb6775111263a002c67ecf198a16aa83bcefe7578d3

See more details on using hashes here.

Supported by

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