Metadata-Version: 2.1
Name: cyberlabrat
Version: 0.1.6
Summary: Contains the classes used for generix data analysis
Home-page: https://github.com/jjb-hub/phd
Author: Jasmine Butler & Remi Corne
Author-email: remi.corne@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: pandas==2.0.3
Requires-Dist: matplotlib==3.7.3
Requires-Dist: outlier-utils
Requires-Dist: pingouin
Requires-Dist: scipy
Requires-Dist: remi-statannotations-fork
Requires-Dist: setuptools
Requires-Dist: statsmodels
Requires-Dist: openpyxl
Requires-Dist: pydantic
Requires-Dist: pytest
Requires-Dist: tqdm
Requires-Dist: ipykernel
Requires-Dist: ipywidgets
Requires-Dist: IPython
Requires-Dist: networkx

# 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(to*correlate, 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
