110 lines
No EOL
4.4 KiB
Python
110 lines
No EOL
4.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed May 25 02:01:19 2022
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@author: tanu
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"""
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# TODO
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# categorical_cols = ['ss_class'
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# , 'wt_prop_water'
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# , 'mut_prop_water'
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# , 'wt_prop_polarity'
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# , 'mut_prop_polarity'
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# , 'wt_calcprop'
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# , 'mut_calcprop']
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my_df['water_change'] = my_df['wt_prop_water'] + str('_to_') + my_df['mut_prop_water']
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my_df['water_change'].value_counts()
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water_prop_changeD = {
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'hydrophobic_to_neutral' : 'change'
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, 'hydrophobic_to_hydrophobic' : 'no_change'
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, 'neutral_to_neutral' : 'no_change'
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, 'neutral_to_hydrophobic' : 'change'
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, 'hydrophobic_to_hydrophilic' : 'change'
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, 'neutral_to_hydrophilic' : 'change'
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, 'hydrophilic_to_neutral' : 'change'
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, 'hydrophilic_to_hydrophobic' : 'change'
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, 'hydrophilic_to_hydrophilic' : 'no_change'
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}
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my_df['water_change'] = my_df['water_change'].map(water_prop_changeD)
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my_df['water_change'].value_counts()
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#%%
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my_df['polarity_change'] = my_df['wt_prop_polarity'] + str('_to_') + my_df['mut_prop_polarity']
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my_df['polarity_change'].value_counts()
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# add a no change category
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polarity_prop_changeD = {
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'non-polar_to_non-polar' : 'no_change'
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, 'non-polar_to_neutral' : 'change'
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, 'neutral_to_non-polar' : 'change'
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, 'neutral_to_neutral' : 'no_change'
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, 'non-polar_to_basic' : 'change'
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, 'acidic_to_neutral' : 'change'
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, 'basic_to_neutral' : 'change'
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, 'non-polar_to_acidic' : 'change'
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, 'neutral_to_basic' : 'change'
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, 'acidic_to_non-polar' : 'change'
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, 'basic_to_non-polar' : 'change'
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, 'neutral_to_acidic' : 'change'
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, 'acidic_to_acidic' : 'no_change'
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, 'basic_to_acidic' : 'change'
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, 'basic_to_basic' : 'no_change'
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, 'acidic_to_basic' : 'change'}
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my_df['polarity_change'] = my_df['polarity_change'].map(polarity_prop_changeD)
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my_df['polarity_change'].value_counts()
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#%%
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my_df['electrostatics_change'] = my_df['wt_calcprop'] + str('_to_') + my_df['mut_calcprop']
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my_df['electrostatics_change'].value_counts()
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calc_prop_changeD = {
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'non-polar_to_non-polar' : 'no_change'
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, 'non-polar_to_polar' : 'change'
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, 'polar_to_non-polar' : 'change'
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, 'non-polar_to_pos' : 'change'
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, 'neg_to_non-polar' : 'change'
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, 'non-polar_to_neg' : 'change'
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, 'pos_to_polar' : 'change'
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, 'pos_to_non-polar' : 'change'
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, 'polar_to_polar' : 'no_change'
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, 'neg_to_neg' : 'no_change'
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, 'polar_to_neg' : 'change'
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, 'pos_to_neg' : 'change'
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, 'pos_to_pos' : 'no_change'
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, 'polar_to_pos' : 'change'
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, 'neg_to_polar' : 'change'
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, 'neg_to_pos' : 'change'
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}
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my_df['electrostatics_change'] = my_df['electrostatics_change'].map(calc_prop_changeD)
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my_df['electrostatics_change'].value_counts()
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#%%
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#https://stackoverflow.com/questions/47181187/finding-string-over-multiple-columns-in-pandas
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detect_change = 'change'
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# if detect_change in my_df['water_change'] | my_df['polarity_change'] | my_df['electrostatics_change']:
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# print('\nChange detected')
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check = ['mutationinformation', 'wild_type', 'water_change', 'polarity_change', 'electrostatics_change']
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check_prop_cols = ['water_change', 'polarity_change', 'electrostatics_change']
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foo = my_df[check]
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foo['aa_prop_change'] = (foo.values == detect_change).any(1).astype(int)
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#foo['aa_prop_change3'] = foo[check_prop_cols].applymap(lambda x: detect_change in x).any(1).astype(int) # lose match so alwasys 1
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foo['aa_prop_change2'] = (foo[check_prop_cols].values == detect_change).any(1).astype(int)
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all(foo['aa_prop_change'] == foo['aa_prop_change2'])
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#%%lineage
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# snp freq and lineage_count_all differ because same mut can be in more than 1 lineage
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lineage_colnames = ['snp_frequency', 'lineage', 'lineage_list_all', 'lineage_count_all', 'lineage_count_unique', 'lineage_list_unique', 'lineage_multimode']
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bar = my_df[lineage_colnames]
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tot_lineage_u = 8
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bar['lineage'].value_counts()
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bar['lineage_proportion'] = bar['lineage_count_unique']/bar['lineage_count_all']
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bar['dist_lineage_proportion'] = bar['lineage_count_unique']/tot_lineage_u |