output from comb script & electrostatic mut changes calculated
This commit is contained in:
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4 changed files with 250 additions and 167 deletions
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@ -1,6 +1,19 @@
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#########################################################
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# TASK: To combine mcsm and meta data with af and or
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# This script doesn't output anything, but can do if needed.
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# TASK: To combine mcsm and meta data with af and or files
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# Input csv files:
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# 1) mcsm output formatted
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# 2) gene associated meta_data_with_AFandOR
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# Output:
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# 1) muts with opposite effects on stability
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# 2) large combined df including NAs for AF, OR,etc
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# Dim: same no. of rows as gene associated meta_data_with_AFandOR
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# 3) small combined df including NAs for AF, OR, etc.
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# Dim: same as mcsm data
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# 4) large combined df excluding NAs
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# Dim: dim(#1) - no. of NAs(AF|OR) + 1
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# 5) small combined df excluding NAs
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# Dim: dim(#2) - no. of unique NAs - 1
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# This script is sourced from other .R scripts for plotting
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#########################################################
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getwd()
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@ -10,7 +23,6 @@ getwd()
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##########################################################
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# Installing and loading required packages
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##########################################################
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source('Header_TT.R')
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#require(data.table)
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#require(arsenal)
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@ -21,19 +33,23 @@ source('Header_TT.R')
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# Read file: normalised file
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# output of step 4 mcsm_pipeline
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#################################
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#%% variable assignment: input and output paths & filenames
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = paste0(gene,'_p.')
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cat(gene_match)
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#===========
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# data dir
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#===========
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datadir = paste0('~/git/Data')
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#===========
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# input
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#===========
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# infile1: mCSM data
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#indir = '~/git/Data/pyrazinamide/input/processed/'
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indir = paste0('~/git/Data', '/', drug, '/', 'output') # revised {TODO: change in mcsm pipeline}
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indir = paste0(datadir, '/', drug, '/', 'output') # revised {TODO: change in mcsm pipeline}
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in_filename = 'mcsm_complex1_normalised.csv'
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infile = paste0(indir, '/', in_filename)
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cat(paste0('Reading infile1: mCSM output file', ' ', infile) )
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@ -105,8 +121,9 @@ changes = mcsm_data[which(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome),]
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dl_i = which(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome)
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ld_i = which(mcsm_data$Lig_outcome != mcsm_data$DUET_outcome)
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cat('Identifying muts with opposite stability effects')
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if(nrow(changes) == (table(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome)[[2]]) & identical(dl_i,ld_i)) {
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cat('PASS: muts with opposite effects on stability and affinity identified correctly'
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cat('PASS: muts with opposite effects on stability and affinity correctly identified'
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, '\nNo. of such muts: ', nrow(changes))
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}else {
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cat('FAIL: unsuccessful in extracting muts with changed stability effects')
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@ -134,6 +151,7 @@ mcsm_data = mcsm_data[order(mcsm_data$Mutationinformation),]
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head(mcsm_data$Mutationinformation)
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orig_col = ncol(mcsm_data)
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# get freq count of positions and add to the df
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setDT(mcsm_data)[, occurrence := .N, by = .(Position)]
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@ -158,6 +176,20 @@ cat('Read mcsm_data file:'
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, '\nNo.of rows: ', nrow(meta_with_afor)
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, '\nNo. of cols:', ncol(meta_with_afor))
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# counting NAs in AF, OR cols
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if (identical(sum(is.na(meta_with_afor$OR))
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, sum(is.na(meta_with_afor$pvalue))
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, sum(is.na(meta_with_afor$AF)))){
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cat('PASS: NA count match for OR, pvalue and AF\n')
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na_count = sum(is.na(meta_with_afor$AF))
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cat('No. of NAs: ', sum(is.na(meta_with_afor$OR)))
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} else{
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cat('FAIL: NA count mismatch'
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, '\nNA in OR: ', sum(is.na(meta_with_afor$OR))
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, '\nNA in pvalue: ', sum(is.na(meta_with_afor$pvalue))
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, '\nNA in AF:', sum(is.na(meta_with_afor$AF)))
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}
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# clear variables
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rm(in_filename_comb, infile_comb)
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@ -172,15 +204,15 @@ head(meta_with_afor$Mutationinformation)
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# 3: merging two dfs: with NA
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###########################
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# link col name = 'Mutationinforamtion'
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head(mcsm_data$Mutationinformation)
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head(meta_with_afor$Mutationinformation)
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cat('Merging dfs with NAs: big df (1-many relationship b/w id & mut)'
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,'\nlinking col: Mutationinforamtion'
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,'\nfilename: merged_df2')
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head(mcsm_data$Mutationinformation)
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head(meta_with_afor$Mutationinformation)
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#########
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# merge 3a: meta data with mcsm
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# merge 3a (merged_df2): meta data with mcsm
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#########
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merged_df2 = merge(x = meta_with_afor
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,y = mcsm_data
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@ -192,6 +224,8 @@ cat('Dim of merged_df2: '
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, '\nNo. of cols: ', ncol(merged_df2))
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head(merged_df2$Position)
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# sanity check
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cat('Checking nrows in merged_df2')
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if(nrow(meta_with_afor) == nrow(merged_df2)){
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cat('nrow(merged_df2) = nrow (gene associated metadata)'
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,'\nExpected no. of rows: ',nrow(meta_with_afor)
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@ -229,9 +263,9 @@ table(merged_df2$Position%in%merged_df2v2$Position)
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rm(merged_df2v2)
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#########
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# merge 3b:remove duplicate mutation information
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# merge 3b (merged_df3):remove duplicate mutation information
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#########
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cat('Merging dfs with NAs: small df (removing duplicate muts)'
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cat('Merging dfs without NAs: small df (removing muts with no AF|OR associated)'
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,'\nCannot trust lineage info from this'
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,'\nlinking col: Mutationinforamtion'
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,'\nfilename: merged_df3')
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@ -244,8 +278,8 @@ cat('Merging dfs with NAs: small df (removing duplicate muts)'
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merged_df3 = merged_df2[!duplicated(merged_df2$Mutationinformation),]
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head(merged_df3$Position); tail(merged_df3$Position) # should be sorted
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# sanity checks
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# nrows of merged_df3 should be the same as the nrows of mcsm_data
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# sanity check
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cat('Checking nrows in merged_df3')
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if(nrow(mcsm_data) == nrow(merged_df3)){
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cat('PASS: No. of rows match with mcsm_data'
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,'\nExpected no. of rows: ', nrow(mcsm_data)
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@ -256,41 +290,51 @@ if(nrow(mcsm_data) == nrow(merged_df3)){
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, '\nNo. of rows merged_df3: ', nrow(merged_df3))
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}
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# uncomment as necessary
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# only need to run this if merged_df2v2 i.e non structural pos included
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#mcsm = mcsm_data$Mutationinformation
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#my_merged = merged_df3$Mutationinformation
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# find the index where it differs
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#diff_n = which(!my_merged%in%mcsm)
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#check if it is indeed pos 186
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#merged_df3[diff_n,]
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# remove this entry
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#merged_df3 = merged_df3[-diff_n,]]
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# counting NAs in AF, OR cols in merged_df3
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# this is becuase mcsm has no AF, OR cols,
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# so you cannot count NAs
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if (identical(sum(is.na(merged_df3$OR))
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, sum(is.na(merged_df3$pvalue))
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, sum(is.na(merged_df3$AF)))){
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cat('PASS: NA count match for OR, pvalue and AF\n')
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na_count_df3 = sum(is.na(merged_df3$AF))
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cat('No. of NAs: ', sum(is.na(merged_df3$OR)))
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} else{
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cat('FAIL: NA count mismatch'
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, '\nNA in OR: ', sum(is.na(merged_df3$OR))
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, '\nNA in pvalue: ', sum(is.na(merged_df3$pvalue))
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, '\nNA in AF:', sum(is.na(merged_df3$AF)))
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}
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###########################
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# 4: merging two dfs: without NA
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###########################
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#########
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# merge 4a (merged_df2_comp): same as merge 1 but excluding NA
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#########
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cat('Merging dfs without any NAs: big df (1-many relationship b/w id & mut)'
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,'\nlinking col: Mutationinforamtion'
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,'\nfilename: merged_df2_comp')
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#########
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# merge 4a: same as merge 1 but excluding NA
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#########
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merged_df2_comp = merged_df2[!is.na(merged_df2$AF),]
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#merged_df2_comp = merged_df2[!duplicated(merged_df2$Mutationinformation),]
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cat('Dim of merged_df2_comp: '
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, '\nNo. of rows: ', nrow(merged_df2_comp)
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, '\nNo. of cols: ', ncol(merged_df2_comp))
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# sanity check
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cat('Checking nrows in merged_df2_comp')
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if(nrow(merged_df2_comp) == (nrow(merged_df2) - na_count + 1)){
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cat('PASS: No. of rows match'
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,'\nDim of merged_df2_comp: '
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,'\nExpected no. of rows: ', nrow(merged_df2) - na_count + 1
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, '\nNo. of rows: ', nrow(merged_df2_comp)
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, '\nNo. of cols: ', ncol(merged_df2_comp))
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}else{
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cat('FAIL: No. of rows mismatch'
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,'\nExpected no. of rows: ', nrow(merged_df2) - na_count + 1
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,'\nGot no. of rows: ', nrow(merged_df2_comp))
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}
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#########
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# merge 4b: remove duplicate mutation information
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# merge 4b (merged_df3_comp): remove duplicate mutation information
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#########
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merged_df3_comp = merged_df2_comp[!duplicated(merged_df2_comp$Mutationinformation),]
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@ -305,38 +349,65 @@ all.equal(foo, merged_df3)
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summary(comparedf(foo, merged_df3))
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# sanity check
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cat('Checking nrows in merged_df3_comp')
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if(nrow(merged_df3_comp) == nrow(merged_df3)){
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cat('NO NAs detected in merged_df3 in AF|OR cols'
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,'\nNo. of rows are identical: ', nrow(merged_df3))
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} else{
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if(nrow(merged_df3_comp) == nrow(merged_df3) - na_count_df3) {
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cat('PASS: NAs detected in merged_df3 in AF|OR cols'
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, '\nNo. of NAs: ', na_count_df3
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, '\nExpected no. of rows in merged_df3_comp: ', nrow(merged_df3) - na_count_df3
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, '\nGot no. of rows: ', nrow(merged_df3_comp))
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}
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}
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#=============== end of combining df
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#*********************
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# write_output files
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# output dir
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#outdir = '~/git/Data/pyrazinamide/output/'
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#uncomment as necessary
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#FIXME
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#out_filenames = c('merged_df2'
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# , 'merged_df3'
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# , 'meregd_df2_comp'
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# , 'merged_df3_comp'
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#)
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# writing 1 file in the style of a loop: merged_df3
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# print(output dir)
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#i = 'merged_df3'
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#out_filename = paste0(i, '.csv')
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#outfile = paste0(outdir, '/', out_filename)
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#cat('Writing output files: '
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# , '\nPath:', outdir)
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#cat('Writing output file: '
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# ,'\nFilename: ', out_filename
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# ,'\nPath: ', outdir)
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#for (i in out_filenames){
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# print(i)
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# print(get(i))
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# outvar = get(i)
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# print(outvar)
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# outfile = paste0(outdir, '/', outvar, '.csv')
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# cat('Writing output file:'
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# ,'\nFilename: ', outfile
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# ,'\n')
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# write.csv(outvar, outfile)
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# cat('Finished writing file:'
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# ,'\nNo. of rows:', nrow(outvar)
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# , '\nNo. of cols:', ncol(outvar))
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#}
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#template: write.csv(merged_df3, 'merged_df3.csv')
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#write.csv(get(i), outfile, row.names = FALSE)
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#cat('Finished writing: ', outfile
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# , '\nNo. of rows: ', nrow(get(i))
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# , '\nNo. of cols: ', ncol(get(i)))
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#sapply(out_filenames, function(x) write.csv(x, 'x.csv'))
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#%% write_output files; all 4 files:
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outvars = c('merged_df2'
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, 'merged_df3'
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, 'merged_df2_comp'
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, 'merged_df3_comp')
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cat('Writing output files: '
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, '\nPath:', outdir)
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for (i in outvars){
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# cat(i, '\n')
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out_filename = paste0(i, '.csv')
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# cat(out_filename, '\n')
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# cat('getting value of variable: ', get(i))
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outfile = paste0(outdir, '/', out_filename)
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# cat('Full output path: ', outfile, '\n')
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cat('Writing output file:'
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,'\nFilename: ', out_filename,'\n')
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write.csv(get(i), outfile, row.names = FALSE)
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cat('Finished writing: ', outfile
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, '\nNo. of rows: ', nrow(get(i))
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, '\nNo. of cols: ', ncol(get(i)), '\n')
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}
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# alternate way to replace with implicit loop
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# FIXME
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#sapply(outvars, function(x, y) write.csv(get(outvars), paste0(outdir, '/', outvars, '.csv')))
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#*************************
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# clear variables
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rm(mcsm_data, meta_with_afor, foo, drug, gene, gene_match, indir, merged_muts_u, meta_muts_u, na_count, orig_col, outdir)
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#########################################################
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# TASK: To combine mcsm and meta data with af and or
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# by filtering for distance to ligand (<10Ang).
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# This script doesn't output anything, but can do if needed.
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# This script is sourced from other .R scripts for plotting
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# This script doesn't output anything.
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# This script is sourced from other .R scripts for plotting ligand plots
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#########################################################
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getwd()
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setwd('~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/')
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@ -9,9 +9,9 @@ Created on Tue Aug 6 12:56:03 2019
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# FIXME: include error checking to enure you only
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# concentrate on positions that have structural info?
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# FIXME: import dirs.py to get the basic dir paths available
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#%% load libraries
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###################
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# load libraries
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import os, sys
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import pandas as pd
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#import numpy as np
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@ -52,19 +52,19 @@ from reference_dict import my_aa_dict #CHECK DIR STRUC THERE!
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#========================================================
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#%% variable assignment: input and output paths & filenames
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = gene + '_p.'
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#=======
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#==========
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# input dir
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#=======
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#==========
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#indir = 'git/Data/pyrazinamide/input/original'
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indir = homedir + '/' + 'git/Data'
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#=========
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#===========
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# output dir
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#=========
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#===========
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# several output files
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# output filenames in respective sections at the time of outputting files
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#outdir = 'git/Data/pyrazinamide/output'
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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'''
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Created on Tue Aug 6 12:56:03 2019
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@author: tanu
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"""
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'''
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# FIXME: include error checking to enure you only
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# concentrate on positions that have structural info
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# FIXME: import dirs.py to get the basic dir paths available
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#%% load libraries
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###################
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@ -16,147 +15,160 @@ import os, sys
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import pandas as pd
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#import numpy as np
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#from pandas.api.types import is_string_dtype
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#from pandas.api.types import is_numeric_dtype
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#====================================================
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# TASK: calculate how many mutations result in
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# electrostatic changes wrt wt
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# Input: mcsm and AF_OR file
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# output: mut_elec_changes_results.txt
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# Output: mut_elec_changes_results.txt
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#========================================================
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#%%
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####################
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#%% specify homedir as python doesn't recognise tilde
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homedir = os.path.expanduser('~')
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# my working dir
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os.getcwd()
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homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
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os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
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os.getcwd()
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#%%
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from reference_dict import my_aa_dict #CHECK DIR STRUC THERE!
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#%%
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############# specify variables for input and output paths and filenames
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drug = "pyrazinamide"
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gene = "pnca"
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datadir = homedir + "/git/Data"
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basedir = datadir + "/" + drug + "/input"
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#========================================================
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#%% variable assignment: input and output paths & filenames
|
||||
drug = 'pyrazinamide'
|
||||
gene = 'pncA'
|
||||
gene_match = gene + '_p.'
|
||||
|
||||
# input
|
||||
inpath = "/processed"
|
||||
#==========
|
||||
# data dir
|
||||
#==========
|
||||
#indir = 'git/Data/pyrazinamide/input/original'
|
||||
datadir = homedir + '/' + 'git/Data'
|
||||
|
||||
# uncomment as necessary
|
||||
in_filename = "/meta_data_with_AFandOR.csv"
|
||||
#in_filename = "/mcsm_complex1_normalised.csv" # probably simpler
|
||||
#==========
|
||||
# input dir
|
||||
#==========
|
||||
indir = datadir + '/' + drug + '/' + 'input'
|
||||
|
||||
infile = basedir + inpath + in_filename
|
||||
#print(infile)
|
||||
#============
|
||||
# output dir
|
||||
#============
|
||||
# several output files
|
||||
outdir = datadir + '/' + drug + '/' + 'output'
|
||||
|
||||
# output file
|
||||
outpath = "/output"
|
||||
outdir = datadir + "/" + drug + outpath
|
||||
out_filename = "/mut_elec_changes_results.txt"
|
||||
outfile = outdir + out_filename
|
||||
# specify output file
|
||||
out_filename = 'mut_elec_changes.txt'
|
||||
outfile = outdir + '/' + out_filename
|
||||
print('Output path: ', outdir)
|
||||
|
||||
#print(outdir)
|
||||
#%% end of variable assignment for input and output files
|
||||
#=============================================================
|
||||
#%% Read input files
|
||||
#in_filename = gene.lower() + '_meta_data_with_AFandOR.csv'
|
||||
in_filename = 'merged_df3.csv'
|
||||
infile = outdir + '/' + in_filename
|
||||
print('Reading input file (merged file):', infile)
|
||||
|
||||
if not os.path.exists(datadir):
|
||||
print('Error!', datadir, 'does not exist. Please ensure it exists. Dir struc specified in README.md')
|
||||
os.makedirs(datadir)
|
||||
exit()
|
||||
comb_df = pd.read_csv(infile, sep = ',')
|
||||
|
||||
if not os.path.exists(outdir):
|
||||
print('Error!', outdir, 'does not exist.Please ensure it exists. Dir struc specified in README.md')
|
||||
exit()
|
||||
|
||||
else:
|
||||
print('Dir exists: Carrying on')
|
||||
|
||||
################## end of variable assignment for input and output files
|
||||
#%%
|
||||
#==============================================================================
|
||||
############
|
||||
# STEP 1: Read file
|
||||
############
|
||||
meta_pnca = pd.read_csv(infile, sep = ',')
|
||||
print('Input filename: ', in_filename,
|
||||
'\nPath :', outdir,
|
||||
'\nNo. of rows: ', len(comb_df),
|
||||
'\nNo. of cols: ', infile)
|
||||
|
||||
# column names
|
||||
list(meta_pnca.columns)
|
||||
list(comb_df.columns)
|
||||
|
||||
#========
|
||||
# Step 2: iterate through the dict, create a lookup dict that i.e
|
||||
# lookup_dict = {three_letter_code: aa_prop_polarity}
|
||||
# Do this for both wild_type and mutant as above.
|
||||
#=========
|
||||
# initialise a sub dict that is lookup dict for three letter code to aa prop
|
||||
lookup_dict = dict()
|
||||
|
||||
for k, v in my_aa_dict.items():
|
||||
lookup_dict[k] = v['aa_calcprop']
|
||||
#print(lookup_dict)
|
||||
wt = meta_pnca['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
|
||||
meta_pnca['wt_calcprop'] = wt.map(lookup_dict)
|
||||
mut = meta_pnca['mutation'].str.extract(r'\d+(\w{3})$').squeeze()
|
||||
meta_pnca['mut_calcprop'] = mut.map(lookup_dict)
|
||||
|
||||
# added two more cols
|
||||
|
||||
# clear variables
|
||||
del(k, v, wt, mut, lookup_dict)
|
||||
del(in_filename, infile, inpath)
|
||||
del(in_filename, infile)
|
||||
|
||||
#%%
|
||||
###########
|
||||
# Step 3: subset unique mutations
|
||||
###########
|
||||
meta_pnca_muts = meta_pnca.drop_duplicates(['Mutationinformation'], keep = 'first')
|
||||
non_struc = meta_pnca_muts[meta_pnca_muts.position == 186]
|
||||
#%% subset unique mutations
|
||||
df = comb_df.drop_duplicates(['Mutationinformation'], keep = 'first')
|
||||
|
||||
# remove pos non_struc 186 : (in case you used file with AF and OR)
|
||||
df = meta_pnca_muts[meta_pnca_muts.position != 186]
|
||||
total_muts = df.Mutationinformation.nunique()
|
||||
#df.Mutationinformation.count()
|
||||
print('Total mutations associated with structure: ', total_muts)
|
||||
|
||||
###########
|
||||
# Step 4: combine cols
|
||||
###########
|
||||
#%% combine aa_calcprop cols so that you can count the changes as value_counts
|
||||
# check if all muts have been categorised
|
||||
print('Checking if all muts have been categorised: ')
|
||||
if df['wt_calcprop'].isna().sum() == 0 & df['mut_calcprop'].isna().sum():
|
||||
print('PASS: No. NA detected i.e all muts have aa prop associated')
|
||||
else:
|
||||
print('FAIL: NAs detected i.e some muts remain unclassified')
|
||||
|
||||
df['aa_calcprop_combined'] = df['wt_calcprop']+ '->' + df['mut_calcprop']
|
||||
df['aa_calcprop_combined']
|
||||
df['wt_calcprop'].head()
|
||||
df['mut_calcprop'].head()
|
||||
|
||||
print('Combining wt_calcprop and mut_calcprop...')
|
||||
#df['aa_calcprop_combined'] = df['wt_calcprop']+ '->' + df['mut_calcprop']
|
||||
df['aa_calcprop_combined'] = df.wt_calcprop.str.cat(df.mut_calcprop, sep = '->')
|
||||
df['aa_calcprop_combined'].head()
|
||||
|
||||
mut_categ = df["aa_calcprop_combined"].unique()
|
||||
print('Total no. of aa_calc properties: ', len(mut_categ))
|
||||
print('Categories are: ', mut_categ)
|
||||
|
||||
# counting no. of muts in each mut categ
|
||||
|
||||
# way1: count values within each combinaton
|
||||
df.groupby('aa_calcprop_combined').size()
|
||||
#df.groupby('aa_calcprop_combined').count()
|
||||
|
||||
# way2: count values within each combinaton
|
||||
#df['aa_calcprop_combined'].value_counts()
|
||||
df['aa_calcprop_combined'].value_counts()
|
||||
|
||||
# comment: the two ways should be identical
|
||||
# groupby result order is similar to pivot table order
|
||||
# groupby result order is similar to pivot table order,
|
||||
# I prefer the value_counts look
|
||||
|
||||
#assign to variable: count values within each combinaton
|
||||
all_prop = df.groupby('aa_calcprop_combined').size()
|
||||
# assign to variable: count values within each combinaton
|
||||
all_prop = df['aa_calcprop_combined'].value_counts()
|
||||
|
||||
# convert to a df from Series
|
||||
ap_df = pd.DataFrame({'aa_calcprop': all_prop.index, 'mut_count': all_prop.values})
|
||||
|
||||
# subset df to contain only the changes in prop
|
||||
all_prop_change = ap_df[ap_df['aa_calcprop'].isin(['neg->neg','non-polar->non-polar','polar->polar', 'pos->pos']) == False]
|
||||
|
||||
|
||||
elec_count = all_prop_change.mut_count.sum()
|
||||
print('Total no.of muts with elec changes: ', elec_count)
|
||||
|
||||
# calculate percentage of electrostatic changes
|
||||
elec_changes = (elec_count/total_muts) * 100
|
||||
|
||||
print("Total number of electrostatic changes resulting from Mutation is (%):", elec_changes)
|
||||
|
||||
print('Total number of electrostatic changes resulting from Mutation is (%):', elec_changes)
|
||||
|
||||
# check no change muts
|
||||
no_change_muts = ap_df[ap_df['aa_calcprop'].isin(['neg->neg','non-polar->non-polar','polar->polar', 'pos->pos']) == True]
|
||||
|
||||
no_change_muts.mut_count.sum()
|
||||
|
||||
|
||||
###########
|
||||
# Step 5: output from console
|
||||
###########
|
||||
#sys.stdout = open(file, 'w')
|
||||
sys.stdout = open(outfile, 'w')
|
||||
|
||||
print(df.groupby('aa_calcprop_combined').size() )
|
||||
print("=======================================================================================")
|
||||
print("Total number of electrostatic changes resulting from Mutation is (%):", elec_changes)
|
||||
print("=======================================================================================")
|
||||
#print(no_change_muts, '\n',
|
||||
# all_prop_change)
|
||||
|
||||
print('======================\n'
|
||||
,'Unchanged muts'
|
||||
,'\n=====================\n'
|
||||
, no_change_muts
|
||||
,'\n=============================\n'
|
||||
, 'Muts with changed prop:'
|
||||
, '\n============================\n'
|
||||
, all_prop_change)
|
||||
|
||||
#print('======================================================================')
|
||||
#print('Total number of electrostatic changes resulting from Mutation is (%):', elec_changes)
|
||||
#print('Total no. of muts: ', total_muts)
|
||||
#print('Total no. of changed muts: ', all_prop_change.mut_count.sum())
|
||||
#print('Total no. of unchanged muts: ', no_change_muts.mut_count.sum() )
|
||||
#print('=======================================================================')
|
||||
|
||||
print('========================================================================'
|
||||
, '\nTotal number of electrostatic changes resulting from Mtation is (%):', elec_changes
|
||||
, '\nTotal no. of muts: ', total_muts
|
||||
, '\nTotal no. of changed muts: ', all_prop_change.mut_count.sum()
|
||||
, '\nTotal no. of unchanged muts: ', no_change_muts.mut_count.sum()
|
||||
, '\n=========================================================================')
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue