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minor edits to format mcsm data like sorting df

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This commit is contained in:
Tanushree Tunstall 2020-07-09 11:15:56 +01:00
parent 01fbc2a87b
commit 6402990154
4 changed files with 127 additions and 85 deletions
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44
mcsm/ind_scripts/format_results.py
View file

@ -264,19 +264,29 @@ def format_mcsm_output(mcsm_outputcsv):
, '\nScaled affinity scores:\n', mcsm_data['affinity_scaled']) , '\nScaled affinity scores:\n', mcsm_data['affinity_scaled'])
#============================================================================= #=============================================================================
# Adding colname: wild_pos: sometimes useful for plotting and db # Adding colname: wild_pos: sometimes useful for plotting and db
print('Creating column: wild_position') print('Creating column: wild_pos')
mcsm_data['wild_position'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str) mcsm_data['wild_pos'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
print(mcsm_data['wild_position'].head()) print(mcsm_data['wild_pos'].head())
# Remove spaces b/w pasted columns # Remove spaces b/w pasted columns
print('removing white space within column: wild_position') print('removing white space within column: wild_pos')
mcsm_data['wild_position'] = mcsm_data['wild_position'].str.replace(' ', '') mcsm_data['wild_pos'] = mcsm_data['wild_pos'].str.replace(' ', '')
print('Correctly formatted column: wild_position\n', mcsm_data['wild_position'].head() print('Correctly formatted column: wild_pos\n', mcsm_data['wild_pos'].head()
, '\n===================================================================') , '\n===================================================================')
#============================================================================= #=============================================================================
# Adding colname: wild_chain_pos: sometimes useful for plotting and db and is explicit
print('Creating column: wild_chain_pos')
mcsm_data['wild_chain_pos'] = mcsm_data['wild_type'] + mcsm_data['chain'] + mcsm_data['position'].astype(str)
print(mcsm_data['wild_chain_pos'].head())
# Remove spaces b/w pasted columns
print('removing white space within column: wild_chain_pos')
mcsm_data['wild_chain_pos'] = mcsm_data['wild_chain_pos'].str.replace(' ', '')
print('Correctly formatted column: wild_chain_pos\n', mcsm_data['wild_chain_pos'].head()
, '\n===================================================================')
#=============================================================================
#%% ensuring dtypes are string for the non-numeric cols #%% ensuring dtypes are string for the non-numeric cols
#) char cols #) char cols
char_cols = ['PredAffLog', 'mutation_information', 'wild_type', 'mutant_type', 'chain' char_cols = ['PredAffLog', 'mutation_information', 'wild_type', 'mutant_type', 'chain'
, 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_position'] , 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_pos', 'wild_chain_pos']
#mcsm_data[char_cols] = mcsm_data[char_cols].astype(str) #mcsm_data[char_cols] = mcsm_data[char_cols].astype(str)
cols_check_char = mcsm_data.select_dtypes(include='object').columns.isin(char_cols) cols_check_char = mcsm_data.select_dtypes(include='object').columns.isin(char_cols)
@ -292,7 +302,12 @@ def format_mcsm_output(mcsm_outputcsv):
#============================================================================= #=============================================================================
# Removing PredAff log column as it is not needed? # Removing PredAff log column as it is not needed?
print('Removing col: PredAffLog since relevant info has been extracted from it') print('Removing col: PredAffLog since relevant info has been extracted from it')
mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog']) mcsm_data_f = mcsm_data.drop(columns = ['PredAffLog'])
#=============================================================================
#sort df by position for convenience
print('Sorting df by position')
mcsm_data_fs = mcsm_data_f.sort_values(by = ['position'])
print('sorted df:\n', mcsm_data_fs.head())
#%%=========================================================================== #%%===========================================================================
############# #############
# sanity check before writing file # sanity check before writing file
@ -300,29 +315,28 @@ def format_mcsm_output(mcsm_outputcsv):
expected_ncols_toadd = 5 # beware of hardcoded numbers expected_ncols_toadd = 5 # beware of hardcoded numbers
dforig_len = dforig_shape[1] dforig_len = dforig_shape[1]
expected_cols = dforig_len + expected_ncols_toadd expected_cols = dforig_len + expected_ncols_toadd
if len(mcsm_dataf.columns) == expected_cols: if len(mcsm_data_fs.columns) == expected_cols:
print('PASS: formatting successful' print('PASS: formatting successful'
, '\nformatted df has expected no. of cols:', expected_cols , '\nformatted df has expected no. of cols:', expected_cols
, '\ncolnames:', mcsm_dataf.columns , '\ncolnames:', mcsm_data_fs.columns
, '\n----------------------------------------------------------------' , '\n----------------------------------------------------------------'
, '\ndtypes in cols:', mcsm_dataf.dtypes , '\ndtypes in cols:', mcsm_data_fs.dtypes
, '\n----------------------------------------------------------------' , '\n----------------------------------------------------------------'
, '\norig data shape:', dforig_shape , '\norig data shape:', dforig_shape
, '\nformatted df shape:', mcsm_dataf.shape , '\nformatted df shape:', mcsm_data_fs.shape
, '\n===============================================================') , '\n===============================================================')
else: else:
print('FAIL: something went wrong in formatting df' print('FAIL: something went wrong in formatting df'
, '\nLen of orig df:', dforig_len , '\nLen of orig df:', dforig_len
, '\nExpected number of cols to add:', expected_ncols_toadd , '\nExpected number of cols to add:', expected_ncols_toadd
, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')' , '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
, '\nGot no. of cols:', len(mcsm_dataf.columns) , '\nGot no. of cols:', len(mcsm_data_fs.columns)
, '\nCheck formatting:' , '\nCheck formatting:'
, '\ncheck hardcoded value:', expected_ncols_toadd , '\ncheck hardcoded value:', expected_ncols_toadd
, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?' , '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
, '\n===============================================================') , '\n===============================================================')
return mcsm_data_fs
return mcsm_dataf
#======================================================================= #=======================================================================
# call function # call function
mcsm_df_formatted = format_mcsm_output(infile) mcsm_df_formatted = format_mcsm_output(infile)

106
mcsm/ind_scripts/format_results_notdef.py
View file

@ -23,45 +23,41 @@ os.chdir(homedir + '/git/LSHTM_analysis/mcsm')
os.getcwd() os.getcwd()
#======================================================================= #=======================================================================
#%% variable assignment: input and output #%% variable assignment: input and output
#drug = 'pyrazinamide' drug = 'pyrazinamide'
#gene = 'pncA' gene = 'pncA'
drug = 'rifampicin'
gene = 'rpoB'
#drug = args.drug #drug = args.drug
#gene = args.gene #gene = args.gene
gene_match = gene + '_p.' gene_match = gene + '_p.'
#========== #==========
# data dir # dirs
#========== #==========
datadir = homedir + '/' + 'git/Data' datadir = homedir + '/' + 'git/Data'
indir = datadir + '/' + drug + '/' + 'input'
outdir = datadir + '/' + drug + '/' + 'output'
#======= #=======
# input: # input:
#======= #=======
# 1) result_urls (from outdir) # 1) result_urls (from outdir)
outdir = datadir + '/' + drug + '/' + 'output'
in_filename = gene.lower() + '_mcsm_output.csv' #(outfile, from mcsm_results.py) in_filename_mcsm_output = gene.lower() + '_mcsm_output.csv' #(outfile, from mcsm_results.py)
infile = outdir + '/' + in_filename infile_mcsm_output = outdir + '/' + in_filename_mcsm_output
print('Input filename:', in_filename print('Input file:', infile_mcsm_output
, '\nInput path(from output dir):', outdir
, '\n=============================================================') , '\n=============================================================')
#======= #=======
# output # output
#======= #=======
outdir = datadir + '/' + drug + '/' + 'output' out_filename_mcsm_norm = gene.lower() + '_complex_mcsm_norm.csv'
out_filename = gene.lower() + '_complex_mcsm_norm.csv' outfile_mcsm_norm = outdir + '/' + out_filename_mcsm_norm
outfile = outdir + '/' + out_filename print('Output file:', out_filename_mcsm_norm
print('Output filename:', out_filename
, '\nOutput path:', outdir
, '\n=============================================================') , '\n=============================================================')
#======================================================================= #=======================================================================
print('Reading input file') print('Reading input file')
mcsm_data = pd.read_csv(infile, sep = ',') mcsm_data = pd.read_csv(infile_mcsm_output, sep = ',')
mcsm_data.columns mcsm_data.columns
# PredAffLog = affinity_change_log # PredAffLog = affinity_change_log
@ -231,19 +227,29 @@ print('Raw affinity scores:\n', mcsm_data['ligand_affinity_change']
, '\nScaled affinity scores:\n', mcsm_data['affinity_scaled']) , '\nScaled affinity scores:\n', mcsm_data['affinity_scaled'])
#============================================================================= #=============================================================================
# Adding colname: wild_pos: sometimes useful for plotting and db # Adding colname: wild_pos: sometimes useful for plotting and db
print('Creating column: wild_position') print('Creating column: wild_pos')
mcsm_data['wild_position'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str) mcsm_data['wild_pos'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
print(mcsm_data['wild_position'].head()) print(mcsm_data['wild_pos'].head())
# Remove spaces b/w pasted columns # Remove spaces b/w pasted columns
print('removing white space within column: wild_position') print('removing white space within column: wild_position')
mcsm_data['wild_position'] = mcsm_data['wild_position'].str.replace(' ', '') mcsm_data['wild_pos'] = mcsm_data['wild_pos'].str.replace(' ', '')
print('Correctly formatted column: wild_position\n', mcsm_data['wild_position'].head() print('Correctly formatted column: wild_pos\n', mcsm_data['wild_pos'].head()
, '\n===================================================================')
#=============================================================================
#%% Adding colname: wild_chain_pos: sometimes useful for plotting and db and is explicit
print('Creating column: wild_chain_pos')
mcsm_data['wild_chain_pos'] = mcsm_data['wild_type'] + mcsm_data['chain'] + mcsm_data['position'].astype(str)
print(mcsm_data['wild_chain_pos'].head())
# Remove spaces b/w pasted columns
print('removing white space within column: wild_chain_pos')
mcsm_data['wild_chain_pos'] = mcsm_data['wild_chain_pos'].str.replace(' ', '')
print('Correctly formatted column: wild_chain_pos\n', mcsm_data['wild_chain_pos'].head()
, '\n===================================================================') , '\n===================================================================')
#============================================================================= #=============================================================================
#%% ensuring dtypes are string for the non-numeric cols #%% ensuring dtypes are string for the non-numeric cols
#) char cols #) char cols
char_cols = ['PredAffLog', 'mutation_information', 'wild_type', 'mutant_type', 'chain' char_cols = ['PredAffLog', 'mutation_information', 'wild_type', 'mutant_type', 'chain'
, 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_position'] , 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_pos', 'wild_chain_pos']
#mcsm_data[char_cols] = mcsm_data[char_cols].astype(str) #mcsm_data[char_cols] = mcsm_data[char_cols].astype(str)
cols_check_char = mcsm_data.select_dtypes(include='object').columns.isin(char_cols) cols_check_char = mcsm_data.select_dtypes(include='object').columns.isin(char_cols)
@ -258,42 +264,48 @@ else:
print(mcsm_data.dtypes) print(mcsm_data.dtypes)
#%% #%%
#============================================================================= #=============================================================================
# Removing PredAff log column as it is not needed? #%% Removing PredAff log column as it is not needed?
print('Removing col: PredAffLog since relevant info has been extracted from it') print('Removing col: PredAffLog since relevant info has been extracted from it')
mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog']) mcsm_data_f = mcsm_data.drop(columns = ['PredAffLog'])
print(mcsm_data_f.head())
#=============================================================================
#%% sort df by position for convenience
print('Sorting df by position')
mcsm_data_fs = mcsm_data_f.sort_values(by = ['position'])
print('sorted df:\n', mcsm_data_fs.head())
#%%=========================================================================== #%%===========================================================================
expected_ncols_toadd = 5 # beware of hardcoded numbers expected_ncols_toadd = 6 # beware of hardcoded numbers
dforig_len = dforig_shape[1] dforig_len = dforig_shape[1]
expected_cols = dforig_len + expected_ncols_toadd expected_cols = dforig_len + expected_ncols_toadd
if len(mcsm_dataf.columns) == expected_cols: if len(mcsm_data_fs.columns) == expected_cols:
print('PASS: formatting successful' print('PASS: formatting successful'
, '\nformatted df has expected no. of cols:', expected_cols , '\nformatted df has expected no. of cols:', expected_cols
, '\ncolnames:', mcsm_dataf.columns , '\ncolnames:', mcsm_data_fs.columns
, '\n----------------------------------------------------------------' , '\n----------------------------------------------------------------'
, '\ndtypes in cols:', mcsm_dataf.dtypes , '\ndtypes in cols:', mcsm_data_fs.dtypes
, '\n----------------------------------------------------------------' , '\n----------------------------------------------------------------'
, '\norig data shape:', dforig_shape , '\norig data shape:', dforig_shape
, '\nformatted df shape:', mcsm_dataf.shape , '\nformatted df shape:', mcsm_data_fs.shape
, '\n===============================================================') , '\n===============================================================')
else: else:
print('FAIL: something went wrong in formatting df' print('FAIL: something went wrong in formatting df'
, '\nLen of orig df:', dforig_len , '\nLen of orig df:', dforig_len
, '\nExpected number of cols to add:', expected_ncols_toadd , '\nExpected number of cols to add:', expected_ncols_toadd
, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')' , '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
, '\nGot no. of cols:', len(mcsm_dataf.columns) , '\nGot no. of cols:', len(mcsm_data_fs.columns)
, '\nCheck formatting:' , '\nCheck formatting:'
, '\ncheck hardcoded value:', expected_ncols_toadd , '\ncheck hardcoded value:', expected_ncols_toadd
, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?' , '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
, '\n===============================================================') , '\n===============================================================')
#%%============================================================================ #%%============================================================================
# writing file # writing file
print('Writing formatted df to csv') print('Writing formatted df to csv')
mcsm_dataf.to_csv(outfile, index = False) mcsm_data_fs.to_csv(outfile_mcsm_norm, index = False)
print('Finished writing file:' print('Finished writing file:'
, '\nFile:', outfile , '\nFile:', outfile_mcsm_norm
, '\nExpected no. of rows:', len(mcsm_dataf) , '\nExpected no. of rows:', len(mcsm_data_fs)
, '\nExpected no. of cols:', len(mcsm_dataf.columns) , '\nExpected no. of cols:', len(mcsm_data_fs.columns)
, '\n=============================================================') , '\n=============================================================')
#%% #%%
#End of script #End of script

54
mcsm/mcsm.py
View file

@ -376,23 +376,34 @@ def format_mcsm_output(mcsm_outputcsv):
# adding column: wild_position # adding column: wild_position
# useful for plots and db # useful for plots and db
############# #############
print('Creating column: wild_position') print('Creating column: wild_pos')
mcsm_data['wild_position'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str) mcsm_data['wild_pos'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
print(mcsm_data['wild_position'].head()) print(mcsm_data['wild_pos'].head())
# Remove spaces b/w pasted columns # Remove spaces b/w pasted columns
print('removing white space within column: wild_position') print('removing white space within created column: wild_pos')
mcsm_data['wild_position'] = mcsm_data['wild_position'].str.replace(' ', '') mcsm_data['wild_pos'] = mcsm_data['wild_pos'].str.replace(' ', '')
print('Correctly formatted column: wild_position\n', mcsm_data['wild_position'].head() print('Correctly formatted column: wild_pos\n', mcsm_data['wild_pos'].head()
, '\n=========================================================') , '\n=========================================================')
#%%=====================================================================
#############
# adding column: wild_chain_pos
# useful for plots and db and its explicit
#############
print('Creating column: wild_chain_pos')
mcsm_data['wild_chain_pos'] = mcsm_data['wild_type'] + mcsm_data['chain'] + mcsm_data['position'].astype(str)
print(mcsm_data['wild_chain_pos'].head())
# Remove spaces b/w pasted columns
print('removing white space within created column: wild_chain_pos')
mcsm_data['wild_chain_pos'] = mcsm_data['wild_chain_pos'].str.replace(' ', '')
print('Correctly formatted column: wild_chain_pos\n', mcsm_data['wild_chain_pos'].head()
, '\n=========================================================')
#%%===================================================================== #%%=====================================================================
############# #############
# ensuring corrrect dtype in non-numeric cols # ensuring corrrect dtype in non-numeric cols
############# #############
#) char cols #) char cols
char_cols = ['PredAffLog', 'mutationinformation', 'wild_type', 'mutant_type', 'chain', 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_position'] char_cols = ['PredAffLog', 'mutationinformation', 'wild_type', 'mutant_type', 'chain', 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_pos', 'wild_chain_pos']
#mcsm_data[char_cols] = mcsm_data[char_cols].astype(str) #mcsm_data[char_cols] = mcsm_data[char_cols].astype(str)
cols_check_char = mcsm_data.select_dtypes(include = 'object').columns.isin(char_cols) cols_check_char = mcsm_data.select_dtypes(include = 'object').columns.isin(char_cols)
@ -408,36 +419,41 @@ def format_mcsm_output(mcsm_outputcsv):
#%%===================================================================== #%%=====================================================================
# Removing PredAff log column as it is not needed? # Removing PredAff log column as it is not needed?
print('Removing col: PredAffLog since relevant info has been extracted from it') print('Removing col: PredAffLog since relevant info has been extracted from it')
mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog']) mcsm_data_f = mcsm_data.drop(columns = ['PredAffLog'])
#%%=====================================================================
# sort df by position for convenience
print('Sorting df by position')
mcsm_data_fs = mcsm_data_f.sort_values(by = ['position'])
print('sorted df:\n', mcsm_data_fs.head())
#%%===================================================================== #%%=====================================================================
############# #############
# sanity check before writing file # sanity check before writing file
############# #############
expected_ncols_toadd = 5 expected_ncols_toadd = 6 # beware hardcoding!
dforig_len = dforig_shape[1] dforig_len = dforig_shape[1]
expected_cols = dforig_len + expected_ncols_toadd expected_cols = dforig_len + expected_ncols_toadd
if len(mcsm_dataf.columns) == expected_cols: if len(mcsm_data_fs.columns) == expected_cols:
print('PASS: formatting successful' print('PASS: formatting successful'
, '\nformatted df has expected no. of cols:', expected_cols , '\nformatted df has expected no. of cols:', expected_cols
, '\n---------------------------------------------------' , '\n---------------------------------------------------'
, '\ncolnames:', mcsm_dataf.columns , '\ncolnames:', mcsm_data_fs.columns
, '\n---------------------------------------------------' , '\n---------------------------------------------------'
, '\ndtypes in cols:', mcsm_dataf.dtypes , '\ndtypes in cols:', mcsm_data_fs.dtypes
, '\n---------------------------------------------------' , '\n---------------------------------------------------'
, '\norig data shape:', dforig_shape , '\norig data shape:', dforig_shape
, '\nformatted df shape:', mcsm_dataf.shape , '\nformatted df shape:', mcsm_data_fs.shape
, '\n===================================================') , '\n===================================================')
else: else:
sys.exit('FAIL: something went wrong in formatting df' print('FAIL: something went wrong in formatting df'
, '\nLen of orig df:', dforig_len , '\nLen of orig df:', dforig_len
, '\nExpected number of cols to add:', expected_ncols_toadd , '\nExpected number of cols to add:', expected_ncols_toadd
, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')' , '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
, '\nGot no. of cols:', len(mcsm_dataf.columns) , '\nGot no. of cols:', len(mcsm_data_fs.columns)
, '\nCheck formatting:' , '\nCheck formatting:'
, '\ncheck hardcoded value:', expected_ncols_toadd , '\ncheck hardcoded value:', expected_ncols_toadd
, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?' , '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
, '\n===================================================') , '\n===================================================')
sys.exit()
return mcsm_data_fs
return mcsm_dataf

6
mcsm/mcsm_wrapper.py
View file

@ -76,7 +76,8 @@ if DEBUG:
print('DEBUG: mCSM output CSV file:', mcsm_output) print('DEBUG: mCSM output CSV file:', mcsm_output)
# format_results globals # format_results globals
out_filename_format = gene.lower() + '_mcsm_processed.csv' #out_filename_format = gene.lower() + '_mcsm_processed.csv'
out_filename_format = gene.lower() + '_complex_mcsm_norm.csv'
outfile_format = outdir + '/' + out_filename_format outfile_format = outdir + '/' + out_filename_format
if DEBUG: if DEBUG:
print('DEBUG: formatted CSV output:', outfile_format) print('DEBUG: formatted CSV output:', outfile_format)
@ -111,7 +112,6 @@ def submit_mcsm():
, 'minutes, but will be longer for more mutations.') , 'minutes, but will be longer for more mutations.')
#%%===================================================================== #%%=====================================================================
def get_results(): def get_results():
output_df = pd.DataFrame() output_df = pd.DataFrame()
url_counter = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1 url_counter = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
success_counter = 1 success_counter = 1
@ -152,7 +152,7 @@ def format_results():
print('Finished writing file:' print('Finished writing file:'
, '\nFile:', outfile_format , '\nFile:', outfile_format
, '\nExpected no. of rows:', len(mcsm_df_formatted) , '\nExpected no. of rows:', len(mcsm_df_formatted)
, '\nExpected no. of cols:', len(mcsm_df_formatted) , '\nExpected no. of cols:', len(mcsm_df_formatted.columns)
, '\n=============================================================') , '\n=============================================================')
#%%===================================================================== #%%=====================================================================
def main(): def main():