minor edits to format mcsm data like sorting df
This commit is contained in:
parent
8931441fa5
commit
6961a9cdb3
4 changed files with 127 additions and 85 deletions
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@ -264,19 +264,29 @@ def format_mcsm_output(mcsm_outputcsv):
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, '\nScaled affinity scores:\n', mcsm_data['affinity_scaled'])
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#=============================================================================
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# Adding colname: wild_pos: sometimes useful for plotting and db
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print('Creating column: wild_position')
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mcsm_data['wild_position'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_position'].head())
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print('Creating column: wild_pos')
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mcsm_data['wild_pos'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_pos'].head())
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# Remove spaces b/w pasted columns
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print('removing white space within column: wild_position')
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mcsm_data['wild_position'] = mcsm_data['wild_position'].str.replace(' ', '')
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print('Correctly formatted column: wild_position\n', mcsm_data['wild_position'].head()
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print('removing white space within column: wild_pos')
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mcsm_data['wild_pos'] = mcsm_data['wild_pos'].str.replace(' ', '')
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print('Correctly formatted column: wild_pos\n', mcsm_data['wild_pos'].head()
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, '\n===================================================================')
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#=============================================================================
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# Adding colname: wild_chain_pos: sometimes useful for plotting and db and is explicit
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print('Creating column: wild_chain_pos')
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mcsm_data['wild_chain_pos'] = mcsm_data['wild_type'] + mcsm_data['chain'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_chain_pos'].head())
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# Remove spaces b/w pasted columns
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print('removing white space within column: wild_chain_pos')
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mcsm_data['wild_chain_pos'] = mcsm_data['wild_chain_pos'].str.replace(' ', '')
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print('Correctly formatted column: wild_chain_pos\n', mcsm_data['wild_chain_pos'].head()
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, '\n===================================================================')
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#=============================================================================
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#%% ensuring dtypes are string for the non-numeric cols
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#) char cols
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char_cols = ['PredAffLog', 'mutation_information', 'wild_type', 'mutant_type', 'chain'
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, 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_position']
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, 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_pos', 'wild_chain_pos']
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#mcsm_data[char_cols] = mcsm_data[char_cols].astype(str)
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cols_check_char = mcsm_data.select_dtypes(include='object').columns.isin(char_cols)
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@ -292,7 +302,12 @@ def format_mcsm_output(mcsm_outputcsv):
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#=============================================================================
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# Removing PredAff log column as it is not needed?
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print('Removing col: PredAffLog since relevant info has been extracted from it')
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mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog'])
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mcsm_data_f = mcsm_data.drop(columns = ['PredAffLog'])
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#=============================================================================
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#sort df by position for convenience
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print('Sorting df by position')
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mcsm_data_fs = mcsm_data_f.sort_values(by = ['position'])
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print('sorted df:\n', mcsm_data_fs.head())
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#%%===========================================================================
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#############
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# sanity check before writing file
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@ -300,29 +315,28 @@ def format_mcsm_output(mcsm_outputcsv):
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expected_ncols_toadd = 5 # beware of hardcoded numbers
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dforig_len = dforig_shape[1]
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expected_cols = dforig_len + expected_ncols_toadd
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if len(mcsm_dataf.columns) == expected_cols:
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if len(mcsm_data_fs.columns) == expected_cols:
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print('PASS: formatting successful'
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, '\nformatted df has expected no. of cols:', expected_cols
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, '\ncolnames:', mcsm_dataf.columns
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, '\ncolnames:', mcsm_data_fs.columns
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, '\n----------------------------------------------------------------'
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, '\ndtypes in cols:', mcsm_dataf.dtypes
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, '\ndtypes in cols:', mcsm_data_fs.dtypes
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, '\n----------------------------------------------------------------'
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, '\norig data shape:', dforig_shape
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, '\nformatted df shape:', mcsm_dataf.shape
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, '\nformatted df shape:', mcsm_data_fs.shape
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, '\n===============================================================')
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else:
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print('FAIL: something went wrong in formatting df'
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, '\nLen of orig df:', dforig_len
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, '\nExpected number of cols to add:', expected_ncols_toadd
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, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
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, '\nGot no. of cols:', len(mcsm_dataf.columns)
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, '\nGot no. of cols:', len(mcsm_data_fs.columns)
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, '\nCheck formatting:'
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, '\ncheck hardcoded value:', expected_ncols_toadd
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, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
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, '\n===============================================================')
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return mcsm_dataf
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return mcsm_data_fs
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#=======================================================================
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# call function
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mcsm_df_formatted = format_mcsm_output(infile)
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@ -23,45 +23,41 @@ os.chdir(homedir + '/git/LSHTM_analysis/mcsm')
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os.getcwd()
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#=======================================================================
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#%% variable assignment: input and output
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#drug = 'pyrazinamide'
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#gene = 'pncA'
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drug = 'rifampicin'
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gene = 'rpoB'
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drug = 'pyrazinamide'
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gene = 'pncA'
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#drug = args.drug
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#gene = args.gene
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gene_match = gene + '_p.'
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#==========
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# data dir
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# dirs
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#==========
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datadir = homedir + '/' + 'git/Data'
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indir = datadir + '/' + drug + '/' + 'input'
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outdir = datadir + '/' + drug + '/' + 'output'
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#=======
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# input:
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#=======
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# 1) result_urls (from outdir)
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outdir = datadir + '/' + drug + '/' + 'output'
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in_filename = gene.lower() + '_mcsm_output.csv' #(outfile, from mcsm_results.py)
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infile = outdir + '/' + in_filename
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print('Input filename:', in_filename
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, '\nInput path(from output dir):', outdir
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in_filename_mcsm_output = gene.lower() + '_mcsm_output.csv' #(outfile, from mcsm_results.py)
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infile_mcsm_output = outdir + '/' + in_filename_mcsm_output
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print('Input file:', infile_mcsm_output
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, '\n=============================================================')
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#=======
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# output
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#=======
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outdir = datadir + '/' + drug + '/' + 'output'
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out_filename = gene.lower() + '_complex_mcsm_norm.csv'
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outfile = outdir + '/' + out_filename
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print('Output filename:', out_filename
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, '\nOutput path:', outdir
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out_filename_mcsm_norm = gene.lower() + '_complex_mcsm_norm.csv'
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outfile_mcsm_norm = outdir + '/' + out_filename_mcsm_norm
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print('Output file:', out_filename_mcsm_norm
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, '\n=============================================================')
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#=======================================================================
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print('Reading input file')
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mcsm_data = pd.read_csv(infile, sep = ',')
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mcsm_data = pd.read_csv(infile_mcsm_output, sep = ',')
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mcsm_data.columns
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# PredAffLog = affinity_change_log
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@ -231,19 +227,29 @@ print('Raw affinity scores:\n', mcsm_data['ligand_affinity_change']
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, '\nScaled affinity scores:\n', mcsm_data['affinity_scaled'])
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#=============================================================================
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# Adding colname: wild_pos: sometimes useful for plotting and db
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print('Creating column: wild_position')
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mcsm_data['wild_position'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_position'].head())
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print('Creating column: wild_pos')
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mcsm_data['wild_pos'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_pos'].head())
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# Remove spaces b/w pasted columns
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print('removing white space within column: wild_position')
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mcsm_data['wild_position'] = mcsm_data['wild_position'].str.replace(' ', '')
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print('Correctly formatted column: wild_position\n', mcsm_data['wild_position'].head()
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mcsm_data['wild_pos'] = mcsm_data['wild_pos'].str.replace(' ', '')
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print('Correctly formatted column: wild_pos\n', mcsm_data['wild_pos'].head()
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, '\n===================================================================')
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#=============================================================================
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#%% Adding colname: wild_chain_pos: sometimes useful for plotting and db and is explicit
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print('Creating column: wild_chain_pos')
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mcsm_data['wild_chain_pos'] = mcsm_data['wild_type'] + mcsm_data['chain'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_chain_pos'].head())
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# Remove spaces b/w pasted columns
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print('removing white space within column: wild_chain_pos')
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mcsm_data['wild_chain_pos'] = mcsm_data['wild_chain_pos'].str.replace(' ', '')
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print('Correctly formatted column: wild_chain_pos\n', mcsm_data['wild_chain_pos'].head()
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, '\n===================================================================')
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#=============================================================================
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#%% ensuring dtypes are string for the non-numeric cols
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#) char cols
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char_cols = ['PredAffLog', 'mutation_information', 'wild_type', 'mutant_type', 'chain'
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, 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_position']
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, 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_pos', 'wild_chain_pos']
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#mcsm_data[char_cols] = mcsm_data[char_cols].astype(str)
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cols_check_char = mcsm_data.select_dtypes(include='object').columns.isin(char_cols)
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@ -258,42 +264,48 @@ else:
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print(mcsm_data.dtypes)
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#%%
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#=============================================================================
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# Removing PredAff log column as it is not needed?
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#%% Removing PredAff log column as it is not needed?
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print('Removing col: PredAffLog since relevant info has been extracted from it')
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mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog'])
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mcsm_data_f = mcsm_data.drop(columns = ['PredAffLog'])
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print(mcsm_data_f.head())
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#=============================================================================
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#%% sort df by position for convenience
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print('Sorting df by position')
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mcsm_data_fs = mcsm_data_f.sort_values(by = ['position'])
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print('sorted df:\n', mcsm_data_fs.head())
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#%%===========================================================================
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expected_ncols_toadd = 5 # beware of hardcoded numbers
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expected_ncols_toadd = 6 # beware of hardcoded numbers
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dforig_len = dforig_shape[1]
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expected_cols = dforig_len + expected_ncols_toadd
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if len(mcsm_dataf.columns) == expected_cols:
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if len(mcsm_data_fs.columns) == expected_cols:
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print('PASS: formatting successful'
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, '\nformatted df has expected no. of cols:', expected_cols
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, '\ncolnames:', mcsm_dataf.columns
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, '\n----------------------------------------------------------------'
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, '\ndtypes in cols:', mcsm_dataf.dtypes
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, '\n----------------------------------------------------------------'
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, '\norig data shape:', dforig_shape
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, '\nformatted df shape:', mcsm_dataf.shape
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, '\n===============================================================')
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, '\nformatted df has expected no. of cols:', expected_cols
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, '\ncolnames:', mcsm_data_fs.columns
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, '\n----------------------------------------------------------------'
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, '\ndtypes in cols:', mcsm_data_fs.dtypes
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, '\n----------------------------------------------------------------'
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, '\norig data shape:', dforig_shape
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, '\nformatted df shape:', mcsm_data_fs.shape
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, '\n===============================================================')
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else:
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print('FAIL: something went wrong in formatting df'
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, '\nLen of orig df:', dforig_len
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, '\nExpected number of cols to add:', expected_ncols_toadd
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, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
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, '\nGot no. of cols:', len(mcsm_dataf.columns)
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, '\nCheck formatting:'
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, '\ncheck hardcoded value:', expected_ncols_toadd
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, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
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, '\n===============================================================')
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, '\nLen of orig df:', dforig_len
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, '\nExpected number of cols to add:', expected_ncols_toadd
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, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
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, '\nGot no. of cols:', len(mcsm_data_fs.columns)
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, '\nCheck formatting:'
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, '\ncheck hardcoded value:', expected_ncols_toadd
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, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
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, '\n===============================================================')
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#%%============================================================================
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# writing file
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print('Writing formatted df to csv')
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mcsm_dataf.to_csv(outfile, index = False)
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mcsm_data_fs.to_csv(outfile_mcsm_norm, index = False)
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print('Finished writing file:'
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, '\nFile:', outfile
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, '\nExpected no. of rows:', len(mcsm_dataf)
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, '\nExpected no. of cols:', len(mcsm_dataf.columns)
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, '\nFile:', outfile_mcsm_norm
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, '\nExpected no. of rows:', len(mcsm_data_fs)
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, '\nExpected no. of cols:', len(mcsm_data_fs.columns)
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, '\n=============================================================')
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#%%
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#End of script
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56
mcsm/mcsm.py
56
mcsm/mcsm.py
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@ -376,23 +376,34 @@ def format_mcsm_output(mcsm_outputcsv):
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# adding column: wild_position
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# useful for plots and db
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#############
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print('Creating column: wild_position')
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mcsm_data['wild_position'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_position'].head())
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print('Creating column: wild_pos')
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mcsm_data['wild_pos'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_pos'].head())
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# Remove spaces b/w pasted columns
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print('removing white space within column: wild_position')
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mcsm_data['wild_position'] = mcsm_data['wild_position'].str.replace(' ', '')
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print('Correctly formatted column: wild_position\n', mcsm_data['wild_position'].head()
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print('removing white space within created column: wild_pos')
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mcsm_data['wild_pos'] = mcsm_data['wild_pos'].str.replace(' ', '')
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print('Correctly formatted column: wild_pos\n', mcsm_data['wild_pos'].head()
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, '\n=========================================================')
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#%%=====================================================================
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#############
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# adding column: wild_chain_pos
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# useful for plots and db and its explicit
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#############
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print('Creating column: wild_chain_pos')
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mcsm_data['wild_chain_pos'] = mcsm_data['wild_type'] + mcsm_data['chain'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_chain_pos'].head())
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# Remove spaces b/w pasted columns
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print('removing white space within created column: wild_chain_pos')
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mcsm_data['wild_chain_pos'] = mcsm_data['wild_chain_pos'].str.replace(' ', '')
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print('Correctly formatted column: wild_chain_pos\n', mcsm_data['wild_chain_pos'].head()
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, '\n=========================================================')
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#%%=====================================================================
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#############
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# ensuring corrrect dtype in non-numeric cols
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#############
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#############
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#) char cols
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char_cols = ['PredAffLog', 'mutationinformation', 'wild_type', 'mutant_type', 'chain', 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_position']
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char_cols = ['PredAffLog', 'mutationinformation', 'wild_type', 'mutant_type', 'chain', 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_pos', 'wild_chain_pos']
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#mcsm_data[char_cols] = mcsm_data[char_cols].astype(str)
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cols_check_char = mcsm_data.select_dtypes(include = 'object').columns.isin(char_cols)
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@ -408,36 +419,41 @@ def format_mcsm_output(mcsm_outputcsv):
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#%%=====================================================================
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# Removing PredAff log column as it is not needed?
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print('Removing col: PredAffLog since relevant info has been extracted from it')
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mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog'])
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mcsm_data_f = mcsm_data.drop(columns = ['PredAffLog'])
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#%%=====================================================================
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# sort df by position for convenience
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print('Sorting df by position')
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mcsm_data_fs = mcsm_data_f.sort_values(by = ['position'])
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print('sorted df:\n', mcsm_data_fs.head())
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#%%=====================================================================
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#############
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# sanity check before writing file
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#############
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expected_ncols_toadd = 5
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expected_ncols_toadd = 6 # beware hardcoding!
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dforig_len = dforig_shape[1]
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expected_cols = dforig_len + expected_ncols_toadd
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if len(mcsm_dataf.columns) == expected_cols:
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if len(mcsm_data_fs.columns) == expected_cols:
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print('PASS: formatting successful'
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, '\nformatted df has expected no. of cols:', expected_cols
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, '\n---------------------------------------------------'
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, '\ncolnames:', mcsm_dataf.columns
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, '\ncolnames:', mcsm_data_fs.columns
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, '\n---------------------------------------------------'
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, '\ndtypes in cols:', mcsm_dataf.dtypes
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, '\ndtypes in cols:', mcsm_data_fs.dtypes
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, '\n---------------------------------------------------'
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, '\norig data shape:', dforig_shape
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, '\nformatted df shape:', mcsm_dataf.shape
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, '\nformatted df shape:', mcsm_data_fs.shape
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, '\n===================================================')
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else:
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sys.exit('FAIL: something went wrong in formatting df'
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print('FAIL: something went wrong in formatting df'
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, '\nLen of orig df:', dforig_len
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, '\nExpected number of cols to add:', expected_ncols_toadd
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, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
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, '\nGot no. of cols:', len(mcsm_dataf.columns)
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, '\nGot no. of cols:', len(mcsm_data_fs.columns)
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, '\nCheck formatting:'
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, '\ncheck hardcoded value:', expected_ncols_toadd
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, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
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, '\n===================================================')
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sys.exit()
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return mcsm_dataf
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return mcsm_data_fs
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@ -76,7 +76,8 @@ if DEBUG:
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print('DEBUG: mCSM output CSV file:', mcsm_output)
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# format_results globals
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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
|
||||
if DEBUG:
|
||||
print('DEBUG: formatted CSV output:', outfile_format)
|
||||
|
|
@ -111,7 +112,6 @@ def submit_mcsm():
|
|||
, 'minutes, but will be longer for more mutations.')
|
||||
#%%=====================================================================
|
||||
def get_results():
|
||||
|
||||
output_df = pd.DataFrame()
|
||||
url_counter = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
|
||||
success_counter = 1
|
||||
|
|
@ -152,7 +152,7 @@ def format_results():
|
|||
print('Finished writing file:'
|
||||
, '\nFile:', outfile_format
|
||||
, '\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=============================================================')
|
||||
#%%=====================================================================
|
||||
def main():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue