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Mckay/pd warnings #45

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merged 5 commits into from
Mar 11, 2024
Merged

Mckay/pd warnings #45

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32f07a7
refactor errors='ignore' to try except
mbowcut2 Mar 8, 2024
b3fbfdb
refactored integer slice to iloc[]
mbowcut2 Mar 8, 2024
70c52bc
moved to_numeric try except to function
mbowcut2 Mar 11, 2024
4fb5831
Refactor to_numeric_ignore_errors to to_numeric_ignore_columns
Colelyman Mar 11, 2024
c1d6dd6
Add documentation to to_numeric_ignore_columns
Colelyman Mar 11, 2024
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33 changes: 28 additions & 5 deletions CRISPResso2/CRISPRessoCORE.py
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Original file line number Diff line number Diff line change
Expand Up @@ -978,6 +978,27 @@ def normalize_name(name, fastq_r1, fastq_r2, bam_input):
return clean_name


def to_numeric_ignore_columns(df, ignore_columns):
"""Convert the columns of a dataframe to numeric, ignoring some columns.

Parameters
----------
df : pandas.DataFrame
The dataframe to convert.
ignore_columns : list or set
The columns to ignore, i.e. not convert to numeric.

Returns
-------
pandas.DataFrame
The dataframe with the columns (except for ignore_columns) converted to numeric.
"""
for col in df.columns:
if col not in ignore_columns:
df[col] = df[col].apply(pd.to_numeric, errors='raise')
return df


def main():

def print_stacktrace_if_debug():
Expand Down Expand Up @@ -3586,7 +3607,7 @@ def count_alternate_alleles(sub_base_vectors, ref_name, ref_sequence, ref_total_
mod_pcts.append(np.concatenate((['All_modifications'], np.array(all_indelsub_count_vectors[ref_name]).astype(float)/tot)))
mod_pcts.append(np.concatenate((['Total'], [counts_total[ref_name]]*refs[ref_name]['sequence_length'])))
colnames = ['Modification']+list(ref_seq)
modification_percentage_summary_df = pd.DataFrame(mod_pcts, columns=colnames).apply(pd.to_numeric, errors='ignore')
modification_percentage_summary_df = to_numeric_ignore_columns(pd.DataFrame(mod_pcts, columns=colnames), {'Modification'})

nuc_df_for_plot = df_nuc_pct_all.reset_index().rename(columns={'index':'Nucleotide'})
nuc_df_for_plot.insert(0, 'Batch', ref_name) #this function was designed for plottin batch... so just add a column in there to make it happy
Expand Down Expand Up @@ -3979,7 +4000,7 @@ def count_alternate_alleles(sub_base_vectors, ref_name, ref_sequence, ref_total_
for nuc in ['A', 'C', 'G', 'T', 'N', '-']:
nuc_pcts.append(np.concatenate(([ref_name_for_hdr, nuc], np.array(ref1_all_base_count_vectors[ref_name_for_hdr+"_"+nuc]).astype(float)/tot)))
colnames = ['Batch', 'Nucleotide']+list(refs[ref_names_for_hdr[0]]['sequence'])
hdr_nucleotide_percentage_summary_df = pd.DataFrame(nuc_pcts, columns=colnames).apply(pd.to_numeric, errors='ignore')
hdr_nucleotide_percentage_summary_df = to_numeric_ignore_columns(pd.DataFrame(nuc_pcts, columns=colnames), {'Batch', 'Nucleotide'})

mod_pcts = []
for ref_name_for_hdr in ref_names_for_hdr:
Expand All @@ -3991,7 +4012,8 @@ def count_alternate_alleles(sub_base_vectors, ref_name, ref_sequence, ref_total_
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'All_modifications'], np.array(ref1_all_indelsub_count_vectors[ref_name_for_hdr]).astype(float)/tot)))
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'Total'], [counts_total[ref_name_for_hdr]]*refs[ref_names_for_hdr[0]]['sequence_length'])))
colnames = ['Batch', 'Modification']+list(refs[ref_names_for_hdr[0]]['sequence'])
hdr_modification_percentage_summary_df = pd.DataFrame(mod_pcts, columns=colnames).apply(pd.to_numeric, errors='ignore')
hdr_modification_percentage_summary_df = to_numeric_ignore_columns(pd.DataFrame(mod_pcts, columns=colnames), {'Batch', 'Modification'})

sgRNA_intervals = refs[ref_names_for_hdr[0]]['sgRNA_intervals']
sgRNA_names = refs[ref_names_for_hdr[0]]['sgRNA_names']
sgRNA_mismatches = refs[ref_names_for_hdr[0]]['sgRNA_mismatches']
Expand Down Expand Up @@ -4574,7 +4596,7 @@ def get_scaffold_len(row, scaffold_start_loc, scaffold_seq):
for nuc in ['A', 'C', 'G', 'T', 'N', '-']:
nuc_pcts.append(np.concatenate(([ref_name, nuc], np.array(ref1_all_base_count_vectors[ref_name+"_"+nuc]).astype(float)/tot)))
colnames = ['Batch', 'Nucleotide']+list(refs[ref_names[0]]['sequence'])
pe_nucleotide_percentage_summary_df = pd.DataFrame(nuc_pcts, columns=colnames).apply(pd.to_numeric,errors='ignore')
pe_nucleotide_percentage_summary_df = to_numeric_ignore_columns(pd.DataFrame(nuc_pcts, columns=colnames), {'Batch', 'Nucleotide'})

mod_pcts = []
for ref_name in ref_names_for_pe:
Expand All @@ -4586,7 +4608,8 @@ def get_scaffold_len(row, scaffold_start_loc, scaffold_seq):
mod_pcts.append(np.concatenate(([ref_name, 'All_modifications'], np.array(ref1_all_indelsub_count_vectors[ref_name]).astype(float)/tot)))
mod_pcts.append(np.concatenate(([ref_name, 'Total'], [counts_total[ref_name]]*refs[ref_names_for_pe[0]]['sequence_length'])))
colnames = ['Batch', 'Modification']+list(refs[ref_names_for_pe[0]]['sequence'])
pe_modification_percentage_summary_df = pd.DataFrame(mod_pcts, columns=colnames).apply(pd.to_numeric,errors='ignore')
pe_modification_percentage_summary_df = to_numeric_ignore_columns(pd.DataFrame(mod_pcts, columns=colnames), {'Batch', 'Modification'})

sgRNA_intervals = refs[ref_names_for_pe[0]]['sgRNA_intervals']
sgRNA_names = refs[ref_names_for_pe[0]]['sgRNA_names']
sgRNA_mismatches = refs[ref_names_for_pe[0]]['sgRNA_mismatches']
Expand Down
2 changes: 1 addition & 1 deletion CRISPResso2/CRISPRessoPlot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -203,7 +203,7 @@ def plot_nucleotide_quilt(nuc_pct_df,mod_pct_df,fig_filename_root, custom_colors
sample_row_start = nNucs * i
y_start = nSamples - i

ins_pct = float(mod_pct_df_indexed.loc[sampleName,'Insertions_Left'][pos_ind-2])
ins_pct = float(mod_pct_df_indexed.loc[sampleName,'Insertions_Left'].iloc[pos_ind-2])

if ins_pct > min_plot_pct:
obs_pct = ins_pct * plotPct
Expand Down