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Fix deprecated numpy type names (fixes #269) #270

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Jan 5, 2023
Merged

Fix deprecated numpy type names (fixes #269) #270

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4 changes: 2 additions & 2 deletions CRISPResso2/CRISPResso2Align.pyx
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Expand Up @@ -166,14 +166,14 @@ def global_align(str pystr_seqj, str pystr_seqi, np.ndarray[DTYPE_INT, ndim=2] m
#init i matrix
for i in range(1,max_j+1):
iScore[0,i] = gap_extend * i + gap_incentive[0]
# iScore[0,1:] = [gap_extend * np.arange(1, max_j+1, dtype=np.int)]
# iScore[0,1:] = [gap_extend * np.arange(1, max_j+1, dtype=int)]
iScore[0:,0] = min_score
iPointer[0,1:] = IARRAY

#init j matrix
for i in range(1,max_i+1):
jScore[i,0] = gap_extend * i + gap_incentive[0]
#jScore[1:,0] = np.vectorize(gap_extend * np.arange(1, max_i+1, dtype=np.int))
#jScore[1:,0] = np.vectorize(gap_extend * np.arange(1, max_i+1, dtype=int))
jScore[0,0:] = min_score
jPointer[1:,0] = JARRAY

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2 changes: 1 addition & 1 deletion CRISPResso2/CRISPRessoBatchCORE.py
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Expand Up @@ -451,7 +451,7 @@ def main():

mod_pcts = {}
for key in mod_freqs:
mod_pcts[key] = np.array(mod_freqs[key]).astype(np.float)/float(mod_freqs['Total'][0])
mod_pcts[key] = np.array(mod_freqs[key]).astype(float)/float(mod_freqs['Total'][0])

amp_found_count += 1

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34 changes: 17 additions & 17 deletions CRISPResso2/CRISPRessoCORE.py
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Expand Up @@ -3514,11 +3514,11 @@ def count_alternate_alleles(sub_base_vectors, ref_name, ref_sequence, ref_total_
if not args.suppress_plots:
mod_pcts = []
tot = float(counts_total[ref_name])
mod_pcts.append(np.concatenate((['Insertions'], np.array(all_insertion_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate((['Insertions_Left'], np.array(all_insertion_left_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate((['Deletions'], np.array(all_deletion_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate((['Substitutions'], np.array(all_substitution_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate((['All_modifications'], np.array(all_indelsub_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate((['Insertions'], np.array(all_insertion_count_vectors[ref_name]).astype(float)/tot)))
mod_pcts.append(np.concatenate((['Insertions_Left'], np.array(all_insertion_left_count_vectors[ref_name]).astype(float)/tot)))
mod_pcts.append(np.concatenate((['Deletions'], np.array(all_deletion_count_vectors[ref_name]).astype(float)/tot)))
mod_pcts.append(np.concatenate((['Substitutions'], np.array(all_substitution_count_vectors[ref_name]).astype(float)/tot)))
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')
Expand Down Expand Up @@ -3897,18 +3897,18 @@ def count_alternate_alleles(sub_base_vectors, ref_name, ref_sequence, ref_total_
for ref_name_for_hdr in ref_names_for_hdr:
tot = float(counts_total[ref_name_for_hdr])
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(np.float)/tot)))
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')

mod_pcts = []
for ref_name_for_hdr in ref_names_for_hdr:
tot = float(counts_total[ref_name_for_hdr])
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'Insertions'], np.array(ref1_all_insertion_count_vectors[ref_name_for_hdr]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'Insertions_Left'], np.array(ref1_all_insertion_left_count_vectors[ref_name_for_hdr]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'Deletions'], np.array(ref1_all_deletion_count_vectors[ref_name_for_hdr]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'Substitutions'], np.array(ref1_all_substitution_count_vectors[ref_name_for_hdr]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'All_modifications'], np.array(ref1_all_indelsub_count_vectors[ref_name_for_hdr]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'Insertions'], np.array(ref1_all_insertion_count_vectors[ref_name_for_hdr]).astype(float)/tot)))
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'Insertions_Left'], np.array(ref1_all_insertion_left_count_vectors[ref_name_for_hdr]).astype(float)/tot)))
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'Deletions'], np.array(ref1_all_deletion_count_vectors[ref_name_for_hdr]).astype(float)/tot)))
mod_pcts.append(np.concatenate(([ref_name_for_hdr, 'Substitutions'], np.array(ref1_all_substitution_count_vectors[ref_name_for_hdr]).astype(float)/tot)))
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')
Expand Down Expand Up @@ -4465,18 +4465,18 @@ def get_scaffold_len(row, scaffold_start_loc, scaffold_seq):
for ref_name in ref_names_for_pe:
tot = float(counts_total[ref_name])
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(np.float)/tot)))
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')

mod_pcts = []
for ref_name in ref_names_for_pe:
tot = float(counts_total[ref_name])
mod_pcts.append(np.concatenate(([ref_name, 'Insertions'], np.array(ref1_all_insertion_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name, 'Insertions_Left'], np.array(ref1_all_insertion_left_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name, 'Deletions'], np.array(ref1_all_deletion_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name, 'Substitutions'], np.array(ref1_all_substitution_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name, 'All_modifications'], np.array(ref1_all_indelsub_count_vectors[ref_name]).astype(np.float)/tot)))
mod_pcts.append(np.concatenate(([ref_name, 'Insertions'], np.array(ref1_all_insertion_count_vectors[ref_name]).astype(float)/tot)))
mod_pcts.append(np.concatenate(([ref_name, 'Insertions_Left'], np.array(ref1_all_insertion_left_count_vectors[ref_name]).astype(float)/tot)))
mod_pcts.append(np.concatenate(([ref_name, 'Deletions'], np.array(ref1_all_deletion_count_vectors[ref_name]).astype(float)/tot)))
mod_pcts.append(np.concatenate(([ref_name, 'Substitutions'], np.array(ref1_all_substitution_count_vectors[ref_name]).astype(float)/tot)))
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')
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8 changes: 4 additions & 4 deletions CRISPResso2/CRISPRessoPlot.py
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Expand Up @@ -2507,7 +2507,7 @@ def __init__(self, data, vmin, vmax, cmap, center, robust, annot, fmt,

if annot is not None:
if per_element_annot_kws is None:
self.per_element_annot_kws=np.empty_like(annot, dtype=np.object)
self.per_element_annot_kws=np.empty_like(annot, dtype=object)
self.per_element_annot_kws[:]=dict()
else:
self.per_element_annot_kws=per_element_annot_kws
Expand Down Expand Up @@ -2641,7 +2641,7 @@ def prep_alleles_table(df_alleles, reference_seq, MAX_N_ROWS, MIN_FREQUENCY):
(row['Reference_Sequence'][i_sub]!=idx[i_sub]) and \
(row['Reference_Sequence'][i_sub]!='-') and\
(idx[i_sub]!='-')]
to_append=np.array([{}]*len(idx), dtype=np.object)
to_append=np.array([{}]*len(idx), dtype=object)
to_append[ idxs_sub]={'weight':'bold', 'color':'black','size':16}
per_element_annot_kws.append(to_append)

Expand Down Expand Up @@ -2693,7 +2693,7 @@ def prep_alleles_table_compare(df_alleles, sample_name_1, sample_name_2, MAX_N_R
(row['Reference_Sequence'][i_sub]!=idx[i_sub]) and \
(row['Reference_Sequence'][i_sub]!='-') and\
(idx[i_sub]!='-')]
to_append=np.array([{}]*len(idx), dtype=np.object)
to_append=np.array([{}]*len(idx), dtype=object)
to_append[ idxs_sub]={'weight':'bold', 'color':'black','size':16}
per_element_annot_kws.append(to_append)

Expand Down Expand Up @@ -3198,7 +3198,7 @@ def plot_nucleotide_quilt_from_folder(crispresso_output_folder,fig_filename_root

mod_pcts = {}
for key in mod_counts:
mod_pcts[key] = np.array(mod_counts[key]).astype(np.float)/float(mod_counts['Total'][0])
mod_pcts[key] = np.array(mod_counts[key]).astype(float)/float(mod_counts['Total'][0])

modification_percentage_summary = []
for mod in ['Insertions', 'Insertions_Left', 'Deletions', 'Substitutions', 'All_modifications']:
Expand Down