diff --git a/scripts/stats.py b/scripts/stats.py
index 0ef81f475bf0522653c6bec1b601f1350d54b965..38b1fc441a29171a8a83449cdd8eefea741d2a79 100644
--- a/scripts/stats.py
+++ b/scripts/stats.py
@@ -25,165 +25,167 @@ else:
size_bp = estSize
def fasta_iter(fasta_file):
- """Takes a FASTA file, and produces a generator of Header and Sequences.
- This is a memory-efficient way of analyzing a FASTA files -- without
- reading the entire file into memory.
+ """
+ Takes a FASTA file, and produces a generator of Header and Sequences.
+ This is a memory-efficient way of analyzing a FASTA files -- without
+ reading the entire file into memory.
- Parameters
- ----------
- fasta_file : str
- The file location of the FASTA file
+ Parameters
+ ----------
+ fasta_file : str
+ The file location of the FASTA file
- Returns
- -------
- header: str
- The string contained in the header portion of the sequence record
- (everything after the '>')
- seq: str
- The sequence portion of the sequence record
- """
+ Returns
+ -------
+ header: str
+ The string contained in the header portion of the sequence record
+ (everything after the '>')
+ seq: str
+ The sequence portion of the sequence record
+ """
- fh = open(fasta_file)
- fa_iter = (x[1] for x in groupby(fh, lambda line: line[0] == ">"))
- for header in fa_iter:
- # drop the ">"
- header = next(header)[1:].strip()
- # join all sequence lines to one.
- seq = "".join(s.upper().strip() for s in next(fa_iter))
- yield header, seq
+ fh = open(fasta_file)
+ fa_iter = (x[1] for x in groupby(fh, lambda line: line[0] == ">"))
+ for header in fa_iter:
+ # drop the ">"
+ header = next(header)[1:].strip()
+ # join all sequence lines to one.
+ seq = "".join(s.upper().strip() for s in next(fa_iter))
+ yield header, seq
def read_genome(fasta_file):
- """Takes a FASTA file, and produces 2 lists of sequence lengths. It also
- calculates the GC Content, since this is the only statistic that is not
- calculated based on sequence lengths.
+ """
+ Takes a FASTA file, and produces 2 lists of sequence lengths. It also
+ calculates the GC Content, since this is the only statistic that is not
+ calculated based on sequence lengths.
- Parameters
- ----------
- fasta_file : str
- The file location of the FASTA file
+ Parameters
+ ----------
+ fasta_file : str
+ The file location of the FASTA file
- Returns
- -------
- contig_lens: list
- A list of lengths of all contigs in the genome.
- scaffold_lens: list
- A list of lengths of all scaffolds in the genome.
- gc_cont: float
- The percentage of total basepairs in the genome that are either G or C.
- """
+ Returns
+ ------
+ contig_lens: list
+ A list of lengths of all contigs in the genome.
+ scaffold_lens: list
+ A list of lengths of all scaffolds in the genome.
+ gc_cont: float
+ The percentage of total basepairs in the genome that are either G or C.
+ """
- gc = 0
- total_len = 0
- contig_lens = []
- scaffold_lens = []
- for _, seq in fasta_iter(fasta_file):
- scaffold_lens.append(len(seq))
- if "NN" in seq:
- contig_list = seq.split("NN")
- else:
- contig_list = [seq]
- for contig in contig_list:
- if len(contig):
- gc += contig.count('G') + contig.count('C')
- total_len += len(contig)
- contig_lens.append(len(contig))
- gc_cont = (gc / total_len) * 100
- return contig_lens, scaffold_lens, gc_cont
+ gc = 0
+ total_len = 0
+ contig_lens = []
+ scaffold_lens = []
+ for _, seq in fasta_iter(fasta_file):
+ scaffold_lens.append(len(seq))
+ if "NN" in seq:
+ contig_list = seq.split("NN")
+ else:
+ contig_list = [seq]
+ for contig in contig_list:
+ if len(contig):
+ gc += contig.count('G') + contig.count('C')
+ total_len += len(contig)
+ contig_lens.append(len(contig))
+ gc_cont = (gc / total_len) * 100
+ return contig_lens, scaffold_lens, gc_cont
def calculate_stats(seq_lens, gc_cont):
- naming = sys.argv[3]
- stats = {}
- seq_array = np.array(seq_lens)
+ naming = sys.argv[3]
+ stats = {}
+ seq_array = np.array(seq_lens)
# stats['Assembly:']=naming
- stats['sequence_count'] = seq_array.size
- testsize=stats['sequence_count']
- stats['number_of_gaps'] = 0
+ stats['sequence_count'] = seq_array.size
+ testsize=stats['sequence_count']
+ stats['number_of_gaps'] = 0
# print("this is the count",naming," ", testsize)
- stats['gc_content (%)'] = gc_cont
- sorted_lens = seq_array[np.argsort(-seq_array)]
- stats['longest (bp)'] = int(sorted_lens[0])
- testlongest= stats['longest (bp)']
+ stats['gc_content (%)'] = gc_cont
+ sorted_lens = seq_array[np.argsort(-seq_array)]
+ stats['longest (bp)'] = int(sorted_lens[0])
+ testlongest= stats['longest (bp)']
# print("this is the longest", naming," ",testlongest)
- stats['shortest (bp)'] = int(sorted_lens[-1])
+ stats['shortest (bp)'] = int(sorted_lens[-1])
# stats['median'] = np.median(sorted_lens)
# stats['mean'] = np.mean(sorted_lens)
- stats['total_bps (bp)'] = int(np.sum(sorted_lens))
- testprint=stats['total_bps (bp)']
+ stats['total_bps (bp)'] = int(np.sum(sorted_lens))
+ testprint=stats['total_bps (bp)']
# print("total_bp is", naming," ",testprint)
- stats['estimated_size (bp)'] = int(size_bp)
- csum = np.cumsum(sorted_lens)
- # if stats['total_bps (bp)'] < stats['estimated_size (bp)']:
+ stats['estimated_size (bp)'] = int(size_bp)
+ csum = np.cumsum(sorted_lens)
+ # if stats['total_bps (bp)'] < stats['estimated_size (bp)']:
# csum_ng = np.append(csum, stats['estimated_size (bp)'])
# else:
# csum_ng=csum
- for level in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, 100]:
- nx = int(stats['total_bps (bp)'] * (level / 100))
- csumn = min(csum[csum >= nx])
- l_level = int(np.where(csum == csumn)[0]) + 1
- stats['L' + str(level)] = l_level
- for level in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, 100]:
+ for level in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, 100]:
+ nx = int(stats['total_bps (bp)'] * (level / 100))
+ csumn = min(csum[csum >= nx])
+ l_level = int(np.where(csum == csumn)[0]) + 1
+ stats['L' + str(level)] = l_level
+ for level in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, 100]:
# print("the totalbps are:", stats['total_bps (bp)'])
- nx = int(stats['total_bps (bp)'] * (level / 100))
+ nx = int(stats['total_bps (bp)'] * (level / 100))
# print("this is the nx", nx)
# print("this is the csum", csum)
- csumn = min(csum[csum >= nx])
- # print("this is the csumn", csumn)
- l_level = int(np.where(csum == csumn)[0])
- n_level = int(sorted_lens[l_level])
- stats['N' + str(level)] = n_level
+ csumn = min(csum[csum >= nx])
+# print("this is the csumn", csumn)
+ l_level = int(np.where(csum == csumn)[0])
+ n_level = int(sorted_lens[l_level])
+ stats['N' + str(level)] = n_level
# print(level, " ", n_level)
- for level in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, 100]:
+ for level in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, 100]:
# print("the estbps are:", stats['estimated_size (bp)'])
- ngx = int(stats['estimated_size (bp)'] * (level / 100))
+ ngx = int(stats['estimated_size (bp)'] * (level / 100))
# print("this is the ngx", ngx)
# print("this is the csum", csum_ng)
# print("this is the csum", csum)
# print("this is the [csum >= ngx]", np.array(csum >= ngx))
- new_array=np.array(csum >= ngx)
+ new_array=np.array(csum >= ngx)
# print(np.any(new_array))
- if np.any(new_array) == False:
- csumng = csum[seq_array.size-1]
- # print("this is the csumng", csumng)
- lg_level = int(np.where(csum == csumng)[0]) + 1
- stats['LG' + str(level)] = lg_level
- elif np.any(new_array) == True:
- csumng = min(csum[csum >= ngx])
- # print("this is the csumng", csumng)
- lg_level = int(np.where(csum == csumng)[0]) + 1
- stats['LG' + str(level)] = lg_level
- for level in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, 100]:
- ngx = int(stats['estimated_size (bp)'] * (level / 100))
- new_array=np.array(csum >= ngx)
+ if np.any(new_array) == False:
+ csumng = csum[seq_array.size-1]
+ # print("this is the csumng", csumng)
+ lg_level = int(np.where(csum == csumng)[0]) + 1
+ stats['LG' + str(level)] = lg_level
+ elif np.any(new_array) == True:
+ csumng = min(csum[csum >= ngx])
+ # print("this is the csumng", csumng)
+ lg_level = int(np.where(csum == csumng)[0]) + 1
+ stats['LG' + str(level)] = lg_level
+ for level in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, 100]:
+ ngx = int(stats['estimated_size (bp)'] * (level / 100))
+ new_array=np.array(csum >= ngx)
# print(np.any(new_array))
- if np.any(new_array) == False:
- csumng = csum[seq_array.size-1]
+ if np.any(new_array) == False:
+ csumng = csum[seq_array.size-1]
# print("this is the csumng", csumng)
- lg_level = int(np.where(csum == csumng)[0])
- ng_level = int(sorted_lens[lg_level])
- stats['NG' + str(level)] = ng_level
- elif np.any(new_array) == True:
- csumng = min(csum[csum >= ngx])
+ lg_level = int(np.where(csum == csumng)[0])
+ ng_level = int(sorted_lens[lg_level])
+ stats['NG' + str(level)] = ng_level
+ elif np.any(new_array) == True:
+ csumng = min(csum[csum >= ngx])
# print("this is the csumng", csumng)
- lg_level = int(np.where(csum == csumng)[0])
- ng_level = int(sorted_lens[lg_level])
- stats['NG' + str(level)] = ng_level
- return stats
+ lg_level = int(np.where(csum == csumng)[0])
+ ng_level = int(sorted_lens[lg_level])
+ stats['NG' + str(level)] = ng_level
+ return stats
if __name__ == "__main__":
# print(size_bp)
# print(type(size_bp))
- naming = sys.argv[3]
- infilename = sys.argv[1]
- contig_lens, scaffold_lens, gc_cont = read_genome(infilename)
+ naming = sys.argv[3]
+ infilename = sys.argv[1]
+ contig_lens, scaffold_lens, gc_cont = read_genome(infilename)
# contig_stats = calculate_stats(contig_lens, gc_cont)
- scaffold_stats = calculate_stats(scaffold_lens, gc_cont)
- contig_stats = calculate_stats(contig_lens, gc_cont)
- gaps=contig_stats.get('sequence_count') - scaffold_stats.get('sequence_count')
- scaffold_stats['number_of_gaps'] = gaps
- contig_stats['number_of_gaps'] = gaps
+ scaffold_stats = calculate_stats(scaffold_lens, gc_cont)
+ contig_stats = calculate_stats(contig_lens, gc_cont)
+ gaps=contig_stats.get('sequence_count') - scaffold_stats.get('sequence_count')
+ scaffold_stats['number_of_gaps'] = gaps
+ contig_stats['number_of_gaps'] = gaps
# print(scaffold_stats)
# df_scaffold_all= pd.DataFrame.from_dict(scaffold_stats, orient= 'index')
# print(df_scaffold_all)
@@ -193,72 +195,72 @@ if __name__ == "__main__":
# s.index.name = 'Assembly:'
# s.reset_index()
# print(s)
- scaff_seq=pd.DataFrame(scaffold_stats.items(), columns=['Assembly:', naming])
- df_scaffold_top=scaff_seq.iloc[0:7,0:2]
- df_scaffold_top[naming]=df_scaffold_top[naming].astype('int64').apply('{:,}'.format)
+ scaff_seq=pd.DataFrame(scaffold_stats.items(), columns=['Assembly:', naming])
+ df_scaffold_top=scaff_seq.iloc[0:7,0:2]
+ df_scaffold_top[naming]=df_scaffold_top[naming].astype('int64').apply('{:,}'.format)
# df_scaffold_top=df_scaffold_top.style.hide_index()
# df_scaffold_top[naming].round(decimals=0)
# df_contig_all=pd.DataFrame(data=contig_stats)
- # df_contig_top=df_contig_all.iloc[0:6,0:2]
- df_scaffold_Nxx=pd.DataFrame(scaffold_stats.items(), columns=['Nxx Level (%)', 'Length of Nxx Scaffold (bp)'])
- df_scaffold_Nxx=df_scaffold_Nxx.iloc[31:55,0:2]
- df_scaffold_Nxx=df_scaffold_Nxx.reset_index()
+ # df_contig_top=df_contig_all.iloc[0:6,0:2]
+ df_scaffold_Nxx=pd.DataFrame(scaffold_stats.items(), columns=['Nxx Level (%)', 'Length of Nxx Scaffold (bp)'])
+ df_scaffold_Nxx=df_scaffold_Nxx.iloc[31:55,0:2]
+ df_scaffold_Nxx=df_scaffold_Nxx.reset_index()
# print(df_scaffold_Nxx)
- df_scaffold_NGxx=pd.DataFrame(scaffold_stats.items(), columns=['NGxx Level (%)', 'Length of NGxx Scaffold (bp)'])
- df_scaffold_NGxx=df_scaffold_NGxx.iloc[79:104,0:2]
- df_scaffold_NGxx=df_scaffold_NGxx.reset_index()
+ df_scaffold_NGxx=pd.DataFrame(scaffold_stats.items(), columns=['NGxx Level (%)', 'Length of NGxx Scaffold (bp)'])
+ df_scaffold_NGxx=df_scaffold_NGxx.iloc[79:104,0:2]
+ df_scaffold_NGxx=df_scaffold_NGxx.reset_index()
# print(df_scaffold_NGxx)
- df_scaffold_N_NG=pd.concat([df_scaffold_Nxx,df_scaffold_NGxx], axis=1)
- df_scaffold_N_NG=df_scaffold_N_NG.drop(df_scaffold_N_NG.columns[[0,3]], axis = 1)
- df_scaffold_N_NG['Length of Nxx Scaffold (bp)']=df_scaffold_N_NG['Length of Nxx Scaffold (bp)'].astype('int64').apply('{:,}'.format)
- df_scaffold_N_NG['Length of NGxx Scaffold (bp)']=df_scaffold_N_NG['Length of NGxx Scaffold (bp)'].astype('int64').apply('{:,}'.format)
+ df_scaffold_N_NG=pd.concat([df_scaffold_Nxx,df_scaffold_NGxx], axis=1)
+ df_scaffold_N_NG=df_scaffold_N_NG.drop(df_scaffold_N_NG.columns[[0,3]], axis = 1)
+ df_scaffold_N_NG['Length of Nxx Scaffold (bp)']=df_scaffold_N_NG['Length of Nxx Scaffold (bp)'].astype('int64').apply('{:,}'.format)
+ df_scaffold_N_NG['Length of NGxx Scaffold (bp)']=df_scaffold_N_NG['Length of NGxx Scaffold (bp)'].astype('int64').apply('{:,}'.format)
# df_scaffold_N_NG=df_scaffold_N_NG.style.hide_index()
- df_scaffold_Lxx=pd.DataFrame(scaffold_stats.items(), columns=['Lxx Level (%)', 'Count of scaffolds (for Lxx Level)'])
- df_scaffold_Lxx=df_scaffold_Lxx.iloc[7:31,0:2]
- df_scaffold_Lxx=df_scaffold_Lxx.reset_index()
+ df_scaffold_Lxx=pd.DataFrame(scaffold_stats.items(), columns=['Lxx Level (%)', 'Count of scaffolds (for Lxx Level)'])
+ df_scaffold_Lxx=df_scaffold_Lxx.iloc[7:31,0:2]
+ df_scaffold_Lxx=df_scaffold_Lxx.reset_index()
# print(df_scaffold_Nxx)
- df_scaffold_LGxx=pd.DataFrame(scaffold_stats.items(), columns=['LGxx Level (%)', 'Count of scaffolds (for LGxx Level)'])
- df_scaffold_LGxx=df_scaffold_LGxx.iloc[55:79,0:2]
- df_scaffold_LGxx=df_scaffold_LGxx.reset_index()
+ df_scaffold_LGxx=pd.DataFrame(scaffold_stats.items(), columns=['LGxx Level (%)', 'Count of scaffolds (for LGxx Level)'])
+ df_scaffold_LGxx=df_scaffold_LGxx.iloc[55:79,0:2]
+ df_scaffold_LGxx=df_scaffold_LGxx.reset_index()
# print(df_scaffold_NGxx)
- df_scaffold_L_LG=pd.concat([df_scaffold_Lxx,df_scaffold_LGxx], axis=1)
- df_scaffold_L_LG=df_scaffold_L_LG.drop(df_scaffold_L_LG.columns[[0,3]], axis = 1)
- df_scaffold_L_LG['Count of scaffolds (for Lxx Level)']=df_scaffold_L_LG['Count of scaffolds (for Lxx Level)'].astype('int64').apply('{:,}'.format)
- df_scaffold_L_LG['Count of scaffolds (for LGxx Level)']=df_scaffold_L_LG['Count of scaffolds (for LGxx Level)'].astype('int64').apply('{:,}'.format)
+ df_scaffold_L_LG=pd.concat([df_scaffold_Lxx,df_scaffold_LGxx], axis=1)
+ df_scaffold_L_LG=df_scaffold_L_LG.drop(df_scaffold_L_LG.columns[[0,3]], axis = 1)
+ df_scaffold_L_LG['Count of scaffolds (for Lxx Level)']=df_scaffold_L_LG['Count of scaffolds (for Lxx Level)'].astype('int64').apply('{:,}'.format)
+ df_scaffold_L_LG['Count of scaffolds (for LGxx Level)']=df_scaffold_L_LG['Count of scaffolds (for LGxx Level)'].astype('int64').apply('{:,}'.format)
################################################################################################################
- contig_seq=pd.DataFrame(contig_stats.items(), columns=['Assembly:', naming])
- df_contig_top=contig_seq.iloc[0:7,0:2]
- df_contig_top[naming]=df_contig_top[naming].astype('int64').apply('{:,}'.format)
+ contig_seq=pd.DataFrame(contig_stats.items(), columns=['Assembly:', naming])
+ df_contig_top=contig_seq.iloc[0:7,0:2]
+ df_contig_top[naming]=df_contig_top[naming].astype('int64').apply('{:,}'.format)
# df_scaffold_top=df_scaffold_top.style.hide_index()
# df_scaffold_top[naming].round(decimals=0)
# df_contig_all=pd.DataFrame(data=contig_stats)
- # df_contig_top=df_contig_all.iloc[0:6,0:2]
- df_contig_Nxx=pd.DataFrame(contig_stats.items(), columns=['Nxx Level (%)', 'Length of Nxx contig (bp)'])
- df_contig_Nxx=df_contig_Nxx.iloc[31:55,0:2]
- df_contig_Nxx=df_contig_Nxx.reset_index()
+ # df_contig_top=df_contig_all.iloc[0:6,0:2]
+ df_contig_Nxx=pd.DataFrame(contig_stats.items(), columns=['Nxx Level (%)', 'Length of Nxx contig (bp)'])
+ df_contig_Nxx=df_contig_Nxx.iloc[31:55,0:2]
+ df_contig_Nxx=df_contig_Nxx.reset_index()
# print(df_scaffold_Nxx)
- df_contig_NGxx=pd.DataFrame(contig_stats.items(), columns=['NGxx Level (%)', 'Length of NGxx contig (bp)'])
- df_contig_NGxx=df_contig_NGxx.iloc[79:104,0:2]
- df_contig_NGxx=df_contig_NGxx.reset_index()
-# print(df_scaffold_NGxx)
- df_contig_N_NG=pd.concat([df_contig_Nxx,df_contig_NGxx], axis=1)
- df_contig_N_NG=df_contig_N_NG.drop(df_contig_N_NG.columns[[0,3]], axis = 1)
- df_contig_N_NG['Length of Nxx contig (bp)']=df_contig_N_NG['Length of Nxx contig (bp)'].astype('int64').apply('{:,}'.format)
- df_contig_N_NG['Length of NGxx contig (bp)']=df_contig_N_NG['Length of NGxx contig (bp)'].astype('int64').apply('{:,}'.format)
- # df_scaffold_N_NG=df_scaffold_N_NG.style.hide_index()
- df_contig_Lxx=pd.DataFrame(contig_stats.items(), columns=['Lxx Level (%)', 'Count of contig (for Lxx Level)'])
- df_contig_Lxx=df_contig_Lxx.iloc[7:31,0:2]
- df_contig_Lxx=df_contig_Lxx.reset_index()
+ df_contig_NGxx=pd.DataFrame(contig_stats.items(), columns=['NGxx Level (%)', 'Length of NGxx contig (bp)'])
+ df_contig_NGxx=df_contig_NGxx.iloc[79:104,0:2]
+ df_contig_NGxx=df_contig_NGxx.reset_index()
+# print(df_scaffold_NGxx)
+ df_contig_N_NG=pd.concat([df_contig_Nxx,df_contig_NGxx], axis=1)
+ df_contig_N_NG=df_contig_N_NG.drop(df_contig_N_NG.columns[[0,3]], axis = 1)
+ df_contig_N_NG['Length of Nxx contig (bp)']=df_contig_N_NG['Length of Nxx contig (bp)'].astype('int64').apply('{:,}'.format)
+ df_contig_N_NG['Length of NGxx contig (bp)']=df_contig_N_NG['Length of NGxx contig (bp)'].astype('int64').apply('{:,}'.format)
+ # df_scaffold_N_NG=df_scaffold_N_NG.style.hide_index()
+ df_contig_Lxx=pd.DataFrame(contig_stats.items(), columns=['Lxx Level (%)', 'Count of contig (for Lxx Level)'])
+ df_contig_Lxx=df_contig_Lxx.iloc[7:31,0:2]
+ df_contig_Lxx=df_contig_Lxx.reset_index()
# print(df_scaffold_Nxx)
- df_contig_LGxx=pd.DataFrame(contig_stats.items(), columns=['LGxx Level (%)', 'Count of contig (for LGxx Level)'])
- df_contig_LGxx=df_contig_LGxx.iloc[55:79,0:2]
- df_contig_LGxx=df_contig_LGxx.reset_index()
+ df_contig_LGxx=pd.DataFrame(contig_stats.items(), columns=['LGxx Level (%)', 'Count of contig (for LGxx Level)'])
+ df_contig_LGxx=df_contig_LGxx.iloc[55:79,0:2]
+ df_contig_LGxx=df_contig_LGxx.reset_index()
# print(df_scaffold_NGxx)
- df_contig_L_LG=pd.concat([df_contig_Lxx,df_contig_LGxx], axis=1)
- df_contig_L_LG=df_contig_L_LG.drop(df_contig_L_LG.columns[[0,3]], axis = 1)
- df_contig_L_LG['Count of contig (for Lxx Level)']=df_contig_L_LG['Count of contig (for Lxx Level)'].astype('int64').apply('{:,}'.format)
- df_contig_L_LG['Count of contig (for LGxx Level)']=df_contig_L_LG['Count of contig (for LGxx Level)'].astype('int64').apply('{:,}'.format)
+ df_contig_L_LG=pd.concat([df_contig_Lxx,df_contig_LGxx], axis=1)
+ df_contig_L_LG=df_contig_L_LG.drop(df_contig_L_LG.columns[[0,3]], axis = 1)
+ df_contig_L_LG['Count of contig (for Lxx Level)']=df_contig_L_LG['Count of contig (for Lxx Level)'].astype('int64').apply('{:,}'.format)
+ df_contig_L_LG['Count of contig (for LGxx Level)']=df_contig_L_LG['Count of contig (for LGxx Level)'].astype('int64').apply('{:,}'.format)
# df_scaffold_L_LG=df_scaffold_L_LG.style.hide_index()
# print(df_contig_top)
# print(scaffold_stats)
@@ -286,27 +288,27 @@ if __name__ == "__main__":
# contig = csv.writer(open(naming + "_contig_stats.tsv", "w"), delimiter='\t')
# for key, val in contig_stats.items():
#
- with open(sys.argv[5], 'w') as outputfile:
+ with open(sys.argv[5], 'w') as outputfile:
# # print('#' + libraryName, file=outputfile)
# # print("Total Reads Processed (Paired): " + total_processed + " ( 100 %)", file=outputfile)
# # print("Discarded reads (Paired): " + discarded + " ( "+str(discarded_perc)+"%)", file=outputfile)
# # print("Successfully Processed reads (Paired): " + successfull + " ( "+str(successfull_perc)+"%)", file=outputfile)
- print(df_scaffold_top.to_string(index=False), file=outputfile)
- print("", file=outputfile)
- print(df_scaffold_N_NG.to_string(index=False), file=outputfile)
- print("", file=outputfile)
- print(df_scaffold_L_LG.to_string(index=False), file=outputfile)
+ print(df_scaffold_top.to_string(index=False), file=outputfile)
+ print("", file=outputfile)
+ print(df_scaffold_N_NG.to_string(index=False), file=outputfile)
+ print("", file=outputfile)
+ print(df_scaffold_L_LG.to_string(index=False), file=outputfile)
- with open(sys.argv[6], 'w') as outputfile2:
+ with open(sys.argv[6], 'w') as outputfile2:
# # print('#' + libraryName, file=outputfile)
# # print("Total Reads Processed (Paired): " + total_processed + " ( 100 %)", file=outputfile)
# # print("Discarded reads (Paired): " + discarded + " ( "+str(discarded_perc)+"%)", file=outputfile)
# # print("Successfully Processed reads (Paired): " + successfull + " ( "+str(successfull_perc)+"%)", file=outputfile)
- print(df_contig_top.to_string(index=False), file=outputfile2)
- print("", file=outputfile2)
- print(df_contig_N_NG.to_string(index=False), file=outputfile2)
- print("", file=outputfile2)
- print(df_contig_L_LG.to_string(index=False), file=outputfile2)
+ print(df_contig_top.to_string(index=False), file=outputfile2)
+ print("", file=outputfile2)
+ print(df_contig_N_NG.to_string(index=False), file=outputfile2)
+ print("", file=outputfile2)
+ print(df_contig_L_LG.to_string(index=False), file=outputfile2)
# with open(sys.argv[4], 'w') as outputRst:
# print(tabulate(df_scaffold_top, headers='keys',tablefmt="rst", showindex=False), file=outputRst)
@@ -324,21 +326,21 @@ if __name__ == "__main__":
# print(tabulate(df_contig_L_LG, headers='keys',tablefmt="rst", showindex=False), file=outputRst2)
# print("", file=outputRst2)
- with open(sys.argv[4], 'w') as outputRst:
- print(tabulate(df_scaffold_top, headers='keys',tablefmt="pipe", showindex=False), file=outputRst)
- print("", file=outputRst)
- print(tabulate(df_scaffold_N_NG, headers='keys',tablefmt="pipe", showindex=False), file=outputRst)
- print("", file=outputRst)
- print(tabulate(df_scaffold_L_LG, headers='keys',tablefmt="pipe", showindex=False), file=outputRst)
- print("", file=outputRst)
+ with open(sys.argv[4], 'w') as outputRst:
+ print(tabulate(df_scaffold_top, headers='keys',tablefmt="pipe", showindex=False), file=outputRst)
+ print("", file=outputRst)
+ print(tabulate(df_scaffold_N_NG, headers='keys',tablefmt="pipe", showindex=False), file=outputRst)
+ print("", file=outputRst)
+ print(tabulate(df_scaffold_L_LG, headers='keys',tablefmt="pipe", showindex=False), file=outputRst)
+ print("", file=outputRst)
#
- with open(sys.argv[5], 'w') as outputRst2:
- print(tabulate(df_contig_top, headers='keys',tablefmt="pipe", showindex=False), file=outputRst2)
- print("", file=outputRst2)
- print(tabulate(df_contig_N_NG, headers='keys',tablefmt="pipe", showindex=False), file=outputRst2)
- print("", file=outputRst2)
- print(tabulate(df_contig_L_LG, headers='keys',tablefmt="pipe", showindex=False), file=outputRst2)
- print("", file=outputRst2)
+ with open(sys.argv[5], 'w') as outputRst2:
+ print(tabulate(df_contig_top, headers='keys',tablefmt="pipe", showindex=False), file=outputRst2)
+ print("", file=outputRst2)
+ print(tabulate(df_contig_N_NG, headers='keys',tablefmt="pipe", showindex=False), file=outputRst2)
+ print("", file=outputRst2)
+ print(tabulate(df_contig_L_LG, headers='keys',tablefmt="pipe", showindex=False), file=outputRst2)
+ print("", file=outputRst2)
# list_of_dfs=[df_scaffold_top,df_scaffold_N_NG,df_scaffold_L_LG]
# for df in list_of_dfs:
# with open('all_dfs.tsv','a') as f: