本文整理匯總了Python中pymodule.PassingData.row_id2NA_mismatch_rate1方法的典型用法代碼示例。如果您正苦於以下問題：Python PassingData.row_id2NA_mismatch_rate1方法的具體用法？Python PassingData.row_id2NA_mismatch_rate1怎麽用？Python PassingData.row_id2NA_mismatch_rate1使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pymodule.PassingData的用法示例。
在下文中一共展示了PassingData.row_id2NA_mismatch_rate1方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: doFilter
# 需要導入模塊: from pymodule import PassingData [as 別名]
# 或者: from pymodule.PassingData import row_id2NA_mismatch_rate1 [as 別名]
#.........這裏部分代碼省略.........
"max_snp_mismatch_rate_%s" % max_snp_mismatch_rate,
"max_snp_NA_rate_%s" % max_snp_NA_rate,
"npute_window_size_%s" % npute_window_size,
]
output_fname = os.path.join(output_dir, "_".join(output_fname_prefix_ls + ["before_imputation.tsv"]))
newSnpData.tofile(output_fname)
# chromosomes = [snpsd_250k_tmp[i].chromosome for i in range(len(snpsd_250k_tmp))]
# snpsdata.writeRawSnpsDatasToFile(output_fname, snpsd_250k_tmp, chromosomes=chromosomes, deliminator=',', withArrayIds = True)
"""
qcdata.no_of_snps_filtered_by_mismatch = 0
qcdata.no_of_snps_filtered_by_na = 0
qcdata.no_of_monomorphic_snps_removed = 0
for snpsd in snpsd_250k_tmp:
qcdata.no_of_snps_filtered_by_mismatch += snpsd.no_of_snps_filtered_by_mismatch
qcdata.no_of_snps_filtered_by_na += snpsd.no_of_snps_filtered_by_na
qcdata.no_of_monomorphic_snps_removed += snpsd.no_of_monomorphic_snps_removed
"""
# snpData0 = RawSnpsData_ls2SNPData(snpsd_250k_tmp)
twoSNPData0 = TwoSNPData(SNPData1=newSnpData, SNPData2=snpData_qc_strain, row_matching_by_which_value=0)
row_id2NA_mismatch_rate0 = twoSNPData0.cmp_row_wise()
col_id2NA_mismatch_rate0 = twoSNPData0.cmp_col_wise()
del twoSNPData0
result = []
# for npute_window_size in npute_window_size_ls:
# snpsd_250k_tmp_1 = copy.deepcopy(snpsd_250k_tmp) #deepcopy, otherwise snpsd_250k_tmp_1[i].snps = [] would clear snpsd_250k_tmp up as well
if len(newSnpData.row_id_ls) > 5:
snps_name_ls = newSnpData.col_id_ls
## 2009-10-8 use NPUTE.samplingImpute()
imputed_matrix, new_snps_name_ls = NPUTE.samplingImpute(
snps_name_ls,
newSnpData.data_matrix,
input_file_format=1,
input_NA_char=0,
lower_case_for_imputation=False,
npute_window_size=int(npute_window_size),
no_of_accessions_per_sampling=300,
coverage=3,
)
snpData_imputed = SNPData(
row_id_ls=newSnpData.row_id_ls, col_id_ls=new_snps_name_ls, data_matrix=imputed_matrix
)
"""
## 2009-10-8 use NPUTE.samplingImpute() instead. comment out below
chr2no_of_snps = NPUTE.get_chr2no_of_snps(snps_name_ls)
chr_ls = chr2no_of_snps.keys()
chr_ls.sort()
snpData_imputed = SNPData(row_id_ls = newSnpData.row_id_ls, col_id_ls=[])
matrix_ls = []
for chromosome in chr_ls:
if chr2no_of_snps[chromosome]>5: #enough for imputation
npute_data_struc = NPUTESNPData(snps_name_ls=snps_name_ls, data_matrix=newSnpData.data_matrix, chromosome=chromosome, \
input_file_format=1, input_NA_char=0)
imputeData(npute_data_struc, int(npute_window_size))
matrix_ls.append(npute_data_struc.snps)
snpData_imputed.col_id_ls += npute_data_struc.chosen_snps_name_ls
if len(matrix_ls)>0:
snpData_imputed.data_matrix = num.transpose(num.concatenate(matrix_ls))
"""
if output_dir: # 2008-05-16 write the data out if output_fname is available
# chromosomes = [snpsd_250k_tmp[i].chromosome for i in range(len(snpsd_250k_tmp))] #already produced in the previous before_imputation output
output_fname = os.path.join(output_dir, "_".join(output_fname_prefix_ls + ["after_imputation.tsv"]))
# snpsdata.writeRawSnpsDatasToFile(output_fname, snpsd_250k_tmp, chromosomes=chromosomes, deliminator=',', withArrayIds = True)
snpData_imputed.tofile(output_fname)
twoSNPData1 = TwoSNPData(
SNPData1=snpData_imputed, SNPData2=snpData_qc_strain, row_matching_by_which_value=0
)
qcdata.row_id2NA_mismatch_rate1 = twoSNPData1.cmp_row_wise()
qcdata.col_id2NA_mismatch_rate1 = twoSNPData1.cmp_col_wise()
del twoSNPData1, snpData_imputed
else:
snpData_imputed = None
# qcdata.row_id2NA_mismatch_rate1 = {}
# qcdata.col_id2NA_mismatch_rate1 = {}
del newSnpData
"""
for i in range(len(snpsd_250k_tmp)):
#snpsd_250k_tmp_1[i].snps = [] #clear it up
if len(snpsd_250k_tmp[i].accessions)>5 and len(snpsd_250k_tmp[i].positions)>5: #not enough for imputation
npute_data_struc = NPUTESNPData(inFile=snpsd_250k_tmp[i], input_NA_char='NA', input_file_format=4, lower_case_for_imputation=0)
imputeData(npute_data_struc, int(npute_window_size))
snpsd_250k_tmp[i].snps = npute_data_struc.snps
del npute_data_struc
"""
qcdata.row_id2NA_mismatch_rate0 = row_id2NA_mismatch_rate0
qcdata.col_id2NA_mismatch_rate0 = col_id2NA_mismatch_rate0
qcdata.min_call_probability = min_call_probability
qcdata.max_call_mismatch_rate = max_call_mismatch_rate
qcdata.max_call_NA_rate = max_call_NA_rate
qcdata.max_snp_mismatch_rate = max_snp_mismatch_rate
qcdata.max_snp_NA_rate = max_snp_NA_rate
qcdata.npute_window_size = npute_window_size
result.append(qcdata)
return result