本文整理汇总了Python中qiime.util.MetadataMap.hasUniqueCategoryValues方法的典型用法代码示例。如果您正苦于以下问题：Python MetadataMap.hasUniqueCategoryValues方法的具体用法？Python MetadataMap.hasUniqueCategoryValues怎么用？Python MetadataMap.hasUniqueCategoryValues使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类qiime.util.MetadataMap的用法示例。
在下文中一共展示了MetadataMap.hasUniqueCategoryValues方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: preprocess_mapping_file
# 需要导入模块: from qiime.util import MetadataMap [as 别名]
# 或者: from qiime.util.MetadataMap import hasUniqueCategoryValues [as 别名]
def preprocess_mapping_file(data, headers, columns, unique=False, single=False, clones=0):
"""Process a mapping file to expand the data or remove unuseful fields
Inputs:
data: mapping file data
headers: mapping file headers
columns: list of headers to keep, if one of these headers includes two
ampersands, this function will create a new column by merging the delimited
columns.
unique: keep columns where all values are unique
single: keep columns where all values are the same
clones: number of times to replicate the metadata
Outputs:
data: processed mapping file data
headers: processed mapping file headers
"""
# The sample ID must always be there, else it's meaningless data
if "SampleID" != columns[0]:
columns = ["SampleID"] + columns
# process concatenated columns if needed
merge = []
for column in columns:
if "&&" in column:
merge.append(column)
# each element needs several columns to be merged
for new_column in merge:
indices = [headers.index(header_name) for header_name in new_column.split("&&")]
# join all the fields of the metadata that are listed in indices
for line in data:
line.append("".join([line[index] for index in indices]))
headers.append(new_column)
# remove all unique or singled valued columns
if unique or single:
columns_to_remove = []
metadata = MetadataMap(mapping_file_to_dict(data, headers), [])
# find columns that have values that are all unique
if unique == True:
columns_to_remove += [
column_name for column_name in headers[1::] if metadata.hasUniqueCategoryValues(column_name)
]
# remove categories where there is only one value
if single == True:
columns_to_remove += [
column_name for column_name in headers[1::] if metadata.hasSingleCategoryValue(column_name)
]
columns_to_remove = list(set(columns_to_remove))
# remove the single or unique columns
data, headers = keep_columns_from_mapping_file(data, headers, columns_to_remove, negate=True)
# remove anything not specified in the input
data, headers = keep_columns_from_mapping_file(data, headers, columns)
# sanitize the mapping file data and headers
data, headers = sanitize_mapping_file(data, headers)
# clones mean: replicate the metadata retagging the sample ids with a suffix
if clones:
out_data = []
for index in range(0, clones):
out_data.extend([[element[0] + "_%d" % index] + element[1::] for element in data])
data = out_data
return data, headers