This is lag sequential analysis for python3
Project description
LSA(lag sequential analysis)
滞后序列分析python版
安装
pip install pyseqlsa
或者
pip3 install pyseqlsa
快速使用
from pyseqlsa import LSA
data = [['A', 'B', 'C', 'B', 'C', 'B', 'C'],
['C', 'C', 'B', 'A', 'C', 'A', 'B', 'C', 'B', 'C']]
lsa = LSA(['A', 'B', 'C'])
lsa.fit(data)
若不想打印输出,可以设置output=False
lsa.fit(data,output=False)
单独查看Z矩阵的方法
# 属性Z即是残差显著性的矩阵,大于1.96即显著
lsa.Z
如果想转换Gseq5为sds文件,可以调用
lsa.to_sds(data, "filename.sds")
通过csv获得seqs,并进行滞后序列分析
注意此处默认csv的格式,第一列为序列的id,第二列为code,标题名可以随意,从第二行开始读取,如下所示
id | code |
---|---|
1 | a |
1 | b |
1 | a |
2 | a |
2 | b |
2 | c |
读取后的数据即可直接用于LSA代码如下所示
from pyseqlsa import read_seqs_from_csv
from pyseqlsa import LSA
data = read_seqs_from_csv('test.csv')
lsa = LSA(['A', 'B', 'C'])
lsa.fit(data)
通过excel获得seqs,并进行滞后序列分析
excel 请按照以下的格式设置,第一列为序列的id,第二列为code,标题名可以随意,从第二行开始读取,如下所示
id | code |
---|---|
1 | a |
1 | b |
1 | a |
2 | a |
2 | b |
2 | c |
读取后的数据即可直接用于LSA代码如下所示
from pyseqlsa import read_seqs_from_excel
from pyseqlsa import LSA
data = read_seqs_from_excel('test.xlsx')
lsa = LSA(['a', 'b', 'c'])
lsa.fit(data)
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