本文整理汇总了Python中clustergrammer.Network.normalize方法的典型用法代码示例。如果您正苦于以下问题：Python Network.normalize方法的具体用法？Python Network.normalize怎么用？Python Network.normalize使用的例子？那么, 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clustergrammer.Network的用法示例。
在下文中一共展示了Network.normalize方法的3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: process_GCT_and_export_tsv
# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import normalize [as 别名]
def process_GCT_and_export_tsv():
from clustergrammer import Network
filename = 'gcts/LDS-1003.gct'
print('exporting processed GCT as tsv file')
df = load_file(filename)
net = Network()
net.df_to_dat(df)
net.swap_nan_for_zero()
# zscore first to get the columns distributions to be similar
net.normalize(axis='col', norm_type='zscore', keep_orig=True)
# filter the rows to keep the perts with the largest normalizes values
net.filter_N_top('row', 200)
net.write_matrix_to_tsv('txt/example_gct_export.txt')示例2: make_json_from_tsv
# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import normalize [as 别名]
def make_json_from_tsv(name):
'''
make a clustergrammer json from a tsv file
'''
from clustergrammer import Network
print('\n' + name)
net = Network()
filename = 'txt/'+ name + '.txt'
net.load_file(filename)
df = net.dat_to_df()
net.swap_nan_for_zero()
# zscore first to get the columns distributions to be similar
net.normalize(axis='col', norm_type='zscore', keep_orig=True)
# filter the rows to keep the perts with the largest normalizes values
net.filter_N_top('row', 1000)
num_rows = net.dat['mat'].shape[0]
num_cols = net.dat['mat'].shape[1]
print('num_rows ' + str(num_rows))
print('num_cols ' + str(num_cols))
if num_cols < 50 or num_rows < 1000:
views = ['N_row_sum']
net.make_clust(dist_type='cos', views=views)
export_filename = 'json/' + name + '.json'
net.write_json_to_file('viz', export_filename)
else:
print('did not cluster, too many columns ')示例3: make_viz_from_df
# 需要导入模块: from clustergrammer import Network [as 别名]
# 或者: from clustergrammer.Network import normalize [as 别名]
def make_viz_from_df(df, filename):
from clustergrammer import Network
net = Network()
net.df_to_dat(df)
net.swap_nan_for_zero()
# zscore first to get the columns distributions to be similar
net.normalize(axis='col', norm_type='zscore', keep_orig=True)
# filter the rows to keep the perts with the largest normalizes values
net.filter_N_top('row', 2000)
num_coluns = net.dat['mat'].shape[1]
if num_coluns < 50:
# views = ['N_row_sum', 'N_row_var']
views = ['N_row_sum']
net.make_clust(dist_type='cos', views=views)
filename = 'json/' + filename.split('/')[1].replace('.gct','') + '.json'
net.write_json_to_file('viz', filename)