def df_filter_row(df, threshold, take_abs=True):
''' filter rows in matrix at some threshold
and remove columns that have a sum below this threshold '''
from copy import deepcopy
from clustergrammer import Network
net = Network()
if take_abs is True:
df_copy = deepcopy(df['mat'].abs())
else:
df_copy = deepcopy(df['mat'])
ini_rows = df_copy.index.values.tolist()
df_copy = df_copy.transpose()
tmp_sum = df_copy.sum(axis=0)
tmp_sum = tmp_sum.abs()
tmp_sum.sort_values(inplace=True, ascending=False)
tmp_sum = tmp_sum[tmp_sum > threshold]
keep_rows = sorted(tmp_sum.index.values.tolist())
if len(keep_rows) < len(ini_rows):
df['mat'] = net.grab_df_subset(df['mat'], keep_rows=keep_rows)
if 'mat_up' in df:
df['mat_up'] = net.grab_df_subset(df['mat_up'], keep_rows=keep_rows)
df['mat_dn'] = net.grab_df_subset(df['mat_dn'], keep_rows=keep_rows)
return df
def df_filter_col(df, threshold, take_abs=True):
''' filter columns in matrix at some threshold
and remove rows that have all zero values '''
from copy import deepcopy
from clustergrammer import Network
net = Network()
if take_abs is True:
df_copy = deepcopy(df['mat'].abs())
else:
df_copy = deepcopy(df['mat'])
df_copy = df_copy.transpose()
df_copy = df_copy[df_copy.sum(axis=1) > threshold]
df_copy = df_copy.transpose()
df_copy = df_copy[df_copy.sum(axis=1) > 0]
if take_abs is True:
inst_rows = df_copy.index.tolist()
inst_cols = df_copy.columns.tolist()
df['mat'] = net.grab_df_subset(df['mat'], inst_rows, inst_cols)
else:
df['mat'] = df_copy
return df
