def imshow_anova_pairs(self, log=True, **kargs):
N = len(self.df.columns)
# could use a dataframe straight way ?
res = np.ones((N, N))
for i, col1 in enumerate(self.df.columns):
for j, col2 in enumerate(self.df.columns):
d1 = self.df[col1]
d2 = self.df[col2]
F, P = scipy.stats.f_oneway(*[d1, d2])
res[i][j] = P
df = pd.DataFrame(res, index=self.df.columns, columns=self.df.columns)
#FIXME: may have na, which are set to 1
df = df.fillna(1)
from biokit.viz import imshow
if log == True:
imshow(-np.log10(df), **kargs)
else:
imshow(df, **kargs)
return df
def imshow_anova_pairs(self, log=True, **kargs):
N = len(self.df.columns)
# could use a dataframe straight way ?
res = np.ones((N, N))
for i, col1 in enumerate(self.df.columns):
for j, col2 in enumerate(self.df.columns):
d1 = self.df[col1]
d2 = self.df[col2]
F, P = scipy.stats.f_oneway(*[d1, d2])
res[i][j] = P
df = pd.DataFrame(res, index=self.df.columns, columns=self.df.columns)
# FIXME: may have na, which are set to 1
df = df.fillna(1)
from biokit.viz import imshow
if log == True:
imshow(-np.log10(df), **kargs)
else:
imshow(df, **kargs)
return df
def test_viz_imshow():
from pandas import DataFrame
df = DataFrame({'a':[1,2], 'b':[3,4]})
imshow(df)
