def __init__(self, cov):
'''
Parameters
----------
cov : Cov Matrix class
Attributes
----------
df : DataFrame
DataFrame of the correlation coefficient matrix
Methods
--------
plot_heatmap
pars
'''
#cov = self._cov().values
d = np.diag(cov.x)
cor = cov.x/np.sqrt(np.multiply.outer(d, d))
self.matrix = Matrix(x=cor, row_names = cov.col_names, col_names = cov.col_names)
# Put into dataframe
self.df = self.matrix.to_dataframe()
class Cor(object):
def __init__(self, cov):
'''
Parameters
----------
cov : Cov Matrix class
Attributes
----------
df : DataFrame
DataFrame of the correlation coefficient matrix
Methods
--------
plot_heatmap
pars
'''
#cov = self._cov().values
d = np.diag(cov.x)
cor = cov.x/np.sqrt(np.multiply.outer(d, d))
self.matrix = Matrix(x=cor, row_names = cov.col_names, col_names = cov.col_names)
# Put into dataframe
self.df = self.matrix.to_dataframe()
def pars(self, par_list, inplace = False):
''' Reduce the correlation coefficient matrix to select parameters
Parameters
----------
par_list : list
list of parameters to show correlation coefficient matrix for
inplace : False, True
If False return a smaller DataFrame of Cor.df
If True, change DataFrame of Cor.df inplace
Returns
-------
df : DataFrame
DataFrame of the correlation coefficient matrix with only select
parameters
'''
reduced_matrix = self.df.loc[par_list][par_list]
if inplace == False:
return reduced_matrix
if inplace == True:
self.df = reduced_matrix
def plot_heatmap(self, label_rows=True, label_cols=True, par_list=None, **kwds):
''' Plot correlation coefficient matrix
Parameters
----------
label_rows : bol, optional
label the rows. Default is True. For large matrices it is often
cleaner to set False
label_cols : bol, optional
label the columns. Default is True. For large matrices it is often
cleaner to set False
par_list : list, optional
list of parameters to show correlation coefficient matrix for.
Useful for large matrices
Returns
-------
Matplotlib plot
Heatmap (pcolormesh) of correlation coefficient matrix
'''
if par_list is None:
df = self.df
else:
df = self.pars(par_list)
plot_obj = plots.HeatMap(df, label_rows=label_rows,
label_cols=label_cols, vmin=-1.0,
vmax=1.0, **kwds)
plot_obj.generate()
plot_obj.draw()
return plot_obj.fig, plot_obj.ax
