def test_missing_data_mask(self):
data = pd.DataFrame(np.arange(4, dtype=float).reshape(2, 2))
data.loc[0, 0] = np.nan
mask = mat._matrix_mask(data, None)
npt.assert_array_equal(mask, [[True, False], [False, False]])
mask_in = np.array([[False, True], [False, False]])
mask_out = mat._matrix_mask(data, mask_in)
npt.assert_array_equal(mask_out, [[True, True], [False, False]])
def test_mask_validation(self):
mask = mat._matrix_mask(self.df_norm, None)
assert mask.shape == self.df_norm.shape
assert mask.values.sum() == 0
with pytest.raises(ValueError):
bad_array_mask = self.rs.randn(3, 6) > 0
mat._matrix_mask(self.df_norm, bad_array_mask)
with pytest.raises(ValueError):
bad_df_mask = pd.DataFrame(self.rs.randn(4, 8) > 0)
mat._matrix_mask(self.df_norm, bad_df_mask)
def __init__(self,
data,
pivot_kws=None,
z_score=None,
standard_scale=None,
figsize=None,
row_colors=None,
col_colors=None,
row_ratios=None,
col_ratios=None,
row_cluster=True,
col_cluster=True,
colorbar=True,
mask=None):
"""Grid object for organizing clustered heatmap input on to axes"""
if isinstance(data, pd.DataFrame):
self.data = data
else:
self.data = pd.DataFrame(data)
self.data2d = self.format_data(self.data, pivot_kws, z_score,
standard_scale)
self.mask = _matrix_mask(self.data2d, mask)
if figsize is None:
width, height = 10, 10
figsize = (width, height)
self.row_colors, self.row_color_labels = \
self._preprocess_colors(data, row_colors, axis=0)
self.col_colors, self.col_color_labels = \
self._preprocess_colors(data, col_colors, axis=1)
self.col_cluster = col_cluster
self.row_cluster = row_cluster
self.dendrogram_row = None
self.dendrogram_col = None
self.colorbar = colorbar
self._setup_axes(figsize, row_ratios, col_ratios)
def __init__(self,
data,
fig,
pivot_kws=None,
z_score=None,
standard_scale=None,
figsize=None,
row_colors=None,
col_colors=None,
mask=None):
"""Grid object for organizing clustered heatmap input on to axes"""
if isinstance(data, pd.DataFrame):
self.data = data
else:
self.data = pd.DataFrame(data)
self.data2d = self.format_data(self.data, pivot_kws, z_score,
standard_scale)
self.mask = _matrix_mask(self.data2d, mask)
if figsize is None:
width, height = 10, 10
figsize = (width, height)
if fig is None:
self.fig = plt.figure(figsize=figsize)
else:
self.fig = fig
self.row_colors, self.row_color_labels = \
self._preprocess_colors(data, row_colors, axis=0)
self.col_colors, self.col_color_labels = \
self._preprocess_colors(data, col_colors, axis=1)
width_ratios = self.dim_ratios(self.row_colors,
figsize=figsize,
axis=1)
height_ratios = self.dim_ratios(self.col_colors,
figsize=figsize,
axis=0)
nrows = 3 if self.col_colors is None else 4
ncols = 3 if self.row_colors is None else 4
self.gs = gridspec.GridSpec(nrows,
ncols,
wspace=0.01,
hspace=0.01,
width_ratios=width_ratios,
height_ratios=height_ratios)
self.ax_row_dendrogram = self.fig.add_subplot(self.gs[nrows - 1, 0:2])
self.ax_col_dendrogram = self.fig.add_subplot(self.gs[0:2, ncols - 1])
self.ax_row_dendrogram.set_axis_off()
self.ax_col_dendrogram.set_axis_off()
self.ax_row_colors = None
self.ax_col_colors = None
if self.row_colors is not None:
self.ax_row_colors = self.fig.add_subplot(self.gs[nrows - 1,
ncols - 2])
if self.col_colors is not None:
self.ax_col_colors = self.fig.add_subplot(self.gs[nrows - 2,
ncols - 1])
self.ax_heatmap = self.fig.add_subplot(self.gs[nrows - 1, ncols - 1])
# colorbar for scale to left corner
self.cax = self.fig.add_subplot(self.gs[0, 0])
self.dendrogram_row = None
self.dendrogram_col = None