Merge pull request #293 from biocore/flake8-again

bump

.github/workflows/master.yml

@@ -35,11 +35,11 @@ jobs:
       - name: Install conda packages
         shell: bash -l {0}
         run: conda install -c conda-forge statsmodels scikit-bio biom-format matplotlib flake8
-        
+
       - name: Install gneiss
         shell: bash -l {0}
         run: pip install -e .[dev]
-        
+
       - name: Run tests
         shell: bash -l {0}
         run: make all

Makefile

@@ -14,7 +14,6 @@ help:
 test:
 	$(TEST_COMMAND)
 pep8:
-	pycodestyle gneiss setup.py
-	flake8 gneiss setup.py
+	flake8 gneiss setup.py --ignore E303,E731,E722
 
 all: pep8 test

gneiss/balances.py

@@ -46,12 +46,13 @@ def _balance_basis(tree_node):
     k = np.array([counts[n]['k'] for n in nds])
     t = np.array([counts[n]['t'] for n in nds])
 
-    a = np.sqrt(s / (r*(r+s)))
-    b = -1*np.sqrt(r / (s*(r+s)))
+    a = np.sqrt(s / (r * (r + s)))
+    b = -1 * np.sqrt(r / (s * (r + s)))
 
-    basis = np.zeros((n_tips-1, n_tips))
+    basis = np.zeros((n_tips - 1, n_tips))
     for i in range(len(nds)):
-        basis[i, :] = np.array([0]*k[i] + [a[i]]*r[i] + [b[i]]*s[i] + [0]*t[i])
+        basis[i, :] = np.array(
+            [0] * k[i] + [a[i]] * r[i] + [b[i]] * s[i] + [0] * t[i])
     # Make sure that the basis is in level order
     basis = basis[:, ::-1]
     nds = [n.name for n in nds]
@@ -243,20 +244,20 @@ def sparse_balance_basis(tree):
             row.append(i)
             # consider tips in reverse order. May want to rethink
             # this orientation in the future.
-            col.append(D-1-j)
+            col.append(D - 1 - j)
             A = np.sqrt(n._s / (n._r * (n._s + n._r)))
 
             value.append(A)
 
         for j in range(n._k + n._r, n._k + n._r + n._s):
             row.append(i)
-            col.append(D-1-j)
+            col.append(D - 1 - j)
             B = -np.sqrt(n._r / (n._s * (n._s + n._r)))
 
             value.append(B)
         i += 1
         nodes.append(n.name)
 
-    basis = coo_matrix((value, (row, col)), shape=(D-1, D))
+    basis = coo_matrix((value, (row, col)), shape=(D - 1, D))
 
     return basis, nodes

gneiss/cluster/tests/test_pba.py

@@ -29,7 +29,7 @@ def test_correlation_linkage_1(self):
                    '(o3:0.574990173931,o4:0.574990173931)y2:0.773481312844)'
                    'y0;\n')
         exp_tree = TreeNode.read([exp_str])
-        res_tree = correlation_linkage(table+0.1)
+        res_tree = correlation_linkage(table + 0.1)
         # only check for tree topology since checking for floating point
         # numbers on the branches is still tricky.
         self.assertEqual(exp_tree.ascii_art(), res_tree.ascii_art())

gneiss/composition/tests/test_variance.py

@@ -29,11 +29,10 @@ def test_varmat_larg(self):
         np.random.seed(123)
         D = 50
         N = 100
-        mean = np.ones(D)*10
+        mean = np.ones(D) * 10
         cov = np.eye(D)
-        X = pd.DataFrame(np.abs(np.random.multivariate_normal(mean, cov,
-                                                              size=N)),
-                         columns=np.arange(D).astype(np.str))
+        n__ = np.random.multivariate_normal(mean, cov, size=N)
+        X = pd.DataFrame(np.abs(n__), columns=np.arange(D).astype(np.str))
         res = variation_matrix(X)
 
         exp = DistanceMatrix.read(get_data_path('exp_varmat.txt'))

gneiss/plot/_decompose.py

@@ -9,6 +9,7 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 from gneiss.util import NUMERATOR, DENOMINATOR
+import warnings
 
 
 def balance_boxplot(balance_name, data, num_color='#FFFFFF',
@@ -49,6 +50,7 @@ def balance_boxplot(balance_name, data, num_color='#FFFFFF',
     --------
     seaborn.boxplot
     """
+    warnings.warn("This visualization are deprecated.", DeprecationWarning)
     import seaborn as sns
     if ax is None:
         f, ax = plt.subplots()
@@ -57,9 +59,9 @@ def balance_boxplot(balance_name, data, num_color='#FFFFFF',
     # resizing to make sure that there is separation between the
     # edges of the plot, and the boxplot
     pad = (data[balance_name].max() - data[balance_name].min()) / 20
-    ax.axvspan(data[balance_name].min()-pad, 0,
+    ax.axvspan(data[balance_name].min() - pad, 0,
                facecolor=num_color, zorder=0)
-    ax.axvspan(0, data[balance_name].max()+pad,
+    ax.axvspan(0, data[balance_name].max() + pad,
                facecolor=denom_color, zorder=0)
 
     if 'hue' in kwargs.keys():
@@ -118,6 +120,7 @@ def balance_barplots(tree, balance_name, header, feature_metadata,
     ax_denom : matplotlib axes object
         Barplot of the features in the denominator of the balance.
     """
+    warnings.warn("This visualization are deprecated.", DeprecationWarning)
     import seaborn as sns
     if axes[0] is None or axes[1] is None:
         f, (ax_num, ax_denom) = plt.subplots(2)
@@ -148,15 +151,15 @@ def balance_barplots(tree, balance_name, header, feature_metadata,
                            color=denom_color)
     ax_denom.set_ylabel(ylabel)
     ax_denom.set_xlabel(xlabel)
-    ax_denom.set_xlim([0,  max([num_.max().values[1],
-                                denom_.max().values[1]])])
+    ax_denom.set_xlim(
+        [0, max([num_.max().values[1], denom_.max().values[1]])])
 
     ax_num = sns.barplot(y='index', x=header, data=num_, ax=ax_num,
                          color=num_color)
     ax_num.set_ylabel(ylabel)
     ax_num.set_xlabel(xlabel)
-    ax_num.set_xlim([0,  max([num_.max().values[1],
-                              denom_.max().values[1]])])
+    ax_num.set_xlim(
+        [0, max([num_.max().values[1], denom_.max().values[1]])])
     return ax_num, ax_denom
 
 
@@ -238,6 +241,7 @@ def proportion_plot(table, metadata, category, left_group, right_group,
     Since this method will return the raw matplotlib object, labels, titles,
     ticks, etc can directly modified using this object.
     """
+    warnings.warn("This visualization are deprecated.", DeprecationWarning)
     import seaborn as sns
     if axes[0] is None or axes[1] is None:
         f, (ax_num, ax_denom) = plt.subplots(1, 2)
@@ -268,16 +272,15 @@ def proportion_plot(table, metadata, category, left_group, right_group,
     denom_data['part'] = 'denominator'
     data = pd.concat((num_data, denom_data))
     if feature_metadata is not None:
-        num_feature_metadata = feature_metadata.loc[num_df.columns,
-                                                    label_col]
-        denom_feature_metadata = feature_metadata.loc[denom_df.columns,
-                                                      label_col]
+        fm = feature_metadata
+        num_feature_metadata = fm.loc[num_df.columns, label_col]
+        denom_feature_metadata = fm.loc[denom_df.columns, label_col]
         # order of the ids to plot
-        order = (list(num_feature_metadata.index) +
-                 list(denom_feature_metadata.index))
+        n__ = list(num_feature_metadata.index)
+        d__ = list(denom_feature_metadata.index)
+        order = n__ + d__
     else:
-        order = (list(num_df.columns) +
-                 list(denom_df.columns))
+        order = list(num_df.columns) + list(denom_df.columns)
 
     less_df = data.loc[data[category] == left_group].dropna()
 
@@ -296,9 +299,9 @@ def proportion_plot(table, metadata, category, left_group, right_group,
                 order=order,
                 ax=ax_num)
     if feature_metadata is not None:
-        ax_denom.set(yticklabels=(list(num_feature_metadata.values) +
-                                  list(denom_feature_metadata.values)),
-                     title=left_group)
+        n__ = list(num_feature_metadata.values)
+        d__ = list(denom_feature_metadata.values)
+        ax_denom.set(yticklabels=(n__ + d__), title=left_group)
     else:
         ax_denom.set(yticklabels=order, title=left_group)
 

gneiss/plot/_dendrogram.py

@@ -276,7 +276,7 @@ def update_coordinates(self, s, x1, y1, a, da):
             for child in self.children:
                 # calculate the arc that covers the subtree.
                 ca = child.leafcount * da
-                points += child.update_coordinates(s, x2, y2, a+ca/2, da)
+                points += child.update_coordinates(s, x2, y2, a + ca / 2, da)
                 a += ca
         return points
 
@@ -375,14 +375,14 @@ class SquareDendrogram(RootedDendrogram):
     def ycoords(self, scale, y1):
         cys = [c.y1 for c in self.children]
         if cys:
-            y2 = (cys[0]+cys[-1]) / 2.0
+            y2 = (cys[0] + cys[-1]) / 2.0
         else:
             y2 = y1 - 0.5 * scale.y
         return (y2, y2)
 
     def xcoords(self, scale, x1):
         if self.is_tip():
-            return (x1, (scale.height-(self.height-self.length))*scale.x)
+            return (x1, (scale.height - (self.height - self.length)) * scale.x)
         else:
             # give some margins for internal nodes
             dx = scale.x * self.length * 0.95

gneiss/plot/_heatmap.py

@@ -11,6 +11,7 @@
 import pandas as pd
 from gneiss.plot._dendrogram import SquareDendrogram
 from gneiss.util import match_tips, NUMERATOR, DENOMINATOR
+import warnings
 
 
 def heatmap(table, tree, mdvar, highlights=None, cmap='viridis',
@@ -112,7 +113,7 @@ def heatmap(table, tree, mdvar, highlights=None, cmap='viridis',
     # heatmap axes
     hstart = xwidth + (h * hwidth)
     [ax1_x, ax1_y, ax1_w, ax1_h] = [hstart, top_buffer,
-                                    cbar_pad-hstart, height]
+                                    cbar_pad - hstart, height]
 
     # plot dendrogram
     ax_dendrogram = fig.add_axes([axm_x, axm_y, axm_w, axm_h],
@@ -182,21 +183,21 @@ def _plot_highlights_dendrogram(ax_highlights, table, t, highlights):
 
         ax_highlights.add_patch(
             patches.Rectangle(
-                (i/num_h, k-offset),  # x, y
-                1/num_h,  # width
+                (i / num_h, k - offset),  # x, y
+                1 / num_h,  # width
                 r,  # height
                 facecolor=highlights.iloc[i, 0]
             ))
 
         ax_highlights.add_patch(
             patches.Rectangle(
-                (i/num_h, k+r-offset),  # x, y
-                1/num_h,  # width
+                (i / num_h, k + r - offset),  # x, y
+                1 / num_h,  # width
                 l,  # height
                 facecolor=highlights.iloc[i, 1]
             ))
-        hcoords.append((i+offset)/num_h)
-    ax_highlights.set_ylim([-offset, table.shape[0]-offset])
+        hcoords.append((i + offset) / num_h)
+    ax_highlights.set_ylim([-offset, table.shape[0] - offset])
     ax_highlights.set_yticks([])
     ax_highlights.set_xticks(hcoords)
     ax_highlights.set_xticklabels(highlights.index, rotation=90)
@@ -219,8 +220,9 @@ def _plot_dendrogram(ax_dendrogram, table, edges, linewidth=1):
     for i in range(len(edges.index)):
         row = edges.iloc[i]
         ax_dendrogram.plot([row.x0, row.x1],
-                           [row.y0-offset, row.y1-offset], '-k', lw=linewidth)
-    ax_dendrogram.set_ylim([-offset, table.shape[0]-offset])
+                           [row.y0 - offset, row.y1 - offset],
+                           '-k', lw=linewidth)
+    ax_dendrogram.set_ylim([-offset, table.shape[0] - offset])
     ax_dendrogram.set_yticks([])
     ax_dendrogram.set_xticks([])
     ax_dendrogram.axis('off')
@@ -278,6 +280,7 @@ def _plot_heatmap(ax_heatmap, table, mdvar, grid_col,
     colorbar_ax : matplotlib axes object
         The colorbar axis.
     """
+    warnings.warn("This visualization are deprecated.", DeprecationWarning)
     # TODO add explicit test for this, since matplotlib orientation
     # is from top to down (i.e. is backwards)
     table, mdvar = _sort_table(table, mdvar)
@@ -293,13 +296,13 @@ def _plot_heatmap(ax_heatmap, table, mdvar, grid_col,
 
     ticks = vcounts.sort_index().cumsum()
     midpoints = ticks - (ticks - np.array([0] + list(ticks.values[:-1]))) / 2.0
-    ax_heatmap.set_xticks(ticks.values-0.5, minor=False)
+    ax_heatmap.set_xticks(ticks.values - 0.5, minor=False)
     ax_heatmap.set_xticklabels([], minor=False)
 
     ax_heatmap.xaxis.grid(True, which='major', color=grid_col,
                           linestyle='-', linewidth=grid_width)
 
-    ax_heatmap.set_xticks(midpoints-0.5, minor=True)
+    ax_heatmap.set_xticks(midpoints - 0.5, minor=True)
     ax_heatmap.set_xticklabels(vcounts.index, minor=True)
     ax_heatmap.set_xlabel(mdvar.name)
     return colorbar_ax

gneiss/plot/_radial.py

@@ -7,10 +7,15 @@
 # ----------------------------------------------------------------------------
 import pandas as pd
 from gneiss.plot._dendrogram import UnrootedDendrogram
-from bokeh.models.glyphs import Circle, Segment
-from bokeh.models import ColumnDataSource, DataRange1d, Plot
-from bokeh.models import (HoverTool, BoxZoomTool, ResetTool,
-                          WheelZoomTool, SaveTool, PanTool)
+import warnings
+try:
+    from bokeh.models.glyphs import Circle, Segment
+    from bokeh.models import ColumnDataSource, DataRange1d, Plot
+    from bokeh.models import (HoverTool, BoxZoomTool, ResetTool,
+                              WheelZoomTool, SaveTool, PanTool)
+except:
+    warnings.warn("Bokeh isn't installed - "
+                  "the interactive visualizations won't work.")
 
 
 def radialplot(tree, node_color='node_color', node_size='node_size',
@@ -56,8 +61,7 @@ def radialplot(tree, node_color='node_color', node_size='node_size',
     --------
     bifurcate
     """
-    # TODO: Add in example doc string
-
+    warnings.warn("This visualization are deprecated.", DeprecationWarning)
     # This entire function was motivated by
     # http://chuckpr.github.io/blog/trees2.html
     t = UnrootedDendrogram.from_tree(tree.copy())