[FIX] Figures of components with index 0 get rendered now

tedana/reporting/dynamic_figures.py

@@ -1,18 +1,18 @@
-import numpy as np
 from math import pi
+
+import numpy as np
 import pandas as pd
+from bokeh import events, models, plotting, transform
 from sklearn.preprocessing import MinMaxScaler
-from bokeh import (events, models, plotting, transform)
 
-color_mapping = {'accepted': '#2ecc71',
-                 'rejected': '#e74c3c',
-                 'ignored': '#3498db'}
+color_mapping = {"accepted": "#2ecc71", "rejected": "#e74c3c", "ignored": "#3498db"}
 
 tap_callback_jscode = """
     // Accessing the selected component ID
     var data          = source_comp_table.data;
     var selected_idx = source_comp_table.selected.indices;
-    if(selected_idx > 0) {
+    console.log('Selected idx is ' + selected_idx)
+    if(selected_idx >= 0) {
         // A component has been selected
         // -----------------------------
         var components = data['component']
@@ -68,56 +68,68 @@ def _create_data_struct(comptable_path, color_mapping=color_mapping):
     cds: bokeh.models.ColumnDataSource
         Data structure with all the fields to plot or hover over
     """
-    unused_cols = ['normalized variance explained',
-                   'countsigFT2', 'countsigFS0',
-                   'dice_FS0', 'countnoise', 'dice_FT2',
-                   'signal-noise_t', 'signal-noise_p',
-                   'd_table_score', 'kappa ratio',
-                   'rationale', 'd_table_score_scrub']
+    unused_cols = [
+        "normalized variance explained",
+        "countsigFT2",
+        "countsigFS0",
+        "dice_FS0",
+        "countnoise",
+        "dice_FT2",
+        "signal-noise_t",
+        "signal-noise_p",
+        "d_table_score",
+        "kappa ratio",
+        "rationale",
+        "d_table_score_scrub",
+    ]
 
     df = pd.read_table(comptable_path)
     n_comps = df.shape[0]
 
     # remove space from column name
-    df.rename(columns={'variance explained': 'var_exp'}, inplace=True)
+    df.rename(columns={"variance explained": "var_exp"}, inplace=True)
 
     # For providing sizes based on Var Explained that are visible
     mm_scaler = MinMaxScaler(feature_range=(4, 20))
-    df['var_exp_size'] = mm_scaler.fit_transform(
-        df[['var_exp', 'normalized variance explained']])[:, 0]
+    df["var_exp_size"] = mm_scaler.fit_transform(
+        df[["var_exp", "normalized variance explained"]]
+    )[:, 0]
 
     # Calculate Kappa and Rho ranks
-    df['rho_rank'] = df['rho'].rank(ascending=False).values
-    df['kappa_rank'] = df['kappa'].rank(ascending=False).values
-    df['var_exp_rank'] = df['var_exp'].rank(ascending=False).values
+    df["rho_rank"] = df["rho"].rank(ascending=False).values
+    df["kappa_rank"] = df["kappa"].rank(ascending=False).values
+    df["var_exp_rank"] = df["var_exp"].rank(ascending=False).values
 
     # Remove unused columns to decrease size of final HTML
     # set errors to 'ignore' in case some columns do not exist in
     # a given data frame
-    df.drop(unused_cols, axis=1, inplace=True, errors='ignore')
+    df.drop(unused_cols, axis=1, inplace=True, errors="ignore")
 
     # Create additional Column with colors based on final classification
-    df['color'] = [color_mapping[i] for i in df['classification']]
+    df["color"] = [color_mapping[i] for i in df["classification"]]
 
     # Create additional column with component ID
-    df['component'] = np.arange(n_comps)
+    df["component"] = np.arange(n_comps)
 
     # Compute angle and re-sort data for Pie plots
-    df['angle'] = df['var_exp'] / df['var_exp'].sum() * 2 * pi
-    df.sort_values(by=['classification', 'var_exp'], inplace=True)
-
-    cds = models.ColumnDataSource(data=dict(
-        kappa=df['kappa'],
-        rho=df['rho'],
-        varexp=df['var_exp'],
-        kappa_rank=df['kappa_rank'],
-        rho_rank=df['rho_rank'],
-        varexp_rank=df['var_exp_rank'],
-        component=[str(i) for i in df['component']],
-        color=df['color'],
-        size=df['var_exp_size'],
-        classif=df['classification'],
-        angle=df['angle']))
+    df["angle"] = df["var_exp"] / df["var_exp"].sum() * 2 * pi
+    df.sort_values(by=["classification", "var_exp"], inplace=True)
+
+    cds = models.ColumnDataSource(
+        data=dict(
+            kappa=df["kappa"],
+            rho=df["rho"],
+            varexp=df["var_exp"],
+            kappa_rank=df["kappa_rank"],
+            rho_rank=df["rho_rank"],
+            varexp_rank=df["var_exp_rank"],
+            component=[str(i) for i in df["component"]],
+            color=df["color"],
+            size=df["var_exp_size"],
+            classif=df["classification"],
+            angle=df["angle"],
+        )
+    )
 
     return cds
 
@@ -137,28 +149,43 @@ def _create_kr_plt(comptable_cds):
         Bokeh scatter plot of kappa vs. rho
     """
     # Create Panel for the Kappa - Rho Scatter
-    kr_hovertool = models.HoverTool(tooltips=[('Component ID', '@component'),
-                                              ('Kappa', '@kappa{0.00}'),
-                                              ('Rho', '@rho{0.00}'),
-                                              ('Var. Expl.', '@varexp{0.00}%')])
-    fig = plotting.figure(plot_width=400, plot_height=400,
-                          tools=["tap,wheel_zoom,reset,pan,crosshair,save", kr_hovertool],
-                          title="Kappa / Rho Plot")
-    diagonal = models.Slope(gradient=1, y_intercept=0, line_color='#D3D3D3')
+    kr_hovertool = models.HoverTool(
+        tooltips=[
+            ("Component ID", "@component"),
+            ("Kappa", "@kappa{0.00}"),
+            ("Rho", "@rho{0.00}"),
+            ("Var. Expl.", "@varexp{0.00}%"),
+        ]
+    )
+    fig = plotting.figure(
+        plot_width=400,
+        plot_height=400,
+        tools=["tap,wheel_zoom,reset,pan,crosshair,save", kr_hovertool],
+        title="Kappa / Rho Plot",
+    )
+    diagonal = models.Slope(gradient=1, y_intercept=0, line_color="#D3D3D3")
     fig.add_layout(diagonal)
-    fig.circle('kappa', 'rho', size='size', color='color', alpha=0.5, source=comptable_cds,
-               legend_group='classif')
-    fig.xaxis.axis_label = 'Kappa'
-    fig.yaxis.axis_label = 'Rho'
+    fig.circle(
+        "kappa",
+        "rho",
+        size="size",
+        color="color",
+        alpha=0.5,
+        source=comptable_cds,
+        legend_group="classif",
+    )
+    fig.xaxis.axis_label = "Kappa"
+    fig.yaxis.axis_label = "Rho"
     fig.toolbar.logo = None
     fig.legend.background_fill_alpha = 0.5
-    fig.legend.orientation = 'horizontal'
-    fig.legend.location = 'bottom_right'
+    fig.legend.orientation = "horizontal"
+    fig.legend.location = "bottom_right"
     return fig
 
 
-def _create_sorted_plt(comptable_cds, n_comps, x_var, y_var, title=None,
-                       x_label=None, y_label=None):
+def _create_sorted_plt(
+    comptable_cds, n_comps, x_var, y_var, title=None, x_label=None, y_label=None
+):
     """
     Create dynamic sorted plots
 
@@ -187,18 +214,26 @@ def _create_sorted_plt(comptable_cds, n_comps, x_var, y_var, title=None,
     fig: bokeh.plotting.figure.Figure
         Bokeh plot of components ranked by a given feature
     """
-    hovertool = models.HoverTool(tooltips=[('Component ID', '@component'),
-                                           ('Kappa', '@kappa{0.00}'),
-                                           ('Rho', '@rho{0.00}'),
-                                           ('Var. Expl.', '@varexp{0.00}%')])
-    fig = plotting.figure(plot_width=400, plot_height=400,
-                          tools=["tap,wheel_zoom,reset,pan,crosshair,save", hovertool],
-                          title=title)
-    fig.line(x=np.arange(1, n_comps + 1),
-             y=comptable_cds.data[y_var].sort_values(ascending=False).values,
-             color='black')
-    fig.circle(x_var, y_var, source=comptable_cds,
-               size=5, color='color', alpha=0.7)
+    hovertool = models.HoverTool(
+        tooltips=[
+            ("Component ID", "@component"),
+            ("Kappa", "@kappa{0.00}"),
+            ("Rho", "@rho{0.00}"),
+            ("Var. Expl.", "@varexp{0.00}%"),
+        ]
+    )
+    fig = plotting.figure(
+        plot_width=400,
+        plot_height=400,
+        tools=["tap,wheel_zoom,reset,pan,crosshair,save", hovertool],
+        title=title,
+    )
+    fig.line(
+        x=np.arange(1, n_comps + 1),
+        y=comptable_cds.data[y_var].sort_values(ascending=False).values,
+        color="black",
+    )
+    fig.circle(x_var, y_var, source=comptable_cds, size=5, color="color", alpha=0.7)
     fig.xaxis.axis_label = x_label
     fig.yaxis.axis_label = y_label
     fig.x_range = models.Range1d(-1, n_comps + 1)
@@ -208,22 +243,34 @@ def _create_sorted_plt(comptable_cds, n_comps, x_var, y_var, title=None,
 
 
 def _create_varexp_pie_plt(comptable_cds, n_comps):
-    fig = plotting.figure(plot_width=400, plot_height=400, title='Variance Explained View',
-                          tools=['hover,tap,save'],
-                          tooltips=[('Component ID', ' @component'),
-                                    ('Kappa', '@kappa{0.00}'),
-                                    ('Rho', '@rho{0.00}'),
-                                    ('Var. Exp.', '@varexp{0.00}%')])
-    fig.wedge(x=0, y=1, radius=.9,
-              start_angle=transform.cumsum('angle', include_zero=True),
-              end_angle=transform.cumsum('angle'),
-              line_color="white",
-              fill_color='color', source=comptable_cds, fill_alpha=0.7)
+    fig = plotting.figure(
+        plot_width=400,
+        plot_height=400,
+        title="Variance Explained View",
+        tools=["hover,tap,save"],
+        tooltips=[
+            ("Component ID", " @component"),
+            ("Kappa", "@kappa{0.00}"),
+            ("Rho", "@rho{0.00}"),
+            ("Var. Exp.", "@varexp{0.00}%"),
+        ],
+    )
+    fig.wedge(
+        x=0,
+        y=1,
+        radius=0.9,
+        start_angle=transform.cumsum("angle", include_zero=True),
+        end_angle=transform.cumsum("angle"),
+        line_color="white",
+        fill_color="color",
+        source=comptable_cds,
+        fill_alpha=0.7,
+    )
     fig.axis.visible = False
     fig.grid.visible = False
     fig.toolbar.logo = None
 
-    circle = models.Circle(x=0, y=1, size=150, fill_color='white', line_color='white')
+    circle = models.Circle(x=0, y=1, size=150, fill_color="white", line_color="white")
     fig.add_glyph(circle)
 
     return fig
@@ -248,10 +295,14 @@ def _tap_callback(comptable_cds, div_content, io_generator):
     CustomJS: bokeh.models.CustomJS
         Javascript function that adds the tapping functionality
     """
-    return models.CustomJS(args=dict(source_comp_table=comptable_cds,
-                                     div=div_content,
-                                     outdir=io_generator.out_dir),
-                           code=tap_callback_jscode)
+    return models.CustomJS(
+        args=dict(
+            source_comp_table=comptable_cds,
+            div=div_content,
+            outdir=io_generator.out_dir,
+        ),
+        code=tap_callback_jscode,
+    )
 
 
 def _link_figures(fig, comptable_ds, div_content, io_generator):
@@ -280,8 +331,5 @@ def _link_figures(fig, comptable_ds, div_content, io_generator):
         its Tap event.
 
     """
-    fig.js_on_event(events.Tap,
-                    _tap_callback(comptable_ds,
-                                  div_content,
-                                  io_generator))
+    fig.js_on_event(events.Tap, _tap_callback(comptable_ds, div_content, io_generator))
     return fig

tedana/reporting/html_report.py

@@ -37,12 +37,14 @@ def _generate_buttons(out_dir):
     with open(str(buttons_template_path), "r") as buttons_file:
         buttons_tpl = Template(buttons_file.read())
 
-    buttons_html = buttons_tpl.substitute(optcomdisp=optcom_nogsr_disp,
-                                          denoiseddisp=denoised_mir_disp,
-                                          accepteddisp=accepted_mir_disp,
-                                          optcomname=optcom_name,
-                                          denoisedname=denoised_name,
-                                          acceptedname=accepted_name)
+    buttons_html = buttons_tpl.substitute(
+        optcomdisp=optcom_nogsr_disp,
+        denoiseddisp=denoised_mir_disp,
+        accepteddisp=accepted_mir_disp,
+        optcomname=optcom_name,
+        denoisedname=denoised_name,
+        acceptedname=accepted_name,
+    )
 
     return buttons_html
 
@@ -69,7 +71,9 @@ def _update_template_bokeh(bokeh_id, about, bokeh_js, buttons):
     body_template_path = resource_path.joinpath(body_template_name)
     with open(str(body_template_path), "r") as body_file:
         body_tpl = Template(body_file.read())
-    body = body_tpl.substitute(content=bokeh_id, about=about, javascript=bokeh_js, buttons=buttons)
+    body = body_tpl.substitute(
+        content=bokeh_id, about=about, javascript=bokeh_js, buttons=buttons
+    )
     return body