ME-ICA · emdupre · Jul 13, 2020 · Nov 11, 2019 · Nov 11, 2019 · Nov 11, 2019
diff --git a/.gitignore b/.gitignore
@@ -104,4 +104,8 @@ ENV/
 .mypy_cache/
 
 # vscode
-.vscode
+.vscode
+
+# jupyter notebooks
+.ipynb_checkpoints/
+*.ipynb
diff --git a/requirements.txt b/requirements.txt
@@ -7,3 +7,4 @@ pandas
 scikit-learn>=0.22
 scipy>=1.3.3
 threadpoolctl
+bokeh
diff --git a/setup.py b/setup.py
@@ -31,6 +31,8 @@ def main():
     pkg_data = {
         'tedana': [
             'tests/data/*',
+            'reporting/data/*',
+            'reporting/data/html/*',
         ]
     }
 

diff --git a/tedana/reporting/__init__.py b/tedana/reporting/__init__.py
@@ -0,0 +1,8 @@
+"""
+Reporting code for tedana
+"""
+
+from .html_report import generate_report
+from .static_figures import comp_figures
+
+__all__ = ['generate_report', 'comp_figures']
diff --git a/tedana/reporting/data/README.md b/tedana/reporting/data/README.md
@@ -0,0 +1,3 @@
+This directory contains data required for tedana reporting.
+
+html/ :                         HTML templates
diff --git a/tedana/reporting/data/html/__init__.py b/tedana/reporting/data/html/__init__.py
diff --git a/tedana/reporting/data/html/report_body_template.html b/tedana/reporting/data/html/report_body_template.html
@@ -0,0 +1,25 @@
+<style type="text/css">
+    .content {
+        padding: 35px 20px 10px 20px;
+        margin-left: 50px;
+        margin-top: 50px;
+        height: 900px;
+    }
+    .about {
+        position: relative;
+        top: -50px;
+        margin-left: 5%;
+        margin-right: 5%;
+        white-space: pre-line;
+        font-family: Arial, Helvetica, sans-serif;
+        font-size: larger;
+    }
+</style>
+<div class="content">
+    $content
+</div>
+<div class="about">
+    $about
+</div>
+
+$javascript
diff --git a/tedana/reporting/data/html/report_head_template.html b/tedana/reporting/data/html/report_head_template.html
@@ -0,0 +1,38 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+<title>tedana report</title>
+<meta name="viewport" content="width = device-width, initial-scale = 1" charset="UTF-8">
+<script src="https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js"
+        crossorigin="anonymous"></script>
+<script src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js"
+        crossorigin="anonymous"></script>
+<script src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js"
+        crossorigin="anonymous"></script>
+<link rel="stylesheet" href="https://unpkg.com/[email protected]/build/pure-min.css"
+    integrity="sha384-oAOxQR6DkCoMliIh8yFnu25d7Eq/PHS21PClpwjOTeU2jRSq11vu66rf90/cZr47" crossorigin="anonymous">
+<!-- CSS for the report -->
+<style type="text/css">
+    body {
+        padding: 10px 20px 10px 20px;
+    }
+</style>
+
+</head>
+
+<nav>
+  <div class="pure-menu pure-menu-horizontal">
+    <a href="https://github.com/ME-ICA/tedana" class="pure-menu-heading pure-menu-link">tedana $version</a>
+    <ul class="pure-menu-list">
+        <li class="pure-menu-item">
+            <a href="https://tedana.readthedocs.io" class="pure-menu-link">Reporting documentation</a>
+        </li>
+    </ul>
+  </div>
+</nav>
+
+<body>
+    $body
+</body>
+</html>
diff --git a/tedana/reporting/dynamic_figures.py b/tedana/reporting/dynamic_figures.py
@@ -0,0 +1,284 @@
+import numpy as np
+from math import pi
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+from bokeh import (events, models, plotting, transform)
+
+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) {
+        // A component has been selected
+        // -----------------------------
+        var components = data['component']
+        var selected = components[selected_idx]
+        var selected_padded = '' + selected;
+        while (selected_padded.length < 2) {
+            selected_padded = '0' + selected_padded;
+        }
+        var selected_padded_forIMG = '0' + selected_padded
+        var selected_padded_C = 'ica_' + selected_padded
+
+        // Find color for selected component
+        var colors = data['color']
+        var this_component_color = colors[selected_idx]
+
+        // Image Below Plots
+        div.text = ""
+        var line = "<span><img src='./figures/comp_"+selected_padded_forIMG+".png'" +
+            " alt='Component Map'><span>\\n";
+        console.log('Linea: ' + line)
+        var text = div.text.concat(line);
+        var lines = text.split("\\n")
+            if (lines.length > 35)
+                lines.shift();
+        div.text = lines.join("\\n");
+
+    } else {
+        // No component has been selected
+        // ------------------------------
+        // Set Component color to Black
+        var this_component_color = '#000000'
+
+        // Image Below Plots
+        div.text = ""
+        var line = "<p>Please select an individual component to view it in more detail</p>\\n"
+        var text = div.text.concat(line);
+
+    }
+    """
+
+
+def _create_data_struct(comptable_path, color_mapping=color_mapping):
+    """
+    Create Bokeh ColumnDataSource with all info dynamic plots need
+
+    Parameters
+    ----------
+    comptable: str
+        file path to component table, JSON format
+
+    Returns
+    -------
+    cds: bokeh.models.ColumnDataSource
+        Data structure with all the fields to plot or hover over
+    """
+    unused_cols = ['normalized variance explained',
+                   'countsigFR2', 'countsigFS0',
+                   'dice_FS0', 'countnoise', 'dice_FR2',
+                   'signal-noise_t', 'signal-noise_p',
+                   'd_table_score', 'kappa ratio',
+                   'rationale', 'd_table_score_scrub']
+
+    df = pd.read_json(comptable_path)
+    df.drop('Description', axis=0, inplace=True)
+    df.drop('Method', axis=1, inplace=True)
+    df = df.T
+    n_comps = df.shape[0]
+
+    # remove space from column name
+    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]
+
+    # 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
+
+    # 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')
+
+    # Create additional Column with colors based on final classification
+    df['color'] = [color_mapping[i] for i in df['classification']]
+
+    # Create additional column with component ID
+    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']))
+
+    return cds
+
+
+def _create_kr_plt(comptable_cds):
+    """
+    Create Dymamic Kappa/Rho Scatter Plot
+
+    Parameters
+    ----------
+    comptable_cds: bokeh.models.ColumnDataSource
+        Data structure containing a limited set of columns from the comp_table
+
+    Returns
+    -------
+    fig: bokeh.plotting.figure.Figure
+        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")
+    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'
+    return fig
+
+
+def _create_sorted_plt(comptable_cds, n_comps, x_var, y_var, title=None,
+                       x_label=None, y_label=None):
+    """
+    Create dynamic sorted plots
+
+    Parameters
+    ----------
+    comptable_ds: bokeh.models.ColumnDataSource
+        Data structure containing a limited set of columns from the comp_table
+
+    x_var: str
+        Name of variable for the x-axis
+
+    y_var: str
+        Name of variable for the y-axis
+
+    title: str
+        Plot title
+
+    x_label: str
+        X-axis label
+
+    y_label: str
+        Y-axis label
+
+    Returns
+    -------
+    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)
+    fig.xaxis.axis_label = x_label
+    fig.yaxis.axis_label = y_label
+    fig.x_range = models.Range1d(-1, n_comps + 1)
+    fig.toolbar.logo = None
+
+    return fig
+
+
+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.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')
+    fig.add_glyph(circle)
+
+    return fig
+
+
+def _tap_callback(comptable_cds, div_content, out_dir):
+    """
+    Javacript function to animate tap events and show component info on the right
+
+    Parameters
+    ----------
+    CDS: bokeh.models.ColumnDataSource
+        Data structure containing a limited set of columns from the comp_table
+    div: bokeh.models.Div
+        Target Div element where component images will be loaded
+
+    Returns
+    -------
+    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=out_dir), code=tap_callback_jscode)
+
+
+def _link_figures(fig, comptable_ds, div_content, out_dir):
+    """
+    Links figures and adds interaction on mouse-click.
+
+    Parameters
+    ----------
+    fig : bokeh.plotting.figure
+        Figure containing a given plot
+
+    comptable_ds : bokeh.models.ColumnDataSource
+        Data structure with a limited version of the comptable
+        suitable for dynamic plot purposes
+
+    div_content : bokeh.models.Div
+        Div element for additional HTML content.
+
+    out_dir : str
+        Output directory of tedana results.
+
+    Returns
+    -------
+    fig : bokeh.plotting.figure
+        Same as input figure, but with a linked method to
+        its Tap event.
+
+    """
+    fig.js_on_event(events.Tap,
+                    _tap_callback(comptable_ds,
+                                  div_content,
+                                  out_dir))
+    return fig
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,3 @@
		This directory contains data required for tedana reporting.

		html/ : HTML templates