Merge pull request #7 from yamada-lab/develop

Develop

Author: omixer

.settings/.gitignore

@@ -0,0 +1 @@
+/ts.eclipse.ide.core.prefs

Dockerfile

@@ -23,10 +23,10 @@ ADD . /app/
 WORKDIR /app/
 
 RUN pip3 install -r requirements.txt
+
 RUN npm install --global yarn && \
   yarn run install-depends && \
   yarn run install-devDepends && \
-  yarn run bower install && \
   yarn run build
 
 EXPOSE 3031 8080

README.md

@@ -13,6 +13,7 @@ $ cd functree-ng
 #### Using Docker and Docker Compose (preferred and supported way)
 Execute the command below. `docker-compose` will automatically set up Docker containers:
 ```bash
+$ docker-compose build
 $ docker-compose up
 ```
 Then open http://localhost:8080 in your web browser.

bower.json

@@ -4,6 +4,16 @@
 from functree.analysis import module_coverage, display, basic_mapping, comparison
 from functools import reduce
 
+def load_input(f):
+    '''
+    Read the csv file and remove duplicated rows
+    '''
+    df = pd.read_csv(f, delimiter='\t', comment='#', header=0, index_col=0)
+    # remove duplicated rows if any
+    if not df.index.is_unique:
+        df = df.groupby(level=0).sum()
+    return df
+
 def map_external_annotations(df):
     '''
     Find KO annotation for the entries of df then create and new df with K Numbers and rows divided by the number of matched KNumbers 
@@ -80,8 +90,9 @@ def calc_abundances(df, nodes, method, results):
             df_dict[node['entry']] = entry_profile.to_dict().values()
 
     df_out = pd.DataFrame.from_dict(df_dict, "index")
-    df_out.columns = df.columns
-    df_out = df_out.dropna(how='all').fillna(0.0)
+    if not df_out.empty:
+        df_out.columns = df.columns
+        df_out = df_out.dropna(how='all').fillna(0.0)
     results[method] = df_out
 
 def calc_distributed_abundances(df, graph, method, results):

functree/analysis/__init__.py

@@ -1,40 +1,42 @@
 import uuid, datetime, multiprocessing
 import pandas as pd
-from functree import app, models, tree, analysis, services
+from functree import app, constants, models, tree, analysis, services
 
 def from_table(form):
     methods=['mean', 'sum']
     if form.modulecoverage.data and services.DefinitionService.has_definition(form.target.data):
         methods.append('modulecoverage')
     result = calc_abundances(f=form.input_file.data, target=form.target.data, methods=methods, distribute=form.distribute.data)
-
-    colors = []
-    if form.color_file.data:
-        colors = pd.read_csv(form.color_file.data, header=None, delimiter='\t').as_matrix().tolist()
-    utcnow = datetime.datetime.utcnow()
-    
 
-    # This inset is 4 seconds
-    # Maybe it is document size Or maybe I shoudl use insert instead of save
+    profile_id = constants.NO_MATCHED_HIERARCHIES
+    # if rows were mapped 
+    if len(result['profile']) > 0:
+        colors = []
+        if form.color_file.data:
+            colors = pd.read_csv(form.color_file.data, header=None, delimiter='\t').as_matrix().tolist()
+        utcnow = datetime.datetime.utcnow()
 
-    return models.Profile(
-        profile_id=uuid.uuid4(),
-        profile=result['profile'],
-        series=result['series'],
-        columns=result['columns'],
-        colors=colors,
-        target=form.target.data,
-        description=form.description.data,
-        added_at=utcnow,
-        expire_at=utcnow + datetime.timedelta(days=app.config['FUNCTREE_PROFILE_TTL_DAYS']),
-        private=form.private.data
-    ).save().profile_id
+        # This inset is 4 seconds
+        # Maybe it is document size Or maybe I shoudl use insert instead of save
+        profile_id = models.Profile(
+            profile_id=uuid.uuid4(),
+            profile=result['profile'],
+            series=result['series'],
+            columns=result['columns'],
+            colors=colors,
+            target=form.target.data,
+            description=form.description.data,
+            added_at=utcnow,
+            expire_at=utcnow + datetime.timedelta(days=app.config['FUNCTREE_PROFILE_TTL_DAYS']),
+            private=form.private.data
+        ).save().profile_id
+    
+    return profile_id
 
 
 def calc_abundances(f, target, methods, distribute):
 
-    df = pd.read_csv(f, delimiter='\t', comment='#', header=0, index_col=0)
-    
+    df = analysis.load_input(f)
     # transform external annotations to kegg KOs
     # runs in 1.66
     if target.lower() in ["kegg", "foam", "enteropathway"]:
@@ -66,21 +68,22 @@ def calc_abundances(f, target, methods, distribute):
     results = dict(shared_data)
     profile = []
     # load KO based entries
-    entries=list(list(results.values())[0].index)
-    if "modulecoverage" in methods:
-        entries += list(list(results.values())[2].index)
-        entries = list(set(entries))
-
-    # This is taking 2 to 4 seconds to run
-    for entry in entries:
-        #values = [results[method].ix[entry].tolist() for method in methods]
-        values = []
-        for method in methods:
-            if entry in results[method].index:
-                values.append(results[method].ix[entry].tolist())
-            else:
-                values.append([0] * df.columns.size)
-        profile.append({'entry': entry, 'layer': analysis.get_layer(entry, entry_to_layer), 'values': values})
+    if len(results) > 0:
+        entries=list(list(results.values())[0].index)
+        if "modulecoverage" in methods:
+            entries += list(list(results.values())[2].index)
+            entries = list(set(entries))
+    
+        # This is taking 2 to 4 seconds to run
+        for entry in entries:
+            #values = [results[method].ix[entry].tolist() for method in methods]
+            values = []
+            for method in methods:
+                if entry in results[method].index:
+                    values.append(results[method].ix[entry].tolist())
+                else:
+                    values.append([0] * df.columns.size)
+            profile.append({'entry': entry, 'layer': analysis.get_layer(entry, entry_to_layer), 'values': values})
     data = {
         'profile': profile,
         'series': methods,

functree/analysis/basic_mapping.py

@@ -24,8 +24,8 @@ def from_table(form):
 
 
 def calc_abundances(f1, f2, target):
-    df1 = pd.read_csv(f1, delimiter='\t', comment='#', header=0, index_col=0)
-    df2 = pd.read_csv(f2, delimiter='\t', comment='#', header=0, index_col=0)
+    df1 = analysis.load_input(f1)
+    df2 = analysis.load_input(f2)
     root = models.Tree.objects().get(source=target)['tree']
     nodes = tree.get_nodes(root)
     entry_to_layer = dict(map(lambda x: (x['entry'], x['layer']), nodes))

functree/analysis/comparison.py

@@ -0,0 +1 @@
+NO_MATCHED_HIERARCHIES = 'NO_MATCHED_HIERARCHIES'

functree/constants.py

@@ -943,3 +943,26 @@ function zoomClick(zoom, config) {
 
     interpolateZoom([view.x, view.y], view.k, zoom);
 }
+
+/**
+ * Matcher for the typeahead plugin
+ * @param strs
+ * @returns
+ */
+var substringMatcher = function (strs) {
+	return function findMatches(q, cb) {
+		// an array that will be populated with substring matches
+		var matches = [];
+		// regex used to determine if a string contains the substring `q`
+		var substrRegex = new RegExp(q, 'i');
+
+		// iterate through the pool of strings and for any string that
+		// contains the substring `q`, add it to the `matches` array
+		$.each(strs, function(i, str) {
+			if (substrRegex.test(str)) {
+				matches.push(str);
+			}
+		});
+		cb(matches);
+	};
+};

functree/static/src/js/functree.js

@@ -1,6 +1,6 @@
 'use strict';
 
-const visualize = (profile, columns, column, color, opacity, width, map) => {
+const visualize = (profile, columns, column, cutoff, color, opacity, width, map) => {
 
     /** iPath */
     {
@@ -13,8 +13,8 @@ const visualize = (profile, columns, column, color, opacity, width, map) => {
         const selection = seriesData.filter((x) => {
             // let layers = ['pathway', 'module', 'ko'];
             let layers = ['ko'];
-            // Match KO layer and ensure that the value is above > 0 
-            return ~layers.indexOf(x.layer) && x.y > 0;
+            // Match KO layer and ensure that the value is above > cutoff
+            return ~layers.indexOf(x.layer) && x.y > cutoff;
         }).map((x) => {
         	return x.name + ' ' + color + ' W' + (width) + ' ' + opacity
         }).join('\n');

functree/static/src/js/pathways.js

@@ -9,4 +9,106 @@ $autocomplete_height: 120px;
 
 * html .ui-autocomplete {
     height: $autocomplete_height;
+}
+
+// enable scrolling for typeahead
+#map-element-search .tt-menu {
+  max-height: 150px;
+  overflow-y: auto;
+}
+
+// the part below is adapted from https://github.com/bassjobsen/typeahead.js-bootstrap-css/blob/master/typeaheadjs.css
+span.twitter-typeahead .tt-menu,
+span.twitter-typeahead .tt-dropdown-menu {
+  cursor: pointer;
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 100%;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  font-size: 14px;
+  text-align: left;
+  background-color: #ffffff;
+  border: 1px solid #cccccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 4px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  background-clip: padding-box;
+}
+span.twitter-typeahead .tt-suggestion {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+span.twitter-typeahead .tt-suggestion.tt-cursor,
+span.twitter-typeahead .tt-suggestion:hover,
+span.twitter-typeahead .tt-suggestion:focus {
+  color: #ffffff;
+  text-decoration: none;
+  outline: 0;
+  background-color: #337ab7;
+}
+.input-group.input-group-lg span.twitter-typeahead .form-control {
+  height: 46px;
+  padding: 10px 16px;
+  font-size: 18px;
+  line-height: 1.3333333;
+  border-radius: 6px;
+}
+.input-group.input-group-sm span.twitter-typeahead .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 3px;
+}
+span.twitter-typeahead {
+  width: 100%;
+}
+.input-group span.twitter-typeahead {
+  display: block !important;
+  height: 34px;
+}
+.input-group span.twitter-typeahead .tt-menu,
+.input-group span.twitter-typeahead .tt-dropdown-menu {
+  top: 32px !important;
+}
+.input-group span.twitter-typeahead:not(:first-child):not(:last-child) .form-control {
+  border-radius: 0;
+}
+.input-group span.twitter-typeahead:first-child .form-control {
+  border-top-left-radius: 4px;
+  border-bottom-left-radius: 4px;
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.input-group span.twitter-typeahead:last-child .form-control {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+  border-top-right-radius: 4px;
+  border-bottom-right-radius: 4px;
+}
+.input-group.input-group-sm span.twitter-typeahead {
+  height: 30px;
+}
+.input-group.input-group-sm span.twitter-typeahead .tt-menu,
+.input-group.input-group-sm span.twitter-typeahead .tt-dropdown-menu {
+  top: 30px !important;
+}
+.input-group.input-group-lg span.twitter-typeahead {
+  height: 46px;
+}
+.input-group.input-group-lg span.twitter-typeahead .tt-menu,
+.input-group.input-group-lg span.twitter-typeahead .tt-dropdown-menu {
+  top: 46px !important;
 }

functree/static/src/scss/functree.scss

@@ -0,0 +1,5 @@
+.action {
+	color: #6b8ed6;
+	border-color: #6b8ed6;
+	font-weight: bold;
+}

functree/static/src/scss/nomapping.scss

@@ -10,4 +10,34 @@
 	height: 16px;
 	background-color: #000;
 	cursor: pointer;
+}
+
+.slider {
+  -webkit-appearance: none;
+  width: 100%;
+  height: 15px;
+  border-radius: 5px;   
+  background: #d3d3d3;
+  outline: none;
+  opacity: 0.7;
+  -webkit-transition: .2s;
+  transition: opacity .2s;
+}
+
+.slider::-webkit-slider-thumb {
+  -webkit-appearance: none;
+  appearance: none;
+  width: 25px;
+  height: 25px;
+  border-radius: 50%; 
+  background: #337ab7;
+  cursor: pointer;
+}
+
+.slider::-moz-range-thumb {
+  width: 25px;
+  height: 25px;
+  border-radius: 50%;
+  background: #337ab7;
+  cursor: pointer;
 }

functree/static/src/scss/pathways.scss

@@ -25,6 +25,13 @@ <h3>Publications</h3>
     <hr />
     <h3>Change log</h3>
     <br />
+    <h5>Version 0.8.1.0 (2019-03-08)</h5>
+	<ul>
+		<li>Enhancement of the search box for map elements.
+		<li>Addition of a cutoff slider for filtering the data submitted to iPath.
+		<li>Handling of matrices with duplicated entries by taking the sum of duplicates.
+		<li>Handling of matrices that result in no mapping to the hierarchy.
+	</ul>
 	<h5>Version 0.8.0.0 (2019-02-28)</h5>
 	<ul>
 		<li>Improvement of iPath integration