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medusa.py
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#!/usr/bin/env python3
import os
import sys
import math
import datetime
import argparse
import h5py
import pandas as pd
import numpy as np
import functools
from tensorflow.keras.models import model_from_json
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.optimizers import Adam
from itertools import product
from sklearn.metrics import matthews_corrcoef
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument("-i", help="Path to the merge feature vector file.", type=str, required=True)
parser.add_argument("-o", "--output", help="Path to the output directory.", type=str, required=True)
parser.add_argument("-m", "--models", help="Path to the models directory.", type=str, required=True)
parser.add_argument("-f", "--fasta", help="Path to the fasta file.", type=str, required=True)
parser.add_argument("-S", "--strict", help="Strict prediction.", action="store_true", default=True)
parser.add_argument("-NS", "--nonstrict", help="Non strict prediction.", action="store_true", default=True)
parser.add_argument("-3", "--three", help="3 classes prediction.", action="store_true", default=True)
parser.add_argument("-5", "--five", help="5 classes prediction.", action="store_true", default=True)
parser.add_argument("-d", "--dim", metavar="D", type=int, nargs=2, help="Rows Columns", required=True)
args = parser.parse_args()
MODEL = args.models
#Check if the merge vector file is valid
merge = args.i
if not os.path.isfile(merge):
sys.exit(f"{merge} does not exist.\n"
"Please enter a valid merge vector file.")
OUTPUT = args.output
fasta = args.fasta
if not os.path.isfile(fasta):
sys.exit(f"{fasta} does not exist.\n"
"Please enter a valid fasta file.")
if not (args.strict or args.nonstrict or three or five):
parser.error("No model selected.")
model_list = []
if args.strict:
model_list.append("S")
if args.nonstrict:
model_list.append("NS")
if args.three:
model_list.append("3")
if args.five:
model_list.append("5")
ROWS, COLS = args.dim
if __name__ == "__main__":
#Get entry files path
ENTRY = merge
########################### LOADING MODELS #################################
if "S" in model_list:
PATH_MODEL_JSON = os.path.join(MODEL, "S", "model.json")
PATH_MODEL_H5 = os.path.join(MODEL, "S", "weights_S.h5")
# Load JSON arch
with open(PATH_MODEL_JSON, "r") as json_file:
loaded_model_json = json_file.read()
model_S = model_from_json(loaded_model_json)
# Load weights from H5
model_S.load_weights(PATH_MODEL_H5)
# Compile model
model_S.compile(loss=CategoricalCrossentropy(),
optimizer=Adam())
if "NS" in model_list:
PATH_MODEL_JSON = os.path.join(MODEL, "NS", "model.json")
PATH_MODEL_H5 = os.path.join(MODEL, "NS", "weights_NS.h5")
# Load JSON arch
with open(PATH_MODEL_JSON, "r") as json_file:
loaded_model_json = json_file.read()
model_NS = model_from_json(loaded_model_json)
# Load weights from H5
model_NS.load_weights(PATH_MODEL_H5)
# Compile model
model_NS.compile(loss=CategoricalCrossentropy(),
optimizer=Adam())
if "3" in model_list:
PATH_MODEL_JSON = os.path.join(MODEL, "3", "model.json")
PATH_MODEL_H5 = os.path.join(MODEL, "3", "weights_3.h5")
# Load JSON arch
with open(PATH_MODEL_JSON, "r") as json_file:
loaded_model_json = json_file.read()
model_3 = model_from_json(loaded_model_json)
# Load weights from H5
model_3.load_weights(PATH_MODEL_H5)
# Compile model
model_3.compile(loss=CategoricalCrossentropy(),
optimizer=Adam())
if "5" in model_list:
PATH_MODEL_JSON = os.path.join(MODEL, "5", "model.json")
PATH_MODEL_H5 = os.path.join(MODEL, "5", "weights_5.h5")
# Load JSON arch
with open(PATH_MODEL_JSON, "r") as json_file:
loaded_model_json = json_file.read()
model_5 = model_from_json(loaded_model_json)
# Load weights from H5
model_5.load_weights(PATH_MODEL_H5)
# Compile model
model_5.compile(loss=CategoricalCrossentropy(),
optimizer=Adam())
#Load X
X = np.loadtxt(ENTRY)
x_test = np.reshape(X, (len(X), ROWS, COLS))
#Load fasta file
fasta_file = np.loadtxt(fasta, dtype="str", skiprows=1)
seq_AA = fasta_file.tolist()
#Create output directory
os.makedirs(OUTPUT, exist_ok=False)
if "S" in model_list:
y_pred = model_S.predict(x_test, verbose=0)
y_pred_classes = np.argmax(y_pred, axis=1)
prob_y_pred = np.amax(y_pred, axis=1)
# Create a dataframe from zipped list
zippedList = list(zip(seq_AA, y_pred_classes, prob_y_pred, y_pred[:, 0], y_pred[:, 1]))
df = pd.DataFrame(zippedList, columns = ["res", "S", "P_max", "P_0", "P_1"])
df.to_csv(os.path.join(OUTPUT, "S_prediction.csv"), index=False, float_format="%.2f", sep="\t")
if "NS" in model_list:
y_pred = model_NS.predict(x_test, verbose=0)
y_pred_classes = np.argmax(y_pred, axis=1)
prob_y_pred = np.amax(y_pred, axis=1)
# Create a dataframe from zipped list
zippedList = list(zip(seq_AA, y_pred_classes, prob_y_pred, y_pred[:, 0], y_pred[:, 1]))
df = pd.DataFrame(zippedList, columns = ["res", "NS", "P_max", "P_0", "P_1"])
df.to_csv(os.path.join(OUTPUT, "NS_prediction.csv"), index=False, float_format="%.2f", sep="\t")
if "3" in model_list:
y_pred = model_3.predict(x_test, verbose=0)
y_pred_classes = np.argmax(y_pred, axis=1)
prob_y_pred = np.amax(y_pred, axis=1)
# Create a dataframe from zipped list
zippedList = list(zip(seq_AA, y_pred_classes, prob_y_pred, y_pred[:, 0], y_pred[:, 1], y_pred[:, 2]))
df = pd.DataFrame(zippedList, columns = ["res", "pred_3", "P_max", "P_0", "P_1", "P_2"])
df.to_csv(os.path.join(OUTPUT, "3_prediction.csv"), index=False, float_format="%.2f", sep="\t")
if "5" in model_list:
y_pred = model_5.predict(x_test, verbose=0)
y_pred_classes = np.argmax(y_pred, axis=1)
prob_y_pred = np.amax(y_pred, axis=1)
# Create a dataframe from zipped list
zippedList = list(zip(seq_AA, y_pred_classes, prob_y_pred, y_pred[:, 0], y_pred[:, 1], y_pred[:, 2], y_pred[:, 3], y_pred[:, 4]))
df = pd.DataFrame(zippedList, columns = ["res", "pred_5", "P_max", "P_0", "P_1", "P_2", "P_3", "P_4"])
df.to_csv(os.path.join(OUTPUT, "5_prediction.csv"), index=False, float_format="%.2f", sep="\t")
header = ""
with open(fasta, "r") as f:
header = f.readline().strip()[1:]
os.makedirs(f"{OUTPUT}/../html", exist_ok=False)
with open(f"{OUTPUT}/../html/results.html", "w") as f:
f.write(f"""
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
<meta name="description" content="" />
<meta name="author" content="" />
<title>MEDUSA | Results</title>
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.0/css/bootstrap.min.css" integrity="sha384-9aIt2nRpC12Uk9gS9baDl411NQApFmC26EwAOH8WgZl5MYYxFfc+NcPb1dKGj7Sk" crossorigin="anonymous"/>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/popper.js@1.16.0/dist/umd/popper.min.js" integrity="sha384-Q6E9RHvbIyZFJoft+2mJbHaEWldlvI9IOYy5n3zV9zzTtmI3UksdQRVvoxMfooAo" crossorigin="anonymous"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.0/js/bootstrap.min.js" integrity="sha384-OgVRvuATP1z7JjHLkuOU7Xw704+h835Lr+6QL9UvYjZE3Ipu6Tp75j7Bh/kR0JKI" crossorigin="anonymous"></script>
<script defer src="https://use.fontawesome.com/releases/v5.13.0/js/all.js" integrity="sha384-ujbKXb9V3HdK7jcWL6kHL1c+2Lj4MR4Gkjl7UtwpSHg/ClpViddK9TI7yU53frPN" crossorigin="anonymous"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.9.3/Chart.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/5.16.0/d3.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/html2canvas@0.5.0-beta4/dist/html2canvas.min.js" integrity="sha256-jP/zNgDdOLDvHgG/IPyrDDazrf7b+P6CMMZ/S3EZ6zc=" crossorigin="anonymous"></script>
<!-- Bootstrap core CSS -->
<link href="https://fonts.googleapis.com/css2?family=Overpass:ital,wght@0,100;0,200;0,300;0,400;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,600;1,700;1,800;1,900&display=swap" rel="stylesheet"/>
<link href="https://fonts.googleapis.com/css2?family=Overpass:ital,wght@0,100;0,200;0,300;0,400;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,600;1,700;1,800;1,900&family=Roboto:ital,wght@0,100;0,300;0,400;0,500;0,700;0,900;1,100;1,300;1,400;1,500;1,700;1,900&display=swap" rel="stylesheet"/>
<link href="https://fonts.googleapis.com/css2?family=Roboto+Mono:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;1,100;1,200;1,300;1,400;1,500;1,600;1,700&display=swap" rel="stylesheet"/>
<!-- Custom styles for this template -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" />
<link href="../../../html/css/custom_features.css" rel="stylesheet" />
</head>
<nav class="navbar navbar-expand-lg navbar-light bg-white fixed-top mb-4 pb-0">
<div class="container shadow-sm" id="nav-container">
<a class="navbar-brand"><img src="../../../html/images/MEDUSA_logo.png" alt=""></a>
</div>
</nav>
<body>
<!-- Page Content -->
<div class="container mt-10" id="principal_container">
<div class="alert alert-success" role="alert">
<h1 class="alert-heading">Results</h1>
<p class="text-break m-0"><strong>Query name: </strong> {header}</p>
<p class="text-break m-0"><strong>Query sequence: </strong> {"".join(seq_AA)}</p>
</div>
<nav id="res-nav">
<div class="nav nav-tabs nav-fill mt-6 mb-5" id="nav-tab" role="tablist">
<a class="nav-item nav-link active" id="nav-s-tab" data-toggle="tab" href="#nav-tabS" role="tab" aria-controls="nav-tabS" aria-selected="true">Strict threshold</a>
<a class="nav-item nav-link" id="nav-ns-tab" data-toggle="tab" href="#nav-tabNS" role="tab" aria-controls="nav-tabNS" aria-selected="false">Non-strict threshold</a>
<a class="nav-item nav-link" id="nav-3pred-tab" data-toggle="tab" href="#nav-tab3pred" role="tab" aria-controls="nav-tab3pred" aria-selected="false">3 classes prediction</a>
<a class="nav-item nav-link" id="nav-5pred-tab" data-toggle="tab" href="#nav-tab5pred" role="tab" aria-controls="nav-tab5pred" aria-selected="false">5 classes prediction</a>
</div>
</nav>
<div class="tab-content">
<div class="tab-pane fade show active" id="nav-tabS" role="tabpanel" aria-labelledby="nav-tabS">
<h1 class="alert-heading">Sequence plot</h1>
<a href="javascript:void(0)" class="btn btn-outline-primary btn-sm float-right mt-4" id="btn-download-chartS">Download PNG</a>
<div class="d-flex justify-content-start">
<p class="d-flex align-items-center">Confidence of prediction: </p>
<img src="../../../html/images/legend-2cl.png" alt="legend-2cl" width="150" height="60">
</div>
<div id="divChartS">
</div>
<h1 class="mt-6 alert-heading">Flexibility class distribution</h1>
<a href="javascript:void(0)" class="btn btn-outline-primary btn-sm float-right mt-4" id="btn-download-pieChartS">Download PNG</a>
<div id="divPieChartS" style="width: 60%">
<canvas id="pie_chart_S" height="150"></canvas>
<div class="d-flex justify-content-left">
<img src="../../../html/images/legend_2cl_piechart.png" alt="legend_2cl_piechart" style="height: 70px" class="ml-5"/>
</div>
</div>
</div>
<div class="tab-pane fade" id="nav-tabNS" role="tabpanel" aria-labelledby="nav-tabNS">
<h1 class="alert-heading">Sequence plot</h1>
<a href="javascript:void(0)" class="btn btn-outline-primary btn-sm float-right mt-4" id="btn-download-chartNS">Download PNG</a>
<div class="d-flex justify-content-start">
<p class="d-flex align-items-center">Confidence of prediction: </p>
<img src="../../../html/images/legend-2cl.png" alt="legend-2cl" width = "150" height="60">
</div>
<div id="divChartNS">
</div>
<h1 class="mt-6 alert-heading">Flexibility class distribution</h1>
<a href="javascript:void(0)" class="btn btn-outline-primary btn-sm float-right mt-4" id="btn-download-pieChartNS">Download PNG</a>
<div id="divPieChartNS" style="width: 60%">
<canvas id="pie_chart_NS" height="150"></canvas>
<div class="d-flex justify-content-left">
<img src="../../../html/images/legend_2cl_piechart.png" alt="legend_2cl_piechart" style="height: 70px" class="ml-5"/>
</div>
</div>
</div>
<div class="tab-pane fade" id="nav-tab3pred" role="tabpanel" aria-labelledby="nav-tab3pred">
<h1 class="alert-heading">Sequence plot</h1>
<a href="javascript:void(0)" class="btn btn-outline-primary btn-sm float-right mt-4" id="btn-download-chart3pred">Download PNG</a>
<div class="d-flex justify-content-start">
<p class="d-flex align-items-center">Confidence of prediction: </p>
<img src="../../../html/images/legend-3cl.png" alt="legend-3cl" width = "150" height="60">
</div>
<div id="divChart3pred">
</div>
<h1 class="mt-6 alert-heading">Flexibility class distribution</h1>
<a href="javascript:void(0)" class="btn btn-outline-primary btn-sm float-right mt-4" id="btn-download-pieChart3pred">Download PNG</a>
<div id="divPieChart3pred" style="width: 60%">
<canvas id="pie_chart_3pred" height="150"></canvas>
<div class="d-flex justify-content-left">
<img src="../../../html/images/legend_3cl_piechart.png" alt="legend_3cl_piechart" style="height: 70px" class="ml-5"/>
</div>
</div>
</div>
<div class="tab-pane fade" id="nav-tab5pred" role="tabpanel" aria-labelledby="nav-tab5pred">
<h1 class="alert-heading">Sequence plot</h1>
<a href="javascript:void(0)" class="btn btn-outline-primary btn-sm float-right mt-4" id="btn-download-chart5pred">Download PNG</a>
<div class="d-flex justify-content-start">
<p class="d-flex align-items-center">Confidence of prediction: </p>
<img src="../../../html/images/legend-5cl.png" alt="legend-5cl" width = "150" height="60">
</div>
<div id="divChart5pred">
</div>
<h1 class="mt-6 alert-heading">Flexibility class distribution</h1>
<a href="javascript:void(0)" class="btn btn-outline-primary btn-sm float-right mt-4" id="btn-download-pieChart5pred">Download PNG</a>
<div id="divPieChart5pred" style="width: 60%">
<canvas id="pie_chart_5pred" height="150"></canvas>
<div class="d-flex justify-content-left">
<img src="../../../html/images/flexible_rigid_legend_arrow.png" alt="legend_5cl_piechart" style="height: 70px" class="ml-5"/>
</div>
</div>
</div>
</div>
</div>
<script>
// When page is scrolled towards bottom, a line appears
// to create separation with nav bar
//
$(window).scroll(function() {{
var scroll = $(window).scrollTop();
//>=, not <=
if (scroll >= 100) {{
$("#nav-container").addClass("shadow-sm");
}} else{{
$("#nav-container").removeClass("shadow-sm");
}}
}});
// Create all "Download PNG" buttons of sequence plot chart
//
$("#btn-download-chartS").click(function() {{
html2canvas($("#divChartS"), {{
onrendered: function(canvas) {{
saveAs(canvas.toDataURL(), 'strict_threshold_pred_chart.png');
}}
}});
}});
$("#btn-download-chartNS").click(function() {{
html2canvas($("#divChartNS"), {{
onrendered: function(canvas) {{
saveAs(canvas.toDataURL(), 'nonstrict_threshold_pred_chart.png');
}}
}});
}});
$("#btn-download-chart3pred").click(function() {{
html2canvas($("#divChart3pred"), {{
onrendered: function(canvas) {{
saveAs(canvas.toDataURL(), '3_classes_pred_chart.png');
}}
}});
}});
$("#btn-download-chart5pred").click(function() {{
html2canvas($("#divChart5pred"), {{
onrendered: function(canvas) {{
saveAs(canvas.toDataURL(), '5_classes_pred_chart.png');
}}
}});
}});
// Create all "Download PNG" buttons of Flexibility class distribution pie chart
//
$("#btn-download-pieChartS").click(function() {{
html2canvas($("#divPieChartS"), {{
onrendered: function(canvas) {{
saveAs(canvas.toDataURL(), 'strict_threshold_pie_chart.png');
}}
}});
}});
$("#btn-download-pieChartNS").click(function() {{
html2canvas($("#divPieChartNS"), {{
onrendered: function(canvas) {{
saveAs(canvas.toDataURL(), 'nonstrict_threshold_pie_chart.png');
}}
}});
}});
$("#btn-download-pieChart3pred").click(function() {{
html2canvas($("#divPieChart3pred"), {{
onrendered: function(canvas) {{
saveAs(canvas.toDataURL(), '3_classes_pie_chart.png');
}}
}});
}});
$("#btn-download-pieChart5pred").click(function() {{
html2canvas($("#divPieChart5pred"), {{
onrendered: function(canvas) {{
saveAs(canvas.toDataURL(), '5_classes_pie_chart.png');
}}
}});
}});
// Create pseudo <a> link to download PNG image
function saveAs(uri, filename) {{
var link = document.createElement('a');
if (typeof link.download === 'string') {{
link.href = uri;
link.download = filename;
//Firefox requires the link to be in the body
document.body.appendChild(link);
//simulate click
link.click();
//remove the link when done
document.body.removeChild(link);
}} else {{
window.open(uri);
}}
}}
// PIE CHART
d3.tsv("../prediction/S_prediction.csv").then(
function(csv_data) {{
var flex = csv_data.map(function(d) {{ return d.S; }});
// parse class column, cast string into numerical value
// and calculate occurences of each classes for pie chart
var values = {{}};
for (var i=0; i < flex.length; i++) {{
values[parseInt(flex[i], 10)] = (values[parseInt(flex[i], 10)] || 0) + 1;
}};
var theHelp = Chart.helpers;
var chart_S = new Chart("pie_chart_S", {{
type: "pie",
data: {{
labels: ["Rigid", "Flexible"],
datasets: [
{{
data: Object.values(values),
backgroundColor: ["rgba(0, 78, 137, 1)", "rgba(234, 115, 23, 1)"],
}}
]
}},
options: {{
legend: {{
display: true,
position: "right",
labels: {{
generateLabels: function(chart) {{
var data = chart.data;
if (data.labels.length && data.datasets.length) {{
return data.labels.map(function(label, i) {{
var meta = chart.getDatasetMeta(0);
var ds = data.datasets[0];
var sum = ds["data"].reduce((pv, cv) => pv + cv, 0);
var arc = meta.data[i];
var custom = arc && arc.custom || {{}};
var getValueAtIndexOrDefault = theHelp.getValueAtIndexOrDefault;
var arcOpts = chart.options.elements.arc;
var fill = custom.backgroundColor ? custom.backgroundColor : getValueAtIndexOrDefault(ds.backgroundColor, i, arcOpts.backgroundColor);
var stroke = custom.borderColor ? custom.borderColor : getValueAtIndexOrDefault(ds.borderColor, i, arcOpts.borderColor);
var bw = custom.borderWidth ? custom.borderWidth : getValueAtIndexOrDefault(ds.borderWidth, i, arcOpts.borderWidth);
return {{
// And finally :
text: ((ds.data[i]*100)/sum).toFixed(0) + "% of class " + label,
fillStyle: fill,
strokeStyle: stroke,
lineWidth: bw,
hidden: isNaN(ds.data[i]) || meta.data[i].hidden,
index: i
}};
}});
}}
return [];
}}
}}
}},
title: {{
display: true,
fontFamily: "Roboto",
fontSize: 16,
fontColor: "black",
fontStyle: "normal",
lineHeight: 1.5,
text: ["Global flexibility of the target sequence:", "residues are grouped into 2 predicted classes of flexibility"]
}},
tooltips: {{
callbacks: {{
label: function(tooltipItem, data) {{
return " "+values[tooltipItem.index]+" residues";
}}
}}
}}
}}
}});
}}
);
// POINT MAP FOR S_PREDICTIONS
d3.tsv("../prediction/S_prediction.csv").then(
function(csv_data) {{
var residues = csv_data.map(function(d) {{ return d.res; }});
var flex = csv_data.map(function(d) {{ return d.S; }});
var pred = csv_data.map(function(d) {{ return d.P_max; }});
var numberOfCharts = Math.ceil(residues.length / 50);
var idx = [];
for (var i=1; i <= residues.length; i++) {{
idx.push(i);
}}
var yLabels = {{
0: "Rigid",
1: "Flexible"
}};
for (var i=1; i<=numberOfCharts; i++) {{
var canvas = document.createElement("canvas");
canvas.id = "canvasChartS_"+i;
canvas.height = 90;
canvas.style = "margin-top: 25px";
canvas.width = document.getElementById("principal_container").offsetWidth - 30;
var mutate1_2 = false,
mutate3 = false,
mutate4 = false;
if ((residues.length > 50) && (i == numberOfCharts) && ((residues.length % 50)!=0)) {{
if (residues.length % 50 >= 5) {{
canvas.width = (document.getElementById("principal_container").offsetWidth - 30)*(residues.length % 50)*1.0 / 50 + 75*(1- (residues.length % 50)*1.0/50) ;
}} else if (residues.length % 50 == 4) {{
canvas.width = 135;
canvas.style = "margin-left: 0.3rem !important";
var mutate4 = true;
}} else if (residues.length % 50 == 3) {{
canvas.width = 135;
canvas.style = "margin-left: -0.7rem !important";
var mutate3 = true;
}} else if (residues.length % 50 == 2 || residues.length % 50 == 1) {{
canvas.width = 135;
canvas.style = "margin-left: -1.7rem !important";
var mutate1_2 = true;
}}
}}
div = document.getElementById("divChartS");
div.appendChild(canvas);
var chart = new Chart("canvasChartS_"+i, {{
type: "line",
data: {{
labels: residues.slice((i*50)-50, (i*50)),
datasets: [
{{
data: flex.slice((i*50)-50, (i*50)),
flex: pred.slice((i*50)-50, (i*50)),
idx: idx.slice((i*50)-50, (i*50)),
tension: 0,
fill: false,
borderWidth: 0,
pointBorderWidth: 0,
pointRadius: 10,
pointHoverRadius: 11,
showLine: false,
pointBackgroundColor: []
}}
]
}},
options: {{
legend: {{
display: false
}},
responsive: false,
elements: {{
point: {{
pointStyle: "rect",
borderWidth: 0,
hoverBorderWidth: 0
}}
}},
scales: {{
xAxes: [{{
ticks: {{
autoSkip: false,
maxRotation: 0,
fontStyle: "bold",
fontSize: 15,
callback: function(value, index, values) {{
if ((index+1) % 10 == 0 || (index+1) % 50 == 1) {{
return [value,index+((i-1)*50)+1];
}}
else {{
return value;
}}
}}
}}
}}],
yAxes: [{{
ticks: {{
beginAtZero: true,
fontSize: 16,
padding: 10,
callback: function(value, index, values) {{
return yLabels[value];
}}
}}
}}]
}},
tooltips: {{
callbacks: {{
title: function(tooltipItems, data) {{
return data.labels[tooltipItems[0].index] + " (" + data.datasets[0].idx[tooltipItems[0].index] + ")";
}},
label: function(tooltipItem, data) {{
return "Probabillity: "+((data.datasets[0].flex[tooltipItem.index]*100).toFixed(0))+" %";
}}
}}
}}
}}
}});
if (mutate4){{
chart.options.layout.padding.left = 21;
chart.update();
}} else if (mutate3) {{
chart.options.layout.padding.left = 53;
chart.update();
}} else if (mutate1_2) {{
chart.options.layout.padding.left = 87;
chart.update();
}}
var chartColors = {{
color1: "rgba(35, 98, 118, 1)",
color2: "rgba(0, 153, 159, 1)",
color3: "rgba(0, 209, 166, 1)",
}};
var dataset = chart.data.datasets[0];
for (var j = 0; j < dataset.data.length; j++) {{
k = j + ((i-1)*50);
if (pred[k] < 0.6) {{
dataset.pointBackgroundColor[j] = chartColors.color3;
}}
else if ((pred[k] >= 0.6) && (pred[k] <= 0.75)){{
dataset.pointBackgroundColor[j] = chartColors.color2;
}}
else {{
dataset.pointBackgroundColor[j] = chartColors.color1;
}}
}}
chart.update();
window.chart = chart;
}};
}});
// PIE CHART
d3.tsv("../prediction/NS_prediction.csv").then(
function(csv_data) {{
var flex = csv_data.map(function(d) {{ return d.NS; }});
// parse class column, cast string into numerical value
// and calculate occurences of each classes for pie chart
var values = {{}};
for (var i=0; i < flex.length; i++) {{
values[parseInt(flex[i], 10)] = (values[parseInt(flex[i], 10)] || 0) + 1;
}};
var theHelp = Chart.helpers;
var chart_NS = new Chart("pie_chart_NS", {{
type: "pie",
data: {{
labels: ["Rigid", "Flexible"],
datasets: [
{{
data: Object.values(values),
backgroundColor: ["rgba(0, 78, 137, 1)", "rgba(234, 115, 23, 1)"],
}}
]
}},
options: {{
legend: {{
display: true,
position: "right",
labels: {{
generateLabels: function(chart) {{
var data = chart.data;
if (data.labels.length && data.datasets.length) {{
return data.labels.map(function(label, i) {{
var meta = chart.getDatasetMeta(0);
var ds = data.datasets[0];
var sum = ds["data"].reduce((pv, cv) => pv + cv, 0);
var arc = meta.data[i];
var custom = arc && arc.custom || {{}};
var getValueAtIndexOrDefault = theHelp.getValueAtIndexOrDefault;
var arcOpts = chart.options.elements.arc;
var fill = custom.backgroundColor ? custom.backgroundColor : getValueAtIndexOrDefault(ds.backgroundColor, i, arcOpts.backgroundColor);
var stroke = custom.borderColor ? custom.borderColor : getValueAtIndexOrDefault(ds.borderColor, i, arcOpts.borderColor);
var bw = custom.borderWidth ? custom.borderWidth : getValueAtIndexOrDefault(ds.borderWidth, i, arcOpts.borderWidth);
return {{
// And finally :
text: ((ds.data[i]*100)/sum).toFixed(0) + "% of class " + label,
fillStyle: fill,
strokeStyle: stroke,
lineWidth: bw,
hidden: isNaN(ds.data[i]) || meta.data[i].hidden,
index: i
}};
}});
}}
return [];
}}
}}
}},
title: {{
display: true,
fontFamily: "Roboto",
fontSize: 16,
fontColor: "black",
fontStyle: "normal",
lineHeight: 1.5,
text: ["Global flexibility of the target sequence:", "residues are grouped into 2 predicted classes of flexibility"]
}},
tooltips: {{
callbacks: {{
label: function(tooltipItem, data) {{
return " "+values[tooltipItem.index]+" residues";
}}
}}
}}
}}
}});
}}
);
// POINT MAP FOR NS_PREDICTIONS
d3.tsv("../prediction/NS_prediction.csv").then(
function(csv_data) {{
var residues = csv_data.map(function(d) {{ return d.res; }});
var flex = csv_data.map(function(d) {{ return d.NS; }});
var pred = csv_data.map(function(d) {{ return d.P_max; }});
var numberOfCharts = Math.ceil(residues.length / 50);
var idx = [];
for (var i=1; i <= residues.length; i++) {{
idx.push(i);
}}
var yLabels = {{
0: "Rigid",
1: "Flexible"
}};
for (var i=1; i<=numberOfCharts; i++) {{
var canvas = document.createElement("canvas");
canvas.id = "canvasChartNS_"+i;
canvas.height = 90;
canvas.style = "margin-top: 25px";
canvas.width = document.getElementById("principal_container").offsetWidth - 30;
var mutate1_2 = false,
mutate3 = false,
mutate4 = false;
if (residues.length > 50 && i == numberOfCharts && ((residues.length % 50)!=0)) {{
if (residues.length % 50 >= 5) {{
canvas.width = (document.getElementById("principal_container").offsetWidth - 30)*(residues.length % 50)*1.0 / 50 + 75*(1- (residues.length % 50)*1.0/50);
}} else if (residues.length % 50 == 4) {{
canvas.width = 135;
canvas.style = "margin-left: 0.3rem !important";
var mutate4 = true;
}} else if (residues.length % 50 == 3) {{
canvas.width = 135;
canvas.style = "margin-left: -0.7rem !important";
var mutate3 = true;
}} else if (residues.length % 50 == 2 || residues.length % 50 == 1) {{
canvas.width = 135;
canvas.style = "margin-left: -1.7rem !important";
var mutate1_2 = true;
}}
}}
div = document.getElementById("divChartNS");
div.appendChild(canvas);
var chart = new Chart("canvasChartNS_"+i, {{
type: "line",
data: {{
labels: residues.slice((i*50)-50, (i*50)),
datasets: [
{{
data: flex.slice((i*50)-50, (i*50)),
flex: pred.slice((i*50)-50, (i*50)),
idx: idx.slice((i*50)-50, (i*50)),
tension: 0,
fill: false,
borderWidth: 0,
pointBorderWidth: 0,
pointRadius: 10,
pointHoverRadius: 11,
showLine: false,
pointBackgroundColor: []
}}
]
}},
options: {{
legend: {{
display: false
}},
responsive: false,
elements: {{
point: {{
pointStyle: "rect",
borderWidth: 0,
hoverBorderWidth: 0
}}
}},
scales: {{
xAxes: [{{
ticks: {{
autoSkip: false,
maxRotation: 0,
fontStyle: "bold",
fontSize: 15,
callback: function(value, index, values) {{
if ((index+1) % 10 == 0 || (index+1) % 50 == 1) {{
return [value,index+((i-1)*50)+1];
}}
else {{
return value;
}}
}}
}}
}}],
yAxes: [{{
ticks: {{
beginAtZero: true,
fontSize: 16,
padding: 10,
callback: function(value, index, values) {{
return yLabels[value];
}}
}}
}}]
}},
tooltips: {{
callbacks: {{
title: function(tooltipItems, data) {{
return data.labels[tooltipItems[0].index] + " (" + data.datasets[0].idx[tooltipItems[0].index] + ")";
}},
label: function(tooltipItem, data) {{
return "Probabillity: "+((data.datasets[0].flex[tooltipItem.index]*100).toFixed(0))+" %";
}}
}}
}}
}}
}});
if (mutate4){{
chart.options.layout.padding.left = 21;
chart.update();
}} else if (mutate3) {{
chart.options.layout.padding.left = 53;
chart.update();
}} else if (mutate1_2) {{
chart.options.layout.padding.left = 87;
chart.update();
}}
var chartColors = {{
color1: "rgba(35, 98, 118, 1)",
color2: "rgba(0, 153, 159, 1)",
color3: "rgba(0, 209, 166, 1)",
}};
var dataset = chart.data.datasets[0];
for (var j = 0; j < dataset.data.length; j++) {{
k = j + ((i-1)*50);
if (pred[k] < 0.6) {{
dataset.pointBackgroundColor[j] = chartColors.color3;
}}
else if ((pred[k] >= 0.6) && (pred[k] <= 0.75)){{
dataset.pointBackgroundColor[j] = chartColors.color2;
}}
else {{
dataset.pointBackgroundColor[j] = chartColors.color1;
}}
}}
chart.update();
window.chart = chart;
}};
}});
// PIE CHART 3PRED
d3.tsv("../prediction/3_prediction.csv").then(
function(csv_data) {{
var flex = csv_data.map(function(d) {{ return d.pred_3; }});
// parse class column, cast string into numerical value
// and calculate occurences of each classes for pie chart
var values = {{0: 0, 1: 0, 2: 0}};
for (var i=0; i < flex.length; i++) {{
values[parseInt(flex[i], 10)] = (values[parseInt(flex[i], 10)] || 0) + 1;
}};
var theHelp = Chart.helpers;
var chart_3pred = new Chart("pie_chart_3pred", {{
type: "pie",
data: {{
labels: ["0", "1", "2"],
datasets: [
{{
data: Object.values(values),
backgroundColor: ["rgba(0, 78, 137, 1)", "rgba(61, 165, 217, 1)", "rgba(234, 115, 23, 1)"],
}}
]
}},
options: {{
legend: {{
display: true,
position: "right",
labels: {{
generateLabels: function(chart) {{
var data = chart.data;
if (data.labels.length && data.datasets.length) {{
return data.labels.map(function(label, i) {{
var meta = chart.getDatasetMeta(0);
var ds = data.datasets[0];
var sum = ds["data"].reduce((pv, cv) => pv + cv, 0);
var arc = meta.data[i];
var custom = arc && arc.custom || {{}};
var getValueAtIndexOrDefault = theHelp.getValueAtIndexOrDefault;
var arcOpts = chart.options.elements.arc;
var fill = custom.backgroundColor ? custom.backgroundColor : getValueAtIndexOrDefault(ds.backgroundColor, i, arcOpts.backgroundColor);
var stroke = custom.borderColor ? custom.borderColor : getValueAtIndexOrDefault(ds.borderColor, i, arcOpts.borderColor);
var bw = custom.borderWidth ? custom.borderWidth : getValueAtIndexOrDefault(ds.borderWidth, i, arcOpts.borderWidth);
return {{
// And finally :
text: ((ds.data[i]*100)/sum).toFixed(0) + "% of class " + label,
fillStyle: fill,
strokeStyle: stroke,
lineWidth: bw,
hidden: isNaN(ds.data[i]) || meta.data[i].hidden,
index: i
}};
}});
}}
return [];
}}
}}
}},
title: {{
display: true,
fontFamily: "Roboto",
fontSize: 16,
fontColor: "black",
fontStyle: "normal",
lineHeight: 1.5,
text: ["Global flexibility of the target sequence:", "residues are grouped into 3 predicted classes of flexibility"]
}},
tooltips: {{
callbacks: {{
label: function(tooltipItem, data) {{
return " "+values[tooltipItem.index]+" residues";
}}
}}
}}
}}