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interactive.py
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import plotly.express as px
from moddotplot.estimate_identity import (
getInteractiveColor,
getMatchingColors,
makeDifferencesEqual,
generateDictionaryFromList,
findValueInRange,
convertMatrixToBed,
)
import numpy as np
import dash
from dash import Input, Output, html, dcc, State
import math
import plotly.graph_objs as go
import logging
import os
# Prevent HTTP protocol requests from showing up in terminal
log = logging.getLogger("werkzeug")
log.setLevel(logging.ERROR)
def find_closest_elements(value, sorted_list):
# Initialize variables to store the indices of the closest elements
closest_index1 = None
closest_index2 = None
# Initialize a variable to store the minimum difference
min_difference = float("inf")
# Iterate through the sorted list
for i in range(len(sorted_list)):
# Calculate the absolute difference between the current element and the target value
current_difference = abs(sorted_list[i] - value)
# Update indices and minimum difference if a closer element is found
if current_difference < min_difference:
min_difference = current_difference
closest_index1 = i
# Check if there is a previous element to avoid index out of range
if i > 0:
closest_index2 = i - 1
if closest_index1 == None:
return closest_index2, closest_index2
elif closest_index2 == None:
return closest_index1, closest_index1
if closest_index1 > closest_index2:
return closest_index2, closest_index1
else:
return closest_index1, closest_index2
def run_dash(matrices, metadata, axes, sparsity, identity, port_number, output_dir):
# Run Dash app
app = dash.Dash(__name__, prevent_initial_callbacks="initial_duplicate")
app.title = "ModDotPlot"
print(
f"{app.title} interactive mode is successfully running on http://127.0.0.1:{port_number}/ \n"
)
# Initialize first sequence
image_pyramid = matrices[0]
current_metadata = metadata[0]
x_name = metadata[0]["x_name"]
y_name = metadata[0]["y_name"]
# Initialize color palette
palette = "Spectral_11"
palette_orientation = "+"
current_color = getInteractiveColor(palette, palette_orientation)
# Initialize axes and titles for all images
titles = []
for i in range(len(metadata)):
titles.append(metadata[i]["title"])
# Get zooming thresholds, adjust sparsity respectively.
def halving_sequence(size, start):
sequence = [start]
for _ in range(1, size):
start /= 2
sequence.append(start)
return sequence
# print(current_metadata)
mod_thresholds_list = halving_sequence(
len(current_metadata["sparsities"]), current_metadata["x_size"]
)
# print(mod_thresholds_list)
# print(current_metadata["min_window_size"]* current_metadata["resolution"])
# print(current_metadata["max_window_size"])
numo = round(
math.log2(
current_metadata["max_window_size"] / current_metadata["min_window_size"]
)
+ 1
)
# print(numo)
important = generateDictionaryFromList(mod_thresholds_list)
# print(f"this is imprtant: {important}")
main_level = image_pyramid[0]
main_x_axis = axes[0][0]
main_y_axis = axes[0][1]
main_x_axis_np = np.array(main_x_axis)
# TODO: modify value here
main_x_axis_np += 3000
# Modify text so that hover shows interval format
# TODO: Add intervals in a way that's speedy
'''X, Y = np.meshgrid(main_x_axis, main_y_axis)
X = np.delete(X, -1, axis=1)
X, Y = np.meshgrid(main_x_axis, main_y_axis)
X = np.delete(X, -1, axis=1)
X_str = np.array([[f"{str(val)}-{str(val + current_metadata['max_window_size'])}" for val in row] for row in X])
Y = np.delete(Y, -1, axis=1)
Y_str = np.array([[f"{str(val)}-{str(val + current_metadata['max_window_size'])}" for val in row] for row in Y])
hover_template_text = "%{text}<br>%{customdata}<br>Identity: %{z:.1f}"'''
hover_template_text = "Identity: %{z:.2f}"
# Create initial heatmap
heatmap = go.Heatmap(
z=main_level,
zmin=identity,
zmax=100,
colorscale=current_color,
x=main_x_axis,
y=main_y_axis,
showscale=True,
colorbar=dict(title="Identity"),
hoverinfo="all",
hovertemplate=hover_template_text,
name="",
x0=0,
dx=current_metadata["max_window_size"],
xtype="scaled",
y0=0,
dy=current_metadata["max_window_size"],
ytype="scaled",
)
fig = go.Figure(data=[heatmap])
fig.update_xaxes(
showspikes=True,
spikemode="across",
ticks="outside",
showline=True,
linewidth=2,
linecolor="black",
mirror=True,
)
fig.update_xaxes(
nticks=10, title_text=metadata[0]["x_name"], title_font=dict(size=18)
)
# Update y-axis properties directly within the heatmap trace
fig.update_yaxes(
showspikes=True,
spikemode="across",
ticks="outside",
showline=True,
linewidth=2,
linecolor="black",
mirror=True,
)
fig.update_yaxes(title_text=metadata[0]["y_name"], title_font=dict(size=18))
fig_title = ""
# TODO: Fine tuning on the names
title_size = 28
if metadata[0]["self"]:
fig_title = f"Self-Identity Plot: {x_name}"
if len(x_name) > 22:
title_size = 20
else:
fig_title = f"Comparative Plot: {x_name} vs. {y_name}"
if len(x_name) + len(y_name) > 22:
title_size = 18
# Set layout properties
fig.update_layout(
height=800,
width=800,
hoverlabel=dict(bgcolor="white", font_size=16, font_family="Helvetica"),
yaxis_scaleanchor="x",
title=fig_title,
title_font=dict(size=title_size, family="Helvetica, Arial, sans-serif"),
title_x=0.5,
title_y=0.95,
)
colorscales = px.colors.named_colorscales()
colornames = px.colors.named_colorscales()
colorscales.insert(0, current_color)
colornames.insert(0, palette)
# HTML for plot webpage
app.layout = html.Div(
id="main_color",
style={
"alignItems": "center",
"backgroundColor": "white",
"color": "black",
"height": "100vh",
},
children=[
html.Link(rel="icon", href="/assets/favicon.png"),
html.Div(
[
dcc.Graph(id="dotplot", figure=fig),
html.Div(
dcc.RangeSlider(
id="threshold-slider",
min=identity,
max=100,
step=0.1,
value=[identity, 100],
marks=None,
vertical=True,
verticalHeight=580,
allowCross=False,
pushable=0.1,
tooltip={"placement": "bottom", "always_visible": False},
className="custom-slider",
),
style={
"display": "flex",
"justifyContent": "center",
"paddingTop": "110px",
},
),
html.Div(
[
html.Div(
[
dcc.Loading(
id="loading-2",
children=[html.Div(id="loading-output-2")],
type="circle",
)
],
style={
"paddingBottom": "40px",
"paddingLeft": "40px",
"width": "100px",
"fontFamily": "Helvetica, Arial, sans-serif",
}, # Added padding to separate the content
),
html.Div(
id="invisible-div",
children=0,
style={"display": "none"},
),
html.Div(
[
html.Label(
"Currently shown plot:",
style={
"marginRight": "10px",
"padding-left": "20px",
},
),
dcc.Dropdown(
id="matrix-dropdown",
options=[
{"label": f"{title}", "value": f"{title}"}
for title in titles # Iterate over each title
],
value=(
titles[0] if titles else None
), # Set default value based on the length of matrices
clearable=False, # Prevent dropdown from clearing values,
style={"width": "300px"},
),
html.Div(id="output-container"),
dcc.Store(id="matrices-store", data=len(matrices)),
],
style={
"display": "none" if len(titles) < 2 else "block",
"width": "fit-content"
* 2, # Set width to fit content
"fontFamily": "Helvetica, Arial, sans-serif",
"paddingTop": "10px",
"paddingLeft": "25px",
"paddingBottom": "30px",
"textAlign": "left",
},
),
html.Div(
html.Button(
"Save Matrix to Bed File",
id="save-bed",
n_clicks=0,
disabled=False,
style={
"marginLeft": "45px",
},
),
),
html.Div(
children=[
f"Current Window Size: {current_metadata['max_window_size']}"
],
id="window-div",
style={
"fontFamily": "Helvetica, Arial, sans-serif",
"paddingTop": "10px",
"paddingLeft": "45px",
"paddingBot": "30px",
"width": "220px",
"textAlign": "left",
},
),
html.Div(
f"Minimum Window Size: {current_metadata['min_window_size']}",
style={
"fontFamily": "Helvetica, Arial, sans-serif",
"paddingTop": "10px",
"paddingLeft": "45px",
"paddingBot": "30px",
"width": "220px",
"textAlign": "left",
},
),
dcc.Checklist(
id="gradient-toggle",
options=[
{
"label": " Readjust Gradient",
"value": "keep-original",
}
],
value=[],
style={
"paddingTop": "10px",
"paddingLeft": "24px",
"paddingBottom": "30px",
"width": "260px",
"textAlign": "left",
"fontFamily": "Helvetica, Arial, sans-serif",
},
),
html.Div(id="content"),
html.Div(
"Color Palette",
id="color-palette-text",
style={
"fontFamily": "Helvetica, Arial, sans-serif",
"paddingLeft": "45px", # Added padding to separate the content
},
),
html.Div(
style={"display": "flex", "height": "10vh"},
children=[
html.Div(
[
dcc.Dropdown(
[
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/diverging/img/Spectral_11_discrete.png",
height=10,
width=100,
),
html.Span(
"Spectral",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "Spectral",
},
{
"label": html.Span(
[
html.Span(
"Sequential Color Palettes",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "Sequential",
"disabled": True,
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/Blues_9_discrete.png",
height=10,
width=100,
),
html.Span(
"Blues",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "Blues",
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/BuGn_9_discrete.png",
height=10,
width=100,
),
html.Span(
"BuGn",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "BuGn",
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/BuPu_9_discrete.png",
height=10,
width=100,
),
html.Span(
"BuPu",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "BuPu",
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/GnBu_9_discrete.png",
height=10,
width=100,
),
html.Span(
"GnBu",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "GnBu",
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/Greens_9_discrete.png",
height=10,
width=100,
),
html.Span(
"Greens",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "Green",
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/Greys_9_discrete.png",
height=10,
width=100,
),
html.Span(
"Greys",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "Greys",
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/OrRd_9_discrete.png",
height=10,
width=100,
),
html.Span(
"OrRd",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "OrRd",
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/Oranges_9_discrete.png",
height=10,
width=100,
),
html.Span(
"Oranges",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "Oranges",
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/PuBu_9_discrete.png",
height=10,
width=100,
),
html.Span(
"PuBu",
style={
"fontSize": 15,
"paddingLeft": 10,
},
),
],
style={
"alignItems": "center",
"justifyContent": "center",
},
),
"value": "PuBu",
},
{
"label": html.Span(
[
html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/sequential/img/PuBuGn_9_discrete.png",
height=10,
width=100,
),
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style={
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style={
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style={
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style={
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style={
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style={
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style={
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style={
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style={
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html.Img(
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html.Span(
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style={
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style={
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"justifyContent": "center",
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{
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style={
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style={
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style={
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style={
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html.Img(
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style={
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"justifyContent": "center",
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{
"label": html.Span(
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html.Span(
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style={
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{
"label": html.Span(
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html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/diverging/img/BrBG_11_discrete.png",
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html.Span(
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style={
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style={
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html.Img(
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html.Span(
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style={
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style={
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{
"label": html.Span(
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html.Img(
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width=100,
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html.Span(
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style={
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],
style={
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{
"label": html.Span(
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html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/diverging/img/PuOr_11_discrete.png",
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html.Span(
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style={
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],
style={
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"justifyContent": "center",
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},
{
"label": html.Span(
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html.Img(
src="https://jiffyclub.github.io/palettable/colorbrewer/diverging/img/RdBu_11_discrete.png",
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width=100,
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html.Span(
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style={
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style={
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{
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html.Span(
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style={
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{
"label": html.Span(
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html.Span(
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style={
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style={
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{
"label": html.Span(
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html.Img(