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single_app.py
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single_app.py
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import os
import random
import base64
import dash
from dash import Dash, dcc, html, Input, Output, no_update
import dash_bootstrap_components as dbc
import plotly.graph_objects as go
import pandas as pd
from src.utils import cpath, load_pkl
url_default_functions={}
analysis_name = 'bhairaviTransposed'
clus_type = 'kmeans'
app = Dash(__name__)
data_dict = {'df': None, 'ap_series': None, 'pp_series': None, 'clusters': None}
svara_header = html.Div([html.H4(id="data-text", children="Svara", style={'font-family':'sans-serif'})], style={'display':'flex', 'height':45})
svara_dd = dcc.Dropdown(
id = 'input-data',
value='All',
placeholder='Select svara type',
options= [
{'label' : 'Sa', 'value' : 'Sa'},
{'label' : 'Ri', 'value' : 'Ri'},
{'label' : 'Ga', 'value' : 'Ga'},
{'label' : 'Ma', 'value' : 'Ma'},
{'label' : 'Pa', 'value' : 'Pa'},
{'label' : 'Dha', 'value' : 'Dha'},
{'label' : 'Ni', 'value' : 'Ni'},
{'label' : 'All', 'value' : 'All'}], style={'width':500, 'font-family':'sans-serif', "margin-bottom": 200})
tooltip = dcc.Tooltip(id="graph-tooltip")
placeholder = html.Div(id="placeholder", style={"display": "none"})
graph = dbc.Card(dcc.Graph(id="scatter-graph", clear_on_unhover=True))
app.layout = dbc.Container(
[
dbc.Row([dbc.Col([svara_header, svara_dd, graph])], style={'display':'flex'}),
tooltip,
placeholder
])
@app.callback(
Output("graph-tooltip", "show"),
Output("graph-tooltip", "bbox"),
Output("graph-tooltip", "children"),
Input("scatter-graph", "hoverData"),
)
def display_hover(hoverData):
if hoverData is None:
return False, no_update, no_update
# demo only shows the first point, but other points may also be available
pt = hoverData["points"][0]
bbox = pt["bbox"]
num = pt["pointNumber"]
df_row = data_dict['df'].iloc[num]
cn = pt['curveNumber']
pi = pt['pointIndex']
img_src = os.path.join(os.getcwd(), data_dict['pp_series'][cn][pi])
encoded_image = base64.b64encode(open(img_src, 'rb').read())
children = [
html.Div([
html.Img(src='data:image/png;base64,{}'.format(encoded_image.decode()), style={"width": "100%"}),
], style={'width': '800px', 'whiteSpace': 'normal'})
]
return True, bbox, children
@app.callback(
Output("placeholder", "children"),
Input("scatter-graph", "clickData"),
)
def fig_click(clickData):
if not clickData:
raise dash.exceptions.PreventUpdate
pt = clickData["points"][0]
cn = pt['curveNumber']
pi = pt['pointIndex']
audio_src = os.path.join(os.getcwd(), data_dict['ap_series'][cn][pi])
encoded_sound = base64.b64encode(open(audio_src, 'rb').read())
return html.Audio(src='data:audio/mpeg;base64,{}'.format(encoded_sound.decode()),
controls=False,
autoPlay=True,
)
@app.callback(
Output('scatter-graph', 'figure'),
[Input('input-data', 'value')]
)
def choose_plot_data(input_data):
disab = False
if input_data is None:
raise dash.exceptions.PreventUpdate()
if input_data == 'Sa':
data_path = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/embedding_kmeans_sa.csv'
this_direc = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/sa/'
if input_data == 'Ri':
data_path = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/embedding_kmeans_ri.csv'
this_direc = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/ri/'
if input_data == 'Ga':
data_path = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/embedding_kmeans_ga.csv'
this_direc = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/ga/'
if input_data == 'Ma':
data_path = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/embedding_kmeans_ma.csv'
this_direc = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/ma/'
if input_data == 'Pa':
data_path = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/embedding_kmeans_pa.csv'
this_direc = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/pa/'
if input_data == 'Dha':
data_path = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/embedding_kmeans_dha.csv'
this_direc = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/dha/'
if input_data == 'Ni':
data_path = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/embedding_kmeans_ni.csv'
this_direc = f'data/analysis/{analysis_name}/clusters_single/{clus_type}/ni/'
if input_data == 'All':
data_path = f'data/analysis/{analysis_name}/clusters_single/embedding_umap.csv'
this_direc = ''
disab = True
data_paths = []
svara_level = 'kmeans' in data_path
title = 'All Svaras' if not svara_level else 'Svara: ' + data_path.split('_')[-1].split('.')[0].capitalize()
df = pd.read_csv(data_path)
data_dict['df'] = df
if svara_level == True:
label_lookup = {i:f'cluster_{i}' if i != -1 else 'Noise' for i in df['label'].unique()}
colours = ['#000000','#696969','#8b4513','#006400','#808000','#483d8b','#008b8b','#9acd32','#00008b','#7f007f','#8fbc8f','#b03060','#ff4500','#ffa500','#ffff00','#7cfc00','#deb887','#8a2be2','#00ff7f','#dc143c','#00ffff','#00bfff','#0000ff','#b0c4de','#ff00ff','#1e90ff','#fa8072','#dda0dd','#90ee90','#ff1493']
random.shuffle(colours)
else:
label_lookup = load_pkl(f'data/analysis/{analysis_name}/label_lookup.pkl')
colours = ['#000000','#66cdaa','#ffa500','#00ff00','#0000ff','#1e90ff','#ff1493']
x_series = []
y_series = []
colours_series = []
names = []
pp_series = []
ap_series= []
for label in df['label'].unique():
this_df = df[df['label']==label]
svara = label_lookup[label]
x_series.append(this_df['x'].values)
y_series.append(this_df['y'].values)
pp_series.append(this_df['plot_path'].values)
ap_series.append(this_df['audio_path'].values)
colours_series.append(colours[label])
names.append(svara)
data_dict['ap_series'] = ap_series
data_dict['pp_series'] = pp_series
fig = go.Figure()
for x, y, c, n in zip(x_series, y_series, colours_series, names):
fig.add_trace(
go.Scatter(
x=x,
y=y,
name=n,
mode='markers',
marker_color=c,
marker=dict(
line={"color": "#444"},
reversescale=True,
sizeref=45,
sizemode="diameter",
opacity=0.8,
)
)
)
# turn off native plotly.js hover effects - make sure to use
# hoverinfo="none" rather than "skip" which also halts events.
fig.update_traces(hoverinfo="none", hovertemplate=None)
fig.update_layout(
plot_bgcolor='rgba(255,255,255,0.1)',
width=700,
height=700
)
return fig
if __name__ == "__main__":
app.run_server(debug=True, port=8010)