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app.py
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app.py
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# Frontend file
# required libraries
from datetime import time
from ntpath import join
from re import S
import socket
from flask import Flask
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix
import json
import pdb
import time
import pickle
import os
import subprocess
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from xgboost import XGBClassifier
# internal files and functions
from utils import app_defaults, process_input_data, read_data_header
from models import Classifier
# dash library
import dash
from dash_bootstrap_components._components.InputGroup import InputGroup
from dash.exceptions import PreventUpdate
from dash import dcc
from dash import html
from dash import Input, Output, State, html, MATCH, ALL
import dash_bootstrap_components as dbc
from dash.exceptions import PreventUpdate
# dash extension
from dash_extensions import Keyboard
import plotly.graph_objs as go
import plotly.express as px
from plotly.subplots import make_subplots
# university logo path
University_Logo = "https://upload.wikimedia.org/wikipedia/de/9/97/Eberhard_Karls_Universit%C3%A4t_T%C3%BCbingen.svg"
temp_ISCAL_font = "https://see.fontimg.com/api/renderfont4/q341/eyJyIjoiZnMiLCJoIjoyMDAsInciOjEwMDAsImZzIjoyMDAsImZnYyI6IiMxMzJFODMiLCJiZ2MiOiIjRkZGRkZGIiwidCI6MX0/SVNDQUw/rapscallion.png"
# START THE MAIN APP
app = dash.Dash(__name__, external_stylesheets=[
dbc.themes.SPACELAB], suppress_callback_exceptions=True)
server = app.server
# storage default params
global params
params = app_defaults()
# create temp. save folder - this folder needs to be deleted after closing app
os.makedirs(os.path.join(
params["current_directory"], "temp_saves"), exist_ok=True)
params["temp_save_path"] = os.path.join(
params["current_directory"], "temp_saves")
# save params to the temp_saves folder
print("Updating software parameters")
pd.DataFrame(params).to_json(os.path.join(
params["temp_save_path"], "app_params.json"))
# define channels & dropdowns
def define_channels(channel_name=["N"], disabled=False, value=None, dd_value=[''], mins_value=[''], maxes_value=[''], transition=False):
options = []
dropdowns = []
mins = []
maxes = []
if isinstance(channel_name[0], list):
channel_name = channel_name[0]
params["transition"] = transition
for nr, i in enumerate(channel_name):
options.append({'label': i, 'value': i, 'disabled': disabled})
dropdowns.append(dbc.Select(
placeholder='N/A',
options=[
{"label": "Raw Data", "value": "raw"},
{"label": "Filtered", "value": "flt"},
{"label": "Both!", "value": "bth"},
],
id={"type": "ddowns", "index": nr},
disabled=disabled,
style={"width": "110px", 'filter': 'blur(150px)', 'opacity': '0'},
class_name='mb-2',
size="sm",
value=dd_value[nr]
))
mins.append(dbc.Input(placeholder="Min", value=mins_value[nr], size="sm", id={"type": "mins", "index": nr}, disabled=disabled,
style={"width": "80px", 'filter': 'blur(150px)', 'opacity': '0'}, class_name='mb-2', inputmode='numeric', type="number",
min=0, max=1000))
maxes.append(dbc.Input(placeholder="Max", value=maxes_value[nr], size="sm", disabled=disabled,
style={"width": "80px", 'filter': 'blur(150px)', 'opacity': '0'}, class_name='mb-2', id={"type": "maxes", "index": nr},
inputmode='numeric', type="number", min=0, max=1000))
components = dbc.Row(children=[
dbc.Col(dbc.Checklist(
id="channel_checklist",
options=options,
value=value,
switch=True,
inputStyle={"margin-right": "0px"},
labelStyle={'display': 'block'},
label_class_name='mb-3',
style={'width': '110px', 'color': '#463d3b'}
),
),
dbc.Col(dropdowns, class_name='me-4'),
dbc.Col(mins, class_name='px-0 g-0 mx-0'),
dbc.Col(maxes, class_name='px-0 g-0 mx-0'),
])
return components
# navigation toolbar with logo, software title and a button
navbar = dbc.NavbarSimple(
dbc.Container(
dbc.Row(
[
# dbc.Col(html.H1("ISCAL", style={'margin-left': '0px',
# 'color': '#003D7F', 'fontSize': 35, 'font-family': 'Garamond'})),
dbc.Col(html.A(html.Img(src=temp_ISCAL_font,
height="40px"), id='for_dummy_use'), width="auto"),
dbc.Col(dbc.Button(["New Session",
dbc.Badge("Next", color="success", pill=True, text_color="white",
className="position-absolute top-0",
style={
"transform": "rotate(40deg)", "width": "30px", 'fontSize': 8, "margin-top": "15px", "margin-left": "-8px"},
)], id="new-button", size="sm"),
width="auto"),
dbc.Col(html.Div(
[
dbc.Button("Import Data",
id="import-offcanvas-button", size="sm", n_clicks=0),
dbc.Offcanvas(children=[
html.P(
"1) Please customize the epoch length below:",
style={'color': '#463d3b'}
),
dbc.InputGroup([dbc.InputGroupText("Epoch length"), dbc.Input(
placeholder="in seconds", autocomplete="off", id="epoch-length-input")],
class_name="mb-4"),
html.P(
"2) Please specify the sampling frequency:",
style={'color': '#463d3b'}
),
dbc.InputGroup([dbc.InputGroupText("Sampling frequency"), dbc.Input(
placeholder="in Hz", autocomplete="off", id="sampling_fr_input")],
class_name="mb-4"),
html.P(
"3) Below are the channels of your dataset. "
"Please select which ones you want to load, "
"and then, press the Load button. "
"This can take a couple of minutes!",
style={'color': '#463d3b'}
),
html.Div(children=define_channels(),
id="channel_def_div"),
dbc.Row(children=[dbc.Button("Load", id="load_button", size="sm", n_clicks=0),
html.Div(children=False, id="second_execution")],
class_name="mt-3"),
html.Br(),
dcc.Loading(id="loading-state",
children=html.Div(id="loading-output")),
dcc.Interval(
id='internal-trigger', interval=100, n_intervals=0, max_intervals=0),
],
id="import-offcanvas",
title="Before you load, there are 3 steps...",
is_open=False,
backdrop='static',
scrollable=True,
style={'width': '500px',
'title-color': '#463d3b', 'background': 'rgba(224, 236, 240, 0.2)', 'backdrop-filter': 'blur(10px)'}
),
]
), width="auto"),
dbc.Col(dbc.Button("Save Scores", id="save-button",
size="sm"), width="auto"),
dbc.Col(dbc.Button(["Save Project", dbc.Badge("New", color="danger", pill=True, text_color="white",
className="position-absolute top-0",
style={
"transform": "rotate(40deg)", "width": "30px", 'fontSize': 8, "margin-top": "15px", "margin-left": "-8px"},
)], id="save-project",
size="sm"), width="auto"),
dbc.Col(dbc.Button(["Open Project", dbc.Badge("New", color="danger", pill=True, text_color="white",
className="position-absolute top-0",
style={
"transform": "rotate(40deg)", "width": "30px", 'fontSize': 8, "margin-top": "15px", "margin-left": "-8px"},
)], id="open-project",
size="sm"), width="auto"),
dbc.Col(html.Div(
[
dbc.Button("Advanced",
id="advparam-button", size="sm", n_clicks=0),
dbc.Offcanvas(children=[
html.Div([
dbc.InputGroup([dbc.InputGroupText("ML algorithm"), dbc.Input(
placeholder="Which ML you want to use?")], class_name="mb-1"),
dbc.InputGroup([dbc.InputGroupText("nThread"), dbc.Input(
placeholder="The number of dedicated threads")], class_name="mb-1"),
dbc.InputGroup([dbc.InputGroupText("GPU"), dbc.Input(
placeholder="1 for yes, 0 for no")], class_name="mb-1"),
dbc.InputGroup([dbc.InputGroupText("Lag time"), dbc.Input(
placeholder="How much you can wait for training")], class_name="mb-1")
]),
dbc.Row(dbc.Button("Apply", id="apply-params", size="sm"),
class_name="mt-3"),
],
id="advparam-offcanvas",
title="Here, you can customize the advance parameters!",
is_open=False,
backdrop='static',
scrollable=True,
placement='bottom',
style={
'title-color': '#463d3b', 'background': 'rgba(224, 236, 240, 0.2)', 'backdrop-filter': 'blur(10px)'}
),
]
), width="auto"),
dbc.Col(html.A(dbc.Button("About Us", id="about-us-button", size="sm"), href="http://www.physiologie2.uni-tuebingen.de/", target="_blank"),
width="auto", style={'margin-left': '50px'}),
dbc.Col(html.A(dbc.Button("Help", id="help-button", size="sm"),
href="https://github.com/NimaMojtahedi/ISCAL/tree/dev", target="_blank"), width="auto"),
dbc.Col(html.A(html.Img(src=University_Logo, height="40px"),
href="http://www.physiologie2.uni-tuebingen.de/", target="_blank"), width="auto"),
],
align="center",
justify="center",
),
fluid=False,
),
links_left=True,
sticky="top",
color="info",
dark=False,
fluid=False,
className="mb-3",
)
inputbar = dbc.Nav(children=[
dbc.Container(children=[
dbc.Row(
[
dbc.Col(
[
dbc.Input(
max=3,
min=1,
inputmode="numeric",
type="number",
id="minus-one_epoch",
placeholder="",
disabled=True,
style={'width': '80px',
'text-align': 'center'},
size='sm'
),
],
class_name="d-flex justify-content-center",
),
dbc.Col(
[
dbc.Input(
max=3,
min=1,
inputmode="numeric",
type="number",
id="null_epoch",
placeholder="",
disabled=True,
style={'width': '80px',
'text-align': 'center'},
size='sm'
),
],
class_name="d-flex justify-content-center",
),
dbc.Col(
[
dbc.Input(
max=3,
min=1,
inputmode="numeric",
type="number",
id="plus-one_epoch",
placeholder="",
disabled=True,
style={'width': '80px',
'text-align': 'center'},
size='sm'
),
],
class_name="d-flex justify-content-center",
),
]),
dbc.Row(
dbc.Col(
[
dbc.Input(
max=3,
min=1,
inputmode="numeric",
value="",
id="null_epoch_act",
placeholder="",
autocomplete="off",
style={'border': '2px solid', 'border-color': '#003D7F',
'width': '80px', 'text-align': 'center', 'hoverinfo': 'none'},
size='sm'
),
],
class_name="d-flex justify-content-center",
),
),
],
fluid=True
)],
fill=True,
pills=True,
navbar=True,
class_name="sticky-top",
)
graph_bar = dbc.Nav(dbc.Container(dbc.Row(
dcc.Graph(id="ch", responsive=True, config={
'displayModeBar': True,
'displaylogo': False,
'modeBarButtonsToRemove': ['zoom', 'resetscale'],
'modeBarButtonsToAdd': ['drawline', 'drawopenpath', 'eraseshape'],
'scrollZoom': True,
'editable': False,
'showLink': False,
'toImageButtonOptions': {
'format': 'png',
'filename': 'EEG-Plot',
'width': 3200,
'scale': 1,
}
},
style={"width": "100%", "height": "600px"},)
),
class_name="fixed-top",
style={"padding-left": "25px", "padding-right": "25px", "margin-top": "150px",
"width": "100%", "height": "100%"},
fluid=True),
style={"margin-top": "0px"},
navbar_scroll=True,
)
# slidebar
sliderbar = dbc.Container(children=[
dbc.Row(
dcc.Slider(
id='epoch-sliderbar',
min=0,
max=10,
step=1,
value=0,
marks={},
#marks=dict((i, str(i)) if i % 100 != 0 else (i, '') for i in range (params["max_possible_epochs"][0])),
tooltip={"placement": "top", "always_visible": True}
)
)
],
fluid=True,
)
def graph_channels(traces, names=['Null Channel'], downsamples=params["downsample"][0], s_fr=1000):
# traces --> n * p array
if downsamples:
traces = traces[::downsamples, :]
s_fr = int(s_fr / downsamples)
# get trace length
trace_len = len(traces[:, 0])
# xaxis
x_axis = np.linspace(0, trace_len/s_fr, trace_len)
# number of channels
nr_ch = traces.shape[1]
# vertical ines positions
x0 = (trace_len/3) / s_fr
x1 = ((2 * trace_len) / 3) / s_fr
y0 = []
y1 = []
y0 = [traces[:, i].min() for i in range(nr_ch)]
y1 = [traces[:, i].max() for i in range(nr_ch)]
fig = make_subplots(rows=nr_ch, cols=1, shared_xaxes=True,
print_grid=False, vertical_spacing=0.05)
# changing px.line(y=trace)["data"][0] to go.Scatter(y=trace, mode="lines")
# increase speed by factor of ~5
for i in range(nr_ch):
fig.add_trace(go.Scatter(x=x_axis, y=traces[:, i], mode="lines", line=dict(
color='#003D7F', width=1), hoverinfo='skip'), row=i+1, col=1)
for i in range(nr_ch):
# adding lines (alternative is box)
try:
split_line1 = go.Scatter(x=[x0, x0], y=[y0[i], y1[i]], mode="lines",
hoverinfo='skip',
line=dict(color='black', width=3, dash='6px,3px,6px,3px'))
split_line2 = go.Scatter(x=[x1, x1], y=[y0[i], y1[i]], mode="lines",
hoverinfo='skip',
line=dict(color='black', width=3, dash='6px,3px,6px,3px'))
fig.add_trace(split_line1, row=i+1, col=1)
fig.add_trace(split_line2, row=i+1, col=1)
except:
pass
fig.update_layout(margin=dict(l=75, r=0, t=1, b=1),
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
showlegend=False,
xaxis=dict(fixedrange=True)
)
fig.update_traces(uirevision='whole')
try:
fig['layout'].update({'yaxis{}'.format(i+1): dict(
ticksuffix=" ",
anchor="free",
automargin=False,
position=0.01,
ticks='',
showgrid=False,
uirevision='yaxes',
title_text='<b>'+names[i]+'</b>', title_standoff=25)})
except:
pass
fig['layout'].update({'xaxis{}'.format(i+1): dict(tickmode='array',
tickvals=[int(params['epoch_length'][0])/int(params["downsample"][0]),
2*int(params['epoch_length'][0])/int(params["downsample"][0])],
ticktext=['{} sec'.format(int(params['epoch_length'][0])),
'{} sec'.format(int(params['epoch_length'][0])*2)],
uirevision='xaxes',
showgrid=False,)})
return fig
# get accuracy plot
def graph_ai_metric(data):
# start plotting
fig = px.line(y=data)
fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
margin=dict(l=0, r=0, t=0, b=0),
font={'size': 10, 'color': '#003D7F'},
xaxis={"automargin": True, "title_standoff": 0, "gridcolor": 'rgba(0,61,127,0.2)', "linewidth": 2, "linecolor": '#003D7F', "tickfont": {
'size': 12, 'color': '#003D7F'}, "title": "Iterations", "showgrid": True, "showline": True},
yaxis={"automargin": True, "title_standoff": 0, "gridcolor": 'rgba(0,61,127,0.2)', "linewidth": 2, "linecolor": '#003D7F', "tickfont": {
'size': 12, 'color': '#003D7F'}, "title": "Accuracy (%)", "showgrid": True, "showline": True},
xaxis_fixedrange=True,
yaxis_fixedrange=True,
showlegend=False,
)
return fig
# spectrum & histograms
def graph_hs_ps(data, names=['Null']):
# list of 2 (power spectrums(by number of channels) and histograms(same))
# first powerspectrum and then histogram
spectrums = data[0] # n*p
histos = data[1] # n*p
nr_ch = histos.shape[1]
# check if it is True
assert spectrums.shape[1] == histos.shape[1]
fig = make_subplots(rows=1, cols=nr_ch*2,
print_grid=False, vertical_spacing=0.0, horizontal_spacing=0.03,
# subplot_titles=("Power Spectrums", "", "", "Amplitude Histograms", "", "")
)
# my_layout=dict(xaxis={"automargin":True,"title_standoff":0, "gridcolor":'rgba(0,61,127,0.2)',"linewidth":2, "linecolor": '#003D7F',"tickfont": {'size':12, 'color': '#003D7F'}, "title": "Iterations","showgrid": True, "showline": True})
for i in range(nr_ch):
# at the moment x is fixed to 30 Hz 120 = 30 * 4 (always 4)
fig.add_trace(go.Scatter(x=np.linspace(0, 30, 120), y=spectrums[:, i], mode="lines", line=dict(
color='black'), hoverinfo='skip'), row=1, col=i+1)
fig.add_trace(go.Histogram(x=histos[:, i], marker_color='LightSkyBlue',
opacity=0.75, histnorm="probability", hoverinfo='skip'), row=1, col=nr_ch+i+1)
# in case it is necessary for histograms xbins=dict(start=-3.0,end=4,size=0.5)
fig.update_layout(margin=dict(l=1, r=1, t=1, b=1),
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)', bargap=0.25,
font={'size': 10, 'color': '#003D7F'},
xaxis={"automargin": True, "title_standoff": 0, "gridcolor": 'rgba(0,61,127,0.2)', "linewidth": 2, "linecolor": '#003D7F', "tickfont": {
'size': 12, 'color': '#003D7F'}, "title": "Frequency (Hz)", "showgrid": True, "showline": True},
yaxis={"automargin": True, "title_standoff": 0, "gridcolor": 'rgba(0,61,127,0.2)', "linewidth": 2, "linecolor": '#003D7F', "tickfont": {
'size': 12, 'color': '#003D7F'}, "title": "Spectral density", "showgrid": True, "showline": True},
showlegend=False,
xaxis_fixedrange=True,
yaxis_fixedrange=True,
)
fig.update_yaxes(fixedrange=True, gridcolor='rgba(0,61,127,0.2)', linewidth=2, linecolor='#003D7F', tickfont={
'size': 12, 'color': '#003D7F'}, showgrid=True, showline=True)
fig.update_xaxes(fixedrange=True, gridcolor='rgba(0,61,127,0.2)', linewidth=2, linecolor='#003D7F', tickfont={
'size': 12, 'color': '#003D7F'}, showgrid=True, showline=True)
for i in range(nr_ch):
fig['layout'].update({'xaxis{}'.format(i+1): dict(title=names[i])})
fig['layout'].update(
{'xaxis{}'.format(nr_ch+i+1): dict(title=names[i])})
return fig
def graph_conf_mat(y_true, y_pred, class_names):
cm = confusion_matrix(y_true, y_pred) # normalize='true'
df = pd.DataFrame(np.round(cm, 2), columns=class_names, index=class_names)
return dbc.Table.from_dataframe(df, striped=False, bordered=False, hover=True, index=True, responsive=True, size="sm", color="info", style={'color': '#003D7F', 'font-size': 14})
# background for the lower row
backgrd = html.Div(
dbc.Container(style={"padding": "0", "margin": "0", "width": "100%", "height": "300px", "border": "0px solid #308fe3",
"background-image": "url(https://previews.123rf.com/images/gonin/gonin1710/gonin171000004/87977156-blue-white-gradient-hexagons-turned-abstract-background-with-geometrical-elements-modern-3d-renderin.jpg)",
'opacity': '0.15', 'filter': 'blur(20px)', "background-size": "100% 100%"},
fluid=True,
class_name="fixed-bottom",
))
# lower row (contains all learning graphs and informations + spectrums and histograms)
conf_matrix_contents = html.Div(children=graph_conf_mat(np.array(
[1, 2, 3]), np.array([1, 2, 3]), ['1', '2', '3']), id="table-contents")
lower_row = dbc.Nav(dbc.Container(children=[
sliderbar,
dbc.Container(dbc.Row(children=[html.H4("Analytics", id="lower-bar-title",
style={"font-weight": "600", "padding-top": "0px"}),
]),
fluid=True,
),
html.Div([dbc.Row([
dbc.Col(html.H6("Confusion Matrix",
style={"font-weight": "600", "margin-left": "15px"}), width={"size": 2}),
dbc.Col(html.H6("AI Acuuracy",
style={"font-weight": "600", "margin-left": "0px"}), width={"size": 1, "offset": 0}),
dbc.Col(html.H6("Power Spectrums",
style={"font-weight": "600", "margin-left": "65px"}), width={"size": 2, "offset": 0}),
dbc.Col(html.H6("Amplitude Histograms",
style={"font-weight": "600", "margin-left": "120px"}), width={"size": 3, "offset": 2}),
]),
]),
dbc.Nav(dbc.Container(dbc.Row([
dbc.Container(dbc.Col(conf_matrix_contents),
style={"width": "220px", "height": "150px", "padding": "0px"}),
dbc.Container(dbc.Col(dcc.Graph(id="accuracy", figure=graph_ai_metric(data=np.array(params["AI_accuracy"])),
responsive=True, style={"width": "200px", "height": "150px"}, config={
'displayModeBar': False,
'displaylogo': False,
'scrollZoom': False,
'editable': False,
'showLink': False,
})),
style={"width": "200px", "height": "150px"}),
dbc.Container(dbc.Col(dcc.Graph(id="hist-graphs", responsive=True,
style={"width": "1350px", "height": "130px"}, config={
'displayModeBar': True,
'modeBarButtonsToRemove': ['zoomin', 'zoomout', 'zoom', 'pan', 'select', 'lasso2d', 'autoscale'],
'displaylogo': False,
'scrollZoom': False,
'editable': False,
'showLink':False,
'toImageButtonOptions': {
'format': 'png',
'filename': 'PowerSpect_Histograms',
'width': 1600,
'scale': 1,
}})),
style={"width": "1350px", "height": "130px"})
],
),
fluid=True,
style={"margin-top": "0px"}
),
fill=True),
dbc.Container(dbc.Row(
dbc.Input(placeholder=" Training information", id="train-info", disabled=True, size='sm',
style={"padding": "0", "margin": "0", 'font-size': 10})),
fluid=True,
class_name="g-0 mb-0 p-0",
style={"margin-top": "0px"}
)
],
fluid=True,
style={"border": "0px", "width": "100%", "height": "300px"},
),
fill=True,
class_name="fixed-bottom",
)
# detecting keyboard keys
my_keyboard = html.Div(Keyboard(id="keyboard"))
# define app layout using dbc container
app.layout = dbc.Container(
html.Div([my_keyboard, navbar, inputbar, graph_bar, backgrd, lower_row]), fluid=True)
# CALLBACKS
# keyboard callback
# 1. reading keyboard keys / epoch-index / max number of possible epoch
# 2. update epoch-index / user pressed key. Both in Storage
@app.callback(
[Output("keyboard", "keydown"),
Output("ch", "figure"),
Output("hist-graphs", "figure"),
Output("epoch-sliderbar", "value"),
Output("minus-one_epoch", "value"),
Output("null_epoch", "value"),
Output("plus-one_epoch", "value"),
Output("null_epoch_act", "value"),
Output("epoch-sliderbar", "max")
],
[Input("keyboard", "keydown"),
Input("keyboard", "n_keydowns"),
Input("import-offcanvas", "is_open"),
Input("null_epoch_act", "value"),
Input('epoch-sliderbar', 'value')
]
)
def keydown(event, n_keydowns, off_canvas, score_value, slider_live_value):
# All UI offcanvases and menus (open situation) should deactivate the keyboard
if params["data_loaded"] == 0:
if off_canvas:
raise PreventUpdate
elif not off_canvas:
try:
event["key"] = ""
except:
pass
return event, graph_channels(np.zeros((1000, 1))), graph_hs_ps([np.zeros((1000, 1)), np.zeros((1000, 1))]), 0, dash.no_update, dash.no_update, dash.no_update, "", dash.no_update
else:
print('404: Something went wrong! :(')
raise PreventUpdate
elif params["data_loaded"] == 1:
assert os.path.exists(os.path.join(
params["temp_save_path"], str(0) + ".json"))
assert os.path.exists(os.path.join(
params["temp_save_path"], str(1) + ".json"))
# getting data
df_mid = pd.read_json(os.path.join(
params["temp_save_path"], str(0) + ".json"))
data_mid = np.stack(df_mid["data"])
ps_mid = np.stack(df_mid["spectrums"]).T
hist_mid = np.stack(df_mid["histograms"]).T
full_ps_hist = [ps_mid, hist_mid]
data_left = np.zeros_like(data_mid)
df_right = pd.read_json(os.path.join(
params["temp_save_path"], str(1) + ".json"))
data_right = np.stack(df_right["data"])
# combine mid_right datasets
full_trace = np.hstack(
[data_left,
data_mid,
data_right])
print("Enjoy scoring!")
# change it for in-use-case
params["data_loaded"] = 2
try:
event["key"] = ""
except:
pass
return event, graph_channels(full_trace.T, names=params["selected_channels"]), graph_hs_ps(full_ps_hist, names=params["selected_channels"]), 0, dash.no_update, dash.no_update, dash.no_update, "", params["max_possible_epochs"][0]
elif params["data_loaded"] == 3:
assert os.path.exists(os.path.join(
params["temp_save_path"], str(int(params["epoch_index"][0])) + ".json"))
assert os.path.exists(os.path.join(
params["temp_save_path"], str(int(params["epoch_index"][0])+1) + ".json"))
# getting data of mid epoch, so-called null epoch
df_mid = pd.read_json(os.path.join(
params["temp_save_path"], str(int(params["epoch_index"][0])) + ".json"))
data_mid = np.stack(df_mid["data"])
ps_mid = np.stack(df_mid["spectrums"]).T
hist_mid = np.stack(df_mid["histograms"]).T
full_ps_hist = [ps_mid, hist_mid]
# trying the minus one epoch data load, to catch th error of opening at 0 epoch
try:
assert os.path.exists(os.path.join(
params["temp_save_path"], str(int(params["epoch_index"][0])-1) + ".json"))
df_left = pd.read_json(os.path.join(
params["temp_save_path"], str(int(params["epoch_index"][0])-1) + ".json"))
data_left = np.stack(df_left["data"])
except:
data_left = np.zeros_like(data_mid)
df_right = pd.read_json(os.path.join(
params["temp_save_path"], str(int(params["epoch_index"][0])+1) + ".json"))
data_right = np.stack(df_right["data"])
# right epoch label
try:
epoch_plus_one_label = str(
params["scoring_labels"][int(params["epoch_index"][0]) + 1])
except:
epoch_plus_one_label = ""
# left epoch label
try:
epoch_minus_one_label = str(
params["scoring_labels"][int(params["epoch_index"][0]) - 1])
except:
epoch_minus_one_label = ""
# mid epoch label
try:
null_score_label = str(
params["scoring_labels"][int(params["epoch_index"][0])])
except:
null_score_label = ""
# combine mid_right datasets
full_trace = np.hstack(
[data_left,
data_mid,
data_right])
print("Enjoy scoring!")
# change it for in-use-case
params["data_loaded"] = 2
try:
event["key"] = ""
except:
pass
return event, graph_channels(full_trace.T, names=params["selected_channels"]), graph_hs_ps(full_ps_hist, names=params["selected_channels"]), params["slider_saved_value"][0], epoch_minus_one_label, null_score_label, epoch_plus_one_label, "", params["max_possible_epochs"][0]
elif params["data_loaded"] == 2:
# read slider saved value
slider_saved_value = params["slider_saved_value"][0]
# It is important False off_canvas / # only in this case enter to this section (later more keys should come here)
functional_keys = (slider_live_value != slider_saved_value or score_value == "1" or
score_value == "2" or score_value == "3" or event["key"] == "ArrowRight" or event["key"] == "ArrowLeft")
if off_canvas:
raise PreventUpdate
elif (functional_keys and not off_canvas):
# read params
epoch_index = params["epoch_index"][0]
max_nr_epochs = params["max_possible_epochs"][0]
score_storage = params["scoring_labels"]
# there is change in slider value / update epoch to current slider value
if (slider_saved_value != slider_live_value):
print("data ready in-use (FUNC: slider)")
# update slider_saved_value
slider_saved_value = epoch_index = int(slider_live_value)
# update figures with only left/right arrow keys
if ((event["key"] == "ArrowRight") or (event["key"] == "ArrowLeft")):
print("data ready in-use (FUNC: arrowkey)")
# check what is user pressed key
if (event["key"] == "ArrowRight"):
if epoch_index < max_nr_epochs:
epoch_index += 1
elif (event["key"] == "ArrowLeft"):
if epoch_index > 0:
epoch_index -= 1
slider_saved_value = slider_live_value = int(epoch_index)
# update figures with score labels
if score_value == "1" or score_value == "2" or score_value == "3":
print("data ready in-use (FUNC: scoring)")
# saving score label to storage
if not score_storage is None:
# re-scoring effect
if epoch_index in score_storage.keys():
score_storage[epoch_index] = int(score_value)
else:
score_storage.update({epoch_index: int(score_value)})
else:
score_storage = {epoch_index: int(score_value)}
params["scoring_labels"] = score_storage
if epoch_index < max_nr_epochs:
epoch_index += 1
slider_saved_value = slider_live_value = int(epoch_index)
# as a general rule: updating epoch index in params
params["epoch_index"] = [int(epoch_index)]
params["slider_saved_value"] = [int(slider_saved_value)]
# read data batch from disk / check if save_path exist
df_mid = pd.read_json(os.path.join(
params["temp_save_path"], str(epoch_index) + ".json"))
data_mid = np.stack(df_mid["data"])
ps_mid = np.stack(df_mid["spectrums"]).T
hist_mid = np.stack(df_mid["histograms"]).T
full_ps_hist = [ps_mid, hist_mid]
if epoch_index == max_nr_epochs:
data_right = np.zeros_like(data_mid)
else:
data_right = np.stack(pd.read_json(os.path.join(
params["temp_save_path"], str(epoch_index + 1) + ".json"))["data"])
if epoch_index == 0:
data_left = np.zeros_like(data_mid)
else:
data_left = np.stack(pd.read_json(os.path.join(
params["temp_save_path"], str(epoch_index - 1) + ".json"))["data"])
# combine mid_right datasets
full_trace = np.hstack(
[data_left,
data_mid,
data_right])
# call for plot functions
fig_traces = graph_channels(
full_trace.T, names=params["selected_channels"], s_fr=params["sampling_fr"][0])
ps_hist_fig = graph_hs_ps(
data=full_ps_hist, names=params["selected_channels"])
print("The current epoch index is ", epoch_index)
# check and update score labels (after key left/right if they exist)
if not score_storage is None:
if epoch_index in score_storage.keys():
null_score_label = str(
score_storage[epoch_index])
else:
null_score_label = ""
if (epoch_index - 1) in score_storage.keys():
epoch_minus_one_label = str(
score_storage[epoch_index - 1])
else:
epoch_minus_one_label = ""
if (epoch_index + 1) in score_storage.keys():
epoch_plus_one_label = str(
score_storage[epoch_index + 1])
else:
epoch_plus_one_label = ""
else:
null_score_label = ""
epoch_minus_one_label = ""
epoch_plus_one_label = ""
# check epoch and score_storage to trigger ml train
if (not epoch_index is None) and (not score_storage is None) and (epoch_index % 10 == 0) and (epoch_index > 0):
# print("data ready in-use (FUNC: ML Trigger)")
ml_trigger = {"epoch_index": [epoch_index],
"score_storage": score_storage,
"save_path": params["temp_save_path"]}
else:
ml_trigger = dash.no_update
try:
event["key"] = ""
except:
pass
return event, fig_traces, ps_hist_fig, slider_live_value, epoch_minus_one_label, null_score_label, epoch_plus_one_label, "", dash.no_update
elif (not functional_keys and not off_canvas):
print('data ready in-use (Unfunctional key!)')
try:
event["key"] = ""
except:
pass
return event, dash.no_update, dash.no_update, dash.no_update, dash.no_update, dash.no_update, dash.no_update, "", dash.no_update
else:
# this condition is not consistent with logic; troubleshoot when happened
print('404: Something went wrong! :(')
raise PreventUpdate
else:
# this condition is not consistent with logic; troubleshoot when happened
print('404: Something went wrong! :(')
raise PreventUpdate
# This part has to merge to above callback after fixing issue
# reading user input for epoch len (custom value)
@app.callback(
Output("epoch-length-input", "value"),
Input("epoch-length-input", "value")
)
def user_custom_epoch_length(value):
if not value is None:
if value.isnumeric():
# catch the error of numeric + string given at the same time, e.g. 1e
try:
params["epoch_length"] = [int(value)]
except:
return ""
time.sleep(.1)
return value
else:
# if this is the case make input invalid @Farzin
params["epoch_length"] = [""]
return ""
else:
# Default value, given by user in utils.py, when app is launching
return str(params["epoch_length"][0])
# Parameters collapse
@ app.callback(
Output("advparam-offcanvas", "is_open"),
[Input("advparam-button", "n_clicks")],
[State("advparam-offcanvas", "is_open")],
)
def toggle_adv_param_offcanvas(n, is_open):
if n:
return not is_open
return is_open
# secondary execution call-back
# channels importing and loading callback
@ app.callback(
[Output("import-offcanvas", "is_open"),
Output("channel_def_div", "children"),
Output("load_button", "children"),
Output("loading-output", "children"),
Output("load_button", "disabled"),
Output("sampling_fr_input", "disabled"),
Output("epoch-length-input", "disabled"),
Output("import-offcanvas-button", "n_clicks"),
Output("load_button", "n_clicks"),
Output("second_execution", "children"),
Output("internal-trigger", "max_intervals"),
Output("internal-trigger", "n_intervals"),
],
[Input("import-offcanvas-button", "n_clicks"),
Input("load_button", "n_clicks"),
Input("second_execution", "children"),
Input("internal-trigger", "n_intervals")]
)
def toggle_import_load_offcanvas(n1, n2, secondary, self_trigger):
if secondary == True and self_trigger == 1:
secondary = False
max_epoch_nr = process_input_data(params=params,
start_index=0,
end_index=-1,