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app.py
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app.py
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import datetime as dt
import dash
import dash_table
import dash_core_components as dcc
import dash_html_components as html
import threading
from time import sleep
import plotly.graph_objects as go
from dash.dependencies import Input, Output, State
from db.db_api import *
import plotly
app = dash.Dash(
__name__,
meta_tags=[{"name": "viewport", "content": "width=device-width, initial-scale=1"}],
)
server = app.server
app_color = {"graph_bg": "#082255", "graph_line": "#309ADE"}
params = [
'L0', 'L1', 'L2', 'R0', 'R1', 'R2'
]
figs = {
'L0': go.Figure(), 'L1': go.Figure(), 'L2': go.Figure(), 'R0': go.Figure(), 'R1': go.Figure(), 'R2': go.Figure()
}
app.layout = html.Div(
[
# header
html.Div(
[
html.Div(
[
html.H4("MONITORING WALKING HABITS", className="app__header__title"),
html.P(
"The application presents a graphical visualization of measurements of a device for monitoring walking habits and patterns and displays live charts of pressure and anomalies in moving.",
className="app__header__title--grey",
),
],
className="app__header__desc",
),
html.Div(
[
html.Img(
src=app.get_asset_url("logo.png"),
className="app__menu__img",
)
],
className="app__header__logo",
),
],
className="app__header",
),
html.Div(
[
#left foot
html.Div(
[
html.Div(
[html.H6("LEFT FOOT", className="graph__title")]
),
dcc.Graph(
id="l0",
figure=figs['L0'],
animate=True,
),
dcc.Graph(
id="l1",
figure=figs['L1'],
animate=True,
),
dcc.Graph(
id="l2",
figure=figs['L2'],
animate=True,
),
],
className="one-third column date__container",
),
# right foot
html.Div(
[
html.Div(
[html.H6("RIGHT FOOT", className="graph__title")]
),
dcc.Graph(
id="r0",
figure=figs['R0'],
animate=True,
),
dcc.Graph(
id="r1",
figure=figs['R1'],
animate=True,
),
dcc.Graph(
id="r2",
figure=figs['R2'],
animate=True,
),
],
className="one-third column date__container",
),
html.Div(
[
# personal data
html.Div(
[
html.Div(
[
html.H6(
"PERSONAL DATA",
className="graph__title",
)
]
),
html.Div(
[
dcc.Dropdown(
id='person-dropdown',
options=[
{'label': 'Janek Grzegorczyk', 'value': '1'},
{'label': 'Elżbieta Kochalska', 'value': '2'},
{'label': 'Albert Lisowski', 'value': '3'},
{'label': 'Ewelina Nosowska', 'value': '4'},
{'label': 'Piotr Fokalski', 'value': '5'},
{'label': 'Bartosz Moskalski', 'value': '6'}
],
value='1'
),
],
className="dropdown",
),
html.Div(
[
html.H2(
"NAME",
className="graph__title",
id="person-name"
)
]
),
html.Div(
[
html.H2(
"SURNAME",
className="graph__title",
id="person-surname"
)
]
),
html.Div(
[
html.H2(
"YEAR OF BIRTH",
className="graph__title",
id="person-year"
)
]
),
html.Div(
[
html.H2(
"DISABLED",
className="graph__title",
id="person-disabled"
)
]
),
],
className="graph__container first",
),
# table of measurements
html.Div(
[
html.Div(
[
html.H6(
"DESCRIPTION OF A GRAPHS", className="graph__title"
)
]
),
# dash_table.DataTable(
# id='table-editing-simple',
# columns=(
# [{'id': 'Model', 'name': 'SENSOR'}] +
# [{'id': p, 'name': p} for p in params]
# ),
# data=[
# dict(Model=i, **{param: 0 for param in params})
# for i in ["VALUE", "ANOMALY", "MEAN", "MIN", "MAX", "QARTILES", "RMS"]
# ],
# style_table={'border': 'thin lightgrey solid'},
# style_header={'backgroundColor':'lightgrey','fontWeight':'bold'},
# style_cell={'textAlign':'center','width':'12%'},
# style_data_conditional=[{
# 'if' : {'filter': 'side eq "bid"' },
# 'color':'blue'
# }
# ]+[
# {
# 'if' : {'filter': 'side eq "ask"' },
# 'color':'rgb(203,24,40)'
# }]+[
# { 'if': {'row_index':'odd'},
# 'backgroundColor':'rgb(242,242,242)'}
# ]+[
# {'if':{'column_id':'price'},
# 'fontWeight':'bold',
# 'border': 'thin lightgrey solid'}
# ]+[{'if':{'column_id':'from_mid'},
# 'fontWeight':'bold'}
# ],
# style_as_list_view=True,
# editable=True,
# ),
html.Div(
[
html.H1(
" ",
className="graph__title",
#id="person-surname"
)
]
),
html.Div(
[
html.H1(
"When NO ANOMALIES are detected points of a graph are violet.",
className="graph__title",
#id="person-surname"
)
]
),
html.Div(
[
html.H1(
"When ANOMALY is detected points of a graph are red.",
className="graph__title",
#id="person-surname"
)
]
),
],
className="graph__container second",
),
],
className="one-third column histogram__direction",
),
],
className="app__content",
),
],
className="app__container",
)
style=dict(
plot_bgcolor=app_color["graph_bg"],
paper_bgcolor=app_color["graph_bg"],
font_color="white",
title_font_size=18,
title_font_color="white",
xaxis=dict(
title="DATE",
linecolor=app_color["graph_bg"], # Sets color of X-axis line
showgrid=False, # Removes X-axis grid lines
),
yaxis=dict(
title="VALUE",
linecolor=app_color["graph_bg"], # Sets color of Y-axis line
showgrid=False, # Removes Y-axis grid lines
),
)
def get_current_date():
now = dt.datetime.now()
total_date = (now.hour * 3600) + (now.minute * 60) + (now.second)
return total_date
@app.callback(
Output('person-name', 'children'),
Output('person-surname', 'children'),
Output('person-year', 'children'),
Output('person-disabled', 'children'),
[dash.dependencies.Input('person-dropdown', 'value')])
def update_output(value):
df = select_people_by_id(value)
#print(df)
return 'NAME: {}'.format(df["name"][0]), 'SURNAME: {}'.format(df["surname"][0]), 'YEAR OF BIRTH: {}'.format(df["birth_year"][0]), 'DISABLED: {}'.format(bool(df["disabled"][0]))
def gen_diag(sensor_name, person_id):
total_date = get_current_date()
traces = select_traces(None, person_id)
df = pd.DataFrame()
for index, row in traces.iterrows():
df = df.append(select_sensor_for_trace(None, row['id'], sensor_name))
return df
@app.callback(
Output("l0", "figure"),
Output("l1", "figure"),
Output("l2", "figure"),
Output("r0", "figure"),
Output("r1", "figure"),
Output("r2", "figure"),
[dash.dependencies.Input('person-dropdown', 'value')])
def draw_diag_for_person(value):
for sensor_name in params:
df = gen_diag(sensor_name, value)
#print(df)
data = {
'time': [],
'Altitude': [],
'value1': [],
'value2': []
}
figs[sensor_name] = go.Figure()
figs[sensor_name].update_layout(title=sensor_name, xaxis_title="Time", yaxis_title="Value")
figs[sensor_name].update_layout(style)
figs[sensor_name].add_trace(go.Scatter(x=df['date'].loc[df['anomaly'] != 1],y=df['value'],mode="markers",name="non-anomaly",))
figs[sensor_name].add_trace(go.Scatter(x=df['date'].loc[df['anomaly'] == 1],y=df['value'],mode="markers",name="anomaly",))
return figs["L0"], figs["L1"], figs["L2"], figs["R0"], figs["R1"], figs["R2"]
# @app.callback(Output('live-update-graph', 'figure'),
# Input('interval-component', 'n_intervals'))
# def update_graph_live(n):
# data = {
# 'time': [],
# 'value1': [],
# 'value2': []
# }
# df = gen_diag('L1', 1)
# # Collect some data
# for i in range(df.shape[0]):
# #df = gen_diag('L1', 1)
# time = dt.datetime.now() - dt.timedelta(seconds=i*20)
# time2 = datetime.now().timestamp()
# #print(df)
# if (df['anomaly'].iloc[i] == 0):
# val1 = df.loc[df['anomaly'] == 0, 'value'].iloc[0]
# data['value1'].append(val1)
# data['time'].append(time2)
# else:
# val2 = df.loc[df['anomaly'] == 1, 'value'].iloc[0]
# data['value2'].append(val2)
# data['time'].append(time2)
# #val = df.loc[df['date'] == time2, 'value'].iloc[i]
# #data['value1'].append(val1)
# #data['value2'].append(val2)
# #data['Altitude'].append(1+i)
# #data['time'].append(time2)
# # Create the graph with subplots
# fig = go.Figure()
# fig.update_layout(style)
# fig.add_trace({
# 'x': data['time'],
# 'y': data['value1'],
# 'name': 'Nonanomaly',
# 'mode': 'markers',
# 'type': 'scatter'
# })
# fig.add_trace({
# 'x': data['time'],
# 'y': data['value2'],
# 'name': 'Anomaly',
# 'mode': 'markers',
# 'type': 'scatter'
# })
# return fig
if __name__ == "__main__":
#t = threading.Thread(target=get_data, args=[])
#t.start()
app.run_server()