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live_visualisations.py
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live_visualisations.py
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import plotly.graph_objects as go
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
import numpy as np
import plotly.express as px
y_axis_neck, y_axis_knee, y_axis_hip, y_axis_ankle, y_axis_kneey = [0], [0], [0], [0], [0]
x_axis = [0]
exponentiation = np.array([2.718 ** i for i in range(10)])
normaliser = np.sum(exponentiation)
exponentiation = exponentiation / normaliser
global data
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div([
dcc.Interval(
id='interval-component',
interval=42, # in milliseconds
n_intervals=0
),
html.H1('NECK'),
dcc.Graph(id='neck-graph'),
html.H1('KNEE'),
dcc.Graph(id='knee-graph'),
html.H1('HIP'),
dcc.Graph(id='hip-graph'),
html.H1('ANKLE'),
dcc.Graph(id='ankle-graph'),
html.H1('KNEE-Y'),
dcc.Graph(id='knee-y-graph')
], style={}
)
@app.callback(
Output('neck-graph', 'figure'),
[Input('interval-component', 'n_intervals')])
def make_figure(n):
global y_axis_neck, exponentiation
global x_axis
global data
data = pd.read_csv("data/visual_plotting.csv")
temp_y = np.dot(np.array(data['neck']), exponentiation)
temp_x = x_axis[-1] + 1
if len(x_axis) > 200:
x_axis.pop(0)
y_axis_neck.pop(0)
x_axis.append(temp_x)
y_axis_neck.append(temp_y)
fig = go.Figure()
fig.update_layout(yaxis_range=[0.5, 1.5])
fig.add_trace((go.Scatter(x=x_axis, y=y_axis_neck, name='neck')))
return fig
@app.callback(
Output('knee-graph', 'figure'),
[Input('interval-component', 'n_intervals')])
def make_figure(n):
global y_axis_knee
global x_axis
global data
temp_y = np.dot(np.array(data['knee']), exponentiation)
temp_x = x_axis[-1] + 1
if len(x_axis) > 200:
x_axis.pop(0)
y_axis_knee.pop(0)
x_axis.append(temp_x)
y_axis_knee.append(temp_y)
fig = go.Figure()
fig.update_layout(yaxis_range=[0.5, 3])
fig.add_trace((go.Scatter(x=x_axis, y=y_axis_knee, name='knee')))
return fig
@app.callback(
Output('hip-graph', 'figure'),
[Input('interval-component', 'n_intervals')])
def make_figure(n):
global y_axis_hip
global x_axis
global data
temp_y = np.dot(np.array(data['hip']), exponentiation)
temp_x = x_axis[-1] + 1
if len(x_axis) > 200:
x_axis.pop(0)
y_axis_hip.pop(0)
x_axis.append(temp_x)
y_axis_hip.append(temp_y)
fig = go.Figure()
fig.update_layout(yaxis_range=[0.5, 3])
fig.add_trace((go.Scatter(x=x_axis, y=y_axis_hip, name='knee')))
return fig
@app.callback(
Output('ankle-graph', 'figure'),
[Input('interval-component', 'n_intervals')])
def make_figure(n):
global y_axis_ankle
global x_axis
global data
temp_y = np.dot(np.array(data['ankle']), exponentiation)
temp_x = x_axis[-1] + 1
if len(x_axis) > 200:
x_axis.pop(0)
y_axis_hip.pop(0)
x_axis.append(temp_x)
y_axis_ankle.append(temp_y)
fig = go.Figure()
fig.update_layout(yaxis_range=[0.5, 3])
fig.add_trace((go.Scatter(x=x_axis, y=y_axis_hip, name='knee')))
return fig
if __name__ == '__main__':
app.run_server(debug=True)