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src/app.py
836
src/app.py
@ -1,4 +1,3 @@
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# %%
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import os
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import base64
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import logging
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@ -14,295 +13,292 @@ from datetime import datetime, timedelta
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import dash_dangerously_set_inner_html
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from urllib.parse import parse_qs, urlparse
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# %%
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log = logging.getLogger(__name__)
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for variable in ['CLIENT_ID','CLIENT_SECRET','REDIRECT_URL'] :
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if variable not in os.environ.keys() :
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log.error(f'Missing required environment variable \'{variable}\', please review the README')
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exit(1)
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if variable not in os.environ.keys() :
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log.error(f'Missing required environment variable \'{variable}\', please review the README')
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exit(1)
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app = dash.Dash(__name__)
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app.title = "Fitbit Wellness Report"
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server = app.server
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app.layout = html.Div(children=[
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dcc.ConfirmDialog(
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id='errordialog',
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message='Invalid Access Token : Unable to fetch data',
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),
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html.Div(id="input-area", className="hidden-print",
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style={
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'display': 'flex',
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'align-items': 'center',
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'justify-content': 'center',
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'gap': '20px',
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'margin': 'auto',
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'flex-wrap': 'wrap',
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'margin-top': '30px'
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},children=[
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dcc.DatePickerRange(
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id='my-date-picker-range',
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display_format='MMMM DD, Y',
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minimum_nights=40,
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max_date_allowed=datetime.today().date() - timedelta(days=1),
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min_date_allowed=datetime.today().date() - timedelta(days=1000),
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end_date=datetime.today().date() - timedelta(days=1),
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start_date=datetime.today().date() - timedelta(days=365)
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),
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html.Button(id='submit-button', type='submit', children='Submit', n_clicks=0, className="button-primary"),
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html.Button("Login to FitBit", id="login-button"),
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]),
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dcc.Location(id="location"),
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dcc.Store(id="oauth-token", storage_type='session'), # Store OAuth token in session storage
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html.Div(id="instruction-area", className="hidden-print", style={'margin-top':'30px', 'margin-right':'auto', 'margin-left':'auto','text-align':'center'}, children=[
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html.P( "Select a date range to generate a report.", style={'font-size':'17px', 'font-weight': 'bold', 'color':'#54565e'}),
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]),
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html.Div(id='loading-div', style={'margin-top': '40px'}, children=[
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dcc.Loading(
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id="loading-progress",
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type="default",
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children=html.Div(id="loading-output-1")
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),
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]),
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dcc.ConfirmDialog(
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id='errordialog',
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message='Invalid Access Token : Unable to fetch data',
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),
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html.Div(id="input-area", className="hidden-print",
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style={
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'display': 'flex',
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'align-items': 'center',
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'justify-content': 'center',
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'gap': '20px',
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'margin': 'auto',
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'flex-wrap': 'wrap',
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'margin-top': '30px'
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},children=[
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dcc.DatePickerRange(
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id='my-date-picker-range',
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display_format='MMMM DD, Y',
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minimum_nights=40,
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max_date_allowed=datetime.today().date() - timedelta(days=1),
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min_date_allowed=datetime.today().date() - timedelta(days=1000),
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end_date=datetime.today().date() - timedelta(days=1),
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start_date=datetime.today().date() - timedelta(days=365)
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),
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html.Button(id='submit-button', type='submit', children='Submit', n_clicks=0, className="button-primary"),
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html.Button("Login to FitBit", id="login-button"),
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]),
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dcc.Location(id="location"),
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dcc.Store(id="oauth-token", storage_type='session'), # Store OAuth token in session storage
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html.Div(id="instruction-area", className="hidden-print", style={'margin-top':'30px', 'margin-right':'auto', 'margin-left':'auto','text-align':'center'}, children=[
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html.P( "Select a date range to generate a report.", style={'font-size':'17px', 'font-weight': 'bold', 'color':'#54565e'}),
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]),
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html.Div(id='loading-div', style={'margin-top': '40px'}, children=[
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dcc.Loading(
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id="loading-progress",
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type="default",
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children=html.Div(id="loading-output-1")
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),
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]),
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html.Div(id='output_div', style={'max-width': '1400px', 'margin': 'auto'}, children=[
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html.Div(id='output_div', style={'max-width': '1400px', 'margin': 'auto'}, children=[
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html.Div(id='report-title-div',
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style={
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'display': 'flex',
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'align-items': 'center',
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'justify-content': 'center',
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'flex-direction': 'column',
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'margin-top': '20px'}, children=[
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html.H2(id="report-title", style={'font-weight': 'bold'}),
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html.H4(id="date-range-title", style={'font-weight': 'bold'}),
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html.P(id="generated-on-title", style={'font-weight': 'bold', 'font-size': '16'})
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]),
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html.Div(style={"height": '40px'}),
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html.H4("Resting Heart Rate 💖", style={'font-weight': 'bold'}),
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html.H6("Resting heart rate (RHR) is derived from a person's average sleeping heart rate. Fitbit tracks heart rate with photoplethysmography. This technique uses sensors and green light to detect blood volume when the heart beats. If a Fitbit device isn't worn during sleep, RHR is derived from daytime sedentary heart rate. According to the American Heart Association, a normal RHR is between 60-100 beats per minute (bpm), but this can vary based upon your age or fitness level."),
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dcc.Graph(
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id='graph_RHR',
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figure=px.line(),
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config= {'displaylogo': False}
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),
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html.Div(id='RHR_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(style={"height": '40px'}),
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html.H4("Steps Count 👣", style={'font-weight': 'bold'}),
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html.H6("Fitbit devices use an accelerometer to track steps. Some devices track active minutes, which includes activities over 3 metabolic equivalents (METs), such as brisk walking and cardio workouts."),
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dcc.Graph(
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id='graph_steps',
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figure=px.bar(),
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config= {'displaylogo': False}
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),
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dcc.Graph(
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id='graph_steps_heatmap',
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figure=px.bar(),
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config= {'displaylogo': False}
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),
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html.Div(id='steps_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(style={"height": '40px'}),
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html.H4("Activity 🏃♂️", style={'font-weight': 'bold'}),
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html.H6("Heart Rate Zones (fat burn, cardio and peak) are based on a percentage of maximum heart rate. Maximum heart rate is calculated as 220 minus age. The Centers for Disease Control recommends that adults do at least 150-300 minutes of moderate-intensity aerobic activity each week or 75-150 minutes of vigorous-intensity aerobic activity each week."),
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dcc.Graph(
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id='graph_activity_minutes',
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figure=px.bar(),
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config= {'displaylogo': False}
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),
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html.Div(id='fat_burn_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(id='cardio_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(id='peak_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(style={"height": '40px'}),
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html.H4("Weight Log ⏲️", style={'font-weight': 'bold'}),
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html.H6("Fitbit connects with the Aria family of smart scales to track weight. Weight may also be self-reported using the Fitbit app. Studies suggest that regular weigh-ins may help people who want to lose weight."),
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dcc.Graph(
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id='graph_weight',
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figure=px.line(),
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config= {'displaylogo': False}
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),
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html.Div(id='weight_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(style={"height": '40px'}),
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html.H4("SpO2 🩸", style={'font-weight': 'bold'}),
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html.H6("A pulse oximeter reading indicates what percentage of your blood is saturated, known as the SpO2 level. A typical, healthy reading is 95–100% . If your SpO2 level is less than 92%, a doctor may recommend you get an ABG. A pulse ox is the most common type of test because it's noninvasive and provides quick readings."),
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dcc.Graph(
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id='graph_spo2',
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figure=px.line(),
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config= {'displaylogo': False}
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),
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html.Div(id='spo2_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(style={"height": '40px'}),
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html.H4("Sleep 💤", style={'font-weight': 'bold'}),
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html.H6("Fitbit estimates sleep stages (awake, REM, light sleep and deep sleep) and sleep duration based on a person's movement and heart-rate patterns. The National Sleep Foundation recommends 7-9 hours of sleep per night for adults"),
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dcc.Checklist(options=[{'label': 'Color Code Sleep Stages', 'value': 'Color Code Sleep Stages','disabled':True}], value=['Color Code Sleep Stages'], style={'max-width': '1330px', 'margin': 'auto'}, inline=True, id="sleep-stage-checkbox", className="hidden-print"),
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dcc.Graph(
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id='graph_sleep',
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figure=px.bar(),
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config= {'displaylogo': False}
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),
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dcc.Graph(
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id='graph_sleep_regularity',
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figure=px.bar(),
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config= {'displaylogo': False}
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),
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html.Div(id='sleep_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(style={"height": '40px'}),
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html.Div(className="hidden-print", style={'margin': 'auto', 'text-align': 'center'}, children=[
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dash_dangerously_set_inner_html.DangerouslySetInnerHTML( '''
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<form action="https://www.paypal.com/donate" method="post" target="_blank">
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html.Div(id='report-title-div',
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style={
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'display': 'flex',
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'align-items': 'center',
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'justify-content': 'center',
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'flex-direction': 'column',
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'margin-top': '20px'}, children=[
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html.H2(id="report-title", style={'font-weight': 'bold'}),
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html.H4(id="date-range-title", style={'font-weight': 'bold'}),
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html.P(id="generated-on-title", style={'font-weight': 'bold', 'font-size': '16'})
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]),
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html.Div(style={"height": '40px'}),
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html.H4("Resting Heart Rate 💖", style={'font-weight': 'bold'}),
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html.H6("Resting heart rate (RHR) is derived from a person's average sleeping heart rate. Fitbit tracks heart rate with photoplethysmography. This technique uses sensors and green light to detect blood volume when the heart beats. If a Fitbit device isn't worn during sleep, RHR is derived from daytime sedentary heart rate. According to the American Heart Association, a normal RHR is between 60-100 beats per minute (bpm), but this can vary based upon your age or fitness level."),
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dcc.Graph(
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id='graph_RHR',
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figure=px.line(),
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config= {'displaylogo': False}
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),
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html.Div(id='RHR_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(style={"height": '40px'}),
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html.H4("Steps Count 👣", style={'font-weight': 'bold'}),
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html.H6("Fitbit devices use an accelerometer to track steps. Some devices track active minutes, which includes activities over 3 metabolic equivalents (METs), such as brisk walking and cardio workouts."),
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dcc.Graph(
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id='graph_steps',
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figure=px.bar(),
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config= {'displaylogo': False}
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),
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dcc.Graph(
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id='graph_steps_heatmap',
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figure=px.bar(),
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config= {'displaylogo': False}
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),
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html.Div(id='steps_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(style={"height": '40px'}),
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html.H4("Activity 🏃♂️", style={'font-weight': 'bold'}),
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html.H6("Heart Rate Zones (fat burn, cardio and peak) are based on a percentage of maximum heart rate. Maximum heart rate is calculated as 220 minus age. The Centers for Disease Control recommends that adults do at least 150-300 minutes of moderate-intensity aerobic activity each week or 75-150 minutes of vigorous-intensity aerobic activity each week."),
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dcc.Graph(
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id='graph_activity_minutes',
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figure=px.bar(),
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config= {'displaylogo': False}
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),
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html.Div(id='fat_burn_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(id='cardio_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(id='peak_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
|
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html.Div(style={"height": '40px'}),
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html.H4("Weight Log ⏲️", style={'font-weight': 'bold'}),
|
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html.H6("Fitbit connects with the Aria family of smart scales to track weight. Weight may also be self-reported using the Fitbit app. Studies suggest that regular weigh-ins may help people who want to lose weight."),
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dcc.Graph(
|
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id='graph_weight',
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figure=px.line(),
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config= {'displaylogo': False}
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),
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html.Div(id='weight_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
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html.Div(style={"height": '40px'}),
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html.H4("SpO2 🩸", style={'font-weight': 'bold'}),
|
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html.H6("A pulse oximeter reading indicates what percentage of your blood is saturated, known as the SpO2 level. A typical, healthy reading is 95–100% . If your SpO2 level is less than 92%, a doctor may recommend you get an ABG. A pulse ox is the most common type of test because it's noninvasive and provides quick readings."),
|
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dcc.Graph(
|
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id='graph_spo2',
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figure=px.line(),
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config= {'displaylogo': False}
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),
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html.Div(id='spo2_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
|
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html.Div(style={"height": '40px'}),
|
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html.H4("Sleep 💤", style={'font-weight': 'bold'}),
|
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html.H6("Fitbit estimates sleep stages (awake, REM, light sleep and deep sleep) and sleep duration based on a person's movement and heart-rate patterns. The National Sleep Foundation recommends 7-9 hours of sleep per night for adults"),
|
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dcc.Checklist(options=[{'label': 'Color Code Sleep Stages', 'value': 'Color Code Sleep Stages','disabled':True}], value=['Color Code Sleep Stages'], style={'max-width': '1330px', 'margin': 'auto'}, inline=True, id="sleep-stage-checkbox", className="hidden-print"),
|
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dcc.Graph(
|
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id='graph_sleep',
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figure=px.bar(),
|
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config= {'displaylogo': False}
|
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),
|
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dcc.Graph(
|
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id='graph_sleep_regularity',
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figure=px.bar(),
|
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config= {'displaylogo': False}
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),
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html.Div(id='sleep_table', style={'max-width': '1200px', 'margin': 'auto', 'font-weight': 'bold'}, children=[]),
|
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html.Div(style={"height": '40px'}),
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html.Div(className="hidden-print", style={'margin': 'auto', 'text-align': 'center'}, children=[
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dash_dangerously_set_inner_html.DangerouslySetInnerHTML( '''
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<form action="https://www.paypal.com/donate" method="post" target="_blank">
|
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<input type="hidden" name="hosted_button_id" value="X4CFTUDJ9ZXX2" />
|
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<input type="image" src="https://pics.paypal.com/00/s/ZjQwZTU5NjktYzM2Ny00MTM3LTkzZWEtNDkwMjE2NGYzNDM4/file.PNG" border="0" name="submit" title="PayPal - The safer, easier way to pay online!" alt="Donate with PayPal button" />
|
||||
<img alt="" border="0" src="https://www.paypal.com/en_CA/i/scr/pixel.gif" width="1" height="1" />
|
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</form>
|
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''')]),
|
||||
html.Div(style={"height": '25px'}),
|
||||
]),
|
||||
''')]),
|
||||
html.Div(style={"height": '25px'}),
|
||||
]),
|
||||
])
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@app.callback(Output('location', 'href'),Input('login-button', 'n_clicks'))
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def authorize(n_clicks):
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"""Authorize the application"""
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if n_clicks :
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client_id = os.environ['CLIENT_ID']
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redirect_uri = os.environ['REDIRECT_URL']
|
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scope = 'profile activity cardio_fitness heartrate sleep weight oxygen_saturation respiratory_rate'
|
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auth_url = f'https://www.fitbit.com/oauth2/authorize?scope={scope}&client_id={client_id}&response_type=code&prompt=none&redirect_uri={redirect_uri}'
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||||
return auth_url
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return dash.no_update
|
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"""Authorize the application"""
|
||||
if n_clicks :
|
||||
client_id = os.environ['CLIENT_ID']
|
||||
redirect_uri = os.environ['REDIRECT_URL']
|
||||
scope = 'profile activity cardio_fitness heartrate sleep weight oxygen_saturation respiratory_rate'
|
||||
auth_url = f'https://www.fitbit.com/oauth2/authorize?scope={scope}&client_id={client_id}&response_type=code&prompt=none&redirect_uri={redirect_uri}'
|
||||
return auth_url
|
||||
return dash.no_update
|
||||
|
||||
@app.callback(Output('oauth-token', 'data'),Input('location', 'href'))
|
||||
def handle_oauth_callback(href):
|
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"""Process the OAuth callback"""
|
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if href:
|
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# Parse the query string from the URL to extract the 'code' parameter
|
||||
parsed_url = urlparse(href)
|
||||
query_params = parse_qs(parsed_url.query)
|
||||
oauth_code = query_params.get('code', [None])[0]
|
||||
if oauth_code :
|
||||
print(f"OAuth code received")
|
||||
else :
|
||||
print("No OAuth code found in URL.")
|
||||
return dash.no_update
|
||||
# Exchange code for a token
|
||||
client_id = os.environ['CLIENT_ID']
|
||||
client_isecret = os.environ['CLIENT_SECRET']
|
||||
redirect_uri = os.environ['REDIRECT_URL']
|
||||
token_url='https://api.fitbit.com/oauth2/token?'
|
||||
payload = {'code': oauth_code, 'grant_type': 'authorization_code', 'client_id': client_id, 'redirect_uri': redirect_uri}
|
||||
token_creds = base64.b64encode(f"{client_id}:{client_isecret}".encode("utf-8")).decode("utf-8")
|
||||
token_headers = {"Authorization": f"Basic {token_creds}"}
|
||||
token_response = requests.post(token_url, data=payload, headers=token_headers)
|
||||
token_response_json = token_response.json()
|
||||
access_token = token_response_json.get('access_token')
|
||||
if access_token :
|
||||
print(f"Acceess token received!")
|
||||
return access_token
|
||||
else :
|
||||
print("No access token found in response.")
|
||||
return dash.no_update
|
||||
"""Process the OAuth callback"""
|
||||
if href:
|
||||
# Parse the query string from the URL to extract the 'code' parameter
|
||||
parsed_url = urlparse(href)
|
||||
query_params = parse_qs(parsed_url.query)
|
||||
oauth_code = query_params.get('code', [None])[0]
|
||||
if oauth_code :
|
||||
print(f"OAuth code received")
|
||||
else :
|
||||
print("No OAuth code found in URL.")
|
||||
return dash.no_update
|
||||
# Exchange code for a token
|
||||
client_id = os.environ['CLIENT_ID']
|
||||
client_isecret = os.environ['CLIENT_SECRET']
|
||||
redirect_uri = os.environ['REDIRECT_URL']
|
||||
token_url='https://api.fitbit.com/oauth2/token?'
|
||||
payload = {'code': oauth_code, 'grant_type': 'authorization_code', 'client_id': client_id, 'redirect_uri': redirect_uri}
|
||||
token_creds = base64.b64encode(f"{client_id}:{client_isecret}".encode("utf-8")).decode("utf-8")
|
||||
token_headers = {"Authorization": f"Basic {token_creds}"}
|
||||
token_response = requests.post(token_url, data=payload, headers=token_headers)
|
||||
token_response_json = token_response.json()
|
||||
access_token = token_response_json.get('access_token')
|
||||
if access_token :
|
||||
print(f"Acceess token received!")
|
||||
return access_token
|
||||
else :
|
||||
print("No access token found in response.")
|
||||
return dash.no_update
|
||||
|
||||
@app.callback(Output('login-button', 'children'),Output('login-button', 'disabled'),Input('oauth-token', 'data'))
|
||||
def update_login_button(oauth_token):
|
||||
if oauth_token:
|
||||
return html.Span("Logged in"), True
|
||||
else:
|
||||
return "Login to FitBit", False
|
||||
if oauth_token:
|
||||
return html.Span("Logged in"), True
|
||||
else:
|
||||
return "Login to FitBit", False
|
||||
|
||||
|
||||
def seconds_to_tick_label(seconds):
|
||||
"""Calculate the number of hours, minutes, and remaining seconds"""
|
||||
hours, remainder = divmod(seconds, 3600)
|
||||
minutes, seconds = divmod(remainder, 60)
|
||||
mult, remainder = divmod(hours, 12)
|
||||
if mult >=2:
|
||||
hours = hours - (12*mult)
|
||||
result_datetime = datetime(1, 1, 1, hour=hours, minute=minutes, second=seconds)
|
||||
if result_datetime.hour >= 12:
|
||||
result_datetime = result_datetime - timedelta(hours=12)
|
||||
else:
|
||||
result_datetime = result_datetime + timedelta(hours=12)
|
||||
return result_datetime.strftime("%H:%M")
|
||||
"""Calculate the number of hours, minutes, and remaining seconds"""
|
||||
hours, remainder = divmod(seconds, 3600)
|
||||
minutes, seconds = divmod(remainder, 60)
|
||||
mult, remainder = divmod(hours, 12)
|
||||
if mult >=2:
|
||||
hours = hours - (12*mult)
|
||||
result_datetime = datetime(1, 1, 1, hour=hours, minute=minutes, second=seconds)
|
||||
if result_datetime.hour >= 12:
|
||||
result_datetime = result_datetime - timedelta(hours=12)
|
||||
else:
|
||||
result_datetime = result_datetime + timedelta(hours=12)
|
||||
return result_datetime.strftime("%H:%M")
|
||||
|
||||
def format_minutes(minutes):
|
||||
return "%2dh %02dm" % (divmod(minutes, 60))
|
||||
return "%2dh %02dm" % (divmod(minutes, 60))
|
||||
|
||||
def calculate_table_data(df, measurement_name):
|
||||
df = df.sort_values(by='Date', ascending=False)
|
||||
result_data = {
|
||||
'Period' : ['30 days', '3 months', '6 months', '1 year'],
|
||||
'Average ' + measurement_name : [],
|
||||
'Max ' + measurement_name : [],
|
||||
'Min ' + measurement_name : []
|
||||
}
|
||||
last_date = df.head(1)['Date'].values[0]
|
||||
for period in [30, 90, 180, 365]:
|
||||
end_date = last_date
|
||||
start_date = end_date - pd.Timedelta(days=period)
|
||||
df = df.sort_values(by='Date', ascending=False)
|
||||
result_data = {
|
||||
'Period' : ['30 days', '3 months', '6 months', '1 year'],
|
||||
'Average ' + measurement_name : [],
|
||||
'Max ' + measurement_name : [],
|
||||
'Min ' + measurement_name : []
|
||||
}
|
||||
last_date = df.head(1)['Date'].values[0]
|
||||
for period in [30, 90, 180, 365]:
|
||||
end_date = last_date
|
||||
start_date = end_date - pd.Timedelta(days=period)
|
||||
|
||||
period_data = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
|
||||
period_data = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
|
||||
|
||||
if len(period_data) >= period:
|
||||
if len(period_data) >= period:
|
||||
|
||||
max_hr = period_data[measurement_name].max()
|
||||
if measurement_name == "Steps Count":
|
||||
min_hr = period_data[period_data[measurement_name] != 0][measurement_name].min()
|
||||
else:
|
||||
min_hr = period_data[measurement_name].min()
|
||||
average_hr = round(period_data[measurement_name].mean(),2)
|
||||
max_hr = period_data[measurement_name].max()
|
||||
if measurement_name == "Steps Count":
|
||||
min_hr = period_data[period_data[measurement_name] != 0][measurement_name].min()
|
||||
else:
|
||||
min_hr = period_data[measurement_name].min()
|
||||
average_hr = round(period_data[measurement_name].mean(),2)
|
||||
|
||||
if measurement_name == "Total Sleep Minutes":
|
||||
result_data['Average ' + measurement_name].append(format_minutes(average_hr))
|
||||
result_data['Max ' + measurement_name].append(format_minutes(max_hr))
|
||||
result_data['Min ' + measurement_name].append(format_minutes(min_hr))
|
||||
else:
|
||||
result_data['Average ' + measurement_name].append(average_hr)
|
||||
result_data['Max ' + measurement_name].append(max_hr)
|
||||
result_data['Min ' + measurement_name].append(min_hr)
|
||||
else:
|
||||
result_data['Average ' + measurement_name].append(pd.NA)
|
||||
result_data['Max ' + measurement_name].append(pd.NA)
|
||||
result_data['Min ' + measurement_name].append(pd.NA)
|
||||
if measurement_name == "Total Sleep Minutes":
|
||||
result_data['Average ' + measurement_name].append(format_minutes(average_hr))
|
||||
result_data['Max ' + measurement_name].append(format_minutes(max_hr))
|
||||
result_data['Min ' + measurement_name].append(format_minutes(min_hr))
|
||||
else:
|
||||
result_data['Average ' + measurement_name].append(average_hr)
|
||||
result_data['Max ' + measurement_name].append(max_hr)
|
||||
result_data['Min ' + measurement_name].append(min_hr)
|
||||
else:
|
||||
result_data['Average ' + measurement_name].append(pd.NA)
|
||||
result_data['Max ' + measurement_name].append(pd.NA)
|
||||
result_data['Min ' + measurement_name].append(pd.NA)
|
||||
|
||||
return pd.DataFrame(result_data)
|
||||
return pd.DataFrame(result_data)
|
||||
|
||||
# Sleep stages checkbox functionality
|
||||
@app.callback(Output('graph_sleep', 'figure', allow_duplicate=True), Input('sleep-stage-checkbox', 'value'), State('graph_sleep', 'figure'), prevent_initial_call=True)
|
||||
def update_sleep_colors(value, fig):
|
||||
if len(value) == 1:
|
||||
fig['data'][0]['marker']['color'] = '#084466'
|
||||
fig['data'][1]['marker']['color'] = '#1e9ad6'
|
||||
fig['data'][2]['marker']['color'] = '#4cc5da'
|
||||
fig['data'][3]['marker']['color'] = '#fd7676'
|
||||
else:
|
||||
fig['data'][0]['marker']['color'] = '#084466'
|
||||
fig['data'][1]['marker']['color'] = '#084466'
|
||||
fig['data'][2]['marker']['color'] = '#084466'
|
||||
fig['data'][3]['marker']['color'] = '#084466'
|
||||
return fig
|
||||
if len(value) == 1:
|
||||
fig['data'][0]['marker']['color'] = '#084466'
|
||||
fig['data'][1]['marker']['color'] = '#1e9ad6'
|
||||
fig['data'][2]['marker']['color'] = '#4cc5da'
|
||||
fig['data'][3]['marker']['color'] = '#fd7676'
|
||||
else:
|
||||
fig['data'][0]['marker']['color'] = '#084466'
|
||||
fig['data'][1]['marker']['color'] = '#084466'
|
||||
fig['data'][2]['marker']['color'] = '#084466'
|
||||
fig['data'][3]['marker']['color'] = '#084466'
|
||||
return fig
|
||||
|
||||
# Limits the date range to one year max
|
||||
@app.callback(Output('my-date-picker-range', 'max_date_allowed'), Output('my-date-picker-range', 'end_date'),
|
||||
[Input('my-date-picker-range', 'start_date')])
|
||||
[Input('my-date-picker-range', 'start_date')])
|
||||
def set_max_date_allowed(start_date):
|
||||
start = datetime.strptime(start_date, "%Y-%m-%d")
|
||||
current_date = datetime.today().date() - timedelta(days=1)
|
||||
max_end_date = min((start + timedelta(days=365)).date(), current_date)
|
||||
return max_end_date, max_end_date
|
||||
start = datetime.strptime(start_date, "%Y-%m-%d")
|
||||
current_date = datetime.today().date() - timedelta(days=1)
|
||||
max_end_date = min((start + timedelta(days=365)).date(), current_date)
|
||||
return max_end_date, max_end_date
|
||||
|
||||
# Disables the button after click and starts calculations
|
||||
@app.callback(Output('errordialog', 'displayed'), Output('submit-button', 'disabled'), Output('my-date-picker-range', 'disabled'), Input('submit-button', 'n_clicks'),State('oauth-token', 'data'),prevent_initial_call=True)
|
||||
def disable_button_and_calculate(n_clicks, oauth_token):
|
||||
headers = {
|
||||
"Authorization": "Bearer " + oauth_token,
|
||||
"Accept": "application/json"
|
||||
}
|
||||
try:
|
||||
token_response = requests.get("https://api.fitbit.com/1/user/-/profile.json", headers=headers)
|
||||
token_response.raise_for_status()
|
||||
except:
|
||||
return True, False, False
|
||||
return False, True, True
|
||||
headers = {
|
||||
"Authorization": "Bearer " + oauth_token,
|
||||
"Accept": "application/json"
|
||||
}
|
||||
try:
|
||||
token_response = requests.get("https://api.fitbit.com/1/user/-/profile.json", headers=headers)
|
||||
token_response.raise_for_status()
|
||||
except:
|
||||
return True, False, False
|
||||
return False, True, True
|
||||
|
||||
# Fetch data and update graphs on click of submit
|
||||
@app.callback(Output('report-title', 'children'), Output('date-range-title', 'children'), Output('generated-on-title', 'children'), Output('graph_RHR', 'figure'), Output('RHR_table', 'children'), Output('graph_steps', 'figure'), Output('graph_steps_heatmap', 'figure'), Output('steps_table', 'children'), Output('graph_activity_minutes', 'figure'), Output('fat_burn_table', 'children'), Output('cardio_table', 'children'), Output('peak_table', 'children'), Output('graph_weight', 'figure'), Output('weight_table', 'children'), Output('graph_spo2', 'figure'), Output('spo2_table', 'children'), Output('graph_sleep', 'figure'), Output('graph_sleep_regularity', 'figure'), Output('sleep_table', 'children'), Output('sleep-stage-checkbox', 'options'), Output("loading-output-1", "children"),
|
||||
@ -310,201 +306,197 @@ Input('submit-button', 'disabled'),State('my-date-picker-range', 'start_date'),
|
||||
prevent_initial_call=True)
|
||||
def update_output(n_clicks, start_date, end_date, oauth_token):
|
||||
|
||||
start_date = datetime.fromisoformat(start_date).strftime("%Y-%m-%d")
|
||||
end_date = datetime.fromisoformat(end_date).strftime("%Y-%m-%d")
|
||||
start_date = datetime.fromisoformat(start_date).strftime("%Y-%m-%d")
|
||||
end_date = datetime.fromisoformat(end_date).strftime("%Y-%m-%d")
|
||||
|
||||
headers = {
|
||||
"Authorization": "Bearer " + oauth_token,
|
||||
"Accept": "application/json"
|
||||
}
|
||||
headers = {
|
||||
"Authorization": "Bearer " + oauth_token,
|
||||
"Accept": "application/json"
|
||||
}
|
||||
|
||||
# Collecting data-----------------------------------------------------------------------------------------------------------------------
|
||||
# Collecting data-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
user_profile = requests.get("https://api.fitbit.com/1/user/-/profile.json", headers=headers).json()
|
||||
response_heartrate = requests.get("https://api.fitbit.com/1/user/-/activities/heart/date/"+ start_date +"/"+ end_date +".json", headers=headers).json()
|
||||
response_steps = requests.get("https://api.fitbit.com/1/user/-/activities/steps/date/"+ start_date +"/"+ end_date +".json", headers=headers).json()
|
||||
response_weight = requests.get("https://api.fitbit.com/1/user/-/body/weight/date/"+ start_date +"/"+ end_date +".json", headers=headers).json()
|
||||
response_spo2 = requests.get("https://api.fitbit.com/1/user/-/spo2/date/"+ start_date +"/"+ end_date +".json", headers=headers).json()
|
||||
user_profile = requests.get("https://api.fitbit.com/1/user/-/profile.json", headers=headers).json()
|
||||
response_heartrate = requests.get("https://api.fitbit.com/1/user/-/activities/heart/date/"+ start_date +"/"+ end_date +".json", headers=headers).json()
|
||||
response_steps = requests.get("https://api.fitbit.com/1/user/-/activities/steps/date/"+ start_date +"/"+ end_date +".json", headers=headers).json()
|
||||
response_weight = requests.get("https://api.fitbit.com/1/user/-/body/weight/date/"+ start_date +"/"+ end_date +".json", headers=headers).json()
|
||||
response_spo2 = requests.get("https://api.fitbit.com/1/user/-/spo2/date/"+ start_date +"/"+ end_date +".json", headers=headers).json()
|
||||
|
||||
# Processing data-----------------------------------------------------------------------------------------------------------------------
|
||||
days_name_list = ('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday','Sunday')
|
||||
report_title = "Wellness Report - " + user_profile["user"]["firstName"] + " " + user_profile["user"]["lastName"]
|
||||
report_dates_range = datetime.fromisoformat(start_date).strftime("%d %B, %Y") + " – " + datetime.fromisoformat(end_date).strftime("%d %B, %Y")
|
||||
generated_on_date = "Report Generated : " + datetime.today().date().strftime("%d %B, %Y")
|
||||
dates_list = []
|
||||
dates_str_list = []
|
||||
rhr_list = []
|
||||
steps_list = []
|
||||
weight_list = []
|
||||
spo2_list = []
|
||||
sleep_record_dict = {}
|
||||
deep_sleep_list, light_sleep_list, rem_sleep_list, awake_list, total_sleep_list, sleep_start_times_list = [],[],[],[],[],[]
|
||||
fat_burn_minutes_list, cardio_minutes_list, peak_minutes_list = [], [], []
|
||||
# Processing data-----------------------------------------------------------------------------------------------------------------------
|
||||
days_name_list = ('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday','Sunday')
|
||||
report_title = "Wellness Report - " + user_profile["user"]["firstName"] + " " + user_profile["user"]["lastName"]
|
||||
report_dates_range = datetime.fromisoformat(start_date).strftime("%d %B, %Y") + " – " + datetime.fromisoformat(end_date).strftime("%d %B, %Y")
|
||||
generated_on_date = "Report Generated : " + datetime.today().date().strftime("%d %B, %Y")
|
||||
dates_list = []
|
||||
dates_str_list = []
|
||||
rhr_list = []
|
||||
steps_list = []
|
||||
weight_list = []
|
||||
spo2_list = []
|
||||
sleep_record_dict = {}
|
||||
deep_sleep_list, light_sleep_list, rem_sleep_list, awake_list, total_sleep_list, sleep_start_times_list = [],[],[],[],[],[]
|
||||
fat_burn_minutes_list, cardio_minutes_list, peak_minutes_list = [], [], []
|
||||
|
||||
for entry in response_heartrate['activities-heart']:
|
||||
dates_str_list.append(entry['dateTime'])
|
||||
dates_list.append(datetime.strptime(entry['dateTime'], '%Y-%m-%d'))
|
||||
try:
|
||||
fat_burn_minutes_list.append(entry["value"]["heartRateZones"][1]["minutes"])
|
||||
cardio_minutes_list.append(entry["value"]["heartRateZones"][2]["minutes"])
|
||||
peak_minutes_list.append(entry["value"]["heartRateZones"][3]["minutes"])
|
||||
except KeyError as E:
|
||||
fat_burn_minutes_list.append(None)
|
||||
cardio_minutes_list.append(None)
|
||||
peak_minutes_list.append(None)
|
||||
if 'restingHeartRate' in entry['value']:
|
||||
rhr_list.append(entry['value']['restingHeartRate'])
|
||||
else:
|
||||
rhr_list.append(None)
|
||||
for entry in response_heartrate['activities-heart']:
|
||||
dates_str_list.append(entry['dateTime'])
|
||||
dates_list.append(datetime.strptime(entry['dateTime'], '%Y-%m-%d'))
|
||||
try:
|
||||
fat_burn_minutes_list.append(entry["value"]["heartRateZones"][1]["minutes"])
|
||||
cardio_minutes_list.append(entry["value"]["heartRateZones"][2]["minutes"])
|
||||
peak_minutes_list.append(entry["value"]["heartRateZones"][3]["minutes"])
|
||||
except KeyError as E:
|
||||
fat_burn_minutes_list.append(None)
|
||||
cardio_minutes_list.append(None)
|
||||
peak_minutes_list.append(None)
|
||||
if 'restingHeartRate' in entry['value']:
|
||||
rhr_list.append(entry['value']['restingHeartRate'])
|
||||
else:
|
||||
rhr_list.append(None)
|
||||
|
||||
for entry in response_steps['activities-steps']:
|
||||
if int(entry['value']) == 0:
|
||||
steps_list.append(None)
|
||||
else:
|
||||
steps_list.append(int(entry['value']))
|
||||
for entry in response_steps['activities-steps']:
|
||||
if int(entry['value']) == 0:
|
||||
steps_list.append(None)
|
||||
else:
|
||||
steps_list.append(int(entry['value']))
|
||||
|
||||
for entry in response_weight["body-weight"]:
|
||||
weight_list.append(float(entry['value']))
|
||||
for entry in response_weight["body-weight"]:
|
||||
weight_list.append(float(entry['value']))
|
||||
|
||||
for entry in response_spo2:
|
||||
spo2_list += [None]*(dates_str_list.index(entry["dateTime"])-len(spo2_list))
|
||||
spo2_list.append(entry["value"]["avg"])
|
||||
spo2_list += [None]*(len(dates_str_list)-len(spo2_list))
|
||||
for entry in response_spo2:
|
||||
spo2_list += [None]*(dates_str_list.index(entry["dateTime"])-len(spo2_list))
|
||||
spo2_list.append(entry["value"]["avg"])
|
||||
spo2_list += [None]*(len(dates_str_list)-len(spo2_list))
|
||||
|
||||
for i in range(0,len(dates_str_list),100):
|
||||
end_index = i+100
|
||||
if i+100 > len(dates_str_list):
|
||||
end_index = len(dates_str_list)
|
||||
temp_start_date = dates_str_list[i]
|
||||
temp_end_date = dates_str_list[end_index-1]
|
||||
for i in range(0,len(dates_str_list),100):
|
||||
end_index = i+100
|
||||
if i+100 > len(dates_str_list):
|
||||
end_index = len(dates_str_list)
|
||||
temp_start_date = dates_str_list[i]
|
||||
temp_end_date = dates_str_list[end_index-1]
|
||||
|
||||
response_sleep = requests.get("https://api.fitbit.com/1.2/user/-/sleep/date/"+ temp_start_date +"/"+ temp_end_date +".json", headers=headers).json()
|
||||
response_sleep = requests.get("https://api.fitbit.com/1.2/user/-/sleep/date/"+ temp_start_date +"/"+ temp_end_date +".json", headers=headers).json()
|
||||
|
||||
for sleep_record in response_sleep["sleep"][::-1]:
|
||||
if sleep_record['isMainSleep']:
|
||||
try:
|
||||
sleep_start_time = datetime.strptime(sleep_record["startTime"], "%Y-%m-%dT%H:%M:%S.%f")
|
||||
if sleep_start_time.hour < 12:
|
||||
sleep_start_time = sleep_start_time + timedelta(hours=12)
|
||||
else:
|
||||
sleep_start_time = sleep_start_time + timedelta(hours=-12)
|
||||
sleep_time_of_day = sleep_start_time.time()
|
||||
sleep_record_dict[sleep_record['dateOfSleep']] = {'deep': sleep_record['levels']['summary']['deep']['minutes'],
|
||||
'light': sleep_record['levels']['summary']['light']['minutes'],
|
||||
'rem': sleep_record['levels']['summary']['rem']['minutes'],
|
||||
'wake': sleep_record['levels']['summary']['wake']['minutes'],
|
||||
'total_sleep': sleep_record["minutesAsleep"],
|
||||
'start_time_seconds': (sleep_time_of_day.hour * 3600) + (sleep_time_of_day.minute * 60) + sleep_time_of_day.second
|
||||
}
|
||||
except KeyError as E:
|
||||
pass
|
||||
for sleep_record in response_sleep["sleep"][::-1]:
|
||||
if sleep_record['isMainSleep']:
|
||||
try:
|
||||
sleep_start_time = datetime.strptime(sleep_record["startTime"], "%Y-%m-%dT%H:%M:%S.%f")
|
||||
if sleep_start_time.hour < 12:
|
||||
sleep_start_time = sleep_start_time + timedelta(hours=12)
|
||||
else:
|
||||
sleep_start_time = sleep_start_time + timedelta(hours=-12)
|
||||
sleep_time_of_day = sleep_start_time.time()
|
||||
sleep_record_dict[sleep_record['dateOfSleep']] = {'deep': sleep_record['levels']['summary']['deep']['minutes'],
|
||||
'light': sleep_record['levels']['summary']['light']['minutes'],
|
||||
'rem': sleep_record['levels']['summary']['rem']['minutes'],
|
||||
'wake': sleep_record['levels']['summary']['wake']['minutes'],
|
||||
'total_sleep': sleep_record["minutesAsleep"],
|
||||
'start_time_seconds': (sleep_time_of_day.hour * 3600) + (sleep_time_of_day.minute * 60) + sleep_time_of_day.second
|
||||
}
|
||||
except KeyError as E:
|
||||
pass
|
||||
|
||||
for day in dates_str_list:
|
||||
if day in sleep_record_dict:
|
||||
deep_sleep_list.append(sleep_record_dict[day]['deep'])
|
||||
light_sleep_list.append(sleep_record_dict[day]['light'])
|
||||
rem_sleep_list.append(sleep_record_dict[day]['rem'])
|
||||
awake_list.append(sleep_record_dict[day]['wake'])
|
||||
total_sleep_list.append(sleep_record_dict[day]['total_sleep'])
|
||||
sleep_start_times_list.append(sleep_record_dict[day]['start_time_seconds'])
|
||||
else:
|
||||
deep_sleep_list.append(None)
|
||||
light_sleep_list.append(None)
|
||||
rem_sleep_list.append(None)
|
||||
awake_list.append(None)
|
||||
total_sleep_list.append(None)
|
||||
sleep_start_times_list.append(None)
|
||||
for day in dates_str_list:
|
||||
if day in sleep_record_dict:
|
||||
deep_sleep_list.append(sleep_record_dict[day]['deep'])
|
||||
light_sleep_list.append(sleep_record_dict[day]['light'])
|
||||
rem_sleep_list.append(sleep_record_dict[day]['rem'])
|
||||
awake_list.append(sleep_record_dict[day]['wake'])
|
||||
total_sleep_list.append(sleep_record_dict[day]['total_sleep'])
|
||||
sleep_start_times_list.append(sleep_record_dict[day]['start_time_seconds'])
|
||||
else:
|
||||
deep_sleep_list.append(None)
|
||||
light_sleep_list.append(None)
|
||||
rem_sleep_list.append(None)
|
||||
awake_list.append(None)
|
||||
total_sleep_list.append(None)
|
||||
sleep_start_times_list.append(None)
|
||||
|
||||
df_merged = pd.DataFrame({
|
||||
"Date": dates_list,
|
||||
"Resting Heart Rate": rhr_list,
|
||||
"Steps Count": steps_list,
|
||||
"Fat Burn Minutes": fat_burn_minutes_list,
|
||||
"Cardio Minutes": cardio_minutes_list,
|
||||
"Peak Minutes": peak_minutes_list,
|
||||
"weight": weight_list,
|
||||
"SPO2": spo2_list,
|
||||
"Deep Sleep Minutes": deep_sleep_list,
|
||||
"Light Sleep Minutes": light_sleep_list,
|
||||
"REM Sleep Minutes": rem_sleep_list,
|
||||
"Awake Minutes": awake_list,
|
||||
"Total Sleep Minutes": total_sleep_list,
|
||||
"Sleep Start Time Seconds": sleep_start_times_list
|
||||
})
|
||||
df_merged = pd.DataFrame({
|
||||
"Date": dates_list,
|
||||
"Resting Heart Rate": rhr_list,
|
||||
"Steps Count": steps_list,
|
||||
"Fat Burn Minutes": fat_burn_minutes_list,
|
||||
"Cardio Minutes": cardio_minutes_list,
|
||||
"Peak Minutes": peak_minutes_list,
|
||||
"weight": weight_list,
|
||||
"SPO2": spo2_list,
|
||||
"Deep Sleep Minutes": deep_sleep_list,
|
||||
"Light Sleep Minutes": light_sleep_list,
|
||||
"REM Sleep Minutes": rem_sleep_list,
|
||||
"Awake Minutes": awake_list,
|
||||
"Total Sleep Minutes": total_sleep_list,
|
||||
"Sleep Start Time Seconds": sleep_start_times_list
|
||||
})
|
||||
|
||||
df_merged['Total Sleep Seconds'] = df_merged['Total Sleep Minutes']*60
|
||||
df_merged["Sleep End Time Seconds"] = df_merged["Sleep Start Time Seconds"] + df_merged['Total Sleep Seconds']
|
||||
df_merged["Total Active Minutes"] = df_merged["Fat Burn Minutes"] + df_merged["Cardio Minutes"] + df_merged["Peak Minutes"]
|
||||
rhr_avg = {'overall': round(df_merged["Resting Heart Rate"].mean(),1), '30d': round(df_merged["Resting Heart Rate"].tail(30).mean(),1)}
|
||||
steps_avg = {'overall': int(df_merged["Steps Count"].mean()), '30d': int(df_merged["Steps Count"].tail(31).mean())}
|
||||
weight_avg = {'overall': round(df_merged["weight"].mean(),1), '30d': round(df_merged["weight"].tail(30).mean(),1)}
|
||||
spo2_avg = {'overall': round(df_merged["SPO2"].mean(),1), '30d': round(df_merged["SPO2"].tail(30).mean(),1)}
|
||||
sleep_avg = {'overall': round(df_merged["Total Sleep Minutes"].mean(),1), '30d': round(df_merged["Total Sleep Minutes"].tail(30).mean(),1)}
|
||||
active_mins_avg = {'overall': round(df_merged["Total Active Minutes"].mean(),2), '30d': round(df_merged["Total Active Minutes"].tail(30).mean(),2)}
|
||||
weekly_steps_array = np.array([0]*days_name_list.index(datetime.fromisoformat(start_date).strftime('%A')) + df_merged["Steps Count"].to_list() + [0]*(6 - days_name_list.index(datetime.fromisoformat(end_date).strftime('%A'))))
|
||||
weekly_steps_array = np.transpose(weekly_steps_array.reshape((int(len(weekly_steps_array)/7), 7)))
|
||||
weekly_steps_array = pd.DataFrame(weekly_steps_array, index=days_name_list)
|
||||
df_merged['Total Sleep Seconds'] = df_merged['Total Sleep Minutes']*60
|
||||
df_merged["Sleep End Time Seconds"] = df_merged["Sleep Start Time Seconds"] + df_merged['Total Sleep Seconds']
|
||||
df_merged["Total Active Minutes"] = df_merged["Fat Burn Minutes"] + df_merged["Cardio Minutes"] + df_merged["Peak Minutes"]
|
||||
rhr_avg = {'overall': round(df_merged["Resting Heart Rate"].mean(),1), '30d': round(df_merged["Resting Heart Rate"].tail(30).mean(),1)}
|
||||
steps_avg = {'overall': int(df_merged["Steps Count"].mean()), '30d': int(df_merged["Steps Count"].tail(31).mean())}
|
||||
weight_avg = {'overall': round(df_merged["weight"].mean(),1), '30d': round(df_merged["weight"].tail(30).mean(),1)}
|
||||
spo2_avg = {'overall': round(df_merged["SPO2"].mean(),1), '30d': round(df_merged["SPO2"].tail(30).mean(),1)}
|
||||
sleep_avg = {'overall': round(df_merged["Total Sleep Minutes"].mean(),1), '30d': round(df_merged["Total Sleep Minutes"].tail(30).mean(),1)}
|
||||
active_mins_avg = {'overall': round(df_merged["Total Active Minutes"].mean(),2), '30d': round(df_merged["Total Active Minutes"].tail(30).mean(),2)}
|
||||
weekly_steps_array = np.array([0]*days_name_list.index(datetime.fromisoformat(start_date).strftime('%A')) + df_merged["Steps Count"].to_list() + [0]*(6 - days_name_list.index(datetime.fromisoformat(end_date).strftime('%A'))))
|
||||
weekly_steps_array = np.transpose(weekly_steps_array.reshape((int(len(weekly_steps_array)/7), 7)))
|
||||
weekly_steps_array = pd.DataFrame(weekly_steps_array, index=days_name_list)
|
||||
|
||||
# Plotting data-----------------------------------------------------------------------------------------------------------------------
|
||||
# Plotting data-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
fig_rhr = px.line(df_merged, x="Date", y="Resting Heart Rate", line_shape="spline", color_discrete_sequence=["#d30f1c"], title=f"<b>Daily Resting Heart Rate<br><br><sup>Overall average : {rhr_avg['overall']} bpm | Last 30d average : {rhr_avg['30d']} bpm</sup></b><br><br><br>")
|
||||
if df_merged["Resting Heart Rate"].dtype != object:
|
||||
fig_rhr.add_annotation(x=df_merged.iloc[df_merged["Resting Heart Rate"].idxmax()]["Date"], y=df_merged["Resting Heart Rate"].max(), text=str(df_merged["Resting Heart Rate"].max()), showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_rhr.add_annotation(x=df_merged.iloc[df_merged["Resting Heart Rate"].idxmin()]["Date"], y=df_merged["Resting Heart Rate"].min(), text=str(df_merged["Resting Heart Rate"].min()), showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_rhr.add_hline(y=df_merged["Resting Heart Rate"].mean(), line_dash="dot",annotation_text="Average : " + str(round(df_merged["Resting Heart Rate"].mean(), 1)) + " BPM", annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_rhr.add_hrect(y0=62, y1=68, fillcolor="green", opacity=0.15, line_width=0)
|
||||
rhr_summary_df = calculate_table_data(df_merged, "Resting Heart Rate")
|
||||
rhr_summary_table = dash_table.DataTable(rhr_summary_df.to_dict('records'), [{"name": i, "id": i} for i in rhr_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#5f040a','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_steps = px.bar(df_merged, x="Date", y="Steps Count", color_discrete_sequence=["#2fb376"], title=f"<b>Daily Steps Count<br><br><sup>Overall average : {steps_avg['overall']} steps | Last 30d average : {steps_avg['30d']} steps</sup></b><br><br><br>")
|
||||
if df_merged["Steps Count"].dtype != object:
|
||||
fig_steps.add_annotation(x=df_merged.iloc[df_merged["Steps Count"].idxmax()]["Date"], y=df_merged["Steps Count"].max(), text=str(df_merged["Steps Count"].max())+" steps", showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_steps.add_annotation(x=df_merged.iloc[df_merged["Steps Count"].idxmin()]["Date"], y=df_merged["Steps Count"].min(), text=str(df_merged["Steps Count"].min())+" steps", showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_steps.add_hline(y=df_merged["Steps Count"].mean(), line_dash="dot",annotation_text="Average : " + str(round(df_merged["Steps Count"].mean(), 1)) + " Steps", annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.8, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_steps_heatmap = px.imshow(weekly_steps_array, color_continuous_scale='YLGn', origin='lower', title="<b>Weekly Steps Heatmap</b>", labels={'x':"Week Number", 'y': "Day of the Week"}, height=350, aspect='equal')
|
||||
fig_steps_heatmap.update_traces(colorbar_orientation='h', selector=dict(type='heatmap'))
|
||||
steps_summary_df = calculate_table_data(df_merged, "Steps Count")
|
||||
steps_summary_table = dash_table.DataTable(steps_summary_df.to_dict('records'), [{"name": i, "id": i} for i in steps_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#072f1c','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_activity_minutes = px.bar(df_merged, x="Date", y=["Fat Burn Minutes", "Cardio Minutes", "Peak Minutes"], title=f"<b>Activity Minutes<br><br><sup>Overall total active minutes average : {active_mins_avg['overall']} minutes | Last 30d total active minutes average : {active_mins_avg['30d']} minutes</sup></b><br><br><br>")
|
||||
fig_activity_minutes.update_layout(yaxis_title='Active Minutes', legend=dict(orientation="h",yanchor="bottom", y=1.02, xanchor="right", x=1, title_text=''))
|
||||
fat_burn_summary_df = calculate_table_data(df_merged, "Fat Burn Minutes")
|
||||
fat_burn_summary_table = dash_table.DataTable(fat_burn_summary_df.to_dict('records'), [{"name": i, "id": i} for i in fat_burn_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#636efa','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
cardio_summary_df = calculate_table_data(df_merged, "Cardio Minutes")
|
||||
cardio_summary_table = dash_table.DataTable(cardio_summary_df.to_dict('records'), [{"name": i, "id": i} for i in cardio_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#ef553b','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
peak_summary_df = calculate_table_data(df_merged, "Peak Minutes")
|
||||
peak_summary_table = dash_table.DataTable(peak_summary_df.to_dict('records'), [{"name": i, "id": i} for i in peak_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#00cc96','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_weight = px.line(df_merged, x="Date", y="weight", line_shape="spline", color_discrete_sequence=["#6b3908"], title=f"<b>Weight<br><br><sup>Overall average : {weight_avg['overall']} Unit | Last 30d average : {weight_avg['30d']} Unit</sup></b><br><br><br>")
|
||||
if df_merged["weight"].dtype != object:
|
||||
fig_weight.add_annotation(x=df_merged.iloc[df_merged["weight"].idxmax()]["Date"], y=df_merged["weight"].max(), text=str(df_merged["weight"].max()), showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_weight.add_annotation(x=df_merged.iloc[df_merged["weight"].idxmin()]["Date"], y=df_merged["weight"].min(), text=str(df_merged["weight"].min()), showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_weight.add_hline(y=round(df_merged["weight"].mean(),1), line_dash="dot",annotation_text="Average : " + str(round(df_merged["weight"].mean(), 1)) + " Units", annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
weight_summary_df = calculate_table_data(df_merged, "weight")
|
||||
weight_summary_table = dash_table.DataTable(weight_summary_df.to_dict('records'), [{"name": i, "id": i} for i in weight_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#4c3b7d','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_spo2 = px.scatter(df_merged, x="Date", y="SPO2", color_discrete_sequence=["#983faa"], title=f"<b>SPO2 Percentage<br><br><sup>Overall average : {spo2_avg['overall']}% | Last 30d average : {spo2_avg['30d']}% </sup></b><br><br><br>", range_y=(90,100), labels={'SPO2':"SpO2(%)"})
|
||||
if df_merged["SPO2"].dtype != object:
|
||||
fig_spo2.add_annotation(x=df_merged.iloc[df_merged["SPO2"].idxmax()]["Date"], y=df_merged["SPO2"].max(), text=str(df_merged["SPO2"].max())+"%", showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_spo2.add_annotation(x=df_merged.iloc[df_merged["SPO2"].idxmin()]["Date"], y=df_merged["SPO2"].min(), text=str(df_merged["SPO2"].min())+"%", showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_spo2.add_hline(y=df_merged["SPO2"].mean(), line_dash="dot",annotation_text="Average : " + str(round(df_merged["SPO2"].mean(), 1)) + "%", annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_spo2.update_traces(marker_size=6)
|
||||
spo2_summary_df = calculate_table_data(df_merged, "SPO2")
|
||||
spo2_summary_table = dash_table.DataTable(spo2_summary_df.to_dict('records'), [{"name": i, "id": i} for i in spo2_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#8d3a18','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_sleep_minutes = px.bar(df_merged, x="Date", y=["Deep Sleep Minutes", "Light Sleep Minutes", "REM Sleep Minutes", "Awake Minutes"], title=f"<b>Sleep Stages<br><br><sup>Overall average : {format_minutes(int(sleep_avg['overall']))} | Last 30d average : {format_minutes(int(sleep_avg['30d']))}</sup></b><br><br>", color_discrete_map={"Deep Sleep Minutes": '#084466', "Light Sleep Minutes": '#1e9ad6', "REM Sleep Minutes": '#4cc5da', "Awake Minutes": '#fd7676',}, height=500)
|
||||
fig_sleep_minutes.update_layout(yaxis_title='Sleep Minutes', legend=dict(orientation="h",yanchor="bottom", y=1.02, xanchor="right", x=1, title_text=''), yaxis=dict(tickvals=[1,120,240,360,480,600,720], ticktext=[f"{m // 60}h" for m in [1,120,240,360,480,600,720]], title="Sleep Time (hours)"))
|
||||
if df_merged["Total Sleep Minutes"].dtype != object:
|
||||
fig_sleep_minutes.add_annotation(x=df_merged.iloc[df_merged["Total Sleep Minutes"].idxmax()]["Date"], y=df_merged["Total Sleep Minutes"].max(), text=str(format_minutes(df_merged["Total Sleep Minutes"].max())), showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_sleep_minutes.add_annotation(x=df_merged.iloc[df_merged["Total Sleep Minutes"].idxmin()]["Date"], y=df_merged["Total Sleep Minutes"].min(), text=str(format_minutes(df_merged["Total Sleep Minutes"].min())), showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_sleep_minutes.add_hline(y=df_merged["Total Sleep Minutes"].mean(), line_dash="dot",annotation_text="Average : " + str(format_minutes(int(df_merged["Total Sleep Minutes"].mean()))), annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_sleep_minutes.update_xaxes(rangeslider_visible=True,range=[dates_str_list[-30], dates_str_list[-1]],rangeslider_range=[dates_str_list[0], dates_str_list[-1]])
|
||||
sleep_summary_df = calculate_table_data(df_merged, "Total Sleep Minutes")
|
||||
sleep_summary_table = dash_table.DataTable(sleep_summary_df.to_dict('records'), [{"name": i, "id": i} for i in sleep_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#636efa','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_sleep_regularity = px.bar(df_merged, x="Date", y="Total Sleep Seconds", base="Sleep Start Time Seconds", title="<b>Sleep Regularity<br><br><sup>The chart time here is always in local time ( Independent of timezone changes )</sup></b>", labels={"Total Sleep Seconds":"Time of Day ( HH:MM )"})
|
||||
fig_sleep_regularity.update_layout(yaxis = dict(tickmode = 'array',tickvals = list(range(0, 120000, 10000)),ticktext = list(map(seconds_to_tick_label, list(range(0, 120000, 10000))))))
|
||||
fig_sleep_regularity.add_hline(y=df_merged["Sleep Start Time Seconds"].mean(), line_dash="dot",annotation_text="Sleep Start Time Trend : "+ str(seconds_to_tick_label(int(df_merged["Sleep Start Time Seconds"].mean()))), annotation_position="bottom right", annotation_bgcolor="#0a3024", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_sleep_regularity.add_hline(y=df_merged["Sleep End Time Seconds"].mean(), line_dash="dot",annotation_text="Sleep End Time Trend : " + str(seconds_to_tick_label(int(df_merged["Sleep End Time Seconds"].mean()))), annotation_position="top left", annotation_bgcolor="#5e060d", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
return report_title, report_dates_range, generated_on_date, fig_rhr, rhr_summary_table, fig_steps, fig_steps_heatmap, steps_summary_table, fig_activity_minutes, fat_burn_summary_table, cardio_summary_table, peak_summary_table, fig_weight, weight_summary_table, fig_spo2, spo2_summary_table, fig_sleep_minutes, fig_sleep_regularity, sleep_summary_table, [{'label': 'Color Code Sleep Stages', 'value': 'Color Code Sleep Stages','disabled': False}], ""
|
||||
fig_rhr = px.line(df_merged, x="Date", y="Resting Heart Rate", line_shape="spline", color_discrete_sequence=["#d30f1c"], title=f"<b>Daily Resting Heart Rate<br><br><sup>Overall average : {rhr_avg['overall']} bpm | Last 30d average : {rhr_avg['30d']} bpm</sup></b><br><br><br>")
|
||||
if df_merged["Resting Heart Rate"].dtype != object:
|
||||
fig_rhr.add_annotation(x=df_merged.iloc[df_merged["Resting Heart Rate"].idxmax()]["Date"], y=df_merged["Resting Heart Rate"].max(), text=str(df_merged["Resting Heart Rate"].max()), showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_rhr.add_annotation(x=df_merged.iloc[df_merged["Resting Heart Rate"].idxmin()]["Date"], y=df_merged["Resting Heart Rate"].min(), text=str(df_merged["Resting Heart Rate"].min()), showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_rhr.add_hline(y=df_merged["Resting Heart Rate"].mean(), line_dash="dot",annotation_text="Average : " + str(round(df_merged["Resting Heart Rate"].mean(), 1)) + " BPM", annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_rhr.add_hrect(y0=62, y1=68, fillcolor="green", opacity=0.15, line_width=0)
|
||||
rhr_summary_df = calculate_table_data(df_merged, "Resting Heart Rate")
|
||||
rhr_summary_table = dash_table.DataTable(rhr_summary_df.to_dict('records'), [{"name": i, "id": i} for i in rhr_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#5f040a','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_steps = px.bar(df_merged, x="Date", y="Steps Count", color_discrete_sequence=["#2fb376"], title=f"<b>Daily Steps Count<br><br><sup>Overall average : {steps_avg['overall']} steps | Last 30d average : {steps_avg['30d']} steps</sup></b><br><br><br>")
|
||||
if df_merged["Steps Count"].dtype != object:
|
||||
fig_steps.add_annotation(x=df_merged.iloc[df_merged["Steps Count"].idxmax()]["Date"], y=df_merged["Steps Count"].max(), text=str(df_merged["Steps Count"].max())+" steps", showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_steps.add_annotation(x=df_merged.iloc[df_merged["Steps Count"].idxmin()]["Date"], y=df_merged["Steps Count"].min(), text=str(df_merged["Steps Count"].min())+" steps", showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_steps.add_hline(y=df_merged["Steps Count"].mean(), line_dash="dot",annotation_text="Average : " + str(round(df_merged["Steps Count"].mean(), 1)) + " Steps", annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.8, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_steps_heatmap = px.imshow(weekly_steps_array, color_continuous_scale='YLGn', origin='lower', title="<b>Weekly Steps Heatmap</b>", labels={'x':"Week Number", 'y': "Day of the Week"}, height=350, aspect='equal')
|
||||
fig_steps_heatmap.update_traces(colorbar_orientation='h', selector=dict(type='heatmap'))
|
||||
steps_summary_df = calculate_table_data(df_merged, "Steps Count")
|
||||
steps_summary_table = dash_table.DataTable(steps_summary_df.to_dict('records'), [{"name": i, "id": i} for i in steps_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#072f1c','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_activity_minutes = px.bar(df_merged, x="Date", y=["Fat Burn Minutes", "Cardio Minutes", "Peak Minutes"], title=f"<b>Activity Minutes<br><br><sup>Overall total active minutes average : {active_mins_avg['overall']} minutes | Last 30d total active minutes average : {active_mins_avg['30d']} minutes</sup></b><br><br><br>")
|
||||
fig_activity_minutes.update_layout(yaxis_title='Active Minutes', legend=dict(orientation="h",yanchor="bottom", y=1.02, xanchor="right", x=1, title_text=''))
|
||||
fat_burn_summary_df = calculate_table_data(df_merged, "Fat Burn Minutes")
|
||||
fat_burn_summary_table = dash_table.DataTable(fat_burn_summary_df.to_dict('records'), [{"name": i, "id": i} for i in fat_burn_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#636efa','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
cardio_summary_df = calculate_table_data(df_merged, "Cardio Minutes")
|
||||
cardio_summary_table = dash_table.DataTable(cardio_summary_df.to_dict('records'), [{"name": i, "id": i} for i in cardio_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#ef553b','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
peak_summary_df = calculate_table_data(df_merged, "Peak Minutes")
|
||||
peak_summary_table = dash_table.DataTable(peak_summary_df.to_dict('records'), [{"name": i, "id": i} for i in peak_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#00cc96','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_weight = px.line(df_merged, x="Date", y="weight", line_shape="spline", color_discrete_sequence=["#6b3908"], title=f"<b>Weight<br><br><sup>Overall average : {weight_avg['overall']} Unit | Last 30d average : {weight_avg['30d']} Unit</sup></b><br><br><br>")
|
||||
if df_merged["weight"].dtype != object:
|
||||
fig_weight.add_annotation(x=df_merged.iloc[df_merged["weight"].idxmax()]["Date"], y=df_merged["weight"].max(), text=str(df_merged["weight"].max()), showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_weight.add_annotation(x=df_merged.iloc[df_merged["weight"].idxmin()]["Date"], y=df_merged["weight"].min(), text=str(df_merged["weight"].min()), showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_weight.add_hline(y=round(df_merged["weight"].mean(),1), line_dash="dot",annotation_text="Average : " + str(round(df_merged["weight"].mean(), 1)) + " Units", annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
weight_summary_df = calculate_table_data(df_merged, "weight")
|
||||
weight_summary_table = dash_table.DataTable(weight_summary_df.to_dict('records'), [{"name": i, "id": i} for i in weight_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#4c3b7d','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_spo2 = px.scatter(df_merged, x="Date", y="SPO2", color_discrete_sequence=["#983faa"], title=f"<b>SPO2 Percentage<br><br><sup>Overall average : {spo2_avg['overall']}% | Last 30d average : {spo2_avg['30d']}% </sup></b><br><br><br>", range_y=(90,100), labels={'SPO2':"SpO2(%)"})
|
||||
if df_merged["SPO2"].dtype != object:
|
||||
fig_spo2.add_annotation(x=df_merged.iloc[df_merged["SPO2"].idxmax()]["Date"], y=df_merged["SPO2"].max(), text=str(df_merged["SPO2"].max())+"%", showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_spo2.add_annotation(x=df_merged.iloc[df_merged["SPO2"].idxmin()]["Date"], y=df_merged["SPO2"].min(), text=str(df_merged["SPO2"].min())+"%", showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_spo2.add_hline(y=df_merged["SPO2"].mean(), line_dash="dot",annotation_text="Average : " + str(round(df_merged["SPO2"].mean(), 1)) + "%", annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_spo2.update_traces(marker_size=6)
|
||||
spo2_summary_df = calculate_table_data(df_merged, "SPO2")
|
||||
spo2_summary_table = dash_table.DataTable(spo2_summary_df.to_dict('records'), [{"name": i, "id": i} for i in spo2_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#8d3a18','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_sleep_minutes = px.bar(df_merged, x="Date", y=["Deep Sleep Minutes", "Light Sleep Minutes", "REM Sleep Minutes", "Awake Minutes"], title=f"<b>Sleep Stages<br><br><sup>Overall average : {format_minutes(int(sleep_avg['overall']))} | Last 30d average : {format_minutes(int(sleep_avg['30d']))}</sup></b><br><br>", color_discrete_map={"Deep Sleep Minutes": '#084466', "Light Sleep Minutes": '#1e9ad6', "REM Sleep Minutes": '#4cc5da', "Awake Minutes": '#fd7676',}, height=500)
|
||||
fig_sleep_minutes.update_layout(yaxis_title='Sleep Minutes', legend=dict(orientation="h",yanchor="bottom", y=1.02, xanchor="right", x=1, title_text=''), yaxis=dict(tickvals=[1,120,240,360,480,600,720], ticktext=[f"{m // 60}h" for m in [1,120,240,360,480,600,720]], title="Sleep Time (hours)"))
|
||||
if df_merged["Total Sleep Minutes"].dtype != object:
|
||||
fig_sleep_minutes.add_annotation(x=df_merged.iloc[df_merged["Total Sleep Minutes"].idxmax()]["Date"], y=df_merged["Total Sleep Minutes"].max(), text=str(format_minutes(df_merged["Total Sleep Minutes"].max())), showarrow=False, arrowhead=0, bgcolor="#5f040a", opacity=0.80, yshift=15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_sleep_minutes.add_annotation(x=df_merged.iloc[df_merged["Total Sleep Minutes"].idxmin()]["Date"], y=df_merged["Total Sleep Minutes"].min(), text=str(format_minutes(df_merged["Total Sleep Minutes"].min())), showarrow=False, arrowhead=0, bgcolor="#0b2d51", opacity=0.80, yshift=-15, borderpad=5, font=dict(family="Helvetica, monospace", size=12, color="#ffffff"), )
|
||||
fig_sleep_minutes.add_hline(y=df_merged["Total Sleep Minutes"].mean(), line_dash="dot",annotation_text="Average : " + str(format_minutes(int(df_merged["Total Sleep Minutes"].mean()))), annotation_position="bottom right", annotation_bgcolor="#6b3908", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_sleep_minutes.update_xaxes(rangeslider_visible=True,range=[dates_str_list[-30], dates_str_list[-1]],rangeslider_range=[dates_str_list[0], dates_str_list[-1]])
|
||||
sleep_summary_df = calculate_table_data(df_merged, "Total Sleep Minutes")
|
||||
sleep_summary_table = dash_table.DataTable(sleep_summary_df.to_dict('records'), [{"name": i, "id": i} for i in sleep_summary_df.columns], style_data_conditional=[{'if': {'row_index': 'odd'},'backgroundColor': 'rgb(248, 248, 248)'}], style_header={'backgroundColor': '#636efa','fontWeight': 'bold', 'color': 'white', 'fontSize': '14px'}, style_cell={'textAlign': 'center'})
|
||||
fig_sleep_regularity = px.bar(df_merged, x="Date", y="Total Sleep Seconds", base="Sleep Start Time Seconds", title="<b>Sleep Regularity<br><br><sup>The chart time here is always in local time ( Independent of timezone changes )</sup></b>", labels={"Total Sleep Seconds":"Time of Day ( HH:MM )"})
|
||||
fig_sleep_regularity.update_layout(yaxis = dict(tickmode = 'array',tickvals = list(range(0, 120000, 10000)),ticktext = list(map(seconds_to_tick_label, list(range(0, 120000, 10000))))))
|
||||
fig_sleep_regularity.add_hline(y=df_merged["Sleep Start Time Seconds"].mean(), line_dash="dot",annotation_text="Sleep Start Time Trend : "+ str(seconds_to_tick_label(int(df_merged["Sleep Start Time Seconds"].mean()))), annotation_position="bottom right", annotation_bgcolor="#0a3024", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
fig_sleep_regularity.add_hline(y=df_merged["Sleep End Time Seconds"].mean(), line_dash="dot",annotation_text="Sleep End Time Trend : " + str(seconds_to_tick_label(int(df_merged["Sleep End Time Seconds"].mean()))), annotation_position="top left", annotation_bgcolor="#5e060d", annotation_opacity=0.6, annotation_borderpad=5, annotation_font=dict(family="Helvetica, monospace", size=14, color="#ffffff"))
|
||||
return report_title, report_dates_range, generated_on_date, fig_rhr, rhr_summary_table, fig_steps, fig_steps_heatmap, steps_summary_table, fig_activity_minutes, fat_burn_summary_table, cardio_summary_table, peak_summary_table, fig_weight, weight_summary_table, fig_spo2, spo2_summary_table, fig_sleep_minutes, fig_sleep_regularity, sleep_summary_table, [{'label': 'Color Code Sleep Stages', 'value': 'Color Code Sleep Stages','disabled': False}], ""
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run_server(debug=True)
|
||||
|
||||
|
||||
|
||||
# %%
|
||||
app.run_server(debug=True)
|
||||
Loading…
Reference in New Issue
Block a user