initial release

apps/home/modules/mountainbikel.py

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+import pandas as pd
+import numpy as np
+import datetime
+import json
+import os
+import plotly
+import plotly.graph_objs as go
+import plotly.express as px
+
+def round_float(s):
+    '''1. if s is float, round it to 0 decimals
+       2. else return s as is
+    '''
+    import re
+    m = re.match("(\d+\.\d+)",s.__str__())
+    try:
+        r = round(float(m.groups(0)[0]),0)
+    except:
+        r = s
+    return r
+
+# https://stackoverflow.com/questions/775049/how-do-i-convert-seconds-to-hours-minutes-and-seconds
+def sec2time(sec, n_msec=3):
+    ''' Convert seconds to 'D days, HH:MM:SS.FFF' '''
+    if hasattr(sec,'__len__'):
+        return [sec2time(s) for s in sec]
+    m, s = divmod(sec, 60)
+    h, m = divmod(m, 60)
+    # d, h = divmod(h, 24)
+    d, h = divmod(h, 72)
+    if n_msec > 0:
+        pattern = '%%02d:%%02d:%%0%d.%df' % (n_msec+3, n_msec)
+    else:
+        pattern = r'%02d:%02d:%02d'
+    # if d == 0:
+    if d < 3:
+        return pattern % (h, m, s)
+    return ('%d Tage, ' + pattern) % (d, h, m, s)
+
+def read_data(csv_file):
+    df = pd.read_csv(csv_file, index_col='startTimeLocal', parse_dates=['startTimeLocal'])
+    df.drop( 'activityId'.split() , axis=1, inplace=True)
+    df.drop( 'Unnamed: 0', axis=1, inplace=True)
+    df['distance'] = round(df['distance'] / 1000, 3)
+    df['maxSpeed'] = round(df['maxSpeed'], 2)
+    df['averageSpeed'] = round(df['averageSpeed']*3.6, 2)
+    df['elevationGain'] = df['elevationGain'].astype(int)
+    df['maxElevation'] = round(df['maxElevation'],0)
+    df['intensityMinutes'] = df['moderateIntensityMinutes'] + df['vigorousIntensityMinutes']*2
+    
+    # print(df.index)
+    
+    df['startzeit'] = df.index
+    # print(df.keys())
+    return(df)
+
+def render_weekly_sum_barchart (datafield, factor):
+    df2 = read_data()
+    df2['startzeit'] = df2.index
+
+    df = df2.sort_values(by="startzeit",ascending=True).set_index("startzeit").last("3M")
+    df['startTimeLocal'] = df.index
+
+    chart_data = pd.concat([
+        pd.Series(df.set_index('startTimeLocal').index.to_period('W').to_timestamp(how='start').values, index=df.index, name='startTimeLocal|W'),
+        df[f'{datafield}'] / factor,], axis=1)
+    chart_data = chart_data.sort_values(['startTimeLocal|W'])
+    chart_data = chart_data.rename(columns={'startTimeLocal|W': 'x'})
+    chart_data_sum = chart_data.groupby(['x'], dropna=True)[[f'{datafield}']].sum()
+    chart_data_sum.columns = [f'{datafield}|sum']
+    chart_data = chart_data_sum.reset_index()
+    chart_data = chart_data.dropna()
+    charts = []
+    charts.append(go.Bar(
+        x=chart_data['x'],
+        y=chart_data[f'{datafield}|sum']
+    ))
+    fig = go.Figure(data=charts, layout=go.Layout({
+        'barmode': 'stack',
+        'legend': {'orientation': 'h', 'y': -0.3},
+        'title': {'text': f'Sum of {datafield} by startTimeLocal (Weekly)'},
+        'xaxis': {'title': {'text': 'startzeit (Weekly)'}},
+        'yaxis': {'title': {'text': f'Sum of {datafield}'}, 'type': 'linear'}
+    }))
+    return(fig)
+
+
+def monthly_sum_barchart (datafield, factor, heading, y_axis, x_axis):
+    df = read_data()
+    df = df.reset_index().drop('index', axis=1, errors='ignore')
+
+    chart_data = pd.concat([
+        pd.Series(df.set_index('startTimeLocal').index.to_period('M').to_timestamp(how='start').values, index=df.index, name='startTimeLocal|M'),
+        df[f'{datafield}'],], axis=1)
+    chart_data = chart_data.sort_values(['startTimeLocal|M'])
+    chart_data = chart_data.rename(columns={'startTimeLocal|M': 'x'})
+    chart_data_sum = chart_data.groupby(['x'], dropna=True)[[f'{datafield}']].sum()
+    chart_data_sum.columns = [f'{datafield}|sum']
+    print(chartdata.info())
+    chart_data = chart_data_sum.reset_index()
+    chart_data = chart_data.dropna()
+    charts = []
+    charts.append(go.Bar(
+        x=chart_data['x'],
+        y=chart_data[f'{datafield}|sum']
+    ))
+    fig = go.Figure(data=charts, layout=go.Layout({
+        'barmode': 'stack',
+        'legend': {'orientation': 'h', 'y': -0.3},
+        'title': {'text': heading},
+        'xaxis': {'title': {'text': x_axis}},
+        'yaxis': {'title': {'text': y_axis}}
+    }))
+    return(fig)
+
+def monthly_sum_barchart_concurrent2 (csv_file, datafield, factor, heading, y_axis, x_axis, grouptitle):
+    df = read_data(csv_file)
+    df = df.reset_index().drop('index', axis=1, errors='ignore')
+    df['month'] = df['startTimeLocal'].dt.month
+    df['year'] = df['startTimeLocal'].dt.year
+    df['movingDuration'] = df['movingDuration'].map(lambda x: round(x/3600,2))
+#    print(df.head())
+    df = df.reset_index().drop('index', axis=1, errors='ignore')
+
+    grouped_df = df.groupby(by=["month", "year"], as_index=False).agg( {f"{datafield}": "sum"} )
+    grouped_df.year = grouped_df['year'].apply(str)
+    grouped_df.sort_values(by=['year'], inplace=True)
+    
+    fig = px.bar(
+        data_frame = grouped_df,
+        x          = "month",
+        y          = f"{datafield}",
+        color      = "year",
+        barmode    = "group",
+        labels     = { "year" : x_axis,
+                       f"{datafield}" : y_axis },
+        title      = heading,
+    )
+    fig.update_layout(
+        font_family="Courier New",
+        font_color="black",
+        xaxis_title="Monat",
+    )
+    fig.update_xaxes( labelalias = { 1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'Mai',6:'Jun',7:'Jul',8:'"Aug"',9:'Sep',10:'Okt',11:'Nov',12:'Dez' } )
+    # fig.update_xaxes( labelalias = dict( 2="Saturday", Sun="Sunday") )
+
+    # fig.update_xaxes(
+    #     tickangle = 90,
+    #     title_text = "Month",
+    #     title_font = {"size": 20},
+    #     title_standoff = 25)
+    return(fig)
+
+def monthly_sum_barchart_concurrent (datafield, factor, heading, y_axis, x_axis):
+    df = read_data()
+    df = df.reset_index().drop('index', axis=1, errors='ignore')
+    df['month'] = df['startTimeLocal'].dt.month
+    df['year'] = df['startTimeLocal'].dt.year
+
+    df = df.reset_index().drop('index', axis=1, errors='ignore')
+    #df.columns = [str(c) for c in df.columns]  # update columns to strings in case they are numbers
+
+    grouped_df = df.groupby(by=["month", "year"], as_index=False).agg( {"elevationGain": "sum"} )
+    grouped_df.year = grouped_df['year'].apply(str)
+    grouped_df.sort_values(by=['year'], inplace=True)
+    # print(data.head())
+    
+    fig = px.bar(
+        data_frame=grouped_df,
+        x="month",
+        y="elevationGain",
+        color="year",
+        barmode="group",
+    )
+    return(fig)
+
+def homepage_data (csv_file):
+    df2 = read_data(csv_file)
+    df2['startzeit'] = df2.index
+    df = df2.sort_values(by="startzeit",ascending=True).set_index("startzeit")
+    df['startzeit'] = df.index
+    df['startTimeLocal'] = df.index
+    wf = df.groupby([pd.Grouper(key='startTimeLocal', freq='W')]).aggregate({
+        'elevationGain': sum,
+        'distance': sum,
+        'averageHR':np.mean,
+        'maxHR':np.max,
+        'movingDuration': sum,
+        'intensityMinutes': sum 
+    })
+    mf = df.groupby([pd.Grouper(key='startzeit', freq='MS')]).aggregate({
+        'elevationGain': sum,
+        'distance': sum,
+        'averageHR':np.mean,
+        'maxHR':np.max,
+        'movingDuration': sum,
+        'intensityMinutes': sum 
+    })
+    yf = df.groupby([pd.Grouper(key='startzeit', freq='YS')]).aggregate({
+        'elevationGain': sum,
+        'distance': sum,
+        'averageHR':np.mean,
+        'maxHR':np.max,
+        'movingDuration': sum,
+        'intensityMinutes': sum 
+    })
+    d = { 'elevationtotal'  : df['elevationGain'].sum(),
+          'elevation0y'    : yf['elevationGain'].take([-1])[0],
+          'elevation1y'    : yf['elevationGain'].take([-2])[0],
+          'elevation1m'     : mf['elevationGain'].take([-2])[0],
+          'elevation0m'     : mf['elevationGain'].take([-1])[0],
+          'elevation1w'     : wf['elevationGain'].take([-2])[0],
+          'elevation0w'     : wf['elevationGain'].take([-1])[0],
+          # '0w'              : wf.take([-1]).keys,
+          'distancetotal'   : round(df['distance'].sum(), 1),
+          'distance0y'     : round(yf['distance'].take([-1])[0], 1),
+          'distance1y'     : round(yf['distance'].take([-2])[0], 1),
+          'distance1m'      : round(mf['distance'].take([-2])[0], 1),
+          'distance0m'      : round(mf['distance'].take([-1])[0], 1),
+          'distance1w'      : round(wf['distance'].take([-2])[0], 1),
+          'distance0w'      : round(wf['distance'].take([-1])[0], 1),
+          'durationtotal'   : sec2time(df['movingDuration'].sum(), 0),
+          'duration0y'     : sec2time(yf['movingDuration'].take([-1])[0], 0),
+          'duration1y'     : sec2time(yf['movingDuration'].take([-2])[0], 0),
+          'duration1m'      : sec2time(mf['movingDuration'].take([-2])[0], 0),
+          'duration0m'      : sec2time(mf['movingDuration'].take([-1])[0], 0),
+          'duration1w'      : sec2time(wf['movingDuration'].take([-2])[0], 0),
+          'duration0w'      : sec2time(wf['movingDuration'].take([-1])[0], 0),
+          'averageHRtotal'  : round(df['averageHR'].mean()),
+          'averageHR0y'    : round(df['averageHR'].take([-1])[0]),
+          'averageHR1y'    : round(df['averageHR'].take([-2])[0]),
+          'averageHR1m'     : round(mf['averageHR'].take([-2])[0]),
+          'averageHR0m'     : round(mf['averageHR'].take([-1])[0]),
+          'averageHR1w'     : round_float(wf['averageHR'].take([-2])[0]),
+          'averageHR0w'     : round_float(wf['averageHR'].take([-1])[0]),
+          'maxHRtotal'      : round(df['maxHR'].max()),
+          'maxHR0y'        : round(df['maxHR'].take([-1])[0]),
+          'maxHR1y'        : round(df['maxHR'].take([-2])[0]),
+          'maxHR1m'         : round(mf['maxHR'].take([-2])[0]),
+          'maxHR0m'         : round(mf['maxHR'].take([-1])[0]),
+          'maxHR1w'         : round_float(wf['maxHR'].take([-2])[0]),
+          'maxHR0w'         : round_float(wf['maxHR'].take([-1])[0]),
+          'intensityMinutestotal'      : round(df['intensityMinutes'].sum()),
+          'intensityMinutes0y'        : round(yf['intensityMinutes'].take([-1])[0]),
+          'intensityMinutes1y'        : round(yf['intensityMinutes'].take([-2])[0]),
+          'intensityMinutes1m'         : round(mf['intensityMinutes'].take([-2])[0]),
+          'intensityMinutes0m'         : round(mf['intensityMinutes'].take([-1])[0]),
+          'intensityMinutes1w'         : round(wf['intensityMinutes'].take([-2])[0]),
+          'intensityMinutes0w'         : round(wf['intensityMinutes'].take([-1])[0]),
+        }
+    return(d)
+    
+if __name__ == "__main__":
+    # render_monthly_sum_barchart ('elevationGain')
+    #print(render_homepage_data())
+    print(monthly_sum_barchart_concurrent2('elevationGain', 1, 'Höhenmeter pro Monat', '(m)', 'Monat', 'grouptitle'))