from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import colorsys
from rowers.models import (
    Workout, User, Rower, WorkoutForm,RowerForm,
    GraphImage,GeoPolygon,GeoCourse,GeoPoint,
    )
from rowers.tasks import handle_setcp
from rowingdata import rower as rrower
from rowingdata import main as rmain
from rowingdata import cumcpdata,histodata

from rowingdata import rowingdata as rrdata
from math import pi,log2
from django.utils import timezone
from rowingdata import make_cumvalues

from bokeh.palettes import Dark2_8 as palette
from bokeh.palettes import Set1_4 as palette2
from bokeh.models.glyphs import MultiLine
import itertools
from bokeh.plotting import figure, ColumnDataSource, Figure,curdoc
from bokeh.models import CustomJS,Slider, TextInput,BoxAnnotation, Band

import arrow

from rowers.utils import myqueue, totaltime_sec_to_string
import django_rq
queue = django_rq.get_queue('default')
queuelow = django_rq.get_queue('low')
queuehigh = django_rq.get_queue('low')

from bokeh.resources import CDN,INLINE
from bokeh.embed import components
from bokeh.layouts import layout,widgetbox
from bokeh.palettes import Category20c,Category10
from bokeh.layouts import row as layoutrow
from bokeh.layouts import column as layoutcolumn
from bokeh.models import (
    LinearAxis,LogAxis,Range1d,DatetimeTickFormatter,HoverTool,Axis,PrintfTickFormatter
    )
#from bokeh.io import output_file, show, vplot
from bokeh.models import (
    GMapPlot, GMapOptions, ColumnDataSource, Circle,
    DataRange1d, PanTool, WheelZoomTool, BoxSelectTool,
    SaveTool,   # ResizeTool,
    ResetTool, TapTool,CrosshairTool,BoxZoomTool,
    Span, Label
)
from  bokeh.transform import cumsum
from bokeh.models.glyphs import ImageURL
from bokeh.models import OpenURL, TapTool
from rowers.opaque import encoder
#from bokeh.models.widgets import Slider, Select, TextInput
from bokeh.core.properties import value

from collections import OrderedDict
from django.conf import settings

from rowers.courses import (
    course_coord_center,course_coord_maxmin,
    polygon_coord_center
    )

from rowers import mytypes
from rowers.models import course_spline,VirtualRaceResult

import datetime
import math
import numpy as np
import pandas as pd
import holoviews as hv
from holoviews import opts
from pytz import timezone as tz,utc
from django.utils.timezone import get_current_timezone
from django.utils.timezone import activate
from django.utils import timezone
activate(settings.TIME_ZONE)
thetimezone = get_current_timezone()

from scipy.stats import linregress,percentileofscore
from scipy.spatial import ConvexHull,Delaunay
from scipy import optimize
from scipy.signal import savgol_filter
from scipy.interpolate import griddata


import rowers.stravastuff as stravastuff
from rowers.metrics import rowingmetrics,metricsdicts

from rowers.dataprep import rdata
import rowers.dataprep as dataprep
import rowers.metrics as metrics
import rowers.c2stuff as c2stuff

from rowers.metrics import axes,axlabels,yaxminima,yaxmaxima,get_yaxminima,get_yaxmaxima

from rowers.utils import lbstoN
from rowers.datautils import p0,rpetotss
import rowers.datautils as datautils

from pandas.core.groupby.groupby import DataError


def workoutname(id):
    try:
        w = Workout.objects.get(id=id)
    except Workout.DoesNotExist:
        return ''

    return str(w)

def all_goldmedalstandards(workouts,startdate,enddate):
    dates = []
    testpowers = []
    testduration = []
    ids = []

    for w in workouts:
        goldmedalstandard, goldmedalseconds = dataprep.workout_goldmedalstandard(w)
        if goldmedalseconds > 60:
            dates.append(arrow.get(w.date).datetime)
            testpowers.append(goldmedalstandard)
            testduration.append(goldmedalseconds)
            ids.append(w.id)

    return dates,testpowers,testduration,ids




def errorbar(fig, x, y, source=ColumnDataSource(),
             xerr=False, yerr=False, color='black',
             point_kwargs={}, error_kwargs={}):


    xvalues = source.data[x]
    yvalues = source.data[y]


    xerrvalues = source.data['xerror']
    yerrvalues = source.data['yerror']
    try:
        colorvalues = source.data['color']
    except KeyError: # pragma: no cover
        colorvalues = ["#%02x%02x%02x" % (255,0,0) for x in xvalues]


    try:
        a = xvalues[0]+1
        if xerr:
            x_err_x = []
            x_err_y = []
            err_color = []
            for px, py, err, color in zip(xvalues, yvalues, xerrvalues, colorvalues):
                x_err_x.append((px - err, px + err))
                x_err_y.append((py, py))
                (r, g, b) = tuple(int(color[i:i+2],16) for i in (1, 3, 5))
                h,s,v = colorsys.rgb_to_hsv(r/255., g/255., b/255.)
                v = v*0.8
                r, g, b = colorsys.hsv_to_rgb(h, s, v)
                color2 = "#%02x%02x%02x" % (int(255.*r), int(255.*g), int(255*b))
                err_color.append(color2)

            fig.multi_line(x_err_x, x_err_y, color=err_color,
                           name='xerr',
                           **error_kwargs)
    except TypeError:  # pragma: no cover
        pass

    try:
        a = yvalues[0]+1
        if yerr:
            y_err_x = []
            y_err_y = []
            err_color = []
            for px, py, err, color in zip(xvalues, yvalues, yerrvalues, colorvalues):
                y_err_x.append((px, px))
                y_err_y.append((py - err, py + err))
                (r, g, b) = tuple(int(color[i:i+2],16) for i in (1, 3, 5))
                h,s,v = colorsys.rgb_to_hsv(r/255., g/255., b/255.)
                v = v*0.8
                r, g, b = colorsys.hsv_to_rgb(h, s, v)
                color2 = "#%02x%02x%02x" % (int(255.*r), int(255.*g), int(255*b))
                err_color.append(color2)

            fig.multi_line(y_err_x, y_err_y, color=err_color,
                           name='yerr',**error_kwargs)
    except TypeError: # pragma: no cover
        pass

    fig.circle(x, y, source=source, name='data',color=color,
               **point_kwargs)


def tailwind(bearing,vwind,winddir):
    """ Calculates head-on head/tailwind in direction of rowing

    positive numbers are tail wind

    """

    b = np.radians(bearing)
    w = np.radians(winddir)

    vtail = -vwind*np.cos(w-b)

    return vtail


from rowers.dataprep import nicepaceformat,niceformat,strfdelta
from rowers.dataprep import timedeltaconv

from math import pi

def interactive_hr_piechart(df,rower,title,totalseconds=0):
    if df.empty:
        return "","Not enough data to make a chart"



    df.sort_values(by='hr',inplace=True)
    df['timehr'] = df['deltat']*df['hr']

    sumtimehr = df['deltat'].sum()

    if totalseconds == 0:
        totalseconds = sumtimehr

    hrzones = rower.hrzones

    qry = 'hr < {ut2}'.format(ut2=rower.ut2)
    qrydata = df.query(qry)
    frac_lut2 = totalseconds*qrydata['deltat'].sum()/sumtimehr


    qry = '{ut2} <= hr < {ut1}'.format(ut1=rower.ut1,ut2=rower.ut2)
    frac_ut2 = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr

    qry = '{ut1} <= hr < {at}'.format(ut1=rower.ut1,at=rower.at)
    frac_ut1 = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr

    qry = '{at} <= hr < {tr}'.format(at=rower.at,tr=rower.tr)
    frac_at = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr

    qry = '{tr} <= hr < {an}'.format(tr=rower.tr,an=rower.an)
    frac_tr = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr

    qry = 'hr >= {an}'.format(an=rower.an)
    frac_an = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr

    datadict = {
        '<{ut2}'.format(ut2=hrzones[1]):frac_lut2,
        '{ut2}'.format(ut2=hrzones[1]): frac_ut2,
        '{ut1}'.format(ut1=hrzones[2]): frac_ut1,
        '{at}'.format(at=hrzones[3]): frac_at,
        '{tr}'.format(tr=hrzones[4]): frac_tr,
        '{an}'.format(an=hrzones[5]): frac_an,
    }



    colors = ['gray','yellow','lime','blue','purple','red']



    data = pd.Series(datadict).reset_index(name='value').rename(columns={'index':'zone'})
    data['angle'] = data['value']/data['value'].sum() * 2*pi
    data['color'] = colors
    data['zone'] = [
        '<{ut2}'.format(ut2=hrzones[1]),
        '{ut2}'.format(ut2=hrzones[1]),
        '{ut1}'.format(ut1=hrzones[2]),
        '{at}'.format(at=hrzones[3]),
        '{tr}'.format(tr=hrzones[4]),
        '{an}'.format(an=hrzones[5])
    ]

    data['totaltime'] = pd.Series([pretty_timedelta(v) for v in data['value']])



    size=350
    TOOLS = 'save,hover'


    z = figure(title="HR "+title, x_range=(-0.5,1), plot_height=375,
               tools=TOOLS,toolbar_location=None,tooltips="@zone: @totaltime",
          )



    z.wedge(x=0,y=1, radius=0.4,
            start_angle=cumsum('angle',include_zero=True), end_angle=cumsum('angle'),
            line_color='white',fill_color='color',source=data,legend_group='zone')



    z.axis.axis_label = None
    z.axis.visible = False
    z.grid.grid_line_color = None
    z.outline_line_color = None
    z.toolbar_location = 'right'


    return components(z)

def pretty_timedelta(secs):
    hours, remainder = divmod(secs,3600)
    minutes, seconds = divmod(remainder,60)

    return '{}:{:02}:{:02}'.format(int(hours),int(minutes),int(seconds))

def mapcolors(x):
    try:
        return mytypes.color_map[x]
    except KeyError: # pragma: no cover
        return mytypes.colors[-1]

def interactive_workouttype_piechart(workouts):
    if len(workouts) == 0:
        return "","Not enough workouts to make a chart"

    datadict = {}


    for w in workouts:
        try:
            # label = mytypes.workouttypes_ordered[w.workouttype]
            label = w.workouttype
        except KeyError: # pragma: no cover
            label = w.workouttype
        try:
            datadict[label] += 60*(60*w.duration.hour+w.duration.minute)+w.duration.second
        except KeyError:
            datadict[label] = 60*(60*w.duration.hour+w.duration.minute)+w.duration.second

    data = pd.Series(datadict).reset_index(name='value').rename(columns={'index':'type'})
    data['angle'] = data['value']/data['value'].sum() * 2*pi




    data = pd.DataFrame(data)

    data['color'] = data['type'].apply(lambda x:mapcolors(x))
    data['totaltime'] = data['value'].apply(lambda x:pretty_timedelta(x))
    try:
        data['type'] = data['type'].apply(lambda x:mytypes.workouttypes_ordered[x])
    except KeyError: # pragma: no cover
        pass




    p = figure(plot_height=350, title="Types", toolbar_location=None,
           tools="hover,save", tooltips="@type: @totaltime", x_range=(-0.5, 1.0))

    p.wedge(x=0, y=1, radius=0.4,
        start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
        line_color="white", fill_color='color', source=data,legend_group='type', )

    p.axis.axis_label=None
    p.axis.visible=False
    p.grid.grid_line_color = None
    p.outline_line_color = None
    p.toolbar_location = 'right'

    return components(p)



def interactive_boxchart(datadf,fieldname,extratitle='',
                         spmmin=0,spmmax=0,workmin=0,workmax=0):

    if datadf.empty: # pragma: no cover
        return '','It looks like there are no data matching your filter'

    columns = datadf.columns

    if not fieldname in columns: # pragma: no cover
        return '','It looks like there are no data matching your filter'

    if not 'date' in columns: # pragma: no cover
        return '','Not enough data'


    tooltips = [
        ('Value', '@'+fieldname),
        ]

    hover = HoverTool(tooltips=tooltips)

    TOOLS = [hover]


    hv.extension('bokeh')

    try:
        boxwhiskers = hv.BoxWhisker(datadf,'date',fieldname)
        boxwhiskers.opts(tools=TOOLS,outlier_color='white')
    except DataError: # pragma: no cover
        return "","Invalid Data"


    plot = hv.render(boxwhiskers)

    yrange1 = Range1d(start=yaxminima[fieldname],end=yaxmaxima[fieldname])
    plot.y_range = yrange1
    plot.sizing_mode = 'stretch_both'

    plot.xaxis.axis_label = 'Date'
    plot.yaxis.axis_label = axlabels[fieldname]


    plot.xaxis.formatter = DatetimeTickFormatter(
        days=["%d %B %Y"],
        months=["%d %B %Y"],
        years=["%d %B %Y"],
        )

    if fieldname == 'pace': # pragma: no cover
        plot.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )


    plot.xaxis.major_label_orientation = pi/4

    plot.plot_width=920
    plot.plot_height=600

    slidertext = 'SPM: {:.0f}-{:.0f}, WpS: {:.0f}-{:.0f}'.format(
        spmmin,spmmax,workmin,workmax
        )
    sliderlabel = Label(x=50,y=20,x_units='screen',y_units='screen',
                        text=slidertext,
                        background_fill_alpha=0.7,
                        background_fill_color='white',
                        text_color='black',text_font_size='10pt',
                        )

    plot.add_layout(sliderlabel)

    script, div = components(plot)

    return script,div


def interactive_planchart(data,startdate,enddate):
    source = ColumnDataSource(data)

    hv.extension('bokeh')


    yaxmaximum = data['executed'].max()
    if data['planned'].max() > yaxmaximum: # pragma: no cover
        yaxmaximum = data['planned'].max()

    if yaxmaximum == 0: # pragma: no cover
        yaxmaximum = 250

    yrange1 = Range1d(start=0,end=1.1*yaxmaximum)


    tidy_df = data.melt(id_vars=['startdate'],value_vars=['executed','planned'])
    bars = hv.Bars(tidy_df,['startdate','variable'],['value'])
    bars.opts(
        opts.Bars(show_legend=True,tools=['tap','hover'],legend_position='bottom',show_frame=True))

    p = hv.render(bars)

    p.plot_width=550
    p.plot_height=350
    p.y_range = yrange1
    p.toolbar_location = 'above'
    p.sizing_mode = 'stretch_both'

    script,div = components(p)

    return script,div

def interactive_activitychart(workouts,startdate,enddate,stack='type',toolbar_location=None,
                              yaxis='trimp'):

    dates = []
    dates_sorting = []
    types = []
    rowers = []
    durations = []
    rscores = []
    trimps = []
    links = []


    rowersinitials = {}
    seen = ['seen']
    idseen = []

    startdate = datetime.datetime(year=startdate.year,month=startdate.month,day=startdate.day)
    enddate = datetime.datetime(year=enddate.year,month=enddate.month,day=enddate.day)

    duration = enddate-startdate

    totaldays = duration.total_seconds()/(24*3600)


    for w in workouts:
        aantal=1
        initials = w.user.user.first_name[0:aantal]+w.user.user.last_name[0:aantal]
        if w.user.id not in idseen:
            while initials in seen: # pragma: no cover
                aantal += 1
                initials = w.user.user.first_name[0:aantal]+w.user.user.last_name[0:aantal]

            seen.append(initials)
            idseen.append(w.user.id)
            rowersinitials[w.user.id] = initials



    for w in workouts:
        dd = w.date.strftime('%m/%d')
        dd2 = w.date.strftime('%Y/%m/%d')
        dd3 = w.date.strftime('%Y/%m')
        du = w.duration.hour*60+w.duration.minute
        rscore = w.rscore
        trimp = w.trimp

        if rscore == 0: # pragma: no cover
            rscore = w.hrtss

        if totaldays<30:
            dates.append(dd)
            dates_sorting.append(dd2)
        else: # pragma: no cover
            dates.append(dd3)
            dates_sorting.append(dd3)
        durations.append(du)
        rscores.append(rscore)
        trimps.append(trimp)
        links.append(
            "{siteurl}/rowers/workout/{code}/".format(
                siteurl = settings.SITE_URL,
                code = encoder.encode_hex(w.id)
                )
        )


        types.append(w.workouttype)
        try:
            rowers.append(rowersinitials[w.user.id])
        except IndexError: # pragma: no cover
            rowers.append(str(w.user))

    try:
        d = utc.localize(startdate)
    except (ValueError,AttributeError): # pragma: no cover
        d = startdate

    try:
        enddate = utc.localize(enddate)
    except (ValueError,AttributeError): # pragma: no cover
        pass

    # add dates with no activity
    while d<=enddate:
        dd = d.strftime('%d')

        if totaldays<30:
            dates.append(d.strftime('%m/%d'))
            dates_sorting.append(d.strftime('%Y/%m/%d'))
        else: # pragma: no cover
            dates.append(d.strftime('%Y/%m'))
            dates_sorting.append(d.strftime('%Y/%m'))
        durations.append(0)
        rscores.append(0)
        trimps.append(0)
        links.append('')
        try:
            types.append(types[0])
        except IndexError:
            types.append('rower')

        try:
            rowers.append(rowers[0])
        except IndexError:
            try:
                rowers.append(str(workouts[0].user))
            except IndexError:
                rowers.append(' ')

        d += datetime.timedelta(days=1)


    thedict = {
        'date':dates,
        'date_sorting':dates_sorting,
        'duration':durations,
        'trimp':trimps,
        'rscore':rscores,
        'type':types,
        'rower':rowers,
        'link':links,
        }

    dim_expr = hv.dim('type').categorize(mytypes.color_map)

    df = pd.DataFrame(thedict)

    source = ColumnDataSource(df)

    df.sort_values('date_sorting',inplace=True)
    #clrs = hv.Cycle(df['colors'].values)

    hv.extension('bokeh')

    if stack == 'type':
        table = hv.Table(df,[('date','Date'),('type','Workout Type')],
                         [('duration','Minutes'),('rscore','rScore'),('trimp','TRIMP'),('link','link')])

    else:
        table = hv.Table(df,[('date','Date'),('rower','Rower')],
                         [('duration','Minutes'),('rscore','rScore'),('trimp','TRIMP'),('link','link')])


    bars=table.to.bars(['date',stack],[yaxis])
    if stack == 'type':
        bars.opts(
            opts.Bars(cmap=mytypes.color_map, show_legend=True, stacked=True,
                    tools=['tap','hover'], width=550, xrotation=45,padding=(0,(0,.1)),
                    legend_position='bottom',show_frame=True))
    else:
        bars.opts(
            opts.Bars(cmap='Category10', show_legend=True, stacked=True,
                    tools=['tap','hover'], width=550, xrotation=45,padding=(0,(0,.1)),
                    legend_position='bottom',show_frame=True))

    p = hv.render(bars)

    p.title.text = 'Activity {d1} to {d2}'.format(
                d1 = startdate.strftime("%Y-%m-%d"),
                d2 = enddate.strftime("%Y-%m-%d"),
                )

    p.plot_width=550
    p.plot_height=350
    p.toolbar_location = toolbar_location
    p.y_range.start = 0
    p.sizing_mode = 'stretch_both'
    url = "http://rowsandall.com/rowers/workout/@duration/"
    taptool = p.select(type=TapTool)



    callback = CustomJS(args={'links':df.link}, code="""
      var index = cb_data.source.selected['1d'].indices[0];
      console.log(links);
      console.log(index);
      console.log(links[index]);
      window.location.href = links[index]
    """)

    taptool.js_on_event('tap',callback)


    script,div = components(p)
    return script,div

def interactive_activitychart2(workouts,startdate,enddate,stack='type',toolbar_location=None,
                               yaxis='duration'):


    dates = []
    dates_sorting = []
    types = []
    rowers = []
    durations = []
    rscores = []
    trimps = []
    links = []


    rowersinitials = {}
    seen = ['seen']
    idseen = []

    startdate = datetime.datetime(year=startdate.year,month=startdate.month,day=startdate.day)
    enddate = datetime.datetime(year=enddate.year,month=enddate.month,day=enddate.day)

    duration = enddate-startdate

    totaldays = duration.total_seconds()/(24*3600)


    for w in workouts:
        aantal=1
        initials = w.user.user.first_name[0:aantal]+w.user.user.last_name[0:aantal]
        if w.user.id not in idseen:
            while initials in seen: # pragma: no cover
                aantal += 1
                initials = w.user.user.first_name[0:aantal]+w.user.user.last_name[0:aantal]

            seen.append(initials)
            idseen.append(w.user.id)
            rowersinitials[w.user.id] = initials



    for w in workouts:
        dd = w.date.strftime('%m/%d')
        dd2 = w.date.strftime('%Y/%m/%d')
        dd3 = w.date.strftime('%Y/%m')

        du = w.duration.hour*60+w.duration.minute
        trimp = w.trimp
        rscore = w.rscore
        if rscore == 0: # pragma: no cover
            rscore = w.hrtss

        if totaldays<=30: # pragma: no cover
            dates.append(dd)
            dates_sorting.append(dd2)
        else:
            dates.append(dd3)
            dates_sorting.append(dd3)
        durations.append(du)
        trimps.append(trimp)
        rscores.append(rscore)
        links.append(
            "{siteurl}/rowers/workout/{code}/".format(
                siteurl = settings.SITE_URL,
                code = encoder.encode_hex(w.id)
                )
        )


        types.append(w.workouttype)
        try:
            rowers.append(rowersinitials[w.user.id])
        except IndexError: # pragma: no cover
            rowers.append(str(w.user))

    try:
        d = utc.localize(startdate)
    except (ValueError,AttributeError): # pragma: no cover
        d = startdate

    try:
        enddate = utc.localize(enddate)
    except (ValueError,AttributeError): # pragma: no cover
        pass

    # add dates with no activity
    while d<=enddate:
        dd = d.strftime('%d')


        if totaldays<=30:
            dates.append(d.strftime('%m/%d'))
            dates_sorting.append(d.strftime('%Y/%m/%d'))
        else:
            dates.append(d.strftime('%Y/%m'))
            dates_sorting.append(d.strftime('%Y/%m'))
        durations.append(0)
        trimps.append(0)
        rscores.append(0)
        links.append('')
        types.append('rower')

        try:
            rowers.append(rowers[0])
        except IndexError:
            try:
                rowers.append(str(workouts[0].user))
            except IndexError:
                rowers.append(' ')

        d += datetime.timedelta(days=1)


    thedict = {
        'date':dates,
        'date_sorting':dates_sorting,
        'duration':durations,
        'trimp':trimps,
        'rscore':rscores,
        'type':types,
        'rower':rowers,
        'link':links,
        }

    dim_expr = hv.dim('type').categorize(mytypes.color_map)



    df = pd.DataFrame(thedict)

    if totaldays>30 and yaxis=='duration': # pragma: no cover
        df['duration'] = df['duration']/60
    elif yaxis == 'TRIMP':
        df.drop('duration',inplace=True,axis='columns')
        df.drop('rscore',inplace=True,axis='columns')
    elif yaxis == 'rScore': # pragma: no cover
        df.drop('duration',inplace=True,axis='columns')
        df.drop('trimp',inplace=True,axis='columns' )

    df['color'] = df['type'].apply(lambda x:mapcolors(x))

    df.sort_values('date_sorting',inplace=True)

    #clrs = hv.Cycle(df['colors'].values)
    source = ColumnDataSource(df)

    hv.extension('bokeh')


    #table = hv.Table(df,[('date','Date'),('type','Workout Type')],
    #                 [('duration','Minutes'),('trimp','TRIMP'),('rscore','rScore'),('link','link')])

    types_order = mytypes.workouttypes_ordered
    #bars=table.to.bars(['date',stack],[yaxis])
    bars = hv.Bars(df, kdims=['date',stack]).aggregate(function=np.sum).redim.values(types=types_order)


    #print(mytypes.color_map)
    bars.opts(
        opts.Bars(cmap=mytypes.color_map, show_legend=True, stacked=True,
                tools=['tap','hover'], width=550, xrotation=45,padding=(0,(0,.1)),
                legend_position='bottom',show_frame=True))


    p = hv.render(bars)

    p.title.text = 'Activity {d1} to {d2}'.format(
                d1 = startdate.strftime("%Y-%m-%d"),
                d2 = enddate.strftime("%Y-%m-%d"),
                )

    p.xaxis.axis_label = 'Period'
    if yaxis == 'duration':
        p.yaxis.axis_label = 'Duration (min)'
        if totaldays>30: # pragma: no cover
            p.yaxis.axis_label = 'Duration (h)'
    elif yaxis == 'TRIMP':
        p.yaxis.axis_label = 'TRIMP'
    else: # pragma: no cover
        p.yaxis.axis_label = 'rScore'



    p.plot_width=550
    p.plot_height=350
    p.toolbar_location = toolbar_location
    p.sizing_mode = 'stretch_both'
    p.y_range.start = 0
    url = "http://rowsandall.com/rowers/workout/@duration/"
    taptool = p.select(type=TapTool)



    callback = CustomJS(args={'links':df['link']}, code="""
      var index = cb_data.source.selected['1d'].indices[0];
      console.log(links);
      console.log(index);
      console.log(links[index]);
      window.location.href = links[index]
    """)

    taptool.js_on_event('tap',callback)


    script,div = components(p)
    return script,div

def interactive_forcecurve(theworkouts,workstrokesonly=True,plottype='scatter'):
    TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'

    ids = [int(w.id) for w in theworkouts]

    boattype = theworkouts[0].boattype

    columns = ['catch','slip','wash','finish','averageforce',
               'peakforceangle','peakforce','spm','distance',
               'workoutstate','driveenergy']


    rowdata = dataprep.getsmallrowdata_db(columns,ids=ids,
                                          workstrokesonly=workstrokesonly)

    rowdata.dropna(axis=1,how='all',inplace=True)
    rowdata.dropna(axis=0,how='any',inplace=True)

    workoutstateswork = [1,4,5,8,9,6,7]
    workoutstatesrest = [3]
    workoutstatetransition = [0,2,10,11,12,13]

    if workstrokesonly:
        try:
            rowdata = rowdata[~rowdata['workoutstate'].isin(workoutstatesrest)]
        except KeyError: # pragma: no cover
            pass

    if rowdata.empty:
        return "","No Valid Data Available","",""


    # quick linear regression
    # peakforce = slope*peakforceangle + intercept
    try:
        slope, intercept, r,p,stderr = linregress(rowdata['peakforceangle'],rowdata['peakforce'])
    except KeyError: # pragma: no cover
        slope = 0
        intercept = 0

    try:
        covariancematrix = np.cov(rowdata['peakforceangle'],y=rowdata['peakforce'])
        eig_vals, eig_vecs = np.linalg.eig(covariancematrix)

        a = rowdata['peakforceangle']-rowdata['peakforceangle'].median()
        F = rowdata['peakforce']-rowdata['peakforce'].median()

        Rinv = eig_vecs
        R = np.linalg.inv(Rinv)

        x = R[0,0]*a+R[0,1]*F
        y = R[1,0]*a+R[1,1]*F


        x05 = x.quantile(q=0.01)
        x25 = x.quantile(q=0.15)
        x75 = x.quantile(q=0.85)
        x95 = x.quantile(q=0.99)

        y05 = y.quantile(q=0.01)
        y25 = y.quantile(q=0.15)
        y75 = y.quantile(q=0.85)
        y95 = y.quantile(q=0.99)

        a25 = Rinv[0,0]*x25 + rowdata['peakforceangle'].median()
        F25 = Rinv[1,0]*x25 + rowdata['peakforce'].median()

        a25b = Rinv[0,1]*y25 + rowdata['peakforceangle'].median()
        F25b = Rinv[1,1]*y25 + rowdata['peakforce'].median()

        a75 = Rinv[0,0]*x75 + rowdata['peakforceangle'].median()
        F75 = Rinv[1,0]*x75 + rowdata['peakforce'].median()

        a75b = Rinv[0,1]*y75 + rowdata['peakforceangle'].median()
        F75b = Rinv[1,1]*y75 + rowdata['peakforce'].median()


        a05 = Rinv[0,0]*x05 + rowdata['peakforceangle'].median()
        F05 = Rinv[1,0]*x05 + rowdata['peakforce'].median()

        a05b = Rinv[0,1]*y05 + rowdata['peakforceangle'].median()
        F05b = Rinv[1,1]*y05 + rowdata['peakforce'].median()

        a95 = Rinv[0,0]*x95 + rowdata['peakforceangle'].median()
        F95 = Rinv[1,0]*x95 + rowdata['peakforce'].median()

        a95b = Rinv[0,1]*y95 + rowdata['peakforceangle'].median()
        F95b = Rinv[1,1]*y95 + rowdata['peakforce'].median()
    except KeyError: # pragma: no cover
        a25 = 0
        F25 = 0

        a25b = 0
        F25b = 0

        a75 = 0
        F75 = 0

        a75b = 0
        F75b = 0


        a05 = 0
        F05 = 0

        a05b = 0
        F05b = 0

        a95 = 0
        F95 = 0

        a95b = 0
        F95b = 0



    try:
        catchav = rowdata['catch'].median()
        catch25 = rowdata['catch'].quantile(q=0.25)
        catch75 = rowdata['catch'].quantile(q=0.75)
        catch05 = rowdata['catch'].quantile(q=0.05)
        catch95 = rowdata['catch'].quantile(q=0.95)
    except KeyError: # pragma: no cover
        catchav = 0
        catch25 = 0
        catch75 = 0
        catch05 = 0
        catch95 = 0

    try:
        finishav = rowdata['finish'].median()
        finish25 = rowdata['finish'].quantile(q=0.25)
        finish75 = rowdata['finish'].quantile(q=0.75)
        finish05 = rowdata['finish'].quantile(q=0.05)
        finish95 = rowdata['finish'].quantile(q=0.95)
    except KeyError: # pragma: no cover
        finishav = 0
        finish25 = 0
        finish75 = 0
        finish05 = 0
        finish95 = 0

    try:
        washav = (rowdata['finish']-rowdata['wash']).median()
        wash25 = (rowdata['finish']-rowdata['wash']).quantile(q=0.25)
        wash75 = (rowdata['finish']-rowdata['wash']).quantile(q=0.75)
        wash05 = (rowdata['finish']-rowdata['wash']).quantile(q=0.05)
        wash95 = (rowdata['finish']-rowdata['wash']).quantile(q=0.95)
    except KeyError: # pragma: no cover
        washav = 0
        wash25 = 0
        wash75 = 0
        wash05 = 0
        wash95 = 0

    try:
        slipav = (rowdata['slip']+rowdata['catch']).median()
        slip25 = (rowdata['slip']+rowdata['catch']).quantile(q=0.25)
        slip75 = (rowdata['slip']+rowdata['catch']).quantile(q=0.75)
        slip05 = (rowdata['slip']+rowdata['catch']).quantile(q=0.05)
        slip95 = (rowdata['slip']+rowdata['catch']).quantile(q=0.95)
    except KeyError: # pragma: no cover
        slipav = 0
        slip25 = 0
        slip75 = 0
        slip05 = 0
        slip95 = 0

    try:
        peakforceav = rowdata['peakforce'].median()
        peakforce25 = rowdata['peakforce'].quantile(q=0.25)
        peakforce75 = rowdata['peakforce'].quantile(q=0.75)
        peakforce05 = rowdata['peakforce'].quantile(q=0.05)
        peakforce95 = rowdata['peakforce'].quantile(q=0.95)
    except KeyError: # pragma: no cover
        peakforceav = 0
        peakforce25 = 0
        peakforce75 = 0
        peakforce05 = 0
        peakforce95 = 0


    try:
        averageforceav = rowdata['averageforce'].median()
    except KeyError: # pragma: no cover
        averageforceav = 0

    try:
        peakforceangleav = rowdata['peakforceangle'].median()
        peakforceangle05 = rowdata['peakforceangle'].quantile(q=0.05)
        peakforceangle25 = rowdata['peakforceangle'].quantile(q=0.25)
        peakforceangle75 = rowdata['peakforceangle'].quantile(q=0.75)
        peakforceangle95 = rowdata['peakforceangle'].quantile(q=0.95)
    except KeyError: # pragma: no cover
        peakforceangleav = 0
        peakforceangle25 = 0
        peakforceangle75 = 0
        peakforceangle05 = 0
        peakforceangle95 = 0


    #thresholdforce /= 4.45 # N to lbs
    thresholdforce = 100 if 'x' in boattype else 200
    points2575 = [
        (catch25,0),  #0
        (slip25,thresholdforce), #1
        (a75,F75),#4
        (a25b,F25b), #9
        (a25,F25), #2
        (wash75,thresholdforce), #5
        (finish75,0), #6
        (finish25,0), #7
        (wash25,thresholdforce), #8
        (a75b,F75b), #3
        (slip75,thresholdforce), #10
        (catch75,0), #11
        ]

    points0595 = [
        (catch05,0),  #0
        (slip05,thresholdforce), #1
        (a95,F95),#4
        (a05b,F05b), #9
        (a05,F05), #2
        (wash95,thresholdforce), #5
        (finish95,0), #6
        (finish05,0), #7
        (wash05,thresholdforce), #8
        (a95b,F95b), #3
        (slip95,thresholdforce), #10
        (catch95,0), #11
        ]



    angles2575 = []
    forces2575 = []

    for x,y in points2575:
        angles2575.append(x)
        forces2575.append(y)


    angles0595 = []
    forces0595 = []

    for x,y in points0595:
        angles0595.append(x)
        forces0595.append(y)





    x = [catchav,
         slipav,
         peakforceangleav,
         washav,
         finishav]

    y = [0,thresholdforce,
         peakforceav,
         thresholdforce,0]



    source = ColumnDataSource(
        data = dict(
            x = x,
            y = y,
        ))

    sourceslipwash = ColumnDataSource(
        data = dict(
            xslip = [slipav,washav],
            yslip = [thresholdforce,thresholdforce]
            )
        )

    sourcetrend = ColumnDataSource(
        data = dict(
            x = [peakforceangle25,peakforceangle75],
            y = [peakforce25,peakforce75]
            )
        )

    sourcefit = ColumnDataSource(
        data = dict(
            x = np.array([peakforceangle25,peakforceangle75]),
            y = slope*np.array([peakforceangle25,peakforceangle75])+intercept
            )
        )

    source2 = ColumnDataSource(
        rowdata
        )

    if plottype == 'scatter': # pragma: no cover
        try:
            sourcepoints = ColumnDataSource(
                data = dict(
                    peakforceangle = rowdata['peakforceangle'],
                    peakforce = rowdata['peakforce']
                )
            )
        except KeyError:
            sourcepoints = ColumnDataSource(
                data = dict(
                    peakforceangle = [],
                    peakforce = []
                    )
                )
    else:
        sourcepoints = ColumnDataSource(
            data = dict(
                peakforceangle = [],
                peakforce = []
                ))


    sourcerange = ColumnDataSource(
        data = dict(
            x2575 = angles2575,
            y2575 = forces2575,
            x0595 = angles0595,
            y0595 = forces0595,
            )
        )

    plot = Figure(tools=TOOLS,
                  toolbar_sticky=False,toolbar_location="above",plot_width=800,plot_height=600)
    plot.sizing_mode = 'stretch_both'

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}


    plot.image_url([watermarkurl],watermarkx,watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    avf = Span(location=averageforceav,dimension='width',line_color='blue',
               line_dash=[6,6],line_width=2)

    plot.patch('x0595','y0595',source=sourcerange,color="red",alpha=0.05)
    plot.patch('x2575','y2575',source=sourcerange,color="red",alpha=0.2)
    plot.line('x','y',source=source,color="red")
    plot.circle('xslip','yslip',source=sourceslipwash,color="red")

    plot.circle('peakforceangle','peakforce',source=sourcepoints,color='black',alpha=0.1)

    if plottype == 'line':
        multilinedatax = []
        multilinedatay = []
        for i in range(len(rowdata)):
            try:
                x = [
                    rowdata['catch'].values[i],
                    rowdata['slip'].values[i]+rowdata['catch'].values[i],
                    rowdata['peakforceangle'].values[i],
                    rowdata['finish'].values[i]-rowdata['wash'].values[i],
                    rowdata['finish'].values[i]
                ]


                y = [
                    0,
                    thresholdforce,
                    rowdata['peakforce'].values[i],
                    thresholdforce,
                    0]
            except KeyError: # pragma: no cover
                x = [0,0]
                y = [0,0]

            multilinedatax.append(x)
            multilinedatay.append(y)

        sourcemultiline = ColumnDataSource(dict(
            x=multilinedatax,
            y=multilinedatay,
            ))

        sourcemultiline2 = ColumnDataSource(dict(
            x=multilinedatax,
            y=multilinedatay,
            ))

        glyph = MultiLine(xs='x',ys='y',line_color='black',line_alpha=0.05)
        plot.add_glyph(sourcemultiline,glyph)
    else: # pragma: no cover
        sourcemultiline = ColumnDataSource(dict(
            x=[],y=[]))

        sourcemultiline2 = ColumnDataSource(dict(
            x=[],y=[]))


    plot.line('x','y',source=source,color="red")

    plot.add_layout(avf)

    peakflabel = Label(x=760,y=460,x_units='screen',y_units='screen',
                       text="Fpeak: {peakforceav:6.2f}".format(peakforceav=peakforceav),
                       background_fill_alpha=.7,
                       background_fill_color='white',
                       text_color='blue',
    )

    avflabel = Label(x=770,y=430,x_units='screen',y_units='screen',
                     text="Favg: {averageforceav:6.2f}".format(averageforceav=averageforceav),
                     background_fill_alpha=.7,
                     background_fill_color='white',
                     text_color='blue',
    )

    catchlabel = Label(x=765,y=400,x_units='screen',y_units='screen',
                       text="Catch: {catchav:6.2f}".format(catchav=catchav),
                       background_fill_alpha=0.7,
                       background_fill_color='white',
                       text_color='red',
                       )

    peakforceanglelabel = Label(x=725,y=370,x_units='screen',y_units='screen',
                                text="Peak angle: {peakforceangleav:6.2f}".format(peakforceangleav=peakforceangleav),
                                background_fill_alpha=0.7,
                                background_fill_color='white',
                                text_color='red',
                       )

    finishlabel = Label(x=760,y=340,x_units='screen',y_units='screen',
                        text="Finish: {finishav:6.2f}".format(finishav=finishav),
                        background_fill_alpha=0.7,
                        background_fill_color='white',
                        text_color='red',
                       )

    sliplabel = Label(x=775,y=310,x_units='screen',y_units='screen',
                      text="Slip: {slipav:6.2f}".format(slipav=slipav-catchav),
                      background_fill_alpha=0.7,
                      background_fill_color='white',
                      text_color='red',
                       )

    washlabel = Label(x=765,y=280,x_units='screen',y_units='screen',
                      text="Wash: {washav:6.2f}".format(washav=finishav-washav),
                      background_fill_alpha=0.7,
                      background_fill_color='white',
                      text_color='red',
    )

    lengthlabel = Label(x=755,y=250, x_units='screen',y_units='screen',
                        text="Length: {length:6.2f}".format(length=finishav-catchav),
                        background_fill_alpha=0.7,
                        background_fill_color='white',
                        text_color='green'
                        )

    efflengthlabel = Label(x=690,y=220, x_units='screen',y_units='screen',
                        text="Effective Length: {length:6.2f}".format(length=washav-slipav),
                        background_fill_alpha=0.7,
                        background_fill_color='white',
                        text_color='green'
                        )

    annolabel = Label(x=50,y=450,x_units='screen',y_units='screen',
                      text='',
                      background_fill_alpha=0.7,
                      background_fill_color='white',
                      text_color='black',
    )

    sliderlabel = Label(x=10,y=470,x_units='screen',y_units='screen',
                        text='',
                        background_fill_alpha=0.7,
                        background_fill_color='white',
                        text_color='black',text_font_size='10pt',
                        )


    plot.add_layout(peakflabel)
    plot.add_layout(peakforceanglelabel)
    plot.add_layout(avflabel)
    plot.add_layout(catchlabel)
    plot.add_layout(sliplabel)
    plot.add_layout(washlabel)
    plot.add_layout(finishlabel)
    plot.add_layout(annolabel)
    plot.add_layout(sliderlabel)
    plot.add_layout(lengthlabel)
    plot.add_layout(efflengthlabel)

    plot.xaxis.axis_label = "Angle"
    plot.yaxis.axis_label = "Force (N)"
    plot.title.text = theworkouts[0].name
    plot.title.text_font_size=value("1.0em")

    yrange1 = Range1d(start=0,end=900)
    plot.y_range = yrange1

    xrange1 = Range1d(start=yaxmaxima['catch'],end=yaxmaxima['finish'])
    plot.x_range = xrange1

    callback = CustomJS(args = dict(
        source=source,
        source2=source2,
        sourceslipwash=sourceslipwash,
        sourcepoints=sourcepoints,
        avf=avf,
        avflabel=avflabel,
        catchlabel=catchlabel,
        finishlabel=finishlabel,
        sliplabel=sliplabel,
        washlabel=washlabel,
        peakflabel=peakflabel,
        peakforceanglelabel=peakforceanglelabel,
        annolabel=annolabel,
        sliderlabel=sliderlabel,
        lengthlabel=lengthlabel,
        efflengthlabel=efflengthlabel,
        plottype=plottype,
        sourcemultiline=sourcemultiline,
        sourcemultiline2=sourcemultiline2
    ), code="""
       var data = source.data
       var data2 = source2.data
       var dataslipwash = sourceslipwash.data
       var datapoints = sourcepoints.data
       var multilines = sourcemultiline.data
       var multilines2 = sourcemultiline2.data
       var plottype = plottype

       var multilinesx = multilines2['x']
       var multilinesy = multilines2['y']

       var x = data['x']
       var y = data['y']

       var xslip = dataslipwash['xslip']

       var spm1 = data2['spm']
       var distance1 = data2['distance']
       var driveenergy1 = data2['driveenergy']

       var thresholdforce = y[1]

       var c = source2.data['catch']
       var finish = data2['finish']
       var slip = data2['slip']
       var wash = data2['wash']
       var peakforceangle = data2['peakforceangle']
       var peakforce = data2['peakforce']
       var averageforce = data2['averageforce']

       var peakforcepoints = datapoints['peakforce']
       var peakforceanglepoints = datapoints['peakforceangle']

       var annotation = annotation.value
       var minspm = minspm.value
       var maxspm = maxspm.value
       var mindist = mindist.value
       var maxdist = maxdist.value
       var minwork = minwork.value
       var maxwork = maxwork.value

       sliderlabel.text = 'SPM: '+minspm.toFixed(0)+'-'+maxspm.toFixed(0)
       sliderlabel.text += ', Dist: '+mindist.toFixed(0)+'-'+maxdist.toFixed(0)
       sliderlabel.text += ', WpS: '+minwork.toFixed(0)+'-'+maxwork.toFixed(0)

       var catchav = 0
       var finishav = 0
       var slipav = 0
       var washav = 0
       var peakforceangleav = 0
       var averageforceav = 0
       var peakforceav = 0
       var count = 0

       datapoints['peakforceangle'] = []
       datapoints['peakforce'] = []
       multilines['x'] = []
       multilines['y'] = []

      for (var i=0; i<c.length; i++) {
          if (spm1[i]>=minspm && spm1[i]<=maxspm) {
             if (distance1[i]>=mindist && distance1[i]<=maxdist) {
                if (driveenergy1[i]>=minwork && driveenergy1[i]<=maxwork) {
                   if (plottype=='scatter') {
                     datapoints['peakforceangle'].push(peakforceangle[i])
                     datapoints['peakforce'].push(peakforce[i])
                   }
                   if (plottype=='line') {
                     multilines['x'].push(multilinesx[i])
                     multilines['y'].push(multilinesy[i])
                   }
                   catchav += c[i]
                   finishav += finish[i]
                   slipav += slip[i]
                   washav += wash[i]
                   peakforceangleav += peakforceangle[i]
                   averageforceav += averageforce[i]
                   peakforceav += peakforce[i]
                   count += 1
                   }
                }
             }
       }

       catchav /= count
       finishav /= count
       slipav /= count
       washav /= count
       peakforceangleav /= count
       peakforceav /= count
       averageforceav /= count

       data['x'] = [catchav,catchav+slipav,peakforceangleav,finishav-washav,finishav]
       data['y'] = [0,thresholdforce,peakforceav,thresholdforce,0]

       dataslipwash['xslip'] = [catchav+slipav,finishav-washav]
       dataslipwash['yslip'] = [thresholdforce,thresholdforce]

       var length = finishav-catchav
       var efflength = length-slipav-washav

       avf.location = averageforceav
       avflabel.text = 'Favg: '+averageforceav.toFixed(2)
       catchlabel.text = 'Catch: '+catchav.toFixed(2)
       finishlabel.text = 'Finish: '+finishav.toFixed(2)
       sliplabel.text = 'Slip: '+slipav.toFixed(2)
       washlabel.text = 'Wash: '+washav.toFixed(2)
       peakflabel.text = 'Fpeak: '+peakforceav.toFixed(2)
       peakforceanglelabel.text = 'Peak angle: '+peakforceangleav.toFixed(2)
       annolabel.text = annotation
       lengthlabel.text = 'Length: '+length.toFixed(2)
       efflengthlabel.text = 'Effective Length: '+efflength.toFixed(2)

       console.log(count);
       console.log(multilines['x'].length);
       console.log(multilines['y'].length);

       // source.trigger('change');
       source.change.emit();
       sourceslipwash.change.emit()
       sourcepoints.change.emit();
       sourcemultiline.change.emit();
       """)

    annotation = TextInput(width=140, title="Type your plot notes here", value="")
    annotation.js_on_change('value',callback)
    callback.args["annotation"] = annotation

    slider_spm_min = Slider(width=140, start=15.0, end=55,value=15.0, step=.1,
                title="Min SPM")
    slider_spm_min.js_on_change('value',callback)
    callback.args["minspm"] = slider_spm_min


    slider_spm_max = Slider(width=140, start=15.0, end=55,value=55.0, step=.1,
                title="Max SPM")
    slider_spm_max.js_on_change('value',callback)
    callback.args["maxspm"] = slider_spm_max

    slider_work_min = Slider(width=140, start=0, end=1500,value=0, step=10,
                title="Min Work per Stroke")
    slider_work_min.js_on_change('value',callback)
    callback.args["minwork"] = slider_work_min


    slider_work_max = Slider(width=140, start=0, end=1500,value=1500, step=10,
                title="Max Work per Stroke")
    slider_work_max.js_on_change('value',callback)
    callback.args["maxwork"] = slider_work_max

    distmax = 100+100*int(rowdata['distance'].max()/100.)

    slider_dist_min = Slider(width=140, start=0,end=distmax,value=0,step=50,
                             title="Min Distance")
    slider_dist_min.js_on_change('value',callback)
    callback.args["mindist"] = slider_dist_min

    slider_dist_max = Slider(width=140, start=0,end=distmax,value=distmax,
                             step=50,
                             title="Max Distance")
    slider_dist_max.js_on_change('value',callback)
    callback.args["maxdist"] = slider_dist_max

    #annotation.sizing_mode = 'fixed'
    #slider_spm_min.sizing_mode = 'fixed'
    #slider_spm_max.sizing_mode = 'fixed'
    #slider_work_min.sizing_mode = 'fixed'
    #slider_work_max.sizing_mode = 'fixed'
    #slider_dist_min.sizing_mode = 'fixed'
    #slider_dist_max.sizing_mode = 'fixed'

    thesliders = layoutcolumn([annotation,
                                      slider_spm_min,
                                      slider_spm_max,
                                      slider_dist_min,
                                      slider_dist_max,
                                      slider_work_min,
                                      slider_work_max,
                               ]
                             )
    #thesliders.sizing_mode = 'fixed'

    layout = layoutrow([thesliders,
                        plot])

    layout.sizing_mode = 'stretch_both'

    script, div = components(layout)
    js_resources = INLINE.render_js()
    css_resources = INLINE.render_css()



    return [script,div,js_resources,css_resources]

def getfatigues(
    fatigues,fitnesses,dates,testpower,testduration,
    impulses,
    startdate,enddate,user,metricchoice,kfatigue,kfitness):

    fatigue = 0
    fitness = 0

    impulses = []
    for f in fatigues:
        impulses.append(0)

    lambda_a = 2/(kfatigue+1)
    lambda_c = 2/(kfitness+1)
    nrdays = (enddate-startdate).days

    for i in range(nrdays+1):
        date = startdate+datetime.timedelta(days=i)
        ws = Workout.objects.filter(user=user.rower,date=date,duplicate=False)
        weight = 0

        for w in ws:
            if getattr(w,metricchoice) > 0:
                weight += getattr(w,metricchoice)
            if getattr(w,metricchoice) <= 0:
                if metricchoice == 'rscore' and w.hrtss > 0: # pragma: no cover
                    weight+= w.hrtss
                else:
                    trimp,hrtss = dataprep.workout_trimp(w)
                    rscore,normp = dataprep.workout_rscore(w)
                    if w.rpe and w.rpe > 0: # pragma: no cover
                        dd = 3600*w.duration.hour+60*w.duration.minute+w.duration.second
                        dd = dd/3600
                        weight += rpetotss[w.rpe]*dd



        impulses.append(weight)

        fatigue = (1-lambda_a)*fatigue+weight*lambda_a
        fitness = (1-lambda_c)*fitness+weight*lambda_c

        fatigues.append(fatigue)
        fitnesses.append(fitness)
        dates.append(arrow.get(date).datetime)
        testpower.append(np.nan)
        testduration.append(np.nan)



    return fatigues,fitnesses,dates,testpower,testduration,impulses

def goldmedalscorechart(user,startdate=None,enddate=None):
    # to avoid data mess later on
    startdate = arrow.get(startdate).datetime.replace(hour=0,minute=0,second=0,microsecond=0)
    enddate = enddate+datetime.timedelta(days=1)
    enddate = arrow.get(enddate).datetime.replace(hour=0,minute=0,second=0,microsecond=0)

    TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'

    # marker workouts
    workouts = Workout.objects.filter(user=user.rower,date__gte=startdate,
                                      date__lte=enddate,
                                      workouttype__in=mytypes.rowtypes,
                                      duplicate=False).order_by('date')

    markerworkouts = workouts.filter(rankingpiece=True)
    outids = [w.id for w in markerworkouts]
    dates = [arrow.get(w.date).datetime for w in markerworkouts]
    testpower = [w.goldmedalstandard if w.rankingpiece else np.nan for w in markerworkouts]

    testduration = [w.goldmedalseconds if w.rankingpiece else 0 for w in markerworkouts]

    df = pd.DataFrame({
        'id':outids,
        'date':dates,
        'testpower':testpower,
        'testduration':testduration,
    })
    df.sort_values(['date'],inplace=True)


    mask = df['testpower'].isnull()
    dates = df.mask(mask)['date'].dropna().values
    testpower = df.mask(mask)['testpower'].dropna().values
    ids = df.mask(mask)['id'].dropna().values

    outids = df.mask(mask)['id'].dropna().unique()

    # all workouts
    alldates,alltestpower,allduration,allids = all_goldmedalstandards(workouts,startdate,enddate)


    nrdays = (enddate-startdate).days

    td = []
    markerscore = []
    score = []
    markerduration = []
    duration = []
    workoutid = []

    previous = 0

    for i in range(len(dates)):
        id = ids[i]
        w = Workout.objects.get(id=id)
        dd = str(dates[i])
        #td.append(arrow.get(dd).datetime)
        td.append(arrow.get(w.date).datetime)
        markerscore.append(testpower[i])
        markerduration.append(testduration[i])
        score.append(testpower[i])
        duration.append(testduration[i])
        workoutid.append(id)

    for i in range(len(alldates)):
        td.append(arrow.get(alldates[i]).datetime)
        markerscore.append(np.nan)
        score.append(alltestpower[i])
        markerduration.append(np.nan)
        duration.append(allduration[i])
        workoutid.append(allids[i])

    for i in range(nrdays+1):
        td.append(arrow.get(startdate+datetime.timedelta(days=i)).datetime)
        markerscore.append(np.nan)
        score.append(np.nan)
        markerduration.append(np.nan)
        duration.append(np.nan)
        workoutid.append(0)


    df = pd.DataFrame({
        'markerscore':markerscore,
        'markerduration':markerduration,
        'score':score,
        'duration':duration,
        'date':td,
        'id':workoutid,
    })

    df['url'] = df['id'].apply(lambda x:settings.SITE_URL+'/rowers/workout/{id}/'.format(id=encoder.encode_hex(x)))
    df['workout'] = df['id'].apply(lambda x:workoutname(x))


    df.sort_values(['date'],inplace=True)

    # find index values where score is max
    idx = df.groupby(['date'])['score'].transform(max) == df['score']
    df = df[idx]


    source = ColumnDataSource(
        data = dict(
            markerscore = df['markerscore'],
            score = df['score'],
            markerduration = df['markerduration'].apply(lambda x:totaltime_sec_to_string(x,shorten=True)),
            duration = df['duration'].apply(lambda x:totaltime_sec_to_string(x,shorten=True)),
            date = df['date'],
            fdate = df['date'].map(lambda x: x.strftime('%d-%m-%Y')),
            url = df['url'],
            workout = df['workout']
        )
    )

    plot = Figure(tools=TOOLS,x_axis_type='datetime',
                  plot_width=900,plot_height=600,
                  toolbar_location='above',
                  toolbar_sticky=False)

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}

    plot.image_url([watermarkurl],watermarkx,watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    plot.xaxis.axis_label = 'Date'
    plot.yaxis.axis_label = 'Gold Medal Score'

    plot.circle('date','score',source=source,fill_color='blue',
                size=10,
                legend_label='Workouts')

    plot.circle('date','markerscore',source=source,fill_color='red',
                size=10,
                legend_label='Marker Workouts')

    plot.legend.location = "bottom_left"

    plot.x_range = Range1d(
        startdate,enddate+datetime.timedelta(days=5),
    )

    hover = plot.select(dict(type=HoverTool))

    hover.tooltips = OrderedDict([
        ('Marker','@markerscore{int}'),
        ('Test', '@markerduration'),
        ('Score','@score{int}'),
        ('Duration', '@duration'),
        ('Date','@fdate'),
        ('Workout','@workout')
    ])

    taptool = plot.select(type=TapTool)
    taptool.callback = OpenURL(url='@url')

    script, div = components(plot)

    return script, div,outids

def performance_chart(user,startdate=None,enddate=None,kfitness=42,kfatigue=7,
                      metricchoice='trimp',doform=False,dofatigue=False,
                      showtests=False):

    TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
    TOOLS2 = 'box_zoom,hover'

    # to avoid data mess later on
    startdate = arrow.get(startdate).datetime.replace(hour=0,minute=0,second=0,microsecond=0)
    enddate = enddate+datetime.timedelta(days=1)
    enddate = arrow.get(enddate).datetime.replace(hour=0,minute=0,second=0,microsecond=0)


    modelchoice = 'coggan'
    p0 = 0
    k1 = 1
    k2 = 1

    dates = []
    testpower = []
    fatigues = []
    fitnesses = []
    testduration = []
    impulses = []

    outids = []


    workouts = Workout.objects.filter(user=user.rower,date__gte=startdate-datetime.timedelta(days=90),
                                      date__lte=enddate,
                                      workouttype__in=mytypes.rowtypes,
                                      duplicate=False).order_by('date')

    markerworkouts = workouts.filter(rankingpiece=True)
    outids = [w.id for w in markerworkouts]
    dates = [arrow.get(w.date).datetime for w in workouts]
    testpower = [w.goldmedalstandard if w.rankingpiece else np.nan for w in workouts]
    impulses = [np.nan for w in workouts]
    testduration = [w.goldmedalseconds if w.rankingpiece else 0 for w in workouts]
    fitnesses = [np.nan for w in workouts]
    fatigues = [np.nan for w in workouts]

    fatigues,fitnesses,dates,testpower,testduration,impulses = getfatigues(fatigues,
                                                     fitnesses,
                                                     dates,
                                                     testpower,testduration,
                                                     impulses,
                                                     startdate-datetime.timedelta(days=90),
                                                     enddate,
                                                     user,metricchoice,
                                                     kfatigue,kfitness)


    df = pd.DataFrame({
        'date':dates,
        'testpower':testpower,
        'testduration': testduration,
        'fatigue':fatigues,
        'fitness':fitnesses,
        'impulse':impulses,
    })


    endfitness = fitnesses[-2]
    endfatigue = fatigues[-2]
    endform = endfitness-endfatigue

    if modelchoice == 'banister': # pragma: no cover
        df['fatigue'] = k2*df['fatigue']
        df['fitness'] = p0+k1*df['fitness']


    df['form'] = df['fitness']-df['fatigue']



    df.sort_values(['date'],inplace=True)
    df = df.groupby(['date']).max()
    df['date'] = df.index.values
    mask = df['date'] > np.datetime64(startdate.astimezone(tz=datetime.timezone.utc).replace(tzinfo=None))
    df = df.loc[mask]



    source = ColumnDataSource(
        data = dict(
            testpower = df['testpower'],
            testduration = df['testduration'].apply(lambda x:totaltime_sec_to_string(x,shorten=True)),
            date = df['date'],
            fdate = df['date'].map(lambda x: x.strftime('%d-%m-%Y')),
            fitness = df['fitness'],
            fatigue = df['fatigue'],
            form = df['form'],
            impulse = df['impulse']
        )
    )


    plot = Figure(tools=TOOLS,x_axis_type='datetime',
                  plot_width=900,plot_height=300,
                  toolbar_location="above",
                  toolbar_sticky=False)


    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}

    plot.image_url([watermarkurl],watermarkx,watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    if modelchoice == 'banister': # pragma: no cover
        fitlabel = 'PTE (fitness)'
        fatiguelabel = 'NTE (fatigue)'
        formlabel = 'Performance'
        rightaxlabel = 'Performance'
        if dofatigue:
            yaxlabel = 'PTE/NTE'
        else:
            yaxlabel = 'PTE'
    else:
        fitlabel = 'Fitness'
        fatiguelabel = 'Fatigue'
        formlabel = 'Freshness'
        rightaxlabel = 'Freshness'
        if dofatigue: # pragma: no cover
            yaxlabel = 'Fitness/Fatigue'
        else:
            yaxlabel = 'Fitness'


    #if showtests:
    #    plot.circle('date','testpower',source=source,fill_color='green',size=10,
    #                legend_label='Your best workouts')

    plot.xaxis.axis_label = None
    plot.yaxis.axis_label = yaxlabel


    y2rangemin = df.loc[:,['form']].min().min()
    y2rangemax = df.loc[:,['form']].max().max()
    #if dofatigue and showtests:
    #    y1rangemin = df.loc[:,['testpower','fitness','fatigue']].min().min()
    #    y1rangemax = df.loc[:,['testpower','fitness','fatigue']].max().max()*1.02
    #elif showtests:
    #    y1rangemin = df.loc[:,['testpower','fitness']].min().min()
    #    y1rangemax = df.loc[:,['testpower','fitness']].max().max()*1.02
    if dofatigue: # pragma: no cover
        y1rangemin = df.loc[:,['fitness','fatigue']].min().min()
        y1rangemax = df.loc[:,['fitness','fatigue']].max().max()*1.02
    else:
        y1rangemin = df.loc[:,['fitness']].min().min()
        y1rangemax = df.loc[:,['fitness']].max().max()*1.02

    if doform: # pragma: no cover
        plot.extra_y_ranges["yax2"] = Range1d(start=y2rangemin,end=y2rangemax)
        plot.add_layout(LinearAxis(y_range_name="yax2",axis_label=rightaxlabel),"right")

    plot.line('date','fitness',source=source,color='blue',
              legend_label=fitlabel)
    band = Band(base='date', upper='fitness', source=source, level='underlay',
            fill_alpha=0.2, fill_color='blue')
    plot.add_layout(band)


    if dofatigue: # pragma: no cover
        plot.line('date','fatigue',source=source,color='red',
                legend_label=fatiguelabel)
    if doform: # pragma: no cover
        plot.line('date','form',source=source,color='green',
                legend_label=formlabel,y_range_name="yax2")

    plot.legend.location = "top_left"

    plot.sizing_mode = 'scale_both'

    #plot.y_range = Range1d(0,1.5*max(df['testpower']))
    startdate = datetime.datetime.combine(startdate,datetime.datetime.min.time())
    enddate = datetime.datetime.combine(enddate,datetime.datetime.min.time())

    xrange = Range1d(
        startdate,enddate,
    )
    plot.x_range = xrange
    plot.y_range = Range1d(
        start=0,end=y1rangemax,
    )
    plot.title.text = 'Performance Manager '+user.first_name


    hover = plot.select(dict(type=HoverTool))

    linked_crosshair = CrosshairTool(dimensions='height')


    hover.tooltips = OrderedDict([
        #(legend_label,'@testpower'),
        ('Date','@fdate'),
        (fitlabel,'@fitness{int}'),
        (fatiguelabel,'@fatigue{int}'),
        (formlabel,'@form{int}'),
        ('Impulse','@impulse{int}')
    ])

    if showtests:
        hover.tooltips = OrderedDict([
            #(legend_label,'@testpower'),
            ('Date','@fdate'),
            (fitlabel,'@fitness{int}'),
            (fatiguelabel,'@fatigue{int}'),
            (formlabel,'@form{int}'),
            ('Impulse','@impulse{int}'),
            ('Gold Medal Score','@testpower{int}'),
            ('Test', '@testduration'),
        ])

    plot2 = Figure(tools=TOOLS2,x_axis_type='datetime',
                  plot_width=900,plot_height=150,
                  toolbar_location=None,
                  toolbar_sticky=False)



    plot2.x_range  = xrange
    plot2.y_range = Range1d(0,df['impulse'].max())

    plot2.vbar(x = df['date'], top = df['impulse'],color='gray')
    plot2.vbar(x = df['date'], top = 0*df['testpower']+df['impulse'], color='red')

    plot2.sizing_mode = 'scale_both'
    plot2.yaxis.axis_label = 'Impulse'
    plot2.xaxis.axis_label = 'Date'

    plot.add_tools(linked_crosshair)
    plot2.add_tools(linked_crosshair)

    layout = layoutcolumn([plot,plot2])
    layout.sizing_mode = 'stretch_both'

    try:
        script,div = components(layout)
    except Exception as e: # pragma: no cover
        df.dropna(inplace=True,axis=0,how='any')
        return (
            '',
            'Something went wrong with the chart ({nrworkouts} workouts, {nrdata} datapoints, error {e})'.format(
                nrworkouts = workouts.count(),
                nrdata = len(df),
                e = e,
            ),0,0,0,[]
            )

    return [script,div,endfitness,endfatigue,endform,outids]




def interactive_histoall(theworkouts,histoparam,includereststrokes,
                         spmmin=0,spmmax=55,
                         workmin=0,workmax=1500):
    TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'

    ids = [int(w.id) for w in theworkouts]

    workstrokesonly = not includereststrokes
    rowdata = dataprep.getsmallrowdata_db([histoparam],ids=ids,doclean=True,workstrokesonly=workstrokesonly)

    rowdata.dropna(axis=0,how='any',inplace=True)

    rowdata = dataprep.filter_df(rowdata,'spm',spmmin,largerthan=True)
    rowdata = dataprep.filter_df(rowdata,'spm',spmmax,largerthan=False)

    rowdata = dataprep.filter_df(rowdata,'driveenergy',workmin,largerthan=True)
    rowdata = dataprep.filter_df(rowdata,'driveenergy',workmax,largerthan=False)

    if rowdata.empty:
        return "","No Valid Data Available"

    try:
        histopwr = rowdata[histoparam].values
    except KeyError:
        return "","No data"
    if len(histopwr) == 0: # pragma: no cover
        return "","No valid data available"

    # throw out nans
    histopwr = histopwr[~np.isinf(histopwr)]
    if histoparam == 'catch': # pragma: no cover
        histopwr = histopwr[histopwr < yaxminima[histoparam]]
        histopwr = histopwr[histopwr > yaxmaxima[histoparam]]
    else:
        histopwr = histopwr[histopwr > yaxminima[histoparam]]
        histopwr = histopwr[histopwr < yaxmaxima[histoparam]]

    plot = Figure(tools=TOOLS,plot_width=900,
                  toolbar_sticky=False,
                  toolbar_location="above"
                  )

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}

    plot.image_url([watermarkurl],watermarkx,watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    hist,edges = np.histogram(histopwr,bins=150)

    histsum = np.cumsum(hist)
    histsum = 100.*histsum/max(histsum)

    hist_norm = 100.*hist/float(hist.sum())

    source = ColumnDataSource(
        data = dict(
            left = edges[:-1],
            right = edges[1:],
            histsum = histsum,
            hist_norm = hist_norm,
            )
        )


#    plot.quad(top='hist_norm',bottom=0,left=edges[:-1],right=edges[1:])
    plot.quad(top='hist_norm',bottom=0,left='left',right='right',source=source)

    plot.xaxis.axis_label = axlabels[histoparam]
    plot.yaxis.axis_label = "% of strokes"
    plot.y_range = Range1d(0,1.05*max(hist_norm))


    hover = plot.select(dict(type=HoverTool))

    hover.tooltips = OrderedDict([
        (axlabels[histoparam],'@left{int}'),
        ('% of strokes','@hist_norm'),
        ('Cumulative %','@histsum{int}'),
        ])

    hover.mode = 'mouse'

    plot.extra_y_ranges["fraction"] = Range1d(start=0,end=105)
    plot.line('right','histsum',source=source,color="red",
              y_range_name="fraction")
    plot.add_layout(LinearAxis(y_range_name="fraction",
                               axis_label="Cumulative % of strokes"),'right')

    plot.sizing_mode = 'stretch_both'

    annolabel = Label(x=50,y=450,x_units='screen',y_units='screen',
                      text='',
                      background_fill_alpha=0.7,
                      background_fill_color='white',
                      text_color='black',
    )

    plot.add_layout(annolabel)

    callback = CustomJS(args = dict(
        annolabel=annolabel,
        ), code="""
           var annotation = annotation.value
           annolabel.text = annotation
           """)

    annotation = TextInput(width=140, title="Type your plot notes here", value="")
    annotation.js_on_change('value',callback)
    callback.args["annotation"] = annotation

    layout = layoutcolumn([annotation,plot])

    try:
        script, div = components(layout)
    except ValueError: # pragma: no cover
        script = ''
        div = ''

    return [script,div]

def course_map(course):
    latmean,lonmean,coordinates = course_coord_center(course)
    lat_min, lat_max, long_min, long_max = course_coord_maxmin(course)

    coordinates = course_spline(coordinates)

    scoordinates = "["

    for index,row in coordinates.iterrows():
        scoordinates += """[{x},{y}],
        """.format(
            x=row['latitude'],
            y=row['longitude']
        )

    scoordinates +="]"

    polygons = GeoPolygon.objects.filter(course=course).order_by("order_in_course")

    plabels = ''

    for p in polygons:
        coords = polygon_coord_center(p)

        plabels += """
        var marker = L.marker([{latbegin}, {longbegin}]).addTo(mymap);
        marker.bindPopup("<b>{name}</b>");

        """.format(
            latbegin = coords[0],
            longbegin = coords[1],
            name = p.name
            )

    pcoordinates = """[
    """

    for p in polygons:
        pcoordinates += """[
        ["""

        points = GeoPoint.objects.filter(polygon=p).order_by("order_in_poly")

        for pt in points:
            pcoordinates += "[{x},{y}],".format(
                x = pt.latitude,
                y = pt.longitude
                )

        # remove last comma
        pcoordinates = pcoordinates[:-1]
        pcoordinates += """]
        ],
        """

    pcoordinates += """
    ]"""



    script = """
    <script>


    var streets = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/streets-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    satellite = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/satellite-v9',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    outdoors = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/outdoors-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    );



    var mymap = L.map('map_canvas', {{
              center: [{latmean}, {lonmean}],
              zoom: 13,
              layers: [outdoors]
              }}).setView([{latmean},{lonmean}], 13);

    var navionics = new JNC.Leaflet.NavionicsOverlay({{
         navKey: 'Navionics_webapi_03205',
         chartType: JNC.NAVIONICS_CHARTS.NAUTICAL,
         isTransparent: true,
         zIndex: 1
    }});


   var osmUrl2='http://tiles.openseamap.org/seamark/{{z}}/{{x}}/{{y}}.png';
   var osmUrl='http://{{s}}.tile.openstreetmap.org/{{z}}/{{x}}/{{y}}.png';


    //create two TileLayer
    var nautical=new L.TileLayer(osmUrl,{{
             maxZoom:18}});


    L.control.layers({{
        "Streets": streets,
        "Satellite": satellite,
        "Outdoors": outdoors,
        "Nautical": nautical,
    }},{{
        "Navionics":navionics,
    }}).addTo(mymap);

    var latlongs = {scoordinates}
    var polyline = L.polyline(latlongs, {{color:'red'}}).addTo(mymap)
    mymap.fitBounds(polyline.getBounds())

    var platlongs = {pcoordinates}
    var polygons = L.polygon(platlongs, {{color:'blue'}}).addTo(mymap)

    {plabels}

    </script>
      """.format(
          latmean=latmean,
          lonmean=lonmean,
          scoordinates=scoordinates,
          pcoordinates=pcoordinates,
          plabels = plabels
          )

    div = """
    <div id="map_canvas" style="width: 100%; height: 400px; margin:0; padding:0;grid-gap:0;"></div>
    """

    return script,div


def get_map_script_course(
                   latmean,
                   lonmean,
                   latbegin,
                   latend,
                   longbegin,
                   longend,
                   scoordinates,
                   course,
                   ):
    latmean,lonmean,coordinates = course_coord_center(course)
    lat_min, lat_max, long_min, long_max = course_coord_maxmin(course)

    coordinates = course_spline(coordinates)

    scoordinates = "["

    for index,row in coordinates.iterrows():
        scoordinates += """[{x},{y}],
        """.format(
            x=row['latitude'],
            y=row['longitude']
        )

    scoordinates +="]"

    polygons = GeoPolygon.objects.filter(course=course).order_by("order_in_course")

    plabels = ''

    for p in polygons:
        coords = polygon_coord_center(p)

        plabels += """
        var marker = L.marker([{latbegin}, {longbegin}]).addTo(mymap);
        marker.bindPopup("<b>{name}</b>");

        """.format(
            latbegin = coords[0],
            longbegin = coords[1],
            name = p.name
            )

    pcoordinates = """[
    """

    for p in polygons:
        pcoordinates += """[
        ["""

        points = GeoPoint.objects.filter(polygon=p).order_by("order_in_poly")

        for pt in points:
            pcoordinates += "[{x},{y}],".format(
                x = pt.latitude,
                y = pt.longitude
                )

        # remove last comma
        pcoordinates = pcoordinates[:-1]
        pcoordinates += """]
        ],
        """

    pcoordinates += """
    ]"""

    script = """
    <script>


    var streets = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/streets-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    satellite = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/satellite-v9',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    outdoors = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/outdoors-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    );



    var mymap = L.map('map_canvas', {{
              center: [{latmean}, {lonmean}],
              zoom: 13,
              layers: [streets, satellite]
              }}).setView([{latmean},{lonmean}], 13);

    var navionics = new JNC.Leaflet.NavionicsOverlay({{
         navKey: 'Navionics_webapi_03205',
         chartType: JNC.NAVIONICS_CHARTS.NAUTICAL,
         isTransparent: true,
         zIndex: 1
    }});


   var osmUrl2='http://tiles.openseamap.org/seamark/{{z}}/{{x}}/{{y}}.png';
   var osmUrl='http://{{s}}.tile.openstreetmap.org/{{z}}/{{x}}/{{y}}.png';


    //create two TileLayer
    var nautical=new L.TileLayer(osmUrl,{{
             maxZoom:18}});


    L.control.layers({{
        "Streets": streets,
        "Satellite": satellite,
        "Outdoors": outdoors,
        "Nautical": nautical,
    }},{{
        "Navionics":navionics,
    }}).addTo(mymap);

    var latlongs = {scoordinates}
    var polyline = L.polyline(latlongs, {{color:'red'}}).addTo(mymap)
    mymap.fitBounds(polyline.getBounds())

    var platlongs = {pcoordinates}
    var polygons = L.polygon(platlongs, {{color:'blue'}}).addTo(mymap)

    {plabels}



    var latlongs = {scoordinates}
    var polyline = L.polyline(latlongs, {{color:'red'}}).addTo(mymap)
    mymap.fitBounds(polyline.getBounds())

    </script>
      """.format(
          latmean=latmean,
          lonmean=lonmean,
          latbegin = latbegin,
          latend=latend,
          longbegin=longbegin,
          longend=longend,
          scoordinates=scoordinates,
          pcoordinates=pcoordinates,
          plabels=plabels
          )

    return script


def get_map_script(
                   latmean,
                   lonmean,
                   latbegin,
                   latend,
                   longbegin,
                   longend,
                   scoordinates,
                   ):
    script = """
    <script>


    var streets = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/streets-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    satellite = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/satellite-v9',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    outdoors = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/outdoors-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    );



    var mymap = L.map('map_canvas', {{
              center: [{latmean}, {lonmean}],
              zoom: 13,
              layers: [streets, satellite]
              }}).setView([{latmean},{lonmean}], 13);

    var navionics = new JNC.Leaflet.NavionicsOverlay({{
         navKey: 'Navionics_webapi_03205',
         chartType: JNC.NAVIONICS_CHARTS.NAUTICAL,
         isTransparent: true,
         zIndex: 1
    }});


   var osmUrl2='http://tiles.openseamap.org/seamark/{{z}}/{{x}}/{{y}}.png';
   var osmUrl='http://{{s}}.tile.openstreetmap.org/{{z}}/{{x}}/{{y}}.png';


    //create two TileLayer
    var nautical=new L.TileLayer(osmUrl,{{
             maxZoom:18}});


    L.control.layers({{
        "Streets": streets,
        "Satellite": satellite,
        "Outdoors": outdoors,
        "Nautical": nautical,
    }},{{
        "Navionics":navionics,
    }}).addTo(mymap);

    var marker = L.marker([{latbegin}, {longbegin}]).addTo(mymap);
    marker.bindPopup("<b>Start</b>");
    var emarker = new L.marker([{latend}, {longend}]).addTo(mymap);
    emarker.bindPopup("<b>End</b>");

    var latlongs = {scoordinates}
    var polyline = L.polyline(latlongs, {{color:'red'}}).addTo(mymap)
    mymap.fitBounds(polyline.getBounds())

    </script>
      """.format(
          latmean=latmean,
          lonmean=lonmean,
          latbegin = latbegin,
          latend=latend,
          longbegin=longbegin,
          longend=longend,
          scoordinates=scoordinates,
          )

    return script

def leaflet_chart(lat,lon,name="",raceresult=0):
    if lat.empty or lon.empty: # pragma: no cover
        return [0,"invalid coordinate data"]


    # Throw out 0,0
    df = pd.DataFrame({
        'lat':lat,
        'lon':lon
        })

    df = df.replace(0,np.nan)
    df = df.loc[(df!=0).any(axis=1)]
    df.fillna(method='bfill',axis=0,inplace=True)
    df.fillna(method='ffill',axis=0,inplace=True)
    lat = df['lat']
    lon = df['lon']
    if lat.empty or lon.empty: # pragma: no cover
        return [0,"invalid coordinate data"]

    latmean = lat.mean()
    lonmean = lon.mean()

    latbegin = lat[lat.index[0]]
    longbegin = lon[lon.index[0]]
    latend = lat[lat.index[-1]]
    longend = lon[lon.index[-1]]

    coordinates = zip(lat,lon)

    scoordinates = "["

    for x,y in coordinates:
        scoordinates += """[{x},{y}],
        """.format(
            x=x,
            y=y
            )

    scoordinates += "]"

    if raceresult == 0:
        script = get_map_script(
            latmean,
            lonmean,
            latbegin,
            latend,
            longbegin,
            longend,
            scoordinates,
            )
    else:
        record = VirtualRaceResult.objects.get(id=raceresult)
        course = record.course
        script = get_map_script_course(
            latmean,
            lonmean,
            latbegin,
            latend,
            longbegin,
            longend,
            scoordinates,
            course,
            )

    div = """
    <div id="map_canvas" style="width: 100%; height: 400px;"><p>&nbsp;</p></div>
    """



    return script,div

def leaflet_chart_compare(course,workoutids,labeldict={},startenddict={}):
    data = []
    for id in workoutids:
        if id != 0 and id is not None:
            try:
                w = Workout.objects.get(id=id)
                rowdata = rdata(w.csvfilename)
                time = rowdata.df['TimeStamp (sec)']
                df = pd.DataFrame({
                    'workoutid':id,
                    'lat':rowdata.df[' latitude'],
                    'lon':rowdata.df[' longitude'],
                    'time':time-time[0],
                    })
                data.append(df)
            except (Workout.DoesNotExist,KeyError): # pragma: no cover
                pass



    df = pd.concat(data,axis=0)

    latmean,lonmean,coordinates = course_coord_center(course)
    lat_min, lat_max, long_min, long_max = course_coord_maxmin(course)

    coordinates = course_spline(coordinates)



    polygons = GeoPolygon.objects.filter(course=course).order_by("order_in_course")

    plabels = ''

    for p in polygons:
        coords = polygon_coord_center(p)

        plabels += """
        var marker = L.marker([{latbegin}, {longbegin}]).addTo(mymap);
        marker.bindPopup("<b>{name}</b>");

        """.format(
            latbegin = coords[0],
            longbegin = coords[1],
            name = p.name
            )

    pcoordinates = """[
    """

    for p in polygons:
        pcoordinates += """[
        ["""

        points = GeoPoint.objects.filter(polygon=p).order_by("order_in_poly")

        for pt in points:
            pcoordinates += "[{x},{y}],".format(
                x = pt.latitude,
                y = pt.longitude
                )

        # remove last comma
        pcoordinates = pcoordinates[:-1]
        pcoordinates += """]
        ],
        """

    pcoordinates += """
    ]"""


    # Throw out 0,0
    df = df.replace(0,np.nan)
    df = df.loc[(df!=0).any(axis=1)]
    df.fillna(method='bfill',axis=0,inplace=True)
    df.fillna(method='ffill',axis=0,inplace=True)


    lat = df['lat']
    lon = df['lon']
    if lat.empty or lon.empty: # pragma: no cover
        return [0,"invalid coordinate data"]

    latbegin = lat.values[0]
    longbegin = lon.values[0]
    latend = lat.values[-1]
    longend = lon.values[-1]

    colors = itertools.cycle(palette)
    try:
        items = itertools.izip(workoutids,colors)
    except AttributeError:
        items = zip(workoutids,colors)



    script = """
    <script>

    var streets = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/streets-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),


    satellite = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/satellite-v9',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    outdoors = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/outdoors-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    );

var navionics = new JNC.Leaflet.NavionicsOverlay({{
     navKey: 'Navionics_webapi_03205',
     chartType: JNC.NAVIONICS_CHARTS.NAUTICAL,
     isTransparent: true,
     zIndex: 1
}});

    var mymap = L.map('map_canvas', {{
              center: [{latmean}, {lonmean}],
              zoom: 13,
              layers: [outdoors]
              }}).setView([{latmean},{lonmean}], 13);




   var osmUrl2='http://tiles.openseamap.org/seamark/{{z}}/{{x}}/{{y}}.png';
   var osmUrl='http://{{s}}.tile.openstreetmap.org/{{z}}/{{x}}/{{y}}.png';


    //create two TileLayer
    var nautical=new L.TileLayer(osmUrl,{{
             maxZoom:18}});


    L.control.layers({{
        "Streets": streets,
        "Satellite": satellite,
        "Outdoors": outdoors,
        "Nautical": nautical,
    }},{{
        "Navionics":navionics,
    }}).addTo(mymap);


    var platlongs = {pcoordinates}
    var polygons = L.polygon(platlongs, {{color:'blue'}}).addTo(mymap)

    {plabels}

      """.format(
          latmean=latmean,
          lonmean=lonmean,
          latbegin = latbegin,
          latend=latend,
          longbegin=longbegin,
          longend=longend,
          pcoordinates=pcoordinates,
          plabels=plabels,
          )

    for id,color in items:
        group = df[df['workoutid']==int(id)].copy()
        try:
            startsecond,endsecond = startenddict[id]
        except KeyError: # pragma: no cover
            startsecond = 0
            endsecond = 0

        try:
            label = labeldict[id]
        except KeyError: # pragma: no cover
            label = str(id)

        group.sort_values(by='time',ascending=True,inplace=True)
        group.dropna(axis=0,how='any',inplace=True)
        if endsecond > 0:
            group['time'] = group['time'] - startsecond
            mask = group['time'] < 0
            group.mask(mask,inplace=True)
            mask = group['time'] > (endsecond-startsecond)
            group.mask(mask,inplace=True)

        lat = group['lat'].dropna()
        lon = group['lon'].dropna()

        coordinates = zip(lat,lon)

        scoordinates = "["
        for x,y in coordinates:
            scoordinates += """[{x},{y}],
            """.format(x=x,y=y)
        scoordinates += "]"

        if not group.empty:
            script += """
        var latlongs = {scoordinates}
        var polyline = L.polyline(latlongs, {{color:'{color}'}}).addTo(mymap)
        polyline.bindPopup("<b>{label}</b>",{{ autoPan: false, autoClose: false }}).openPopup()
        polyline.on('mouseover',function(ev) {{
            ev.target.openPopup();
            }});
        polyline.on('dblclick', function (e) {{
            mymap.removeLayer(this);
            }});
        mymap.fitBounds(polyline.getBounds())
        """.format(
            scoordinates=scoordinates,
            color=color,
            label=label,
            id=id,
        )

    script += """

        </script>
    """




    div = """
    <div id="map_canvas" style="width: 100%; height: 100%; min-height: 100vh"><p>&nbsp;</p></div>
    """



    return script,div

def leaflet_chart2(lat,lon,name=""):
    if lat.empty or lon.empty: # pragma: no cover
        return [0,"invalid coordinate data"]


    # Throw out 0,0
    df = pd.DataFrame({
        'lat':lat,
        'lon':lon
        })

    df = df.replace(0,np.nan)
    df = df.loc[(df!=0).any(axis=1)]
    df.fillna(method='bfill',axis=0,inplace=True)
    df.fillna(method='ffill',axis=0,inplace=True)
    lat = df['lat']
    lon = df['lon']
    if lat.empty or lon.empty: # pragma: no cover
        return [0,"invalid coordinate data"]

    latmean = lat.mean()
    lonmean = lon.mean()
    latbegin = lat[lat.index[0]]
    longbegin = lon[lon.index[0]]
    latend = lat[lat.index[-1]]
    longend = lon[lon.index[-1]]

    coordinates = zip(lat,lon)

    scoordinates = "["

    for x,y in coordinates:
        scoordinates += """[{x},{y}],
        """.format(
            x=x,
            y=y
            )

    scoordinates += "]"

    script = """
    <script>

    var streets = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/streets-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),


    satellite = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/satellite-v9',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    outdoors = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/outdoors-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    );



    var mymap = L.map('map_canvas', {{
              center: [{latmean}, {lonmean}],
              zoom: 13,
              layers: [streets, satellite]
              }}).setView([{latmean},{lonmean}], 13);

    var navionics = new JNC.Leaflet.NavionicsOverlay({{
         navKey: 'Navionics_webapi_03205',
         chartType: JNC.NAVIONICS_CHARTS.NAUTICAL,
         isTransparent: true,
         zIndex: 1
    }});


   var osmUrl2='http://tiles.openseamap.org/seamark/{{z}}/{{x}}/{{y}}.png';
   var osmUrl='http://{{s}}.tile.openstreetmap.org/{{z}}/{{x}}/{{y}}.png';


    //create two TileLayer
    var nautical=new L.TileLayer(osmUrl,{{
             maxZoom:18}});


    L.control.layers({{
        "Streets": streets,
        "Satellite": satellite,
        "Outdoors": outdoors,
        "Nautical": nautical,
    }},{{
        "Navionics":navionics,
    }}).addTo(mymap);

    var marker = L.marker([{latbegin}, {longbegin}]).addTo(mymap);
    marker.bindPopup("<b>Start</b>");
    var emarker = new L.marker([{latend}, {longend}]).addTo(mymap);
    emarker.bindPopup("<b>End</b>");

    var latlongs = {scoordinates}
    var polyline = L.polyline(latlongs, {{color:'red'}}).addTo(mymap)
    mymap.fitBounds(polyline.getBounds())

    </script>
      """.format(
          latmean=latmean,
          lonmean=lonmean,
          latbegin = latbegin,
          latend=latend,
          longbegin=longbegin,
          longend=longend,
          scoordinates=scoordinates,
          )

    div = """
    <div id="map_canvas" style="width: 100%; height: 100%; min-height: 100vh"><p>&nbsp;</p></div>
    """



    return script,div

def leaflet_chart_video(lat,lon,name=""):
    if not len(lat) or not len(lon): # pragma: no cover
        return [0,"invalid coordinate data"]


    # Throw out 0,0
    df = pd.DataFrame({
        'lat':lat,
        'lon':lon
        })

    df = df.replace(0,np.nan)
    df = df.loc[(df!=0).any(axis=1)]
    df.fillna(method='bfill',axis=0,inplace=True)
    df.fillna(method='ffill',axis=0,inplace=True)
    lat = df['lat']
    lon = df['lon']
    if lat.empty or lon.empty: # pragma: no cover
        return [0,"invalid coordinate data"]

    latmean = lat.mean()
    lonmean = lon.mean()
    latbegin = lat[lat.index[0]]
    longbegin = lon[lon.index[0]]
    latend = lat[lat.index[-1]]
    longend = lon[lon.index[-1]]

    coordinates = zip(lat,lon)

    scoordinates = "["

    for x,y in coordinates:
        scoordinates += """[{x},{y}],
        """.format(
            x=x,
            y=y
            )

    scoordinates += "]"

    script = """


    var streets = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/streets-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    satellite = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/satellite-v9',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    ),

    outdoors = L.tileLayer(
    'https://api.mapbox.com/styles/v1/{{id}}/tiles/{{z}}/{{x}}/{{y}}?access_token={{accessToken}}', {{
    attribution: '© <a href="https://www.mapbox.com/about/maps/">Mapbox</a> © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> <strong><a href="https://www.mapbox.com/map-feedback/" target="_blank">Improve this map</a></strong>',
    tileSize: 512,
    maxZoom: 18,
    zoomOffset: -1,
    id: 'mapbox/outdoors-v11',
    accessToken: 'pk.eyJ1Ijoic2FuZGVycm9vc2VuZGFhbCIsImEiOiJjajY3aTRkeWQwNmx6MzJvMTN3andlcnBlIn0.MFG8Xt0kDeSA9j7puZQ9hA'
}}
    );



    var mymap = L.map('map_canvas', {{
              center: [{latmean}, {lonmean}],
              zoom: 13,
              layers: [streets, satellite]
              }}).setView([{latmean},{lonmean}], 13);

    var navionics = new JNC.Leaflet.NavionicsOverlay({{
         navKey: 'Navionics_webapi_03205',
         chartType: JNC.NAVIONICS_CHARTS.NAUTICAL,
         isTransparent: true,
         zIndex: 1
    }});


   var osmUrl2='http://tiles.openseamap.org/seamark/{{z}}/{{x}}/{{y}}.png';
   var osmUrl='http://{{s}}.tile.openstreetmap.org/{{z}}/{{x}}/{{y}}.png';


    //create two TileLayer
    var nautical=new L.TileLayer(osmUrl,{{
             maxZoom:18}});


    L.control.layers({{
        "Streets": streets,
        "Satellite": satellite,
        "Outdoors": outdoors,
        "Nautical": nautical,
    }},{{
        "Navionics":navionics,
    }},
    {{
    position:'topleft'
    }}).addTo(mymap);

    var marker = L.marker([{latbegin}, {longbegin}]).addTo(mymap);
    marker.bindPopup("<b>Start</b>");

    var latlongs = {scoordinates}
    var polyline = L.polyline(latlongs, {{color:'red'}}).addTo(mymap)
    mymap.fitBounds(polyline.getBounds())

      """.format(
          latmean=latmean,
          lonmean=lonmean,
          latbegin = latbegin,
          latend=latend,
          longbegin=longbegin,
          longend=longend,
          scoordinates=scoordinates,
          )

    div = """
    <div id="map_canvas" style="width: 640px; height: 390px;"><p>&nbsp;</p></div>
    """



    return script,div


def interactive_agegroupcpchart(age,normalized=False):
    durations = [1,4,30,60]
    distances = [100,500,1000,2000,5000,6000,10000,21097,42195]

    fhduration = []
    fhpower = []

    for distance in distances:
        worldclasspower = c2stuff.getagegrouprecord(
            age,
            sex='female',
            distance=distance,
            weightcategory='hwt'
        )
        velo = (worldclasspower/2.8)**(1./3.)
        try: # pragma: no cover
            duration = distance/velo
            fhduration.append(duration)
            fhpower.append(worldclasspower)
        except ZeroDivisionError:
            pass
    for duration in durations:
        worldclasspower = c2stuff.getagegrouprecord(
            age,
            sex='female',
            duration=duration,
            weightcategory='hwt'
            )
        try:
            velo = (worldclasspower/2.8)**(1./3.)
            distance = int(60*duration*velo)
            fhduration.append(60.*duration)
            fhpower.append(worldclasspower)
        except ValueError: # pragma: no cover
            pass

    flduration = []
    flpower = []

    for distance in distances:
        worldclasspower = c2stuff.getagegrouprecord(
            age,
            sex='female',
            distance=distance,
            weightcategory='lwt'
        )
        velo = (worldclasspower/2.8)**(1./3.)
        try: # pragma: no cover
            duration = distance/velo
            flduration.append(duration)
            flpower.append(worldclasspower)
        except ZeroDivisionError:
            pass
    for duration in durations:
        worldclasspower = c2stuff.getagegrouprecord(
            age,
            sex='female',
            duration=duration,
            weightcategory='lwt'
            )
        try:
            velo = (worldclasspower/2.8)**(1./3.)
            distance = int(60*duration*velo)
            flduration.append(60.*duration)
            flpower.append(worldclasspower)
        except ValueError: # pragma: no cover
            pass

    mlduration = []
    mlpower = []

    for distance in distances:
        worldclasspower = c2stuff.getagegrouprecord(
            age,
            sex='male',
            distance=distance,
            weightcategory='lwt'
        )
        velo = (worldclasspower/2.8)**(1./3.)
        try: # pragma: no cover
            duration = distance/velo
            mlduration.append(duration)
            mlpower.append(worldclasspower)
        except ZeroDivisionError:
            mlduration.append(duration)
            mlpower.append(np.nan)
    for duration in durations:
        worldclasspower = c2stuff.getagegrouprecord(
            age,
            sex='male',
            duration=duration,
            weightcategory='lwt'
            )
        try:
            velo = (worldclasspower/2.8)**(1./3.)
            distance = int(60*duration*velo)
            mlduration.append(60.*duration)
            mlpower.append(worldclasspower)
        except ValueError: # pragma: no cover
            mlduration.append(60.*duration)
            mlpower.append(np.nan)


    mhduration = []
    mhpower = []

    for distance in distances:
        worldclasspower = c2stuff.getagegrouprecord(
            age,
            sex='male',
            distance=distance,
            weightcategory='hwt'
        )
        velo = (worldclasspower/2.8)**(1./3.)
        try: # pragma: no cover
            duration = distance/velo
            mhduration.append(duration)
            mhpower.append(worldclasspower)
        except ZeroDivisionError:
            mhduration.append(duration)
            mhpower.append(np.nan)
    for duration in durations:
        worldclasspower = c2stuff.getagegrouprecord(
            age,
            sex='male',
            duration=duration,
            weightcategory='hwt'
            )
        try:
            velo = (worldclasspower/2.8)**(1./3.)
            distance = int(60*duration*velo)
            mhduration.append(60.*duration)
            mhpower.append(worldclasspower)
        except ValueError: # pragma: no cover
            mhduration.append(60.*duration)
            mhpower.append(np.nan)



    fitfunc = lambda pars,x: pars[0]/(1+(x/pars[2])) + pars[1]/(1+(x/pars[3]))
    errfunc = lambda pars,x,y: fitfunc(pars,x)-y

    #    p0 = [500,350,10,8000]

    # fitting WC data to three parameter CP model
    if len(fhduration)>=4:
        p1fh, success = optimize.leastsq(errfunc, p0[:],
                                       args = (fhduration,fhpower))
    else: # pragma: no cover
        p1fh = None

    # fitting WC data to three parameter CP model
    if len(flduration)>=4:
        p1fl, success = optimize.leastsq(errfunc, p0[:],
                                       args = (flduration,flpower))
    else: # pragma: no cover
        p1fl = None

    # fitting WC data to three parameter CP model
    if len(mlduration)>=4:
        p1ml, success = optimize.leastsq(errfunc, p0[:],
                                       args = (mlduration,mlpower))
    else: # pragma: no cover
        p1ml = None

    if len(mhduration)>=4:
        p1mh, success = optimize.leastsq(errfunc, p0[:],
                                       args = (mhduration,mhpower))
    else: # pragma: no cover
        p1mh = None

    fitt = pd.Series(10**(4*np.arange(100)/100.))

    fitpowerfh = fitfunc(p1fh,fitt)
    fitpowerfl = fitfunc(p1fl,fitt)
    fitpowerml = fitfunc(p1ml,fitt)
    fitpowermh = fitfunc(p1mh,fitt)

    if normalized:
        facfh = fitfunc(p1fh,60)
        facfl = fitfunc(p1fl,60)
        facml = fitfunc(p1ml,60)
        facmh = fitfunc(p1mh,60)
        fitpowerfh /= facfh
        fitpowerfl /= facfl
        fitpowermh /= facmh
        fitpowerml /= facml
        fhpower /= facfh
        flpower /= facfl
        mlpower /= facml
        mhpower /= facmh



    sourcemh = ColumnDataSource(
        data = dict(
            mhduration = mhduration,
            mhpower = mhpower,
            )
        )

    sourcefl = ColumnDataSource(
        data = dict(
            flduration = flduration,
            flpower = flpower,
            )
        )

    sourcefh = ColumnDataSource(
        data = dict(
            fhduration = fhduration,
            fhpower = fhpower,
            )
        )

    sourceml = ColumnDataSource(
        data = dict(
            mlduration = mlduration,
            mlpower = mlpower,
            )
        )

    sourcefit = ColumnDataSource(
        data = dict(
            duration = fitt,
            fitpowerfh = fitpowerfh,
            fitpowerfl = fitpowerfl,
            fitpowerml = fitpowerml,
            fitpowermh = fitpowermh,
        )
    )

    x_axis_type = 'log'
    y_axis_type = 'linear'

    TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'

    plot = Figure(plot_width=900,x_axis_type=x_axis_type,
                  tools=TOOLS)
    plot.sizing_mode = 'stretch_both'

    plot.line('duration','fitpowerfh',source=sourcefit,
              legend_label='Female HW',color='blue')
    plot.line('duration','fitpowerfl',source=sourcefit,
              legend_label='Female LW',color='red')

    plot.line('duration','fitpowerml',source=sourcefit,
              legend_label='Male LW',color='green')

    plot.line('duration','fitpowermh',source=sourcefit,
              legend_label='Male HW',color='orange')


    plot.circle('flduration','flpower',source=sourcefl,
                fill_color='red',size=15)

    plot.circle('fhduration','fhpower',source=sourcefh,
                fill_color='blue',size=15)

    plot.circle('mlduration','mlpower',source=sourceml,
                fill_color='green',size=15)

    plot.circle('mhduration','mhpower',source=sourcemh,
                fill_color='orange',size=15)

    plot.title.text = 'age '+str(age)

    plot.xaxis.axis_label = "Duration (seconds)"
    if normalized:
        plot.yaxis.axis_label = "Power (normalized)"
    else:
        plot.yaxis.axis_label = "Power (W)"

    script,div = components(plot)

    return script,div


def interactive_otwcpchart(powerdf,promember=0,rowername="",r=None,cpfit='data',
                           title='',type='water',
                           wcpower=[],wcdurations=[],cpoverlay=False):

    powerdf2 = powerdf[~(powerdf == 0).any(axis=1)].copy()
    # plot tools
    if (promember==1): # pragma: no cover
        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
    else:
        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'


    x_axis_type = 'log'
    y_axis_type = 'linear'

    deltas = powerdf2['Delta'].apply(lambda x: timedeltaconv(x))
    powerdf2['ftime'] = deltas.apply(lambda x:strfdelta(x))
    powerdf2['Deltaminutes'] = powerdf2['Delta']/60.


    source = ColumnDataSource(
        data = powerdf2
        )



    # there is no Paul's law for OTW

    thesecs = powerdf2['Delta']
    theavpower = powerdf2['CP']


    p1,fitt,fitpower,ratio = datautils.cpfit(powerdf2)
    if cpfit == 'automatic' and r is not None:
        if type == 'water':
            p1 = [r.p0,r.p1,r.p2,r.p3]
            ratio = r.cpratio
        elif type == 'erg' : # pragma: no cover
            p1 = [r.ep0,r.ep1,r.ep2,r.ep3]
            ratio = r.ecpratio

        fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
        fitpower = fitfunc(p1,fitt)

    message = ""
    #if len(fitpower[fitpower<0]) > 0:
        # message = "CP model fit didn't give correct results"


    deltas = fitt.apply(lambda x: timedeltaconv(x))
    ftime = niceformat(deltas)
    workouts = powerdf2['workout']
    urls = powerdf2['url']

    # add world class
    wcpower = pd.Series(wcpower,dtype='float')
    wcdurations = pd.Series(wcdurations,dtype='float')


    # fitting WC data to three parameter CP model
    if len(wcdurations)>=4: # pragma: no cover
        fitfunc = lambda pars,x: pars[0]/(1+(x/pars[2])) + pars[1]/(1+(x/pars[3]))
        errfunc = lambda pars,x,y: fitfunc(pars,x)-y
        p1wc, success = optimize.leastsq(errfunc, p0[:],
                                       args = (wcdurations,wcpower))
    else:
        p1wc = None

    if p1wc is not None and cpoverlay: # pragma: no cover
        fitpowerwc = fitfunc(p1wc,fitt)
        fitpowerexcellent = 0.7*fitfunc(p1wc,fitt)
        fitpowergood = 0.6*fitfunc(p1wc,fitt)
        fitpowerfair = 0.5*fitfunc(p1wc,fitt)
        fitpoweraverage = 0.4*fitfunc(p1wc,fitt)

    else:
        fitpowerwc = 0*fitpower
        fitpowerexcellent = 0*fitpower
        fitpowergood = 0*fitpower
        fitpowerfair = 0*fitpower
        fitpoweraverage = 0*fitpower


    sourcecomplex = ColumnDataSource(
        data = dict(
            CP = fitpower,
            CPmax = ratio*fitpower,
            duration = fitt/60.,
            ftime = ftime,
#            workout = workouts,
            fitpowerwc = fitpowerwc,
            fitpowerexcellent = fitpowerexcellent,
            fitpowergood = fitpowergood,
            fitpowerfair = fitpowerfair,
            fitpoweraverage = fitpoweraverage,
#            url = urls,
            )
        )

    sourceannot= ColumnDataSource(
        data = dict(
            workout = workouts,
            url = urls,
        )
    )

        # making the plot
    plot = Figure(tools=TOOLS,x_axis_type=x_axis_type,
                  plot_width=900,
                  toolbar_location="above",
                  toolbar_sticky=False)

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.sizing_mode = 'scale_both'



    plot.image_url([watermarkurl],1.8*max(thesecs),watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   y_range_name = "watermark",
                   )

    plot.circle('Deltaminutes','CP',source=source,fill_color='red',size=15,
                legend_label='Power Data')
    plot.xaxis.axis_label = "Duration (minutes)"
    plot.yaxis.axis_label = "Power (W)"

    plot.y_range = Range1d(0,1.5*max(theavpower))
    plot.x_range = Range1d(0.5*min(thesecs)/60.,2*max(thesecs)/60.)
    plot.legend.orientation = "vertical"
    if not title:
        title = "Critical Power for "+rowername
    plot.title.text = title

    xaxis = plot.select(dict(type=Axis, layout="below"))[0]
    xaxis.formatter = PrintfTickFormatter()


    hover = plot.select(dict(type=HoverTool))

    hover.tooltips = OrderedDict([
        ('Duration ','@ftime'),
        ('Power (W)','@CP{int}'),
        ('Power (W) upper','@CPmax{int}'),
        ('Workout','@workout'),
        ('World Class','@fitpowerwc{int}')
        ])

    hover.mode = 'mouse'

    taptool = plot.select(type=TapTool)
    taptool.callback = OpenURL(url='@url')

    plot.line('duration','CP',source=sourcecomplex,legend_label="CP Model",
              color='green')

    plot.line('duration','CPmax',source=sourcecomplex,legend_label="CP Model",
              color='red')

    if p1wc is not None: # pragma: no cover
        plot.line('duration','fitpowerwc',source=sourcecomplex,
                  legend_label="Gold Medal Standard",
                  color='darkgoldenrod',line_dash='dotted')

        plot.line('duration','fitpowerexcellent',source=sourcecomplex,
                  legend_label="90% percentile",
                  color='goldenrod',line_dash='dotted')

        plot.line('duration','fitpowergood',source=sourcecomplex,
                  legend_label="75% percentile",
                  color='sandybrown',line_dash='dotted')

        plot.line('duration','fitpowerfair',source=sourcecomplex,
                  legend_label="50% percentile",
                  color='rosybrown',line_dash='dotted')

        plot.line('duration','fitpoweraverage',source=sourcecomplex,
                  legend_label="25% percentile",
                  color='tan',line_dash='dotted')

    script, div = components(plot)

    return [script,div,p1,ratio,message]

def interactive_agegroup_plot(df,distance=2000,duration=None,
                              sex='male',weightcategory='hwt'):

    if df.empty:
        return '',''

    age = df['age']
    power = df['power']
    name = df['name']
    season = df['season']

    if duration: # pragma: no cover
        duration2 = int(duration/60.)
        plottitle = sex+' '+weightcategory+' %s min' % duration2
    else:
        plottitle = sex+' '+weightcategory+' %s m' % distance

#    poly_coefficients = np.polyfit(age,power,6)

    age2 = np.linspace(11,95)
#    poly_vals = np.polyval(poly_coefficients,age2)
#    poly_vals = 0.5*(np.abs(poly_vals)+poly_vals)

    fitfunc = lambda pars, x: np.abs(pars[0])*(1-x/max(120,pars[1]))-np.abs(pars[2])*np.exp(-x/np.abs(pars[3]))+np.abs(pars[4])*(np.sin(np.pi*x/max(50,pars[5])))
    errfunc = lambda pars, x,y: fitfunc(pars,x)-y

    p0age = [700,120,700,10,100,100]

    p1, success = optimize.leastsq(errfunc,p0age[:],
                                   args = (age,power))

    expo_vals = fitfunc(p1, age2)
    expo_vals = 0.5*(np.abs(expo_vals)+expo_vals)


    source = ColumnDataSource(
        data = dict(
            age = age,
            power = power,
            #age2 = age2,
            #expo_vals = expo_vals,
            season = season,
            name=name,
            )
    )

    sourcefit = ColumnDataSource(
        data = dict(
            age2 = age2,
            expo_vals = expo_vals,
        )
    )

    TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'

    plot = Figure(tools=TOOLS,plot_width=900)
    plot.sizing_mode='stretch_both'
    plot.circle('age','power',source=source,fill_color='red',size=15,
                legend_label='World Record')

    plot.line('age2','expo_vals',source=sourcefit)
    plot.xaxis.axis_label = "Age"
    plot.yaxis.axis_label = "Concept2 power"
    plot.title.text = plottitle

    hover = plot.select(dict(type=HoverTool))

    hover.tooltips = OrderedDict([
        ('Name ','@name'),
        ('Season ','@season'),
        ])

    hover.mode = 'mouse'

    script,div = components(plot)

    return script,div

def interactive_cpchart(rower,thedistances,thesecs,theavpower,
                        theworkouts,promember=0,
                        wcpower=[],wcdurations=[]):

    message = 0
    # plot tools
    if (promember==1):
        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
    else: # pragma: no cover
        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'


    x_axis_type = 'log'
    y_axis_type = 'linear'

    thesecs = pd.Series(thesecs)

    velo = thedistances/thesecs
    p = pd.Series(500./velo)

    p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x))

    source = ColumnDataSource(
        data = dict(
            dist = thedistances,
            duration = thesecs,
            spm = 0*theavpower,
            tim = niceformat(
                thesecs.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
                ),

            power = theavpower,
            fpace = nicepaceformat(p2),
            )
        )

    # fitting the data to Paul
    if len(thedistances)>=2:
        paulslope, paulintercept,r,p,stderr = linregress(np.log10(thedistances),p)
    else: # pragma: no cover
        paulslope = 5.0/np.log10(2.0)
        paulintercept = p[0]-paulslope*np.log10(thedistances[0])


    fitx = pd.Series(np.arange(100)*2*max(np.log10(thedistances))/100.)

    fitp = paulslope*fitx+paulintercept

    fitvelo = 500./fitp
    fitpower = 2.8*(fitvelo**3)
    fitt = 10**fitx/fitvelo
    fitp2 = fitp.fillna(method='ffill').apply(lambda x: timedeltaconv(x))


    sourcepaul = ColumnDataSource(
        data = dict(
            dist = 10**fitx,
            duration = fitt,
            power = fitpower,
            spm = 0*fitpower,
            tim = niceformat(
                fitt.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
                ),
            fpace = nicepaceformat(fitp2),
            )
        )


    fitfunc = lambda pars,x: pars[0]/(1+(x/pars[2])) + pars[1]/(1+(x/pars[3]))
    errfunc = lambda pars,x,y: fitfunc(pars,x)-y

    # p0 = [500,350,10,8000]
    wcpower = pd.Series(wcpower,dtype='float')
    wcdurations = pd.Series(wcdurations,dtype='float')

    # fitting WC data to three parameter CP model
    if len(wcdurations)>=4:
        p1wc, success = optimize.leastsq(errfunc, p0[:],
                                       args = (wcdurations,wcpower))
    else: # pragma: no cover
        p1wc = None

    # fitting the data to three parameter CP model

    success = 0
    p1 = p0
    if len(thesecs)>=4:
        try:
            p1, success = optimize.leastsq(errfunc, p0[:], args = (thesecs,theavpower))
        except (RuntimeError,RuntimeWarning): # pragma: no cover
            factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
            p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
            success = 0
    else: # pragma: no cover
        factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
        p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
        success = 0


    # Get stayer score
    if success == 1: # pragma: no cover
        power1min = fitfunc(p1,60.)
        power4min = fitfunc(p1,240.)
        power6min = fitfunc(p1,360.)
        power30min = fitfunc(p1,1800.)
        power1h = fitfunc(p1,3600.)
        power10sec = fitfunc(p1,10.)
        r10sec4min = 100.*power10sec/power4min
        r1h4min = 100.*power1h/power4min
        r1min6min = 100.*power1min/power6min
        r30min6min = 100.*power30min/power6min

        combined = r1h4min-0.2*(r10sec4min-100)
        combined2 = r30min6min-1.5*(r1min6min-100)

        dataset = pd.read_csv('static/stats/combined_set.csv')
        dataset2 = pd.read_csv('static/stats/combined_set6min.csv')

        stayerscore = int(percentileofscore(dataset['combined'],combined))
        stayerscore2 = int(percentileofscore(dataset2['combined'],combined2))
    else:
        stayerscore = None
        stayerscore2 = None


    fitt = pd.Series(10**(4*np.arange(100)/100.))

    fitpower = fitfunc(p1,fitt)
    if p1wc is not None:
        fitpowerwc = 0.95*fitfunc(p1wc,fitt)
        fitpowerexcellent = 0.7*fitfunc(p1wc,fitt)
        fitpowergood = 0.6*fitfunc(p1wc,fitt)
        fitpowerfair = 0.5*fitfunc(p1wc,fitt)
        fitpoweraverage = 0.4*fitfunc(p1wc,fitt)

    else: # pragma: no cover
        fitpowerwc = 0*fitpower
        fitpowerexcellent = 0*fitpower
        fitpowergood = 0*fitpower
        fitpowerfair = 0*fitpower
        fitpoweraverage = 0*fitpower



    message = ""
    if len(fitpower[fitpower<0]) > 0: # pragma: no cover
        message = "CP model fit didn't give correct results"

    fitvelo = (fitpower/2.8)**(1./3.)
    fitdist = fitt*fitvelo
    fitp = 500./fitvelo
    fitp2 = fitp.fillna(method='ffill').apply(lambda x: timedeltaconv(x))

    sourcecomplex = ColumnDataSource(
        data = dict(
            dist = fitdist,
            duration = fitt,
            tim = niceformat(
                fitt.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
                ),
            spm = 0*fitpower,
            power = fitpower,
            fitpowerwc = fitpowerwc,
            fitpowerexcellent = fitpowerexcellent,
            fitpowergood = fitpowergood,
            fitpowerfair = fitpowerfair,
            fitpoweraverage = fitpoweraverage,
            fpace = nicepaceformat(fitp2),
            )
        )



    # making the plot
    plot = Figure(tools=TOOLS,x_axis_type=x_axis_type,
                  plot_width=900,
                  toolbar_location="above",
                  toolbar_sticky=False)

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.sizing_mode = 'scale_both'


    plot.image_url([watermarkurl],1.8*max(thesecs),watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   y_range_name = "watermark",
                   )

    plot.circle('duration','power',source=source,fill_color='red',size=15,
                legend_label='Power')
    plot.xaxis.axis_label = "Duration (seconds)"
    plot.yaxis.axis_label = "Power (W)"

    if stayerscore is not None: # pragma: no cover
        plot.add_layout(
            Label(x=100,y=100,x_units='screen',y_units='screen',
                  text='Stayer Score '+str(stayerscore)+'%',
                  background_fill_alpha=0.7,
                  background_fill_color='white',
                  text_color='black')
            )
#        plot.add_layout(
#            Label(x=100,y=120,x_units='screen',y_units='screen',
#                  text='Stayer Score (6min) '+str(stayerscore2)+'%',
#                  background_fill_alpha=0.7,
#                  background_fill_color='white',
#                  text_color='black')
#            )

    cpdata = dataprep.fetchcperg(rower, theworkouts)


    if cpdata.empty: # pragma: no cover
        message = 'Calculations are running in the background. Please refresh this page to see updated results'
        return ['','',paulslope,paulintercept,p1,message,p1wc]

    velo = cpdata['distance']/cpdata['delta']

    p = 500./velo

    p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x))

    source2 = ColumnDataSource(
        data = dict(
            duration = cpdata['delta'],
            power = cpdata['cp'],
            tim = niceformat(
                cpdata['delta'].fillna(method='ffill').apply(lambda x: timedeltaconv(x))
                ),
            dist = cpdata['distance'],
            pace = nicepaceformat(p2),
            )
        )

    plot.circle('duration','power',source=source2,
                fill_color='blue',size=3,
                legend_label = 'Power from segments')

    hover = plot.select(dict(type=HoverTool))

    hover.tooltips = OrderedDict([
        ('Duration ','@tim'),
        ('Power (W)','@power{int}'),
        ('Distance (m)','@dist{int}'),
        ('Pace (/500m)','@fpace'),
        ])

    hover.mode = 'mouse'

    plot.y_range = Range1d(0,1.5*max(theavpower))
    plot.x_range = Range1d(1,2*max(thesecs))
    plot.legend.orientation = "vertical"


    plot.line('duration','power',source=sourcepaul,legend_label="Paul's Law")
    plot.line('duration','power',source=sourcecomplex,legend_label="CP Model",
              color='green')
    if p1wc is not None:
        plot.line('duration','fitpowerwc',source=sourcecomplex,
                  legend_label="World Class",
                  color='Maroon',line_dash='dotted')

        plot.line('duration','fitpowerexcellent',source=sourcecomplex,
                  legend_label="90% percentile",
                  color='Purple',line_dash='dotted')

        plot.line('duration','fitpowergood',source=sourcecomplex,
                  legend_label="75% percentile",
                  color='Olive',line_dash='dotted')

        plot.line('duration','fitpowerfair',source=sourcecomplex,
                  legend_label="50% percentile",
                  color='Gray',line_dash='dotted')

        plot.line('duration','fitpoweraverage',source=sourcecomplex,
                  legend_label="25% percentile",
                  color='SkyBlue',line_dash='dotted')


    script, div = components(plot)


    return [script,div,paulslope,paulintercept,p1,message,p1wc]

def interactive_windchart(id=0,promember=0):
    # check if valid ID exists (workout exists)
    row = Workout.objects.get(id=id)
    # g = GraphImage.objects.filter(workout=row).order_by("-creationdatetime")

    f1 = row.csvfilename

    # create interactive plot
    plot = Figure(plot_width=400,plot_height=300)

    # get user
    # u = User.objects.get(id=row.user.id)
    r = row.user
    u = r.user



    rr = rrower(hrmax=r.max,hrut2=r.ut2,
                hrut1=r.ut1,hrat=r.at,
                hrtr=r.tr,hran=r.an,ftp=r.ftp)

    rowdata = rdata(f1,rower=rr)
    if rowdata == 0: # pragma: no cover
        return 0

    try:
        dist = rowdata.df.loc[:,'cum_dist']
    except KeyError:
        return ['','No Data Found']

    try: # pragma: no cover
        vwind = rowdata.df.loc[:,'vwind']
        winddirection = rowdata.df.loc[:,'winddirection']
        bearing = rowdata.df.loc[:,'bearing']
    except KeyError:
        rowdata.add_wind(0,0)
        rowdata.add_bearing()
        vwind = rowdata.df.loc[:,'vwind']
        winddirection = rowdata.df.loc[:,'winddirection']
        bearing = rowdata.df.loc[:,'winddirection']
        rowdata.write_csv(f1,gzip=True)
        dataprep.update_strokedata(id,rowdata.df)

    winddirection = winddirection % 360
    winddirection = (winddirection + 360) % 360

    tw = tailwind(bearing,vwind,1.0*winddirection)


    source = ColumnDataSource(
        data = dict(
            dist=dist,
            vwind=vwind,
            tw=tw,
            winddirection=winddirection,
            )
        )

    # plot tools
    if (promember==1):
        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair'
    else: # pragma: no cover
        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,crosshair'



    # making the plot
    plot = Figure(tools=TOOLS,plot_width=400,height=500,
#                 toolbar_location="below",
                  toolbar_sticky=False,
                  )
    plot.line('dist','vwind',source=source,legend_label="Wind Speed (m/s)")
    plot.line('dist','tw',source=source,legend_label="Tail (+)/Head (-) Wind (m/s)",color='black')
    plot.title.text = row.name
    #    plot.title.text_font_size=value("1.0em")
    plot.title.text_font="1.0em"
    plot.xaxis.axis_label = "Distance (m)"
    plot.yaxis.axis_label = "Wind Speed (m/s)"
    plot.y_range = Range1d(-7,7)
    plot.sizing_mode = 'stretch_both'


    plot.extra_y_ranges = {"winddirection": Range1d(start=0,end=360)}
    plot.line('dist','winddirection',source=source,
              legend_label='Wind Direction',color="red",
              y_range_name="winddirection")
    plot.add_layout(LinearAxis(y_range_name="winddirection",axis_label="Wind Direction (degree)"),'right')


    script, div = components(plot)

    return [script,div]

def interactive_streamchart(id=0,promember=0):
    # check if valid ID exists (workout exists)
    row = Workout.objects.get(id=id)
    # g = GraphImage.objects.filter(workout=row).order_by("-creationdatetime")

    f1 = row.csvfilename

    # create interactive plot
    plot = Figure(plot_width=400,
                  )
    # get user
    # u = User.objects.get(id=row.user.id)
    r = row.user
    u = r.user



    rr = rrower(hrmax=r.max,hrut2=r.ut2,
                hrut1=r.ut1,hrat=r.at,
                hrtr=r.tr,hran=r.an,ftp=r.ftp)

    rowdata = rdata(f1,rower=rr)
    if rowdata == 0: # pragma: no cover
        return "","No Valid Data Available"

    try:
        dist = rowdata.df.loc[:,'cum_dist']
    except KeyError:
        return ['','No Data found']

    try:
        vstream = rowdata.df.loc[:,'vstream']
    except KeyError:
        rowdata.add_stream(0)
        vstream = rowdata.df.loc[:,'vstream']
        rowdata.write_csv(f1,gzip=True)
        dataprep.update_strokedata(id,rowdata.df)

    # plot tools
    if (promember==1):
        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair'
    else: # pragma: no cover
        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,crosshair'



    # making the plot
    plot = Figure(tools=TOOLS,plot_width=400,height=500,
#                 toolbar_location="below",
                  toolbar_sticky=False,
                  )
    plot.line(dist,vstream,legend_label="River Stream Velocity (m/s)")
    plot.title.text = row.name
    plot.title.text_font_size=value("1.0em")
    plot.xaxis.axis_label = "Distance (m)"
    plot.yaxis.axis_label = "River Current (m/s)"
    plot.y_range = Range1d(-2,2)
    plot.sizing_mode = 'stretch_both'


    script, div = components(plot)

    return [script,div]

def interactive_chart(id=0,promember=0,intervaldata = {}):

    # Add hover to this comma-separated string and see what changes
    if (promember==1):
        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
    else:
        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'


    columns = ['time','pace','hr','fpace','ftime','spm']
    datadf = dataprep.getsmallrowdata_db(columns,ids=[id])

    datadf.dropna(axis=0,how='any',inplace=True)

    row = Workout.objects.get(id=id)
    if datadf.empty:
        return "","No Valid Data Available"

    try:
        spm = datadf['spm']
    except KeyError: # pragma: no cover
        datadf['spm'] = 0

    try:
        pace = datadf['pace']
    except KeyError: # pragma: no cover
        datadf['pace'] = 0

    source = ColumnDataSource(
        datadf
        )

    plot = Figure(x_axis_type="datetime",y_axis_type="datetime",
                  plot_width=400,
                  plot_height=400,
                  toolbar_sticky=False,
                  tools=TOOLS)

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}

    plot.image_url([watermarkurl],0.01,0.99,
                   0.5*watermarkw,0.5*watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor='top_left',
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    plot.line('time','pace',source=source,legend_label="Pace",name="pace")
    plot.title.text = row.name
    plot.title.text_font_size=value("1.0em")
    plot.sizing_mode = 'stretch_both'
    plot.xaxis.axis_label = "Time"
    plot.yaxis.axis_label = "Pace (/500m)"
    plot.xaxis[0].formatter = DatetimeTickFormatter(
            hours = ["%H"],
            minutes = ["%M"],
            seconds = ["%S"],
            days = ["0"],
            months = [""],
            years = [""]
        )
    plot.yaxis[0].formatter = DatetimeTickFormatter(
            seconds = ["%S"],
            minutes = ["%M"]
        )
    ymax = 90.
    ymin = 150.

    if row.workouttype == 'water':
        ymax = 90.
        ymin = 210.

    plot.y_range = Range1d(1.e3*ymin,1.e3*ymax)


    plot.extra_y_ranges["spmax"] = Range1d(start=10,end=45)
    plot.line('time','spm',source=source,color="red",
              y_range_name="spmax", legend_label="Stroke Rate",name="spm")
    plot.add_layout(LinearAxis(y_range_name="spmax",axis_label="SPM"),'right')

    plot.legend.location = "bottom_right"

    # add shaded bar chart areas
    if intervaldata != {}:
        intervaldf = pd.DataFrame(intervaldata)
        intervaldf['itime'] = intervaldf['itime']*1.e3
        intervaldf['time'] = intervaldf['itime'].cumsum()
        intervaldf['time'] = intervaldf['time'].shift(1)
        intervaldf.loc[0,'time'] = 0
        intervaldf['time_r'] = intervaldf['time'] +intervaldf['itime']
        intervaldf['value'] = 100
        mask = intervaldf['itype'] == 3
        intervaldf.loc[mask,'value'] = 0
        intervaldf['bottom'] = 10

        intervalsource = ColumnDataSource(
            intervaldf
        )

        plot.quad(left='time',top='value',bottom='bottom',
                  right='time_r',source=intervalsource,color='mediumvioletred',
                  y_range_name='spmax',fill_alpha=0.2,line_alpha=0.2)


    hover = plot.select(dict(type=HoverTool))


    hover.tooltips = OrderedDict([
        ('Time','@ftime'),
        ('Pace','@fpace'),
        ('HR','@hr{int}'),
        ('SPM','@spm{1.1}'),
        ])

    hover.mode = 'mouse'
    hover.names = ["spm","pace"]

    script, div = components(plot)

    return [script,div]

def interactive_chart_video(videodata):
    try:
        spm = videodata['spm']
    except KeyError: # pragma: no cover
        return "","No SPM data"

    time = range(len(spm))

    data = zip(time,spm)

    data2 = "["

    for t,s in data:
        data2 += "{x: %s, y: %s}, " % (t,s)

    data2 = data2[:-2] + "]"

    markerpoint = {
        'x': time[0],
        'y': spm[0],
        'r': 10,
    }

    div = """
    <canvas id="myChart">
    </canvas>
    """

    script = """
    var ctx = document.getElementById("myChart").getContext('2d');
    var data = %s

   var myChart = new Chart(ctx, {
        type: 'scatter',
        label: 'SPM',
        animationSteps: 10,
        options: {
            legend: {
                display: false,
            },
            animation: {
                duration: 100,
            },
            scales: {
                yAxes: [{
                    scaleLabel: {
                        display: true,
                        labelString: 'Stroke Rate'
                    }
                }],
                xAxes: [{
                    scaleLabel: {
                        type: 'linear',
                        display: true,
                        labelString: 'Time (seconds)'
                    }
                }],
            }
        },
        data:
        {
            datasets: [
            {
                type: 'bubble',
                label: 'now',
                data: [ %s ],
                backgroundColor: '#36a2eb',
            },
            {
                label: 'spm',
                data: data,
                backgroundColor: "#ff0000",
                borderColor: "#ff0000",
                fill: false,
                borderDash: [0, 0],
                pointRadius: 1,
                pointHoverRadius: 1,
                showLine: true,
                tension: 0,
            },

            ]
        },

    });

    var marker = {
     datapoint: %s ,
     setLatLng: function (LatLng) {
       var lat = LatLng.lat;
       var lng = LatLng.lng;
       this.datapoint = {
          'x': lat,
          'y': lng,
          'r': 10,
       }
       myChart.data.datasets[0].data[0] = this.datapoint;
       myChart.update();
    }
   }
   marker.setLatLng({
   'lat': data[0]['x'],
   'lng': data[0]['y']
   })

    """ % (data2, markerpoint, markerpoint)

    return [script,div]



def interactive_multiflex(datadf,xparam,yparam,groupby,extratitle='',
                          ploterrorbars=False,
                          title=None,binsize=1,colorlegend=[],
                          spmmin=0,spmmax=0,workmin=0,workmax=0):

    if datadf.empty: # pragma: no cover
        return ['','<p>No non-zero data in selection</p>']

    if xparam == 'workoutid': # pragma: no cover
        xparamname = 'Workout'
    else:
        xparamname = axlabels[xparam]

    if yparam == 'workoutid': # pragma: no cover
        yparamname = 'Workout'
    else:
        yparamname = axlabels[yparam]

    if groupby == 'workoutid': # pragma: no cover
        groupname = 'Workout'
    elif groupby == 'date': # pragma: no cover
        groupname = 'Date'
    else:
        groupname = axlabels[groupby]


    if title==None:
        title = '{y} vs {x} grouped by {gr}'.format(
            x = xparamname,
            y = yparamname,
            gr = groupname,
            )

    if xparam == 'cumdist': # pragma: no cover
        res = make_cumvalues(datadf[xparam])
        datadf[xparam] = res[0]
    if xparam=='distance': # pragma: no cover
        xaxmax = datadf[xparam].max()
        xaxmin = datadf[xparam].min()
    elif xparam=='time': # pragma: no cover
        tseconds = datadf.loc[:,'time']
        xaxmax = tseconds.max()
        xaxmin = 0
    elif xparam == 'workoutid': # pragma: no cover
        xaxmax = datadf[xparam].max()-5
        xaxmin = datadf[xparam].min()+5
    else:
        xaxmax = yaxmaxima[xparam]
        xaxmin = yaxminima[xparam]

    if yparam=='distance':# pragma: no cover
        yaxmax = datadf[yparam].max()
        yaxmin = datadf[yparam].min()
    elif yparam=='time': # pragma: no cover
        tseconds = datadf.loc[:,'time']
        yaxmax = tseconds.max()
        yaxmin = 0
    elif yparam == 'workoutid': # pragma: no cover
        yaxmax = datadf[yparam].max()-5
        yaxmin = datadf[yparam].min()+5
    else:
        yaxmax = yaxmaxima[yparam]
        yaxmin = yaxminima[yparam]

    x_axis_type = 'linear'
    y_axis_type = 'linear'
    if xparam == 'time': # pragma: no cover
        x_axis_type = 'datetime'
    if yparam == 'pace':
        y_axis_type = 'datetime'

    datadf.index.names = ['index']

    source = ColumnDataSource(
        datadf,
        )


    TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap'

    if groupby != 'date':
        hover = HoverTool(names=['data'],
                          tooltips = [
                              (groupby,'@groupval{1.1}'),
                              (xparamname,'@x{1.1}'),
                              (yparamname,'@y')
                          ])
    else: # pragma: no cover
        hover = HoverTool(names=['data'],
                          tooltips = [
                              (groupby,'@groupval'),
                              (xparamname,'@x{1.1}'),
                              (yparamname,'@y')
                              ,
                          ])

    hover.mode = 'mouse'
    TOOLS = [SaveTool(),PanTool(),BoxZoomTool(),WheelZoomTool(),
             ResetTool(),TapTool(),hover]


    plot = Figure(x_axis_type=x_axis_type,y_axis_type=y_axis_type,
                  tools=TOOLS,
                  toolbar_location="above",
                  toolbar_sticky=False,plot_width=920)

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}

    plot.title.text = title
    plot.title.text_font_size=value("1.0em")
    plot.sizing_mode = 'stretch_both'

    plot.image_url([watermarkurl],watermarkx,watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    errorbar(plot,xparam,yparam,source=source,
             xerr=ploterrorbars,
             yerr=ploterrorbars,
             point_kwargs={
                 'line_color':"#969696",
                 'size':"groupsize",
                 'fill_color':"color",
                 'fill_alpha':1.0,
             },
    )



    for nr, gvalue, color in colorlegend:
        box = BoxAnnotation(bottom=75+20*nr,left=50,top=95+20*nr,
                            right=70,
                            bottom_units='screen',
                            top_units='screen',
                            left_units='screen',
                            right_units='screen',
                            fill_color=color,
                            fill_alpha=1.0,
                            line_color=color)
        legendlabel = Label(x=71,y=78+20*nr,x_units='screen',
                            y_units='screen',
                            text = "{gvalue:3.0f}".format(gvalue=gvalue),
                            background_fill_alpha=1.0,
                            text_color='black',
                            text_font_size="0.7em")
        plot.add_layout(box)
        plot.add_layout(legendlabel)

    if colorlegend:
        legendlabel = Label(x=322,y=250,x_units='screen',
                            y_units='screen',
                            text = 'group legend',
                            text_color='black',
                            text_font_size="0.7em",
                            angle=90,
                            angle_units='deg')

    if xparam == 'workoutid': # pragma: no cover
        plot.xaxis.axis_label = 'Workout'
    else:
        plot.xaxis.axis_label = axlabels[xparam]

    if yparam == 'workoutid': # pragma: no cover
        plot.xaxis.axis_label = 'Workout'
    else:
        plot.yaxis.axis_label = axlabels[yparam]

    binlabel = Label(x=50,y=50,x_units='screen',
                     y_units='screen',
                     text="Bin size {binsize:3.1f}".format(binsize=binsize),
                     background_fill_alpha=0.7,
                     background_fill_color='white',
                     text_color='black',text_font_size='10pt',
                     )

    slidertext = 'SPM: {:.0f}-{:.0f}, WpS: {:.0f}-{:.0f}'.format(
        spmmin,spmmax,workmin,workmax
        )
    sliderlabel = Label(x=50,y=20,x_units='screen',y_units='screen',
                        text=slidertext,
                        background_fill_alpha=0.7,
                        background_fill_color='white',
                        text_color='black',text_font_size='10pt',
                        )


    plot.add_layout(binlabel)
    plot.add_layout(sliderlabel)

    yrange1 = Range1d(start=yaxmin,end=yaxmax)
    plot.y_range = yrange1

    xrange1 = Range1d(start=xaxmin,end=xaxmax)
    plot.x_range = xrange1

    if yparam == 'pace':
        plot.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )


    script,div = components(plot)


    return [script,div]

def interactive_cum_flex_chart2(theworkouts,promember=0,
                                xparam='spm',
                                yparam1='power',
                                yparam2='spm',
                                workstrokesonly=False):

    # datadf = dataprep.smalldataprep(theworkouts,xparam,yparam1,yparam2)
    ids = [int(w.id) for w in theworkouts]
    columns = [xparam,yparam1,yparam2,'spm','driveenergy','distance']
    datadf = dataprep.getsmallrowdata_db(columns,ids=ids,doclean=True,
                                         workstrokesonly=workstrokesonly)

    try:
        tests = datadf[yparam2]
    except KeyError: # pragma: no cover
        yparam2 = 'None'

    try:
        tests = datadf[yparam1]
    except KeyError:
        yparam1 = 'None'

    datadf.dropna(axis=1,how='all',inplace=True)
    datadf.dropna(axis=0,how='any',inplace=True)

    # test if we have drive energy
    nowork = 1
    try: # pragma: no cover
        test = datadf['driveenergy'].mean()
        nowork = 0
    except KeyError: # pragma: no cover
        datadf['driveenergy'] = 500.

    # test if we have power
    nopower = 1
    try: # pragma: no cover
        test = datadf['power'].mean()
        nopower = 0
    except KeyError: # pragma: no cover
        datadf['power'] = 50.


    yparamname1 = axlabels[yparam1]
    if yparam2 != 'None':
        yparamname2 = axlabels[yparam2]


    # check if dataframe not empty
    if datadf.empty: # pragma: no cover
        return ['','<p>No non-zero data in selection</p>','','']


    try:
        datadf['x1'] = datadf.loc[:,xparam]
    except KeyError: # pragma: no cover
        try:
            datadf['x1'] = datadf['distance']
        except KeyError:
            try:
                datadf['x1'] = datadf['time']
            except KeyError: # pragma: no cover
                return ['','<p>No non-zero data in selection</p>','','']

    try:
        datadf['y1'] = datadf.loc[:,yparam1]
    except KeyError:
        try:
            datadf['y1'] = datadf['pace']
        except KeyError: # pragma: no cover
            return ['','<p>No non-zero data in selection</p>','','']
    if yparam2 != 'None':
        try:
            datadf['y2'] = datadf.loc[:,yparam2]
        except KeyError: # pragma: no cover
            datadf['y2'] = datadf['y1']
    else: # pragma: no cover
        datadf['y2'] = datadf['y1']


    if xparam=='distance': # pragma: no cover
        xaxmax = datadf['x1'].max()
        xaxmin = datadf['x1'].min()
    else:
        xaxmax = yaxmaxima[xparam]
        xaxmin = yaxminima[xparam]

    # average values
    x1mean = datadf['x1'].mean()

    y1mean = datadf['y1'].mean()
    y2mean = datadf['y2'].mean()


    xvals = pd.Series(xaxmin+np.arange(100)*(xaxmax-xaxmin)/100.)

    x_axis_type = 'linear'
    y_axis_type = 'linear'
    if xparam == 'time':
        x_axis_type = 'datetime'

    if yparam1 == 'pace': # pragma: no cover
        y_axis_type = 'datetime'
        y1mean = datadf.loc[:,'pace'].mean()

    datadf['xname'] = axlabels[xparam]
    datadf['yname1'] = axlabels[yparam1]
    if yparam2 != 'None':
        datadf['yname2'] = axlabels[yparam2]
    else: # pragma: no cover
        datadf['yname2'] = axlabels[yparam1]


    source = ColumnDataSource(
        datadf
        )

    source2 = ColumnDataSource(
        datadf.copy()
        )

    # Add hover to this comma-separated string and see what changes
    if (promember==1):
        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair'
    else: # pragma: no cover
        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,crosshair'

    plot = Figure(x_axis_type=x_axis_type,y_axis_type=y_axis_type,
                  tools=TOOLS,
                  toolbar_location="above",
                  toolbar_sticky=False)

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}
    plot.sizing_mode = 'stretch_both'

    plot.image_url([watermarkurl],watermarkx,watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    x1means = Span(location=x1mean,dimension='height',line_color='green',
                   line_dash=[6,6], line_width=2)

    y1means = Span(location=y1mean,dimension='width',line_color='blue',
                   line_dash=[6,6],line_width=2)
    y2means = y1means

    xlabel = Label(x=50,y=80,x_units='screen',y_units='screen',
                   text=axlabels[xparam]+": {x1mean:6.2f}".format(x1mean=x1mean),
                   background_fill_alpha=.7,
                   background_fill_color='white',
                   text_color='green',
    )

    sliderlabel = Label(x=10,y=470,x_units='screen',y_units='screen',
                        text='',
                        background_fill_alpha=0.7,
                        background_fill_color='white',
                        text_color='black',text_font_size='10pt',
                        )


    plot.add_layout(x1means)
    plot.add_layout(xlabel)
    plot.add_layout(y1means)
    plot.add_layout(sliderlabel)

    y1label = Label(x=50,y=50,x_units='screen',y_units='screen',
                    text=axlabels[yparam1]+": {y1mean:6.2f}".format(y1mean=y1mean),
                    background_fill_alpha=.7,
                    background_fill_color='white',
                    text_color='blue',
    )

    if yparam1 != 'time' and yparam1 != 'pace':
        plot.add_layout(y1label)

    y2label = y1label
    plot.circle('x1','y1',source=source2,fill_alpha=0.3,line_color=None,
                 legend_label=yparamname1,
                 )

    plot.xaxis.axis_label = axlabels[xparam]
    plot.yaxis.axis_label = axlabels[yparam1]


    yrange1 = Range1d(start=yaxminima[yparam1],end=yaxmaxima[yparam1])
    plot.y_range = yrange1

    xrange1 = Range1d(start=yaxminima[xparam],end=yaxmaxima[xparam])
    plot.x_range = xrange1

    if yparam1 == 'pace': # pragma: no cover
        plot.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )

    if yparam2 != 'None':
        yrange2 = Range1d(start=yaxminima[yparam2],end=yaxmaxima[yparam2])
        plot.extra_y_ranges["yax2"] = yrange2

        plot.circle('x1','y2',color="red",y_range_name="yax2",
                    legend_label=yparamname2,
                    source=source2,fill_alpha=0.3,line_color=None)

        plot.add_layout(LinearAxis(y_range_name="yax2",
                                   axis_label=axlabels[yparam2]),'right')

        y2means = Span(location=y2mean,dimension='width',line_color='red',
                       line_dash=[6,6],line_width=2,y_range_name="yax2")


        plot.add_layout(y2means)
        y2label = Label(x=50,y=20,x_units='screen',y_units='screen',
                        text=axlabels[yparam2]+": {y2mean:6.2f}".format(y2mean=y2mean),
                        background_fill_alpha=.7,
                        background_fill_color='white',
                        text_color='red',
        )
        if yparam2 != 'pace' and yparam2 != 'time':
            plot.add_layout(y2label)

    callback = CustomJS(args = dict(source=source,source2=source2,
                                    x1means=x1means,
                                    y1means=y1means,
                                    y1label=y1label,
                                    y2label=y2label,
                                    xlabel=xlabel,
                                    sliderlabel=sliderlabel,
                                    y2means=y2means), code="""
       var data = source.data
       var data2 = source2.data
       var x1 = data['x1']
       var y1 = data['y1']
       var y2 = data['y2']
       var spm1 = data['spm']

       var index1 = data['index']

       var distance1 = data['distance']
       var power1 = data['power']
       var driveenergy1 = data['driveenergy']
       var xname = data['xname']
       var yname1 = data['yname1']
       var yname2 = data['yname2']
       var workoutid1 = data['workoutid']

       var minspm = minspm.value
       var maxspm = maxspm.value
       var mindist = mindist.value
       var maxdist = maxdist.value
       var minwork = minwork.value
       var maxwork = maxwork.value

       sliderlabel.text = 'SPM: '+minspm.toFixed(0)+'-'+maxspm.toFixed(0)
       sliderlabel.text += ', Dist: '+mindist.toFixed(0)+'-'+maxdist.toFixed(0)
       sliderlabel.text += ', WpS: '+minwork.toFixed(0)+'-'+maxwork.toFixed(0)

       var xm = 0
       var ym1 = 0
       var ym2 = 0

       data2['x1'] = []
       data2['y1'] = []
       data2['y2'] = []
       data2['distance'] = []
       data2['power'] = []
       data2['x1mean'] = []
       data2['y1mean'] = []
       data2['y2mean'] = []
       data2['driveenergy'] = []
       data2['workoutid'] = []
       data2['xname'] = []
       data2['yname1'] = []
       data2['yname2'] = []
       data2['spm'] = []

       for (var i=0; i<x1.length; i++) {
          if (spm1[i]>=minspm && spm1[i]<=maxspm) {
             if (distance1[i]>=mindist && distance1[i]<=maxdist) {
                if (driveenergy1[i]>=minwork && driveenergy1[i]<=maxwork) {
                   data2['x1'].push(x1[i])
                   data2['y1'].push(y1[i])
                   data2['y2'].push(y2[i])
                   data2['spm'].push(spm1[i])
                   data2['driveenergy'].push(driveenergy1[i])
                   data2['distance'].push(distance1[i])
                   data2['power'].push(power1[i])
                   data2['workoutid'].push(0)
                   data2['xname'].push(0)
                   data2['yname1'].push(0)
                   data2['yname2'].push(0)

                   xm += x1[i]
                   ym1 += y1[i]
                   ym2 += y2[i]
                   }
                }
             }
       }


       xm /= data2['x1'].length
       ym1 /= data2['x1'].length
       ym2 /= data2['x1'].length

       for (var i=0; i<data2['x1'].length; i++) {
          data2['x1mean'].push(xm)
          data2['y1mean'].push(ym1)
          data2['y2mean'].push(ym2)
       }

       x1means.location = xm
       y1means.location = ym1
       y2means.location = ym2
       y1label.text = yname1[0]+': '+(ym1).toFixed(2)
       y2label.text = yname2[0]+': '+(ym2).toFixed(2)
       xlabel.text = xname[0]+': '+(xm).toFixed(2)

       source2.change.emit();
       """)

    slider_spm_min = Slider(width=140, start=15.0, end=55,value=15.0, step=.1,
                title="Min SPM")
    slider_spm_min.js_on_change('value',callback)
    callback.args["minspm"] = slider_spm_min


    slider_spm_max = Slider(width=140, start=15.0, end=55,value=55.0, step=.1,
                title="Max SPM")
    slider_spm_max.js_on_change('value',callback)
    callback.args["maxspm"] = slider_spm_max

    slider_work_min = Slider(width=140, start=0.0, end=1500,value=0.0, step=10,
                title="Min Work per Stroke")
    slider_work_min.js_on_change('value',callback)
    callback.args["minwork"] = slider_work_min


    slider_work_max = Slider(width=140, start=0.0, end=1500,value=1500.0, step=10,
                title="Max Work per Stroke")
    slider_work_max.js_on_change('value',callback)
    callback.args["maxwork"] = slider_work_max

    try:
        distmax = 100+100*int(datadf['distance'].max()/100.)
    except KeyError: # pragma: no cover
        distmax = 1000.

    slider_dist_min = Slider(width=140, start=0,end=distmax,value=0,step=50,
                             title="Min Distance")
    slider_dist_min.js_on_change('value',callback)
    callback.args["mindist"] = slider_dist_min

    slider_dist_max = Slider(width=140, start=0,end=distmax,value=distmax,
                             step=50,
                             title="Max Distance")
    slider_dist_max.js_on_change('value',callback)
    callback.args["maxdist"] = slider_dist_max

    #slider_spm_min.sizing_mode = 'fixed'
    #slider_spm_max.sizing_mode = 'fixed'
    #slider_work_min.sizing_mode = 'fixed'
    #slider_work_max.sizing_mode = 'fixed'
    #slider_dist_min.sizing_mode = 'fixed'
    #slider_dist_max.sizing_mode = 'fixed'

    thesliders = layoutcolumn([slider_spm_min,
                                      slider_spm_max,
                                      slider_dist_min,
                                      slider_dist_max,
                                      slider_work_min,
                                      slider_work_max,
                               ],
                                     )

    #thesliders.sizing_mode = 'fixed'

    layout = layoutrow([thesliders,
                        plot])

    layout.sizing_mode = 'stretch_both'

    script, div = components(layout)
    js_resources = INLINE.render_js()
    css_resources = INLINE.render_css()



    return [script,div,js_resources,css_resources]


def interactive_flexchart_stacked(id,r,xparam='time',
                                  yparam1='pace',
                                  yparam2='power',
                                  yparam3='hr',
                                  yparam4='spm',
                                  mode='erg'):

    columns = [xparam,yparam1,yparam2,yparam3,yparam4,
               'ftime','distance','fpace',
               'power','hr','spm','driveenergy',
               'time','pace','workoutstate']

    comment = None

    rowdata = dataprep.getsmallrowdata_db(columns,ids=[id],doclean=True,
                                          workstrokesonly=False)


    if r.usersmooth > 1: # pragma: no cover
        for column in columns:
            try:
                if metricsdicts[column]['maysmooth']:
                    nrsteps = int(log2(r.usersmooth))
                    for i in range(nrsteps):
                        rowdata[column] = stravastuff.ewmovingaverage(rowdata[column],5)
            except KeyError:
                pass

    if len(rowdata)<2:
        rowdata = dataprep.getsmallrowdata_db(columns,ids=[id],
                                              doclean=False,
                                              workstrokesonly=False)

    row = Workout.objects.get(id=id)
    if rowdata.empty:
        return "","No valid data",'','',comment

    try:
        tseconds = rowdata.loc[:,'time']
    except KeyError: # pragma: no cover
        return '','No time data - cannot make flex plot','','',comment

    try:
        rowdata['x1'] = rowdata.loc[:,xparam]
        rowmin = rowdata[xparam].min()
    except KeyError: # pragma: no cover
        rowdata['x1'] = 0*rowdata.loc[:,'time']

    try:
        rowdata['y1'] = rowdata.loc[:,yparam1]
        rowmin = rowdata[yparam1].min()
    except KeyError: # pragma: no cover
        rowdata['y1'] = 0*rowdata.loc[:,'time']
        rowdata[yparam1] = rowdata['y1']

    try: # pragma: no cover
        rowdata['y2'] = rowdata.loc[:,yparam2]
        rowmin = rowdata[yparam2].min()
    except KeyError:
        rowdata['y2'] = 0*rowdata.loc[:,'time']
        rowdata[yparam2] = rowdata['y2']

    try:
        rowdata['y3'] = rowdata.loc[:,yparam3]
        rowmin = rowdata[yparam3].min()
    except KeyError: # pragma: no cover
        rowdata['y3'] = 0*rowdata.loc[:,'time']
        rowdata[yparam3] = rowdata['y3']

    try:
        rowdata['y4'] = rowdata.loc[:,yparam4]
        rowmin = rowdata[yparam4].min()
    except KeyError: # pragma: no cover
        rowdata['y4'] = 0*rowdata.loc[:,'time']
        rowdata[yparam4] = rowdata['y4']

    if xparam=='time':
        xaxmax = tseconds.max()
        xaxmin = tseconds.min()
    elif xparam=='distance' or xparam=='cumdist': # pragma: no cover
        xaxmax = rowdata['x1'].max()
        xaxmin = rowdata['x1'].min()
    else: # pragma: no cover
        try:
            xaxmax = get_yaxmaxima(r,xparam,mode)
            xaxmin = get_yaxminima(r,xparam,mode)
        except KeyError:
            xaxmax = rowdata['x1'].max()
            xaxmin = rowdata['x1'].min()


    x_axis_type = 'linear'
    y1_axis_type = 'linear'
    y2_axis_type = 'linear'
    y3_axis_type = 'linear'
    y4_axis_type = 'linear'
    if xparam == 'time':
        x_axis_type = 'datetime'

    if yparam1 == 'pace': # pragma: no cover
        y1_axis_type = 'datetime'

    if yparam2 == 'pace': # pragma: no cover
        y2_axis_type = 'datetime'

    if yparam3 == 'pace': # pragma: no cover
        y3_axis_type = 'datetime'

    if yparam4 == 'pace': # pragma: no cover
        y4_axis_type = 'datetime'

    try:
        rowdata['xname'] = axlabels[xparam]
    except KeyError: # pragma: no cover
        rowdata['xname'] = xparam

    try:
        rowdata['yname1'] = axlabels[yparam1]
    except KeyError: # pragma: no cover
        rowdata['yname1'] = yparam1

    try:
        rowdata['yname2'] = axlabels[yparam2]
    except KeyError: # pragma: no cover
        rowdata['yname2'] = yparam2

    try:
        rowdata['yname3'] = axlabels[yparam3]
    except KeyError: # pragma: no cover
        rowdata['yname3'] = yparam3

    try:
        rowdata['yname4'] = axlabels[yparam4]
    except KeyError: # pragma: no cover
        rowdata['yname4'] = yparam4

    # prepare data
    source = ColumnDataSource(
        rowdata
        )

    TOOLS = 'box_zoom,wheel_zoom,reset,tap,hover'
    TOOLS2 = 'box_zoom,hover'

    plot1 = Figure(x_axis_type=x_axis_type,y_axis_type=y1_axis_type,plot_width=920,plot_height=150,
                   tools=TOOLS,toolbar_location='above')
    plot2 = Figure(x_axis_type=x_axis_type,y_axis_type=y2_axis_type,plot_width=920,plot_height=150,
                   tools=TOOLS2,toolbar_location=None)
    plot3 = Figure(x_axis_type=x_axis_type,y_axis_type=y3_axis_type,plot_width=920,plot_height=150,
                   tools=TOOLS2,toolbar_location=None)
    plot4 = Figure(x_axis_type=x_axis_type,y_axis_type=y4_axis_type,plot_width=920,plot_height=150,
                   tools=TOOLS2,toolbar_location=None)

    plot1.xaxis.visible=False
    plot2.xaxis.visible=False
    plot3.xaxis.visible=False

    plot1.sizing_mode = 'stretch_both'
    plot2.sizing_mode = 'stretch_both'
    plot3.sizing_mode = 'stretch_both'
    plot4.sizing_mode = 'stretch_both'

    linked_crosshair = CrosshairTool(dimensions="height")
    plot1.add_tools(linked_crosshair)
    plot2.add_tools(linked_crosshair)
    plot3.add_tools(linked_crosshair)
    plot4.add_tools(linked_crosshair)

    xaxlabel = axlabels.get(xparam,xparam)
    yax1label = axlabels.get(yparam1,yparam1)

    plot1.yaxis.axis_label = yax1label

    yax2label = axlabels.get(yparam2,yparam2)

    plot2.yaxis.axis_label = yax2label

    yax3label = axlabels.get(yparam3,yparam3)


    plot3.yaxis.axis_label = yax3label

    yax4label = axlabels.get(yparam4,yparam4)

    plot4.yaxis.axis_label = yax4label

    plot4.xaxis.axis_label = xaxlabel


    xrange1 = Range1d(start=xaxmin,end=xaxmax)
    plot1.x_range = xrange1
    plot2.x_range = xrange1
    plot3.x_range = xrange1
    plot4.x_range = xrange1

    if xparam == 'time':
        plot4.xaxis[0].formatter = DatetimeTickFormatter(
                hours = ["%H"],
                minutes = ["%M"],
                seconds = ["%S"],
                days = ["0"],
                months = [""],
                years = [""]
            )

    hover1 = plot1.select(dict(type=HoverTool))
    hover2 = plot2.select(dict(type=HoverTool))
    hover3 = plot3.select(dict(type=HoverTool))
    hover4 = plot4.select(dict(type=HoverTool))

    if yparam1 == 'pace':
        y1tooltip = '@fpace'
    elif yparam1 != 'None': # pragma: no cover
        y1tooltip = '@{yparam1}'.format(yparam1=yparam1)
        if metricsdicts[yparam1]['numtype'] == 'integer' or yparam1 == 'power':
            y1tooltip+='{int}'
        else: # pragma: no cover
            y1tooltip+='{0.00}'
    else: # pragma: no cover
        y1tooltip = ''
        comment = 'The metric in the first chart is only accessible with a Pro plan or higher'

    if yparam2 == 'pace': # pragma: no cover
        y2tooltip = '@fpace'
    elif yparam2 != 'None':
        y2tooltip = '@{yparam2}'.format(yparam2=yparam2)
        if metricsdicts[yparam2]['numtype'] == 'integer' or yparam2 == 'power':
            y2tooltip+='{int}'
        else: # pragma: no cover
            y2tooltip+='{0.00}'
    else: # pragma: no cover
        y2tooltip = ''
        comment = 'The metric in the second chart is only accessible with a Pro plan or higher'

    if yparam3 == 'pace': # pragma: no cover
        y3tooltip = '@fpace'
    elif yparam3 != 'None':
        y3tooltip = '@{yparam3}'.format(yparam3=yparam3)
        if metricsdicts[yparam3]['numtype'] == 'integer' or yparam3 == 'power':
            y3tooltip+='{int}'
        else: # pragma: no cover
            y3tooltip+='{0.00}'
    else: # pragma: no cover
        y3tooltip = ''
        comment = 'The metric in the third chart is only accessible with a Pro plan or higher'

    if yparam4 == 'pace': # pragma: no cover
        y4tooltip = '@fpace'
    elif yparam4 != 'None':
        y4tooltip = '@{yparam4}'.format(yparam4=yparam4)
        if metricsdicts[yparam4]['numtype'] == 'integer' or yparam4 == 'power': # pragma: no cover
            y4tooltip+='{int}'
        else: # pragma: no cover
            y4tooltip+='{0.00}'
    else: # pragma: no cover
        y4tooltip = ''
        comment = 'The metric in the fourth chart is only accessible with a Pro plan or higher'

    if yparam1 != 'None':
        hover1.tooltips = OrderedDict([
            ('Time','@ftime'),
            ('Distance','@distance{int}'),
            (axlabels[yparam1],y1tooltip),
            (axlabels[yparam2],y2tooltip),
            (axlabels[yparam3],y3tooltip),
            (axlabels[yparam4],y4tooltip),
            ])
    if yparam2 != 'None':
        hover2.tooltips = OrderedDict([
            ('Time','@ftime'),
            ('Distance','@distance{int}'),
            (axlabels[yparam1],y1tooltip),
            (axlabels[yparam2],y2tooltip),
            (axlabels[yparam3],y3tooltip),
            (axlabels[yparam4],y4tooltip),
            ])

    if yparam3 != 'None':
        hover3.tooltips = OrderedDict([
            ('Time','@ftime'),
            ('Distance','@distance{int}'),
            (axlabels[yparam1],y1tooltip),
            (axlabels[yparam2],y2tooltip),
            (axlabels[yparam3],y3tooltip),
            (axlabels[yparam4],y4tooltip),
            ])

    if yparam4 != 'None':
        hover4.tooltips = OrderedDict([
            ('Time','@ftime'),
            ('Distance','@distance{int}'),
            (axlabels[yparam1],y1tooltip),
            (axlabels[yparam2],y2tooltip),
            (axlabels[yparam3],y3tooltip),
            (axlabels[yparam4],y4tooltip),
            ])

    hover1.mode = 'vline'
    hover2.mode = 'vline'
    hover3.mode = 'vline'
    hover4.mode = 'vline'

    y1min = get_yaxminima(r,yparam1,mode)
    y2min = get_yaxminima(r,yparam2,mode)
    y3min = get_yaxminima(r,yparam3,mode)
    y4min = get_yaxminima(r,yparam4,mode)

    y1max = get_yaxmaxima(r,yparam1,mode)
    y2max = get_yaxmaxima(r,yparam2,mode)
    y3max = get_yaxmaxima(r,yparam3,mode)
    y4max = get_yaxmaxima(r,yparam4,mode)

    plot1.y_range = Range1d(start=y1min,end=y1max)
    plot2.y_range = Range1d(start=y2min,end=y2max)
    plot3.y_range = Range1d(start=y3min,end=y3max)
    plot4.y_range = Range1d(start=y4min,end=y4max)

    if yparam1 == 'pace':
        plot1.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )
        plot1.y_range = Range1d(y1min,y1max)

    if yparam2 == 'pace': # pragma: no cover
        plot2.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )
        plot2.y_range = Range1d(y2min,y2max)


    if yparam3 == 'pace': # pragma: no cover
        plot3.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )
        plot3.y_range = Range1d(y3min,y3max)

    if yparam4 == 'pace': # pragma: no cover
        plot4.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )
        plot4.y_range = Range1d(y4min,y4max)

    plot1.line('x1','y1',source=source,color=palette2[1])
    plot2.line('x1','y2',source=source,color=palette2[3])
    plot3.line('x1','y3',source=source,color=palette2[0])
    plot4.line('x1','y4',source=source,color=palette2[2])

    layout = layoutcolumn([
        plot1,
        plot2,
        plot3,
        plot4,
    ])

    layout.sizing_mode = 'stretch_both'


    script, div = components(layout)
    js_resources = INLINE.render_js()
    css_resources = INLINE.render_css()

    return script,div,js_resources,css_resources,comment



def interactive_flex_chart2(id,r,promember=0,
                           xparam='time',
                           yparam1='pace',
                           yparam2='hr',
                           plottype='line',
                           workstrokesonly=False,
                           mode='rower'):

    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'

    #rowdata,row = dataprep.getrowdata_db(id=id)
    columns = [xparam,yparam1,yparam2,
               'ftime','distance','fpace',
               'power','hr','spm','driveenergy',
               'time','pace','workoutstate']

    rowdata = dataprep.getsmallrowdata_db(columns,ids=[id],doclean=True,
                                          workstrokesonly=workstrokesonly)


    if r.usersmooth > 1: # pragma: no cover
        for column in columns:
            try:
                if metricsdicts[column]['maysmooth']:
                    nrsteps = int(log2(r.usersmooth))
                    for i in range(nrsteps):
                        rowdata[column] = stravastuff.ewmovingaverage(rowdata[column],5)
            except KeyError:
                pass



    if len(rowdata)<2:
        rowdata = dataprep.getsmallrowdata_db(columns,ids=[id],doclean=True,
                                              workstrokesonly=False)
        workstrokesonly=False
    if len(rowdata)<2:
        rowdata = dataprep.getsmallrowdata_db(columns,ids=[id],
                                              doclean=False,
                                              workstrokesonly=False)
        workstrokesonly=False
    try:
        tests = rowdata[yparam2]
    except KeyError: # pragma: no cover
        yparam2 = 'None'



    try:
        tests = rowdata[yparam1]
    except KeyError: # pragma: no cover
        yparam1 = 'None'


    # test if we have drive energy
    nowork = 1
    try:
        test = rowdata['driveenergy'].mean()
        nowork = 0
    except KeyError:
        rowdata['driveenergy'] = 500.

    # test if we have power
    nopower = 1
    try:
        test = rowdata['power'].mean()
        nopower = 0
    except KeyError:
        rowdata['power'] = 50.

    # replace nans
    rowdata.fillna(value=0,inplace=True)



    row = Workout.objects.get(id=id)
    if rowdata.empty:
        return "","No valid data",'','',workstrokesonly


    workoutstateswork = [1,4,5,8,9,6,7]
    workoutstatesrest = [3]
    workoutstatetransition = [0,2,10,11,12,13]

    if workstrokesonly: # pragma: no cover
        try:
            rowdata = rowdata[~rowdata['workoutstate'].isin(workoutstatesrest)]
        except KeyError:
            pass

    try:
        tseconds = rowdata.loc[:,'time']
    except KeyError: # pragma: no cover
        return '','No time data - cannot make flex plot','','',workstrokesonly


    try:
        rowdata['x1'] = rowdata.loc[:,xparam]
        rowmin = rowdata[xparam].min()
    except KeyError: # pragma: no cover
        rowdata['x1'] = 0*rowdata.loc[:,'time']

    try:
        rowdata['y1'] = rowdata.loc[:,yparam1]
        rowmin = rowdata[yparam1].min()
    except KeyError: # pragma: no cover
        rowdata['y1'] = 0*rowdata.loc[:,'time']
        rowdata[yparam1] = rowdata['y1']

    if yparam2 != 'None':
        try:
            rowdata['y2'] = rowdata.loc[:,yparam2]
            rowmin = rowdata[yparam2].min()
        except KeyError: # pragma: no cover
            rowdata['y2'] = 0*rowdata.loc[:,'time']
            rowdata[yparam2] = rowdata['y2']
    else: # pragma: no cover
        rowdata['y2'] = rowdata['y1']

    if xparam=='time':
        xaxmax = tseconds.max()
        xaxmin = tseconds.min()
    elif xparam=='distance' or xparam=='cumdist':
        xaxmax = rowdata['x1'].max()
        xaxmin = rowdata['x1'].min()
    else: # pragma: no cover
        try:
            xaxmax = get_yaxmaxima(r,xparam,mode)
            xaxmin = get_yaxminima(r,xparam,mode)
        except KeyError:
            xaxmax = rowdata['x1'].max()
            xaxmin = rowdata['x1'].min()

    # average values
    if xparam != 'time':
        try:
            x1mean = rowdata['x1'].mean()
        except TypeError: # pragma: no cover
            x1mean = 0
    else: # pragma: no cover
        x1mean = 0

    y1mean = rowdata['y1'].mean()
    y2mean = rowdata['y2'].mean()


    if xparam != 'time':
        xvals = xaxmin+np.arange(100)*(xaxmax-xaxmin)/100.
    else:
        xvals = np.arange(100)

    # constant power plot
    if yparam1 == 'driveenergy':
        if xparam == 'spm': # pragma: no cover
            yconstantpower = rowdata['y1'].mean()*rowdata['x1'].mean()/xvals

    x_axis_type = 'linear'
    y_axis_type = 'linear'
    if xparam == 'time':
        x_axis_type = 'datetime'

    if yparam1 == 'pace':
        y_axis_type = 'datetime'
        try:
            y1mean = rowdata.loc[:,'pace'].mean()
        except KeyError: # pragma: no cover
            y1mean = 0

    try:
        rowdata['xname'] = axlabels[xparam]
    except KeyError: # pragma: no cover
        rowdata['xname'] = xparam
    try:
        rowdata['yname1'] = axlabels[yparam1]
    except KeyError: # pragma: no cover
        rowdata['yname1'] = yparam1
    if yparam2 != 'None':
        try:
            rowdata['yname2'] = axlabels[yparam2]
        except KeyError: # pragma: no cover
            rowdata['yname2'] = yparam2
    else: # pragma: no cover
        rowdata['yname2'] = rowdata['yname1']


    # prepare data
    source = ColumnDataSource(
        rowdata
        )



    # second source for filtering
    source2 = ColumnDataSource(
        rowdata.copy()
        )

    # Add hover to this comma-separated string and see what changes
    if (promember==1):
        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
    else:
        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'



    plot = Figure(x_axis_type=x_axis_type,y_axis_type=y_axis_type,
                  tools=TOOLS,toolbar_location='above',
                  toolbar_sticky=False,plot_width=800,plot_height=600,
                  )
    plot.sizing_mode = 'stretch_both'
    #plot.width_policy = 'max'


    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}
    plot.sizing_mode = 'stretch_both'

    plot.image_url([watermarkurl],watermarkx,watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    x1means = Span(location=x1mean,dimension='height',line_color='green',
                   line_dash=[6,6], line_width=2)

    y1means = Span(location=y1mean,dimension='width',line_color='blue',
                   line_dash=[6,6],line_width=2)
    y2means = y1means

    try:
        xlabeltext = axlabels[xparam]+": {x1mean:6.2f}".format(
            x1mean=x1mean
        )
    except KeyError: # pragma: no cover
        xlabeltext = xparam+": {x1mean:6.2f}".format(x1mean=x1mean)

    xlabel = Label(x=50,y=80,x_units='screen',y_units='screen',
                   text=xlabeltext,
                   background_fill_alpha=.7,
                   background_fill_color='white',
                   text_color='green',
    )

    annolabel = Label(x=50,y=450,x_units='screen',y_units='screen',
                      text='',
                      background_fill_alpha=0.7,
                      background_fill_color='white',
                      text_color='black',
    )

    sliderlabel = Label(x=10,y=470,x_units='screen',y_units='screen',
                        text='',
                        background_fill_alpha=0.7,
                        background_fill_color='white',
                        text_color='black',text_font_size='10pt',
                        )


    if (xparam != 'time') and (xparam != 'distance') and (xparam != 'cumdist'): # pragma: no cover
        plot.add_layout(x1means)
        plot.add_layout(xlabel)


    plot.add_layout(y1means)
    plot.add_layout(annolabel)
    plot.add_layout(sliderlabel)

    try:
        yaxlabel = axlabels[yparam1]
    except KeyError: # pragma: no cover
        yaxlabel = str(yparam1)+' '

    try:
        xaxlabel = axlabels[xparam]
    except KeyError: # pragma: no cover
        xaxlabel = xparam

    y1label = Label(x=50,y=50,x_units='screen',y_units='screen',
                    text=yaxlabel+": {y1mean:6.2f}".format(y1mean=y1mean),
                    background_fill_alpha=.7,
                    background_fill_color='white',
                    text_color='blue',
    )
    if yparam1 != 'time' and yparam1 != 'pace': # pragma: no cover
        plot.add_layout(y1label)
    y2label = y1label

    # average values
    if yparam1 == 'driveenergy': # pragma: no cover
        if xparam == 'spm':
            plot.line(xvals,yconstantpower,color="green",legend_label="Constant Power")

    if plottype=='line':
        plot.line('x1','y1',source=source2,legend_label=yaxlabel)
    elif plottype=='scatter': # pragma: no cover
        plot.scatter('x1','y1',source=source2,legend_label=yaxlabel,fill_alpha=0.4,
                     line_color=None)

    plot.title.text = row.name
    plot.title.text_font_size=value("1.0em")

    plot.sizing_mode = 'stretch_both'
    plot.xaxis.axis_label = xaxlabel

    plot.yaxis.axis_label = yaxlabel



    try:
        yrange1 = Range1d(start=get_yaxminima(r,yparam1,mode),
                              end=get_yaxmaxima(r,yparam1,mode))
    except KeyError: # pragma: no cover
        yrange1 = Range1d(start=rowdata[yparam1].min(),
                          end=rowdata[yparam1].max())

    plot.y_range = yrange1

    if (xparam != 'time') and (xparam != 'distance') and (xparam != 'cumdist'): # pragma: no cover
        try:
            xrange1 = Range1d(start=get_yaxminima(r,xparam,mode),
                              end=get_yaxmaxima(r,xparam,mode))
        except KeyError:
            xrange1 = Range1d(start=rowdata[xparam].min(),
                              end=rowdata[xparam].max())

        plot.x_range = xrange1

    if xparam == 'time':
        xrange1 = Range1d(start=xaxmin,end=xaxmax)
        plot.x_range = xrange1
        plot.xaxis[0].formatter = DatetimeTickFormatter(
                hours = ["%H"],
                minutes = ["%M"],
                seconds = ["%S"],
                days = ["0"],
                months = [""],
                years = [""]
            )


    if yparam1 == 'pace':
        plot.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )


    if yparam2 != 'None':
        try:
            yrange2 = Range1d(start=get_yaxminima(r,yparam2,mode),
                              end=get_yaxmaxima(r,yparam2,mode))
        except KeyError: # pragma: no cover
            yrange2 = Range1d(start=rowdata[yparam2].min(),
                              end=rowdata[yparam2].max())

        plot.extra_y_ranges["yax2"] = yrange2
        #= {"yax2": yrange2}
        try:
            axlegend = axlabels[yparam2]
        except KeyError: # pragma: no cover
            axlegend = str(yparam2)+' '

        if plottype=='line':
            plot.line('x1','y2',color="red",y_range_name="yax2",
                      legend_label=axlegend,
                      source=source2)

        elif plottype=='scatter': # pragma: no cover
            plot.scatter('x1','y2',source=source2,legend_label=axlegend,
                         fill_alpha=0.4,
                         line_color=None,color="red",y_range_name="yax2")

        plot.add_layout(LinearAxis(y_range_name="yax2",
                                   axis_label=axlegend),'right')

        y2means = Span(location=y2mean,dimension='width',line_color='red',
                       line_dash=[6,6],line_width=2,y_range_name="yax2")


        plot.add_layout(y2means)
        y2label = Label(x=50,y=20,x_units='screen',y_units='screen',
                        text=axlegend+": {y2mean:6.2f}".format(y2mean=y2mean),
                        background_fill_alpha=.7,
                        background_fill_color='white',
                        text_color='red',
        )
        if yparam2 != 'pace' and yparam2 != 'time':
            plot.add_layout(y2label)

    hover = plot.select(dict(type=HoverTool))


    hover.tooltips = OrderedDict([
        ('Time','@ftime'),
        ('Distance','@distance{int}'),
        ('Pace','@fpace'),
        ('HR','@hr{int}'),
        ('SPM','@spm{1.1}'),
        ('Power','@power{int}'),
        ])

    hover.mode = 'mouse'


    callback = CustomJS(args = dict(source=source,source2=source2,
                                    x1means=x1means,
                                    y1means=y1means,
                                    y1label=y1label,
                                    y2label=y2label,
                                    xlabel=xlabel,
                                    annolabel=annolabel,
                                    sliderlabel=sliderlabel,
                                    y2means=y2means,
    ), code="""
       var data = source.data
       var data2 = source2.data
       var x1 = data['x1']
       var y1 = data['y1']
       var y2 = data['y2']
       var spm1 = data['spm']
       var time1 = data['time']
       var ftime1 = data['ftime']
       var pace1 = data['pace']
       var hr1 = data['hr']
       var fpace1 = data['fpace']
       var distance1 = data['distance']
       var power1 = data['power']
       var driveenergy1 = data['driveenergy']
       var xname = data['xname']
       var yname1 = data['yname1']
       var yname2 = data['yname2']
       var workoutid1 = data['workoutid']
       var workoutstate1 = data['workoutstate']

       var annotation = annotation.value
       var minspm = minspm.value
       var maxspm = maxspm.value
       var mindist = mindist.value
       var maxdist = maxdist.value
       var minwork = minwork.value
       var maxwork = maxwork.value

       sliderlabel.text = 'SPM: '+minspm.toFixed(0)+'-'+maxspm.toFixed(0)
       sliderlabel.text += ', Dist: '+mindist.toFixed(0)+'-'+maxdist.toFixed(0)
       sliderlabel.text += ', WpS: '+minwork.toFixed(0)+'-'+maxwork.toFixed(0)

       var xm = 0
       var ym1 = 0
       var ym2 = 0

       data2['x1'] = []
       data2['y1'] = []
       data2['y2'] = []
       data2['spm'] = []
       data2['time'] = []
       data2['ftime'] = []
       data2['pace'] = []
       data2['hr'] = []
       data2['fpace'] = []
       data2['distance'] = []
       data2['power'] = []
       data2['x1mean'] = []
       data2['y1mean'] = []
       data2['y2mean'] = []
       data2['driveenergy'] = []
       data2['workoutid'] = []
       data2['workoutstate'] = []
       data2['xname'] = []
       data2['yname1'] = []
       data2['yname2'] = []


       for (var i=0; i<x1.length; i++) {
          if (spm1[i]>=minspm && spm1[i]<=maxspm) {
             if (distance1[i]>=mindist && distance1[i]<=maxdist) {
                if (driveenergy1[i]>=minwork && driveenergy1[i]<=maxwork) {
                   data2['x1'].push(x1[i])
                   data2['y1'].push(y1[i])
                   data2['y2'].push(y2[i])
                   data2['spm'].push(spm1[i])
                   data2['time'].push(time1[i])
                   data2['ftime'].push(ftime1[i])
                   data2['fpace'].push(fpace1[i])
                   data2['driveenergy'].push(driveenergy1[i])
                   data2['pace'].push(pace1[i])
                   data2['hr'].push(hr1[i])
                   data2['distance'].push(distance1[i])
                   data2['power'].push(power1[i])
                   data2['workoutid'].push(0)
                   data2['workoutstate'].push(0)
                   data2['xname'].push(0)
                   data2['yname1'].push(0)
                   data2['yname2'].push(0)


                   xm += x1[i]
                   ym1 += y1[i]
                   ym2 += y2[i]
                 }
                }
             }
       }

       xm /= data2['x1'].length
       ym1 /= data2['x1'].length
       ym2 /= data2['x1'].length

       for (var i=0; i<data2['x1'].length; i++) {
          data2['x1mean'].push(xm)
          data2['y1mean'].push(ym1)
          data2['y2mean'].push(ym2)
       }



       x1means.location = xm
       y1means.location = ym1
       y2means.location = ym2
       y1label.text = yname1[0]+': '+ym1.toFixed(2)
       y2label.text = yname2[0]+': '+ym2.toFixed(2)
       xlabel.text = xname[0]+': '+xm.toFixed(2)
       annolabel.text = annotation

       source2.change.emit();
       """)

    annotation = TextInput(width=140, title="Type your plot notes here", value="")
    annotation.js_on_change('value',callback)
    callback.args["annotation"] = annotation

    slider_spm_min = Slider(width=140, start=15.0, end=55,value=15.0, step=.1,
                title="Min SPM")
    slider_spm_min.js_on_change('value',callback)
    callback.args["minspm"] = slider_spm_min


    slider_spm_max = Slider(width=140, start=15.0, end=55,value=55.0, step=.1,
                title="Max SPM")
    slider_spm_max.js_on_change('value',callback)
    callback.args["maxspm"] = slider_spm_max

    slider_work_min = Slider(width=140, start=0.0, end=1500,value=0.0, step=10,
                title="Min Work per Stroke")
    slider_work_min.js_on_change('value',callback)
    callback.args["minwork"] = slider_work_min


    slider_work_max = Slider(width=140, start=0.0, end=1500,value=1500.0, step=10,
                title="Max Work per Stroke")
    slider_work_max.js_on_change('value',callback)
    callback.args["maxwork"] = slider_work_max

    try:
        distmax = 100+100*int(rowdata['distance'].max()/100.)
    except (KeyError,ValueError): # pragma: no cover
        distmax = 100

    slider_dist_min = Slider(width=140, start=0,end=distmax,value=0,step=50,
                             title="Min Distance")
    slider_dist_min.js_on_change('value',callback)
    callback.args["mindist"] = slider_dist_min

    slider_dist_max = Slider(width=140, start=0,end=distmax,value=distmax,
                             step=50,
                             title="Max Distance")
    slider_dist_max.js_on_change('value',callback)
    callback.args["maxdist"] = slider_dist_max

    #annotation.sizing_mode = 'fixed'
    #slider_spm_min.sizing_mode = 'fixed'
    #slider_spm_max.sizing_mode = 'fixed'
    #slider_work_min.sizing_mode = 'fixed'
    #slider_work_max.sizing_mode = 'fixed'
    #slider_dist_min.sizing_mode = 'fixed'
    #slider_dist_max.sizing_mode = 'fixed'

    thesliders = layoutcolumn([
        annotation,
        slider_spm_min,
        slider_spm_max,
        slider_dist_min,
        slider_dist_max,
        slider_work_min,
        slider_work_max,
    ])

    #thesliders.sizing_mode = 'fixed'

    layout = layoutrow([thesliders,
                        plot])

    #    layout.sizing_mode = 'stretch_both'
    layout.sizing_mode = 'stretch_both'

    script, div = components(layout)
    js_resources = INLINE.render_js()
    css_resources = INLINE.render_css()

    return [script,div,js_resources,css_resources,workstrokesonly]

def thumbnails_set(r,id,favorites):
    charts = []

    columns = [f.xparam for f in favorites]
    columns += [f.yparam1 for f in favorites]
    columns += [f.yparam2 for f in favorites]

    columns += ['time']
    rowdata = dataprep.getsmallrowdata_db(columns,ids=[id],doclean=True)

    rowdata.dropna(axis=1,how='all',inplace=True)

    if rowdata.empty:
        try:
            rowdata = dataprep.getsmallrowdata_db(columns,ids=[id],doclean=False,
                                                  workstrokesonly=False)
        except: # pragma: no cover
            return [
                {'script':"",
                 'div':"",
                 'notes':""
                }]

    if rowdata.empty:
        return [
            {'script':"",
             'div':"",
             'notes':""
             }]

    #    else:
    #        try:
    #            rowdata.sort_values(by='time',ascending=True,inplace=True)
    #        except KeyError:
    #            pass

    l = len(rowdata)
    maxlength = 50
    if l > maxlength:
        try:
            bins = np.linspace(rowdata['time'].min(),rowdata['time'].max(),maxlength)
            groups = rowdata.groupby(np.digitize(rowdata['time'],bins))
            rowdata = groups.mean()
        except KeyError: # pragma: no cover
            pass

    for f in favorites:
        workstrokesonly = not f.reststrokes
        script,div = thumbnail_flex_chart(
            rowdata,
            id=id,
            xparam=f.xparam,
            yparam1=f.yparam1,
            yparam2=f.yparam2,
            plottype=f.plottype,
        )


        charts.append({
            'script':script,
            'div':div,
            'notes':f.notes})

    return charts


def thumbnail_flex_chart(rowdata,id=0,promember=0,
                         xparam='time',
                         yparam1='pace',
                         yparam2='hr',
                         plottype='line',
                         workstrokesonly=False):


    try:
        tests = rowdata[yparam2]
    except KeyError:
        yparam2 = 'None'

    try:
        tests = rowdata[yparam1]
    except KeyError:
        yparam1 = 'None'



    try:
        tseconds = rowdata.loc[:,'time']
    except KeyError: # pragma: no cover
        return '','No time data - cannot make flex plot'


    try:
        rowdata['x1'] = rowdata.loc[:,xparam]
    except KeyError: # pragma: no cover
        rowdata['x1'] = 0*rowdata.loc[:,'time']

    try:
        rowdata['y1'] = rowdata.loc[:,yparam1]
    except KeyError: # pragma: no cover
        rowdata['y1'] = 0*rowdata.loc[:,'time']

    if yparam2 != 'None':
        try:
            rowdata['y2'] = rowdata.loc[:,yparam2]
        except KeyError: # pragma: no cover
            rowdata['y2'] = 0*rowdata.loc[:,'time']
    else:
        rowdata['y2'] = rowdata['y1']

    if xparam=='time':
        xaxmax = tseconds.max()
        xaxmin = tseconds.min()
    elif xparam=='distance' or xparam=='cumdist': # pragma: no cover
        xaxmax = rowdata['x1'].max()
        xaxmin = rowdata['x1'].min()
    else:
        xaxmax = yaxmaxima[xparam]
        xaxmin = yaxminima[xparam]

    x_axis_type = 'linear'
    y_axis_type = 'linear'
    if xparam == 'time':
        x_axis_type = 'datetime'

    if yparam1 == 'pace': # pragma: no cover
        y_axis_type = 'datetime'
        y1mean = rowdata.loc[:,'pace'].mean()


    rowdata['xname'] = axlabels[xparam]
    try:
        rowdata['yname1'] = axlabels[yparam1]
    except KeyError: # pragma: no cover
        rowdata['yname1'] = axlabels[xparam]
    if yparam2 != 'None':
        rowdata['yname2'] = axlabels[yparam2]
    else:
        rowdata['yname2'] = axlabels[yparam1]


    # prepare data
    source = ColumnDataSource(
        rowdata
        )


    sizing_mode = 'fixed'  # 'stretch_both' also looks nice with this example
    plot = Figure(x_axis_type=x_axis_type,y_axis_type=y_axis_type,
                  plot_width=200,plot_height=150,
                  )



#    plot.sizing_mode = 'stretch_both'
    plot.sizing_mode = 'fixed'
    plot.toolbar.logo = None
    plot.toolbar_location = None
    #plot.yaxis.visible = False
    plot.xaxis.axis_label_text_font_size = "7pt"
    plot.yaxis.axis_label_text_font_size = "7pt"
    plot.xaxis.major_label_text_font_size = "7pt"
    plot.yaxis.major_label_text_font_size = "7pt"

    if plottype=='line':
        plot.line('x1','y1',source=source)
    elif plottype=='scatter':
        plot.scatter('x1','y1',source=source,fill_alpha=0.4,
                     line_color=None)

    try:
        plot.xaxis.axis_label = axlabels[xparam]
    except KeyError: # pragma: no cover
        plot.xaxis.axis_label = 'X'
    try:
        plot.yaxis.axis_label = axlabels[yparam1]
    except KeyError: # pragma: no cover
        plot.yaxis.axis_label = 'Y'


    try:
        yrange1 = Range1d(start=yaxminima[yparam1],end=yaxmaxima[yparam1])
    except KeyError: # pragma: no cover
        yrange1 = Range1d(start=yparam1.min(), end=yparam1.max())

    plot.y_range = yrange1

    if (xparam != 'time') and (xparam != 'distance') and (xparam != 'cumdist'):
        xrange1 = Range1d(start=yaxminima[xparam],end=yaxmaxima[xparam])
        plot.x_range = xrange1

    if xparam == 'time':
        xrange1 = Range1d(start=xaxmin,end=xaxmax)
        plot.x_range = xrange1
        plot.xaxis[0].formatter = DatetimeTickFormatter(
                hours = ["%H"],
                minutes = ["%M"],
                seconds = ["%S"],
                days = ["0"],
                months = [""],
                years = [""]
            )


    if yparam1 == 'pace': # pragma: no cover
        plot.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )


    if yparam2 != 'None':
        yrange2 = Range1d(start=yaxminima[yparam2],end=yaxmaxima[yparam2])
        plot.extra_y_ranges["yax2"] = yrange2
        #= {"yax2": yrange2}

        if plottype=='line':
            plot.line('x1','y2',color="red",y_range_name="yax2",
                      source=source)

        elif plottype=='scatter': # pragma: no cover
            plot.scatter('x1','y2',source=source,
                         fill_alpha=0.4,
                         line_color=None,color="red",y_range_name="yax2")

        plot.add_layout(LinearAxis(y_range_name="yax2",
                                   axis_label=axlabels[yparam2],
                                   major_label_text_font_size="7pt",
                                   axis_label_text_font_size="7pt",
        ),'right',
        )


    script, div = components(plot)

    return [script,div]

def interactive_multiple_compare_chart(ids,xparam,yparam,plottype='line',
                                       promember=0,workstrokesonly=True,
                                       labeldict=None,startenddict={}):

    message = ''
    errormessage = ''

    columns = [xparam,yparam,
               'ftime','distance','fpace',
               'power','hr','spm',
               'time','pace','workoutstate',
               'workoutid']

    compute = False
    doclean = False
    if workstrokesonly:
        compute = True
        doclean = True
    datadf = dataprep.getsmallrowdata_db(columns,ids=ids,doclean=doclean,compute=compute,
                                         workstrokesonly=workstrokesonly)
    datadf.dropna(axis=1,how='all',inplace=True)
    datadf.dropna(axis=0,how='any',inplace=True)


    nrworkouts = len(ids)

    try:
        tseconds = datadf.loc[:,'time']
    except KeyError: # pragma: no cover
        try:
            tseconds = datadf.loc[:,xparam]
        except:
            return ['','<p>A chart data error occurred</p>','','A chart data error occurred']

    yparamname = axlabels[yparam]

    #datadf = datadf[datadf[yparam] > 0]

    #datadf = datadf[datadf[xparam] > 0]

    # check if dataframe not empty
    if datadf.empty: # pragma: no cover
        return ['','<p>No non-zero data in selection</p>','','No non-zero data in selection']

    if xparam != 'distance' and xparam != 'time' and xparam !=  'cumdist': # pragma: no cover
        xaxmax = yaxmaxima[xparam]
        xaxmin = yaxminima[xparam]
    elif xparam == 'time' and not startenddict:
        xaxmax = tseconds.max()
        xaxmin = tseconds.min()
    elif xparam == 'time' and startenddict: # pragma: no cover
        deltas = [pair[1]-pair[0] for key,pair in startenddict.items()]
        xaxmin = 0
        xaxmax = pd.Series(deltas).max()*1000.
        if xaxmax == 0:
            xaxmax = tseconds.max()
    else:
        xaxmax = datadf['distance'].max()
        xaxmin = datadf['distance'].min()

    if yparam == 'distance': # pragma: no cover
        yaxmin = datadf['distance'].min()
        yaxmax = datadf['distance'].max()
    elif yparam == 'cumdist': # pragma: no cover
        yaxmin = datadf['cumdist'].min()
        yaxmax = datadf['cumdist'].max()
    else:
        yaxmin = yaxminima[yparam]
        yaxmax = yaxmaxima[yparam]

    x_axis_type = 'linear'
    y_axis_type = 'linear'

    # Add hover to this comma-separated string and see what changes
    if (promember==1):
        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair'
    else: # pragma: no cover
        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,crosshair'

    if yparam == 'pace':
        y_axis_type = 'datetime'
        yaxmax = 90.*1e3
        yaxmin = 150.*1e3

    if xparam == 'time':
        x_axis_type = 'datetime'

    if xparam != 'time':
        xvals = xaxmin+np.arange(100)*(xaxmax-xaxmin)/100.
    else:
        xvals = np.arange(100)

    plot = Figure(x_axis_type=x_axis_type,y_axis_type=y_axis_type,
                  tools=TOOLS,
                  toolbar_location="above",
                  plot_width=920,plot_height=500,
                  toolbar_sticky=False)

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}
    plot.sizing_mode = 'stretch_both'


    plot.image_url([watermarkurl],0.05,0.9,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor='top_left',
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    colors = itertools.cycle(palette)

    cntr = 0
    l1 = []

    try:
        items = itertools.izip(ids,colors)
    except AttributeError:
        items = zip(ids,colors)

    for id,color in items:
        group = datadf[datadf['workoutid']==int(id)].copy()
        try:
            startsecond,endsecond = startenddict[id]
        except KeyError:
            startsecond = 0
            endsecond = 0

        group.sort_values(by='time',ascending=True,inplace=True)

        if endsecond > 0:
            group['time'] = group['time'] - 1.e3*startsecond
            mask = group['time'] < 0
            group.mask(mask,inplace=True)
            mask = group['time'] > 1.e3*(endsecond-startsecond)
            group.mask(mask,inplace=True)

        if xparam == 'cumdist':
            group['cumdist'] = group['cumdist'] - group['cumdist'].min()
            res = make_cumvalues(group[xparam])
            group[xparam] = res[0]
        elif xparam == 'distance':
            group['distance'] = group['distance'] - group['distance'].min()


        try:
            group['x'] = group[xparam]
        except KeyError: # pragma: no cover
            group['x'] = group['time']
            errormessage = xparam+' has no values. Plot invalid'
        try:
            group['y'] = group[yparam]
        except KeyError:
            group['y'] = 0.0*group['x']


        ymean = group['y'].mean()
        f = group['time'].diff().mean()
        if f != 0 and not np.isnan(f):
            windowsize = 2* (int(20000./(f))) + 1
        else:
            windowsize = 1

        if windowsize > 3 and windowsize < len(group['y']):
            try:
                group['y'] = savgol_filter(group['y'],windowsize,3)
            except ValueError: # pragma: no cover
                pass

        ylabel = Label(x=100,y=60+nrworkouts*20-20*cntr,
                       x_units='screen',y_units='screen',
                       text=axlabels[yparam]+": {ymean:6.2f}".format(ymean=ymean),
                       background_fill_alpha=.7,
                       background_fill_color='white',
                       text_color=color,
        )
        if yparam != 'time' and yparam != 'pace':
            plot.add_layout(ylabel)

        source = ColumnDataSource(
            group
            )

        TIPS = OrderedDict([
            ('time','@ftime'),
            ('pace','@fpace'),
            ('hr','@hr'),
            ('spm','@spm{1.1}'),
            ('distance','@distance{5}'),
        ])

        hover = plot.select(type=HoverTool)
        hover.tooltips = TIPS

        if labeldict:
            try:
                legend_label=labeldict[id]
            except KeyError: # pragma: no cover
                legend = str(id)
        else: # pragma: no cover
            legend_label=str(id)

        if plottype=='line':
            l1.append(plot.line('x','y',source=source,color=color,legend_label=legend_label,line_width=2))
        else:
            l1.append(plot.scatter('x','y',source=source,color=color,legend_label=legend_label,
                         fill_alpha=0.4,line_color=None))

        plot.add_tools(HoverTool(renderers=[l1[cntr]],tooltips=TIPS))
        cntr += 1

    plot.legend.location='top_right'
    plot.xaxis.axis_label = axlabels[xparam]
    plot.yaxis.axis_label = axlabels[yparam]

    if (xparam != 'time') and (xparam != 'distance') and (xparam != 'cumdist'): # pragma: no cover
        xrange1 = Range1d(start=yaxminima[xparam],end=yaxmaxima[xparam])
        plot.x_range = xrange1

    yrange1 = Range1d(start=yaxmin,end=yaxmax)
    plot.y_range = yrange1

    if xparam == 'time':
        xrange1 = Range1d(start=xaxmin,end=xaxmax)
        plot.x_range = xrange1
        plot.xaxis[0].formatter = DatetimeTickFormatter(
                hours = ["%H"],
                minutes = ["%M"],
                seconds = ["%S"],
                days = ["0"],
                months = [""],
                years = [""]
            )


    if yparam == 'pace':
        plot.yaxis[0].formatter = DatetimeTickFormatter(
                seconds = ["%S"],
                minutes = ["%M"]
            )

    script, div = components(plot)

    return [script,div,message,errormessage]




def interactive_otw_advanced_pace_chart(id=0,promember=0):
    # check if valid ID exists (workout exists)
    rowdata,row = dataprep.getrowdata_db(id=id)
    rowdata.dropna(axis=1,how='all',inplace=True)
    rowdata.dropna(axis=0,how='any',inplace=True)

    if rowdata.empty:
        return "","No Valid Data Available"

    # Add hover to this comma-separated string and see what changes
    if (promember==1):
        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
    else: # pragma: no cover
        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'

    source = ColumnDataSource(
        rowdata
        )





    plot = Figure(x_axis_type="datetime",y_axis_type="datetime",
                  tools=TOOLS,
                  plot_width=920,
                  toolbar_sticky=False)

    # add watermark
    watermarkurl = "/static/img/logo7.png"
    watermarksource = ColumnDataSource(dict(
        url = [watermarkurl],))

    watermarkrange = Range1d(start=0,end=1)
    watermarkalpha = 0.6
    watermarkx = 0.99
    watermarky = 0.01
    watermarkw = 184
    watermarkh = 35
    watermarkanchor = 'bottom_right'
    plot.extra_y_ranges = {"watermark": watermarkrange}
    plot.extra_x_ranges = {"watermark": watermarkrange}
    plot.sizing_mode = 'scale_both'


    plot.image_url([watermarkurl],watermarkx,watermarky,
                   watermarkw,watermarkh,
                   global_alpha=watermarkalpha,
                   w_units='screen',
                   h_units='screen',
                   anchor=watermarkanchor,
                   dilate=True,
                   x_range_name = "watermark",
                   y_range_name = "watermark",
                   )

    plot.title.text = row.name
    plot.title.text_font_size=value("1.2em")
    plot.xaxis.axis_label = "Time"
    plot.yaxis.axis_label = "Pace (/500m)"
    plot.xaxis[0].formatter = DatetimeTickFormatter(
            hours = ["%H"],
            minutes = ["%M"],
            seconds = ["%S"],
            days = ["0"],
            months = [""],
            years = [""]
        )
    plot.yaxis[0].formatter = DatetimeTickFormatter(
            seconds = ["%S"],
            minutes = ["%M"]
        )

    ymax = 1.0e3*90
    ymin = 1.0e3*210

    plot.y_range = Range1d(ymin,ymax)


    hover = plot.select(dict(type=HoverTool))

    plot.line('time','pace',source=source,legend_label="Pace",color="black")
    plot.line('time','nowindpace',source=source,legend_label="Corrected Pace",color="red")

    hover.tooltips = OrderedDict([
        ('Time','@ftime'),
        ('Pace','@fpace'),
        ('Corrected Pace','@fnowindpace'),
        ('HR','@hr{int}'),
        ('SPM','@spm{1.1}'),
        ])

    hover.mode = 'mouse'

    try:
        script, div = components(plot)
    except: # pragma: no cover
        script = ''
        div = ''

    return [script,div]

def get_zones_report(rower,startdate,enddate,trainingzones='hr',date_agg='week',
                     yaxis='time'):

    duration = enddate-startdate

    totaldays = duration.total_seconds()/(24*3600)

    dates = []
    dates_sorting = []
    minutes = []
    hours = []
    zones = []

    enddate = enddate + datetime.timedelta(days=1)


    workouts = Workout.objects.filter(
        user=rower,
        startdatetime__gte=startdate,
        startdatetime__lte=enddate,
        duplicate=False,
    ).order_by("-startdatetime")

    ids = [w.id for w in workouts]

    columns = ['workoutid','hr','power','time']

    df = dataprep.getsmallrowdata_db(columns,ids=ids)
    try:
        df['deltat'] = df['time'].diff().clip(lower=0).clip(upper=20*1e3)
    except KeyError: # pragma: no cover
        pass

    df = dataprep.clean_df_stats(df,workstrokesonly=False,
                                     ignoreadvanced=True,ignorehr=False)

    #totalmeters,totalhours, totalminutes, totalseconds = get_totals(workouts)

    hrzones = rower.hrzones
    powerzones = rower.powerzones

    for w in workouts:
        dd = w.date.strftime('%m/%d')
        dd2 = w.date.strftime('%Y/%m/%d')
        dd3 = w.date.strftime('%Y/%m')
        dd4 = '{year}/{week:02d}'.format(
            week=arrow.get(w.date).isocalendar()[1],
            year= w.date.strftime('%y')
            )
        dd4 = (w.date - datetime.timedelta(days = w.date.weekday())).strftime('%y/%m/%d')

        #print(w.date,arrow.get(w.date),arrow.get(w.date).isocalendar())

        qryw = 'workoutid == {workoutid}'.format(workoutid=w.id)

        qry = 'hr < {ut2}'.format(ut2=rower.ut2)
        if trainingzones == 'power':
            qry = 'power < {ut2}'.format(ut2=rower.pw_ut2)
        timeinzone = df.query(qry).query(qryw)['deltat'].sum()/(60*1e3)
        if date_agg == 'week':
            dates.append(dd4)
            dates_sorting.append(dd4)
        else: # pragma: no cover
            dates.append(dd3)
            dates_sorting.append(dd3)
        minutes.append(timeinzone)
        hours.append(timeinzone/60.)
        if trainingzones == 'hr':
            zones.append('<{ut2}'.format(ut2=hrzones[1]))
        else:
            zones.append('<{ut2}'.format(ut2=powerzones[1]))
        #print(w,dd,timeinzone,'<UT2')

        qry = '{ut2} <= hr < {ut1}'.format(ut1=rower.ut1,ut2=rower.ut2)
        if trainingzones == 'power':
            qry = '{ut2} <= power < {ut1}'.format(ut1=rower.pw_ut1,ut2=rower.pw_ut2)
        timeinzone = df.query(qry).query(qryw)['deltat'].sum()/(60*1e3)
        if date_agg == 'week':
            dates.append(dd4)
            dates_sorting.append(dd4)
        else: # pragma: no cover
            dates.append(dd3)
            dates_sorting.append(dd3)
        minutes.append(timeinzone)
        hours.append(timeinzone/60.)
        if trainingzones == 'hr':
            zones.append(hrzones[1])
        else:
            zones.append(powerzones[1])
        #print(w,dd,timeinzone,'UT2')

        qry = '{ut1} <= hr < {at}'.format(ut1=rower.ut1,at=rower.at)
        if trainingzones == 'power':
            qry = '{ut1} <= power < {at}'.format(ut1=rower.pw_ut1,at=rower.pw_at)
        timeinzone = df.query(qry).query(qryw)['deltat'].sum()/(60*1e3)
        if date_agg == 'week':
            dates.append(dd4)
            dates_sorting.append(dd4)
        else: # pragma: no cover
            dates.append(dd3)
            dates_sorting.append(dd3)
        minutes.append(timeinzone)
        hours.append(timeinzone/60.)
        if trainingzones == 'hr':
            zones.append(hrzones[2])
        else:
            zones.append(powerzones[2])
        #print(w,dd,timeinzone,'UT1')

        qry = '{at} <= hr < {tr}'.format(at=rower.at,tr=rower.tr)
        if trainingzones == 'power':
            qry = '{at} <= power < {tr}'.format(at=rower.pw_at,tr=rower.pw_tr)
        timeinzone = df.query(qry).query(qryw)['deltat'].sum()/(60*1e3)
        if date_agg == 'week':
            dates.append(dd4)
            dates_sorting.append(dd4)
        else: # pragma: no cover
            dates.append(dd3)
            dates_sorting.append(dd3)
        minutes.append(timeinzone)
        hours.append(timeinzone/60.)
        if trainingzones == 'hr':
            zones.append(hrzones[3])
        else:
            zones.append(powerzones[3])
        #print(w,dd,timeinzone,'AT')

        qry = '{tr} <= hr < {an}'.format(tr=rower.tr,an=rower.an)
        if trainingzones == 'power':
            qry = '{tr} <= power < {an}'.format(tr=rower.pw_tr,an=rower.pw_an)
        timeinzone = df.query(qry).query(qryw)['deltat'].sum()/(60*1e3)
        if date_agg == 'week':
            dates.append(dd4)
            dates_sorting.append(dd4)
        else: # pragma: no cover
            dates.append(dd3)
            dates_sorting.append(dd3)
        minutes.append(timeinzone)
        hours.append(timeinzone/60.)
        if trainingzones == 'hr':
            zones.append(hrzones[4])
        else:
            zones.append(powerzones[4])
        #print(w,dd,timeinzone,'TR')

        qry = 'hr >= {an}'.format(an=rower.an)
        if trainingzones == 'power':
            qry = 'power >= {an}'.format(an=rower.pw_an)
        timeinzone = df.query(qry).query(qryw)['deltat'].sum()/(60*1e3)
        if date_agg == 'week':
            dates.append(dd4)
            dates_sorting.append(dd4)
        else: # pragma: no cover
            dates.append(dd3)
            dates_sorting.append(dd3)
        minutes.append(timeinzone)
        hours.append(timeinzone/60.)
        if trainingzones == 'hr':
            zones.append(hrzones[5])
        else:
            zones.append(powerzones[5])
        #print(w,dd,timeinzone,'AN')

    try:
        d = utc.localize(startdate)
    except (ValueError,AttributeError): # pragma: no cover
        d = startdate

    try:
        enddate = utc.localize(enddate)
    except (ValueError,AttributeError): # pragma: no cover
        pass

    while d<=enddate:
        dd = d.strftime('%d')
        if date_agg == 'week':
            dd4 = '{year}/{week:02d}'.format(
                week=arrow.get(d).isocalendar()[1],
                year= d.strftime('%y')
                )
            dd4 = (d - datetime.timedelta(days = d.weekday())).strftime('%y/%m/%d')

            dates.append(dd4)
            dates_sorting.append(dd4)
        else: # pragma: no cover
            dates.append(d.strftime('%Y/%m'))
            dates_sorting.append(d.strftime('%Y/%m'))

        minutes.append(0)
        hours.append(0)
        if trainingzones == 'hr':
            zones.append(hrzones[1])
        else:
            zones.append(powerzones[1])

        d += datetime.timedelta(days=1)



    # this should be renamed with rower zones
    data = {
        'date':dates,
        'date_sorting':dates_sorting,
        'minutes': minutes,
        'zones':zones,
        'hours':hours,
        }

    #print(pd.DataFrame(data).head())

    return data

def interactive_zoneschart(rower,data,startdate,enddate,trainingzones='hr',date_agg='week',
                           yaxis='time'):
    if startdate >= enddate:
        st = startdate
        startdate = enddate
        enddate = st

    duration = enddate-startdate

    totaldays = duration.total_seconds()/(24*3600)

    colors = ['gray','yellow','lime','blue','purple','red']

    hrzones = rower.hrzones
    powerzones = rower.powerzones


    color_map = {
        '<{ut2}'.format(ut2=hrzones[1]):'green',
        hrzones[1]:'lime',
        hrzones[2]:'yellow',
        hrzones[3]:'blue',
        hrzones[4]:'purple',
        hrzones[5]:'red',
    }
    if trainingzones == 'power':
        color_map = {
            '<{ut2}'.format(ut2=powerzones[1]):'green',
            powerzones[1]:'lime',
            powerzones[2]:'yellow',
            powerzones[3]:'blue',
            powerzones[4]:'purple',
            powerzones[5]:'red',
        }

    zones_order = [
        '<{ut2}'.format(ut2=hrzones[1]),
        hrzones[1],
        hrzones[2],
        hrzones[3],
        hrzones[4],
        hrzones[5]
        ]

    if trainingzones == 'power':
        zones_order = [
            '<{ut2}'.format(ut2=powerzones[1]),
            powerzones[1],
            powerzones[2],
            powerzones[3],
            powerzones[4],
            powerzones[5]
            ]


    df = pd.DataFrame(data)
    df2 = pd.DataFrame(data)

    df.drop('minutes',inplace=True,axis='columns')

    #df.drop('hours',inplace=True,axis='columns')


    source = ColumnDataSource(df)

    df.sort_values('date_sorting',inplace=True)
    df.drop('date_sorting',inplace=True,axis='columns')
    df['totaltime']  = 0
    if df.empty: # pragma: no cover
        return '','No Data Found'

    if yaxis == 'percentage':
        dates = list(set(df['date'].values))
        for date in dates:
            qry = 'date == "{d}"'.format(d=date)

            totaltime = df.query(qry)['hours'].sum()

            mask = df['date'] == date
            df.loc[mask,'totaltime'] = totaltime

        df['percentage'] = 100.*df['hours']/df['totaltime']
        df.drop('hours',inplace=True,axis='columns')
        df.drop('totaltime',inplace=True,axis='columns')



    hv.extension('bokeh')

    xrotation = 0
    nrdates = len(list(set(df['date'].values)))
    if nrdates > 10:
        xrotation = 45

    bars = hv.Bars(df, kdims = ['date','zones']).aggregate(function=np.sum).redim.values(zones=zones_order)

    #bars = table.to.bars(['date','zones'],['minutes'])
    bars.opts(
        opts.Bars(cmap=color_map,show_legend=True,stacked=True,
                  tools=['tap','hover'],width=550,padding=(0,(0,.1)),
                  legend_position='bottom',
                  xrotation=xrotation,
                  show_frame=False)
    )

    p = hv.render(bars)

    p.title.text = 'Activity {d1} to {d2} for {r}'.format(
                d1 = startdate.strftime("%Y-%m-%d"),
                d2 = enddate.strftime("%Y-%m-%d"),
                r = str(rower),
                )

    if date_agg == 'week':
        p.xaxis.axis_label = 'Week'
    else: # pragma: no cover
        p.xaxis.axis_label = 'Month'

    if yaxis == 'percentage':
        p.yaxis.axis_label = 'Percentage'

    p.plot_width=550
    p.plot_height=350
    p.toolbar_location = 'right'
    p.y_range.start = 0
    p.sizing_mode = 'stretch_both'

    if yaxis == 'percentage':
        tidy_df = df2.groupby(['date']).sum()

        source2 = ColumnDataSource(tidy_df)
        y2rangemax = tidy_df.loc[:,'hours'].max()*1.1
        p.extra_y_ranges["yax2"] = Range1d(start=0,end=y2rangemax)
        p.line('date','hours',source=source2,y_range_name="yax2",color="black",width=5)
        p.circle('date','hours',source=source2,y_range_name="yax2",color="black",size=10,
                 legend_label='Hours')
        p.add_layout(LinearAxis(y_range_name="yax2",
                                axis_label='Hours'),'right')


    script,div = components(p)

    return script,div