# pylint: disable=C0103, C0303
from __future__ import absolute_import
import numpy as np
import pandas as pd
from pandas import DataFrame
from lxml import objectify
from fitparse import FitFile
try:
    from . import tcxtools,gpxtools
    from .utils import totimestamp, geo_distance
except (ValueError,ImportError): # pragma: no cover
    import tcxtools,gpxtools
    from utils import totimestamp, geo_distance

import sys
if sys.version_info[0]<=2: # pragma: no cover
    pythonversion = 2
else:
    pythonversion = 3

import gzip
import arrow
import shutil
from datetime import datetime
from six.moves import range
import json

NAMESPACE = 'http://www.garmin.com/xmlschemas/TrainingCenterDatabase/v2'

def strip_non_ascii(string):
    ''' Returns the string without non ASCII characters'''
    stripped = (c for c in string if 0 < ord(c) < 127)
    return ''.join(stripped)

def tofloat(x):
    try:
        return float(x)
    except ValueError: # pragma: no cover
        return np.nan

class ExcelTemplate(object):
    def __init__(self,readfile): # pragma: no cover
        self.readfile = readfile
        xls_f = pd.ExcelFile(self.readfile)
        self.xls_df = xls_f.parse('workout')

        self.df = pd.DataFrame()
        now = datetime.utcnow()
        unixnow = arrow.get(now).timestamp
        time = 0
        totdistance = 0

        for nr,row in self.xls_df.iterrows():
            duration = row['Interval Time']
            #duration = datetime.strptime(durationstring,"%M:%S.%f")
            try:
                seconds = 60*duration.minute+duration.second+duration.microsecond/1.e6
            except AttributeError:
                seconds = 0
            if seconds:
                spm = row['SPM']
                if spm == np.nan:
                    spm = 10.
                deltat = 60./spm
                aantal = max(2,int(seconds/deltat)+1)
                time_list = time+np.arange(aantal)*deltat
                distance = row['Interval Distance']
                deltad = distance/float(aantal-1)
                d_list = np.arange(aantal)*deltad

                velo = distance/float(seconds)
                pace = 500./velo

                data = pd.DataFrame({
                    'time':time_list,
                    'distance':d_list,
                    'hr':row['Avg HR'],
                    'spm':spm,
                    'pace':pace,
                    'velo':velo,
                    'type':4,
                    ' lapIdx':nr
                })



                self.df = self.df.append(data)
                time += seconds
                totdistance = distance
            if row['Rest Time'] != np.nan:
                try:
                    restseconds = 60.*row['Rest Time'].minute
                    restseconds += row['Rest Time'].second
                    restseconds += row['Rest Time'].microsecond/1.0e6
                except AttributeError:
                    restseconds = 0

                if restseconds:
                    restdistance = row['Rest Distance']
                    deltat = 60./spm
                    aantal = int(restseconds/deltat)
                    time_list = time+np.arange(aantal)*deltat
                    try:
                        deltad = restdistance/float(aantal)
                    except ZeroDivisionError:
                        deltad = 0
                    d_list = totdistance+np.arange(aantal)*deltad

                    if restseconds:
                        try:
                            velo = restdistance/restseconds
                            pace = 500./velo
                        except ZeroDivisionError:
                            velo = 0
                            pace = 0
                    else:
                        velo = 0
                        pace = 0

                    data = pd.DataFrame({
                        'time':time_list,
                        'distance':d_list,
                        'pace':pace,
                        'velo':velo,
                        ' lapIdx':nr,
                        'type':3,
                    })

                    self.df = self.df.append(data)
                    time += restseconds
                    totdistance += restdistance

        self.df['TimeStamp (sec)'] = unixnow+self.df['time']
        self.df['power'] = 2.8*self.df['velo']**3
        mapping = {
            'time': ' ElapsedTime (sec)',
            'distance': ' Horizontal (meters)',
            'hr': ' HRCur (bpm)',
            'spm': ' Cadence (stokes/min)',
            'power': ' Power (watts)',
            }

        self.df.rename(columns = mapping, inplace=True)

    def write_csv(self, *args, **kwargs): # pragma: no cover
        isgzip = kwargs.pop('gzip', False)
        writeFile = args[0]

        data = self.df

        if isgzip:
            return data.to_csv(writeFile + '.gz', index_label='index',
                               compression='gzip')
        else:
            return data.to_csv(writeFile, index_label='index')

def fitsummarydata(*args, **kwargs): # pragma: no cover
    from warnings import warn
    warn("fitsummarydata was renamed to FitSummaryData")
    return FitSummaryData(*args, **kwargs)

class FitSummaryData(object):
    def __init__(self, readfile):
        self.readfile = readfile
        self.fitfile = FitFile(readfile, check_crc=False)
        self.records = self.fitfile.messages

        recorddicts = []
        lapdict = []
        lapcounter = 0
        for record in self.records:
            if record.name ==  'record':
                values = record.get_values()
                values['lapid'] = lapcounter
                recorddicts.append(values)
            if record.name == 'lap':
                lapcounter += 1
                values = record.get_values()
                values['lapid'] = lapcounter
                lapdict.append(values)

        self.df = pd.DataFrame(recorddicts)
        self.lapdf = pd.DataFrame(lapdict)

        self.summarytext = 'Work Details\n'


    def setsummary(self, separator="|"):
        lapcount = 0
        self.summarytext += "#-{sep}SDist{sep}-Split-{sep}-SPace-{sep}-SPM-{sep}-Pwr-{sep}AvgHR{sep}MaxHR{sep}DPS-\n".format(
            sep=separator
            )

        totaldistance = 0
        totaltime = 0

        dfgrouped = self.df.groupby('lapid')
        for lapcount,group in dfgrouped:
            intdist = int(
                self.lapdf[self.lapdf['lapid']==lapcount+1]['total_distance']
            )
            if np.isnan(intdist): # pragma: no cover
                intdist = 1
            else:
                intdist = int(intdist)
            timestamps = group['timestamp'].apply(totimestamp)
            inttime = self.lapdf[self.lapdf['lapid']==lapcount+1][
                'total_elapsed_time'
            ]
            inttime = float(inttime)
            try:
                intpower = int(group['power'].mean())
            except KeyError:
                intpower = 0
            lapmin = int(inttime/60)
            lapsec = int(int(10*(inttime-lapmin*60.))/10.)
            try:
                intvelo = group['enhanced_speed'].mean()
                intpace = 500./intvelo
            except KeyError: # pragma: no cover
                try:
                    intvelo = group['speed'].mean()
                    intpace = 500./intvelo
                except KeyError:
                    intvelo = 0
                    intpace = 0

            pacemin = int(intpace/60)
            pacesec = int(10*(intpace-pacemin*60.))/10.
            pacestring = str(pacemin)+":"+str(pacesec)
            intspm = group['cadence'].mean()
            inthr = int(group['heart_rate'].mean())
            intmaxhr = int(group['heart_rate'].max())
            strokecount = intspm*inttime/60.
            try:
                intdps = intdist/float(strokecount)
            except ZeroDivisionError: # pragma: no cover
                intdps = 0.0

            summarystring = "{nr:0>2}{sep}{intdist:0>5d}{sep}".format(
                nr=lapcount+1,
                sep=separator,
                intdist=intdist
            )

            summarystring += " {lapmin:0>2}:{lapsec:0>2} {sep}".format(
                lapmin=lapmin,
                lapsec=lapsec,
                sep=separator,
            )

            summarystring += "{pacemin:0>2}:{pacesec:0>3.1f}".format(
                pacemin=pacemin,
                pacesec=pacesec,
            )

            summarystring += "{sep} {intspm:0>4.1f}{sep}".format(
                intspm=intspm,
                sep=separator
            )

            summarystring += " {intpower:0>3d} {sep}".format(
                intpower=intpower,
                sep=separator
            )

            summarystring += " {inthr:0>3d} {sep}".format(
                inthr=inthr,
                sep=separator
            )

            summarystring += " {intmaxhr:0>3d} {sep}".format(
                intmaxhr=intmaxhr,
                sep=separator
            )

            summarystring += " {dps:0>3.1f}".format(
                dps=intdps
            )

            summarystring += "\n"
            self.summarytext += summarystring

        # add total summary
        try:
            overallvelo = self.df['enhanced_speed'].mean()
        except KeyError: # pragma: no cover
            overallvelo = self.df['speed'].mean()

        timestamps = self.df['timestamp'].apply(totimestamp)
        totaltime = timestamps.max()-timestamps.min()

        overallpace = 500./overallvelo

        minutes = int(overallpace/60)
        sec = int(10*(overallpace-minutes*60.))/10.
        pacestring = str(minutes)+":"+str(sec)

        totmin = int(totaltime/60)
        totsec = int(int(10*(totaltime-totmin*60.))/10.)

        avghr = self.df['heart_rate'].mean()
        grandmaxhr = self.df['heart_rate'].max()
        try:
            avgpower = self.df['power'].mean()
        except KeyError:
            avgpower = 0
        try:
            avgspm = self.df['cadence'].mean()
        except KeyError: # pragma: no cover
            avgspm = 0
        totaldistance = self.df['distance'].max()-self.df['distance'].min()
        if np.isnan(totaldistance): # pragma: no cover
            totaldistance = 1

        strokecount = avgspm*totaltime/60.
        try:
            avgdps = totaldistance/strokecount
        except ZeroDivisionError: # pragma: no cover
            avgdps = 0


        summarystring = "Workout Summary\n"
        summarystring += "--{sep}{totaldistance:0>5}{sep}".format(
            totaldistance=int(totaldistance),
            sep=separator
            )

        summarystring += " {totmin:0>2}:{totsec:0>2} {sep} ".format(
            totmin=totmin,
            totsec=totsec,
            sep=separator,
            )

        summarystring += pacestring+separator

        summarystring += " {avgspm:0>4.1f}{sep}".format(
            sep=separator,
            avgspm=avgspm
            )

        summarystring += " {avgpower:0>3} {sep}".format(
            sep=separator,
            avgpower=int(avgpower)
            )

        summarystring += " {avghr:0>3} {sep} {grandmaxhr:0>3} {sep}".format(
            avghr=int(avghr),
            grandmaxhr=int(grandmaxhr),
            sep=separator
            )

        summarystring += " {avgdps:0>3.1f}".format(
            avgdps=avgdps
            )

        self.summarytext += summarystring

class FITParser(object):

    def __init__(self, readfile):
        extension = readfile[-3:].lower()
        if extension == '.gz':
            newfile = readfile[-3:]
            with gzip.open(readfile,'rb') as f_in, open(newfile,'wb') as f_out:
                shutil.copyfileobj(f_in, f_out)
            self.readfile = newfile
        else:
            self.readfile = readfile

        self.fitfile = FitFile(self.readfile, check_crc=False)

        self.records = self.fitfile.messages

        recorddicts = []
        lapcounter = 0

        for record in self.records:
            if record.name == 'record':
                values = record.get_values()
                values['lapid'] = lapcounter
                recorddicts.append(values)
            if record.name == 'lap':
                lapcounter += 1



        self.df = pd.DataFrame(recorddicts)

        # columns to lowercase - this should be easier
        self.df.columns = [strip_non_ascii(x) for x in self.df.columns]
        self.df.columns = [x.encode('ascii','ignore') for x in self.df.columns]
        if pythonversion == 3:
            #        self.df.columns = [str(x) for x in self.df.columns]
            self.df.columns = [x.decode('ascii') for x in self.df.columns]

        self.df.rename(columns = str.lower,inplace=True)


        # check column dimensions

        for c in self.df.columns:
            x = self.df[c]
            if len(x.shape)>1: # pragma: no cover
                newdf = pd.DataFrame({
                    c: x.iloc[:,0].values
                    })
                self.df.drop(labels=c,axis=1,inplace=True)
                self.df[c] = newdf[c]

        try:
            latitude = self.df['position_lat']*(180./2**31)
            longitude = self.df['position_long']*(180./2**31)
        except KeyError: # pragma: no cover
            # no coordinates
            latitude = 0
            longitude = 0

        try:
            distance = self.df['distance']
        except KeyError: # pragma: no cover
            distance = pd.Series(np.zeros(len(self.df)))

        self.df['position_lat'] = latitude
        self.df['position_long'] = longitude


        if pd.isnull(distance).all():
            dist2 = np.zeros(len(distance))
            for i in range(len(distance)-1):
                res = geo_distance(
                    latitude[i],
                    longitude[i],
                    latitude[i+1],
                    longitude[i+1]
                )
                deltal = 1000.*res[0]
                dist2[i+1] = dist2[i]+deltal
            self.df['distance'] = dist2

        try:
            velo = self.df['enhanced_speed']
        except KeyError: # pragma: no cover
            try:
                velo = self.df['speed']
            except KeyError:
                velo = pd.Series(np.zeros(len(self.df)))

        try:
            if velo.mean() >= 1000: # pragma: no cover
                velo = velo/1000.
        except TypeError: # pragma: no cover
            pass


        try:
            timestamps = self.df['timestamp'].apply(totimestamp)
        except AttributeError: # pragma: no cover
            pass

        try:
            pace = 500./velo
        except TypeError: # pragma: no cover
            pace = pd.Series(np.zeros(len(self.df)))
        elapsed_time = timestamps-timestamps.values[0]

        self.df['TimeStamp (sec)'] = timestamps
        self.df[' Stroke500mPace (sec/500m)'] = pace
        self.df[' ElapsedTime (sec)'] = elapsed_time

        hrname = 'heart_rate'
        spmname = 'cadence'

        if 'heart rate' in self.df.columns: # pragma: no cover
            hrname = 'heart rate'

        if 'stroke rate' in self.df.columns: # pragma: no cover
            spmname = 'stroke rate'

        newcolnames = {
            'power': ' Power (watts)',
            hrname: ' HRCur (bpm)',
            'position_long': ' longitude',
            'position_lat': ' latitude',
            spmname: ' Cadence (stokes/min)',
            'lapid': ' lapIdx',
            'distance': ' Horizontal (meters)'
            }

        self.df.rename(columns=newcolnames,inplace=True)

        # timestamp
        # distance
        # pace
        # elapsedtime


    def write_csv(self, writefile="fit_o.csv", gzip=False):

        if gzip: # pragma: no cover
            return self.df.to_csv(writefile+'.gz', index_label='index',
                                  compression='gzip')
        else:
            return self.df.to_csv(writefile, index_label='index')

class JSONParser(object): # pragma: no cover
    def __init__(self, json_file):
        df = pd.DataFrame()
        with open(json_file,'r') as f:
            data = json.load(f)

        laps = data['laps']

        for lap in laps:
            points = lap['points']
            ldf = pd.DataFrame.from_records(points)
            df=df.append(ldf,ignore_index=True)


        self.df = df

        newcolnames = {
            'time':'TimeStamp (sec)',
            'hr':' HRCur (bpm)',
            }

        self.df.rename(columns=newcolnames,inplace=True)


    def write_csv(self,writefile="json_o.csv", gzip = False):
        if gzip:
            return self.df.to_csv(writefile+'.gz', index_label='index',compression='gzip')
        else:
            return self.df.to_csv(writefile, index_label='index')

class TCXParserTester(object): # pragma: no cover
    def __init__(self, tcx_file):
        tree = objectify.parse(tcx_file)
        self.root = tree.getroot()
        self.activity = self.root.Activities.Activity

        # need to select only trackpoints with Cadence, Distance,
        # Time & HR data
        self.selectionstring = '//ns:Trackpoint[descendant::ns:HeartRateBpm]'
        self.selectionstring += '[descendant::ns:Cadence]'
        self.selectionstring += '[descendant::ns:DistanceMeters]'
        self.selectionstring += '[descendant::ns:Time]'


        self.hr_values = self.root.xpath(self.selectionstring
                                         +'//ns:HeartRateBpm/ns:Value',
                                         namespaces={'ns': NAMESPACE})



        self.distance_values = self.root.xpath(self.selectionstring
                                               +'/ns:DistanceMeters',
                                               namespaces={'ns': NAMESPACE})

        self.spm_values = self.root.xpath(self.selectionstring
                                          +'/ns:Cadence',
                                          namespaces={'ns': NAMESPACE})

    def getarray(self, str1, str2=''):
        selectionstring = self.selectionstring
        selectionstring = selectionstring+'//ns:'+str1
        if str2 != '':
            selectionstring = selectionstring+'/ns:'+str2

        the_array = self.root.xpath(selectionstring,
                                    namespaces={'ns': NAMESPACE})

        return the_array


class GPXParser(object): # pragma: no cover
    def __init__(self, gpx_file, *args, **kwargs):
        self.df = gpxtools.gpxtodf2(gpx_file)

    def write_csv(self, writefile='example.csv', window_size=5, gzip=False):
        data = self.df
        data = data.sort_values(by='TimeStamp (sec)', ascending=True)
        data = data.fillna(method='ffill')

        # drop all-zero columns
        for c in data.columns:
            if (data[c] == 0).any() and data[c].mean() == 0:
                data = data.drop(c, axis=1)
            if c == 'Position':
                data = data.drop(c, axis=1)
            if c == 'Extensions':
                data = data.drop(c, axis=1)

        if gzip:
            return data.to_csv(writefile+'.gz', index_label='index',
                               compression='gzip')
        else:
            return data.to_csv(writefile, index_label='index')

class TCXParser(object):
    def __init__(self, tcx_file, *args, **kwargs):
        if 'alternative' in kwargs: # pragma: no cover
            alternative = kwargs['alternative']
        else:
            alternative = False

        if alternative: # pragma: no cover
            self.df = tcxtools.tcxtodf(tcx_file)
        else:
            self.df = tcxtools.tcxtodf2(tcx_file)

        try:
            lat = self.df['latitude'].apply(tofloat).values
            longitude = self.df['longitude'].apply(tofloat).values
        except KeyError: # pragma: no cover
            self.df['latitude'] = 0
            self.df['longitude'] = 0
            lat = self.df['latitude'].apply(tofloat).values
            longitude = self.df['longitude'].apply(tofloat).values


        unixtimes = self.df['timestamp'].values
        try:
            spm = self.df['Cadence'].apply(tofloat).values
        except KeyError: # pragma: no cover
            try:
                spm = self.df['StrokeRate'].apply(tofloat).values
                self.df['Cadence'] = self.df['StrokeRate']
            except KeyError:
                try:
                    spm = 0.0*self.df['Speed'].apply(tofloat).values
                except KeyError:
                    spm = 0.0*unixtimes


        try:
            velo = self.df['Speed'].apply(tofloat)
            dist2 = self.df['DistanceMeters'].apply(tofloat)
            strokelength = velo*60./spm
        except KeyError: # pragma: no cover
            nr_rows = len(lat)
            dist2 = np.zeros(nr_rows)
            velo = np.zeros(nr_rows)
            strokelength = np.zeros(nr_rows)
            for i in range(nr_rows-1):
                res = geo_distance(lat[i], longitude[i], lat[i+1], longitude[i+1])
                deltal = 1000.*res[0]
                dist2[i+1] = dist2[i]+deltal
                try:
                    velo[i+1] = deltal/(1.0*(unixtimes[i+1]-unixtimes[i]))
                except ZeroDivisionError:
                    velo[i+1] = velo[i]
                if spm[i] != 0:
                    strokelength[i] = deltal*60/spm[i]
                else: # pragma: no cover
                    strokelength[i] = 0.

        try:
            power = self.df['Watts']
        except KeyError: # pragma: no cover
            try:
                power = self.df['ns3:Watts']
            except KeyError:
                power = 0*spm

            self.df['Watts'] = power

        p = 500./velo

        self.df[' Horizontal (meters)'] = dist2
        self.df[' StrokeDistance (meters)'] = strokelength
        self.df[' Stroke500mPace (sec/500m)'] = p

        # translate from standard TCX names to our naming convention
        self.columns = {
            'timestamp':'TimeStamp (sec)',
            'Cadence': ' Cadence (stokes/min)',
            'HeartRateBpm' : ' HRCur (bpm)',
            'Watts': ' Power (watts)',
            'lapid': ' lapIdx',
            'latitude': ' latitude',
            'longitude': ' longitude',
        }

        self.df.rename(columns=self.columns, inplace=True)

        cc = [value for key, value in self.columns.items()]

        for c in cc:
            if c != 'lapIdx':
                try:
                    self.df[c] = self.df[c].astype(float)
                except KeyError: # pragma: no cover
                    pass


    def write_csv(self, writefile='example.csv', window_size=5, gzip=False):
        data = self.df
        data = data.sort_values(by='TimeStamp (sec)', ascending=True)
        data = data.fillna(method='ffill')

        # drop all-zero columns
        for c in data.columns:
            if (data[c] == 0).any() and data[c].mean() == 0:
                data = data.drop(c, axis=1)
            if c == 'Position': # pragma: no cover
                data = data.drop(c, axis=1)
            if c == 'Extensions': # pragma: no cover
                data = data.drop(c, axis=1)

        if gzip: # pragma: no cover
            return data.to_csv(writefile+'.gz', index_label='index',
                               compression='gzip')
        else:
            return data.to_csv(writefile, index_label='index')

# pylint: disable=C0103, C0303
from __future__ import absolute_import
import numpy as np
import pandas as pd
from pandas import DataFrame
from lxml import objectify
from fitparse import FitFile
try:
    from . import tcxtools,gpxtools
    from .utils import totimestamp, geo_distance
except (ValueError,ImportError): # pragma: no cover
    import tcxtools,gpxtools
    from utils import totimestamp, geo_distance

import sys
if sys.version_info[0]<=2: # pragma: no cover
    pythonversion = 2
else:
    pythonversion = 3

import gzip
import arrow
import shutil
from datetime import datetime
from six.moves import range
import json

NAMESPACE = 'http://www.garmin.com/xmlschemas/TrainingCenterDatabase/v2'

def strip_non_ascii(string):
    ''' Returns the string without non ASCII characters'''
    stripped = (c for c in string if 0 < ord(c) < 127)
    return ''.join(stripped)

def tofloat(x):
    try:
        return float(x)
    except ValueError: # pragma: no cover
        return np.nan

class ExcelTemplate(object):
    def __init__(self,readfile): # pragma: no cover
        self.readfile = readfile
        xls_f = pd.ExcelFile(self.readfile)
        self.xls_df = xls_f.parse('workout')

        self.df = pd.DataFrame()
        now = datetime.utcnow()
        unixnow = arrow.get(now).timestamp
        time = 0
        totdistance = 0

        for nr,row in self.xls_df.iterrows():
            duration = row['Interval Time']
            #duration = datetime.strptime(durationstring,"%M:%S.%f")
            try:
                seconds = 60*duration.minute+duration.second+duration.microsecond/1.e6
            except AttributeError:
                seconds = 0
            if seconds:
                spm = row['SPM']
                if spm == np.nan:
                    spm = 10.
                deltat = 60./spm
                aantal = max(2,int(seconds/deltat)+1)
                time_list = time+np.arange(aantal)*deltat
                distance = row['Interval Distance']
                deltad = distance/float(aantal-1)
                d_list = np.arange(aantal)*deltad

                velo = distance/float(seconds)
                pace = 500./velo

                data = pd.DataFrame({
                    'time':time_list,
                    'distance':d_list,
                    'hr':row['Avg HR'],
                    'spm':spm,
                    'pace':pace,
                    'velo':velo,
                    'type':4,
                    ' lapIdx':nr
                })



                self.df = self.df.append(data)
                time += seconds
                totdistance = distance
            if row['Rest Time'] != np.nan:
                try:
                    restseconds = 60.*row['Rest Time'].minute
                    restseconds += row['Rest Time'].second
                    restseconds += row['Rest Time'].microsecond/1.0e6
                except AttributeError:
                    restseconds = 0

                if restseconds:
                    restdistance = row['Rest Distance']
                    deltat = 60./spm
                    aantal = int(restseconds/deltat)
                    time_list = time+np.arange(aantal)*deltat
                    try:
                        deltad = restdistance/float(aantal)
                    except ZeroDivisionError:
                        deltad = 0
                    d_list = totdistance+np.arange(aantal)*deltad

                    if restseconds:
                        try:
                            velo = restdistance/restseconds
                            pace = 500./velo
                        except ZeroDivisionError:
                            velo = 0
                            pace = 0
                    else:
                        velo = 0
                        pace = 0

                    data = pd.DataFrame({
                        'time':time_list,
                        'distance':d_list,
                        'pace':pace,
                        'velo':velo,
                        ' lapIdx':nr,
                        'type':3,
                    })

                    self.df = self.df.append(data)
                    time += restseconds
                    totdistance += restdistance

        self.df['TimeStamp (sec)'] = unixnow+self.df['time']
        self.df['power'] = 2.8*self.df['velo']**3
        mapping = {
            'time': ' ElapsedTime (sec)',
            'distance': ' Horizontal (meters)',
            'hr': ' HRCur (bpm)',
            'spm': ' Cadence (stokes/min)',
            'power': ' Power (watts)',
            }

        self.df.rename(columns = mapping, inplace=True)

    def write_csv(self, *args, **kwargs): # pragma: no cover
        isgzip = kwargs.pop('gzip', False)
        writeFile = args[0]

        data = self.df

        if isgzip:
            return data.to_csv(writeFile + '.gz', index_label='index',
                               compression='gzip')
        else:
            return data.to_csv(writeFile, index_label='index')

def fitsummarydata(*args, **kwargs): # pragma: no cover
    from warnings import warn
    warn("fitsummarydata was renamed to FitSummaryData")
    return FitSummaryData(*args, **kwargs)

class FitSummaryData(object):
    def __init__(self, readfile):
        self.readfile = readfile
        self.fitfile = FitFile(readfile, check_crc=False)
        self.records = self.fitfile.messages

        recorddicts = []
        lapdict = []
        lapcounter = 0
        for record in self.records:
            if record.name ==  'record':
                values = record.get_values()
                values['lapid'] = lapcounter
                recorddicts.append(values)
            if record.name == 'lap':
                lapcounter += 1
                values = record.get_values()
                values['lapid'] = lapcounter
                lapdict.append(values)

        self.df = pd.DataFrame(recorddicts)
        self.lapdf = pd.DataFrame(lapdict)

        self.summarytext = 'Work Details\n'


    def setsummary(self, separator="|"):
        lapcount = 0
        self.summarytext += "#-{sep}SDist{sep}-Split-{sep}-SPace-{sep}-SPM-{sep}-Pwr-{sep}AvgHR{sep}MaxHR{sep}DPS-\n".format(
            sep=separator
            )

        totaldistance = 0
        totaltime = 0

        dfgrouped = self.df.groupby('lapid')
        for lapcount,group in dfgrouped:
            intdist = int(
                self.lapdf[self.lapdf['lapid']==lapcount+1]['total_distance']
            )
            if np.isnan(intdist): # pragma: no cover
                intdist = 1
            else:
                intdist = int(intdist)
            timestamps = group['timestamp'].apply(totimestamp)
            inttime = self.lapdf[self.lapdf['lapid']==lapcount+1][
                'total_elapsed_time'
            ]
            inttime = float(inttime)
            try:
                intpower = int(group['power'].mean())
            except KeyError:
                intpower = 0
            lapmin = int(inttime/60)
            lapsec = int(int(10*(inttime-lapmin*60.))/10.)
            try:
                intvelo = group['enhanced_speed'].mean()
                intpace = 500./intvelo
            except KeyError: # pragma: no cover
                try:
                    intvelo = group['speed'].mean()
                    intpace = 500./intvelo
                except KeyError:
                    intvelo = 0
                    intpace = 0

            pacemin = int(intpace/60)
            pacesec = int(10*(intpace-pacemin*60.))/10.
            pacestring = str(pacemin)+":"+str(pacesec)
            intspm = group['cadence'].mean()
            inthr = int(group['heart_rate'].mean())
            intmaxhr = int(group['heart_rate'].max())
            strokecount = intspm*inttime/60.
            try:
                intdps = intdist/float(strokecount)
            except ZeroDivisionError: # pragma: no cover
                intdps = 0.0

            summarystring = "{nr:0>2}{sep}{intdist:0>5d}{sep}".format(
                nr=lapcount+1,
                sep=separator,
                intdist=intdist
            )

            summarystring += " {lapmin:0>2}:{lapsec:0>2} {sep}".format(
                lapmin=lapmin,
                lapsec=lapsec,
                sep=separator,
            )

            summarystring += "{pacemin:0>2}:{pacesec:0>3.1f}".format(
                pacemin=pacemin,
                pacesec=pacesec,
            )

            summarystring += "{sep} {intspm:0>4.1f}{sep}".format(
                intspm=intspm,
                sep=separator
            )

            summarystring += " {intpower:0>3d} {sep}".format(
                intpower=intpower,
                sep=separator
            )

            summarystring += " {inthr:0>3d} {sep}".format(
                inthr=inthr,
                sep=separator
            )

            summarystring += " {intmaxhr:0>3d} {sep}".format(
                intmaxhr=intmaxhr,
                sep=separator
            )

            summarystring += " {dps:0>3.1f}".format(
                dps=intdps
            )

            summarystring += "\n"
            self.summarytext += summarystring

        # add total summary
        try:
            overallvelo = self.df['enhanced_speed'].mean()
        except KeyError: # pragma: no cover
            overallvelo = self.df['speed'].mean()

        timestamps = self.df['timestamp'].apply(totimestamp)
        totaltime = timestamps.max()-timestamps.min()

        overallpace = 500./overallvelo

        minutes = int(overallpace/60)
        sec = int(10*(overallpace-minutes*60.))/10.
        pacestring = str(minutes)+":"+str(sec)

        totmin = int(totaltime/60)
        totsec = int(int(10*(totaltime-totmin*60.))/10.)

        avghr = self.df['heart_rate'].mean()
        grandmaxhr = self.df['heart_rate'].max()
        try:
            avgpower = self.df['power'].mean()
        except KeyError:
            avgpower = 0
        try:
            avgspm = self.df['cadence'].mean()
        except KeyError: # pragma: no cover
            avgspm = 0
        totaldistance = self.df['distance'].max()-self.df['distance'].min()
        if np.isnan(totaldistance): # pragma: no cover
            totaldistance = 1

        strokecount = avgspm*totaltime/60.
        try:
            avgdps = totaldistance/strokecount
        except ZeroDivisionError: # pragma: no cover
            avgdps = 0


        summarystring = "Workout Summary\n"
        summarystring += "--{sep}{totaldistance:0>5}{sep}".format(
            totaldistance=int(totaldistance),
            sep=separator
            )

        summarystring += " {totmin:0>2}:{totsec:0>2} {sep} ".format(
            totmin=totmin,
            totsec=totsec,
            sep=separator,
            )

        summarystring += pacestring+separator

        summarystring += " {avgspm:0>4.1f}{sep}".format(
            sep=separator,
            avgspm=avgspm
            )

        summarystring += " {avgpower:0>3} {sep}".format(
            sep=separator,
            avgpower=int(avgpower)
            )

        summarystring += " {avghr:0>3} {sep} {grandmaxhr:0>3} {sep}".format(
            avghr=int(avghr),
            grandmaxhr=int(grandmaxhr),
            sep=separator
            )

        summarystring += " {avgdps:0>3.1f}".format(
            avgdps=avgdps
            )

        self.summarytext += summarystring

class FITParser(object):

    def __init__(self, readfile):
        extension = readfile[-3:].lower()
        if extension == '.gz':
            newfile = readfile[-3:]
            with gzip.open(readfile,'rb') as f_in, open(newfile,'wb') as f_out:
                shutil.copyfileobj(f_in, f_out)
            self.readfile = newfile
        else:
            self.readfile = readfile

        self.fitfile = FitFile(self.readfile, check_crc=False)

        self.records = self.fitfile.messages

        recorddicts = []
        lapcounter = 0

        for record in self.records:
            if record.name == 'record':
                values = record.get_values()
                values['lapid'] = lapcounter
                recorddicts.append(values)
            if record.name == 'lap':
                lapcounter += 1



        self.df = pd.DataFrame(recorddicts)

        # columns to lowercase - this should be easier
        self.df.columns = [strip_non_ascii(x) for x in self.df.columns]
        self.df.columns = [x.encode('ascii','ignore') for x in self.df.columns]
        if pythonversion == 3:
            #        self.df.columns = [str(x) for x in self.df.columns]
            self.df.columns = [x.decode('ascii') for x in self.df.columns]

        self.df.rename(columns = str.lower,inplace=True)


        # check column dimensions

        for c in self.df.columns:
            x = self.df[c]
            if len(x.shape)>1: # pragma: no cover
                newdf = pd.DataFrame({
                    c: x.iloc[:,0].values
                    })
                self.df.drop(labels=c,axis=1,inplace=True)
                self.df[c] = newdf[c]

        try:
            latitude = self.df['position_lat']*(180./2**31)
            longitude = self.df['position_long']*(180./2**31)
        except KeyError: # pragma: no cover
            # no coordinates
            latitude = 0
            longitude = 0

        try:
            distance = self.df['distance']
        except KeyError: # pragma: no cover
            distance = pd.Series(np.zeros(len(self.df)))

        self.df['position_lat'] = latitude
        self.df['position_long'] = longitude


        if pd.isnull(distance).all():
            dist2 = np.zeros(len(distance))
            for i in range(len(distance)-1):
                res = geo_distance(
                    latitude[i],
                    longitude[i],
                    latitude[i+1],
                    longitude[i+1]
                )
                deltal = 1000.*res[0]
                dist2[i+1] = dist2[i]+deltal
            self.df['distance'] = dist2

        try:
            velo = self.df['enhanced_speed']
        except KeyError: # pragma: no cover
            try:
                velo = self.df['speed']
            except KeyError:
                velo = pd.Series(np.zeros(len(self.df)))

        try:
            if velo.mean() >= 1000: # pragma: no cover
                velo = velo/1000.
        except TypeError: # pragma: no cover
            pass


        try:
            timestamps = self.df['timestamp'].apply(totimestamp)
        except AttributeError: # pragma: no cover
            pass

        try:
            pace = 500./velo
        except TypeError: # pragma: no cover
            pace = pd.Series(np.zeros(len(self.df)))
        elapsed_time = timestamps-timestamps.values[0]

        self.df['TimeStamp (sec)'] = timestamps
        self.df[' Stroke500mPace (sec/500m)'] = pace
        self.df[' ElapsedTime (sec)'] = elapsed_time

        hrname = 'heart_rate'
        spmname = 'cadence'

        if 'heart rate' in self.df.columns: # pragma: no cover
            hrname = 'heart rate'

        if 'stroke rate' in self.df.columns: # pragma: no cover
            spmname = 'stroke rate'

        newcolnames = {
            'power': ' Power (watts)',
            hrname: ' HRCur (bpm)',
            'position_long': ' longitude',
            'position_lat': ' latitude',
            spmname: ' Cadence (stokes/min)',
            'lapid': ' lapIdx',
            'distance': ' Horizontal (meters)'
            }

        self.df.rename(columns=newcolnames,inplace=True)

        # timestamp
        # distance
        # pace
        # elapsedtime


    def write_csv(self, writefile="fit_o.csv", gzip=False):

        if gzip: # pragma: no cover
            return self.df.to_csv(writefile+'.gz', index_label='index',
                                  compression='gzip')
        else:
            return self.df.to_csv(writefile, index_label='index')

class JSONParser(object): # pragma: no cover
    def __init__(self, json_file):
        df = pd.DataFrame()
        with open(json_file,'r') as f:
            data = json.load(f)

        laps = data['laps']

        for lap in laps:
            points = lap['points']
            ldf = pd.DataFrame.from_records(points)
            df=df.append(ldf,ignore_index=True)


        self.df = df

        newcolnames = {
            'time':'TimeStamp (sec)',
            'hr':' HRCur (bpm)',
            }

        self.df.rename(columns=newcolnames,inplace=True)


    def write_csv(self,writefile="json_o.csv", gzip = False):
        if gzip:
            return self.df.to_csv(writefile+'.gz', index_label='index',compression='gzip')
        else:
            return self.df.to_csv(writefile, index_label='index')

class TCXParserTester(object): # pragma: no cover
    def __init__(self, tcx_file):
        tree = objectify.parse(tcx_file)
        self.root = tree.getroot()
        self.activity = self.root.Activities.Activity

        # need to select only trackpoints with Cadence, Distance,
        # Time & HR data
        self.selectionstring = '//ns:Trackpoint[descendant::ns:HeartRateBpm]'
        self.selectionstring += '[descendant::ns:Cadence]'
        self.selectionstring += '[descendant::ns:DistanceMeters]'
        self.selectionstring += '[descendant::ns:Time]'


        self.hr_values = self.root.xpath(self.selectionstring
                                         +'//ns:HeartRateBpm/ns:Value',
                                         namespaces={'ns': NAMESPACE})



        self.distance_values = self.root.xpath(self.selectionstring
                                               +'/ns:DistanceMeters',
                                               namespaces={'ns': NAMESPACE})

        self.spm_values = self.root.xpath(self.selectionstring
                                          +'/ns:Cadence',
                                          namespaces={'ns': NAMESPACE})

    def getarray(self, str1, str2=''):
        selectionstring = self.selectionstring
        selectionstring = selectionstring+'//ns:'+str1
        if str2 != '':
            selectionstring = selectionstring+'/ns:'+str2

        the_array = self.root.xpath(selectionstring,
                                    namespaces={'ns': NAMESPACE})

        return the_array


class GPXParser(object): # pragma: no cover
    def __init__(self, gpx_file, *args, **kwargs):
        self.df = gpxtools.gpxtodf2(gpx_file)

    def write_csv(self, writefile='example.csv', window_size=5, gzip=False):
        data = self.df
        data = data.sort_values(by='TimeStamp (sec)', ascending=True)
        data = data.fillna(method='ffill')

        # drop all-zero columns
        for c in data.columns:
            if (data[c] == 0).any() and data[c].mean() == 0:
                data = data.drop(c, axis=1)
            if c == 'Position':
                data = data.drop(c, axis=1)
            if c == 'Extensions':
                data = data.drop(c, axis=1)

        if gzip:
            return data.to_csv(writefile+'.gz', index_label='index',
                               compression='gzip')
        else:
            return data.to_csv(writefile, index_label='index')

class TCXParser(object):
    def __init__(self, tcx_file, *args, **kwargs):
        if 'alternative' in kwargs: # pragma: no cover
            alternative = kwargs['alternative']
        else:
            alternative = False

        if alternative: # pragma: no cover
            self.df = tcxtools.tcxtodf(tcx_file)
        else:
            self.df = tcxtools.tcxtodf2(tcx_file)

        try:
            lat = self.df['latitude'].apply(tofloat).values
            longitude = self.df['longitude'].apply(tofloat).values
        except KeyError: # pragma: no cover
            self.df['latitude'] = 0
            self.df['longitude'] = 0
            lat = self.df['latitude'].apply(tofloat).values
            longitude = self.df['longitude'].apply(tofloat).values


        unixtimes = self.df['timestamp'].values
        try:
            spm = self.df['Cadence'].apply(tofloat).values
        except KeyError: # pragma: no cover
            try:
                spm = self.df['StrokeRate'].apply(tofloat).values
                self.df['Cadence'] = self.df['StrokeRate']
            except KeyError:
                try:
                    spm = 0.0*self.df['Speed'].apply(tofloat).values
                except KeyError:
                    spm = 0.0*unixtimes


        try:
            velo = self.df['Speed'].apply(tofloat)
            dist2 = self.df['DistanceMeters'].apply(tofloat)
            strokelength = velo*60./spm
        except KeyError: # pragma: no cover
            nr_rows = len(lat)
            dist2 = np.zeros(nr_rows)
            velo = np.zeros(nr_rows)
            strokelength = np.zeros(nr_rows)
            for i in range(nr_rows-1):
                res = geo_distance(lat[i], longitude[i], lat[i+1], longitude[i+1])
                deltal = 1000.*res[0]
                dist2[i+1] = dist2[i]+deltal
                try:
                    velo[i+1] = deltal/(1.0*(unixtimes[i+1]-unixtimes[i]))
                except ZeroDivisionError:
                    velo[i+1] = velo[i]
                if spm[i] != 0:
                    strokelength[i] = deltal*60/spm[i]
                else: # pragma: no cover
                    strokelength[i] = 0.

        try:
            power = self.df['Watts']
        except KeyError: # pragma: no cover
            try:
                power = self.df['ns3:Watts']
            except KeyError:
                power = 0*spm

            self.df['Watts'] = power

        p = 500./velo

        self.df[' Horizontal (meters)'] = dist2
        self.df[' StrokeDistance (meters)'] = strokelength
        self.df[' Stroke500mPace (sec/500m)'] = p

        # translate from standard TCX names to our naming convention
        self.columns = {
            'timestamp':'TimeStamp (sec)',
            'Cadence': ' Cadence (stokes/min)',
            'HeartRateBpm' : ' HRCur (bpm)',
            'Watts': ' Power (watts)',
            'lapid': ' lapIdx',
            'latitude': ' latitude',
            'longitude': ' longitude',
        }

        self.df.rename(columns=self.columns, inplace=True)

        cc = [value for key, value in self.columns.items()]

        for c in cc:
            if c != 'lapIdx':
                try:
                    self.df[c] = self.df[c].astype(float)
                except KeyError: # pragma: no cover
                    pass


    def write_csv(self, writefile='example.csv', window_size=5, gzip=False):
        data = self.df
        data = data.sort_values(by='TimeStamp (sec)', ascending=True)
        data = data.fillna(method='ffill')

        # drop all-zero columns
        for c in data.columns:
            if (data[c] == 0).any() and data[c].mean() == 0:
                data = data.drop(c, axis=1)
            if c == 'Position': # pragma: no cover
                data = data.drop(c, axis=1)
            if c == 'Extensions': # pragma: no cover
                data = data.drop(c, axis=1)

        if gzip: # pragma: no cover
            return data.to_csv(writefile+'.gz', index_label='index',
                               compression='gzip')
        else:
            return data.to_csv(writefile, index_label='index')

# pylint: disable=C0103, C0303 # pragma: no cover # pragma: no cover Returns the string without non ASCII characters # pragma: no cover # pragma: no cover #duration = datetime.strptime(durationstring,"%M:%S.%f") # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # add total summary # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # columns to lowercase - this should be easier #        self.df.columns = [str(x) for x in self.df.columns] # check column dimensions # pragma: no cover # pragma: no cover # no coordinates # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # timestamp # distance # pace # elapsedtime # pragma: no cover # pragma: no cover # pragma: no cover # need to select only trackpoints with Cadence, Distance, # Time & HR data # pragma: no cover # drop all-zero columns # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # pragma: no cover # translate from standard TCX names to our naming convention # pragma: no cover # drop all-zero columns # pragma: no cover # pragma: no cover # pragma: no cover