| 1 | import os |
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| 2 | from datetime import datetime, timedelta, timezone |
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| 3 | from decimal import Decimal |
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| 4 | |
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| 5 | import pytz |
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| 6 | import gpxpy |
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| 7 | from repoze.folder import Folder |
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| 8 | from pyramid.security import Allow, Everyone |
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| 9 | |
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| 10 | from ow.utilities import ( |
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| 11 | GPXMinidomParser, |
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| 12 | copy_blob, |
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| 13 | create_blob, |
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| 14 | mps_to_kmph, |
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| 15 | save_map_screenshot, |
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| 16 | timedelta_to_hms |
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| 17 | ) |
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| 18 | |
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| 19 | from ow.fit import Fit |
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| 20 | |
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| 21 | |
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| 22 | class Workout(Folder): |
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| 23 | |
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| 24 | __parent__ = __name__ = None |
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| 25 | |
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| 26 | def __acl__(self): |
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| 27 | """ |
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| 28 | If the workout is owned by a given user, only that user have access to |
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| 29 | it (for now). If not, everybody can view it, only admins can edit it. |
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| 30 | """ |
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| 31 | # Default permissions |
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| 32 | permissions = [ |
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| 33 | (Allow, Everyone, 'view'), |
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| 34 | (Allow, 'group:admins', 'edit') |
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| 35 | ] |
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| 36 | |
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| 37 | uid = getattr(self.__parent__, 'uid', None) |
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| 38 | if uid is not None: |
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| 39 | # Change permissions in case this workout has an owner |
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| 40 | permissions = [ |
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| 41 | (Allow, str(uid), 'view'), |
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| 42 | (Allow, str(uid), 'edit'), |
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| 43 | ] |
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| 44 | return permissions |
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| 45 | |
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| 46 | def __init__(self, **kw): |
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| 47 | super(Workout, self).__init__() |
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| 48 | # we do store datetime objects with UTC timezone |
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| 49 | self.start = kw.get('start', datetime.now(timezone.utc)) |
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| 50 | self.sport = kw.get('sport', 'unknown') # string |
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| 51 | self.title = kw.get('title', '') # unicode string |
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| 52 | self.notes = kw.get('notes', '') # unicode string |
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| 53 | self.duration = kw.get('duration', None) # a timedelta object |
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| 54 | self.distance = kw.get('distance', None) # kilometers, Decimal |
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| 55 | self.speed = kw.get('speed', {}) |
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| 56 | self.hr_min = kw.get('hr_min', None) # bpm, Decimal |
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| 57 | self.hr_max = kw.get('hr_max', None) # bpm, Decimal |
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| 58 | self.hr_avg = kw.get('hr_avg', None) # bpm, Decimal |
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| 59 | self.uphill = kw.get('uphill', None) |
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| 60 | self.downhill = kw.get('downhill', None) |
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| 61 | self.cad_min = kw.get('cad_min', None) |
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| 62 | self.cad_max = kw.get('cad_max', None) |
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| 63 | self.cad_avg = kw.get('cad_avg', None) |
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| 64 | self.atemp_min = kw.get('atemp_min', None) |
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| 65 | self.atemp_max = kw.get('atemp_max', None) |
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| 66 | self.atemp_avg = kw.get('atemp_avg', None) |
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| 67 | self.tracking_file = kw.get('tracking_file', None) # Blob |
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| 68 | self.tracking_filetype = '' # unicode string |
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| 69 | # attr to store ANT fit files. For now this file is used to |
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| 70 | # generate a gpx-encoded tracking file we then use through |
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| 71 | # the whole app |
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| 72 | self.fit_file = kw.get('fit_file', None) # Blob |
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| 73 | |
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| 74 | @property |
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| 75 | def workout_id(self): |
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| 76 | return self.__name__ |
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| 77 | |
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| 78 | @property |
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| 79 | def owner(self): |
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| 80 | return self.__parent__ |
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| 81 | |
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| 82 | @property |
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| 83 | def end(self): |
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| 84 | if not self.duration: |
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| 85 | return None |
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| 86 | return self.start + self.duration |
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| 87 | |
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| 88 | @property |
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| 89 | def start_date(self): |
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| 90 | return self.start.strftime('%d/%m/%Y') |
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| 91 | |
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| 92 | @property |
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| 93 | def start_time(self): |
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| 94 | return self.start.strftime('%H:%M') |
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| 95 | |
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| 96 | def start_in_timezone(self, timezone): |
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| 97 | """ |
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| 98 | Return a string representation of the start date and time, |
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| 99 | localized into the given timezone |
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| 100 | """ |
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| 101 | _start = self.start.astimezone(pytz.timezone(timezone)) |
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| 102 | return _start.strftime('%d/%m/%Y %H:%M (%Z)') |
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| 103 | |
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| 104 | def end_in_timezone(self, timezone): |
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| 105 | """ |
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| 106 | Return a string representation of the end date and time, |
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| 107 | localized into the given timezone |
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| 108 | """ |
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| 109 | _end = self.end.astimezone(pytz.timezone(timezone)) |
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| 110 | return _end.strftime('%d/%m/%Y %H:%M (%Z)') |
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| 111 | |
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| 112 | def split_duration(self): |
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| 113 | return timedelta_to_hms(self.duration) |
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| 114 | |
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| 115 | @property |
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| 116 | def duration_hours(self): |
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| 117 | return str(self.split_duration()[0]).zfill(2) |
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| 118 | |
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| 119 | @property |
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| 120 | def duration_minutes(self): |
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| 121 | return str(self.split_duration()[1]).zfill(2) |
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| 122 | |
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| 123 | @property |
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| 124 | def duration_seconds(self): |
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| 125 | return str(self.split_duration()[2]).zfill(2) |
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| 126 | |
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| 127 | @property |
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| 128 | def _duration(self): |
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| 129 | return ':'.join( |
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| 130 | [self.duration_hours, self.duration_minutes, self.duration_seconds] |
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| 131 | ) |
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| 132 | |
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| 133 | @property |
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| 134 | def rounded_distance(self): |
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| 135 | """ |
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| 136 | Return rounded value for distance, '-' if the workout has no distance |
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| 137 | data (weight lifting, martial arts, table tennis, etc) |
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| 138 | """ |
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| 139 | if self.distance: |
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| 140 | return round(self.distance, 1) |
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| 141 | return '-' |
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| 142 | |
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| 143 | @property |
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| 144 | def has_hr(self): |
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| 145 | """ |
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| 146 | True if this workout has heart rate data, False otherwise |
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| 147 | """ |
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| 148 | data = [self.hr_min, self.hr_max, self.hr_avg] |
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| 149 | return data.count(None) == 0 |
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| 150 | |
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| 151 | @property |
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| 152 | def hr(self): |
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| 153 | """ |
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| 154 | Return a dict with rounded values for hr min, max and average, |
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| 155 | return None if there is no heart rate data for this workout |
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| 156 | """ |
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| 157 | if self.has_hr: |
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| 158 | return {'min': round(self.hr_min), |
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| 159 | 'max': round(self.hr_max), |
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| 160 | 'avg': round(self.hr_avg)} |
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| 161 | return None |
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| 162 | |
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| 163 | @property |
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| 164 | def has_cad(self): |
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| 165 | """ |
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| 166 | True if this workout has cadence data, False otherwise |
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| 167 | """ |
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| 168 | data = [self.cad_min, self.cad_max, self.cad_avg] |
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| 169 | return data.count(None) == 0 |
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| 170 | |
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| 171 | @property |
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| 172 | def cad(self): |
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| 173 | """ |
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| 174 | Return a dict with rounded values for cadence min, max and average |
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| 175 | return None if there is no cadence data for this workout |
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| 176 | """ |
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| 177 | if self.has_cad: |
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| 178 | return {'min': round(self.cad_min), |
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| 179 | 'max': round(self.cad_max), |
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| 180 | 'avg': round(self.cad_avg)} |
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| 181 | return None |
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| 182 | |
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| 183 | @property |
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| 184 | def has_atemp(self): |
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| 185 | """ |
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| 186 | True if this workout has temperature data, False otherwise |
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| 187 | """ |
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| 188 | data = [self.atemp_min, self.atemp_max, self.atemp_avg] |
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| 189 | return data.count(None) == 0 |
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| 190 | |
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| 191 | @property |
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| 192 | def atemp(self): |
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| 193 | """ |
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| 194 | Return a dict with rounded values for temperature min, max and average |
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| 195 | return None if there is no temperature data for this workout |
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| 196 | """ |
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| 197 | if self.has_atemp: |
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| 198 | return {'min': round(self.atemp_min), |
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| 199 | 'max': round(self.atemp_max), |
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| 200 | 'avg': round(self.atemp_avg)} |
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| 201 | return None |
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| 202 | |
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| 203 | @property |
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| 204 | def tracking_file_path(self): |
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| 205 | """ |
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| 206 | Get the path to the blob file attached as a tracking file. |
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| 207 | |
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| 208 | First check if the file was not committed to the db yet (new workout |
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| 209 | not saved yet) and use the path to the temporary file on the fs. |
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| 210 | If none is found there, go for the real blob file in the blobs |
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| 211 | directory |
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| 212 | """ |
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| 213 | path = None |
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| 214 | if self.tracking_file: |
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| 215 | path = self.tracking_file._uncommitted() |
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| 216 | if path is None: |
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| 217 | path = self.tracking_file.committed() |
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| 218 | return path |
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| 219 | |
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| 220 | @property |
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| 221 | def fit_file_path(self): |
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| 222 | """ |
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| 223 | Get the path to the blob file attached as a fit file. |
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| 224 | |
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| 225 | First check if the file was not committed to the db yet (new workout |
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| 226 | not saved yet) and use the path to the temporary file on the fs. |
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| 227 | If none is found there, go for the real blob file in the blobs |
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| 228 | directory |
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| 229 | """ |
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| 230 | path = None |
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| 231 | if self.fit_file: |
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| 232 | path = self.fit_file._uncommitted() |
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| 233 | if path is None: |
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| 234 | path = self.fit_file.committed() |
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| 235 | return path |
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| 236 | |
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| 237 | def load_from_file(self): |
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| 238 | """ |
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| 239 | Check which kind of tracking file we have for this workout, then call |
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| 240 | the proper method to load info from the tracking file |
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| 241 | """ |
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| 242 | if self.tracking_filetype == 'gpx': |
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| 243 | self.load_from_gpx() |
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| 244 | elif self.tracking_filetype == 'fit': |
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| 245 | self.load_from_fit() |
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| 246 | |
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| 247 | def load_from_gpx(self): |
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| 248 | """ |
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| 249 | Load some information from an attached gpx file. Return True if data |
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| 250 | had been succesfully loaded, False otherwise |
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| 251 | """ |
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| 252 | with self.tracking_file.open() as gpx_file: |
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| 253 | gpx_contents = gpx_file.read() |
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| 254 | gpx_contents = gpx_contents.decode('utf-8') |
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| 255 | gpx = gpxpy.parse(gpx_contents) |
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| 256 | if gpx.tracks: |
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| 257 | track = gpx.tracks[0] |
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| 258 | # Start time comes in UTC/GMT/ZULU |
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| 259 | time_bounds = track.get_time_bounds() |
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| 260 | self.start = time_bounds.start_time |
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| 261 | # ensure this datetime start object is timezone-aware |
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| 262 | self.start = self.start.replace(tzinfo=timezone.utc) |
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| 263 | # get_duration returns seconds |
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| 264 | self.duration = timedelta(seconds=track.get_duration()) |
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| 265 | # length_3d returns meters |
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| 266 | self.distance = Decimal(track.length_3d()) / Decimal(1000.00) |
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| 267 | ud = track.get_uphill_downhill() |
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| 268 | self.uphill = Decimal(ud.uphill) |
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| 269 | self.downhill = Decimal(ud.downhill) |
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| 270 | # If the user did not provide us with a title, and the gpx has |
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| 271 | # one, use that |
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| 272 | if not self.title and track.name: |
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| 273 | self.title = track.name |
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| 274 | |
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| 275 | # Hack to calculate some values from the GPX 1.1 extensions, |
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| 276 | # using our own parser (gpxpy does not support those yet) |
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| 277 | tracks = self.parse_gpx() |
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| 278 | hr = [] |
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| 279 | cad = [] |
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| 280 | atemp = [] |
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| 281 | for t in tracks: |
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| 282 | hr += [ |
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| 283 | d['hr'] for d in tracks[t] if d['hr'] is not None] |
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| 284 | cad += [ |
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| 285 | d['cad'] for d in tracks[t] if d['cad'] is not None] |
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| 286 | atemp += [ |
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| 287 | d['atemp'] for d in tracks[t] |
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| 288 | if d['atemp'] is not None] |
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| 289 | |
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| 290 | if hr: |
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| 291 | self.hr_min = Decimal(min(hr)) |
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| 292 | self.hr_avg = Decimal(sum(hr)) / Decimal(len(hr)) |
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| 293 | self.hr_max = Decimal(max(hr)) |
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| 294 | |
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| 295 | if cad: |
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| 296 | self.cad_min = Decimal(min(cad)) |
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| 297 | self.cad_avg = Decimal(sum(cad)) / Decimal(len(cad)) |
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| 298 | self.cad_max = Decimal(max(cad)) |
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| 299 | |
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| 300 | if atemp: |
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| 301 | self.atemp_min = Decimal(min(atemp)) |
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| 302 | self.atemp_avg = Decimal(sum(atemp)) / Decimal(len(atemp)) |
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| 303 | self.atemp_max = Decimal(max(atemp)) |
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| 304 | |
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| 305 | return True |
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| 306 | |
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| 307 | return False |
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| 308 | |
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| 309 | def parse_gpx(self): |
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| 310 | """ |
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| 311 | Parse the gpx using minidom. |
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| 312 | |
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| 313 | This method is needed as a workaround to get HR/CAD/TEMP values from |
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| 314 | gpx 1.1 extensions (gpxpy does not handle them correctly so far) |
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| 315 | """ |
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| 316 | if not self.has_gpx: |
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| 317 | # No gpx, nothing to return |
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| 318 | return {} |
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| 319 | |
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| 320 | # Get the path to the blob file, first check if the file was not |
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| 321 | # committed to the db yet (new workout not saved yet) and use the |
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| 322 | # path to the temporary file on the fs. If none is found there, go |
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| 323 | # for the final blob file |
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| 324 | gpx_path = self.tracking_file._p_blob_uncommitted |
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| 325 | if gpx_path is None: |
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| 326 | gpx_path = self.tracking_file._p_blob_committed |
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| 327 | |
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| 328 | # Create a parser, load the gpx and parse the tracks |
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| 329 | parser = GPXMinidomParser(gpx_path) |
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| 330 | parser.load_gpx() |
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| 331 | parser.parse_tracks() |
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| 332 | return parser.tracks |
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| 333 | |
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| 334 | def load_from_fit(self): |
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| 335 | """ |
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| 336 | Try to load data from an ANT-compatible .fit file (if any has been |
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| 337 | added to this workout). |
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| 338 | |
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| 339 | "Load data" means: |
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| 340 | |
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| 341 | 1. Copy over the uploaded fit file to self.fit_file, so we can keep |
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| 342 | that copy around for future use |
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| 343 | |
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| 344 | 2. generate a gpx object from the fit file |
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| 345 | |
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| 346 | 3. save the gpx object as the tracking_file, which then will be used |
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| 347 | by the current code to display and gather data to be displayed/shown |
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| 348 | to the user. |
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| 349 | |
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| 350 | 4. Grab some basic info from the fit file and store it in the Workout |
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| 351 | """ |
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| 352 | |
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| 353 | # we can call load_from_fit afterwards for updates. In such case, check |
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| 354 | # if the tracking file is a fit file uploaded to override the previous |
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| 355 | # one. If not, just reuse the existing fit file |
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| 356 | if self.tracking_filetype == 'fit': |
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| 357 | # backup the fit file |
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| 358 | self.fit_file = copy_blob(self.tracking_file) |
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| 359 | |
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| 360 | # create an instance of our Fit class |
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| 361 | fit = Fit(self.fit_file_path) |
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| 362 | fit.load() |
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| 363 | |
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| 364 | # fit -> gpx and store that as the main tracking file |
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| 365 | self.tracking_file = create_blob(fit.gpx, 'gpx') |
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| 366 | self.tracking_filetype = 'gpx' |
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| 367 | |
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| 368 | # grab the needed data from the fit file, update the workout |
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| 369 | self.sport = fit.data['sport'] |
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| 370 | self.start = fit.data['start'] |
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| 371 | # ensure this datetime start object is timezone-aware |
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| 372 | self.start = self.start.replace(tzinfo=timezone.utc) |
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| 373 | # duration comes in seconds, store a timedelta |
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| 374 | self.duration = timedelta(seconds=fit.data['duration']) |
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| 375 | if fit.data['distance']: |
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| 376 | # distance comes in meters |
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| 377 | self.distance = Decimal(fit.data['distance']) / Decimal(1000.00) |
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| 378 | if fit.data['uphill']: |
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| 379 | self.uphill = Decimal(fit.data['uphill']) |
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| 380 | if fit.data['downhill']: |
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| 381 | self.downhill = Decimal(fit.data['downhill']) |
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| 382 | # If the user did not provide us with a title, build one from the |
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| 383 | # info in the fit file |
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| 384 | if not self.title: |
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| 385 | self.title = fit.name |
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| 386 | |
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| 387 | if fit.data['max_speed']: |
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| 388 | self.speed['max'] = mps_to_kmph(fit.data['max_speed']) |
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| 389 | |
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| 390 | if fit.data['avg_speed']: |
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| 391 | self.speed['avg'] = mps_to_kmph(fit.data['avg_speed']) |
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| 392 | |
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| 393 | if fit.data['avg_hr']: |
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| 394 | self.hr_avg = Decimal(fit.data['avg_hr']) |
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| 395 | self.hr_min = Decimal(fit.data['min_hr']) |
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| 396 | self.hr_max = Decimal(fit.data['max_hr']) |
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| 397 | |
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| 398 | if fit.data['avg_cad']: |
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| 399 | self.cad_avg = Decimal(fit.data['avg_cad']) |
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| 400 | self.cad_min = Decimal(fit.data['min_cad']) |
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| 401 | self.cad_max = Decimal(fit.data['max_cad']) |
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| 402 | |
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| 403 | if fit.data['avg_atemp']: |
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| 404 | self.atemp_avg = Decimal(fit.data['avg_atemp']) |
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| 405 | self.atemp_min = Decimal(fit.data['min_atemp']) |
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| 406 | self.atemp_max = Decimal(fit.data['max_atemp']) |
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| 407 | |
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| 408 | return True |
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| 409 | |
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| 410 | @property |
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| 411 | def has_tracking_file(self): |
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| 412 | return self.tracking_file is not None |
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| 413 | |
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| 414 | @property |
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| 415 | def has_gpx(self): |
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| 416 | return self.has_tracking_file and self.tracking_filetype == 'gpx' |
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| 417 | |
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| 418 | @property |
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| 419 | def has_fit(self): |
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| 420 | return self.fit_file is not None |
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| 421 | |
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| 422 | @property |
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| 423 | def map_screenshot(self): |
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| 424 | """ |
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| 425 | Return the static path to the screenshot image of the map for |
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| 426 | this workout (works only for workouts with gps tracking) |
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| 427 | """ |
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| 428 | if not self.has_gpx: |
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| 429 | return None |
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| 430 | |
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| 431 | current_path = os.path.abspath(os.path.dirname(__file__)) |
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| 432 | screenshot_path = os.path.join( |
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| 433 | current_path, '../static/maps', |
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| 434 | str(self.owner.uid), str(self.workout_id)) + '.png' |
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| 435 | |
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| 436 | if not os.path.exists(screenshot_path): |
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| 437 | # screenshot does not exist, generate it |
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| 438 | save_map_screenshot(self) |
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| 439 | |
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| 440 | # the value returned is relative to the static files served |
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| 441 | # by the app, so we can use request.static_url() with it |
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| 442 | static_path = os.path.join('static/maps', str(self.owner.uid), |
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| 443 | str(self.workout_id)) |
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| 444 | return 'ow:' + static_path + '.png' |
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