def build_lift_selected(self, lift=None):
# TODO: Clean this mess up and make it pretty
# TODO: Clear current workout stats
if lift:
if isinstance(lift, QtWidgets.QTreeWidgetItem):
txt = lift.text(1)
elif isinstance(lift, str):
txt = lift
else:
txt = ''
else:
txt = self.build_lifts.currentItem().text(1)
if txt:
if txt == self._build_cur_lift:
return
else:
self._build_cur_lift = txt
else:
return
pre_workouts = analysis.get_max_e1RM(self.db.workout_log[self.db.workout_log.Lift == self._build_cur_lift])
pre_e1RM = pre_workouts[pre_workouts.notnull()][-1]
self._build_tgt_e1rm = pre_e1RM
self.build_populate_rpe_table()
self.build_target.setValue(int(self._build_tgt_e1rm))
self.build_btn_add.setEnabled(True)
self.build_workout_switch_tables()
self.build_data_changed.emit()
def log_populate_stats(self, lift=None):
if pd.isnull(self.log_date) or self._log_table_view.empty:
return
self.log_tree.clear()
cur_wkt = self.db.workout_log[self.db.workout_log.index == self._log_date]
pre_30d = self.db.workout_log[self.db.workout_log.index >= self._log_date - pd.Timedelta(days=30)]
pre_30d = pre_30d[pre_30d.index < self._log_date]
e1RM_node = QTreeWidgetItem()
e1RM_node.setText(0, 'e1RMs:')
for lift, lift_data in cur_wkt.groupby('Lift'):
e1RM = max(analysis.get_max_e1RM(lift_data))
if pre_30d.Lift.isin([lift]).any():
e1RM_pre_30d = max(analysis.get_max_e1RM(pre_30d[pre_30d.Lift == lift]))
else:
e1RM_pre_30d = ''
child_node = QTreeWidgetItem()
child_node.setText(0, str(lift))
child_node.setText(1, str(e1RM))
child_node.setText(2, str(e1RM_pre_30d))
e1RM_node.addChild(child_node)
volume_node = QTreeWidgetItem()
volume_node.setText(0, 'Volume:')
for cat, cat_data in cur_wkt.groupby('Category'):
vol = sum(cat_data.Volume)
if pre_30d.Category.isin([cat]).any():
vol_pre_30d = round(pre_30d[pre_30d.Category == cat].resample('1d', how=np.sum).Volume.mean())
else:
vol_pre_30d = ''
child_node = QTreeWidgetItem()
child_node.setText(0, str(cat))
child_node.setText(1, str(vol))
child_node.setText(2, str(vol_pre_30d))
volume_node.addChild(child_node)
nt_node = QTreeWidgetItem()
nt_node.setText(0, 'Normalized Tonnage:')
for cat, cat_data in cur_wkt.groupby('Category'):
nt = round(analysis.calc_normalized_tonnage(cat_data).sum())
if pre_30d.Category.isin([cat]).any():
nt_pre_30d = round(analysis.calc_normalized_tonnage(
pre_30d[pre_30d.Category == cat]).resample('1d', how=np.sum).mean())
else:
nt_pre_30d = ''
child_node = QTreeWidgetItem()
child_node.setText(0, str(cat))
child_node.setText(1, str(nt))
child_node.setText(2, str(nt_pre_30d))
nt_node.addChild(child_node)
# TODO: Figure out why fatigue isn't being normalized to weekly average
fatigue_node = QTreeWidgetItem()
fatigue_node.setText(0, 'Fatigue:')
for slot, slot_data in cur_wkt.groupby('Slot'):
fatigue = analysis.get_fatigue(slot_data)
fatigue = fatigue.resample('1d', how=np.sum)
fatigue = fatigue.fillna(0).iloc[-7:].sum()
if pre_30d.Slot.isin([slot]).any():
fatigue_30d = analysis.get_fatigue(pre_30d[pre_30d.Slot == slot])
fatigue_30d = fatigue_30d.resample('1d', how=np.sum)
fatigue_30d = round(fatigue_30d.fillna(0).mean() * 7.0, 1)
else:
fatigue_30d = ''
child_node = QTreeWidgetItem()
child_node.setText(0, str(slot))
child_node.setText(1, str(fatigue))
child_node.setText(2, str(fatigue_30d))
fatigue_node.addChild(child_node)
self.log_tree.addTopLevelItems([e1RM_node, volume_node, fatigue_node, nt_node])
self.log_tree.expandAll()
