From aa1d4ef039963d92b93ee67fd22f770daffab43d Mon Sep 17 00:00:00 2001 From: Christopher <110344005+RobotPsychologist@users.noreply.github.com> Date: Tue, 26 Nov 2024 20:03:18 -0500 Subject: [PATCH 1/6] Side by side histogram (#185) * Add chunk spilt function * Updated notebook to plot percentiles of chunks * Changed the labeling of the axes made better presentation. * Plot fan chart * Remove old dataset * Add simulated data * Update title * Update some minor stuff * Bug fixing and making the y-axis symmetric. * Updates to notebook --------- Co-authored-by: Tony Chan --- ...5mins_dayStart4hrs_minCarb10g_3hrMealW.csv | 26497 ---------------- ...5mins_dayStart4hrs_minCarb10g_3hrMealW.csv | 26497 ---------------- ...5mins_dayStart4hrs_minCarb10g_3hrMealW.csv | 26497 ---------------- ...5mins_dayStart4hrs_minCarb10g_3hrMealW.csv | 26497 ---------------- ...5mins_dayStart4hrs_minCarb10g_3hrMealW.csv | 26497 ---------------- ...5mins_dayStart4hrs_minCarb10g_3hrMealW.csv | 26497 ---------------- ...1-18_adu001__i5mins_d4hrs_c5g_l2hrs_n3.csv | 2018 ++ ...1-18_adu001__i5mins_d4hrs_c5g_l3hrs_n3.csv | 2018 ++ ...1-18_adu003__i5mins_d4hrs_c5g_l2hrs_n3.csv | 2018 ++ ...1-18_adu003__i5mins_d4hrs_c5g_l3hrs_n3.csv | 2018 ++ ...5mins_dayStart4hrs_minCarb10g_3hrMealW.csv | 26497 ---------------- ...5mins_dayStart4hrs_minCarb10g_3hrMealW.csv | 26497 ---------------- .../meal_identification/datasets/plots.py | 283 + .../notebooks/1.04-tc-change-point-plot.ipynb | 692 +- 14 files changed, 8545 insertions(+), 212478 deletions(-) delete mode 100644 0_meal_identification/meal_identification/data/interim/2024-10-30_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv delete mode 100644 0_meal_identification/meal_identification/data/interim/2024-10-30_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv delete mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-07_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv delete mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-07_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv delete mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-10_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv delete mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-10_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv create mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-18_adu001__i5mins_d4hrs_c5g_l2hrs_n3.csv create mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-18_adu001__i5mins_d4hrs_c5g_l3hrs_n3.csv create mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-18_adu003__i5mins_d4hrs_c5g_l2hrs_n3.csv create mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-18_adu003__i5mins_d4hrs_c5g_l3hrs_n3.csv delete mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-23_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv delete mode 100644 0_meal_identification/meal_identification/data/interim/2024-11-23_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv diff --git a/0_meal_identification/meal_identification/data/interim/2024-10-30_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv b/0_meal_identification/meal_identification/data/interim/2024-10-30_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv deleted file mode 100644 index 03f601f..0000000 --- a/0_meal_identification/meal_identification/data/interim/2024-10-30_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv +++ /dev/null @@ -1,26497 +0,0 @@ -date,bgl,msg_type,affects_fob,affects_iob,dose_units,food_g,food_glycemic_index,food_g_keep,day_start_shift -2024-07-01 00:00:00-05:00,98.0,,,,,,,,2024-06-30 -2024-07-01 00:05:00-05:00,100.0,,,,,,,,2024-06-30 -2024-07-01 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14:45:00-05:00,111.0,,,,,0.0,,,2024-07-11 -2024-07-11 14:50:00-05:00,111.0,,,,,0.0,,,2024-07-11 -2024-07-11 14:55:00-05:00,114.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:00:00-05:00,119.0,,False,True,,0.0,,,2024-07-11 -2024-07-11 15:05:00-05:00,125.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:10:00-05:00,125.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:15:00-05:00,119.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:20:00-05:00,107.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:25:00-05:00,96.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:30:00-05:00,87.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:35:00-05:00,81.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:40:00-05:00,77.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:45:00-05:00,75.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:50:00-05:00,73.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:55:00-05:00,72.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:00:00-05:00,72.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:05:00-05:00,71.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:10:00-05:00,70.0,,,,,0.0,,,2024-07-11 -2024-07-11 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17:45:00-05:00,110.0,,,,,,,,2024-07-11 -2024-07-11 17:50:00-05:00,110.0,ANNOUNCE_MEAL,,,,79.0,,40.0,2024-07-11 -2024-07-11 17:55:00-05:00,104.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:00:00-05:00,99.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:05:00-05:00,95.0,,True,False,,0.0,1.0,,2024-07-11 -2024-07-11 18:10:00-05:00,84.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:15:00-05:00,75.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:20:00-05:00,76.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:25:00-05:00,80.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:30:00-05:00,84.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:35:00-05:00,81.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:40:00-05:00,81.0,,,,,0.0,,15.0,2024-07-11 -2024-07-11 18:45:00-05:00,70.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:50:00-05:00,67.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:55:00-05:00,67.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:00:00-05:00,67.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:05:00-05:00,68.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:10:00-05:00,68.0,,,,,0.0,,,2024-07-11 -2024-07-11 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23:55:00-05:00,141.0,,,,,,,,2024-07-11 -2024-07-12 00:00:00-05:00,142.0,,,,,,,,2024-07-11 -2024-07-12 00:05:00-05:00,142.0,DOSE_INSULIN,False,True,2.0,,,,2024-07-11 -2024-07-12 00:10:00-05:00,137.0,,,,,,,,2024-07-11 -2024-07-12 00:15:00-05:00,136.0,,,,,,,,2024-07-11 -2024-07-12 00:20:00-05:00,135.0,,,,,,,,2024-07-11 -2024-07-12 00:25:00-05:00,141.0,,,,,,,,2024-07-11 -2024-07-12 00:30:00-05:00,141.0,,,,,,,,2024-07-11 -2024-07-12 00:35:00-05:00,141.0,,,,,,,,2024-07-11 -2024-07-12 00:40:00-05:00,142.0,,,,,,,,2024-07-11 -2024-07-12 00:45:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 00:50:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 00:55:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 01:00:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 01:05:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 01:10:00-05:00,142.0,,,,,,,,2024-07-11 -2024-07-12 01:15:00-05:00,140.0,,,,,,,,2024-07-11 -2024-07-12 01:20:00-05:00,136.0,,,,,,,,2024-07-11 -2024-07-12 01:25:00-05:00,134.0,,,,,,,,2024-07-11 -2024-07-12 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05:55:00-05:00,191.0,,,,,,,,2024-07-13 -2024-07-13 06:00:00-05:00,192.0,,,,,,,,2024-07-13 -2024-07-13 06:05:00-05:00,197.0,,,,,,,,2024-07-13 -2024-07-13 06:10:00-05:00,197.0,,,,,,,,2024-07-13 -2024-07-13 06:15:00-05:00,201.0,,,,,,,,2024-07-13 -2024-07-13 06:20:00-05:00,203.0,,,,,,,,2024-07-13 -2024-07-13 06:25:00-05:00,204.0,,,,,,,,2024-07-13 -2024-07-13 06:30:00-05:00,207.0,,,,,,,,2024-07-13 -2024-07-13 06:35:00-05:00,210.0,,,,,,,,2024-07-13 -2024-07-13 06:40:00-05:00,211.0,,,,,,,,2024-07-13 -2024-07-13 06:45:00-05:00,213.0,,,,,,,,2024-07-13 -2024-07-13 06:50:00-05:00,214.0,,,,,,,,2024-07-13 -2024-07-13 06:55:00-05:00,216.0,,,,,,,,2024-07-13 -2024-07-13 07:00:00-05:00,216.0,,,,,,,,2024-07-13 -2024-07-13 07:05:00-05:00,218.0,,,,,,,,2024-07-13 -2024-07-13 07:10:00-05:00,216.0,,,,,,,,2024-07-13 -2024-07-13 07:15:00-05:00,213.0,,,,,,,,2024-07-13 -2024-07-13 07:20:00-05:00,205.0,,,,,,,,2024-07-13 -2024-07-13 07:25:00-05:00,200.0,,,,,,,,2024-07-13 -2024-07-13 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19:30:00-05:00,92.0,ANNOUNCE_MEAL,,,,73.0,,65.0,2024-07-14 -2024-07-14 19:35:00-05:00,82.0,,,,,0.0,,,2024-07-14 -2024-07-14 19:40:00-05:00,92.0,,,,,0.0,,,2024-07-14 -2024-07-14 19:45:00-05:00,93.0,,,,,0.0,,,2024-07-14 -2024-07-14 19:50:00-05:00,80.0,,,,,0.0,,,2024-07-14 -2024-07-14 19:55:00-05:00,69.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:00:00-05:00,78.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:05:00-05:00,81.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:10:00-05:00,82.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:15:00-05:00,110.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:20:00-05:00,130.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:25:00-05:00,132.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:30:00-05:00,137.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:35:00-05:00,137.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:40:00-05:00,137.0,,,,,0.0,,8.0,2024-07-14 -2024-07-14 20:45:00-05:00,130.0,,,,,0.0,,,2024-07-14 -2024-07-14 20:50:00-05:00,122.0,,False,True,22.0,0.0,,,2024-07-14 -2024-07-14 20:55:00-05:00,121.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:00:00-05:00,135.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:05:00-05:00,157.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:10:00-05:00,145.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:15:00-05:00,146.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:20:00-05:00,167.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:25:00-05:00,183.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:30:00-05:00,174.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:35:00-05:00,178.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:40:00-05:00,178.0,,False,False,,0.0,,,2024-07-14 -2024-07-14 21:45:00-05:00,126.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:50:00-05:00,132.0,,,,,0.0,,,2024-07-14 -2024-07-14 21:55:00-05:00,130.0,,,,,0.0,,,2024-07-14 -2024-07-14 22:00:00-05:00,134.0,,,,,0.0,,,2024-07-14 -2024-07-14 22:05:00-05:00,136.0,,,,,0.0,,,2024-07-14 -2024-07-14 22:10:00-05:00,123.0,,,,,0.0,,,2024-07-14 -2024-07-14 22:15:00-05:00,126.0,,,,,0.0,,,2024-07-14 -2024-07-14 22:20:00-05:00,138.0,,,,,0.0,,,2024-07-14 -2024-07-14 22:25:00-05:00,135.0,,,,,0.0,,,2024-07-14 -2024-07-14 22:30:00-05:00,140.0,,,,,0.0,,,2024-07-14 -2024-07-14 22:35:00-05:00,136.0,,,,,,,,2024-07-14 -2024-07-14 22:40:00-05:00,131.0,,,,,,,,2024-07-14 -2024-07-14 22:45:00-05:00,142.0,,,,,,,,2024-07-14 -2024-07-14 22:50:00-05:00,142.0,ANNOUNCE_MEAL,,,,25.0,,25.0,2024-07-14 -2024-07-14 22:55:00-05:00,153.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:00:00-05:00,163.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:05:00-05:00,173.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:10:00-05:00,173.0,,False,False,,0.0,,,2024-07-14 -2024-07-14 23:15:00-05:00,112.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:20:00-05:00,138.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:25:00-05:00,146.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:30:00-05:00,119.0,,False,False,5.0,0.0,,,2024-07-14 -2024-07-14 23:35:00-05:00,190.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:40:00-05:00,198.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:45:00-05:00,197.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:50:00-05:00,214.0,,,,,0.0,,,2024-07-14 -2024-07-14 23:55:00-05:00,204.0,,,,,0.0,,,2024-07-14 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23:25:00-05:00,134.0,,,,,,,,2024-07-17 -2024-07-17 23:30:00-05:00,126.0,,,,,,,,2024-07-17 -2024-07-17 23:35:00-05:00,130.0,,,,,,,,2024-07-17 -2024-07-17 23:40:00-05:00,130.0,,,,,,,,2024-07-17 -2024-07-17 23:45:00-05:00,126.0,,,,,,,,2024-07-17 -2024-07-17 23:50:00-05:00,125.0,,,,,,,,2024-07-17 -2024-07-17 23:55:00-05:00,122.0,,,,,,,,2024-07-17 -2024-07-18 00:00:00-05:00,118.0,,,,,,,,2024-07-17 -2024-07-18 00:05:00-05:00,107.0,,,,,,,,2024-07-17 -2024-07-18 00:10:00-05:00,104.0,,,,,,,,2024-07-17 -2024-07-18 00:15:00-05:00,96.0,,,,,,,,2024-07-17 -2024-07-18 00:20:00-05:00,88.0,,,,,,,,2024-07-17 -2024-07-18 00:25:00-05:00,86.0,,,,,,,,2024-07-17 -2024-07-18 00:30:00-05:00,87.0,,,,,,,,2024-07-17 -2024-07-18 00:35:00-05:00,82.0,,,,,,,,2024-07-17 -2024-07-18 00:40:00-05:00,76.0,,,,,,,,2024-07-17 -2024-07-18 00:45:00-05:00,85.0,,,,,,,,2024-07-17 -2024-07-18 00:50:00-05:00,84.0,,,,,,,,2024-07-17 -2024-07-18 00:55:00-05:00,83.0,,,,,,,,2024-07-17 -2024-07-18 01:00:00-05:00,76.0,,,,,,,,2024-07-17 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23:50:00-05:00,219.0,,,,,,,,2024-08-11 -2024-08-11 23:55:00-05:00,214.0,,,,,,,,2024-08-11 -2024-08-12 00:00:00-05:00,204.0,,,,,,,,2024-08-11 -2024-08-12 00:05:00-05:00,190.0,,,,,,,,2024-08-11 -2024-08-12 00:10:00-05:00,180.0,,,,,,,,2024-08-11 -2024-08-12 00:15:00-05:00,172.0,,,,,,,,2024-08-11 -2024-08-12 00:20:00-05:00,164.0,,,,,,,,2024-08-11 -2024-08-12 00:25:00-05:00,163.0,,,,,,,,2024-08-11 -2024-08-12 00:30:00-05:00,152.0,,,,,,,,2024-08-11 -2024-08-12 00:35:00-05:00,145.0,,,,,,,,2024-08-11 -2024-08-12 00:40:00-05:00,145.0,,,,,,,,2024-08-11 -2024-08-12 00:45:00-05:00,161.0,,,,,,,,2024-08-11 -2024-08-12 00:50:00-05:00,172.0,,,,,,,,2024-08-11 -2024-08-12 00:55:00-05:00,169.0,,,,,,,,2024-08-11 -2024-08-12 01:00:00-05:00,164.0,,,,,,,,2024-08-11 -2024-08-12 01:05:00-05:00,155.0,,,,,,,,2024-08-11 -2024-08-12 01:10:00-05:00,156.0,,,,,,,,2024-08-11 -2024-08-12 01:15:00-05:00,163.0,,,,,,,,2024-08-11 -2024-08-12 01:20:00-05:00,166.0,,,,,,,,2024-08-11 -2024-08-12 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21:45:00-05:00,142.0,,,,,0.0,,,2024-08-16 -2024-08-16 21:50:00-05:00,148.0,,,,,0.0,,,2024-08-16 -2024-08-16 21:55:00-05:00,157.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:00:00-05:00,171.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:05:00-05:00,175.0,,False,True,5.0,0.0,,,2024-08-16 -2024-08-16 22:10:00-05:00,173.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:15:00-05:00,169.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:20:00-05:00,165.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:25:00-05:00,171.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:30:00-05:00,166.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:35:00-05:00,174.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:40:00-05:00,171.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:45:00-05:00,169.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:50:00-05:00,176.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:55:00-05:00,174.0,,,,,0.0,,,2024-08-16 -2024-08-16 23:00:00-05:00,,,,,,0.0,,,2024-08-16 -2024-08-16 23:05:00-05:00,175.0,,,,,0.0,,,2024-08-16 -2024-08-16 23:10:00-05:00,146.0,,,,,0.0,,,2024-08-16 -2024-08-16 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00:55:00-05:00,,,,,,,,,2024-08-16 -2024-08-17 01:00:00-05:00,94.0,,,,,,,,2024-08-16 -2024-08-17 01:05:00-05:00,94.0,,,,,,,,2024-08-16 -2024-08-17 01:10:00-05:00,93.0,,,,,,,,2024-08-16 -2024-08-17 01:15:00-05:00,95.0,,,,,,,,2024-08-16 -2024-08-17 01:20:00-05:00,95.0,,,,,,,,2024-08-16 -2024-08-17 01:25:00-05:00,94.0,,,,,,,,2024-08-16 -2024-08-17 01:30:00-05:00,92.0,,,,,,,,2024-08-16 -2024-08-17 01:35:00-05:00,91.0,,,,,,,,2024-08-16 -2024-08-17 01:40:00-05:00,90.0,,,,,,,,2024-08-16 -2024-08-17 01:45:00-05:00,92.0,,,,,,,,2024-08-16 -2024-08-17 01:50:00-05:00,91.0,,,,,,,,2024-08-16 -2024-08-17 01:55:00-05:00,90.0,,,,,,,,2024-08-16 -2024-08-17 02:00:00-05:00,90.0,,,,,,,,2024-08-16 -2024-08-17 02:05:00-05:00,87.0,,,,,,,,2024-08-16 -2024-08-17 02:10:00-05:00,87.0,,,,,,,,2024-08-16 -2024-08-17 02:15:00-05:00,88.0,,,,,,,,2024-08-16 -2024-08-17 02:20:00-05:00,87.0,,,,,,,,2024-08-16 -2024-08-17 02:25:00-05:00,89.0,,,,,,,,2024-08-16 -2024-08-17 02:30:00-05:00,87.0,,,,,,,,2024-08-16 -2024-08-17 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00:55:00-05:00,86.0,,,,,,,,2024-08-26 -2024-08-27 01:00:00-05:00,84.0,,,,,,,,2024-08-26 -2024-08-27 01:05:00-05:00,81.0,,,,,,,,2024-08-26 -2024-08-27 01:10:00-05:00,78.0,,,,,,,,2024-08-26 -2024-08-27 01:15:00-05:00,79.0,,,,,,,,2024-08-26 -2024-08-27 01:20:00-05:00,76.0,,,,,,,,2024-08-26 -2024-08-27 01:25:00-05:00,71.0,INTERVENTION_SNACK,True,False,,6.0,1.0,,2024-08-26 -2024-08-27 01:30:00-05:00,68.0,,,,,,,,2024-08-26 -2024-08-27 01:35:00-05:00,68.0,,,,,,,,2024-08-26 -2024-08-27 01:40:00-05:00,62.0,,,,,,,,2024-08-26 -2024-08-27 01:45:00-05:00,59.0,,,,,,,,2024-08-26 -2024-08-27 01:50:00-05:00,58.0,,,,,,,,2024-08-26 -2024-08-27 01:55:00-05:00,60.0,,,,,,,,2024-08-26 -2024-08-27 02:00:00-05:00,63.0,,,,,,,,2024-08-26 -2024-08-27 02:05:00-05:00,69.0,,,,,,,,2024-08-26 -2024-08-27 02:10:00-05:00,74.0,,,,,,,,2024-08-26 -2024-08-27 02:15:00-05:00,87.0,,,,,,,,2024-08-26 -2024-08-27 02:20:00-05:00,97.0,,,,,,,,2024-08-26 -2024-08-27 02:25:00-05:00,98.0,,,,,,,,2024-08-26 -2024-08-27 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23:35:00-05:00,95.0,,,,,,,,2024-09-30 -2024-09-30 23:40:00-05:00,87.0,,,,,,,,2024-09-30 -2024-09-30 23:45:00-05:00,86.0,,,,,,,,2024-09-30 -2024-09-30 23:50:00-05:00,89.0,,,,,,,,2024-09-30 -2024-09-30 23:55:00-05:00,88.0,,,,,,,,2024-09-30 diff --git a/0_meal_identification/meal_identification/data/interim/2024-10-30_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv b/0_meal_identification/meal_identification/data/interim/2024-10-30_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv deleted file mode 100644 index 18642dc..0000000 --- a/0_meal_identification/meal_identification/data/interim/2024-10-30_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv +++ /dev/null @@ -1,26497 +0,0 @@ -date,bgl,msg_type,affects_fob,affects_iob,dose_units,food_g,food_glycemic_index,food_g_keep,day_start_shift -2024-07-01 00:00:00-04:00,115.0,,,,,,,,2024-06-30 -2024-07-01 00:05:00-04:00,112.0,,,,,,,,2024-06-30 -2024-07-01 00:10:00-04:00,121.0,,,,,,,,2024-06-30 -2024-07-01 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21:35:00-04:00,121.0,,,,,0.0,,,2024-08-03 -2024-08-03 21:40:00-04:00,121.0,,True,False,,0.0,1.0,,2024-08-03 -2024-08-03 21:45:00-04:00,100.0,,,,,0.0,,,2024-08-03 -2024-08-03 21:50:00-04:00,105.0,,,,,0.0,,,2024-08-03 -2024-08-03 21:55:00-04:00,,,,,,0.0,,,2024-08-03 -2024-08-03 22:00:00-04:00,112.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:05:00-04:00,141.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:10:00-04:00,141.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:15:00-04:00,157.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:20:00-04:00,158.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:25:00-04:00,159.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:30:00-04:00,158.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:35:00-04:00,,,,,,,,,2024-08-03 -2024-08-03 22:40:00-04:00,133.0,,,,,,,,2024-08-03 -2024-08-03 22:45:00-04:00,125.0,,,,,,,,2024-08-03 -2024-08-03 22:50:00-04:00,115.0,,,,,,,,2024-08-03 -2024-08-03 22:55:00-04:00,112.0,,,,,,,,2024-08-03 -2024-08-03 23:00:00-04:00,103.0,,,,,,,,2024-08-03 -2024-08-03 23:05:00-04:00,103.0,,,,,,,,2024-08-03 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22:40:00-04:00,134.0,,,,,,,,2024-08-16 -2024-08-16 22:45:00-04:00,129.0,,,,,,,,2024-08-16 -2024-08-16 22:50:00-04:00,132.0,,,,,,,,2024-08-16 -2024-08-16 22:55:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-16 23:00:00-04:00,128.0,,,,,,,,2024-08-16 -2024-08-16 23:05:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-16 23:10:00-04:00,136.0,,,,,,,,2024-08-16 -2024-08-16 23:15:00-04:00,135.0,,,,,,,,2024-08-16 -2024-08-16 23:20:00-04:00,135.0,,,,,,,,2024-08-16 -2024-08-16 23:25:00-04:00,139.0,,,,,,,,2024-08-16 -2024-08-16 23:30:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-16 23:35:00-04:00,134.0,,,,,,,,2024-08-16 -2024-08-16 23:40:00-04:00,132.0,,,,,,,,2024-08-16 -2024-08-16 23:45:00-04:00,128.0,,,,,,,,2024-08-16 -2024-08-16 23:50:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-16 23:55:00-04:00,126.0,,,,,,,,2024-08-16 -2024-08-17 00:00:00-04:00,134.0,,,,,,,,2024-08-16 -2024-08-17 00:05:00-04:00,142.0,,,,,,,,2024-08-16 -2024-08-17 00:10:00-04:00,120.0,,,,,,,,2024-08-16 -2024-08-17 00:15:00-04:00,123.0,,,,,,,,2024-08-16 -2024-08-17 00:20:00-04:00,130.0,,,,,,,,2024-08-16 -2024-08-17 00:25:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-17 00:30:00-04:00,134.0,,,,,,,,2024-08-16 -2024-08-17 00:35:00-04:00,136.0,,,,,,,,2024-08-16 -2024-08-17 00:40:00-04:00,133.0,,,,,,,,2024-08-16 -2024-08-17 00:45:00-04:00,133.0,,,,,,,,2024-08-16 -2024-08-17 00:50:00-04:00,153.0,,,,,,,,2024-08-16 -2024-08-17 00:55:00-04:00,150.0,,,,,,,,2024-08-16 -2024-08-17 01:00:00-04:00,144.0,,,,,,,,2024-08-16 -2024-08-17 01:05:00-04:00,144.0,,,,,,,,2024-08-16 -2024-08-17 01:10:00-04:00,143.0,,,,,,,,2024-08-16 -2024-08-17 01:15:00-04:00,148.0,,,,,,,,2024-08-16 -2024-08-17 01:20:00-04:00,144.0,,,,,,,,2024-08-16 -2024-08-17 01:25:00-04:00,127.0,,,,,,,,2024-08-16 -2024-08-17 01:30:00-04:00,138.0,,,,,,,,2024-08-16 -2024-08-17 01:35:00-04:00,132.0,,,,,,,,2024-08-16 -2024-08-17 01:40:00-04:00,110.0,,,,,,,,2024-08-16 -2024-08-17 01:45:00-04:00,128.0,,,,,,,,2024-08-16 -2024-08-17 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-2024-08-31 09:25:00-04:00,153.0,,,,,,,,2024-08-31 -2024-08-31 09:30:00-04:00,148.0,,,,,,,,2024-08-31 -2024-08-31 09:35:00-04:00,146.0,,,,,,,,2024-08-31 -2024-08-31 09:40:00-04:00,141.0,,,,,,,,2024-08-31 -2024-08-31 09:45:00-04:00,140.0,,,,,,,,2024-08-31 -2024-08-31 09:50:00-04:00,139.0,,,,,,,,2024-08-31 -2024-08-31 09:55:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 10:00:00-04:00,146.0,,,,,,,,2024-08-31 -2024-08-31 10:05:00-04:00,148.0,,,,,,,,2024-08-31 -2024-08-31 10:10:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 10:15:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 10:20:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 10:25:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 10:30:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 10:35:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 10:40:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 10:45:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 10:50:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 10:55:00-04:00,128.0,,,,,,,,2024-08-31 -2024-08-31 11:00:00-04:00,132.0,,,,,,,,2024-08-31 -2024-08-31 11:05:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 11:10:00-04:00,143.0,,,,,,,,2024-08-31 -2024-08-31 11:15:00-04:00,145.0,,,,,,,,2024-08-31 -2024-08-31 11:20:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 11:25:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 11:30:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 11:35:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 11:40:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 11:45:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 11:50:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 11:55:00-04:00,146.0,,,,,,,,2024-08-31 -2024-08-31 12:00:00-04:00,141.0,,,,,,,,2024-08-31 -2024-08-31 12:05:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 12:10:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 12:15:00-04:00,136.0,,,,,,,,2024-08-31 -2024-08-31 12:20:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 12:25:00-04:00,112.0,,,,,,,,2024-08-31 -2024-08-31 12:30:00-04:00,112.0,,,,,,,,2024-08-31 -2024-08-31 12:35:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 12:40:00-04:00,138.0,,,,,,,,2024-08-31 -2024-08-31 12:45:00-04:00,128.0,,,,,,,,2024-08-31 -2024-08-31 12:50:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 12:55:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 13:00:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 13:05:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 13:10:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 13:15:00-04:00,127.0,DOSE_BASAL_INSULIN,False,True,12.0,,,,2024-08-31 -2024-08-31 13:20:00-04:00,137.0,,,,,,,,2024-08-31 -2024-08-31 13:25:00-04:00,138.0,,,,,,,,2024-08-31 -2024-08-31 13:30:00-04:00,130.0,MEDICAL_TEST_RESULT,False,False,,,,,2024-08-31 -2024-08-31 13:35:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 13:40:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 13:45:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 13:50:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 13:55:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 14:00:00-04:00,124.0,,,,,,,,2024-08-31 -2024-08-31 14:05:00-04:00,118.0,,,,,,,,2024-08-31 -2024-08-31 14:10:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 14:15:00-04:00,148.0,,,,,,,,2024-08-31 -2024-08-31 14:20:00-04:00,133.0,,,,,,,,2024-08-31 -2024-08-31 14:25:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 14:30:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 14:35:00-04:00,124.0,,,,,,,,2024-08-31 -2024-08-31 14:40:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 14:45:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 14:50:00-04:00,121.0,,,,,,,,2024-08-31 -2024-08-31 14:55:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 15:00:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 15:05:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 15:10:00-04:00,116.0,,,,,,,,2024-08-31 -2024-08-31 15:15:00-04:00,116.0,,,,,,,,2024-08-31 -2024-08-31 15:20:00-04:00,115.0,,,,,,,,2024-08-31 -2024-08-31 15:25:00-04:00,119.0,,,,,,,,2024-08-31 -2024-08-31 15:30:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 15:35:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 15:40:00-04:00,119.0,,,,,,,,2024-08-31 -2024-08-31 15:45:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 15:50:00-04:00,121.0,,,,,,,,2024-08-31 -2024-08-31 15:55:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 16:00:00-04:00,122.0,ANNOUNCE_MEAL,,,,35.0,,35.0,2024-08-31 -2024-08-31 16:05:00-04:00,117.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:10:00-04:00,119.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:15:00-04:00,124.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:20:00-04:00,101.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:25:00-04:00,119.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:30:00-04:00,156.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:35:00-04:00,168.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:40:00-04:00,186.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:45:00-04:00,195.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:50:00-04:00,199.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:55:00-04:00,203.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:00:00-04:00,188.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:05:00-04:00,208.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:10:00-04:00,198.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:15:00-04:00,187.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:20:00-04:00,183.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:25:00-04:00,182.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:30:00-04:00,165.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:35:00-04:00,155.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:40:00-04:00,155.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:45:00-04:00,168.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:50:00-04:00,161.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:55:00-04:00,162.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:00:00-04:00,125.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:05:00-04:00,122.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:10:00-04:00,109.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:15:00-04:00,101.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:20:00-04:00,99.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:25:00-04:00,95.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:30:00-04:00,102.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:35:00-04:00,104.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:40:00-04:00,104.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:45:00-04:00,109.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:50:00-04:00,108.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:55:00-04:00,106.0,,,,,0.0,,,2024-08-31 -2024-08-31 19:00:00-04:00,108.0,,,,,0.0,,,2024-08-31 -2024-08-31 19:05:00-04:00,108.0,,,,,,,,2024-08-31 -2024-08-31 19:10:00-04:00,106.0,,,,,,,,2024-08-31 -2024-08-31 19:15:00-04:00,105.0,,,,,,,,2024-08-31 -2024-08-31 19:20:00-04:00,105.0,,,,,,,,2024-08-31 -2024-08-31 19:25:00-04:00,107.0,,,,,,,,2024-08-31 -2024-08-31 19:30:00-04:00,117.0,,,,,,,,2024-08-31 -2024-08-31 19:35:00-04:00,109.0,,,,,,,,2024-08-31 -2024-08-31 19:40:00-04:00,115.0,,,,,,,,2024-08-31 -2024-08-31 19:45:00-04:00,118.0,,,,,,,,2024-08-31 -2024-08-31 19:50:00-04:00,134.0,,,,,,,,2024-08-31 -2024-08-31 19:55:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 20:00:00-04:00,153.0,,,,,,,,2024-08-31 -2024-08-31 20:05:00-04:00,154.0,DOSE_INSULIN,False,True,2.0,,,,2024-08-31 -2024-08-31 20:10:00-04:00,152.0,,,,,,,,2024-08-31 -2024-08-31 20:15:00-04:00,165.0,,,,,,,,2024-08-31 -2024-08-31 20:20:00-04:00,165.0,,,,,,,,2024-08-31 -2024-08-31 20:25:00-04:00,157.0,,,,,,,,2024-08-31 -2024-08-31 20:30:00-04:00,152.0,,,,,,,,2024-08-31 -2024-08-31 20:35:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 20:40:00-04:00,128.0,,,,,,,,2024-08-31 -2024-08-31 20:45:00-04:00,107.0,,,,,,,,2024-08-31 -2024-08-31 20:50:00-04:00,99.0,,,,,,,,2024-08-31 -2024-08-31 20:55:00-04:00,95.0,INTERVENTION_SNACK,True,False,,7.5,1.0,,2024-08-31 -2024-08-31 21:00:00-04:00,91.0,,,,,,,,2024-08-31 -2024-08-31 21:05:00-04:00,95.0,,,,,,,,2024-08-31 -2024-08-31 21:10:00-04:00,87.0,,,,,,,,2024-08-31 -2024-08-31 21:15:00-04:00,90.0,,,,,,,,2024-08-31 -2024-08-31 21:20:00-04:00,97.0,,,,,,,,2024-08-31 -2024-08-31 21:25:00-04:00,90.0,,,,,,,,2024-08-31 -2024-08-31 21:30:00-04:00,93.0,,,,,,,,2024-08-31 -2024-08-31 21:35:00-04:00,85.0,,,,,,,,2024-08-31 -2024-08-31 21:40:00-04:00,91.0,,,,,,,,2024-08-31 -2024-08-31 21:45:00-04:00,95.0,,,,,,,,2024-08-31 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23:25:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 23:30:00-04:00,132.0,,,,,,,,2024-08-31 -2024-08-31 23:35:00-04:00,133.0,,,,,,,,2024-08-31 -2024-08-31 23:40:00-04:00,136.0,,,,,,,,2024-08-31 -2024-08-31 23:45:00-04:00,136.0,,,,,,,,2024-08-31 -2024-08-31 23:50:00-04:00,153.0,,,,,,,,2024-08-31 -2024-08-31 23:55:00-04:00,146.0,,,,,,,,2024-08-31 -2024-09-01 00:00:00-04:00,137.0,,,,,,,,2024-08-31 -2024-09-01 00:05:00-04:00,135.0,,,,,,,,2024-08-31 -2024-09-01 00:10:00-04:00,138.0,,,,,,,,2024-08-31 -2024-09-01 00:15:00-04:00,141.0,,,,,,,,2024-08-31 -2024-09-01 00:20:00-04:00,153.0,,,,,,,,2024-08-31 -2024-09-01 00:25:00-04:00,150.0,,,,,,,,2024-08-31 -2024-09-01 00:30:00-04:00,147.0,,,,,,,,2024-08-31 -2024-09-01 00:35:00-04:00,148.0,,,,,,,,2024-08-31 -2024-09-01 00:40:00-04:00,146.0,,,,,,,,2024-08-31 -2024-09-01 00:45:00-04:00,153.0,,,,,,,,2024-08-31 -2024-09-01 00:50:00-04:00,145.0,,,,,,,,2024-08-31 -2024-09-01 00:55:00-04:00,144.0,,,,,,,,2024-08-31 -2024-09-01 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05:15:00-04:00,102.0,,,,,,,,2024-09-16 -2024-09-16 05:20:00-04:00,100.0,,,,,,,,2024-09-16 -2024-09-16 05:25:00-04:00,103.0,,,,,,,,2024-09-16 -2024-09-16 05:30:00-04:00,104.0,,,,,,,,2024-09-16 -2024-09-16 05:35:00-04:00,107.0,,,,,,,,2024-09-16 -2024-09-16 05:40:00-04:00,111.0,,,,,,,,2024-09-16 -2024-09-16 05:45:00-04:00,111.0,,,,,,,,2024-09-16 -2024-09-16 05:50:00-04:00,95.0,,,,,,,,2024-09-16 -2024-09-16 05:55:00-04:00,88.0,,,,,,,,2024-09-16 -2024-09-16 06:00:00-04:00,118.0,,,,,,,,2024-09-16 -2024-09-16 06:05:00-04:00,100.0,,,,,,,,2024-09-16 -2024-09-16 06:10:00-04:00,87.0,,,,,,,,2024-09-16 -2024-09-16 06:15:00-04:00,78.0,,,,,,,,2024-09-16 -2024-09-16 06:20:00-04:00,69.0,,,,,,,,2024-09-16 -2024-09-16 06:25:00-04:00,78.0,INTERVENTION_SNACK,True,False,,7.5,1.0,,2024-09-16 -2024-09-16 06:30:00-04:00,141.0,,,,,,,,2024-09-16 -2024-09-16 06:35:00-04:00,133.0,,,,,,,,2024-09-16 -2024-09-16 06:40:00-04:00,128.0,,,,,,,,2024-09-16 -2024-09-16 06:45:00-04:00,97.0,,,,,,,,2024-09-16 -2024-09-16 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14:45:00-04:00,103.0,,,,,,,,2024-09-16 -2024-09-16 14:50:00-04:00,104.0,,,,,,,,2024-09-16 -2024-09-16 14:55:00-04:00,98.0,,,,,,,,2024-09-16 -2024-09-16 15:00:00-04:00,96.0,,,,,,,,2024-09-16 -2024-09-16 15:05:00-04:00,97.0,,,,,,,,2024-09-16 -2024-09-16 15:10:00-04:00,97.0,,,,,,,,2024-09-16 -2024-09-16 15:15:00-04:00,99.0,,,,,,,,2024-09-16 -2024-09-16 15:20:00-04:00,100.0,,,,,,,,2024-09-16 -2024-09-16 15:25:00-04:00,95.0,,,,,,,,2024-09-16 -2024-09-16 15:30:00-04:00,92.0,,,,,,,,2024-09-16 -2024-09-16 15:35:00-04:00,86.0,,,,,,,,2024-09-16 -2024-09-16 15:40:00-04:00,87.0,,,,,,,,2024-09-16 -2024-09-16 15:45:00-04:00,83.0,INTERVENTION_SNACK,True,False,,7.5,1.0,,2024-09-16 -2024-09-16 15:50:00-04:00,86.0,,,,,,,,2024-09-16 -2024-09-16 15:55:00-04:00,98.0,,,,,,,,2024-09-16 -2024-09-16 16:00:00-04:00,99.0,,,,,,,,2024-09-16 -2024-09-16 16:05:00-04:00,97.0,,,,,,,,2024-09-16 -2024-09-16 16:10:00-04:00,111.0,,,,,,,,2024-09-16 -2024-09-16 16:15:00-04:00,113.0,,,,,,,,2024-09-16 -2024-09-16 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-2024-09-30 23:20:00-04:00,132.0,,,,,,,,2024-09-30 -2024-09-30 23:25:00-04:00,122.0,,,,,,,,2024-09-30 -2024-09-30 23:30:00-04:00,115.0,,,,,,,,2024-09-30 -2024-09-30 23:35:00-04:00,113.0,,,,,,,,2024-09-30 -2024-09-30 23:40:00-04:00,112.0,,,,,,,,2024-09-30 -2024-09-30 23:45:00-04:00,101.0,,,,,,,,2024-09-30 -2024-09-30 23:50:00-04:00,96.0,,,,,,,,2024-09-30 -2024-09-30 23:55:00-04:00,103.0,,,,,,,,2024-09-30 diff --git a/0_meal_identification/meal_identification/data/interim/2024-11-07_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv b/0_meal_identification/meal_identification/data/interim/2024-11-07_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv deleted file mode 100644 index 4294d1d..0000000 --- a/0_meal_identification/meal_identification/data/interim/2024-11-07_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv +++ /dev/null @@ -1,26497 +0,0 @@ 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b/0_meal_identification/meal_identification/data/interim/2024-11-07_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv deleted file mode 100644 index 3ad0145..0000000 --- a/0_meal_identification/meal_identification/data/interim/2024-11-07_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv +++ /dev/null @@ -1,26497 +0,0 @@ -date,bgl,msg_type,affects_fob,affects_iob,dose_units,food_g,food_glycemic_index,food_g_keep,date,day_start_shift -2024-07-01 00:00:00-04:00,115.0,,,,,,,0,2024-07-01 00:00:00-04:00,2024-06-30 -2024-07-01 00:05:00-04:00,112.0,,,,,,,0,2024-07-01 00:05:00-04:00,2024-06-30 -2024-07-01 00:10:00-04:00,121.0,,,,,,,0,2024-07-01 00:10:00-04:00,2024-06-30 -2024-07-01 00:15:00-04:00,118.0,,,,,,,0,2024-07-01 00:15:00-04:00,2024-06-30 -2024-07-01 00:20:00-04:00,115.0,,,,,,,0,2024-07-01 00:20:00-04:00,2024-06-30 -2024-07-01 00:25:00-04:00,119.0,,,,,,,0,2024-07-01 00:25:00-04:00,2024-06-30 -2024-07-01 00:30:00-04:00,119.0,,,,,,,0,2024-07-01 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23:30:00-04:00,2024-09-30 -2024-09-30 23:35:00-04:00,113.0,,,,,,,0,2024-09-30 23:35:00-04:00,2024-09-30 -2024-09-30 23:40:00-04:00,112.0,,,,,,,0,2024-09-30 23:40:00-04:00,2024-09-30 -2024-09-30 23:45:00-04:00,101.0,,,,,,,0,2024-09-30 23:45:00-04:00,2024-09-30 -2024-09-30 23:50:00-04:00,96.0,,,,,,,0,2024-09-30 23:50:00-04:00,2024-09-30 -2024-09-30 23:55:00-04:00,103.0,,,,,,,0,2024-09-30 23:55:00-04:00,2024-09-30 diff --git a/0_meal_identification/meal_identification/data/interim/2024-11-10_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv b/0_meal_identification/meal_identification/data/interim/2024-11-10_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv deleted file mode 100644 index 03f601f..0000000 --- a/0_meal_identification/meal_identification/data/interim/2024-11-10_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv +++ /dev/null @@ -1,26497 +0,0 @@ -date,bgl,msg_type,affects_fob,affects_iob,dose_units,food_g,food_glycemic_index,food_g_keep,day_start_shift -2024-07-01 00:00:00-05:00,98.0,,,,,,,,2024-06-30 -2024-07-01 00:05:00-05:00,100.0,,,,,,,,2024-06-30 -2024-07-01 00:10:00-05:00,98.0,,,,,,,,2024-06-30 -2024-07-01 00:15:00-05:00,94.0,,,,,,,,2024-06-30 -2024-07-01 00:20:00-05:00,94.0,,,,,,,,2024-06-30 -2024-07-01 00:25:00-05:00,99.0,,,,,,,,2024-06-30 -2024-07-01 00:30:00-05:00,107.0,,,,,,,,2024-06-30 -2024-07-01 00:35:00-05:00,114.0,,,,,,,,2024-06-30 -2024-07-01 00:40:00-05:00,118.0,,,,,,,,2024-06-30 -2024-07-01 00:45:00-05:00,119.0,,,,,,,,2024-06-30 -2024-07-01 00:50:00-05:00,120.0,,,,,,,,2024-06-30 -2024-07-01 00:55:00-05:00,120.0,,,,,,,,2024-06-30 -2024-07-01 01:00:00-05:00,115.0,,,,,,,,2024-06-30 -2024-07-01 01:05:00-05:00,112.0,,,,,,,,2024-06-30 -2024-07-01 01:10:00-05:00,108.0,,,,,,,,2024-06-30 -2024-07-01 01:15:00-05:00,105.0,,,,,,,,2024-06-30 -2024-07-01 01:20:00-05:00,108.0,,,,,,,,2024-06-30 -2024-07-01 01:25:00-05:00,94.0,,,,,,,,2024-06-30 -2024-07-01 01:30:00-05:00,86.0,,,,,,,,2024-06-30 -2024-07-01 01:35:00-05:00,92.0,,,,,,,,2024-06-30 -2024-07-01 01:40:00-05:00,90.0,,,,,,,,2024-06-30 -2024-07-01 01:45:00-05:00,91.0,,,,,,,,2024-06-30 -2024-07-01 01:50:00-05:00,93.0,,,,,,,,2024-06-30 -2024-07-01 01:55:00-05:00,97.0,,,,,,,,2024-06-30 -2024-07-01 02:00:00-05:00,100.0,,,,,,,,2024-06-30 -2024-07-01 02:05:00-05:00,102.0,,,,,,,,2024-06-30 -2024-07-01 02:10:00-05:00,102.0,,,,,,,,2024-06-30 -2024-07-01 02:15:00-05:00,101.0,,,,,,,,2024-06-30 -2024-07-01 02:20:00-05:00,101.0,,,,,,,,2024-06-30 -2024-07-01 02:25:00-05:00,101.0,,,,,,,,2024-06-30 -2024-07-01 02:30:00-05:00,101.0,,,,,,,,2024-06-30 -2024-07-01 02:35:00-05:00,100.0,,,,,,,,2024-06-30 -2024-07-01 02:40:00-05:00,99.0,,,,,,,,2024-06-30 -2024-07-01 02:45:00-05:00,98.0,,,,,,,,2024-06-30 -2024-07-01 02:50:00-05:00,97.0,,,,,,,,2024-06-30 -2024-07-01 02:55:00-05:00,95.0,,,,,,,,2024-06-30 -2024-07-01 03:00:00-05:00,96.0,,,,,,,,2024-06-30 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07:55:00-05:00,99.0,,,,,,,,2024-07-01 -2024-07-01 08:00:00-05:00,99.0,,,,,,,,2024-07-01 -2024-07-01 08:05:00-05:00,98.0,,,,,,,,2024-07-01 -2024-07-01 08:10:00-05:00,103.0,,,,,,,,2024-07-01 -2024-07-01 08:15:00-05:00,104.0,,,,,,,,2024-07-01 -2024-07-01 08:20:00-05:00,109.0,,,,,,,,2024-07-01 -2024-07-01 08:25:00-05:00,109.0,,,,,,,,2024-07-01 -2024-07-01 08:30:00-05:00,106.0,,,,,,,,2024-07-01 -2024-07-01 08:35:00-05:00,110.0,,,,,,,,2024-07-01 -2024-07-01 08:40:00-05:00,108.0,,,,,,,,2024-07-01 -2024-07-01 08:45:00-05:00,103.0,,,,,,,,2024-07-01 -2024-07-01 08:50:00-05:00,100.0,,,,,,,,2024-07-01 -2024-07-01 08:55:00-05:00,102.0,,,,,,,,2024-07-01 -2024-07-01 09:00:00-05:00,104.0,,,,,,,,2024-07-01 -2024-07-01 09:05:00-05:00,109.0,,,,,,,,2024-07-01 -2024-07-01 09:10:00-05:00,105.0,,,,,,,,2024-07-01 -2024-07-01 09:15:00-05:00,113.0,,,,,,,,2024-07-01 -2024-07-01 09:20:00-05:00,114.0,,,,,,,,2024-07-01 -2024-07-01 09:25:00-05:00,116.0,,,,,,,,2024-07-01 -2024-07-01 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11:05:00-05:00,97.0,,,,,,,,2024-07-01 -2024-07-01 11:10:00-05:00,95.0,,,,,,,,2024-07-01 -2024-07-01 11:15:00-05:00,94.0,,,,,,,,2024-07-01 -2024-07-01 11:20:00-05:00,91.0,,,,,,,,2024-07-01 -2024-07-01 11:25:00-05:00,89.0,,,,,,,,2024-07-01 -2024-07-01 11:30:00-05:00,87.0,,,,,,,,2024-07-01 -2024-07-01 11:35:00-05:00,86.0,,,,,,,,2024-07-01 -2024-07-01 11:40:00-05:00,87.0,,,,,,,,2024-07-01 -2024-07-01 11:45:00-05:00,86.0,,,,,,,,2024-07-01 -2024-07-01 11:50:00-05:00,87.0,,,,,,,,2024-07-01 -2024-07-01 11:55:00-05:00,85.0,,,,,,,,2024-07-01 -2024-07-01 12:00:00-05:00,82.0,,,,,,,,2024-07-01 -2024-07-01 12:05:00-05:00,82.0,,,,,,,,2024-07-01 -2024-07-01 12:10:00-05:00,78.0,,,,,,,,2024-07-01 -2024-07-01 12:15:00-05:00,80.0,,,,,,,,2024-07-01 -2024-07-01 12:20:00-05:00,81.0,,,,,,,,2024-07-01 -2024-07-01 12:25:00-05:00,81.0,,,,,,,,2024-07-01 -2024-07-01 12:30:00-05:00,80.0,,,,,,,,2024-07-01 -2024-07-01 12:35:00-05:00,80.0,,,,,,,,2024-07-01 -2024-07-01 12:40:00-05:00,78.0,,,,,,,,2024-07-01 -2024-07-01 12:45:00-05:00,79.0,,,,,,,,2024-07-01 -2024-07-01 12:50:00-05:00,78.0,,,,,,,,2024-07-01 -2024-07-01 12:55:00-05:00,77.0,,,,,,,,2024-07-01 -2024-07-01 13:00:00-05:00,74.0,,,,,,,,2024-07-01 -2024-07-01 13:05:00-05:00,71.0,0,,,,0.0,,7.0,2024-07-01 -2024-07-01 13:10:00-05:00,71.0,,,,,,,,2024-07-01 -2024-07-01 13:15:00-05:00,71.0,,,,,,,,2024-07-01 -2024-07-01 13:20:00-05:00,70.0,,,,,,,,2024-07-01 -2024-07-01 13:25:00-05:00,72.0,,,,,,,,2024-07-01 -2024-07-01 13:30:00-05:00,72.0,INTERVENTION_SNACK,True,False,,6.0,1.0,,2024-07-01 -2024-07-01 13:35:00-05:00,79.0,,,,,,,,2024-07-01 -2024-07-01 13:40:00-05:00,85.0,,,,,,,,2024-07-01 -2024-07-01 13:45:00-05:00,85.0,TEXT,False,False,6.0,,,,2024-07-01 -2024-07-01 13:50:00-05:00,103.0,,,,,,,,2024-07-01 -2024-07-01 13:55:00-05:00,103.0,ANNOUNCE_MEAL,,,,45.0,,45.0,2024-07-01 -2024-07-01 14:00:00-05:00,115.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:05:00-05:00,114.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:10:00-05:00,110.0,,,,,0.0,,,2024-07-01 -2024-07-01 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15:45:00-05:00,144.0,,,,,0.0,,,2024-07-01 -2024-07-01 15:50:00-05:00,143.0,,,,,0.0,,,2024-07-01 -2024-07-01 15:55:00-05:00,143.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:00:00-05:00,141.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:05:00-05:00,143.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:10:00-05:00,142.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:15:00-05:00,151.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:20:00-05:00,151.0,,False,True,3.0,0.0,,,2024-07-01 -2024-07-01 16:25:00-05:00,154.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:30:00-05:00,156.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:35:00-05:00,156.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:40:00-05:00,154.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:45:00-05:00,152.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:50:00-05:00,149.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:55:00-05:00,145.0,,,,,0.0,,,2024-07-01 -2024-07-01 17:00:00-05:00,140.0,,,,,,,,2024-07-01 -2024-07-01 17:05:00-05:00,134.0,,,,,,,,2024-07-01 -2024-07-01 17:10:00-05:00,133.0,,,,,,,,2024-07-01 -2024-07-01 17:15:00-05:00,128.0,,,,,,,,2024-07-01 -2024-07-01 17:20:00-05:00,121.0,,,,,,,,2024-07-01 -2024-07-01 17:25:00-05:00,114.0,,,,,,,,2024-07-01 -2024-07-01 17:30:00-05:00,105.0,,,,,,,,2024-07-01 -2024-07-01 17:35:00-05:00,97.0,,,,,,,,2024-07-01 -2024-07-01 17:40:00-05:00,97.0,TEXT,False,False,,50.0,0.5,,2024-07-01 -2024-07-01 17:45:00-05:00,89.0,,,,,,,,2024-07-01 -2024-07-01 17:50:00-05:00,86.0,,,,,,,,2024-07-01 -2024-07-01 17:55:00-05:00,86.0,DOSE_INSULIN,False,True,8.0,,,,2024-07-01 -2024-07-01 18:00:00-05:00,78.0,,,,,,,,2024-07-01 -2024-07-01 18:05:00-05:00,78.0,,,,,,,,2024-07-01 -2024-07-01 18:10:00-05:00,79.0,,,,,,,,2024-07-01 -2024-07-01 18:15:00-05:00,83.0,,,,,,,,2024-07-01 -2024-07-01 18:20:00-05:00,85.0,,,,,,,,2024-07-01 -2024-07-01 18:25:00-05:00,94.0,,,,,,,,2024-07-01 -2024-07-01 18:30:00-05:00,98.0,,,,,,,,2024-07-01 -2024-07-01 18:35:00-05:00,93.0,,,,,,,,2024-07-01 -2024-07-01 18:40:00-05:00,86.0,,,,,,,,2024-07-01 -2024-07-01 18:45:00-05:00,75.0,,,,,,,,2024-07-01 -2024-07-01 18:50:00-05:00,66.0,0,,,,0.0,,8.0,2024-07-01 -2024-07-01 18:55:00-05:00,62.0,,,,,,,,2024-07-01 -2024-07-01 19:00:00-05:00,61.0,,,,,,,,2024-07-01 -2024-07-01 19:05:00-05:00,62.0,,,,,,,,2024-07-01 -2024-07-01 19:10:00-05:00,71.0,,,,,,,,2024-07-01 -2024-07-01 19:15:00-05:00,80.0,,,,,,,,2024-07-01 -2024-07-01 19:20:00-05:00,90.0,,,,,,,,2024-07-01 -2024-07-01 19:25:00-05:00,97.0,,,,,,,,2024-07-01 -2024-07-01 19:30:00-05:00,104.0,,,,,,,,2024-07-01 -2024-07-01 19:35:00-05:00,104.0,,,,,,,,2024-07-01 -2024-07-01 19:40:00-05:00,102.0,,,,,,,,2024-07-01 -2024-07-01 19:45:00-05:00,101.0,,,,,,,,2024-07-01 -2024-07-01 19:50:00-05:00,107.0,,,,,,,,2024-07-01 -2024-07-01 19:55:00-05:00,113.0,,,,,,,,2024-07-01 -2024-07-01 20:00:00-05:00,117.0,,,,,,,,2024-07-01 -2024-07-01 20:05:00-05:00,117.0,,,,,,,,2024-07-01 -2024-07-01 20:10:00-05:00,109.0,,,,,,,,2024-07-01 -2024-07-01 20:15:00-05:00,102.0,,,,,,,,2024-07-01 -2024-07-01 20:20:00-05:00,98.0,,,,,,,,2024-07-01 -2024-07-01 20:25:00-05:00,99.0,,,,,,,,2024-07-01 -2024-07-01 20:30:00-05:00,97.0,,,,,,,,2024-07-01 -2024-07-01 20:35:00-05:00,97.0,ANNOUNCE_MEAL,,,,43.0,,15.0,2024-07-01 -2024-07-01 20:40:00-05:00,94.0,,,,,0.0,,,2024-07-01 -2024-07-01 20:45:00-05:00,90.0,,,,,0.0,,,2024-07-01 -2024-07-01 20:50:00-05:00,85.0,,,,,0.0,,,2024-07-01 -2024-07-01 20:55:00-05:00,79.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:00:00-05:00,77.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:05:00-05:00,77.0,,False,True,24.0,0.0,0.5,,2024-07-01 -2024-07-01 21:10:00-05:00,78.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:15:00-05:00,76.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:20:00-05:00,76.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:25:00-05:00,77.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:30:00-05:00,75.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:35:00-05:00,78.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:40:00-05:00,79.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:45:00-05:00,76.0,,,,,0.0,,,2024-07-01 -2024-07-01 21:50:00-05:00,79.0,,,,,0.0,,,2024-07-01 -2024-07-01 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22:55:00-05:00,101.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:00:00-05:00,112.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:05:00-05:00,122.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:10:00-05:00,122.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:15:00-05:00,122.0,,False,True,3.0,0.0,,,2024-07-10 -2024-07-10 23:20:00-05:00,119.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:25:00-05:00,119.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:30:00-05:00,124.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:35:00-05:00,127.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:40:00-05:00,131.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:45:00-05:00,135.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:50:00-05:00,135.0,,,,,0.0,,,2024-07-10 -2024-07-10 23:55:00-05:00,141.0,,False,True,1.0,0.0,,,2024-07-10 -2024-07-11 00:00:00-05:00,146.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:05:00-05:00,141.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:10:00-05:00,141.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:15:00-05:00,145.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:20:00-05:00,150.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:25:00-05:00,154.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:30:00-05:00,157.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:35:00-05:00,159.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:40:00-05:00,157.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:45:00-05:00,156.0,,,,,0.0,,,2024-07-10 -2024-07-11 00:50:00-05:00,153.0,,,,,,,,2024-07-10 -2024-07-11 00:55:00-05:00,154.0,,,,,,,,2024-07-10 -2024-07-11 01:00:00-05:00,160.0,,,,,,,,2024-07-10 -2024-07-11 01:05:00-05:00,159.0,,,,,,,,2024-07-10 -2024-07-11 01:10:00-05:00,152.0,,,,,,,,2024-07-10 -2024-07-11 01:15:00-05:00,155.0,,,,,,,,2024-07-10 -2024-07-11 01:20:00-05:00,154.0,,,,,,,,2024-07-10 -2024-07-11 01:25:00-05:00,154.0,,,,,,,,2024-07-10 -2024-07-11 01:30:00-05:00,151.0,,,,,,,,2024-07-10 -2024-07-11 01:35:00-05:00,151.0,,,,,,,,2024-07-10 -2024-07-11 01:40:00-05:00,151.0,,,,,,,,2024-07-10 -2024-07-11 01:45:00-05:00,151.0,,,,,,,,2024-07-10 -2024-07-11 01:50:00-05:00,155.0,,,,,,,,2024-07-10 -2024-07-11 01:55:00-05:00,160.0,,,,,,,,2024-07-10 -2024-07-11 02:00:00-05:00,155.0,,,,,,,,2024-07-10 -2024-07-11 02:05:00-05:00,154.0,,,,,,,,2024-07-10 -2024-07-11 02:10:00-05:00,156.0,,,,,,,,2024-07-10 -2024-07-11 02:15:00-05:00,158.0,,,,,,,,2024-07-10 -2024-07-11 02:20:00-05:00,156.0,,,,,,,,2024-07-10 -2024-07-11 02:25:00-05:00,154.0,,,,,,,,2024-07-10 -2024-07-11 02:30:00-05:00,152.0,,,,,,,,2024-07-10 -2024-07-11 02:35:00-05:00,152.0,,,,,,,,2024-07-10 -2024-07-11 02:40:00-05:00,150.0,,,,,,,,2024-07-10 -2024-07-11 02:45:00-05:00,150.0,,,,,,,,2024-07-10 -2024-07-11 02:50:00-05:00,149.0,,,,,,,,2024-07-10 -2024-07-11 02:55:00-05:00,146.0,,,,,,,,2024-07-10 -2024-07-11 03:00:00-05:00,149.0,,,,,,,,2024-07-10 -2024-07-11 03:05:00-05:00,150.0,,,,,,,,2024-07-10 -2024-07-11 03:10:00-05:00,151.0,,,,,,,,2024-07-10 -2024-07-11 03:15:00-05:00,151.0,,,,,,,,2024-07-10 -2024-07-11 03:20:00-05:00,145.0,,,,,,,,2024-07-10 -2024-07-11 03:25:00-05:00,146.0,,,,,,,,2024-07-10 -2024-07-11 03:30:00-05:00,141.0,,,,,,,,2024-07-10 -2024-07-11 03:35:00-05:00,139.0,,,,,,,,2024-07-10 -2024-07-11 03:40:00-05:00,136.0,,,,,,,,2024-07-10 -2024-07-11 03:45:00-05:00,137.0,,,,,,,,2024-07-10 -2024-07-11 03:50:00-05:00,134.0,,,,,,,,2024-07-10 -2024-07-11 03:55:00-05:00,135.0,,,,,,,,2024-07-10 -2024-07-11 04:00:00-05:00,138.0,,,,,,,,2024-07-11 -2024-07-11 04:05:00-05:00,138.0,,,,,,,,2024-07-11 -2024-07-11 04:10:00-05:00,135.0,,,,,,,,2024-07-11 -2024-07-11 04:15:00-05:00,133.0,,,,,,,,2024-07-11 -2024-07-11 04:20:00-05:00,132.0,,,,,,,,2024-07-11 -2024-07-11 04:25:00-05:00,131.0,,,,,,,,2024-07-11 -2024-07-11 04:30:00-05:00,131.0,,,,,,,,2024-07-11 -2024-07-11 04:35:00-05:00,140.0,,,,,,,,2024-07-11 -2024-07-11 04:40:00-05:00,140.0,,,,,,,,2024-07-11 -2024-07-11 04:45:00-05:00,140.0,,,,,,,,2024-07-11 -2024-07-11 04:50:00-05:00,139.0,,,,,,,,2024-07-11 -2024-07-11 04:55:00-05:00,137.0,,,,,,,,2024-07-11 -2024-07-11 05:00:00-05:00,131.0,,,,,,,,2024-07-11 -2024-07-11 05:05:00-05:00,125.0,,,,,,,,2024-07-11 -2024-07-11 05:10:00-05:00,122.0,,,,,,,,2024-07-11 -2024-07-11 05:15:00-05:00,121.0,,,,,,,,2024-07-11 -2024-07-11 05:20:00-05:00,121.0,,,,,,,,2024-07-11 -2024-07-11 05:25:00-05:00,120.0,,,,,,,,2024-07-11 -2024-07-11 05:30:00-05:00,118.0,,,,,,,,2024-07-11 -2024-07-11 05:35:00-05:00,112.0,,,,,,,,2024-07-11 -2024-07-11 05:40:00-05:00,110.0,,,,,,,,2024-07-11 -2024-07-11 05:45:00-05:00,111.0,,,,,,,,2024-07-11 -2024-07-11 05:50:00-05:00,113.0,,,,,,,,2024-07-11 -2024-07-11 05:55:00-05:00,114.0,,,,,,,,2024-07-11 -2024-07-11 06:00:00-05:00,110.0,,,,,,,,2024-07-11 -2024-07-11 06:05:00-05:00,109.0,,,,,,,,2024-07-11 -2024-07-11 06:10:00-05:00,112.0,,,,,,,,2024-07-11 -2024-07-11 06:15:00-05:00,111.0,,,,,,,,2024-07-11 -2024-07-11 06:20:00-05:00,111.0,,,,,,,,2024-07-11 -2024-07-11 06:25:00-05:00,110.0,,,,,,,,2024-07-11 -2024-07-11 06:30:00-05:00,111.0,,,,,,,,2024-07-11 -2024-07-11 06:35:00-05:00,112.0,,,,,,,,2024-07-11 -2024-07-11 06:40:00-05:00,110.0,,,,,,,,2024-07-11 -2024-07-11 06:45:00-05:00,111.0,,,,,,,,2024-07-11 -2024-07-11 06:50:00-05:00,108.0,,,,,,,,2024-07-11 -2024-07-11 06:55:00-05:00,102.0,,,,,,,,2024-07-11 -2024-07-11 07:00:00-05:00,103.0,,,,,,,,2024-07-11 -2024-07-11 07:05:00-05:00,103.0,,,,,,,,2024-07-11 -2024-07-11 07:10:00-05:00,106.0,,,,,,,,2024-07-11 -2024-07-11 07:15:00-05:00,110.0,,,,,,,,2024-07-11 -2024-07-11 07:20:00-05:00,109.0,,,,,,,,2024-07-11 -2024-07-11 07:25:00-05:00,107.0,,,,,,,,2024-07-11 -2024-07-11 07:30:00-05:00,104.0,,,,,,,,2024-07-11 -2024-07-11 07:35:00-05:00,102.0,,,,,,,,2024-07-11 -2024-07-11 07:40:00-05:00,101.0,,,,,,,,2024-07-11 -2024-07-11 07:45:00-05:00,101.0,,,,,,,,2024-07-11 -2024-07-11 07:50:00-05:00,100.0,,,,,,,,2024-07-11 -2024-07-11 07:55:00-05:00,100.0,,,,,,,,2024-07-11 -2024-07-11 08:00:00-05:00,99.0,,,,,,,,2024-07-11 -2024-07-11 08:05:00-05:00,97.0,,,,,,,,2024-07-11 -2024-07-11 08:10:00-05:00,95.0,,,,,,,,2024-07-11 -2024-07-11 08:15:00-05:00,93.0,,,,,,,,2024-07-11 -2024-07-11 08:20:00-05:00,91.0,,,,,,,,2024-07-11 -2024-07-11 08:25:00-05:00,92.0,,,,,,,,2024-07-11 -2024-07-11 08:30:00-05:00,92.0,,,,,,,,2024-07-11 -2024-07-11 08:35:00-05:00,94.0,,,,,,,,2024-07-11 -2024-07-11 08:40:00-05:00,98.0,,,,,,,,2024-07-11 -2024-07-11 08:45:00-05:00,99.0,,,,,,,,2024-07-11 -2024-07-11 08:50:00-05:00,101.0,,,,,,,,2024-07-11 -2024-07-11 08:55:00-05:00,102.0,,,,,,,,2024-07-11 -2024-07-11 09:00:00-05:00,103.0,,,,,,,,2024-07-11 -2024-07-11 09:05:00-05:00,99.0,,,,,,,,2024-07-11 -2024-07-11 09:10:00-05:00,96.0,,,,,,,,2024-07-11 -2024-07-11 09:15:00-05:00,97.0,,,,,,,,2024-07-11 -2024-07-11 09:20:00-05:00,97.0,TEXT,False,False,,,,,2024-07-11 -2024-07-11 09:25:00-05:00,95.0,,,,,,,,2024-07-11 -2024-07-11 09:30:00-05:00,98.0,,,,,,,,2024-07-11 -2024-07-11 09:35:00-05:00,97.0,,,,,,,,2024-07-11 -2024-07-11 09:40:00-05:00,98.0,,,,,,,,2024-07-11 -2024-07-11 09:45:00-05:00,101.0,,,,,,,,2024-07-11 -2024-07-11 09:50:00-05:00,101.0,,,,,,,,2024-07-11 -2024-07-11 09:55:00-05:00,101.0,DOSE_INSULIN,False,True,8.0,,,,2024-07-11 -2024-07-11 10:00:00-05:00,105.0,,,,,,,,2024-07-11 -2024-07-11 10:05:00-05:00,107.0,,,,,,,,2024-07-11 -2024-07-11 10:10:00-05:00,110.0,ANNOUNCE_MEAL,,,,40.0,,40.0,2024-07-11 -2024-07-11 10:15:00-05:00,115.0,,,,,0.0,,,2024-07-11 -2024-07-11 10:20:00-05:00,118.0,,,,,0.0,,,2024-07-11 -2024-07-11 10:25:00-05:00,121.0,,,,,0.0,,,2024-07-11 -2024-07-11 10:30:00-05:00,123.0,,,,,0.0,,,2024-07-11 -2024-07-11 10:35:00-05:00,121.0,,,,,0.0,,,2024-07-11 -2024-07-11 10:40:00-05:00,121.0,,,,,0.0,,,2024-07-11 -2024-07-11 10:45:00-05:00,125.0,,,,,0.0,,,2024-07-11 -2024-07-11 10:50:00-05:00,128.0,,,,,0.0,,,2024-07-11 -2024-07-11 10:55:00-05:00,132.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:00:00-05:00,138.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:05:00-05:00,140.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:10:00-05:00,142.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:15:00-05:00,143.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:20:00-05:00,143.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:25:00-05:00,143.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:30:00-05:00,143.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:35:00-05:00,142.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:40:00-05:00,144.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:45:00-05:00,139.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:50:00-05:00,136.0,,,,,0.0,,,2024-07-11 -2024-07-11 11:55:00-05:00,135.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:00:00-05:00,135.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:05:00-05:00,136.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:10:00-05:00,137.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:15:00-05:00,137.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:20:00-05:00,139.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:25:00-05:00,141.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:30:00-05:00,143.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:35:00-05:00,143.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:40:00-05:00,143.0,,False,True,3.0,0.0,,,2024-07-11 -2024-07-11 12:45:00-05:00,141.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:50:00-05:00,139.0,,,,,0.0,,,2024-07-11 -2024-07-11 12:55:00-05:00,140.0,,,,,0.0,,,2024-07-11 -2024-07-11 13:00:00-05:00,141.0,,,,,0.0,,,2024-07-11 -2024-07-11 13:05:00-05:00,143.0,,,,,0.0,,,2024-07-11 -2024-07-11 13:10:00-05:00,140.0,,,,,0.0,,,2024-07-11 -2024-07-11 13:15:00-05:00,137.0,,,,,,,,2024-07-11 -2024-07-11 13:20:00-05:00,135.0,,,,,,,,2024-07-11 -2024-07-11 13:25:00-05:00,130.0,,,,,,,,2024-07-11 -2024-07-11 13:30:00-05:00,130.0,,,,,,,,2024-07-11 -2024-07-11 13:35:00-05:00,128.0,,,,,,,,2024-07-11 -2024-07-11 13:40:00-05:00,125.0,,,,,,,,2024-07-11 -2024-07-11 13:45:00-05:00,126.0,,,,,,,,2024-07-11 -2024-07-11 13:50:00-05:00,126.0,,,,,,,,2024-07-11 -2024-07-11 13:55:00-05:00,126.0,,,,,,,,2024-07-11 -2024-07-11 14:00:00-05:00,126.0,,,,,,,,2024-07-11 -2024-07-11 14:05:00-05:00,124.0,,,,,,,,2024-07-11 -2024-07-11 14:10:00-05:00,122.0,,,,,,,,2024-07-11 -2024-07-11 14:15:00-05:00,121.0,,,,,,,,2024-07-11 -2024-07-11 14:20:00-05:00,119.0,,,,,,,,2024-07-11 -2024-07-11 14:25:00-05:00,116.0,,,,,,,,2024-07-11 -2024-07-11 14:30:00-05:00,115.0,,,,,,,,2024-07-11 -2024-07-11 14:35:00-05:00,114.0,,,,,,,,2024-07-11 -2024-07-11 14:40:00-05:00,114.0,ANNOUNCE_MEAL,,,,33.0,,25.0,2024-07-11 -2024-07-11 14:45:00-05:00,111.0,,,,,0.0,,,2024-07-11 -2024-07-11 14:50:00-05:00,111.0,,,,,0.0,,,2024-07-11 -2024-07-11 14:55:00-05:00,114.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:00:00-05:00,119.0,,False,True,,0.0,,,2024-07-11 -2024-07-11 15:05:00-05:00,125.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:10:00-05:00,125.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:15:00-05:00,119.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:20:00-05:00,107.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:25:00-05:00,96.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:30:00-05:00,87.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:35:00-05:00,81.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:40:00-05:00,77.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:45:00-05:00,75.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:50:00-05:00,73.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:55:00-05:00,72.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:00:00-05:00,72.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:05:00-05:00,71.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:10:00-05:00,70.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:15:00-05:00,69.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:20:00-05:00,70.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:25:00-05:00,68.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:30:00-05:00,67.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:35:00-05:00,66.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:40:00-05:00,66.0,,True,False,,0.0,1.0,,2024-07-11 -2024-07-11 16:45:00-05:00,,,,,,0.0,,,2024-07-11 -2024-07-11 16:50:00-05:00,72.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:55:00-05:00,73.0,,,,,0.0,,,2024-07-11 -2024-07-11 17:00:00-05:00,77.0,,,,,0.0,,,2024-07-11 -2024-07-11 17:05:00-05:00,81.0,,,,,0.0,,,2024-07-11 -2024-07-11 17:10:00-05:00,90.0,,,,,0.0,,,2024-07-11 -2024-07-11 17:15:00-05:00,96.0,,,,,0.0,,,2024-07-11 -2024-07-11 17:20:00-05:00,103.0,,,,,0.0,,,2024-07-11 -2024-07-11 17:25:00-05:00,108.0,,False,True,8.0,0.0,,,2024-07-11 -2024-07-11 17:30:00-05:00,110.0,,,,,0.0,,,2024-07-11 -2024-07-11 17:35:00-05:00,111.0,,,,,0.0,,,2024-07-11 -2024-07-11 17:40:00-05:00,112.0,,,,,0.0,,,2024-07-11 -2024-07-11 17:45:00-05:00,110.0,,,,,,,,2024-07-11 -2024-07-11 17:50:00-05:00,110.0,ANNOUNCE_MEAL,,,,79.0,,40.0,2024-07-11 -2024-07-11 17:55:00-05:00,104.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:00:00-05:00,99.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:05:00-05:00,95.0,,True,False,,0.0,1.0,,2024-07-11 -2024-07-11 18:10:00-05:00,84.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:15:00-05:00,75.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:20:00-05:00,76.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:25:00-05:00,80.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:30:00-05:00,84.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:35:00-05:00,81.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:40:00-05:00,81.0,,,,,0.0,,15.0,2024-07-11 -2024-07-11 18:45:00-05:00,70.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:50:00-05:00,67.0,,,,,0.0,,,2024-07-11 -2024-07-11 18:55:00-05:00,67.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:00:00-05:00,67.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:05:00-05:00,68.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:10:00-05:00,68.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:15:00-05:00,68.0,,,,,0.0,,12.0,2024-07-11 -2024-07-11 19:20:00-05:00,63.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:25:00-05:00,59.0,,True,False,,0.0,1.0,,2024-07-11 -2024-07-11 19:30:00-05:00,59.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:35:00-05:00,59.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:40:00-05:00,63.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:45:00-05:00,70.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:50:00-05:00,75.0,,,,,0.0,,,2024-07-11 -2024-07-11 19:55:00-05:00,75.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:00:00-05:00,76.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:05:00-05:00,78.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:10:00-05:00,82.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:15:00-05:00,84.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:20:00-05:00,86.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:25:00-05:00,91.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:30:00-05:00,92.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:35:00-05:00,97.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:40:00-05:00,100.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:45:00-05:00,102.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:50:00-05:00,107.0,,,,,0.0,,,2024-07-11 -2024-07-11 20:55:00-05:00,111.0,,,,,,,,2024-07-11 -2024-07-11 21:00:00-05:00,111.0,DOSE_BASAL_INSULIN,False,True,22.0,,,,2024-07-11 -2024-07-11 21:05:00-05:00,118.0,,,,,,,,2024-07-11 -2024-07-11 21:10:00-05:00,123.0,,,,,,,,2024-07-11 -2024-07-11 21:15:00-05:00,125.0,,,,,,,,2024-07-11 -2024-07-11 21:20:00-05:00,128.0,,,,,,,,2024-07-11 -2024-07-11 21:25:00-05:00,128.0,,,,,,,,2024-07-11 -2024-07-11 21:30:00-05:00,130.0,,,,,,,,2024-07-11 -2024-07-11 21:35:00-05:00,135.0,,,,,,,,2024-07-11 -2024-07-11 21:40:00-05:00,137.0,,,,,,,,2024-07-11 -2024-07-11 21:45:00-05:00,133.0,,,,,,,,2024-07-11 -2024-07-11 21:50:00-05:00,131.0,,,,,,,,2024-07-11 -2024-07-11 21:55:00-05:00,131.0,,,,,,,,2024-07-11 -2024-07-11 22:00:00-05:00,134.0,,,,,,,,2024-07-11 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23:40:00-05:00,134.0,,,,,,,,2024-07-11 -2024-07-11 23:45:00-05:00,137.0,,,,,,,,2024-07-11 -2024-07-11 23:50:00-05:00,139.0,,,,,,,,2024-07-11 -2024-07-11 23:55:00-05:00,141.0,,,,,,,,2024-07-11 -2024-07-12 00:00:00-05:00,142.0,,,,,,,,2024-07-11 -2024-07-12 00:05:00-05:00,142.0,DOSE_INSULIN,False,True,2.0,,,,2024-07-11 -2024-07-12 00:10:00-05:00,137.0,,,,,,,,2024-07-11 -2024-07-12 00:15:00-05:00,136.0,,,,,,,,2024-07-11 -2024-07-12 00:20:00-05:00,135.0,,,,,,,,2024-07-11 -2024-07-12 00:25:00-05:00,141.0,,,,,,,,2024-07-11 -2024-07-12 00:30:00-05:00,141.0,,,,,,,,2024-07-11 -2024-07-12 00:35:00-05:00,141.0,,,,,,,,2024-07-11 -2024-07-12 00:40:00-05:00,142.0,,,,,,,,2024-07-11 -2024-07-12 00:45:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 00:50:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 00:55:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 01:00:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 01:05:00-05:00,143.0,,,,,,,,2024-07-11 -2024-07-12 01:10:00-05:00,142.0,,,,,,,,2024-07-11 -2024-07-12 01:15:00-05:00,140.0,,,,,,,,2024-07-11 -2024-07-12 01:20:00-05:00,136.0,,,,,,,,2024-07-11 -2024-07-12 01:25:00-05:00,134.0,,,,,,,,2024-07-11 -2024-07-12 01:30:00-05:00,133.0,,,,,,,,2024-07-11 -2024-07-12 01:35:00-05:00,134.0,,,,,,,,2024-07-11 -2024-07-12 01:40:00-05:00,134.0,,,,,,,,2024-07-11 -2024-07-12 01:45:00-05:00,135.0,,,,,,,,2024-07-11 -2024-07-12 01:50:00-05:00,135.0,,,,,,,,2024-07-11 -2024-07-12 01:55:00-05:00,134.0,,,,,,,,2024-07-11 -2024-07-12 02:00:00-05:00,134.0,,,,,,,,2024-07-11 -2024-07-12 02:05:00-05:00,134.0,,,,,,,,2024-07-11 -2024-07-12 02:10:00-05:00,134.0,,,,,,,,2024-07-11 -2024-07-12 02:15:00-05:00,121.0,,,,,,,,2024-07-11 -2024-07-12 02:20:00-05:00,112.0,,,,,,,,2024-07-11 -2024-07-12 02:25:00-05:00,120.0,,,,,,,,2024-07-11 -2024-07-12 02:30:00-05:00,122.0,,,,,,,,2024-07-11 -2024-07-12 02:35:00-05:00,125.0,,,,,,,,2024-07-11 -2024-07-12 02:40:00-05:00,122.0,,,,,,,,2024-07-11 -2024-07-12 02:45:00-05:00,121.0,,,,,,,,2024-07-11 -2024-07-12 02:50:00-05:00,122.0,,,,,,,,2024-07-11 -2024-07-12 02:55:00-05:00,123.0,,,,,,,,2024-07-11 -2024-07-12 03:00:00-05:00,120.0,,,,,,,,2024-07-11 -2024-07-12 03:05:00-05:00,118.0,,,,,,,,2024-07-11 -2024-07-12 03:10:00-05:00,121.0,,,,,,,,2024-07-11 -2024-07-12 03:15:00-05:00,119.0,,,,,,,,2024-07-11 -2024-07-12 03:20:00-05:00,117.0,,,,,,,,2024-07-11 -2024-07-12 03:25:00-05:00,114.0,,,,,,,,2024-07-11 -2024-07-12 03:30:00-05:00,113.0,,,,,,,,2024-07-11 -2024-07-12 03:35:00-05:00,112.0,,,,,,,,2024-07-11 -2024-07-12 03:40:00-05:00,111.0,,,,,,,,2024-07-11 -2024-07-12 03:45:00-05:00,107.0,,,,,,,,2024-07-11 -2024-07-12 03:50:00-05:00,106.0,,,,,,,,2024-07-11 -2024-07-12 03:55:00-05:00,107.0,,,,,,,,2024-07-11 -2024-07-12 04:00:00-05:00,110.0,,,,,,,,2024-07-12 -2024-07-12 04:05:00-05:00,112.0,,,,,,,,2024-07-12 -2024-07-12 04:10:00-05:00,113.0,,,,,,,,2024-07-12 -2024-07-12 04:15:00-05:00,108.0,,,,,,,,2024-07-12 -2024-07-12 04:20:00-05:00,104.0,,,,,,,,2024-07-12 -2024-07-12 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11:30:00-05:00,110.0,,,,,,,,2024-08-11 -2024-08-11 11:35:00-05:00,109.0,ANNOUNCE_MEAL,,,,15.0,,15.0,2024-08-11 -2024-08-11 11:40:00-05:00,109.0,,,,,0.0,,,2024-08-11 -2024-08-11 11:45:00-05:00,107.0,,,,,0.0,,,2024-08-11 -2024-08-11 11:50:00-05:00,109.0,,,,,0.0,,,2024-08-11 -2024-08-11 11:55:00-05:00,113.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:00:00-05:00,110.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:05:00-05:00,107.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:10:00-05:00,108.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:15:00-05:00,,,,,,0.0,,,2024-08-11 -2024-08-11 12:20:00-05:00,109.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:25:00-05:00,109.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:30:00-05:00,108.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:35:00-05:00,108.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:40:00-05:00,108.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:45:00-05:00,117.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:50:00-05:00,118.0,,,,,0.0,,,2024-08-11 -2024-08-11 12:55:00-05:00,124.0,,,,,0.0,,,2024-08-11 -2024-08-11 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21:45:00-05:00,142.0,,,,,0.0,,,2024-08-16 -2024-08-16 21:50:00-05:00,148.0,,,,,0.0,,,2024-08-16 -2024-08-16 21:55:00-05:00,157.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:00:00-05:00,171.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:05:00-05:00,175.0,,False,True,5.0,0.0,,,2024-08-16 -2024-08-16 22:10:00-05:00,173.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:15:00-05:00,169.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:20:00-05:00,165.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:25:00-05:00,171.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:30:00-05:00,166.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:35:00-05:00,174.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:40:00-05:00,171.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:45:00-05:00,169.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:50:00-05:00,176.0,,,,,0.0,,,2024-08-16 -2024-08-16 22:55:00-05:00,174.0,,,,,0.0,,,2024-08-16 -2024-08-16 23:00:00-05:00,,,,,,0.0,,,2024-08-16 -2024-08-16 23:05:00-05:00,175.0,,,,,0.0,,,2024-08-16 -2024-08-16 23:10:00-05:00,146.0,,,,,0.0,,,2024-08-16 -2024-08-16 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00:55:00-05:00,,,,,,,,,2024-08-16 -2024-08-17 01:00:00-05:00,94.0,,,,,,,,2024-08-16 -2024-08-17 01:05:00-05:00,94.0,,,,,,,,2024-08-16 -2024-08-17 01:10:00-05:00,93.0,,,,,,,,2024-08-16 -2024-08-17 01:15:00-05:00,95.0,,,,,,,,2024-08-16 -2024-08-17 01:20:00-05:00,95.0,,,,,,,,2024-08-16 -2024-08-17 01:25:00-05:00,94.0,,,,,,,,2024-08-16 -2024-08-17 01:30:00-05:00,92.0,,,,,,,,2024-08-16 -2024-08-17 01:35:00-05:00,91.0,,,,,,,,2024-08-16 -2024-08-17 01:40:00-05:00,90.0,,,,,,,,2024-08-16 -2024-08-17 01:45:00-05:00,92.0,,,,,,,,2024-08-16 -2024-08-17 01:50:00-05:00,91.0,,,,,,,,2024-08-16 -2024-08-17 01:55:00-05:00,90.0,,,,,,,,2024-08-16 -2024-08-17 02:00:00-05:00,90.0,,,,,,,,2024-08-16 -2024-08-17 02:05:00-05:00,87.0,,,,,,,,2024-08-16 -2024-08-17 02:10:00-05:00,87.0,,,,,,,,2024-08-16 -2024-08-17 02:15:00-05:00,88.0,,,,,,,,2024-08-16 -2024-08-17 02:20:00-05:00,87.0,,,,,,,,2024-08-16 -2024-08-17 02:25:00-05:00,89.0,,,,,,,,2024-08-16 -2024-08-17 02:30:00-05:00,87.0,,,,,,,,2024-08-16 -2024-08-17 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23:35:00-05:00,95.0,,,,,,,,2024-09-30 -2024-09-30 23:40:00-05:00,87.0,,,,,,,,2024-09-30 -2024-09-30 23:45:00-05:00,86.0,,,,,,,,2024-09-30 -2024-09-30 23:50:00-05:00,89.0,,,,,,,,2024-09-30 -2024-09-30 23:55:00-05:00,88.0,,,,,,,,2024-09-30 diff --git a/0_meal_identification/meal_identification/data/interim/2024-11-10_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv b/0_meal_identification/meal_identification/data/interim/2024-11-10_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv deleted file mode 100644 index 18642dc..0000000 --- a/0_meal_identification/meal_identification/data/interim/2024-11-10_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv +++ /dev/null @@ -1,26497 +0,0 @@ -date,bgl,msg_type,affects_fob,affects_iob,dose_units,food_g,food_glycemic_index,food_g_keep,day_start_shift -2024-07-01 00:00:00-04:00,115.0,,,,,,,,2024-06-30 -2024-07-01 00:05:00-04:00,112.0,,,,,,,,2024-06-30 -2024-07-01 00:10:00-04:00,121.0,,,,,,,,2024-06-30 -2024-07-01 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04:35:00-04:00,347.0,,,,,,,,2024-08-31 -2024-08-31 04:40:00-04:00,342.0,,,,,,,,2024-08-31 -2024-08-31 04:45:00-04:00,337.0,,,,,,,,2024-08-31 -2024-08-31 04:50:00-04:00,342.0,,,,,,,,2024-08-31 -2024-08-31 04:55:00-04:00,339.0,,,,,,,,2024-08-31 -2024-08-31 05:00:00-04:00,336.0,,,,,,,,2024-08-31 -2024-08-31 05:05:00-04:00,336.0,,,,,,,,2024-08-31 -2024-08-31 05:10:00-04:00,336.0,,,,,,,,2024-08-31 -2024-08-31 05:15:00-04:00,340.0,,,,,,,,2024-08-31 -2024-08-31 05:20:00-04:00,342.0,,,,,,,,2024-08-31 -2024-08-31 05:25:00-04:00,344.0,,,,,,,,2024-08-31 -2024-08-31 05:30:00-04:00,343.0,,,,,,,,2024-08-31 -2024-08-31 05:35:00-04:00,345.0,,,,,,,,2024-08-31 -2024-08-31 05:40:00-04:00,342.0,,,,,,,,2024-08-31 -2024-08-31 05:45:00-04:00,346.0,,,,,,,,2024-08-31 -2024-08-31 05:50:00-04:00,348.0,,,,,,,,2024-08-31 -2024-08-31 05:55:00-04:00,348.0,,,,,,,,2024-08-31 -2024-08-31 06:00:00-04:00,347.0,,,,,,,,2024-08-31 -2024-08-31 06:05:00-04:00,362.0,,,,,,,,2024-08-31 -2024-08-31 06:10:00-04:00,354.0,,,,,,,,2024-08-31 -2024-08-31 06:15:00-04:00,342.0,,,,,,,,2024-08-31 -2024-08-31 06:20:00-04:00,340.0,,,,,,,,2024-08-31 -2024-08-31 06:25:00-04:00,346.0,,,,,,,,2024-08-31 -2024-08-31 06:30:00-04:00,352.0,,,,,,,,2024-08-31 -2024-08-31 06:35:00-04:00,363.0,,,,,,,,2024-08-31 -2024-08-31 06:40:00-04:00,340.0,,,,,,,,2024-08-31 -2024-08-31 06:45:00-04:00,333.0,,,,,,,,2024-08-31 -2024-08-31 06:50:00-04:00,349.0,,,,,,,,2024-08-31 -2024-08-31 06:55:00-04:00,346.0,,,,,,,,2024-08-31 -2024-08-31 07:00:00-04:00,347.0,,,,,,,,2024-08-31 -2024-08-31 07:05:00-04:00,341.0,,,,,,,,2024-08-31 -2024-08-31 07:10:00-04:00,339.0,,,,,,,,2024-08-31 -2024-08-31 07:15:00-04:00,337.0,,,,,,,,2024-08-31 -2024-08-31 07:20:00-04:00,339.0,,,,,,,,2024-08-31 -2024-08-31 07:25:00-04:00,,,,,,,,,2024-08-31 -2024-08-31 07:30:00-04:00,,,,,,,,,2024-08-31 -2024-08-31 07:35:00-04:00,316.0,,,,,,,,2024-08-31 -2024-08-31 07:40:00-04:00,328.0,,,,,,,,2024-08-31 -2024-08-31 07:45:00-04:00,323.0,,,,,,,,2024-08-31 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11:00:00-04:00,132.0,,,,,,,,2024-08-31 -2024-08-31 11:05:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 11:10:00-04:00,143.0,,,,,,,,2024-08-31 -2024-08-31 11:15:00-04:00,145.0,,,,,,,,2024-08-31 -2024-08-31 11:20:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 11:25:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 11:30:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 11:35:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 11:40:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 11:45:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 11:50:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 11:55:00-04:00,146.0,,,,,,,,2024-08-31 -2024-08-31 12:00:00-04:00,141.0,,,,,,,,2024-08-31 -2024-08-31 12:05:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 12:10:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 12:15:00-04:00,136.0,,,,,,,,2024-08-31 -2024-08-31 12:20:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 12:25:00-04:00,112.0,,,,,,,,2024-08-31 -2024-08-31 12:30:00-04:00,112.0,,,,,,,,2024-08-31 -2024-08-31 12:35:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 12:40:00-04:00,138.0,,,,,,,,2024-08-31 -2024-08-31 12:45:00-04:00,128.0,,,,,,,,2024-08-31 -2024-08-31 12:50:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 12:55:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 13:00:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 13:05:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 13:10:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 13:15:00-04:00,127.0,DOSE_BASAL_INSULIN,False,True,12.0,,,,2024-08-31 -2024-08-31 13:20:00-04:00,137.0,,,,,,,,2024-08-31 -2024-08-31 13:25:00-04:00,138.0,,,,,,,,2024-08-31 -2024-08-31 13:30:00-04:00,130.0,MEDICAL_TEST_RESULT,False,False,,,,,2024-08-31 -2024-08-31 13:35:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 13:40:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 13:45:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 13:50:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 13:55:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 14:00:00-04:00,124.0,,,,,,,,2024-08-31 -2024-08-31 14:05:00-04:00,118.0,,,,,,,,2024-08-31 -2024-08-31 14:10:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 14:15:00-04:00,148.0,,,,,,,,2024-08-31 -2024-08-31 14:20:00-04:00,133.0,,,,,,,,2024-08-31 -2024-08-31 14:25:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 14:30:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 14:35:00-04:00,124.0,,,,,,,,2024-08-31 -2024-08-31 14:40:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 14:45:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 14:50:00-04:00,121.0,,,,,,,,2024-08-31 -2024-08-31 14:55:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 15:00:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 15:05:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 15:10:00-04:00,116.0,,,,,,,,2024-08-31 -2024-08-31 15:15:00-04:00,116.0,,,,,,,,2024-08-31 -2024-08-31 15:20:00-04:00,115.0,,,,,,,,2024-08-31 -2024-08-31 15:25:00-04:00,119.0,,,,,,,,2024-08-31 -2024-08-31 15:30:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 15:35:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 15:40:00-04:00,119.0,,,,,,,,2024-08-31 -2024-08-31 15:45:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 15:50:00-04:00,121.0,,,,,,,,2024-08-31 -2024-08-31 15:55:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 16:00:00-04:00,122.0,ANNOUNCE_MEAL,,,,35.0,,35.0,2024-08-31 -2024-08-31 16:05:00-04:00,117.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:10:00-04:00,119.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:15:00-04:00,124.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:20:00-04:00,101.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:25:00-04:00,119.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:30:00-04:00,156.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:35:00-04:00,168.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:40:00-04:00,186.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:45:00-04:00,195.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:50:00-04:00,199.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:55:00-04:00,203.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:00:00-04:00,188.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:05:00-04:00,208.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:10:00-04:00,198.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:15:00-04:00,187.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:20:00-04:00,183.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:25:00-04:00,182.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:30:00-04:00,165.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:35:00-04:00,155.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:40:00-04:00,155.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:45:00-04:00,168.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:50:00-04:00,161.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:55:00-04:00,162.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:00:00-04:00,125.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:05:00-04:00,122.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:10:00-04:00,109.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:15:00-04:00,101.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:20:00-04:00,99.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:25:00-04:00,95.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:30:00-04:00,102.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:35:00-04:00,104.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:40:00-04:00,104.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:45:00-04:00,109.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:50:00-04:00,108.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:55:00-04:00,106.0,,,,,0.0,,,2024-08-31 -2024-08-31 19:00:00-04:00,108.0,,,,,0.0,,,2024-08-31 -2024-08-31 19:05:00-04:00,108.0,,,,,,,,2024-08-31 -2024-08-31 19:10:00-04:00,106.0,,,,,,,,2024-08-31 -2024-08-31 19:15:00-04:00,105.0,,,,,,,,2024-08-31 -2024-08-31 19:20:00-04:00,105.0,,,,,,,,2024-08-31 -2024-08-31 19:25:00-04:00,107.0,,,,,,,,2024-08-31 -2024-08-31 19:30:00-04:00,117.0,,,,,,,,2024-08-31 -2024-08-31 19:35:00-04:00,109.0,,,,,,,,2024-08-31 -2024-08-31 19:40:00-04:00,115.0,,,,,,,,2024-08-31 -2024-08-31 19:45:00-04:00,118.0,,,,,,,,2024-08-31 -2024-08-31 19:50:00-04:00,134.0,,,,,,,,2024-08-31 -2024-08-31 19:55:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 20:00:00-04:00,153.0,,,,,,,,2024-08-31 -2024-08-31 20:05:00-04:00,154.0,DOSE_INSULIN,False,True,2.0,,,,2024-08-31 -2024-08-31 20:10:00-04:00,152.0,,,,,,,,2024-08-31 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02:40:00,61.23619691929304,,,,,,,,2024-01-01 +2024-01-02 02:45:00,61.33227329669894,,,,,,,,2024-01-01 +2024-01-02 02:50:00,63.95321733739936,,,,,,,,2024-01-01 +2024-01-02 02:55:00,65.24402361701446,,,,,,,,2024-01-01 +2024-01-02 03:00:00,64.1451021797174,,,,,,,,2024-01-01 +2024-01-02 03:05:00,59.39201380573057,,,,,,,,2024-01-01 +2024-01-02 03:10:00,53.84428925319428,,,,,,,,2024-01-01 +2024-01-02 03:15:00,51.75209558317312,,,,,,,,2024-01-01 +2024-01-02 03:20:00,50.89041110199949,,,,,,,,2024-01-01 +2024-01-02 03:25:00,56.94026748460208,,,,,,,,2024-01-01 +2024-01-02 03:30:00,61.7207215313647,,,,,,,,2024-01-01 +2024-01-02 03:35:00,66.43337627965418,,,,,,,,2024-01-01 +2024-01-02 03:40:00,66.57497202498817,,,,,,,,2024-01-01 +2024-01-02 03:45:00,65.64161963917951,,,,,,,,2024-01-01 +2024-01-02 03:50:00,63.18664670890266,,,,,,,,2024-01-01 +2024-01-02 03:55:00,61.817402382448606,,,,,,,,2024-01-01 +2024-01-02 04:00:00,62.06034644198455,,,,,,,,2024-01-02 +2024-01-02 04:05:00,64.11773294480004,,,,,,,,2024-01-02 +2024-01-02 04:10:00,67.36676715555794,,,,,,,,2024-01-02 +2024-01-02 04:15:00,69.12622439547005,,,,,,,,2024-01-02 +2024-01-02 04:20:00,72.48846653252676,,,,,,,,2024-01-02 +2024-01-02 04:25:00,75.15099044667379,,,,,,,,2024-01-02 +2024-01-02 04:30:00,76.02871487143496,,,,,,,,2024-01-02 +2024-01-02 04:35:00,76.44286335301837,,,,,,,,2024-01-02 +2024-01-02 04:40:00,74.4874153728255,,,,,,,,2024-01-02 +2024-01-02 04:45:00,72.54648150757725,,,,,,,,2024-01-02 +2024-01-02 04:50:00,67.63851947937147,,,,,,,,2024-01-02 +2024-01-02 04:55:00,62.98117433288436,,,,,,,,2024-01-02 +2024-01-02 05:00:00,61.34643963001424,,,,,,,,2024-01-02 +2024-01-02 05:05:00,60.61292927844648,,,,,,,,2024-01-02 +2024-01-02 05:10:00,64.71083079122545,,,,,,,,2024-01-02 +2024-01-02 05:15:00,68.65067438643243,,,,,,,,2024-01-02 +2024-01-02 05:20:00,77.06762966058118,,,,,,,,2024-01-02 +2024-01-02 05:25:00,80.60103081250233,,,,,,,,2024-01-02 +2024-01-02 05:30:00,79.09670372724142,,,,,,,,2024-01-02 +2024-01-02 05:35:00,71.50146388491422,,,,,,,,2024-01-02 +2024-01-02 05:40:00,62.32918708611827,,,,,,,,2024-01-02 +2024-01-02 05:45:00,58.85357004864832,,,,,,,,2024-01-02 +2024-01-02 05:50:00,57.52721321317705,,,,,,,,2024-01-02 +2024-01-02 05:55:00,64.63813484989842,,,,,,,,2024-01-02 +2024-01-02 06:00:00,63.93732449947299,,,,,,,,2024-01-02 +2024-01-02 06:05:00,63.67395757681743,,,,,,,,2024-01-02 +2024-01-02 06:10:00,64.57302454131322,,,,,,,,2024-01-02 +2024-01-02 06:15:00,65.32526162187479,,,,,,,,2024-01-02 +2024-01-02 06:20:00,67.16873622803372,,,,,,,,2024-01-02 +2024-01-02 06:25:00,69.12233489906912,,,,,,,,2024-01-02 +2024-01-02 06:30:00,71.29255007691545,ANNOUNCE_MEAL,,,,,10.666666666666666,10.666666666666666,2024-01-02 +2024-01-02 06:35:00,72.84011146221826,,,,,,0.0,,2024-01-02 +2024-01-02 06:40:00,77.7742538442041,,,,,,0.0,,2024-01-02 +2024-01-02 06:45:00,81.28546426037045,,,,,,0.0,,2024-01-02 +2024-01-02 06:50:00,88.84896617407905,,,,,,0.0,,2024-01-02 +2024-01-02 06:55:00,94.16633246456828,,,,,,0.0,,2024-01-02 +2024-01-02 07:00:00,95.23029848590213,,,,,,0.0,,2024-01-02 +2024-01-02 07:05:00,95.97365803896848,,,,,,0.0,,2024-01-02 +2024-01-02 07:10:00,98.39098710220982,,,,,,0.0,,2024-01-02 +2024-01-02 07:15:00,101.10280919439174,,,,,,0.0,,2024-01-02 +2024-01-02 07:20:00,108.1717130922955,,,,,,0.0,,2024-01-02 +2024-01-02 07:25:00,114.57447218500542,,,,,,0.0,,2024-01-02 +2024-01-02 07:30:00,116.7629057987088,,,,,,0.0,,2024-01-02 +2024-01-02 07:35:00,119.53949120190072,,,,,,0.0,,2024-01-02 +2024-01-02 07:40:00,121.5238647190598,,,,,,0.0,,2024-01-02 +2024-01-02 07:45:00,122.56488745037971,,,,,,0.0,,2024-01-02 +2024-01-02 07:50:00,124.24885837629458,,,,,,0.0,,2024-01-02 +2024-01-02 07:55:00,124.27542188433544,,,,,,0.0,,2024-01-02 +2024-01-02 08:00:00,123.37682498844111,,,,,,0.0,,2024-01-02 +2024-01-02 08:05:00,120.1613463438867,,,,,,0.0,,2024-01-02 +2024-01-02 08:10:00,115.98214502429023,,,,,,0.0,,2024-01-02 +2024-01-02 08:15:00,113.8758997174294,,,,,,0.0,,2024-01-02 +2024-01-02 08:20:00,110.31374264978488,,,,,,0.0,,2024-01-02 +2024-01-02 08:25:00,108.14648546629084,,,,,,0.0,,2024-01-02 +2024-01-02 08:30:00,106.8243746678771,,,,,,0.0,,2024-01-02 +2024-01-02 08:35:00,104.03355231240714,,,,,,,,2024-01-02 +2024-01-02 08:40:00,101.44968114846152,,,,,,,,2024-01-02 +2024-01-02 08:45:00,100.38542794400487,,,,,,,,2024-01-02 +2024-01-02 08:50:00,99.01090701307412,,,,,,,,2024-01-02 +2024-01-02 08:55:00,99.02989363374624,,,,,,,,2024-01-02 +2024-01-02 09:00:00,99.64172941536218,,,,,,,,2024-01-02 +2024-01-02 09:05:00,101.74992656997556,,,,,,,,2024-01-02 +2024-01-02 09:10:00,104.42315825328646,,,,,,,,2024-01-02 +2024-01-02 09:15:00,105.77449849939173,,,,,,,,2024-01-02 +2024-01-02 09:20:00,108.35579927227523,,,,,,,,2024-01-02 +2024-01-02 09:25:00,110.6663321234804,,,,,,,,2024-01-02 +2024-01-02 09:30:00,111.63325391071324,,,,,,,,2024-01-02 +2024-01-02 09:35:00,112.67816622892846,LOW_CARB_MEAL,,,,,2.333333333333333,2.333333333333333,2024-01-02 +2024-01-02 09:40:00,111.80378580115604,,,,,,,,2024-01-02 +2024-01-02 09:45:00,110.74721819067692,,,,,,,,2024-01-02 +2024-01-02 09:50:00,109.34935909892772,,,,,,,,2024-01-02 +2024-01-02 09:55:00,111.44771831280472,,,,,,,,2024-01-02 +2024-01-02 10:00:00,114.3517550460395,,,,,,,,2024-01-02 +2024-01-02 10:05:00,122.71070000485312,,,,,,,,2024-01-02 +2024-01-02 10:10:00,132.21597746615592,,,,,,,,2024-01-02 +2024-01-02 10:15:00,136.61264044966637,,,,,,,,2024-01-02 +2024-01-02 10:20:00,143.2373842136879,,,,,,,,2024-01-02 +2024-01-02 10:25:00,145.1236850481729,,,,,,,,2024-01-02 +2024-01-02 10:30:00,143.87344558019657,,,,,,,,2024-01-02 +2024-01-02 10:35:00,138.1405116656289,,,,,,,,2024-01-02 +2024-01-02 10:40:00,130.42207905482204,,,,,,,,2024-01-02 +2024-01-02 10:45:00,126.68651459189007,,,,,,,,2024-01-02 +2024-01-02 10:50:00,121.19124848194906,,,,,,,,2024-01-02 +2024-01-02 10:55:00,119.24173084041227,,,,,,,,2024-01-02 +2024-01-02 11:00:00,117.25220980169247,,,,,,,,2024-01-02 +2024-01-02 11:05:00,116.87044538094248,,,,,,,,2024-01-02 +2024-01-02 11:10:00,119.63240662580291,,,,,,,,2024-01-02 +2024-01-02 11:15:00,121.57132669422896,,,,,,,,2024-01-02 +2024-01-02 11:20:00,125.32092204148222,,,,,,,,2024-01-02 +2024-01-02 11:25:00,127.26807189462014,,,,,,,,2024-01-02 +2024-01-02 11:30:00,127.1826091316722,,,,,,,,2024-01-02 +2024-01-02 11:35:00,125.80484527434209,,,,,,,,2024-01-02 +2024-01-02 11:40:00,124.67345077296908,,,,,,,,2024-01-02 +2024-01-02 11:45:00,124.88968662301929,ANNOUNCE_MEAL,,,,,22.33333333333333,22.33333333333333,2024-01-02 +2024-01-02 11:50:00,125.09006642390568,,,,,,0.0,,2024-01-02 +2024-01-02 11:55:00,124.60929490895856,,,,,,0.0,,2024-01-02 +2024-01-02 12:00:00,123.81691313900812,,,,,,0.0,,2024-01-02 +2024-01-02 12:05:00,122.27833939699664,,,,,,0.0,,2024-01-02 +2024-01-02 12:10:00,122.99553144395962,,,,,,0.0,,2024-01-02 +2024-01-02 12:15:00,124.92857039766812,,,,,,0.0,,2024-01-02 +2024-01-02 12:20:00,132.58380397906316,,,,,,0.0,,2024-01-02 +2024-01-02 12:25:00,145.1474730712106,,,,,,0.0,,2024-01-02 +2024-01-02 12:30:00,152.78715214213085,,,,,,0.0,,2024-01-02 +2024-01-02 12:35:00,167.71949854991635,,,,,,0.0,,2024-01-02 +2024-01-02 12:40:00,177.74072413957649,,,,,,0.0,,2024-01-02 +2024-01-02 12:45:00,179.86331056528422,,,,,,0.0,,2024-01-02 +2024-01-02 12:50:00,180.01821139851813,,,,,,0.0,,2024-01-02 +2024-01-02 12:55:00,178.49876863868153,,,,,,0.0,,2024-01-02 +2024-01-02 13:00:00,178.2394382794282,,,,,,0.0,,2024-01-02 +2024-01-02 13:05:00,178.72141720419762,,,,,,0.0,,2024-01-02 +2024-01-02 13:10:00,179.61955027015318,,,,,,0.0,,2024-01-02 +2024-01-02 13:15:00,179.87321818595132,,,,,,0.0,,2024-01-02 +2024-01-02 13:20:00,179.26766506189767,,,,,,0.0,,2024-01-02 +2024-01-02 13:25:00,177.3076877327078,,,,,,0.0,,2024-01-02 +2024-01-02 13:30:00,177.8198378715971,,,,,,0.0,,2024-01-02 +2024-01-02 13:35:00,176.05350369277713,,,,,,0.0,,2024-01-02 +2024-01-02 13:40:00,171.36469214935508,,,,,,0.0,,2024-01-02 +2024-01-02 13:45:00,168.2195366007654,,,,,,0.0,,2024-01-02 +2024-01-02 13:50:00,160.91001602630513,,,,,,,,2024-01-02 +2024-01-02 13:55:00,153.0073611711594,,,,,,,,2024-01-02 +2024-01-02 14:00:00,149.1028975511221,,,,,,,,2024-01-02 +2024-01-02 14:05:00,141.80739938047785,,,,,,,,2024-01-02 +2024-01-02 14:10:00,135.7027361377489,,,,,,,,2024-01-02 +2024-01-02 14:15:00,133.26076610615382,,,,,,,,2024-01-02 +2024-01-02 14:20:00,129.74039168639945,,,,,,,,2024-01-02 +2024-01-02 14:25:00,128.1487266740735,,,,,,,,2024-01-02 +2024-01-02 14:30:00,128.0767119190764,,,,,,,,2024-01-02 +2024-01-02 14:35:00,129.08348784470488,,,,,,,,2024-01-02 +2024-01-02 14:40:00,131.10448202443018,,,,,,,,2024-01-02 +2024-01-02 14:45:00,132.24819850942745,,,,,,,,2024-01-02 +2024-01-02 14:50:00,133.92355111934478,,,,,,,,2024-01-02 +2024-01-02 14:55:00,133.501364272546,,,,,,,,2024-01-02 +2024-01-02 15:00:00,132.31289779090832,,,,,,,,2024-01-02 +2024-01-02 15:05:00,129.70626763457722,,,,,,,,2024-01-02 +2024-01-02 15:10:00,129.90021880861258,,,,,,,,2024-01-02 +2024-01-02 15:15:00,131.79977319852205,,,,,,,,2024-01-02 +2024-01-02 15:20:00,137.62940224243715,,,,,,,,2024-01-02 +2024-01-02 15:25:00,142.82324930051124,,,,,,,,2024-01-02 +2024-01-02 15:30:00,144.21886644678403,,,,,,,,2024-01-02 +2024-01-02 15:35:00,145.01131928899062,,,,,,,,2024-01-02 +2024-01-02 15:40:00,144.55748747552315,,,,,,,,2024-01-02 +2024-01-02 15:45:00,144.395053474623,,,,,,,,2024-01-02 +2024-01-02 15:50:00,145.31478062855294,,,,,,,,2024-01-02 +2024-01-02 15:55:00,148.28839912774055,,,,,,,,2024-01-02 +2024-01-02 16:00:00,148.11863245230822,,,,,,,,2024-01-02 +2024-01-02 16:05:00,147.55928954651424,,,,,,,,2024-01-02 +2024-01-02 16:10:00,146.79740171319892,,,,,,,,2024-01-02 +2024-01-02 16:15:00,146.38352735501945,,,,,,,,2024-01-02 +2024-01-02 16:20:00,145.58652388838476,,,,,,,,2024-01-02 +2024-01-02 16:25:00,144.9629053215494,,,,,,,,2024-01-02 +2024-01-02 16:30:00,144.76892221978375,,,,,,,,2024-01-02 +2024-01-02 16:35:00,144.74777886923624,,,,,,,,2024-01-02 +2024-01-02 16:40:00,145.3744631657138,,,,,,,,2024-01-02 +2024-01-02 16:45:00,145.83456556764597,,,,,,,,2024-01-02 +2024-01-02 16:50:00,145.73072526530188,,,,,,,,2024-01-02 +2024-01-02 16:55:00,142.1102397728438,,,,,,,,2024-01-02 +2024-01-02 17:00:00,138.64071060332392,,,,,,,,2024-01-02 +2024-01-02 17:05:00,131.1280690380656,,,,,,,,2024-01-02 +2024-01-02 17:10:00,127.78102112010905,,,,,,,,2024-01-02 +2024-01-02 17:15:00,128.7842872298835,,,,,,,,2024-01-02 +2024-01-02 17:20:00,133.00547240703537,,,,,,,,2024-01-02 +2024-01-02 17:25:00,133.94508773062597,,,,,,,,2024-01-02 +2024-01-02 17:30:00,131.5967059501651,,,,,,,,2024-01-02 +2024-01-02 17:35:00,123.58703258759444,,,,,,,,2024-01-02 +2024-01-02 17:40:00,116.38687096382353,,,,,,,,2024-01-02 +2024-01-02 17:45:00,113.68808356097016,ANNOUNCE_MEAL,,,,,17.333333333333332,17.333333333333332,2024-01-02 +2024-01-02 17:50:00,113.44375381325052,,,,,,0.0,,2024-01-02 +2024-01-02 17:55:00,113.19885885851367,,,,,,0.0,,2024-01-02 +2024-01-02 18:00:00,112.6719189159721,,,,,,0.0,,2024-01-02 +2024-01-02 18:05:00,111.20321489770215,,,,,,0.0,,2024-01-02 +2024-01-02 18:10:00,110.49765847495593,,,,,,0.0,,2024-01-02 +2024-01-02 18:15:00,110.78001038564356,,,,,,0.0,,2024-01-02 +2024-01-02 18:20:00,112.74726778630432,,,,,,0.0,,2024-01-02 +2024-01-02 18:25:00,117.0560270309505,,,,,,0.0,,2024-01-02 +2024-01-02 18:30:00,121.50191099148245,,,,,,0.0,,2024-01-02 +2024-01-02 18:35:00,127.97387191232777,,,,,,0.0,,2024-01-02 +2024-01-02 18:40:00,131.68211343378886,,,,,,0.0,,2024-01-02 +2024-01-02 18:45:00,132.7732482562214,,,,,,0.0,,2024-01-02 +2024-01-02 18:50:00,134.12791421200197,,,,,,0.0,,2024-01-02 +2024-01-02 18:55:00,135.3959433568174,,,,,,0.0,,2024-01-02 +2024-01-02 19:00:00,136.27054640477135,,,,,,0.0,,2024-01-02 +2024-01-02 19:05:00,138.16092438771832,,,,,,0.0,,2024-01-02 +2024-01-02 19:10:00,139.43780524229516,,,,,,0.0,,2024-01-02 +2024-01-02 19:15:00,139.6845396427546,,,,,,0.0,,2024-01-02 +2024-01-02 19:20:00,140.1160352401319,,,,,,0.0,,2024-01-02 +2024-01-02 19:25:00,141.80311455991583,,,,,,0.0,,2024-01-02 +2024-01-02 19:30:00,143.393904732418,,,,,,0.0,,2024-01-02 +2024-01-02 19:35:00,147.00006176569502,,,,,,0.0,,2024-01-02 +2024-01-02 19:40:00,149.11480381109945,,,,,,0.0,,2024-01-02 +2024-01-02 19:45:00,149.02767149786777,,,,,,0.0,,2024-01-02 +2024-01-02 19:50:00,146.77487918064276,,,,,,,,2024-01-02 +2024-01-02 19:55:00,142.5787874047984,,,,,,,,2024-01-02 +2024-01-02 20:00:00,140.05830472011925,,,,,,,,2024-01-02 +2024-01-02 20:05:00,134.76273036241125,,,,,,,,2024-01-02 +2024-01-02 20:10:00,129.89677642882603,,,,,,,,2024-01-02 +2024-01-02 20:15:00,127.85703767942616,,,,,,,,2024-01-02 +2024-01-02 20:20:00,124.62034299402524,,,,,,,,2024-01-02 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21:50:00,127.8029368047298,,,,,,,,2024-01-02 +2024-01-02 21:55:00,124.0527994438257,,,,,,,,2024-01-02 +2024-01-02 22:00:00,120.53063937246624,,,,,,,,2024-01-02 +2024-01-02 22:05:00,112.20825876468962,,,,,,,,2024-01-02 +2024-01-02 22:10:00,106.18765712030049,,,,,,,,2024-01-02 +2024-01-02 22:15:00,105.07439686244118,,,,,,,,2024-01-02 +2024-01-02 22:20:00,105.24301648184456,,,,,,,,2024-01-02 +2024-01-02 22:25:00,105.39582087607457,,,,,,,,2024-01-02 +2024-01-02 22:30:00,104.585449003641,,,,,,,,2024-01-02 +2024-01-02 22:35:00,101.7946619697135,,,,,,,,2024-01-02 +2024-01-02 22:40:00,99.1704179720374,,,,,,,,2024-01-02 +2024-01-02 22:45:00,98.42678812467292,,,,,,,,2024-01-02 +2024-01-02 22:50:00,98.06361339689232,,,,,,,,2024-01-02 +2024-01-02 22:55:00,98.76906418196349,,,,,,,,2024-01-02 +2024-01-02 23:00:00,99.36215936838406,,,,,,,,2024-01-02 +2024-01-02 23:05:00,100.72407601305198,,,,,,,,2024-01-02 +2024-01-02 23:10:00,101.91743947376432,,,,,,,,2024-01-02 +2024-01-02 23:15:00,102.2988110150969,,,,,,,,2024-01-02 +2024-01-02 23:20:00,102.32471530766068,,,,,,,,2024-01-02 +2024-01-02 23:25:00,100.969347806366,,,,,,,,2024-01-02 +2024-01-02 23:30:00,99.95745638481137,,,,,,,,2024-01-02 +2024-01-02 23:35:00,99.03633507463552,,,,,,,,2024-01-02 +2024-01-02 23:40:00,98.98631972049952,,,,,,,,2024-01-02 +2024-01-02 23:45:00,99.06301877097052,,,,,,,,2024-01-02 +2024-01-02 23:50:00,98.96937289149363,,,,,,,,2024-01-02 +2024-01-02 23:55:00,97.96412482532476,,,,,,,,2024-01-02 +2024-01-03 00:00:00,97.04805010038449,,,,,,,,2024-01-02 +2024-01-03 00:05:00,95.32099449547964,,,,,,,,2024-01-02 +2024-01-03 00:10:00,95.35544614192308,,,,,,,,2024-01-02 +2024-01-03 00:15:00,96.24917211442522,,,,,,,,2024-01-02 +2024-01-03 00:20:00,97.35075128768652,,,,,,,,2024-01-02 +2024-01-03 00:25:00,93.23243195602686,,,,,,,,2024-01-02 +2024-01-03 00:30:00,88.32266145925693,,,,,,,,2024-01-02 +2024-01-03 00:35:00,76.29803628380297,,,,,,,,2024-01-02 +2024-01-03 00:40:00,68.01074138596412,,,,,,,,2024-01-02 +2024-01-03 00:45:00,67.0445264044152,,,,,,,,2024-01-02 +2024-01-03 00:50:00,69.68719360339519,,,,,,,,2024-01-02 +2024-01-03 00:55:00,73.97302711638113,,,,,,,,2024-01-02 +2024-01-03 01:00:00,75.41385433840776,,,,,,,,2024-01-02 +2024-01-03 01:05:00,76.8402066618296,,,,,,,,2024-01-02 +2024-01-03 01:10:00,77.28243008314801,,,,,,,,2024-01-02 +2024-01-03 01:15:00,77.42918551999549,,,,,,,,2024-01-02 +2024-01-03 01:20:00,77.69530199945399,,,,,,,,2024-01-02 +2024-01-03 01:25:00,77.90273062115068,,,,,,,,2024-01-02 +2024-01-03 01:30:00,77.99937452110815,,,,,,,,2024-01-02 +2024-01-03 01:35:00,78.37855593271958,,,,,,,,2024-01-02 +2024-01-03 01:40:00,79.34030450256634,,,,,,,,2024-01-02 +2024-01-03 01:45:00,80.16517212318479,,,,,,,,2024-01-02 +2024-01-03 01:50:00,82.7539889331211,,,,,,,,2024-01-02 +2024-01-03 01:55:00,84.35943227587,,,,,,,,2024-01-02 +2024-01-03 02:00:00,80.14119618573201,,,,,,,,2024-01-02 +2024-01-03 02:05:00,78.61461880250604,,,,,,,,2024-01-02 +2024-01-03 02:10:00,83.50261667495309,,,,,,,,2024-01-02 +2024-01-03 02:15:00,87.30261179253819,,,,,,,,2024-01-02 +2024-01-03 02:20:00,95.5156826187557,,,,,,,,2024-01-02 +2024-01-03 02:25:00,101.78258088206506,,,,,,,,2024-01-02 +2024-01-03 02:30:00,103.42035199850464,,,,,,,,2024-01-02 +2024-01-03 02:35:00,104.15345403454911,,,,,,,,2024-01-02 +2024-01-03 02:40:00,102.87647417743165,,,,,,,,2024-01-02 +2024-01-03 02:45:00,101.92546237793168,,,,,,,,2024-01-02 +2024-01-03 02:50:00,99.72302581226629,,,,,,,,2024-01-02 +2024-01-03 02:55:00,97.3316084300433,,,,,,,,2024-01-02 +2024-01-03 03:00:00,96.156290517678,,,,,,,,2024-01-02 +2024-01-03 03:05:00,94.11619473908596,,,,,,,,2024-01-02 +2024-01-03 03:10:00,92.87233567717294,,,,,,,,2024-01-02 +2024-01-03 03:15:00,92.59655667113778,,,,,,,,2024-01-02 +2024-01-03 03:20:00,92.131196626295,,,,,,,,2024-01-02 +2024-01-03 03:25:00,90.7140995332778,,,,,,,,2024-01-02 +2024-01-03 03:30:00,89.41554144521672,,,,,,,,2024-01-02 +2024-01-03 03:35:00,86.39423760161468,,,,,,,,2024-01-02 +2024-01-03 03:40:00,84.28379295409943,,,,,,,,2024-01-02 +2024-01-03 03:45:00,83.97310010899429,,,,,,,,2024-01-02 +2024-01-03 03:50:00,84.54308053133994,,,,,,,,2024-01-02 +2024-01-03 03:55:00,85.8717190981404,,,,,,,,2024-01-02 +2024-01-03 04:00:00,86.5703980339182,,,,,,,,2024-01-03 +2024-01-03 04:05:00,87.98321064186207,,,,,,,,2024-01-03 +2024-01-03 04:10:00,89.52416217335688,,,,,,,,2024-01-03 +2024-01-03 04:15:00,90.3830195912119,,,,,,,,2024-01-03 +2024-01-03 04:20:00,92.35816773731396,,,,,,,,2024-01-03 +2024-01-03 04:25:00,94.786827314342,,,,,,,,2024-01-03 +2024-01-03 04:30:00,96.0775617564512,,,,,,,,2024-01-03 +2024-01-03 04:35:00,97.84654620794912,,,,,,,,2024-01-03 +2024-01-03 04:40:00,98.45842282969365,,,,,,,,2024-01-03 +2024-01-03 04:45:00,98.30277642529252,,,,,,,,2024-01-03 +2024-01-03 04:50:00,97.01298454028908,,,,,,,,2024-01-03 +2024-01-03 04:55:00,94.35289804043568,,,,,,,,2024-01-03 +2024-01-03 05:00:00,92.54741151910906,,,,,,,,2024-01-03 +2024-01-03 05:05:00,88.52389697371785,,,,,,,,2024-01-03 +2024-01-03 05:10:00,84.85358802211317,,,,,,,,2024-01-03 +2024-01-03 05:15:00,83.49834727422355,,,,,,,,2024-01-03 +2024-01-03 05:20:00,82.50659044269185,,,,,,,,2024-01-03 +2024-01-03 05:25:00,84.81472212354893,,,,,,,,2024-01-03 +2024-01-03 05:30:00,87.35830923605891,,,,,,,,2024-01-03 +2024-01-03 05:35:00,93.87349385215488,,,,,,,,2024-01-03 +2024-01-03 05:40:00,99.85913091193532,,,,,,,,2024-01-03 +2024-01-03 05:45:00,101.96476050528872,,,,,,,,2024-01-03 +2024-01-03 05:50:00,104.66449993388304,,,,,,,,2024-01-03 +2024-01-03 05:55:00,106.33788642972034,,,,,,,,2024-01-03 +2024-01-03 06:00:00,107.03236856062838,,,,,,,,2024-01-03 +2024-01-03 06:05:00,107.6834394541626,,,,,,,,2024-01-03 +2024-01-03 06:10:00,106.39629391418254,,,,,,,,2024-01-03 +2024-01-03 06:15:00,104.86360607009746,,,,,,,,2024-01-03 +2024-01-03 06:20:00,101.12772169269348,,,,,,,,2024-01-03 +2024-01-03 06:25:00,98.5171726658384,,,,,,,,2024-01-03 +2024-01-03 06:30:00,98.1610266256805,,,,,,,,2024-01-03 +2024-01-03 06:35:00,98.71610986133508,,,,,,,,2024-01-03 +2024-01-03 06:40:00,99.4467906553526,,,,,,,,2024-01-03 +2024-01-03 06:45:00,99.3101435273726,,,,,,,,2024-01-03 +2024-01-03 06:50:00,96.8972054724806,,,,,,,,2024-01-03 +2024-01-03 06:55:00,91.52817671841376,,,,,,,,2024-01-03 +2024-01-03 07:00:00,90.88337412208372,ANNOUNCE_MEAL,,,,,12.0,12.0,2024-01-03 +2024-01-03 07:05:00,90.78736540623044,,,,,,0.0,,2024-01-03 +2024-01-03 07:10:00,91.85328147039024,,,,,,0.0,,2024-01-03 +2024-01-03 07:15:00,92.80000402126647,,,,,,0.0,,2024-01-03 +2024-01-03 07:20:00,95.58313089022836,,,,,,0.0,,2024-01-03 +2024-01-03 07:25:00,99.24058426627792,,,,,,0.0,,2024-01-03 +2024-01-03 07:30:00,101.25823082424516,,,,,,0.0,,2024-01-03 +2024-01-03 07:35:00,106.21259246731216,,,,,,0.0,,2024-01-03 +2024-01-03 07:40:00,113.44279920492016,,,,,,0.0,,2024-01-03 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09:05:00,120.31223016837396,,,,,,,,2024-01-03 +2024-01-03 09:10:00,123.07109548484004,,,,,,,,2024-01-03 +2024-01-03 09:15:00,123.39505079994174,,,,,,,,2024-01-03 +2024-01-03 09:20:00,120.29055658275723,,,,,,,,2024-01-03 +2024-01-03 09:25:00,113.92730144954676,,,,,,,,2024-01-03 +2024-01-03 09:30:00,117.6170622471058,,,,,,,,2024-01-03 +2024-01-03 09:35:00,122.22988257204584,,,,,,,,2024-01-03 +2024-01-03 09:40:00,124.05133892220933,,,,,,,,2024-01-03 +2024-01-03 09:45:00,124.25199381624012,,,,,,,,2024-01-03 +2024-01-03 09:50:00,123.90719267131618,,,,,,,,2024-01-03 +2024-01-03 09:55:00,123.4606151458621,,,,,,,,2024-01-03 +2024-01-03 10:00:00,123.47756361016378,,,,,,,,2024-01-03 +2024-01-03 10:05:00,124.1318017457646,,,,,,,,2024-01-03 +2024-01-03 10:10:00,125.62884317682672,,,,,,,,2024-01-03 +2024-01-03 10:15:00,126.62704121138847,,,,,,,,2024-01-03 +2024-01-03 10:20:00,128.44779066215693,,,,,,,,2024-01-03 +2024-01-03 10:25:00,128.95677854750744,,,,,,,,2024-01-03 +2024-01-03 10:30:00,128.5482298881157,,,,,,,,2024-01-03 +2024-01-03 10:35:00,127.74173752195884,,,,,,,,2024-01-03 +2024-01-03 10:40:00,129.28809148558213,,,,,,,,2024-01-03 +2024-01-03 10:45:00,131.43784623033147,,,,,,,,2024-01-03 +2024-01-03 10:50:00,136.8382465074638,,,,,,,,2024-01-03 +2024-01-03 10:55:00,140.5217836120081,,,,,,,,2024-01-03 +2024-01-03 11:00:00,140.91518067515312,,,,,,,,2024-01-03 +2024-01-03 11:05:00,139.99028844850537,,,,,,,,2024-01-03 +2024-01-03 11:10:00,139.46625586792177,,,,,,,,2024-01-03 +2024-01-03 11:15:00,140.0573696599404,,,,,,,,2024-01-03 +2024-01-03 11:20:00,142.07174668216447,,,,,,,,2024-01-03 +2024-01-03 11:25:00,143.15220759289952,,,,,,,,2024-01-03 +2024-01-03 11:30:00,142.78996200100454,,,,,,,,2024-01-03 +2024-01-03 11:35:00,140.71177079919107,,,,,,,,2024-01-03 +2024-01-03 11:40:00,138.0493178269,,,,,,,,2024-01-03 +2024-01-03 11:45:00,136.95558727693884,,,,,,,,2024-01-03 +2024-01-03 11:50:00,136.1531071957497,,,,,,,,2024-01-03 +2024-01-03 11:55:00,137.22646171261349,,,,,,,,2024-01-03 +2024-01-03 12:00:00,136.20237437028663,,,,,,,,2024-01-03 +2024-01-03 12:05:00,135.68943807418836,,,,,,,,2024-01-03 +2024-01-03 12:10:00,136.16298786117284,,,,,,,,2024-01-03 +2024-01-03 12:15:00,136.37104980176102,,,,,,,,2024-01-03 +2024-01-03 12:20:00,135.9305424569463,,,,,,,,2024-01-03 +2024-01-03 12:25:00,133.3115103661344,,,,,,,,2024-01-03 +2024-01-03 12:30:00,131.0389587063375,,,,,,,,2024-01-03 +2024-01-03 12:35:00,126.0546963427194,,,,,,,,2024-01-03 +2024-01-03 12:40:00,123.1310208050412,,,,,,,,2024-01-03 +2024-01-03 12:45:00,123.0752613950362,,,,,,,,2024-01-03 +2024-01-03 12:50:00,124.41590389053424,,,,,,,,2024-01-03 +2024-01-03 12:55:00,124.87674747934392,,,,,,,,2024-01-03 +2024-01-03 13:00:00,124.0840045365996,ANNOUNCE_MEAL,,,,,27.66666666666666,24.33333333333333,2024-01-03 +2024-01-03 13:05:00,121.72349996519984,,,,,,0.0,,2024-01-03 +2024-01-03 13:10:00,121.0992891065856,,,,,,0.0,,2024-01-03 +2024-01-03 13:15:00,122.16881218945528,,,,,,0.0,,2024-01-03 +2024-01-03 13:20:00,125.84188453459066,,,,,,0.0,,2024-01-03 +2024-01-03 13:25:00,128.9496388811778,,,,,,0.0,,2024-01-03 +2024-01-03 13:30:00,129.61845352772764,,,,,,0.0,,2024-01-03 +2024-01-03 13:35:00,130.44188588225694,,,,,,0.0,,2024-01-03 +2024-01-03 13:40:00,132.67666035982307,,,,,,0.0,,2024-01-03 +2024-01-03 13:45:00,134.795239264174,,,,,,0.0,,2024-01-03 +2024-01-03 13:50:00,140.62856308117378,,,,,,0.0,,2024-01-03 +2024-01-03 13:55:00,147.92718001415065,,,,,,0.0,,2024-01-03 +2024-01-03 14:00:00,151.97644163339396,,,,,,0.0,,2024-01-03 +2024-01-03 14:05:00,160.50365923047514,,,,,,0.0,,2024-01-03 +2024-01-03 14:10:00,169.13817393810055,,,,,,0.0,,2024-01-03 +2024-01-03 14:15:00,173.3414520986717,,,,,,0.0,,2024-01-03 +2024-01-03 14:20:00,181.222210285856,,,,,,0.0,,2024-01-03 +2024-01-03 14:25:00,187.75188035627667,,,,,,0.0,,2024-01-03 +2024-01-03 14:30:00,189.70542082757524,,,,,,0.0,,2024-01-03 +2024-01-03 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15:55:00,180.52127620451,,,,,,,,2024-01-03 +2024-01-03 16:00:00,179.64364384979686,,,,,,,,2024-01-03 +2024-01-03 16:05:00,176.3576912047451,,,,,,,,2024-01-03 +2024-01-03 16:10:00,170.7668959520797,,,,,,,,2024-01-03 +2024-01-03 16:15:00,167.30172108792104,,,,,,,,2024-01-03 +2024-01-03 16:20:00,161.6137681861903,,,,,,,,2024-01-03 +2024-01-03 16:25:00,161.80550789827873,,,,,,,,2024-01-03 +2024-01-03 16:30:00,165.0313130577086,,,,,,,,2024-01-03 +2024-01-03 16:35:00,174.79460969489713,,,,,,,,2024-01-03 +2024-01-03 16:40:00,183.1205845577344,,,,,,,,2024-01-03 +2024-01-03 16:45:00,184.54744113253287,,,,,,,,2024-01-03 +2024-01-03 16:50:00,167.3257371767648,ANNOUNCE_MEAL,,,,,27.666666666666668,27.666666666666668,2024-01-03 +2024-01-03 16:55:00,156.56386998223817,,,,,,0.0,,2024-01-03 +2024-01-03 17:00:00,152.12589467064748,,,,,,0.0,,2024-01-03 +2024-01-03 17:05:00,146.1921000890573,,,,,,0.0,,2024-01-03 +2024-01-03 17:10:00,144.15056988390853,,,,,,0.0,,2024-01-03 +2024-01-03 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03:05:00,68.27901518747925,,,,,,,,2024-01-04 +2024-01-05 03:10:00,69.50796192851924,,,,,,,,2024-01-04 +2024-01-05 03:15:00,69.69585736348891,,,,,,,,2024-01-04 +2024-01-05 03:20:00,69.50946586015309,,,,,,,,2024-01-04 +2024-01-05 03:25:00,69.28014741360109,,,,,,,,2024-01-04 +2024-01-05 03:30:00,69.3878311136582,,,,,,,,2024-01-04 +2024-01-05 03:35:00,70.33025108542785,,,,,,,,2024-01-04 +2024-01-05 03:40:00,72.54090152206948,,,,,,,,2024-01-04 +2024-01-05 03:45:00,74.23388657174002,,,,,,,,2024-01-04 +2024-01-05 03:50:00,79.01935280022859,,,,,,,,2024-01-04 +2024-01-05 03:55:00,83.73649795831517,,,,,,,,2024-01-04 +2024-01-05 04:00:00,81.41596424021215,,,,,,,,2024-01-05 +2024-01-05 04:05:00,78.35027727153326,,,,,,,,2024-01-05 +2024-01-05 04:10:00,76.62608359093323,,,,,,,,2024-01-05 +2024-01-05 04:15:00,75.9824266754603,,,,,,,,2024-01-05 +2024-01-05 04:20:00,74.56277541364476,,,,,,,,2024-01-05 +2024-01-05 04:25:00,72.22579312462952,,,,,,,,2024-01-05 +2024-01-05 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05:55:00,70.91323512471173,,,,,,,,2024-01-05 +2024-01-05 06:00:00,72.59571266606369,,,,,,,,2024-01-05 +2024-01-05 06:05:00,76.38225538190272,,,,,,,,2024-01-05 +2024-01-05 06:10:00,80.02365550729725,,,,,,,,2024-01-05 +2024-01-05 06:15:00,81.52470907366477,,,,,,,,2024-01-05 +2024-01-05 06:20:00,83.35709682624368,,,,,,,,2024-01-05 +2024-01-05 06:25:00,83.73968120312969,,,,,,,,2024-01-05 +2024-01-05 06:30:00,84.96942267181836,,,,,,,,2024-01-05 +2024-01-05 06:35:00,86.04763727769355,,,,,,,,2024-01-05 +2024-01-05 06:40:00,85.84262228483641,,,,,,,,2024-01-05 +2024-01-05 06:45:00,85.4683303299165,,,,,,,,2024-01-05 +2024-01-05 06:50:00,84.5950389042479,,,,,,,,2024-01-05 +2024-01-05 06:55:00,84.11459019390725,,,,,,,,2024-01-05 +2024-01-05 07:00:00,84.23621440324764,,,,,,,,2024-01-05 +2024-01-05 07:05:00,85.66781646454123,,,,,,,,2024-01-05 +2024-01-05 07:10:00,89.28081197076406,,,,,,,,2024-01-05 +2024-01-05 07:15:00,91.84365199351788,,,,,,,,2024-01-05 +2024-01-05 07:20:00,96.26785361632632,,,,,,,,2024-01-05 +2024-01-05 07:25:00,96.04875472268932,,,,,,,,2024-01-05 +2024-01-05 07:30:00,93.42210915498973,,,,,,,,2024-01-05 +2024-01-05 07:35:00,83.09073361203342,ANNOUNCE_MEAL,,,,,20.666666666666668,18.666666666666668,2024-01-05 +2024-01-05 07:40:00,81.61066488256918,,,,,,0.0,,2024-01-05 +2024-01-05 07:45:00,81.91565554609076,,,,,,0.0,,2024-01-05 +2024-01-05 07:50:00,85.84367018208478,,,,,,0.0,,2024-01-05 +2024-01-05 07:55:00,90.41608239386164,,,,,,0.0,,2024-01-05 +2024-01-05 08:00:00,92.08206237401686,,,,,,0.0,,2024-01-05 +2024-01-05 08:05:00,95.350444877229,,,,,,0.0,,2024-01-05 +2024-01-05 08:10:00,100.54316105276462,,,,,,0.0,,2024-01-05 +2024-01-05 08:15:00,104.11368833746477,,,,,,0.0,,2024-01-05 +2024-01-05 08:20:00,111.56280965174386,,,,,,0.0,,2024-01-05 +2024-01-05 08:25:00,116.62194378674636,,,,,,0.0,,2024-01-05 +2024-01-05 08:30:00,117.56369122940836,,,,,,0.0,,2024-01-05 +2024-01-05 08:35:00,116.9381289151032,,,,,,0.0,,2024-01-05 +2024-01-05 08:40:00,114.8254791015647,,,,,,0.0,,2024-01-05 +2024-01-05 08:45:00,113.95822554146108,,,,,,0.0,,2024-01-05 +2024-01-05 08:50:00,114.15262623044086,,,,,,0.0,,2024-01-05 +2024-01-05 08:55:00,120.31138053632878,,,,,,0.0,,2024-01-05 +2024-01-05 09:00:00,127.85470024510217,,,,,,0.0,,2024-01-05 +2024-01-05 09:05:00,137.47256324692376,,,,,,0.0,,2024-01-05 +2024-01-05 09:10:00,141.262099084494,LOW_CARB_MEAL,,,,,0.0,2.0,2024-01-05 +2024-01-05 09:15:00,141.515086973631,,,,,,0.0,,2024-01-05 +2024-01-05 09:20:00,140.35467409804218,,,,,,0.0,,2024-01-05 +2024-01-05 09:25:00,139.976577962202,,,,,,0.0,,2024-01-05 +2024-01-05 09:30:00,140.47216307299365,,,,,,0.0,,2024-01-05 +2024-01-05 09:35:00,142.32001076337193,,,,,,0.0,,2024-01-05 +2024-01-05 09:40:00,144.78546397103463,,,,,,,,2024-01-05 +2024-01-05 09:45:00,146.14392100369113,,,,,,,,2024-01-05 +2024-01-05 09:50:00,148.49037782915582,,,,,,,,2024-01-05 +2024-01-05 09:55:00,149.45003297295025,,,,,,,,2024-01-05 +2024-01-05 10:00:00,149.2190024583499,,,,,,,,2024-01-05 +2024-01-05 10:05:00,147.8160054915934,,,,,,,,2024-01-05 +2024-01-05 10:10:00,146.12285210737122,,,,,,,,2024-01-05 +2024-01-05 10:15:00,145.51457220502311,,,,,,,,2024-01-05 +2024-01-05 10:20:00,145.40158882179327,,,,,,,,2024-01-05 +2024-01-05 10:25:00,147.52008261008078,,,,,,,,2024-01-05 +2024-01-05 10:30:00,149.4472020074328,,,,,,,,2024-01-05 +2024-01-05 10:35:00,153.27781068417687,,,,,,,,2024-01-05 +2024-01-05 10:40:00,154.04812349698688,,,,,,,,2024-01-05 +2024-01-05 10:45:00,152.558886222654,,,,,,,,2024-01-05 +2024-01-05 10:50:00,147.3926621729758,,,,,,,,2024-01-05 +2024-01-05 10:55:00,142.5497715521546,,,,,,,,2024-01-05 +2024-01-05 11:00:00,141.14364463483992,,,,,,,,2024-01-05 +2024-01-05 11:05:00,139.80808838929676,,,,,,,,2024-01-05 +2024-01-05 11:10:00,138.76580062778527,,,,,,,,2024-01-05 +2024-01-05 11:15:00,137.7256109449107,,,,,,,,2024-01-05 +2024-01-05 11:20:00,133.34804737439623,,,,,,,,2024-01-05 +2024-01-05 11:25:00,127.18623138769652,,,,,,,,2024-01-05 +2024-01-05 11:30:00,129.59199729005832,,,,,,,,2024-01-05 +2024-01-05 11:35:00,133.25696680495116,,,,,,,,2024-01-05 +2024-01-05 11:40:00,135.16423626070184,,,,,,,,2024-01-05 +2024-01-05 11:45:00,135.37340338262166,,,,,,,,2024-01-05 +2024-01-05 11:50:00,134.13411764735463,,,,,,,,2024-01-05 +2024-01-05 11:55:00,130.48723815887553,,,,,,,,2024-01-05 +2024-01-05 12:00:00,127.869119076878,,,,,,,,2024-01-05 +2024-01-05 12:05:00,122.58610791406682,,,,,,,,2024-01-05 +2024-01-05 12:10:00,119.8006006451996,,,,,,,,2024-01-05 +2024-01-05 12:15:00,121.19834281742888,ANNOUNCE_MEAL,,,,,24.0,24.0,2024-01-05 +2024-01-05 12:20:00,123.04886588282125,,,,,,0.0,,2024-01-05 +2024-01-05 12:25:00,127.76772155242008,,,,,,0.0,,2024-01-05 +2024-01-05 12:30:00,130.27084250427566,,,,,,0.0,,2024-01-05 +2024-01-05 12:35:00,135.53952013085674,,,,,,0.0,,2024-01-05 +2024-01-05 12:40:00,141.6832696988153,,,,,,0.0,,2024-01-05 +2024-01-05 12:45:00,145.16651985654073,,,,,,0.0,,2024-01-05 +2024-01-05 12:50:00,152.08727078377393,,,,,,0.0,,2024-01-05 +2024-01-05 12:55:00,156.76942492982906,,,,,,0.0,,2024-01-05 +2024-01-05 13:00:00,157.7369694613976,,,,,,0.0,,2024-01-05 +2024-01-05 13:05:00,158.97630994625055,,,,,,0.0,,2024-01-05 +2024-01-05 13:10:00,163.60105460475046,,,,,,0.0,,2024-01-05 +2024-01-05 13:15:00,168.25349630508384,,,,,,0.0,,2024-01-05 +2024-01-05 13:20:00,179.58165426248567,,,,,,0.0,,2024-01-05 +2024-01-05 13:25:00,188.2779038948274,,,,,,0.0,,2024-01-05 +2024-01-05 13:30:00,190.1920522308856,,,,,,0.0,,2024-01-05 +2024-01-05 13:35:00,190.16046279515345,,,,,,0.0,,2024-01-05 +2024-01-05 13:40:00,187.8661398245814,,,,,,0.0,,2024-01-05 +2024-01-05 13:45:00,186.9436282896868,,,,,,0.0,,2024-01-05 +2024-01-05 13:50:00,187.67219304836897,,,,,,0.0,,2024-01-05 +2024-01-05 13:55:00,191.3086584456692,,,,,,0.0,,2024-01-05 +2024-01-05 14:00:00,187.38829724597213,,,,,,0.0,,2024-01-05 +2024-01-05 14:05:00,180.3321052746256,,,,,,0.0,,2024-01-05 +2024-01-05 14:10:00,174.2154266366611,,,,,,0.0,,2024-01-05 +2024-01-05 14:15:00,171.46497087833444,,,,,,0.0,,2024-01-05 +2024-01-05 14:20:00,166.48234174246204,,,,,,,,2024-01-05 +2024-01-05 14:25:00,162.05878538832712,,,,,,,,2024-01-05 +2024-01-05 14:30:00,160.0602897294747,,,,,,,,2024-01-05 +2024-01-05 14:35:00,156.8990809130135,,,,,,,,2024-01-05 +2024-01-05 14:40:00,155.53613336478682,,,,,,,,2024-01-05 +2024-01-05 14:45:00,155.6335404903927,,,,,,,,2024-01-05 +2024-01-05 14:50:00,156.48915212634495,,,,,,,,2024-01-05 +2024-01-05 14:55:00,156.60131546263275,,,,,,,,2024-01-05 +2024-01-05 15:00:00,155.87800670647152,,,,,,,,2024-01-05 +2024-01-05 15:05:00,152.66850946018542,,,,,,,,2024-01-05 +2024-01-05 15:10:00,147.1721552332627,,,,,,,,2024-01-05 +2024-01-05 15:15:00,143.7141292958185,,,,,,,,2024-01-05 +2024-01-05 15:20:00,136.57721428387515,,,,,,,,2024-01-05 +2024-01-05 15:25:00,131.09512308431195,,,,,,,,2024-01-05 +2024-01-05 15:30:00,129.5772599377699,,,,,,,,2024-01-05 +2024-01-05 15:35:00,129.2367200424894,,,,,,,,2024-01-05 +2024-01-05 15:40:00,132.44001022944195,,,,,,,,2024-01-05 +2024-01-05 15:45:00,135.19982404034442,,,,,,,,2024-01-05 +2024-01-05 15:50:00,141.5180874186969,,,,,,,,2024-01-05 +2024-01-05 15:55:00,146.37842278852352,,,,,,,,2024-01-05 +2024-01-05 16:00:00,147.54880393230752,,,,,,,,2024-01-05 +2024-01-05 16:05:00,147.87653472420112,,,,,,,,2024-01-05 +2024-01-05 16:10:00,146.9205813492736,,,,,,,,2024-01-05 +2024-01-05 16:15:00,146.48823667637768,,,,,,,,2024-01-05 +2024-01-05 16:20:00,146.82685494504477,,,,,,,,2024-01-05 +2024-01-05 16:25:00,148.74929616489135,,,,,,,,2024-01-05 +2024-01-05 16:30:00,146.91861838159446,,,,,,,,2024-01-05 +2024-01-05 16:35:00,142.44740538687716,,,,,,,,2024-01-05 +2024-01-05 16:40:00,137.36710729984975,,,,,,,,2024-01-05 +2024-01-05 16:45:00,134.78442484089436,,,,,,,,2024-01-05 +2024-01-05 16:50:00,129.9164203242351,,,,,,,,2024-01-05 +2024-01-05 16:55:00,125.94887472452618,,,,,,,,2024-01-05 +2024-01-05 17:00:00,124.42363267613663,,,,,,,,2024-01-05 +2024-01-05 17:05:00,122.0362746161988,,,,,,,,2024-01-05 +2024-01-05 17:10:00,119.99737433265386,,,,,,,,2024-01-05 +2024-01-05 17:15:00,118.96665891720102,,,,,,,,2024-01-05 +2024-01-05 17:20:00,117.04658220779685,,,,,,,,2024-01-05 +2024-01-05 17:25:00,115.69656571408264,,,,,,,,2024-01-05 +2024-01-05 17:30:00,115.29944532031068,,,,,,,,2024-01-05 +2024-01-05 17:35:00,114.6241844478504,,,,,,,,2024-01-05 +2024-01-05 17:40:00,113.31885643802576,,,,,,,,2024-01-05 +2024-01-05 17:45:00,112.3114103980584,,,,,,,,2024-01-05 +2024-01-05 17:50:00,110.65632793988328,,,,,,,,2024-01-05 +2024-01-05 17:55:00,111.3819557647376,,,,,,,,2024-01-05 +2024-01-05 18:00:00,113.00398026942574,,,,,,,,2024-01-05 +2024-01-05 18:05:00,116.96736360862772,,,,,,,,2024-01-05 +2024-01-05 18:10:00,118.5358749844741,ANNOUNCE_MEAL,,,,,22.666666666666668,22.666666666666668,2024-01-05 +2024-01-05 18:15:00,117.60690364855787,,,,,,0.0,,2024-01-05 +2024-01-05 18:20:00,113.94613326694385,,,,,,0.0,,2024-01-05 +2024-01-05 18:25:00,111.51507300640564,,,,,,0.0,,2024-01-05 +2024-01-05 18:30:00,111.92824688833026,,,,,,0.0,,2024-01-05 +2024-01-05 18:35:00,116.00151940117355,,,,,,0.0,,2024-01-05 +2024-01-05 18:40:00,123.07309167455956,,,,,,0.0,,2024-01-05 +2024-01-05 18:45:00,126.95819144017756,,,,,,0.0,,2024-01-05 +2024-01-05 18:50:00,133.1806646463043,,,,,,0.0,,2024-01-05 +2024-01-05 18:55:00,138.66195393651432,,,,,,0.0,,2024-01-05 +2024-01-05 19:00:00,149.66724612987963,,,,,,0.0,,2024-01-05 +2024-01-05 19:05:00,161.28985782406156,,,,,,0.0,,2024-01-05 +2024-01-05 19:10:00,162.31954572993456,,,,,,0.0,,2024-01-05 +2024-01-05 19:15:00,160.18106408392538,,,,,,0.0,,2024-01-05 +2024-01-05 19:20:00,153.36736520513165,,,,,,0.0,,2024-01-05 +2024-01-05 19:25:00,146.90276590741405,,,,,,0.0,,2024-01-05 +2024-01-05 19:30:00,144.91697195327666,,,,,,0.0,,2024-01-05 +2024-01-05 19:35:00,143.53502851875493,,,,,,0.0,,2024-01-05 +2024-01-05 19:40:00,144.85224742864227,,,,,,0.0,,2024-01-05 +2024-01-05 19:45:00,146.14677216942815,,,,,,0.0,,2024-01-05 +2024-01-05 19:50:00,148.60968317266455,,,,,,0.0,,2024-01-05 +2024-01-05 19:55:00,148.80915462830183,,,,,,0.0,,2024-01-05 +2024-01-05 20:00:00,147.58890729507493,,,,,,0.0,,2024-01-05 +2024-01-05 20:05:00,143.9025143580868,,,,,,0.0,,2024-01-05 +2024-01-05 20:10:00,141.29609682893027,,,,,,0.0,,2024-01-05 +2024-01-05 20:15:00,141.09356921315353,,,,,,,,2024-01-05 +2024-01-05 20:20:00,141.86731151348312,,,,,,,,2024-01-05 +2024-01-05 20:25:00,141.91605300073627,,,,,,,,2024-01-05 +2024-01-05 20:30:00,141.0783648581591,,,,,,,,2024-01-05 +2024-01-05 20:35:00,138.4935173054399,,,,,,,,2024-01-05 +2024-01-05 20:40:00,136.54970216581572,,,,,,,,2024-01-05 +2024-01-05 20:45:00,136.30073340670378,,,,,,,,2024-01-05 +2024-01-05 20:50:00,136.661859760086,,,,,,,,2024-01-05 +2024-01-05 20:55:00,136.78281355190734,,,,,,,,2024-01-05 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22:20:00,148.97354148078776,,,,,,,,2024-01-05 +2024-01-05 22:25:00,143.2738061630832,,,,,,,,2024-01-05 +2024-01-05 22:30:00,140.4501143937623,,,,,,,,2024-01-05 +2024-01-05 22:35:00,134.88495167629347,,,,,,,,2024-01-05 +2024-01-05 22:40:00,129.04051030878514,,,,,,,,2024-01-05 +2024-01-05 22:45:00,126.01783535231736,,,,,,,,2024-01-05 +2024-01-05 22:50:00,120.9008443388158,,,,,,,,2024-01-05 +2024-01-05 22:55:00,118.9074438489616,,,,,,,,2024-01-05 +2024-01-05 23:00:00,119.37154155116266,,,,,,,,2024-01-05 +2024-01-05 23:05:00,120.7730647925962,,,,,,,,2024-01-05 +2024-01-05 23:10:00,118.3992524289853,,,,,,,,2024-01-05 +2024-01-05 23:15:00,114.71948697622148,,,,,,,,2024-01-05 +2024-01-05 23:20:00,104.4499428284144,,,,,,,,2024-01-05 +2024-01-05 23:25:00,94.70517019169225,,,,,,,,2024-01-05 +2024-01-05 23:30:00,91.3013637616344,,,,,,,,2024-01-05 +2024-01-05 23:35:00,87.1997229600519,,,,,,,,2024-01-05 +2024-01-05 23:40:00,85.28806958603417,,,,,,,,2024-01-05 +2024-01-05 23:45:00,84.62116116898991,,,,,,,,2024-01-05 +2024-01-05 23:50:00,82.79953465441173,,,,,,,,2024-01-05 +2024-01-05 23:55:00,82.46766931440291,,,,,,,,2024-01-05 +2024-01-06 00:00:00,88.70324398131987,,,,,,,,2024-01-05 +2024-01-06 00:05:00,89.00864924740102,,,,,,,,2024-01-05 +2024-01-06 00:10:00,79.19632143706312,,,,,,,,2024-01-05 +2024-01-06 00:15:00,72.78488615035542,,,,,,,,2024-01-05 +2024-01-06 00:20:00,61.5299014323574,,,,,,,,2024-01-05 +2024-01-06 00:25:00,58.35614224901772,,,,,,,,2024-01-05 +2024-01-06 00:30:00,61.15291247206356,,,,,,,,2024-01-05 +2024-01-06 00:35:00,71.67588281751476,,,,,,,,2024-01-05 +2024-01-06 00:40:00,81.14899422206865,,,,,,,,2024-01-05 +2024-01-06 00:45:00,83.44567448068818,,,,,,,,2024-01-05 +2024-01-06 00:50:00,85.00457345467134,,,,,,,,2024-01-05 +2024-01-06 00:55:00,87.29019437887904,,,,,,,,2024-01-05 +2024-01-06 01:00:00,89.9979861805511,,,,,,,,2024-01-05 +2024-01-06 01:05:00,97.47426702962068,,,,,,,,2024-01-05 +2024-01-06 01:10:00,104.7942467275658,,,,,,,,2024-01-05 +2024-01-06 01:15:00,107.50190972649928,,,,,,,,2024-01-05 +2024-01-06 01:20:00,110.75562898209397,,,,,,,,2024-01-05 +2024-01-06 01:25:00,111.44740596068768,,,,,,,,2024-01-05 +2024-01-06 01:30:00,111.20694302244146,,,,,,,,2024-01-05 +2024-01-06 01:35:00,112.11880244252814,,,,,,,,2024-01-05 +2024-01-06 01:40:00,118.93868033834504,,,,,,,,2024-01-05 +2024-01-06 01:45:00,125.22199957571864,,,,,,,,2024-01-05 +2024-01-06 01:50:00,138.50918039094944,,,,,,,,2024-01-05 +2024-01-06 01:55:00,143.655770479618,,,,,,,,2024-01-05 +2024-01-06 02:00:00,140.77670498353322,,,,,,,,2024-01-05 +2024-01-06 02:05:00,126.72885145165448,,,,,,,,2024-01-05 +2024-01-06 02:10:00,108.07434156578722,,,,,,,,2024-01-05 +2024-01-06 02:15:00,99.43614343720544,,,,,,,,2024-01-05 +2024-01-06 02:20:00,88.368814191654,,,,,,,,2024-01-05 +2024-01-06 02:25:00,89.58659236614812,,,,,,,,2024-01-05 +2024-01-06 02:30:00,89.741019520129,,,,,,,,2024-01-05 +2024-01-06 02:35:00,88.31786788220741,,,,,,,,2024-01-05 +2024-01-06 02:40:00,85.48255165194772,,,,,,,,2024-01-05 +2024-01-06 02:45:00,83.87116415424943,,,,,,,,2024-01-05 +2024-01-06 02:50:00,80.93138453398865,,,,,,,,2024-01-05 +2024-01-06 02:55:00,79.2403118888494,,,,,,,,2024-01-05 +2024-01-06 03:00:00,79.02490823173417,,,,,,,,2024-01-05 +2024-01-06 03:05:00,78.97252224904071,,,,,,,,2024-01-05 +2024-01-06 03:10:00,77.79991892192382,,,,,,,,2024-01-05 +2024-01-06 03:15:00,76.43970219849818,,,,,,,,2024-01-05 +2024-01-06 03:20:00,73.3354102108458,,,,,,,,2024-01-05 +2024-01-06 03:25:00,71.98454996803683,,,,,,,,2024-01-05 +2024-01-06 03:30:00,72.55211854001605,,,,,,,,2024-01-05 +2024-01-06 03:35:00,75.41148731539305,,,,,,,,2024-01-05 +2024-01-06 03:40:00,78.79726278256761,,,,,,,,2024-01-05 +2024-01-06 03:45:00,80.14027294914409,,,,,,,,2024-01-05 +2024-01-06 03:50:00,81.89211594038407,,,,,,,,2024-01-05 +2024-01-06 03:55:00,82.4360582519511,,,,,,,,2024-01-05 +2024-01-06 04:00:00,82.32123613851854,,,,,,,,2024-01-06 +2024-01-06 04:05:00,81.83151680841038,,,,,,,,2024-01-06 +2024-01-06 04:10:00,81.81707524389515,,,,,,,,2024-01-06 +2024-01-06 04:15:00,82.18103975402485,,,,,,,,2024-01-06 +2024-01-06 04:20:00,83.19288878170417,,,,,,,,2024-01-06 +2024-01-06 04:25:00,83.65017568687159,,,,,,,,2024-01-06 +2024-01-06 04:30:00,83.44016723299579,,,,,,,,2024-01-06 +2024-01-06 04:35:00,82.554508696105,,,,,,,,2024-01-06 +2024-01-06 04:40:00,81.94559916902168,,,,,,,,2024-01-06 +2024-01-06 04:45:00,82.0815347715939,,,,,,,,2024-01-06 +2024-01-06 04:50:00,83.90742465492093,,,,,,,,2024-01-06 +2024-01-06 04:55:00,87.82308617255276,,,,,,,,2024-01-06 +2024-01-06 05:00:00,88.43671423802321,,,,,,,,2024-01-06 +2024-01-06 05:05:00,87.9200341901619,,,,,,,,2024-01-06 +2024-01-06 05:10:00,86.08952024135667,,,,,,,,2024-01-06 +2024-01-06 05:15:00,85.06086421495843,,,,,,,,2024-01-06 +2024-01-06 05:20:00,83.5353503859074,,,,,,,,2024-01-06 +2024-01-06 05:25:00,83.7124037786771,,,,,,,,2024-01-06 +2024-01-06 05:30:00,84.6725915538214,,,,,,,,2024-01-06 +2024-01-06 05:35:00,87.78213363672248,,,,,,,,2024-01-06 +2024-01-06 05:40:00,91.34960850977484,,,,,,,,2024-01-06 +2024-01-06 05:45:00,93.02836320784806,,,,,,,,2024-01-06 +2024-01-06 05:50:00,96.69265790503673,,,,,,,,2024-01-06 +2024-01-06 05:55:00,101.83946174239878,,,,,,,,2024-01-06 +2024-01-06 06:00:00,105.0381780947632,,,,,,,,2024-01-06 +2024-01-06 06:05:00,110.38525460345952,,,,,,,,2024-01-06 +2024-01-06 06:10:00,110.3681913807636,,,,,,,,2024-01-06 +2024-01-06 06:15:00,107.4721445568372,,,,,,,,2024-01-06 +2024-01-06 06:20:00,98.62285232621738,,,,,,,,2024-01-06 +2024-01-06 06:25:00,91.03138914937593,,,,,,,,2024-01-06 +2024-01-06 06:30:00,89.12660581772411,,,,,,,,2024-01-06 +2024-01-06 06:35:00,87.68284683779547,,,,,,,,2024-01-06 +2024-01-06 06:40:00,85.85846213274368,,,,,,,,2024-01-06 +2024-01-06 06:45:00,83.88414196420393,,,,,,,,2024-01-06 +2024-01-06 06:50:00,78.94719924854101,,,,,,,,2024-01-06 +2024-01-06 06:55:00,75.36311381370044,,,,,,,,2024-01-06 +2024-01-06 07:00:00,74.83056108301554,,,,,,,,2024-01-06 +2024-01-06 07:05:00,75.96320051363536,,,,,,,,2024-01-06 +2024-01-06 07:10:00,78.86870532761664,,,,,,,,2024-01-06 +2024-01-06 07:15:00,80.55223832680623,,,,,,,,2024-01-06 +2024-01-06 07:20:00,83.51278671660313,,,,,,,,2024-01-06 +2024-01-06 07:25:00,83.56498658634099,ANNOUNCE_MEAL,,,,,8.0,8.0,2024-01-06 +2024-01-06 07:30:00,80.61479541915756,,,,,,0.0,,2024-01-06 +2024-01-06 07:35:00,81.20869811366374,,,,,,0.0,,2024-01-06 +2024-01-06 07:40:00,87.64508878201502,,,,,,0.0,,2024-01-06 +2024-01-06 07:45:00,92.0928623194304,,,,,,0.0,,2024-01-06 +2024-01-06 07:50:00,101.4558798504316,,,,,,0.0,,2024-01-06 +2024-01-06 07:55:00,108.4762496528806,,,,,,0.0,,2024-01-06 +2024-01-06 08:00:00,110.2238201175015,,,,,,0.0,,2024-01-06 +2024-01-06 08:05:00,110.77657245180208,,,,,,0.0,,2024-01-06 +2024-01-06 08:10:00,109.21703397014214,,,,,,0.0,,2024-01-06 +2024-01-06 08:15:00,108.3104966967508,,,,,,0.0,,2024-01-06 +2024-01-06 08:20:00,107.207269514699,,,,,,0.0,,2024-01-06 +2024-01-06 08:25:00,108.22329759707534,,,,,,0.0,,2024-01-06 +2024-01-06 08:30:00,109.76971184910738,,,,,,0.0,,2024-01-06 +2024-01-06 08:35:00,113.8615180072983,,,,,,0.0,,2024-01-06 +2024-01-06 08:40:00,117.2511424793357,,,,,,0.0,,2024-01-06 +2024-01-06 08:45:00,118.1440924003627,,,,,,0.0,,2024-01-06 +2024-01-06 08:50:00,118.89133758512811,,,,,,0.0,,2024-01-06 +2024-01-06 08:55:00,119.63691173417976,,,,,,0.0,,2024-01-06 +2024-01-06 09:00:00,120.37505211796396,,,,,,0.0,,2024-01-06 +2024-01-06 09:05:00,122.21045230587865,,,,,,0.0,,2024-01-06 +2024-01-06 09:10:00,123.59810662690458,,,,,,0.0,,2024-01-06 +2024-01-06 09:15:00,123.88319000356016,,,,,,0.0,,2024-01-06 +2024-01-06 09:20:00,123.94472693032044,,,,,,0.0,,2024-01-06 +2024-01-06 09:25:00,124.0378657300543,,,,,,0.0,,2024-01-06 +2024-01-06 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13:35:00,196.7119339312168,,,,,,0.0,,2024-01-06 +2024-01-06 13:40:00,201.3441880375325,,,,,,,,2024-01-06 +2024-01-06 13:45:00,203.78203214928004,,,,,,,,2024-01-06 +2024-01-06 13:50:00,207.2160926814652,,,,,,,,2024-01-06 +2024-01-06 13:55:00,206.02688064516812,,,,,,,,2024-01-06 +2024-01-06 14:00:00,203.07852211319664,,,,,,,,2024-01-06 +2024-01-06 14:05:00,194.12068516390585,,,,,,,,2024-01-06 +2024-01-06 14:10:00,184.39939494213144,,,,,,,,2024-01-06 +2024-01-06 14:15:00,180.2782114116356,,,,,,,,2024-01-06 +2024-01-06 14:20:00,173.97059792505834,,,,,,,,2024-01-06 +2024-01-06 14:25:00,170.20014805971928,,,,,,,,2024-01-06 +2024-01-06 14:30:00,169.14925567672466,,,,,,,,2024-01-06 +2024-01-06 14:35:00,167.74082434695742,,,,,,,,2024-01-06 +2024-01-06 14:40:00,165.66416963730836,,,,,,,,2024-01-06 +2024-01-06 14:45:00,163.78026442089282,ANNOUNCE_MEAL,,,,,6.0,6.0,2024-01-06 +2024-01-06 14:50:00,157.4046564851431,,,,,,0.0,,2024-01-06 +2024-01-06 14:55:00,149.16980198266592,,,,,,0.0,,2024-01-06 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03:55:00,153.8572751621051,,,,,,,,2023-12-31 +2024-01-01 04:00:00,159.32613776339718,,,,,,,,2024-01-01 +2024-01-01 04:05:00,169.474904641771,,,,,,,,2024-01-01 +2024-01-01 04:10:00,176.321405079608,,,,,,,,2024-01-01 +2024-01-01 04:15:00,177.8993365801646,,,,,,,,2024-01-01 +2024-01-01 04:20:00,177.8853581753867,,,,,,,,2024-01-01 +2024-01-01 04:25:00,175.02874168073555,,,,,,,,2024-01-01 +2024-01-01 04:30:00,173.02662171868866,,,,,,,,2024-01-01 +2024-01-01 04:35:00,168.66320628145672,,,,,,,,2024-01-01 +2024-01-01 04:40:00,164.79390622800938,,,,,,,,2024-01-01 +2024-01-01 04:45:00,163.40943049472727,,,,,,,,2024-01-01 +2024-01-01 04:50:00,162.47058966181766,,,,,,,,2024-01-01 +2024-01-01 04:55:00,167.7242051135255,,,,,,,,2024-01-01 +2024-01-01 05:00:00,176.8073077700847,,,,,,,,2024-01-01 +2024-01-01 05:05:00,181.3023915660056,,,,,,,,2024-01-01 +2024-01-01 05:10:00,173.6765615502015,,,,,,,,2024-01-01 +2024-01-01 05:15:00,167.60852194114807,,,,,,,,2024-01-01 +2024-01-01 05:20:00,155.54262967502666,,,,,,,,2024-01-01 +2024-01-01 05:25:00,149.5635516682375,,,,,,,,2024-01-01 +2024-01-01 05:30:00,150.22040164980805,,,,,,,,2024-01-01 +2024-01-01 05:35:00,155.08410786245312,,,,,,,,2024-01-01 +2024-01-01 05:40:00,157.21172506850502,,,,,,,,2024-01-01 +2024-01-01 05:45:00,155.26459048340325,,,,,,,,2024-01-01 +2024-01-01 05:50:00,147.35476883562296,,,,,,,,2024-01-01 +2024-01-01 05:55:00,139.10699399547468,,,,,,,,2024-01-01 +2024-01-01 06:00:00,136.3813343181509,,,,,,,,2024-01-01 +2024-01-01 06:05:00,134.16893994243983,,,,,,,,2024-01-01 +2024-01-01 06:10:00,135.79954261329448,,,,,,,,2024-01-01 +2024-01-01 06:15:00,137.6910084464456,,,,,,,,2024-01-01 +2024-01-01 06:20:00,141.52980448089903,,,,,,,,2024-01-01 +2024-01-01 06:25:00,142.1628222199201,,,,,,,,2024-01-01 +2024-01-01 06:30:00,140.51855748653966,,,,,,,,2024-01-01 +2024-01-01 06:35:00,135.2438077436095,,,,,,,,2024-01-01 +2024-01-01 06:40:00,131.22486278672366,,,,,,,,2024-01-01 +2024-01-01 06:45:00,130.7215801788915,ANNOUNCE_MEAL,,,,,12.0,12.0,2024-01-01 +2024-01-01 06:50:00,131.66297981811294,,,,,,0.0,,2024-01-01 +2024-01-01 06:55:00,132.7194080825664,,,,,,0.0,,2024-01-01 +2024-01-01 07:00:00,132.74974539573617,,,,,,0.0,,2024-01-01 +2024-01-01 07:05:00,132.67512798581393,,,,,,0.0,,2024-01-01 +2024-01-01 07:10:00,133.88582743066797,,,,,,0.0,,2024-01-01 +2024-01-01 07:15:00,135.34513560467332,,,,,,0.0,,2024-01-01 +2024-01-01 07:20:00,140.39413755119875,,,,,,0.0,,2024-01-01 +2024-01-01 07:25:00,146.19190793877573,,,,,,0.0,,2024-01-01 +2024-01-01 07:30:00,143.80421512159637,,,,,,0.0,,2024-01-01 +2024-01-01 07:35:00,139.3527396563323,,,,,,0.0,,2024-01-01 +2024-01-01 07:40:00,135.57644623760186,,,,,,0.0,,2024-01-01 +2024-01-01 07:45:00,134.07363468591413,,,,,,0.0,,2024-01-01 +2024-01-01 07:50:00,132.15344665950985,,,,,,0.0,,2024-01-01 +2024-01-01 07:55:00,132.12173006092374,,,,,,0.0,,2024-01-01 +2024-01-01 08:00:00,132.95985019476592,,,,,,0.0,,2024-01-01 +2024-01-01 08:05:00,136.479271418232,,,,,,0.0,,2024-01-01 +2024-01-01 08:10:00,142.54185137508398,,,,,,0.0,,2024-01-01 +2024-01-01 08:15:00,146.38280244799654,,,,,,0.0,,2024-01-01 +2024-01-01 08:20:00,154.1280241689696,,,,,,0.0,,2024-01-01 +2024-01-01 08:25:00,159.40773704972568,,,,,,0.0,,2024-01-01 +2024-01-01 08:30:00,160.45936546260018,,,,,,0.0,,2024-01-01 +2024-01-01 08:35:00,160.21675135857976,,,,,,0.0,,2024-01-01 +2024-01-01 08:40:00,158.7982404169953,,,,,,0.0,,2024-01-01 +2024-01-01 08:45:00,158.27259866798724,,,,,,0.0,,2024-01-01 +2024-01-01 08:50:00,158.05978010620873,,,,,,0.0,,2024-01-01 +2024-01-01 08:55:00,159.2610383253971,,,,,,0.0,,2024-01-01 +2024-01-01 09:00:00,160.35064732501786,,,,,,0.0,,2024-01-01 +2024-01-01 09:05:00,162.31406558315294,,,,,,0.0,,2024-01-01 +2024-01-01 09:10:00,161.9929421987451,,,,,,0.0,,2024-01-01 +2024-01-01 09:15:00,160.5331396655896,,,,,,0.0,,2024-01-01 +2024-01-01 09:20:00,156.37463444726504,,,,,,0.0,,2024-01-01 +2024-01-01 09:25:00,153.2370652347779,,,,,,0.0,,2024-01-01 +2024-01-01 09:30:00,152.77056097690848,,,,,,0.0,,2024-01-01 +2024-01-01 09:35:00,153.4096358026043,,,,,,0.0,,2024-01-01 +2024-01-01 09:40:00,154.47563443735442,,,,,,0.0,,2024-01-01 +2024-01-01 09:45:00,154.5436624467939,,,,,,0.0,,2024-01-01 +2024-01-01 09:50:00,152.50281865144674,,,,,,,,2024-01-01 +2024-01-01 09:55:00,146.33627407334052,,,,,,,,2024-01-01 +2024-01-01 10:00:00,142.86875015960197,,,,,,,,2024-01-01 +2024-01-01 10:05:00,138.20099290634116,,,,,,,,2024-01-01 +2024-01-01 10:10:00,136.25909624912532,,,,,,,,2024-01-01 +2024-01-01 10:15:00,136.1990671564595,,,,,,,,2024-01-01 +2024-01-01 10:20:00,137.65264001066683,,,,,,,,2024-01-01 +2024-01-01 10:25:00,140.79350585099056,,,,,,,,2024-01-01 +2024-01-01 10:30:00,142.80420661108192,,,,,,,,2024-01-01 +2024-01-01 10:35:00,147.2020551238952,,,,,,,,2024-01-01 +2024-01-01 10:40:00,151.40538564648725,,,,,,,,2024-01-01 +2024-01-01 10:45:00,153.17736700611667,,,,,,,,2024-01-01 +2024-01-01 10:50:00,155.5215216049822,,,,,,,,2024-01-01 +2024-01-01 10:55:00,155.59696354131003,,,,,,,,2024-01-01 +2024-01-01 11:00:00,154.81460833681615,,,,,,,,2024-01-01 +2024-01-01 11:05:00,153.67469361191792,ANNOUNCE_MEAL,,,,,30.666666666666668,30.666666666666668,2024-01-01 +2024-01-01 11:10:00,156.5846564420813,,,,,,0.0,,2024-01-01 +2024-01-01 11:15:00,160.3003421310745,,,,,,0.0,,2024-01-01 +2024-01-01 11:20:00,169.74787943548398,,,,,,0.0,,2024-01-01 +2024-01-01 11:25:00,177.42533033012285,,,,,,0.0,,2024-01-01 +2024-01-01 11:30:00,179.5989289439251,,,,,,0.0,,2024-01-01 +2024-01-01 11:35:00,182.7628439850454,,,,,,0.0,,2024-01-01 +2024-01-01 11:40:00,187.9839508892453,,,,,,0.0,,2024-01-01 +2024-01-01 11:45:00,192.2230680947192,,,,,,0.0,,2024-01-01 +2024-01-01 11:50:00,204.2633952665088,,,,,,0.0,,2024-01-01 +2024-01-01 11:55:00,221.1842039612652,,,,,,0.0,,2024-01-01 +2024-01-01 12:00:00,231.21550379501863,,,,,,0.0,,2024-01-01 +2024-01-01 12:05:00,250.7293460833568,,,,,,0.0,,2024-01-01 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13:30:00,185.6089237026496,,,,,,0.0,,2024-01-01 +2024-01-01 13:35:00,184.745342400166,,,,,,0.0,,2024-01-01 +2024-01-01 13:40:00,183.19588693947924,,,,,,0.0,,2024-01-01 +2024-01-01 13:45:00,181.9462980676637,,,,,,0.0,,2024-01-01 +2024-01-01 13:50:00,179.50192757770466,,,,,,0.0,,2024-01-01 +2024-01-01 13:55:00,179.01398591686905,,,,,,0.0,,2024-01-01 +2024-01-01 14:00:00,179.96847055566116,,,,,,0.0,,2024-01-01 +2024-01-01 14:05:00,183.43560017688296,,,,,,0.0,,2024-01-01 +2024-01-01 14:10:00,187.177588564738,,,,,,,,2024-01-01 +2024-01-01 14:15:00,188.61440850654944,,,,,,,,2024-01-01 +2024-01-01 14:20:00,190.55135314971133,,,,,,,,2024-01-01 +2024-01-01 14:25:00,191.55547702177824,,,,,,,,2024-01-01 +2024-01-01 14:30:00,191.7494320717942,,,,,,,,2024-01-01 +2024-01-01 14:35:00,191.00063941768323,,,,,,,,2024-01-01 +2024-01-01 14:40:00,187.8134336340031,,,,,,,,2024-01-01 +2024-01-01 14:45:00,184.94846995185992,,,,,,,,2024-01-01 +2024-01-01 14:50:00,175.85553370043758,,,,,,,,2024-01-01 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16:20:00,144.7296235875964,,,,,,,,2024-01-01 +2024-01-01 16:25:00,139.50958162150667,,,,,,,,2024-01-01 +2024-01-01 16:30:00,137.09293302827794,,,,,,,,2024-01-01 +2024-01-01 16:35:00,132.16312123176192,,,,,,,,2024-01-01 +2024-01-01 16:40:00,126.15515727288204,,,,,,,,2024-01-01 +2024-01-01 16:45:00,122.57616415624732,,,,,,,,2024-01-01 +2024-01-01 16:50:00,115.29137196750808,,,,,,,,2024-01-01 +2024-01-01 16:55:00,109.71996796723106,,,,,,,,2024-01-01 +2024-01-01 17:00:00,108.08711521875296,,,,,,,,2024-01-01 +2024-01-01 17:05:00,106.71943197427352,,,,,,,,2024-01-01 +2024-01-01 17:10:00,106.7675426068387,,,,,,,,2024-01-01 +2024-01-01 17:15:00,106.90408649470976,,,,,,,,2024-01-01 +2024-01-01 17:20:00,106.56465928266424,,,,,,,,2024-01-01 +2024-01-01 17:25:00,103.04888424589367,,,,,,,,2024-01-01 +2024-01-01 17:30:00,98.04887527279158,,,,,,,,2024-01-01 +2024-01-01 17:35:00,93.61328779063746,,,,,,,,2024-01-01 +2024-01-01 17:40:00,94.20428222705232,,,,,,,,2024-01-01 +2024-01-01 17:45:00,95.48745684961062,,,,,,,,2024-01-01 +2024-01-01 17:50:00,98.23452315570998,,,,,,,,2024-01-01 +2024-01-01 17:55:00,98.85669072628814,,,,,,,,2024-01-01 +2024-01-01 18:00:00,97.69312562679956,,,,,,,,2024-01-01 +2024-01-01 18:05:00,92.59429887440628,,,,,,,,2024-01-01 +2024-01-01 18:10:00,84.67145359409233,,,,,,,,2024-01-01 +2024-01-01 18:15:00,80.15096574209153,,,,,,,,2024-01-01 +2024-01-01 18:20:00,72.25916159341205,,,,,,,,2024-01-01 +2024-01-01 18:25:00,69.35882142157242,,,,,,,,2024-01-01 +2024-01-01 18:30:00,72.88011139566586,ANNOUNCE_MEAL,,,,,34.0,34.0,2024-01-01 +2024-01-01 18:35:00,75.66370286109336,,,,,,0.0,,2024-01-01 +2024-01-01 18:40:00,79.69854910467761,,,,,,0.0,,2024-01-01 +2024-01-01 18:45:00,80.07164943252585,,,,,,0.0,,2024-01-01 +2024-01-01 18:50:00,79.09164914760514,,,,,,0.0,,2024-01-01 +2024-01-01 18:55:00,79.72030159872483,,,,,,0.0,,2024-01-01 +2024-01-01 19:00:00,81.86886538283527,,,,,,0.0,,2024-01-01 +2024-01-01 19:05:00,90.0598862366381,,,,,,0.0,,2024-01-01 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20:30:00,168.134737905002,,,,,,0.0,,2024-01-01 +2024-01-01 20:35:00,167.2310218842888,,,,,,0.0,,2024-01-01 +2024-01-01 20:40:00,164.08589573918746,,,,,,0.0,,2024-01-01 +2024-01-01 20:45:00,161.77875854381583,,,,,,0.0,,2024-01-01 +2024-01-01 20:50:00,155.9936361208361,,,,,,0.0,,2024-01-01 +2024-01-01 20:55:00,149.07997847323136,,,,,,0.0,,2024-01-01 +2024-01-01 21:00:00,145.40185048061426,,,,,,0.0,,2024-01-01 +2024-01-01 21:05:00,138.3088003731717,,,,,,0.0,,2024-01-01 +2024-01-01 21:10:00,132.60204255529777,,,,,,0.0,,2024-01-01 +2024-01-01 21:15:00,130.46810117778102,,,,,,0.0,,2024-01-01 +2024-01-01 21:20:00,126.66140381661609,,,,,,0.0,,2024-01-01 +2024-01-01 21:25:00,121.66541092429742,,,,,,0.0,,2024-01-01 +2024-01-01 21:30:00,118.42214313247231,,,,,,0.0,,2024-01-01 +2024-01-01 21:35:00,112.60765123804322,,,,,,,,2024-01-01 +2024-01-01 21:40:00,111.60516734565836,,,,,,,,2024-01-01 +2024-01-01 21:45:00,113.78912547528476,,,,,,,,2024-01-01 +2024-01-01 21:50:00,120.9711839815604,,,,,,,,2024-01-01 +2024-01-01 21:55:00,126.8602393941731,,,,,,,,2024-01-01 +2024-01-01 22:00:00,127.95750193974295,,,,,,,,2024-01-01 +2024-01-01 22:05:00,127.55662923648984,,,,,,,,2024-01-01 +2024-01-01 22:10:00,126.68593535005974,,,,,,,,2024-01-01 +2024-01-01 22:15:00,127.17130672109236,,,,,,,,2024-01-01 +2024-01-01 22:20:00,132.02917697486947,,,,,,,,2024-01-01 +2024-01-01 22:25:00,140.74444542965847,,,,,,,,2024-01-01 +2024-01-01 22:30:00,138.56994551049576,,,,,,,,2024-01-01 +2024-01-01 22:35:00,135.84784821097895,,,,,,,,2024-01-01 +2024-01-01 22:40:00,134.99170215443937,,,,,,,,2024-01-01 +2024-01-01 22:45:00,135.19404490070417,,,,,,,,2024-01-01 +2024-01-01 22:50:00,136.81649853130028,,,,,,,,2024-01-01 +2024-01-01 22:55:00,140.0777711718208,,,,,,,,2024-01-01 +2024-01-01 23:00:00,142.16571991532413,,,,,,,,2024-01-01 +2024-01-01 23:05:00,145.62371972717733,,,,,,,,2024-01-01 +2024-01-01 23:10:00,145.48378153176174,,,,,,,,2024-01-01 +2024-01-01 23:15:00,143.51658585611776,,,,,,,,2024-01-01 +2024-01-01 23:20:00,137.91792438295562,,,,,,,,2024-01-01 +2024-01-01 23:25:00,134.15108983297256,,,,,,,,2024-01-01 +2024-01-01 23:30:00,133.9344806548743,,,,,,,,2024-01-01 +2024-01-01 23:35:00,134.86617183880304,,,,,,,,2024-01-01 +2024-01-01 23:40:00,133.47252585407767,,,,,,,,2024-01-01 +2024-01-01 23:45:00,130.79228342541057,,,,,,,,2024-01-01 +2024-01-01 23:50:00,122.50625859162416,,,,,,,,2024-01-01 +2024-01-01 23:55:00,112.99027026054368,,,,,,,,2024-01-01 +2024-01-02 00:00:00,108.65024724562448,,,,,,,,2024-01-01 +2024-01-02 00:05:00,101.68220741310544,,,,,,,,2024-01-01 +2024-01-02 00:10:00,97.7840205275497,,,,,,,,2024-01-01 +2024-01-02 00:15:00,97.08551499400002,,,,,,,,2024-01-01 +2024-01-02 00:20:00,96.96243838671208,,,,,,,,2024-01-01 +2024-01-02 00:25:00,96.35981043077764,,,,,,,,2024-01-01 +2024-01-02 00:30:00,95.29018810226005,,,,,,,,2024-01-01 +2024-01-02 00:35:00,92.11335435063278,,,,,,,,2024-01-01 +2024-01-02 00:40:00,88.88697576238394,,,,,,,,2024-01-01 +2024-01-02 00:45:00,87.75506369340997,,,,,,,,2024-01-01 +2024-01-02 00:50:00,87.45285907815274,,,,,,,,2024-01-01 +2024-01-02 00:55:00,88.30724760550818,,,,,,,,2024-01-01 +2024-01-02 01:00:00,83.44806579538587,,,,,,,,2024-01-01 +2024-01-02 01:05:00,75.81683121268637,,,,,,,,2024-01-01 +2024-01-02 01:10:00,71.42662488803781,,,,,,,,2024-01-01 +2024-01-02 01:15:00,70.618681575053,,,,,,,,2024-01-01 +2024-01-02 01:20:00,72.12060154994113,,,,,,,,2024-01-01 +2024-01-02 01:25:00,78.19389996782118,,,,,,,,2024-01-01 +2024-01-02 01:30:00,82.76187226263293,,,,,,,,2024-01-01 +2024-01-02 01:35:00,92.11729506668772,,,,,,,,2024-01-01 +2024-01-02 01:40:00,97.05878205906276,,,,,,,,2024-01-01 +2024-01-02 01:45:00,96.6807026695137,,,,,,,,2024-01-01 +2024-01-02 01:50:00,92.02347228174544,,,,,,,,2024-01-01 +2024-01-02 01:55:00,86.19315196611211,,,,,,,,2024-01-01 +2024-01-02 02:00:00,84.0525687139895,,,,,,,,2024-01-01 +2024-01-02 02:05:00,81.58084053472865,,,,,,,,2024-01-01 +2024-01-02 02:10:00,80.98767794123924,,,,,,,,2024-01-01 +2024-01-02 02:15:00,81.07954526975652,,,,,,,,2024-01-01 +2024-01-02 02:20:00,80.56033904120822,,,,,,,,2024-01-01 +2024-01-02 02:25:00,76.84876852883546,,,,,,,,2024-01-01 +2024-01-02 02:30:00,73.38856272295612,,,,,,,,2024-01-01 +2024-01-02 02:35:00,65.65466436977503,,,,,,,,2024-01-01 +2024-01-02 02:40:00,61.23619691929304,,,,,,,,2024-01-01 +2024-01-02 02:45:00,61.33227329669894,,,,,,,,2024-01-01 +2024-01-02 02:50:00,63.95321733739936,,,,,,,,2024-01-01 +2024-01-02 02:55:00,65.24402361701446,,,,,,,,2024-01-01 +2024-01-02 03:00:00,64.1451021797174,,,,,,,,2024-01-01 +2024-01-02 03:05:00,59.39201380573057,,,,,,,,2024-01-01 +2024-01-02 03:10:00,53.84428925319428,,,,,,,,2024-01-01 +2024-01-02 03:15:00,51.75209558317312,,,,,,,,2024-01-01 +2024-01-02 03:20:00,50.89041110199949,,,,,,,,2024-01-01 +2024-01-02 03:25:00,56.94026748460208,,,,,,,,2024-01-01 +2024-01-02 03:30:00,61.7207215313647,,,,,,,,2024-01-01 +2024-01-02 03:35:00,66.43337627965418,,,,,,,,2024-01-01 +2024-01-02 03:40:00,66.57497202498817,,,,,,,,2024-01-01 +2024-01-02 03:45:00,65.64161963917951,,,,,,,,2024-01-01 +2024-01-02 03:50:00,63.18664670890266,,,,,,,,2024-01-01 +2024-01-02 03:55:00,61.817402382448606,,,,,,,,2024-01-01 +2024-01-02 04:00:00,62.06034644198455,,,,,,,,2024-01-02 +2024-01-02 04:05:00,64.11773294480004,,,,,,,,2024-01-02 +2024-01-02 04:10:00,67.36676715555794,,,,,,,,2024-01-02 +2024-01-02 04:15:00,69.12622439547005,,,,,,,,2024-01-02 +2024-01-02 04:20:00,72.48846653252676,,,,,,,,2024-01-02 +2024-01-02 04:25:00,75.15099044667379,,,,,,,,2024-01-02 +2024-01-02 04:30:00,76.02871487143496,,,,,,,,2024-01-02 +2024-01-02 04:35:00,76.44286335301837,,,,,,,,2024-01-02 +2024-01-02 04:40:00,74.4874153728255,,,,,,,,2024-01-02 +2024-01-02 04:45:00,72.54648150757725,,,,,,,,2024-01-02 +2024-01-02 04:50:00,67.63851947937147,,,,,,,,2024-01-02 +2024-01-02 04:55:00,62.98117433288436,,,,,,,,2024-01-02 +2024-01-02 05:00:00,61.34643963001424,,,,,,,,2024-01-02 +2024-01-02 05:05:00,60.61292927844648,,,,,,,,2024-01-02 +2024-01-02 05:10:00,64.71083079122545,,,,,,,,2024-01-02 +2024-01-02 05:15:00,68.65067438643243,,,,,,,,2024-01-02 +2024-01-02 05:20:00,77.06762966058118,,,,,,,,2024-01-02 +2024-01-02 05:25:00,80.60103081250233,,,,,,,,2024-01-02 +2024-01-02 05:30:00,79.09670372724142,,,,,,,,2024-01-02 +2024-01-02 05:35:00,71.50146388491422,,,,,,,,2024-01-02 +2024-01-02 05:40:00,62.32918708611827,,,,,,,,2024-01-02 +2024-01-02 05:45:00,58.85357004864832,,,,,,,,2024-01-02 +2024-01-02 05:50:00,57.52721321317705,,,,,,,,2024-01-02 +2024-01-02 05:55:00,64.63813484989842,,,,,,,,2024-01-02 +2024-01-02 06:00:00,63.93732449947299,,,,,,,,2024-01-02 +2024-01-02 06:05:00,63.67395757681743,,,,,,,,2024-01-02 +2024-01-02 06:10:00,64.57302454131322,,,,,,,,2024-01-02 +2024-01-02 06:15:00,65.32526162187479,,,,,,,,2024-01-02 +2024-01-02 06:20:00,67.16873622803372,,,,,,,,2024-01-02 +2024-01-02 06:25:00,69.12233489906912,,,,,,,,2024-01-02 +2024-01-02 06:30:00,71.29255007691545,ANNOUNCE_MEAL,,,,,10.666666666666666,10.666666666666666,2024-01-02 +2024-01-02 06:35:00,72.84011146221826,,,,,,0.0,,2024-01-02 +2024-01-02 06:40:00,77.7742538442041,,,,,,0.0,,2024-01-02 +2024-01-02 06:45:00,81.28546426037045,,,,,,0.0,,2024-01-02 +2024-01-02 06:50:00,88.84896617407905,,,,,,0.0,,2024-01-02 +2024-01-02 06:55:00,94.16633246456828,,,,,,0.0,,2024-01-02 +2024-01-02 07:00:00,95.23029848590213,,,,,,0.0,,2024-01-02 +2024-01-02 07:05:00,95.97365803896848,,,,,,0.0,,2024-01-02 +2024-01-02 07:10:00,98.39098710220982,,,,,,0.0,,2024-01-02 +2024-01-02 07:15:00,101.10280919439174,,,,,,0.0,,2024-01-02 +2024-01-02 07:20:00,108.1717130922955,,,,,,0.0,,2024-01-02 +2024-01-02 07:25:00,114.57447218500542,,,,,,0.0,,2024-01-02 +2024-01-02 07:30:00,116.7629057987088,,,,,,0.0,,2024-01-02 +2024-01-02 07:35:00,119.53949120190072,,,,,,0.0,,2024-01-02 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09:00:00,99.64172941536218,,,,,,0.0,,2024-01-02 +2024-01-02 09:05:00,101.74992656997556,,,,,,0.0,,2024-01-02 +2024-01-02 09:10:00,104.42315825328646,,,,,,0.0,,2024-01-02 +2024-01-02 09:15:00,105.77449849939173,,,,,,0.0,,2024-01-02 +2024-01-02 09:20:00,108.35579927227523,,,,,,0.0,,2024-01-02 +2024-01-02 09:25:00,110.6663321234804,,,,,,0.0,,2024-01-02 +2024-01-02 09:30:00,111.63325391071324,,,,,,0.0,,2024-01-02 +2024-01-02 09:35:00,112.67816622892846,LOW_CARB_MEAL,,,,,2.333333333333333,2.333333333333333,2024-01-02 +2024-01-02 09:40:00,111.80378580115604,,,,,,,,2024-01-02 +2024-01-02 09:45:00,110.74721819067692,,,,,,,,2024-01-02 +2024-01-02 09:50:00,109.34935909892772,,,,,,,,2024-01-02 +2024-01-02 09:55:00,111.44771831280472,,,,,,,,2024-01-02 +2024-01-02 10:00:00,114.3517550460395,,,,,,,,2024-01-02 +2024-01-02 10:05:00,122.71070000485312,,,,,,,,2024-01-02 +2024-01-02 10:10:00,132.21597746615592,,,,,,,,2024-01-02 +2024-01-02 10:15:00,136.61264044966637,,,,,,,,2024-01-02 +2024-01-02 10:20:00,143.2373842136879,,,,,,,,2024-01-02 +2024-01-02 10:25:00,145.1236850481729,,,,,,,,2024-01-02 +2024-01-02 10:30:00,143.87344558019657,,,,,,,,2024-01-02 +2024-01-02 10:35:00,138.1405116656289,,,,,,,,2024-01-02 +2024-01-02 10:40:00,130.42207905482204,,,,,,,,2024-01-02 +2024-01-02 10:45:00,126.68651459189007,,,,,,,,2024-01-02 +2024-01-02 10:50:00,121.19124848194906,,,,,,,,2024-01-02 +2024-01-02 10:55:00,119.24173084041227,,,,,,,,2024-01-02 +2024-01-02 11:00:00,117.25220980169247,,,,,,,,2024-01-02 +2024-01-02 11:05:00,116.87044538094248,,,,,,,,2024-01-02 +2024-01-02 11:10:00,119.63240662580291,,,,,,,,2024-01-02 +2024-01-02 11:15:00,121.57132669422896,,,,,,,,2024-01-02 +2024-01-02 11:20:00,125.32092204148222,,,,,,,,2024-01-02 +2024-01-02 11:25:00,127.26807189462014,,,,,,,,2024-01-02 +2024-01-02 11:30:00,127.1826091316722,,,,,,,,2024-01-02 +2024-01-02 11:35:00,125.80484527434209,,,,,,,,2024-01-02 +2024-01-02 11:40:00,124.67345077296908,,,,,,,,2024-01-02 +2024-01-02 11:45:00,124.88968662301929,ANNOUNCE_MEAL,,,,,22.33333333333333,22.33333333333333,2024-01-02 +2024-01-02 11:50:00,125.09006642390568,,,,,,0.0,,2024-01-02 +2024-01-02 11:55:00,124.60929490895856,,,,,,0.0,,2024-01-02 +2024-01-02 12:00:00,123.81691313900812,,,,,,0.0,,2024-01-02 +2024-01-02 12:05:00,122.27833939699664,,,,,,0.0,,2024-01-02 +2024-01-02 12:10:00,122.99553144395962,,,,,,0.0,,2024-01-02 +2024-01-02 12:15:00,124.92857039766812,,,,,,0.0,,2024-01-02 +2024-01-02 12:20:00,132.58380397906316,,,,,,0.0,,2024-01-02 +2024-01-02 12:25:00,145.1474730712106,,,,,,0.0,,2024-01-02 +2024-01-02 12:30:00,152.78715214213085,,,,,,0.0,,2024-01-02 +2024-01-02 12:35:00,167.71949854991635,,,,,,0.0,,2024-01-02 +2024-01-02 12:40:00,177.74072413957649,,,,,,0.0,,2024-01-02 +2024-01-02 12:45:00,179.86331056528422,,,,,,0.0,,2024-01-02 +2024-01-02 12:50:00,180.01821139851813,,,,,,0.0,,2024-01-02 +2024-01-02 12:55:00,178.49876863868153,,,,,,0.0,,2024-01-02 +2024-01-02 13:00:00,178.2394382794282,,,,,,0.0,,2024-01-02 +2024-01-02 13:05:00,178.72141720419762,,,,,,0.0,,2024-01-02 +2024-01-02 13:10:00,179.61955027015318,,,,,,0.0,,2024-01-02 +2024-01-02 13:15:00,179.87321818595132,,,,,,0.0,,2024-01-02 +2024-01-02 13:20:00,179.26766506189767,,,,,,0.0,,2024-01-02 +2024-01-02 13:25:00,177.3076877327078,,,,,,0.0,,2024-01-02 +2024-01-02 13:30:00,177.8198378715971,,,,,,0.0,,2024-01-02 +2024-01-02 13:35:00,176.05350369277713,,,,,,0.0,,2024-01-02 +2024-01-02 13:40:00,171.36469214935508,,,,,,0.0,,2024-01-02 +2024-01-02 13:45:00,168.2195366007654,,,,,,0.0,,2024-01-02 +2024-01-02 13:50:00,160.91001602630513,,,,,,0.0,,2024-01-02 +2024-01-02 13:55:00,153.0073611711594,,,,,,0.0,,2024-01-02 +2024-01-02 14:00:00,149.1028975511221,,,,,,0.0,,2024-01-02 +2024-01-02 14:05:00,141.80739938047785,,,,,,0.0,,2024-01-02 +2024-01-02 14:10:00,135.7027361377489,,,,,,0.0,,2024-01-02 +2024-01-02 14:15:00,133.26076610615382,,,,,,0.0,,2024-01-02 +2024-01-02 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11:10:00,139.46625586792177,,,,,,,,2024-01-03 +2024-01-03 11:15:00,140.0573696599404,,,,,,,,2024-01-03 +2024-01-03 11:20:00,142.07174668216447,,,,,,,,2024-01-03 +2024-01-03 11:25:00,143.15220759289952,,,,,,,,2024-01-03 +2024-01-03 11:30:00,142.78996200100454,,,,,,,,2024-01-03 +2024-01-03 11:35:00,140.71177079919107,,,,,,,,2024-01-03 +2024-01-03 11:40:00,138.0493178269,,,,,,,,2024-01-03 +2024-01-03 11:45:00,136.95558727693884,,,,,,,,2024-01-03 +2024-01-03 11:50:00,136.1531071957497,,,,,,,,2024-01-03 +2024-01-03 11:55:00,137.22646171261349,,,,,,,,2024-01-03 +2024-01-03 12:00:00,136.20237437028663,,,,,,,,2024-01-03 +2024-01-03 12:05:00,135.68943807418836,,,,,,,,2024-01-03 +2024-01-03 12:10:00,136.16298786117284,,,,,,,,2024-01-03 +2024-01-03 12:15:00,136.37104980176102,,,,,,,,2024-01-03 +2024-01-03 12:20:00,135.9305424569463,,,,,,,,2024-01-03 +2024-01-03 12:25:00,133.3115103661344,,,,,,,,2024-01-03 +2024-01-03 12:30:00,131.0389587063375,,,,,,,,2024-01-03 +2024-01-03 12:35:00,126.0546963427194,,,,,,,,2024-01-03 +2024-01-03 12:40:00,123.1310208050412,,,,,,,,2024-01-03 +2024-01-03 12:45:00,123.0752613950362,,,,,,,,2024-01-03 +2024-01-03 12:50:00,124.41590389053424,,,,,,,,2024-01-03 +2024-01-03 12:55:00,124.87674747934392,,,,,,,,2024-01-03 +2024-01-03 13:00:00,124.0840045365996,ANNOUNCE_MEAL,,,,,27.66666666666666,24.33333333333333,2024-01-03 +2024-01-03 13:05:00,121.72349996519984,,,,,,0.0,,2024-01-03 +2024-01-03 13:10:00,121.0992891065856,,,,,,0.0,,2024-01-03 +2024-01-03 13:15:00,122.16881218945528,,,,,,0.0,,2024-01-03 +2024-01-03 13:20:00,125.84188453459066,,,,,,0.0,,2024-01-03 +2024-01-03 13:25:00,128.9496388811778,,,,,,0.0,,2024-01-03 +2024-01-03 13:30:00,129.61845352772764,,,,,,0.0,,2024-01-03 +2024-01-03 13:35:00,130.44188588225694,,,,,,0.0,,2024-01-03 +2024-01-03 13:40:00,132.67666035982307,,,,,,0.0,,2024-01-03 +2024-01-03 13:45:00,134.795239264174,,,,,,0.0,,2024-01-03 +2024-01-03 13:50:00,140.62856308117378,,,,,,0.0,,2024-01-03 +2024-01-03 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05:30:00,76.98016235401556,,,,,,,,2024-01-04 +2024-01-04 05:35:00,78.50788309984074,,,,,,,,2024-01-04 +2024-01-04 05:40:00,75.26366283882618,,,,,,,,2024-01-04 +2024-01-04 05:45:00,72.7140816709924,,,,,,,,2024-01-04 +2024-01-04 05:50:00,67.48399813966093,,,,,,,,2024-01-04 +2024-01-04 05:55:00,64.35150878591617,,,,,,,,2024-01-04 +2024-01-04 06:00:00,64.2181755317313,,,,,,,,2024-01-04 +2024-01-04 06:05:00,66.60336128983027,,,,,,,,2024-01-04 +2024-01-04 06:10:00,71.62516749236397,,,,,,,,2024-01-04 +2024-01-04 06:15:00,74.6935247718401,,,,,,,,2024-01-04 +2024-01-04 06:20:00,80.39363166728543,,,,,,,,2024-01-04 +2024-01-04 06:25:00,83.20771895646193,,,,,,,,2024-01-04 +2024-01-04 06:30:00,82.97843980627157,,,,,,,,2024-01-04 +2024-01-04 06:35:00,80.40827246343319,,,,,,,,2024-01-04 +2024-01-04 06:40:00,77.44757949966062,,,,,,,,2024-01-04 +2024-01-04 06:45:00,76.50202376525257,,,,,,,,2024-01-04 +2024-01-04 06:50:00,75.37253342999196,,,,,,,,2024-01-04 +2024-01-04 06:55:00,74.1971301722394,,,,,,,,2024-01-04 +2024-01-04 07:00:00,73.32124134647196,,,,,,,,2024-01-04 +2024-01-04 07:05:00,71.38153103111159,ANNOUNCE_MEAL,,,,,10.666666666666666,10.666666666666666,2024-01-04 +2024-01-04 07:10:00,70.07748532860428,,,,,,0.0,,2024-01-04 +2024-01-04 07:15:00,69.96372427338954,,,,,,0.0,,2024-01-04 +2024-01-04 07:20:00,71.23074763910158,,,,,,0.0,,2024-01-04 +2024-01-04 07:25:00,75.01896991461965,,,,,,0.0,,2024-01-04 +2024-01-04 07:30:00,77.88506967586747,,,,,,0.0,,2024-01-04 +2024-01-04 07:35:00,84.7725921317604,,,,,,0.0,,2024-01-04 +2024-01-04 07:40:00,91.5668870262106,,,,,,0.0,,2024-01-04 +2024-01-04 07:45:00,94.27671468640182,,,,,,0.0,,2024-01-04 +2024-01-04 07:50:00,97.119929698279,,,,,,0.0,,2024-01-04 +2024-01-04 07:55:00,95.41999698297882,,,,,,0.0,,2024-01-04 +2024-01-04 08:00:00,93.9162094347214,,,,,,0.0,,2024-01-04 +2024-01-04 08:05:00,93.9198503343312,,,,,,0.0,,2024-01-04 +2024-01-04 08:10:00,96.32543434594352,,,,,,0.0,,2024-01-04 +2024-01-04 08:15:00,97.83237315617482,,,,,,0.0,,2024-01-04 +2024-01-04 08:20:00,100.2652467915955,,,,,,0.0,,2024-01-04 +2024-01-04 08:25:00,100.37042824218298,,,,,,0.0,,2024-01-04 +2024-01-04 08:30:00,99.26915082162962,,,,,,0.0,,2024-01-04 +2024-01-04 08:35:00,96.42067013072992,,,,,,0.0,,2024-01-04 +2024-01-04 08:40:00,95.7581798848804,,,,,,0.0,,2024-01-04 +2024-01-04 08:45:00,97.00576706504648,,,,,,0.0,,2024-01-04 +2024-01-04 08:50:00,101.38463513879874,,,,,,0.0,,2024-01-04 +2024-01-04 08:55:00,105.48155827821869,,,,,,0.0,,2024-01-04 +2024-01-04 09:00:00,106.56566931518606,,,,,,0.0,,2024-01-04 +2024-01-04 09:05:00,106.48132159026072,,,,,,0.0,,2024-01-04 +2024-01-04 09:10:00,103.5349557753476,,,,,,0.0,,2024-01-04 +2024-01-04 09:15:00,101.18705112284678,,,,,,0.0,,2024-01-04 +2024-01-04 09:20:00,96.20933713813427,,,,,,0.0,,2024-01-04 +2024-01-04 09:25:00,93.2029116218659,,,,,,0.0,,2024-01-04 +2024-01-04 09:30:00,93.03900997924208,,,,,,0.0,,2024-01-04 +2024-01-04 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11:00:00,119.65528568721284,,,,,,,,2024-01-04 +2024-01-04 11:05:00,120.44172771231824,,,,,,,,2024-01-04 +2024-01-04 11:10:00,121.4923443998936,,,,,,,,2024-01-04 +2024-01-04 11:15:00,121.78945110468972,,,,,,,,2024-01-04 +2024-01-04 11:20:00,121.81660069893476,,,,,,,,2024-01-04 +2024-01-04 11:25:00,121.17114978279528,,,,,,,,2024-01-04 +2024-01-04 11:30:00,120.65796408316432,,,,,,,,2024-01-04 +2024-01-04 11:35:00,119.5746162855554,,,,,,,,2024-01-04 +2024-01-04 11:40:00,118.91788810762743,,,,,,,,2024-01-04 +2024-01-04 11:45:00,118.87038492328304,,,,,,,,2024-01-04 +2024-01-04 11:50:00,119.08545429504645,,,,,,,,2024-01-04 +2024-01-04 11:55:00,119.19514566923073,,,,,,,,2024-01-04 +2024-01-04 12:00:00,119.1439454448844,,,,,,,,2024-01-04 +2024-01-04 12:05:00,119.64035716991792,,,,,,,,2024-01-04 +2024-01-04 12:10:00,122.34638755419869,,,,,,,,2024-01-04 +2024-01-04 12:15:00,124.7100075418642,,,,,,,,2024-01-04 +2024-01-04 12:20:00,129.28536372475097,,,,,,,,2024-01-04 +2024-01-04 12:25:00,129.8072349762699,,,,,,,,2024-01-04 +2024-01-04 12:30:00,127.5785624563186,,,,,,,,2024-01-04 +2024-01-04 12:35:00,119.68129335387744,,,,,,,,2024-01-04 +2024-01-04 12:40:00,110.6383312005472,,,,,,,,2024-01-04 +2024-01-04 12:45:00,106.97719364547729,,,,,,,,2024-01-04 +2024-01-04 12:50:00,103.95489785228725,,,,,,,,2024-01-04 +2024-01-04 12:55:00,107.07536055533976,,,,,,,,2024-01-04 +2024-01-04 13:00:00,103.96839630450646,ANNOUNCE_MEAL,,,,,20.0,20.0,2024-01-04 +2024-01-04 13:05:00,103.89493005542826,,,,,,0.0,,2024-01-04 +2024-01-04 13:10:00,105.75150895571537,,,,,,0.0,,2024-01-04 +2024-01-04 13:15:00,107.36268687982434,,,,,,0.0,,2024-01-04 +2024-01-04 13:20:00,111.49906565905714,,,,,,0.0,,2024-01-04 +2024-01-04 13:25:00,116.5581841563799,,,,,,0.0,,2024-01-04 +2024-01-04 13:30:00,119.27418532401704,,,,,,0.0,,2024-01-04 +2024-01-04 13:35:00,124.64365745335704,,,,,,0.0,,2024-01-04 +2024-01-04 13:40:00,128.80039022455023,,,,,,0.0,,2024-01-04 +2024-01-04 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05:10:00,92.35868522685728,,,,,,,,2024-01-07 +2024-01-07 05:15:00,93.90135501467516,,,,,,,,2024-01-07 +2024-01-07 05:20:00,98.6120170578296,,,,,,,,2024-01-07 +2024-01-07 05:25:00,101.2000555904909,,,,,,,,2024-01-07 +2024-01-07 05:30:00,100.57720817817264,,,,,,,,2024-01-07 +2024-01-07 05:35:00,96.8276866361307,,,,,,,,2024-01-07 +2024-01-07 05:40:00,92.93951166060911,,,,,,,,2024-01-07 +2024-01-07 05:45:00,92.14334849569462,,,,,,,,2024-01-07 +2024-01-07 05:50:00,95.24671190631358,,,,,,,,2024-01-07 +2024-01-07 05:55:00,103.5290054486168,,,,,,,,2024-01-07 +2024-01-07 06:00:00,100.9060565463837,,,,,,,,2024-01-07 +2024-01-07 06:05:00,98.89936499003755,,,,,,,,2024-01-07 +2024-01-07 06:10:00,99.77800773110025,,,,,,,,2024-01-07 +2024-01-07 06:15:00,100.76172760946292,,,,,,,,2024-01-07 +2024-01-07 06:20:00,102.74276274942476,,,,,,,,2024-01-07 +2024-01-07 06:25:00,103.34199667431588,,,,,,,,2024-01-07 +2024-01-07 06:30:00,102.8594167872804,,,,,,,,2024-01-07 +2024-01-07 06:35:00,101.7126565075952,,,,,,,,2024-01-07 +2024-01-07 06:40:00,102.87925155704072,,,,,,,,2024-01-07 +2024-01-07 06:45:00,104.8896392875936,,,,,,,,2024-01-07 +2024-01-07 06:50:00,109.9640478107518,,,,,,,,2024-01-07 +2024-01-07 06:55:00,112.9474280220793,,,,,,,,2024-01-07 +2024-01-07 07:00:00,112.89447739687807,,,,,,,,2024-01-07 +2024-01-07 07:05:00,112.2089735338756,,,,,,,,2024-01-07 +2024-01-07 07:10:00,115.6020688725664,ANNOUNCE_MEAL,,,,,12.333333333333334,12.333333333333334,2024-01-07 +2024-01-07 07:15:00,119.83560432698422,,,,,,0.0,,2024-01-07 +2024-01-07 07:20:00,129.4692025808911,,,,,,0.0,,2024-01-07 +2024-01-07 07:25:00,133.48551137134362,,,,,,0.0,,2024-01-07 +2024-01-07 07:30:00,131.81765481513196,,,,,,0.0,,2024-01-07 +2024-01-07 07:35:00,125.1561526972771,,,,,,0.0,,2024-01-07 +2024-01-07 07:40:00,121.70046428828876,,,,,,0.0,,2024-01-07 +2024-01-07 07:45:00,123.0977842862124,,,,,,0.0,,2024-01-07 +2024-01-07 07:50:00,130.12672105610213,,,,,,0.0,,2024-01-07 +2024-01-07 07:55:00,137.96056295361578,,,,,,0.0,,2024-01-07 +2024-01-07 08:00:00,140.79290960122484,,,,,,0.0,,2024-01-07 +2024-01-07 08:05:00,144.2659631620172,,,,,,0.0,,2024-01-07 +2024-01-07 08:10:00,145.98638782648055,,,,,,0.0,,2024-01-07 +2024-01-07 08:15:00,146.66129712491875,,,,,,0.0,,2024-01-07 +2024-01-07 08:20:00,148.62252096053902,,,,,,0.0,,2024-01-07 +2024-01-07 08:25:00,152.91665049825164,,,,,,0.0,,2024-01-07 +2024-01-07 08:30:00,156.43874415230067,,,,,,0.0,,2024-01-07 +2024-01-07 08:35:00,162.7699084678157,,,,,,0.0,,2024-01-07 +2024-01-07 08:40:00,167.35238998968725,,,,,,0.0,,2024-01-07 +2024-01-07 08:45:00,168.68431863270632,,,,,,0.0,,2024-01-07 +2024-01-07 08:50:00,168.81328583588413,,,,,,0.0,,2024-01-07 +2024-01-07 08:55:00,164.7702390547063,,,,,,0.0,,2024-01-07 +2024-01-07 09:00:00,161.19360537054257,,,,,,0.0,,2024-01-07 +2024-01-07 09:05:00,153.2080567631707,,,,,,0.0,,2024-01-07 +2024-01-07 09:10:00,147.98689601010028,,,,,,0.0,,2024-01-07 +2024-01-07 09:15:00,147.38443528619746,,,,,,0.0,,2024-01-07 +2024-01-07 09:20:00,148.62237936665235,,,,,,0.0,,2024-01-07 +2024-01-07 09:25:00,149.65373422276343,,,,,,0.0,,2024-01-07 +2024-01-07 09:30:00,149.10506406713623,,,,,,0.0,,2024-01-07 +2024-01-07 09:35:00,146.19423223213232,,,,,,0.0,,2024-01-07 +2024-01-07 09:40:00,142.1987099748954,,,,,,0.0,,2024-01-07 +2024-01-07 09:45:00,140.29043159207035,,,,,,0.0,,2024-01-07 +2024-01-07 09:50:00,137.61964141053426,,,,,,0.0,,2024-01-07 +2024-01-07 09:55:00,137.71982954710498,,,,,,0.0,,2024-01-07 +2024-01-07 10:00:00,138.9989519383306,,,,,,0.0,,2024-01-07 +2024-01-07 10:05:00,142.3713324397055,,,,,,0.0,,2024-01-07 +2024-01-07 10:10:00,143.8753890619124,,,,,,0.0,,2024-01-07 +2024-01-07 10:15:00,143.16555581343232,,,,,,,,2024-01-07 +2024-01-07 10:20:00,139.83250141392395,,,,,,,,2024-01-07 +2024-01-07 10:25:00,136.29007122539417,,,,,,,,2024-01-07 +2024-01-07 10:30:00,135.14053179541014,,,,,,,,2024-01-07 +2024-01-07 10:35:00,134.1657395472111,,,,,,,,2024-01-07 +2024-01-07 10:40:00,134.5435592061441,,,,,,,,2024-01-07 +2024-01-07 10:45:00,135.08134295631666,,,,,,,,2024-01-07 +2024-01-07 10:50:00,136.4981833725325,,,,,,,,2024-01-07 +2024-01-07 10:55:00,138.3503596602901,,,,,,,,2024-01-07 +2024-01-07 11:00:00,140.48021545528383,,,,,,,,2024-01-07 +2024-01-07 11:05:00,144.80487205405018,,,,,,,,2024-01-07 +2024-01-07 11:10:00,148.65883768715446,,,,,,,,2024-01-07 +2024-01-07 11:15:00,150.21534122912885,,,,,,,,2024-01-07 +2024-01-07 11:20:00,152.2354404046797,,,,,,,,2024-01-07 +2024-01-07 11:25:00,152.38062536871848,,,,,,,,2024-01-07 +2024-01-07 11:30:00,151.7001662105995,,,,,,,,2024-01-07 +2024-01-07 11:35:00,149.4618469322911,,,,,,,,2024-01-07 +2024-01-07 11:40:00,147.16848343328476,,,,,,,,2024-01-07 +2024-01-07 11:45:00,146.27998057921107,ANNOUNCE_MEAL,,,,,21.0,21.0,2024-01-07 +2024-01-07 11:50:00,144.5517550628486,,,,,,0.0,,2024-01-07 +2024-01-07 11:55:00,142.02975617830944,,,,,,0.0,,2024-01-07 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13:20:00,191.38121015692303,,,,,,0.0,,2024-01-07 +2024-01-07 13:25:00,182.3201388125153,,,,,,0.0,,2024-01-07 +2024-01-07 13:30:00,183.7928872279506,,,,,,0.0,,2024-01-07 +2024-01-07 13:35:00,180.46732341163573,,,,,,0.0,,2024-01-07 +2024-01-07 13:40:00,171.9836739753842,,,,,,0.0,,2024-01-07 +2024-01-07 13:45:00,167.0057743736272,,,,,,0.0,,2024-01-07 +2024-01-07 13:50:00,157.96831426860987,,,,,,0.0,,2024-01-07 +2024-01-07 13:55:00,153.27015651179426,,,,,,0.0,,2024-01-07 +2024-01-07 14:00:00,153.20232159307508,,,,,,0.0,,2024-01-07 +2024-01-07 14:05:00,155.18193046075748,,,,,,0.0,,2024-01-07 +2024-01-07 14:10:00,155.28163680522078,,,,,,0.0,,2024-01-07 +2024-01-07 14:15:00,153.36297042180527,,,,,,0.0,,2024-01-07 +2024-01-07 14:20:00,146.72020869947636,,,,,,0.0,,2024-01-07 +2024-01-07 14:25:00,139.29128386735334,,,,,,0.0,,2024-01-07 +2024-01-07 14:30:00,136.2706841962614,,,,,,0.0,,2024-01-07 +2024-01-07 14:35:00,132.7223166762575,,,,,,0.0,,2024-01-07 +2024-01-07 14:40:00,133.57372411528158,,,,,,0.0,,2024-01-07 +2024-01-07 14:45:00,135.65897355324998,,,,,,0.0,,2024-01-07 +2024-01-07 14:50:00,140.27941782914056,,,,,,,,2024-01-07 +2024-01-07 14:55:00,140.61822341349875,,,,,,,,2024-01-07 +2024-01-07 15:00:00,138.05296899437548,,,,,,,,2024-01-07 +2024-01-07 15:05:00,130.31411914822823,,,,,,,,2024-01-07 +2024-01-07 15:10:00,124.90126940788544,,,,,,,,2024-01-07 +2024-01-07 15:15:00,124.67575280912988,,,,,,,,2024-01-07 +2024-01-07 15:20:00,128.18915904881376,,,,,,,,2024-01-07 +2024-01-07 15:25:00,133.9099465307486,,,,,,,,2024-01-07 +2024-01-07 15:30:00,136.64064915160776,,,,,,,,2024-01-07 +2024-01-07 15:35:00,141.64201225301332,,,,,,,,2024-01-07 +2024-01-07 15:40:00,146.46046041778575,,,,,,,,2024-01-07 +2024-01-07 15:45:00,148.97845715674066,,,,,,,,2024-01-07 +2024-01-07 15:50:00,154.64728045551175,,,,,,,,2024-01-07 +2024-01-07 15:55:00,160.08481495010614,,,,,,,,2024-01-07 +2024-01-07 16:00:00,158.82963427618813,,,,,,,,2024-01-07 +2024-01-07 16:05:00,154.16756953923897,,,,,,,,2024-01-07 +2024-01-07 16:10:00,148.33467597032717,,,,,,,,2024-01-07 +2024-01-07 16:15:00,145.62074895382543,,,,,,,,2024-01-07 +2024-01-07 16:20:00,141.88515342334176,,,,,,,,2024-01-07 +2024-01-07 16:25:00,142.07353512720368,,,,,,,,2024-01-07 +2024-01-07 16:30:00,143.89791763358392,,,,,,,,2024-01-07 +2024-01-07 16:35:00,148.8465229299668,,,,,,,,2024-01-07 +2024-01-07 16:40:00,151.62374527412197,,,,,,,,2024-01-07 +2024-01-07 16:45:00,151.13113546463197,,,,,,,,2024-01-07 +2024-01-07 16:50:00,147.17127691825516,ANNOUNCE_MEAL,,,,,22.666666666666668,22.666666666666668,2024-01-07 +2024-01-07 16:55:00,141.44467783022844,,,,,,0.0,,2024-01-07 +2024-01-07 17:00:00,138.63600293597003,,,,,,0.0,,2024-01-07 +2024-01-07 17:05:00,133.8769276230911,,,,,,0.0,,2024-01-07 +2024-01-07 17:10:00,131.33777161325747,,,,,,0.0,,2024-01-07 +2024-01-07 17:15:00,131.28809533450308,,,,,,0.0,,2024-01-07 +2024-01-07 17:20:00,133.7003712945599,,,,,,0.0,,2024-01-07 +2024-01-07 17:25:00,138.5131058401936,,,,,,0.0,,2024-01-07 +2024-01-07 17:30:00,141.28357484096114,,,,,,0.0,,2024-01-07 +2024-01-07 17:35:00,146.75417409629293,,,,,,0.0,,2024-01-07 +2024-01-07 17:40:00,151.59857240792445,,,,,,0.0,,2024-01-07 +2024-01-07 17:45:00,153.75003347252232,,,,,,0.0,,2024-01-07 +2024-01-07 17:50:00,157.79948147931512,,,,,,0.0,,2024-01-07 +2024-01-07 17:55:00,162.12910737218652,,,,,,0.0,,2024-01-07 +2024-01-07 18:00:00,164.5638863140747,,,,,,0.0,,2024-01-07 +2024-01-07 18:05:00,169.58999850006992,,,,,,0.0,,2024-01-07 +2024-01-07 18:10:00,174.0172859900011,,,,,,0.0,,2024-01-07 +2024-01-07 18:15:00,175.6982040873214,,,,,,0.0,,2024-01-07 +2024-01-07 18:20:00,177.36060516356434,,,,,,0.0,,2024-01-07 +2024-01-07 18:25:00,174.62999662603232,,,,,,0.0,,2024-01-07 +2024-01-07 18:30:00,169.45122545229273,,,,,,0.0,,2024-01-07 +2024-01-07 18:35:00,162.7703941835679,,,,,,0.0,,2024-01-07 +2024-01-07 18:40:00,159.50514454334845,,,,,,0.0,,2024-01-07 +2024-01-07 18:45:00,158.5912509036322,,,,,,0.0,,2024-01-07 +2024-01-07 18:50:00,157.06783205393162,,,,,,0.0,,2024-01-07 +2024-01-07 18:55:00,154.46046691944423,,,,,,0.0,,2024-01-07 +2024-01-07 19:00:00,152.42537697401357,,,,,,0.0,,2024-01-07 +2024-01-07 19:05:00,147.9675281159275,,,,,,0.0,,2024-01-07 +2024-01-07 19:10:00,145.07499942847852,,,,,,0.0,,2024-01-07 +2024-01-07 19:15:00,144.70634728105694,,,,,,0.0,,2024-01-07 +2024-01-07 19:20:00,144.98726525773867,,,,,,0.0,,2024-01-07 +2024-01-07 19:25:00,144.29785336169067,,,,,,0.0,,2024-01-07 +2024-01-07 19:30:00,142.98907181267538,,,,,,0.0,,2024-01-07 +2024-01-07 19:35:00,139.24305697050798,,,,,,0.0,,2024-01-07 +2024-01-07 19:40:00,135.83598239296262,,,,,,0.0,,2024-01-07 +2024-01-07 19:45:00,134.77822140014013,,,,,,0.0,,2024-01-07 +2024-01-07 19:50:00,133.72559137051468,,,,,,0.0,,2024-01-07 +2024-01-07 19:55:00,133.2795526435405,,,,,,,,2024-01-07 +2024-01-07 20:00:00,132.94612016522876,,,,,,,,2024-01-07 +2024-01-07 20:05:00,131.12368525926288,,,,,,,,2024-01-07 +2024-01-07 20:10:00,126.46892488425296,,,,,,,,2024-01-07 +2024-01-07 20:15:00,122.93286563049836,,,,,,,,2024-01-07 +2024-01-07 20:20:00,115.58157213339996,,,,,,,,2024-01-07 +2024-01-07 20:25:00,111.61008980055392,,,,,,,,2024-01-07 +2024-01-07 20:30:00,111.85750032510047,,,,,,,,2024-01-07 +2024-01-07 20:35:00,115.59405746251684,,,,,,,,2024-01-07 +2024-01-07 20:40:00,120.6611899841507,,,,,,,,2024-01-07 +2024-01-07 20:45:00,122.55267345721064,,,,,,,,2024-01-07 +2024-01-07 20:50:00,122.79083910889756,,,,,,,,2024-01-07 +2024-01-07 20:55:00,119.0133858273454,,,,,,,,2024-01-07 +2024-01-07 21:00:00,122.89979282056882,,,,,,,,2024-01-07 +2024-01-07 21:05:00,127.29712309989245,,,,,,,,2024-01-07 +2024-01-07 21:10:00,128.12594999525945,,,,,,,,2024-01-07 +2024-01-07 21:15:00,127.56365138362077,LOW_CARB_MEAL,,,,,5.0,5.0,2024-01-07 +2024-01-07 21:20:00,125.22223493234742,,,,,,,,2024-01-07 +2024-01-07 21:25:00,122.32046066660982,,,,,,,,2024-01-07 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22:55:00,139.93343362963105,,,,,,,,2024-01-07 +2024-01-07 23:00:00,142.7603955820982,,,,,,,,2024-01-07 +2024-01-07 23:05:00,148.08131507066395,,,,,,,,2024-01-07 +2024-01-07 23:10:00,150.2381446518481,,,,,,,,2024-01-07 +2024-01-07 23:15:00,149.10934618759958,,,,,,,,2024-01-07 +2024-01-07 23:20:00,140.39477472461792,,,,,,,,2024-01-07 +2024-01-07 23:25:00,121.60086562089944,,,,,,,,2024-01-07 +2024-01-07 23:30:00,112.4622265923918,,,,,,,,2024-01-07 +2024-01-07 23:35:00,102.93045537785,,,,,,,,2024-01-07 +2024-01-07 23:40:00,101.6230232894746,,,,,,,,2024-01-07 +2024-01-07 23:45:00,102.76473085793998,,,,,,,,2024-01-07 +2024-01-07 23:50:00,106.06154610545288,,,,,,,,2024-01-07 +2024-01-07 23:55:00,107.323524210139,,,,,,,,2024-01-07 +2024-01-08 00:00:00,106.3123079834055,,,,,,,,2024-01-07 diff --git a/0_meal_identification/meal_identification/data/interim/2024-11-18_adu003__i5mins_d4hrs_c5g_l2hrs_n3.csv 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10:05:00,140.54571965153465,,,,,,,,2024-01-02 +2024-01-02 10:10:00,148.87203425591676,,,,,,,,2024-01-02 +2024-01-02 10:15:00,152.64731326117928,,,,,,,,2024-01-02 +2024-01-02 10:20:00,158.08580506714603,,,,,,,,2024-01-02 +2024-01-02 10:25:00,158.94299432184638,,,,,,,,2024-01-02 +2024-01-02 10:30:00,157.25058957171882,,,,,,,,2024-01-02 +2024-01-02 10:35:00,150.780520667111,,,,,,,,2024-01-02 +2024-01-02 10:40:00,142.50806058563137,,,,,,,,2024-01-02 +2024-01-02 10:45:00,138.5568740290744,,,,,,,,2024-01-02 +2024-01-02 10:50:00,132.73708512319314,,,,,,,,2024-01-02 +2024-01-02 10:55:00,130.5850580842097,,,,,,,,2024-01-02 +2024-01-02 11:00:00,128.53320017667792,,,,,,,,2024-01-02 +2024-01-02 11:05:00,128.09300567064545,,,,,,,,2024-01-02 +2024-01-02 11:10:00,130.8721050956058,,,,,,,,2024-01-02 +2024-01-02 11:15:00,132.84392284092726,,,,,,,,2024-01-02 +2024-01-02 11:20:00,136.70148384958227,,,,,,,,2024-01-02 +2024-01-02 11:25:00,138.80580971917146,,,,,,,,2024-01-02 +2024-01-02 11:30:00,138.81517611827098,,,,,,,,2024-01-02 +2024-01-02 11:35:00,137.65589273620614,,,,,,,,2024-01-02 +2024-01-02 11:40:00,136.7772868167676,,,,,,,,2024-01-02 +2024-01-02 11:45:00,137.27633414707142,ANNOUNCE_MEAL,,,,,22.33333333333333,22.33333333333333,2024-01-02 +2024-01-02 11:50:00,137.62813519054745,,,,,,0.0,,2024-01-02 +2024-01-02 11:55:00,137.5442235802276,,,,,,0.0,,2024-01-02 +2024-01-02 12:00:00,137.15431995120488,,,,,,0.0,,2024-01-02 +2024-01-02 12:05:00,137.3037934761098,,,,,,0.0,,2024-01-02 +2024-01-02 12:10:00,141.23135752624236,,,,,,0.0,,2024-01-02 +2024-01-02 12:15:00,145.07725862088816,,,,,,0.0,,2024-01-02 +2024-01-02 12:20:00,155.3655501450609,,,,,,0.0,,2024-01-02 +2024-01-02 12:25:00,167.4888355686681,,,,,,0.0,,2024-01-02 +2024-01-02 12:30:00,173.7741121628244,,,,,,0.0,,2024-01-02 +2024-01-02 12:35:00,184.43493036811017,,,,,,0.0,,2024-01-02 +2024-01-02 12:40:00,189.082209320807,,,,,,0.0,,2024-01-02 +2024-01-02 12:45:00,188.3932066412541,,,,,,0.0,,2024-01-02 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14:10:00,108.68380548596556,,,,,,,,2024-01-02 +2024-01-02 14:15:00,106.07970013145334,,,,,,,,2024-01-02 +2024-01-02 14:20:00,102.33029917771768,,,,,,,,2024-01-02 +2024-01-02 14:25:00,100.62360948919957,,,,,,,,2024-01-02 +2024-01-02 14:30:00,100.53214890168564,,,,,,,,2024-01-02 +2024-01-02 14:35:00,101.56687834998644,,,,,,,,2024-01-02 +2024-01-02 14:40:00,103.69200160534322,,,,,,,,2024-01-02 +2024-01-02 14:45:00,104.91104471220277,,,,,,,,2024-01-02 +2024-01-02 14:50:00,106.77101932864343,,,,,,,,2024-01-02 +2024-01-02 14:55:00,106.55734441757082,,,,,,,,2024-01-02 +2024-01-02 15:00:00,105.47168221533512,,,,,,,,2024-01-02 +2024-01-02 15:05:00,103.03844040168704,,,,,,,,2024-01-02 +2024-01-02 15:10:00,103.30772623270786,,,,,,,,2024-01-02 +2024-01-02 15:15:00,105.1824819414201,,,,,,,,2024-01-02 +2024-01-02 15:20:00,110.7902302811522,,,,,,,,2024-01-02 +2024-01-02 15:25:00,115.54823080293798,,,,,,,,2024-01-02 +2024-01-02 15:30:00,116.70760855560336,,,,,,,,2024-01-02 +2024-01-02 15:35:00,117.27437883310972,,,,,,,,2024-01-02 +2024-01-02 15:40:00,117.42847202243524,,,,,,,,2024-01-02 +2024-01-02 15:45:00,118.05981175019116,,,,,,,,2024-01-02 +2024-01-02 15:50:00,121.72568721210828,,,,,,,,2024-01-02 +2024-01-02 15:55:00,128.85058630631806,,,,,,,,2024-01-02 +2024-01-02 16:00:00,131.12484964891615,,,,,,,,2024-01-02 +2024-01-02 16:05:00,135.76883897015298,,,,,,,,2024-01-02 +2024-01-02 16:10:00,140.131501468429,,,,,,,,2024-01-02 +2024-01-02 16:15:00,142.11223135158292,,,,,,,,2024-01-02 +2024-01-02 16:20:00,145.6147286284156,,,,,,,,2024-01-02 +2024-01-02 16:25:00,148.5620294930242,,,,,,,,2024-01-02 +2024-01-02 16:30:00,149.88111539004592,,,,,,,,2024-01-02 +2024-01-02 16:35:00,152.38492137040146,,,,,,,,2024-01-02 +2024-01-02 16:40:00,154.95304362577474,,,,,,,,2024-01-02 +2024-01-02 16:45:00,156.19978101630468,,,,,,,,2024-01-02 +2024-01-02 16:50:00,157.3694304323417,,,,,,,,2024-01-02 +2024-01-02 16:55:00,154.7058642351571,,,,,,,,2024-01-02 +2024-01-02 17:00:00,151.6238099889573,,,,,,,,2024-01-02 +2024-01-02 17:05:00,144.7510101639779,,,,,,,,2024-01-02 +2024-01-02 17:10:00,141.91507768310382,,,,,,,,2024-01-02 +2024-01-02 17:15:00,143.14165709453664,,,,,,,,2024-01-02 +2024-01-02 17:20:00,147.77033013785723,,,,,,,,2024-01-02 +2024-01-02 17:25:00,149.09148497241097,,,,,,,,2024-01-02 +2024-01-02 17:30:00,146.93192503466918,,,,,,,,2024-01-02 +2024-01-02 17:35:00,139.3075586668889,,,,,,,,2024-01-02 +2024-01-02 17:40:00,132.5140305266605,,,,,,,,2024-01-02 +2024-01-02 17:45:00,130.25241921281338,ANNOUNCE_MEAL,,,,,17.333333333333332,17.333333333333332,2024-01-02 +2024-01-02 17:50:00,130.23991243146344,,,,,,0.0,,2024-01-02 +2024-01-02 17:55:00,130.4968833605675,,,,,,0.0,,2024-01-02 +2024-01-02 18:00:00,130.2965046283189,,,,,,0.0,,2024-01-02 +2024-01-02 18:05:00,130.10702688115543,,,,,,0.0,,2024-01-02 +2024-01-02 18:10:00,132.0479041632729,,,,,,0.0,,2024-01-02 +2024-01-02 18:15:00,133.9340777486012,,,,,,0.0,,2024-01-02 +2024-01-02 18:20:00,138.29931986663684,,,,,,0.0,,2024-01-02 +2024-01-02 18:25:00,142.82572274552876,,,,,,0.0,,2024-01-02 +2024-01-02 18:30:00,146.57310025086144,,,,,,0.0,,2024-01-02 +2024-01-02 18:35:00,150.53127588166416,,,,,,0.0,,2024-01-02 +2024-01-02 18:40:00,150.92139625211323,,,,,,0.0,,2024-01-02 +2024-01-02 18:45:00,150.25177250600427,,,,,,0.0,,2024-01-02 +2024-01-02 18:50:00,148.13539814189562,,,,,,0.0,,2024-01-02 +2024-01-02 18:55:00,146.1863067582848,,,,,,0.0,,2024-01-02 +2024-01-02 19:00:00,145.58365221918083,,,,,,0.0,,2024-01-02 +2024-01-02 19:05:00,144.80366401152628,,,,,,0.0,,2024-01-02 +2024-01-02 19:10:00,143.7807700376807,,,,,,0.0,,2024-01-02 +2024-01-02 19:15:00,143.00731521938854,,,,,,0.0,,2024-01-02 +2024-01-02 19:20:00,141.63311244047694,,,,,,0.0,,2024-01-02 +2024-01-02 19:25:00,141.7933661956689,,,,,,0.0,,2024-01-02 +2024-01-02 19:30:00,142.71326847563174,,,,,,0.0,,2024-01-02 +2024-01-02 19:35:00,145.14133011602047,,,,,,0.0,,2024-01-02 +2024-01-02 19:40:00,146.2718037728954,,,,,,0.0,,2024-01-02 +2024-01-02 19:45:00,145.7573150467345,,,,,,0.0,,2024-01-02 +2024-01-02 19:50:00,142.76611860752172,,,,,,,,2024-01-02 +2024-01-02 19:55:00,137.97185584440865,,,,,,,,2024-01-02 +2024-01-02 20:00:00,135.20004293907894,,,,,,,,2024-01-02 +2024-01-02 20:05:00,129.48901024737484,,,,,,,,2024-01-02 +2024-01-02 20:10:00,124.31413349531267,,,,,,,,2024-01-02 +2024-01-02 20:15:00,122.156553025768,,,,,,,,2024-01-02 +2024-01-02 20:20:00,118.75126740800556,,,,,,,,2024-01-02 +2024-01-02 20:25:00,116.46110879979229,,,,,,,,2024-01-02 +2024-01-02 20:30:00,115.68299888574091,,,,,,,,2024-01-02 +2024-01-02 20:35:00,114.65333259144904,,,,,,,,2024-01-02 +2024-01-02 20:40:00,114.0077522452054,,,,,,,,2024-01-02 +2024-01-02 20:45:00,113.71004299367183,,,,,,,,2024-01-02 +2024-01-02 20:50:00,112.92796961748964,,,,,,,,2024-01-02 +2024-01-02 20:55:00,111.91695897169105,,,,,,,,2024-01-02 +2024-01-02 21:00:00,112.43722802415392,,,,,,,,2024-01-02 +2024-01-02 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22:30:00,122.87588221548836,,,,,,,,2024-01-02 +2024-01-02 22:35:00,122.05337969449128,,,,,,,,2024-01-02 +2024-01-02 22:40:00,120.98330932487552,,,,,,,,2024-01-02 +2024-01-02 22:45:00,120.88533184980238,,,,,,,,2024-01-02 +2024-01-02 22:50:00,121.5978892509833,,,,,,,,2024-01-02 +2024-01-02 22:55:00,123.14939513235456,,,,,,,,2024-01-02 +2024-01-02 23:00:00,124.0987090892168,,,,,,,,2024-01-02 +2024-01-02 23:05:00,126.07219268841637,,,,,,,,2024-01-02 +2024-01-02 23:10:00,127.77865463544646,,,,,,,,2024-01-02 +2024-01-02 23:15:00,128.39090417927224,,,,,,,,2024-01-02 +2024-01-02 23:20:00,128.8450687679386,,,,,,,,2024-01-02 +2024-01-02 23:25:00,127.89182302212906,,,,,,,,2024-01-02 +2024-01-02 23:30:00,127.0766998603444,,,,,,,,2024-01-02 +2024-01-02 23:35:00,126.54874388778369,,,,,,,,2024-01-02 +2024-01-02 23:40:00,126.8993323723883,,,,,,,,2024-01-02 +2024-01-02 23:45:00,127.18130432840222,,,,,,,,2024-01-02 +2024-01-02 23:50:00,127.51100639676665,,,,,,,,2024-01-02 +2024-01-02 23:55:00,126.9484222189256,,,,,,,,2024-01-02 +2024-01-03 00:00:00,126.26136478405073,,,,,,,,2024-01-02 +2024-01-03 00:05:00,125.00786135753884,,,,,,,,2024-01-02 +2024-01-03 00:10:00,125.53601124317876,,,,,,,,2024-01-02 +2024-01-03 00:15:00,126.68375546575854,,,,,,,,2024-01-02 +2024-01-03 00:20:00,128.3066204037323,,,,,,,,2024-01-02 +2024-01-03 00:25:00,124.72550508437304,,,,,,,,2024-01-02 +2024-01-03 00:30:00,120.0895849709046,,,,,,,,2024-01-02 +2024-01-03 00:35:00,108.6216165688043,,,,,,,,2024-01-02 +2024-01-03 00:40:00,100.90079842223784,,,,,,,,2024-01-02 +2024-01-03 00:45:00,100.2207017614612,,,,,,,,2024-01-02 +2024-01-03 00:50:00,103.43977035719448,,,,,,,,2024-01-02 +2024-01-03 00:55:00,108.30547521981052,,,,,,,,2024-01-02 +2024-01-03 01:00:00,110.03677888505688,,,,,,,,2024-01-02 +2024-01-03 01:05:00,112.04370442378308,,,,,,,,2024-01-02 +2024-01-03 01:10:00,113.06413864301936,,,,,,,,2024-01-02 +2024-01-03 01:15:00,113.49844956374756,,,,,,,,2024-01-02 +2024-01-03 01:20:00,114.33532769084536,,,,,,,,2024-01-02 +2024-01-03 01:25:00,115.10616074756668,,,,,,,,2024-01-02 +2024-01-03 01:30:00,115.48119864011562,,,,,,,,2024-01-02 +2024-01-03 01:35:00,116.40953459042493,,,,,,,,2024-01-02 +2024-01-03 01:40:00,117.90901106559896,,,,,,,,2024-01-02 +2024-01-03 01:45:00,118.99802320359402,,,,,,,,2024-01-02 +2024-01-03 01:50:00,122.10489816765872,,,,,,,,2024-01-02 +2024-01-03 01:55:00,124.21380145605092,,,,,,,,2024-01-02 +2024-01-03 02:00:00,120.2414928210957,,,,,,,,2024-01-02 +2024-01-03 02:05:00,119.19457778928506,,,,,,,,2024-01-02 +2024-01-03 02:10:00,124.54528538699412,,,,,,,,2024-01-02 +2024-01-03 02:15:00,128.5700440352629,,,,,,,,2024-01-02 +2024-01-03 02:20:00,137.21905076788318,,,,,,,,2024-01-02 +2024-01-03 02:25:00,143.90329391204685,,,,,,,,2024-01-02 +2024-01-03 02:30:00,145.74261268517265,,,,,,,,2024-01-02 +2024-01-03 02:35:00,146.86430474664317,,,,,,,,2024-01-02 +2024-01-03 02:40:00,145.95629679503105,,,,,,,,2024-01-02 +2024-01-03 02:45:00,145.18232915345672,,,,,,,,2024-01-02 +2024-01-03 02:50:00,143.31896935196687,,,,,,,,2024-01-02 +2024-01-03 02:55:00,141.2464949284753,,,,,,,,2024-01-02 +2024-01-03 03:00:00,140.22306964365382,,,,,,,,2024-01-02 +2024-01-03 03:05:00,138.47157839346625,,,,,,,,2024-01-02 +2024-01-03 03:10:00,137.4960945556988,,,,,,,,2024-01-02 +2024-01-03 03:15:00,137.34693423174386,,,,,,,,2024-01-02 +2024-01-03 03:20:00,137.1197353308272,,,,,,,,2024-01-02 +2024-01-03 03:25:00,135.92081366559344,,,,,,,,2024-01-02 +2024-01-03 03:30:00,134.723902146064,,,,,,,,2024-01-02 +2024-01-03 03:35:00,131.89113775840173,,,,,,,,2024-01-02 +2024-01-03 03:40:00,129.9497587331312,,,,,,,,2024-01-02 +2024-01-03 03:45:00,129.71637791297331,,,,,,,,2024-01-02 +2024-01-03 03:50:00,130.42672116424723,,,,,,,,2024-01-02 +2024-01-03 03:55:00,131.87696795771134,,,,,,,,2024-01-02 +2024-01-03 04:00:00,132.62952147182594,,,,,,,,2024-01-03 +2024-01-03 04:05:00,134.13644330237867,,,,,,,,2024-01-03 +2024-01-03 04:10:00,135.75362416760518,,,,,,,,2024-01-03 +2024-01-03 04:15:00,136.6440104735422,,,,,,,,2024-01-03 +2024-01-03 04:20:00,138.66929184750916,,,,,,,,2024-01-03 +2024-01-03 04:25:00,141.13119127513804,,,,,,,,2024-01-03 +2024-01-03 04:30:00,142.4323406914414,,,,,,,,2024-01-03 +2024-01-03 04:35:00,144.2100109756851,,,,,,,,2024-01-03 +2024-01-03 04:40:00,144.81474194091288,,,,,,,,2024-01-03 +2024-01-03 04:45:00,144.64972283230207,,,,,,,,2024-01-03 +2024-01-03 04:50:00,143.32986284622007,,,,,,,,2024-01-03 +2024-01-03 04:55:00,140.6249839666545,,,,,,,,2024-01-03 +2024-01-03 05:00:00,138.79172015865305,,,,,,,,2024-01-03 +2024-01-03 05:05:00,134.7021710553676,,,,,,,,2024-01-03 +2024-01-03 05:10:00,130.95223127690443,,,,,,,,2024-01-03 +2024-01-03 05:15:00,129.55221793296224,,,,,,,,2024-01-03 +2024-01-03 05:20:00,128.4612839698895,,,,,,,,2024-01-03 +2024-01-03 05:25:00,130.65777129730463,,,,,,,,2024-01-03 +2024-01-03 05:30:00,133.14100122656333,,,,,,,,2024-01-03 +2024-01-03 05:35:00,139.5266801888568,,,,,,,,2024-01-03 +2024-01-03 05:40:00,145.37145876437444,,,,,,,,2024-01-03 +2024-01-03 05:45:00,147.4025391191058,,,,,,,,2024-01-03 +2024-01-03 05:50:00,149.94521204828155,,,,,,,,2024-01-03 +2024-01-03 05:55:00,151.45126670754584,,,,,,,,2024-01-03 +2024-01-03 06:00:00,152.05836656235047,,,,,,,,2024-01-03 +2024-01-03 06:05:00,152.5275030744944,,,,,,,,2024-01-03 +2024-01-03 06:10:00,151.04920907349927,,,,,,,,2024-01-03 +2024-01-03 06:15:00,149.41761972983616,,,,,,,,2024-01-03 +2024-01-03 06:20:00,145.4775298939972,,,,,,,,2024-01-03 +2024-01-03 06:25:00,142.6545694405147,,,,,,,,2024-01-03 +2024-01-03 06:30:00,142.1892626339896,,,,,,,,2024-01-03 +2024-01-03 06:35:00,142.52035428137788,,,,,,,,2024-01-03 +2024-01-03 06:40:00,143.019797586479,,,,,,,,2024-01-03 +2024-01-03 06:45:00,142.764930389066,,,,,,,,2024-01-03 +2024-01-03 06:50:00,140.1105767600978,,,,,,,,2024-01-03 +2024-01-03 06:55:00,134.49379531191033,,,,,,,,2024-01-03 +2024-01-03 07:00:00,133.722849248555,ANNOUNCE_MEAL,,,,,12.0,12.0,2024-01-03 +2024-01-03 07:05:00,133.37263898627378,,,,,,0.0,,2024-01-03 +2024-01-03 07:10:00,134.3257068409034,,,,,,0.0,,2024-01-03 +2024-01-03 07:15:00,135.46980097450796,,,,,,0.0,,2024-01-03 +2024-01-03 07:20:00,139.4427731207235,,,,,,0.0,,2024-01-03 +2024-01-03 07:25:00,144.660336480093,,,,,,0.0,,2024-01-03 +2024-01-03 07:30:00,147.1104740874571,,,,,,0.0,,2024-01-03 +2024-01-03 07:35:00,151.69064951479834,,,,,,0.0,,2024-01-03 +2024-01-03 07:40:00,157.03666185035456,,,,,,0.0,,2024-01-03 +2024-01-03 07:45:00,160.27926425529822,,,,,,0.0,,2024-01-03 +2024-01-03 07:50:00,166.16180745671815,,,,,,0.0,,2024-01-03 +2024-01-03 07:55:00,168.05221149249218,,,,,,0.0,,2024-01-03 +2024-01-03 08:00:00,166.74427829473157,,,,,,0.0,,2024-01-03 +2024-01-03 08:05:00,161.30744721492783,,,,,,0.0,,2024-01-03 +2024-01-03 08:10:00,155.5600913899832,,,,,,0.0,,2024-01-03 +2024-01-03 08:15:00,153.52299476373116,,,,,,0.0,,2024-01-03 +2024-01-03 08:20:00,150.59602360895346,,,,,,0.0,,2024-01-03 +2024-01-03 08:25:00,147.53127108506695,,,,,,0.0,,2024-01-03 +2024-01-03 08:30:00,145.4682202352494,,,,,,0.0,,2024-01-03 +2024-01-03 08:35:00,140.42614392892563,,,,,,0.0,,2024-01-03 +2024-01-03 08:40:00,134.797010170403,,,,,,0.0,,2024-01-03 +2024-01-03 08:45:00,132.0409158979138,,,,,,0.0,,2024-01-03 +2024-01-03 08:50:00,127.51498288586173,,,,,,0.0,,2024-01-03 +2024-01-03 08:55:00,125.55606716458344,,,,,,0.0,,2024-01-03 +2024-01-03 09:00:00,125.7840371369411,,,,,,0.0,,2024-01-03 +2024-01-03 09:05:00,127.5056089138175,,,,,,,,2024-01-03 +2024-01-03 09:10:00,128.72612281142742,,,,,,,,2024-01-03 +2024-01-03 09:15:00,128.32704121042244,,,,,,,,2024-01-03 +2024-01-03 09:20:00,123.86587481473508,,,,,,,,2024-01-03 +2024-01-03 09:25:00,116.26117422062995,,,,,,,,2024-01-03 +2024-01-03 09:30:00,119.37184871343344,,,,,,,,2024-01-03 +2024-01-03 09:35:00,122.9063476417126,,,,,,,,2024-01-03 +2024-01-03 09:40:00,123.75088420623092,,,,,,,,2024-01-03 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11:10:00,136.52822620050284,,,,,,,,2024-01-03 +2024-01-03 11:15:00,138.27995827945506,,,,,,,,2024-01-03 +2024-01-03 11:20:00,142.59294891860478,,,,,,,,2024-01-03 +2024-01-03 11:25:00,145.77496925610626,,,,,,,,2024-01-03 +2024-01-03 11:30:00,146.35208730444032,,,,,,,,2024-01-03 +2024-01-03 11:35:00,145.90182670955028,,,,,,,,2024-01-03 +2024-01-03 11:40:00,144.5373307988216,,,,,,,,2024-01-03 +2024-01-03 11:45:00,143.97863169024396,,,,,,,,2024-01-03 +2024-01-03 11:50:00,144.04778677345945,,,,,,,,2024-01-03 +2024-01-03 11:55:00,145.77153720232872,,,,,,,,2024-01-03 +2024-01-03 12:00:00,145.00564588209647,,,,,,,,2024-01-03 +2024-01-03 12:05:00,144.90432417009816,,,,,,,,2024-01-03 +2024-01-03 12:10:00,145.6837317820923,,,,,,,,2024-01-03 +2024-01-03 12:15:00,146.01606687153327,,,,,,,,2024-01-03 +2024-01-03 12:20:00,145.78489495106453,,,,,,,,2024-01-03 +2024-01-03 12:25:00,143.34232646969951,,,,,,,,2024-01-03 +2024-01-03 12:30:00,141.1516153495329,,,,,,,,2024-01-03 +2024-01-03 12:35:00,136.32741769354226,,,,,,,,2024-01-03 +2024-01-03 12:40:00,133.56821154358704,,,,,,,,2024-01-03 +2024-01-03 12:45:00,133.59899452169714,,,,,,,,2024-01-03 +2024-01-03 12:50:00,135.1250980573237,,,,,,,,2024-01-03 +2024-01-03 12:55:00,135.79126954637678,,,,,,,,2024-01-03 +2024-01-03 13:00:00,135.10947350302197,ANNOUNCE_MEAL,,,,,27.66666666666666,24.33333333333333,2024-01-03 +2024-01-03 13:05:00,132.98827297623626,,,,,,0.0,,2024-01-03 +2024-01-03 13:10:00,132.66428521647694,,,,,,0.0,,2024-01-03 +2024-01-03 13:15:00,134.00574246103298,,,,,,0.0,,2024-01-03 +2024-01-03 13:20:00,138.95916614081884,,,,,,0.0,,2024-01-03 +2024-01-03 13:25:00,144.89112667546928,,,,,,0.0,,2024-01-03 +2024-01-03 13:30:00,147.2833010549144,,,,,,0.0,,2024-01-03 +2024-01-03 13:35:00,150.37665125082611,,,,,,0.0,,2024-01-03 +2024-01-03 13:40:00,151.8046747170656,,,,,,0.0,,2024-01-03 +2024-01-03 13:45:00,152.37739123996428,,,,,,0.0,,2024-01-03 +2024-01-03 13:50:00,153.53427598521452,,,,,,0.0,,2024-01-03 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16:40:00,146.09559025813988,,,,,,,,2024-01-03 +2024-01-03 16:45:00,148.50501411817936,,,,,,,,2024-01-03 +2024-01-03 16:50:00,134.41799196812337,ANNOUNCE_MEAL,,,,,27.666666666666668,27.666666666666668,2024-01-03 +2024-01-03 16:55:00,124.75897011707094,,,,,,0.0,,2024-01-03 +2024-01-03 17:00:00,121.5673376576475,,,,,,0.0,,2024-01-03 +2024-01-03 17:05:00,119.43527590012678,,,,,,0.0,,2024-01-03 +2024-01-03 17:10:00,122.902934430967,,,,,,0.0,,2024-01-03 +2024-01-03 17:15:00,126.32168512947344,,,,,,0.0,,2024-01-03 +2024-01-03 17:20:00,135.50525510937825,,,,,,0.0,,2024-01-03 +2024-01-03 17:25:00,144.42630213558874,,,,,,0.0,,2024-01-03 +2024-01-03 17:30:00,147.64423305314747,,,,,,0.0,,2024-01-03 +2024-01-03 17:35:00,151.9633635045339,,,,,,0.0,,2024-01-03 +2024-01-03 17:40:00,155.6878056107737,,,,,,0.0,,2024-01-03 +2024-01-03 17:45:00,157.97100030666232,,,,,,0.0,,2024-01-03 +2024-01-03 17:50:00,162.0540043637823,,,,,,0.0,,2024-01-03 +2024-01-03 17:55:00,162.64679254119926,,,,,,0.0,,2024-01-03 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19:20:00,115.65265492696,,,,,,,,2024-01-03 +2024-01-03 19:25:00,105.35874363748016,,,,,,,,2024-01-03 +2024-01-03 19:30:00,99.05056420910302,,,,,,,,2024-01-03 +2024-01-03 19:35:00,93.45768868756694,,,,,,,,2024-01-03 +2024-01-03 19:40:00,94.36660089812356,,,,,,,,2024-01-03 +2024-01-03 19:45:00,96.1862910392432,,,,,,,,2024-01-03 +2024-01-03 19:50:00,100.41232485181176,,,,,,,,2024-01-03 +2024-01-03 19:55:00,102.6059748326891,,,,,,,,2024-01-03 +2024-01-03 20:00:00,102.1965592929788,,,,,,,,2024-01-03 +2024-01-03 20:05:00,99.15014573771796,,,,,,,,2024-01-03 +2024-01-03 20:10:00,95.0790102609596,,,,,,,,2024-01-03 +2024-01-03 20:15:00,93.26244028621342,,,,,,,,2024-01-03 +2024-01-03 20:20:00,90.33155115511188,,,,,,,,2024-01-03 +2024-01-03 20:25:00,88.3490112549607,,,,,,,,2024-01-03 +2024-01-03 20:30:00,87.83991121119186,,,,,,,,2024-01-03 +2024-01-03 20:35:00,88.88072422825898,,,,,,,,2024-01-03 +2024-01-03 20:40:00,94.45433453365222,,,,,,,,2024-01-03 +2024-01-03 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07:35:00,137.8145242209237,ANNOUNCE_MEAL,,,,,20.666666666666668,18.666666666666668,2024-01-05 +2024-01-05 07:40:00,136.19489005695328,,,,,,0.0,,2024-01-05 +2024-01-05 07:45:00,136.35796934593958,,,,,,0.0,,2024-01-05 +2024-01-05 07:50:00,140.0365582319406,,,,,,0.0,,2024-01-05 +2024-01-05 07:55:00,144.81434516727995,,,,,,0.0,,2024-01-05 +2024-01-05 08:00:00,147.02556297144065,,,,,,0.0,,2024-01-05 +2024-01-05 08:05:00,152.30569675864737,,,,,,0.0,,2024-01-05 +2024-01-05 08:10:00,159.1841506073805,,,,,,0.0,,2024-01-05 +2024-01-05 08:15:00,162.80306144860003,,,,,,0.0,,2024-01-05 +2024-01-05 08:20:00,168.39424814666586,,,,,,0.0,,2024-01-05 +2024-01-05 08:25:00,169.53173359410033,,,,,,0.0,,2024-01-05 +2024-01-05 08:30:00,168.03080455935398,,,,,,0.0,,2024-01-05 +2024-01-05 08:35:00,162.06303096349023,,,,,,0.0,,2024-01-05 +2024-01-05 08:40:00,154.4635328213771,,,,,,0.0,,2024-01-05 +2024-01-05 08:45:00,150.9118984192622,,,,,,0.0,,2024-01-05 +2024-01-05 08:50:00,145.97679358231915,,,,,,0.0,,2024-01-05 +2024-01-05 08:55:00,147.39694525685283,,,,,,0.0,,2024-01-05 +2024-01-05 09:00:00,152.72670670424182,,,,,,0.0,,2024-01-05 +2024-01-05 09:05:00,158.22327402380716,,,,,,0.0,,2024-01-05 +2024-01-05 09:10:00,158.2766822135954,LOW_CARB_MEAL,,,,,0.0,2.0,2024-01-05 +2024-01-05 09:15:00,156.7981153015238,,,,,,0.0,,2024-01-05 +2024-01-05 09:20:00,153.3125276630695,,,,,,0.0,,2024-01-05 +2024-01-05 09:25:00,152.97279590238782,,,,,,0.0,,2024-01-05 +2024-01-05 09:30:00,153.25750889594477,,,,,,0.0,,2024-01-05 +2024-01-05 09:35:00,153.12016641057994,,,,,,0.0,,2024-01-05 +2024-01-05 09:40:00,151.90034660473538,,,,,,,,2024-01-05 +2024-01-05 09:45:00,151.05487451299427,,,,,,,,2024-01-05 +2024-01-05 09:50:00,148.74382925199248,,,,,,,,2024-01-05 +2024-01-05 09:55:00,145.1229780002716,,,,,,,,2024-01-05 +2024-01-05 10:00:00,142.72384135870297,,,,,,,,2024-01-05 +2024-01-05 10:05:00,137.31614422526724,,,,,,,,2024-01-05 +2024-01-05 10:10:00,132.0973244754872,,,,,,,,2024-01-05 +2024-01-05 10:15:00,129.90504109147244,,,,,,,,2024-01-05 +2024-01-05 10:20:00,126.96200476465658,,,,,,,,2024-01-05 +2024-01-05 10:25:00,126.66529487504994,,,,,,,,2024-01-05 +2024-01-05 10:30:00,127.52624827167094,,,,,,,,2024-01-05 +2024-01-05 10:35:00,129.48022091872207,,,,,,,,2024-01-05 +2024-01-05 10:40:00,128.68192059536122,,,,,,,,2024-01-05 +2024-01-05 10:45:00,126.51277685123895,,,,,,,,2024-01-05 +2024-01-05 10:50:00,120.17613146114375,,,,,,,,2024-01-05 +2024-01-05 10:55:00,114.39282171536934,,,,,,,,2024-01-05 +2024-01-05 11:00:00,112.59523040234028,,,,,,,,2024-01-05 +2024-01-05 11:05:00,110.62109000314324,,,,,,,,2024-01-05 +2024-01-05 11:10:00,109.1171938942827,,,,,,,,2024-01-05 +2024-01-05 11:15:00,107.90730608104104,,,,,,,,2024-01-05 +2024-01-05 11:20:00,103.30309089898611,,,,,,,,2024-01-05 +2024-01-05 11:25:00,97.05343195430837,,,,,,,,2024-01-05 +2024-01-05 11:30:00,99.46328565550526,,,,,,,,2024-01-05 +2024-01-05 11:35:00,103.22480708165892,,,,,,,,2024-01-05 +2024-01-05 11:40:00,105.33651898160367,,,,,,,,2024-01-05 +2024-01-05 11:45:00,105.68458491061756,,,,,,,,2024-01-05 +2024-01-05 11:50:00,104.7883308175098,,,,,,,,2024-01-05 +2024-01-05 11:55:00,101.55890891426432,,,,,,,,2024-01-05 +2024-01-05 12:00:00,99.17175488357384,,,,,,,,2024-01-05 +2024-01-05 12:05:00,94.38027196038256,,,,,,,,2024-01-05 +2024-01-05 12:10:00,92.09981300918884,,,,,,,,2024-01-05 +2024-01-05 12:15:00,93.9827450709596,ANNOUNCE_MEAL,,,,,24.0,24.0,2024-01-05 +2024-01-05 12:20:00,96.06594102846464,,,,,,0.0,,2024-01-05 +2024-01-05 12:25:00,101.5261508720645,,,,,,0.0,,2024-01-05 +2024-01-05 12:30:00,104.84505548554802,,,,,,0.0,,2024-01-05 +2024-01-05 12:35:00,113.08296953309548,,,,,,0.0,,2024-01-05 +2024-01-05 12:40:00,123.98739223683754,,,,,,0.0,,2024-01-05 +2024-01-05 12:45:00,130.05689712995692,,,,,,0.0,,2024-01-05 +2024-01-05 12:50:00,140.51853616956416,,,,,,0.0,,2024-01-05 +2024-01-05 12:55:00,145.30422508657298,,,,,,0.0,,2024-01-05 +2024-01-05 13:00:00,145.1587298803682,,,,,,0.0,,2024-01-05 +2024-01-05 13:05:00,142.67912207735148,,,,,,0.0,,2024-01-05 +2024-01-05 13:10:00,142.65353049606878,,,,,,0.0,,2024-01-05 +2024-01-05 13:15:00,144.9298600365517,,,,,,0.0,,2024-01-05 +2024-01-05 13:20:00,151.76515528671325,,,,,,0.0,,2024-01-05 +2024-01-05 13:25:00,156.55968520937276,,,,,,0.0,,2024-01-05 +2024-01-05 13:30:00,156.7850240124054,,,,,,0.0,,2024-01-05 +2024-01-05 13:35:00,153.90528772465785,,,,,,0.0,,2024-01-05 +2024-01-05 13:40:00,149.42306899223624,,,,,,0.0,,2024-01-05 +2024-01-05 13:45:00,147.62896875910712,,,,,,0.0,,2024-01-05 +2024-01-05 13:50:00,147.0030174703182,,,,,,0.0,,2024-01-05 +2024-01-05 13:55:00,149.73073392959958,,,,,,0.0,,2024-01-05 +2024-01-05 14:00:00,145.49798517011348,,,,,,0.0,,2024-01-05 +2024-01-05 14:05:00,138.05691458878846,,,,,,0.0,,2024-01-05 +2024-01-05 14:10:00,131.82530745586877,,,,,,0.0,,2024-01-05 +2024-01-05 14:15:00,129.10258270552498,,,,,,0.0,,2024-01-05 +2024-01-05 14:20:00,124.31928692704724,,,,,,,,2024-01-05 +2024-01-05 14:25:00,120.25774641097708,,,,,,,,2024-01-05 +2024-01-05 14:30:00,118.4919995748584,,,,,,,,2024-01-05 +2024-01-05 14:35:00,115.88442482468714,,,,,,,,2024-01-05 +2024-01-05 14:40:00,115.17550152489484,,,,,,,,2024-01-05 +2024-01-05 14:45:00,115.63199492397771,,,,,,,,2024-01-05 +2024-01-05 14:50:00,117.26031801121476,,,,,,,,2024-01-05 +2024-01-05 14:55:00,118.20654164770252,,,,,,,,2024-01-05 +2024-01-05 15:00:00,117.91930280006692,,,,,,,,2024-01-05 +2024-01-05 15:05:00,115.61237102025108,,,,,,,,2024-01-05 +2024-01-05 15:10:00,111.04855916378985,,,,,,,,2024-01-05 +2024-01-05 15:15:00,108.0636625969962,,,,,,,,2024-01-05 +2024-01-05 15:20:00,101.87602877780743,,,,,,,,2024-01-05 +2024-01-05 15:25:00,97.32823531367885,,,,,,,,2024-01-05 +2024-01-05 15:30:00,96.26231191145688,,,,,,,,2024-01-05 +2024-01-05 15:35:00,96.76727122799856,,,,,,,,2024-01-05 +2024-01-05 15:40:00,100.68466153905028,,,,,,,,2024-01-05 +2024-01-05 15:45:00,103.72741576421232,,,,,,,,2024-01-05 +2024-01-05 15:50:00,110.42157459497083,,,,,,,,2024-01-05 +2024-01-05 15:55:00,115.44199706179865,,,,,,,,2024-01-05 +2024-01-05 16:00:00,116.68811872417224,,,,,,,,2024-01-05 +2024-01-05 16:05:00,117.48209738819432,,,,,,,,2024-01-05 +2024-01-05 16:10:00,117.96062122788322,,,,,,,,2024-01-05 +2024-01-05 16:15:00,118.7890365361263,,,,,,,,2024-01-05 +2024-01-05 16:20:00,122.87277212030256,,,,,,,,2024-01-05 +2024-01-05 16:25:00,129.93264435441742,,,,,,,,2024-01-05 +2024-01-05 16:30:00,131.00017736237615,,,,,,,,2024-01-05 +2024-01-05 16:35:00,132.5263246332809,,,,,,,,2024-01-05 +2024-01-05 16:40:00,133.20349823567997,,,,,,,,2024-01-05 +2024-01-05 16:45:00,133.2759983846775,,,,,,,,2024-01-05 +2024-01-05 16:50:00,133.1338617202023,,,,,,,,2024-01-05 +2024-01-05 16:55:00,133.06132132280356,,,,,,,,2024-01-05 +2024-01-05 17:00:00,133.18114675551766,,,,,,,,2024-01-05 +2024-01-05 17:05:00,133.5366001044613,,,,,,,,2024-01-05 +2024-01-05 17:10:00,133.61038985060986,,,,,,,,2024-01-05 +2024-01-05 17:15:00,133.4389767467611,,,,,,,,2024-01-05 +2024-01-05 17:20:00,132.91874419196742,,,,,,,,2024-01-05 +2024-01-05 17:25:00,132.63371512632082,,,,,,,,2024-01-05 +2024-01-05 17:30:00,132.67330656026948,,,,,,,,2024-01-05 +2024-01-05 17:35:00,132.72982881474462,,,,,,,,2024-01-05 +2024-01-05 17:40:00,132.02185316378217,,,,,,,,2024-01-05 +2024-01-05 17:45:00,131.27955589424232,,,,,,,,2024-01-05 +2024-01-05 17:50:00,130.11428440497315,,,,,,,,2024-01-05 +2024-01-05 17:55:00,131.30310456652563,,,,,,,,2024-01-05 +2024-01-05 18:00:00,133.15482458788944,,,,,,,,2024-01-05 +2024-01-05 18:05:00,137.5857300984736,,,,,,,,2024-01-05 +2024-01-05 18:10:00,139.6439895314767,ANNOUNCE_MEAL,,,,,22.666666666666668,22.666666666666668,2024-01-05 +2024-01-05 18:15:00,138.97130369050493,,,,,,0.0,,2024-01-05 +2024-01-05 18:20:00,135.85166467203896,,,,,,0.0,,2024-01-05 +2024-01-05 18:25:00,134.1630121621691,,,,,,0.0,,2024-01-05 +2024-01-05 18:30:00,135.2235975387154,,,,,,0.0,,2024-01-05 +2024-01-05 18:35:00,141.71071286836226,,,,,,0.0,,2024-01-05 +2024-01-05 18:40:00,152.40280476014448,,,,,,0.0,,2024-01-05 +2024-01-05 18:45:00,157.82952748913053,,,,,,0.0,,2024-01-05 +2024-01-05 18:50:00,165.14583270385742,,,,,,0.0,,2024-01-05 +2024-01-05 18:55:00,168.8148749515859,,,,,,0.0,,2024-01-05 +2024-01-05 19:00:00,178.08525446332936,,,,,,0.0,,2024-01-05 +2024-01-05 19:05:00,185.2931179250368,,,,,,0.0,,2024-01-05 +2024-01-05 19:10:00,181.41326830686813,,,,,,0.0,,2024-01-05 +2024-01-05 19:15:00,176.83665134051148,,,,,,0.0,,2024-01-05 +2024-01-05 19:20:00,165.41371027686716,,,,,,0.0,,2024-01-05 +2024-01-05 19:25:00,154.8476774485272,,,,,,0.0,,2024-01-05 +2024-01-05 19:30:00,151.0246291688138,,,,,,0.0,,2024-01-05 +2024-01-05 19:35:00,146.39271503577595,,,,,,0.0,,2024-01-05 +2024-01-05 19:40:00,144.98965077438868,,,,,,0.0,,2024-01-05 +2024-01-05 19:45:00,145.1040868645338,,,,,,0.0,,2024-01-05 +2024-01-05 19:50:00,145.52623786603692,,,,,,0.0,,2024-01-05 +2024-01-05 19:55:00,144.05859731763388,,,,,,0.0,,2024-01-05 +2024-01-05 20:00:00,142.12689943126554,,,,,,0.0,,2024-01-05 +2024-01-05 20:05:00,137.23028416010803,,,,,,0.0,,2024-01-05 +2024-01-05 20:10:00,133.6612430065921,,,,,,0.0,,2024-01-05 +2024-01-05 20:15:00,133.05856391399502,,,,,,,,2024-01-05 +2024-01-05 20:20:00,133.1746293146963,,,,,,,,2024-01-05 +2024-01-05 20:25:00,132.73368178673678,,,,,,,,2024-01-05 +2024-01-05 20:30:00,131.70691108577347,,,,,,,,2024-01-05 +2024-01-05 20:35:00,128.84300898768848,,,,,,,,2024-01-05 +2024-01-05 20:40:00,126.73784829042,,,,,,,,2024-01-05 +2024-01-05 20:45:00,126.44745512098636,,,,,,,,2024-01-05 +2024-01-05 20:50:00,126.79532784937084,,,,,,,,2024-01-05 +2024-01-05 20:55:00,126.9846388906789,,,,,,,,2024-01-05 +2024-01-05 21:00:00,126.62771134703522,,,,,,,,2024-01-05 +2024-01-05 21:05:00,125.19049126036478,,,,,,,,2024-01-05 +2024-01-05 21:10:00,123.41463288721552,,,,,,,,2024-01-05 +2024-01-05 21:15:00,122.63501390682404,,,,,,,,2024-01-05 +2024-01-05 21:20:00,121.76499543080848,,,,,,,,2024-01-05 +2024-01-05 21:25:00,123.36332148164286,,,,,,,,2024-01-05 +2024-01-05 21:30:00,126.75342071709042,,,,,,,,2024-01-05 +2024-01-05 21:35:00,131.09560442770356,,,,,,,,2024-01-05 +2024-01-05 21:40:00,133.20559294331713,,,,,,,,2024-01-05 +2024-01-05 21:45:00,133.81009743327178,LOW_CARB_MEAL,,,,,3.6666666666666665,3.6666666666666665,2024-01-05 +2024-01-05 21:50:00,135.24268167374905,,,,,,,,2024-01-05 +2024-01-05 21:55:00,139.76204591745042,,,,,,,,2024-01-05 +2024-01-05 22:00:00,143.04781727048902,,,,,,,,2024-01-05 +2024-01-05 22:05:00,148.85967222808264,,,,,,,,2024-01-05 +2024-01-05 22:10:00,150.88359429379568,,,,,,,,2024-01-05 +2024-01-05 22:15:00,149.9959234633744,,,,,,,,2024-01-05 +2024-01-05 22:20:00,145.4930040308461,,,,,,,,2024-01-05 +2024-01-05 22:25:00,139.5132232609589,,,,,,,,2024-01-05 +2024-01-05 22:30:00,136.61000451110766,,,,,,,,2024-01-05 +2024-01-05 22:35:00,131.07914670443063,,,,,,,,2024-01-05 +2024-01-05 22:40:00,125.552884519738,,,,,,,,2024-01-05 +2024-01-05 22:45:00,122.79235635051988,,,,,,,,2024-01-05 +2024-01-05 22:50:00,118.38318356015948,,,,,,,,2024-01-05 +2024-01-05 22:55:00,117.30496325720522,,,,,,,,2024-01-05 +2024-01-05 23:00:00,118.29031031024437,,,,,,,,2024-01-05 +2024-01-05 23:05:00,120.83582943370462,,,,,,,,2024-01-05 +2024-01-05 23:10:00,119.71172903399852,,,,,,,,2024-01-05 +2024-01-05 23:15:00,116.68699496591567,,,,,,,,2024-01-05 +2024-01-05 23:20:00,107.77251170686576,,,,,,,,2024-01-05 +2024-01-05 23:25:00,99.42646742922658,,,,,,,,2024-01-05 +2024-01-05 23:30:00,96.73340097161216,,,,,,,,2024-01-05 +2024-01-05 23:35:00,94.0681542088269,,,,,,,,2024-01-05 +2024-01-05 23:40:00,93.604600270755,,,,,,,,2024-01-05 +2024-01-05 23:45:00,93.66364552594658,,,,,,,,2024-01-05 +2024-01-05 23:50:00,93.29383855304732,,,,,,,,2024-01-05 +2024-01-05 23:55:00,94.40957263252406,,,,,,,,2024-01-05 +2024-01-06 00:00:00,101.36609750182572,,,,,,,,2024-01-05 +2024-01-06 00:05:00,103.10565114640784,,,,,,,,2024-01-05 +2024-01-06 00:10:00,94.71483628108696,,,,,,,,2024-01-05 +2024-01-06 00:15:00,89.00864069979541,,,,,,,,2024-01-05 +2024-01-06 00:20:00,79.15158932997215,,,,,,,,2024-01-05 +2024-01-06 00:25:00,77.35678010939353,,,,,,,,2024-01-05 +2024-01-06 00:30:00,80.83486211888003,,,,,,,,2024-01-05 +2024-01-06 00:35:00,92.70114838517472,,,,,,,,2024-01-05 +2024-01-06 00:40:00,103.48572315712892,,,,,,,,2024-01-05 +2024-01-06 00:45:00,106.42287763668448,,,,,,,,2024-01-05 +2024-01-06 00:50:00,109.22286680076884,,,,,,,,2024-01-05 +2024-01-06 00:55:00,112.68439931404204,,,,,,,,2024-01-05 +2024-01-06 01:00:00,115.95600320462492,,,,,,,,2024-01-05 +2024-01-06 01:05:00,124.5454863368856,,,,,,,,2024-01-05 +2024-01-06 01:10:00,133.0758077029966,,,,,,,,2024-01-05 +2024-01-06 01:15:00,136.48746872250376,,,,,,,,2024-01-05 +2024-01-06 01:20:00,141.47127612861803,,,,,,,,2024-01-05 +2024-01-06 01:25:00,144.39697466655588,,,,,,,,2024-01-05 +2024-01-06 01:30:00,145.4433635300922,,,,,,,,2024-01-05 +2024-01-06 01:35:00,149.13725578577413,,,,,,,,2024-01-05 +2024-01-06 01:40:00,158.80081730668485,,,,,,,,2024-01-05 +2024-01-06 01:45:00,166.45704426578726,,,,,,,,2024-01-05 +2024-01-06 01:50:00,182.2930461348345,,,,,,,,2024-01-05 +2024-01-06 01:55:00,189.65628399218457,,,,,,,,2024-01-05 +2024-01-06 02:00:00,187.7510927540363,,,,,,,,2024-01-05 +2024-01-06 02:05:00,175.38848836187296,,,,,,,,2024-01-05 +2024-01-06 02:10:00,158.097581271719,,,,,,,,2024-01-05 +2024-01-06 02:15:00,150.0343774488556,,,,,,,,2024-01-05 +2024-01-06 02:20:00,139.93350210865694,,,,,,,,2024-01-05 +2024-01-06 02:25:00,141.911869413545,,,,,,,,2024-01-05 +2024-01-06 02:30:00,142.3828545800037,,,,,,,,2024-01-05 +2024-01-06 02:35:00,141.4889549147954,,,,,,,,2024-01-05 +2024-01-06 02:40:00,139.07117298614216,,,,,,,,2024-01-05 +2024-01-06 02:45:00,137.6352373733596,,,,,,,,2024-01-05 +2024-01-06 02:50:00,134.99377502949744,,,,,,,,2024-01-05 +2024-01-06 02:55:00,133.54601263579812,,,,,,,,2024-01-05 +2024-01-06 03:00:00,133.43629782974028,,,,,,,,2024-01-05 +2024-01-06 03:05:00,133.57068731501855,,,,,,,,2024-01-05 +2024-01-06 03:10:00,132.55964934738614,,,,,,,,2024-01-05 +2024-01-06 03:15:00,131.2730270336274,,,,,,,,2024-01-05 +2024-01-06 03:20:00,128.30495953482526,,,,,,,,2024-01-05 +2024-01-06 03:25:00,127.0790654275504,,,,,,,,2024-01-05 +2024-01-06 03:30:00,127.7058456645046,,,,,,,,2024-01-05 +2024-01-06 03:35:00,130.67841298481895,,,,,,,,2024-01-05 +2024-01-06 03:40:00,134.1715737728704,,,,,,,,2024-01-05 +2024-01-06 03:45:00,135.5663753269169,,,,,,,,2024-01-05 +2024-01-06 03:50:00,137.41827414559168,,,,,,,,2024-01-05 +2024-01-06 03:55:00,138.05765529229396,,,,,,,,2024-01-05 +2024-01-06 04:00:00,137.98878438044554,,,,,,,,2024-01-06 +2024-01-06 04:05:00,137.58723714046465,,,,,,,,2024-01-06 +2024-01-06 04:10:00,137.65562412932812,,,,,,,,2024-01-06 +2024-01-06 04:15:00,138.05883580159195,,,,,,,,2024-01-06 +2024-01-06 04:20:00,139.14448154003338,,,,,,,,2024-01-06 +2024-01-06 04:25:00,139.66879250371508,,,,,,,,2024-01-06 +2024-01-06 04:30:00,139.48956291603537,,,,,,,,2024-01-06 +2024-01-06 04:35:00,138.65961298251284,,,,,,,,2024-01-06 +2024-01-06 04:40:00,138.09811945372604,,,,,,,,2024-01-06 +2024-01-06 04:45:00,138.2544759200589,,,,,,,,2024-01-06 +2024-01-06 04:50:00,140.11430063861675,,,,,,,,2024-01-06 +2024-01-06 04:55:00,144.0542782480201,,,,,,,,2024-01-06 +2024-01-06 05:00:00,144.6763154822605,,,,,,,,2024-01-06 +2024-01-06 05:05:00,144.1687008558181,,,,,,,,2024-01-06 +2024-01-06 05:10:00,142.3366137631033,,,,,,,,2024-01-06 +2024-01-06 05:15:00,141.3030812232613,,,,,,,,2024-01-06 +2024-01-06 05:20:00,139.7594624477871,,,,,,,,2024-01-06 +2024-01-06 05:25:00,139.90708330607174,,,,,,,,2024-01-06 +2024-01-06 05:30:00,140.84824685260315,,,,,,,,2024-01-06 +2024-01-06 05:35:00,143.9110308253736,,,,,,,,2024-01-06 +2024-01-06 05:40:00,147.4200434668659,,,,,,,,2024-01-06 +2024-01-06 05:45:00,149.06515412912864,,,,,,,,2024-01-06 +2024-01-06 05:50:00,152.65331009881714,,,,,,,,2024-01-06 +2024-01-06 05:55:00,157.71216997857223,,,,,,,,2024-01-06 +2024-01-06 06:00:00,160.8624944617503,,,,,,,,2024-01-06 +2024-01-06 06:05:00,166.10398066975992,,,,,,,,2024-01-06 +2024-01-06 06:10:00,165.96965296603406,,,,,,,,2024-01-06 +2024-01-06 06:15:00,163.0106286266008,,,,,,,,2024-01-06 +2024-01-06 06:20:00,154.02677125956157,,,,,,,,2024-01-06 +2024-01-06 06:25:00,146.28938886182243,,,,,,,,2024-01-06 +2024-01-06 06:30:00,144.30744098738398,,,,,,,,2024-01-06 +2024-01-06 06:35:00,142.70106073327153,,,,,,,,2024-01-06 +2024-01-06 06:40:00,140.70316988209612,,,,,,,,2024-01-06 +2024-01-06 06:45:00,138.6380833275874,,,,,,,,2024-01-06 +2024-01-06 06:50:00,133.5117272470633,,,,,,,,2024-01-06 +2024-01-06 06:55:00,129.727926417994,,,,,,,,2024-01-06 +2024-01-06 07:00:00,129.09173286026322,,,,,,,,2024-01-06 +2024-01-06 07:05:00,130.00969109099506,,,,,,,,2024-01-06 +2024-01-06 07:10:00,132.69087843897793,,,,,,,,2024-01-06 +2024-01-06 07:15:00,134.25872739773493,,,,,,,,2024-01-06 +2024-01-06 07:20:00,136.98103797432657,,,,,,,,2024-01-06 +2024-01-06 07:25:00,136.78608638223912,ANNOUNCE_MEAL,,,,,8.0,8.0,2024-01-06 +2024-01-06 07:30:00,133.70907061496828,,,,,,0.0,,2024-01-06 +2024-01-06 07:35:00,134.05017618930307,,,,,,0.0,,2024-01-06 +2024-01-06 07:40:00,140.44164910573426,,,,,,0.0,,2024-01-06 +2024-01-06 07:45:00,145.1003400755836,,,,,,0.0,,2024-01-06 +2024-01-06 07:50:00,155.33711697640672,,,,,,0.0,,2024-01-06 +2024-01-06 07:55:00,163.1003079821556,,,,,,0.0,,2024-01-06 +2024-01-06 08:00:00,164.87360640993427,,,,,,0.0,,2024-01-06 +2024-01-06 08:05:00,164.63852536261695,,,,,,0.0,,2024-01-06 +2024-01-06 08:10:00,161.4088606402272,,,,,,0.0,,2024-01-06 +2024-01-06 08:15:00,159.45895275838998,,,,,,0.0,,2024-01-06 +2024-01-06 08:20:00,156.0594821507739,,,,,,0.0,,2024-01-06 +2024-01-06 08:25:00,154.6852194136802,,,,,,0.0,,2024-01-06 +2024-01-06 08:30:00,155.04544049594068,,,,,,0.0,,2024-01-06 +2024-01-06 08:35:00,156.82880213590647,,,,,,0.0,,2024-01-06 +2024-01-06 08:40:00,158.02194484859135,,,,,,0.0,,2024-01-06 +2024-01-06 08:45:00,157.86208335445554,,,,,,0.0,,2024-01-06 +2024-01-06 08:50:00,156.59317106578268,,,,,,0.0,,2024-01-06 +2024-01-06 08:55:00,155.43550211678087,,,,,,0.0,,2024-01-06 +2024-01-06 09:00:00,155.26157549684194,,,,,,0.0,,2024-01-06 +2024-01-06 09:05:00,155.3466976161215,,,,,,0.0,,2024-01-06 +2024-01-06 09:10:00,155.0767769651755,,,,,,0.0,,2024-01-06 +2024-01-06 09:15:00,154.56611701451436,,,,,,0.0,,2024-01-06 +2024-01-06 09:20:00,153.09975679623,,,,,,0.0,,2024-01-06 +2024-01-06 09:25:00,151.74742857433125,,,,,,0.0,,2024-01-06 +2024-01-06 09:30:00,151.25836125214187,,,,,,,,2024-01-06 +2024-01-06 09:35:00,150.0612885245162,,,,,,,,2024-01-06 +2024-01-06 09:40:00,147.42820173946558,,,,,,,,2024-01-06 +2024-01-06 09:45:00,145.14366455312827,,,,,,,,2024-01-06 +2024-01-06 09:50:00,137.7787181729289,,,,,,,,2024-01-06 +2024-01-06 09:55:00,127.80403861342268,,,,,,,,2024-01-06 +2024-01-06 10:00:00,126.78602836493228,,,,,,,,2024-01-06 +2024-01-06 10:05:00,124.82746120827343,,,,,,,,2024-01-06 +2024-01-06 10:10:00,123.08787553514792,,,,,,,,2024-01-06 +2024-01-06 10:15:00,122.34352262891004,,,,,,,,2024-01-06 +2024-01-06 10:20:00,121.1923478035911,,,,,,,,2024-01-06 +2024-01-06 10:25:00,120.60238718207452,,,,,,,,2024-01-06 +2024-01-06 10:30:00,120.5235920534414,,,,,,,,2024-01-06 +2024-01-06 10:35:00,120.5458182952286,,,,,,,,2024-01-06 +2024-01-06 10:40:00,120.37663978062584,,,,,,,,2024-01-06 +2024-01-06 10:45:00,120.13475373987478,,,,,,,,2024-01-06 +2024-01-06 10:50:00,119.72011637957254,,,,,,,,2024-01-06 +2024-01-06 10:55:00,120.12689631180804,,,,,,,,2024-01-06 +2024-01-06 11:00:00,120.85269858867548,,,,,,,,2024-01-06 +2024-01-06 11:05:00,123.3361034085265,,,,,,,,2024-01-06 +2024-01-06 11:10:00,126.99806721916828,,,,,,,,2024-01-06 +2024-01-06 11:15:00,129.10085946270533,,,,,,,,2024-01-06 +2024-01-06 11:20:00,132.77732264703053,,,,,,,,2024-01-06 +2024-01-06 11:25:00,134.031115755617,,,,,,,,2024-01-06 +2024-01-06 11:30:00,133.4046160217988,,,,,,,,2024-01-06 +2024-01-06 11:35:00,130.6673061936171,ANNOUNCE_MEAL,,,,,26.666666666666668,26.666666666666668,2024-01-06 +2024-01-06 11:40:00,130.94723259982777,,,,,,0.0,,2024-01-06 +2024-01-06 11:45:00,132.22390701743234,,,,,,0.0,,2024-01-06 +2024-01-06 11:50:00,136.85049429549315,,,,,,0.0,,2024-01-06 +2024-01-06 11:55:00,143.35063935903892,,,,,,0.0,,2024-01-06 +2024-01-06 12:00:00,147.30171015923935,,,,,,0.0,,2024-01-06 +2024-01-06 12:05:00,157.38624943199744,,,,,,0.0,,2024-01-06 +2024-01-06 12:10:00,169.02257606037864,,,,,,0.0,,2024-01-06 +2024-01-06 12:15:00,174.44590207725832,,,,,,0.0,,2024-01-06 +2024-01-06 12:20:00,183.3648228349697,,,,,,0.0,,2024-01-06 +2024-01-06 12:25:00,187.6004167210521,,,,,,0.0,,2024-01-06 +2024-01-06 12:30:00,184.28389427873933,,,,,,0.0,,2024-01-06 +2024-01-06 12:35:00,179.99359551648794,,,,,,0.0,,2024-01-06 +2024-01-06 12:40:00,177.8741208389551,,,,,,0.0,,2024-01-06 +2024-01-06 12:45:00,177.25082629081146,,,,,,0.0,,2024-01-06 +2024-01-06 12:50:00,176.0461993651743,,,,,,0.0,,2024-01-06 +2024-01-06 12:55:00,173.71125772227649,,,,,,0.0,,2024-01-06 +2024-01-06 13:00:00,171.8253194018206,,,,,,0.0,,2024-01-06 +2024-01-06 13:05:00,167.2818371284799,,,,,,0.0,,2024-01-06 +2024-01-06 13:10:00,163.0557678139198,,,,,,0.0,,2024-01-06 +2024-01-06 13:15:00,161.46873413619923,,,,,,0.0,,2024-01-06 +2024-01-06 13:20:00,159.513516590677,,,,,,0.0,,2024-01-06 +2024-01-06 13:25:00,159.13595066857633,,,,,,0.0,,2024-01-06 +2024-01-06 13:30:00,159.51309353251773,,,,,,0.0,,2024-01-06 +2024-01-06 13:35:00,161.31543181992703,,,,,,0.0,,2024-01-06 +2024-01-06 13:40:00,164.36430444067977,,,,,,,,2024-01-06 +2024-01-06 13:45:00,166.12738134034876,,,,,,,,2024-01-06 +2024-01-06 13:50:00,168.42166747894288,,,,,,,,2024-01-06 +2024-01-06 13:55:00,166.34386644230085,,,,,,,,2024-01-06 +2024-01-06 14:00:00,163.0357345580794,,,,,,,,2024-01-06 +2024-01-06 14:05:00,153.51004878789703,,,,,,,,2024-01-06 +2024-01-06 14:10:00,143.4022026325248,,,,,,,,2024-01-06 +2024-01-06 14:15:00,139.1484129509858,,,,,,,,2024-01-06 +2024-01-06 14:20:00,132.68325472332089,,,,,,,,2024-01-06 +2024-01-06 14:25:00,128.88128764118636,,,,,,,,2024-01-06 +2024-01-06 14:30:00,127.85535467296076,,,,,,,,2024-01-06 +2024-01-06 14:35:00,126.5646555225642,,,,,,,,2024-01-06 +2024-01-06 14:40:00,124.6759528667204,,,,,,,,2024-01-06 +2024-01-06 14:45:00,122.9035703505497,ANNOUNCE_MEAL,,,,,6.0,6.0,2024-01-06 +2024-01-06 14:50:00,117.16785934904205,,,,,,0.0,,2024-01-06 +2024-01-06 14:55:00,111.77684518395682,,,,,,0.0,,2024-01-06 +2024-01-06 15:00:00,112.76568664619712,,,,,,0.0,,2024-01-06 +2024-01-06 15:05:00,114.5347782645321,,,,,,0.0,,2024-01-06 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16:30:00,93.39740401881522,,,,,,0.0,,2024-01-06 +2024-01-06 16:35:00,88.97125405562183,,,,,,0.0,,2024-01-06 +2024-01-06 16:40:00,87.88166266459481,,,,,,0.0,,2024-01-06 +2024-01-06 16:45:00,88.11134687384984,,,,,,0.0,,2024-01-06 +2024-01-06 16:50:00,89.77018147053197,,,,,,,,2024-01-06 +2024-01-06 16:55:00,92.7984911299032,,,,,,,,2024-01-06 +2024-01-06 17:00:00,94.69872954770152,,,,,,,,2024-01-06 +2024-01-06 17:05:00,98.71341589755464,,,,,,,,2024-01-06 +2024-01-06 17:10:00,102.14941080113287,,,,,,,,2024-01-06 +2024-01-06 17:15:00,103.32663745941494,,,,,,,,2024-01-06 +2024-01-06 17:20:00,103.95307169778238,,,,,,,,2024-01-06 +2024-01-06 17:25:00,101.7816925231496,,,,,,,,2024-01-06 +2024-01-06 17:30:00,100.8133451677696,,,,,,,,2024-01-06 +2024-01-06 17:35:00,101.38269580092444,,,,,,,,2024-01-06 +2024-01-06 17:40:00,103.69462550777132,,,,,,,,2024-01-06 +2024-01-06 17:45:00,104.90632259189135,,,,,,,,2024-01-06 +2024-01-06 17:50:00,106.25153296837352,,,,,,,,2024-01-06 +2024-01-06 17:55:00,104.63549540704246,,,,,,,,2024-01-06 +2024-01-06 18:00:00,102.54845761866164,,,,,,,,2024-01-06 +2024-01-06 18:05:00,98.40815764109723,,,,,,,,2024-01-06 +2024-01-06 18:10:00,98.87387884307992,,,,,,,,2024-01-06 +2024-01-06 18:15:00,101.90335923542584,,,,,,,,2024-01-06 +2024-01-06 18:20:00,110.94332063992894,,,,,,,,2024-01-06 +2024-01-06 18:25:00,118.56724688457744,,,,,,,,2024-01-06 +2024-01-06 18:30:00,120.42835671786963,,,,,,,,2024-01-06 +2024-01-06 18:35:00,122.10263166215007,,,,,,,,2024-01-06 +2024-01-06 18:40:00,125.32041510850708,,,,,,,,2024-01-06 +2024-01-06 18:45:00,128.5724748151237,,,,,,,,2024-01-06 +2024-01-06 18:50:00,136.49701117244214,,,,,,,,2024-01-06 +2024-01-06 18:55:00,142.36984467790745,,,,,,,,2024-01-06 +2024-01-06 19:00:00,143.54612857628683,,,,,,,,2024-01-06 +2024-01-06 19:05:00,143.81443825793087,,,,,,,,2024-01-06 +2024-01-06 19:10:00,144.55074107745793,,,,,,,,2024-01-06 +2024-01-06 19:15:00,145.95248550040557,,,,,,,,2024-01-06 +2024-01-06 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00:50:00,119.90890968936816,,,,,,,,2024-01-01 +2024-01-02 00:55:00,121.33984448897006,,,,,,,,2024-01-01 +2024-01-02 01:00:00,116.78915806642806,,,,,,,,2024-01-01 +2024-01-02 01:05:00,109.81371903915696,,,,,,,,2024-01-01 +2024-01-02 01:10:00,106.12807508133886,,,,,,,,2024-01-01 +2024-01-02 01:15:00,105.68940315683396,,,,,,,,2024-01-01 +2024-01-02 01:20:00,107.96095652882568,,,,,,,,2024-01-01 +2024-01-02 01:25:00,114.84125170692782,,,,,,,,2024-01-01 +2024-01-02 01:30:00,119.8251581746963,,,,,,,,2024-01-01 +2024-01-02 01:35:00,130.0341371851332,,,,,,,,2024-01-01 +2024-01-02 01:40:00,135.85388319221283,,,,,,,,2024-01-01 +2024-01-02 01:45:00,135.92264257752208,,,,,,,,2024-01-01 +2024-01-02 01:50:00,132.17148919174147,,,,,,,,2024-01-01 +2024-01-02 01:55:00,127.25990415357464,,,,,,,,2024-01-01 +2024-01-02 02:00:00,125.58203760841776,,,,,,,,2024-01-01 +2024-01-02 02:05:00,124.03978440477132,,,,,,,,2024-01-01 +2024-01-02 02:10:00,124.37813883781368,,,,,,,,2024-01-01 +2024-01-02 02:15:00,124.93533983949531,,,,,,,,2024-01-01 +2024-01-02 02:20:00,125.34372171768293,,,,,,,,2024-01-01 +2024-01-02 02:25:00,122.5528624914777,,,,,,,,2024-01-01 +2024-01-02 02:30:00,119.54945833663828,,,,,,,,2024-01-01 +2024-01-02 02:35:00,112.7202513570644,,,,,,,,2024-01-01 +2024-01-02 02:40:00,109.19237710153888,,,,,,,,2024-01-01 +2024-01-02 02:45:00,109.72769151381628,,,,,,,,2024-01-01 +2024-01-02 02:50:00,113.2135841768482,,,,,,,,2024-01-01 +2024-01-02 02:55:00,115.3495920602117,,,,,,,,2024-01-01 +2024-01-02 03:00:00,114.66527729322588,,,,,,,,2024-01-01 +2024-01-02 03:05:00,110.72435296944278,,,,,,,,2024-01-01 +2024-01-02 03:10:00,105.96479332585666,,,,,,,,2024-01-01 +2024-01-02 03:15:00,104.25725267465386,,,,,,,,2024-01-01 +2024-01-02 03:20:00,104.1452562312308,,,,,,,,2024-01-01 +2024-01-02 03:25:00,110.91775725926117,,,,,,,,2024-01-01 +2024-01-02 03:30:00,116.0490984613756,,,,,,,,2024-01-01 +2024-01-02 03:35:00,121.4421540417332,,,,,,,,2024-01-01 +2024-01-02 03:40:00,122.23508752934652,,,,,,,,2024-01-01 +2024-01-02 03:45:00,121.61632545851668,,,,,,,,2024-01-01 +2024-01-02 03:50:00,119.76808654767945,,,,,,,,2024-01-01 +2024-01-02 03:55:00,118.9753487820568,,,,,,,,2024-01-01 +2024-01-02 04:00:00,119.49512516291212,,,,,,,,2024-01-02 +2024-01-02 04:05:00,122.08322059433011,,,,,,,,2024-01-02 +2024-01-02 04:10:00,125.83227548622256,,,,,,,,2024-01-02 +2024-01-02 04:15:00,127.83022606191584,,,,,,,,2024-01-02 +2024-01-02 04:20:00,131.64644938893326,,,,,,,,2024-01-02 +2024-01-02 04:25:00,134.73237549427543,,,,,,,,2024-01-02 +2024-01-02 04:30:00,135.81038648640248,,,,,,,,2024-01-02 +2024-01-02 04:35:00,136.60242092767663,,,,,,,,2024-01-02 +2024-01-02 04:40:00,134.9948432598134,,,,,,,,2024-01-02 +2024-01-02 04:45:00,133.21668965380036,,,,,,,,2024-01-02 +2024-01-02 04:50:00,128.61220871100392,,,,,,,,2024-01-02 +2024-01-02 04:55:00,124.22924689644526,,,,,,,,2024-01-02 +2024-01-02 05:00:00,122.72093034099672,,,,,,,,2024-01-02 +2024-01-02 05:05:00,122.2190069322894,,,,,,,,2024-01-02 +2024-01-02 05:10:00,126.5205853838558,,,,,,,,2024-01-02 +2024-01-02 05:15:00,130.55196702893377,,,,,,,,2024-01-02 +2024-01-02 05:20:00,139.13174474201463,,,,,,,,2024-01-02 +2024-01-02 05:25:00,142.80144454376392,,,,,,,,2024-01-02 +2024-01-02 05:30:00,141.35550594735,,,,,,,,2024-01-02 +2024-01-02 05:35:00,133.85790318699418,,,,,,,,2024-01-02 +2024-01-02 05:40:00,124.7582647200346,,,,,,,,2024-01-02 +2024-01-02 05:45:00,121.30979106214173,,,,,,,,2024-01-02 +2024-01-02 05:50:00,120.0197734619908,,,,,,,,2024-01-02 +2024-01-02 05:55:00,127.14365008682378,,,,,,,,2024-01-02 +2024-01-02 06:00:00,126.44075462267584,,,,,,,,2024-01-02 +2024-01-02 06:05:00,126.15650848217152,,,,,,,,2024-01-02 +2024-01-02 06:10:00,127.01297551199708,,,,,,,,2024-01-02 +2024-01-02 06:15:00,127.7359771737072,,,,,,,,2024-01-02 +2024-01-02 06:20:00,129.50553026478195,,,,,,,,2024-01-02 +2024-01-02 06:25:00,131.36516651180602,,,,,,,,2024-01-02 +2024-01-02 06:30:00,133.42204877657247,ANNOUNCE_MEAL,,,,,10.666666666666666,10.666666666666666,2024-01-02 +2024-01-02 06:35:00,134.90589323526586,,,,,,0.0,,2024-01-02 +2024-01-02 06:40:00,139.71311251774162,,,,,,0.0,,2024-01-02 +2024-01-02 06:45:00,143.22736728977185,,,,,,0.0,,2024-01-02 +2024-01-02 06:50:00,151.3505297462674,,,,,,0.0,,2024-01-02 +2024-01-02 06:55:00,158.04327455254847,,,,,,0.0,,2024-01-02 +2024-01-02 07:00:00,159.80389494378832,,,,,,0.0,,2024-01-02 +2024-01-02 07:05:00,161.1134278173986,,,,,,0.0,,2024-01-02 +2024-01-02 07:10:00,162.62662044845575,,,,,,0.0,,2024-01-02 +2024-01-02 07:15:00,164.41558432847756,,,,,,0.0,,2024-01-02 +2024-01-02 07:20:00,169.0249068476183,,,,,,0.0,,2024-01-02 +2024-01-02 07:25:00,172.5306221574452,,,,,,0.0,,2024-01-02 +2024-01-02 07:30:00,173.21090072823395,,,,,,0.0,,2024-01-02 +2024-01-02 07:35:00,172.97858048908412,,,,,,0.0,,2024-01-02 +2024-01-02 07:40:00,172.0545993691951,,,,,,0.0,,2024-01-02 +2024-01-02 07:45:00,171.69660821413495,,,,,,0.0,,2024-01-02 +2024-01-02 07:50:00,170.7042403677018,,,,,,0.0,,2024-01-02 +2024-01-02 07:55:00,168.2157388472272,,,,,,0.0,,2024-01-02 +2024-01-02 08:00:00,166.11725609206684,,,,,,0.0,,2024-01-02 +2024-01-02 08:05:00,160.6095766036976,,,,,,0.0,,2024-01-02 +2024-01-02 08:10:00,154.27151891437722,,,,,,0.0,,2024-01-02 +2024-01-02 08:15:00,151.13251648078247,,,,,,0.0,,2024-01-02 +2024-01-02 08:20:00,145.59283408316264,,,,,,0.0,,2024-01-02 +2024-01-02 08:25:00,141.55990087192464,,,,,,0.0,,2024-01-02 +2024-01-02 08:30:00,139.3452911033868,,,,,,0.0,,2024-01-02 +2024-01-02 08:35:00,134.84772587054314,,,,,,0.0,,2024-01-02 +2024-01-02 08:40:00,130.65889808228533,,,,,,0.0,,2024-01-02 +2024-01-02 08:45:00,128.82950411805592,,,,,,0.0,,2024-01-02 +2024-01-02 08:50:00,125.99783001279845,,,,,,0.0,,2024-01-02 +2024-01-02 08:55:00,124.65505698154028,,,,,,0.0,,2024-01-02 +2024-01-02 09:00:00,124.6209324624778,,,,,,0.0,,2024-01-02 +2024-01-02 09:05:00,125.50511063658678,,,,,,0.0,,2024-01-02 +2024-01-02 09:10:00,127.04184278007928,,,,,,0.0,,2024-01-02 +2024-01-02 09:15:00,127.8564429002822,,,,,,0.0,,2024-01-02 +2024-01-02 09:20:00,129.42411882656398,,,,,,0.0,,2024-01-02 +2024-01-02 09:25:00,130.7956073058719,,,,,,0.0,,2024-01-02 +2024-01-02 09:30:00,131.31856453768603,,,,,,0.0,,2024-01-02 +2024-01-02 09:35:00,131.5203261677019,LOW_CARB_MEAL,,,,,2.333333333333333,2.333333333333333,2024-01-02 +2024-01-02 09:40:00,129.92068862282153,,,,,,,,2024-01-02 +2024-01-02 09:45:00,128.78683663173223,,,,,,,,2024-01-02 +2024-01-02 09:50:00,127.93210956387094,,,,,,,,2024-01-02 +2024-01-02 09:55:00,130.29479492687062,,,,,,,,2024-01-02 +2024-01-02 10:00:00,133.02412170616043,,,,,,,,2024-01-02 +2024-01-02 10:05:00,140.54571965153465,,,,,,,,2024-01-02 +2024-01-02 10:10:00,148.87203425591676,,,,,,,,2024-01-02 +2024-01-02 10:15:00,152.64731326117928,,,,,,,,2024-01-02 +2024-01-02 10:20:00,158.08580506714603,,,,,,,,2024-01-02 +2024-01-02 10:25:00,158.94299432184638,,,,,,,,2024-01-02 +2024-01-02 10:30:00,157.25058957171882,,,,,,,,2024-01-02 +2024-01-02 10:35:00,150.780520667111,,,,,,,,2024-01-02 +2024-01-02 10:40:00,142.50806058563137,,,,,,,,2024-01-02 +2024-01-02 10:45:00,138.5568740290744,,,,,,,,2024-01-02 +2024-01-02 10:50:00,132.73708512319314,,,,,,,,2024-01-02 +2024-01-02 10:55:00,130.5850580842097,,,,,,,,2024-01-02 +2024-01-02 11:00:00,128.53320017667792,,,,,,,,2024-01-02 +2024-01-02 11:05:00,128.09300567064545,,,,,,,,2024-01-02 +2024-01-02 11:10:00,130.8721050956058,,,,,,,,2024-01-02 +2024-01-02 11:15:00,132.84392284092726,,,,,,,,2024-01-02 +2024-01-02 11:20:00,136.70148384958227,,,,,,,,2024-01-02 +2024-01-02 11:25:00,138.80580971917146,,,,,,,,2024-01-02 +2024-01-02 11:30:00,138.81517611827098,,,,,,,,2024-01-02 +2024-01-02 11:35:00,137.65589273620614,,,,,,,,2024-01-02 +2024-01-02 11:40:00,136.7772868167676,,,,,,,,2024-01-02 +2024-01-02 11:45:00,137.27633414707142,ANNOUNCE_MEAL,,,,,22.33333333333333,22.33333333333333,2024-01-02 +2024-01-02 11:50:00,137.62813519054745,,,,,,0.0,,2024-01-02 +2024-01-02 11:55:00,137.5442235802276,,,,,,0.0,,2024-01-02 +2024-01-02 12:00:00,137.15431995120488,,,,,,0.0,,2024-01-02 +2024-01-02 12:05:00,137.3037934761098,,,,,,0.0,,2024-01-02 +2024-01-02 12:10:00,141.23135752624236,,,,,,0.0,,2024-01-02 +2024-01-02 12:15:00,145.07725862088816,,,,,,0.0,,2024-01-02 +2024-01-02 12:20:00,155.3655501450609,,,,,,0.0,,2024-01-02 +2024-01-02 12:25:00,167.4888355686681,,,,,,0.0,,2024-01-02 +2024-01-02 12:30:00,173.7741121628244,,,,,,0.0,,2024-01-02 +2024-01-02 12:35:00,184.43493036811017,,,,,,0.0,,2024-01-02 +2024-01-02 12:40:00,189.082209320807,,,,,,0.0,,2024-01-02 +2024-01-02 12:45:00,188.3932066412541,,,,,,0.0,,2024-01-02 +2024-01-02 12:50:00,183.0450800360288,,,,,,0.0,,2024-01-02 +2024-01-02 12:55:00,176.4554755637908,,,,,,0.0,,2024-01-02 +2024-01-02 13:00:00,173.8760259905326,,,,,,0.0,,2024-01-02 +2024-01-02 13:05:00,170.17807704826754,,,,,,0.0,,2024-01-02 +2024-01-02 13:10:00,167.49501912363735,,,,,,0.0,,2024-01-02 +2024-01-02 13:15:00,166.16755884095167,,,,,,0.0,,2024-01-02 +2024-01-02 13:20:00,162.77883217266452,,,,,,0.0,,2024-01-02 +2024-01-02 13:25:00,158.48584493655136,,,,,,0.0,,2024-01-02 +2024-01-02 13:30:00,157.9805269022906,,,,,,0.0,,2024-01-02 +2024-01-02 13:35:00,154.44177423213688,,,,,,0.0,,2024-01-02 +2024-01-02 13:40:00,148.28932842925718,,,,,,0.0,,2024-01-02 +2024-01-02 13:45:00,144.5146174791712,,,,,,0.0,,2024-01-02 +2024-01-02 13:50:00,136.12751634052782,,,,,,0.0,,2024-01-02 +2024-01-02 13:55:00,127.36326247082724,,,,,,0.0,,2024-01-02 +2024-01-02 14:00:00,123.10045652527027,,,,,,0.0,,2024-01-02 +2024-01-02 14:05:00,115.21848043116368,,,,,,0.0,,2024-01-02 +2024-01-02 14:10:00,108.68380548596556,,,,,,0.0,,2024-01-02 +2024-01-02 14:15:00,106.07970013145334,,,,,,0.0,,2024-01-02 +2024-01-02 14:20:00,102.33029917771768,,,,,,0.0,,2024-01-02 +2024-01-02 14:25:00,100.62360948919957,,,,,,0.0,,2024-01-02 +2024-01-02 14:30:00,100.53214890168564,,,,,,0.0,,2024-01-02 +2024-01-02 14:35:00,101.56687834998644,,,,,,0.0,,2024-01-02 +2024-01-02 14:40:00,103.69200160534322,,,,,,0.0,,2024-01-02 +2024-01-02 14:45:00,104.91104471220277,,,,,,0.0,,2024-01-02 +2024-01-02 14:50:00,106.77101932864343,,,,,,,,2024-01-02 +2024-01-02 14:55:00,106.55734441757082,,,,,,,,2024-01-02 +2024-01-02 15:00:00,105.47168221533512,,,,,,,,2024-01-02 +2024-01-02 15:05:00,103.03844040168704,,,,,,,,2024-01-02 +2024-01-02 15:10:00,103.30772623270786,,,,,,,,2024-01-02 +2024-01-02 15:15:00,105.1824819414201,,,,,,,,2024-01-02 +2024-01-02 15:20:00,110.7902302811522,,,,,,,,2024-01-02 +2024-01-02 15:25:00,115.54823080293798,,,,,,,,2024-01-02 +2024-01-02 15:30:00,116.70760855560336,,,,,,,,2024-01-02 +2024-01-02 15:35:00,117.27437883310972,,,,,,,,2024-01-02 +2024-01-02 15:40:00,117.42847202243524,,,,,,,,2024-01-02 +2024-01-02 15:45:00,118.05981175019116,,,,,,,,2024-01-02 +2024-01-02 15:50:00,121.72568721210828,,,,,,,,2024-01-02 +2024-01-02 15:55:00,128.85058630631806,,,,,,,,2024-01-02 +2024-01-02 16:00:00,131.12484964891615,,,,,,,,2024-01-02 +2024-01-02 16:05:00,135.76883897015298,,,,,,,,2024-01-02 +2024-01-02 16:10:00,140.131501468429,,,,,,,,2024-01-02 +2024-01-02 16:15:00,142.11223135158292,,,,,,,,2024-01-02 +2024-01-02 16:20:00,145.6147286284156,,,,,,,,2024-01-02 +2024-01-02 16:25:00,148.5620294930242,,,,,,,,2024-01-02 +2024-01-02 16:30:00,149.88111539004592,,,,,,,,2024-01-02 +2024-01-02 16:35:00,152.38492137040146,,,,,,,,2024-01-02 +2024-01-02 16:40:00,154.95304362577474,,,,,,,,2024-01-02 +2024-01-02 16:45:00,156.19978101630468,,,,,,,,2024-01-02 +2024-01-02 16:50:00,157.3694304323417,,,,,,,,2024-01-02 +2024-01-02 16:55:00,154.7058642351571,,,,,,,,2024-01-02 +2024-01-02 17:00:00,151.6238099889573,,,,,,,,2024-01-02 +2024-01-02 17:05:00,144.7510101639779,,,,,,,,2024-01-02 +2024-01-02 17:10:00,141.91507768310382,,,,,,,,2024-01-02 +2024-01-02 17:15:00,143.14165709453664,,,,,,,,2024-01-02 +2024-01-02 17:20:00,147.77033013785723,,,,,,,,2024-01-02 +2024-01-02 17:25:00,149.09148497241097,,,,,,,,2024-01-02 +2024-01-02 17:30:00,146.93192503466918,,,,,,,,2024-01-02 +2024-01-02 17:35:00,139.3075586668889,,,,,,,,2024-01-02 +2024-01-02 17:40:00,132.5140305266605,,,,,,,,2024-01-02 +2024-01-02 17:45:00,130.25241921281338,ANNOUNCE_MEAL,,,,,17.333333333333332,17.333333333333332,2024-01-02 +2024-01-02 17:50:00,130.23991243146344,,,,,,0.0,,2024-01-02 +2024-01-02 17:55:00,130.4968833605675,,,,,,0.0,,2024-01-02 +2024-01-02 18:00:00,130.2965046283189,,,,,,0.0,,2024-01-02 +2024-01-02 18:05:00,130.10702688115543,,,,,,0.0,,2024-01-02 +2024-01-02 18:10:00,132.0479041632729,,,,,,0.0,,2024-01-02 +2024-01-02 18:15:00,133.9340777486012,,,,,,0.0,,2024-01-02 +2024-01-02 18:20:00,138.29931986663684,,,,,,0.0,,2024-01-02 +2024-01-02 18:25:00,142.82572274552876,,,,,,0.0,,2024-01-02 +2024-01-02 18:30:00,146.57310025086144,,,,,,0.0,,2024-01-02 +2024-01-02 18:35:00,150.53127588166416,,,,,,0.0,,2024-01-02 +2024-01-02 18:40:00,150.92139625211323,,,,,,0.0,,2024-01-02 +2024-01-02 18:45:00,150.25177250600427,,,,,,0.0,,2024-01-02 +2024-01-02 18:50:00,148.13539814189562,,,,,,0.0,,2024-01-02 +2024-01-02 18:55:00,146.1863067582848,,,,,,0.0,,2024-01-02 +2024-01-02 19:00:00,145.58365221918083,,,,,,0.0,,2024-01-02 +2024-01-02 19:05:00,144.80366401152628,,,,,,0.0,,2024-01-02 +2024-01-02 19:10:00,143.7807700376807,,,,,,0.0,,2024-01-02 +2024-01-02 19:15:00,143.00731521938854,,,,,,0.0,,2024-01-02 +2024-01-02 19:20:00,141.63311244047694,,,,,,0.0,,2024-01-02 +2024-01-02 19:25:00,141.7933661956689,,,,,,0.0,,2024-01-02 +2024-01-02 19:30:00,142.71326847563174,,,,,,0.0,,2024-01-02 +2024-01-02 19:35:00,145.14133011602047,,,,,,0.0,,2024-01-02 +2024-01-02 19:40:00,146.2718037728954,,,,,,0.0,,2024-01-02 +2024-01-02 19:45:00,145.7573150467345,,,,,,0.0,,2024-01-02 +2024-01-02 19:50:00,142.76611860752172,,,,,,0.0,,2024-01-02 +2024-01-02 19:55:00,137.97185584440865,,,,,,0.0,,2024-01-02 +2024-01-02 20:00:00,135.20004293907894,,,,,,0.0,,2024-01-02 +2024-01-02 20:05:00,129.48901024737484,,,,,,0.0,,2024-01-02 +2024-01-02 20:10:00,124.31413349531267,,,,,,0.0,,2024-01-02 +2024-01-02 20:15:00,122.156553025768,,,,,,0.0,,2024-01-02 +2024-01-02 20:20:00,118.75126740800556,,,,,,0.0,,2024-01-02 +2024-01-02 20:25:00,116.46110879979229,,,,,,0.0,,2024-01-02 +2024-01-02 20:30:00,115.68299888574091,,,,,,0.0,,2024-01-02 +2024-01-02 20:35:00,114.65333259144904,,,,,,0.0,,2024-01-02 +2024-01-02 20:40:00,114.0077522452054,,,,,,0.0,,2024-01-02 +2024-01-02 20:45:00,113.71004299367183,,,,,,0.0,,2024-01-02 +2024-01-02 20:50:00,112.92796961748964,,,,,,,,2024-01-02 +2024-01-02 20:55:00,111.91695897169105,,,,,,,,2024-01-02 +2024-01-02 21:00:00,112.43722802415392,,,,,,,,2024-01-02 +2024-01-02 21:05:00,116.72904541765408,,,,,,,,2024-01-02 +2024-01-02 21:10:00,123.2559422563334,,,,,,,,2024-01-02 +2024-01-02 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02:55:00,141.2464949284753,,,,,,,,2024-01-02 +2024-01-03 03:00:00,140.22306964365382,,,,,,,,2024-01-02 +2024-01-03 03:05:00,138.47157839346625,,,,,,,,2024-01-02 +2024-01-03 03:10:00,137.4960945556988,,,,,,,,2024-01-02 +2024-01-03 03:15:00,137.34693423174386,,,,,,,,2024-01-02 +2024-01-03 03:20:00,137.1197353308272,,,,,,,,2024-01-02 +2024-01-03 03:25:00,135.92081366559344,,,,,,,,2024-01-02 +2024-01-03 03:30:00,134.723902146064,,,,,,,,2024-01-02 +2024-01-03 03:35:00,131.89113775840173,,,,,,,,2024-01-02 +2024-01-03 03:40:00,129.9497587331312,,,,,,,,2024-01-02 +2024-01-03 03:45:00,129.71637791297331,,,,,,,,2024-01-02 +2024-01-03 03:50:00,130.42672116424723,,,,,,,,2024-01-02 +2024-01-03 03:55:00,131.87696795771134,,,,,,,,2024-01-02 +2024-01-03 04:00:00,132.62952147182594,,,,,,,,2024-01-03 +2024-01-03 04:05:00,134.13644330237867,,,,,,,,2024-01-03 +2024-01-03 04:10:00,135.75362416760518,,,,,,,,2024-01-03 +2024-01-03 04:15:00,136.6440104735422,,,,,,,,2024-01-03 +2024-01-03 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05:45:00,147.4025391191058,,,,,,,,2024-01-03 +2024-01-03 05:50:00,149.94521204828155,,,,,,,,2024-01-03 +2024-01-03 05:55:00,151.45126670754584,,,,,,,,2024-01-03 +2024-01-03 06:00:00,152.05836656235047,,,,,,,,2024-01-03 +2024-01-03 06:05:00,152.5275030744944,,,,,,,,2024-01-03 +2024-01-03 06:10:00,151.04920907349927,,,,,,,,2024-01-03 +2024-01-03 06:15:00,149.41761972983616,,,,,,,,2024-01-03 +2024-01-03 06:20:00,145.4775298939972,,,,,,,,2024-01-03 +2024-01-03 06:25:00,142.6545694405147,,,,,,,,2024-01-03 +2024-01-03 06:30:00,142.1892626339896,,,,,,,,2024-01-03 +2024-01-03 06:35:00,142.52035428137788,,,,,,,,2024-01-03 +2024-01-03 06:40:00,143.019797586479,,,,,,,,2024-01-03 +2024-01-03 06:45:00,142.764930389066,,,,,,,,2024-01-03 +2024-01-03 06:50:00,140.1105767600978,,,,,,,,2024-01-03 +2024-01-03 06:55:00,134.49379531191033,,,,,,,,2024-01-03 +2024-01-03 07:00:00,133.722849248555,ANNOUNCE_MEAL,,,,,12.0,12.0,2024-01-03 +2024-01-03 07:05:00,133.37263898627378,,,,,,0.0,,2024-01-03 +2024-01-03 07:10:00,134.3257068409034,,,,,,0.0,,2024-01-03 +2024-01-03 07:15:00,135.46980097450796,,,,,,0.0,,2024-01-03 +2024-01-03 07:20:00,139.4427731207235,,,,,,0.0,,2024-01-03 +2024-01-03 07:25:00,144.660336480093,,,,,,0.0,,2024-01-03 +2024-01-03 07:30:00,147.1104740874571,,,,,,0.0,,2024-01-03 +2024-01-03 07:35:00,151.69064951479834,,,,,,0.0,,2024-01-03 +2024-01-03 07:40:00,157.03666185035456,,,,,,0.0,,2024-01-03 +2024-01-03 07:45:00,160.27926425529822,,,,,,0.0,,2024-01-03 +2024-01-03 07:50:00,166.16180745671815,,,,,,0.0,,2024-01-03 +2024-01-03 07:55:00,168.05221149249218,,,,,,0.0,,2024-01-03 +2024-01-03 08:00:00,166.74427829473157,,,,,,0.0,,2024-01-03 +2024-01-03 08:05:00,161.30744721492783,,,,,,0.0,,2024-01-03 +2024-01-03 08:10:00,155.5600913899832,,,,,,0.0,,2024-01-03 +2024-01-03 08:15:00,153.52299476373116,,,,,,0.0,,2024-01-03 +2024-01-03 08:20:00,150.59602360895346,,,,,,0.0,,2024-01-03 +2024-01-03 08:25:00,147.53127108506695,,,,,,0.0,,2024-01-03 +2024-01-03 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11:15:00,138.27995827945506,,,,,,,,2024-01-03 +2024-01-03 11:20:00,142.59294891860478,,,,,,,,2024-01-03 +2024-01-03 11:25:00,145.77496925610626,,,,,,,,2024-01-03 +2024-01-03 11:30:00,146.35208730444032,,,,,,,,2024-01-03 +2024-01-03 11:35:00,145.90182670955028,,,,,,,,2024-01-03 +2024-01-03 11:40:00,144.5373307988216,,,,,,,,2024-01-03 +2024-01-03 11:45:00,143.97863169024396,,,,,,,,2024-01-03 +2024-01-03 11:50:00,144.04778677345945,,,,,,,,2024-01-03 +2024-01-03 11:55:00,145.77153720232872,,,,,,,,2024-01-03 +2024-01-03 12:00:00,145.00564588209647,,,,,,,,2024-01-03 +2024-01-03 12:05:00,144.90432417009816,,,,,,,,2024-01-03 +2024-01-03 12:10:00,145.6837317820923,,,,,,,,2024-01-03 +2024-01-03 12:15:00,146.01606687153327,,,,,,,,2024-01-03 +2024-01-03 12:20:00,145.78489495106453,,,,,,,,2024-01-03 +2024-01-03 12:25:00,143.34232646969951,,,,,,,,2024-01-03 +2024-01-03 12:30:00,141.1516153495329,,,,,,,,2024-01-03 +2024-01-03 12:35:00,136.32741769354226,,,,,,,,2024-01-03 +2024-01-03 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17:55:00,162.64679254119926,,,,,,0.0,,2024-01-03 +2024-01-03 18:00:00,160.96676630589755,,,,,,0.0,,2024-01-03 +2024-01-03 18:05:00,155.41044217389253,,,,,,0.0,,2024-01-03 +2024-01-03 18:10:00,150.38450971354612,,,,,,0.0,,2024-01-03 +2024-01-03 18:15:00,148.975862211459,,,,,,0.0,,2024-01-03 +2024-01-03 18:20:00,147.4649622290697,,,,,,0.0,,2024-01-03 +2024-01-03 18:25:00,145.42212217722914,,,,,,0.0,,2024-01-03 +2024-01-03 18:30:00,143.63334569835578,,,,,,0.0,,2024-01-03 +2024-01-03 18:35:00,139.52769151247955,,,,,,0.0,,2024-01-03 +2024-01-03 18:40:00,136.87286721582694,,,,,,0.0,,2024-01-03 +2024-01-03 18:45:00,136.61678242208112,,,,,,0.0,,2024-01-03 +2024-01-03 18:50:00,137.2615141178092,,,,,,0.0,,2024-01-03 +2024-01-03 18:55:00,137.3376889105075,,,,,,0.0,,2024-01-03 +2024-01-03 19:00:00,136.52586418394225,,,,,,0.0,,2024-01-03 +2024-01-03 19:05:00,133.06821666473803,,,,,,0.0,,2024-01-03 +2024-01-03 19:10:00,127.44077089978867,,,,,,0.0,,2024-01-03 +2024-01-03 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09:15:00,132.173822445414,,,,,,0.0,,2024-01-04 +2024-01-04 09:20:00,125.45080072616038,,,,,,0.0,,2024-01-04 +2024-01-04 09:25:00,120.80798440927964,,,,,,0.0,,2024-01-04 +2024-01-04 09:30:00,119.86583686744262,,,,,,0.0,,2024-01-04 +2024-01-04 09:35:00,119.88941086009352,,,,,,0.0,,2024-01-04 +2024-01-04 09:40:00,120.52782563234308,,,,,,0.0,,2024-01-04 +2024-01-04 09:45:00,120.54156683236988,,,,,,0.0,,2024-01-04 +2024-01-04 09:50:00,120.25916016873433,,,,,,0.0,,2024-01-04 +2024-01-04 09:55:00,120.4991188630286,,,,,,0.0,,2024-01-04 +2024-01-04 10:00:00,121.05503599286166,,,,,,0.0,,2024-01-04 +2024-01-04 10:05:00,122.70757236591486,,,,,,0.0,,2024-01-04 +2024-01-04 10:10:00,124.31638224130096,,,,,,,,2024-01-04 +2024-01-04 10:15:00,124.81818844504834,,,,,,,,2024-01-04 +2024-01-04 10:20:00,124.64606796201112,,,,,,,,2024-01-04 +2024-01-04 10:25:00,125.09126447672926,,,,,,,,2024-01-04 +2024-01-04 10:30:00,131.08747704176176,,,,,,,,2024-01-04 +2024-01-04 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16:05:00,105.96353392183948,,,,,,,,2024-01-04 +2024-01-04 16:10:00,102.5102283639115,,,,,,,,2024-01-04 +2024-01-04 16:15:00,100.5400850802662,,,,,,,,2024-01-04 +2024-01-04 16:20:00,96.96884485692692,,,,,,,,2024-01-04 +2024-01-04 16:25:00,95.43512804178955,,,,,,,,2024-01-04 +2024-01-04 16:30:00,95.7487214553209,,,,,,,,2024-01-04 +2024-01-04 16:35:00,97.23013842693776,,,,,,,,2024-01-04 +2024-01-04 16:40:00,97.40834683779806,,,,,,,,2024-01-04 +2024-01-04 16:45:00,96.56083257653697,,,,,,,,2024-01-04 +2024-01-04 16:50:00,95.485191555167,,,,,,,,2024-01-04 +2024-01-04 16:55:00,99.83172677413155,,,,,,,,2024-01-04 +2024-01-04 17:00:00,105.01729554463262,,,,,,,,2024-01-04 +2024-01-04 17:05:00,117.87419553465682,,,,,,,,2024-01-04 +2024-01-04 17:10:00,127.04879360904911,,,,,,,,2024-01-04 +2024-01-04 17:15:00,128.35737401533063,,,,,,,,2024-01-04 +2024-01-04 17:20:00,126.3692420134548,,,,,,,,2024-01-04 +2024-01-04 17:25:00,123.1788291372874,,,,,,,,2024-01-04 +2024-01-04 17:30:00,122.60387014492034,,,,,,,,2024-01-04 +2024-01-04 17:35:00,123.42798251047904,,,,,,,,2024-01-04 +2024-01-04 17:40:00,125.6643792270448,,,,,,,,2024-01-04 +2024-01-04 17:45:00,126.87963894391952,,,,,,,,2024-01-04 +2024-01-04 17:50:00,128.67296753812877,,,,,,,,2024-01-04 +2024-01-04 17:55:00,130.12108091381626,,,,,,,,2024-01-04 +2024-01-04 18:00:00,133.500138387735,,,,,,,,2024-01-04 +2024-01-04 18:05:00,136.2617751262464,,,,,,,,2024-01-04 +2024-01-04 18:10:00,135.4560129510996,ANNOUNCE_MEAL,,,,,31.666666666666668,31.666666666666668,2024-01-04 +2024-01-04 18:15:00,134.36967105748454,,,,,,0.0,,2024-01-04 +2024-01-04 18:20:00,132.1673163574406,,,,,,0.0,,2024-01-04 +2024-01-04 18:25:00,132.36474753866824,,,,,,0.0,,2024-01-04 +2024-01-04 18:30:00,134.27856394085794,,,,,,0.0,,2024-01-04 +2024-01-04 18:35:00,141.68068628208846,,,,,,0.0,,2024-01-04 +2024-01-04 18:40:00,152.10141446877927,,,,,,0.0,,2024-01-04 +2024-01-04 18:45:00,157.10234995513363,,,,,,0.0,,2024-01-04 +2024-01-04 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03:10:00,117.3750383170926,,,,,,,,2024-01-04 +2024-01-05 03:15:00,117.9422536073483,,,,,,,,2024-01-04 +2024-01-05 03:20:00,118.49109637668182,,,,,,,,2024-01-04 +2024-01-05 03:25:00,118.96546841413146,,,,,,,,2024-01-04 +2024-01-05 03:30:00,119.4130696043404,,,,,,,,2024-01-04 +2024-01-05 03:35:00,121.0113312672536,,,,,,,,2024-01-04 +2024-01-05 03:40:00,123.84572273336684,,,,,,,,2024-01-04 +2024-01-05 03:45:00,125.8385216992815,,,,,,,,2024-01-04 +2024-01-05 03:50:00,131.19951329678642,,,,,,,,2024-01-04 +2024-01-05 03:55:00,136.4601211037414,,,,,,,,2024-01-04 +2024-01-05 04:00:00,134.39934069588395,,,,,,,,2024-01-05 +2024-01-05 04:05:00,131.8293338350086,,,,,,,,2024-01-05 +2024-01-05 04:10:00,130.56927130595523,,,,,,,,2024-01-05 +2024-01-05 04:15:00,130.145945459502,,,,,,,,2024-01-05 +2024-01-05 04:20:00,129.14371000607568,,,,,,,,2024-01-05 +2024-01-05 04:25:00,127.19348163946552,,,,,,,,2024-01-05 +2024-01-05 04:30:00,125.7250420004691,,,,,,,,2024-01-05 +2024-01-05 04:35:00,122.55001762370644,,,,,,,,2024-01-05 +2024-01-05 04:40:00,120.45274399358404,,,,,,,,2024-01-05 +2024-01-05 04:45:00,120.2398316941552,,,,,,,,2024-01-05 +2024-01-05 04:50:00,121.67731459377684,,,,,,,,2024-01-05 +2024-01-05 04:55:00,125.56134499936812,,,,,,,,2024-01-05 +2024-01-05 05:00:00,128.20834019055843,,,,,,,,2024-01-05 +2024-01-05 05:05:00,133.03537592940597,,,,,,,,2024-01-05 +2024-01-05 05:10:00,134.21400681352512,,,,,,,,2024-01-05 +2024-01-05 05:15:00,132.7396535776806,,,,,,,,2024-01-05 +2024-01-05 05:20:00,127.48829150354892,,,,,,,,2024-01-05 +2024-01-05 05:25:00,122.8405233838114,,,,,,,,2024-01-05 +2024-01-05 05:30:00,121.78336973080764,,,,,,,,2024-01-05 +2024-01-05 05:35:00,121.81339401080557,,,,,,,,2024-01-05 +2024-01-05 05:40:00,123.21362746006535,,,,,,,,2024-01-05 +2024-01-05 05:45:00,123.9965957956302,,,,,,,,2024-01-05 +2024-01-05 05:50:00,125.79885876579056,,,,,,,,2024-01-05 +2024-01-05 05:55:00,128.37310249007453,,,,,,,,2024-01-05 +2024-01-05 06:00:00,130.0452700896791,,,,,,,,2024-01-05 +2024-01-05 06:05:00,133.7958985714241,,,,,,,,2024-01-05 +2024-01-05 06:10:00,137.38155890651268,,,,,,,,2024-01-05 +2024-01-05 06:15:00,138.84751955905998,,,,,,,,2024-01-05 +2024-01-05 06:20:00,140.59569566842362,,,,,,,,2024-01-05 +2024-01-05 06:25:00,140.87592302877275,,,,,,,,2024-01-05 +2024-01-05 06:30:00,142.04788821049303,,,,,,,,2024-01-05 +2024-01-05 06:35:00,142.9977639015371,,,,,,,,2024-01-05 +2024-01-05 06:40:00,142.6479009538144,,,,,,,,2024-01-05 +2024-01-05 06:45:00,142.1951942788731,,,,,,,,2024-01-05 +2024-01-05 06:50:00,141.1534869561118,,,,,,,,2024-01-05 +2024-01-05 06:55:00,140.4896930311707,,,,,,,,2024-01-05 +2024-01-05 07:00:00,140.5142383916545,,,,,,,,2024-01-05 +2024-01-05 07:05:00,141.74124878009508,,,,,,,,2024-01-05 +2024-01-05 07:10:00,145.13623705582498,,,,,,,,2024-01-05 +2024-01-05 07:15:00,147.5852260572055,,,,,,,,2024-01-05 +2024-01-05 07:20:00,151.77239118465872,,,,,,,,2024-01-05 +2024-01-05 07:25:00,151.304281501229,,,,,,,,2024-01-05 +2024-01-05 07:30:00,148.54881416151173,,,,,,,,2024-01-05 +2024-01-05 07:35:00,137.8145242209237,ANNOUNCE_MEAL,,,,,20.666666666666668,18.666666666666668,2024-01-05 +2024-01-05 07:40:00,136.19489005695328,,,,,,0.0,,2024-01-05 +2024-01-05 07:45:00,136.35796934593958,,,,,,0.0,,2024-01-05 +2024-01-05 07:50:00,140.0365582319406,,,,,,0.0,,2024-01-05 +2024-01-05 07:55:00,144.81434516727995,,,,,,0.0,,2024-01-05 +2024-01-05 08:00:00,147.02556297144065,,,,,,0.0,,2024-01-05 +2024-01-05 08:05:00,152.30569675864737,,,,,,0.0,,2024-01-05 +2024-01-05 08:10:00,159.1841506073805,,,,,,0.0,,2024-01-05 +2024-01-05 08:15:00,162.80306144860003,,,,,,0.0,,2024-01-05 +2024-01-05 08:20:00,168.39424814666586,,,,,,0.0,,2024-01-05 +2024-01-05 08:25:00,169.53173359410033,,,,,,0.0,,2024-01-05 +2024-01-05 08:30:00,168.03080455935398,,,,,,0.0,,2024-01-05 +2024-01-05 08:35:00,162.06303096349023,,,,,,0.0,,2024-01-05 +2024-01-05 08:40:00,154.4635328213771,,,,,,0.0,,2024-01-05 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10:05:00,137.31614422526724,,,,,,0.0,,2024-01-05 +2024-01-05 10:10:00,132.0973244754872,,,,,,0.0,,2024-01-05 +2024-01-05 10:15:00,129.90504109147244,,,,,,0.0,,2024-01-05 +2024-01-05 10:20:00,126.96200476465658,,,,,,0.0,,2024-01-05 +2024-01-05 10:25:00,126.66529487504994,,,,,,0.0,,2024-01-05 +2024-01-05 10:30:00,127.52624827167094,,,,,,0.0,,2024-01-05 +2024-01-05 10:35:00,129.48022091872207,,,,,,0.0,,2024-01-05 +2024-01-05 10:40:00,128.68192059536122,,,,,,,,2024-01-05 +2024-01-05 10:45:00,126.51277685123895,,,,,,,,2024-01-05 +2024-01-05 10:50:00,120.17613146114375,,,,,,,,2024-01-05 +2024-01-05 10:55:00,114.39282171536934,,,,,,,,2024-01-05 +2024-01-05 11:00:00,112.59523040234028,,,,,,,,2024-01-05 +2024-01-05 11:05:00,110.62109000314324,,,,,,,,2024-01-05 +2024-01-05 11:10:00,109.1171938942827,,,,,,,,2024-01-05 +2024-01-05 11:15:00,107.90730608104104,,,,,,,,2024-01-05 +2024-01-05 11:20:00,103.30309089898611,,,,,,,,2024-01-05 +2024-01-05 11:25:00,97.05343195430837,,,,,,,,2024-01-05 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12:50:00,140.51853616956416,,,,,,0.0,,2024-01-05 +2024-01-05 12:55:00,145.30422508657298,,,,,,0.0,,2024-01-05 +2024-01-05 13:00:00,145.1587298803682,,,,,,0.0,,2024-01-05 +2024-01-05 13:05:00,142.67912207735148,,,,,,0.0,,2024-01-05 +2024-01-05 13:10:00,142.65353049606878,,,,,,0.0,,2024-01-05 +2024-01-05 13:15:00,144.9298600365517,,,,,,0.0,,2024-01-05 +2024-01-05 13:20:00,151.76515528671325,,,,,,0.0,,2024-01-05 +2024-01-05 13:25:00,156.55968520937276,,,,,,0.0,,2024-01-05 +2024-01-05 13:30:00,156.7850240124054,,,,,,0.0,,2024-01-05 +2024-01-05 13:35:00,153.90528772465785,,,,,,0.0,,2024-01-05 +2024-01-05 13:40:00,149.42306899223624,,,,,,0.0,,2024-01-05 +2024-01-05 13:45:00,147.62896875910712,,,,,,0.0,,2024-01-05 +2024-01-05 13:50:00,147.0030174703182,,,,,,0.0,,2024-01-05 +2024-01-05 13:55:00,149.73073392959958,,,,,,0.0,,2024-01-05 +2024-01-05 14:00:00,145.49798517011348,,,,,,0.0,,2024-01-05 +2024-01-05 14:05:00,138.05691458878846,,,,,,0.0,,2024-01-05 +2024-01-05 14:10:00,131.82530745586877,,,,,,0.0,,2024-01-05 +2024-01-05 14:15:00,129.10258270552498,,,,,,0.0,,2024-01-05 +2024-01-05 14:20:00,124.31928692704724,,,,,,0.0,,2024-01-05 +2024-01-05 14:25:00,120.25774641097708,,,,,,0.0,,2024-01-05 +2024-01-05 14:30:00,118.4919995748584,,,,,,0.0,,2024-01-05 +2024-01-05 14:35:00,115.88442482468714,,,,,,0.0,,2024-01-05 +2024-01-05 14:40:00,115.17550152489484,,,,,,0.0,,2024-01-05 +2024-01-05 14:45:00,115.63199492397771,,,,,,0.0,,2024-01-05 +2024-01-05 14:50:00,117.26031801121476,,,,,,0.0,,2024-01-05 +2024-01-05 14:55:00,118.20654164770252,,,,,,0.0,,2024-01-05 +2024-01-05 15:00:00,117.91930280006692,,,,,,0.0,,2024-01-05 +2024-01-05 15:05:00,115.61237102025108,,,,,,0.0,,2024-01-05 +2024-01-05 15:10:00,111.04855916378985,,,,,,0.0,,2024-01-05 +2024-01-05 15:15:00,108.0636625969962,,,,,,0.0,,2024-01-05 +2024-01-05 15:20:00,101.87602877780743,,,,,,,,2024-01-05 +2024-01-05 15:25:00,97.32823531367885,,,,,,,,2024-01-05 +2024-01-05 15:30:00,96.26231191145688,,,,,,,,2024-01-05 +2024-01-05 15:35:00,96.76727122799856,,,,,,,,2024-01-05 +2024-01-05 15:40:00,100.68466153905028,,,,,,,,2024-01-05 +2024-01-05 15:45:00,103.72741576421232,,,,,,,,2024-01-05 +2024-01-05 15:50:00,110.42157459497083,,,,,,,,2024-01-05 +2024-01-05 15:55:00,115.44199706179865,,,,,,,,2024-01-05 +2024-01-05 16:00:00,116.68811872417224,,,,,,,,2024-01-05 +2024-01-05 16:05:00,117.48209738819432,,,,,,,,2024-01-05 +2024-01-05 16:10:00,117.96062122788322,,,,,,,,2024-01-05 +2024-01-05 16:15:00,118.7890365361263,,,,,,,,2024-01-05 +2024-01-05 16:20:00,122.87277212030256,,,,,,,,2024-01-05 +2024-01-05 16:25:00,129.93264435441742,,,,,,,,2024-01-05 +2024-01-05 16:30:00,131.00017736237615,,,,,,,,2024-01-05 +2024-01-05 16:35:00,132.5263246332809,,,,,,,,2024-01-05 +2024-01-05 16:40:00,133.20349823567997,,,,,,,,2024-01-05 +2024-01-05 16:45:00,133.2759983846775,,,,,,,,2024-01-05 +2024-01-05 16:50:00,133.1338617202023,,,,,,,,2024-01-05 +2024-01-05 16:55:00,133.06132132280356,,,,,,,,2024-01-05 +2024-01-05 17:00:00,133.18114675551766,,,,,,,,2024-01-05 +2024-01-05 17:05:00,133.5366001044613,,,,,,,,2024-01-05 +2024-01-05 17:10:00,133.61038985060986,,,,,,,,2024-01-05 +2024-01-05 17:15:00,133.4389767467611,,,,,,,,2024-01-05 +2024-01-05 17:20:00,132.91874419196742,,,,,,,,2024-01-05 +2024-01-05 17:25:00,132.63371512632082,,,,,,,,2024-01-05 +2024-01-05 17:30:00,132.67330656026948,,,,,,,,2024-01-05 +2024-01-05 17:35:00,132.72982881474462,,,,,,,,2024-01-05 +2024-01-05 17:40:00,132.02185316378217,,,,,,,,2024-01-05 +2024-01-05 17:45:00,131.27955589424232,,,,,,,,2024-01-05 +2024-01-05 17:50:00,130.11428440497315,,,,,,,,2024-01-05 +2024-01-05 17:55:00,131.30310456652563,,,,,,,,2024-01-05 +2024-01-05 18:00:00,133.15482458788944,,,,,,,,2024-01-05 +2024-01-05 18:05:00,137.5857300984736,,,,,,,,2024-01-05 +2024-01-05 18:10:00,139.6439895314767,ANNOUNCE_MEAL,,,,,22.666666666666668,22.666666666666668,2024-01-05 +2024-01-05 18:15:00,138.97130369050493,,,,,,0.0,,2024-01-05 +2024-01-05 18:20:00,135.85166467203896,,,,,,0.0,,2024-01-05 +2024-01-05 18:25:00,134.1630121621691,,,,,,0.0,,2024-01-05 +2024-01-05 18:30:00,135.2235975387154,,,,,,0.0,,2024-01-05 +2024-01-05 18:35:00,141.71071286836226,,,,,,0.0,,2024-01-05 +2024-01-05 18:40:00,152.40280476014448,,,,,,0.0,,2024-01-05 +2024-01-05 18:45:00,157.82952748913053,,,,,,0.0,,2024-01-05 +2024-01-05 18:50:00,165.14583270385742,,,,,,0.0,,2024-01-05 +2024-01-05 18:55:00,168.8148749515859,,,,,,0.0,,2024-01-05 +2024-01-05 19:00:00,178.08525446332936,,,,,,0.0,,2024-01-05 +2024-01-05 19:05:00,185.2931179250368,,,,,,0.0,,2024-01-05 +2024-01-05 19:10:00,181.41326830686813,,,,,,0.0,,2024-01-05 +2024-01-05 19:15:00,176.83665134051148,,,,,,0.0,,2024-01-05 +2024-01-05 19:20:00,165.41371027686716,,,,,,0.0,,2024-01-05 +2024-01-05 19:25:00,154.8476774485272,,,,,,0.0,,2024-01-05 +2024-01-05 19:30:00,151.0246291688138,,,,,,0.0,,2024-01-05 +2024-01-05 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20:55:00,126.9846388906789,,,,,,0.0,,2024-01-05 +2024-01-05 21:00:00,126.62771134703522,,,,,,0.0,,2024-01-05 +2024-01-05 21:05:00,125.19049126036478,,,,,,0.0,,2024-01-05 +2024-01-05 21:10:00,123.41463288721552,,,,,,0.0,,2024-01-05 +2024-01-05 21:15:00,122.63501390682404,,,,,,,,2024-01-05 +2024-01-05 21:20:00,121.76499543080848,,,,,,,,2024-01-05 +2024-01-05 21:25:00,123.36332148164286,,,,,,,,2024-01-05 +2024-01-05 21:30:00,126.75342071709042,,,,,,,,2024-01-05 +2024-01-05 21:35:00,131.09560442770356,,,,,,,,2024-01-05 +2024-01-05 21:40:00,133.20559294331713,,,,,,,,2024-01-05 +2024-01-05 21:45:00,133.81009743327178,LOW_CARB_MEAL,,,,,3.6666666666666665,3.6666666666666665,2024-01-05 +2024-01-05 21:50:00,135.24268167374905,,,,,,,,2024-01-05 +2024-01-05 21:55:00,139.76204591745042,,,,,,,,2024-01-05 +2024-01-05 22:00:00,143.04781727048902,,,,,,,,2024-01-05 +2024-01-05 22:05:00,148.85967222808264,,,,,,,,2024-01-05 +2024-01-05 22:10:00,150.88359429379568,,,,,,,,2024-01-05 +2024-01-05 22:15:00,149.9959234633744,,,,,,,,2024-01-05 +2024-01-05 22:20:00,145.4930040308461,,,,,,,,2024-01-05 +2024-01-05 22:25:00,139.5132232609589,,,,,,,,2024-01-05 +2024-01-05 22:30:00,136.61000451110766,,,,,,,,2024-01-05 +2024-01-05 22:35:00,131.07914670443063,,,,,,,,2024-01-05 +2024-01-05 22:40:00,125.552884519738,,,,,,,,2024-01-05 +2024-01-05 22:45:00,122.79235635051988,,,,,,,,2024-01-05 +2024-01-05 22:50:00,118.38318356015948,,,,,,,,2024-01-05 +2024-01-05 22:55:00,117.30496325720522,,,,,,,,2024-01-05 +2024-01-05 23:00:00,118.29031031024437,,,,,,,,2024-01-05 +2024-01-05 23:05:00,120.83582943370462,,,,,,,,2024-01-05 +2024-01-05 23:10:00,119.71172903399852,,,,,,,,2024-01-05 +2024-01-05 23:15:00,116.68699496591567,,,,,,,,2024-01-05 +2024-01-05 23:20:00,107.77251170686576,,,,,,,,2024-01-05 +2024-01-05 23:25:00,99.42646742922658,,,,,,,,2024-01-05 +2024-01-05 23:30:00,96.73340097161216,,,,,,,,2024-01-05 +2024-01-05 23:35:00,94.0681542088269,,,,,,,,2024-01-05 +2024-01-05 23:40:00,93.604600270755,,,,,,,,2024-01-05 +2024-01-05 23:45:00,93.66364552594658,,,,,,,,2024-01-05 +2024-01-05 23:50:00,93.29383855304732,,,,,,,,2024-01-05 +2024-01-05 23:55:00,94.40957263252406,,,,,,,,2024-01-05 +2024-01-06 00:00:00,101.36609750182572,,,,,,,,2024-01-05 +2024-01-06 00:05:00,103.10565114640784,,,,,,,,2024-01-05 +2024-01-06 00:10:00,94.71483628108696,,,,,,,,2024-01-05 +2024-01-06 00:15:00,89.00864069979541,,,,,,,,2024-01-05 +2024-01-06 00:20:00,79.15158932997215,,,,,,,,2024-01-05 +2024-01-06 00:25:00,77.35678010939353,,,,,,,,2024-01-05 +2024-01-06 00:30:00,80.83486211888003,,,,,,,,2024-01-05 +2024-01-06 00:35:00,92.70114838517472,,,,,,,,2024-01-05 +2024-01-06 00:40:00,103.48572315712892,,,,,,,,2024-01-05 +2024-01-06 00:45:00,106.42287763668448,,,,,,,,2024-01-05 +2024-01-06 00:50:00,109.22286680076884,,,,,,,,2024-01-05 +2024-01-06 00:55:00,112.68439931404204,,,,,,,,2024-01-05 +2024-01-06 01:00:00,115.95600320462492,,,,,,,,2024-01-05 +2024-01-06 01:05:00,124.5454863368856,,,,,,,,2024-01-05 +2024-01-06 01:10:00,133.0758077029966,,,,,,,,2024-01-05 +2024-01-06 01:15:00,136.48746872250376,,,,,,,,2024-01-05 +2024-01-06 01:20:00,141.47127612861803,,,,,,,,2024-01-05 +2024-01-06 01:25:00,144.39697466655588,,,,,,,,2024-01-05 +2024-01-06 01:30:00,145.4433635300922,,,,,,,,2024-01-05 +2024-01-06 01:35:00,149.13725578577413,,,,,,,,2024-01-05 +2024-01-06 01:40:00,158.80081730668485,,,,,,,,2024-01-05 +2024-01-06 01:45:00,166.45704426578726,,,,,,,,2024-01-05 +2024-01-06 01:50:00,182.2930461348345,,,,,,,,2024-01-05 +2024-01-06 01:55:00,189.65628399218457,,,,,,,,2024-01-05 +2024-01-06 02:00:00,187.7510927540363,,,,,,,,2024-01-05 +2024-01-06 02:05:00,175.38848836187296,,,,,,,,2024-01-05 +2024-01-06 02:10:00,158.097581271719,,,,,,,,2024-01-05 +2024-01-06 02:15:00,150.0343774488556,,,,,,,,2024-01-05 +2024-01-06 02:20:00,139.93350210865694,,,,,,,,2024-01-05 +2024-01-06 02:25:00,141.911869413545,,,,,,,,2024-01-05 +2024-01-06 02:30:00,142.3828545800037,,,,,,,,2024-01-05 +2024-01-06 02:35:00,141.4889549147954,,,,,,,,2024-01-05 +2024-01-06 02:40:00,139.07117298614216,,,,,,,,2024-01-05 +2024-01-06 02:45:00,137.6352373733596,,,,,,,,2024-01-05 +2024-01-06 02:50:00,134.99377502949744,,,,,,,,2024-01-05 +2024-01-06 02:55:00,133.54601263579812,,,,,,,,2024-01-05 +2024-01-06 03:00:00,133.43629782974028,,,,,,,,2024-01-05 +2024-01-06 03:05:00,133.57068731501855,,,,,,,,2024-01-05 +2024-01-06 03:10:00,132.55964934738614,,,,,,,,2024-01-05 +2024-01-06 03:15:00,131.2730270336274,,,,,,,,2024-01-05 +2024-01-06 03:20:00,128.30495953482526,,,,,,,,2024-01-05 +2024-01-06 03:25:00,127.0790654275504,,,,,,,,2024-01-05 +2024-01-06 03:30:00,127.7058456645046,,,,,,,,2024-01-05 +2024-01-06 03:35:00,130.67841298481895,,,,,,,,2024-01-05 +2024-01-06 03:40:00,134.1715737728704,,,,,,,,2024-01-05 +2024-01-06 03:45:00,135.5663753269169,,,,,,,,2024-01-05 +2024-01-06 03:50:00,137.41827414559168,,,,,,,,2024-01-05 +2024-01-06 03:55:00,138.05765529229396,,,,,,,,2024-01-05 +2024-01-06 04:00:00,137.98878438044554,,,,,,,,2024-01-06 +2024-01-06 04:05:00,137.58723714046465,,,,,,,,2024-01-06 +2024-01-06 04:10:00,137.65562412932812,,,,,,,,2024-01-06 +2024-01-06 04:15:00,138.05883580159195,,,,,,,,2024-01-06 +2024-01-06 04:20:00,139.14448154003338,,,,,,,,2024-01-06 +2024-01-06 04:25:00,139.66879250371508,,,,,,,,2024-01-06 +2024-01-06 04:30:00,139.48956291603537,,,,,,,,2024-01-06 +2024-01-06 04:35:00,138.65961298251284,,,,,,,,2024-01-06 +2024-01-06 04:40:00,138.09811945372604,,,,,,,,2024-01-06 +2024-01-06 04:45:00,138.2544759200589,,,,,,,,2024-01-06 +2024-01-06 04:50:00,140.11430063861675,,,,,,,,2024-01-06 +2024-01-06 04:55:00,144.0542782480201,,,,,,,,2024-01-06 +2024-01-06 05:00:00,144.6763154822605,,,,,,,,2024-01-06 +2024-01-06 05:05:00,144.1687008558181,,,,,,,,2024-01-06 +2024-01-06 05:10:00,142.3366137631033,,,,,,,,2024-01-06 +2024-01-06 05:15:00,141.3030812232613,,,,,,,,2024-01-06 +2024-01-06 05:20:00,139.7594624477871,,,,,,,,2024-01-06 +2024-01-06 05:25:00,139.90708330607174,,,,,,,,2024-01-06 +2024-01-06 05:30:00,140.84824685260315,,,,,,,,2024-01-06 +2024-01-06 05:35:00,143.9110308253736,,,,,,,,2024-01-06 +2024-01-06 05:40:00,147.4200434668659,,,,,,,,2024-01-06 +2024-01-06 05:45:00,149.06515412912864,,,,,,,,2024-01-06 +2024-01-06 05:50:00,152.65331009881714,,,,,,,,2024-01-06 +2024-01-06 05:55:00,157.71216997857223,,,,,,,,2024-01-06 +2024-01-06 06:00:00,160.8624944617503,,,,,,,,2024-01-06 +2024-01-06 06:05:00,166.10398066975992,,,,,,,,2024-01-06 +2024-01-06 06:10:00,165.96965296603406,,,,,,,,2024-01-06 +2024-01-06 06:15:00,163.0106286266008,,,,,,,,2024-01-06 +2024-01-06 06:20:00,154.02677125956157,,,,,,,,2024-01-06 +2024-01-06 06:25:00,146.28938886182243,,,,,,,,2024-01-06 +2024-01-06 06:30:00,144.30744098738398,,,,,,,,2024-01-06 +2024-01-06 06:35:00,142.70106073327153,,,,,,,,2024-01-06 +2024-01-06 06:40:00,140.70316988209612,,,,,,,,2024-01-06 +2024-01-06 06:45:00,138.6380833275874,,,,,,,,2024-01-06 +2024-01-06 06:50:00,133.5117272470633,,,,,,,,2024-01-06 +2024-01-06 06:55:00,129.727926417994,,,,,,,,2024-01-06 +2024-01-06 07:00:00,129.09173286026322,,,,,,,,2024-01-06 +2024-01-06 07:05:00,130.00969109099506,,,,,,,,2024-01-06 +2024-01-06 07:10:00,132.69087843897793,,,,,,,,2024-01-06 +2024-01-06 07:15:00,134.25872739773493,,,,,,,,2024-01-06 +2024-01-06 07:20:00,136.98103797432657,,,,,,,,2024-01-06 +2024-01-06 07:25:00,136.78608638223912,ANNOUNCE_MEAL,,,,,8.0,8.0,2024-01-06 +2024-01-06 07:30:00,133.70907061496828,,,,,,0.0,,2024-01-06 +2024-01-06 07:35:00,134.05017618930307,,,,,,0.0,,2024-01-06 +2024-01-06 07:40:00,140.44164910573426,,,,,,0.0,,2024-01-06 +2024-01-06 07:45:00,145.1003400755836,,,,,,0.0,,2024-01-06 +2024-01-06 07:50:00,155.33711697640672,,,,,,0.0,,2024-01-06 +2024-01-06 07:55:00,163.1003079821556,,,,,,0.0,,2024-01-06 +2024-01-06 08:00:00,164.87360640993427,,,,,,0.0,,2024-01-06 +2024-01-06 08:05:00,164.63852536261695,,,,,,0.0,,2024-01-06 +2024-01-06 08:10:00,161.4088606402272,,,,,,0.0,,2024-01-06 +2024-01-06 08:15:00,159.45895275838998,,,,,,0.0,,2024-01-06 +2024-01-06 08:20:00,156.0594821507739,,,,,,0.0,,2024-01-06 +2024-01-06 08:25:00,154.6852194136802,,,,,,0.0,,2024-01-06 +2024-01-06 08:30:00,155.04544049594068,,,,,,0.0,,2024-01-06 +2024-01-06 08:35:00,156.82880213590647,,,,,,0.0,,2024-01-06 +2024-01-06 08:40:00,158.02194484859135,,,,,,0.0,,2024-01-06 +2024-01-06 08:45:00,157.86208335445554,,,,,,0.0,,2024-01-06 +2024-01-06 08:50:00,156.59317106578268,,,,,,0.0,,2024-01-06 +2024-01-06 08:55:00,155.43550211678087,,,,,,0.0,,2024-01-06 +2024-01-06 09:00:00,155.26157549684194,,,,,,0.0,,2024-01-06 +2024-01-06 09:05:00,155.3466976161215,,,,,,0.0,,2024-01-06 +2024-01-06 09:10:00,155.0767769651755,,,,,,0.0,,2024-01-06 +2024-01-06 09:15:00,154.56611701451436,,,,,,0.0,,2024-01-06 +2024-01-06 09:20:00,153.09975679623,,,,,,0.0,,2024-01-06 +2024-01-06 09:25:00,151.74742857433125,,,,,,0.0,,2024-01-06 +2024-01-06 09:30:00,151.25836125214187,,,,,,0.0,,2024-01-06 +2024-01-06 09:35:00,150.0612885245162,,,,,,0.0,,2024-01-06 +2024-01-06 09:40:00,147.42820173946558,,,,,,0.0,,2024-01-06 +2024-01-06 09:45:00,145.14366455312827,,,,,,0.0,,2024-01-06 +2024-01-06 09:50:00,137.7787181729289,,,,,,0.0,,2024-01-06 +2024-01-06 09:55:00,127.80403861342268,,,,,,0.0,,2024-01-06 +2024-01-06 10:00:00,126.78602836493228,,,,,,0.0,,2024-01-06 +2024-01-06 10:05:00,124.82746120827343,,,,,,0.0,,2024-01-06 +2024-01-06 10:10:00,123.08787553514792,,,,,,0.0,,2024-01-06 +2024-01-06 10:15:00,122.34352262891004,,,,,,0.0,,2024-01-06 +2024-01-06 10:20:00,121.1923478035911,,,,,,0.0,,2024-01-06 +2024-01-06 10:25:00,120.60238718207452,,,,,,0.0,,2024-01-06 +2024-01-06 10:30:00,120.5235920534414,,,,,,,,2024-01-06 +2024-01-06 10:35:00,120.5458182952286,,,,,,,,2024-01-06 +2024-01-06 10:40:00,120.37663978062584,,,,,,,,2024-01-06 +2024-01-06 10:45:00,120.13475373987478,,,,,,,,2024-01-06 +2024-01-06 10:50:00,119.72011637957254,,,,,,,,2024-01-06 +2024-01-06 10:55:00,120.12689631180804,,,,,,,,2024-01-06 +2024-01-06 11:00:00,120.85269858867548,,,,,,,,2024-01-06 +2024-01-06 11:05:00,123.3361034085265,,,,,,,,2024-01-06 +2024-01-06 11:10:00,126.99806721916828,,,,,,,,2024-01-06 +2024-01-06 11:15:00,129.10085946270533,,,,,,,,2024-01-06 +2024-01-06 11:20:00,132.77732264703053,,,,,,,,2024-01-06 +2024-01-06 11:25:00,134.031115755617,,,,,,,,2024-01-06 +2024-01-06 11:30:00,133.4046160217988,,,,,,,,2024-01-06 +2024-01-06 11:35:00,130.6673061936171,ANNOUNCE_MEAL,,,,,26.666666666666668,26.666666666666668,2024-01-06 +2024-01-06 11:40:00,130.94723259982777,,,,,,0.0,,2024-01-06 +2024-01-06 11:45:00,132.22390701743234,,,,,,0.0,,2024-01-06 +2024-01-06 11:50:00,136.85049429549315,,,,,,0.0,,2024-01-06 +2024-01-06 11:55:00,143.35063935903892,,,,,,0.0,,2024-01-06 +2024-01-06 12:00:00,147.30171015923935,,,,,,0.0,,2024-01-06 +2024-01-06 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08:50:00,169.5993765611304,,,,,,0.0,,2024-01-07 +2024-01-07 08:55:00,162.5925925354749,,,,,,0.0,,2024-01-07 +2024-01-07 09:00:00,157.6133024472078,,,,,,0.0,,2024-01-07 +2024-01-07 09:05:00,146.97598170200226,,,,,,0.0,,2024-01-07 +2024-01-07 09:10:00,139.3016437902957,,,,,,0.0,,2024-01-07 +2024-01-07 09:15:00,137.54485646046436,,,,,,0.0,,2024-01-07 +2024-01-07 09:20:00,136.6143423653274,,,,,,0.0,,2024-01-07 +2024-01-07 09:25:00,135.6583718161149,,,,,,0.0,,2024-01-07 +2024-01-07 09:30:00,134.1817502750566,,,,,,0.0,,2024-01-07 +2024-01-07 09:35:00,129.54185679371213,,,,,,0.0,,2024-01-07 +2024-01-07 09:40:00,123.98000903238788,,,,,,0.0,,2024-01-07 +2024-01-07 09:45:00,121.34709166432545,,,,,,0.0,,2024-01-07 +2024-01-07 09:50:00,117.33876082534276,,,,,,0.0,,2024-01-07 +2024-01-07 09:55:00,116.24278069117685,,,,,,0.0,,2024-01-07 +2024-01-07 10:00:00,116.97375270943334,,,,,,0.0,,2024-01-07 +2024-01-07 10:05:00,119.34279305560965,,,,,,0.0,,2024-01-07 +2024-01-07 10:10:00,119.95672350780458,,,,,,0.0,,2024-01-07 +2024-01-07 10:15:00,118.83952484784828,,,,,,,,2024-01-07 +2024-01-07 10:20:00,114.75501754100296,,,,,,,,2024-01-07 +2024-01-07 10:25:00,110.52387324339064,,,,,,,,2024-01-07 +2024-01-07 10:30:00,109.04289857431117,,,,,,,,2024-01-07 +2024-01-07 10:35:00,107.40304809025989,,,,,,,,2024-01-07 +2024-01-07 10:40:00,107.07277364252334,,,,,,,,2024-01-07 +2024-01-07 10:45:00,107.23214425007755,,,,,,,,2024-01-07 +2024-01-07 10:50:00,107.87759039721256,,,,,,,,2024-01-07 +2024-01-07 10:55:00,109.09118313665758,,,,,,,,2024-01-07 +2024-01-07 11:00:00,111.03769693482518,,,,,,,,2024-01-07 +2024-01-07 11:05:00,115.45818940535696,,,,,,,,2024-01-07 +2024-01-07 11:10:00,120.2011557705368,,,,,,,,2024-01-07 +2024-01-07 11:15:00,122.51273017450112,,,,,,,,2024-01-07 +2024-01-07 11:20:00,126.5487217990718,,,,,,,,2024-01-07 +2024-01-07 11:25:00,129.11556382793367,,,,,,,,2024-01-07 +2024-01-07 11:30:00,129.69316934207887,,,,,,,,2024-01-07 +2024-01-07 11:35:00,129.89782774175654,,,,,,,,2024-01-07 +2024-01-07 11:40:00,129.808859114588,,,,,,,,2024-01-07 +2024-01-07 11:45:00,129.9044210346917,ANNOUNCE_MEAL,,,,,21.0,21.0,2024-01-07 +2024-01-07 11:50:00,129.86534821322394,,,,,,0.0,,2024-01-07 +2024-01-07 11:55:00,128.73847449661528,,,,,,0.0,,2024-01-07 +2024-01-07 12:00:00,127.98108900792973,,,,,,0.0,,2024-01-07 +2024-01-07 12:05:00,128.0956066222204,,,,,,0.0,,2024-01-07 +2024-01-07 12:10:00,132.21500318238247,,,,,,0.0,,2024-01-07 +2024-01-07 12:15:00,135.48913355819147,,,,,,0.0,,2024-01-07 +2024-01-07 12:20:00,142.3733028976709,,,,,,0.0,,2024-01-07 +2024-01-07 12:25:00,147.7092602083077,,,,,,0.0,,2024-01-07 +2024-01-07 12:30:00,149.44976724044707,,,,,,0.0,,2024-01-07 +2024-01-07 12:35:00,151.10482879344914,,,,,,0.0,,2024-01-07 +2024-01-07 12:40:00,150.7003248279036,,,,,,0.0,,2024-01-07 +2024-01-07 12:45:00,149.98141161142817,,,,,,0.0,,2024-01-07 +2024-01-07 12:50:00,148.74922584732585,,,,,,0.0,,2024-01-07 +2024-01-07 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15:40:00,109.0669737348583,,,,,,,,2024-01-07 +2024-01-07 15:45:00,112.83573341031943,,,,,,,,2024-01-07 +2024-01-07 15:50:00,121.98543573683496,,,,,,,,2024-01-07 +2024-01-07 15:55:00,131.89383925513965,,,,,,,,2024-01-07 +2024-01-07 16:00:00,133.0705484158885,,,,,,,,2024-01-07 +2024-01-07 16:05:00,133.31400169061828,,,,,,,,2024-01-07 +2024-01-07 16:10:00,132.0906237989631,,,,,,,,2024-01-07 +2024-01-07 16:15:00,131.48490781835784,,,,,,,,2024-01-07 +2024-01-07 16:20:00,131.49614931515038,,,,,,,,2024-01-07 +2024-01-07 16:25:00,134.7924327589274,,,,,,,,2024-01-07 +2024-01-07 16:30:00,137.94464001836758,,,,,,,,2024-01-07 +2024-01-07 16:35:00,145.14732087422334,,,,,,,,2024-01-07 +2024-01-07 16:40:00,149.72271575440053,,,,,,,,2024-01-07 +2024-01-07 16:45:00,149.9883646869191,,,,,,,,2024-01-07 +2024-01-07 16:50:00,147.31967173344913,ANNOUNCE_MEAL,,,,,22.666666666666668,22.666666666666668,2024-01-07 +2024-01-07 16:55:00,142.63860020082035,,,,,,0.0,,2024-01-07 +2024-01-07 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23:55:00,125.6795779585376,,,,,,,,2024-01-07 +2024-01-08 00:00:00,125.08371463970458,,,,,,,,2024-01-07 diff --git a/0_meal_identification/meal_identification/data/interim/2024-11-23_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv b/0_meal_identification/meal_identification/data/interim/2024-11-23_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv deleted file mode 100644 index 03f601f..0000000 --- a/0_meal_identification/meal_identification/data/interim/2024-11-23_500030__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv +++ /dev/null @@ -1,26497 +0,0 @@ -date,bgl,msg_type,affects_fob,affects_iob,dose_units,food_g,food_glycemic_index,food_g_keep,day_start_shift -2024-07-01 00:00:00-05:00,98.0,,,,,,,,2024-06-30 -2024-07-01 00:05:00-05:00,100.0,,,,,,,,2024-06-30 -2024-07-01 00:10:00-05:00,98.0,,,,,,,,2024-06-30 -2024-07-01 00:15:00-05:00,94.0,,,,,,,,2024-06-30 -2024-07-01 00:20:00-05:00,94.0,,,,,,,,2024-06-30 -2024-07-01 00:25:00-05:00,99.0,,,,,,,,2024-06-30 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10:10:00-05:00,128.0,,,,,,,,2024-07-01 -2024-07-01 10:15:00-05:00,127.0,,,,,,,,2024-07-01 -2024-07-01 10:20:00-05:00,128.0,,,,,,,,2024-07-01 -2024-07-01 10:25:00-05:00,124.0,,,,,,,,2024-07-01 -2024-07-01 10:30:00-05:00,119.0,,,,,,,,2024-07-01 -2024-07-01 10:35:00-05:00,116.0,,,,,,,,2024-07-01 -2024-07-01 10:40:00-05:00,110.0,,,,,,,,2024-07-01 -2024-07-01 10:45:00-05:00,107.0,,,,,,,,2024-07-01 -2024-07-01 10:50:00-05:00,103.0,,,,,,,,2024-07-01 -2024-07-01 10:55:00-05:00,100.0,,,,,,,,2024-07-01 -2024-07-01 11:00:00-05:00,99.0,,,,,,,,2024-07-01 -2024-07-01 11:05:00-05:00,97.0,,,,,,,,2024-07-01 -2024-07-01 11:10:00-05:00,95.0,,,,,,,,2024-07-01 -2024-07-01 11:15:00-05:00,94.0,,,,,,,,2024-07-01 -2024-07-01 11:20:00-05:00,91.0,,,,,,,,2024-07-01 -2024-07-01 11:25:00-05:00,89.0,,,,,,,,2024-07-01 -2024-07-01 11:30:00-05:00,87.0,,,,,,,,2024-07-01 -2024-07-01 11:35:00-05:00,86.0,,,,,,,,2024-07-01 -2024-07-01 11:40:00-05:00,87.0,,,,,,,,2024-07-01 -2024-07-01 11:45:00-05:00,86.0,,,,,,,,2024-07-01 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13:25:00-05:00,72.0,,,,,,,,2024-07-01 -2024-07-01 13:30:00-05:00,72.0,INTERVENTION_SNACK,True,False,,6.0,1.0,,2024-07-01 -2024-07-01 13:35:00-05:00,79.0,,,,,,,,2024-07-01 -2024-07-01 13:40:00-05:00,85.0,,,,,,,,2024-07-01 -2024-07-01 13:45:00-05:00,85.0,TEXT,False,False,6.0,,,,2024-07-01 -2024-07-01 13:50:00-05:00,103.0,,,,,,,,2024-07-01 -2024-07-01 13:55:00-05:00,103.0,ANNOUNCE_MEAL,,,,45.0,,45.0,2024-07-01 -2024-07-01 14:00:00-05:00,115.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:05:00-05:00,114.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:10:00-05:00,110.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:15:00-05:00,110.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:20:00-05:00,115.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:25:00-05:00,120.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:30:00-05:00,125.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:35:00-05:00,129.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:40:00-05:00,129.0,,,,,0.0,,,2024-07-01 -2024-07-01 14:45:00-05:00,131.0,,,,,0.0,,,2024-07-01 -2024-07-01 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16:20:00-05:00,151.0,,False,True,3.0,0.0,,,2024-07-01 -2024-07-01 16:25:00-05:00,154.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:30:00-05:00,156.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:35:00-05:00,156.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:40:00-05:00,154.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:45:00-05:00,152.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:50:00-05:00,149.0,,,,,0.0,,,2024-07-01 -2024-07-01 16:55:00-05:00,145.0,,,,,0.0,,,2024-07-01 -2024-07-01 17:00:00-05:00,140.0,,,,,,,,2024-07-01 -2024-07-01 17:05:00-05:00,134.0,,,,,,,,2024-07-01 -2024-07-01 17:10:00-05:00,133.0,,,,,,,,2024-07-01 -2024-07-01 17:15:00-05:00,128.0,,,,,,,,2024-07-01 -2024-07-01 17:20:00-05:00,121.0,,,,,,,,2024-07-01 -2024-07-01 17:25:00-05:00,114.0,,,,,,,,2024-07-01 -2024-07-01 17:30:00-05:00,105.0,,,,,,,,2024-07-01 -2024-07-01 17:35:00-05:00,97.0,,,,,,,,2024-07-01 -2024-07-01 17:40:00-05:00,97.0,TEXT,False,False,,50.0,0.5,,2024-07-01 -2024-07-01 17:45:00-05:00,89.0,,,,,,,,2024-07-01 -2024-07-01 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14:45:00-05:00,111.0,,,,,0.0,,,2024-07-11 -2024-07-11 14:50:00-05:00,111.0,,,,,0.0,,,2024-07-11 -2024-07-11 14:55:00-05:00,114.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:00:00-05:00,119.0,,False,True,,0.0,,,2024-07-11 -2024-07-11 15:05:00-05:00,125.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:10:00-05:00,125.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:15:00-05:00,119.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:20:00-05:00,107.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:25:00-05:00,96.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:30:00-05:00,87.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:35:00-05:00,81.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:40:00-05:00,77.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:45:00-05:00,75.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:50:00-05:00,73.0,,,,,0.0,,,2024-07-11 -2024-07-11 15:55:00-05:00,72.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:00:00-05:00,72.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:05:00-05:00,71.0,,,,,0.0,,,2024-07-11 -2024-07-11 16:10:00-05:00,70.0,,,,,0.0,,,2024-07-11 -2024-07-11 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07:30:00-05:00,194.0,,,,,,,,2024-07-13 -2024-07-13 07:35:00-05:00,206.0,,,,,,,,2024-07-13 -2024-07-13 07:40:00-05:00,210.0,,,,,,,,2024-07-13 -2024-07-13 07:45:00-05:00,206.0,,,,,,,,2024-07-13 -2024-07-13 07:50:00-05:00,208.0,,,,,,,,2024-07-13 -2024-07-13 07:55:00-05:00,208.0,,,,,,,,2024-07-13 -2024-07-13 08:00:00-05:00,207.0,,,,,,,,2024-07-13 -2024-07-13 08:05:00-05:00,200.0,,,,,,,,2024-07-13 -2024-07-13 08:10:00-05:00,197.0,,,,,,,,2024-07-13 -2024-07-13 08:15:00-05:00,201.0,,,,,,,,2024-07-13 -2024-07-13 08:20:00-05:00,200.0,,,,,,,,2024-07-13 -2024-07-13 08:25:00-05:00,197.0,,,,,,,,2024-07-13 -2024-07-13 08:30:00-05:00,197.0,DOSE_INSULIN,False,True,8.0,,,,2024-07-13 -2024-07-13 08:35:00-05:00,193.0,TEXT,False,False,,,,,2024-07-13 -2024-07-13 08:40:00-05:00,195.0,,,,,,,,2024-07-13 -2024-07-13 08:45:00-05:00,197.0,,,,,,,,2024-07-13 -2024-07-13 08:50:00-05:00,197.0,,,,,,,,2024-07-13 -2024-07-13 08:55:00-05:00,191.0,,,,,,,,2024-07-13 -2024-07-13 09:00:00-05:00,179.0,,,,,,,,2024-07-13 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10:40:00-05:00,104.0,,,,,,,,2024-07-13 -2024-07-13 10:45:00-05:00,103.0,,,,,,,,2024-07-13 -2024-07-13 10:50:00-05:00,100.0,,,,,,,,2024-07-13 -2024-07-13 10:55:00-05:00,100.0,DOSE_INSULIN,False,True,4.0,35.0,0.5,,2024-07-13 -2024-07-13 11:00:00-05:00,98.0,,,,,,,,2024-07-13 -2024-07-13 11:05:00-05:00,99.0,,,,,,,,2024-07-13 -2024-07-13 11:10:00-05:00,97.0,,,,,,,,2024-07-13 -2024-07-13 11:15:00-05:00,100.0,,,,,,,,2024-07-13 -2024-07-13 11:20:00-05:00,106.0,,,,,,,,2024-07-13 -2024-07-13 11:25:00-05:00,111.0,,,,,,,,2024-07-13 -2024-07-13 11:30:00-05:00,114.0,,,,,,,,2024-07-13 -2024-07-13 11:35:00-05:00,113.0,,,,,,,,2024-07-13 -2024-07-13 11:40:00-05:00,105.0,,,,,,,,2024-07-13 -2024-07-13 11:45:00-05:00,99.0,,,,,,,,2024-07-13 -2024-07-13 11:50:00-05:00,94.0,,,,,,,,2024-07-13 -2024-07-13 11:55:00-05:00,88.0,,,,,,,,2024-07-13 -2024-07-13 12:00:00-05:00,81.0,,,,,,,,2024-07-13 -2024-07-13 12:05:00-05:00,74.0,,,,,,,,2024-07-13 -2024-07-13 12:10:00-05:00,70.0,INTERVENTION_SNACK,True,False,,6.0,1.0,,2024-07-13 -2024-07-13 12:15:00-05:00,71.0,,,,,,,,2024-07-13 -2024-07-13 12:20:00-05:00,71.0,,,,,,,,2024-07-13 -2024-07-13 12:25:00-05:00,68.0,,,,,,,,2024-07-13 -2024-07-13 12:30:00-05:00,63.0,INTERVENTION_SNACK,True,False,,6.0,1.0,,2024-07-13 -2024-07-13 12:35:00-05:00,59.0,,,,,,,,2024-07-13 -2024-07-13 12:40:00-05:00,57.0,,,,,,,,2024-07-13 -2024-07-13 12:45:00-05:00,61.0,,,,,,,,2024-07-13 -2024-07-13 12:50:00-05:00,68.0,,,,,,,,2024-07-13 -2024-07-13 12:55:00-05:00,68.0,ANNOUNCE_MEAL,,,,10.0,,10.0,2024-07-13 -2024-07-13 13:00:00-05:00,83.0,,,,,,,,2024-07-13 -2024-07-13 13:05:00-05:00,84.0,,,,,,,,2024-07-13 -2024-07-13 13:10:00-05:00,84.0,,,,,,,,2024-07-13 -2024-07-13 13:15:00-05:00,86.0,,,,,,,,2024-07-13 -2024-07-13 13:20:00-05:00,84.0,,,,,,,,2024-07-13 -2024-07-13 13:25:00-05:00,84.0,,,,,,,,2024-07-13 -2024-07-13 13:30:00-05:00,85.0,,,,,,,,2024-07-13 -2024-07-13 13:35:00-05:00,85.0,DOSE_INSULIN,False,True,6.0,,,,2024-07-13 -2024-07-13 13:40:00-05:00,79.0,,,,,,,,2024-07-13 -2024-07-13 13:45:00-05:00,79.0,ANNOUNCE_MEAL,,,,91.0,,40.0,2024-07-13 -2024-07-13 13:50:00-05:00,74.0,,,,,0.0,,,2024-07-13 -2024-07-13 13:55:00-05:00,76.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:00:00-05:00,79.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:05:00-05:00,78.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:10:00-05:00,75.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:15:00-05:00,74.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:20:00-05:00,68.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:25:00-05:00,64.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:30:00-05:00,64.0,,True,False,,0.0,1.0,,2024-07-13 -2024-07-13 14:35:00-05:00,51.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:40:00-05:00,45.0,,True,False,,0.0,1.0,,2024-07-13 -2024-07-13 14:45:00-05:00,43.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:50:00-05:00,44.0,,,,,0.0,,,2024-07-13 -2024-07-13 14:55:00-05:00,45.0,,,,,0.0,,,2024-07-13 -2024-07-13 15:00:00-05:00,51.0,,,,,0.0,,,2024-07-13 -2024-07-13 15:05:00-05:00,52.0,,,,,0.0,,,2024-07-13 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18:15:00-05:00,160.0,DOSE_INSULIN,False,True,5.0,,,,2024-07-13 -2024-07-13 18:20:00-05:00,169.0,,,,,,,,2024-07-13 -2024-07-13 18:25:00-05:00,172.0,,,,,,,,2024-07-13 -2024-07-13 18:30:00-05:00,175.0,,,,,,,,2024-07-13 -2024-07-13 18:35:00-05:00,182.0,,,,,,,,2024-07-13 -2024-07-13 18:40:00-05:00,188.0,,,,,,,,2024-07-13 -2024-07-13 18:45:00-05:00,191.0,,,,,,,,2024-07-13 -2024-07-13 18:50:00-05:00,193.0,,,,,,,,2024-07-13 -2024-07-13 18:55:00-05:00,194.0,,,,,,,,2024-07-13 -2024-07-13 19:00:00-05:00,197.0,,,,,,,,2024-07-13 -2024-07-13 19:05:00-05:00,198.0,,,,,,,,2024-07-13 -2024-07-13 19:10:00-05:00,193.0,,,,,,,,2024-07-13 -2024-07-13 19:15:00-05:00,193.0,DOSE_INSULIN,False,True,6.0,,,,2024-07-13 -2024-07-13 19:20:00-05:00,191.0,,,,,,,,2024-07-13 -2024-07-13 19:25:00-05:00,193.0,,,,,,,,2024-07-13 -2024-07-13 19:30:00-05:00,194.0,,,,,,,,2024-07-13 -2024-07-13 19:35:00-05:00,194.0,ANNOUNCE_MEAL,,,,50.0,,35.0,2024-07-13 -2024-07-13 19:40:00-05:00,197.0,,,,,0.0,,,2024-07-13 -2024-07-13 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21:50:00-05:00,134.0,,,,,,,,2024-07-17 -2024-07-17 21:55:00-05:00,136.0,,,,,,,,2024-07-17 -2024-07-17 22:00:00-05:00,138.0,,,,,,,,2024-07-17 -2024-07-17 22:05:00-05:00,136.0,,,,,,,,2024-07-17 -2024-07-17 22:10:00-05:00,150.0,,,,,,,,2024-07-17 -2024-07-17 22:15:00-05:00,144.0,,,,,,,,2024-07-17 -2024-07-17 22:20:00-05:00,147.0,,,,,,,,2024-07-17 -2024-07-17 22:25:00-05:00,147.0,,,,,,,,2024-07-17 -2024-07-17 22:30:00-05:00,145.0,,,,,,,,2024-07-17 -2024-07-17 22:35:00-05:00,148.0,,,,,,,,2024-07-17 -2024-07-17 22:40:00-05:00,152.0,,,,,,,,2024-07-17 -2024-07-17 22:45:00-05:00,153.0,,,,,,,,2024-07-17 -2024-07-17 22:50:00-05:00,153.0,,,,,,,,2024-07-17 -2024-07-17 22:55:00-05:00,152.0,,,,,,,,2024-07-17 -2024-07-17 23:00:00-05:00,142.0,,,,,,,,2024-07-17 -2024-07-17 23:05:00-05:00,142.0,DOSE_INSULIN,False,True,3.0,,,,2024-07-17 -2024-07-17 23:10:00-05:00,128.0,,,,,,,,2024-07-17 -2024-07-17 23:15:00-05:00,132.0,,,,,,,,2024-07-17 -2024-07-17 23:20:00-05:00,133.0,,,,,,,,2024-07-17 -2024-07-17 23:25:00-05:00,134.0,,,,,,,,2024-07-17 -2024-07-17 23:30:00-05:00,126.0,,,,,,,,2024-07-17 -2024-07-17 23:35:00-05:00,130.0,,,,,,,,2024-07-17 -2024-07-17 23:40:00-05:00,130.0,,,,,,,,2024-07-17 -2024-07-17 23:45:00-05:00,126.0,,,,,,,,2024-07-17 -2024-07-17 23:50:00-05:00,125.0,,,,,,,,2024-07-17 -2024-07-17 23:55:00-05:00,122.0,,,,,,,,2024-07-17 -2024-07-18 00:00:00-05:00,118.0,,,,,,,,2024-07-17 -2024-07-18 00:05:00-05:00,107.0,,,,,,,,2024-07-17 -2024-07-18 00:10:00-05:00,104.0,,,,,,,,2024-07-17 -2024-07-18 00:15:00-05:00,96.0,,,,,,,,2024-07-17 -2024-07-18 00:20:00-05:00,88.0,,,,,,,,2024-07-17 -2024-07-18 00:25:00-05:00,86.0,,,,,,,,2024-07-17 -2024-07-18 00:30:00-05:00,87.0,,,,,,,,2024-07-17 -2024-07-18 00:35:00-05:00,82.0,,,,,,,,2024-07-17 -2024-07-18 00:40:00-05:00,76.0,,,,,,,,2024-07-17 -2024-07-18 00:45:00-05:00,85.0,,,,,,,,2024-07-17 -2024-07-18 00:50:00-05:00,84.0,,,,,,,,2024-07-17 -2024-07-18 00:55:00-05:00,83.0,,,,,,,,2024-07-17 -2024-07-18 01:00:00-05:00,76.0,,,,,,,,2024-07-17 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23:50:00-05:00,140.0,,,,,,,,2024-07-24 -2024-07-24 23:55:00-05:00,141.0,,,,,,,,2024-07-24 -2024-07-25 00:00:00-05:00,124.0,,,,,,,,2024-07-24 -2024-07-25 00:05:00-05:00,119.0,,,,,,,,2024-07-24 -2024-07-25 00:10:00-05:00,114.0,,,,,,,,2024-07-24 -2024-07-25 00:15:00-05:00,117.0,,,,,,,,2024-07-24 -2024-07-25 00:20:00-05:00,114.0,,,,,,,,2024-07-24 -2024-07-25 00:25:00-05:00,124.0,,,,,,,,2024-07-24 -2024-07-25 00:30:00-05:00,144.0,,,,,,,,2024-07-24 -2024-07-25 00:35:00-05:00,125.0,,,,,,,,2024-07-24 -2024-07-25 00:40:00-05:00,97.0,,,,,,,,2024-07-24 -2024-07-25 00:45:00-05:00,103.0,,,,,,,,2024-07-24 -2024-07-25 00:50:00-05:00,117.0,,,,,,,,2024-07-24 -2024-07-25 00:55:00-05:00,91.0,,,,,,,,2024-07-24 -2024-07-25 01:00:00-05:00,70.0,INTERVENTION_SNACK,True,False,,12.0,1.0,,2024-07-24 -2024-07-25 01:05:00-05:00,78.0,,,,,,,,2024-07-24 -2024-07-25 01:10:00-05:00,93.0,,,,,,,,2024-07-24 -2024-07-25 01:15:00-05:00,74.0,,,,,,,,2024-07-24 -2024-07-25 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18:50:00-05:00,131.0,,,,,0.0,,,2024-08-06 -2024-08-06 18:55:00-05:00,128.0,,,,,,,,2024-08-06 -2024-08-06 19:00:00-05:00,131.0,ANNOUNCE_MEAL,,,,56.0,,45.0,2024-08-06 -2024-08-06 19:05:00-05:00,129.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:10:00-05:00,128.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:15:00-05:00,119.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:20:00-05:00,118.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:25:00-05:00,122.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:30:00-05:00,126.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:35:00-05:00,126.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:40:00-05:00,128.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:45:00-05:00,136.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:50:00-05:00,134.0,,,,,0.0,,,2024-08-06 -2024-08-06 19:55:00-05:00,138.0,,,,,0.0,,,2024-08-06 -2024-08-06 20:00:00-05:00,161.0,,,,,0.0,,,2024-08-06 -2024-08-06 20:05:00-05:00,161.0,,False,True,4.0,0.0,,,2024-08-06 -2024-08-06 20:10:00-05:00,177.0,,,,,0.0,,,2024-08-06 -2024-08-06 20:15:00-05:00,172.0,,,,,0.0,,,2024-08-06 -2024-08-06 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13:45:00-05:00,82.0,,,,,,,,2024-08-17 -2024-08-17 13:50:00-05:00,83.0,,,,,,,,2024-08-17 -2024-08-17 13:55:00-05:00,79.0,,,,,,,,2024-08-17 -2024-08-17 14:00:00-05:00,76.0,,,,,,,,2024-08-17 -2024-08-17 14:05:00-05:00,81.0,,,,,,,,2024-08-17 -2024-08-17 14:10:00-05:00,81.0,,,,,,,,2024-08-17 -2024-08-17 14:15:00-05:00,89.0,,,,,,,,2024-08-17 -2024-08-17 14:20:00-05:00,91.0,,,,,,,,2024-08-17 -2024-08-17 14:25:00-05:00,90.0,,,,,,,,2024-08-17 -2024-08-17 14:30:00-05:00,,,,,,,,,2024-08-17 -2024-08-17 14:35:00-05:00,94.0,,,,,,,,2024-08-17 -2024-08-17 14:40:00-05:00,96.0,,,,,,,,2024-08-17 -2024-08-17 14:45:00-05:00,102.0,,,,,,,,2024-08-17 -2024-08-17 14:50:00-05:00,109.0,,,,,,,,2024-08-17 -2024-08-17 14:55:00-05:00,108.0,,,,,,,,2024-08-17 -2024-08-17 15:00:00-05:00,109.0,,,,,,,,2024-08-17 -2024-08-17 15:05:00-05:00,109.0,,,,,,,,2024-08-17 -2024-08-17 15:10:00-05:00,109.0,,,,,,,,2024-08-17 -2024-08-17 15:15:00-05:00,106.0,,,,,,,,2024-08-17 -2024-08-17 15:20:00-05:00,106.0,,,,,,,,2024-08-17 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19:55:00-05:00,139.0,,,,,,,,2024-08-17 -2024-08-17 20:00:00-05:00,152.0,,,,,,,,2024-08-17 -2024-08-17 20:05:00-05:00,162.0,,,,,,,,2024-08-17 -2024-08-17 20:10:00-05:00,157.0,,,,,,,,2024-08-17 -2024-08-17 20:15:00-05:00,160.0,,,,,,,,2024-08-17 -2024-08-17 20:20:00-05:00,163.0,,,,,,,,2024-08-17 -2024-08-17 20:25:00-05:00,168.0,,,,,,,,2024-08-17 -2024-08-17 20:30:00-05:00,174.0,,,,,,,,2024-08-17 -2024-08-17 20:35:00-05:00,179.0,,,,,,,,2024-08-17 -2024-08-17 20:40:00-05:00,187.0,,,,,,,,2024-08-17 -2024-08-17 20:45:00-05:00,189.0,,,,,,,,2024-08-17 -2024-08-17 20:50:00-05:00,194.0,DOSE_INSULIN,False,True,6.0,,,,2024-08-17 -2024-08-17 20:55:00-05:00,201.0,,,,,,,,2024-08-17 -2024-08-17 21:00:00-05:00,197.0,,,,,,,,2024-08-17 -2024-08-17 21:05:00-05:00,193.0,,,,,,,,2024-08-17 -2024-08-17 21:10:00-05:00,201.0,,,,,,,,2024-08-17 -2024-08-17 21:15:00-05:00,207.0,,,,,,,,2024-08-17 -2024-08-17 21:20:00-05:00,215.0,,,,,,,,2024-08-17 -2024-08-17 21:25:00-05:00,216.0,,,,,,,,2024-08-17 -2024-08-17 21:30:00-05:00,213.0,,,,,,,,2024-08-17 -2024-08-17 21:35:00-05:00,208.0,,,,,,,,2024-08-17 -2024-08-17 21:40:00-05:00,219.0,,,,,,,,2024-08-17 -2024-08-17 21:45:00-05:00,216.0,DOSE_INSULIN,False,True,3.0,,,,2024-08-17 -2024-08-17 21:50:00-05:00,201.0,,,,,,,,2024-08-17 -2024-08-17 21:55:00-05:00,185.0,,,,,,,,2024-08-17 -2024-08-17 22:00:00-05:00,165.0,,,,,,,,2024-08-17 -2024-08-17 22:05:00-05:00,155.0,,,,,,,,2024-08-17 -2024-08-17 22:10:00-05:00,139.0,,,,,,,,2024-08-17 -2024-08-17 22:15:00-05:00,145.0,,,,,,,,2024-08-17 -2024-08-17 22:20:00-05:00,164.0,,,,,,,,2024-08-17 -2024-08-17 22:25:00-05:00,161.0,,,,,,,,2024-08-17 -2024-08-17 22:30:00-05:00,150.0,,,,,,,,2024-08-17 -2024-08-17 22:35:00-05:00,148.0,,,,,,,,2024-08-17 -2024-08-17 22:40:00-05:00,146.0,,,,,,,,2024-08-17 -2024-08-17 22:45:00-05:00,144.0,,,,,,,,2024-08-17 -2024-08-17 22:50:00-05:00,144.0,DOSE_INSULIN,False,True,2.0,,,,2024-08-17 -2024-08-17 22:55:00-05:00,139.0,,,,,,,,2024-08-17 -2024-08-17 23:00:00-05:00,140.0,,,,,,,,2024-08-17 -2024-08-17 23:05:00-05:00,121.0,,,,,,,,2024-08-17 -2024-08-17 23:10:00-05:00,118.0,,,,,,,,2024-08-17 -2024-08-17 23:15:00-05:00,104.0,,,,,,,,2024-08-17 -2024-08-17 23:20:00-05:00,102.0,,,,,,,,2024-08-17 -2024-08-17 23:25:00-05:00,99.0,,,,,,,,2024-08-17 -2024-08-17 23:30:00-05:00,100.0,,,,,,,,2024-08-17 -2024-08-17 23:35:00-05:00,105.0,,,,,,,,2024-08-17 -2024-08-17 23:40:00-05:00,105.0,,,,,,,,2024-08-17 -2024-08-17 23:45:00-05:00,97.0,,,,,,,,2024-08-17 -2024-08-17 23:50:00-05:00,87.0,,,,,,,,2024-08-17 -2024-08-17 23:55:00-05:00,81.0,,,,,,,,2024-08-17 -2024-08-18 00:00:00-05:00,76.0,,,,,,,,2024-08-17 -2024-08-18 00:05:00-05:00,73.0,,,,,,,,2024-08-17 -2024-08-18 00:10:00-05:00,73.0,,,,,,,,2024-08-17 -2024-08-18 00:15:00-05:00,71.0,,,,,,,,2024-08-17 -2024-08-18 00:20:00-05:00,73.0,,,,,,,,2024-08-17 -2024-08-18 00:25:00-05:00,68.0,,,,,,,,2024-08-17 -2024-08-18 00:30:00-05:00,75.0,,,,,,,,2024-08-17 -2024-08-18 00:35:00-05:00,73.0,,,,,,,,2024-08-17 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09:10:00-05:00,145.0,,,,,,,,2024-09-18 -2024-09-18 09:15:00-05:00,150.0,,,,,,,,2024-09-18 -2024-09-18 09:20:00-05:00,152.0,,,,,,,,2024-09-18 -2024-09-18 09:25:00-05:00,153.0,DOSE_INSULIN,False,True,8.0,,,,2024-09-18 -2024-09-18 09:30:00-05:00,161.0,,,,,,,,2024-09-18 -2024-09-18 09:35:00-05:00,164.0,,,,,,,,2024-09-18 -2024-09-18 09:40:00-05:00,168.0,,,,,,,,2024-09-18 -2024-09-18 09:45:00-05:00,170.0,,,,,,,,2024-09-18 -2024-09-18 09:50:00-05:00,167.0,,,,,,,,2024-09-18 -2024-09-18 09:55:00-05:00,164.0,,,,,,,,2024-09-18 -2024-09-18 10:00:00-05:00,164.0,TEXT,False,False,,,,,2024-09-18 -2024-09-18 10:05:00-05:00,125.0,,,,,,,,2024-09-18 -2024-09-18 10:10:00-05:00,154.0,,,,,,,,2024-09-18 -2024-09-18 10:15:00-05:00,152.0,,,,,,,,2024-09-18 -2024-09-18 10:20:00-05:00,138.0,,,,,,,,2024-09-18 -2024-09-18 10:25:00-05:00,133.0,DOSE_MEDICINE,False,False,,,,,2024-09-18 -2024-09-18 10:30:00-05:00,126.0,,,,,,,,2024-09-18 -2024-09-18 10:35:00-05:00,125.0,DOSE_INSULIN,False,True,9.0,,,,2024-09-18 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23:35:00-05:00,95.0,,,,,,,,2024-09-30 -2024-09-30 23:40:00-05:00,87.0,,,,,,,,2024-09-30 -2024-09-30 23:45:00-05:00,86.0,,,,,,,,2024-09-30 -2024-09-30 23:50:00-05:00,89.0,,,,,,,,2024-09-30 -2024-09-30 23:55:00-05:00,88.0,,,,,,,,2024-09-30 diff --git a/0_meal_identification/meal_identification/data/interim/2024-11-23_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv b/0_meal_identification/meal_identification/data/interim/2024-11-23_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv deleted file mode 100644 index 18642dc..0000000 --- a/0_meal_identification/meal_identification/data/interim/2024-11-23_679372__timeInter5mins_dayStart4hrs_minCarb10g_3hrMealW.csv +++ /dev/null @@ -1,26497 +0,0 @@ -date,bgl,msg_type,affects_fob,affects_iob,dose_units,food_g,food_glycemic_index,food_g_keep,day_start_shift -2024-07-01 00:00:00-04:00,115.0,,,,,,,,2024-06-30 -2024-07-01 00:05:00-04:00,112.0,,,,,,,,2024-06-30 -2024-07-01 00:10:00-04:00,121.0,,,,,,,,2024-06-30 -2024-07-01 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21:35:00-04:00,121.0,,,,,0.0,,,2024-08-03 -2024-08-03 21:40:00-04:00,121.0,,True,False,,0.0,1.0,,2024-08-03 -2024-08-03 21:45:00-04:00,100.0,,,,,0.0,,,2024-08-03 -2024-08-03 21:50:00-04:00,105.0,,,,,0.0,,,2024-08-03 -2024-08-03 21:55:00-04:00,,,,,,0.0,,,2024-08-03 -2024-08-03 22:00:00-04:00,112.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:05:00-04:00,141.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:10:00-04:00,141.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:15:00-04:00,157.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:20:00-04:00,158.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:25:00-04:00,159.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:30:00-04:00,158.0,,,,,0.0,,,2024-08-03 -2024-08-03 22:35:00-04:00,,,,,,,,,2024-08-03 -2024-08-03 22:40:00-04:00,133.0,,,,,,,,2024-08-03 -2024-08-03 22:45:00-04:00,125.0,,,,,,,,2024-08-03 -2024-08-03 22:50:00-04:00,115.0,,,,,,,,2024-08-03 -2024-08-03 22:55:00-04:00,112.0,,,,,,,,2024-08-03 -2024-08-03 23:00:00-04:00,103.0,,,,,,,,2024-08-03 -2024-08-03 23:05:00-04:00,103.0,,,,,,,,2024-08-03 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22:40:00-04:00,134.0,,,,,,,,2024-08-16 -2024-08-16 22:45:00-04:00,129.0,,,,,,,,2024-08-16 -2024-08-16 22:50:00-04:00,132.0,,,,,,,,2024-08-16 -2024-08-16 22:55:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-16 23:00:00-04:00,128.0,,,,,,,,2024-08-16 -2024-08-16 23:05:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-16 23:10:00-04:00,136.0,,,,,,,,2024-08-16 -2024-08-16 23:15:00-04:00,135.0,,,,,,,,2024-08-16 -2024-08-16 23:20:00-04:00,135.0,,,,,,,,2024-08-16 -2024-08-16 23:25:00-04:00,139.0,,,,,,,,2024-08-16 -2024-08-16 23:30:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-16 23:35:00-04:00,134.0,,,,,,,,2024-08-16 -2024-08-16 23:40:00-04:00,132.0,,,,,,,,2024-08-16 -2024-08-16 23:45:00-04:00,128.0,,,,,,,,2024-08-16 -2024-08-16 23:50:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-16 23:55:00-04:00,126.0,,,,,,,,2024-08-16 -2024-08-17 00:00:00-04:00,134.0,,,,,,,,2024-08-16 -2024-08-17 00:05:00-04:00,142.0,,,,,,,,2024-08-16 -2024-08-17 00:10:00-04:00,120.0,,,,,,,,2024-08-16 -2024-08-17 00:15:00-04:00,123.0,,,,,,,,2024-08-16 -2024-08-17 00:20:00-04:00,130.0,,,,,,,,2024-08-16 -2024-08-17 00:25:00-04:00,131.0,,,,,,,,2024-08-16 -2024-08-17 00:30:00-04:00,134.0,,,,,,,,2024-08-16 -2024-08-17 00:35:00-04:00,136.0,,,,,,,,2024-08-16 -2024-08-17 00:40:00-04:00,133.0,,,,,,,,2024-08-16 -2024-08-17 00:45:00-04:00,133.0,,,,,,,,2024-08-16 -2024-08-17 00:50:00-04:00,153.0,,,,,,,,2024-08-16 -2024-08-17 00:55:00-04:00,150.0,,,,,,,,2024-08-16 -2024-08-17 01:00:00-04:00,144.0,,,,,,,,2024-08-16 -2024-08-17 01:05:00-04:00,144.0,,,,,,,,2024-08-16 -2024-08-17 01:10:00-04:00,143.0,,,,,,,,2024-08-16 -2024-08-17 01:15:00-04:00,148.0,,,,,,,,2024-08-16 -2024-08-17 01:20:00-04:00,144.0,,,,,,,,2024-08-16 -2024-08-17 01:25:00-04:00,127.0,,,,,,,,2024-08-16 -2024-08-17 01:30:00-04:00,138.0,,,,,,,,2024-08-16 -2024-08-17 01:35:00-04:00,132.0,,,,,,,,2024-08-16 -2024-08-17 01:40:00-04:00,110.0,,,,,,,,2024-08-16 -2024-08-17 01:45:00-04:00,128.0,,,,,,,,2024-08-16 -2024-08-17 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-2024-08-31 09:25:00-04:00,153.0,,,,,,,,2024-08-31 -2024-08-31 09:30:00-04:00,148.0,,,,,,,,2024-08-31 -2024-08-31 09:35:00-04:00,146.0,,,,,,,,2024-08-31 -2024-08-31 09:40:00-04:00,141.0,,,,,,,,2024-08-31 -2024-08-31 09:45:00-04:00,140.0,,,,,,,,2024-08-31 -2024-08-31 09:50:00-04:00,139.0,,,,,,,,2024-08-31 -2024-08-31 09:55:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 10:00:00-04:00,146.0,,,,,,,,2024-08-31 -2024-08-31 10:05:00-04:00,148.0,,,,,,,,2024-08-31 -2024-08-31 10:10:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 10:15:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 10:20:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 10:25:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 10:30:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 10:35:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 10:40:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 10:45:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 10:50:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 10:55:00-04:00,128.0,,,,,,,,2024-08-31 -2024-08-31 11:00:00-04:00,132.0,,,,,,,,2024-08-31 -2024-08-31 11:05:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 11:10:00-04:00,143.0,,,,,,,,2024-08-31 -2024-08-31 11:15:00-04:00,145.0,,,,,,,,2024-08-31 -2024-08-31 11:20:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 11:25:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 11:30:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 11:35:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 11:40:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 11:45:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 11:50:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 11:55:00-04:00,146.0,,,,,,,,2024-08-31 -2024-08-31 12:00:00-04:00,141.0,,,,,,,,2024-08-31 -2024-08-31 12:05:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 12:10:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 12:15:00-04:00,136.0,,,,,,,,2024-08-31 -2024-08-31 12:20:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 12:25:00-04:00,112.0,,,,,,,,2024-08-31 -2024-08-31 12:30:00-04:00,112.0,,,,,,,,2024-08-31 -2024-08-31 12:35:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 12:40:00-04:00,138.0,,,,,,,,2024-08-31 -2024-08-31 12:45:00-04:00,128.0,,,,,,,,2024-08-31 -2024-08-31 12:50:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 12:55:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 13:00:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 13:05:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 13:10:00-04:00,127.0,,,,,,,,2024-08-31 -2024-08-31 13:15:00-04:00,127.0,DOSE_BASAL_INSULIN,False,True,12.0,,,,2024-08-31 -2024-08-31 13:20:00-04:00,137.0,,,,,,,,2024-08-31 -2024-08-31 13:25:00-04:00,138.0,,,,,,,,2024-08-31 -2024-08-31 13:30:00-04:00,130.0,MEDICAL_TEST_RESULT,False,False,,,,,2024-08-31 -2024-08-31 13:35:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 13:40:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 13:45:00-04:00,131.0,,,,,,,,2024-08-31 -2024-08-31 13:50:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 13:55:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 14:00:00-04:00,124.0,,,,,,,,2024-08-31 -2024-08-31 14:05:00-04:00,118.0,,,,,,,,2024-08-31 -2024-08-31 14:10:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 14:15:00-04:00,148.0,,,,,,,,2024-08-31 -2024-08-31 14:20:00-04:00,133.0,,,,,,,,2024-08-31 -2024-08-31 14:25:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 14:30:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 14:35:00-04:00,124.0,,,,,,,,2024-08-31 -2024-08-31 14:40:00-04:00,126.0,,,,,,,,2024-08-31 -2024-08-31 14:45:00-04:00,125.0,,,,,,,,2024-08-31 -2024-08-31 14:50:00-04:00,121.0,,,,,,,,2024-08-31 -2024-08-31 14:55:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 15:00:00-04:00,129.0,,,,,,,,2024-08-31 -2024-08-31 15:05:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 15:10:00-04:00,116.0,,,,,,,,2024-08-31 -2024-08-31 15:15:00-04:00,116.0,,,,,,,,2024-08-31 -2024-08-31 15:20:00-04:00,115.0,,,,,,,,2024-08-31 -2024-08-31 15:25:00-04:00,119.0,,,,,,,,2024-08-31 -2024-08-31 15:30:00-04:00,123.0,,,,,,,,2024-08-31 -2024-08-31 15:35:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 15:40:00-04:00,119.0,,,,,,,,2024-08-31 -2024-08-31 15:45:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 15:50:00-04:00,121.0,,,,,,,,2024-08-31 -2024-08-31 15:55:00-04:00,122.0,,,,,,,,2024-08-31 -2024-08-31 16:00:00-04:00,122.0,ANNOUNCE_MEAL,,,,35.0,,35.0,2024-08-31 -2024-08-31 16:05:00-04:00,117.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:10:00-04:00,119.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:15:00-04:00,124.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:20:00-04:00,101.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:25:00-04:00,119.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:30:00-04:00,156.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:35:00-04:00,168.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:40:00-04:00,186.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:45:00-04:00,195.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:50:00-04:00,199.0,,,,,0.0,,,2024-08-31 -2024-08-31 16:55:00-04:00,203.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:00:00-04:00,188.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:05:00-04:00,208.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:10:00-04:00,198.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:15:00-04:00,187.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:20:00-04:00,183.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:25:00-04:00,182.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:30:00-04:00,165.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:35:00-04:00,155.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:40:00-04:00,155.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:45:00-04:00,168.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:50:00-04:00,161.0,,,,,0.0,,,2024-08-31 -2024-08-31 17:55:00-04:00,162.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:00:00-04:00,125.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:05:00-04:00,122.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:10:00-04:00,109.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:15:00-04:00,101.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:20:00-04:00,99.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:25:00-04:00,95.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:30:00-04:00,102.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:35:00-04:00,104.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:40:00-04:00,104.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:45:00-04:00,109.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:50:00-04:00,108.0,,,,,0.0,,,2024-08-31 -2024-08-31 18:55:00-04:00,106.0,,,,,0.0,,,2024-08-31 -2024-08-31 19:00:00-04:00,108.0,,,,,0.0,,,2024-08-31 -2024-08-31 19:05:00-04:00,108.0,,,,,,,,2024-08-31 -2024-08-31 19:10:00-04:00,106.0,,,,,,,,2024-08-31 -2024-08-31 19:15:00-04:00,105.0,,,,,,,,2024-08-31 -2024-08-31 19:20:00-04:00,105.0,,,,,,,,2024-08-31 -2024-08-31 19:25:00-04:00,107.0,,,,,,,,2024-08-31 -2024-08-31 19:30:00-04:00,117.0,,,,,,,,2024-08-31 -2024-08-31 19:35:00-04:00,109.0,,,,,,,,2024-08-31 -2024-08-31 19:40:00-04:00,115.0,,,,,,,,2024-08-31 -2024-08-31 19:45:00-04:00,118.0,,,,,,,,2024-08-31 -2024-08-31 19:50:00-04:00,134.0,,,,,,,,2024-08-31 -2024-08-31 19:55:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 20:00:00-04:00,153.0,,,,,,,,2024-08-31 -2024-08-31 20:05:00-04:00,154.0,DOSE_INSULIN,False,True,2.0,,,,2024-08-31 -2024-08-31 20:10:00-04:00,152.0,,,,,,,,2024-08-31 -2024-08-31 20:15:00-04:00,165.0,,,,,,,,2024-08-31 -2024-08-31 20:20:00-04:00,165.0,,,,,,,,2024-08-31 -2024-08-31 20:25:00-04:00,157.0,,,,,,,,2024-08-31 -2024-08-31 20:30:00-04:00,152.0,,,,,,,,2024-08-31 -2024-08-31 20:35:00-04:00,135.0,,,,,,,,2024-08-31 -2024-08-31 20:40:00-04:00,128.0,,,,,,,,2024-08-31 -2024-08-31 20:45:00-04:00,107.0,,,,,,,,2024-08-31 -2024-08-31 20:50:00-04:00,99.0,,,,,,,,2024-08-31 -2024-08-31 20:55:00-04:00,95.0,INTERVENTION_SNACK,True,False,,7.5,1.0,,2024-08-31 -2024-08-31 21:00:00-04:00,91.0,,,,,,,,2024-08-31 -2024-08-31 21:05:00-04:00,95.0,,,,,,,,2024-08-31 -2024-08-31 21:10:00-04:00,87.0,,,,,,,,2024-08-31 -2024-08-31 21:15:00-04:00,90.0,,,,,,,,2024-08-31 -2024-08-31 21:20:00-04:00,97.0,,,,,,,,2024-08-31 -2024-08-31 21:25:00-04:00,90.0,,,,,,,,2024-08-31 -2024-08-31 21:30:00-04:00,93.0,,,,,,,,2024-08-31 -2024-08-31 21:35:00-04:00,85.0,,,,,,,,2024-08-31 -2024-08-31 21:40:00-04:00,91.0,,,,,,,,2024-08-31 -2024-08-31 21:45:00-04:00,95.0,,,,,,,,2024-08-31 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23:25:00-04:00,130.0,,,,,,,,2024-08-31 -2024-08-31 23:30:00-04:00,132.0,,,,,,,,2024-08-31 -2024-08-31 23:35:00-04:00,133.0,,,,,,,,2024-08-31 -2024-08-31 23:40:00-04:00,136.0,,,,,,,,2024-08-31 -2024-08-31 23:45:00-04:00,136.0,,,,,,,,2024-08-31 -2024-08-31 23:50:00-04:00,153.0,,,,,,,,2024-08-31 -2024-08-31 23:55:00-04:00,146.0,,,,,,,,2024-08-31 -2024-09-01 00:00:00-04:00,137.0,,,,,,,,2024-08-31 -2024-09-01 00:05:00-04:00,135.0,,,,,,,,2024-08-31 -2024-09-01 00:10:00-04:00,138.0,,,,,,,,2024-08-31 -2024-09-01 00:15:00-04:00,141.0,,,,,,,,2024-08-31 -2024-09-01 00:20:00-04:00,153.0,,,,,,,,2024-08-31 -2024-09-01 00:25:00-04:00,150.0,,,,,,,,2024-08-31 -2024-09-01 00:30:00-04:00,147.0,,,,,,,,2024-08-31 -2024-09-01 00:35:00-04:00,148.0,,,,,,,,2024-08-31 -2024-09-01 00:40:00-04:00,146.0,,,,,,,,2024-08-31 -2024-09-01 00:45:00-04:00,153.0,,,,,,,,2024-08-31 -2024-09-01 00:50:00-04:00,145.0,,,,,,,,2024-08-31 -2024-09-01 00:55:00-04:00,144.0,,,,,,,,2024-08-31 -2024-09-01 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05:15:00-04:00,102.0,,,,,,,,2024-09-16 -2024-09-16 05:20:00-04:00,100.0,,,,,,,,2024-09-16 -2024-09-16 05:25:00-04:00,103.0,,,,,,,,2024-09-16 -2024-09-16 05:30:00-04:00,104.0,,,,,,,,2024-09-16 -2024-09-16 05:35:00-04:00,107.0,,,,,,,,2024-09-16 -2024-09-16 05:40:00-04:00,111.0,,,,,,,,2024-09-16 -2024-09-16 05:45:00-04:00,111.0,,,,,,,,2024-09-16 -2024-09-16 05:50:00-04:00,95.0,,,,,,,,2024-09-16 -2024-09-16 05:55:00-04:00,88.0,,,,,,,,2024-09-16 -2024-09-16 06:00:00-04:00,118.0,,,,,,,,2024-09-16 -2024-09-16 06:05:00-04:00,100.0,,,,,,,,2024-09-16 -2024-09-16 06:10:00-04:00,87.0,,,,,,,,2024-09-16 -2024-09-16 06:15:00-04:00,78.0,,,,,,,,2024-09-16 -2024-09-16 06:20:00-04:00,69.0,,,,,,,,2024-09-16 -2024-09-16 06:25:00-04:00,78.0,INTERVENTION_SNACK,True,False,,7.5,1.0,,2024-09-16 -2024-09-16 06:30:00-04:00,141.0,,,,,,,,2024-09-16 -2024-09-16 06:35:00-04:00,133.0,,,,,,,,2024-09-16 -2024-09-16 06:40:00-04:00,128.0,,,,,,,,2024-09-16 -2024-09-16 06:45:00-04:00,97.0,,,,,,,,2024-09-16 -2024-09-16 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14:45:00-04:00,103.0,,,,,,,,2024-09-16 -2024-09-16 14:50:00-04:00,104.0,,,,,,,,2024-09-16 -2024-09-16 14:55:00-04:00,98.0,,,,,,,,2024-09-16 -2024-09-16 15:00:00-04:00,96.0,,,,,,,,2024-09-16 -2024-09-16 15:05:00-04:00,97.0,,,,,,,,2024-09-16 -2024-09-16 15:10:00-04:00,97.0,,,,,,,,2024-09-16 -2024-09-16 15:15:00-04:00,99.0,,,,,,,,2024-09-16 -2024-09-16 15:20:00-04:00,100.0,,,,,,,,2024-09-16 -2024-09-16 15:25:00-04:00,95.0,,,,,,,,2024-09-16 -2024-09-16 15:30:00-04:00,92.0,,,,,,,,2024-09-16 -2024-09-16 15:35:00-04:00,86.0,,,,,,,,2024-09-16 -2024-09-16 15:40:00-04:00,87.0,,,,,,,,2024-09-16 -2024-09-16 15:45:00-04:00,83.0,INTERVENTION_SNACK,True,False,,7.5,1.0,,2024-09-16 -2024-09-16 15:50:00-04:00,86.0,,,,,,,,2024-09-16 -2024-09-16 15:55:00-04:00,98.0,,,,,,,,2024-09-16 -2024-09-16 16:00:00-04:00,99.0,,,,,,,,2024-09-16 -2024-09-16 16:05:00-04:00,97.0,,,,,,,,2024-09-16 -2024-09-16 16:10:00-04:00,111.0,,,,,,,,2024-09-16 -2024-09-16 16:15:00-04:00,113.0,,,,,,,,2024-09-16 -2024-09-16 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-2024-09-30 23:20:00-04:00,132.0,,,,,,,,2024-09-30 -2024-09-30 23:25:00-04:00,122.0,,,,,,,,2024-09-30 -2024-09-30 23:30:00-04:00,115.0,,,,,,,,2024-09-30 -2024-09-30 23:35:00-04:00,113.0,,,,,,,,2024-09-30 -2024-09-30 23:40:00-04:00,112.0,,,,,,,,2024-09-30 -2024-09-30 23:45:00-04:00,101.0,,,,,,,,2024-09-30 -2024-09-30 23:50:00-04:00,96.0,,,,,,,,2024-09-30 -2024-09-30 23:55:00-04:00,103.0,,,,,,,,2024-09-30 diff --git a/0_meal_identification/meal_identification/meal_identification/datasets/plots.py b/0_meal_identification/meal_identification/meal_identification/datasets/plots.py index 670959d..d94ff0e 100644 --- a/0_meal_identification/meal_identification/meal_identification/datasets/plots.py +++ b/0_meal_identification/meal_identification/meal_identification/datasets/plots.py @@ -1,4 +1,9 @@ +import os +import pandas as pd import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +from dataset_operations import get_root_dir def plot_announce_meal_histogram(df, hours_or_15minutes='hours'): """ @@ -51,3 +56,281 @@ def plot_announce_meal_histogram(df, hours_or_15minutes='hours'): plt.grid(True) plt.tight_layout() plt.show() + +def load_data(file='2024-11-15_500030__i5mins_d4hrs_c5g_l2hrs_n3.csv') -> pd.DataFrame: + data_path = '0_meal_identification/meal_identification/data/interim' + project_root = get_root_dir() + full_raw_loc_path = os.path.join(project_root, data_path) + file_path = os.path.join(full_raw_loc_path, file) + return pd.read_csv(file_path, usecols=['date', 'bgl', 'msg_type', 'day_start_shift'], parse_dates=['date']) + +def generate_meal_periods(df: pd.DataFrame, meal_length=6, n_steps=1) -> pd.DataFrame: + """ + Add meal time "meal_length" after each ANNOUNCE_MEAL and calculate derivative. 1 meal length is one time step + Basically, we wanted to create a period of meal time. + + Parameters + ---------- + df: DataFrame containing the BGL data + meal_length: Number of rows to label as ANNOUNCE_MEAL after each ANNOUNCE_MEAL + n_steps: Number of time steps (time delta) for second derivative calculation + Returns + ------- + DataFrame with processed and smoothed data + """ + copied_df = df.copy() + + print(f"Each meal lasts {meal_length * 5} mins ({meal_length * 10} rows)") + meal_indices = copied_df[copied_df['msg_type'] == 'ANNOUNCE_MEAL'].index + for idx in meal_indices: + next_indices = range(idx, min(idx + meal_length, len(df))) + + # Set those rows' msg_type to ANNOUNCE_MEAL + copied_df.loc[next_indices, 'msg_type'] = 'ANNOUNCE_MEAL' + + copied_df['bgl_rate'] = copied_df['bgl'].diff(periods=n_steps) / ( + 5.0 * n_steps) # divide by total minutes to get rate + normalized_df = normalize_df(copied_df) + return normalized_df + +def normalize_df(df: pd.DataFrame) -> pd.DataFrame: + """ + Normalize numeric columns in the DataFrame using z-score normalization (StandardScaler). + + Parameters + ---------- + df : pd.DataFrame + Input DataFrame to normalize + + Returns + ------- + pd.DataFrame + Normalized DataFrame with non-numeric columns preserved + """ + # Create a copy to avoid modifying the original + df_normalized = df.copy() + + # Calculate mean and std for each numeric column + for column in df.columns: + if column in ['bgl', 'bgl_rate']: # Only normalize BGL-related columns + mean = df[column].mean() + std = df[column].std() + + # Avoid division by zero + if std == 0: + df_normalized[column] = 0 + else: + df_normalized[column] = (df[column] - mean) / std + + return df_normalized + +def to_chunk(df: pd.DataFrame, meal_length: int) -> list[pd.DataFrame]: + """ + Creates temporal fan chart data from glucose measurements around meal announcements. + + Parameters: + ----------- + df : pandas.DataFrame + DataFrame with datetime index and columns: 'bgl', 'msg_type' + meal_length : float + Length of a meal + percentiles : list + List of percentiles to calculate for the fan chart + + Returns: + -------- + dict containing: + - time_points: array of time points (in hours) relative to meal announcement + - percentile_values: dictionary of percentile curves + - count_per_timepoint: number of observations at each time point + """ + chunks = [] + i = 0 + + while i < len(df): + # Exit if not enough rows left + if i + meal_length > len(df): + break + + # Check if current position contains ANNOUNCE_MEAL + if df.iloc[i]['msg_type'] == 'ANNOUNCE_MEAL': + # Extract chunk starting from meal announcement + if i + meal_length <= len(df): + chunk = df.iloc[i: (i + meal_length)].copy() + chunks.append(chunk) + i += meal_length + else: + # For non-meal periods, check if the next meal_length rows contain any ANNOUNCE_MEAL + if i + meal_length <= len(df): + potential_chunk = df.iloc[i: i + meal_length] + # Check if there's a meal announcement in this potential chunk + meal_indices = potential_chunk.index[potential_chunk['msg_type'] == 'ANNOUNCE_MEAL'] + + if len(meal_indices) > 0: + # If there's a meal announcement, move i to that position + # Find the position of the first meal announcement relative to i + first_meal_pos = potential_chunk.index.get_loc(meal_indices[0]) + i += first_meal_pos # Move i to the meal announcement + else: + # Only add the chunk if it's a complete chunk + if len(potential_chunk) == meal_length: + chunks.append(potential_chunk.copy()) + i += meal_length + return chunks + +def plot_histograms(df: pd.DataFrame, file, bgl_bins=30, rate_bins=20): + meal_df = df[df['msg_type'] == 'ANNOUNCE_MEAL'] + non_meal_df = df[df['msg_type'] != 'ANNOUNCE_MEAL'] + + # Common axis parameters + def style_axis(ax, title, xlabel): + ax.set_title(title, pad=15, fontsize=12, fontweight='bold') + ax.set_xlabel(xlabel, fontsize=11) + ax.set_ylabel('Density', fontsize=11) + ax.grid(True, linestyle='--', alpha=0.3) + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.tick_params(labelsize=10) + + # Set fixed width for histogram + bgl_min = min(meal_df['bgl'].min(), non_meal_df['bgl'].min()) + bgl_max = max(meal_df['bgl'].max(), non_meal_df['bgl'].max()) + rate_min = min(meal_df['bgl_rate'].min(), non_meal_df['bgl_rate'].min()) + rate_max = max(meal_df['bgl_rate'].max(), non_meal_df['bgl_rate'].max()) + + fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6)) + plt.title = file + + bgl_bins = np.linspace(bgl_min, bgl_max, bgl_bins + 1) + rate_bins = np.linspace(rate_min, rate_max, rate_bins + 1) + hist_params = dict( + stat='density', + alpha=0.5, # Reduced alpha for better overlay visibility + edgecolor='white', + linewidth=1, + ) + + # Plot both distributions on the same axes + sns.histplot(data=meal_df['bgl'], ax=ax1, color='r', bins=bgl_bins, + label=f'Meal Events (n={len(meal_df):,})', **hist_params) + sns.histplot(data=non_meal_df['bgl'], ax=ax1, color='b', bins=bgl_bins, + label=f'Non-Meal Events (n={len(non_meal_df):,})', **hist_params) + + style_axis(ax1, 'Blood Glucose Level Distribution - Meal vs Non-Meal Events', 'Blood Glucose Level (mg/dL)') + ax1.set_xlim(bgl_min, bgl_max) + ax1.legend(frameon=True, fontsize=10) + + sns.histplot(data=meal_df['bgl_rate'], ax=ax2, color='r', bins=rate_bins, + label=f'Meal Events (n={len(meal_df):,})', **hist_params) + sns.histplot(data=non_meal_df['bgl_rate'], ax=ax2, color='b', bins=rate_bins, + label=f'Non-Meal Events (n={len(non_meal_df):,})', **hist_params) + + style_axis(ax1, 'Blood Glucose Level Distribution - Meal vs Non-Meal Events', 'Blood Glucose Level') + ax1.set_xlim(bgl_min, bgl_max) + ax1.legend(frameon=True, fontsize=10) + + style_axis(ax2, 'Blood Glucose Rate Distribution - Meal vs Non-Meal Events', 'Blood Glucose Rate') + ax2.set_xlim(rate_min, rate_max) + ax2.legend(frameon=True, fontsize=10) + + plt.tight_layout() + + # statistics + print(f"BGL range: {bgl_min:.1f} to {bgl_max:.1f}") + print(f"Rate range: {rate_min:.1f} to {rate_max:.1f}") + print(f"\nNumber of meal rows: {len(meal_df)}") + print(f"Number of non-meal rows: {len(non_meal_df)}") + +def plot_fan_chart(chunks: list[pd.DataFrame], column='bgl'): + meal_plot = [] + non_meal_plot = [] + chunk_length = len(chunks[0]) + for chunk in chunks: + meal_plot.append(chunk[column]) if 'ANNOUNCE_MEAL' in chunk['msg_type'].values else non_meal_plot.append( + chunk['bgl']) + + meal_plot = np.array(meal_plot) + non_meal_plot = np.array(non_meal_plot) + + # Define the percentiles to plot + percentiles = [5, 25, 50, 75, 95] + + # Calculate the percentiles for each timestep + percentile_data_meal = np.nanpercentile(meal_plot, percentiles, axis=0) + percentile_data_non_meal = np.nanpercentile(non_meal_plot, percentiles, axis=0) + + fig, ax = plt.subplots(figsize=(15, 6)) + plt.title(f"Prandial vs Non-Prandial Glucose DynamicsChunks {column.upper()} in {chunk_length*5} mins (Orange = Meal, Blue = Non-meal)") + plt.xlabel('Minutes After Meal') + plt.ylabel('Normalized Blood Glucose Level') + plt.grid(True, alpha=0.3) + + # Define alpha values for the filled areas + alpha_values = [0.33, 0.66, 1, 0.66, 0.33] + + for i, percentile in enumerate(percentiles): + ax.plot(range(chunk_length), percentile_data_meal[i, :], label=f'meal: {percentile}th percentile', + color='orange', alpha=alpha_values[i]) + + for i, percentile in enumerate(percentiles): + ax.plot(range(chunk_length), percentile_data_non_meal[i, :], label=f'non-meal: {percentile}th percentile', + color='blue', alpha=alpha_values[i]) + + ax.fill_between(range(chunk_length), percentile_data_meal[0, :], percentile_data_meal[-1, :], color='orange', + alpha=0.2) + ax.fill_between(range(chunk_length), percentile_data_meal[1, :], percentile_data_meal[-2, :], color='orange', + alpha=0.4) + ax.fill_between(range(chunk_length), percentile_data_meal[2, :], percentile_data_meal[2, :], color='orange', + alpha=0.6) + + # Set the x-tick labels to be multiplied by 5 + xticks = np.arange(0, chunk_length, step=1) + ax.set_xticks(xticks) + ax.set_xticklabels(xticks * 5) + ax.set_facecolor('white') + + ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left') + + # Adjust layout to make room for the legend + plt.tight_layout(rect=(0, 0, 0.85, 1)) + + plt.show() + +def plot_chart(file_name='', meal_length=6 , n_step=1, bgl_bins=50, rate_bins=40, use_fan_chart=True): + """ + Parameters + ---------- + meal_length: Number of rows to label as ANNOUNCE_MEAL after each ANNOUNCE_MEAL. Each row is 5 mins. Default to 6 rows (30 mins) + file_name: Name of the file you want to plot in data/interim + n_step: Number of time steps (time delta) for second derivative calculation + bgl_bins: Number of bins for bgl (only used in histogram) + rate_bins: Number of bins of derivative (only used in histogram) + use_fan_chart: Set false to use histogram chart. Default to fan chart + + Returns + ------- + """ + csv_files = [f for f in os.listdir('../../data/interim') if f.endswith('.csv')] + print("csv files: ", csv_files) + print(f"File selected: {file_name}") + df = load_data(file_name) + + normalized_df = generate_meal_periods(df, meal_length=meal_length, n_steps=n_step) + chunks = to_chunk(normalized_df, meal_length=meal_length) + + # use only one because graphs are quite expensive to generate + if use_fan_chart: + plot_fan_chart(chunks, column='bgl') + plot_fan_chart(chunks, column='bgl_rate') + else: + plot_histograms(normalized_df, file_name, bgl_bins=bgl_bins, rate_bins=rate_bins) + + +plot_chart( + file_name='2024-11-15_679372__i5mins_d4hrs_c10g_l3hrs_n4.csv', + meal_length=30, + n_step=1, + bgl_bins=50, + rate_bins=40, + use_fan_chart=True +) \ No newline at end of file diff --git a/0_meal_identification/meal_identification/notebooks/1.04-tc-change-point-plot.ipynb b/0_meal_identification/meal_identification/notebooks/1.04-tc-change-point-plot.ipynb index ecf4150..cfb64b0 100644 --- a/0_meal_identification/meal_identification/notebooks/1.04-tc-change-point-plot.ipynb +++ b/0_meal_identification/meal_identification/notebooks/1.04-tc-change-point-plot.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "793403fbb7451220", "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T05:46:26.205884Z", - "start_time": "2024-11-25T05:46:26.186825Z" + "end_time": "2024-11-26T05:13:41.262493Z", + "start_time": "2024-11-26T05:13:41.225686Z" } }, "outputs": [], @@ -202,20 +202,63 @@ " print(f\"\\nNumber of meal rows: {len(meal_df)}\")\n", " print(f\"Number of non-meal rows: {len(non_meal_df)}\")\n", "\n", - "def plot_fan_chart(chunks: [pd.DataFrame]):\n", - " # Create time points (x-axis) based on chunk length\n", - " plt.figure(figsize=(12, 6))\n", "\n", - " # If it is a meal chunk, use red line instead, else blue\n", + "def plot_fan_chart(chunks: list[pd.DataFrame], ylim: tuple[float, float], column='bgl', apply_chunk_recentering=False):\n", + " meal_plot = []\n", + " non_meal_plot = []\n", + " chunk_length = len(chunks[0])\n", " for chunk in chunks:\n", - " color = 'r' if 'ANNOUNCE_MEAL' in chunk['msg_type'].values else 'b'\n", - " plt.plot(range(len(chunk)), chunk['bgl'].values, color=color, alpha=0.1)\n", + " meal_plot.append(chunk[column]) if 'ANNOUNCE_MEAL' in chunk['msg_type'].values else non_meal_plot.append(chunk[column])\n", "\n", - " plt.title('Fan Chart Glucose Chunks (Red = Meal, Blue = Non-meal)')\n", - " plt.xlabel('Time Point')\n", - " plt.ylabel('Blood Glucose Level')\n", + " meal_plot = np.array(meal_plot)\n", + " non_meal_plot = np.array(non_meal_plot)\n", + "\n", + " print(meal_plot[0])\n", + "\n", + " if apply_chunk_recentering:\n", + " meal_plot = meal_plot - meal_plot[:,0].reshape(-1, 1)\n", + " non_meal_plot = non_meal_plot - non_meal_plot[:,0].reshape(-1, 1)\n", + " print(meal_plot[0])\n", + "\n", + " # Define the percentiles to plot\n", + " percentiles = [5, 25, 50, 75, 95]\n", + "\n", + " # Calculate the percentiles for each timestep\n", + " percentile_data_meal = np.nanpercentile(meal_plot, percentiles, axis=0)\n", + " percentile_data_non_meal = np.nanpercentile(non_meal_plot, percentiles, axis=0)\n", + "\n", + " fig, ax = plt.subplots(figsize=(15, 6))\n", + " plt.title(f\"Prandial vs Non-Prandial Glucose DynamicsChunks {column.upper()} (Orange = Meal, Blue = Non-meal)\")\n", + " plt.xlabel('Minutes After Meal')\n", + " plt.ylabel('Normalized Blood Glucose Level')\n", " plt.grid(True, alpha=0.3)\n", "\n", + " # Define alpha values for the filled areas\n", + " alpha_values = [0.33, 0.66, 1, 0.66, 0.33]\n", + "\n", + " for i, percentile in enumerate(percentiles):\n", + " ax.plot(range(chunk_length), percentile_data_meal[i, :], label=f'meal: {percentile}th percentile', color='orange', alpha=alpha_values[i])\n", + "\n", + " for i, percentile in enumerate(percentiles):\n", + " ax.plot(range(chunk_length), percentile_data_non_meal[i, :], label=f'non-meal: {percentile}th percentile', color='blue', alpha=alpha_values[i])\n", + "\n", + "\n", + " ax.fill_between(range(chunk_length), percentile_data_meal[0, :], percentile_data_meal[-1, :], color='orange', alpha=0.2)\n", + " ax.fill_between(range(chunk_length), percentile_data_meal[1, :], percentile_data_meal[-2, :], color='orange', alpha=0.4)\n", + " ax.fill_between(range(chunk_length), percentile_data_meal[2, :], percentile_data_meal[2, :], color='orange', alpha=0.6)\n", + "\n", + " # Set the x-tick labels to be multiplied by 5\n", + " xticks = np.arange(0, chunk_length, step=1)\n", + " ax.set_xticks(xticks)\n", + " ax.set_xticklabels(xticks * 5)\n", + " ax.set_facecolor('white')\n", + " ax.set_ylim(ylim)\n", + " ax.grid(True, linestyle='--', alpha=0.3)\n", + " ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "\n", + " # Adjust layout to make room for the legend\n", + " plt.tight_layout(rect=(0, 0, 0.85, 1))\n", + "\n", " plt.show()" ] }, @@ -235,12 +278,11 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 110, "id": "8ad7651445286bff", "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T05:47:01.385545Z", - "start_time": "2024-11-25T05:46:59.408480Z" + "end_time": "2024-11-26T05:54:43.007126Z", } }, "outputs": [ @@ -248,17 +290,116 @@ "name": "stdout", "output_type": "stream", "text": [ + "csv files: ['2024-11-18_adu003__i5mins_d4hrs_c5g_l2hrs_n3.csv', '2024-11-18_adu003__i5mins_d4hrs_c5g_l3hrs_n3.csv', '2024-11-15_500030__i5mins_d4hrs_c5g_l5hrs_n3.csv', '2024-11-15_500030__i5mins_d4hrs_c10g_l3hrs_n3.csv', '2024-11-15_500030__i5mins_d4hrs_c10g_l3hrs_n4.csv', '2024-11-15_500030__i5mins_d4hrs_c5g_l5hrs_n4.csv', '2024-11-15_500030__i5mins_d4hrs_c10g_l2hrs_n3.csv', '2024-11-15_500030__i5mins_d4hrs_c10g_l2hrs_n4.csv', '2024-11-15_500030__i5mins_d4hrs_c10g_l5hrs_n4.csv', '2024-11-15_500030__i5mins_d4hrs_c5g_l3hrs_n4.csv', '2024-11-15_500030__i5mins_d4hrs_c5g_l3hrs_n3.csv', '2024-11-15_500030__i5mins_d4hrs_c10g_l5hrs_n3.csv', '2024-11-15_500030__i5mins_d4hrs_c5g_l2hrs_n4.csv', 'meal_annotation_plus_2hr_meal.csv', '2024-11-15_500030__i5mins_d4hrs_c5g_l2hrs_n3.csv', '2024-11-15_679372__i5mins_d4hrs_c10g_l5hrs_n4.csv', '2024-11-15_679372__i5mins_d4hrs_c10g_l5hrs_n3.csv', '2024-11-15_679372__i5mins_d4hrs_c10g_l3hrs_n3.csv', '2024-11-15_679372__i5mins_d4hrs_c10g_l3hrs_n4.csv', '2024-11-15_679372__i5mins_d4hrs_c10g_l2hrs_n3.csv', '2024-11-15_679372__i5mins_d4hrs_c10g_l2hrs_n4.csv', '2024-11-15_679372__i5mins_d4hrs_c5g_l5hrs_n3.csv', '2024-11-15_679372__i5mins_d4hrs_c5g_l5hrs_n4.csv', '2024-11-18_adu001__i5mins_d4hrs_c5g_l2hrs_n3.csv', '2024-11-18_adu001__i5mins_d4hrs_c5g_l3hrs_n3.csv', '2024-11-15_679372__i5mins_d4hrs_c5g_l3hrs_n4.csv', '2024-11-15_679372__i5mins_d4hrs_c5g_l3hrs_n3.csv', '2024-11-15_679372__i5mins_d4hrs_c5g_l2hrs_n4.csv', '2024-11-15_679372__i5mins_d4hrs_c5g_l2hrs_n3.csv']\n", "---------------\n", "\n", - "File selected: 2024-11-15_679372__i5mins_d4hrs_c5g_l3hrs_n3.csv\n", - "Each meal lasts 150 mins (300 rows)\n" + "File selected: 2024-11-15_679372__i5mins_d4hrs_c5g_l3hrs_n4.csv\n", + "Each meal lasts 150 mins (300 rows)\n", + "[0.22827034 0.42028477 0.51629199 0.70830643 0.70830643 0.61229921\n", + " 0.64430161 0.51629199 0.74030883 0.86831845 0.48428959 0.93232326\n", + " 0.99632808 1.22034492 1.41235935 1.86039303 1.98840265 2.53244355\n", + " 2.62845076 3.3965085 3.14048925 3.10848685 3.36450609 2.69245558\n", + " 2.94847482 3.04448204 3.26849888 2.88447001 2.91647241 3.23649647]\n", + "[0. 0.19201443 0.28802165 0.48003608 0.48003608 0.38402887\n", + " 0.41603127 0.28802165 0.51203849 0.64004811 0.25601925 0.70405292\n", + " 0.76805774 0.99207457 1.18408901 1.63212269 1.76013231 2.30417321\n", + " 2.40018042 3.16823816 2.91221891 2.88021651 3.13623575 2.46418523\n", + " 2.72020448 2.8162117 3.04022854 2.65619967 2.68820207 3.00822613]\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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RUVGMGjWKTz/9lG3btvHEE0+wdOlSnnrqKYvts5SidRMXF8fixYv57bffStzLb4fPPvuM++67D1dXV9avX8+mTZvw9PSkf//+FjnkLDl3BQUFlbrHLViwAI1Gw0svvVTudl988QUPPvggrq6ubNiwgdWrV5OcnExERAQHDhwosX1Fz80VcerUKZ5//nnmzp3LN998g5ubGw899BAvv/wyq1atYtGiRXz++eekpqYyePBgsrOzTfvu3buXHj16kJKSwocffsh3331Hhw4dGDFiBOvWrTNtl5SUBMDLL7/MDz/8wNq1a2natCkREREWzVFpb29P9+7d+eGHHywul0QikUjKQEhsgrVr1wpAHDlyROTn54v09HSxbds24ePjI1xcXERsbKwQQojHH39cAGLNmjXlHs9gMIj8/Hxx5coVAYjvvvvOtO7ll18WgFiyZInZPpMnTxaOjo7CYDAIIYTYsWOHAMS7775rtt2///1vAYiXX365hP3R0dGVtqeifY3MmjVL6HQ6kZKSYlp27tw5AYj33ntPCCFEQkKCAMQ777xT7rFKw1gvN2/eFAkJCcLNzU0MHz7ctL5x48bi/vvvN33/8MMPBSA2bdpkdpw33nhDAGLXrl2mZYDw8/MTaWlppmWxsbFCrVaL119/vULb9u7dKwDx5Zdfivz8fJGVlSX++9//iubNmwuNRiNOnTplVob58+dXeMyCggKRkZEhnJ2dzc6x8XxMnjzZbPslS5YIQMTExAghhDh//rwAxMyZM822+/zzzwUgHn/88RL27927t9L2WLKvEEI4OjqKrl27llheWFgo8vPzTZ/CwsISZS3a9ho3bmxmu5Hw8HARHh5u+v7KK68IQOzevbtMmyxtI2+++aYAzNp2cV5//XWhVqvFsWPHzJZ//fXXAhDbt28vc18hlL7D2dm5zPXG8zlp0iTTsvDwcNGhQwez7SZNmiRcXV1Fenq6aVlV23dBQYHIy8sTLVq0MGtHxnM+ZMgQs+1nzJghADF9+nSz5UOHDhWenp5my4qfR0vO19SpU4W7u3uZ64Uou52VRXh4uADEL7/8Yra8TZs2on///qbvZfWDpbV/S49p3Perr74SJ06cEIGBgaJnz54iMTHRbD+9Xi9mzJhhcZmK21z84+DgIP7zn/+YbXs712fxNl9ZwsPDRdu2bU0aMTExYv78+QIQH374YYnt+/TpIxwdHUVycrKZHatXry71+MXvDUUpq44AodFoKrR94MCBolWrViWWW9IO586da9ZOjLaMHTu2Qt2Krs1BgwaZbb9p0yYBiMOHDwshhEhKShIODg5ixIgRZtsdPnxYAGZ96e32bcb7XkWfxo0bV1juos8Ba9euFQ4ODiIxMVEUFBSIgIAAsWDBAiGEEM7Ozmb9S1XLYGz/n3zyidBoNCIpKcm07vHHH7fI5tIwnqfiH7VaLV588cUS2xfvLy3tjzIzM4Wnp2eJvrqwsFC0b99e3HXXXRXaasm5A8TatWstOtaUKVOEEEK8+OKLQq1Wm56PivcnhYWFIjAwULRr186s30lPTxe+vr6ie/fupmWWPjeXR+PGjYVOpxPXr183LTt58qQAREBAgMjMzDQt37JliwDE1q1bTctatWolOnbsKPLz882OO3jwYBEQEGBWhqIUFBSI/Px80bdvXzFs2LAS9VX0Wd6Ise4yMjIqLJdEIpFIykaOjLMxunbtip2dHS4uLgwePBh/f3927NiBn5+f2XbDhw8vsW98fDxPP/00QUFBaLVa7OzsaNy4MUCpGTeNI2GMhIWFkZOTQ3x8PKC8hQNKzFVXdLRDeVTWnoqYMGEC2dnZfPnll6Zla9euxcHBwWSTp6cnzZo1Y+nSpbz11lucOHGiwrCt0vDy8mLu3Lls3ryZX375pdRtfv75Z5ydnfnnP/9pttwYGlf8jXDv3r1xcXExfffz88PX19csjKKiN8EjRozAzs4OJycnevXqRWFhIV9//bVpVKCR0tpHRkYGc+fOpXnz5mi1WrRaLXq9nszMTIvbB/wdEltW+3jkkUfQaiuerrKy9lSVhx56CDs7O9Nn+vTp1XLcHTt2EBISwr333lvmNpa2kS5dugBK3W3atKnUTGbbtm0jNDSUDh06mLWR/v37VzlTbVGKtzVQRsedPHmSgwcPAkoY1qeffsrjjz+OXq8329bS9r1o0SLatGmDvb09Wq0We3t7Ll68WOo5L569uHXr1oAyj1fx5UlJSeWOXLLkfN11112kpKQwcuRIvvvuuyqN0CwNf39/0yg0I2FhYVUOya7sMX/88Ud69uxJr1692L17d4lJze+66y7WrVvHa6+9xpEjRyo9cvmTTz7h2LFjHDt2jB07dvD4448zZcoU3n///Qr3ranrszhnz541aQQEBPDKK6/w/PPPlxiNFB0dzd69e3nooYdwd3cHlDnYXFxcbitUtWgdGT9l3VuKcuPGDXx9faukabymi88HV9r9obLXZkX3hyNHjpCbm8sjjzxitl3Xrl1LZAi93b5t4sSJJeq2tM/3339f7nGK8/DDD2Nvb8/nn3/O9u3biY2NLTPjaGXKcOLECR544AG8vLzQaDTY2dkxduxYCgsLyx2NXxXeeOMNU/l3797NnDlzWLx4Mf/3f/9XLcc/dOgQSUlJPP7442blNhgMDBgwgGPHjlUYamzJuTt27JhZMhhLmDNnDp6ensydO7fU9X/88Qc3btxgzJgxZvMC6vV6hg8fzpEjR0pM01DRc3PxUZrFR2N26NDBbO5H4z0tIiLCbG5f43Lj9XTp0iV+//130/NWUY1BgwYRExNjNoL1ww8/pFOnTjg6Opqev/fs2WPxs5Wvry8Gg6HEqFqJRCKRVA6ZwMHG+OSTT2jdujVarRY/P79Ss5M6OTmVmHPHYDBw3333cePGDV566SXatWuHs7MzBoOBrl27mg11N+Ll5WX23cHBAcC0bWJiIlqttsR2liQ1qIo9FdG2bVu6dOnC2rVrmThxIoWFhXz22Wc8+OCDph+XxnnlXnnlFZYsWcLs2bPx9PRk9OjR/Pvf/zZzFlTEjBkzeP/995kzZw779+8vsT4xMRF/f/8SP3R8fX3RarUlQryK1yModW6si8uXL9OkSROz9Xv37jWbW+eNN96gT58+aDQavL29y5xct7R2M2rUKPbs2cNLL71Ely5dcHV1RaVSMWjQoCq3DyjZHkprM6VRWXsqolGjRqU6IpYtW8a8efOAv51e1cHNmzdp1KhRudtY2kZ69erFli1bWL58OWPHjiU3N5e2bdvy4osvMnLkSADi4uK4dOlSmXP93K7jyFh3gYGBpmUPPvggwcHBfPDBB/To0YN169aRmZlZIkQVKm7fALNmzeKDDz5g7ty5hIeH4+HhgVqt5l//+lep57y408je3r7c5Tk5OSWchEYsOV9jxoyhoKCAjz/+mOHDh2MwGOjSpQuvvfYa/fr1A5R2Fh0dXe5ximNJ3VSWyhxzy5YtZGdnM2nSJNN1XJQvv/yS1157zZRhVK/XM2zYMJYsWWJRf9+6desSCRyuXLnCnDlzeOyxx3B3d6/167M4zZo1Y+PGjQghuHLlCq+99hqvv/46YWFhZgkO1qxZgxCCf/7zn2ZTGzzwwAN8/vnn/P7776VmRa2I4nVkKdnZ2SVexoFl7dA431fx+0Rp94fKXpuW3h9Ks734stvt2/z9/S1yWFY2SYWzszMjRoxgzZo1NG7cmHvvvdf0QrE4lpbh6tWr9OzZk5YtW/Luu+8SHByMo6MjR48eZcqUKbfVJ5RG06ZNzdrdvffeS3JyMsuWLeOJJ56oUlsuSlxcHECJF05FSUpKKnVeQyMdOnSwSEuj0VTKNldXV+bNm8eMGTNMLw+LYmyjpV0PgYGBGAwGkpOTzZxkFbX7CRMmmGUhDQ8PN3PEVuWeBn/X87PPPmvK7FwcYxt76623mD17Nk8//TSvvvoq3t7eppBdS51xxoQ21d0eJRKJ5E5DOuNsDEse2Et7oPztt984deoU69at4/HHHzctN84pVhW8vLwoKCggMTHR7AHEkjdlNWEPwPjx45k8eTLnz5/nzz//JCYmhvHjx5tt07hxY1avXg0oEy5v2rSJBQsWkJeXZ5o/zRJ0Oh0LFixg4sSJpc6d4eXlxS+//IIQwuycxMfHU1BQgLe3d6XKFhgYyLFjx8yWtWzZ0ux78QfrsijeRlJTU9m2bRsvv/wyzz33nGl5bm6uaX6RymJsE7GxsWZveo1tpjxqwp5+/frxwQcf8Ouvv5rVkTGBhaU4OjqWSK4AyoNu0XPq4+PD9evXyz1WZdrIgw8+yIMPPkhubi5Hjhzh9ddfZ9SoUQQHB9OtWze8vb3R6XRljs6pbHsrjjE7YFHnr1qtZsqUKbzwwgssW7aM//znP/Tt27dEu7SUzz77jLFjx7Jo0SKz5QkJCaZRSDWFJecLlD5m/PjxZGZm8t///peXX36ZwYMHc+HCBRo3bkz//v157733OHLkSLVmCDb++Cne9qpjdN7bb7/Nl19+ycCBA/n222+57777zNZ7e3vzzjvv8M4773D16lW2bt3Kc889R3x8PDt37qySZlhYGD/++CMXLlzgrrvuqrbrs6oYEwiA4vTr3bs3bdu2ZcaMGQwePBi9Xo/BYDDNvVTavJ+gOOuWLFlSKzaDcm5K6xON9VlWO8zKymL37t2EhoaWcKiW9gxR3dem8f5gdCIUJTY21mx03O32ba+88opp7tzyaNy4cYmEBBUxYcIEVq1axenTp/n888/L3M7SMmzZsoXMzEy++eYbM8eepQmmqoOwsDCEEJw+fbpMZ5yl/ZGxXO+9916Z/WFpDtmiVJRMwsjatWvLHJlYFpMmTeLdd99l7ty5TJo0yWydsY0a5wwtyo0bN1Cr1ZXO7rxgwQKmTp1q+l6ZF8DlYazn559/vsy+yXhf/uyzz4iIiGDFihVm69PT0y3WM/Y5t/tcIZFIJHc6Mkz1DsH4cF181MNHH31U5WP27t0boMQDaPEJ3WvLHoCRI0fi6OjIunXrWLduHQ0aNCjxw7IoISEhzJs3j3bt2nH8+PFK602YMIHWrVvz3HPPlQh37du3LxkZGWzZssVs+SeffGJaXxns7e3p3Lmz2ae6HuRUKhVCiBLnY9WqVZWa1LooRqdN8faxadMms8QDtWXPzJkzcXJyYsqUKZV66CxOcHCwKTOvkQsXLpSYxHzgwIFcuHCBn3/+ucxjVaWNODg4EB4ezhtvvAEoIU2ghGxGRUXh5eVVop107ty5ROhXZdi9ezerVq2ie/fu3HPPPWbr/vWvf2Fvb8/o0aP5448/zH5oVBaVSlXinP/www+lhuVWN5acr6I4OzszcOBAXnzxRfLy8jh79iygtDNnZ2cmT55cahY9IQTffvttpe0znr/ibc/oJL0dHB0d+eabbxg8eDAPPPAA3333XZnbNmrUiKlTp9KvX78q9ZlGjM4FY3KE6ro+qwtjsoK4uDjee+89QAnnvX79OlOmTGHv3r0lPm3btuWTTz6psH+rTlq1alVqkp+ZM2ei0+mYNm1aqWGAzz77LMnJyaZRhxVR3dfm3XffjYODg9m0EqCErxYfIXm7fVtNhakCdOvWjQkTJjBs2DCGDRtW5naWlqG0ZyMhRK1mrjRem+WNJrS0P+rRowfu7u6cO3eu1HJ37tzZNMqrLGoqTBWU56rXXnuNY8eO8dVXX5mta9myJQ0aNOCLL74wm6YhMzOTzZs3mzKsVobg4GCzslf1xVVxWrZsSYsWLTh16lSZ9Wx8XiztWj59+nSJDLHl8eeff+Ll5VWhI1UikUgk5SNHxt0htGrVimbNmvHcc88hhMDT05Pvv/+e3bt3V/mY9913H7169WLOnDlkZmbSuXNnDh48yKeffmoVewDc3d0ZNmwY69atIyUlhWeffdZsro/Tp08zdepUHn74YVq0aIG9vT0///wzp0+fNhuBZSkajYZFixaZHsKLzs02duxYPvjgAx5//HEuX75Mu3btOHDgAIsWLWLQoEHlzk1V27i6utKrVy+WLl2Kt7c3wcHB7N+/n9WrV1d5RFLr1q157LHHeOedd7Czs+Pee+/lt99+48033ywRRl0b9jRr1owNGzYwcuRI2rVrx6RJk+jUqRMODg7Ex8eza9cuk3Z5jBkzhscee4zJkyczfPhwrly5wpIlS0pkXJwxYwZffvklDz74IM899xx33XUX2dnZ7N+/n8GDB9O7d2+L28j8+fO5fv06ffv2pWHDhqSkpPDuu+9iZ2dHeHi4SW/z5s306tWLmTNnEhYWhsFg4OrVq+zatYvZs2dz9913l1s2g8HAkSNHAGXEw9WrV9mxYwebNm2idevWbNq0qcQ+7u7ujB07lhUrVtC4ceMq/SAyMnjwYNatW0erVq0ICwsjMjKSpUuX0rBhwyof01IsOV9PPvkkOp2OHj16EBAQQGxsLK+//jpubm6mEMomTZqwceNGRowYQYcOHZg6dSodO3YElEyzxjDH8n64l0aXLl1o2bIlzz77LAUFBXh4ePDtt9+WmtGvKtjZ2bFhwwb+9a9/8c9//pNPPvmEkSNHkpqaSu/evRk1ahStWrXCxcWFY8eOsXPnzjJHYBTnt99+MzmoEhMT+eabb9i9ezfDhg0zhd7fzvX5888/lzqiadCgQZX+sVyUsWPH8tZbb/Hmm28yZcoUVq9ejVar5YUXXjAL1zby1FNPMX36dH744QcefPDBSmkVraOiNGvWrETfUpSIiAjWrFnDhQsXCAkJMdvv008/ZfTo0XTp0oVZs2bRsmVL4uLiWLNmDTt27ODZZ59lxIgRFtlX3demp6cns2bN4vXXX8fDw4Nhw4Zx/fp1Fi5cSEBAgNl9+3b7tsDAwFLPV3VhHGlfHpaWoV+/ftjb2zNy5EjmzJlDTk4OK1assCgDOSgjrxYuXFhiCouyuHjxoqnPT01N5aeffmL16tV07tyZnj17lrmfpf2RXq/nvffe4/HHHycpKYl//vOf+Pr6cvPmTU6dOsXNmzdLjNAqTk1nWB45ciRvvvkmO3bsMFuuVqtZsmQJo0ePZvDgwTz11FPk5uaydOlSUlJSWLx4cY3aVVk++ugjBg4cSP/+/Rk3bhwNGjQgKSmJ8+fPc/z4cZOzcfDgwbz66qu8/PLLhIeH88cff/DKK6/QpEkTi18kHDlyhPDw8EqHdkskEomkGLWfM0JSFSzNGldeRsRz586Jfv36CRcXF+Hh4SEefvhhcfXq1RLZkopmCyvNhqLZs1JSUsSECROEu7u7cHJyEv369RO///67RdlULbXH0myqRnbt2mXKrnXhwgWzdXFxcWLcuHGiVatWwtnZWej1ehEWFibefvttUVBQUO5xy6oXIYTo3r27AEpkzEtMTBRPP/20CAgIEFqtVjRu3Fg8//zzIicnx2w7imT4KkpZmTuLUzQrYlXLcP36dTF8+HDh4eEhXFxcxIABA8Rvv/1WZha14m2xtKyOubm5Yvbs2cLX19eU3e/w4cMljlnavpbaY2k2VSNRUVFi2rRpomXLlkKn0wkHBwfRuHFj8fDDD4tvv/3WLOtZaW3PYDCIJUuWiKZNmwpHR0fRuXNn8fPPP5fIpiqEEMnJyeKZZ54RjRo1EnZ2dsLX11fcf//94vfffzdtY0kb2bZtmxg4cKBo0KCBsLe3F76+vmLQoEHif//7n5leRkaGmDdvnmjZsqWwt7cXbm5uol27dmLmzJmmjMtlYczEbPzodDrRqFEjMWTIELFmzRqRm5tb5r779u0TgFi8eHGp6y1t38nJyeKJJ54Qvr6+wsnJSdxzzz3if//7X4m6Lau9l9U2S2v3pV1bFZ2v9evXi969ews/Pz9hb28vAgMDxSOPPCJOnz5domxRUVFi8uTJonnz5sLBwUHodDrRpk0bMWvWLLP2ZMzkWZzSMiVeuHBB3HfffcLV1VX4+PiIadOmiR9++KHUbKqWHLO0ejQYDGL69OlCrVaLjz/+WOTk5Iinn35ahIWFCVdXV6HT6UTLli3Fyy+/bJbdrzRKyxTq5uYmOnToIN56660S/aCx3ip7fZb1sfSeUVZ9CSFM9btw4UJhb28vhg4dWuZxkpOThU6nK5E5sqrZVAHx8ccfl2t7amqq0Ov1JbI4Gjl79qx4/PHHRcOGDYWdnZ3w9PQUAwYMED/88EOZtpT2nHG712Z0dHSJjJcGg0G89tpromHDhsLe3l6EhYWJbdu2ifbt25fI6ng7fVt1Ut49tCjFs6kKYXkZvv/+e9G+fXvh6OgoGjRoIP7v//7PlL2+6HVeWh8xe/ZsoVKpxPnz58u1r7Rsqs7OzqJNmzbi5ZdfFqmpqWbbl9ZfWtofCSHE/v37xf333y88PT2FnZ2daNCggbj//vsrfGapbsq6FxV9bize/rds2SLuvvtu4ejoKJydnUXfvn3FwYMHzbapzHNzWZTVT5Rms/F6Wrp0qdnyU6dOiUceeUT4+voKOzs74e/vL/r06WOWFTo3N1c8++yzokGDBsLR0VF06tRJbNmypdT2VPx5XAghLl26JACxefPmCsskkUgkkvJRCVFKijyJRCKRSCxk9uzZrFixgmvXrlmUnEMikVQf06ZNY8+ePZw9e9bmR6pER0fTqlUrXn75ZV544QVrm2Nz3HXXXTRu3LhEyKVEUl289NJLfPLJJ0RFRaHVygAriUQiuR2kM04ikUgkVeLIkSNcuHCBp556iqeeeop33nnH2iZJJHcccXFxhISEsHr16nKzVtY1Tp06xYYNG+jevTuurq788ccfLFmyhLS0NH777Tc5H1UlSUtLw8fHh5MnT9K6dWtrmyOph6SkpNC0aVPee+89Ro8ebW1zJBKJxOaRzjiJRCKRVAmVSoWTkxODBg1i7dq16PV6a5skkZgoLCykvEcclUqFRqOpRYtqjm3btpGcnMyYMWOsbYrFXLp0iaeffppTp06RkpKCm5sbERER/Pvf/662ie0lEkn1ceLECX766SeeffZZmx+FK5FIJHUB6YyTSCQSiURS7wgODi6RmbMo4eHh7Nu3r/YMkkgkEolEIpFIbmEzwf4rVqxgxYoVpoxpbdu2Zf78+QwcONC6hkkkEolEIqlzfP/99+Tm5pa53sXFpRatkUgkEolEIpFI/sZmRsZ9//33aDQamjdvDsD69etZunQpJ06coG3btla2TiKRSCQSiUQikUgkEolEIqkYm3HGlYanpydLly7liSeesLYpEolEIpFIJBKJRCKRSCQSSYXYTJhqUQoLC/nqq6/IzMykW7duZW6Xm5trFqJiMBhISkrCy8tLTjwqkUgkEolEIpFIJHcQQgjS09MJDAxErVZb2xyJRHIHY1POuDNnztCtWzdycnLQ6/V8++23tGnTpsztX3/9dRYuXFiLFkokEolEIpFIJBKJpC5z7do1GjZsaG0zJBLJHYxNhanm5eVx9epVUlJS2Lx5M6tWrWL//v1lOuSKj4xLTU2lUaNGXLlyBVdXVwBUKhUqlQohBEWroqLlBoPBTKuyy9VqdYljG5cXFhaWGLlX2vZVtb34ciEEGo2mRstUdLnBYECtVtdomYraaNSryTIVX15YWFhCszrLVNz20i7j6i5TaW2mJstUdLnxHNZkmYraWB3nr7LXU0FBgdkb2uouU/HlQogSbacm+z2DwYBWq63RMhVdXrzN1HRfXrTN1FSZii8vfq+o6fuTcZ+aLFPxe4VGo6nxe27xNlOTZSq6vPg1Xxv3p7LaTE315ZV9vrjdfq9om6mN+5Mlzxe2fn8qrR+oyftTVZ8vqtpHlPdMWl1lqur5s9X7U3nPF7X1W6O85enp6TRq1IiUlBTc3NyQSCQSa2FTzrji3HvvvTRr1oyPPvrIou3T0tJwc3MjNTXV5IyrawghKCgoQKvVmj0wSz3b0LOGptSzbT1raEo929azhqbUs209a2hKPdvXlHq2rWcNTWuUsbLYwu9BiURyZ2DTgfJCCLORb/UBIQSJiYmlvomUenVfzxqaUs+29ayhKfVsW88amlLPtvWsoSn1bF9T6tm2njU0rVFGiUQisVVsZs64F154gYEDBxIUFER6ejobN25k37597Ny509qmSSQSiUQikUgkEolEIpFIJBZhM864uLg4xowZQ0xMDG5uboSFhbFz50769etnbdMkEkl9wlCIKj8JhI+1LZFIJBKJRCKRSCQSST3EZpxxq1evtrYJtUZtz7Eg9WxfU+pVE8IAN/+HQ+J5MJwHzw7gHAxqTY1L19s6lXr1RlPq2baeNTSlnu1rSj3b1rOGZl2dK66+YzAYyMvLs7YZEskdj52dHRqNZb8dbTqBQ2WRE3ZKJJJySToBqb9B+p9gyAcHL9AFKk45fVNQ21nbQolEIpFIJBJJFamPvwfz8vKIjo4ukX1WIpFYB3d3d/z9/St8OWEzI+PuFIQQ5OXlYW9vX2tZj6SebWtKvWoi8wqk/YHIvEaeXQPsHRxRpf8OuQmQdR10fuDeHlxDQONQrdL1tk6lXr3RlHq2rWcNTaln+5pSz7b1rKFpjTLe6QghiImJQaPREBQUhFpt0/kZJRKbRghBVlYW8fHxAAQEBJS7vXTG1TGEECQnJ+Pr61trN02pZ9uaUq8ayEuGxGOQdQOhcSZZ0wJfN2dUbi0hIxpSz0FqAmTFQNJx8AgD15agdaoW+XpZp1KvXmlKPdvWs4am1LN9Taln23rW0LRGGe90CgoKyMrKIjAwECen6nkulUgkVUen0wEQHx+Pr69vuSGr0hknkUjubApz4eZhyIkHQx749IC0Ww+QKjW4NFNCVLOuQ8oZSDsPObeccm5twb0N2NWPMAeJRCKRSCQSie1QWFgIgL29vZUtkUgkRoyO8fz8fOmMk0gkklIRAhJ/gZxYyE0Er7tAqwNyzLdTqcA5SPlkx95yyl1Q9ks5DW6twC0UHDytUgyJRCKRSCQSyZ2LHIkokdQdLL0epTOuDqLV1u5pkXq2ryn1qkjKGci8Ctk3wLUVOHqDEGg15XSgOn/lk5sIyach/RLkxCmhrPoW4NEOHH0rbUq9qVOpV281pZ5t61lDU+rZvqbUs209a2hao4wSiURii8hsqhKJ5M4k8xokHISMy+DgDR4dlBFwlSUvVXHqZf0FWmdw9AF9E3APUzKxyjeVEolEIpFIJHWC+vZ7MCcnh+joaJo0aYKjo6O1zanzXL58mSZNmnDixAk6dOhQ43rBwcHMmDGDGTNm1LhWXWbBggVs2bKFkydPAjBu3DhSUlLYsmWLVe2qKSy9LmW6lTqGMQNHbflIpZ7ta0q9KpCXeithQwyoHcC9nclpJoQgK6fAcj17N/C9BxoOVkbEZVyBhCNw7Vv463slS6soP9V8vahTqSf7GalXZ/WsoSn1bF9T6tm2njU0rVFGiaQ2UKlUJT4ffvihaf26detwd3e3noF1CJVKVcLJ9uyzz7Jnzx7rGFSHkc64OoYQgrS0tFq9aUo929aUepWkMA8SDkNuHBRmg2dnUP8dUiGAtMx8Kq2mdQbvuyDoQXBqBNnXFYfftS1w7RslnNVQWOquNl+nUk/2M1KvTutZQ1Pq2b6m1LNtPWtoWqOMEkltsXbtWmJiYkyfxx9/3NomWUxhYSEGQ/mDA2oSvV6Pl5eX1fTrKtIZJ5FI7hyEgMSjkB0DOYng2RHsqjkNvMYBPDtAg6Hg0lyZTy7pJFz/Hq5ugtTzYMivXk2JRCKRSCQSicRGiIiIYNq0acyYMQMPDw/8/PxYuXIlmZmZjB8/HhcXF5o1a8aOHTvM9jt37hyDBg1Cr9fj5+fHmDFjSEhIMK3fuXMn99xzD+7u7nh5eTF48GCioqKqxWZ3d3f8/f1NH51OB8C+ffsYP348qampplFzCxYsMO2XlZXFhAkTcHFxoVGjRqxcubLCupk6dSpTp041lWPevHlmTu68vDzmzJlDgwYNcHZ25u6772bfvn2m9caRetu2baNNmzY4ODhw5coVcnNzmTNnDkFBQTg4ONCiRQtWr15t2q+i+o2IiGD69OnMmTMHT09P/P39zcoaHBwMwLBhw1CpVKbvCxYsKDcsWAjBkiVLaNq0KTqdjvbt2/P111+XW0/1AemMk0gkdw6p55Sw0ay/wLV5lRItWIzGDtxDIWgouLWBvCRI+Q3++gEub1SSPxTmVHgYiUQikUgkEonEYgwF1vlUkvXr1+Pt7c3Ro0eZNm0akyZN4uGHH6Z79+4cP36c/v37M2bMGLKysgCIiYkhPDycDh068Ouvv7Jz507i4uJ45JFHTMfMzMxk1qxZHDt2jD179qBWqxk2bFi5o8KCg4PNHEplMXXqVLy9venSpQsffvih6Zjdu3fnnXfewdXV1TRq7tlnnzXtt2zZMjp37syJEyeYPHkykyZN4vfff6+wbrRaLb/88gvLly/n7bffZtWqVab148eP5+DBg2zcuJHTp0/z8MMPM2DAAC5evGjaJisri9dff51Vq1Zx9uxZfH19GTt2LBs3bmT58uWcP3+eDz/8EL1eb3H9Gm1zdnbml19+YcmSJbzyyivs3r0bgGPHjgF/jyI0fq+IefPmsXbtWlasWMHZs2eZOXMmjz32GPv377dof1tFprupY6hUKuzt7WstPbXUs31NqWchWTcg9TfIuq444fTNStcD7O3UVFvpVBpwawmuIZB5WXHIpSUqGVyTIsG9HSrXVrZZp1LPKnrW0JR6tq1nDU2pZ/uaUs+29ayhaY0ySophKIDrW6yj3XCo2dQvFdG+fXvmzZsHwPPPP8/ixYvx9vbmySefBGD+/PmsWLGC06dP07VrV1asWEGnTp1YtGiR6Rhr1qwhKCiICxcuEBISwvDhw800Vq9eja+vL+fOnSM0NLRUO5o1a4a3t3e5tr766qv07dsXnU7Hnj17mD17NgkJCcybNw97e3vc3NxQqVT4+/uX2HfQoEFMnjwZgLlz5/L222+zb98+WrVqVaZeUFAQb7/9NiqVipYtW3LmzBnefvttnnzySaKiotiwYQPXr18nMDAQUOZk27lzJ2vXrjXVT35+Pv/5z39o3749ABcuXGDTpk3s3r2be++9F4CmTZuaNC2pX4CwsDBefvllAFq0aMH777/Pnj176NevHz4+PsDfowgtITMzk7feeouff/6Zbt26mew6cOAAH330EeHh4RYdxxaRzrg6hkqlwtPTU+rZqJ41NKWeBeSnQ+IvkB2rPCR4hIGq9IHBKpUKT1eH29Mr/cBKllXnYMj+65ZT7nfIjkWVfBJPvz6gqp16tclzKPWsqin1bFvPGppSz/Y1pZ5t61lD0xpllNguYWFhpv81Gg1eXl60a9fOtMzPzw+A+Ph4ACIjI9m7d69pJFdRoqKiCAkJISoqipdeeokjR46QkJBgGr129erVMp1xliQWMDoNAVO45SuvvGK2vCyKltPosDOWqSy6du1q5tTu1q0by5Yto7CwkOPHjyOEMDnHjOTm5prNy2Zvb2+mffLkSTQaTZnOLUvqt3h5AAICAiosT3mcO3eOnJwc+vXrZ7Y8Ly+Pjh07Vvm4toB0xtUxhBBkZGSg1+tr5a2S1LN9TalXAYYCJWFDTjwUZIBPD1Dbla+XXYBep62Z8qlU4NRQ+eTEQfJpRNpFsjPT0IVORaWxr37NYtjcOZR6VteUeratZw1NqWf7mlLPtvWsoWmNMkqKodYqI9SspV0J7OzMn8dVKpXZMmMbMjrUDAYDQ4YM4Y033ihxrICAAACGDBlCUFAQH3/8MYGBgRgMBkJDQ8nLy6uUbRXRtWtX0tLSiIuLMzkNy6K0ct5OMgWDwYBGoyEyMhKNRmO2rqgjTafTmV2HxjnuyjtuRfULNVMegB9++IEGDRqYrXNwqIEBEnUI6YyrYwghyMzMxNnZudZumlLPtjWlXgUkHlNCVHPiwaMT2LmUrwdkZhfgrNNWX6hqWTj6gf+9iLj95CbH45hwDJVfj5pWtb1zKPWsrin1bFvPGppSz/Y1pZ5t61lD0xpllJRCJZ1itkKnTp3YvHkzwcHBaLUly5iYmMj58+f56KOP6NmzJwAHDhyoEVtOnDiBo6Mj7u7ugDIKrbCwsNqOf+TIkRLfW7RogUajoWPHjhQWFhIfH28qpyW0a9cOg8HA/v37TWGqRamofi3Fzs6uUnVhTDBx9erVeh2SWhoygYNEIqm/pP4OGdFKWKi+GThZNndBraJSgVtrJdNrwmEoyLa2RRKJRCKRSCQSSZ1iypQpJCUlMXLkSI4ePcqff/7Jrl27mDBhAoWFhXh4eODl5cXKlSu5dOkSP//8M7NmzarwuH379uX9998vc/3333/Pxx9/zG+//UZUVBSrVq3ixRdfZOLEiaaRW8HBwWRkZLBnzx4SEhJMSSeqyrVr15g1axZ//PEHGzZs4L333uOZZ54BICQkhNGjRzN27Fi++eYboqOjOXbsGG+88Qbbt28v85jBwcE8/vjjTJgwgS1bthAdHc2+ffvYtGkTUHH9WkpwcDB79uwhNjaW5OTkCrd3cXHh2WefZebMmaxfv56oqChOnDjBBx98wPr16y3WtUWkM04ikdRPsuMg5bSSOdXeE1xbWNuisnH0RTj4Qu5NSDhkbWskEolEIpFIJJI6RWBgIAcPHqSwsJD+/fsTGhrKM888g5ubG2q1GrVazcaNG4mMjCQ0NJSZM2eydOnSCo8bFRVFQkJCmevt7Oz4z3/+Q7du3QgLC+Pdd9/llVdeYdmyZaZtunfvztNPP82IESPw8fFhyZIlt1XWsWPHkp2dzV133cWUKVOYNm0aEydONK1fu3YtY8eOZfbs2bRs2ZIHHniAX375haCgoHKPu2LFCv75z38yefJkWrVqxZNPPklmZiZQcf1ayrJly9i9ezdBQUEWz/n26quvMn/+fF5//XVat25N//79+f7772nSpInFuraISgghrG1EbZGWloabmxupqam4urpa25xSEUKQlpaGq6trrQ0nl3q2rSn1SqEgE2J+gswrYMgF33tAbdlcbEII0jLzcXW2q9U2k54Sj0vqflQO7tB8Mji41ahenT+HUq9OaUo929azhqbUs31NqWfbetbQtEYZK4st/B6sDDk5OURHR9OkSRMcHR2tbY6kGoiIiKBDhw6888471jZFUkUsvS7lyLg6hkqlMqVGlnq2p2cNTalXDEMh3DykJEfITwOvzhY74kx6evtabzOuHn6oXJpCbgok1Mz8FkX16vQ5lHp1TlPq2baeNTSlnu1rSj3b1rOGpjXKKJFIJLaKdMbVMYQQpKamUlsDFqWe7WuKgmxSkxPqbZ1WWi8pErJvKM44jzCwq9xbTyEEqRl5td9mMvIQLiGg1kDSCcgpe7h8tejV5XMo9eqcptSzbT1raEo929eUeratZw1Na5RRIpFIbBXpjKtjCCHIzs6u1Zum1LNhzbxkxPWtGC5/ibi2FRKOKEkLsuOgsHpTeBup0+cw7SJkXIKs66APBqcGFe5SQg/Izi2kNh8jTZp2LuDSXBnRF7+v5vTq8jmUenVSU+rZtp41NKWe7WtKPdvWs4amNcookdQ39u3bJ0NU7xDqZ95jieROISMa8pIh+zokxUGaI2j1oHEEjQPYuYOjL9i7g72H8ldTT+eTyLkJySeVhA127uDa0toWVQ2XEOW8ppwFr67g3NDaFkkkEolEIpFIJBKJpBqRzjiJxFYRBsi8BnnJ5OtCwNMN8pIgNxlyE0Dkg8pOcb5pnYs56HxuOec8lO9anbVLc3sUZCmjArNjAQGeHUGlsbZVVUOrA9dWimMxfj80GW1tiyQSiUQikUgkEolEUo1IZ1wdQ6VS4ezsXKsTrUo9G9XMiYOCNFSGXHQeTVA560Hf+O/1BTmKUy43QXHS5SVBYS6ojQ66WyPo1A5g7woOvn+PnrP3AK2TdctnqZ6hEBIOm+oD766K07GqeoCzTkttTj1cQlPfRAm3Tb8AaZfAtXn16tW1cyj16rym1LNtPWtoSj3b15R6tq1nDU1rlFEikUhsFZW4g4L661sqa8kdTsJRSI6E/Czwj7Bsn4IcyEuA3MRbDrp0MOTeGkHnoIyg0+oUB52di+Kgc/As4qBzrskSVY3EXyHtHGRcAfd24BxkbYuqh/RLStlcW0Oz8SAfbCUSiUQikUhui/r2ezAnJ4fo6GiaNGmCo2M9nYpGIrExLL0uZQKHOoYQgqSkpFqdaFXq2aCmoRCy/4LcZIRTQ5LSci3T0zqCU0PwaA9+vSHoAWj4APjcBc4NAIMS6pn2BySdUsIkY3bDX9vgyia4sgkRu4/UK4cQuck1V74ilFufGX9C+kXIvA5OjarFESeEsLw+q4lSNZ2Dwd5NmT8u9Wz169Xj67C+61lDU+rZtp41NKWe7WtKPdvWs4amNcookUgktooMU61jCCHIy8tDCFErQ7ylno1q5sRAfjqIAoRTA/JSDQioWmilxgF0gcrHiCEPcpNuhbkmQnY8GLIALUJthyHHHpH3GypHH9A3VfZ18K6R0Vtl1mduIiQdh6wbyig+99bVowfk5d9GfVaXploLbm3h5iGI/58yQk5dPfPg1ffrsL7rWUNT6tm2njU0pZ7ta0o929azhqY1yiiRSCS2ihwZJ5HYIplXoSAdtK41kx1VbQ86f3APBb9waHg/NBwKPt3BuQkGoYb0PxVn2I1dcO0buPqVElaZHaOM3KtJCnOUeeKy40AUgGcn203YUB5ODcHRG7KuQ/IJa1sjkUgkEolEIpHYLJcvX0alUnHy5Elrm2JCpVKxZcsWa5thdcaNG8fQoUNN3yMiIpgxY4bV7KkNpDNOIrE1DPmQfUMZuebcqPZ01Xag8wP3NuR79oCgoeDRQRmtlXkZkk8rIa3Xt8DVL5URXZlXFXurE2G45YiLhfxk8PxHzTgk6wIqtTI6zpAPNw9AQa61LZJIJBKJRCKRSO4YTp06xciRIwkKCkKn09G6dWveffdds22MTr7in507d5q2WbBgAR06dKhl6+seZTlE3333XdatW2cVm6yFDFOtY6hUKlxdXWs165HUszHNrL+gIAMQ4BSACnB1tqu1kEqTnloD+mDlYyiEnFjIvKKM4hLXlLnO7N2VrK1OjZX53HSBSoKIyugVr8/kU4pGdiy4tQIHj5opX7Ue9TY0Hf2VUYo5cZB0FHx73r5ePb8O67ueNTSlnm3rWUNT6tm+ptSzbT1raFqjjBJJTRMZGYmPjw+fffYZQUFBHDp0iIkTJ6LRaJg6darZtj/99BNt27Y1fff09Kxtc6tMfn4+dnZ2VtN3c3Ozmra1kCPj6hgqlQonJ6davWlKPRvTzLqmZEG19wC1vaLnqK3dOi2up9aAUwMljDXoIcVhZO+tzDWX9ruSCOKv7XBlgzJ6Lu0PZc47S/WM9Zl5Rdk38zo4BSpJDmqjfDVMuZoqlTI6Thgg4Rcle2516NXj67C+61lDU+rZtp41NKWe7WtKPdvWs4amNcoosU0iIiKYNm0aM2bMwMPDAz8/P1auXElmZibjx4/HxcWFZs2asWPHDrP9zp07x6BBg9Dr9fj5+TFmzBgSEhJM63fu3Mk999yDu7s7Xl5eDB48mKioqNuydcKECSxfvpzw8HCaNm3KY489xvjx4/nmm29KbOvl5YW/v7/pY29vD8C6detYuHAhp06dMo2aKzoKLCEhgWHDhuHk5ESLFi3YunVruTYFBwfz6quvMmrUKPR6PYGBgbz33ntm26SmpjJx4kR8fX1xdXWlT58+nDp1yrTeOFJvzZo1NG3aFAcHB4QQpKSkMHHiRPz8/HB0dCQ0NJRt27aZ9jt06BC9evVCp9MRFBTE9OnTyczMNLNt0aJFTJgwARcXFxo1asTKlStN65s0aQJAx44dUalUREREACXDVIuTl5fHnDlzaNCgAc7Oztx9993s27ev3Hqq60hnXB3DYDCQkJCAwWCQejaoV+OahTnKiLC8JCV7KGAQgoSUHAy1lLnKIj1HX/DurGRr9e8Lzg2VkNK0P+DmwVuOuS+VLK0pZyGv7MyspvrMSYTEY0rCBq2T4qCqgYe92q5PizQdvZX543ITIOHQ7evV8+uwvutZQ1Pq2baeNTSlnu1rSj3b1rOGpjXKKCmGEFCYa51PJZ+d169fj7e3N0ePHmXatGlMmjSJhx9+mO7du3P8+HH69+/PmDFjyMpSXkTHxMQQHh5Ohw4d+PXXX9m5cydxcXE88sgjpmNmZmYya9Ysjh07xp49e1Cr1QwbNqzcNhkcHMyCBQsqZXtqamqpo94eeOABfH196dGjB19//bVp+YgRI5g9ezZt27YlJiaGmJgYRowYYVq/cOFCHnnkEU6fPs2gQYMYPXo0SUlJ5dqwdOlSwsLCOH78OM8//zwzZ85k9+7dgJJM5f777yc2Npbt27cTGRlJp06d6Nu3r9lxL126xKZNm9i8eTMnT57EYDAwcOBADh06xGeffca5c+dYvHgxGo0yN/eZM2fo378/Dz30EKdPn+bLL7/kwIEDJUYILlu2jM6dO3PixAkmT57MpEmT+P333wE4evQooIwijImJKdWpWRrjx4/n4MGDbNy4kdOnT/Pwww8zYMAALl68aNH+dREZploHKSgokHo2rFejmlnXlRBVlUYJXTTqFdZuCvlK6dl7KB+PDpCfocwjl3XtVljrNUj9A+xdlWys+iaga1AiM2tBXiYknIHcm2DIBe+eSrbRGqK269MiTbc2ylyBScfAszM4uN+eXj2/Duu7njU0pZ5t61lDU+rZvqbUs209a2hao4ySIhjy4PDj1tHuth40DhZv3r59e+bNmwfA888/z+LFi/H29ubJJ58EYP78+axYsYLTp0/TtWtXVqxYQadOnVi0aJHpGGvWrCEoKIgLFy4QEhLC8OHDzTRWr16Nr68v586dIzQ0tFQ7mjVrhre3t8V2Hz58mE2bNvHDDz+Ylun1et566y169OiBWq1m69atjBgxgvXr1/PYY4+h0+nQ6/VotVr8/f1LHHPcuHGMHDkSgEWLFvHee+9x9OhRBgwYUKYdPXr04LnnngMgJCSEgwcP8vbbb9OvXz/27t3LmTNniI+Px8FBOSdvvvkmW7Zs4euvv2bixImAMtrs008/xcfHB4Bdu3Zx9OhRzp8/T0hICABNmzY1aS5dupRRo0aZEiu0aNHCNGpwxYoVODoq83gPGjSIyZMnAzB37lzefvtt9u3bR6tWrUxaxlGElhAVFcWGDRu4fv06gYGBADz77LPs3LmTtWvXmrUJW0I64yQSWyLrGuSngYNnjTqjagw7Pbi3UT6FOUp5Mq9CehRkXIW0i2Dvpsw159xEGQ1m741d6nHQJkFOInh1qfS8c/UCY52kR8HN/0HDIda2SCKRSCQSiUQiqRJhYWGm/zUaDV5eXrRr1860zM/PD4D4+HhAmbtt79696PX6EseKiooiJCSEqKgoXnrpJY4cOWI2SvPq1atlOuP27Nljsc1nz57lwQcfZP78+fTr18+03Nvbm5kzZ5q+d+7cmeTkZJYsWcJjjz1W4XGL1oWzszMuLi6mcpdFt27dSnx/5513AKWuMjIy8PLyMtsmOzvbLGy3cePGJucYwMmTJ2nYsKHJEVecyMhILl26xOeff25aJoTAYDAQHR1N69atS5RHpVLh7+9fYXnK4/jx4wghStiVm5tbooy2hA3+mpdI7lAKspRJ/POSwesua1tz+2gcwaWF8jEUQPZfimMu8zJkqiH9T2X0l9oJdU4GaJPBtaUSsnmn4tpSmTcv+SR4362EA0skEolEIpFIJABqe2WEmrW0K0HxZAEqlcpsmXHuQaNDzWAwMGTIEN54440SxwoICABgyJAhBAUF8fHHHxMYGIjBYCA0NJS8vLxK2VYa586do0+fPjz55JOmEX3l0bVrV1atWmXRsUuri6qEexets4CAgFLnVHN3dzf97+zsbLZOpyt/wIPBYOCpp55i+vTpJdY1atTI9H91laeorkajITIy0hQya6Q056ytIJ1xdQyVSoWHh0etTrQq9WxEM+saFGSCyg4c/n4DoAI8XOxrNZtqteupteDcWPkIAdkxkHUFsv5CJQrRY4/KMUgJY61hars+K6VppwfXFso8e3H/hcb/rJpePb8O67ueNTSlnm3rWUNT6tm+ptSzbT1raFqjjJJiqFSVChW1JTp16sTmzZsJDg5Gqy3pxkhMTOT8+fN89NFH9OzZE4ADBw5Ui/bZs2fp06cPjz/+OP/+978t2ufEiRMmJyGAvb09hYWF1WIPwJEjR0p8b9WqFaDUVWxsLFqtluDgYIuPGRYWxvXr101hv8Xp1KkTZ8+epXnz5lW225jUojJ10bFjRwoLC4mPjzed2/qATOBQx1CpVDg4ONTqTVPq2Yhm5lXISwNHH7MQVZVKhYO9pnbrtCb1VColU6p3Nwgahso3HDuv9qg82tVIwoaS8rVbn5XW1DcHrSOknlXaRFX16vF1WN/1rKEp9WxbzxqaUs/2NaWebetZQ9MaZZTcOUyZMoWkpCRGjhzJ0aNH+fPPP9m1axcTJkygsLAQDw8PvLy8WLlyJZcuXeLnn39m1qxZFR63b9++vP/++2WuP3v2LL1796Zfv37MmjWL2NhYYmNjuXnzpmmb9evX88UXX3D+/Hn++OMP3nzzTZYvX860adNM2wQHBxMdHc3JkydJSEggNzf3turj4MGDLFmyhAsXLvDBBx/w1Vdf8cwzzwBw77330q1bN4YOHcqPP/7I5cuXOXToEPPmzePXX38t85jh4eH06tWL4cOHs3v3bqKjo9mxYwc7d+4ElPnfDh8+zJQpUzh58iQXL15k69atZuWsCF9fX3Q6nSkBR2pqaoX7hISEMHr0aMaOHcs333xDdHQ0x44d44033mD79u0Wa9c1pDOujmEwGIiLi6vVrEdSzwY089MgNxHyU5XRY0X1hCAuKbtWs6nWqp6DN3F5vhhUmoo3rg69Wi5fpTW1OnBppSTyiNtf6cxVUP+vw/quZw1NqWfbetbQlHq2ryn1bFvPGprWKKPkziEwMJCDBw9SWFhI//79CQ0N5ZlnnsHNzQ21Wo1arWbjxo1ERkYSGhrKzJkzWbp0aYXHjYqKIiEhocz1X331FTdv3uTzzz8nICDA9OnSpYvZdq+99hqdO3emS5cubNy4kTVr1pjNIzd8+HAGDBhA79698fHxYcOGDVWvDGD27NlERkbSsWNHXn31VZYtW0b//v0BxTG+fft2evXqxYQJEwgJCeHRRx/l8uXLprn4ymLz5s106dKFkSNH0qZNG+bMmWMaxRYWFsb+/fu5ePEiPXv2pGPHjrz00ktmIwArQqvVsnz5cj766CMCAwN58MEHLdpv7dq1jB07ltmzZ9OyZUseeOABfvnlF4KCgizWrmuohKjFX5xWJi0tDTc3N1JTU3F1dbW2OaViMBiIj4/H19cXtbrmfaVSz0Y0U85CwmHISYCAvqD6+7gGIYhPysHX0xF1LbyJlHp1QNOQBzG7wVAITR5TQlcro1fPr8P6rmcNTaln23rW0JR6tq8p9Wxbzxqa1ihjZbGF34OVIScnh+joaJo0aWLKZCmp/wQHBzNjxgxTVlNJ3cLS67Ju9pISicScrGuQl6KEqKrkZXvHo7YH11ZQmAXx+0HIN9ASiUQikUgkEolEYivIX/USSV0nLxnykqAgHZyDrW2NpK7g3Bjs3SHjCqT8Zm1rJBKJRCKRSCQSiURiITJMtY4hhKCgoACtVlsrk59KPRvQTD4NCUeUkXH+fUokMRBCUFAo0GpUtVenUq9uaGZeg5sHFSdti6fMEntUqFePr8P6rmcNTaln23rW0JR6tq8p9Wxbzxqa1ihjZbGF34OVQYapSiR1DxmmaqOoVCo0mtrNjCn16rCmELdCVFPB0bfUbKKKXu05jqReHdJ0aqCELmffgKTjldSrv9dhfdezhqbUs209a2hKPdvXlHq2rWcNTWuUUSKRSGwV6YyrYxgnPq3NrEdSrw5r5iYoI+IKM0HfuNRNjJP/12Y2ValXRzRVanBrC4YCSDgIBZalSK/v12F917OGptSzbT1raEo929eUeratZw1Na5RRIpFIbBXpjJNI6jJZ16AgA7ROoHWxtjWSuoijHzgFQE48JB6xtjUSiUQikUgkEolEIqkA6YyTSOoqwgBZ15UEDroGpYaoSiSoVODWWmkviUchP8PaFkkkEolEIpFIJBKJpBykM04iqavkxEN+KhTmglOQta2R1GUcvMG5kRLWfPOgta2RSCQSiUQikUgkEkk5yGyqdRCDwYBaXXt+UqlXRzUTjkJyJORngX9E+XpCoK7FkXNSrw5q5qVC7G4lnLnFJHDwKF+vnl+H9V3PGppSz7b1rKEp9WxfU+rZtp41NK1RxspgK78HLUVmU5VI6h4ym6qNIoSgsLCQ2vKRSr06qmkohOy/IDcZnBtaoCdquU6lXp3TtHcDfVPFKXfzfxbo1d/rsL7rWUNT6tm2njU0pZ7ta0o929azhqY1yiiRVIbLly+jUqk4efKktU0xoVKp2LJli7XNsDrjxo1j6NChpu8RERHMmDHDavbUBtIZV8cQQpCYmFirN02pVwc1c2IgPx1EPjhV4IwDElNzqa0SSr06rOkaAhotJJ+C7Liy9er5dVjf9ayhKfVsW88amlLP9jWlnm3rWUPTGmWUSGoDlUpV4vPhhx+abXPmzBnCw8PR6XQ0aNCAV155xexaWLBgAR06dKhly+seZTlE3333XdatW2cVm6yF1toGSCSSUsi8BgXpoHUFjRxyLrEQrR5cQiDlN4jfD40fsbZFEolEIpFIJBKJzbN27VoGDBhg+u7m5mb6Py0tjX79+tG7d2+OHTvGhQsXGDduHM7OzsyePdsa5laa/Px87OzsrKZftD7vFOTIOImkrmHIvxWimgTOja1tjcTWcGkOWh2knoeMK9a2RiKRSCQSiUQiMSMiIoJp06YxY8YMPDw88PPzY+XKlWRmZjJ+/HhcXFxo1qwZO3bsMNvv3LlzDBo0CL1ej5+fH2PGjCEhIcG0fufOndxzzz24u7vj5eXF4MGDiYqKqhab3d3d8ff3N310Op1p3eeff05OTg7r1q0jNDSUhx56iBdeeIG33noLIQTr1q1j4cKFnDp1yjSyrugosISEBIYNG4aTkxMtWrRg69at5doSHBzMq6++yqhRo9Dr9QQGBvLee++ZbZOamsrEiRPx9fXF1dWVPn36cOrUKdN640i9NWvW0LRpUxwcHBBCkJKSwsSJE/Hz88PR0ZHQ0FC2bdtm2u/QoUP06tULnU5HUFAQ06dPJzMz08y2RYsWMWHCBFxcXGjUqBErV640rW/SpAkAHTt2RKVSERERAZQMUy1OXl4ec+bMoUGDBjg7O3P33Xezb9++cuupriOdcXUQVS1PHC/16phm1l9QkKH87xRgoV7V5aqC1KvDmhpHcG2ltKH4fVBGqEh9vw7ru541NKWebetZQ1Pq2b6m1LNtPWtoWqOMkiIIAQWZ1vlUMjx5/fr1eHt7c/ToUaZNm8akSZN4+OGH6d69O8ePH6d///6MGTOGrKwsAGJiYggPD6dDhw78+uuv7Ny5k7i4OB555O9IkMzMTGbNmsWxY8fYs2cParWaYcOGYTAYyrQjODiYBQsWVGjv1KlT8fb2pkuXLnz44Ydmxzx8+DDh4eE4ODiYlvXv358bN25w+fJlRowYwezZs2nbti0xMTHExMQwYsQI07YLFy7kkUce4fTp0wwaNIjRo0eTlJRUrj1Lly4lLCyM48eP8/zzzzNz5kx2794NKCHj999/P7GxsWzfvp3IyEg6depE3759zY576dIlNm3axObNmzl58iQGg4GBAwdy6NAhPvvsM86dO8fixYvRaDSAEorbv39/HnroIU6fPs2XX37JgQMHmDp1qplty5Yto3Pnzpw4cYLJkyczadIkfv/9dwCOHj0KwE8//URMTAzffPNNhXUPMH78eA4ePMjGjRs5ffo0Dz/8MAMGDODixYsW7V8XkdlUJZK6Rvz/IOkkqACf7ta2RmKLGPIhZhcYCiB4NLi1tLZFEolEIpFIJFanvv0eLJG1sSATNumtY8wjGaB1tmjTiIgICgsL+d//lKRjhYWFuLm58dBDD/HJJ58AEBsbS0BAAIcPH6Zr167Mnz+fX375hR9//NF0nOvXrxMUFMQff/xBSEhICZ2bN2/i6+vLmTNnCA0N5fLlyzRp0oQTJ06Y5m/r27cvw4YNK+FQKsprr71G37590el07Nmzh/nz5/P8888zb948AO677z6Cg4PNRoDduHGDBg0acOjQIbp168aCBQvYsmVLibnSVCoV8+bN49VXXwUUh6KLiwvbt283C4stSnBwMK1btzYbOfjoo4+SlpbG9u3b+fnnnxk2bBjx8fFmDsLmzZszZ84cJk6cyIIFC1i0aBF//fUXPj4+AOzatYuBAwdy/vz5Uutz7Nix6HQ6PvroI9OyAwcOEB4eTmZmJo6OjgQHB9OzZ08+/fRTQHEM+vv7s3DhQp5++ulSzwEoI+NSUlJMySwiIiLo0KED77zzDlFRUbRo0YLr168TGBho2ufee+/lrrvuYtGiRWWeO2tgaTZVOWdcHUMIQV5eHvb29rXyZknq1THNwlzIjoW8JPDoYLlevgF7O3Xt1anUq9uaajtwbQ2JR+Hmf8G1Baj+Hghd36/D+q5nDU2pZ9t61tCUeravKfVsW88amtYoo8R2CQsLM/2v0Wjw8vKiXbt2pmV+fn4AxMfHAxAZGcnevXvR60s6G6OioggJCSEqKoqXXnqJI0eOkJCQYBq9dvXqVUJDQ0u1Y8+ePRXaanS6ASYH0iuvvGK2vHibN455suRaKFoXzs7OuLi4mMpdFt26dSvx/Z133gGUusrIyMDLy8tsm+zsbLOw3caNG5sccQAnT56kYcOGpTrijMe9dOkSn3/+uWmZEAKDwUB0dDStW7cuUR6VSoW/v3+F5SmP48ePI4QoYVdubm6JMtoS0hlXxxBCkJycjK+vb63dNKVeHdLMuq6EF6rUoPO3TA9ITs/D19OR2iih1LMRTedGkHFJmTcu5Qx4tP9br55fh/VdzxqaUs+29ayhKfVsX1Pq2baeNTStUUZJMTROygg1a2lXguLJAlQqldkyYxsyOtQMBgNDhgzhjTfeKHGsgABlap8hQ4YQFBTExx9/TGBgIAaDgdDQUPLy8iplW0V07dqVtLQ04uLi8PPzw9/fn9jYWLNtjM4no1OxPEqri/JCa8uiaJ0FBASUOqeau7u76X9nZ/ORjEXnwSsNg8HAU089xfTp00usa9Soken/6ipPUV2NRkNkZKQpZNZIac5ZW0E64ySSukTWVchPA3tPUMvLU3IbqLXg1gbiDygft7bWaVMF2ZB1A3VeFuBb+/oSiUQikUgkdwoqlcWhorZGp06d2Lx5M8HBwWi1JZ9pExMTOX/+PB999BE9e/YElBDKmuDEiRM4OjqaHFvdunXjhRdeMI0MBSXkMzAwkODgYADs7e0pLCysNhuOHDlS4nurVq0Apa5iY2PRarUmfUsICwvj+vXrXLhwodTRcZ06deLs2bM0b968ynYb66cyddGxY0cKCwuJj483ndv6gEzgIJHUFQqyICce8lJAH2xtayT1AV0gOPoq2XmTfq0dTSGUTMApZyFmN/z1HcTtx+7mbshNqR0bJBKJRCKRSCT1iilTppCUlMTIkSM5evQof/75J7t27WLChAkUFhbi4eGBl5cXK1eu5NKlS/z888/MmjWrwuP27duX999/v8z133//PR9//DG//fYbUVFRrFq1ihdffJGJEyea5mMbNWoUDg4OjBs3jt9++41vv/2WRYsWMWvWLNNoteDgYKKjozl58iQJCQnk5ubeVn0cPHiQJUuWcOHCBT744AO++uornnnmGUCZS61bt24MHTqUH3/8kcuXL3Po0CHmzZvHr7+W/ZsgPDycXr16MXz4cHbv3k10dDQ7duxg586dAMydO5fDhw8zZcoUTp48ycWLF9m6dSvTpk2z2G5fX190Op0pAUdqamqF+4SEhDB69GjGjh3LN998Q3R0NMeOHeONN95g+/btFmvXNaQzrg5Smqdf6tmOXpU1s64pk66qtODgXTk9Te2GAkg9G9FUqZXRcYZCSDgMBTl/61XndWHIV0KsE4/BX9vgxna4eRCST0PqeciMQpMbB4m/VJ9mBch+RupJPetrSj3b15R6tq1nDU1rlFFyZxAYGMjBgwcpLCykf//+hIaG8swzz+Dm5oZarUatVrNx40YiIyMJDQ1l5syZLF26tMLjRkVFkZCQUOZ6Ozs7/vOf/9CtWzfCwsJ49913eeWVV1i2bJlpGzc3N3bv3s3169fp3LkzkydPZtasWWbOwOHDhzNgwAB69+6Nj48PGzZsuK36mD17NpGRkXTs2JFXX32VZcuW0b9/f0AJC92+fTu9evViwoQJhISE8Oijj3L58uUKw2Y3b95Mly5dGDlyJG3atGHOnDmmUWxhYWHs37+fixcv0rNnTzp27MhLL71kChO2BK1Wy/Lly/noo48IDAzkwQcftGi/tWvXMnbsWGbPnk3Lli154IEH+OWXXwgKCrJYu64hs6lKJHWF2J8g+Ywy34J3Z2tbI6kvCAE3D0BOHPj3B/+I6jlufjpkxyifnHgozFRGd+algCEPNDpw8AKnhopTMG4vaF0gZCrYy/5XIpFIJBJJ7VPffg9amrVRUr8IDg5mxowZzJgxw9qmSErB0utSjoyrYwghyMrKorZ8pFKvjmjmp0NOAuSnVDpEVQhBVk5B7dap1LMdTZVKGR0nBCQdhfyMqrVRYVCcbsmn4MZOZQTczf9B8klIPQeZV5VswPqm4Ncb/PuCZ0dw9EHYe5JjF4TIS4XEIxVK3S6yn5F6Us/6mlLP9jWlnm3rWUPTGmWUSCQSW0U64+oYQgjS0tJq9aYp9eqAZtY1JYuq2hEcPCqnB6Rl5lNbJZR6Nqjp4KVkV81JhJsHLG+jhTmQcRluHobr30HMj5BwRMnOmvY7ZMeB2gE8O0HAfeDXC1xbgJ1ecQIWKV+qqhFCpYak45Bfs1m+ZD8j9aSe9TWlnu1rSj3b1rOGpjXKKJFIJLaKDOqXSOoCmVchL1WZbF+lqXh7iaSyuLZSnL5JkeDRpfRthFBGZxrDT3MTlNDTgkzIT1XmhtM6K+1U10kJN1VZ9k5H2LmDJgiyrkDCLxDQt9qKJpFIJBKJRCKR3ClcvnzZ2iZIqgHpjJNIrE1eMuQlQUE6eHSwtjWS+oq9G+ibQdoFJbzUvquy3FCghJ/m3HLA5aVCYZYyei0/RUkoYucOLi1B5w9aXdVtcGkOWVchORJ8uoHWqTpKJpFIJBKJRCKRSCQ2hXTG1TFUKhX29vamFMhSz7b0qqSZeU1xfGh0VZrYXgXY26mprRJKPRvWdA2BzMuoUk6jc/VAdfMC5MYro98KMxVHXEG24nCz91JG0zl4gvr2bhWm8jl4gnNDpc0nHgW/iGopVgk92c9IPalndU2pZ/uaUs+29ayhaY0ySiQSia0is6lKJNZECLixHVLOgaMPeLSztkWS+k7Kb8rHKQhQKaPfhAHsXJTwU6eGoDWf861ayU2A2L3g4K1kVtXKzF8SiUQikUhqh/r2e1BmU5VI6h4ym6qNIoQgPT29VidalXpW1MxNhLwUZVSSvlHV9bLya7dOpZ7tarq0QDj6kJWZqiRUcG8HAf3ALxzcWitOuWp2xJmVz8EbdIGKUy7xWLXqmOnJfkbqST2rako929eUeratZw1Na5RRIpFIbBXpjKtjCCHIzMys1Zum1LOipjGLqtYZtFV7OyeAzOyCWs02KvVsWFPjgPDrQ5pLBML7bnAOAo1DjUqWKJ9riLI08RgU5Fa/nuxnpJ7Us7qm1LN9Taln23rW0LRGGSUSicRWkc44icRaCANkXVdGxukCai4sUCIpDWu2N0cfpc3n3VSSOUgkEolEIpFIJBLJHYR0xkkk1iInHvJToTAbnKoWoiqR2CyuIYpDOuEXKMyztjUSiUQikUgkknrO5cuXUalUnDx50tqmmFCpVGzZssXaZlidcePGMXToUNP3iIgIZsyYYTV7agPpjKtjqFQqdDpdrWY9knpW0sy6BgXpymT5ds5V1wN0DppazTYq9Wxbs07oOfiCzl/J5pp0vHr1ZD8j9aSe1TWlnu1rSj3b1rOGpjXKKJHUNOvWrUOlUpX6iY+PB/528hX/7Ny503ScBQsW0KFDByuVou5QlkP03XffZd26dVaxyVporW2AxByVSoWbm5vUs1E9izUNhUqIam4yuDS9fT29/W0dQ+pZT88amnVCT6UClxDIioXEX8CrM6ir55Yk+xmpJ/Wsryn1bF9T6tm2njU0rVFGiaSmGTFiBAMGDDBbNm7cOHJycvD19TVb/tNPP9G2bVvTd09Pz1qxsTrIz8/Hzs7Oavp3Yt8hR8bVMYQQpKam1upEq1LPCpo5sZCfDiIfdA1vXy8jr3brVOrZtGad0XP0U+aPy4mD5BPVqyf7Gakn9ayqKfVsX1Pq2baeNTStUUaJbRIREcG0adOYMWMGHh4e+Pn5sXLlSjIzMxk/fjwuLi40a9aMHTt2mO137tw5Bg0ahF6vx8/PjzFjxpCQkGBav3PnTu655x7c3d3x8vJi8ODBREVF3ZatOp0Of39/00ej0fDzzz/zxBNPlNjWy8vLbFt7e+Vl9Lp161i4cCGnTp0yjZorOgosISGBYcOG4eTkRIsWLdi6dWu5NgUHB/Pqq68yatQo9Ho9gYGBvPfee2bbpKamMnHiRHx9fXF1daVPnz6cOnXKtN44Um/NmjU0bdoUBwcHhBCkpKQwceJE/Pz8cHR0JDQ0lG3btpn2O3ToEL169UKn0xEUFMT06dPJzMw0s23RokVMmDABFxcXGjVqxMqVK03rmzRpAkDHjh1RqVREREQAJcNUi5OXl8ecOXNo0KABzs7O3H333ezbt6/ceqrr2Iwz7vXXX6dLly64uLjg6+vL0KFD+eOPP6xtVrUjhCA7O7tWb5pSzwqamVeVLKp2bqDV3Z4ekJ1bWKvZRqWebWvWGT2VClxbgqFAmTvOUFg9erKfkXpSz+qaUs/2NaWebetZQ9MaZZQUQwgozLXOp5Lnff369Xh7e3P06FGmTZvGpEmTePjhh+nevTvHjx+nf//+jBkzhqysLABiYmIIDw+nQ4cO/Prrr+zcuZO4uDgeeeQR0zEzMzOZNWsWx44dY8+ePajVaoYNG4bBYCjTjuDgYBYsWGCx3Z988glOTk7885//LLHugQcewNfXlx49evD111+blo8YMYLZs2fTtm1bYmJiiImJYcSIEab1Cxcu5JFHHuH06dMMGjSI0aNHk5SUVK4dS5cuJSwsjOPHj/P8888zc+ZMdu/eDSjX4v33309sbCzbt28nMjKSTp060bdvX7PjXrp0iU2bNrF582ZOnjyJwWBg4MCBHDp0iM8++4xz586xePFiNBoNAGfOnKF///489NBDnD59mi+//JIDBw4wdepUM9uWLVtG586dOXHiBJMnT2bSpEn8/vvvABw9ehRQRhHGxMTwzTffWFTv48eP5+DBg2zcuJHTp0/z8MMPM2DAAC5evGjR/nURmwlT3b9/P1OmTKFLly4UFBTw4osvct9993Hu3Dmcnas+35ZEUusY8iH7L8hNAtfW1rZGIrEuOn9w9IbsGEg5DZ4drW2RRCKRSCQSiW1iyIPDj1tHu9t60DhYvHn79u2ZN28eAM8//zyLFy/G29ubJ598EoD58+ezYsUKTp8+TdeuXVmxYgWdOnVi0aJFpmOsWbOGoKAgLly4QEhICMOHDzfTWL16Nb6+vpw7d47Q0NBS7WjWrBne3t4W271mzRpGjRqFTvf3gAq9Xs9bb71Fjx49UKvVbN26lREjRrB+/Xoee+wxdDoder0erVaLv79/iWOOGzeOkSNHArBo0SLee+89jh49WiI8tig9evTgueeeAyAkJISDBw/y9ttv069fP/bu3cuZM2eIj4/HwUE5J2+++SZbtmzh66+/ZuLEiYAy2uzTTz/Fx8cHgF27dnH06FHOnz9PSEgIAE2b/j2l0tKlSxk1apQpsUKLFi1Yvnw54eHhrFixAkdHRwAGDRrE5MmTAZg7dy5vv/02+/bto1WrViYt4yhCS4iKimLDhg1cv36dwMBAAJ599ll27tzJ2rVrzdqELWEzzriikx8CrF27Fl9fXyIjI+nVq5eVrJJIqkD2DWVUHAZwCrC2NRKJdVGpldFx8Qch4TB4tFeWSSQSiUQikUjqLWFhYab/NRoNXl5etGvXzrTMz88PwJQkITIykr1796LX60scKyoqipCQEKKionjppZc4cuQICQkJphFxV69eLdMZt2fPHottPnz4MOfOneOTTz4xW+7t7c3MmTNN3zt37kxycjJLlizhscceq/C4RevC2dkZFxcXU7nLolu3biW+v/POO4BSVxkZGXh5eZltk52dbRa227hxY5NzDODkyZM0bNjQ5IgrTmRkJJcuXeLzzz83LRNCYDAYiI6OpnXr1iXKo1Kp8Pf3r7A85XH8+HGEECXsys3NLVFGW8JmnHHFSU1NBWxrUkRLUKlUODs712rWI6lXy5qZ1yAvHezcK/X2qEw9wFmnrdXMmFLPtjXrnJ4uEBw9IesGpJxRHHK3oyf7Gakn9ayuKfVsX1Pq2baeNTStUUZJMdT2ygg1a2lXguLJAlQqldkyYzsyOtQMBgNDhgzhjTfeKHGsgABlgMOQIUMICgri448/JjAwEIPBQGhoKHl5eZWyrSxWrVpFhw4d+Mc//lHhtl27dmXVqlUWHbe0uigvtLYsitZZQEBAqXOqubu7m/4vHmFYdLRfaRgMBp566immT59eYl2jRo1M/1dXeYrqajQaIiMjTSGzRkpzztoKNumME0Iwa9Ys7rnnnjI93KB4SnNzc03f09LSAOVkGhuDcQJFIYTZ/AYVLS/emCq7XK1Wlzi2cblery+xrrTtq2p78eV6vb7Gy1R0ubOzM0KIarHdkuVGPaNmTZSp6PKiZSyqKYRAFORA1l+Ql4TKvT0qlPZc9CiqW/aUtdxQTFMFuDjZYSheB2Vsry7l2JVdrtdpy7WxOspUdLmzTqvsd6s+a6JMRs2iekKIGitT8eVFNau7TKUtd3GyU95kldJmqqtMRZc7l9FmFNvVCH1LRMIhiD8Erm1Bpb6tfs94DRrX1WRfDubXvHG5pX1HVfu34veKmr4/ubi4mN5+1lSZit8rjNTG/anoOazJe65xefE2Uxv3p7LaTHWVqbiNlX2+qEqZymozNVWmyj5f3G6Zimoay1jq80U1lKm05S4uLhgMBrPjVGeZii83tpmaLFPR5eU9k1ZXmap6/mz1/lTavaKm70+VWV58m3qHSlUtL/vrIp06dWLz5s0EBwej1ZZ0YyQmJnL+/Hk++ugjevbsCcCBAweqTT8jI4NNmzbx+uuvW7T9iRMnTE5CAHt7ewoLq2d+ZIAjR46U+N6qVStAqavY2Fi0Wi3BwcEWHzMsLIzr16+bwn6L06lTJ86ePUvz5s2rbLcxqUVl6qJjx44UFhYSHx9vOrf1AZt0xk2dOpXTp09XeHG9/vrrLFy4sMTymzdvkpOTAyjeXzc3N9LS0sjOzjZtYxwempycbOZJd3V1xcnJiaSkJAoKCkzLPTw8cHBw4ObNm2advJeXFxqNpsSwTF9fXwoLC0lMTDQtU6lU+Pr6EhcXZ7pJA2i1Wry9vcnOzjY5FEFpyJ6enmRkZJhlMKlMmYw3qICAgBork5+fH3l5eSQnJyOEICMjA3d3d3x8fGqkTEXPU2LCTVKTYnB280OlUtVImYwYz1NWVhY3btwwPVAWLVPOzbNoU+IQuWo0bl64AWmZ+WTn/t0ZOeu0uDjZkZyeR17+3w8hrs52ODlqSUrNpaDwb9vdXezIyik02xbAy80BjQbik3LMy+TpSGGhIDH1b0e1SgV+njry8g0kp/9dj1qNCm93R7JzC0nLzAeUNpObb6CxvzMZ2QVkZv/dZnQOGtz09rddJg8XexzsNdxMzsFgEGRkF6DXafF2d6yRMgHY26nxdHUgIyuf2KQck8OxJspU9DnQy80BtVrw518ZJs1qL1Ox8+Ror8EgBGqVipy8milT0fMkhHIOmzbQYzBQepns/MnKd4PEaxRoDqNyb13lfi8rK4uMjAz0ej16vb5W+vLLly+brvmK+ojq6Pf0ej1XrlzBwcHB1GZq8v5ktNPJyYmUlJQaKVPR82S8VwQGBuLs7Fzj96ekpCRTm7Gzs6uxe67xPOl0Oi5fvoxOpzOdv5q+P3l5eRETE2PmGKjue27R82R8jvHz86vRe67xPBnbjKenJ15eXjVSpqLnKT4+nvT0dNN1XxNlMlL0PMXGxpo0a+MZNi0tjby8PFObqYkyGc+TEIL8/HyCgoJq5blcFOaSmZ6Ms5sffn5+NVIm+LvtpaamcvPmTdP5q63fGrV9f8rPzzedy5oq0+2cJ+NvQIntMWXKFD7++GNGjhzJ//3f/+Ht7c2lS5fYuHEjH3/8MR4eHnh5ebFy5UoCAgK4evWqaU618ujbty/Dhg0rkYSgOF9++SUFBQWMHj26xLr169djZ2dHx44dUavVfP/99yxfvtxsFF9wcDDR0dGmUFAXFxfTfG5V4eDBgyxZsoShQ4eye/duvvrqK3744QcA7r33Xrp168bQoUN54403aNmyJTdu3GD79u0MHTqUzp07l3rM8PBwevXqxfDhw3nrrbdo3rw5v//+OyqVigEDBjB37ly6du3KlClTePLJJ3F2dub8+fPs3r27RDbXsvD19UWn07Fz504aNmyIo6Mjbm5u5e4TEhLC6NGjGTt2LMuWLaNjx44kJCTw888/065dOwYNGlS5yqsj2Jwzbtq0aWzdupX//ve/NGzYsNxtn3/+eWbNmmX6npaWRlBQED4+Pri6ugKY/YhxcXExbVv04bj4Gx8oGR5rXF405tq43OhkK4parS51udE55uPjY3qjZUSn05kmRSyqqdfrzd4GV6ZMBoPBdAOsqTKBcjP39fU1veHy8PCosTKZLReX0GafxMW9PWrvu1FptNVepuI4Ojqi1+tN59CsTBlZkCvA2ROVXhkG7Opsh4tzkSHZt/56uNiXGJ0E4Olm3mkLIcjLz8fbwwF1kYdl4+gkX09Hs+3VKhUqDSWWg+K8KW25zkGDo4MyJNggBDeTchAoI+ScdX93I0b12y2TcbmPh6MyuiopBx9PRzTG81TNZSqq6aTTotdp8fF0VI5bA2UqvtxYl0bN6i5T8fMkhOBmci4+Hg646mumTEXPk/EcqlQqtGWVyd4Ou4DWkHAEDOfBo7tSpir0EcblPj4+pqHsNdmXCyHMrvm/y1R6H1Ed/Z4QAgcHBzPNmrw/GQwGEhIScHNzq7Eywd/nyXivMB6zpu9PxuMUrc+avD8JIdDpdKWev5q6PxmdY6VpVtc9t+h5Kvp8UZP3XON5MrYZY/hNTZSp6HJvb2+z57WaKFNxTScnp1KfL2rqGdboUCnet1VnmYqep6Jtpsafy10F4uZx0rKu4+LVA7XKH5VWW+1lKrrcxcWF7OzsEuevPt2fhBDcvHkTHx8fMwduTf9+qkyZ0tPTS2wjsQ0CAwM5ePAgc+fOpX///uTm5tK4cWMGDBhgahsbN25k+vTphIaG0rJlS5YvX05ERES5x42KiiIhIaFC/dWrV/PQQw+ZfscW57XXXuPKlStoNBpCQkJYs2aN2Xxxw4cP55tvvqF3796kpKSwdu1axo0bV5kqMGP27NlERkaycOFCXFxcWLZsGf379weUa2j79u28+OKLTJgwgZs3b+Lv70+vXr1Mc/GVxebNm3n22WcZOXIkmZmZNG/enMWLFwPKyLn9+/fz4osv0rNnT4QQNGvWzCwzbEVotVqWL1/OK6+8wvz58+nZs2ep4bTFWbt2La+99hqzZ8/mr7/+wsvLi27dutmsIw5AJWxkrK4QgmnTpvHtt9+yb98+WrRoUeljpKWlmd5MGZ1xdQ2DwUB8fDy+vr4lnHFSr5LkZ2C4vo3U2PO46e1Ru7eDgHtB61SjsmWWsSAL/voeUn8Hr85KFsnq0BOC+KQcfIs4cmoSqWf7mnVWz1AAsT9BQQ40GQVuVcs2XK/7NStpSj3b1rOGptSzfc16q5f6O6ScwZAVQ2pyDG6u7qg924NvOGjLny/pdpBtpm5gC78HK0NOTg7R0dE0adLEzOkoqd8EBwczY8YMU1ZTSd3C0uuybvaSpTBlyhQ+++wzvvjiC1xcXIiNjSU2NtZsaLREYkbGJShIR6BRHGFJkXB1E+RUPZPLbZF1HQoyQa0FB8vTZ0skdwxqLbi2AkMO3DwItvGuSCKpNNHRsH27A9euWdsSieQOwpAPNw9D0nHIuAyGXPKdW0N+CiT8Ale/huw4a1spkUgkkjsEm3HGrVixgtTUVCIiIggICDB9vvzyS2ubVq2oVCpcXV3NhnZLvSpgyIeMaFS5iTh6BKNqMFB525n2B1zZBCnnaky6zDJmXYW8VLD3VpwO1aWHEkJZO2dQ6tUHzTqt59QQ7Fwh8wqkX6yaXn3t16yoKfWqj9xcOHNGhYODEydOqMjKqnFJoH7X6Z2gZw3NeqWXnwaxPyvPgZnRYO+CyrcHep+WqAIGKM9laeeUZ8TkUzXyMki2GYlEIpEUxWbCVKuD+jYsWVIO6Zfg5gHIvAr+fUHjCKIQkk4o6xx8wasL+N5TrY6xMslPh79+gLTz4N0VHEvOaSGRSG6RHgWJx8CtLTR9XMnyIJHUE06cgKiov7/7+UE9SgwmkdQ9sv6CxKOQE6dER+hbgGtzUBUZkyAKIfm04qxz8Ab3MPCLqNGwVYl1qG+/B2WYqkRS96h3Yap3CsZJsoun8pZ6lUAIxeGWm4zB3oeE9FsTyKs0ylxt3ndD7k24+T+4tkVxlFUjpZYx6xoUZIDaERy8qldPCBJScpQy1gJSz/Y167yecyOwc4GMP5VPZfXqY79mZU2pVz2kpcGffyp6wcFJgIG4OCVstaapr3V6p+hZQ9Pm9YSAlDPK817mFchNAs9/gFsIqNTm9yaVBjw7gm9PyEuBxF/g6lfVGrYq24xEIpFIiiKdcXWQoim/pV4VyImD3EQlJMGlBQWFxRwA+iYQcJ8Sypp8Cq58CZnXq9WEEmXMvKaEqDr6KA981UyJMtYwUs/2Neu0ntoOXEKgMBtuHqqaXn3r1+qAptS7fU6fVvwDgYHQsGEeoaHK8lOnqJVw1fpYp3eSnjU0bVavMFdxwiWfUuaHEwbwuadE8qwS9yanBhA4ENT2SjTDlS8h6WS1ha3KNiORSCQSI9IZJ6l/pF+C/FTQuoC9W+nbOHhA4ACwd1fmpbq2WQlhrYmRQnkpkJcIBengHFz9x5dI6iP6xmCnVxKxZFy2tjUSyW0TFwexsaBWQ1iYsqxFC/DygoICiIy0rn0SSb0hLxli9yjPg5mXlbBTnx5g52zZ/nbOEHAv6JspkQ0xO+HGTiiQSeMkEolEUn1IZ5ykfpGfDtl/Qc5NcG1W/lxTGgfw6wWurZVMpzE/QuxuKMyrXpuyrkF+Bmh0ZTsHJRKJOWp7ZXRcQZaSWVUisWGEUEa/ATRrBnq98r9KBZ07g0ZDrYWrSiT1mozLiiMu87IyV5xLS/DoUPn5gU1hq72UyIbEX+DqJsiOrQGjJRKJRHInIp1xdQyVSoWHh0etZj2qV3rpl5TwVI0jOPqjAjxc7MvO4qhSg0e7W3OEJCsp769tVkazVRGzMgqhhKjmp4Kjn/lkwdVEhWWUenVazxqaNqPn3FgZoZB+QbmOLNWrb/1aHdCUerdHdLQyX5y9PbRuba7n4gJt2yrb1WS4an2r0ztNzxqaNqUnDJB0HBKOQMYV5eWs113g0rTMF7MW3ZucAouErf5+W2Grss1IJBKJpCjSGVfHUKlUODg41OpNs97oGfKVN6G5icqcH2qtomevqVjPqQEEDFQe2FJ+Ux62Mqo2RMGsjLmJipOvIBP0jap0PIv0LCmj1KuTetbQtBk9jYOS9a6So+PqVb9WRzSlXtXJz4ezZ5X/27RRHHLF9WojXLU+1emdqGcNTZvRK8iGuH2Q+puS9EetVcJSHb0r1rPk3mTnDAH9wKXZrUiKnXBjh3JvqgSyzUgkEomkKNIZV8cwGAzExcXVatajeqOXcVkJJTDkK0kaULI4xiVlW5bF0d4FAvqDoy+k/wlXv4GEo8rb1kpgVkZjFlWtE2hrJn16pcoo9eqcnjU0bUpPHwxaHaT9AVk3LNOrT/1aHdGUelXnjz8gNxdcXKBp09L1aiNctT7V6Z2oZw1Nm9DLuQmxP93Kvn0ZdIHg3VW5b1SkV5l7k0oNHkXDVo/eyrZqediqbDMSifW5fPkyKpWKkydPWtsUEyqVii1btljbDKszbtw4hg4davoeERHBjBkzrGZPbSCdcXUQUYtOgHqjJ4Qy0XteMjj4KGGqRVZZjPFtqkc7yIlVHvBu7Kj0pL1CCEU467oS8qoLLH/+utuklk+h1KsHmjajp3EElxaKUzvB8syq9aJfq2OaUq/yZGbChQvK/2FhSvKGsvRqI1y1PtTpnaxnDc06rZd2URkRl3FZcYq5tQP30ErND1fp4hnDVjUORcJWT1j84la2GYnENtmzZw/du3fHxcWFgIAA5s6da5Y52OjkK/7ZuXOnaZsFCxbQoUMHK1hftyjLIfruu++ybt06q9hkLaQzTlI/yIlVQkLzU5Uf7reDSgVurcEvQnEAJB6Fq19DTkLljpMbr9hTmANONROiKpHcETg3UZxyKWchqw5Onm3IR5VXyf5Bckfw229gMICvLwQEVLy9zK4qkViAoQASfoGkY8r0JIZs8OmmTAdSG+GRds7gf2+RsNUfb2VbraEJHyUSiVU5ffo0gwYNYsCAAZw4cYKNGzeydetWnnvuuRLb/vTTT8TExJg+ffr0sYLFVSM/P9+q+m5ubri7u1vVhtpGOuMk9QNj4gY71+rLWKrzh4ABtybtPae8/Uz9w/L9M69CQTpo9cqDm0QiqRpaHbg0r/TouFohLxlid2OfsFeZPFwiuUViIly7lXekfXvL9ikervrnnzVnn0Rik+RnQNzPyqi09GjQOIHPPWDvXrt2FA1bzS+abTWmdu2QSGyUiIgIpk2bxowZM/Dw8MDPz4+VK1eSmZnJ+PHjcXFxoVmzZuzYscNsv3PnzjFo0CD0ej1+fn6MGTOGhIS/X4ju3LmTe+65B3d3d7y8vBg8eDBRUVG3ZevGjRsJCwtj/vz5NG/enPDwcF5//XU++OAD0tPTzbb18vLC39/f9LG3twdg3bp1LFy4kFOnTplGzRUdBZaQkMCwYcNwcnKiRYsWbN26tVybgoODefXVVxk1ahR6vZ7AwEDee+89s21SU1OZOHEivr6+uLq60qdPH04ZU7vz90i9NWvW0LRpUxwcHBBCkJKSwsSJE/Hz88PR0ZHQ0FC2bdtm2u/QoUP06tULnU5HUFAQ06dPJzMz08y2RYsWMWHCBFxcXGjUqBErV640rW/SRJlOqmPHjqhUKiIiIoCSYarFycvLY86cOTRo0ABnZ2fuvvtu9u3bV2491XWkM66OoVKp8PLyqtWJVm1eLz8Nsv+C3ATlB3uRY6sALzeHqmeNNL79dGoEmVfgr60Q/z8wFJa5i0qlwsvTHVX2DchNVkIaapDbLqPUs6qeNTRtUk/fVBkdl3pWmSOoPL3a6tfSoyB2D6rMK7gQiypuX6VD2qtKvei767GeEEqoKUCTJuBW7B1ReXouLhAaqvx/+nT1havaep3e6XrW0Kxzetkxt+aHi1ZeeOobg3cXJWS0KnpUw73JKRACBin3p7Q/4MqmMsNWZZuR1BqGAut8Ksn69evx9vbm6NGjTJs2jUmTJvHwww/TvXt3jh8/Tv/+/RkzZgxZt26EMTExhIeH06FDB3799Vd27txJXFwcjzzyiOmYmZmZzJo1i2PHjrFnzx7UajXDhg0rdx7D4OBgFixYUOb63NxcHB0dzZbpdDpycnKILDaM/YEHHsDX15cePXrw9ddfm5aPGDGC2bNn07ZtW9OouREjRpjWL1y4kEceecQ0Cm/06NEkJSWVW39Lly4lLCyM48eP8/zzzzNz5kx2794NKOHi999/P7GxsWzfvp3IyEg6depE3759zY576dIlNm3axObNmzl58iQGg4GBAwdy6NAhPvvsM86dO8fixYvRaDQAnDlzhv79+/PQQw9x+vRpvvzySw4cOMDUqVPNbFu2bBmdO3fmxIkTTJ48mUmTJvH7778DcPToUeDvUYTffPNNueU0Mn78eA4ePMjGjRs5ffo0Dz/8MAMGDODixYsW7V8XsXxSBUmtoFKp0GhqN8uhzeulRykp7NX2ymi2Enrcnp5aC953gb0nJP0K8f9VHH/+/cBOX2JzlUqFJu8mqsJ0EPngFFR1bQuoljJaSGEh/HnZEa2dgSaNamcoc22Wzxp61tC0ST2tk+KQSz2vjI5r+GAFejXYrxkKlFFwGVGQdQOVyEdt74Iq76aS9TXg3prRLUK96Lvrsd7165CUBFrt3/PAVUaveXPlGImJ8Ouv0KvX7dtk63V6p+tZQ7PO6AkBaech5YwyN1xBOnh0uO2XndV2L7RzUl7cppxSIihifoTsG+DXW7l3menJNiOpYQwFcH2LdbQbDq3UnI3t27dn3rx5ADz//PMsXrwYb29vnnzySQDmz5/PihUrOH36NF27dmXFihV06tSJRYsWmY6xZs0agoKCuHDhAiEhIQwfPtxMY/Xq1fj6+nLu3DlCjW+6itGsWTO8vcvOvty/f3/eeecdNmzYwCOPPEJsbCyvvfYaoDgIAfR6PW+99RY9evRArVazdetWRowYwfr163nsscfQ6XTo9Xq0Wi3+/v4lNMaNG8fIkSMBWLRoEe+99x5Hjx5lwIABZdrVo0cPU6hsSEgIBw8e5O2336Zfv37s3buXM2fOEB8fj4OD8sLizTffZMuWLXz99ddMnDgRUEabffrpp/j4+ACwa9cujh49yvnz5wkJCQGgqTH7FIoDcNSoUabECi1atGD58uWEh4ezYsUKk9Ny0KBBTJ48GYC5c+fy9ttvs2/fPlq1amXSMo4itISoqCg2bNjA9evXCQxU+v5nn32WnTt3snbtWrM2YUvIkXF1DIPBQHx8fK1mPbJpvcI85Q1pToIyek2lMdcTgviknOrJGunaXEltX5it/BC/ugmySoYjGAwGkv86jSE3HezcLMrodTtUaxnL4a8bdvz4sysnftOx5392RJ5yrJUkALVVPmvpWUPTZvVcmoLaDpJPQ05i2Xo12a/lp98KkTqnZO/TOGDwuYeb6lAMqJQ5jHKTq1+3GDbfd9djvcJCOHNG+b9lSyj2Mt0iPZUKunRRwlXj46snXNWW61TqWUezTugV5ikvYJJOKIkaRL4SlloNUQfVei8sEbZ69NZz4t9ZwGWbkUjMCQsLM/2v0Wjw8vKiXbt2pmV+fn4AxMfHAxAZGcnevXvR6/WmT6tWrQBMoahRUVGMGjWKpk2b4urqagqJvHr1apl27Nmzp8TIrqLcd999LF26lKeffhoHBwdCQkK4//77TXYDeHt7M3PmTO666y46d+7MK6+8wuTJk1myZEml68LZ2RkXFxdTucuiW7duJb6fP38eUOoqIyMDLy8vs/qKjo42C9tt3LixyTkGcPLkSRo2bGhyxBUnMjKSdevWmR2zf//+GAwGooukgi9aHpVKhb+/f4XlKY/jx48jhCAkJMRMe//+/bcdhmxN5Mg4iW2TeVkJUxUFoA+ueT0HLyWL1s0DkHZB+WHu10fJ3mV8C2jIR50bC6pkcG1V8zbVMGlpak7+5kR8gtJdODoYSMuAqMuO5ORouPsfmWhlTyKpDbR6ZXRc2h+3RscNqV39rOuQeAxy4iE7TnEOuoYAaoSdO9g1VUbqxv8XgsoeuSep31y8qISWOjlBGc+yFqHXK+Gqp04p4ap+fuAspx+V3EnkpULCYSU8NfsvcPBWRsSp7axtWdkYw1YTDkDq70oZfHsqdksktYFaq4xQs5Z2JbCzM7+WVSqV2TLjCEujc9dgMDBkyBDeeOONEscKuJUlaciQIQQFBfHxxx8TGBiIwWAgNDSUvLy8StlWnFmzZjFz5kxiYmLw8PDg8uXLPP/88yZnX2l07dqVVatWWXT80uqiKk7tonUWEBBQ6pxqRZMkOBd7sNDpyh9EYjAYeOqpp5g+fXqJdY0a/Z2wsLrKU1RXo9EQGRlpcoAa0etLRqrZCvIntMR2EUIJE8tLBke/Ks8ZUmk0jooDLvmU4hS4sV3JnOrbS3lAzL6hZNTSGmp8vriaJD8fzl/QcTHKAQGoVdCyRQ4hzbP57Q8Df0Z7EhNnx74DLvS4OwOdTqayl9QCLs2V6z7ltDI6wsGj5jWFQdFL+10ZDVuYDZ7/AKdbQ+uNoypcW0LWFWVbry42ff1LqkZODtyaEoXQUGVkW6kIA6qCdMC33OM1bw5//QUJCUp21eoIV5VIbILMq5D4K+TGKdEPri1A30wZgVbXMYWtnlGmVojZpTwb+kRY2zLJnUIlnWK2QqdOndi8eTPBwcFoSxkJkJiYyPnz5/noo4/o2bMnAAcOHKg2fZVKZQqR3LBhA0FBQXTq1KnM7U+cOGFyEgLY29tTWFj2vOOV5ciRIyW+G0cKdurUidjYWLRaLcHBwRYfMywsjOvXr5vCfovTqVMnzp49S/PmzatstzGpRWXqomPHjhQWFhIfH286t/UBG7ijSSRlkB0DuYnKyDiXqncIVUKlBs+O4NMd8pLg5iG49q1iS9Y1MOQomb1qy0FYjQgBV67Z8+PPbly45YgL9M+nf5802rbKQaOBAP88wrun42AvSEnTsPeAC6lpsjuR1AJ2etA3gbw0SDxS8fa3S0EWxO1TflSlRwOqWyFSpcxxoXVWHHL5aRC/v+Ztk9Q5zp6FggLw9ISgsqYLzY67lYF3txLKVg5Fs6tWV7iqRFKnEQblZWfCISVxVm6y8nLDpYVtOOKMqNTg0R58w2+FrR6Dq1+jzom1tmUSic0yZcoUkpKSGDlyJEePHuXPP/9k165dTJgwgcLCQjw8PPDy8mLlypVcunSJn3/+mVmzZlV43L59+/L++++Xu83SpUs5c+YMZ8+e5dVXX2Xx4sUsX77cNEpr/fr1fPHFF5w/f54//viDN998k+XLlzNt2jTTMYKDg4mOjubkyZMkJCSQm5t7W/Vx8OBBlixZwoULF/jggw/46quveOaZZwC499576datG0OHDuXHH3/k8uXLHDp0iHnz5vHrr7+Weczw8HB69erF8OHD2b17N9HR0ezYsYOdO3cCyvxvhw8fZsqUKZw8eZKLFy+ydetWs3JWhK+vLzqdzpSAIzU1tcJ9QkJCGD16NGPHjuWbb74hOjqaY8eO8cYbb7B9+3aLtesaNnRXuzNQq9X4+vqiVtfOqbFpvYxLyvB/e1ewdyt1E7VKha+nI+qamkjWuREE3KeEySafhssbUefE4mafhdq5UcX7VwPVWcaUVA37Dug5dsKJnFwVemcD99ydQfe7MnF2NpjpeXsa6NMzHRe9gaxsNfsOuBB/s/rfxNX4ObSynjU0bV7PpTmo1cocQrklb+DV1s9kx0HsHmVuuIxo0PmBTzcly3JRvaLl0zdXJuxO+x3Sai67k0333fVULzUVjNOltG9vlthboSATbh6GuL2oM6JwIwZ13N4K24kxXBWUcNXMzKrZZ4t1KvWsq1nreiIPX35HnXb21ssPwKcnOPqUv2NV9WrjXugUcCvbqhPq9N/xzNiHSL9GYSG18oH63WYkdxaBgYEcPHiQwsJC+vfvT2hoKM888wxubm6o1WrUajUbN24kMjKS0NBQZs6cydKlSys8blRUFAkJCeVus2PHDnr27Ennzp354Ycf+O677xg6dKjZNq+99hqdO3emS5cubNy4kTVr1jBz5kzT+uHDhzNgwAB69+6Nj48PGzZsqFI9GJk9ezaRkZF07NiRV199lWXLltG/f39AGcW3fft2evXqxYQJEwgJCeHRRx/l8uXLprn4ymLz5s106dKFkSNH0qZNG+bMmWMaxRYWFsb+/fu5ePEiPXv2pGPHjrz00ktmIwArQqvVsnz5cj766CMCAwN58EHLpnZZu3YtY8eOZfbs2bRs2ZIHHniAX375haAy337WfVRC1OIs5VYmLS0NNzc3UlNTcXV1tbY5pSKEoKCgAK1WWyuZiGxWLz9NCQ9Nu6BMmFtGOJgQgoJCgVajqtnyGfKUH1nZcQgHLwoLctAE9kOlsa85zVtURxnz8lT8dt6RP68oI/m0GkHrkBxaNMul+PNUcb28PBWHjjqTkKRFBfyjQxbBjW5vXoby9Gqa2tazhma90Ev8FdL/BP8+ENi/pN7t9DNmmfvioSAV3ELBqWEpHpZSypcepYyCcG0JzSbUyGgOm+2767Hef/+rjF5r2BC6di2ywlAI6X8o4Wp5Scp9QuNEgcYZbcYlVM4NockYcPAsxz7Yv18JV/X1hZ49S22KNV5GS7l5E44eFdjZFdK9uwa93jbOYV3Ws4ZmreoVZCPifqYw8waa3FhUukBlPt4aDLerzXthQb6B4weuE30pHbWjH6qAvrUSSiiEoG3bAlq3rodtporYwu/BypCTk0N0dDRNmjQxZbKU1H+Cg4OZMWOGKauppG5h6XUpX1vUMYQQJCYmUls+UpvVS7+khKmp7ZXRKmXpAYmpudR46dT2ypxx7m0RuYkkFQYgammC4dspoxAQFW3Pzj2uJkdcUGAe/fuk0bJFSUdcaXr29oJe3TNo1CAPAfx60omzv1ffw0CtnUMr6VlDs17oGUfHJZ+A/AxzvdvpZwpzlQQtxsx9hlzw7g7OQWV6P0qUz7mxEqaecUVx6NUANtt311O9mBjFEadWQ5FEcJD1F8Tu+tt5nHMT3FohfHuQqGqDcGmiZFv8a6vS9sqgOsJVa6tOL1xQHJOZmYKrV9P56SdBXFyNSgK212ZsQbPW9AwFkHAQkXWDtKQYhGsrJcSzhp1VtXUvzMhQs/eAG5dTQ8k26BF5icqcw7WAEILDhzNIT69nbUYikUjqAfVzdkdJ/aYwT/mRnJsI+sagKmuG7FpGpQL3tuDSEkPy7c0BUBskJGo4ecaJlDSl/txcCukYloW3V+UnFlWr4a5/ZOHsbOD8BUfOX3AkM1NN545ZpTr0JJLbxt4dnBopGZUTjkDAvbd/zNwk5Vg5sYqDxNHn1g/CSjrW1VplREf8AYj7L7i1qdvZ/yS3hcGghI8CtGhxK+Npfjokn1Qy8ObchPwU0DUAt9bKXKJC3LpndFTmk0q7CDE/QoMhZTp9i2ZXPXMG/P3rVnbVggIlycS1a8r3oCC4fl2Qlwf/+59ieyvbTzAuqW6EUOZOzLoO2THkO7e99WxXN0dVVZaYWC1HjzuTX6DC0dFAaE9nmtsfQ21/FZr7gaN3jWkLAYcOQVqacm327l1jUhKJRCKpAtIZJ7E9MqOVHy+iAJyDrW1NSdSaOj3JcE6OitNndVz9SwmhtdMKQltn0zQ477affdu2ysHZyUDkSSeu/mVPVraa7ndlYm8v35BKagDXFpB1FZIiwbvkXG6VIv2S4jzJjlNCCV1bgr5p1X8Q6gKVUbs5MUrIqk/3qtsmqdP8+Sekp4ODA7QKKfh/9t48Pqr63v9/ntknM9l3QjZ2gkRWBREQRUTtVdSr1o1yba39ttZbW6reuhSKVqq2UrmtXWjVKrf1Z1VqqVURBRWRfSdsISEkZN9nMvs5vz8+yZA9k2SWBM/z8ZhHkpMz5/X5fM7nbO/zXqD+qAhLddWBswp00ZA0B4xxXb+s0QvPy4oPhTemKRWSLu1Rq3111d27RXXVoWCzsNnOP/RLksiZN2oUZGW5OXcOzpyBw4ehrg5mzgS9aptWaaPxsMjJ2VIK1lwUX++5jIYLigIFx00cPSEiBRLjvVwy3Uazw4JOGoOm6ThUfwg5d4T0IJ42DQoLReh4cTH0o6iiiorKEKa4uDjSTVAJAkPXYvAVJtw5FoaVnqKIfEyuOvHQEkC10kg8qIRbMxA9WYbjJ428vznGb4jLzXKz+KomRuf2zxDX27o5WW7mzrah1ynU1On45PNo7PbBnWqG4ngOd80LQs8QL/K4ueu6VKUM+Dwje6Fmh/i+rRi8zZB4KUSP7leju6wqaSB2ksgXVr0dPC0BbytwzWF07r5A9dxuOHpU/D4ptwx9zftQt/f8dSr2Ikjp3hDnl9NbIXG2KPBQ+YkwTPTYxvPhqtXV/Q9XDcWYlpfD5s3CEGcywfz5wmgIoNNJTJ8O06cLD+pz5+Djj8W6oWA4zJnhphlSPVsRNBwRVeiNKRAz/oK4NrXl0m0zxI3OcTF/jg2zWRF6MRNE9e2m4yFLZdCGxQLjx4uIh4MHwekMqRwQmeNCRUVFZTiiFnBQGV60lImHleZCSJnbYxVVlY5UVunYfziKZpswiiXEiZDU+Lj+h6QGSlOThs93WGlxaDAaFOZcaiMhPnR6Kl9RXLWi4qkxEcY9CLp+5Cv0NImwVMc5sJeJ80nCVNAGMQFy9RfC4yNtIaRfHbztqgwJDh6EE0ftxEgnuXrmUSR3tZhXUSMhdoLIJxoojQVQf0CEX49a2uv17dQp2L8fdDq4+urIhKsqChQUnDdGJibC7NnCINcddXWwfTs4HKLdM2dCRkb42qsyxHBWQ9UWsJWI0P6kWWEpaBBqGps0bN9lxWbXoNXAtItbyM7spqiV/SxUb4OoDBj9bdBHhaxNiiIM5g0NInz80p6db78SXGjPg2oBBxWVoYdawGGYoigKLpcrrMl5h5Ve8ylwN4iHlAAMcYqi4HL7wp5gOZyavenZ7Rq+2Gnhsy+tNNuEUWzGlBYWzG0esCEu0P7FxMgsuLyZuBgfLrfE1m3RlJ3rf2zSUBrPC0XzgtIzJgrDh6sOanec1+vrPGM/K4x4ttPid2s2JF0yIENcr/2LzRNecnW7wVXf7233qjmczt0XoJ6t0c2pfaeh/gAXZx1Asp8CJEi5HOLzezXEdTtnYiaAJUsYb0vfBdnT4/dHj4akJJGnbfdu8bAdij72hNsN27adN8SNGSM84trfb3bWS0iAhQshOVm0e/t2kfsuWLt4OMyZ4aYZMj1Pc+uLigpQfJA4AzS6YX9tOlum5+NPY7DZNUSZZRbMbe5giOugFzVSFAZyVAijZIhQFAW328W0acIr7+xZqKgImVxEjgsVFRWV4YpqjBtiKIpCfX19WG9Eho2euxEc5eCuBeuYwPSA+mZ32CtjhlOzOz2fD44eM/HhJzGcq9AjAWNHuVh8VSM5WYPLDdef/pnNCldc3kx6qgefDNt3WzhZ2Hdo8UD1gsFXdc4Ma73ocUKldjd4Xb2fZxRZ5Oaq3tZaCKYOEqaJxPoDLAbTa/8McSL3nKseqj4d0Pa71RxO5+4LTU9RwHaaQ1t2IDcXk2YpJDWmAuKniNyA+r49LbqdM5IkjBKGGBG+Vr6pR0vVQMJVgzWmjY3Cy6aiQujPnAlTptClWE93ekajyHM3bpz4+/hxUdzBFYSaR0N6zgxTzZDo+dzi/OusEPl/E2f4X4IM12uTLMOBw2Z27LHgkyE12cvC+c3ExXZ86dlBT5Ig7uLWlzV7hIdgCGjbh3Fxij98fO9eYRAPpZ5qjFNRUVHpG9UYpzJ8sJ0S4T8aE5hTIt2aIUvZOT0ffBzD0RMmfDKkJHm5+oomLr7IEZGk2TodXHaJndE54mnrwBEz+w6ag+YNMdzxeKClRT0VDwpTEpjTwV0D9bt7Xs/bApVboPFQa14uCZLngjkttO2LGQ9aAzQcFFVaVYYvrlqo3Ez1sT2UFTUiuWvJv9gAqVeIcLPB5krSGFqLffjEA3rd3h5XtVph8mTx+6FDYLcPTjoQSkpEzje7XYTGLlgA2dn924YkQX6+CJXT6aCqShj36oPnOKoyVFFkqNkuUgM4qyB+6rBPN+J0Snz6hZWTp8WLxgljnVw+yxZY4Sq9FeImiXvbik0ix2gImTQJoqKgpeW8V6uKioqKSuQY/skZVL4a+FytXiw1YM0dsAfLhUxzs4aDRyxUVovDOsosk5/nYGRGz6FO4UKSYGq+A0uUzMGjZgqLjbQ4NFw63Y7uAjoL+Xzgckm4PRpcLgmXW8Lt1uByS2J52+9ty10SCiAr4JUNTJ2kkJvlwWRSLZX9Jma88Jyt2Qlx07v+31EhijQ4K8XvUSNFCGk4chTpLBAzDuoPCWNg7p2h11QJLj6nSLRuO43iquPAXitI0YzKH0VMZpC19DGiOnDVZ1DxEZhSRDhbN4weLaqrVleHtrqqLAuD38mT4u/UVGFMM/QjJV5nMjMhNlZUYbXZ4JNPROVHtdrjBUzdXlEB23FOhGVHhfhFSIipq9eyfZcFh1ODTqswc2oLGSP6ec9lHQP2EvGCqHZHSCtv63TiGPv8c3EsZ2ZCfHzI5FRUVFRU+uACegy+cNCF2ToxLPRsReLNoeIDa07/9LThr+oUTk2PR+L4SQtVVdFISGgkGD/WyfgxzpAZugbav3FjXFiiZHbutVBeqWfL59FcPsvWp/Ep3PtQp5WQZXB7JNxuCVer4cztkXC5NK0/uy73yQPTkyQFp1PHoQI9R4/BiHQPuVkuUlO8IassF4kxDSnGZOHh5qyCun3odGPFckWBpgJhSHFUgNcGcZOFMS6Ig9tn/6xjoPk0NB+HppMQM3bwmsPh3D3c9RRZ5CptPCKq9joqKKlIooFJ6BNiyJvUBAMMcut1zkRlQNxF0HBY5I/LvUeEr3aiLVz1ww/Ph6uOHt2L5gDG1OmEL7+Emhrx98SJkJcX2OHTl15MDFx1FezaJSqt7t4tCj10F/YaCENizlxgmkHTazoOzSdEjk7zSPFitTu9YXJtOl1sYP+hKGQFoq0yl820ER3d901AFz2NTngIVnwCVZ+Ll0SGuAG1qUfNdvswLU0Y4c6ehT17xPEX7PuMSBwXKirDgS1btrBgwYLW0PG4kOtJksQ777zDkiVLQq41lFm2bBkNDQ1s2LABgCuuuIIpU6awZs2aiLYLVGPckEOj0ZCUlBR6IY+N6pIyDu6x0eKxQnQChOX+RwP0s3+KAnXNYI8D3QiITu7X13U6hZEj3ORmubFaB2gt6QcaSSIpLvTVjOx2DcVnDZwuNuJyS0hAeqqHiyc5QtrPwfYvY4SH+eZmtu2w0tCk5ePPorn8UhsxMd23OVzj2dSkoaJKT2W1nvqGONyegR0QGgmMRhmDXsFoVFp/yhgNCgbD+d/b1jEYFBQFzpYZKDqjoa5BS1m5nrJyPVFmmZxMN7nZLszm4HnLhWtMw6onSSJ3XEsFmvodJI2bCYpHeMrZz4hKzBptwDm9+kNA/dMaxUNW7S6o2grRo0WuoIFqhutaEQk9nxtN/V6SXHVQHQXaKNC1frTtfgbRq7Hb/jmroH5/qzdlJficeM1jOFw1FQw6JoxzYDQO7LgMaM7E5oG7HlrOwrl/Qtbt3fbZYhHhqvv3i+quaWndV1cdyD6srRWGuLYKqJdcAiNGBPbdQPX0elGF9dgxOHJEGBQbGsQysznwtg6VY0JRRD4un+/8p/3fg/ldljUYjUlYLCLc0Gymw+8mU3ANK0Eb05YycSzZy0AfC3HdW3OHw7XJ54N9B6MoPivcQjPSPcycGpiXf496pmSIGQ1NhSJXZNZ/Bm1HdrcPL74YKivFcXby5PkcjqHSU1FRGTjFxcXk5nZ9efHvf/+bxYsXA7BixQo2bNjA/v37w9y6oUXbWO3bt48pU6b4l//6178esnksVWPcEENRFBwOB2azGSnYr6p8TmgpxVlbysEDbkpKNCjeFrxeN7p4H1JcXnD1ukFUdXJjMBgC75+rDuwOcHogNgPcgY+LooDbpmCzmzh+ykRyopdROS5GpHnQhijSVVEUHC4fZqM26PtQluFcuZ6iEqM/HFVRQG/wcOlUJ+lpoc03IvQG37+EeB8LLm9m205R5fWTz6OZPdNOSnLXjMKhGk+3W6KyWkdllZ7Kah0Op6ZVD9weGYNeQpLwG9WMBlkY09oZ0jovNxjkAeXlUxSF1NQWcrJcNDVrKTpj5MxZAy0ODUdPmCg4YSI1xcOobDdpqZ4BeYx01gvVHI2onikVzCkozkpcZzdh1PqQXFUiT5sppbXCZfATJwbcP0u2yH1pKxb54+KnDE4zVNeKSOp5W6D6cxR7Ke6msxh0GiSdCTRG0OpB0omPRicMcvoYEQbc2VCnNYuk8AG2tUP/fA6oPyCMuK4acQ0yp0DSLE6cisfh1mGJkhk7euBVBwKaM5IGEi8RoaoNx8D4MaQv6nbVQMJV+7sPCwvhwAFx3YmJEcax6Oh+9jFAPUkSHnfx8bBjh/CO++gjmDVLVF8Ntt5gUBRhpCwpUSgrc6HTGZFlyW80k0P4zk9RFBobe76H0miEUS4qqudPf+59gjKm7gao2SHSCGgkSJzWY6qRoX5tammR2L7LSn2jFgm4aKKD8WMDPw/0qhc7SRgtG49A02SIndDP3vSi2Wkfmkwib+Pu3cIAnpHRvQE/WHoqKiqD56OPPmLSpEn+vxMSEiLYmv7h8XjQRyJxeSuxsUM3N6lqjBtiKIpCU1MTJpMpOBcx2QuOMrCXoLRUcPo0HC4w4XE6QNIxKsdFUlQBsbFFaEb4IH7y4DV7a46sUF3dQHJyMhpNgP2rOgqNBSKxbbIHCDwfh4xC0VkvzU0xVFXrqa7VUV2rw6BXyM50k5vl6tEja6AoQJPdg8moDZqzYXOzhqISYaBxtTNGpiR5ycl2ojM0k5pkIhzujcHqn9Uqs+DyZr7YaaGmTsdn261Mn9JCTpY7JHqyLPK7VFbrqajUU9/Y8WFAq4GkRC8pyW7Q2shKN2Ayhib/Umfa9zE2RmbKZAeT8xyUlespOmOkulZHRZWeiio9JqNCTpaL3Cw3FsvA5m4o5uiQ0JMkiBmPUlmFo3IfhpgYJG+DyCdnzQ3Zzgy4fxqdeOCq+hwqPxO/D9A4GPRrxVDQczdA9efQcg7FUUGjlEWSVY8kt4DXDi47yC6QW88RkrbVMGcQnoda43lDXdtyXTToo7sa6/zeddrz/Wusx+Q6jdR8wh+SitYMSZeCKQmHQ+L4KeHVMnmiY1BG8YDnjNYIyXOEQa5mBxhTIeHiLqu1hatu2iQMcoWF+Csn+jUD3Ic+H+zbB8XF4u+RI8W2+xt5NpA5k5Ymwua2bxdVWz/9VBgNxgYQ1R3qOdrUJApYlJSIBPiyrNDU5CAmxtDj/YxOJ4xfWm1gv/f0v7afiqJQWtpIVFQSTqdESwv+j8MhrnN2e+/FPIzG8wY7i6Xr76Z2jluDHlOv43zlVG8LJF8ujsseGMrXpqpqHV/utuD2SBj0CrNmdP8CccB6WhPE5UPNl+J4t+aK43+Q9LQPc3LgzBlxvti7F+bOHbRUr3oqKp254ooryM/Px2QysW7dOgwGA9/5zndYsWKFf52SkhK+//3vs3nzZjQaDYsXL2bt2rWkpqYC5z3CfvSjH/HEE09QX1/Ptddeyx//+Eeie3l7lJOTw7e+9S1OnDjB22+/TWJiIi+++CKXXXYZ3/rWt9i8eTO5ubm8/PLLzJgxw/+9L774gkcffZRdu3aRlJTETTfdxDPPPIOl1Zr9+uuvs2bNGo4fP47FYuHKK69kzZo1pKQMvvBgYmIiaWld82y+8sorrFy5EsB/zL388sssW7YMgJqaGm666SY++OADMjIy+OUvf8kNN9zQ69h885vfpKCggHfffZeYmBj+53/+h+9///v+dRobG/nxj3/Mhg0bcDqdzJgxgxdeeIGLLxb3J2375cEHH+Spp56iuLgYn89HY2MjDz/8MP/4xz9obGxkzJgxrF69mq997WsBjW9OTg7f/va3OXXqFG+++Sbx8fE8/vjjfPvb3wbwexBOnToVgPnz57Nly5YuYaqdcbvdPP7446xfv56GhgYuuugifvGLX3DFFVcEsmsGhWqMuxBRZBFWYy+BllLw2qivcbNnv5GG5ijQ64kfmcq0GVpi4xRqyyDGsxeN7X1IiBLhUyFClsHpVIiJCTAfjLsBdGVgqICEGWDun/FBVhTS09xcnGfD6dBQXGKkuNXj6ORpIydPG0mM95Kb7WbkCPeQKibg80HpOQNFZwzU1J1vmMmokJvtIidTGGNkRaGqLoINHQQGg8K8y2zs3hdFSZmB3fujsLdomDTBGZTt22wa4f1WraeqWofX1/HGMDbaR2qKl9RkD0mJXrRaWsfTh9GoRPRGUquFrJEeskZ6aG4WIcnFJUacLoljJ00cO2kiJclLbraLjPTBe8tdMJhSRXXV+lrwaiHhUjANobeH5hGtue3KRchqCJN1DysclVDzhXhod9ZAfD6yMwliu/FuU2Th6e21gccG3mbxoO+1g88tDHaKF9AKY5ukEw+63XrXWYSxTmvGUHcGTB5wVosQ59jxwpux1YvncIEZnwyJ8d7wFsYxxEHipcKoUb5JhLRFdY0VbQtX3bdPFFtISxMVV/uD3S4MYQ0NYtgvugjGjw9KLwLGaoUrrxT5rEpKhHdeXR1Mn95/g+BgaWkRubVKSoRxsA2dDtLTwWj0kJEhQm27M6wFG1kGt1smJaXrPZSiCINcewNd54/XCy6X+DQ0dK+h0Zz3ojOZQJa1REUJ78j+NdYLNduER5yrVsxhfZDcr8LM8ZNGDheYUYD4WB+zZ9qIigpByJMlS3jkOspEAZf0hcHXaMf06SLfZGWlmONZWSGVUwkj3v7ZiYNGf8/Rr776Kj/84Q/ZsWMH27dvZ9myZcyZM4err74aRVFYsmQJFouFrVu34vV6+e53v8vtt9/Oli1b/NsoLCxkw4YNbNy4kfr6em677TZWr17N008/3av2Cy+8wM9//nOeeOIJXnjhBe655x7mzJnDvffey3PPPccjjzzC0qVLOXLkCJIkcejQIa655hpWrVrFn/70J6qrq3nggQd44IEHePnllwFh1Fm1ahXjx4+nqqqKhx56iGXLlvHee+/12A5JkjoYz3rihhtuwOl0MnbsWB566CH+8z//E4Dbb7+dw4cP8/777/PRRx8BHT3BVq5cybPPPstzzz3H2rVrueuuuzhz5kyvnnXPPfccP/nJT1ixYgUffPABDz30EBMmTPDvl+uvv56EhATee+89YmNj+f3vf89VV13FiRMn/Ns9deoU/9//9//x1ltvodVqkWWZa6+9lubmZl5//XVGjx7N0aNH0bZeLAMZX4Bf/vKXrFq1ip/85Cf8/e9/5//9v//HvHnzmDBhAjt37uSSSy7xexEaAqwy9V//9V8UFxfzt7/9jREjRvDOO++wePFiDh06xNhA3gYOgiFkelAZFIoibnZaSkRyXG8TeG24bQ0cOZlIYflIMCahT4nlojw3o3LcSJKo4ugzZ4LVBw0HoOyfkH0HmFMj3SNB8ynwNILGLB5ABkFUlELeBCcTxzuprNJx+oyR8go9tfU6aut1HDhsJjNDjE1cbOjDPXuisUnjD1P0eMVDqASkpXrIzXKTnuYJi7dWuNBo4JLpLVgsMgWtIZl2u4YZU1v6bWDyeKC6Ru83wNnsHTdgNCikJHn8Brhg5mELJdHRMpPznEya4OwQplxVIz4GvUJOlpuczOB7eg47JA0kXYbbVwYpGaALX/6hgJA0IhdYxRao3g5xU0AfFelWRRb7GVHptqVcGNgSLwFjoqgc0B2S5nwOOVMPb5t9HrGtDp8WcNmEZ53sAqR23nV6pBZFvAWJGiH2UTuvlPoGLWdKxU3dlMmOIA9AAFgywT1ReImXvguj7hHhuZ0YNQpKS8+Hq86fH7hDaGWlCBF1u4X31KWXQhBe5g8IrVbkp0tIEMa4s2eFZ9rs2f03MPYXt1uM4dmzYhzb0GiEgTMrSxjiJAmqqmQSEwdWbCLYSNJ5I1pPeDy9G+vavOtsNvGRZWhq0lFSIoxxqanik5xM7ykZFKX1mC4Txri4yWBKDHqfQ43XC7v2WSgrF53NyXQzNb8lZGlOkDQQf7HwjKvdAXGTwJweIjFxLOXlweHD4jhLTRXHvsrwxuuFHhyAQs6SJf0zyOXn5/PTn/4UgLFjx/K///u/bN68mauvvpqPPvqIgwcPUlRURGamqCj+2muvMWnSJHbt2sXMmTMBkGWZV155xe8Jd88997B58+Y+jXHXXXcd999/PwBPPvkkL730EjNnzuTWW28F4JFHHmH27NlUVlaSlpbGc889x5133skPfvADf3tffPFF5s+fz0svvYTJZOLee+/1b3/UqFG8+OKLXHLJJdhsNqw9XLzGjx/faxil1WrlV7/6FXPmzEGj0fDuu+9y++238+qrr3L33XdjNpuxWq3odLpuPeeWLVvGHXfcAcDPf/5z1q5dy86dO/355rpjzpw5PProowCMGzeObdu28cILL3D11VfzySefcOjQIaqqqjC2njCef/55NmzYwN///ne/l5rb7ea1114juTXXxIcffsjOnTspKChgXGuiylGjRvk1AxlfEPvtu9/9rn8fvfDCC2zZsoUJEyb4tXryIuyOwsJC/vrXv1JaWsqI1qS4y5cv5/333+fll1/m5z//eUDbGSiqMW6IIUlS//KpeZqEB5y9RCR69trET5+LM1UjOHhmLi4pGWJ1ZI90MznP3qFypQQY9Bok63jw2YXx6+zbkHMnGIIfX92v/vlcYC8WRkbrqB5zjPSqR2v/OrQB0lK9pKV6cToliksMFJ81YrNrOH3GyOkzRuJifIzKcZGZ4e53DrDuNPvC621L4G+kruF8P6PMMrlZbnKyek7gPxC9wRAqvUkTnESZZfYeEF5yDqeG2TPt6PVKj3qKAg2NWioqhQGutk7Xoa6hBCQmeElLEQa4uFhfnw+m4R7P/mhqNDAyw8PIDI+/gEdxiRirE4VGThQaSUo47+nZ0wPDhTJnetTTGtFHj0TSBvZGbLCcPGXk8HEzkyfIjB3t7tv4YUoBy0jhuVyzDdKv7rdmv68VgyRkeo3HoGE/2M8Jb7Tk2aCPQVJ6Pu4DQqsHbTwY47v/vyKLEDpfM3jsSB4bkuxDSsrp1pPywGFRSSArw0183OBf1gzomIi7CDwN0HIGSv8JWbf6w2z9220XrlpT0zFctbd92FY8QVFE3rbZs3s36gRCMObMmDEQFyeKSDQ2wubNwkiX3o19YjB6Ph+UlwvvoIqKjnnfkpKEAW7kSGj/kl1RwnsMwuDHVK+H2Fjx6Q5ZFgY5h0N4SdpsEoWFWtxuyW+gKywU8ywxURhv0tLEPurQpMbDYCsSxUesucKYHEj/GDrXpuZmDdt3W2hq1qKRYMrkFkbluLtZMzh6fgxxEDNBVFI+96EwvA+i2E9fc2bcODHvm5pEAZhW+0bI9FRU2pOfn9/h7/T0dKqqqgAoKCggMzPTb4gDyMvLIy4ujoKCAr8xLicnp0NIavttrF+/3m9wA1H0YG5rTHZ77baw18mTJ3dZVlVVRVpaGnv27OHUqVOsX7/ev46iKMiyTFFRERMnTmTfvn2sWLGC/fv3U1dXh9x6MSkpKSEvr/vc7MeOHet1jJKSknjooYf8f8+YMYP6+nqeffZZ7r777l6/27mfFouF6Oho//j0xOzZs7v83VZ5dM+ePdhsNhITO75gcTgcFBYW+v/Ozs72G8cA9u/fz8iRI/2GuM4EMr6d+yNJEmlpaX32pzf27t2Loihd2uVyubr0MRSoxrghhiRJfSdk9LaIGxx7iUgu7bWJcE6PHfRRNPly2XtqIjVNFtBCTLSPqZNtJCd19VmWJImEmNbXYAlTwecQXgql74jKbbp+lDILVv/asJ0WxkZFBmv2wPVien7NZzIpTBjnYvxYF9U1OorOGCgrN9DQpGXvwSgOHI4iM0NUs0xMCOwBrC/N9tTVi2T9Z8v0/hBKCRiR7mFUtouUZG/fxqN+6AWDUOrlZruJMst8udtCda2OTz4XlVbb6zkcEpXVeiqrhPdb56qnVotMarKH1GQvKcmefrvMh3s8B6ppschMmuAkb7yTikodRSXC07OmTkdNnY79h8xkjXSTm93V0/NCmjOR1JNl2HvgfFW9g0ehpNTItItbSIjv43wRmweOc1C3W4Tg92Q06oF+nUuDQND1FEVUV2w6BvZSYVRKmuO/5oR8H0oaETKnt4BJnHfjeuhe2TlxXGk1ImF7UOQH0j9JK0L9yj+CxqNQtQXSruqyWk/hqt3tQ49HeNCVlYm/c3NhypTghFgGa84kJYk8cl9+KQonbNsmvHkmTuxoAOqvnqJAVZXwgCst7RjWFRsrDHCZmT0bJcN9DIZDU6MR88diEeMOEpMmxQiv82rhPVlZKYxyNTXic+SIMFK2ec2lWoox24+I+1RjisjZ2Z/+DYFrRdk5Pbv2ReH1SZhNMrNn2vs+pw9Crwsx48X4NZ+Cun2QOH1wmr3MGY1GGPA//ljkkMvOHpxHbCSOC5WO6HTCQy1S2v2hc1J/SZL8BixF6T5dTOflvW3jhhtu4NJLL/X/LyMjo9vvtW2vu2Vt25Jlmfvvv58HH3ywS5uysrKw2+0sWrSIRYsW8frrr5OcnExJSQnXXHMNbvfgDPmdmTVrFuvWrQto3d7Gpz+0H4/09PQOocJtxMXF+X+3dKoKY+6jPHpf49tGsPrTXler1bJnzx5/yGwbPXkzBhPVGDfEUBTF78ra4QTkc4OjVBjgnJUiN467UYRwak1gSsEbM4OCohROFJpQEAnp88Y7GDva1WMIhaIo2BxerGYdkqSFpFlQuQWaTkLZRsi8SeTVCXX/uqwoC2Ocqw5Mab0m/O1Tz9+/nvUkCVKSvaQke3G5HJSUGigqMdDUrBXeR2cNxET7yM1yk53pxmDoObyxL02PB86cNVJ0xkBj8/mD3mqRyc1ykZPlxmgMPHwy0D4Gi1DrpaZ4ueLyZrbtEJVWN39qZfToZrxuI5XVepqaO54odVqFlORW77dk74CLGrQR7vEcrKYkQXqal/Q0Lw6H5M+LaG/RUFhspLDYSHysj9xsF1kjRV7EC23ORELP7Zb8xUdAVMOtqzXT0KTl48+iGZXt4qKJzp7PFYY4sI6GphNQtRUyl/RLP+BzaZAIqp7sEyFYttMirYI+GhJndChmMVTmjCzDoQJxAzlujDNoeaIG3D+tCVLmiNxxNV+K62PcpC6rdReuCh33YXMzfPEFNDeLB/IpU8T3gkUw54zZLPpw4IDwzDp6FOrrhRdPm7daoHr19cIT6OzZjpHQUVHCAJeVFVh+tHAfg5HQbK83YoREawQPdrvwIKysFAZNt1uM59nCRmg8R4xOR1pKJqljJ5AkB27cjfRxryhw5JjIyQqQlOBl1oyOESXB1OsRjR7ip4r78cotEDNOnCcHqtnHnElIEF6op06JXI2LFg3cIB+J40KlK0MpB/ZAycvLo6SkhLNnz/q9444ePUpjY6PfS6ovoqOjey3k0B+mTZvGkSNHGNO5OlIrhw4doqamhtWrV/vbu3v37qBod2bfvn2kt3MRNxgM+HzBS7H05Zdfdvl7wgRR4XnatGlUVFSg0+nIyckJeJv5+fmUlpZy4sSJbr3j+hrfQGjLEdefsZg6dSo+n4+qqiq/12Q4uQAO1QsLRVGw2+1YLBYkRRZJvu0lwoPCYwNPs6jyJunAmCS8K4wJnKswsn+3mRaHsLqNSPMw5aKWPh8cFMDu8GIx64TbvEYvql1VfAQNh0BvhfTFQatC2KF/vW2zpUz009syqDeCXfoXAEajwtjRLsaOdlFbp+V0sZHSc8Iwd+CImUNHzWSki9xy3Xkb9qRZUyu84ErPGfC12os0EowcIbyXuttWqPo4GMKhFxsjKq1u22GlrlHL9l1mYqwG2grWJcT5SE3xkJrsISHeF9R8PeEez2Bqms0KE8c7mTDOSVW1jqIzRs5ViMqx9QejOHjETGaGh+wsJ17FeUHNmXDqNTdr2LbTis2uQa9TuGS6DUlnI/ZiD0cKhKfc6TNGysoNTM5zkJ3ZQ+hqzDgRit9wCBJmgiWjm5W6J+BzaZAImp7PJQoRtJwVH1OayCfV6aXPUJkzp06LFAYmo8L4McEpLNObXkAY4iFxpqiuWv5vcS/QKc9rd+Gqo0ad34fnzkns2iW8wcxmEZYabGeWYM9RjQamThXt3LtXhJV+/LFoe2xs73o223kDXHPz+eUGgwg/zcoSYZf9aWa4j8FIaPakZ7HA6NHiI8uiwEZlqZ2KgsPUuxtpcupo0l7EiXq9v1J5arKHtBRPr3lNI3nce9wSO/ZYqKwW56Kxo1xMzhtc1eTe9PrsnzlNhPjazkDF5n6/sPFrBjhnJk0SHrJ2uzB2t4vWC4meikpfLFy4kPz8fO666y7WrFnjL+Awf/78DhVOw8UjjzzCrFmz+N73vsd9992HxWKhoKCATZs2sXbtWrKysjAYDKxdu5bvfOc7HD58mFWrVvW53QkTJvDMM89w0003dfv/V199Fb1ez9SpU9FoNPzzn//kxRdf5Be/+IV/nZycHIqKivyhoNHR0f58bgNh27ZtPPvssyxZsoRNmzbx5ptv8q9//QsQ+2X27NksWbKEX/ziF4wfP55z587x3nvvsWTJkh73zfz585k3bx633HILv/rVrxgzZgzHjh1DkiQWL17c5/gGQkpKCmazmffff5+RI0diMpl6zccHIifeXXfdxdKlS/nlL3/J1KlTqamp4eOPP2by5Mlcd911/Ru8fqIa44YaioLGXQ21Z8B5ThjfvM3CQwzAkCCMU8Zk0Oiw2zXs32mmvFJ4FFiiZKZc1EJ62iDK6OjMkDq/9c37TtBZISXMlmLbKXA1iLx1+v6W8AoeiQk+EhNamDK5RXjLnTHS0KTl7DkDZ88Z/N5s2Znubt+culwSZ84aKCox0mw7f0cXE+1jVLabrJG9e9l9lTGbFa64vJmd+6IoOiuTk+kiPdVLarJXHbM+kCThYZia4u0yB4tKDBSeMdBsN5GSqMNkUDAaFYwGGYNBwWhQxE+jjLH1p0Ev1hkKScojTXWNju27LLg9EpYomTmX2LBG+6iqE4b8GVNbyMlysfdgFE3NWnbvj6K4xMDU/BZiOz+E6izCIFd/SHjH5d4ZmU6FC68dqj4XlQJbysCaI3IjDSIfUihxuSQKTggPmYsmOoaWp4E1V6SnaDoBpf+A3Hu6pJXoHK6akiJCMw8fhhMnxDrJyaJQg2mI1TnpjexsYXz74gthZPv4Y2F4zOhky3Y6hXdgSYkwFrWh1cKIEcIAl5o6NIovDGc0GkiKd5Pk/oxJljO4m2upVOZT2aBQWS3jcLZVNddx8KgZs0kmNdnb+kJtaFzPGxq17Nhtxd6iQauBGVPtZIazYnJPxF0kXsbXHxAvLaJHh0xKrxfG7i++EOeHrKyecwuqqIQDSZLYsGED3//+95k3bx4ajYbFixcHbJgJNvn5+WzdupXHHnuMuXPnoigKo0eP5vbbbwcgOTmZV155hZ/85Ce8+OKLTJs2jeeff54bbrih1+0eP36cxvblurvhqaee4syZM2i1WsaNG8ef//znDvnibrnlFt5++20WLFhAQ0NDQNVZe+NHP/oRe/bsYeXKlURHR/PLX/6Sa665BhD75b333uOxxx7j3nvvpbq6mrS0NObNm+fPs9cTb731FsuXL+eOO+7AbrczZswYVq9eDfQ9voGg0+l48cUX+dnPfsaTTz7J3Llzuw2n7czLL7/MU089xY9+9CPKyspITExk9uzZITfEAUiKokT+KhgmmpqaiI2NpbGxkZh+12gPA/YS5Lr9NNacJTZKg8ZTLxJa62JFufOodH+4pizDiVNGCk6Y8cnCw2rcGCcTxzn75VouKwpVdU5SEkxoOr/BclSJnDT6GMj4mqjwNEhkWaaqqoqUlBQ0Pd0Bu+vh3AfQdFy8/R9EZdde+zdA6hu0FJ0xUFJq6JLnLTfLRXKyh6MnfTQ3xVBRYUBuPcK0GlqrtbqCknukjVD0UdULn144Ndu8M0vO6alv9HTwNgwEnVYY5YRxTj5vuDPIHZa3Ge8MBqW1avOFsQ+LSwzs2R+FAiTGe7nsEjtGo9KtniwLr6qjx014fRISMHa0i7zxnYw6PhdUbBKh+Tl3Q0xgJdQDOpcGkUHruetbDXHlItVCzARhUOph/wyFObP/kJlTRaKgz1Xzm4NaxToo/VN8UPkpuGshYTpk3tzFsKko8OmnIlw1Pl6mqakejycejUbDuHHCWBeq4Q31HHW7RfXXykrx9+jRMomJVfh8KZSWaqiqEv2H1lQUKcLAkJERnBCucB+DkdAMSE+RoeozsBUKI3v8NIg6X8WuqUlDZbWeiio9NbU6f2RAG/GxPtJShad7XJyXmobwHvd7DimUlMSjyBJWi8zsmbauL06CqNfv4775FNTuFoa40d/sEM4fkGY/58z27cJDLj4erryy/+eHSBwX/WXIPw/2E6fTSVFREbm5uf6KkyoqAyUnJ4cf/OAH/qqmKgMj0ONyKL3nVdHokVxVGHzVSF6reFCJyuzytruqWse+Q1F+T6vkRC/T8luIjh5AMkbAbNR27y5vToGkS6F6u6jopLMO+q2cJEmYzebeXdebT7XmwjODKbnn9QLRo5f+DZD4OB/xcQ7yJzkoPWfgdLGogFpWrqesXI9Go2Cz+zCbdEgS/nxdA6nMGgih6ONXVk9RkGp3E1e9D8meBFEZ4mNKC2ruxM6Eq49JiT6SEluYdrFCVZ0Pk96A263B5Zb8P12udr+3LXdJKIDXJ+FtkbADEJjV36BXMBhkjCYDMVM9g67QGAjBHk9FgcMFJo6fEhfTzBFuZkxt8b/46E5Po4FxY1yMHOHmwJEoysr1nCgUxVqmXOQgY0Sr14XWCDEToXaX8I6LHh2Qp1hA59IgMig9RwXUbBeGOFcdxE+BqBHdrqoo4hpXfNZAQ5OJ3EyFtBRvr6FtwaDzPmxq0lBYJEI88ic5gm6wCsocbcvz2pZWwpQMKfM7rtIuXLWuTsLhMGG1SsycKYoThJJQz1GDAS6/XBQQOHYMTp2SOHQoGrNZ8u+vhITzlVCD/Ywa7mMwEpoB6dXthZbWdCox4zsY4gBiYmRiYkTqD58Pamp1rcY5HU3NWpFGoVFLwQkTWq2CRusmyqRFCsNVX1agokrBbJIYkebhkmn2kNyntTGg496aKwqr2c9C9RcicqU/mv2cM1OnijyA9fUih9zYwN4PDVhPRUVF5auM6hk3lFBk4YpuOw2W7C6vo5xOiQOHzZw9J7zjTEaF/EktZI0MsSt9Y4FoV1QGZN8xKE+1PvE5ReGIpuMQPUZ8hgFNTRpOnzFSUmrA7ZHQ65QeK1mqDFFkjwj3qt0BrlrhdaI1CW9UjRGiRgrjeFQGmEeIfC4DLCwyHPF4wOXS4PYIg52r1Ugn/u643O2WulS5BeFZNznPwaicHnKoDUG8Xti518K5CvGEljfOSd6E/ucOq6gUL1HsLcLQlpbiYcpFDqxWGWQvVG4GTwtk/6cwVl0o2IqFodFxToSpJswAU9dS8Z2Lj3SmLbQtLcVDShhC27btsFBeqWdEmofLLrGHVGvQOKuh4hMwxEDmLRA7ocsqRUUiMbvVCpddFliBguFEWRn+HHjR0cLQmJUl+qsSQpqOQ90ecZybR4iwyn6c3NtXR6+q0eNyR+bCkDfOycTxzqF7XXLVCaO7IQ5G3wempJDKnT4t8jLqdKKYQzheooWTIf882E9UzziVYKJ6xgWHQI9L1Rg3xFDsZTSV7iImaaT/rZKiQGGRkSPHTHi84l3h6FwXkyY4Bv0GT1EUmuweYiz6nt9iKYq42Wo+BdZRkHOnyOU2UL2mJmJiYrrXazwqKsS1nIP0qwZt7Aiof0HE54PGJg1onMTHhEcz3H28IPXc9VC0HmyFKM5qHNoRmHVuJG8jeOyALIqmaAzCY1OjB60BzBkihNw8QvxuTheeTv3kQhxTRREVR10uiRaHxM79elxOE5IkCnBMn2IPWShQsPrncEhs22GloUmLRoKZ07rPIxSons8Hx06aOH7ShKyI9AITxjoZP9aJ1lUqwjijsmDcd/oMRerzXBpkBqTXeBQaDoK9TBi3ky7pUBFQUYSR8vQZIxWVetpuRsTLDBcybuw2M7V1+i6hbe2LuCQm+Ab9EN1+H1ZV6/nsSysSsGhB04C8zvujF5T911wojJ7mdMhd2u3DemOjgs/XRHz8EJ4zg6ClRaGmponMzAuzf5HQ7FWv5RxUfwa2EtBFQdJM4a05YC1oaNRQXecjOip81VR9uMhI1Q79a2/9AWg8BglTxIvxAL8/kDmjKLB1qyj+kp4Oc+YE3sxIHBf9ZTg8D/YH1RinojL0UMNUhymKAg43RCPc2evqtew9EEVDk7jBiY/1Me3iFuLjguNtpQAOl49oi75nt3lJgoSp4HMIV/nSdyDr9i7hswHpKQoOh4Po6OiuF2lFFl6BrrqgeR0F1L8gotVCXJyPqjofCuHRDHcfLzi95lNQ/FdwVoCnEcUyiibjPEyWJnGvK8vgaQBnFbhqRAi1q0EYF1rKxA2y34POIB6GLZlgHilC8cwjxP8j2ccI6EmSKGhgNCpYoxVmTrfR3BjL0WNm6hq0fLQlhvFj+5/nMhCC0b+GRi3bdlhwODUYDQqXXWIjMaH7826gelotTJrgJCvDzb5DUVTV6Dh6wkRJmYGpF2WRak4TFbRrd0HyZb33sbdzaQjol56iQP1e4TVjPys8S5Nn+Y8Du11D8VkDxSUGHM7zXnBJCV5G5bjISPcgadpyK/lQZKlLaFtdg/gUnDCh1ymkJItqjanJXiyW/hvP2vahNUrPwSPi2jZmlCskhrj2ekE7BqNHi5cKzYWtBR3u6nLeiY5WqKpyoChDcM4EAZNJwWC4cPsXCc0e9dwN4sWpo0K8VUicNihDHIhrRmysD5cv3LkiPSiEJxXGoI77mAnifNp4XISlx+cHpjmAOSNJMG0afPSRqFpcWipCvUOlp6KiovJVRTXGDVHcbomjx8ycPiO8bPQ6Ed6Vmx2h8K623DSVW6DppAglzbwpuHm0WkrFw4TXDonhL1mt8hVDUUSOrvIPhJFNdrcmns6GdgYCNBowJohPG7IM3iZwVIK7RjyYuKqFgc5RJryB2jzoNAZhXI7KajXOtYa56i6wuI8+kCRh3Mgc4WH/YZFD7dhJE6XnDEzLbyEleRAVoIPMuXI9O/ZY8Mmi8vGcS+wDMvD0RHS0zLzLbJwt03PgcBQ2u4bPdsQwMvEyLk7aiLn6C4ibAvphOEdkrwj1thVBy1nQxUHiVGT0nCvTU1RipLL6/HXDaFDIznSTm9XR8CW389nXas9XB86f1DG0rbJaj9sj+XN2AlgtMqnJHtJSPCQnefuVrL+4xEBjsxaDXmHiuP6HI0eU+CniXGQrhHP/hpE3DtlKtSrDGK8DqreJF1heOyRf/pVK2RAxtEZRSK16G1R+DNYxIb1GxMTAhAlw9Cjs3y8KoBjU3ayioqISVFRj3BDk7DkzOw7G4PGIm+jskW4m5zkwmSIcUazRi5uutmTReiukLw5eKbbmU8LjyJDQIZRJRSXo+JxQ8neo3y+MaBoTpMwDQzwEcphpNCJ3iyEOGH9+ubsJXJXgrAFPfWvuOY/Il9VwuKMHnTEZrNnnjXNKRki6OtQwmxVmz7RzrlzPvkNmbHYNn263kj3STf4kB0ZjZM9zJ04ZOXhUeEalJnuZNcMWsoTemRke0lIaOXrczKnTRkprU6gouYxJI48wJvZzpIxFoREOFT4XVH8uXqy0nAXTCJp1+RQVRHHmrKFDPqjUZC+5WS5GpHvob8E9s1khJ8tNTpa7NbRNS0WlnspqHbV1Omx2DTa7kcJio3DaSWj1mkvxEhfbc0irxyNxuEDs+4njnCHPSxd0NDpIng3lm4THrikFkvsRX6ai0heyF2q2iWIs7jpIuAT0lki36qtD1Ejhed9yDqq2QMZ1IZWbMAHOnoXmZjh0CKZPD+BLzmokTwOQEtK2qaioqFwIqMa4IURTE+zZZqD0VDwmq4bYGB9TJztITgqdx4gEWMy6wN3ldWZRyal8E9TsFBVWU+YGridJWCyWrq7rrjpwVoK7FhIvDXh7ferRz/4NQ01Vr584q6HoNbCXCEOcOQ3ip4POdF5P7xyYniFGfKLblR9z24SBzlUNrnox12W38KBrOgpaE5JkIN46CSn+PpGLLsQMhX04It1DcpKHI8fMnCoycqbUQHmlnosvcpCd6Q66Xl/IMuw7GEVRiRj/0TkupkwOrIrmYMZTr8ff570HoqjzZnHgRAPFVbVMu76BxBFx3Wv2dC4NEX3qeWzCEOcow9dcTqltEkW1edTUn7/NMJtkYUTLdPfpaRjomEpSW4VrHxPHi0Ij1TXCMFdZrcdm11Bdq6O6VsfhY8ITLyXJQ1qql5QkD2az4tc7d86K260h2iozOtcV+OAMgJAdgzoLJF0GlZ9A5adgTIUYUQgp7HNG9mCJMg2dOTrM9SKh2UFPUaB2p0jP4KgQxRq6KcYyKD0if20a0nqSBHEXi/Gv2wOxF4E1q4+vDHzOaDTCALdliygCk5UFyck9rOxtgYaDSLZior1aJMb1W09FRUXlq4ZawGEIceIEHNxVh7blJHn5FsaOdvXbYyBsOKrEWzl9DGR8TbjOD4aanVC/D9yNkLZADa1RCQ0Nh6HkTWGQ8zaBdRzE5YV/vnlt4hhy1YjQbHet8DxNnAXj/t9XLuSnrl7Lnv1RNDaLnEMpSV6m5beISqNhwO2W2L7LQnWteEi6+CIHY0aF1hjTHYoiwiQP7SzH3VgJMePJnXkJkycP8fAgVx1Ub6OxqoaiQi9nGvPw6NIA8fCZluphVLaLtFRv2NMs2Gwav2GuqlqH19exAbHRPlJTvCTEedm514KswGUz7YxID3GV8lDTdALq9gpPmtylYIwPjY6iiFBFbzN4msHTJH53N4GvtQqtJUe0w5QWlpcNKiGi4RDUHwR7MViyIXZipFv01aXpGNTtF3nkRi0DTZATr3Zi715RYTU6GhYupGOeV9kHzSegsUB4SzoqW887d4e8XQNlqD8P9he1gIOKytBDLeAwDBkzBlrqPCQZq8jISg9bct76Zjfx0Yb+6ZlTIOlSqN4O5z4UHnLRowPTq68nPj7+vJ7PCS0lwjARPTaohpEB928Yaap6gWxEhvIPhbeIq0b8nXAJWLpmJFYUqHdZiDfaQ2c40Fkh2grRo4SmowZ3+VYMNV8iKT4Y/0BIDXJDbR8mxPu4an4zJwuNHD1upqpGx4efxJA33sG4Mf1/KdGf/tlsGrbttNJs06DTKlw63U56Wv+8kYM1npIEudluRiSZOfRZM8XlxRQVjKasLJH8fMjOPp8VoNtzaQjpSc/bXM7ZAwcoKvRRVxsjDC/GOCxRMjmZbnKyXH7vs37rBWFMrVYZq9XN6Fw3siwMv5XVeioq9dQ3amlsFh9FMWJ3eMkZqYTFEBfyYzB6rDD0285A2T8g+04UjX7gc0b2ipcIniZhdPO2Gt7cTSA7QfYIj1/ZI4o9eVtQZA82pw+r5TSSMV6c96KyzlegHkARqN4YKsfEhaTp19M3IjUeEeHnxmSIGd/3lweqN4SuTUNWzzpGePfbikSOzl4K/gRjzkyeDOfOiXDVY8dg0qTWfzjKz6f7cFSC7EGJGkk9mcRLmrBGpaioDAe2bNnCggULqK+vJy4uLtLNobi4mNzcXPbt28eUKVMi3ZyIcsUVVzBlyhTWrFkDQE5ODj/4wQ/4wQ9+EFJd1Rg3hNBoIP8iL1VFPhQIW2Unt0cemJ4lW7il1x+Asn+KUuvm1N71FAW3242iKOdvCmynxU09irhJDyKD6t8w0VT1+sBrhzN/g8ajoiKqPgYSZopw0p70ZH1454wpiaaE/yCp4W0RBnR8LYx7QCRsDoUeQ28fajQwfqyopLnvUBSV1ToOHzNTUmZg+sUtPVYyHageQE2tli92WnF7JKLMMnMutREbM7BKnMEcT6Mlihmzo8k5fYS9ZYk0ua9i924RJjRtGsTG9nAuDSGd9erqoOhIKWcLzuB1+MAno4kdw4iRRnKzbaQkD84LLhRzVKOBpEQfSYk+Jk1w4nJJVNXoqKzSU16lw+70cfHk8HhEhvwYlCRImCaqPzedhPIPUNKv63vO+JztjG3N7YxvTe0Mbm7wucX1X3aKlxsaHWjMoI1qNdbEo+iiaam1YdGXIznPgVwKtuLWvLAWiMps/WQEJU9spI+JcBCJPnps51Dko0gtZSK/avzFIfMmH4rXpiGpp9FB/FSo+EQUdIjNa81h241mEOaMXg9TpsCXX8Lx45CZZiPGt18YZ9ty5JrSIS4PxduCu1EO63GhonIhs2XLFl544QV27txJU1MTY8eO5cc//jF33XVXh3UWLFjQ5bsFBQVMmDABgGXLltHQ0MCGDRvC1fQhSU8G0bfffht9qJJE94JqjFMZHDEThLGj+RScfRty7gRDbODfV2RoLhSJ7s3pIlRPRSVYtJRC8XqR48ZVK0InEqYNyXnmM6SgpF2NVLEJanfB8Rdh/PdF0YevEFarzNzZNkpKRaXRpmYtn3wezegcFxdNdAStmMKZswb27I9CViAhzsdll9giXySnPdbRJKWcZmHiZ5ySR3O0JIfaWvjoIxg7ViTWDjduN5w6BWfOQOO5s9ByBpx1RFtc5EzKJmeUD6PRHv6GDRCjUSEzw0NmhgdZUaisdRIbYyJ8ZvgQo9ELj5nyD0XIqjEZyBHuvx7bee82v9GtCXwtrUa3VsObz9H6ac3jqDGIStH6VqObIRb0seLFQWcDjaKgGAyQMAKkGSINhb1YFLRxlIL9DOjjhCHOnNYazpohCumoDA08NgyNe0DXBIoXkmcJQ5BK5DElQ8xoaCoUeZyzbw2p3MiRMCLNy7nCc+zZVMQV044juSqFx2vSZedD4b0tIW2HispXjS+++IL8/HweeeQRUlNT+de//sXSpUuJiYnhP/7jPzqse/z48Q6h18k9JnkcerjdbgwRzMmSkJAQEV31iqoyOCQJEqaKm3X7GSh9B7JuDzz8pOWseKPmc4C17zBXlQgje6DiY0w2F0TNEuHKQ5Xa3VC6QYSleu0QNwmixwev+m8oMCZC+iJxY123p9Ug9+BXziAHkDXSQ1pKEwePmCk+a6Cw2EhZuYGpk1vIGDHwMEJFgSPHTBw7KcZ0ZLqHmdPsHXPgDAW0RoiZiKZ2F+NiNpG56JscOKihtFTkFz1zBkBPfDxhyS3q9cKpU0asVgWNvRCNs4yR8cXkZjlJHjupNYnQEDJmDoChfGoYMG0PypVbofITDGSC0wCKu53BzS083LwtIhxVoxVGN12UMLRZsluNZhaQ9AMfKEMsGC4WnlUeuzDm2s+Cs0KE3DUeE57LxiQRwm8eIX5Xc8iKvFyOUrCXoW+sBU18eMbFVSdymtIiDLtfwWvRkCZ2knjp2HgEGidDbAjf0tjPMiXtMFWHjdTWNXG60MfoSZNEdVf1GFUZAFdccQX5+fmYTCbWrVuHwWDgO9/5DitWrPCvU1JSwve//302b96MRqNh8eLFrF27ltRUEYm1YsUKNmzYwI9+9COeeOIJ6uvrufbaa/njH/9IdHTPHtc5OTl861vf4sSJE7z99tskJiby4osvctlll/Gtb32LzZs3k5uby8svv8yMGTP83/viiy949NFH2bVrF0lJSdx0000888wzWCyiqvTrr7/OmjVrOH78OBaLhSuvvJI1a9aQkjLw56Wf/OQnHf5+8MEH+eCDD3jnnXe6GONSUlK6DX9dsWIFr776KoDfY/WTTz4hJycHgNOnT/PQQw+xY8cOxo4dy+9+9ztmz57dY5skSeK3v/0t7777Llu2bCEtLY1nn32WW289/1KgrKyMH/7wh3z44YdoNBouv/xyfv3rX/s12zz1Lr30UtauXYvBYKC4uJjS0lKWL1/Ohx9+iMvlYuLEifzmN7/h0ktFgcd//vOfrFixgiNHjjBixAi+8Y1v8Nhjj6HT6fxt++Mf/8i//vUvPvjgAzIyMvjlL3/JDTfcQHFxsd+DMD5evED4xje+wSuvvNIlTLUzjY2N/PjHP2bDhg04nU5mzJjBCy+8wMUXDy5vvmqMG2JIEsREhe+9vATEWPSD05O0kDQLKreIcJiyjZB5U7dvTyVJIiYm5rzrevMpcDWefzseZILSvyGuGTY9dz0UvY5kO43VbUNq/FAkcE66TFRVC5G3Wb/7J3vh3Eao/kLkMUEDSbOF50WgeoaW8M+ZNk1jAqRfDRWbhDfLsTUw/r+Dml9puMxRg0FhxtQWska62XswCptdw/bdFtJTPUyd3EJUVPfGn570fD7YuddCWbmYqxPHOckb7xy0ESZk42nJBtspsJ3B7DrIrFlTqKiAffvAZpNwu604nVJYjEiKImE0GInjCKNyTpMVdwyDJbbV0zT4txLDZY4OCz1zGsTnI9UdwKLxIjV6AVmcszUm4elmShFhbvoYEWoahH3aax/1FhFaF5snjIEtJedzYNnPinsDQ6xokyVXeMyZ0npNCN/l/iLEhEXP3dA6JmfA04TkbsTqtCN5NWGxHkuKgllyIsVN61/Uw0D1uICPw1DoaU2iumrNl1DxEVhzu6S3GPQ8dTeKvHCOMqK81VyUqWX/6TwOVU9ghLYFs9TxOiz6qFNDVFUC4tVXX+WHP/whO3bsYPv27Sxbtow5c+Zw9dVXoygKS5YswWKxsHXrVrxeL9/97ne5/fbb2bJli38bhYWFbNiwgY0bN1JfX89tt93G6tWrefrpp3vVfuGFF/j5z3/OE088wQsvvMA999zDnDlzuPfee3nuued45JFHWLp0KUeOHEGSJA4dOsQ111zDqlWr+NOf/kR1dTUPPPAADzzwAC+//DIgvLtWrVrF+PHjqaqq4qGHHmLZsmW89957PbZDkiRefvllli1bFvC4NTY2MnFi1yI6U6dOxel0kpeXx+OPP+43PC1fvpyCggKampr8bU1ISODcuXMAPPbYYzz//POMHTuWxx57jDvuuINTp075DVzd8cQTT7B69Wp+/etf89prr3HHHXdw0UUXMXHiRFpaWliwYAFz587l008/RafT8dRTT7F48WIOHjzo94DbvHkzMTExbNq0CUVRsNlszJ8/n4yMDN59913S0tLYu3cvsixSyHzwwQfcfffdvPjii8ydO5fCwkK+/e1vA/DTn/7U37aVK1fy7LPP8txzz7F27Vruuusuzpw5Q2ZmJm+99Ra33HKL34vQbO77+UpRFK6//noSEhJ47733iI2N5fe//z1XXXUVJ06cGJRXnVpNdajRck7kjLJkRrol/cfrEDcDigzJsyF9ce83i65aET7TdFwUgzANYS+rrzrNJ6H4b8KDwdMIkk6ENGkMrZ4MicIgm3iJCDeOFO5GOPN/wijsqgJ9vGiT3hK5Ng0UVz1UfCh+j78Yxj8U9ITnwwmfD46dNHH8pAlZAZ1WYdIEJ2NGuQJ6JnU6JbbtsFLfqEUjwfQpLWRnukPf8MHSUgpVn4vE9+O+Axo9Pp9Ipu0JZ8FPn4s473YSjMVgL4WoEcIILw01l0KVblEUMZe8jtbQ0pjuQ0sjjewFR1mrx1wlIIHOIkLgdNEilNWSKfJTXaiVWWWvME7aikTlb0+TqFIpu8WLS50ljJHUGmHMVe/Phi6KDFWfiZePqVdB+sLgbFf2CI+7ppNi/jmrwBCLEnMRn+wYSV2Dlox0D7NndkpN4K4X94Yp84LTjhAwLJ4H+0Hnqo2KItJKRAKDIfD3BFdccQU+n4/PPvvMv+ySSy7hyiuvZPXq1WzatIlrr72WoqIiMjPFc/HRo0eZNGkSO3fuZObMmaxYsYLnnnuOiooKvyfcww8/zKeffsqXX37Zo3ZOTg5z587ltddeA6CiooL09HSeeOIJfvaznwHw5ZdfMnv2bMrLy0lLS2Pp0qWYzWZ+//vf+7fz+eefM3/+fOx2e7cVM3ft2sUll1xCc3MzVqu123xlEyZM4JlnnuGmm24KaNz+/ve/c9ddd7F3714mtVZTOX78OJ9++inTp0/H5XLx2muv8bvf/Y4tW7Ywb544FrvLGddWwGHdunV885vf7DDG7fPNdUaSJL7zne/w0ksv+ZfNmjWLadOm8dvf/pY///nPPPvssxQUFPgN8263m7i4ODZs2MCiRYtYtmwZ77//PiUlJX7j3B/+8AeWL19OcXFxtwauefPmce211/I///M//mWvv/46Dz/8sN+wKEkSjz/+OKtWrQLAbrcTHR3Ne++9x+LFi3vMGddbAYePP/6Ym266iaqqKozG8y88xowZw8MPP+w3CLYnqNVU+1OBp66uLqD1VLpHlhXqmiEhSkEThrdKsqJQ1+giIdY4eD2dGVLnixC7mp0iPCZlbkc9Waauro6EhAQ0zaeEYUdnEaEoISCo/RuimiHVUxSo2grl7wvjqexGjptOnW4SCdoKNE1HRQ4gd5140Cv/EKLHCWNsXH5QQloC7p+tCIr/T1T3cjeANVu8Me6nh4esQJ0zmgRTM5owPfB0q2mMh7RrhEGu/gAc+yVMeEgcL4PWG35zVKuFSROcZI5ws+dAFLX1Og4cMVNSamD6lBbiYs8XeOis19ik4fMvrTicGgx6hcsusZGUGHhBiL4I6XiaM8TDsPOcOK+mzEGrhYyMdufSUMWp+pzCGOCqRraXYqstRvY0ookeAzFjQ+qZMxzn6FDWQ5KQo0YKTdMQ7qNGJzxCLdni+uOoEOGsLedEzjJbkcgpp7dCVLYwzJkzQGfueH8RhtjtoOu56sBeJDwEPY3i5ZK7XnguRo0ESw6y1hSZeWochvekXxU9SSNe2JVvEpVV4y7qEAnQ73mqKOK+ruGwSPPhrBTL4/PBnIEkSUyfYuejLTGUlespO6fvkDpC9NFDQpIcluNQpStuN3zjG5HRfvVVMPaj9lh+fn6Hv9PT06mqqgJE8YHMzEy/IQ4gLy+PuLg4CgoKmDlzJiCMJu1DUttvY/369dx///3+//373/9m7ty5XbTbwl4nT57cZVlVVRVpaWns2bOHU6dOsX79ev86iqIgyzJFRUVMnDiRffv2sWLFCvbv309dXZ3fo6ukpIS8vLxux+DYsWMBjRWI4gPLli3jj3/8o98QBzB+/HjGjz9f5Xr27NmcPXuW559/3m+M6432Y5Genu7vd0/GuDaNzn/v378fwD9WnUOFnU4nhYWF/r8nT57cIU/c/v37mTp1ao+eZnv27GHXrl0dvB59Ph9Op5OWlhaioqK69MdisRAdHe2fEwNhz5492Gw2EhMTOyx3OBwd+jMQAnpK7Sl2ViU0eIP3jBigXhCdI/Ux4m1Y5SdQ9an4O75jLLXX6xVv5+0lwsATPS6kb+eD2r8hqhkSPZ8TSv5+vmy9xiT2rT4er0MnHoaSLoHEmSKEpum4MIS5aqDxkKiYl3ip8EyLGjmoB/de+6coULMNyt4T2rJTzDnrqAFrepXwe/t0q2mMg/RroPwDaDgIBb+CiT8MikFuuM7RmBiZKy63UXTGwKGjZuobtWzeGs3Y0S7yxjto86hv0yuv0LFjjwWvTyLaKjPnEhtWa/8rpvZFyMZTkkReoMpPoGa7qKCnFzcbXq83uFo+tzjWXdXCC8JdB14nyA5wNeJ1umDERWDNCa5uDwzXOTpU9SKhOSg9SYKodPEBUbWx5YwoyNNSCrYzraG10cIgF5WJz6kDJT4obQ+EQR+DbSG6tiJx3HmahVFO8YAhUUQNtM+bpyjDax+qeuHRM8SJtCENh+HchzDq7g731QHPU1cd1O8TRnBnFXhtwjAeM65DKpLYGJnxY50cO2li/2EzKcmeDsWVInFuUxmedK5eKUmS34DVU0Xezst728YNN9zgzzUGkJGR0e332rbX3bK2bcmyzP3338+DDz7YpU1ZWVnY7XYWLVrEokWLeP3110lOTqakpIRrrrkGdxBcFbdu3cp//Md/8Ktf/YqlS5f2uf6sWbN4/fXXA9p2b/3uD+2/O3369A6GyzbaF5Voy7XXRl8ho7Iss3LlSm6++eYu/2vvfdbbnBgIsiyTnp7eITy6je5y9PWHgIxx34iUeV1leGJKFjeQ1dvFTYHOCtGdijPYT4tKbjA8Q3IvdJxVUPR6q8G0BsypkDBDhDV1vseSJPFwbs0RCcCbCsSDhbteGOcqN4NlFCTPgfgpwQ21lN2iim/tnlaDoR6S5oIpse/vDhcMsTBisTDINR6Cgl+2GuSskW5ZxJAkGJXjZkSah/2Hoigt13Oi0EhZuZ6pk1tISRFv6U8WGjl0VBiuUpK8zJphx2AYhg8JpmRh0G4pg+rPYMQ1wdmu7Gn1fKgWYd2u2vPVMz028dFoRXigeQRubSpYLqBjS2V4YUoSn4TprZVZWw1zjnPid10sBocGvOmtOeaShSHLED/0wnGd1eI62VLSeqw1Co9urVl4dVuy1WIJKv0jZrwoimY7CXX7IHF64N/1uaDhENgKW0NSq4UxOGVuj/mcJ45zUnrOgM2u4XCBman5jiB1RGWwGAzCQy1S2sEiLy+PkpISzp492yFMtad8ad0RHR3dayGH/jBt2jSOHDnCmDFjuv3/oUOHqKmpYfXq1f727t69OyjaW7Zs4Wtf+xq/+MUvug2J7I59+/b5vdwADAYDPl/wPH6+/PLLDkbBL7/8kqlTpwJirN544w1SUlL6FQqen5/PunXr/N68nZk2bRrHjx/vcR8EQpsnXn/GYtq0aVRUVKDT6fwFKILFgDL0FhYW8vLLL1NYWMivf/1rUlJSeP/998nMzOzgMqnyFcaSLQwz9Qeg7J+QfYcw6AAoPrCdFg9+5hEhS/yvMkAaDkPJm+JmzNsk3ojG5gX2MKOLEg9K8dPEQ1LzMXBUin3ddEwYlhJnCo85S87gwtxcNVC8HmzFwpBgTBEGwwsxr5o+RuRgLP8AGg/D0edh4nLhnfgVxmRSmDXTTnmFjn2HorC3aPh8h5WMEW6a7FoaG6LQSDAq28WUyY6wVB0NGbF5wuhQt6c1D+IAkqnLPnHctHm+uWrB13Le+OZtFjngdFaRIyr+YqGj0QkP1Dpn8PulojIQDLFgyBehcx57awGIs+BogoZqaD4hzpu6KOFJbEoDU6rIb2pMjMx9h88lwv9sxeLY8zaJn4osjIZJs0QBn6FmOFQZHmj0wnO6cotILxIzvu97BEURBriGw8II5ygXFZPjp4t79l7u0bRamH5xC1u/sFJYbCRrpJvEhDCH9qh0iyT1L1R0qLJw4ULy8/O56667WLNmjb+Aw/z58ztUOA0XjzzyCLNmzeJ73/se9913HxaLhYKCAjZt2sTatWvJysrCYDCwdu1avvOd73D48GF/3rLe6Ctn3JYtW7j++uv57//+b2655RYqKioAYVRqM1itWbOGnJwcJk2ahNvt5vXXX+ett97irbfe8m8nJyeHDz74gOPHj5OYmEhs7OCK8rz55pvMmDGDyy+/nPXr17Nz507+9Kc/AXDXXXfx3HPPceONN/Kzn/2MkSNHUlJSwttvv82Pf/xjRo4c2e0277jjDn7+85+zZMkSnnnmGdLT09m3bx8jRoxg9uzZPPnkk3zta18jMzOTW2+9FY1Gw8GDBzl06BBPPfVUQO3Ozs5GkiQ2btzIddddh9lsxmrt/Vy5cOFCZs+ezZIlS/jFL37B+PHjOXfuHO+99x5LliwZ1Hzs9xV/69atTJ48mR07dvD2229js9kAOHjwYIcqFioDQ5IgPoy5eSUgPtoQGr2YCSKvUEuZ8F5yNyJJEvFGG5KnUTwAWkf3vZ1BENL+DRHNoOkpPjj3PhS9JkKAfA5hNIu7qMPDgQRiH/baKAksIyFtIWTdAtHjRdirrRhK/wlHn4WjvxBJh722/vev6Ric+I0IjXVVg3UMJF8WFENcQP0LMgFp6qNbi6LooekoFDwrQpoGqjcc52gPpKd5WbSgibGjXEhAaZmBulorEpCf52DaxaE1xIVlPA1x4nzpboCqreJc2lc+V0UWRvXGo+IB7ew7wqBbvU14TjQeaa3SaBMGisTZIiw6db447o2J/pyLF9qc+arpRUIzbHp6C8ROREq/mqjRNyAlXSY843xOaC6ChiPiWlP+IZT+A4r/Cuc+ECkYWkrFegMgsGNQES+kar4ULyart4njsfmEOH9HjxUJ95MuEV5/fRjiLth9qOoFB3OaqKjqqBQF1ehlnjqrRdX2mi+huVC87LHmivN/VFpAL0uTk7zktBZC2nMgCllu66NaTVVl8EiSxIYNG4iPj2fevHksXLiQUaNG8cYbb0SkPfn5+WzdupWTJ08yd+5cpk6dyhNPPOH3PktOTuaVV17hzTffJC8vj9WrV/P888/3ud3jx4/T2NjY4/9feeUVWlpa/Maptk/7UE23283y5cvJz89n7ty5fP755/zrX//qsM59993H+PHjmTFjBsnJyWzbtm0QoyEqlv7tb38jPz+fV199lfXr1/vz4kVFRfHpp5+SlZXFzTffzMSJE7n33ntxOBy9esoZDAY+/PBDUlJSuO6665g8eTKrV69GqxWpfK655ho2btzIpk2bmDlzJrNmzeJXv/oV2dnZAbc7IyODlStX8uijj5KamsoDDzzQ53ckSeK9995j3rx53HvvvYwbN46vf/3rFBcX+3MLDpR+V1OdPXs2t956Kz/84Q+Jjo7mwIEDjBo1il27drFkyRLKysoG1aBQMiyq5wznaqrdofig+guRfyJ2AmTdLvJ7NRwVN53Js/vehkro8drhzF+hsUB4zOhjIGEmGIJ4nCiKqMbaeEwko2/zwNHHCI+2pFnC2NDXg03lZij/SHj4KF4R+mrJCl47hzpem3iQlN3C4D1xufASUQGgvkHL3gNR2OwaZk5tYUR6OEuOhhivXRgUJD2M/hZYMjr+X5FFeHhb2KmzWni+eR3iu54mQBHeQoYEUfnYmCAq36moXKi46sS1x1kljNmKV8x5nVV4DmnMwvBsThMeasakHkPzAsbrEF5w9mKh721qzQWnCK9T6yhhYFcNFirBpv11IvfurmlivA6Rg9ZeBM5acNeCMRliL/LnI+0PbrfEBx/H4HJLTBrvZGJuuVpNNcwEWrVRRSVYSJLEO++8w5IlSyLdlCFLUKuptufQoUP83//9X5flycnJ1NbW9ndzKp2QZYXqRkgOYzXV6nonyfGm0OhJWmFkqdwCTSeRS96mqbmZGOrQJM8Kvl4nQt6/IaA5aL2WUhHu2VImQmaiMiFhao9hPLIC1Y5Yks2N/as2Kkni4d+cLpJWN5+A5lMitMhZLTwGojJac8tN8xuY/P2LVtCcfVPkNXFVgTYKEucE3RA14P6FS1NnPZ9DrukYFDwHE3/cr3EYdnO0H8TH+Vgwr4mqOicpCSbC4Wcctv7pLCL0qP4gcuUnVEctJDnOgMZd0xp2Wi0exHwOkSbA3QAoIg+VIR6ixwijg7b/8SsX8pz5KuhFQnPI6BkTxCe2tZKdp7k1fUKV+OlzgMYorimGaHG86GNEUQhja546fVwXw5ksy1RXV5OcnCyqRra9cLKdFi9Wvc3gqhc/dTHi5UlUxqBCZIfMmKp6Q1dPZ4G4yVC7Gyo+QjaPpLq2geSkRDS2U8Iz010n5r7OKNIemJL73m4PGAwKUya3sGOPhYITJkYkaXDKHpLVaqoqKioqfdJvY1xcXBzl5eXk5uZ2WL5v374OFUpUBk7/fBWHgZ5GD8mXC5f5pmMobgtYreKhMAyEezwjoTlgvdrdULpBeJl57RA3SYSU9nGTqAzWwKE1iDC4uIvOh9E5ysQb2uZC0L8jcp8kzYLosUjOSqSKN4Xh0FUjDHoJM8R2QsCg+xdqTZ2ltcrqhyJU9+gvIO9h4WkRqN5wmaPDhLD1zzpaPOw3n8DQ3AItZpBd5z3fFJ9I/G6IEznfTMlBSwR/oc+ZC10vEppDUk8f3er51poA2usEZyW4KkXFVq8dJB3ojgsjms4szrnmEe2KQiQAGhRFEeu3lAgvOHe9OA5dtcL735QiXm7pY4LmBTckx1TVG1p61lyRgsBeAtXbkdzxULEH3DXCCCe7IWaMWE8afPX4zAwPZ856qKjSs+dgDBPzGgbfBxUVFZWvAP02xt1555088sgjvPnmm/4ysdu2bWP58uUBldlV+YqiM4s8FOc+FOEalmlqkuJIInvh3EYRQuyqBjSQdNn5IhvhxJQMpvng84DtFDSfPG90q92BZEoj2uUBpU6EaMZMFCHPX/X5094g13yynUEuPtItUwklWiPE5Il0Bq5SQGo1vsVC7CTx8K81qeFvKiqBojOJCqbW1pwzsld4mjpbjXPOc4BGnGd10SLMW2sGYyr6Jjs43OCzCUOct0V418Vd1FqgakB10lRUBoekFSk8KjZD7Xb03jgwesFTL15mxuUFvVrv1MkOPvxET02dnrNlJlLHB3XzKioqQ4h+ZjlT6YV+3yU8/fTTLFu2jIyMDBRFIS8vD5/Px5133snjjz8eijaqXCjoYyB9Ee6qqgsnJ95wxN0IZ/4Pmk6KMB19fGt1Rktk26XVQ+xE8XHVi+TyLWdbq81Fo+hBSrpUPOCoCHRRrQa5D8SD4pFWg5yxazlwlQsIay6gxd3ogZQ0ceyqxjcVleCg0UHUCPEBkGVhxHBUCANdS2lr3rlCNA4DeLwiAsCcBokzxL2OikqkMSZC9LjWQldO8aIu+TIwhuaFncUiM2mCg/0HoOCElSnzwKCmI1VRUVHplX4XcGijsLCQffv2IcsyU6dOZezYscFuW9AZDgk7FXsZ3upd6KJHhqUSkaIoeH0KOq2k6g1TzX7p2U6LSnKOcpFPypoNcRf36+29ooBX0aCT5NA//8teFFsRPrcNbcxYJEPvpaeDQVj7FyxNn0N4yHntIjF43sMilKpHvSE8R1W9Iamp6g1vvUhoXtB6niaUlnK87mZ0UWlI5rSweMFd0GOq6gUf2YvSdBKvFCWeKzSDD0ntDUWBHV96ycr2kT5p9pCtqDocngf7g1rAQUVl6BGyAg5bt25l/vz5jB49mtGjR/f9BZV+IUkSWg1hu4BJkoRWq+oNZ82A9BRFVLEt+5fwNJOdIp+UdVS/PWokCbSEyVCl0SHFjEWjhM/xJ6z9C5am1nw+ZNV2utVD7hGReLxbvSE4R1W9Ia2p6g1vvUhoXtB6+hik2Bi0iqLuQ1VvyOqh0SHFTQzbPJUkmDW9GVnSD1lDnIqKispQot9Jl66++mqysrJ49NFHOXz4cCja9JVGlhWqGkXFpbDoKQpVdU5Vbxhr9qknu6HkDTj7D+ERhwJJc0W5+wHcLMkKVDnikMM0pBe6XtA0tSZIXyRyGtmL4OhqURyjW70hNkdVvSGvqeoNb71IaKp6w19T1RveepHQlBWFqnoPsiyHRU9FRUVlONNvY9y5c+d4+OGH+eyzz8jPzyc/P59nn32W0tLSULRPRUVlMLhq4ORLULMDnOUiZ0jKFWAKTyVblTDTZpDTx7QzyFVGulUqKioqKioqKioqKioq7ei3MS4pKYkHHniAbdu2UVhYyO23385f/vIXcnJyuPLKK0PRRhUVlYHQdAxO/KY1eW81WEeL5L06c6RbphJKtEZIuxr0sWAvFiGrjopIt0pFRUVFRUVFRUVFRUWllX4b49qTm5vLo48+yurVq5k8eTJbt24NVrtUVFQGiqJAxUdQ+DLYz4K3BRJmiBxxUmiT96oMEbRGSFskKuXaz8DRX0DLuUi3SkVFRUVFRUVFReUryZYtW5AkiYaGhkg3BYDi4mIkSWL//v2RbkrEueKKK/jBD37g/zsnJ4c1a9aEXHfAxrht27bx3e9+l/T0dO68804mTZrExo0bg9m2ryQajURKLGjClPhUI0mkJJhUvWGs2UHP64Civ8C590VYqkYHKfPBkhVEPUgxN6AJV/7hC1wvZJpaA6QvBGM82EtaDXJlrXoX9nF4oetFQlPVG956kdBU9Ya/pqo3vPUioamRJFLi9Wg0g/L3UFFRaaXNYNb58/7773dYb+vWrUyfPh2TycSoUaP43e9+1+H/y5YtY8mSJWFs+dCkJ4Po22+/zapVq8Lenn5XU/3JT37CX//6V86dO8fChQtZs2YNS5YsISoqKhTt+8qhKAo+GaQwVT5SFAWfT0EKU3WnC10vEpp+PXcF0pn1whvOVQPmEZAwXRhlgqoHPkWDFKaKoxe6Xkg1NQZIWwgVH0NLqTDITfwxStTIC/o4vND1IqGp6g1vvUhoqnrDX1PVG956kdBUFAWfEr7nGBWVrwofffQRkyZN8v+dkJDg/72oqIjrrruO++67j9dff93vNJWcnMwtt9wSieb2G7fbjcEQ3GfW/tB+PMNJv19bbNmyheXLl1NWVsa//vUv7rzzTtUQF0QUBWqbIVx1lhSgttGl6g1jTQVoKt0lCjU0F4K7DmImQtKlQTfEtenVOmPCuw8vYL2Qa2oMkHYVGJOEZ9zRZ1HsJRf0cXih60VCU9Ub3nqR0FT1hr+mqje89SKhKfQ8KGGsGKsyPLniiit48MEHefjhh0lISCAtLY0VK1Z0WKekpIQbb7wRq9VKTEwMt912G5WV5wuTrVixgilTpvDaa6+Rk5NDbGwsX//612lubu5VOycnh6eeeoqlS5ditVrJzs7mH//4B9XV1X69yZMns3v37g7f++KLL5g3bx5ms5nMzEwefPBB7Ha7//+vv/46M2bMIDo6mrS0NO68806qqqoGP1hAYmIiaWlp/k97w9Xvfvc7srKyWLNmDRMnTuRb3/oW9957L88//7x/nF599VX+8Y9/+D3rtmzZ4v/+6dOnWbBgAVFRUVx88cVs376917ZIksRLL73Etddei9lsJjc3lzfffLPDOmVlZdx+++3Ex8eTmJjIjTfeSHFxsf//bZ56zzzzDCNGjGDcuHEAlJaW8vWvf52EhAQsFgszZsxgx44d/u/985//7OABuHLlSrxeb4e2rVu3jptuuomoqCjGjh3Lu+++CwgvwwULFgAQHx+PJEksW7YM6Bqm2pnGxka+/e1vk5KSQkxMDFdeeSUHDhzodZwCod/GuC+++ILvfe97JCUlDVpcRUVlkHjtULaRqMo3wVEKsksY4eLyQFJDBFRa0egh7UowJYOjDKngObSOkki3SkVFRUVFRUVF5SvKq6++isViYceOHTz77LP87Gc/Y9OmTYDwslyyZAl1dXVs3bqVTZs2+YtHtqewsJANGzawceNGNm7cyNatW1m9enWf2i+88AJz5sxh3759XH/99dxzzz0sXbqUu+++m7179zJmzBiWLl3qNywfOnSIa665hptvvpmDBw/yxhtv8Pnnn/PAAw/4t+l2u1m1ahUHDhxgw4YNFBUV+Y09PSFJEq+88kqf7b3hhhtISUlhzpw5/P3vf+/wv+3bt7No0aIOy6655hp2796Nx+Nh+fLl3HbbbSxevJjy8nLKy8u57LLL/Os+9thjLF++nP379zNu3DjuuOOODgau7njiiSe45ZZbOHDgAHfffTd33HEHBQUFALS0tLBgwQKsViuffvopn3/+OVarlcWLF+N2u/3b2Lx5MwUFBWzatImNGzdis9mYP38+586d49133+XAgQM8/PDDyLIMwAcffMDdd9/Ngw8+yNGjR/n973/PK6+8wtNPP92hbStXruS2227j4MGDXHfdddx1113U1dWRmZnJW2+9BcDx48cpLy/n17/+dZ9jrygK119/PRUVFbz33nvs2bOHadOmcdVVV1FXV9fn93uj32GqAK+99hq/+93vKCoqYvv27WRnZ7NmzRpyc3O58cYbB9UgFRWVPlAUsJ2G2l3QeAjJYwNXM4rZgpQ4E/TWSLdQZSii0UPqlVD5MTjKsZasBePdkDwr0i1TUVFRUVFRUVEJAooCLS2R0Y6Kol/pVvLz8/npT38KwNixY/nf//1fNm/ezNVXX81HH33EwYMHKSoqIjMzExA2iEmTJrFr1y5mzpwJgCzLvPLKK0RHRwNwzz33sHnz5i4Gms5cd9113H///QA8+eSTvPTSS8ycOZNbb70VgEceeYTZs2dTWVlJWloazz33HHfeeaffe2rs2LG8+OKLzJ8/n5deegmTycS9997r3/6oUaN48cUXueSSS7DZbFit3T+fjR8/ntjY2B7babVa+dWvfsWcOXPQaDS8++673H777bz66qvcfffdAFRUVJCamtrhe6mpqXi9XmpqakhPT8dsNuNyuUhLS+uisXz5cq6//npAGLImTZrEqVOnmDBhQo/tuvXWW/nWt74FwKpVq9i0aRNr167lt7/9LX/729/QaDSsW7fOH67+8ssvExcXx5YtW/yGQ4vFwrp16/xefn/4wx+orq5m165d/rDRMWPG+DWffvppHn30Ub7xjW/4x3jVqlU8/PDD/nkEwuvujjvuAODnP/85a9euZefOnSxevNi/3ZSUFOLi4nrsX3s++eQTDh06RFVVFUajEYDnn3+eDRs28Pe//51vf/vbAW2nO/ptjHvppZd48skn+cEPfsDTTz+Nz+cDIC4ujjVr1oTUGPfpp5/y3HPPsWfPHsrLy3nnnXcuyESE4U6xoOoNE01PM9Ttgbrd4KgQXnGeJpB0tERdjDEpA7T6EAh3RQprkMWFrxc2TY0OUq9EqfoMyV6LdOr30HIGMm8R/wshF/pxf8GcZ1S9C1YvEpqq3vDXVPWGt14kNNVccZGlpQV6sPuEHJsNLJbA18/Pz+/wd3p6uj+ss6CggMzMTL8hDiAvL4+4uDgKCgr8xricnBy/Ia7zNtavX+83uAH8+9//Zu7cuV202wxZkydP7rKsqqqKtLQ09uzZw6lTp1i/fr1/HUVRkGWZoqIiJk6cyL59+1ixYgX79++nrq7O79FVUlJCXl5et2Nw7NixXscoKSmJhx56yP/3jBkzqK+v59lnn/Ub46Drcdfm0RfI8dh+LNLT0/397s0YN3v27C5/t1VlbRur9vsFwOl0UlhY6P978uTJHcJt9+/fz9SpU3vM37Znzx527drVwdDq8/lwOp20tLT4U6e174/FYiE6OnpQ4cJ79uzBZrORmJjYYbnD4ejQn4HQ76evtWvX8sc//pElS5Z0cAGdMWMGy5cvH1Rj+sJut3PxxRfzX//1X8MmGWF/0WgkUmMJ25VTI0mkJpjDovVV0Au6piJD8wmo3Q2NR1oNcM0gO0EfB3EXIVlySNQag6MXABoJUqMaVb3hqqnRoUm9AlPDIWg4AGX/FNVWx3wbDD2/mRuU5AV+3A/784yqd8HrRUJT1Rv+mqre8NaLhKZGkkiN14NaTVUlAPT6jk4EkiT5DVhKD0VAOi/vbRs33HADl156qf9/GRkZ3X6vbXvdLWvblizL3H///Tz44INd2pSVlYXdbmfRokUsWrSI119/neTkZEpKSrjmmms6hGYGg1mzZrFu3Tr/32lpaVRUVHRYp6qqCp1O18WA1B299bs/tP/u9OnTOxgu20hOTvb/bulkuTWbez9XybLMypUrufnmm7v8z2Qy+X/vbU4MBFmWSU9P75Bnr41Avet6ot/GuKKiIqZOndpludFo7JDAMBRce+21XHvttSHViDSKouD2gCGM1VTdHhmDXqPqDSVNd4PwgKvbA86q815wGgOYUsE6BowJIEkoCrh9Ogwab9iqjbplVW84aypIuGOmYTAkIVVvFXPtSBWM/X9gHRV8vQv8uB+25xlV7yujFwlNVW/4a6p6w1svEpp+PbWaasSIihIeapHSDhZ5eXmUlJRw9uxZv3fc0aNHaWxsZOLEiQFtIzo6uot31kCZNm0aR44c6RA22Z5Dhw5RU1PD6tWr/e3tXAAiWOzbt8/vwQbCK+2f//xnh3U+/PBDZsyY4TdMGQwGf0RjMPjyyy9ZunRph7/bbETTpk3jjTfe8Bc7CJT8/HzWrVtHXV1dt95x06ZN4/jx4z3ug0Bo88Trz1hMmzaNiooKdDodOTk5A9bujn6/tsjNzfW7ILbn3//+d4/ul5HC5XLR1NTU4QPCutn2aXPhbHMzDXR5+2UDWd7dtsX6UGcDn6Igt/v412/3UYKw3Kco1DWJKktyD+sHurw7zc7L2/R8IexT++Xt9ULVp87L5R40+2y77EVuOIxS+DIcXY1y9h8ojQUoLeXIaFDipsKIxciJlyIbE5GRkBWQFah3WfG1/t72ad18h2Vy6zJFGfhynwJ1TitKN+srPWynr+WdNdsvb9PzhbBPnTXb9ELVp87LFTpqBrtP3a1f77Iim0cgj/gPFG0USnMRypFfQOVWFFkO6PgIdHn780x/j6eBnAvaH/d9rR+M817nYz4Ufeq8XIEumsHsU2dNn6JQ3+zusm4w+9R+eds+bD/Gwe5T++WdrxWh6FOw919/5pjch2aw+tp5zvT3/qK/feptzoSiT+2XB3p/MZg+ddeWAd1fDHCOyYo47gO5Jx1on3q6Jw1Vn9ov7+ueNBh9Gsz+G0ifOrexs2aw+9TdduqbvYN+TuppuV9zEMvbtn2hIkkiVDQSn2DaXxcuXEh+fj533XUXe/fuZefOnSxdupT58+czY8aM4AkFyCOPPML27dv53ve+x/79+zl58iTvvvsu3//+9wHhHWcwGFi7di2nT5/m3XffZdWqVX1ud8KECbzzzjs9/v/VV1/l//7v/ygoKOD48eM8//zzvPjii35dgO985zucOXOGH/7whxQUFPDnP/+ZP/3pTx2iFnNycjh48CDHjx+npqYGj8cziNGAN998kz//+c+cOHGCn/70p+zcudNfzOKuu+4iKSmJG2+8kc8++4yioiK2bt3Kf//3f1NaWtrjNu+44w7S0tJYsmQJ27Zt4/Tp07z11lv+6q5PPvkkf/nLX1ixYgVHjhyhoKCAN954g8cffzzgdmdnZyNJEhs3bqS6uhpbAJbrhQsXMnv2bJYsWcIHH3xAcXExX3zxBY8//vigDa799oz78Y9/zPe+9z2cTieKorBz507++te/8swzz3RwlxwKPPPMM6xcubLL8urqapxOJyDcIWNjY2lqasLhcPjXaYsvrq+v7+BaGhMTQ1RUFHV1dR2qjMTHx2M0Gqmuru5wkk9MTESr1XaJU05JScHn81FbW+tfJkkSyRbwylBV50TTekbTaSWS4kw4XD6a7OcPHINeQ0KMEZvDi91xvi1mo5ZYq4EmuweH67zV12LWER2lp77ZjdvT6nKrKHi84ve6Rhde3/m2x0cbMBq0VNc7aX/dSow1otWKNnboU4IJn0+httHVrk+QmmDG7ZH9D3KNNg9arYaU+ND0CSDGoifKpKOu0U2DzYOCcJ0PRZ/aaL+f2mv21afm+grkml0YmvcjeerRKQ70ciMOEnDoL8ITPRZ0VmKMDqI0buoc0XgVrX87sQZxEql2xiJx/iqYaGpCi0yVI65jn8wN+BQNtc7zbyokFFKjGnHLOupd5xNN6CQfSeZmHD4DTW7xuktWwO41kQrYvCbsnvNuwWadi1iDgyaPGYf3fOisRe8kWu+k3mXBLZ93HY4xtBClc1Pn7NineKMNo9ZLtSMWnyLR6LagAMnm0PQJwKDxkGCyY/eaaGjV00ih6ZPSaT9JyB00g92nzvvJqBXzucljxuWLg/g7iGr4DLPzOPrTL9NSe4rm5P8Unpi0P54Gdo5oO+6TE0woMv0+nvp7jrA7vTS2HoPRUfo+zhHBOe+1P+ZD0afO5z2LWYfN4UVpd60Idp/a7ycFkAC3V6ax+XzbQ3V9apsz0RY9VrMm5Nen2iaXf84YdJqQXXPb9pPJqKW5xYvC+f0X6utTQqwRt1fucH8R/Gvu+f0kKwq+1t9Dfc1tsnv8c8ag15IYG5o+ddxPrg7HfSj61EbbfrI7vB00g9+njvspIcaAoigd5kyw+9R+P8mK4u9HqPrUfj+1zRkFSEs0h6RP0H7uddx/oehTeyJxfYrSioY1NDR0eNgP9fNTamoqbreb+vr6833S6UhKSsLhcPgdMgD/M6DK0EaSJDZs2MD3v/995s2bh0ajYfHixaxduzYi7cnPz2fr1q089thjzJ07F0VRGD16tL+6a3JyMq+88go/+clPePHFF5k2bRrPP/88N9xwQ6/bPX78OI2Nvaeqeeqppzhz5gxarZZx48bx5z//uUO+uNzcXN577z0eeughfvOb3zBixAhefPHFDmm97rvvPrZs2cKMGTOw2Wx88skng/LyWrlyJX/729/47ne/S1paGuvXr/c7ZkVFRfHpp5/yyCOPcPPNN9Pc3ExGRgZXXXVVr55yBoOBDz/8kB/96Edcd911eL1e8vLy+M1vfgOICrEbN27kZz/7Gc8++yx6vZ4JEyb4C0kEQkZGBitXruTRRx/lv/7rv1i6dGmf1WwlSeK9997jscce495776W6upq0tDTmzZvXpXBGf5GUAbwe+OMf/8hTTz3F2bNnAdGpFStW8M1vfnNQjekPkiT1WcDB5XLhcp2/qDY1NZGZmUl9fb1/IkiShCRJ4k11u6Hoa3nnuOP+LtdoNF22DUBLOZWnd5E8IqPDjY+mrS3tt926p1xgugABAABJREFU/cEslxWF6jonqYldY7Tb1pc7tbGn5d21sfPyNr3kBBO6tjEIcp/aL/fKsl9PI0kh6VPn5T5ZPOx01uywvuxFajyCVLcbpfkEeFtELjjFC/p4pOhcFPNIFE27GH7EjaHcSVRRoNoZR5KpwW/I6W19jXTeG2sgy2UFqh1xpEY1IEE34951O30t79zG9svb9JLNDWil0PSpvaZPhqpWPY0Umj51Xq4AlS3nNYPdp87L2+ZMsqnh/FtMRUFqPi7mpNYMMRNRxt4PxqR+Hzedl7c/z3SeM6LtgR9ngZwLfO3OM9r25/IBtL2v5d0d86HoU+flClBZ6+igGcw+ddaUFYWaehfJ8cYuoUehuD61zZmUBBNajSYkfWq/vP2c0UhSyK65bcuDsf/6e32iF81g9bV9GwdyfzGQa25PcyYUfWq/PND7i8H0qbNmQPcXg+hT5+WKolBd7yIp3tjnPelA+9TTPWn39xfBPZf3dU8ajD61X97f/Tccr0+Kq47qJg3JYxd0uFaE+vmpP8ubm5uJi4ujsbGxXyF0QxWn00lRURG5ubkd8mapqISKQOwwX3UCPS4HVD7vvvvu47777qOmpgZZlklJScFut/Ppp58yb968ATc62BiNRn/52fZoNBo0nRKLtp30O9PT8s7fH8jy7rYtA3od/oeBLut3s+3BLtfrRNs66/nb3o/lgWjqdZrzDwIh6lP7Nrbpdb6R7I6B9qnz8u40JUlCclZC7S6o2wfuevDakDzNoDNDVCZEj/Yn0ZdaP13b2PFvGeEZpZG6/q+79UVbut92oMv1Gl9QttNbG9sv12tE/9qGMxR9ar+8Ta+DcTPIfWqPogRHM9DlbXNGaq8nSRA7AYwJSJVboPEg0pGfi8IOsRNb2z7wc0TbeSbUx7wkSWg4f55pO8f2do7oq+19Le/1mO9n2wNdrihKt5r9bXtPy7vT1GnFeAbrPBnItaJt/4X6+tR+zoTj+hSs/dcfTbkXzWBec9szkPuLYM2ZUPWp/fJA7y+COZeCca4JdH/IiOM+EvekoepT5+XhvCft7/4bjtcnWZLOXyu6efYJ1fNTf5Z3t46KiopKJBiQMa6NpKQk/++nTp1iwYIFQU0M+FVEo5FIiua81SHUepJwSw8XF7pet5qyG+oPiiT5ttPCC87bBLIPjIkQmwfmEaAZ2OGokSDJ3Byk1qt64daLhGaveqYUGHkDVH4iqqwW/BKyboH0xQM+L13ox/2QOM+oeqreENNU9Ya/pqo3vPUioamRJJJi1WqqKioqKoEwKGNcuLHZbJw6dcr/d1FREfv37ychIYGsrKwItix4KIqCww3mqPBVU3W4fJiNWlUv2Jq+CqT63VC3HzyN4LWJUFSdFaKyIXos6K19bq9vPXD4DJi17rDYcFW94a/Zp57WBOnXCC/O5hNw5q9gPwO53xBenP3Wu7CP+4ieZy7QPqp6w19T1Rv+mqre8NaLhKbQkzErajVVFZULlQu9CEo4GVavLXbv3s3UqVP9ZXN/+MMfMnXqVJ588skItyx4KAo0tXTNqRQyPUSSXFUvWII+lNodyMfWwvFfQ/kmsBWCqxq0UZA8Rxg5EqYGxRAHrX10R4V3TC9kPZ8LR1N1WC80Q3JMJQ0kXQpJc8Bjh6qtcOTn4KgYmN4FfNyH/TwTAU1Vb3jrRUJT1Rv+mqre8NaLhKbQ86oP6yoqKioBMKw846644gr15K4ydFEUKHkLqWYHWocNaBIGN+sYsI4CvSXSLVTpC0cF0tm3SPQ0otgnQeaNkW5R5IkeBYY4EbbadAyOPA2j/gsSpkW6ZSoqKioqKioqKioqKsOSgI1x7777bq//LyoqGnRjVFSGNbU7RFifsxKnbhzG+IuRzCnCw0hl6NNwCM79G0nxAiA1HYGqREi5PMINGwIYEyDja1D1KbSUwYn/hRFfE8ZKSRvp1qmoqKioqKioqKioqAwrAjbGBVK6Vs0NMHgkCQz67isihkQPMOg1qt5gsZ+B0ndFOKoxGSVmJpLJEZYdKQEGjSe8Y3oh6ck+qPwI6vYAoOhicOrSMTuPQ/WnYEqGmPGhUgeGyZhqjZC2EOr3Q+NhKH1HzPsx3wJ9dN96F+JxHyG9SGiqesNbLxKaqt7w11T1hrdeJDT9euozoYqKikqfSMpXKO6zqamJ2NhYGhsbiYmJiXRzuqflHNTuBEtmpFuiEigeG5xYC7ZiUGRInQ+6qEi3SiUQPM1w9h1wlIq/TamQuhAsWVDyhqh+K2lg1DeFUU5FYD8L1Z+BpANrDoz9f2DJjnSrVFRUVFRUVCKJux40BkiZF+mW9MiweB7sB06nk6KiInJzczGZwlutV0VFpXsCPS7V+LkhhqIoNDvDV6VEURSaWzyq3oAFfKLSpOMc+ByQOANFG0Wzx0S4zNyKgqo3EOxn4fSfhSFO0oAlF7JvR7Fk0+w1o4z8TzAkCANr0avgbQlyA84z7MbUkinCVDUGaD4FR56B6m30tMEL7riPsF4kNFW94a0XCU1Vb/hrqnrDWy8Smoqi0OzwqTm+VVRUVAJANcYNMRQF7M7wVlO1O7yq3kAp/0AktXfVQ+wkMCULTY8pvH1U9fqxQUXk9iteD147aEwQPR6ybwed9byeRge594DGCLJbGO5kb7Ba0bFJDMMxNcRAxvVgHgHOKjj1RzjzfyB7ute7kI77COtFQlPVG956kdBU9Ya/pqo3vPUioSn0VGOcikp3bNmyBUmSaGhoiHRTACguLkaSJPbv3x/ppkScK664gh/84Af+v3NyclizZk3IdVVjnIrKQGk4DJVbhSEiaiREj4l0i1T6QvZA2T+hYhMggz4OUuZD5k3dF9rQWSB3KaABTxOc+VuP3l9fSTR6MX7xU8DTCGXvQcEvRZiKioqKioqKioqKisqAWbFiBZIkdflYLBb/Om1Gvs6fY8eO+ddZtmxZQDUALnR6Moi+/fbbrFq1KuztUY1xKioDwVkNJW+Cqwb0MZAwVVTfUBm6uOtFuGnjYfG3eQSMvAkSp/f+PVMyZN0qfm8pgfIPQ9vO4YYkQdxFkHo1KF6o3wuHn4Kmk5FumYqKioqKioqKisqwZfny5ZSXl3f45OXlceutt3ZZ9/jx4x3WGzt2bARaPDDcbndE9RMSEoiO7r0gXShQjXFDDEkCsyG81VTNRq2q1x98Tih+XRjkFBkSZgoPofaaOld4+6jq9U5zIRS+LLwYJR1YR0P21yEqPTC96NGQeqX4vX4P1O0bTGsC0wwhIdGLShN55HRWUcyk4Fmo+AgU5cI47oeQXiQ0Vb3hrRcJTVVv+GuqesNbLxKaQk+tpqrSN1dccQUPPvggDz/8MAkJCaSlpbFixYoO65SUlHDjjTditVqJiYnhtttuo7Ky0v//FStWMGXKFF577TVycnKIjY3l61//Os3Nzb1q5+Tk8NRTT7F06VKsVivZ2dn84x//oLq62q83efJkdu/e3eF7X3zxBfPmzcNsNpOZmcmDDz6I3W73///1119nxowZREdHk5aWxp133klVVdWgxslqtZKWlub/VFZWcvToUb75zW92WTclJaXDulqt1j9Or776Kv/4xz/8XnNbtmzxf+/06dMsWLCAqKgoLr74YrZv395rmyRJ4qWXXuLaa6/FbDaTm5vLm2++2WGdsrIybr/9duLj40lMTOTGG2+kuLjY//82T71nnnmGESNGMG7cOABKS0v5+te/TkJCAhaLhRkzZrBjxw7/9/75z38yffp0TCYTo0aNYuXKlXi959MISZLEunXruOmmm4iKimLs2LG8++67gAjLXbBgAQDx8fFIksSyZcuArmGqnWlsbOTb3/42KSkpxMTEcOWVV3LgwIFexykQBmSMa2hoYN26dfzP//wPdXV1AOzdu5eysrJBN+irjiRJxEYRtouYJEnEWg2qXqAoCpx9G+xnwNsM8VNF7qwOmhBrcITNUU7V6wVFgerPRWVU2QnaKJHbL+s20HZf2aZHvaRZEHuR+L38fVEAIkgMqzHtDb0FRlwL1lzhNVr0GhSuQ5Jdw/u4H2J6kdBU9Ya3XiQ0Vb3hr6nqDW+9SGhKkkSsRaca41QC4tVXX8VisbBjxw6effZZfvazn7Fp0yZAFANZsmQJdXV1bN26lU2bNlFYWMjtt9/eYRuFhYVs2LCBjRs3snHjRrZu3crq1av71H7hhReYM2cO+/bt4/rrr+eee+5h6dKl3H333ezdu5cxY8awdOlSf/7DQ4cOcc0113DzzTdz8OBB3njjDT7//HMeeOAB/zbdbjerVq3iwIEDbNiwgaKiIr+xpyckSeKVV14JeMzWrVvHuHHjmDt3bpf/TZ06lfT0dK666io++eQT//Lly5dz2223sXjxYr/X3GWXXeb//2OPPcby5cvZv38/48aN44477uhg4OqOJ554gltuuYUDBw5w9913c8cdd1BQUABAS0sLCxYswGq18umnn/L5559jtVpZvHhxBw+4zZs3U1BQwKZNm9i4cSM2m4358+dz7tw53n33XQ4cOMDDDz+MLMsAfPDBB9x99908+OCDHD16lN///ve88sorPP300x3atnLlSm677TYOHjzIddddx1133UVdXR2ZmZm89dZbwHkvwl//+td9jrmiKFx//fVUVFTw3nvvsWfPHqZNm8ZVV13lt4UNFF1/v3Dw4EEWLlxIbGwsxcXF3HfffSQkJPDOO+9w5swZ/vKXvwyqQV91FEWhqQViopSwXMgURaHJ7iHGolf1AqF6m/CKclZD9DiwjOxGE5o8ZmL04TGuqHo94HOK/HDNreGShkRIuVwY4waql/E1cNeJ6rln/gpj7gdDbL/602/NEBBSPUkLyZeBMQlqd0LlJygt52jO+CbRCRnD87gfYnqR0FT1hrdeJDRVveGvqeoNb71IaAo9LzFKeJ5jVLqiKBCpiD+DoX9Ze/Lz8/npT38KwNixY/nf//1fNm/ezNVXX81HH33EwYMHKSoqIjMzE4DXXnuNSZMmsWvXLmbOnAmALMu88sor/hDDe+65h82bN3cx0HTmuuuu4/777wfgySef5KWXXmLmzJn+8M9HHnmE2bNnU1lZSVpaGs899xx33nmn33tq7NixvPjii8yfP5+XXnoJk8nEvffe69/+qFGjePHFF7nkkkuw2WxYrdZu2zF+/HhiYwN7lnC5XKxfv55HH320w/L09HT+8Ic/MH36dFwuF6+99hpXXXUVW7ZsYd68eVitVsxmMy6Xi7S0tC7bXb58Oddffz0gDFmTJk3i1KlTTJgwoce23HrrrXzrW98CYNWqVWzatIm1a9fy29/+lr/97W9oNBrWrVvnPw+8/PLLxMXFsWXLFhYtWgSAxWJh3bp1GAwGAP7whz9QXV3Nrl27SEhIAGDMmPM52Z9++mkeffRRvvGNbwBijFetWsXDDz/sn0cgvO7uuOMOAH7+85+zdu1adu7cyeLFi/3bTUlJIS4uLqBx/+STTzh06BBVVVUYjUYAnn/+eTZs2MDf//53vv3tbwe0ne7otzHuhz/8IcuWLePZZ5/tEFd77bXXcueddw64ISoCRQGHG6IJT6iqAjhcPqItelWvL2yn4dy/wFUNplSI696oowAOr5FovSN8fVT1OuKshrNvCcMZkiiwMeI6MCYOTk/SQPYdcOr34LWJCqtjvwdaQ3+61D/NEBAWvZhxYrwrPobGI+htq1FSpiIlz4KYCcJoFyKG9XlmiGqqesNbLxKaqt7w11T1hrdeJDSFnky0aoyLGG43tNoqws6rr0KrrSIg8vPzO/ydnp7uD+ssKCggMzPTb4gDyMvLIy4ujoKCAr8xLicnp4NNov021q9f7ze4Afz73//2e5S1105NTQVg8uTJXZZVVVWRlpbGnj17OHXqFOvXr/evoygKsixTVFTExIkT2bdvHytWrGD//v3U1dX5PbpKSkrIy8vrdgzaF1noi7fffpvm5maWLl3aYfn48eMZP368/+/Zs2dz9uxZnn/+eebNm9fndtuPRXp6ur/fvRnjZs+e3eXvtqqsbWPVOQeb0+mksLDQ//fkyZP9hjiA/fv3M3XqVL/BrDN79uxh165dHQytPp8Pp9NJS0sLUVFRXfpjsViIjo4eVLjwnj17sNlsJCZ2fI50OBwd+jMQ+m2M27VrF7///e+7LM/IyKCiomJQjVFRGbK4G6H4r+CqBY0BEqZ3X31TJfI0HoWyf4HiEbn8LLkw8sYOef0GhdYIud8QBjmfA4r+AqPvVedDdxgTYeR/oFR9Bi31SJWbhbecKRVS5kDCjIAMpCoqKioqKioqKhcWen3He3NJkvwGLKUHg27n5b1t44YbbuDSSy/1/y8jI6Pb77Vtr7tlbduSZZn777+fBx98sEubsrKysNvtLFq0iEWLFvH666+TnJxMSUkJ11xzTdCKE6xbt46vfe1r3Xq3dWbWrFm8/vrrAW23t373h/bfnT59egfDZRvJycn+39tXhAUwm829bl+WZVauXMnNN9/c5X8m0/n0Q73NiYEgyzLp6ekd8uy1Eah3XU/02xhnMploamrqsvz48eMdBldF5YJB9oqQREe5yDuWNBd0vZ8sVCKAIkPlJ1DbmuRTZ4XYyZB6RfAr3RpiIftOKH4NXFVQ+g/IvCm4GhcKWhOkXU1zo4zR/SVSS4nwWGwpgbKNIg9f0myIzQNNvy9JKioqKioqKioqrRgMwkMtUtrBIi8vj5KSEs6ePev3jjt69CiNjY1MnDgxoG1ER0cHrULmtGnTOHLkSIewyfYcOnSImpoaVq9e7W9v5wIQg6GoqIhPPvnEX4ygL/bt2+f3cgMwGAz4fL6gtefLL7/s4KH35ZdfMnXqVECM1RtvvOEvdhAo+fn5rFu3jrq6um6946ZNm8bx48d73AeB0OaJ15+xmDZtGhUVFeh0OnJycgas3R39duW48cYb+dnPfobH4wGEpbGkpIRHH32UW265JaiN+yoiSWAxhbeaqsWsU/V649y/oekEuBsg5iIw9e7JIwEWvTO8ffyq63ntwmDaZogzpsCI6yFtQb8NcQH3zzJS5JADaCoQ+QQHyJAc0yDrmS1RSMmXQdatwiNO0kJLGVRugRP/C4d+CqUbRcXbIOgNu/PMENdU9Ya3XiQ0Vb3hr6nqDW+9SGgKPa0aohpBJEmEikbiE8zdvnDhQvLz87nrrrvYu3cvO3fuZOnSpcyfP58ZM2YETyhAHnnkEbZv3873vvc99u/fz8mTJ3n33Xf5/ve/DwjvOIPBwNq1azl9+jTvvvsuq1at6nO7EyZM4J133ulzvT//+c+kp6dz7bXXdvnfmjVr2LBhAydPnuTIkSP8z//8D2+99VaH4hI5OTkcPHiQ48ePU1NT47flDJQ333yTP//5z5w4cYKf/vSn7Ny506931113kZSUxI033shnn31GUVERW7du5b//+78pLS3tcZt33HEHaWlpLFmyhG3btnH69Gneeustf3XXJ598kr/85S+sWLGCI0eOUFBQwBtvvMHjjz8ecLuzs7ORJImNGzdSXV2NzWbr8zsLFy5k9uzZLFmyhA8++IDi4mK++OILHn/88UEbXPttjHv++eeprq4mJSUFh8PB/PnzGTNmDNHR0X0mSlTpG0mSiDaFt5pqdFT4EskOO736/VD9mfB+smRB9OgANCFa7wxrZcyvtF7LOSj8s6hwiwYsOZB9W0D7akB67YmbDImt7u9VW4XRNtSaQSCiehotxIyFjOsh4wawZIKnEZqOw9n/Dw79DE78Bur2gDwwt/5hd54ZBpqq3vDWi4Smqjf8NVW94a0XCU1JkohWjXEqQUCSJDZs2EB8fDzz5s1j4cKFjBo1ijfeeCMi7cnPz2fr1q2cPHmSuXPnMnXqVJ544gm/91lycjKvvPIKb775Jnl5eaxevZrnn3++z+0eP36cxsbGXtdpK1KxbNkytNquOZfdbjfLly8nPz+fuXPn8vnnn/Ovf/2rQzjnfffdx/jx45kxYwbJycls2zZwJwIQhR7+9re/kZ+fz6uvvsr69ev9efGioqL49NNPycrK4uabb2bixInce++9OByOXj3lDAYDH374ISkpKVx33XVMnjyZ1atX+/t8zTXXsHHjRjZt2sTMmTOZNWsWv/rVr8jOzg643RkZGaxcuZJHH32U1NTUDgbLnpAkiffee4958+Zx7733Mm7cOL7+9a9TXFzszy04UCSlrV5vP/n444/Zu3cvsiwzbdo0Fi5cOKiGhIOmpiZiY2NpbGzsl8tkOFHsZdSf3UV88siwVT2qb3YTHx2esufDSs9RASd/Cy2loLVAytyAwugUBepdFuKN9rBVxvzK6tXvh/IPQPGBxgjWXMi4URh8QqHX0xdK3hAFPiQtjLoXTP0L2R9SYxoJPUUGe7EwyLXlZdRHi3xyibMg8RKIGtEPvWF0nhkmmqre8NaLhKaqN/w1Vb3hrRcJTcVVR32LlvhRC4asQW44PA/2B6fTSVFREbm5uR3yZqmohApJknjnnXdYsmRJpJsyZAn0uBxwgp4rr7ySK6+8EoCGhoaBbkalE4oCbo+oRhSOS5gCuD2yqtcZnxOK14uqnACJMwPOZ6UAblkf3j5+1fRkL1R8KIxxAPpYiJ8OybNCo9cbkgSZ/wmF60QutKJXYex3QRcVOs1BMuT0JA1YR4mPxw5NR8FWBO56EcpasQmix0DyHIjLF3no+tIbDueZYaSp6g1vvUhoqnrDX1PVG956kdD066nVVFVUVFT6pN9hqr/4xS86uIfedtttJCYmkpGRwYEDB4LaOBWViKAoUPIm2EvAa4OEaaC3RrpVKm14mkThhDZDnCldeMMFwRA3YDQ6yL1HeOfJbjj9Z2EwVOk/eoswfmfdCsmXg84iiqfUfAkn/wAHnoCSv4vjc2CO3SoqKioqKioqKioqKhGl38a43//+9/4KIZs2bWLTpk38+9//5tprr+XHP/5x0BuoohJ2qrZC/QFw10L0ODAHHh6nEmLsxSI/nKNchIRaR0H27aKYQqTRWSB3KaARBsMzf1ONRYNBkkSexvRFkHkLRI8FXwvYT0PpBji6Ggqeh+ovwNsS6daqqKioqKioqKioXPAoiqKGqAaJfoeplpeX+41xGzdu5LbbbmPRokXk5ORw6aWXBr2BXzUkCWKiwltNNcaiV/XaaD4F5e+DqxpMaRAbWOnsLpqGlvD28ULX09uRar6Eqk8ABbRmUahh5JLglm5ikP0zJUPWLcKzsqVEhNKmXxNazQEw7PR0ZuGhGj9VFOxoLgBHJbhqoOkYGOJEhdakS8GSO/TPM8NQU9Ub3nqR0FT1hr+mqje89SKhKfR0aoiqioqKSgD02xgXHx/P2bNnyczM5P333+epp54ChIXU9/+zd97hcV1l/v/c6SNp1LtsS5bc4thO3GQ7jh2XkIQESAgLgYQEyBLaslnYHwvssiwJLBAWlgTCAsuGEhKWDoENoQSX2GnuLbHjJrmo9zZ95t7fH++oxbKkkaZopPN5nnnsGc3c7zm3nHvOe98SDse8gTMNTdNIsxFzA8Ooeo4Jpw6cXnqBLjj3U0kgb3ZC7krJZRW1JqRZJlYFciJMez09QFrTb8ToAmDLgfxrIOeq+OhNtn+u+VC0GZp3SEVQexHkXh1fzShJWT1Ng/QyeYV9UvCh9yz0nZOCKy27IH0OWsF60nJXgJaY8PJEj2vJ0FR6qa2XDE2ll/qaSi+19ZKhqWkaaXZTwtYxCoVCkcpEbWm4/fbbufPOO3nDG95Ae3s7b3zjGwE4fPgw8+bNi3kDZxq6btDWC3qCwtt0w6Cty6f09FCkYEOz5PzKWw1m+wQ1oc3rQk9QhOK01fN3QPMOjNPfgZ7XMACcs2DW7XEzxEGM+pe/DrKWyP8b/wjui/HXjIJpoWd2yHkw+61QvBVseeLR2nkIo+ZH+A48gN5bG0PBy5PocS0ZmkovtfWSoan0Ul9T6aW2XjI0dcOgrTuIrusJ0VMoFIpUJupHJQ8//DAVFRVcvHiR//iP/yAjQzwPGhsb+chHPhLzBs5EQgl2MAyFE5vXakrq1T8tHjbBLgmFs+VMTtMwT+r3M1ZPD4m3U9dhcJ8HJOQhrKWhpc9Cm/XmCRtJoyEm/St7k3hZ+hrh/E9h3gfBlhVfzSiYNnqaBs5ieYWD0HsKek5hBJvQTj4MSz4Hjrz4aA8h0eNaMjSVXmrrJUNT6aW+ptJLbb1kaCajjwqFQpGKRG2Ms1qtfOITn7jk84997GOxaI9CkXja90Pbi+BvkTxk6RXJbtHMw9cq1VG7j0n4YT8WF7qtgFbXLRTmZoAphcIeNBNU3Aln/luq8tb8AOb/HZhtyW7Z9MVshewrMVwLCde/AJ56OPkILPmMeNIpFAqFQqFQKBQKxRRgQkkEzp49yyOPPMKJEyfQNI0rrriCj33sY1RWVsa6fQpFfPHUS2VGfytYcyB7mcpzkSj0AHSfECOct37wc80K9lxwzobC9WBOB282aF1JaugkMNulwuqZ70HYC7U/hqp7J5SLUBEFJgvunDeQ3vkz6D0Jp78DC++XCrwKhUKhUCgUCoVCkWSiXhH++c9/ZvHixezdu5dly5axZMkS9uzZw+LFi3n22Wfj0cYZhaZBTnpiq6nmuGwzUy/kkTxx/jbABHnVYJp8klsNyLH3JbaPqaTnbYKGP8LJb0LDHwYNcdYsyKiUaqSV90LpDWBJT73+vR5bNpS/S7bsb4G638Vfcwxmgl52mg5FW8AA2vfC+Z/FVS+R41oyNJVeauslQ1Pppb6m0kttvWRoip6qpqpQjMTOnTvRNI2urq5kNwWAc+fOoWkahw8fTnZTks6mTZuGRXpWVFTwyCOPxF03amPcpz/9aT7+8Y+zZ88evv71r/Pwww+zZ88ePvaxj/GpT30qHm2cUWiaht1Kwm5imqZht5lnnp5hwPmfg6cOQm6pnGpNj5Em2M2hhDnYpYRe2AcdB+HsDyRcs/OQeMaZbOAsgbx1UPW3UP5OMcgN2XhK9G8s0mdD6S3y/54T0PpC/DVHYcbo2bOhcAOE/WIAbt4eJ73EjmvJ0FR6qa2XDE2ll/qaSi+19ZKhKesYkzLGKRQx5Be/+AVXX301aWlplJeX89WvfnXY3/uNfK9/vfbaawPfee9738ttt92W4JZPPS5nEP3Nb37DF77whYS3J2pj3IkTJ/jbv/3bSz6/9957OX78eEwaNZPRdYPm7sRWU23u8M48veZt0P2qJNjPXCjJ32OmCc2erIRWqpySeoYhxs76p+Hko9D4J/A1ARpYsyGjCubcAZXvg+LNl83pNWX7Fy05y8T7EqDlOeg5HX/NyzCj9NLKxNgeckPNj6Hr1TjoJXZcS4am0kttvWRoKr3U11R6qa2XDE3dMGjuVNVUFYpY8cc//pG77rqLD33oQ7zyyit8+9vf5utf/zrf+ta3LvnuyZMnaWxsHHjNnz8/CS2eGIFAIKn6ubm5uFyuhOtGbYwrKCgY0ZXx8OHDFBYWxqJNM54E3qNnpl7Pa9D4rIQMOkshc1HsNRMahDDF9EIeCQs8+z+SI63rKBhBMNllfxdsgKr3Q/kd4jE2Wb04EDe9oq2QPlf+X/cbKVwRb83LMKP0MheBayEEu+HUt8DTGHu9JBSPS/pYqvRSSi8Zmkov9TWVXmrrJUPTSEYnFSnHpk2buP/++/nkJz9Jbm4uxcXFPPDAA8O+c+HCBW699VYyMjLIzMzkHe94B83NzQN/f+CBB7j66qt54oknqKioICsri3e+85309vaOql1RUcG///u/c88995CRkUF5eTm/+93vaG1tHdBbunQp+/fvH/a7F198kY0bN+J0Opk9ezb3338/brd74O9PPvkkq1atwuVyUVxczJ133klLS8uk9tMTTzzBbbfdxoc+9CEqKyu55ZZb+NSnPsVXvvKVS661wsJCiouLB15ms3lgPz3++OP87ne/G/Ca27lz58Dvampq2Lx5M2lpaVx11VW89NJLo7ZJ0zS+853v8MY3vhGn08ncuXP55S9/Oew79fX13HHHHeTk5JCXl8ett97KuXPnBv7e76n35S9/mdLSUhYsWABAXV0d73znO8nNzSU9PZ1Vq1axZ8+egd/93//9HytXrsThcFBZWcmDDz5IKBQa1rbHHnuMt771raSlpTF//nx+//vfAxKWu3nzZgBycnLQNI33vve9wKVhqq+nu7ubD3zgAxQWFpKZmcmWLVs4cuTIqPtpPERtjLvvvvv4wAc+wFe+8hV2797N888/z0MPPcQHP/hBPvCBD0y6QQpFXPF3wPlfiEecOU08ZlQy/cljGOA+J8UwTj0KTX+N5OLTwJYLrvlQfidUvhcKr525FUU1Dea8HWw5YISh9nExXirii6ZB3ioxBvua4eTXIdiX7FYpFAqFQqFQxJxQKDmvaHn88cdJT09nz549/Md//Aef//znB3LQG4bBbbfdRkdHB8899xzPPvssZ8+e5Y477hi2jbNnz/LUU0/x9NNP8/TTT/Pcc8/x0EMPjan98MMPs379eg4dOsQtt9zC3XffzT333MO73/1uDh48yLx587jnnnsGDF7Hjh3jxhtv5Pbbb+fo0aP8/Oc/5/nnn+ejH/3owDYDgQBf+MIXOHLkCE899RS1tbUDxp7LoWkaP/rRjy77d7/fj8MxPHrI6XRSV1fH+fPnh32+fPlySkpK2Lp1Kzt27Bj4/BOf+ATveMc7uOmmmwa85q655pqBv3/mM5/hE5/4BIcPH2bBggW8613vGmbgGonPfvazvO1tb+PIkSO8+93v5l3vehcnTpwAwOPxsHnzZjIyMti1axfPP/88GRkZ3HTTTcM84LZt28aJEyd49tlnefrpp+nr6+O6666joaGB3//+9xw5coRPfvKTA562f/7zn3n3u9/N/fffz/Hjx/nv//5vfvSjH/HFL35xWNsefPBB3vGOd3D06FFuvvlm7rrrLjo6Opg9eza//vWvgUEvwm984xuj9hPkXLzllltoamrimWee4cCBA6xYsYKtW7fS0dEx5u9HI+ps9Z/97GdxuVz853/+J//8z/8MQGlpKQ888AD333//pBqjUMQVPSgFG3zNYIQgb/3MNQrFimAfdB2DrsMQ6Bz83OwEe554I+WujElhjGmDySIVVk9/F3Q/1PwQ5n0o2a2a/mgm8cps/BP01cLJb8DiT4LJmuyWKRQKhUKhUMSEUAieeio52rfdBpYopvzLli3jc5/7HADz58/nW9/6Ftu2beMNb3gDf/3rXzl69Ci1tbXMni2RNE888QRXXnkl+/btY/Xq1QDous6PfvSjgRDDu+++m23btl1ioHk9N998Mx/84AcB+Ld/+ze+853vsHr1at7+9rcD8KlPfYp169bR3NxMcXExX/3qV7nzzjsHvKfmz5/PN7/5Ta677jq+853v4HA4uPfeewe2X1lZyTe/+U2qq6vp6+sjIyNjxHYsXLiQrKysy7bzxhtv5OMf/zjvfe972bx5M2fOnBkoLNDY2EhFRQUlJSV873vfY+XKlfj9fp544gm2bt3Kzp072bhxIxkZGTidTvx+P8XFl6Zm+sQnPsEtt0hu6wcffJArr7ySM2fOsGjR5aPH3v72t/P+978fgC984Qs8++yzPProo3z729/mZz/7GSaTiccee2wgf+QPf/hDsrOz2blzJzfccAMA6enpPPbYY9hssh7/3ve+R2trK/v27SM3NxeAefPmDWh+8Ytf5NOf/jTvec97BvbxF77wBT75yU8OnEcgXnfvete7APjSl77Eo48+yt69e7npppsGtltYWEh2dvZl+zeUHTt2cOzYMVpaWrDb7QB87Wtf46mnnuJXv/rVpBzSol4ha5rGxz/+cT7+8Y8PuIAmI752uqJpkOdKbDXVvCz79NczDKli2VcjoWo5q8B2+YFv0pqOnsT2MZF6hk5+6CDaxYPQewbozwtiEo8vex4UbQJ7fmz0mIb705IOFXdLMYtgN9r5n5E3593Tq49TUc9skwqrDc+IEfnsD2HefcMKhkxYL4HjWjI0lV5q6yVDU+mlvqbSS229ZGiKnlUVcFCMi2XLlg17X1JSMhDWeeLECWbPnj1giANYvHgx2dnZnDhxYsAYV1FRMcwWMXQbP/nJTwYMbiD51zZs2HCJdlFREQBLly695LOWlhaKi4s5cOAAZ86c4Sc/+cnAdwzDQNd1amtrueKKKzh06BAPPPAAhw8fpqOjY8Cj68KFCyxevHjEfTC0yMJI3HfffZw9e5Y3velNBINBMjMz+Yd/+AceeOCBgTDUhQsXsnDhwoHfrFu3josXL/K1r32NjRs3jrr91++LkpKSgX6PZoxbt27dJe/7U5n176vX24h8Ph9nz54deL906dIBQxxI2rPly5cPGMxez4EDB9i3b98wQ2s4HMbn8+HxeEhLS7ukP+np6bhcrkmFCx84cIC+vj7y8vKGfe71eof1ZyJEbYyrra0lFAoxf/78YTv49OnTWK1WKioqJtWgmY6maZhNia2majbPAL32vdC+B/ytkF4JGXPiqAlm9IRWjkyIXrAHOo+gdR3BEuwZ/NycJga4rCsh5yrQzDGVnbb701kIc94GF36J5jmPpeF3aLPexASyB0TNtN2n49GzZkDhJmh6VgppOIth1lsmqZfYcS0ZmgnTC/VBx0G0joNY9ABa7krIjd/Dk34S1j9Dh95TaO37sfTVorkqIW+NFLSJs/a0PWdmiF4yNKetXsgLXUfkOgz1oeVcLakMYvQQ8XLMmHNGS2wfFcOxWMRDLVna0WC1Do9O0DRtwIBlGMaI59HrPx9tG295y1tYs2bNwN/KyspG/F3/9kb6rH9buq7zwQ9+cMRIwDlz5uB2u7nhhhu44YYbePLJJykoKODChQvceOONkypOoGkaX/nKV/jSl75EU1MTBQUFbNu2DWBUu8vatWt58sknx6UxWr+jbWv/b1euXDnMcNlPQUHBwP/T09OH/c3pdI66fV3XefDBB7n99tsv+dvQUN7RzomJoOs6JSUlw/Ls9TNe77rLEbUx7r3vfS/33nvvJdU59uzZw2OPPTZiIxXjR9cNWrqhMN3AlIAbmW4YtHT4KMx1TFu99oYz5Lf+Ds3XCrZ8yF469g8npQkt3mwKnV2YEjAXiaueERbvt87D4lWI5E3QNRuaNQvNkQdFm8UjLk5Mq/35elzzoXALtGxH63kF42wrWsU7xXMujkzrfToePWchFKyD1hfg4q/EINdf6XZCeokd15KhGVc9w4C+M1L4petVCPVhhPrw+cM4uo+j1f1ODP3566T6dYwN/pCA/Rnogo790L4f/K0YITc+nw9Hz2toLc9L1d+CayWs3xqfaINpdc7MQL1kaE4rPcMA93no2CeFpQK9kXEmiKP7BFr905C9BPLXQtaSuKTXmDHnTFeQwgIdk0nlZE4W0RrFpiKLFy/mwoULXLx4ccA77vjx43R3d3PFFVeMaxsulytmEXwrVqzg1VdfHRY2OZRjx47R1tbGQw89NNDe1xeAmAxms3nAmPjTn/6UdevWjVo889ChQwNebgA2m41wOByz9rz88svcc889w94vX74ckH3185//fKDYwXhZtmwZjz32GB0dHSN6x61YsYKTJ09e9hiMh35PvGj2xYoVK2hqasJiscTc8SzqS/XQoUOsX7/+ks/Xrl07LIGhQjElCPWR3vRzCLWDZhEPC1PsF3LTjkAndB6BriMQGqwShCUD3ZZPS/pNFOZno6mJ1uQpWItuskHTXzH5myWXXPk7ZXGuiB8ZleLt2XUMzvyPGOpdlclu1cwi0A0dB8RI5WsRr7hgL2gWDEchHsss7MHDaJ468Wpu2wuOIihcD7mrwT5yGMOUwQhD9wlo3we9J2UsDfaCHsCwZuPOqMaun0Tz1kOgHXrPQt1vIWcF5K8B1wJVYEiRPAxDil3pFsAx5tenLCE3dBwSI5ynQd6HegAThq0Aj3Uu9sBhNG+D9LfjoHjI5V8D+atlzFEoFAnn+uuvZ9myZdx111088sgjhEIhPvKRj3DdddexatWqhLfnU5/6FGvXruXv/u7vuO+++0hPTx8oPvDoo48yZ84cbDYbjz76KB/60Id45ZVX+MIXvjDmdhctWsSXv/xl3vrWt47497a2Nn71q1+xadMmfD4fP/zhD/nlL3/Jc889N/CdRx55hIqKCq688koCgQBPPvkkv/71rweKFYB40f35z3/m5MmT5OXljZqnbjz88pe/ZNWqVVx77bX85Cc/Ye/evXz/+98H4K677uKrX/0qt956K5///OeZNWsWFy5c4De/+Q3/9E//xKxZs0bc5rve9S6+9KUvDVRZLSkp4dChQ5SWlrJu3Tr+7d/+jTe96U3Mnj2bt7/97ZhMJo4ePcqxY8f493//93G1u7y8HE3TePrpp7n55ptxOp2XzefXz/XXX8+6deu47bbb+MpXvsLChQtpaGjgmWee4bbbbpvU+TihnHEjlQvu7u6OqbVVoZgwgU7oOQndJ9D6zqL5uwGfeFVY05LduqmLHpIFY+dheXrcj2aRiqjOYijcCJZM8GaD1pWkhk5DclfQrl1BfvO30XQ/1P4YSm4QLxlF/Mi+Sgxy7gtw8hFY+sDUN/CkOkYYek6JF1zPaxEDXJ8UM7FmQc4ySCsHk42gNxucs2VM7z4OnosQ6ADPBah/GrKWRrxYFk+tIjH+NvGA6zggi/v+xb/JAY4SyJwHlmxCvhwZV/Ug9J4W70D3RfC1QttL4CiW6tO5q8CWnexeKWYCegB6a+Ta7HkNzd9GVsCArkWQfaV4ptrzxt5OsjEM6DsrhvCuYzLOhPog7JU5TOYVkF4BZmdknCmVe0HPCZn/BDrAWycFfzIXyvwxZxmYVNEvhSJRaJrGU089xd///d+zceNGTCYTN910E48++mhS2rNs2TKee+45PvOZz7BhwwYMw6CqqmqgumtBQQE/+tGP+Jd/+Re++c1vsmLFCr72ta/xlreMngrl5MmTdHd3j/qdxx9/nE984hMYhsG6devYuXMn1dWDER2BQIBPfOIT1NfX43Q6ufLKK/nDH/7AzTffPPCd++67j507d7Jq1Sr6+vrYsWPHpLy8HnzwQX72s5/xkY98hOLiYn7yk58M5MVLS0tj165dfOpTn+L222+nt7eXsrIytm7dOqqnnM1m4y9/+Qv/7//9P26++WZCoRCLFy/mv/7rvwApZvH000/z+c9/nv/4j//AarWyaNGigUIS46GsrIwHH3yQT3/607zvfe/jnnvuGbWaLci5+Mwzz/CZz3yGe++9l9bWVoqLi9m4ceNAbsGJohn99XrHyZve9CbS0tL46U9/OpA0MBwOc8cdd+B2u/njH/84qQbFk56eHrKysuju7o7KZTKR6H31tNTuo7B0VuqHBCRKzwjLAiYyecTbAGEfhL0YITcePRNnzlxMWfPH3lYMmHIheWPhbxMDXNcxmaj2Y3HJpDt3ZcRDQ4uNXpRMd71hmrYmTOeeEC8gkAVD2ZtjbmiY7vs0Kj09JPnjAh2QuRiWfAbM0XmBzJjQo8no+TvEA67jgIw5Ibcsfk12cBRKyLYtZ/RxRg9LuHzvKTHQme0yTjkKxIslr1r+n4z+6SHofiXiBXcmYoDrFUObLRcy5kLarIHqvZc9R/3tEcNjnby3pIM1E7KXRQyPV0w4TDflzhmllxjNQNfg/Kn/3A37IOTG0P34QlYcFh3Nmi5jY9osSfeRuQjSy2MaNj7p/gV7ZYxp3we+5sFxRrPI2OCaL15vo40zhgHuc/JQ198mBjirS67j/LUyzkzQc33anDOj6fk7aOnWKFywZcqGqabCejAafD4ftbW1zJ07d1jeLIUiXmiaxm9/+1tuS1ZywhRgvNdl1Ma448ePs3HjRrKzsweqkezevZuenh62b9/OkiVLJtfyOJISg6+nAb1tL6aM2WN/N0boRmLy08VUL+SRBVnPa+JlEewWQ1LII5NIs1O8LNJno9uLMVlHTwgZa3SDhBlyJqSnB+UJcOfhwUUfgGYVzyBnGRRuuGzusinfvxTTG6ZpGPI0vvOQ/MGaA3PvkgV5PPQSxJTWC3mh4Y9gBMSos/Dvo15gJnocTYZm1Hp6SAxLHftknA57BsI0seVA+lxInz1goLpUb5RjGOiBnuPixWKEwZImHi9ZV4gXS/aSqL1YJrQ/fc2y8O84JAbd/lBUS5p4wbnmgW3ka3fU/ukh8ezpv7+Z7dI/RyEUrJ9wsvkpf84ovfhrGrp4A/eelDmUp37gAaakpdDE+OQoljmUHsbkPicPOoO9kurDnCbnuDVbrrXMKyBzQUzynUbdv0hRFNr3yXgzEA7ui8wDK8RoaLZfRm+U6zDolrlSXy0YwcjcMhNcVXKvyLl6Qg9vUu6cGZdISMbDnpPoJgemRX8fX71JkBLrwShQxjhFolHGuLEZ73UZtbvF4sWLOXr0KN/61rc4cuQITqeTe+65h49+9KOXLUOrGD+GYRDWQbtMBZm46IUNtARVWpqwnmGAv0Umjt2vgbs2Mnn0SeiBERYPCWcppM8BWx6YzBgGhA0TmpG4So4DmgmqHhmVnrdJDHDdr8iCuB9rZsQLrlpyaY2yoSndvxTUu1RTg9I3yuKh/v8g2Cl55Oa8AzIq4qAXk02mtp7FCcWboeFPUnX5fCFU3BmFXmLH0WRoRqXnaxUDXPsB8WAL9YmXmNkpY7Rrvow5kxlnbJmDXip958Sw4G0Y9PS150HeWsm95iwZYQOT6J8egM6j4unXVyMPgUI9oOtgz5GwWWfpqB6tY/bPZJHwuMyF4rk0UphulMnmp/Q5o/TiqxnyRh5gnpRXsCsyf/KIkdzsFMNa5kIxIlucEb3IeWrPl/M05BOvMc9F8LVJrkf3eWh+Tn7jmi+V1TMXSa61KPdLVP0LdEUM4QfEm3zA29Ym2hnz5XqczDhjTRfDd+5KeXDZc0LmUf426DouDxXyVss4kzZnzP6m1DkzGnoIvI3grZdx19MQiUrxY4TdGLZCjIUfVRVVFQqFYgyi9oxLZVLhSYgKUx365ZAsdHpOiBHO1yZPbvuf3pqsYMuSiWPabHki+7ptJjXkcKqE5IX90P2qLFB9TYOfm2xitEwvl8qS5vF5D065/qW43qia/k6o/dFg+HDhRshfH/UCZ9x6cSJl9Dx10LxDxpKqv5VKwePSmwGhR2Pp6UHoekVywfXVDAnTDEXCNCsltGucIdcTOoZBt4x17nNghIZ4scwTb7LsZZf1YhnX/vTUSS64zsPirdaf786SHjEyzgPr6EmAJ9U/QxcPnZ6TYpQz2SIPUvLFGzC/etRk81PunFF68dM0DDFQ9Yef9tVG5k9DH2BmSOGajHK5Rke4Nkc9T3UdfA1ijPM1i8HPZJXrweyUczEnEs6aUXVZD9io+je0KErPSQgPFkXBmi3h4Omzx+0VO6HrMOSN7NMa8b7rH2fSK6DgGshZPkpUwRQ+Zy67gYAY3jwNEeNbvRgkw375mxGMPBz3Axq6ZqHHtpjMN/xChakmCOUZp1BMPeLmGbdr165R/75x48ZoN6lQDBLoHgyd6D0jC52wVyZc4UAkf04O5FwF9kIwq2S6I2IY8pSy87B4VBjByB80Cduw58niLX1OMlupGAt7Diz4KJz7qSSTbtkl+RFnv02d+/EgbZYky+/YDzU/lsVk1uJkt2pq422UhXHn4YgXXMQ7xZIuD0kyLh+mGXOs6WKQylstnjvdJ6R9/jboelW8WPLXQN4aOdbjWZiGfdK39n3ikdYfAochhozsZVKEIYZ5sy6LZpLwOFcVBHojYbrnXpdsfpFKNj9T0UMSNdAdMcD5WsRYFOp/gGmR+79rkZz/VtfkHuyYTLKdtEhVvGC3GP08DRHvsRYpTGJJFyN15mIJI89cFH1BkssWRXGK56trnhjjEmHgsjghd7mEqHobh3vL9ZwE62/Emy5vzZiRBlOOsG/Q463f283bJEa3/lfYJ/9qmux/SzqkF8lDAWsu+NvxhStIfROXQqFQxJ+ojXGbNm265LOhbsiqoqoiKgwj4vofmTx66oY/ve3PXZJWISEA9mxZkChGJuSF7mPQeWSwCABIknR7njydzqu+bO4UxRTEZIW5d0PLc9D2oiy2znwXKt6tKn/Gg8xFYkzqPQUnH4Ul/wZpY4c5zijCPug6KgYq9/khBipdPGyyroyEaSbAQDUSmiYPGtLnRLxYTogXi7sTfI3QvB3SK4d4sbzOK9gwpF8d+6HriPQtGKnGaHXJwj+9Uox/ycLmihgWq6WtPa9FDALtcmxikGxekQIEeyOhp6/JmBXsi3gpucVTyZIm50L2EskBF8+HONYsMVDlXC2esu4LYsD2tcp8xFMv9zCzUzzys5fIeJs2e+R53WWLooTEsJ67OuJtO7bHXVzQNEgrlVfYL8eh7+ygp2DLbjFUFqyXMFerKzntvBwhb8TYFvF489RHDLgBOX4Dhje/PGwwO8Sb0lkC9gI5BmbHpcZGf0dy+qNQKBQpSNTGuM7OzmHvg8Eghw4d4rOf/Sxf/OIXY9awmUyiH6IlXM/wQ9eZQQ+4QNeQ4gteMRRZc8SjwlkK1rTJ6ZH4SOyEahoGNu8ZtPYXoPc1CeOItAJbdsQLbkNMDQqJ3qfTXW9MTU2Dok1ikL74KzEKnPkezH6r5PiJtV4cSBk9TRPPqlCvGDdOfh2Wfk4WIWP8LNEkVNMwsPjr4OJRMfYMDdO0uiQ8LKNq3GGa4yEm54zFCbkrxOjmaYDeE+BtFqNVzwkZI3NXQ95qTHo2tB4QI5ynPhIC1wOYxOsjd3kkD1ZsHgjFpH+aJrkkMyokTLc3kmw+0CmL7KZnxXiYfw1kXzX95xfTXE+uw3poOidzKPeFIQ8w3fIdq0vuFWnlMXmAOaHz1GQd9OLsz/nrPg+eRrn2fM0S1j7UWJh5BWTMwxxogfqjr/O2jRRFiYO3bUyuQ7NdvFFzlsn40nNCrr9Auxjo6p6SMShvDRqJK9DWj0n3QG/dkBxv9WIg7Te6DfN4M0dCbzNkDu4olHHSZB/3CZ+MOZRC8gMqFIqpwXivx5jljNu1axcf//jHOXDgQCw2FxdSIkeAp0Fy7qTH92bt9Wq8uDeDXS9l0NkVtU12YugBCanxt8mTTSMkEwHDkImb2SGTSLMzpjNchy3EuqsusmlVDTmZvphtN+mE+qAr4gUXGPIk0uyUXHCZCyTkbpw5msbCHzDz0pE5PH+4HKslzHUrz7FqcT0Wix6T7SvGSaBX8siFeuV9bjUUb02tUJhUIOyHxj/LQjBnOSz+ZMyupUkR6Ip4bL0qHgtxxu218/zRRew+soCeXlNk8RYEjMhYkx3zMTvu6GFZ5Ae75T6kWSSk02SVhxl6UD432cQIa82Ki5efxayz8ooGtlTXUFLQG7sND/U497XIeWtxiSdLyQ1QfH1cj5fXq/HCngxe3JdBmlNn0/peli/zYI6no2TYJ0Ut+mphOhsCwj4xEId9Ungh5Il4vmeDozTyADOJHpvjIeiOFIGoG5i7+PUMXjx1DS+cWIXVonPdkj2sqtyHRQtKuoaMqjGLokw5wkHxVuw7gxHo40zLAra/uomLHZVcvbCBzSteJS+rL/7t0IMy1g3zePPK/zXLoOHNmhsxvGVNLnrCfVFCV6u/E7s+xJiUWA9GQTAY5MyZM5SWlpKVlZXs5igUCqC9vZ2WlhYWLFiAeZQJUMyMcSdOnGD16tX09SXgxjJBUmHwNdz1BJr3YcuaFfMqRIYBZ2rsbN/t4sW9GfgDGhhDSp7Hcy0V7ImEoPowwgFZB5isYHLI007NGh99A3Q0TBiYTAbLFzWwdU0NVy1oxGyOz4TdMCCgW7CZQrFf7/Qn8O48LLlYEEOYgQlsOWj2PCjYCM7CmEnW1uewfW8lzx8qx+u3DtunmRl+Nq6sZcvqGkoLY7iYHEJc9+cU0JuQph6Gi7+GvjPy3lkK5XeMuwjHdN+nMdML9kLDH+X/xVuh6v0jGjEMwyAQ1LFZTfGpHjdC0nIj2IseCmDS9JgPnYYBJ+sr2XZkHS+fuppgSBbABhqayQLmNFlsxXNhPGScidu9yQB0rxznsFf6p5kGw7Gi8ASZiPbQ/i2ubGFLdQ1rllzEao3hA44hyeaNsA/dkoEpfy1a5XsvDc+dBGPNL7Kzw2xa38uWDb0UFoRipguIl1Xtkxjui+i+dkwE4zqd6ccAdMMUl2twVE3MmCxOud+nzRYDSpyuxXiP3TUXstj+Qi4vHCrF6zXA0DEwoZksZLoMNla3svWa+tgaq4cQ7/71eWw8f6icbS+WcrHODGGPjDPI0HL13BNsWfYSKypfxWyOz4NNOU81TCYzmtkphnlHroSaWrNiHrps9F0kYMrGtvYbU7aaaiqsB6PBMAwuXLhAMBiktLR0yhbOUChmAoZh4PF4aGlpITs7m5KS0SPTojbGHT169BLBxsZGHnroIYLBIC+88EL0rU4QqTD4xqOaal+fief3ZLBtl4uL9YM33dLiIJs39JCX301+tj0+lZ3CfqlO2HUEAt0YepAO20pystMxJSABvW7AK3WlHDlayMlzBQOf52Z62bSqhi3VNeTneGKuGfPKkcEe8YDrOhIJm4pgTkO35dOafgMF+XmYYuR64PFaeeHwHLbvq6K2Pmfg8+K8Xq5bVUurO5ODR4ro6h1czC2qaGXrmrOsWVqHzRq73JEpU4kzGZpteyT/FYYYECruGrWa4qT1JkhK63lbJNTP7BCD56y3jKAXpwp5oyQt1x1FtGsLyLN1iEEnBvS4new6vIjtB6+koW3wup9T1M6mla+SU2wlL8PAlICDqBvQ7s8kz96TmHMmHKLDDbnpesLuTWdaijh6LJ8jp0owDOlkujPAhuXn2FJdw5yS7tgJGgZ67xn8rcdwWHQ0VxUs+DspPjEJRptfbLq2h7omnUOHc+ntG7w3LVnkZet1vay62o11sum+ul6BC78AXytGsI922ypy00MxuyZGQ0ejPZAb02twbE0TLfo8Cl3BhF2HsR67++cX2/ZWca7hdfOLq1+hpc/FwVcr6e4bTFFyxdxWtlSnxvzCMOBETQHb91Wx59gsgiE5962WMGuXnKMov5FXTxRw4txgLsdsl4dNVx9n84rjFOXG8Loncs6EK+ScMcc/v57eV0dLuJzCax+YskahVFgPRksgEKC2thZdV9EqCsVUIDs7m+Li4jEfSkRtjDOZ5Mn/63+2du1afvCDH7Bo0aLoW5sgUmHwjZUxzjDgxEkH23e72HMgnWBItmW1GKxb7WbLhh4WzvdjEMcy6321cOGXkp/C3wbWLPSclTIpSMKivLElk+37Ktl1oIJej7jga5rBsvlNbKmuYeUVDTEJuYzZ5M4IQ+9Z8YLrO8tA6I1mlnAjez4UbUa35sREzzDg9IU8tu2p4uWjs/EH5Um7xayzZulFtqyuYXFVCwbSvzxbN0dPlbB9byUHXysdtpi89urzbF1zNiaLyZQ25CRC01MP538qoSdoUHqzVBuOl94ESHm9vhpofUHC6Bd8VJLiD9OL4Tg6VtLyjEpIK0PXrDG77l85U8S2PVXse7WMsC6LJ7s1xPqrL7Cl+ixVszsGrvuUPYZTWK+rx8mOfZXs2FdJW9egAWLe7Ha2rjnLumUXcdgn702mG9DeHSK/53doekAMcZXvk5x6UdA/v9i2y8Xeg4PzC5vVYO2qS+cXuZkODh1JZ/vuTI4ed9I/fXRlhNm4ro8tG3opKw2OojhSI8LQ+Kw87PO3gaHL/EJbPG3PmWRoxkrPMODU+Xy2763kpaNzCAQHDVTVS+rYWn2WKypbL5lfbNtTxaGT8TNWx3J/9vTZee7AXLbvraSxbbBYQ3lJF1urz7J++XmcjuCAXkt7Btv3VrJzfyU97sGw0CurWthafZbVV9bFxEs24eeMMsYlDV3XCQQCyW6GQjHjsVqto4amDiVqY9z58+eHvTeZTBQUFOBwOKLZTFJIhcF3ssa4nh4Tz73oYvtuF43Ng0/AymcF2HpdD+ur3aSnD97c4+LRoYeg6S/QskuqKoW9ku8jazG6Zkn6RDIYNLH/eBnb9lbxyplBL6LMdD/XrayddP6eSU98Ap0RL7ijkYqyESzpkgsu5yqpVhhJyjxZvV63jd0HK9i+r5K65sFcE7OKutmyuoYNK87hSh+8uY+k19HtZOf+uezYV0lr52C+mnmz29m8uoZrrrqA0zGxxWSqLj4SqhnyQu3jg7kDs5eKUU4b+UYw3fdpXPQ6DkulYns+LP5ncFUO0YvBOOptEgNc56FLk5Y7Sy5JWj7ZPnZ0O3nuwFx27JtLS8dg0YWqWR1sqT57yTU7LY7hFNfTdY1jp4vYvreS/ccHDaMOW4j1V59nS3UNlbM6JhxON6Bpa8bUukvCO23ZUPommH3rZceLfrp7TDz3gswvmloG5xcVswNs2Ti++UVrm4WdL2SwY7eLjiH5ahfN97H52l7WrnJjt48xLQ31wfmfSei2rwWsmZC7Gt2amfRjON004zm/2Fpdw7XLxz+/2L636hJj9ZZqmV9M1Fg92f4ZBhw9VTzua3YkvXBY48DxMrbtreTo6eIBw6Mrzc+GFefYWl1DWVHPSPIJ6WPUesoYp1AoFOMmZjnjUoEpP/jqYfSaH9PpdZBTOn/cizrDgKOvOtm+28X+w2mEw/I7h11n/Ro3Wzf2MLc8MOIEXjcMOrr95GbFKEzV0yDecP2FGsxp8tTdURDRgw6fi1xHb8ImkqPpNbens2OfPJns6h00KE8mf8+E+qiHJNFv52HZd/1oFqk05iiCoutk0REDPcOA42cL2ba3ir2vzCIUlgmTzRpm3bILbF1zlvlz2i9zzlxezzDg2Gnxsnn9xPSaqwa9bKI51abaOTNlNQ1dEph3vyLv7flQfueI1S2n+z6Ni55hQOtuqV6YNguWPSieakxiHNUD0HkUOvaJJ/GAF5wO9txI0vKSEfNBTaSP4bDG4ZOXerOmOYJcu/w8W6vPUl7aNXJTp8MxTCG97t5BL5um9pG9bNKd0XmTDdPEkPtN9yuSHy9nBcx7v3h/Dv2NDseOO9m2y8WBI7GbX+g6HHnFybZdmRw8mkZ/ZFWaU+fateItVzFnBA8PTx2c+0mkGmS75EzLXQ4m65Q7htNBM5bzC7tVimltqZ74/CIaw1e8+gejGwhH82YdS6+tM23AS7ajZzANyMLyNrZUn2XtsovYbdGF6Sb8nOmro0OfRe76LyhjnEKhUIzBuIxx3/zmN8e9wfvvv39SDYonU37wbdkFx/9DJpilN0PRRpkkX4aOTjM7n5en1G0dg4u1eXP9bN3Yw7rVbhyOBNlaDV3a3/iXSJW6HkgvlzLvpvjn35ks4bDGoddK2ba3ksNxDIm4BH+bLIi6jokHYT8WF9jzIHcluBbELIl4V49jYIHXPMQbZm5ZJ1tWn2X91RdIi3KBdzl6+uzsOljBtj1Vw0I25hR3sXXNWa6dwGJSMQ46j0SKDuhSJGXOO+NenXnGoIckf1ygAzIXw5LPSC65aDAM8NZHvOCOSJW7UB8E+8T7Na1MjHAjGFEnSmtH2sBDh6ELvHjleVTEjv78U/2Gjf78UzZrmLVLxbCxsKJt4rcI90UxMmsWyKiA+R+G9HI6Os3s2O1ix/Pxn190dpl57oUMtu/OpKVtUKuy3M/Wjb1cU92H02lI/sS6p+S+GXJD1uKY3h8Vk6OrxzHgIT90flFZ1sGW6hrWX31+wh7yr6c/JHTbntgZq8ei/2FGvENnQbxkj5wqZtueKg6eKEWPaDntgw9OKsq6YqIVc1Q1VYVCoRg34zLGzZ07d3wb0zRqamom3ah4MeUH365XMI59nnDrPsy2NDRHARRthsLrBp5Wh8Nw+Fga23a5OHQsbSD3Snqazoa1fWzZ2MOcWeOfgBiGgdcfxmk3T7zqkb8NLvxKqnv626Qyas5Vsqi8RA+8YRtO88hP0mPNRPTau5zs3D/x/D1jaupB6DkhRjhP3eDnmlW8YZxlUHjtqIbYaPR0XePoqWK27a3k4InSgafJ/ZO6LdU1zC3rHJfWuPo3wvdfqy1g+95KXj42e3gy46UX2VJdw6K5rZfdViqcM1NO09cGtT8G3QdoULRF8pxFNj7d92lc9UJeMXYaAci/Bhb+PQamscfRsE+u+fZ94LkwGIYKEn7uqpIcXmOECvYzVh9DIRMHTpSybU8Vx84UDQt9um5V9BWQp9UxTFG9Po+E/G3bOzzkr6ywhy2ra9i4snZYyN+4NQM90LydcMDHobr1bLvwQQ7XLMKI1AhN1PzCMOCVEw62785k78FBLzy7Ncw1C15ky7xfMy/vMJrJBLmrwFl0ye+n+jFMNc3xzC+ORLzVDhwvm7TRaCLzi8sWS1h6ka1rRjdWj0evpSN9ILfb0AiKiRSVmMjx6zdybt9XOSylwHiNnAk/Z/ou4jXl4Vz7dVVNVaFQKMZAhalOMfSLf6Dj2A/J00+ihXrl6ZI9jxbLm9h+6hZ2vlxIV/fgYm3xQh9bNvRQvcKDzRb9oZxUriPDgPa90PAH8eYLdIGzFHKuBotzxJ+kUr6T/vw92/ZWceB46bhDIi6r6W2KhAW9Crp/8HNrpizG86olOXuUx+Fyem2daQNPqdu7B42KC8rb2LK6hnVXXYg63GHU/o0Dt9cq+WP2VnKhKXvg89KCHrZU17BxxTkyM/zDfpNK58yU0gwH4NyT4GuS9675MOu2gXCu6bxP464X6ISGP0n4aNlb0MvfOfI4ahjgPg8d+yOVkHvFAy7UJw9Y0mZBeiVY0y+vdRku18eGFlekUM3cYUnBl85rZkv1WVYtrp9QUvBpdwxTWM8w4MyFPLbvq+TFw3MGiu2YTTrVS+rYUl3DknnN4743tXSks31POTt3p9PVY5IHQ85iFi/PZ8tGd1LmFz09Jna95GL7ThsNNU1y7eh+Zhd2snVjL9eubiYjbbjhMZWOYapojja/GCmcckF5G1snGE45mt546PPYeP5QOdv2VHFxiLF6IvOL/tzC2/fKw4x+MtP9bFwZ/cOMWPTPMODVs4WRYjvDw3+vufoCW1bXMG+E8F+VM+5SUmE9qFAoZgbKGDfF0OueoeX40xQWZBPubWT/3iDbD13FsYtXgskOjgIy8/O4bkOALRt6KCmenMv/hCfLwV64+GvoekUMcSBJ49PLRzUmTZWJZLREk79nmKbuF+Nb5+FBowhI6K4tD9LnQME1YB7ZeDkehuoZusbBE6Vs21vFkVODiYAz0iSMYuuas8yaRCLgS/o3wX1qGHD2Yi7b9lZdsphcfWU9W9ecHVhMpuo5MyU0DQOa/io5yQCsWVBxF7o1e1rv04ToeeqkkqMlHb3yb2mxrh0cR0Nu6Dgk+93TEMkF1wOYJJefax44CgeKsEyEoX0MhczsOTaL7XurOFFbMPCdbJePTatq2FJdQ2Gue1LdnZbHcBroeX0WXjhczva9ldTU5w58Xpjbx5bVNWxaVUt2pu8SzXBohEJGBmTaGrlu3h/YcvUhSiqLYf4HB3IjRt/HSRY26avBqP0pJ0/b2H5gMS+dWU/QlA+ahtUSZs3SOrasliqc6l4Rfz1DH7nQQEZagI0rpPjVVJlfjNdY/Xq9/ocZz+2fS69HHmZomsHSec1sXXOWlVc0YLFMvMJprI5fr9vGrgNz2ba3kobWwfXM7KJutlRL4a1+Y7Uyxl1KKqwHFQrFzGDcxriuri5++tOf8uEPfxiAu+66C693MMeV2Wzmf/7nf8jOzo5LQ2NBKgy+et0zHN29myNnV/D8wQp63XYIe9CCnSydfYytyw+ycnEjlpL1ULxVKqFNRm8ik+XOo1D3O/C3SLVUR6Ekfx6Hd0eqT5bHk79n/pxWurp7yPU+j9Z9HIz+sB5NjCH2PMhbBxlzJt8gpI+v1pdy9Gg+uw/OHRZGcWVVC1tWn6V6Sd2EvGEupxfLfer1WXjxyBy2763ibN3wxeTm1bVsWFFLyGZP2XNmSmj2nJRcS0YYNDP6rL+hxbJy2u7ThOl1n4CO/RjWbNrL7ic3OwNTx35Jih/sk2IMYS9YMiVvX/rcy3oNR4tuwMHa2Rw9ls8LhypweyU3p6YZXL2wka3VNSxf1IDZHJvnbdP2GE4jvXP12WzbW8Xzh8rx+qXaqUkzWHFFA1vXnGXp/CaO1ZVx9Gi+zC8uZ2wINEDrc4AhBRLmfQgy50+gjxM0xhkGtL0A9RGve90P2UtxmxbwwuEKtu2t4nxj9sDXS/J7IwaIWnxmZ0ofw6mmqRvwSl0ZR4/lsfvAXLr7UnN+sW1P1SXG6s2ra9m4soY+I53aMy527qvktXODDzNyMr1sXlXDptW1k36Y0U885qQnz+WzfW8VLx+bTSA4GKZbvaSOrdVnWTi3lVafMsYNJRXWgwqFYmYwbmPcV7/6VY4cOcKTTz4JgMvl4sYbb8TlEi+hl156iXe+85088MADcWvsZJnqg+/Ro/DrH53m2MEObHYnaJCb6WXTqho2r6qhwHkGOg9KOKglTapsFm6UfFD23DG3PxKGYdDZGyDHZRs7t0PIC/W/g46DMkE2QpB5hXh5jNPDwzCg059Ojt2dsHwn8dIbMX9PyE2R4yh5jvrBL2oWWYDbcsE5C0zjywk1Xrx+CycvFGIzhUCDrAwf162Up9TF+X0x1YL47tPzDYOLSY9PFpMaBmWlvbjsvoSdM76wDUeC8wDFXTPsg85DoAcxgLCtCLMtA82SIfkJY3xeDiXR+zRhegbgrcPwtxM2pWO22ND0YCQM3RRJM5ArVaVj3JBej42z9fkD131+toct1WfZtKqW3Czv2BuIkuk0dk93PX/AzMtHZ7N9bxUnz+cPfJ7mCNDlThs4ZwbmF6trKMj1DN9I0A0tOyDQLedw+R1QtDWq8ziq+UU/egAu/AY6DoC/VbzIc1eDI2/IdqGmLpfteyt54XA5vsCg51NpSR8ZNnWviBUjzS82rapl8+rUm1+cq89m+74qdh8cbqzGDHpQA00M0ysWNbCluoarFzbG7GFGP/Hsn9tr5YVD5WzfV8m5hkFv1oKcPjJcocTdfwPduHI0Pv4fb1U54xQKhWIMxm2MW7NmDZ/73Oe4+eabATHGHTlyhMrKSgB++9vf8vnPf55Dhw7Fr7WTZKoPvrt3w399rQFT3ymWL/OydU0NVy0YYTLgaZBFdaAzYpTLgYJrxVPOnj/yxidL7xkp0uBtgEA7WHMhdwXYssb+7TTHMOD0+Vx27DTx4oFc/MFIniaTDcw2sBXEzBvmcmiawbL5TWytrmHl4vqYTyATjT9gZs+x2WzfO/xJtWKSGMZg8YDXY7JJZVCzM/KvY9zFBGY0hiFewmGv7C/NKkY4S1rc95/ZpLNqcT1bqmtYOr8Zkym1r3tF7KlrzmT73kp2RzzhTJrB8kUNl59fDMUIQ9se6DsruU0LroW5d0dfQXi8+Nug9ifgrpX/2wukUMMo90+fv9+zupIzF/Mu+z3FxNE0g6sWNLG1+iwrroidt22yGMlYXZDjZnMkrDseDzMSiWFAbX0O2/ZUDTNWJ4yQhzklPfzHEzckVjcKpvp6UKFQzBzGbYzLz8/nxRdfZMGCBQCsWrWKp556ilmzZgFQU1PDsmXL6OuL/ZOyWDHVB99AAJ55Yj8rcv+b2XMLx36C5W0SL7VAhyz8rNlQsB6KrwfH+AwYhmHQ5w2R4bSM/ARLD0ii8tYXRCfsA9cCyFw0IU8aw4C+kIMMS+KeXMddTw9IOE3PCbwBB682r6Xbvoq0nHI0UyImQQbFJW7K8zunzz4dQn2zi9fqC0lLoFeVJ2xLmF4yNI3eswR7zmIN9aDpPvFyHQlLuiyIbXkSXm3PndBCPOH9S7SeHsbn7sRht6FZXTH3ghsJTTMom9VLWXb3tLzulV5sCQZNnDqfhysnyOzcrvFrGoZUSm/fK9d+5kKY/2FJTzHmT8eYXwyl+zW48HPwNUOwBzKqIHtJVAbtC41ZnG7Mn7bjTHI0p//8oqXPxVVzG0hERGWi++fzW3jlTCHdfmfirgtPMzgzWHv3PyrPOIVCoRiDcVsKPB4PgcBg5ar9+/cP+7vb7UbXY5MzYqZis8Fbbmyh5bgVAxjzFuYshrKbZfLacUi81uqfFsNZ/looeQM4ikbdhAG4vSHSnZZL9dwX4cIvJFG5v1VC2Qo2DgsXiRYDcAcdpFt8Y/cvBsRdz98hhSz8rQA4s/JYsXAeLRRS6GxMaN6acZ0zMSDRx7CksBezy6zyAMVUz0aLd53oYcg13n0CvPUQ9oiXlx4Y+ceWDHCWyfjjKJZ/LaPni5wO+bjGr9cdf0Gm/3Wv9GKL1apzRVVr9OeMpkHmAjHGN22Xok2vfBGq3g85S0f96ajzi4EvGdC8DRqfHUx/kbtacixGyazibmxZ2rQdZ5KhOd3Hmf75RULESHz/HPYQKxY3JKWAg2EYU9YYp1AoFFOFcRvjKisrOXjwIEuWLBnx7/v372fu3Lkxa5giChxFUHoT+FolfNXbAA1/hLaXIG8NlNwgC+bxYoRl0t28XUJhg32QUSHVUk3WuHUj5eg9DXW/l/xQmkX2UdmtUvU2taMcFDMJTYsUFxiy+DUM8DZCz4lIWKtXjHR6AEJ90HtSXv1Y0sFRKuNMv5HO6rpUS6FQpCb2PJj1ZmjeCZ6LcPIRmHUblN0y8arAIa94w3W9IqHe5jTIW6/SXygUCoVCoZgRjNsY99a3vpV//dd/5YYbbqC4eLhhp7Gxkc997nPcc889MW+gIgocBWJ487dJ+Kq3ERr/BO0vy5PmkhshrXT0bfia4fwvoa8GAm1iWCq4Jjpj3nTHMKB1N7Q+L+/N6RK6U3KjGDZSO52KQiHncVrp8PHCMMTg33Mc3OfEey7kEWN0yA19p+XVj9kJzoiBzl6MSVso21BPyhWK1MTskDlGxwHoeU08593noep9Y3rHXoK3Cc49KR74/jYZK3JXSp5VhUKhUCgUihnAuI1xn/zkJ/n1r3/NggULuPvuu1mwYAGapvHaa6/x5JNPUlZWxqc+9al4tnVGoAFOUx8aOWN+97LY8yNGuQ6pvuptgqZnoWOfJEMuuQHSZg3q2c1ohgFtL0h+OH8HBLvlOzlXg9kei64N6ln8iYoIiL1e2CvecH1n5b09HwqvE2NcvDTHQOmltl4yNCekp2ngLJTXUPxt0B0x0IXc4u2i++Ra6TsLfWcxAYWAYXKAsyTy6vegy4q5gS4l9meKaSq91NaLmaZmgrzVkkuy7UVJi+FrlDxykXnFMD27+VK9zsOS3sHXJp62mYsha+HEPeyG6qljqPSUXlI1NQycZp8KUVUoFIpxMO4CDgCdnZ388z//M7/4xS/o6uoCIDs7m3e84x186UtfIjc3N17tjAkpkbCz/o9w6r8g96rYbdPfORi+arZLoYfcFeLJlT4HAl1SKbXnNVlYaybIvkom1upmOoivRfZTsAvQIG22hOjYJmE4VSimI4FOMdD11cpiO+yR4i8jYbKJcc4xxEBny1Fjj0Ix1Ql0SUoL3SfpMua+B/KrL/99IywpNFp2yVwDZC7iHMNjX6FQpA7ui+IpW/2dZLfksqTEelChUMwIojLG9WMYBq2tkrC+oKAgZZ5+pMLga9Q9Q89rPyMzf3bs16L+bug6CJ76AaOckbWEoLsZa7gDLdApE+qcFWBNi7G4YBjQE3SSafUmrDJXTPS6XoWGP0hyaZMN0iskX84I1VJTto9KLyl6ydBMip47TKZ3P5r7LAR7hxjoRrgFadZI/rmSwSIRttxxe85M9/2ZDE2ll9p6cdPUA9CyW9Ji2LKg5I0w+3YwWTAMgx53kMx0K1rIDef/F7pPSn44axbkVYM1I0YNUcdQ6Sm9qaBp9F2kh2Iyr/nKlF0fpsJ6UKFQzAzGHaY6FE3TKCwcu6y9InoMwKtn4CIOxZ3sWVC0GQI9Er7qqYNAF2EysJqDkHMVZFTF1SPFALwhOy6rN2GVuSal11/MomOfvLe4xGuwaGP8NKNtotJLab1kaCZFz5SHK38NWsGawT+E3BEPuhrxOA15JbzVCEqSeM/Fwe9qFnlYMDTE1Z4/ooFuuu/PZGgqvdTWi5umyQZFW6DrqLzqfy9FX6ruw7Bm4vWHcdGIdv5/xTvf3yle97nLY14QSh1Dpaf0kq9poOENO3CpaqoKhUIxJhMyxilSHFsmFG2CYC9G51G8oVzs+bPRbLF7Qj0tCPZB3W8HDQKOIll0ZMxNbrsUiumCJV3yT+WtHvws5IGek1IMItA1xEAXAm+9vPrRzGAvHGKgK4kY6MwJ74pCMWPRNHmYZ8+XENT2fVIMat6HsXXXofX8Cfztch1nLwXXPBWGrlAoFAqFYsajjHEzGasLCtYT8GaDtSvZrZlaeOrg4m8k3xUmSC+HsjfJPpvJBHvBfRFHwAGGFWzZYMmYHgursE+MP4EuHH47WIviFq6tGAVLmnjN5C4f/Czsl5yWvWcg0CEhriGveK76GuXV2f9lE5qjAJdtIZSsBkvsCtAoFIpRSCuDsjdD83boq0E7/hBOwwVGh3i2FqyXqu8KhUKhUCgUCmWMm2poQLqpe3LVVKPVs/oSWklqSusZhoTwNj4L6GByQEaF5IcbZ76qKd/HaNHD4o3kPg/+VrSwn3RdQ3OHwWQGk13yedlzJQ+QLVvCeWNkoIt5/wxDDG/BLjG+9f8b8oARRtNDpBsmNJ8VCjcmZPE47c6ZWOuZ7eJ5kzOksE04CL2n5OVvE6+bsAcMHc3XTLqvGcN9ACrujPsxTPT+TIam0kttvYRpWjOg9GaptNp3HovJA848yF0d94cb6hgqPaWXfE0Ng3SLR4WoKhQKxTiYUAGHVCUlEnbGo5qqYnzoQWj8s+S9ATEs5VZD/urRfzddCXSD+5yE6YY8kqgbTbwf0srB1wDeJvncCEd+pA0x0OVEXtlSwdfqGrdBM2YYhhhphhrdAl2DYY9GGAwdMCS00Zopucj0IHjOiyEydzVkzp8e3n/TnXAI+s5EvOhOS+45TDDrLZC1ONmtUyhmDoYhuePCfslFa1Kh4wrFjEBVU1UoFIpxMy7PuG9+85vj3uD9998/4cYopFJtZ6iQHCMxa3/DgE5/Ojl298zWC3RJWKqvSd47S6H4BkgrjZ9mjIipnh6SEF33OcnxowfEaGXJkLxeRddjZF1Bp9sgx2VD0wPQd07ye/WdE+85X6N4nnnqIvn2TLIQ06xgzxs0ztmyxfg1hoFu3P0zDAi7xYg41PgWjoQzDhjeiBjesiTXWPpccFVAxnxwFGCY7HR295HT9jO05p3QsQcCrZC3ZsTqubEgpc+ZqaRntkDWIozMRXS5DbIbvo0W7Ia6p8B9AUpuiItBONH7MxmaSi+19RKuqWkYaeWip7kTkzheHUOlp/SSrmkY0BnIIkcVcFAoFIoxGdfK8uGHHx72vrW1FY/HQ3Z2NgBdXV2kpaVRWFiojHGTxAAChgODOFRTvZyebp3Zen21slgPe8VIk14BZW8BizN+mjFk0nqGIYarvnNiQAt7I15wkVx5RRuh4FoxpCEG40DQJ3pmO2QtlFc/elCMeb1nIga6c1JFb5iBThPDlmZ9nQddFtiyhiXgH7F/hiH5/IJdw41vYV/E6BaKGN40yVVkixjeMqoixrcqCV002UbYHQaBsAWj8m/RMqrg3JNyjgS6oPA6CcOKMSl3zqSAnt+UgzHvw2j1T4mnXOdBCbcuf5fkpYuxXiL7lwxNpZfaesnQVHqpr6n0UlsvGZoGGgHdhqGMcQqFQjEm4zLG1dbWDvz/f//3f/n2t7/N97//fRYulAX4yZMnue+++/jgBz8Yn1YqFPHAMKDtJWh5DjDA7ISMeVKoYSZMIPSAGMf6zkGgE3S/GLKsmVCwDoquh8yF0XuDmazgmi+vAa2weM31nhbjnPs8eOskCb+3IVIhUxv0oLNmSw46WzZYsjCFEM+mULe0NdA16LWnh4GI4c1kkd+mlUFGpVS+Ta8CR760Kxo0DYq3iEHy1DfBUw8Nf4CCDRPymFQkAc0Es2+H9v3Q9Fep8Hj6O2KQU8dQoVAoFAqFQqFQJImoY64++9nP8qtf/WrAEAewcOFCHn74Yf7mb/6Gu+66K6YNVCjiQtgP9U9D70l5b8uVSm/ZS5PbrnhjGBBoFwOct148yfSAeI9lVELhZshfB/bs2OqazOCqlFc/ejhiDDwNfTVibPPUSRJ+X5OEu6KhaRZcuhPNFAk3JZLm0mSV4+acJUa3jMj2bXmxDSd1VcGyf4dTj0LnUakUmHM1ZF05M4y204G8VeAsg/P/K0bn2seh5EbIXZHslikUCoVCoVAoFIoZSNQr1sbGRoLB4CWfh8NhmpubY9KomYwGZJrbE1pNNdPmSWglqaTr+dvgwq/FKIUGabOg9I1gz4+fZhwZl17YL8m03eeHeJWFJTw0d5N4gGVUjcuIpQGZ6dbJ989khoxyefWj62KE6zkF7tqB0FlrOAi2fDlWGZEw0/S50v4Y53EbsX9WF1zxSTj/c2j8I3Qekpx6BetGDHWdkOZUO2emm15aCSz4O6j9sYwBjX+S66HszZM+hxLdv2RoKr3U1kuGptJLfU2ll9p6ydDUMMi09qoQVYVCoRgHUVdTffOb38yFCxf4/ve/z8qVK9E0jf3793Pfffcxe/Zsfv/738errQB8+9vf5qtf/SqNjY1ceeWVPPLII2zYsGFcv02J6jmqmmp86XlNPOL0gIRDZlRA2a1gnrxBZcphGOBvjVREbRCPID0gHmUZ88QAl1sNtil6LfTTXxHVbB+WSy5ptL4ENT8QY5w1U/LI2bKS3SrFeDEMaPgjdB2W97YcqLhLjqVCoVAoFIqJo6qpKhQKxbiJuqzcD37wA8rKyqiursbhcGC321mzZg0lJSU89thj8WjjAD//+c/52Mc+xmc+8xkOHTrEhg0beOMb38iFCxfiqptIdMOgLVSCHpWJdDJ60OZ1TX89XYfmHVIxVQ/IRCH3apj9NzE3xCV9n4a90HMSmp6Fll3QcxpCveLdVXoLXPVluOrfofj6CRnidMOgrcuHHp0df8LoQFufCT364WpiemP1r2AdLP038SQMuaHhGfGwmpTmDLkOp4KepkHZzWKE18ySg/D0d6Fv4scw0f1LhqbSS229ZGgqvdTXVHqprZcMTd3QaPPnyrxboVAoFKMSdWxOQUEBzzzzDKdOneLEiRMAXHHFFSxYsCDmjXs9X//61/nbv/1b3v/+9wPwyCOP8Oc//5nvfOc7fPnLX467fqIIGVEmmp+0XmK9jRKtFw750Jp/LmGPAPYCKNwEmfNH/d1kSPg+1TXwNoHnHHgbI15wQTDZIWuJeMHlrRIjZCz0wgmcSU5FvbTZsOxBMeJ07BOjZ9aVkktOm5jRcLpfh1NOL/tKqbBb+yPJnXj+J+LlmH/NhHIBJrp/ydBUeqmtlwxNpZf6mkovtfWSoZmMPioUCkUqMuFEOQsWLGD+fDFmJCIvQCAQ4MCBA3z6058e9vkNN9zAiy++GHf9RGAEvXgOfA1/uBS3qxBTAtIt6AZ4vFbcWBOj13serecwHqsvYWXknZ0n8YQ7gQxInwUFb5SQNG98NBO6T0Ne9L7zmHtb8NCBFvYDBjhKIW8NFG6UxPWaCfzIa5LohoHHY8JtN2FKwLU/dfUyYPY/gvlpqPs/aK6Bbg8UXCMhtVFpJvg6VHoRiuQYnv+FVPW9sA/a22D2W6LKBZjo/uFvR+89R1CrwJ2TiykBolP3GCq9qaqZUD09iN5zGs3tTuj8gnAm7txyTLEuenQZErpPA93o3afQAiR0n2pBh9JLYU0j0Idh6iZBwRMKhUKR0kzIGPfjH/+Yr371q5w+fRoQw9w//dM/cffdd8e0cUNpa2sjHA5TVFQ07POioiKamppG/I3f78fvH7Q+9PT0AKDr+oD7tKZpaJqGYRgMTZ831uevd7+O9nOTyXTJtj17/x3XbdvH3BcKhWIqsRL4XLIboZgUX0x2AxQKhUKhmDZ0d4fJyBhc+8Rz/RTt51GmS1coFIq4EbUx7utf/zqf/exn+ehHP8r69esxDIMXXniBD33oQ7S1tfHxj388Hu0c4PVeeIZhXNYz78tf/jIPPvjgJZ+3trbi8/kAcDqdZGVl0dPTg9c76CqVnp6Oy+Wis7OTQCAw8HlmZiZpaWl0dHQQCoUGPs/JycFut9Pa2jpskM/Ly8NsNtPS0jKsDYWFhYTDYdrb2wc+8xa8Zzy7QKFQKBQKhUKhUCimJF1dXXg8wYH38Vw/aZpGUVERgUCAzs7Ogc8tFgv5+fl4vd4BhwxgYA2oUCgUySbqaqpz587lwQcf5J577hn2+eOPP84DDzxAbW1tTBvYTyAQIC0tjV/+8pe89a1vHfj8H/7hHzh8+DDPPffcJb8ZyTNu9uzZdHZ2DlTPmUqecYYBvrN/wThwP5q3Xj6z5WLMuQOTLQPDiIRF9G8bSW0Uq89fn9w12s9NI2wbbyOmut9DqAcDE0baHCh9I1gzRvx+SvQpys8n1ScjLDng3BfQ/G1ouh9DD4ElHSPzCijajJa1GM3ivKTggGxHu+RzU/95fUnbx/95/7Zj9fl42z7l+xT2oZ17Aq3tJYyQFyNjPuQtB82ceufeONo4LfsU6EU7/ySE+gAwcldjFF4XGcsT1KdwAHpOoHUdRfMNen4bJhuGLRfNORutYC0GYLTsROutQQu7B79nTsfIuQqyl4HVNT2Pk+rTzOoTBoa3CbqOQvcJNCM4sCXDmoVhy5Pq4OllA9dqwvrkbUJr3g6+liHtAsNRjJGzAlPWAgzNOvWOkx6EntNo3UfQPHWD7dasYMvFcBSjFaxHs6XP7HNP9Sm6trvr0KzpODc8wtC/TCXPuN7eXrKzs1U1VYVCkXSiNsY5HA5eeeUV5s2bN+zz06dPs3Tp0rg+bVizZg0rV67k29/+9sBnixcv5tZbbx1XAYdUKGWt1z1D6/HfU2BrwNT4BzB00Kww5w7ImBN7PQNavVkUOLtjn3+k8zA0/hmMsBQSyJiLXnorrf7c+OiNQFz7F0/NYA/0nQPPBamOGvYDGqSVQcFGKNwAzqKInkFrp4+CHEfCcqopvVEwDGj6C5z7KQS7IwVDNoxaPCPR56nSG2sDYbj4K+g7K++dZVD+DjA746MHct54G2Tc7D4OQ4wNWLPAngd56wbuA5do9p6RitGBDhlz+0mvgNyV4JoH2sSTaqfcMVR6SdeMiV7YC12vynXhH+IhY7LLNZFRKflRzfbk908PQ9tL0HVY7uH9aBYxjOcsH7hvx0xzIvhaZH92vQL6kDm7NRNseZC7KjJeaMnfp0ov5TT1vjpaw3MouPZBTKaJFbSKN6mwHlQoFDODqMNU582bxy9+8Qv+5V/+ZdjnP//5zwcKOsSLf/zHf+Tuu+9m1apVrFu3ju9973tcuHCBD33oQ3HVTTQGJsheCmmlUPO4TJbOPwmFmyF/LROp9De6XozvznpIjBGdh+W9NRNyVkhieyMOemOQaL0Ja+oh8NZB33nwt4EeACMElgwoWAlFW6UC5AiJ5ROd/kLpjYKmQcmNYgQ59S3wNEDDM2JEHWUhNt2vi5TSM5mh/A5ZWDfvBG+9VM6tuAschbHVC3tlUdx5GPytQ9pwqbFhVE3XPHmFA9CyA3pOinef+5y8TA4xBuReDbacCTU1pY6h0psSmhPSMwzwXJRrouc1uQ8CoIEtWwxGBevl4VQs9CbBMD2TGQqvlZevFRqfBV+D3Ms7D8rLXiDGrqzFURf6GVFzvIQD0HNc9qm3YUibrbI/02bLPrWkxUZvEii91NdMRh8VCoUiFYnaGPfggw9yxx13sGvXLtavX4+maTz//PNs27aNX/ziF/Fo4wB33HEH7e3tfP7zn6exsZElS5bwzDPPUF5eHlfdpGHPg4UfhXP/K5Onlh3iKTX7dplATUWCPXDx1xJaCeAogeI3SBVTxaUYBgS7Il5wdbIw1wOgmSBtDhRdB/nrwZGf7JYqoiVzISz9PJx6FLpfhaZnxUMpc1HMDeqKOJG/Thap538m1+bZ70PpLZCzbHLbNQwZyweMDf2ebEONDdfKA5loMdvEGFxyI7gvQNM2MfLpPmh/SV5psyPecgvANOGi6gpFbAm5oeuYXBeBjsHPzQ65JlzzIW/11J3/DMVRAHPvlGu9fS90HBBPaX8rNP5RHlhmLobcFeAsjc89wTDA1zjobasP5j8e9LZdIw+O1D1JoVAoFIqEE/Us/G1vext79uzh4Ycf5qmnnsIwDBYvXszevXtZvnx5PNo4jI985CN85CMfibvOlMFkg7nvgebt0L5HwqbO/Ld4aEzQuyFuuM/Bxacg7JFwqPRyKHvLiE9aZzx6UJ78u8+DvwN0vyzIrZliACi+Xow5aqGc2thz4Mp/gdonofmv0LEf/O2QvyY1FpQKSJsF8z8CtY9DoBManhZDWukbow/7HNPYsADyVsXu3EifA1XvE6/b1t3igRfqlbHHc1HuL9lXQe5ysCuDvyIJGIbMHToPQc8poD8/lEkM0/a8Mb2KpzSaJuN9/hrwd8pDGc9Fued3H5OXLUe85bKXXDYUPirCPrnWuw5LSGo/JjvYcyG9UtpjdkxeS6FQKBQKxYSJOmdcKpMKOQKMumcInfofLLmLL31Q2XsaLv5WQjY0M8y6HTInFxpsGBAyTFg0feIPRvuf/DZvBwyZTKZXwKzbLnnaGhO9KJuWSL1RNQ0DAu1igPPUSR443R8xXFZC0SYxxNmjM7IahkEobGAxa5etLBxLlN4EaXkOan4sBh1rtng9Wl0Rzel9XUwLPUOH+v8TL0eQcLPyd4E1Y3Q9wwB3bcQL7vXGhpyIsWFD1MaGCffR2wCNfwVf85C8dICjWAyBmVeMaAycFsdwBuslQ3NUvWCvFGPoPCLe4f2Y0+SayLwCcq6O6oHUlOrfWD/sPAztL0Ogi8Ek9yZ5CJe7QjzjR9joqPMLT11knDkxQmhvrnjZRxmlkDL7VOlNGU2j7yIhcyaWNY8mZM42EVJhPahQKGYGEzLGhcNhnnrqKU6cOIGmaSxevJi3vOUtmM0TTw6dCFJi8K3/I/rJb2PKu0wYVKAban4E/ZXz8tdB4aZJhRjoBpNIrhyAhj/I5A9kcZm3TvISxUNvAiRa7xLNsD/iBXdOjl+/F1z/0/CireCqmpQXnG4YCSluoPQmSV8tnHxUPKs0cyQUsSyiOb2vi2mj13EYGv8E6OJZNucOSJ99qV6wVwwNXUckPK2fAWPD4oixYXJFFSbcxwkkm582x3CG6iVDc5ieoYt3f+dhKTjSb4TSTGIssuXJQyl7Xmz0EsCk9YK9klvOc0482vqxZka85ZZeUvxnmGbII962XYfF67ofk0P2o2t+xNv20lyz4yXl9qnSS66m+yK6OQPTmm+P/d0kkRLrQYVCMSOI2hh35swZbrnlFurq6li4cCGGYXDq1Clmz57NH/7wB6qqquLV1kmTCoOvXvcMLcefprAw5/I3Tj0EF34p3hYgYVRz3jGhkAPdgBZvNoXOruhv1P52yQ/nb5P3zjIouWlUD49J6U2AROsNaHqyKDSdxuQ5J/nzwj4JTTVZIKMKirZIrhbb5M9D3TBo6fBRmJu4aqNKbxKE+uDkf0lYlu6H7KvQs5bS4suZttfFtNPztUDtE3L80NCLttLifAOFzk5MfWfE2NB3lkFjg1mM77Y8KNosoWKTJKZ9fH2y+X6GJJvXTfbpdQxnmF4yNAf0zOcwdR0RT7hQ7+AXLOlyTWQvlZc2ucqLKX0MDUPyurU+L97TAx60mhjUcldA+lx0NFo8mRTqRzB1HYbeU0NyTg4N7d0AzuJJNirF96nSS4qm3ldHS7icwmsfUNVUFQqFYgyidsW5//77qays5KWXXiI3VxYU7e3tvPvd7+b+++/nD3/4Q8wbqXgdJgtUvAtaX5TQN09dpNLfnZet9Bdzek5JyJbuF0+KjAoou3XC1cGmDXoI+mpwdTeiGa3iFYcuk+PcNVC8RfZVtLmmFNMHSwYs/gRc+BXUPy2eU/52tPQ3JLtlivHiKIQFH4VzT4KvGVPzX8mx16CFW8TY2k+/sSFnGWQtmbSxIW4MSza/T3Ibvi7ZvJa5GKdlPvi8CWuWM+BUeqmsaYTJ6T6H5j01+JlmFi84R6F49duyEtOWqY6mSbX07Csh6IHmbWLQD3vE4NZ7CszpaJkLye89hyk0NOekM+IFt0iMdirXrEKhUCgUKUHUd+znnnuOl19+ecAQB5CXl8dDDz3E+vXrY9o4xRgUXCNV8S78TCZsZ38AZW+SJMDxwtAlEXjrC/LenC45TkpuVNW4gr3QvgfN34EpZAJLGLKuFANc3upLQk0UMxjNDOV3iJfkmf9B8zaQ6fkleAsgYy44S6au4UYhmO1QeS80/QWj4wB2f418rlnEC85RBIXXpZaxQdMgv1per0s2r3UfI4tjCWuKCUjknpvuesnQNAEDj+csLjEY5Vwt+eBm+nxhNKxpMOvN8v+e09CyQ7zlwm60zoNYAAMTmj1XDJtFm1QBFoVCoVAoUpCojXF2u53e3t5LPu/r68Nmm3hOCsUESZ8N8yKV/oJdUP97cF+A0ptiv5gPeWX7fWflvT1fFpuZC2Ork4p4G6D9gITgmOx48m/BXrEeLX22MqooLk/eKnCWYdT8ANpPobnPSW5BqwsyKiF9Llgzkt1KxeXQNCi5ESNtLt7m/TisGqbcqyFzUeobG+w5UP4O8ZbrOoLRthe/bseu9ZGInhmA38hQeimsaQBuUylpDgemog1qLJsImfPlFfZD8w4M93m6zVVkZuWj5S5VXvYKhUKhUKQwUeeMu+eeezh48CDf//73qa6uBmDPnj3cd999rFy5kh/96EfxaGdMSIkcAWMVcLgchg51vxsspGAvlFDWcXhjjSuxq68ZLvw6UvVME4+8slvEAyRKplWyXMOQfd7zGoTcUpFw/ofQs5dNj4IDSi9xmp5GTE1/kaT6/vZI1WQLOEvFMJdWGtOF17S6DmegXjI0lV5q6yVDU+mlvqbSS229hGuqAg4KhUIxbqJ22fnmN79JVVUV69atw+Fw4HA4WL9+PfPmzeMb3/hGPNo4ozAMgzAWoq5xq5lg9lulgAIm8LfA6e+Ap34MPQgbptH1ul6BmojnnckmyYTL3zkhQ9y49GJIXPXCfjGcdB8XQ1zWMlj6AEb2MsJhgwkUKp4QhmEovRTWG9C0FWHMvQdW/xcs+jjkrASTVcIEW56Dut9Ldc5A99gbHFNvGl2HM1AvGZpKL7X1kqGp9FJfU+mltl4yNAf1EthJhUKhSFGiNsZlZ2fzu9/9jpMnT/KrX/2KX/7yl5w8eZLf/va3ZGWlUG6cKYoBtIdKmPAtLHcFVL0PTHapilf7Y2jfP7qeL3NkPSMMjX+R0FQjJDlfcqthzt9MOEHwqHpxIG56gS5o2QmeC7KfS2+BKz8NziLR7PYnto9KL2X1LtHUzJIPcum/wqpvyvXmLJGKvF1HoeEZaNou4ax6aOJ60+E6nKF6ydBUeqmtlwxNpZf6mkovtfWSoWmg0R7IVcY4hUKhGAcTLrk0f/585s+fH8u2KGKFo0gq/dU+IR5yTX8Ro1HZW8ZvRAv2Qd1vpFJr/zaLtkhy+ZmO+wJ0HpJ9ZEmHyvdIAmWVu0URa+z5UiW5/J3QcQCat0PnMclR6G0Ac5pckxkVYM1O/TxlCoVCoVAoFAqFQjEDGJdl5h//8R/HvcGvf/3rE26MIoaY7VD1t9D4Z+g8KDnNvE0w991gHSM/gqcOLv4GQn2ACdLLpUqr1ZWQpk9ZDB26jkHvGalemzYbFvydKmChiD+aSSry5q0Gf5dUuWzdDb4m6H5F8hbaC8BVBWmzJJxcoVAoFAqFQqFQKBRTknEZ4w4dOjSujWnKKyMmaOgx2pAmVVXTyyXUNNgFp78Lc94hnjQDehFXcsMQw13js4AOJod8b9ZtMa0IqiXUQT9GemEvtO8FX4uEC+ZVw7wPScXBkTQTfCkovdTWi0rTng3lb5fw1a6j0PRXySXnaxHjnNkJ6RVy7dryLrvhlLwOlV5SNZVeauslQ1Pppb6m0kttvWRoJqOPCoVCkYpEXU01lUmJ6jn1f4RT/wW5V8V2u/5OqP2RGJUACjZAwbWDC3U9CA1/gu5j8t6aDbmrIX91bNuRivjboH0fBCOJ88tuFUOI2Z7cdikU/QR6JIS1ZRd46+R61sxgywXXPPHiVOerQqFQKBSKeOK+KClcqr+T7JZclpRYDyoUihlB7NydFDHBMAz8uiP2VY/sOZJHzjlL3rfuhvM/xQgH8Ht7MWp/PGiIc5aKN1wcDHGGAf7wBKrFJkPPMKD3LLQ8D/52MKfDgvslh9cohg3DMPAHwgmt/qn0UlcvJpq2TJh9G6z4T1j6IBRtlslwoF0q/tb9Dtr2iPecYaTWdaj0poSm0kttvWRoKr3U11R6qa2XDE3Rs6oCDgqFQjEOojLG7dixg//8z//khRdeAOC///u/mTNnDgUFBdx33314vd64NHImYQCd4cL4OHibrDD3bshfL+/d5+D0d7Ce+z6ar1k8aTKqoPwOSCuNRwto7UzjeF0xeqImBUCnPyP6/amHJGF+52EI9UJGJSz9HBReO2Y8oQF09gYSWp1L6aWuXkw1NQ2yroCF98Pqb0PlveBaIAq9pyQEveGPGD0n6fKYE7tPJ3IdKr0poWkYcKEpi5rW/Gm7T6e7XjI0lV7qayq91NZLhqaBRmcwWxnjFAqFYhyMu5rq//zP//DhD3+YiooKPvOZz/C5z32OL37xi9x9992YTCaefPJJ8vLyeOihh+LZXsVk0TQouk7C1i7+Ci3sRgMMcxpaRpUUaohxwqxg0MT+42Vs21vFsdNF+MNWFsxu5frqs6xffp50ZzCmepMm5BYvIn8b6AEo2CjFMGzKlV2RQljSoewWefWekaIPbfsg2IXWvpdMXgNvAbgqpVqyyvmpeB29bhu7Dsxl+75K6pozCehW1iw+z9bqGlZc0YDZrBZbCoVCoVAoFArFRBi3Me4b3/gGDz/8MH//93/Pn/70J9785jfz2GOP8Z73vAeATZs28c///M/KGJcquCph/ocxzv0UD7k48xeh5SyJqUR9cybb91Wy60AFvR4J69Q0A7PZ4HxDNj/43Uqe+MPVrF16ka1rzrKwoi359gBvM3Tsg2AvmMxQcRfMeot4FSoUqYprnrwq3yd55Zqfg+46tL4acNeKoTljnhR7saQlu7WKJGIY8OrZQrbtqWLfq7MIhcWB3moN4/dZOfRaKYdPlpLt8rFpVQ2bV9dQlOdOcqsVCoVCoVAoFIrUYtzGuJqaGt7ylrcAcNNNN6FpGtXV1QN/X7NmDRcvXox9C2cgFi1BnmJWF8a8D+D1uXA6emOySX/AzMtHZ7NjXyWvnSsY+Dw308umVTVct6oGLw6On8hjx94qLjZnsftQBbsPVVBa0MOW6ho2rjhHZoY/Ju0BsGjhsb9kGNB7ErqPQ8gD9nyY90HIWzkxTXNirYpKL7X1EqZpdkDJDRjFb8DbVIO9bztaxx4IdEHHfug6InklM+aCsySmVZTHdR3GkOmuF2vNrh4HO/eLF1xLR8bA55VlHWyprmHdVec5157PoaOF7NpfSVevg6d2LOapHYu5sqqFrdVnWX1lHVZrjKqBM/2PYaqfM0pPHUOlN/X1kqGZjD4qFApFKjLuaqomk4mmpiYKCwsBcLlcHDlyhMrKSgCam5spLS0lHJ66A3BKVM+JVzXVOHOuPpvt+6p4/lA5Hp94kZk0g+WLGti6poarFjReEtJkGHDmQh7b91Xy4uE5+INiGzabdKqX1LGluoYl85rj7y2nByU/nKcOQl7IWgwLPgJpZXEWViimAOEgtD0PzTuh5xTofkADa8agt5w1Y6ytKFIQXdc4cqqYbXuqOHiiFN2QwdZpD3Lt8vNsrT5LRVnXJb8LhUwcOFHK9r2VHD1djBH5nSvNz4YV59haXUNZUU8iu6JQKBSKqYCqpqpQKBTjZtzGOLPZzKlTpygoKMAwDGbPns3zzz9PRUUFIMa4RYsWKWPcJDHqnsF78nGcefMSErJpGOAN23CaA1HreX0WXjwyh217qqipzx34vDC3j82ra9m0qoacTN+49Lw+Cy8cLmf73spxbysm/Qv2SH64QAcYISi6HirvkYnEBDEMA68/jNNuRkvAQVR6qa2XDM3L6nkaoOkv0PrS4DWhWaTCsqtS/tXME9Cb+DgzEaa73mQ12zrT2LGvkh37KunocQ58vqC8ja3VZ1m77CJ22/B7+eX0LretheVtbLnMtuLdv4kw3fWSoan0Ul9T6aW2XjI0jb6LeE15ONd+PWFzqGhJhfWgQqGYGYw7TNUwDBYsWDDs/fLly4e9n6qDbiphAD3hPBxAIvamAfQE0nA4A+PSm6w32+X0nI4Q1689y/Vrzw542e0+WE5LRwY///NSfvmXJaN62U2of5466DgIoT4wOWDue6HkDWAa92VxeU13EIfdnLhjqPRSVi8ZmpfVSyuFyvdK1eXWl6F5B/QcB89FeVnSpbJwxlywjn8CG+04M1mmu95ENMNhjf3Hyy7rzbaluoZZo3izXU4vP8fD2294hbdd/+owL7uT5/M5eT6fH/1+xahedrHq32SZ7nrJ0FR6qa+p9FJbLxmaBho9QRcOtS5UKBSKMRm31WHHjh3xbIdiitPnsfH8oXK27ZE8b/3EI89bRVkX95Yd4K6bD/Py0dls31vFyfP5HDhRxoETZQP55zavrqEg1xO9gKFLbrjeU1I51VkK8z8C2VfGpP0KRcqjmaFwvbx8rdD4Z2h7Uf7fdVSuH0dRxFtu1qQN2Ir40dSWwfa9lezcX0mP2z7weazzvJlMBssXNbJ8USOdPQ6eG5J/7tmX5/Hsy/MG8s+tv/o8Tkdo0poKhUKhUCgUCkWqMu4V1HXXXRfPdiimIIYBJ2oK2L6vij3HZhEMSXia1RJOSAVUuy3MdavOcd2qc9Q1Z7J9byW7D1bQ0ePkN9uv5Lc7FrN0XjNb15xl5RUNWCzjWFCG/dC+D3yNEPZBzgqY/0FwFManEwpFquMogLnvhoo7Jbdi03YxyHkbwdsA5jTxlMuoAFtOslurAIJBE3temc32vZUcrxkc27IyfGxaVcvm1TUU5/fFTT8n08dtW05w6+YTA5VZ974yi5r6XGp+m8sTT1/NuqsusqX6LPPntCe/irZCoVAoFAqFQpFglDvDFEMDbJovYe7rGmAzBYfp9fTZee7AXLbvraSxzTXweXlJF1urz7J++XnSnROr+DqS3niYVdTDPW8+zLtuOsr+42Vs21vFK2eKOHq6mKOni8lM93Pdylq2VNdQUjBYGXaYnr8D2vdCsEssjWW3QsU7pcJkDNEAm9WU2GOo9FJWLxmaE9LTTJC3Wl7+Tmj6K7TuBl8TdL8CPSfAXgCuKkibDSbrcL0JXPcTZbrrXU7zYlMW2/ZUsvtQBW6vTb6nGVy9sJEtq2tYcUXDuEP8x6M35m80WDKvhSXzWuh129h1YC7b9lbS0JrJzv1z2bl/LrOLutlSXcOGFefISAtMSm8yTHe9ZGgqvdTXVHqprZcMTQ0DmymgQlQVCoViHIy7gMN0ICUSdiapmqphwNFTxWzfW8n+42WEdRMADluI9VefZ0t1DZWzOqaUB0Nzezo79kn4VVfvoEFtcWULW6prqF5Sh80aSRzedw66jkCwDywZkher6DoxMCgUiolhGNB5BJq3QedhCHkAQwzc6RXiMWfLZUoNHNMMf8DMi0fmsH1vFacv5A18npflYfNqCefPy/YmsYXDMQw4eS6f7XurePnYbALBQY/r6iV1bK0+yxWVreqUUSgUilREVVNVKBSKcaOMcVMItxs69n4fd+N+0vMqErIYCYZMvPhKBS8enE17V9rA5/Nmt7OluoZrrrqAwx673D6GAX0hBxkWX8z6Fw5rHHqtlG17Kzl8smQgMXm6M8C1V9WwqmIH2cZxtLBXPHbmvkdC6uKEyWSQkenFlWZJWGXMPm+IDGdi9Hp6NBpaId1hRkvAs1YDA7cvPG31QLyXXFkpfs4EuqF5O7TsBm8d6EHJPWfLo8+8mHrvPNLMwYRVrHMbLtJtRuL0QnbSLf6EGZF63Ta2HZjHgWOleP3ihWg26ay4ooGt1TUsW9CEyRS723s8xm6318oLh8rZvq+Scw2DIc7Feb1sXl3DvKpOMm3eaXkMk3HOTPc+Tne9ZGgaoQCeIKQlsNqoJ2xTeimsaXiaCFhyqbztoSnrHTfV14MKhWLmoIxxU4jdv32Z//qGm2BYx5JWmKBFOfjDVuzmIBlpATYsl6p6c0q646KnG9DizabQ2YUp1t3Tg7S3hdi5p4wd++fT1uUEPUhY1zBrQbDlSyVIsy3GwsMxDHBl+rjleg+br+0jOyscVz3dMGjp8FGY68AUp3NG1+Hoq0627XJx4EgaHq+O3WZO2OTVHwhPW71+zfR0Hze/wcOWa/vIzUnhc8YwoOcERuNfeeVQD9sPXc3eU1cR0G2YtckXChgvYcyYbJloFlfcS1MPHUcTec70axbn9bGluobrVtaSnemLi148x27DgNr6HLbtqeKFw+X4ApaE79PprpcMTaWX4pohLwTaI3Oo+N6ThhI2zEovlTUNneLCHr7+qzsxmaZm9MlUXw8qFIqZgzLGTSFe3l7PY187QjjQi9nmQnMUxF3TAHLz/Nyy7jjrlg0J64wTMVvQ6QHxxAl2QSDyCvWCHgIjjB7WOXZ+EX89up5X6pdgTi9DyyhPSFiq12eizy3GKovFYMUyD1s39rLsSi/xmJfE07DS1m5m5wsudj7voq1DUkwaBlisAZz2xBnHvP7wtNUD8Pk1evsM7DYzJhMsXyrnzNVLPZjNsdeL5znT1W1m5/MZbN+dSUsL4G8BXysOSxcWUyghvoYGyAKSIJo1U4pQxFHZALwhG05LIHF5eUwG5XN6eMv6V1lS1RL3czWuD1KG4PNbeOmoFJ+oacpL2D5N9DFMxjkz3fs43fUSqqn7wduEoQcJY8dsCidu7I4YjpReamoahkFRoZcv/PhuZYxTKBSKMRiXMe72228f9wZ/85vfTKpB8SQVBl/9wv/hP/ivOIxOtLxqyFoYX70ELbAmpRf2Q7BbDG79xrdQL+hhMCIvDMAEljSwF0oYakYlekYVLf4iCguy4+Y19no8XvjjdhuHj+RxumYwl11eTojNG3rZtL6X/LzYGT1jbVgJh+HgkTS27XJx5NU0+keI9DSdjet62bShB7uzN66eeENJhOdfMvUAfH740w4rh4/k8dpp58Dn2VlhNq3vZcuGXgoLYhcuHo9z5sgrTrbtyuTQsTT0iAOc06GzYV0fm67tIS29k8Ica2KOoa7TVbOdnPZfogW7wJ4PhRslj0089BI8jiZDU+mltl4yNJVeimqGPNCyE/yd6M5ZtMz6NIWFuYm7/3YGKMyxKb0U1dT9HbT0WilccL0yxikUCsUYjKuaalZW1sD/DcPgt7/9LVlZWaxatQqAAwcO0NXVFZXRTjEyWt4K9OKboO0P0LEXrJmQVhI/PcBp8Se0ktSoemFfxOOtc9DzLdgXMbqFwNAZMLxZM4YZ3siYB2mlwxbcmmHgdCe2cpXTYXD9dX3cfrOfugYb23e52PWSi/ZOC7/6fQ6//r8crrpSPJ9WXDV5zycNxItrku1ubrGwfbeL51500dU92KjFC31s3dhD9Qo3VquMAT3uyeuNl1j1b6rqAdhtBpuvdXPrjQEam2yR45BBV7eZp57J5qlnsll6hZctG3tZdbUch8kQqz62tZvZvjuTnc9n0NE1eDtZOM/Hlg29rF3lxm43IueMDc1kTUgxB00zMJdeD0Xz4PS3wFsPDc9AwQZwFsdej8SOo8nQVHqprZcMTaWXgpp6ENpelvmXLRNt4f04TXmJG7sNA6dDU3oprKmZbJHIgqmZL06hUCimElGHqX7qU5+io6OD7373u5gjVoRwOMxHPvIRMjMz+epXvxqXhsaClHgS4mmAtj3QulsWj3oASm8GW9bYv00lDEMMb/2ebsFuCHTKE9lhhjdk8mBxgaMI0ueCqxIyqsBZIp5wKUAwCHsPprNtVybHTw56y2VnhbnuGvF8KiqMnedTNO3adyidbbtcvPraoEdWpmvQI6u4KPHtUkAoBPsjx+bYicFj48oIc901fWzZ0EtpSTAp7TpwOI3tuzM5etw54DnpygizYa20a1ZZ4tt1WQLdcOpR6DomC82cFZB1harwqlAoFEMxDGh/Waphahos/EfIX53sVilSjUAnmGzijT5FSYn1oEKhmBFEbYwrKCjg+eefZ+HC4eGTJ0+e5JprrqG9vT2mDYwlqTD4Gu56eur2kZlbjHb6v+QJpWaGsjeB2R57PQN6gk4yrQmqWOe+iL+3CXu4VaqbGqEhoaZIXy0u8V7JqIT0SnBVyXuzY/SNj6RnGPS4g2SmWxP2lG4szaZm8UDb+YKLnt5BD7QrF3nZsqF3wAMtVnojUVdvZftu8djrc0sYgabBssVetm7sYeXVl/fYS/Q+ne5649FsabWw8wUXO3a76BzitbhovnigrVkpHmix0huJxiYL23dn8tyLGcPO2yWLvGy9bnSPvaQfQz0E538KjX+GoBsyyiF/HZgm6WI4oJfYcTQZmkovtfWSoan0Ukyz6xj0nJR8cXPfB7PelPyxW+mlnKbh76DHZyazYvOU9Y5LhfWgQqGYGYwrTHUooVCIEydOXGKMO3HiBLqeuEp50xXDAG8AXCYL2rz75AlT93Fo3g4lN4ixKpZ6gDdkx2X1xj/Uovc0dB7FCAImrxRTsGaBs1Q83jIqwDUPHIUTMryNhIEk43elWxObYHkUzeKiEHf+TSd3vLWTA4fT2LZLPIxefU1eGemSm228Hkbj7aPfr/HyfvG0OnV2cP/mZksuu83Xji+XXaL36XTXG49mYUGId9zWydve3MnhY2ls3+3i4NE0Xjvt4LXTDn700zyuXdvH1o29lM8OTFqvn2AQ9hxIZ/vuSz06N62Xc2Y8Hp1JP4YmC8y9W4z7tT8E9wXxyC3aJKkAYqGXqHE0SZpKL7X1kqGp9FJIs68Wek5B2Atlb4ayNw7qTeP773TXS4am6Om4DGPKGuMUCoViqhC1Me5973sf9957L2fOnGHt2rUAvPzyyzz00EO8733vi3kDZzSWdFjwUXj1S7J4bH0RCq5NzfCqnpPQ9QqEPPhd12Avq0bLnCeJ1ePg8ZcKmM1QvdJD9UoPbe1mdjwvnk8dXRae+WsWz/w1iwVVPrZuHMy9NRHOXbCxbZeL51/OwOsTLziTiUiV1x6uWhKfKq+K2GM2w8qrPay82kNHp5mdz7vY8byL1nYLf9mRyV92ZFJV4WfLhl6uqe7D6ZzYOXOx3sq25zLZ/XIGbs+g5+TVSzxs2RCbXIdJoXA9pM+Gk98A93lJBZAf+UyhUChmIr4W6DwMYQ/krYGKO2P+4FehUCgUCsWlRG2M+9rXvkZxcTEPP/wwjY2NAJSUlPDJT36S//f//l/MGzjjcRTAgr+DE18F9znx4si5Ktmtio6e16DrVQh7MPLX4Ct4P5kF2alpVIwT+Xlh3n5rF297c9dAVcqDR9M4ddbBqbODnk9bNvQyt3xszyevV+P5lzPYvttF7YVBY2dhfogtG3rYdG0f2Vmxq+iqSDy5OWFuf3MXb31TF8eOO9m+28W+Q2mcPWfn7Dk7P/55HtdUi7dc1Vz/mJebz6fx0j7xgjtdM3jO5OeG2HxtL5uu7SUvdxqcM+lzYNnn4fR3oP2AVA3MXgrZy8RbV6FQKGYKwV5o3wshN7jmw/wPxywyQaFQKBQKxehEnTNuKD09PQApE2+fCjkCDHc9fQ37yMidNdy9u/VlOPPfUvCg4FrImBsbPQP6Qg4yLL7Y28YMQwxx3celMEPBOoz5f0df0EaG05Kw/Bx93lDC9GKp2dVtZufzGWzfnUlL26DdfO4c8Xy6dq14PvXrpTssnKlxsH23i5f2ZeAPiLbFbLB6hXjBXblo8sc50ft0uuvFUrOnx8Sul1xs2+WisXkwH9qcWQG2bOhlw9o+0tP1YefMuQt2tu3K5MW96QOek2azwcqrpOLv0sWT95ycksfQ0OHib6HuKQj1Sbh8wbUT8tSN6zg6RTSVXmrrJUNT6U1xzbAfWp4DfxvY8mDpv0Fa2ev0puDYrfSmtKbh76AvYCFjzqYpG6aaCutBhUIxM5iQMS4UCrFz507Onj3LnXfeicvloqGhgczMTDIyMuLRzpiQEoOvp0GeUo4UNnXxKbj4azFsldwIjvyEN2/cGAb0nIDuExL6kL8eFnwkZaqfTiUMA159zcG2XZnsO5hGKCyTG7vNYN3qPjas6+NCnYSi1jXYBn5XWhxk68YeNq7rw+VS+RxnEoYBr50Sw+zL+9MJhuScsVoM1qx0s+naXhqarGzflcm5i4PnTHFhkC0berlufS9ZmTPknOk4CKf/G/ytkhqg8Dqw5ya7VWNjGMq7WKEYimFIUahEoplT06PWCEPrC+BtAJMdrvgk5CxNdqsU0wFVTVWhUCjGTdTGuPPnz3PTTTdx4cIF/H4/p06dorKyko997GP4fD6++93vxqutkyYVBl/DXU/nxX3kFMy69ImSocOZ/5EnmYYOZbfI4nEyegZ0+tPJsbtjt64zDOg5Dt2viSGuYIOEPlicGIZBZ2+AHJctYU8hE6kXb83eXhO7Xspg265MGpoink8GBEI6NosJW8RAt2VDLwvmjR2aOBGm+zGcbueM221idyRk+UJdxPA25JyxWg2qV7jZsrGXxQvj4+Ex5Y+htwlOflOKzGBA3lpwVUahF4dx9HJCvmZwn8PwNuGxVpJWsBjNHHXGiQlJJ6SPSm/aaCZML+QB93kM93kCAT82UzBhieoDuhNb3hVoUYwXk9KMxT41DOg8CH01YpSb92Eo3nyZr07xsVvpTTlNw99Bp8dMTqWqpqpQKBRjEfUM/h/+4R9YtWoVR44cIS8vb+Dzt771rbz//e+PaeNmIoYBgaBM8i65hWkmqHwvBNol2W7TNih9I5isl2xn3HpAQLeOrDehDRrQ/aoUbAh7xMtk/ocGcpAYQCCox05vrOYkWC/emi6Xzi039HDzG3o4dcbO9t0u9u63UFAQ5JYbAmxY4yY9Pb4eTdP9GE63cyY9XeemrT3cuKWHs7Vyzrx8II1ch59b3uBh41p33D0np/wxdBZLiNaZx6DtRWh7QcbZ3BXjSmQe83H09YQ8kjPUfQFCvRAOgGFgCpyCcD0UbQBLfL3S495HpTftNOOqZ+hiRHefA18T6AEIh9CNNGDsKuSxQtfN4mEWxXgxGWKyT3tPQd85CVOdcwcUXTe63lQeu5XelNMc0FPVVBUKhWJMojbGPf/887zwwgvYbLZhn5eXl1NfXx+zhikug9kO8/8OXv2iPNVs2QVFm6dGmIRhQPcr0HNKDHFFm2HeB1Qy4DigabBwvp+FZWf54E2v0N0bJGv2KkxpJclummKKomkwr9LPvEo/77/HoKXDR2GuA5OaLAtmhxTLyaiEC7+QfJeBDvHsTUZ4vaGDt1GqvvYbG/QgmByQcxVG9nKMuh0QuAD1f5R8d+r6V0x3gn1yTXjOS9GBcAAwwFGCkX8N3fZrsOemJ8jLScd3/kUcHb9L/ngxXjz18sA05IHiLTDn9qkxf1QoFAqFYgYStTFO13XC4Usr6tXV1eFyuWLSKMUY2LJg4f3w6pdlsdaxH/Kqk9smw4CuY/LENeyFoq0w7/3KEBdPQl7oOQ2BLjBsYpjNugKyFoMp/mFrCsW0Q9Og7GYpkHPqv2R8bXgGCjaCszAxbbicscFZAgXrxdvYWQpAr60aZ8v/yEOQ5m2Qs1yuf2VgVUwnjLDkNus7D/4W0P2gh8DshNyVUHw95CyT3GcdPnA4EnMNGAb+gpswihegnf62eOoleryIhkCnzBdDbqkeXXXvpCIrFAqFQqFQTI6oV+xveMMbeOSRR/je974HgKZp9PX18bnPfY6bb7455g2caWgaZKaNw5U8bRbM+yCc/IaEhFqzIHNh9HpAps0zOdd1w4CuI9B7RgxxxddD1ftHrEqoAZnp1oS55ydaL6GaPa9BoAvNUYIj/yq0nv1SudbfKsZZa3yM49P9GE7rc0bpjY+sK2DZ5yWPXM9xaPoL5K6GzAUjLvInPY4OGBvOyfUb9ksienMa5K6Ckushe+mwhxuaYeDKyoP8T8GFn0Pjn6QYhb8dCtZKAu0YEpN7hdJLml4yNCetF+yJhGdfFG/7sF+2mlYmnqCF14GzaFDPMJIzztgXw7IvROZjJ8YcLyatOZF9GvJA28ti7E+bDQs/Oq6cwyk3diu9pGuKXuKqxSoUCkUqE3UBh4aGBjZv3ozZbOb06dOsWrWK06dPk5+fz65duygsnIJPAyOkRMLO0aqpjkTjX+HcEzJpLbo+8WFKhiH56/rORgxxb4Cqvx3REKeIIZ566DoKQTdUvU8m/Z4maHhawpfRxGMgozzZLVUoUhc9CLVPSH7OsEdCWPOqY+dNEuwRA5zngoyfw4wNG6Qa3RBjw6i0vgg1PwB/B1gzxVBhy4pNOxWKRKGHwFsvRjh/WyQ/YkhyImYvg+KtkL0k5sbmmKAHoeZxaN4xZLxYk3xPdT0knvO+ZrBmwJX/Cq6q5LZJMX1R1VQVCoVi3ERtjAPwer389Kc/5eDBg+i6zooVK7jrrrtwOp3xaGPMSIXBV++rp+PCPnKLZo0vl5NhwLmfQMMfJadQ6c1RLcB0Azp8LnIdvZiifYhlGNB5SIw/YS+U3CSGoVEmybph0NHtJzfLnpBcVYnWS4hm2CdPuL1NUHgtesktdHR2kZubi8kIQtNfxUMm2CMVIXOujunCZbofw2l5zii9iWMYUsG69scSEm7LkUXGEM/TqMZRPQTeukjIXZuM28OMDddD9pVjXrMj9tF9Qbxz3OcBk4S1jvfBzljNnsy9QuklXS8ZmlHpBTrlvHVflPmEHhCvsrQ5ULhJzmVH/hh6U2CcMQxo3ikPSQOdYMsVw7g1NgVWoj6GhgHte2Rs0DRY+HHIH39akymxT5VeSmnq/g46+kzkVm3GZJqa+QhTYT2oUChmBhN6XOd0Orn33nu59957Y90eBRC6NCXf5dE0KH+nhDa17YHmv0Lpm6LyTAsZE6j+ZRjQeRD6amXiXHoLVL5nXB4joXDU9t9JkWi9uGv2vCaTfEeR5ObTTIRCIfmb2Q5lt0BauYTK9J4TT5n8NWJEiBHT/RhOu3NG6U0cTYOiTWIUOPVN8NRF8kJtgLTSQb2xxtERjQ0m2W7RdZA/trHh9VzSx/Q5El57+jvQfgBadkLWEsi5KiZJ2id0r1B6U0YvGZqj6ulBuZ7c5+Q+pfslZNuaCflrxTCduTAqT9SkjzOaBsWbIb0cTn1DvNgb/nDJeDEpzWiOYfcrso+NIFS8LypD3IBesvep0ks5zWT0UaFQKFKRqI1xZrOZjRs38utf/5rc3NyBz5ubmyktLR2xuIMizpgsUrXU3yGGmubtUHIDaHGadBu6eF65z4mXVtmbYO49KhFwIvA2ikecHhKjmzUDdP3S7+UskYl/wx+g+xQ0Pgu5yyFjnkrurlBMBFel5IU69S3oPBIpmHC1GLsul41HD4LnohjhLjE2rBtibIhhGJslHRb9I1z8LdT9ThbjgQ7xLFIFdRTJxjDkfHSfE0NV2DdomE6fK4bv/GvAHruHR0nBVQnL/v1148VyyLoycffgvtpIdXsvlL0Zyt6YGF2FQqFQKBTjIuoVgGEY+P1+Vq1axe9//3uWLFky7G+KJGFJj1RY/aJ4XrS+KAmOYz3pM3ToOBAxxPllgjf33coQlwjCfinWEeiCvHVjF+yw50L5u6BlN7S9KJ4y3mbIW6UW5QrFRLC6YPEn4fwvxDuu87AUTMi/ZvA7w4wNdXLd6n55OJIoY4Nmgjlvk6qwp/87UhX2jxIuZ88d+/cKRawJ+wcN04GuQcO0LQdyN4iXt2te8vOrxZL+8eLcz6Hxj5LWw98m1785zjnvfC1iBAx7JG9dxZ3xe0CrUCgUCoViQkSdM85sNlNXV8dDDz3ED3/4Q5544gluvfXWlPCMS4UcAYa7nkDzPmxZsyZWiajnNJz4GvhbIPsqyFk2up4BAd2CzRQa225n6NCxXybTYT/MuhUq7opq8mwYBoGgjs1qSkilpUTrxVWz84g86bZkwvwPgc01qBcIYLPZLq/Xe1YW456LYHZKEvrxJoZ/HdP9GE6rc0bpxU+vbQ+cfQz87RhWF8HcjVhD7WiekYwNqyLGhqqYGRvG3Udvk1SF7T0tA37+WjHSRblforpXxACll/qahm4Q9HVi9ZxF8zVEvOCCcg1kVEHRFjEU2WIzH5vS48yQ8QKrK1JgJXsCmuM4hsFeyXMZ6JIxZ/FnwB69luhN4X2q9KakpuHvIKBbsZVeN2UrqqbCelChUMwMojbGmUwmmpqaKCws5Hvf+x73338///qv/8r73/9+ysrKlDFuskRbTXUkWl+CM9+TiVjhBsiomHy7DB3a90Wq/vlh9u2Sq246PcWeynibxAsn2Atz75YE79ES7BNvnu5XIeSVbWRdoZ6WKxQTxVMvBRP6amWMNFniamyYMGGfGAJaX5SQNddCyFs5Na/9gTxiF+T/ziJwFIM9LyZ575JOyAu+JqlsGewGewGkV4jRdoouXKMm7JPj5z4vfQz7AR1sefIgqHirzEum4vkXTzx1Yhjvq5X3+dfEvuJ52C+GOH+b7O8ln4X0WbHVUChGQ1VTVSgUinEzKUvKBz7wARYsWMDf/M3f8Nxzz8WqTTMaXTdo7YaCNGPiVY8K1slE/+KvofUFqdJ3mcTgugGt3iwKnN2Xr8xl6GIg9FyEcABmvw3K75iQIU43DFo7fRTkOBJWuSqRenHR1APQ2x+eulYMaEP/rOu0trZSUFAweuUqawbM+RtonyPV3rpekVCW/Go5R8bbnGl+DKfFOaP0EqOXVgZLH0Q//V0CrYexW2xoedVQvCXuxoao+mh2wPy/g/QquPBzGU+CnZJU3pI2Tr1x3CsmykBo7/lIaK8PQw/g1x3YvY1oJrN49DpLwVkixWuiKFI0HuLWP8OQxWm/AS7QCXoAQw9F+teC1vMa2PMlTDNtVkyrXw8l7sfQ1yLh2d5G0H0Y4SA+srBnz8NUvEVSJERxr4mWKT/OpM2CpQ/A6e9Cxz5o3QX+KyH36nEbmkc9hkZYKqcGOuR6WfDRSRvipvw+VXpTTlP0ghTk61O2mqpCoVBMFaK2ppSXl2M2Dy4wNm3axMsvv8yb3/zmmDZsJhOT1Huz3iKT/5bnpKBD2Zsuu+gyLpd8HCKTu72yQNIDMPtvoPztk/KIS3RqwWSkMoypZn+eOHs+lFw/4qR93A6umknC1NJmQ/0fwF0LjX+B3NVReWNO92OY8ueM0kucnsUJC/+B7uwGCvKz0azxMza8nqj6qGmSwD2jAk79VySP3DNQsBGchePTG+1eMREul0fMmo1RsAG3VoUtfByt+1UI9kDvKQm3NdnEoyytVLzmrJkx8SqLWf/0oBimfE2SpzPskc+MEGACRz5G5pX0mRZhC7+K1nkIfK1irDM7xFMuY654NsV48RzzYxjyRKoEn4dQX8QLzgB7AUbxWrodmyksnQ2mxHjBTflxxpIGiz4Gdb+Hut9Az6sQaJcohnHmch3xGBqGeM/7muV95fsgZ2mUjbuM3lTfp0pvymmqHOIKhUIxPqK2qNTW1l7y2bx58zh06BDNzc0xaZQiBmgmmYwFOmSC1vRXKH1jdIUWjLDkOfHWiyFuzjvEs2qmhZYkE19LpOKcH+a8HWxZsdluWhlUvkcMcR0HxYPSN19yDKpiHApFdGgahi0PLClQGCXrClj2eTj5qBgCmv4iOe0yFyYmTNIwJITOfQ68DUOqaVrBNR8KN8kDA2smwQ4f5N4o3tk9x6F9P3Qdk3tS/0szSw6utFmRcNb85KRPCPZFjG9N0j89IAY4dDDZIb1cqu/mrhaDqMlGqMMHuW+Q3zZvF08pT51URe89DbZcyfmVNifmnoCTwtClr33n5N/+vpoccg8puh5yV4hxqcM3PcKLY4lmgtm3RQqsfFfu8w3PRAqs5E1sm72n5XiE/TDnDikUo1AoFAqFYkoTsxmrw+GgvDzGuS8Uk8Nsl9CkV78IfTXQsguKNo9vYqyHof1lWSzpQZjzTphzuzLEJRI9KIuyQLeE92Quju32zXbxmEwvh6ZnofdMpNLbmgklllYoFCmCPReW/AvUPgFN2yRkzt8uIevxMsa/Po+Y7hejji0P8lZH8ojNHbzHDPWsMJkhe6m8QIwXbS9LUZveU+I113UMeFXGNWcpOIvFODfOMNyoMXQZL33NYoAL9oARBD0kf7dmQfYyyF0pRjhHwfB779D+2Vww+1bxaO85KeNxxwHxmGprBdMhGacz5opHYLJyy4X6Il5wFyJecAHAAEcJFFwjBiBn6WD7lHfM6ORcBcsejBRYOQUNf5ZrMKMqumPsqYfuV8QDs2hzZK6mDKAKhUKhUEx1xlXAITc3l1OnTpGfn09OTs6o1XE6Ojpi2sBYkgoJOw13PaHWfVhcE6ymOhKeOnj1yxKWlLlAEij36xkQMkxYNH1w7qeHxBDnaZCwmvJ3yVPcGBjiDMMgFDawmLWEVa5KpF5MNbteESOq2QHzP3xZA5lhGIRCISwWy8T1/O1Q/7RU4zVCksMmY96IC4LpfgxT+pxRejNCL2aahiGpDGp/LGGithxJum11jfjVS+4V49n+6/KIiQeVDTLmS269vNWX0Rtn/8I+8e7tOCgGiUDHoEHMZBFjX384qy33skaOcfUv7IsY35rl3/7+GGG5PzqKxWCYuxKyFo2aH23M/oU8ktuzZZekE9ADgEnuA64qSCuXEOkomNgxDMtDub7zUqVd98v+NTshawkUXy/ecCOEWE736zBmemE/1PwAWp4Xg5prvnhQjhDae8kxDHTKORLsgaylcOWnwJI+8bZcopei+1TpJU3T8HcQwoaleKOqpqpQKBRjMC5j3OOPP8473/lO7HY7jz/++Kjffc973hOzxsWalBh8PQ3obXsxZUyimupIdB6Rp6+BDjHGZS4c+JNuMJgIWA+Jx4G3QSbhFXfCrFtj+pRVNyZRnCIF9GKi6WsTz4hgtxyDnKtG19NjkChXD8nCvO1lWZinzYG8FSMusqb7MUzJc0bpzSi9mGr21sCpRyV/m2aGgmsllP0SPcaX+L8/j5jnglSAHpJHjPy14r2TPmfM+0rU/TMMeYDRvgc6j4kRUA8AuvTLkj6kCEThJYUSLumfYcgY3B9+GugQ45selP6Y0yS/W+4KyFkheTejCJEdd/96z0o4cfu+iAdeWNqeNlu85RxFURUAGNcxDPYMesGFPZFjqIlhM38DFG0U78Mx9ab3dRgzPcMQj8hz/ztYZbdww4iGtYFjGPJCy07wd8j1uuSz4oEZY1J2nyq95GgGOtE1K6ai6xKjNwFSYj2oUChmBOMyxk0XUmHw1fvqaandR2HprNjfOBufhXNPyuKoaCuklaAb0OLNptDZhckIQdtL4sFg6DD3Lih7c8wNcS0dPgpzE1fVKZF6MdHUI8fB0ygeanPeMWrya13XaWlpobCwMDaVq3pOQcOfJB+TxSkVXIdM8Kf7MUzJc0bpzSi9uGgGe6WwQ+dh8X7Kvkq8vCLbHnavGEluII/YefA1Ds8jlrlIvOByV0ZRvTUG/Qt0Sp65zkPQfUJCK/uLKJisYsSKeM3p5gxafDkU2tswBVrF+OZrgpB7SPEFTTztshZJnr3sZeKpNoH2Tah/YZ94T7XslLQCekDaZM0Ub7n08lG9osY8hnpIxn33OQnBDQek35Z0OR+Kt0L2knFXe53u12Fc9HpORgqs1Mt+LtgwzOg5cAztbZjadst5asmAKz8DmfNi04YhTIt9OoP1kqGp+zto6dYoXLBlylZTTYX1oEKhmBmM6xFuT0/PuDeoBrUpTPH1ElrT8EeZzJfeLHltQBYb7S/JxM7QYe67JZ+YyjuSeHpPySLSlgklNyasCt0AmQskB1DjM9B1HJp2yAIsa6HKGahQTFesLlj8T3D+l9DwB+g6IjnL8q8ZvXjAZfOIFUsescJN4rWTjHAlWw6UvEFeekjyynXslxQAvibxBPRcAJMVzZJJOkVoel0kFLO/+ELEAy17meTudM0bd9XLmGN2SEXtkuvBfREa/yxegIFO8aTuOgrOMsioFO+/8d6/A11igHNfhLA3UlBDE+/owo1iEHLkx7Nnin4yF8LSB+H0t+Q8bfqreF1mXTEkF58u57GvVeYH8z8cF0OcQqFQKBSK+DIuY1x2dvaYcf+GYaBpGuFwOCYNU8QBTYPyd0oOn/a90PxXKHkT6AG0thdlcYIBc++BspuVIS4Z+NvAXSdhXuW3SqL1ZGBzSfXWtD0Sutp1VM6b/FVgjl0+GoVCMYXQzFDxTgl9PPuYJIZv/JNUebRmD37PCIsHdd+5S/OI5a4UD6qcq5JntBoJkwVyl8sLpG/teyJFIM5AoAuL7gGTD6wZkLVYjCC5y8Uzaao9iEifDfPeD3PfA20vQvMO6D0ZMYyeF+NqeqVUbh0hJx96UPLJus9JmKPul+NqdUHeGjFgZi5U1bWTgT0HFv8znPuJhK52Rop55K8FzYrDfRjNVycFQyreI0UfFAqFQqFQpBzjMsbt2LEj3u1QJAqTBeZ/UJ6k97yG1ryDDCMbws3y97nvg9KbkletbSajh6D7NQh2yUI2Z1ly26OZoGCdeIU0PC0LvMZnIWcVoLwkFIppS/4aSJsFJ78BfbXiTZ2/DpNeId5lnpHyiF0rRjtnUWrcP9LKIO12mH07BN0YHfvxtjVhLyxHy1osnsn/n733jo/rvA60nzt9MOgdIAo7KRZJbJJIyWqWJctFiu24xUW2Emed2IkVp9jZbNb2JlnH2TibTbx24o3bxlnL+WLZie04tnqjJFIsYpFEkWIn0dsA02fu+/3xAiAAoswA987FBc/z+0EiLoB57pnbz5z3PW7A64eGW/RXolNXy/U+p5vyDB7UTS3CTaPVcsvwZnogcRgS5/WwVzOtz/WR5Xouv9pdOhkkOIvHByvv09vt5Lf19Tc9CGWrCcZOgEpCy1th2VudXlNBEARBEOaJzBm32LCrgcNUkj1w9M8gdg6FgeHxwsqP6mGRNj9IyWS5MzD0qh6iavhhzccLGhZkSQOH2cgl9UNe/0HIjmCGm/F485s3yApMTwDP2KTrRahQcc0+I74r1lcUZzYBJ/5eV5DlkihvGGNs/jRfRA/dbLwDKjbOPpR1niz1bWibT+Wg9wVdLTd0VJ+/AbwhFGCYaf07gUo9913DG/Xw2wKaT+TLknlPnfSNnNYNVmJnAFBmDqP2Olj/O0WpPl2S7+kV5Cu6Uxo4CIIg5M2877zi8Thnz54lnU5PWn711Q5X87gcpRQ5E4zRYb+2EaqDNZ9AvfKXqEwMVt6H0XSH7Yk4pRS5nMLwUrQ28sX0zduZ6h+dcymuh4cWkIjTvhyGYWPbem8Ilt0DJe2ojocx450YhqIY76gCTBOM6AmMQLmu4Chp0UPJ7PC5ZZ8R3xXrK5rTF4Z1n4ILP0ad/RdUNgklLRgNN+uOqzZ0bhxjqW9DW32GV8/XV7dLT33Q8QvoeRaV7EbhgdI1GA236mGPgQpr3RNYUu+pk77S5XD1F+C1r6L692OWrcKz6uMYRUjELdn39ArxOeFUSpFTRXiOEQRBWAIUnIzr6enhox/9KD/72c+m/bnMGbcwlIK+Yagvw/5ER/ka1DV/RnffMPWN7cW5SAN9Qynqq0NFS+QU0zcvp5mF6Ct6eGrlZqjeWphPKfr6+qivr7d3GxoGVF+Liqyk/9xRamvKi7PPmCYDXaep8ZzFSHTobo9DL+t5nCLtuiOihRUdrthnxHdF+4rqNAxouQdVtZWe/hh1zSswilAVu9S3YdF8wVpY/ivQ/j5U9DV6hv3UNbdj2FAFN5Ul+5464fOVwlW/h4qeoCdRRX2wcunFKL4l4dS+DPUNkowTBEGYi4Lvxh544AEGBgZ4/vnnue222/jhD39IV1cXf/qnf8qXv/xlO9ZRsJNgDYQi7pjjZ6ky8rqujPNFoPluW4YKWYq/FLN0BVTUQzHa1psmuXQt1N0NibN6wvWhl/UE7LHzECiFkuV6DigbqzwE4YqmZBkqmZQJ/d2K4YHydbq6cbE1oxDyw/BA+RrIJp1eE0EQBEEQLKDgp/7HHnuMf/3Xf2XHjh14PB7a29t505veRHl5OV/84hd561tlMllByJv0IIycgWwMWt9l67Av12MYustj6Qo9t+HAIRh8CeLndLfX6Ct6TrnIcl01J0kDQRAEQRAEQRAEYRFScDIuFotRX18PQHV1NT09Paxdu5bNmzezf/9+y1fwSqTYRWric8ipcrrCKzMElRuhZtsCfMUN0nGfLwx11+uv2LnRarkjkOyC+EU9n1xJG0Ra9STlBfusWe/F7BSfu31OOMXnbp8TTvG53yk+d/uccMrwVEEQhPwouJvqjh07+NM//VPuuusufumXfmm8Iu5v/uZv+Jd/+Rdef/11u9Z1wbiie078IvTt0UkEYWkzfByGjunJAVd/HMINTq+Ru8kmdUJu4CXdDCMX10N+g3V6AuxwE3iK1wFWEARBEAThiiI9oO+16m92ek1mxBXPg4IgXBHMa864jo4OAD73uc9x11138U//9E8EAgG+/e1vW71+VxxKKdIZCBSpC5H2mQT8HvEV05kegpFTkB2BlncsKBGnlCKdThMIBIr3ni5Gny8ENdv1V7wDBg/C4GFI9UCiQ8/JF2nTnVgD1TN+VLxo9xnxic9Bp/jc7XPCKT73O8Xnbp8TznGfdFMVBEGYk4JnX//ABz7ARz7yEQC2bNnC6dOn2bt3L+fOneO9732v1et3xaEUDMR0N6Ki+ICB4bT4iukcG56aHoLy9VC7Y2E+pRgYGKDAItel7Stp0s0w1v0OtL9XDwNGwdCr0PU4dD+hk6G5yyfCXpT7jPjE57BTfO72OeEUn/ud4nO3zwmn9mWLdo8oCILgZhbctrGkpIStW7dasS6CcGUwcgpSfeANjXZPlUYDtuH1Q9W1+ivRAwMHYOiwfv8TXeAr0ZVyJW26s7B8iisIgiAIgiAIgiDYTMHJOKUU//Iv/8Ljjz9Od3c3pmlO+vlDDz1k2coJwpIjE50wPPXtUNLs9BpdOYTrIHwnNNyuO68OHIKRExA9AcOvQ7Bad2INNaEn8hMEQRAEQRAEQRAE6yk4GfepT32Kr3/969x22200NDTIfAA24PMW21fcbbjUfTM6lXmpe2rZGqi5wTqfb8FFrleOz+uDqs36K9UPAwd1Yi7VA8m94AkSUvXgq9HVi74weELgDYInaFv13FI/LsTnfqf43O1zwik+9zvF526fE04nYhQEQXAjBXdTra6u5rvf/S5vectb7Fon23BF9xzpprp0GTkJQ6+AmYXVvw6RFqfXSBhDmRB9TQ9jjb4G2TigdOLN8I5++fSQYm8Y/BGdqPOE9P8nfhlFzqYLgiAIgiAsBqSbqiAIQt4UXG5SUVHBypUr7VgXAT0MOJGGcEnxuqkmUjnCQa/47HRmhmH4pP5/81stTcQppUgkEoTD4eK9p0vNZ3igYj1UrEelBkn2HiFkxDAyQ/rGMjMEmRhkY3qocWLC8HyPF/Dq/xtenazzlej/e8O6os4b0v/2BPX308W4hI8L8bnfKT53+5xwis/9TvG52+eEU/tMwtJNVRAEYU4KTsZ9/vOf5wtf+ALf/OY3CYfDdqzTtPzZn/0ZP/3pTzl48CCBQIDBwcGiuYuJUhCNQ6iyOLNWKSAayxAKesVnl3N8eOoglK6Cup3W+pQiGo0SCoWKdqO1pH3+coa8qwnW12N4JjSczmX0NsxEIT36/+ywHuqaGYJsFLJpyCUg2ct4T2TDGK2qG0va+cEXGU3Y6Wo65QkRjZcTCkaW5HEhPvc7xedunxNO8bnfKT53+5xwal+WkCTjBEEQ5qTgZNy73/1uvve971FfX8/y5cvx+yd3gty/f79lKzeRdDrNu9/9bnbu3Mk3vvENWxyCYAuxM7p7p+GHZW+ZtjJKcAFeP3jrIFQ3/c+VOVo5Nwip0eRcJgrpIZ28yw5CJn6puk4p9G2rAYYHf7YWqm+Wjq6CIAiCIAiCIAhLnIKTcR/5yEfYt28fH/zgB4vawOELX/gCAN/+9reL4hMES8iO6LniMlFougsibU6vkWAXhgf8ZfqrZIY5H3Op0SGvo0m6zJAeujzyOp6+Lhg8DNXXFHe9BUEQBEEQBEEQhKJScDLupz/9KT//+c+56aab7FifKx7DgIC/OENUQXsCfo/47HAqpYenpgehdAXUv8Een2EQCASKlhgX3wLwBsFbD6H6yc6Rc/gSD2JEX4Fgje3NPZb6cbjUfU44xedunxNO8bnfKT53+5xwjvukyl8QBGFOCk7Gtba2uqbzTCqVIpVKjX8fjUYBME0T09QTsBuGgWEYKKWY2Fh2ruVjfz/f5R6P57LXBvAYBlUlevDaxJ95xtZl4muPvv5Cl1eV6cSDOWVdxn4/3+XTreN0yyvLAmOD82yLaXz5BJ9SyraYJi4fjzF2GpXoxcDAaH4LyhNATdgPrNz3qqurMU0zr9+fcd8rYHlVVdW81n2+x1NlZeX439kV05hzok+Nznli+zmitJVI+x2ojl+genaD7814guUF73uFHB/V5UGUUpP2+bmOp4WcIypHzzN2xjRx+dhxz+g2tCOmsXUci2/ieduOmKYur5ritDKm6dZ9un3G6pimXivGKMb1aeI2tPOaO7Z86j5TjOvTTPuMVTFNXcdC7y/mE9NM+4xdMRV6f7HQmCY6x2Kc6rQqpumWV5cHMadc56yMaaZ7Ujtjmrh8tntSq2Ka7/Zz6/WpusyHgkn3O7Y/PxWwfOrvCIIgOEXBybgvf/nL/MEf/AF/93d/x/LlyxckH2sGMRt79+5l+/bt83r9L37xi9O+fk9PD8lkEoBwOExFRQXRaJREIjH+O5FIhLKyMgYGBkin0+PLy8vLKSkpob+/n2w2O768qqqKYDBIT0/PpJN8TU0NXq+X7u7uSetQX19PLpejr69vfJlhGNSXKvpjkE4mxi/cPq9BbWWIRCpHNJYZ//2A30N1eZCRRJZY4tK6hINeKkoDRGMZEqncpZjCPspK/AwMp0ln9MVNKYXP56G2Ikj/UIpsbkLCpSxAMOClZyDJxOtWTUUQrxe6+5OTY6oOkcsp+oYuJUANAxqqw6QzJgPDaZRSJNMmpWEfdVX2xARQHvFTEvLRN5hiJJElFPCMP7xaHdMYY9spnszS0ztARfJVyEQx6m+mMtLOyMgIsVjsUkwW7XuVlZVkMplJrw2F73sNDQ2k02kGBgYuxeTzUVtbSyKRGE9mK6XI5XIsW7bMtpgmHk+maZJMJgmFQtTW1toSE0AgEKC6upqRkRF6e3vHG0YU4xzh8Xg4O1RBmbEcbyqGOv0klavvIqd8Be17+R5PoYAXr9cgl1Mk0/kfT/M9R4wd922NJZgmtsQ08RwRT2ZJpk1CAQ+lJX5bYhqjvjpELmtyvicxfp6xI6ap573SsI8LPXG8nktJZEtjmnLeG/P6fQaDw5fW3a7r09g+U18VJBL223596o+mxvcZv89j2zV3bDuFg17Od8fxey9tP7uvTzUVQXqHUmSz5rjT6mvuxO2klCLg91JdHrD1mju2ncb2mfKIn5oKe2KauJ26+5MkUrnx496OmMYY307xDL1D6XGn1TFdtp3KA8TTOeKJ7Pg+Y3lME7bTWGKnqSZsX0wTttPYPhMKeGioCdsSE1za94ZGMgwMX9p+dl1zJ22nYl+fvCZ4c6T7+8lkLr2O3c9PhdzvjT0DCoIgOI2hCvx4oKqqing8TjabpaSk5LIGDv39/Xm/Vm9vL729vbP+zvLlywmFQuPff/vb3+aBBx7Iq5vqdJVxra2tDAwMjFf3FaXqpYBPdoh30HVyL3XNy8Y/0QL7Kg9MpejpT9JQc3lnXDs+pR/z1VWH8I29BxbHNHF51jTHfR7DKErlQS6XY/DMc1R6OzHCTRirP4bhCxe8j+W77yml6Onpoba2Fs+E7p92fapomiY9PT3jc0baEdPE5WO+uro6vF6vbZ+UjjlzuRzd3d3U1dXh8XhsiWnqcqUUXV1d1FVX4Ln4bzB0GE+oDlV7I2rCeQCs+URbKUXPQIq6quCkByy7PqWfeJ4xwNbKA6UUuQnnGe/E7WRhTBPXMWeadE84z9gR09TlCujqS0xy2lkZZypF7zT7jJUxTVw+ts/UV4fwejy2V8ZN3Gc8hmF7ZZwV26/Q6xOzOO2oIpvP/cVCKq6m7jN2xDRxeb73F1ZWkU0919hdGTd2raitCs55TzrfmGa6J516rbDj+jTXPakVMU1cXuj2c+P1SaX66Yl6qFtz2+T7i3ncG9l1vzc8PExlZSVDQ0OuGe0lCMLSpODKuL/+67+2TF5bW0ttba1lrzeVYDBIMHh550qPxzMpaQGXTvpTmWn51L+fz/LpXtsEDIPxh4HLfn+a117ocmPCxXnadS9geT5OY0JsdsU0cR3HfFNvJKdjvjFNWp44jyfTj+EFz7K3gS986fcL2Mfy3ffGblSm26+n+30r1sWYuP1siGnqcsMwxhNjdsU0dfnU99POc4RSeiilxx/Cs+xu3Yl1+HWM6CsYlRund07z2vkuNycsL2SfX8g5YtI+s4B1z2e5h0uxzeW17Lw3zXnGzljH95lptqHd53JLzpN5XiuKdX2auM8U4/pk1fYrxGnO4rTymjv192Za95mWW7XP2BXTxOX53l9Yui9ZcK7Jd3uYE5YX+57UrpimLi/mPWmh28+N1ydz4vua5z1pocutuocVBEFwmoKScZlMhieeeII//uM/ZuXKlXat07ScPXuW/v5+zp49Sy6X4+DBgwCsXr2a0tLSoq6LMH8udvg53wX11U6viU2kB2H4BOTiUHebbtwgCIUQqICmN0P2B7q7aqAKSpqdXitBEARBEARBEATBIqb/GGIG/H4/P/zhD+1al1n5r//1v7JlyxY+97nPMTIywpYtW9iyZQsvvviiI+tjF4YB4QDTfvpkiw8970MxfBc7/Dy3t5TXXivj3PnA3H9gAUWJTylIdkPfi9D7PEaqD3/pMoyGW/UGtRnD0POaFeuTPvEVwVnaprvvBmugZzdkhq31UbzjXnxLwyk+d/uccIrP/U7xudvnhFP7pJuqIAhCPhQ8Z9xHP/pRNm/ezKc//Wm71sk2otEoFRUVi3uOgPhF6NsDkVan18RSBoe8PP50GbnRMQ/BgOLO26IEgwXtfosLlYNEB8TO6oq47AgYHihbC013STWTsDCUgo6fQ+8LOqnbdBd4Cp5ZQBAEQRAEoTikB8ATgPqbnV6TGXHF86AgCFcEBT/ZrV69mj/5kz9h9+7dbNu2jUgkMunnv/3bv23Zyl2JKKWIxqG8RBXlUyWlFNFYhvKI3zZfImHw7AsRcibU12boHzJJpoIcOhpmx9a4Lc4xbIkvl4L4eUich/QwZGPgC0H1Dqi9HhVuJjo8Qrkq4jaMRikvLxefC30zOg0DGm6DVB8MvayT9LU7Lam2LMZxL76l5RSfu31OOMXnfqf43O1zwql92aLdAwuCILiZgpNx//AP/0BlZSX79u1j3759k35mGIYk4xaIUpBIQxnFGaqqgEQqR1nEb4svm4VnXyglkfRQXpbj+u0xTp7LcPhIHWfOB2hvTVNfl537heaJpfFlY7oKLnFR/zsbB3+lTpjU7NBDCg0DZZokEgnKysqKduMjPvf6ZnV6g7DsLZCJwshpCByDivUL92HvcS++pecUn7t9TjjF536n+Nztc8KpfSZlkowTBEGYk4KTcadOnbJjPYQliFKw90CEwaiXYEBx43Ux/H5FVWWW1SuSnDwdYt9LJdx5WxSv1+m1nYX0IMROQ6JbJ+HMFIQaoPFNUHUt+KWBiGAjgSo9RPXcD2HgoP4+3OD0WgmCIAiCIAiCIAjzZEETEI1NNyeffAjTcfTVEBc6/HgM2LljhEjExBydIm7TVQk6OgPE4h5ePhZi84aksys7FaUg1Q2xM5Dq1/PBmSaUtkPtLqi4SlctCUIxKFsJdTdB16PQ84zutuqPzP13giAIgiAIgiAIwqKjoG6qY/zf//t/2bx5M+FwmHA4zNVXX80//uM/Wr1uVySGAZFQcbupRsI+y32nzwZ49XgIgO1bYtTW5Cb5/D7YerWeL+61EyGGovPaFeek4PjMLMTOQe9u6NunhwZmR6B8Paz+NVj961B97ayJOMMwiEQiRe3+KT73+vJ21u6AqqvB44eep/W+Ol8f9hz34iseSz1G8bnfKT73O8Xnbp8TTu3zSqGGIAhCHhTcTfWv/uqv+OM//mM++clPcuONN6KU4tlnn+V//+//zZ/+6Z/yO7/zO3at64JxRfecJdBNtafXx9PPlWIquGptko3rZ656e35vhPMdfqoqctx+87AVc9PPj1wK4ud0Y4bMyGhThjBUXg01N0BJk+6UKghOkk3A2X+B6MtQ0gq11zu9RoIgCIIgCBrppioIgpA3BWcX/vZv/5avfe1rfOlLX+Kee+7h3nvv5S/+4i/46le/yt/8zd/YsY5XFEop+mOXhgAXxRdNWeYbGfHw3N4IpoKWpgwb1k1OxE31Xbs5jt+nGBjycuKk9cM+54wvO6I7VfY8A0NHIdEJhg8a3wjrPgVt74LIsoIScUop+vv7i7sNxedaX0FOXxia74ZwC4ycgujx+fssPO7FV1yfE07xudvnhFN87neKz90+J5xKKfqHs0WNURAEwa0UPGdcR0cHu3btumz5rl276OjosGSlrmSUgnRGdyMqVtejdMa0xJdOGzy7p5R0xqC6MseOrbHLKt2m+kIhxeYNCfYfKuHoqyGWNaUpKbHuAj5jfOkBPR9cont0Prg0hBv1XFyVVy+oKYNSinQ6jSpSJynxudtXsDNUC013wvl/hf59EKiEUF1hPqw77sVXfJ8TTvG52+eEU3zud4rP3T4nnOM+6aYqCIIwJwVXxq1evZp//ud/vmz597//fdasWWPJSgnuwzThub0Rhkc8lIRNdl03kneH1BXtaWqrs2RzBgcOl9i3ksrUlW+9L+iv4RO6U2pJK6z4EKz9JNTtku6owuKnfA3U7tSJuJ6nIbfIGqAIgiAIgiAIgiAIM1JwZdwXvvAF3vve9/LUU09x4403YhgGzzzzDI8++ui0STrhyuDAoRJ6+nz4vIobrx8hFJppWGgcT6oHkiEwvICBYRhs2xDl4Sdr6LhgcO50mtZlad3NAmN0iKgx+r1nyv/zQGUhdhYS5yAd1ZVwHh9UbNQJjchy8OSZORSExULtDZDqhf790P00NN4+ekwJgiAIgiAIgiAIi5mCk3Hvete7eOGFF/if//N/8qMf/QilFBs2bGDPnj1s2bLFjnW8ojAMKC8pbjfV8oh/Qb7XTgQ5dTaAAVy/LUZFuTn9L5pZjP4XqUgNYPSYl5JtQJlhsL66npdP1nHwmSwNN54k4DcvrSSXfvdSEm5ios576XvDg07WeTAwqErGMJIxyMbBF9FJjNobINSYf0KvAAzDoLy8vKjdP8XnXt+8nR4vNL0JMoMQfRX6D0DN9vx8LPy4LwTxud8pPnf7nHCKz/1O8bnb54RT+3wyRFUQBCEPCu6m6mZc0T3HZd1UL3b42b03AsA1GxOsWZWa+ZdHTsLgEcCAUL0eNqpMUDnAxMzleGR3G9FYgBXN/WzbcFFPPkFu9Pdg9D9T/j9hF1YTvh9frCBQBTU7oGab/rcgLBWS3XDmn/UciLXXQelKp9dIEARBEIQrEemmKgiCkDcFV8YJ9mKaiv5hqC5ReIrwqZKpFP1DKaorggX7Boe8vLBPJ+JWtqdmT8SZGRg5g5mNM1h6B5Wr34THM3nKQg+wtUHxxBOKU0rRVq+oq1OXEmzKBDPHpeRcbjyRp5dPSO6N/t/MZRkYjlPVeBWeYFlB8c0X0zTp7++nurr6shjFJz7LnaF6aHgjXPwJ9O4FfyUEq2f3LeC4nw/ic79TfO72OeEUn/ud4nO3zwmn9mWorjWLdg8lCILgVvJOxnk8njlLjg3DIJvNLnilrnSyuWL7Ci+OTCQMnn0hQs6Ehros125OzP4HsTOQHYZADemSdTP+Wm2dwcpVBidPwv6DcMcd5N0IYlpMk0y6G/yRBbxI4RT7OBCfu30LdlZeBaluPXdc91PQ/Gbwhmb3zeO4Xwjic79TfO72OeEUn/ud4nOxz8xCsotspgwIFk3rxHEhCILgRvJOxv3whz+c8We7d+/mb//2b7mCRrxe0eRysHtPKYmkh/KyHDdsH2HWD79yKd1AIRuHZXeBmj1JsHkzXLwIw8Pw6quwcaO16y8IS5K6G3VDh4GD0PMsNNw2On+iIAiCIAhXFErB0BGIncefqYLaXU6vkSAIgjCFvJNx995772XLXn31Vf7wD/+QH//4x3zgAx/gT/7kTyxdOWHxoRTs2R9hYMhLwK+48boYfv8cfxQ7raviQg1QfS30RWf9db8ftmyB557TybjWVpApHQRhDjw+aLoLMkMQPQYDL0G1NNURBEEQhCuOkdf1PNTZOJ5EHLIx8Jc6vVaCIAjCBOZVNnHx4kU+9rGPcfXVV5PNZjl48CDf+c53aGtrs3r9rjgMA6oixe2mWlUWyNt39NUQFzr8eAzYdd0IkcgMnVPHyCUhfh6yCWi4FcMXpqqqas4hz8uWQXOzTv7t2zc6bdw8MAwjL5+VFNspPnf7LHX6S6Hpbgg364Rc7Oz0Pgo77heK+NzvFJ+7fU44xed+p/hc6kt0wPBJyEQxStoojYQwho/bbQXGYpRuqoIgCPlQUDJuaGiIz3zmM6xevZqjR4/y6KOP8uMf/5hNmzbZtX5XHIZhEPRT1MRDMODNy3f6bIBXj+shptuujVNbk8fkdiOnIDMMJcugcrP2BYN5+bZsAZ8P+vrg5Mm5VdNRiM8qiu0Un7t9ljtLmqDhdgjWQe/zkB6c3pfncW8F4nO/U3zu9jnhFJ/7neJzoS89BEMvQ7ofqrdjLLsbf7gWY+Skbm5mM/o5Zu55xgVBEIQCknF/8Rd/wcqVK/nJT37C9773PXbv3s0b3vAGO9ftisQ0FV1DuhtRUXxK0dWfmNPX0+tj/0slAFy1Nkl7a3ruF8/GdVVcLgkNt4I3iGmadHV1YZpz3xCEw3r+OIDDhyExR4+I6SjEZxXFdorP3T5bnJWboGY7+Eqh6ykwJx+v+R73ViE+9zvF526fE07xud8pPpf5ckkYPASpfihdAcvehlnSxkC6BBNDN1OzGVMpugYyRb2HEgRBcCt5zxn32c9+lnA4zOrVq/nOd77Dd77znWl/76GHHrJs5a5Uit0HYy7fyIiH5/ZGMBW0NGXYsC6Z3wuPnITMCETaoHzDBF/+Aa5cCWfOQH8/HDgAu+Yx/6wTjUWK7RSfe32mCd3dkM1a6DQMqH+DviEfOAg9u6H+Fr18lMV2nhFf/mSz0Nntp6vfJBn34ylCBYKpFDnS1FXZrhpnKW5D04REwsNI3KC7309NBXgW0jG8QIr5nvYPeBmKemWfcbmzmL6hqJdYzCP7zLxeOAeDhyHVB/4KaH0X+CNgmmQjq/X9QPRVnaSzkTPnQ8QyJnVrbNUIgiAsCfJOxn34wx+WkuMrkHTa4Nk9paQzBtWVOXZsjZHXbpAd0RPHmimovxW8gXn5DQO2b4eHH9YdVi9c0PPJCcJSIJGA55+Hnh7I5QLcfTdUVlr04h4/NN8JmUEYPq5v0quutujFBScYGPRy6kyAs+cDpLMG0ZEA5aUBPEW4NJsKYgk//psztDZn7Re6lEwG4gkP8bhH/3/KvxNJPSDBVBAdCdLZ6eHGHTFKSpZON3ql4PDLYV49ESQ6EuLMGQ+r2tO0taTx5X3XKVxJKAUvHwtx9FiIWCJI5ZtTVFdKZVVBRF+FRBegoO1dEG4Y/1Eu1Abm6zByDFIDELQ+2zky4mH/oRI6O3xkMVm3AzzS0F0QBGFW8r4t+va3v23jagiLEdOE51+MMDzioSRssuu6Ebz5foI/fFIn5EpXQcX6Ba1HeTmsXw+vvKKr4+rrmbuDqyAscnp7dSIuOVpoGosZPPYYXHcdtLRYJPGXQ9Ob9bE49DIEqiFi1YsLxSCTgbPnA5w6E2QweukEHCnJ4Q9mqCozilIZF08aREcMnttbyuCaJJuuSub3wcwSQilIJIxpk2xj32dzc78pHgNKSnLEk4qBQR+PPlXO9dti1Ne5P8mZShk8/2KEnr7R20sDBgZ97B/ycehomNZlGVa0p6iuymPOWeGKIJ022LO/hM5ufWOXyxk8+0Ipb7plmFBo6SSpbSV2BmLndMfU1ndC+drJP/eG9bJUh07a1e20TG2acOx4iFdeC2Eq8HoVzU2JK+76IAiCMB8M5cQYPoeIRqNUVFQwNDREeXm506szLSp2gWzPXnxlLUWpRFRKkc0pfF7jMt++gyWcOhvA51Xc9oZhKsrz/JQyE4We5yE9AKvun5SMU0qRzWbx+QrrtJTL6eq4kRFYtUo3d8iH+foWQrGd4nOf78QJeOkl/XBfXg5btyoOHcrR16cndl67Vs+XaJm+/wB0/EJP6Nx0N8pfOuNxbweznWfENz19/V5Ong5y/mKA3Oip12PAsqY0K5enqanOkDOLF6NpKg4cDnHydFiPgq7Ncv22GMGgPbcQTmzDRBIyGQ+JhJdYXFexTapqS3jIJ9pgQBEOmUQiJiVhk3BI/z8S0f/W75liKGqw90ApQ1EfBrB5Q4K1q1O2xmjne9o/4OW5vRESSQ8+r2L7lhiVFRkuXAxy+lyI4ZFLZTLlZTlWjlbLBQLW7ENL4bhfbE67fUNRD7v3lBKLe/B64OqNMV49ESQe91FdmePWm4bz/xB4HiyJ9zPVq6/xqV6ovxma3zpp7Pv4PU12AOP8D2DkNLS8A7wL/1S7r9/LvpdKiA5rX0NdlmvXXSAU8eJrvHnRjqhyw/OgIAhXBpKMW2zEL2L27sFT2lo0panUZZUVr50IcujlMAaw67oRmhoL+MR+4CAMn4DS1bDyPvBMLsA0TRPPPGrXe3rgySf1v2+7DWpq8vu7+foWQrGd4nOHL5eDffvg7Fn9fWsrbNumuwbnciYvv+zh2DH9s/p6uP56CAYtECsFnQ9Dz3P6++a7MA1fUSqqxpjuPCO+yaRSBmfOBTh9LjD+cAM6cbGiLU176+TEhRMxXrwY4MWDJWRzBiVhk507YlRV2lPlVKz44nGD/YdKuNjln3PIr8eAcFgn10rCJiUlE/49+n2+yQNTKZRpcOBQCafP6akcWpoybN8Ss204p13v6akzAQ4cKsFUUFZqsnP7COXl5iRfb5+XU2cuTzC3NKdZ0Z6mrnbhlYFuPO4Xu9Mu39nzfvYdjJAzIVKizyWVFTmiwwZPPFNOOmPQ0pTh+u15To8yT1z9fmZj0LcXEt36g+/lvwLey28axu9pLvwEevdA2Wqo3DhvbSajh6KfPKNdwYDimk1x2loykB7ANPx4Gm6Z9+vbjSueBwVBuCKQZNwiwxy5QPepvdQ3txRtUu7u/iT11aFx38UOP7v3RgC4ZmOCNasK+KQ+PQi9L0BmCFZ/TF/wJ/pMk+7uburr6+eV7HjxRTh9WlcT3XHH3PNRLNQ3H4rtFJ87fLEY7N4NQ0O64u3qq2HNmsudFy96ePFFPUl/SQns3AlVVkzvkkvDuYdg8DBmsJZu73bqa8KOnWfEp1FKd6s+dSbAhY4A5ugV2euB1mVpVrSnqKm+PNnlZIwjw1527y1lJObBY8DWa+Isb8ujw/Y8fXbGd+pMgENHw6QyBtGRNLVVPkpL1LRJtpKwrmqzYnWmxvf6qQAvHdHJrPKyHDu3xygrs3bOLDve01wODh7WVfQAzY0ZdmyJ4ffP7EunjdGh1wGGJiSdSyMmK9pSLG9Lz6vi0k3HvVucdvhMEw4dDXPilE7kNNZnuG5rnEBAjfs8qpRnnivDVLB+dFi8Hbj6/TSzo4m4ixCs1SNRApWX/9rEe5r4aTj/Y93koeWeeZXfn7/g5+CREpIp/bfLW9NcvTEx/kGRmeqne8igfu3tRf8gPF/c8DwoCMKVgUylK0xicMjLnv0lAKxsTxWWiAMYeR2yw1BxFUSs79h09dXQ0QHRKLz6KmzYMPffCILTdHbCnj2QTutKtxtugLq66X+3pUUnm597DoaH4fHH9bDsFQs9nLwBaL5bJ8qHT+LjKFRda8lQFaFwkkmD02cDnD4XZCR26YGlsjzHyuUpWpelF+3cmOXlJm+8OcreAxEudvp58WAJ/QNert2ccM2E3YmEwb6XLs1TVVOVZeu1g6xsK0532qmsWpGmsiLHc3tLiQ57eezpMnZsidPclCn6uuRLPK7nEBwY0gm1TesTrFuTmvP5PhBQrF6ZYvXKFAODejj2uQt+RmIeDr8S5sgrYZqbMqxsT1Ffl5W5p5YQyaSeU7C3Xz9+XLU2yYZ1l88/WVuTZdu1cfYeKOHV4yHKSk3aW61N+LsapWDoCKR6wBuCtl+eNhF3GSVtEKqDZCckOqCkOW9lPG5w4HAJHV36nFkaMdl2TdySilZBEIQrFUnGCeMkkwbPvhAhmzP0vA+bE4W9QKofkj3633W3TJqzwioCAbj2WnjhBZ2Ma22FsjLLNYJgCUrp/fToUf19dbWudAuHZ/+78nK4/XbYu1d3Ed63DwYG9L6/oGRHoEI3dMj8AG//69A1ANVb9M25YDtKQWeXj1Nng3R0+sfnH/N5FW0tei64ygp3TGzv98POHTFefU13QDx5JsjgkI+dO0YIhxd3wf3pswFeOhImkzXwGLDpqgSrVibpGcgBzmVAa6pz3HFLdDxZsXtvZMZkhdN09/h4YV+EVNog4Fdcvy1GQ33hD+VVlTm2XRvnmk1w7oJuVNI/6OVCh58LHX5KwiYr2tIsb0st+v1KmJ2+fi/P7S0lmTLw+9Scyeb21jTDIx5ePR5i38ESIiU5amvccX60nZETEL+oK95XvB8ibfn9nccH5VfpZg/RV/JKxikFJ04GOfpqiGxOnzPXrUmyfk3S1vn8BEEQrgQkGScAeqjJsy+Ukkh6KCs1uWH7SOEP/SOvQ2YYKjdDabst6wk6AXfmjK422rcPbrnFwonuBcEiMhldDdfRob9fubKwZJrfrxN3Y8m8kydhcFBX1ZWULGDFStuh9Z2oxI8h2wmdj0PZSqjYCL45soTCvIjHDU6fDXLqbIBE8tIOUFOVZUV7mpbmtG1zhNmJYcBV65JUVWbZsz9C/6CXR54s54btsUVZLTG1Gq66MseOLXo4qLlI8jyhkOLmXSMcfjnM8ZNBXnktRP+Aj+u3xSxrdLBQXjsR5PDLYRS6knPnjhiRyMKG1Pp8sKJdzx03FPVw6kyQM+cCxBMejh4L8fKxEI0NGVa0pWlqzMg132XMdxj2xvVJRka8nO/ws3tPKbe/YZjSUmuHb7uORAcMn9LN0pruhorNhf192WrofxGiJyA9DIGZP9EeHPKy72DJePVrTVWWbdfEKc+3oZsgCIIwKzJn3GLDgQYOOVOxZ18pFzr8BPxqfjc7yV59cc/GYM1vQGTm9bdicvx4HH7+c51E3LZt9iF80sBBfMX2RaN6friREZ1827oVli+fv7OrS1eD5jPMNV/MbAbPwF7oeVYfv97gaCJ9ORjWv9euniR7Hj7ThI5OP6fOBsaTPwABv6K9Nc2KttSCH2icjnEisZiH3XsiDA17MYCrC51vtEBfoZw5F+Dg4UvVcBvX686lE19+Mb2fMPME93Y6ZyObhRcPRDjfoffn9pY0W6+Jz1odsxBfLgcXOvycPB0cH9YIEAoqlrelWNGWviwJuNi24VJwLnQb7n+phDPn829QMtWXy8ETz5QxMOSlrNTktpuGLU1Mu+n9JD2k77WTPVCzA1rfAZ65q3kvu7/ofAy6n4ZwI9Rsv+z3s1l4+ViY468HUYDfp9i8IcGK9vTciXBp4CAIgpA3koxbZKjYBbI9e/GVtRStzfrBIyFOnAzj9cDNu4YLHwagFPS9ALGzULUV2t8zY6naeIt1n2/B8R0/Di+9pCuI7roLQiF7fflSbKf4Fpfv/HkKbsCQjzMW0/PIDQ7qw2vzZli7tuDVu9yXiepOq4NHITsC4XqovBaC1fN78Zl8OYXPaxRvGzrki8W8nDob4My54PgE1wB1NVlWLk/R3JixZGjPYnxPcznYd7CEsxf0g3drc5pt18bnVfVnVXzJpK6GG5vnqKpCV8NNTYQuxvcTYCjqYfeeUmJxD16PbpYx37mzFhLj8LCH516MEB1Ntl67Oc6qFbOvh5Xv6fCwh1NndbVcKn3pteprs6xsT9HclMEwFuc2dLNzIb5YzMNzeyMMRvU+s3mDToDPx5dMGjz6VBmJpIf62iw33TCP0RsF+OxiQb5ccrRhQ6celrriPvBH8nNOvb+IX4TzP4T4eZ3QMy5dlDq7fOw/VEI8od/glqYM126OEwrl97ioUv1kCeBrvLlox0WhuOF5UBCEKwNJxi0iUikY6uig5+wB6hqb8RThGtbb7+WZPT7KSwNcv3WeN/nJLujfr28U1vzmrHNQWNmpUil47DE9l1ZLi64WstOXL0ul++dMjIyYXLzYQ3t7HcHg0otvvj6l4PBheO01/X19PVx/va5ks8qZy8H+/XqYNugh29u2UXCyY1pf9DXofBTi50DloGytbsTizSOAuXxF7liXTMHJcxlqK0NFOY+aCs52ZBgeKqOv/1KVwlgFz/LWtOVDqxZzF8ATJ4O8dEQPYywvy7FrR6zg+K2I7+x5PwcPl5DO6Gq4DetmbjCwmN/PdNpgz/5Lw2tXr0hx9cbCm2XMN8aLHX72HighkzUIBRU7d4xM2+HXKt+sr2nq9Tl1NkhXz6UTXzCgaG1J4QsOU19VvOO+ZyBJXZF8ABiKdC5GY+3i208n0tWt5xRMZwyCAT2nYH3d3EPXZ/MNRT08/nQZ2ZzByvYUW68pcF7jAn12kDMVp85lWN7ix+ctwKdy0L8P4hfAF4FVv5b3XK/TXu+V0sm4vv1QuQnK15BMGrx0JMy5i/rDlJKwyZbNcZoaC5tyQLqpCoIg5I8LZ6lZunR1wfPPBIh21VJeXVa0m0lIc9XaxPwScUrB8Ek970TtdRBusnoVZ8QwdCLi0Ud1NVJHBzQVT39FkcvBhQtw6pTeT6PRAOXlOtEUDkMkoqvAwmH9/0hE/zscvjLm80ul4PnnoWe0f8m6dbBpk/Wxe72wY4duBHHwIJw7B0NDsGsXlJYu8MXL10LpCj1stfc5iB7Tn5pXXq2HnbtgQ/b0+jh1JsDZi34GoxnKSwNFO49GR9KUl/rxGNBYf2luK9ueRczFNyfbGKtXpqisyPL8i7oz6KNPlXHd1ljBD3XzJZk02H+ohIuds1fDuYVAQHHj9TFeORbi5ddCnDgVZGDQyw3bY7Y2NVAKXj4W4pXXdNl5bXWWG7bH8q6QsQOPB1qWZWhZliEW83D6XIDTo3MxvvZ6iOiIp8jHfbBovjFnKhPg6qtgVbv1SX4rePW1IEde1fOPVlXk2LljhJKShe8zFeUm12+L8eyeUk6eCVJWai54KHwxicU8PLMnwrmLOaoq/NRU5qityVJbnaWmOjt79+yhVyDRrf/d+ssLb7pkGFC+AYZPoYZe4/TARg4d1cP4DfQ5fOP6hCvnMhUEQXATcppdRPh8UBpRZEuylEZyRfvUs64uwYZ1JjAPX7IT0gO6eqbupqI/sFdW6qF6x47piqG77iq8SkiYmWhUJ+DOnNHzlY0xdtOYyeivaHT6vzeMSwm6mb7cvr36+/Xw0URCx7JjByxbZq9z1Sq97z/3nH7vH30UrrvOgmS0xw8Nt0LFJuj8hU7I9e6G2DKoulp3Y11kJJMGZ84FOHU2yEhMZ75MBaGgWdTzqDJybFibYGVb2pIHz1lJ9sDAQXzZBqgucPLuIlFbk+ONN+vOoH0DPp7dU8qGtUmusrkz6LkLfg4culQNd9XaJOvWJO1LihYJw4AN6y81y+gb8PHoU+XcsH3Elg6T6bTBC/si49Vn863Gs5NIxGTjet1ttrPLx+tnAmQv5op63GfN4vlAV/1GRzwcOxHg+Osh6mqyrGhPsazJmuHvCyGTgRcPRrgwOqfgirY0126efU7BQmlqzHL1hgSHXg7z0tEwpZFc0ZL8C6Gzy8ee/RGSaQPIkcsZ9Pb7xudCNICK8snJufFE+8hp/cFYNgat74TyNdasVOkKoulG9u+N0UsO/AaV5bq7cVWldK0VBEEoBi5/DF5aNDdD45tS9Jzupq4pULSbyZ6BJIYxzYRrc6FMGDmpO6jW3QThhrz+zOo5JDZs0JVxsRgcOaI7Vtrpy4diO630ZbP6/Tx5Uieaxigp0U0I2togFktTVQXJpE5CxePTfyl16d8zEQhcXlE3MVkXCCze9/PUKThwQA+dKivT88PNd8RDoTHW1MAdd+iKvN5eePZZuOoqfTzk81Kz+kK10P5+GDqqJ3pOduh55crXQ/m6vCaMvtxX8J/MiFJ6GNSps0EudvgZS335vIrWZRmWtyfJqtjo8LHinUeL4jOzOkmaGsAX74fMSggstCwyPwoNLRxW3HLjCC8dCfP66SAvvxZiYMjLji3xvCZgL8SXSulquLFEQGV5jh1bY1QUUA1X7EvFfHxNjVneePMwu/fq+duefLaMazYlWL0yvwqhfJyDQ16e2xsZn6du27Ux2loyha9snr6FYhj6fWloyLCmWMchRT7uR8nmFEeOmUSHyunu8dPT56Onz2dpY5ip5BNaNOrhuRdLGR7x4DFgy9VxVrTPb27DuXxrV6cYiXk4eSbIC/si3PaG4YKO80J9C0EpePW1EEeP6Xvs6sosO7YPUF0Won/AT99oQm4k5mEw6mUw6uXEKT01RKTEpLasl1pPF7UlCcpWvEE3bZgHU6/3pgmvHgvy6sEdmMOH8frPsfGaFaxZNf0w/oX6BEEQhOmROeMWG/GL0Ldn1m6ki4b4eRh4SSfl1v0WBGscW5WuLnj6af3v22/Xw/iEwhgY0Mmls2d1Qg70TWpzs+5W29BQ2E2rUjpZN1OiLpGYXG03Ex6PHoJZX6/Xoa7O+Wq6XE4PEz11Sn/f3Kwr4mYdZmITpgmHDsGJE/r7xkZdJRcIWCTIJnTXtf69kB7U1XFV1xZ1SPoYiYTBqTNBTp8LjE8uDVBdmWNFe4rWZWnH9w3bGT6hhyyZWZ0UDVZD3S6n12pOzpwLsP+lEnImlEZMdu4YWdAD9ETOX/Bz4HAJqbQeYnXV2iTr1xZYDZdLQS4OgTm6rSwSslndLGNsfqd8Opvmw9QOrruus247CdYSjxucPhvk1Ogw3TFqqrKsaE/T0lyc8+GFi3pOwWzOIBzSXX+rq+ytrDJNeOb5Urp7fZSETW5/w7Cjw6enI5OBvQci48PlV7anuHbz9NWlyaRBb59OzPX2+RiKelG5lD7fp6NQ0kyw5Q3U1PuprYXaWl0dP59K1Z4ePZJkeBjIDNPIY2xp20tk7d3gnccH81NJD4AnAPU3L/y1bMIVz4OCIFwRSDJukaFiF0h37SVQUbxuqumMScDvKcynctDznE7INdwOy+7O35dOEwgELI9v7149nLKiAt74Rn2TYqdvJortXIgvk9HJt1OndJfOMUpLdRXc8uWXd6m1Mr5MZvbKukQCTHNyJzCPR9+INjTor4oKq6uuZo8vHtfDQwcG9PebNuk54hayDla8p2fPwr59OlEYieh55CpmGFU6L1/8InQ8DCMndOIi0qqHrvrmrsqa93kGndTt6PRz6myAzq5LVXB+32glSHvqsmTBQnzzoWi+bEyfd1O9qPrbyEZP4kucwWh5C/jtvaZZEWMhFVf5+FIpgwOHSjg/Wg1XUZZjx9Y4lRUFJAKUgsQFVPQ1sslhfJF6jKprwRcuNLyCsGqfOf56kENHdbOMirIcu66LEYlMnzybzWmacOhoeLwqp7E+w3Vb86tgnIklexw65JvJOVYpfPJMkI7OyefI1mVpVi5PF3ZMzOG79DM48kqIYyf0TUJdjZ5TMBgscJ9ROT3CIj2ISg+SyYE/XIkRqAJ/GRjTZ5zSaYPHnyljeMRDVUWOW28aLjgZbdc2nKlSMF9fJpWl/+QRejuH6Y010x9+KzljcudUr1dXyI8l56qrp/+gcux6DwGOHDHGP0QMhfRIkhbPz6D3eYgs19f0BaJS/aRNP4HmWxZthZwbngcFQbgykGTcIsMcuUD3qb3UN7cs6k5ZxM7CwCF9k7TutyBQmZ/Pxs6YqRT8/Oe62mrTJli/XrqpzkRfnx6Gev68Tt6ATl4uWwYrV+obu5l2h2LGpxTEYibHj/eSy9XS0+MhFpv8O6GQrpprbNT/n5o8LJTZ4uvp0cNCUyldeXb99TohuFCsek8HB3WiMBbTN+rbtulhxZb5lAkDB6HrCUh2g8cLFRuhdBV4Zi7BmM95JhbzcOpsgNNngyRTl/6mtjrLyuWzz5G0mDtjLoj+/Xr+oJJmzBX3M3DiP6jKHMYTKIf6N9jnxboYp85FtmZlis0bLq8Wmct34aKf/YcWWA2XjUP0FUh2Y6aHGEoGqAhl8XiDuvoz0mbbGDYr95meXh/PvxghlTYI+BXXbY3R2HD5PFozOZNJg+f26nnoQL+XGyyY22/JHocO+fJxJpMGp88GOH3u0hyaoIdtr1yuq4cLqeCeyZdK6eO4u1fvM2tX6eM4r7fBzEB6CDKDuto6MwS5BOTSmGaGobiiosSDx+sHbxiCtboC2F+pP3SYcK0ZGfHw2NNlpDMGLU0Zrt8eK2i/tWMbnr/g58WDulKwJKwrBcfmYMvLp5S+zsbPgeGFlR/FDLcyMKCnpOjt1fdwU0cWGIaulhtLztXW6iZbpmly4EAv58/XksnofWLlSn2fHAigrynnfwypLmj5pQWf86SbqiAIQv4s9QE9gh2YWYidhuwINL4p70Sc3QSDcM01ukLulVegpUXPOSZoUqlLVXATGy6Ul+thqO3tFg5ttAjD0Nuwqcmkvl4nDEdGoLNTD03u6dFDYc+e1V+gb0bHquZqa+c3jGM6XnsNDh/W98mVlXp+uEhkzj8rKpWVuip0zx79Hu3Zo+f9u/pqi94HwwPVW6FsHXQ9ph8Y+g9A7IxOXoTqF/TypgkXOvycOhMcf8gDCAYUy9tSLG9NU1Z2hQ6ZS3bpBKjKQeNd4A2SLd0AiS49d2dqEIKVTq/lnAQCiptuGOHoqyFePR7i+MlLnUHzGWaWShkcPBweH54572q4+NnRIWBDgAl1N5POLQeOQfSo7igcPw/V1+RV/ekkdbVZ7rglynN7S+kf9PLMC6VsXKeTk3M9V/f1e3lubynJlIHfp4ra9VawnlBIsX5tinVrUuPdpS90BBiMetl/qISXjpTQukxXFNdUz69abmBQV7jGEx58XsX2a+O0LJtlTsFccjTpNqj/n46CmRr9ygIe8EV0N+9wO9loCoJRSJwbrZh7HYZf18PyvUE9JUqgGgKVlJZUsus6g6d2l3G+w0/ZsRAb1yfnFddCUQoOvxzmtdd1dWl9bZbrt82jUnDkBCQ6IJeGFe+HSCsedBVcTY2uxFdKDzMdS8719uqq/YEB/XX8uH6psjL9wdzp037Ky/U9wtat+t5onJJWPU9sslOf89wwTY4gCMISQZJxQuHEz+sHGH8l1N7g9NpMor1dD1Xt7tZzYtx0k9Nr5CxK6YTVqVNw4YJOdoC+OWtt1Um4Guem+psXpaWwerX+Mk39CXFXl04+DQ5e+jp2TMdZV6er5hoa9I1poWSz8OKLuooQ9D62dSuOd66biUAAbrwRXn5ZJ6VPnNDvxw03LLxqcBx/BFreDpVXQ+cjMHIKOp+AshW6E2uBQ/yiUQ+nzwU5cy5AKn0pe9BQl2VFW4rmpsyi6uJYdMwsDB/XD7E110HZSlCg/BUQ2AipHhg8qDvhugDDgE1XJamuzLFnfwm9/T4efapszrmmplbDrV+b5KpCq+EyIxB9GZK9kInqB9Flb4WS5aieHqjbAMNX6f06cRE6uqDyGp0omGG43GIgHFbcetMwBw+HOXkmyNFjY80yYjNWQp04GeSlI3qIa3lZjl07YpSWXqHJ7iWGYUB9XZb6uiypVIKz5wOcOhsgOuzl9LkAp88FKC/LsaItTXtrOu/hyKfP6rkfTaXnfty1Y2RywwilIBcbTboN6gRcZgTM9OhXVld7+St1RXXpCihpgXCjnq/MNMl5u3WZO0CqWyfO4xd0BVe6X4/MGDmtK+Q8AWoDVWxb1c7eo6288kqE0kiA9tb5NY+YL6mUwfMvRujp049V61Yn2XTVPKpLEx0wfFKfm5ruhorpu2Ubhv4gtbxcV7mBTsaNVc319OgPXYeH9X2SYcDGjbrJ02XnS48XKjdB7Lyej1SScYIgCEVDknGLEF+RH/J93kJq+seq4mKw7C0QKLy822fzjMJbt8LDD+uE3OnTUF5e/N3c7hjn8iWTOvbTp3Ul2RiVlfrGrbV1Yc0GnI5vDI9HJ9vq6vSQi1RKJ+bGvpJJnaTr7NS/X1JyqWquvn7mSsAx3/CwHvYZjWrXNdfAqlV2RGjtezp2411VpStFe3vhkUd0Nd9Y8tUSX2k7rPyIbjrT8wwMn9Jzy1Vuuix5MfU8k8vB+YsBTp0J0Nt/aV3CIZPlbWmWt6ZnnPsqHwo6r1mArb7YaV355i+Fxlv1+6pMvQ3LNkP0VV3lleqztZGO1TE2N2V4483DPPei7gz6xDNlXLs5zsrl6Um+dNrgwKFL1XDlZTl2bImPD/3KC2Xq93H4pE4QGF5d2V3/Bp08NkffT8OAyo0QWQFdj+jKz769eshY9VZL5+az+v30eGDrNQmqKnMcOFTCxU4/jz5VPilh4vMa5HKw71AJZ87r97O1Oc22a+O2TPa/pI7DReCbjzMYVKxZlWLNqhR9/V5OnQly7oJOzL10NMzhl8Msa9Jzy9XVXl4V6fMamCYcPKITvQDNjRmd6PWZ+kOCiZVvuYSeV9RM6+Scx6fPSyWtel6ykhY99NQ7/QV4/Nrk8egkXbgRxk5rqYHR5Nx5XZGd7IJkF+0lFxiubuLVs8vY90SQyI0JahvCeuSGr3TWoZcL3YaFVgrO6EsPwdDLugFCzQ5oeENBQ0ZLSvS0FGNTU6TTOjEXjUIwaNLWNkuFfOlKCFZB9Lhej8AME87miRPHhSAIghuROeMWG4u9m+rw6zB0VM/jsfaT+uFwEXLsmB5SGAjAXXfpIaxLHaV0AurkSejo0N+DntC3rU0n4SorHV3FojM0dGlIa2/vpcpA0Pe41dWXknPV1ZPvey9e1ImsTEZXlE1MZLmJoiUU00O6mmjwiB7CHqqDqi16rp8JDA55OXUmwNnzATJZ/YYbQFNjhhVtKRobsnZN0+VOsnE9ZDLZAy33TD83XM/z0PUoGD5ouqP467hAsll48UBkvBHD8tY0W67WnUEvduhquGRKV8OtW6PnMyuoGi49pOeGS/XqKp3SldD8lvzmhIu+Bh2/gMQFwNCJ5rLVOpm3iJkpQRCLeXhub4TBqBcD2LwhwdrVKadXVygymQy6Wu5MkMHopX25NGKyoi1Fe2t6fNh4ImGMD4HGNNm4upv1bRcwsoOjybekHlJpZgClh5KG6qGkXX9gE27RSTGPDcdMJqYT5fFzqJGzPP+CwYWOEAFfmtuve53SUvT9aqhOd0oOVOqEukXH76kzAQ4c0pWCZaUmO7dPqRTMl1xSJ/0Tnfq8tOI+XYFeTLqegu4nIVADddfP/3Wkm6ogCELeSDJukaH6DpAYOE24qrVo3cASqRzhoHdun5mG7mf1J5HL7oWGwseAKqVIJBKEw2Fb4zNNePRRGBxU1Nen2L49WNRuZ8WIcYxMRnHiRJLOzhCJxCVfTY0ehtrSMn2HrflS7Pis8uVyeujGWNXcxHnzQFcK1tdDQ4Oiry/J6dMhDMOgttbiIZ7TYPd7OnWobVubYsWKBCUlNviGT0Dn47pyQeWgbDVm6VrOdJbQ0RlmcOjSzhgpMVnemmZ5W4pw2LpLUUHntcXuGzioq7nCjbD61/WDLlP2mVwcTn9PVzU03qYfPC2mGO/pseNBjryih01WVmQJBDJ0dYcxDP2gu2PL7MNYL8PM6vn0Yqd1Qs4T0N2/63aOv49jzHoM5lLQ9Tj0vairf0INukpuAfOlFuP9nDrJftuyFGcueEH5CAUVN2yPTVsJZRVL6jhcBD67nAODlz4gyeYufUDS3JShvjrGgYM5VDpJ0DPCdRtO0Fg1MDrfWwY931uJPj9FVugPksPL9Ae18xjWvdBrYS6d4Ylf9DLQ2U+Zv4vbr96L3xjW96+g553zhXTCKViD8leQMEsJh0MF+UwTDhwq4dRZXd03Xik4x4iDabefykH/Pj0U1xeBVb9m6Tk87/c00QXnH4KRs9ByL3jnN3xCpfpJZP2EW6SbqiAIwlzIMNXFRPwCqm8Pib4eQhUNGF77y7kUEI1lCAX1p+SzEjsD2WEI1kPNtvn5lCIajRIKFXbjUygeD2zfDo88ojh+PEFXVwCPp0jdzkxFNJqivDxUFKf2JSkvDxIKGbS36yScXfcXxdqGVvu8Xj13XGOj/j4ev5SY6+7WQzouXIBz5y69n2vXGmzebF0TiJmw+z31+XRCcawJxenTikOH7NpHV4O5HKLHYPg1yAxjes4xQgOl5Qa+QJjmJpMV7Snq6+ypgivovLaYfckeXSmhctD05kkJpEn7jC+iG2gke2DgADTdaeVaaB/2v6fr1qSoqszx/IsR+gd9REdMKsoU61an2LAuWdg8jan+0Wq4AV2pWb4emu+GkuZpf33WY9AbhOY369fo+Lmes6rjYajYAOXrZu0kPBPFeD+DQcUbdo5w5JUQx06EOH0+SHQkzfKWLDdeF7M0AT4dS+Y4XCQ+u5xVlTmqKhNcvTExPnVA34CPC2eSnHv5HPF4kqbqGDddfZJISRY8FRBpH53vre3SfG8WnMwXei30BvzceEcTjz7axHBiI89Hb+XGbR14EudGK+jOQGoI0mdg5BTK8JFK+ghV1mCEqsFfoSvnfDMnE+Nxg+dfHK0UBDatT7BuTSqv8KfdfkOvQKIbMKD13ZZ/mJL3exqqh1CTHqETO6nPbfPxAdFYlpBSizYZJwiCsFiQZNyiwgBPEI85oj+Fr9tl6fw0CyKX1A8g2Tg0vaXgCdqdoKoKNmyAF17QyZRiTgBvGMVz6uGWJtu26Sq4xdpYYLFRUqKTlitW6CG9AwN6SGtHh5577rrrYPlyp9fSWtauvTSP3PCwjfuoxwfVG6G0TTcWiJ+nguNsbBhmxbI4wdJyPXw1Ua4ffnwRSx7klhQqB8PH9HxM1VuhbI6xxRUbYeCQ/ptEp35AdiH1dboz6Av7S8DIcttNSWqrCxj2NdbsInZOV7H5ItDyDqjdoatiFkLpclh1P3Q/Bb0vwOBhXc1Ss1XPgbUIMQzYvCFJVUWOl14OUVWd5NZdOZnTSbgMnw89X2dbmmh/glOHXudsIkdNM9z8hlL8lW/T874F6y6rLF1MhEK6idETT0BXj4eDry1j69ZlwGjDsVSfvp+Nn9eNIBJdupNo4uLozVtAJxcD1XoeNf/odcoboqfXx/MvRkilDQJ+xfXbYjTUL6C6dOS0Xo9cHFreCeWrF/4GzBfD0NeRkVN6eP48k3GCIAhC/kgybjFR0gwt96KGk5A9pz95r7luccwfFzutW8yHm6H6GqfXJm+uugpqalLU1xcvGWea0N1dPKf2ZYoa41JjbP646mpYv/7S9luK1NXBm99crH20DNRNmIOvMHD2Rar8UTyZtH7oiZ+f8OAT1hN8ByovVSYs4oe9ohA7M9q0IQKNb5x7yJcvDNVbdPfB/gOw7O6irKYdlJQobrlxhO7+JNVVIci3BijZq5tZpPv1B0eVm6HpLgg3WLdyHr/eHuXr4eLP9TDYzsf09xVXLTzhZzXKhPQgLWU9NF/bT1+6Go9nrdNrJSxmcinKc/u4ZmUnm69qpLv0bXiXrXDVDUZlJVx/PTz7rJ5Ht6wM1qwZ/WFQD1GleguYJqmLp6E0DckOfV2Kn9dNytKndWLK4wVPgNfOt3L41EqUJ0FlTYCd1+eIlC3gPUn16qRXehDqb4Ga7QuOe8GULtfvTarb1R/qCIIguAVJxi0yjGAltL4DI7Uf+l+EnmchswEqNs1r/o05fUDA75n9USeb0J/+55LQcIv+xHC+PsMgEAgUrXS92D4nnOJzt88JZ1F9hoFReRWGasSoqtJJ/fg5SIw++CQu6MRJelD//uiDD/5y/VAwlpzzl+U96XZe5zULsdyXjeuHwOwwLLsHQpdXXU27DSs2wOAhPewpdh4iLVat0eJ+T820fqiNX9Bzw/nLoe3tUH1t3kNICz4mSpbByg/r5ho9z+ouiIkLuooxNHfyz9b3U+V09U+qRw9dzsYhl8AwM4RSZzBGglC23A7zJBb1PuNCX1GcKqfPIcle8JVitP0ygVRx5ocFa69NTU1w9dVw6BC89BKUlupll/lKKjEqqqByvV5omvrYSVyARAfZofO8uN/L+Y4wqPO0Nw2ydVU33mgAkpV6/jl/OQTKwTt7lff49suO6GZH6UHdFKbpTfY0uKDA99Tj19Vx8Qv6g415JOPGY5Rqd0EQhDmRBg6LFaVg4CVdHZfq1lVzNdctKBE2b4Ze1vM/BRtgzX+asR29IAhCwZgmpLpGE3MdenhhqlsnWMws4NETSXtDE7rhVY7O61Pi8MrbxMBBGD6l5/BZ8+uFnfcHDsPF/4BcDJa9bekP/010jg7nHdQfGFVt0dVwU7r42rsOPdDxMz08NpfSQ80qNhW3utPM6EqbZLf+fzYBZlIfQ75SiCzXSd3BI/rnDbdAuGnOlxWuIJSCoSMwcgZUBlZ8GMrdX0W5bx+cOqWH4d52G1RU5P+3493IBzIY2X6uXXOOVQ2nIH5Wn3PMtK4+NTz6ePeW6OGtY1XegXL94dJEzMxo59SLeg7mVfdDoICVspv0EJz5Poyc0NcQX4FdXaWbqiAIQt5IZdwiQynFyMgIpaWlGNXX6oex8/+qhyx1PAL1N+oHUit9iSylYd/0n2JlY6NVcanRqriFJeImxVekyf+L6XPCKT53+5xwLiqfx6OTAhMTA2PVuImLujohfg7Sw5A5qx8Ux6rnvCWjw1srLlXQefxzn9fsiM8qX7J3tGlDVieVZkjEzfielq/T8/QNvawfGCPtC1ufib7F9J7mkrpyI9GpHx6DNXry84oN86owWdAxEa6DFR/U1eydT0D0dT0JevVWXUE3n/jyIZfUlW+pHl0Jl0voZUrpY6LsWqjcoCfa95eilCKeC1Ginsfofhoa77A1abno9hmX+2x3jpycMn/Z2sV1rZgnW7ZALKYbNT37LNx++6Xu6LP5Ll7U86tmMhCK+Nm5s4GamgZgdDhpahAS5y9Veccv6Mrv1ACgdFWuJ6CvTUHdHEL5ykj0Hyec7cHwhqHtl21PxBX8ngYq9Dkj0aE/YKi6tnBfIkepNHAQBEGYE0nGLTKUUsRiMSKRiL6IlTTroTDnf6wfrjoehpod+kJphQ+IJbJEwr7phz2MnNI3F6Ur9Hw4C/VNjc9miu1zwik+d/uccC56ny+sq4smTmad6ruUoIuf1//PRPVy0MNrPAEIVKIClSTipUTC7UXrqjjreTTvFxpt2pCJQtVWKF8z86/O9J56A1C1TSep+l/S3Q6t6HKIRTEu1KeUTtBGj+sGDWYGaq6HpjsW9FC74GPC8Ojq9dI10PEfMPQqdD+tr52VV1/W9Gje72d2RCdsU906GWAmIJvSPwvVQOUWqFiv536akshVSjHs30C4WmH0PacbRTXeCYGywuPNg0WzzywRn63ORCcMv64T2w1v1A1PcMG1Ig88Ht1N/PHHdaXb7t1wyy262dV0PqXg5ZfhlVf039fW6r8PTf1cJFipvyo36e/N3GgjiNHrVOz8aKK8Wye2DEN3b40bhEI5jPZfLsqc0PN6T8vXj3ZEf12fvwqYJkfvozkikowTBEGYE0nGuQFfBNrePdrBbbfu4pbqh6pr8p4PZ15kRvSn+2YaGm5dfBNTC4Jw5TA26XbV1fp7MzuhIuGirlBI9Y0OdT2PP+GBkhhUbnR2vQshdkZXVfhKoPH2+c8TWr4WBl/S3T5HTs7didUtZOMQfUUPxUwP6bnZlr1VVwPaMKfqvAhWQfv79FDjrsd1JWeiS1eXRFoLT4wqNZp0Hp3/LTOkq99ySR1zuAlq1ukEXHjZ3NXrhgF1t+ghrP37oPsxnZBzQYd0wSbSgzB0VDc+qd4GTXk0jHEZgYDusPrYY9Dfryverr/+8t9Lp2HPHt1ZHWD1aj3vXF69KzxeXQk7sRo2G7/04VH8ou7iyoieI65isxWh2UPJMj0yJ9Gpr0sWFQAIgiAIk5FknFvweKHxNt0V7uJ/wPAJXRVQe0Ph8znky8jregLxsjVQ5v55QwRBWEJ4fDq5MbGyID0MyQswfAbO79Xzp3kD+hy22MkmRps2jEDzWyBUN//X8vj0Q3X8Igwc0g9Sbn64VkoPuR1+fbTRh6nnI2q4DfylTq/d5RiG7tRYugo6fq6r2nt3Q7xVdyP3zbHOytTzLqV6dBVcdnh0Dri0rv4saYbyDTrpGmoo/EM5jxea7wIzpZOG3U/q5O/Uua2EpU82oRs2pPr0/rrs7Uv2g9fSUti1C556Cs6f1x1Wr5ow4GNoSFfNxWK6am7bNmhrW6DUV6KP07G593I5Up1nod6aimXbMDy64i92Vk8HIMk4QRAEW5Bk3CLDMAzC4Vk6V1VsgGCdnkdu5HXofBhqdkG4fn4+IBz0Xj7cIT2kP81XOai/1bIKvDnjs5hi+5xwis/dPiecS9YXKIPAeoyydfjMCoyhp6BvH3iCEFnoU9XMzHgeLYTh1yAdhZIWqNs5t3Ou97R0lX6tdL+e96d83ULWzpoY5+PLjMDwKzoplYlCSauuhitdaenDrC37aKBczwk19DJ0PqKHr3V0QeU1GJHlk99PM6u31XgDhpieA87M6uGmpSv09b9sja4QnUdydVKMhl8nfXNJvX7dT0P9LZZW2zu2zyxRn+VOM6sraJO9EKjV+6p/8oe7S+1aUVurk2x79+phqKWlBpWVYc6dM9i/H3I5iER00q6QRg/5Yng8hEurMfIqtbPIOd/3NLJCz3UXPaYrtoP5zVet91HppioIgpAP0k3VreSScOHf9Sea2bj+FL5sjXUPJ/0HdLKvdA2svM+2luuCIKGPIHkAAEfdSURBVAi20bdPf2CR6tND7cONTq/R9KR69bpmhmDlRyyZnxPQlWTn/02/fuu9YLjoPK5MiJ2G4ZO6ChyProarv9mdQyozMZ2QGzioqx/DjXqqiWxidP63Pv3vXEJ/COYrg7IVUH6Vvrb7y+2ppMkMw9n/TydsQ41Qt8vdVZRCfigTBl7SzXEMA1bcd0VVPx05Aq++qoefLlsG587p5Y2NcN11elirAHQ/C91P6PNP3a78/ka6qQqCIOSN3HEtMpRSDA0NMWeO1BuC1l/S804EqqB/P/Tt0ZNZF+obSU/2pQf0p/NKjc4VZ90DXN7xudTnhFN87vY54bxifNVboe4NEKiErqcuNXuwwzf1PJr3H5sQfU0n4qquyXtKgLze09IVo8N4DV3dsAAWFGOhmFlU315i3UdQyR5dDbf6Y9Bk39xmtu+j/ohOiC5/P0TaUcleYmceRfW9qLd/snd0ePFW/QHYVZ+GFR+Cmu26MYUVTTimi9FfBi2/pBt9xC/qewmL3oOi7jNXgM9SZ/Q1Pb9mLg2t75wxEbdUrxUbN+okXC6nOHYshlKKq67S88rZmYhz3f1F+TpdiRs/r/eVfH2xbFFjFARBcCuSjFtkKKVIJBL5XcQMD9TdqJs7lLToiaK7HteNF/L1AYlUjkm24dG54io26I5sFlJQfC70OeEUn7t9TjivKF/dLqjdqecW63xcD8G32sc059F8iZ3VwxO9Yd3FMM8PP/J6Tw0PVG/X888NvqyHpc2TBcVYkEjB0BFUsot0Kotqegus+lV9LbJx2FPR9tHytbD6Y6i6m0mbQZQvArW7YPWvwVW/C+3v0XM12TAX3owxBqt1Qi6yTF//h45Y46NI+8wV4rPMGTurq04zUWi+e9ZGAkv1WmEYugKutlahVJqdOxUbN9o/jZvr7i9Ctfr5whPS1bP5+IBEypRknCAIQh5IMm4pUL5aDzGo3KATcZ2/gHjH/F4r1au/MKDhFhmuIgiCuzEMPRdWzXZdUdz1hJ6PazGQS+pup5kRPaQn3GC9I9KuvwyfnhtssTN8XFdoZdOk6+6ExlvBG3R6razFG4Tmu0i1/Rqs+5SumCtfq/dPpyhpgmX36O6sg0fzfvAWXEayV1fJpgeh9sbRYclX5txeXi/ccgvceWeK5man12YRU7FBJ+yHj1tWNSsIgiBoJNOyVAhWQvv7dQWIEdDd0QaP6iFQhTByUs8hU3m1HhokCILgdjxeaHwTVF0NGND5mE6EOc3Y8NSSZfrcbQeGoTurhur0Q3ieQ40cIX5ed5TNRKHxDpQbuuAuBK9vcXWuLF0OTXdBqF7PYRg76/QaCVaSGYHBw3oy/ooNsOzNljbscCtXaC4yf0ra9PVDZSFx0em1EQRBWFJIMm6RYRgGkUhkfl2IvAHdZW7ZWyFYq2+6enZDLjWzD4iEfbozV3J0EmmPV8+zZENV3ILic4HPCaf43O1zwnlF+jw+3T2ycoMertn1BJjWJKYmnUfzJdWn52wyM/OaC62g97SkRc8J5QnA0NGCPOM+5hFjIaT6YegVPWdpzQ0YDTcTKS27svbRxeCs3KCHSwdroPc5SHbN34fN+8wV5luQM5eCwYOQ7tNVkK3vzKsSc6kfF0vdZ4nT44PyDeCvhOirc/uASNgr3VQFQRDyQLqpLlVi53QXvfg58JXoueUClTP/vlLQ94L+JLx6B7S9Sz4uFARh6ZGNw7kf6MRPoFIPYS12dYgyofcFfX6u3Kyrmu3uWB2/COce0teGlnvA5+CQyKlkR6DvRUh06+GaKz7g7JDNKx2loOcZ6H5KV8o33gHBKqfXSpgvKqcbc8Qv6ONq5UehRMZlCgWQGYYz34focT3PYKBs5t+VbqqCIAh5I5VxiwylFP39/Quf+DTSCis+rIdl5TLQ+ci0Q06UUvRHU6hEp65M8ASg/ibbEnGWxbdIfU44xedunxPOK9rnK4GWe6F0lX5o6H2u8OH80/miqfzji53VFSrekE50zCMRV/B7WtIMZat1Em4eE/QXHGO+mGkYeEnPVRpu0B8EeUOLa59ZAr6CnIahP8Cr2aEbi3Q/rhOm8/HZsc8sFl8uw0BfF8pc2PmjIGehMSql54pMdAEK2n65oETcUj8ulrrPMqe/DEpX6+vn8OyduZVS9A9LN1VBEIR8kGTcIkMpRTptQdt60J9ctb0HGt4A3hI9ZHXgoP6UdMwHpNNZ1Ngk4tVbIWTDJOJjPivjW4Q+J5zic7fPCecV7/OXQ9s7oaRdN7vp27ugiakVkM6Y+XU4zCVH50Ub0dMBhBvn55zPe1q1BYL1ox2z44X5KCDGvF/UhIFDOhHnLdHXq9EK7kW3z7jcV7DT8EDD7VC9RTf/6Hy84HkWbdlnFosvm0D1vQC9z6F6ntHDzotAwTGOnNRzMebi0Px2KF9XmG+JHxdL3Weps3yt7q4aOz3pOeIyH6P7qCTjBEEQ5kSScUsdj0/PR9T6S7rqYOhV6H4asonxX/GmO3VnLV8I6uyrihMEQVg0BKqg9R26injktK7OKgbDJ3TThnAj1O4qjnOMcIN+oPJFYPBQcd3TEX0Fkp1g5qD1XRBpcXqNhIl4fND0Zj3Zv5nVjaHMjNNr5TzZGPTv0/Psqqz+f+djeqh1pvAKQttIdOrEe3oI6m+D2h1Or5HgZsJN+gsvDJ90em0EQRCWBJKMu1Ko3AztH9TDlFK9ethqsheUiTdxRg9Bqd6hOyYJgiBcCYQb9JDVcLOemHroZXt96QE9d5uZ0l0r/SX2+qajaguE6yF2Rs8D5BQjp/X8dZmYbjpUcZVz6yLMjDcAy94OFet0oqnn2VmrYpY8meHRRFwneCOkG94BjXdCsFonKDp/AYNHnO/WnB7UzVrS/XrEQ9MbbWnKJVxBGIZu5BCo0p3ABUEQhAUjV+ZFhmEYlJeX29OFqKQRVnwIqrcBCjofw+jfR8Qbw/BFoM7+Kg1b41sEPiec4nO3zwmn+CYQaYVlb4NQox4yOfx64T6gPOKfvcOhMiF6TFfFVWyC8oUln+b9noZqoWw9eCM63nx95BFjviS79MNcelA30Ki5/rKK7EW9z7jQtyDn+DyLK3UVWO8LeQ3rtnSfyQPbfelBnYhLdIG/GmPFhyhr2oTReCus/rieEsQT0sm4jl/oKlgza+kq5BVjNqErX1N9em7MZfeAxz8/3xI/Lpa6z3Jn6Qp9DcnF9Qf70/mA8ohPuqkKgiDkgXRTvRJRJvQ8Dd3P6koNlJ5EvPnNTq+ZIAiCMwwegQs/g1S3/mAi0mbt68fOaIeZhbW/MTrcxyHSA7oz3vDrsOxu8FcUz52J6uF8yR7dYKjtPbr6Slj8JHvg7L/oqsby1bri6koh1a/n3E316sT98l/RlbVTSXRC1+O60jYb13MgVm6C8LLiTAFiZqH/RV2B66+AVffr5IkgWEXvC9D5KPjC03dMlW6qgiAIeSOVcYsM0zTp7e3FtLM7l+HR1Qit78QMNTGUq8Osvt4+3wSKEp+DPiec4nO3zwmn+KahchM03g7BGt1hNdGZv08pegeTmDN9tpVL6SFsmWE9L6cFibgFvaeBKqjYqBtZ9Oc3V96cMeZDLjnaObVPVyS2/NKMiThX7DMu8lniDNXpIasly3Rl4xzDui3ZZwrANl+yBwYO6Kk9wi2w8sMQbpj+/Qw3wvL362725eshm4TuZ/R8e8meBa/KrDEqBYOHdfWi1w/t715wIm6pHxdL3WeLs3ytvk7GO6cdjm0qRe9QpqgxCoIguBVJxi1CsllrhzXMSMV6WPObJBrfBYHiVUYULT6HfE44xedunxNO8U1DzTadLPNXQNdTBXVIzOZmSQAMH9fDU0MNUHdj4es1k3Mh72nVNRCqh0QHpAby880W41yYWV1ZlOzV1UJt7wV/6ew+N+wzLvJZ4oy0QPNbJgzrnn0i9wXtM/PAcl+i89J+W7oCVn5IJyLGfDO9n2WrYNVHof1dUNKqK+q6Hoee53V16AKYMcbhY/p4zqWh9Z16fS1gqR8XS91nuTNQpSvHfaEZ544r9nEvCILgViQZd6XjDcz5QCQIgnDFUHejnsPMXwqdj+tOhAshPaiHjOXGmjZELFnNBeMv1419AhUweNBel1IwdERXBnm80PZu3URCcCflq6H5TgjWQ98eiF9weo3sIX5eJxxTfVC+DpZ/QCeS88XwQNW1elh689sgWAfxs3o+uf6Dk7raL5jYOT18OBOF5ruhYrN1ry0IU6nYAIFaGHk9r/kjBUEQhOmRZJwgCIIgjGEY0HCbrpLzhnQ1SzY2v9dSSs8dlRmCyo2Lr2No5dW6Wi/RA8n8qwALZuTEpYTksnt11ZDgbio3Q8MtEKjWHVYtGIK5qIidgcGXdTfSys3Q/j4IlM3vtTx+qL8R1nxcn1t8pbqZS+fP9f/NzMLWNdWrzzPpQajdpee8lMnzBTsJt0C4Tl/j4uecXhtBEATXIsm4RYZhGFRVVRW1s5P43O0Un7t9TjjFNwceLzS+STcYwKMTctPMjTPuA6rKApd3OIyf0xO/ewL69Ty++a3PdE4r3lN/qU7IBSpgYP/sPmaIcS7i50fny4vq96D62rz+zHX7zCL32eKs2QF1O8FXBl1PXlZFOu99Zp5Y5ht+HYZe1Ym46m3Q/p5pK1oLfj/9pbpR1ur/BDXXAT7oPwAdD0PsrG6uNQeXxZgZgYHDeqh5xQbdkGWxnWfE55jPNqfHOzrvaKU+Vib6gKoy6aYqCIKQD5KMW2QYhkEwGCzqjYH43O0Un7t9TjjFlwcev54bq3ID5DJ6AnYzPbMv4J3sy6VgZDQJVbcLSprnvy4zOa14Tys36fm/0gOzNq2YNsa5SPXD0Cv6tWuuh4ab9dC9PHDlPrOIfbY4DUN3TByvIn1CdxCd6Ct0n1nQ6ljgi76m53hMD0DNTmh9h45tJt983s9QLbT/Mqz8iE5omGno2a2T/smuWf90Uoy5lB5inu6DkiY9T9wM6zpflvpxsdR9tjpLV0KwWldkTkjEG4ZB0O+RZJwgCEIeSDJukWGaJl1dXUXt7CQ+dzvF526fE07x5Yk3qLtHVqzTFSjdT+tGBFN9StHVn5jc4XDkhH5ICdbrphAWY1mMvhJdrRao0lU6M/mmi3E2sjEYPKQrdsrXwbK3FlSx49p9ZpH6bHMaHmi8Qw/lxBitIk1pX6H7zAJZkE8pnTgeeV0ft/VvgNa363PATL6Fvp+l7bDyPj0ENrJCJzQ6n9TDftOD0zvHYjSz+vhK9uphr23vsaUR11I/Lpa6z1anL6I7q/pLJ1XHmUrRNSDdVAVBEPJBknGLEFXkyVDF536n+Nztc8IpvjzxlUDLvVC6Sld69T437XCySbr0IMQu6KGtzXfa1iTHshgrNujquOzwrJPx560z07oDZapXN2poexf4wgWvlmv3mUXqs83p8cOyt+g5EXPp0SrS7KjPet1szMunTN1gZOQUpKM6udj8Fh3XnL4FBmgYej7JNb8OLW/XczjGL0LnIzo5Ps18lcocTRwmugAFbb9seeXtJN8SPy6Wus9WZ9laXekZP6MryO32CYIgLDEkGScIgiAIs+Ev18PVIssh3gH9e2d+6ldKT8o+3rRhY1FXdV54Q1C9VU/GP3BwYRkUZY52oOwFbwm0vVdX3QlLG28Ilr0Nytfoodk9u0HlnF6ruVE5XWEWOwvZEd2JtPEOS+ddywuPD2pv0J1XG+/Q55zocd15dejlSUPkvcnTej7KXBya364TIoLgBKF6CDfreVFHTji9NoIgCK7DFcm406dP86u/+qusWLGCcDjMqlWr+NznPkc6Pf38PYIgCIJgKcHq0YRcKwyfhoGXpv+9xAVI9emqmgYHHurnS/l6CDdBLrGw7njRVyDZCWYOWt8FkRbr1lFY3PhLR6tIV+h9oP/F4pfGFYKZ1cnn2HnIJvSQ9IZb9OT0TuELQ9MdsPrjujmGEdDJ7Y5fwPApiF/AFz+pK/jqb4PaHdI5VXAOw4DyDfqDnGFJxgmCIBSKK54SXn31VUzT5O///u9ZvXo1R44c4WMf+xixWIy//Mu/dHr1LMUwDGpqaoo6maz43O0Un7t9TjjFN0/CDTrZcPYHEH0VvAGo2IAB1FQEMcy0fiDJjD4olyyz1j8By2P0BnTnyESXTjSWtE56yB+PcbbXGDkNsXOQiUHrL+lhi/Nkyewzi8RXNGegEpbdC2f/P4yRM9SWeDDSayFYaZ9zlLz20THMjE7EJTp11VnrO3R32ALeG1vfz2CVPoaqd0DXY/q80vcChi9CmS+GUb0FGm/PuyHKfFnqx8VS9xXFWbocgjWQ6oZEB4YnSE2FXxo4CIIg5IGhXDqw/3/8j//B1772NU6ePJn330SjUSoqKhgaGqK8vNzGtVsYpmni8RSvaFF87neKz90+J5ziWwDRE3D+X3X1T80OKFuFqRSe6Cu6E6O3FNb+pm1zxY1heYxmBs78s54/q2oblK2Y/GOl8Mz0gJXsgv6XIN0P9bfo4X4LrDBaUvvMIvAV1Rk7C2d/gJnowOPx67kDS9oh3JjXXGzzZdZ9dIxcCgYOQLJbD1NteZduYjKP5EFR3k+ldJfX7icgdg6zpBnPivvAH7HXO8pSPy6Wuq8ozr59unrTG4CqazANP56GW+zzLRC3PA8KgrD0cUVl3HQMDQ1RXV096++kUilSqdT499FoFNAXpbEuP4ZhYBgGSqlJE47OtXxql6BCl3s8nstee4yuri7q6uomXTin+/35rvvE5aZp0tPTQ0NDw2XrYVVME5eP+erq6vD5fLbENHF5Npsd93k8Hltimro8l8vR3d19mdOqmKauo1KKnp4eamtrJ+0zVsY00z4ztp5WxzRx+cR9xuv12hLTRKdV26+Qc4RS6rLj3sqYpi4f22fq6uomfXpt13lvtn3GqpgmLs/lcpftM5bGVLoSGt8EF/8DT99ecvjoiQWpy53FyCRg2Tvw+EstjWnq8un2mYVfn7xQtRVPogM1cAgVaRuvvDGVoncgRV1V8LKKB092GDVwBJUe0F01G+7AMDwYMO9Yx/aZ+vp6vF6vbdfcseUT9xmPx2PbNXdsuRXbr9BzBFx+f2Hb9Sncgtn6PgZPPka154yeazF2QXdgjLRjlLRgBKsu63zqGXvtKeudz3JTKXr6k9RXh/COvQcT30fAMFOovn2oZI9OvrW+D6Nq47zOEfneX1hyLi9fi1G2mtzwKbqjHuqMIB7TtP36NNP9hV3Xp4XcX8znHDHXPakVMU1cXuj9hRXnvalOq2NSSqEiK/VQ1ZGTqMgIPfFS6mpz095f2H0uz2f5dOdGQRAEJ3BlMu7111/nb//2b/nyl7886+998Ytf5Atf+MJly3t6ekgmkwCEw2EqKiqIRqMkEonx34lEIpSVlTEwMDBpbrry8nJKSkro7+8nm82OL6+qqiIYDNLT0zPpJF9TU4PX66W7u3vSOtTX15PL5ejr6xtfZhgGdXV1ZLNZuru7xy+aPp+P2tpaEonEeEIRIBAIUF1dzcjICLHYpY5bhcRkmiaZjO6AZFdMDQ0NpNNpBgYGME2ToaEhvF4v9fX1tsQ0dTsNDg6ilMLj8dgS0xgTt9NEpx0xTdxOFRUVgN6vJ974WB3TxGR2LBajoaHBtpgmbqdcLsfQ0BBKqfHkitUxwaV9LxaLTdp+xThHGIYxyWl1TFO3UzAYBPQHFBM/sLDrvDd23NfV1aGUsiWmidspFouN7zNlZWU2ncvr8QWupZp9mD3Pk45VMuSNYpasJpduoAEsjWnqvheJRBgZGZm0z1hyfTLLqA+3YCYPMXDhGLkSXR2n0MmMdNZkaPhS1zwfKWpzh0nH+xkxmskE3gB9gws+743tM2VlZZSWltp+ferr6xvfZwKBgG3X3LHtFAqFGB4enrT97L4+VVdXk06nJ91f2Hl9Mk1FrupmaKih/8xzeKKvYIx0QvRlykqO4QnX0pdtIhdo1BPAA/XVIXI5Rd/QpfOSYUBDdZh0xmRg+JLT5zWorQyRSOWIxjKYSjE0kiHg91JTEWQkkSWWuLTPhH0pKtJHiEe7SZl+MvVvRaVriYyMzPscMfG8bdc1d+J2ihn1DI5cQI1uQ7uvT9XV1SilJu0zVsc0cd8zTXM8jmLcl4+dZ5RSNDY22nbNnbidJu4zxXrWmOi0I6ZEIkUgU42ROo+/7ySEr2ZwcHD8+cKOmBayncaeAQVBEJzG0WGqn//856dNlk1k7969bN++ffz7ixcvcsstt3DLLbfwD//wD7P+7XSVca2trQwMDIyXJdv9qWKhn+yAVMZZ+Sm9VMZJZVyhn/5KZZxUxhV0jujdTa7zCaJDQ5SXBTBW/SeItFhfeTBl+XT7jGUxxU6izv2rrnRreTsY3ukr48ws9O/TlXS+CGrFR/VwxHnGNHG5VMYtPKZ87i/svD5Ne3+RuAgDL2EMHcHIDGKaWfCVQEkblLTgCdagwPrKuMwIxsABjFQPyhtBtb1vfBj2fM97Ra2Mc+j6NNP9hVTGSWXcZcvj5+H8v6Ji5+gpuYO6tW+c9v5iMVTGDQ8PU1lZKcNUBUFwHEcr4z75yU/yvve9b9bfWb58+fi/L168yG233cbOnTv5+te/PufrB4PB8QqQiYzdaE9k7KQ/lZmWzzT3QiHLp3ttc3TYgRXrmO/ysX/bFdPU5WPxWbHucy0fu9mZ+n5aHdN0y6c6rYxpImM3KtPtM1bHNPF7K15ntnWcuHzs/ZxtX7V6X1ro9itkH1NK2brPTF0+cZh+Ie/lQs4RVu8zsy2feNzP5V3wea/uRoxsEmJPY9Rej6f0UuMDO2OdaZ+xJKbSlRil7Rj9A3ri+Ir1k9bFYxij81gdhVQPGB6M9vdgRBotjXXiv+2+Pk13rXDD9ivEWYz7i+led9LySIv+aroTho7gGXwJRk5B9BiMnIBgDUZkOUa4Cbyhy52XGScvv2z/AUgP6TniUr3gr8RY/isYkdZ5xzRxeb73F269Ps12f2HX8THfa8V8zxETr4PFuD4Vsv2sPO/Zfn8RaYFwA2ayE0+md8b7i2I9a8y2fLrfEQRBcALXNHC4cOECt912G9u2beO73/0uXm/hE0O7ZcLOpT6Z7FL3OeEUn7t9TjjFZ7Ev2Y8nUIadk9Nf5rQzxpFTcO5HOtnWci94fJMnxx8+DtHjkI1B23ugZqvlq7Dk9xk5z2gS3bqz6dAhSPXrRiK+Eihp0RVzwZrxBPecPjWlgUN6APoP6kRcsBZWfADCTfOO5zKfbEPxLXJfUZ1DL8OFf8c0fHiuesB+3zxxy/OgIAhLH1fMGXfx4kVuvfVW2tra+Mu//Et6enrGf9bYePkn8W5mbLjMTJ/uiG9x+5xwis/dPiec4rPB5y3DMHzTVu7Y5rQzxkg7lC7XyZHoMVTFBnI5heEFI3EBhk9CJgpNd+lOlBZzRewzcp7RhOshfCc03A7RV2DgkK6Si56A4dchWA2R5RBuBl94Dt/oPmoYOgE38BKk+iC8DJa/H0J1xY/PQhbtNhTfovQV3Vm6EhWsx1QejFwWw+uKx0xBEATHKO5HM/PkF7/4BSdOnOCxxx6jpaWFpqam8a+lhlJ6cvNiFSyKz/1O8bnb54RTfO72FcVpeKB6G4RqYehlVC5D31AKleqHoVd0xVHNddBwK2MdV61kqW/DJbnPLNTn9UHVZlj5AVj3SWi+E0INephp317o+Dn0vQjJLlDmZX+uQO+joH+n/yAke3ViecUHLU3EgWxD8S1+X9Gd3hCq/b30lt6BsuG6IAiCsNRwxZnyIx/5yPjkm1O/BEEQBEGwgZJWKF2hh95GX8bIxWHwEKQGoHwdLHsbeKTyQbCBYA00vhHWf0oPLa3eCoZPV2R2PQWdj+ih0tnY5X+buKir61J9UL4Gln9AV9cJgmA/RZyqQRAEwe3IXbQgCIIgCJdjGLo6buQMDL+GP9sP/n49rLDtXbMOGRQESzA8ULFBf401Yhg4BMlu6NsH3iN6+GqkHYK1eJPnIHVKV25WbIC2d4O/1OkoBEEQBEEQLkOScYuQYnf5EZ/7neJzt88Jp/jc7Suas2QZlK2EZA+eVA8ES6HtvRCosl291Lfhkt1n7PIFKvSw6PpbdJffgZf0HHOxM/rLV4Y/ngPfCNRsgZZ32J4wlm0ovsXuc8Ip3UoFQRDywzXdVK1AuucIgiAIQoEkOuHsDyDVBW3vg8oNTq+RIGjSIzB4QCfmEp1gpvRchi33gDfo9NoJgrAIkedBQRAWC66YM+5KQilFKpUq6mSy4nO3U3zu9jnhFJ+7fUV3hhtRy95Oqvn9qPL19vtY+ttwye8zxfIFSqH+DbD2E6hVv0pq2XtRy4qTiJNtKL7F7nPC6USMgiAIbkWScYsMpRQDAwNFvWiKz91O8bnb54RTfO72OeFUJS0MZKsoVoRLfRteEftMMX2GgYq0M6BaUEWaQF62ofgWu88JpxMxCoIguBVJxgmCIAiCIAiCIAiCIAhCkZBknCAIgiAIgiAIgiAIgiAUCUnGLUJ8vuI2uRWf+53ic7fPCaf43O1zwik+d/uccIrP/U7xudvnhNOJGAVBENyIdFMVBEEQBEEQBEEQljzyPCgIwmJBKuMWGUop4vF4USdaFZ+7neJzt88Jp/jc7XPCKT53+5xwis/9TvG52+eE04kYBUEQ3Iok4xYZSimi0WhRL5ric7dTfO72OeEUn7t9TjjF526fE07xud8pPnf7nHA6EaMgCIJbkWScIAiCIAiCIAiCIAiCIBQJScYJgiAIgiAIgiAIgiAIQpGQZNwiwzAMAoEAhmGIz4U+J5zic7fPCaf43O1zwik+d/uccIrP/U7xudvnhNOJGAVBENyKdFMVBEEQBEEQBEEQljzyPCgIwmJBKuMWGUophoeHizrRqvjc7RSfu31OOMXnbp8TTvG52+eEU3zud4rP3T4nnE7EKAiC4FYkGbfIUEoRi8WKetEUn7ud4nO3zwmn+Nztc8IpPnf7nHCKz/1O8bnb54TTiRgFQRDciiTjBEEQBEEQBEEQBEEQBKFISDJOEARBEARBEARBEARBEIqEJOMWGYZhEA6Hi9r1SHzudorP3T4nnOJzt88Jp/jc7XPCKT73O8Xnbp8TTidiFARBcCvSTVUQBEEQBEEQBEFY8sjzoCAIiwWpjFtkKKUYGhoq6kSr4nO3U3zu9jnhFJ+7fU44xedunxNO8bnfKT53+5xwOhGjIAiCW5Fk3CJDKUUikSjqRVN87naKz90+J5zic7fPCaf43O1zwik+9zvF526fE04nYhQEQXArkowTBEEQBEEQBEEQBEEQhCIhyThBEARBEARBEARBEARBKBKSjFtkGIZBJBIpatcj8bnbKT53+5xwis/dPiec4nO3zwmn+NzvFJ+7fU44nYhREATBrUg3VUEQBEEQBEEQBGHJI8+DgiAsFqQybpGhlKK/v7+oE62Kz91O8bnb54RTfO72OeEUn7t9TjjF536n+Nztc8LpRIyCIAhuRZJxiwylFOl0uqgXTfG52yk+d/uccIrP3T4nnOJzt88Jp/jc7xSfu31OOJ2IURAEwa1IMk4QBEEQBEEQBEEQBEEQioQk4wRBEARBEARBEARBEAShSEgybpFhGAbl5eVF7XokPnc7xedunxNO8bnb54RTfO72OeEUn/ud4nO3zwmnEzEKgiC4FemmKgiCIAiCIAiCICx55HlQEITFglTGLTJM06S3txfTNMXnQp8TTvG52+eEU3zu9jnhFJ+7fU44xed+p/jc7XPC6USMgiAIbkWScYuQbDYrPhf7nHCKz90+J5zic7fPCaf43O1zwik+9zvF526fE04nYhQEQXAjkowTBEEQBEEQBEEQBEEQhCIhyThBEARBEARBEARBEARBKBLSwGGRoZQinU4TCASK0olIfO53is/dPiec4nO3zwmn+Nztc8IpPvc7xedunxNOJ2IsFDc8DwqCcGUgyThBEARBEARBEARhySPPg4IgLBZkmOoiwzRNurq6itr1SHzudorP3T4nnOJzt88Jp/jc7XPCKT73O8Xnbp8TTidiFARBcCuSjFuEFLtYUXzud4rP3T4nnOJzt88Jp/jc7XPCKT73O8Xnbp8Tzito0JUgCMKCkGScIAiCIAiCIAiCIAiCIBQJScYJgiAIgiAIgiAIgiAIQpGQBg6LDKUU2WwWn89XtK5H4nO3U3zu9jnhFJ+7fU44xedunxNO8bnfKT53+5xwOhFjobjheVAQhCsDqYxbZBiGgdfrLdoFTHzud4rP3T4nnOJzt88Jp/jc7XPCKT73O8Xnbp8TTidiFARBcCuSjFtkmKZJd3d3Ubseic/dTvG52+eEU3zu9jnhFJ+7fU44xed+p/jc7XPC6USMgiAIbkWScYIgCIIgCIIgCIIgCIJQJCQZJwiCIAiCIAiCIAiCIAhFQpJxgiAIgiAIgiAIgiAIglAkpJvqIsQ0TTye4uVJxed+p/jc7XPCKT53+5xwis/dPiec4nO/U3zu9jnhdCLGQnDL86AgCEufxXumvEJRSpHL5ShWjlR87neKz90+J5zic7fPCaf43O1zwik+9zvF526fE04nYhQEQXArkoxbZCil6OvrK+pFU3zudorP3T4nnOJzt88Jp/jc7XPCKT73O8Xnbp8TTidiFARBcCuSjBMEQRAEQRAEQRAEQRCEIiHJOEEQBEEQBEEQBEEQBEEoEpKMW4QYhiE+F/uccIrP3T4nnOJzt88Jp/jc7XPCKT73O8Xnbp8TTidiFARBcCPSTVUQBEEQBEEQBEFY8sjzoCAIiwWpjFtkKKVIpVJFnWhVfO52is/dPiec4nO3zwmn+Nztc8IpPvc7xedunxNOJ2IUBEFwK5KMW2QopRgYGCjqRVN87naKz90+J5zic7fPCaf43O1zwik+9zvF526fE04nYhQEQXArkowTBEEQBEEQBEEQBEEQhCIhyThBEARBEARBEARBEARBKBKSjFuE+Hw+8bnY54RTfO72OeEUn7t9TjjF526fE07xud8pPnf7nHA6EaMgCIIbkW6qgiAIgiAIgiAIwpJHngcFQVgsSGXcIkMpRTweL+pEq+Jzt1N87vY54RSfu31OOMXnbp8TTvG53yk+d/uccDoRoyAIgluRZNwiQylFNBot6kVTfO52is/dPiec4nO3zwmn+Nztc8IpPvc7xedunxNOJ2IUBEFwK5KMEwRBEARBEARBEARBEIQiIck4QRAEQRAEQRAEQRAEQSgSkoxbZBiGQSAQwDAM8bnQ54RTfO72OeEUn7t9TjjF526fE07xud8pPnf7nHA6EaMgCIJbkW6qgiAIgiAIgiAIwpJHngcFQVgsSGXcIkMpxfDwcFEnWhWfu53ic7fPCaf43O1zwik+d/uccIrP/U7xudvnhNOJGAVBENyKJOMWGUopYrFYUS+a4nO3U3zu9jnhFJ+7fU44xedunxNO8bnfKT53+5xwOhGjIAiCW5FknCAIgiAIgiAIgiAIgiAUCUnGCYIgCIIgCIIgCIIgCEKRkGTcIsMwDMLhcFG7HonP3U7xudvnhFN87vY54RSfu31OOMXnfqf43O1zwulEjIIgCG7FNd1U77nnHg4ePEh3dzdVVVXccccdfOlLX6K5uTnv15DuOYIgCIIgCIIgCFcm8jwoCMJiwTWVcbfddhv//M//zLFjx/jBD37A66+/zi//8i87vVqWo5RiaGioqBOtis/dTvG52+eEU3zu9jnhFJ+7fU44xed+p/jc7XPC6USMgiAIbsU1ybjf+Z3f4YYbbqC9vZ1du3bx2c9+lueff55MJuP0qlmKUopEIlHUi6b43O0Un7t9TjjF526fE07xudvnhFN87neKz90+J5xOxCgIguBWXJOMm0h/fz//9E//xK5du/D7/U6vjiAIgiAIgiAIgiAIgiDkhc/pFSiEz3zmM3zlK18hHo9zww038JOf/GTW30+lUqRSqfHvh4aGABgcHMQ0TUBPNGoYBkqpSZ/izLV87O/nu9zj8Vz22mNEo1ECgQAej2fW35/vuk9cbpom0WiUUCh02XpYFdPE5WO+QCCAz+ezJaaJy7PZ7KT3046Ypi7P5XLTOq2Kaeo6KqUYHh6+bJ+xMqaZ9pmx9bQ6ponLJ+4zXq/XlpgmOq3afoWcI5RSlx33VsY0dfnYPhMMBidNsmzXeW+2fcaqmCYun7gNx/YZO8/lU/cZO2Kauny6fcbO65NpmtPuM1bGNHH52D4TDAbxer22XXPHlk/dhnZdc8eWW7H9Cj1HwOX3F3Zen+Zzf7GQ897Ufcaua26h9xduvj7NdH9h1/VpIfcX8zlHzHVPakVME5cXuv3ceH2a6/7C7nN5PsuHh4fH11UQBMFJHG3g8PnPf54vfOELs/7O3r172b59OwC9vb309/dz5swZvvCFL1BRUcFPfvKTyx4MCnl9QRAEQRAEQRAE4crh3LlztLS0OL0agiBcwTiajOvt7aW3t3fW31m+fPm0n6qeP3+e1tZWdu/ezc6dO6f926mVcaZp0t/fT01NzYwJPKeJRqO0trZy7ty5onT4EZ/7neJzt88Jp/jc7XPCKT53+5xwis/9TvG52+eE04kYC2Wseq+5uXlSxacgCEKxcXSYam1tLbW1tfP627Ec4sRk21SCwSDBYHDSssrKynn5ik15eXlRL2Lic79TfO72OeEUn7t9TjjF526fE07xud8pPnf7nHA6EWMhVFRUOL0KgiAI7pgzbs+ePezZs4ebbrqJqqoqTp48yX/9r/+VVatWzVgVJwiCIAiCIAiCIAiCIAiLDVfU5obDYR566CHe+MY3sm7dOu6//342bdrEk08+eVnlmyAIgiAIgiAIgiAIgiAsVlxRGbd582Yee+wxp1ejKASDQT73uc8VLckoPvc7xedunxNO8bnb54RTfO72OeEUn/ud4nO3zwmnEzEKgiC4FUcbOAiCIAiCIAiCIAiCIAjClYQrhqkKgiAIgiAIgiAIgiAIwlJAknGCIAiCIAiCIAiCIAiCUCQkGScIgiAIgiAIgiAIgiAIRUKScYuIr371q6xYsYJQKMS2bdt4+umnbXN9/vOfxzCMSV+NjY2Wvf5TTz3F29/+dpqbmzEMgx/96EeTfq6U4vOf/zzNzc2Ew2FuvfVWjh49apvvIx/5yGXx3nDDDfP2ffGLX2THjh2UlZVRX1/PL/3SL3Hs2LFJv2NljPn4rIzxa1/7GldffTXl5eWUl5ezc+dOfvazn9kSWz4+q7ffVL74xS9iGAYPPPDA+DKrY5zLZ3WMcx3jVsc3l8+ObXjhwgU++MEPUlNTQ0lJCddeey379u2zLcZ8nFbGuXz58steyzAMPvGJT9gS31w+q7dhNpvlv/yX/8KKFSsIh8OsXLmS//bf/humaY7/jpUx5uOzOsbh4WEeeOAB2tvbCYfD7Nq1i71799oSX77OhcRoxbU9lUrxW7/1W9TW1hKJRLjnnns4f/68bb5bb731snjf9773zTvGhx56iLvuuova2loMw+DgwYOXvYaVMebjKyTG2XyZTIbPfOYzbN68mUgkQnNzMx/+8Ie5ePGiLfHl67MqPtDXqvXr1xOJRKiqquKOO+7ghRdemHd8VjmtjHEi/+k//ScMw+Cv//qv5x2jFb5Cj0NBEIQrAUnGLRK+//3v88ADD/BHf/RHHDhwgDe84Q3cfffdnD171jbnxo0b6ejoGP86fPiwZa8di8W45ppr+MpXvjLtz//iL/6Cv/qrv+IrX/kKe/fupbGxkTe96U0MDw/b4gN485vfPCnef//3f5+XC+DJJ5/kE5/4BM8//zwPP/ww2WyWO++8k1gsNv47VsaYj8/KGFtaWvjzP/9zXnzxRV588UVuv/127r333vGHHKu331w+K2Obyt69e/n617/O1VdfPWm51THO5QPrY5ztGLcjvrnOKVbGNzAwwI033ojf7+dnP/sZL7/8Ml/+8peprKwc/x2rY8zHaWWce/funfQ6Dz/8MADvfve7bYlvLp+VsQF86Utf4u/+7u/4yle+wiuvvMJf/MVf8D/+x//gb//2b8d/x8oY8/FZHeOv/dqv8fDDD/OP//iPHD58mDvvvJM77riDCxcuWB5fvs6FxGjFtf2BBx7ghz/8IQ8++CDPPPMMIyMjvO1tbyOXy9niA/jYxz42Kd6///u/n3eMsViMG2+8kT//8z+f8TWsjDEfXyExzuaLx+Ps37+fP/7jP2b//v089NBDvPbaa9xzzz22xJevz6r4ANauXctXvvIVDh8+zDPPPMPy5cu588476enpmVd8VjmtjHGMH/3oR7zwwgs0Nzdf9jMr99F8fIXEJwiCcMWghEXBddddpz7+8Y9PWrZ+/Xr12c9+1hbf5z73OXXNNdfY8tpTAdQPf/jD8e9N01SNjY3qz//8z8eXJZNJVVFRof7u7/7Ocp9SSt13333q3nvvXfBrz0R3d7cC1JNPPqmUsj/GqT6l7I+xqqpK/cM//IPtsU31KWVfbMPDw2rNmjXq4YcfVrfccov61Kc+pZSyb/vN5FPK+hhnO8btiG+uc4rV8X3mM59RN91004w/tyPGuZxK2XscfupTn1KrVq1SpmkW5Tic6FPK+tje+ta3qvvvv3/Ssne+853qgx/8oFLK+m04l08pa2OMx+PK6/Wqn/zkJ5OWX3PNNeqP/uiPbNmGczmVsi7G+VzbBwcHld/vVw8++OD471y4cEF5PB71H//xH5b7lFKXnWsXEuNETp06pQB14MCBScutjDEfn1Lzj3E23xh79uxRgDpz5oxSyr74ZvIpZW98Q0NDClCPPPKIUmph8c3XqZT1MZ4/f14tW7ZMHTlyRLW3t6v/+T//5/jP7NiGs/mUWthxKAiCsFSRyrhFQDqdZt++fdx5552Tlt95553s3r3bNu/x48dpbm5mxYoVvO997+PkyZO2uSZy6tQpOjs7J8UbDAa55ZZbbI33iSeeoL6+nrVr1/Kxj32M7u5uy157aGgIgOrqasD+GKf6xrAjxlwux4MPPkgsFmPnzp22xzbVN4YdsX3iE5/grW99K3fcccek5XbFOJNvDKtjnOkYtyu+uc4pVsb3b//2b2zfvp13v/vd1NfXs2XLFv7P//k/4z+3I8a5nGPYsa+m02m++93vcv/992MYhu3H4VTfGFbGdtNNN/Hoo4/y2muvAfDSSy/xzDPP8Ja3vAWwfhvO5RvDqhiz2Sy5XI5QKDRpeTgc5plnnrFlG87lHMOOfTSfePbt20cmk5n0O83NzWzatKngmAt5//7pn/6J2tpaNm7cyO/93u8tuMJ5NqyMsRDsinFoaAjDMMYrgO2Ob6pvDDviS6fTfP3rX6eiooJrrrkGsD++6ZxjWBWjaZp86EMf4vd///fZuHHjZT+3Osa5fGMU8zgUBEFwAz6nV0CA3t5ecrkcDQ0Nk5Y3NDTQ2dlpi/P666/n//7f/8vatWvp6uriT//0T9m1axdHjx6lpqbGFucYYzFNF++ZM2dscd599928+93vpr29nVOnTvHHf/zH3H777ezbt49gMLig11ZK8elPf5qbbrqJTZs2AfbGOJ0PrI/x8OHD7Ny5k2QySWlpKT/84Q/ZsGHD+I2a1bHN5LMjNoAHH3yQ/fv3T5pLaQw7tt9sPrA+xtmOcTvim+ucYnV8J0+e5Gtf+xqf/vSn+c//+T+zZ88efvu3f5tgMMiHP/xhW2Kcywn2nWt+9KMfMTg4yEc+8hHA/vPoVB9YH9tnPvMZhoaGWL9+PV6vl1wux5/92Z/x/ve/H7A+xrl8VsdYVlbGzp07+ZM/+ROuuuoqGhoa+N73vscLL7zAmjVrbNmGczmtjnEi+cTT2dlJIBCgqqrqst8p9H4n3/fvAx/4ACtWrKCxsZEjR47wh3/4h7z00kvjw7CtxsoY88WuGJPJJJ/97Gf5lV/5FcrLywF745vOB9bH95Of/IT3ve99xONxmpqaePjhh6mtrQXsi282p9UxfulLX8Ln8/Hbv/3b0/7c6hjn8kHxj0NBEAQ3IMm4RcTE6gPQSZepy6zi7rvvHv/35s2b2blzJ6tWreI73/kOn/70p21xTqWY8b73ve8d//emTZvYvn077e3t/PSnP+Wd73zngl77k5/8JIcOHZpUdTCGHTHO5LM6xnXr1nHw4EEGBwf5wQ9+wH333ceTTz45/nOrY5vJt2HDBstjO3fuHJ/61Kf4xS9+cVkFyUSsijEfn9UxznaMj03WbuU2nOucYnV8pmmyfft2/vt//+8AbNmyhaNHj/K1r31tPDEG1saYj9Ouc803vvEN7r777svm4rHrPDqdz+rYvv/97/Pd736X//f//h8bN27k4MGDPPDAAzQ3N3PfffeN/55VMebjszrGf/zHf+T+++9n2bJleL1etm7dyq/8yq+wf/9+y+PL12nn9RDmF89CYp7L97GPfWz835s2bWLNmjVs376d/fv3s3Xr1nk554Od9zh2xJjJZHjf+96HaZp89atfnfP3FxrfbD6r47vttts4ePAgvb29/J//8394z3vewwsvvEB9ff2Mf7PQ+OZyWhXjvn37+F//63+xf//+gtd3PjHm61ssx6EgCMJiQoapLgJqa2vxer2XfRrV3d192Se+dhGJRNi8eTPHjx+33TXWYdHJeJuammhvb19wvL/1W7/Fv/3bv/H444/T0tIyvtyuGGfyTcdCYwwEAqxevZrt27fzxS9+kWuuuYb/9b/+l22xzeSbjoXGtm/fPrq7u9m2bRs+nw+fz8eTTz7J3/zN3+Dz+cbjsCrGuXzTTZhs1T46xsRjvBjH4FznlIXG19TUNF45OcZVV1013vTGjhjncs70NwvdjmfOnOGRRx7h137t18aX2bkNp/NNx0Jj+/3f/30++9nP8r73vY/NmzfzoQ99iN/5nd/hi1/8ImB9jHP5pmOhMa5atYonn3ySkZERzp07x549e8hkMuMVImD9NpzNOR1WnWvyiaexsZF0Os3AwMCMv2Olbzq2bt2K3++37X7Hyhjny0JjzGQyvOc97+HUqVM8/PDDk6rU7IhvNt90LDS+SCTC6tWrueGGG/jGN76Bz+fjG9/4BmDf9pvNOR3zjfHpp5+mu7ubtra28fuNM2fO8Lu/+7ssX74csDbGfHxWxicIgrCUkGTcIiAQCLBt27bLSrUffvhhdu3aVZR1SKVSvPLKKzQ1NdnuGnsImRhvOp3mySefLFq8fX19nDt3bt7xKqX45Cc/yUMPPcRjjz122UOO1THO5ZuOhcY43TqkUqmibb8x33QsNLY3vvGNHD58mIMHD45/bd++nQ984AMcPHiQlStXWhrjXD6v12t5jFOZeIwXYxvOdU5ZaHw33ngjx44dm7Tstddeo729HbDnPDOXczqs2I7f+ta3qK+v561vfev4Mju34XS+6VhobPF4HI9n8m2I1+vFNE3A+hjn8k2HVcdhJBKhqamJgYEBfv7zn3PvvffafhxO55wOq2LMJ55t27bh9/sn/U5HRwdHjhwpOOb5vn9Hjx4lk8nYdr9jZYzzZSExjiXGjh8/ziOPPHLZ1CVWxzeXbzqs3oYT7zeKtf1mu8eB+cf4oQ99iEOHDk2632hubub3f//3+fnPfw5YG2M+PivjEwRBWFIUrVWEMCsPPvig8vv96hvf+IZ6+eWX1QMPPKAikYg6ffq0Lb7f/d3fVU888YQ6efKkev7559Xb3vY2VVZWZplveHhYHThwQB04cEAB6q/+6q/UgQMHxrtj/fmf/7mqqKhQDz30kDp8+LB6//vfr5qamlQ0GrXcNzw8rH73d39X7d69W506dUo9/vjjaufOnWrZsmXz9v3Gb/yGqqioUE888YTq6OgY/4rH4+O/Y2WMc/msjvEP//AP1VNPPaVOnTqlDh06pP7zf/7PyuPxqF/84heWxzaXz47tNx1TO31ZHeNsPjtinOsYtzq+2Xx2xLdnzx7l8/nUn/3Zn6njx4+rf/qnf1IlJSXqu9/97vjvWB3jXE474szlcqqtrU195jOfuexnduyjM/nsiO2+++5Ty5YtUz/5yU/UqVOn1EMPPaRqa2vVH/zBH9gS41w+O2L8j//4D/Wzn/1MnTx5Uv3iF79Q11xzjbruuutUOp22PL58nAuN0Ypr+8c//nHV0tKiHnnkEbV//351++23q2uuuUZls1nLfSdOnFBf+MIX1N69e9WpU6fUT3/6U7V+/Xq1ZcuWaX35OPv6+tSBAwfUT3/6UwWoBx98UB04cEB1dHTYEuNcvkJjnM2XyWTUPffco1paWtTBgwcn3W+kUinL48vHZ2V8IyMj6g//8A/Vc889p06fPq327dunfvVXf1UFg0F15MiRecVnhdPKGKdjuu6mVu6jc/nmcxwKgiBcCUgybhHxv//3/1bt7e0qEAiorVu3qieffNI213vf+17V1NSk/H6/am5uVu985zvV0aNHLXv9xx9/XAGXfd13331KKaVM01Sf+9znVGNjowoGg+rmm29Whw8ftsUXj8fVnXfeqerq6pTf71dtbW3qvvvuU2fPnp23bzoXoL71rW+N/46VMc7lszrG+++/f3xfrKurU2984xvHE3FWxzaXz47tNx1Tk3FWxzibz44Y5zrGrY5vNp9d2/DHP/6x2rRpkwoGg2r9+vXq61//+qSf27ENZ3PaEefPf/5zBahjx45d9jM74pvJZ0ds0WhUfepTn1JtbW0qFAqplStXqj/6oz+a9NBvZYxz+eyI8fvf/75auXKlCgQCqrGxUX3iE59Qg4ODtsSXj3OhMVpxbU8kEuqTn/ykqq6uVuFwWL3tbW+b0b9Q39mzZ9XNN9+sqqurVSAQUKtWrVK//du/rfr6+uYd47e+9a1pf/65z33Olhjn8hUa42y+U6dOzXi/8fjjj1seXz4+K+NLJBLqHe94h2publaBQEA1NTWpe+65R+3Zs2fSaxQSnxVOK2OcjumScVbuo3P55nMcCoIgXAkYSik1XcWcIAiCIAiCIAiCIAiCIAjWInPGCYIgCIIgCIIgCIIgCEKRkGScIAiCIAiCIAiCIAiCIBQJScYJgiAIgiAIgiAIgiAIQpGQZJwgCIIgCIIgCIIgCIIgFAlJxgmCIAiCIAiCIAiCIAhCkZBknCAIgiAIgiAIgiAIgiAUCUnGCYIgCIIgCIIgCIIgCEKRkGScIAiCIAiCIAiCIAiCIBQJScYJgiAIwizceuutPPDAA06vhm3E43He9a53UV5ejmEYDA4OOr1KtrB8+XL++q//2unVEARBEARBEARJxgmCIAhXFh/5yEcwDIOPf/zjl/3sN3/zNzEMg4985CPjyx566CH+5E/+xNJ1+Pa3v01lZaWlrzkb/+///T+8Xu+0MX/nO9/h6aefZvfu3XR0dDAwMIBhGBw8eNC29Vm+fDmGYfDggw9e9rONGzdiGAbf/va3bfMLgiAIgiAIgpNIMk4QBEG44mhtbeXBBx8kkUiML0smk3zve9+jra1t0u9WV1dTVlZW7FW0lG9+85v8wR/8AQ8++CDxeHzSz15//XWuuuoqNm3aRGNjI4ZhWObNZDIz/qy1tZVvfetbk5Y9//zzdHZ2EolELFsHQRAEQRAEQVhsSDJOEARBuOLYunUrbW1tPPTQQ+PLHnroIVpbW9myZcuk3506THX58uX89//+37n//vspKyujra2Nr3/96+M/f+KJJy4b7nnw4EEMw+D06dM88cQTfPSjH2VoaAjDMDAMg89//vMApNNp/uAP/oBly5YRiUS4/vrreeKJJ8Zf58yZM7z97W+nqqqKSCTCxo0b+fd///dZYz19+jS7d+/ms5/9LOvXr+df/uVfJsX25S9/maeeegrDMLj11ltZsWIFAFu2bBlfNsa3vvUtrrrqKkKhEOvXr+erX/3qJI9hGPzzP/8zt956K6FQiO9+97szrtcHPvABnnzySc6dOze+7Jvf/CYf+MAH8Pl8k353aGiIX//1X6e+vp7y8nJuv/12XnrppfGfv/7669x77700NDRQWlrKjh07eOSRR2Z9XwRBEARBEATBKSQZJwiCIFyRfPSjH51UmfXNb36T+++/P6+//fKXv8z27ds5cOAAv/mbv8lv/MZv8Oqrr+b1t7t27eKv//qvKS8vp6Ojg46ODn7v935vfJ2effZZHnzwQQ4dOsS73/1u3vzmN3P8+HEAPvGJT5BKpXjqqac4fPgwX/rSlygtLZ3V981vfpO3vvWtVFRU8MEPfpBvfOMb4z976KGH+NjHPsbOnTvp6OjgoYceYs+ePQA88sj/3969hUS5vXEc/832QEpopqZZgmeRMFOEyNIuksZjeSgUTAkrtJPYCYmQDM0wHIKgQCxTurAwisKspAw6EELmRRcGZZiBmZneaGKosy/EodlZ2b/2sPn3/VzNrHe9az3veu8envWuu5Y2SaqtrdWRI0d0/PhxdXV1qbKyUqWlpWpoaLCar6SkREVFRerq6pLRaPxmXF5eXjIajZb7P336pMuXL3/1Dsxms5KTk9Xf36+WlhZ1dHQoKipK69at09DQkCRpZGRESUlJunv3rjo7O2U0GpWamqre3t4fvg8AAADA1kjGAQD+SLm5uXr06JF6enr05s0bPX78WFu2bJnTvUlJSdq1a5eCgoJUUlIiDw8Pqwq273F0dJSrq6sMBoO8vb3l7e2t+fPnq7u7W42NjWpqalJsbKwCAwN18OBBrVmzxpI07O3t1erVqxUeHq6AgAClpKQoLi7um3NNTU2pvr7e8lzZ2dl68uSJXr16JWl6C66zs7McHR3l7e2thQsXytPTU5Lk7u5uaZOk8vJymUwmZWRkyN/fXxkZGdq3b59qamqs5iwuLrb08fHx+e5a5Ofnq76+XmazWVeuXFFgYKBWrFhh1ef+/ft6/vy5mpqaFB0dreDgYFVXV2vBggWWKr+IiAgVFBQoPDxcwcHBqqioUEBAgG7cuDGndwIAAADYkv2PuwAA8P/Hw8NDycnJamhosFRfeXh4zOne5cuXW37PJNUGBgZ+KZ5nz57JbDYrJCTEqn18fFzu7u6SpKKiIu3cuVOtra2Kj49XZmamVSz/1NraqtHRUSUmJkqafub169errq5OlZWVc47tw4cPevv2rbZt26YdO3ZY2icmJuTq6mrVNzo6es7jJicnq6CgQA8ePPhmZWJHR4dGRkYsazBjbGxM3d3dkqTR0VEdO3ZMzc3N6uvr08TEhMbGxqiMAwAAwH8SyTgAwB8rPz9fe/bskSSdOXNmzvc5ODhY/TcYDJqampIk/fXXdNG52Wy2XP/eQQYzpqamZGdnp46ODtnZ2Vldm9mKun37dhmNRt28eVOtra06ceKETCaT9u7dO+uYdXV1GhoakrOzs9U8nZ2dKi8v/2qe78UmTW9VXblypdW1f47xM4cv2NvbKzc3V0ePHlV7e7uuXbs269yLFy+etfJw5kTaQ4cO6c6dO6qurlZQUJCcnJy0adMmff78ec6xAAAAALZCMg4A8MdKSEiwJGy+932znzGzzfPdu3dyc3OTNH2Aw5ccHR01OTlp1RYZGanJyUkNDAwoNjb2m+P7+vqqsLBQhYWFOnz4sGpra2dNxn38+FHXr1/XpUuXtGzZMkv71NSUYmNjdevWLaWkpHx1n6OjoyRZxefl5aUlS5bo9evXysnJ+cEK/Jz8/HxVV1crKyvLsl5fioqKUn9/v+zt7eXn5zfrGA8fPtTWrVuVnp4uafobcj09Pb81TgAAAOB3IRkHAPhj2dnZqaury/L7dwgKCpKvr6/KyspUUVGhly9fymQyWfXx8/PTyMiI7t27p4iICDk7OyskJEQ5OTnKy8uTyWRSZGSkBgcH1dbWpvDwcCUlJam4uFiJiYkKCQnR8PCw2traFBYWNmscFy9elLu7uzZv3myp1puRkpKi8+fPz5qMW7RokZycnHT79m0tXbpU8+bNk6urq8rKylRUVCQXFxclJiZqfHxcT58+1fDwsPbv3/8/r1dYWJgGBwetqve+FB8fr1WrViktLU1VVVUKDQ1VX1+fWlpalJaWpujoaAUFBenq1atKTU2VwWBQaWmppZoPAAAA+K/hAAcAwB/NxcVFLi4uv208BwcHNTY26sWLF4qIiFBVVZUqKiqs+sTExKiwsFBZWVny9PTUyZMnJUkXLlxQXl6eDhw4oNDQUG3YsEHt7e3y9fWVNF2ttnv3boWFhSkhIUGhoaE6e/bsrHHU1dUpPT39q0ScJGVmZqq5uVnv37//6pq9vb1Onz6tmpoa+fj4aOPGjZKmt8ieO3dO9fX1Cg8P19q1a1VfXy9/f/9fWi9p+rAIJyenWa8ZDAa1tLQoLi5O+fn5CgkJUXZ2tnp6euTl5SVJOnXqlNzc3BQTE6PU1FQZjUZFRUX9clwAAADAv8Fg/vKjNgAAAAAAAAD+NVTGAQAAAAAAADZCMg4AAAAAAACwEZJxAAAAAAAAgI2QjAMAAAAAAABshGQcAAAAAAAAYCMk4wAAAAAAAAAbIRkHAAAAAAAA2AjJOAAAAAAAAMBGSMYBAAAAAAAANkIyDgAAAAAAALARknEAAAAAAACAjZCMAwAAAAAAAGzkb3VRNeYr2FXhAAAAAElFTkSuQmCC", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -275,13 +416,13 @@ "\n", "# Choose an index of the file\n", "csv_files = [f for f in os.listdir('../data/interim') if f.endswith('.csv')]\n", - "# print(\"csv files: \", csv_files)\n", + "print(\"csv files: \", csv_files)\n", "\n", "\n", "print('---------------\\n')\n", "file = csv_files[index]\n", "# Or use the file name directly\n", - "# file = '2024-11-18_adu001__i5mins_d4hrs_c5g_l3hrs_n3.csv'\n", + "#file = '2024-11-15_500030__i5mins_d4hrs_c5g_l3hrs_n4.csv'\n", "print(f\"File selected: {file}\")\n", "df = load_data(file)\n", "\n", @@ -290,509 +431,56 @@ "\n", "# use only one because graphs are quite expensive to generate\n", "if use_fan_chart:\n", - " plot_fan_chart(chunks)\n", + " plot_fan_chart(chunks, ylim=[-3,3], column = 'bgl', apply_chunk_recentering=True)\n", + " plot_fan_chart(chunks, ylim=[-3,3], column = 'bgl', apply_chunk_recentering=False)\n", + " plot_fan_chart(chunks, ylim=[-3,3], column = 'bgl_rate', apply_chunk_recentering=True)\n", + " plot_fan_chart(chunks, ylim=[-3,3], column = 'bgl_rate', apply_chunk_recentering=False)\n", "else:\n", " plot_histograms(normalized_df, file, bgl_bins=50, rate_bins=50)" ] }, { "cell_type": "code", - "execution_count": 72, - "id": "5e448f31", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datebglmsg_typeday_start_shiftbgl_rate
1632024-07-01 13:35:00-04:000.228270ANNOUNCE_MEAL2024-07-010.000034
1642024-07-01 13:40:00-04:000.420285ANNOUNCE_MEAL2024-07-010.790342
1652024-07-01 13:45:00-04:000.516292ANNOUNCE_MEAL2024-07-010.395188
1662024-07-01 13:50:00-04:000.708306ANNOUNCE_MEAL2024-07-010.790342
1672024-07-01 13:55:00-04:000.708306ANNOUNCE_MEAL2024-07-010.000034
1682024-07-01 14:00:00-04:000.612299ANNOUNCE_MEAL2024-07-01-0.395119
1692024-07-01 14:05:00-04:000.644302ANNOUNCE_MEAL2024-07-010.131752
1702024-07-01 14:10:00-04:000.516292ANNOUNCE_MEAL2024-07-01-0.526837
1712024-07-01 14:15:00-04:000.740309ANNOUNCE_MEAL2024-07-010.922060
1722024-07-01 14:20:00-04:000.868318ANNOUNCE_MEAL2024-07-010.526906
1732024-07-01 14:25:00-04:000.484290ANNOUNCE_MEAL2024-07-01-1.580580
1742024-07-01 14:30:00-04:000.932323ANNOUNCE_MEAL2024-07-011.844085
1752024-07-01 14:35:00-04:000.996328ANNOUNCE_MEAL2024-07-010.263470
1762024-07-01 14:40:00-04:001.220345ANNOUNCE_MEAL2024-07-010.922060
1772024-07-01 14:45:00-04:001.412359ANNOUNCE_MEAL2024-07-010.790342
1782024-07-01 14:50:00-04:001.860393ANNOUNCE_MEAL2024-07-011.844085
1792024-07-01 14:55:00-04:001.988403ANNOUNCE_MEAL2024-07-010.526906
1802024-07-01 15:00:00-04:002.532444ANNOUNCE_MEAL2024-07-012.239239
1812024-07-01 15:05:00-04:002.628451ANNOUNCE_MEAL2024-07-010.395188
1822024-07-01 15:10:00-04:003.396508ANNOUNCE_MEAL2024-07-013.161264
1832024-07-01 15:15:00-04:003.140489ANNOUNCE_MEAL2024-07-01-1.053709
1842024-07-01 15:20:00-04:003.108487ANNOUNCE_MEAL2024-07-01-0.131684
1852024-07-01 15:25:00-04:003.364506ANNOUNCE_MEAL2024-07-011.053777
1862024-07-01 15:30:00-04:002.692456ANNOUNCE_MEAL2024-07-01-2.766041
1872024-07-01 15:35:00-04:002.948475ANNOUNCE_MEAL2024-07-011.053777
1882024-07-01 15:40:00-04:003.044482ANNOUNCE_MEAL2024-07-010.395188
1892024-07-01 15:45:00-04:003.268499ANNOUNCE_MEAL2024-07-010.922060
1902024-07-01 15:50:00-04:002.884470ANNOUNCE_MEAL2024-07-01-1.580580
1912024-07-01 15:55:00-04:002.916472ANNOUNCE_MEAL2024-07-010.131752
1922024-07-01 16:00:00-04:003.236496ANNOUNCE_MEAL2024-07-011.317213
\n", - "
" - ], - "text/plain": [ - " date bgl msg_type day_start_shift \\\n", - "163 2024-07-01 13:35:00-04:00 0.228270 ANNOUNCE_MEAL 2024-07-01 \n", - "164 2024-07-01 13:40:00-04:00 0.420285 ANNOUNCE_MEAL 2024-07-01 \n", - "165 2024-07-01 13:45:00-04:00 0.516292 ANNOUNCE_MEAL 2024-07-01 \n", - "166 2024-07-01 13:50:00-04:00 0.708306 ANNOUNCE_MEAL 2024-07-01 \n", - "167 2024-07-01 13:55:00-04:00 0.708306 ANNOUNCE_MEAL 2024-07-01 \n", - "168 2024-07-01 14:00:00-04:00 0.612299 ANNOUNCE_MEAL 2024-07-01 \n", - "169 2024-07-01 14:05:00-04:00 0.644302 ANNOUNCE_MEAL 2024-07-01 \n", - "170 2024-07-01 14:10:00-04:00 0.516292 ANNOUNCE_MEAL 2024-07-01 \n", - "171 2024-07-01 14:15:00-04:00 0.740309 ANNOUNCE_MEAL 2024-07-01 \n", - "172 2024-07-01 14:20:00-04:00 0.868318 ANNOUNCE_MEAL 2024-07-01 \n", - "173 2024-07-01 14:25:00-04:00 0.484290 ANNOUNCE_MEAL 2024-07-01 \n", - "174 2024-07-01 14:30:00-04:00 0.932323 ANNOUNCE_MEAL 2024-07-01 \n", - "175 2024-07-01 14:35:00-04:00 0.996328 ANNOUNCE_MEAL 2024-07-01 \n", - "176 2024-07-01 14:40:00-04:00 1.220345 ANNOUNCE_MEAL 2024-07-01 \n", - "177 2024-07-01 14:45:00-04:00 1.412359 ANNOUNCE_MEAL 2024-07-01 \n", - "178 2024-07-01 14:50:00-04:00 1.860393 ANNOUNCE_MEAL 2024-07-01 \n", - "179 2024-07-01 14:55:00-04:00 1.988403 ANNOUNCE_MEAL 2024-07-01 \n", - "180 2024-07-01 15:00:00-04:00 2.532444 ANNOUNCE_MEAL 2024-07-01 \n", - "181 2024-07-01 15:05:00-04:00 2.628451 ANNOUNCE_MEAL 2024-07-01 \n", - "182 2024-07-01 15:10:00-04:00 3.396508 ANNOUNCE_MEAL 2024-07-01 \n", - "183 2024-07-01 15:15:00-04:00 3.140489 ANNOUNCE_MEAL 2024-07-01 \n", - "184 2024-07-01 15:20:00-04:00 3.108487 ANNOUNCE_MEAL 2024-07-01 \n", - "185 2024-07-01 15:25:00-04:00 3.364506 ANNOUNCE_MEAL 2024-07-01 \n", - "186 2024-07-01 15:30:00-04:00 2.692456 ANNOUNCE_MEAL 2024-07-01 \n", - "187 2024-07-01 15:35:00-04:00 2.948475 ANNOUNCE_MEAL 2024-07-01 \n", - "188 2024-07-01 15:40:00-04:00 3.044482 ANNOUNCE_MEAL 2024-07-01 \n", - "189 2024-07-01 15:45:00-04:00 3.268499 ANNOUNCE_MEAL 2024-07-01 \n", - "190 2024-07-01 15:50:00-04:00 2.884470 ANNOUNCE_MEAL 2024-07-01 \n", - "191 2024-07-01 15:55:00-04:00 2.916472 ANNOUNCE_MEAL 2024-07-01 \n", - "192 2024-07-01 16:00:00-04:00 3.236496 ANNOUNCE_MEAL 2024-07-01 \n", - "\n", - " bgl_rate \n", - "163 0.000034 \n", - "164 0.790342 \n", - "165 0.395188 \n", - "166 0.790342 \n", - "167 0.000034 \n", - "168 -0.395119 \n", - "169 0.131752 \n", - "170 -0.526837 \n", - "171 0.922060 \n", - "172 0.526906 \n", - "173 -1.580580 \n", - "174 1.844085 \n", - "175 0.263470 \n", - "176 0.922060 \n", - "177 0.790342 \n", - "178 1.844085 \n", - "179 0.526906 \n", - "180 2.239239 \n", - "181 0.395188 \n", - "182 3.161264 \n", - "183 -1.053709 \n", - "184 -0.131684 \n", - "185 1.053777 \n", - "186 -2.766041 \n", - "187 1.053777 \n", - "188 0.395188 \n", - "189 0.922060 \n", - "190 -1.580580 \n", - "191 0.131752 \n", - "192 1.317213 " - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chunks[5]" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "106c5bf9", + "execution_count": 97, + "id": "1f1e8e3fbac73ed6", "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. -0.67694743 1.44072623 ... 0.60262563 -0.32698168\n", + " -0.13628288]\n", + " [ 0. 1.26850213 -0.58148579 ... 0.69359972 -0.82667855\n", + " 0.12587647]\n", + " [ 0. 0.31805214 0.24753713 ... 0.09511455 -0.8928023\n", + " 0.69070346]\n", + " ...\n", + " [ 0. -0.71332097 -0.22290693 ... -0.87205757 -0.30487227\n", + " -1.51762486]\n", + " [ 0. -0.10806892 0.6449397 ... 0.83729447 -0.83550169\n", + " 0.70583824]\n", + " [ 0. -1.45775263 0.20609169 ... -1.90251234 -0.15001824\n", + " -1.0981055 ]]\n" + ] } ], "source": [ - "# Create time points (x-axis) based on chunk length\n", - "meal_plot = []\n", - "non_meal_plot = []\n", - "chunk_length = len(chunks[0])\n", - "# If it is a meal chunk, use red line instead, else blue\n", - "for chunk in chunks:\n", - " meal_plot.append(chunk['bgl']) if 'ANNOUNCE_MEAL' in chunk['msg_type'].values else non_meal_plot.append(chunk['bgl'])\n", - "\n", - "meal_plot = np.array(meal_plot)\n", - "non_meal_plot = np.array(non_meal_plot)\n", - "\n", - "# Define the percentiles to plot\n", - "percentiles = [5, 25, 50, 75, 95]\n", - "\n", - "# Calculate the percentiles for each timestep\n", - "percentile_data_meal = np.nanpercentile(meal_plot, percentiles, axis=0)\n", - "percentile_data_non_meal = np.nanpercentile(non_meal_plot, percentiles, axis=0)\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "plt.title('Fan Chart Glucose Chunks (Orange = Meal, Blue = Non-meal)')\n", - "plt.xlabel('Time Point')\n", - "plt.ylabel('Blood Glucose Level')\n", - "plt.grid(True, alpha=0.3)\n", - "\n", - "# Define alpha values for the filled areas\n", - "alpha_values = [0.33, 0.66, 1, 0.66, 0.33]\n", - "\n", - "for i, percentile in enumerate(percentiles):\n", - " ax.plot(range(chunk_length), percentile_data_meal[i, :], label=f'meal: {percentile}th percentile', color='orange', alpha=alpha_values[i])\n", - "\n", - "for i, percentile in enumerate(percentiles):\n", - " ax.plot(range(chunk_length), percentile_data_non_meal[i, :], label=f'non-meal: {percentile}th percentile', color='blue', alpha=alpha_values[i])\n", - "\n", - "\n", - "ax.fill_between(range(chunk_length), percentile_data_meal[0, :], percentile_data_meal[-1, :], color='orange', alpha=0.2)\n", - "ax.fill_between(range(chunk_length), percentile_data_meal[1, :], percentile_data_meal[-2, :], color='orange', alpha=0.4)\n", - "ax.fill_between(range(chunk_length), percentile_data_meal[2, :], percentile_data_meal[2, :], color='orange', alpha=0.6)\n", - "\n", - "\n", - "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "import numpy as np\n", "\n", - "# Adjust layout to make room for the legend\n", - "plt.tight_layout(rect=[0, 0, 0.85, 1])" - ] - }, - { - "cell_type": "markdown", - "id": "3c085000", - "metadata": {}, - "source": [ - "### Example Code:" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "0ed01651", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ "# Generate sample data\n", "# Assume we have 100 time series chunks, each of length 50\n", "num_chunks = 100\n", "chunk_length = 50\n", "\n", "# Generate random data for demonstration purposes\n", - "# Replace this with your actual data loading/generation code\n", "data = np.random.randn(num_chunks, chunk_length)\n", - "# Define the percentiles to plot\n", - "percentiles = [5, 25, 50, 75, 95]\n", - "\n", - "# Calculate the percentiles for each timestep\n", - "percentile_data = np.percentile(data, percentiles, axis=0)\n", - "\n", - "# Create the fan chart\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "\n", - "# Plot the percentiles\n", - "for i, percentile in enumerate(percentiles):\n", - " ax.plot(range(chunk_length), percentile_data[i, :], label=f'{percentile}th percentile')\n", "\n", - "# Fill between the percentiles to create the fan chart effect\n", - "ax.fill_between(range(chunk_length), percentile_data[0, :], percentile_data[-1, :], color='gray', alpha=0.2)\n", - "ax.fill_between(range(chunk_length), percentile_data[1, :], percentile_data[-2, :], color='gray', alpha=0.4)\n", - "ax.fill_between(range(chunk_length), percentile_data[2, :], percentile_data[2, :], color='gray', alpha=0.6)\n", + "# Subtract the 0th element from every element in each vector\n", + "data_centered = data - data[:, 0].reshape(-1, 1)\n", "\n", - "# Add labels and title\n", - "ax.set_xlabel('Timestep')\n", - "ax.set_ylabel('Value')\n", - "ax.set_title('Fan Chart with Percentiles for Each Timestep')\n", - "ax.legend()\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "bf99c756", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100, 50)" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.shape" + "# Check the result\n", + "print(data_centered)" ] } ], From ac67dec7deeb01cb773a90ed126fa6d1d0f89132 Mon Sep 17 00:00:00 2001 From: Andrew Yang Date: Wed, 27 Nov 2024 19:28:23 -0500 Subject: [PATCH 2/6] Module doc + remove_day_of_num_meals function for cleaning (#188) * change discussed during spring * unit tests for data generation * Add pytest mock to env * add custom pip install script * refine custom_pip_install script, automatically manage and clean up yaml file * add environment * add doc md for clean, generate, operation, utils, and plot [wip] * remove installation * add remove_num_meal to cleaner and fix recommended docstring mismatch * fix spelling * Custom pip install script (#175) * change discussed during spring * unit tests for data generation * Add pytest mock to env * add custom pip install script * refine custom_pip_install script, automatically manage and clean up yaml file * add environment --------- Co-authored-by: Christopher <110344005+RobotPsychologist@users.noreply.github.com> Co-authored-by: Tony Chan <88695195+Tony911029@users.noreply.github.com> Co-authored-by: Tony Chan --------- Co-authored-by: Christopher <110344005+RobotPsychologist@users.noreply.github.com> Co-authored-by: Tony Chan <88695195+Tony911029@users.noreply.github.com> Co-authored-by: Tony Chan --- .../docs/docs/how-to-use-functions.md | 403 ++++++++++++++++++ .../datasets/dataset_cleaner.py | 38 ++ .../datasets/dataset_operations.py | 4 +- .../data_cleaning/test_remove_num_meal.py | 40 ++ 4 files changed, 483 insertions(+), 2 deletions(-) create mode 100644 0_meal_identification/meal_identification/docs/docs/how-to-use-functions.md create mode 100644 0_meal_identification/meal_identification/meal_identification/tests/data_cleaning/test_remove_num_meal.py diff --git a/0_meal_identification/meal_identification/docs/docs/how-to-use-functions.md b/0_meal_identification/meal_identification/docs/docs/how-to-use-functions.md new file mode 100644 index 0000000..87adf79 --- /dev/null +++ b/0_meal_identification/meal_identification/docs/docs/how-to-use-functions.md @@ -0,0 +1,403 @@ +## Introduction + +Managing and cleaning patient meal data is crucial for accurate analysis, especially in studies related to diabetes management, dietary impacts, and metabolic research. This toolkit offers robust functions to preprocess raw meal data, ensuring overlaps are handled, top carbohydrate meals are retained, and missing values are appropriately managed. Additionally, it provides utilities for dataset generation and visualization to aid researchers and data scientists in their workflows. + +## Project Structure + +The project is organized into several modules, each responsible for specific tasks: + +- **dataset_cleaner.py**: Functions to clean and preprocess meal data. +- **dataset_generator.py**: Functions to generate and save processed datasets. +- **dataset_operations.py**: Core operations for loading, saving, and labeling data. +- **utils.py**: Utility functions for file handling and path management. +- **plots.py**: Visualization functions for analyzing meal data. + +You can find these modules under `0_meal_identification/meal_identification/meal_identification/datasets`. + + +## Usage + +### Dataset Cleaner + +This module provides functions to clean and preprocess the raw meal data. + + + +#### `erase_meal_overlap_fn` + +**Purpose**: Handles overlapping meal events by merging them within a specified time window and applying carbohydrate thresholds. + +**Parameters**: + +- `patient_df` (`pd.DataFrame`): The input DataFrame containing at least `'msg_type'`, `'food_g'`, and a datetime index. +- `meal_length` (`pd.Timedelta`): Duration to look ahead for overlapping meal events. +- `min_carbs` (`int`): Minimum carbohydrate threshold to consider an event as a valid meal. + +**Returns**: + +- `pd.DataFrame`: Processed DataFrame with meal overlaps handled. + +**Behaviour**: + +1. Identifies all `'ANNOUNCE_MEAL'` events. +2. For each meal: + - If `food_g` ≤ `min_carbs`, labels it as `'LOW_CARB_MEAL'`. + - Otherwise, sums up `food_g` within the `meal_length` window and adds it to the original meal. + - Erases overlapping events within the window by setting `'food_g'` to `0` and `'msg_type'` to an empty string. + +#### `remove_num_meal` + +**Purpose**: Removes all days that contain a specific number of meals (`ANNOUNCE_MEAL` events). + +**Parameters**: + +- `patient_df` (`pd.DataFrame`): The input DataFrame containing at least `'msg_type'`, `'food_g'`, and a datetime index. +- `num_meal` (`int`): The specific number of meals in a day to identify and remove. + +**Returns**: + +- `pd.DataFrame`: The processed DataFrame with days containing the specified number of meals removed. + +**Behaviour**: + +1. Extracts the date part of the datetime index and adds it as a temporary `'day'` column. +2. Filters to include only `'ANNOUNCE_MEAL'` events. +3. Groups the data by day and counts the number of meals per day. +4. Identifies days where the count of meals matches the specified `num_meal`. +5. Removes rows from the DataFrame corresponding to these identified days. +6. Drops the temporary `'day'` column. + + +#### `keep_top_n_carb_meals` + +**Purpose**: Retains only the top N carbohydrate-rich meals per day, filtering out lesser meals. + +**Parameters**: + +- `patient_df` (`pd.DataFrame`): Input DataFrame with columns `'msg_type'`, `'food_g'`, and a datetime index. Must include `'day_start_shift'`. +- `n_top_carb_meals` (`int`): Number of top carbohydrate meals to retain per day. + +**Returns**: + +- `pd.DataFrame`: Processed DataFrame with only the top N carbohydrate meals per day retained. + +**Behaviour**: + +1. Verifies the existence of the `'day_start_shift'` column; raises a `KeyError` if absent. +2. Filters DataFrame to include only `'ANNOUNCE_MEAL'` events. +3. Groups meals by `'day_start_shift'` and selects the top N meals based on `'food_g'`. +4. Sets `'food_g'` to `0` and `'msg_type'` to `'0'` for meals not in the top N. + + +#### `erase_consecutive_nan_values` + +**Purpose**: Manages consecutive missing (`NaN`) values in the dataset by either removing entire days or individual `NaN` entries based on specified thresholds. + +**Parameters**: + +- `patient_df` (`pd.DataFrame`): Input DataFrame with a datetime index. +- `max_consecutive_nan_values_per_day` (`int`): Maximum allowed consecutive `NaN` values per day. Days exceeding this threshold are removed entirely; otherwise, `NaN` values are dropped. + +**Returns**: + +- `pd.DataFrame`: Processed DataFrame with consecutive `NaN` values handled as per the threshold. + +**Behaviour**: + +1. Adds a temporary `'day'` column based on the date part of the datetime index. +2. Iterates through each day: + - Counts the maximum number of consecutive `NaN` values in `'food_g'`. + - If the maximum is within the allowed limit, retains the day; otherwise, excludes it. +3. Drops the temporary `'day'` column. +4. Removes remaining `NaN` values that do not form a long enough consecutive chain. + +### Dataset Generator + +This module orchestrates the dataset creation process, integrating loading, cleaning, transformation, and saving operations. + +#### `ensure_datetime_index` + +**Purpose**: Ensures that the DataFrame has a datetime index, either by verifying the existing index or converting a `'date'` column. + +**Parameters**: + +- `data` (`pd.DataFrame`): Input DataFrame that either has a datetime index or a `'date'` column. + +**Returns**: + +- `pd.DataFrame`: DataFrame with a sorted datetime index. + +**Behaviour**: + +1. Checks if the DataFrame's index is a `DatetimeIndex`. +2. If not, attempts to set the `'date'` column as the index. +3. Converts the index to a `DatetimeIndex`. +4. Sorts the DataFrame based on the datetime index. + +**Notes**: + +- **Docstring Accuracy**: The docstring correctly describes the function. However, it should clarify that the function **sets** the `'date'` column as the index if it's not already a `DatetimeIndex`. + +#### `dataset_creator` + +**Purpose**: Creates a processed dataset from raw data by applying various cleaning and transformation functions, then saves the result. + +**Parameters**: + +- `raw_data_path` (`str`, optional): Path to raw data files. Default: `'0_meal_identification/meal_identification/data/raw'`. +- `output_dir` (`str`, optional): Directory to save processed data. Default: `'0_meal_identification/meal_identification/data/interim'`. +- `use_auto_label` (`bool`, optional): Whether to use automatic labeling. Default: `True`. +- `keep_cols` (`list` of `str`, optional): Columns to retain from raw data. Default: `['date', 'bgl', 'msg_type', 'affects_fob', 'affects_iob', 'dose_units', 'food_g', 'food_glycemic_index']`. +- `day_start_index_change` (`bool`, optional): Whether to adjust day start index based on a specific time. Default: `True`. +- `day_start_time` (`pd.Timedelta`, optional): Time of day to start the day index. Default: `pd.Timedelta(hours=4)`. +- `max_consecutive_nan_values_per_day` (`int`, optional): Max allowed consecutive `NaN` values per day. Default: `-1` (disabled). +- `min_carbs` (`int`, optional): Minimum carbohydrates to consider a meal. Default: `5`. +- `n_top_carb_meals` (`int`, optional): Number of top carbohydrate meals to retain per day. Default: `3`. +- `meal_length` (`pd.Timedelta`, optional): Duration to consider for meal overlaps. Default: `pd.Timedelta(hours=2)`. +- `erase_meal_overlap` (`bool`, optional): Whether to handle meal overlaps. Default: `True`. +- `coerce_time` (`bool`, optional): Whether to coerce time intervals. Default: `True`. +- `coerse_time_interval` (`pd.Timedelta`, optional): Interval for time coercion. Default: `pd.Timedelta(minutes=5)`. +- `return_data` (`bool`, optional): Whether to return processed DataFrames. Default: `False`. +- `over_write` (`bool`, optional): Whether to overwrite existing processed datasets. Default: `False`. + +**Returns**: + +- `list` of `pd.DataFrame` or `None`: List of processed DataFrames if `return_data` is `True`; otherwise, `None`. + +**Behaviour**: + +1. **Data Loading**: Utilizes `load_data` to read raw CSV files from `raw_data_path`, retaining only specified columns. +2. **Processing Each Patient's Data**: + - Ensures the DataFrame has a datetime index using `ensure_datetime_index`. + - Applies time coercion if `coerce_time` is `True` via `coerce_time_fn`. + - Adjusts day start index based on `day_start_time` if `day_start_index_change` is `True`. + - Handles consecutive `NaN` values using `erase_consecutive_nan_values` if `max_consecutive_nan_values_per_day` is set. + - Manages meal overlaps with `erase_meal_overlap_fn` if `erase_meal_overlap` is `True`. + - Retains top N carbohydrate meals per day using `keep_top_n_carb_meals` if `n_top_carb_meals` is set. +3. **Saving Processed Data**: Saves the cleaned DataFrame using `save_data` with appropriate labeling. +4. **Handling Overwrites**: Skips saving if the file already exists and `over_write` is `False`. +5. **Returning Data**: Optionally appends processed DataFrames to a list for return if `return_data` is `True`. + +**Notes**: + +- **Docstring Accuracy**: The docstring mostly aligns with the function's Behaviour. However, there is a typo in the parameter description: `"over_write : False: bool, optional"` should be `"over_write : bool, optional"`. + +#### `run_dataset_combinations` + +**Purpose**: Generates datasets by iterating through various combinations of parameters to explore different preprocessing scenarios. + +**Parameters**: + +- `raw_data_path` (`str`, optional): Path to raw data files. Default: `'0_meal_identification/meal_identification/data/raw'`. +- `output_dir` (`str`, optional): Directory to save processed data. Default: `'0_meal_identification/meal_identification/data/interim'`. +- `over_write` (`bool`, optional): Whether to overwrite existing processed datasets. Default: `False`. + +**Returns**: + +- `None` + +**Behaviour**: + +1. **Parameter Combinations**: Defines ranges for `min_carbs` (5, 10), `meal_length` (2, 3, 5 hours), and `n_top_meals` (3, 4). +2. **Iteration**: For each combination, calls `dataset_creator` with the respective parameters. +3. **Error Handling**: Catches and logs any exceptions during processing, allowing the loop to continue with remaining combinations. +4. **Completion Message**: Prints a message upon completing all combinations. + +### Dataset Operations + +Core functions responsible for data loading, saving, labeling, and time coercion. + +#### `get_root_dir` + +**Purpose**: Determines the root directory of the project by searching for a unique identifier directory (e.g., `.github`). + +**Parameters**: + +- `current_dir` (`str`, optional): Starting directory for the search. Defaults to the current working directory if `None`. + +**Returns**: + +- `str`: Absolute path to the project root directory. + +**Behaviour**: + +1. Starts from `current_dir` or the current working directory. +2. Iteratively moves up the directory hierarchy. +3. Checks for the presence of the `unique_dir` (default `.github`) to identify the root. +4. Raises a `FileNotFoundError` if the root directory isn't found. + + +#### `load_data` + +**Purpose**: Loads raw CSV data from the specified directory, retaining only specified columns. + +**Parameters**: + +- `raw_data_path` (`str`): Path to the directory containing raw CSV data files. +- `keep_cols` (`list` of `str`): Columns to retain from the raw data. + +**Returns**: + +- `dict`: Dictionary mapping filenames to their corresponding `pd.DataFrame` objects. + +**Behaviour**: + +1. Determines the full path to `raw_data_path` relative to the project root. +2. Validates the existence of the directory and the presence of CSV files. +3. Iterates through each CSV file, loading it into a DataFrame while retaining only `keep_cols`. +4. Handles and logs any errors encountered during file loading. +5. Returns a dictionary of loaded DataFrames. + + +#### `find_file_loc` + +**Purpose**: Constructs the file path and filename for saving processed data based on provided parameters. + +**Parameters**: + +- `output_dir` (`str`): Directory to save the processed data. +- `data_label` (`str`): Label describing the dataset transformations. +- `patient_id` (`str`): Identifier for the patient. +- `data_gen_date` (`str`): Date when the data was generated (formatted as `'YYYY-MM-DD'`). +- `include_gen_date_label` (`bool`): Whether to include the generation date in the filename. + +**Returns**: + +- `tuple[str, str]`: Tuple containing the full file path and the filename. + +**Behaviour**: + +1. Determines the full path to `output_dir` relative to the project root and ensures the directory exists. +2. Constructs the filename based on whether `include_gen_date_label` is `True`: + - If `True`: `"{data_gen_date}_{patient_id}_{data_label}.csv"` + - If `False`: `"{patient_id}_{data_label}.csv"` +3. Returns the full file path and the filename. + +#### `save_data` + +**Purpose**: Saves the processed DataFrame to a CSV file in the specified directory with appropriate labeling. + +**Parameters**: + +- `data` (`pd.DataFrame`): The DataFrame to save. +- `output_dir` (`str`): Directory to save the data. +- `data_label` (`str`): Label describing the dataset transformations. +- `patient_id` (`str`): Identifier for the patient. +- `data_gen_date` (`str`): Date when the data was generated (formatted as `'YYYY-MM-DD'`). +- `include_gen_date_label` (`bool`, optional): Whether to include the generation date in the filename. Default: `True`. + +**Returns**: + +- `None` + +**Behaviour**: + +1. Calls `find_file_loc` to determine the full file path and filename. +2. Saves the DataFrame to the specified path using `to_csv`, including the datetime index. +3. Logs a success message with the file location and dataset label. + +#### `dataset_label_modifier_fn` + +**Purpose**: Generates a dataset label based on the transformations applied to the data, facilitating easy identification of preprocessing steps. + +**Parameters**: + +- `base_label_modifier` (`str`, optional): Base string to start the label. Default: `''`. +- `coerce_time` (`bool`, optional): Whether time intervals were coerced. Default: `False`. +- `coerce_time_interval` (`pd.Timedelta`, optional): Interval used for time coercion. +- `day_start_index_change` (`bool`, optional): Whether the day start index was adjusted based on a specific time. Default: `False`. +- `day_start_time` (`pd.Timedelta`, optional): Time of day when the day starts. +- `erase_meal_overlap` (`bool`, optional): Whether meal overlaps were handled. Default: `False`. +- `min_carbs` (`int`, optional): Minimum carbohydrate threshold used in overlap handling. +- `meal_length` (`pd.Timedelta`, optional): Duration considered for meal overlaps. +- `n_top_carb_meals` (`int`, optional): Number of top carbohydrate meals retained per day. + +**Returns**: + +- `str`: Modified dataset label reflecting the applied transformations. + +**Behaviour**: + +1. Starts with `base_label_modifier`. +2. Appends specific labels based on which transformations were applied: + - Time coercion (`i{minutes}mins_`) + - Day start index change (`d{hours}hrs_`) + - Meal overlap handling (`c{min_carbs}g_l{hours}hrs_`) + - Top N carbohydrate meals (`n{n_top_carb_meals}`) +3. Returns the concatenated label string. + + +#### `coerce_time_fn` + +**Purpose**: Coerces the time intervals of the dataset to a specified frequency, ensuring consistent time steps. + +**Parameters**: + +- `data` (`pd.DataFrame`): Input DataFrame with a datetime index named `'date'`. +- `coerse_time_interval` (`pd.Timedelta`): Desired time interval for coercion. + +**Returns**: + +- `pd.DataFrame`: Coerced DataFrame with resampled time intervals. + +**Behaviour**: + +1. Validates that the DataFrame's index is named `'date'`; raises a `KeyError` if not. +2. Converts `coerse_time_interval` to a frequency string. +3. Separates meal announcements (`'ANNOUNCE_MEAL'`) from non-meal data. +4. Resamples non-meal data based on the specified frequency, taking the first entry in each bin. +5. Aligns meal announcements with the resampled non-meal data. +6. Combines the two DataFrames, ensuring meal announcements are retained. +7. Drops temporary columns used for merging. + +### Utilities + +#### `get_path` + +**Purpose**: Infers the correct file path by ensuring the presence of a specified file suffix. + +**Parameters**: + +- `path` (`str` or `pathlib.Path`): The provided path or filename. +- `suffix` (`str`): The expected file extension (e.g., `'.csv'`). + +**Returns**: + +- `str`: Resolved file path with the appropriate suffix. + +**Behaviour**: + +1. Converts `path` to an absolute `Path` object. +2. Checks if the path has the specified suffix. +3. If not, verifies if a file with the original name exists. +4. If the file doesn't exist, appends the suffix to the path. +5. Returns the resolved path as a string. + + +### Plots + +#### `plot_announce_meal_histogram` + +**Purpose**: Visualizes the distribution of `'ANNOUNCE_MEAL'` events throughout the day, either by hour or in 15-minute intervals. + +**Parameters**: + +- `df` (`pd.DataFrame`): Input DataFrame containing `'msg_type'` and a datetime index. +- `hours_or_15minutes` (`str`, optional): Aggregation level for the histogram. Accepts `'hours'` or `'minutes'`. Default: `'hours'`. + +**Returns**: + +- `None` + +**Behaviour**: + +1. Filters the DataFrame to include only `'ANNOUNCE_MEAL'` events. +2. Extracts the hour and minute from each meal event's timestamp. +3. Depending on `hours_or_15minutes`: + - **'minutes'**: + - Converts time to fractional hours. + - Plots a histogram with 15-minute interval bins (96 bins for 24 hours). + - **'hours'**: + - Plots a histogram with hourly bins (24 bins for 24 hours). +4. Configures plot aesthetics (labels, title, grid, layout). +5. Displays the histogram. diff --git a/0_meal_identification/meal_identification/meal_identification/datasets/dataset_cleaner.py b/0_meal_identification/meal_identification/meal_identification/datasets/dataset_cleaner.py index 131134c..3334958 100644 --- a/0_meal_identification/meal_identification/meal_identification/datasets/dataset_cleaner.py +++ b/0_meal_identification/meal_identification/meal_identification/datasets/dataset_cleaner.py @@ -1,5 +1,43 @@ import pandas as pd +def remove_num_meal(patient_df, num_meal): + """ + Remove all days that have meals with the specified num_meal number of meals. + + Parameters + ---------- + patient_df : pd.DataFrame + The input DataFrame with columns 'msg_type', 'food_g', and a datetime index. + num_meal : int + The specific number of meals in a day to identify and remove. + + Returns + ------- + pd.DataFrame + The processed DataFrame with days containing num_meal meals removed. + """ + # Ensure a 'day' column based on the date from the datetime index + patient_df = patient_df.copy() + patient_df['day'] = patient_df.index.date + + # Filter to only ANNOUNCE_MEAL rows + announce_meal_df = patient_df[patient_df['msg_type'] == 'ANNOUNCE_MEAL'] + + # Count the number of meals per day + meal_counts = announce_meal_df.groupby('day').size() + + # Identify days with the specified number of meals + days_to_remove = meal_counts[meal_counts == num_meal].index + + # Remove rows corresponding to these days + result_df = patient_df[~patient_df['day'].isin(days_to_remove)] + + # Drop the temporary 'day' column + result_df.drop(columns=['day'], inplace=True) + + return result_df + + def erase_meal_overlap_fn(patient_df, meal_length, min_carbs): """ Process the DataFrame to handle meal overlaps. diff --git a/0_meal_identification/meal_identification/meal_identification/datasets/dataset_operations.py b/0_meal_identification/meal_identification/meal_identification/datasets/dataset_operations.py index 53a2cfb..a1a4a1d 100644 --- a/0_meal_identification/meal_identification/meal_identification/datasets/dataset_operations.py +++ b/0_meal_identification/meal_identification/meal_identification/datasets/dataset_operations.py @@ -155,13 +155,13 @@ def dataset_label_modifier_fn( Time interval for coercion day_start_index_change : bool, optional Whether to create a day index starting at a specific time - day_start_time : time, optional + day_start_time : pd.Timedelta, optional Time object specifying when the day starts erase_meal_overlap : bool, optional Whether to erase overlapping meals min_carbs : int, optional Minimum carbs threshold for meal detection - meal_length : time, optional + meal_length : pd.Timedelta, optional Length of time for meal window n_top_carb_meals : int, optional Number of top carb meals to consider diff --git a/0_meal_identification/meal_identification/meal_identification/tests/data_cleaning/test_remove_num_meal.py b/0_meal_identification/meal_identification/meal_identification/tests/data_cleaning/test_remove_num_meal.py new file mode 100644 index 0000000..a7742df --- /dev/null +++ b/0_meal_identification/meal_identification/meal_identification/tests/data_cleaning/test_remove_num_meal.py @@ -0,0 +1,40 @@ +from pydantic import BaseModel, ValidationError +import pandas as pd +from meal_identification.datasets.dataset_cleaner import remove_num_meal + + +class TestData(BaseModel): + msg_type: list + food_g: list + timestamps: list + +def test_remove_num_meal(): + # Mock data + mock_data = TestData( + msg_type=['ANNOUNCE_MEAL', 'ANNOUNCE_MEAL', 'ANNOUNCE_MEAL', 'OTHER', 'ANNOUNCE_MEAL', 'ANNOUNCE_MEAL'], + food_g=[50, 60, 70, 0, 80, 90], + timestamps=[ + '2024-11-01 08:00:00', '2024-11-01 08:05:00', '2024-11-01 08:10:00', + '2024-11-01 08:15:00', '2024-11-02 08:00:00', '2024-11-02 08:05:00' + ] + ) + + # Create DataFrame + df = pd.DataFrame({ + 'msg_type': mock_data.msg_type, + 'food_g': mock_data.food_g, + }, index=pd.to_datetime(mock_data.timestamps)) + + # Apply the function to remove days with 3 meals + processed_df = remove_num_meal(df, num_meal=3) + + # Assert the correct days were removed + # 2024-11-01 has 3 meals and should be removed + # 2024-11-02 remains + expected_timestamps = ['2024-11-02 08:00:00', '2024-11-02 08:05:00'] + expected_df = pd.DataFrame({ + 'msg_type': ['ANNOUNCE_MEAL', 'ANNOUNCE_MEAL'], + 'food_g': [80, 90], + }, index=pd.to_datetime(expected_timestamps)) + + assert processed_df.equals(expected_df), f"Test failed: {processed_df} does not match {expected_df}" From 35e53e69934475160b8e96a0d37edae70c1b46ad Mon Sep 17 00:00:00 2001 From: Shivam Singal <64049823+ShivamJ07@users.noreply.github.com> Date: Wed, 27 Nov 2024 19:35:40 -0500 Subject: [PATCH 3/6] Created notebook for information gain segmentation (#189) Co-authored-by: Christopher <110344005+RobotPsychologist@users.noreply.github.com> --- .../3.08-ss-information-gain-segmentation.ipynb | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 0_meal_identification/meal_identification/notebooks/3.08-ss-information-gain-segmentation.ipynb diff --git a/0_meal_identification/meal_identification/notebooks/3.08-ss-information-gain-segmentation.ipynb b/0_meal_identification/meal_identification/notebooks/3.08-ss-information-gain-segmentation.ipynb new file mode 100644 index 0000000..d3e2dea --- /dev/null +++ b/0_meal_identification/meal_identification/notebooks/3.08-ss-information-gain-segmentation.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f3c97e56ced4f775b5952537d5bb38a78b084d8c Mon Sep 17 00:00:00 2001 From: Christopher <110344005+RobotPsychologist@users.noreply.github.com> Date: Wed, 27 Nov 2024 22:29:03 -0500 Subject: [PATCH 4/6] Fitbit data for causal modeling. 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- 2024-04-01.csv new file mode 100644 index 0000000..ff8670e --- /dev/null +++ b/data/fitness/DailyReadiness/Daily Readiness Score - 2024-04-01.csv @@ -0,0 +1,31 @@ +date,readiness_score_value,readiness_state,activity_subcomponent,sleep_subcomponent,hrv_subcomponent,activity_state,sleep_state,hrv_state +2024-05-01,71.0,HIGH,71.0,94.0,55.0,LOW,HIGH,MEDIUM +2024-04-30,39.0,MEDIUM,39.0,99.0,49.0,MEDIUM,HIGH,MEDIUM +2024-04-29,100.0,HIGH,100.0,95.0,46.0,LOW,HIGH,MEDIUM +2024-04-28,98.0,HIGH,98.0,99.0,58.0,LOW,HIGH,MEDIUM +2024-04-27,87.0,HIGH,87.0,94.0,51.0,LOW,HIGH,MEDIUM +2024-04-26,61.0,MEDIUM,61.0,97.0,56.0,MEDIUM,HIGH,MEDIUM +2024-04-25,17.0,LOW,17.0,82.0,51.0,HIGH,HIGH,MEDIUM +2024-04-24,44.0,MEDIUM,44.0,87.0,54.0,MEDIUM,HIGH,MEDIUM +2024-04-23,1.0,LOW,0.0,73.0,43.0,HIGH,HIGH,MEDIUM +2024-04-22,91.0,HIGH,91.0,91.0,44.0,LOW,HIGH,MEDIUM +2024-04-21,88.0,HIGH,88.0,97.0,42.0,LOW,HIGH,MEDIUM +2024-04-20,78.0,HIGH,78.0,99.0,54.0,LOW,HIGH,MEDIUM 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diff --git a/data/fitness/DailyReadiness/Daily Readiness Score - 2024-05-01.csv b/data/fitness/DailyReadiness/Daily Readiness Score - 2024-05-01.csv new file mode 100644 index 0000000..1aa4984 --- /dev/null +++ b/data/fitness/DailyReadiness/Daily Readiness Score - 2024-05-01.csv @@ -0,0 +1,31 @@ +date,readiness_score_value,readiness_state,activity_subcomponent,sleep_subcomponent,hrv_subcomponent,activity_state,sleep_state,hrv_state +2024-06-01,94.0,HIGH,94.0,90.0,54.0,LOW,HIGH,MEDIUM +2024-05-31,80.0,HIGH,80.0,84.0,69.0,LOW,HIGH,MEDIUM +2024-05-30,42.0,MEDIUM,42.0,95.0,49.0,MEDIUM,HIGH,MEDIUM +2024-05-28,48.0,MEDIUM,48.0,99.0,46.0,MEDIUM,HIGH,MEDIUM +2024-05-27,96.0,HIGH,96.0,100.0,56.0,LOW,HIGH,MEDIUM +2024-05-26,90.0,HIGH,90.0,97.0,52.0,LOW,HIGH,MEDIUM +2024-05-25,78.0,HIGH,78.0,98.0,45.0,LOW,HIGH,MEDIUM +2024-05-24,59.0,MEDIUM,59.0,88.0,51.0,MEDIUM,HIGH,MEDIUM +2024-05-23,23.0,LOW,23.0,66.0,55.0,HIGH,MEDIUM,MEDIUM +2024-05-22,23.0,LOW,55.0,23.0,55.0,MEDIUM,LOW,MEDIUM 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a/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-04-21.csv b/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-04-21.csv new file mode 100644 index 0000000..5ae884f --- /dev/null +++ b/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-04-21.csv @@ -0,0 +1,70 @@ +timestamp,rmssd,coverage,low_frequency,high_frequency +2024-04-21T05:45:00,18.91,0.747,766.361,56.468 +2024-04-21T05:50:00,26.646,0.758,1218.07,87.129 +2024-04-21T05:55:00,23.538,0.925,359.061,99.537 +2024-04-21T06:00:00,17.82,1.0,523.117,71.365 +2024-04-21T06:05:00,14.239,1.003,201.522,40.377 +2024-04-21T06:10:00,17.056,1.005,395.031,59.203 +2024-04-21T06:15:00,16.978,1.0,226.844,72.618 +2024-04-21T06:20:00,17.583,0.952,473.339,50.531 +2024-04-21T06:35:00,18.736,0.726,467.856,41.909 +2024-04-21T06:40:00,14.416,0.774,493.65,44.679 +2024-04-21T06:45:00,16.495,0.901,575.154,45.951 +2024-04-21T06:50:00,16.239,0.942,521.182,67.35 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at end of file diff --git a/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-05-16.csv b/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-05-16.csv new file mode 100644 index 0000000..7000923 --- /dev/null +++ b/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-05-16.csv @@ -0,0 +1,81 @@ +timestamp,rmssd,coverage,low_frequency,high_frequency +2024-05-16T01:35:00,21.291,0.813,632.948,86.04 +2024-05-16T01:40:00,27.421,0.751,575.429,104.632 +2024-05-16T01:45:00,25.276,0.999,926.556,130.413 +2024-05-16T01:50:00,25.68,0.943,773.282,148.076 +2024-05-16T01:55:00,21.281,1.002,528.047,102.701 +2024-05-16T02:00:00,23.742,0.966,650.426,128.809 +2024-05-16T02:05:00,24.146,1.004,643.627,134.53 +2024-05-16T02:10:00,22.478,1.003,459.265,132.788 +2024-05-16T02:15:00,25.133,0.96,1254.801,156.182 +2024-05-16T02:20:00,26.16,0.977,809.456,180.241 +2024-05-16T02:25:00,25.217,0.957,922.665,107.945 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newline at end of file diff --git a/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-10-27.csv b/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-10-27.csv new file mode 100644 index 0000000..8bbcf7e --- /dev/null +++ b/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-10-27.csv @@ -0,0 +1,95 @@ +timestamp,rmssd,coverage,low_frequency,high_frequency +2024-10-27T03:30:00,19.547,0.939,332.625,54.742 +2024-10-27T03:35:00,25.043,0.942,572.705,159.274 +2024-10-27T03:40:00,24.563,0.988,649.444,132.462 +2024-10-27T03:45:00,27.399,0.912,865.309,173.245 +2024-10-27T03:50:00,25.604,1.002,402.383,270.32 +2024-10-27T03:55:00,27.539,0.99,462.509,309.285 +2024-10-27T04:00:00,25.706,1.001,789.092,243.116 +2024-10-27T04:05:00,25.63,1.005,502.403,274.741 +2024-10-27T04:10:00,27.258,1.001,653.73,291.615 +2024-10-27T04:15:00,25.343,0.993,257.448,244.213 +2024-10-27T04:20:00,24.812,1.004,465.629,271.073 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a/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-11-12.csv b/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-11-12.csv new file mode 100644 index 0000000..d4817b9 --- /dev/null +++ b/data/fitness/HeartRateVariability/HRVDetails/Heart Rate Variability Details - 2024-11-12.csv @@ -0,0 +1,90 @@ +timestamp,rmssd,coverage,low_frequency,high_frequency +2024-11-12T02:00:00,27.275,0.92,936.969,141.789 +2024-11-12T02:05:00,23.659,0.983,509.844,102.166 +2024-11-12T02:10:00,23.376,0.94,489.488,86.11 +2024-11-12T02:15:00,21.605,1.002,524.24,61.674 +2024-11-12T02:20:00,19.789,1.003,245.703,122.159 +2024-11-12T02:25:00,19.405,1.001,245.553,104.434 +2024-11-12T02:30:00,19.714,1.005,523.306,119.948 +2024-11-12T02:35:00,24.375,0.997,1414.832,160.738 +2024-11-12T02:40:00,24.538,0.987,776.925,71.988 +2024-11-12T02:45:00,25.449,0.995,652.265,117.866 +2024-11-12T02:50:00,19.703,1.005,746.299,73.614 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a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-03.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-03.csv new file mode 100644 index 0000000..eef74c3 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-03.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-03T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-04.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-04.csv new file mode 100644 index 0000000..67ec4f7 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-04.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-04T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-05.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-05.csv new file mode 100644 index 0000000..be83151 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-05.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-05T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-06.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-06.csv new file mode 100644 index 0000000..a18969d --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-06.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-06T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-07.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-07.csv new file mode 100644 index 0000000..55ec7d1 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-07.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-07T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-09.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-09.csv new file mode 100644 index 0000000..54ebc06 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-09.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-09T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-11.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-11.csv new file mode 100644 index 0000000..a3eb5a8 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-11.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-11T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-12.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-12.csv new file mode 100644 index 0000000..b901b36 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-12.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-12T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-13.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-13.csv new file mode 100644 index 0000000..27b2e2f --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-13.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-13T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-14.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-14.csv new file mode 100644 index 0000000..7a6e3df --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-14.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-14T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-15.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-15.csv new file mode 100644 index 0000000..5e181da --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-15.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-15T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-16.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-16.csv new file mode 100644 index 0000000..125fe7a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-16.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-16T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-17.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-17.csv new file mode 100644 index 0000000..34d01d7 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-17.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-17T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-18.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-18.csv new file mode 100644 index 0000000..10dcb17 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-18.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-18T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-19.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-19.csv new file mode 100644 index 0000000..fac1712 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-19.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-19T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-20.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-20.csv new file mode 100644 index 0000000..e6b98fb --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-20.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-20T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-21.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-21.csv new file mode 100644 index 0000000..d4e99de --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-21.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-21T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-22.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-22.csv new file mode 100644 index 0000000..c6f170b --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-22.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-22T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-23.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-23.csv new file mode 100644 index 0000000..5c32bb9 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-23.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-23T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-24.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-24.csv new file mode 100644 index 0000000..b4c104c --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-24.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-24T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-25.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-25.csv new file mode 100644 index 0000000..b43c845 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-25.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-25T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-26.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-26.csv new file mode 100644 index 0000000..c28b082 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-26.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-26T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-27.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-27.csv new file mode 100644 index 0000000..25c7442 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-27.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-27T00:00:00,12.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-28.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-28.csv new file mode 100644 index 0000000..e3d3ff2 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-28.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-28T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-29.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-29.csv new file mode 100644 index 0000000..c8fa75a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-29.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-29T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-30.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-30.csv new file mode 100644 index 0000000..de44280 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-04-30.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-04-30T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-01.csv new file mode 100644 index 0000000..d127938 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-01.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-01T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-02.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-02.csv new file mode 100644 index 0000000..b63385a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-02.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-02T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-03.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-03.csv new file mode 100644 index 0000000..cfd1a89 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-03.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-03T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-04.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-04.csv new file mode 100644 index 0000000..a7d8725 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-04.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-04T00:00:00,14.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-05.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-05.csv new file mode 100644 index 0000000..ad53618 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-05.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-05T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-07.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-07.csv new file mode 100644 index 0000000..ae788ed --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-07.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-07T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-08.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-08.csv new file mode 100644 index 0000000..dade2e6 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-08.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-08T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-09.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-09.csv new file mode 100644 index 0000000..2a1d3ba --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-09.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-09T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-10.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-10.csv new file mode 100644 index 0000000..7962b8b --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-10.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-10T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-11.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-11.csv new file mode 100644 index 0000000..daef283 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-11.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-11T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-12.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-12.csv new file mode 100644 index 0000000..3fd0e14 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-12.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-12T00:00:00,15.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-13.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-13.csv new file mode 100644 index 0000000..949ceb8 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-13.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-13T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-14.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-14.csv new file mode 100644 index 0000000..8a39bd2 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-14.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-14T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-15.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-15.csv new file mode 100644 index 0000000..776fe2b --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-15.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-15T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-16.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-16.csv new file mode 100644 index 0000000..3dd66ad --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-16.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-16T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-17.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-17.csv new file mode 100644 index 0000000..664397f --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-17.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-17T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-18.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-18.csv new file mode 100644 index 0000000..c69c4e6 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-18.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-18T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-19.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-19.csv new file mode 100644 index 0000000..4550a9a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-19.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-19T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-22.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-22.csv new file mode 100644 index 0000000..0486112 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-22.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-22T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-23.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-23.csv new file mode 100644 index 0000000..f1d6e83 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-23.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-23T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-24.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-24.csv new file mode 100644 index 0000000..1725034 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-24.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-24T00:00:00,12.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-25.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-25.csv new file mode 100644 index 0000000..bd6d0e2 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-25.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-25T00:00:00,15.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-26.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-26.csv new file mode 100644 index 0000000..d28d035 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-26.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-26T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-27.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-27.csv new file mode 100644 index 0000000..0b6e6e4 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-27.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-27T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-28.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-28.csv new file mode 100644 index 0000000..580319b --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-28.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-28T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-30.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-30.csv new file mode 100644 index 0000000..980a0a3 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-30.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-30T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-31.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-31.csv new file mode 100644 index 0000000..0064bc5 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-05-31.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-05-31T00:00:00,12.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-01.csv new file mode 100644 index 0000000..0844d94 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-01.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-01T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-02.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-02.csv new file mode 100644 index 0000000..2d85fb9 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-02.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-02T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-03.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-03.csv new file mode 100644 index 0000000..1e745dd --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-03.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-03T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-04.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-04.csv new file mode 100644 index 0000000..64d4063 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-04.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-04T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-05.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-05.csv new file mode 100644 index 0000000..451fbb9 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-05.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-05T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-06.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-06.csv new file mode 100644 index 0000000..89db694 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-06.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-06T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-07.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-07.csv new file mode 100644 index 0000000..285413f --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-07.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-07T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-08.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-08.csv new file mode 100644 index 0000000..efaf856 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-08.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-08T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-10.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-10.csv new file mode 100644 index 0000000..1d696be --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-10.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-10T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-11.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-11.csv new file mode 100644 index 0000000..e873c9a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-11.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-11T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-12.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-12.csv new file mode 100644 index 0000000..7d7bfed --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-12.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-12T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-13.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-13.csv new file mode 100644 index 0000000..fa88e87 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-13.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-13T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-14.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-14.csv new file mode 100644 index 0000000..c5b087d --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-14.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-14T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-15.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-15.csv new file mode 100644 index 0000000..bb16460 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-15.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-15T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-16.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-16.csv new file mode 100644 index 0000000..08d40c7 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-16.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-16T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-17.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-17.csv new file mode 100644 index 0000000..8882138 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-17.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-17T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-18.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-18.csv new file mode 100644 index 0000000..f2e8d84 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-18.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-18T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-19.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-19.csv new file mode 100644 index 0000000..6a5a77e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-19.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-19T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-21.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-21.csv new file mode 100644 index 0000000..c02da9e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-21.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-21T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-23.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-23.csv new file mode 100644 index 0000000..6c35514 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-23.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-23T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-24.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-24.csv new file mode 100644 index 0000000..c7765ad --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-24.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-24T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-25.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-25.csv new file mode 100644 index 0000000..87a5475 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-25.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-25T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-26.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-26.csv new file mode 100644 index 0000000..fbf3900 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-26.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-26T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-27.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-27.csv new file mode 100644 index 0000000..b9a22cc --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-27.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-27T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-28.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-28.csv new file mode 100644 index 0000000..18803a9 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-28.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-28T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-30.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-30.csv new file mode 100644 index 0000000..bee27f6 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-06-30.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-06-30T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-01.csv new file mode 100644 index 0000000..9df4812 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-01.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-01T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-02.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-02.csv new file mode 100644 index 0000000..c438982 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-02.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-02T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-04.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-04.csv new file mode 100644 index 0000000..febc1ce --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-04.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-04T00:00:00,12.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-05.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-05.csv new file mode 100644 index 0000000..e564541 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-05.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-05T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-06.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-06.csv new file mode 100644 index 0000000..3e8bb5f --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-06.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-06T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-07.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-07.csv new file mode 100644 index 0000000..bbc6fca --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-07.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-07T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-08.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-08.csv new file mode 100644 index 0000000..abb060b --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-08.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-08T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-09.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-09.csv new file mode 100644 index 0000000..a0be100 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-09.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-09T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-10.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-10.csv new file mode 100644 index 0000000..6f26c76 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-10.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-10T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-11.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-11.csv new file mode 100644 index 0000000..c5720f9 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-11.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-11T00:00:00,12.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-12.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-12.csv new file mode 100644 index 0000000..65206ee --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-12.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-12T00:00:00,12.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-13.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-13.csv new file mode 100644 index 0000000..a3c2a0e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-13.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-13T00:00:00,12.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-14.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-14.csv new file mode 100644 index 0000000..b10d873 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-14.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-14T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-15.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-15.csv new file mode 100644 index 0000000..cb0d14e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-15.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-15T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-16.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-16.csv new file mode 100644 index 0000000..d44d39f --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-16.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-16T00:00:00,12.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-17.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-17.csv new file mode 100644 index 0000000..67629c8 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-17.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-17T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-18.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-18.csv new file mode 100644 index 0000000..02a08c8 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-18.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-18T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-19.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-19.csv new file mode 100644 index 0000000..6310423 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-19.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-19T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-20.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-20.csv new file mode 100644 index 0000000..0865b47 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-20.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-20T00:00:00,12.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-21.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-21.csv new file mode 100644 index 0000000..49adc44 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-21.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-21T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-22.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-22.csv new file mode 100644 index 0000000..144696d --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-22.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-22T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-23.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-23.csv new file mode 100644 index 0000000..917a52e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-23.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-23T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-24.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-24.csv new file mode 100644 index 0000000..71d6964 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-24.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-24T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-25.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-25.csv new file mode 100644 index 0000000..0575634 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-25.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-25T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-26.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-26.csv new file mode 100644 index 0000000..34896e5 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-26.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-26T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-27.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-27.csv new file mode 100644 index 0000000..069b927 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-27.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-27T00:00:00,12.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-28.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-28.csv new file mode 100644 index 0000000..7552a6a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-28.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-28T00:00:00,12.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-29.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-29.csv new file mode 100644 index 0000000..b60d181 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-29.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-29T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-30.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-30.csv new file mode 100644 index 0000000..0223a1a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-30.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-30T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-31.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-31.csv new file mode 100644 index 0000000..0174202 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-07-31.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-07-31T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-01.csv new file mode 100644 index 0000000..0081b2e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-01.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-01T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-02.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-02.csv new file mode 100644 index 0000000..3a2956e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-02.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-02T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-03.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-03.csv new file mode 100644 index 0000000..d56b502 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-03.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-03T00:00:00,12.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-04.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-04.csv new file mode 100644 index 0000000..84fd439 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-04.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-04T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-05.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-05.csv new file mode 100644 index 0000000..42f29bf --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-05.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-05T00:00:00,12.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-06.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-06.csv new file mode 100644 index 0000000..739a265 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-06.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-06T00:00:00,17.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-07.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-07.csv new file mode 100644 index 0000000..5622c9e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-07.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-07T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-08.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-08.csv new file mode 100644 index 0000000..c9635df --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-08.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-08T00:00:00,12.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-09.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-09.csv new file mode 100644 index 0000000..8e6a0b6 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-09.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-09T00:00:00,11.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-10.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-10.csv new file mode 100644 index 0000000..03f5912 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-10.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-10T00:00:00,15.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-11.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-11.csv new file mode 100644 index 0000000..c4f9126 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-11.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-11T00:00:00,14.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-12.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-12.csv new file mode 100644 index 0000000..9f58848 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-12.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-12T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-13.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-13.csv new file mode 100644 index 0000000..0313646 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-13.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-13T00:00:00,15.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-14.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-14.csv new file mode 100644 index 0000000..410819c --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-14.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-14T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-15.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-15.csv new file mode 100644 index 0000000..6b101f1 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-15.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-15T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-16.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-16.csv new file mode 100644 index 0000000..fe5bb37 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-16.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-16T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-17.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-17.csv new file mode 100644 index 0000000..186d0a4 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-17.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-17T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-18.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-18.csv new file mode 100644 index 0000000..5880723 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-18.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-18T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-19.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-19.csv new file mode 100644 index 0000000..c84ce62 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-19.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-19T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-20.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-20.csv new file mode 100644 index 0000000..589e094 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-20.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-20T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-21.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-21.csv new file mode 100644 index 0000000..0b25b7c --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-21.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-21T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-22.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-22.csv new file mode 100644 index 0000000..c603f9c --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-22.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-22T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-23.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-23.csv new file mode 100644 index 0000000..c0d0d6a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-23.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-23T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-24.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-24.csv new file mode 100644 index 0000000..71cd5d3 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-24.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-24T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-25.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-25.csv new file mode 100644 index 0000000..0498fcf --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-25.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-25T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-27.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-27.csv new file mode 100644 index 0000000..9bb68f3 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-27.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-27T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-28.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-28.csv new file mode 100644 index 0000000..1ebca41 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-28.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-28T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-29.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-29.csv new file mode 100644 index 0000000..6f5068f --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-29.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-29T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-30.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-30.csv new file mode 100644 index 0000000..5acf83f --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-30.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-30T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-31.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-31.csv new file mode 100644 index 0000000..9567284 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-08-31.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-08-31T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-01.csv new file mode 100644 index 0000000..3bfc2cd --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-01.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-01T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-02.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-02.csv new file mode 100644 index 0000000..ca763aa --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-02.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-02T00:00:00,12.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-03.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-03.csv new file mode 100644 index 0000000..83384a4 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-03.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-03T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-04.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-04.csv new file mode 100644 index 0000000..56b0aa8 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-04.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-04T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-05.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-05.csv new file mode 100644 index 0000000..a187eaa --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-05.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-05T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-07.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-07.csv new file mode 100644 index 0000000..6feb06a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-07.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-07T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-08.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-08.csv new file mode 100644 index 0000000..f61f91c --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-08.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-08T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-10.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-10.csv new file mode 100644 index 0000000..b338d70 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-10.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-10T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-11.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-11.csv new file mode 100644 index 0000000..0bbd274 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-11.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-11T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-12.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-12.csv new file mode 100644 index 0000000..89b0c92 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-12.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-12T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-13.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-13.csv new file mode 100644 index 0000000..143dffc --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-13.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-13T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-14.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-14.csv new file mode 100644 index 0000000..8ee5f41 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-14.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-14T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-15.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-15.csv new file mode 100644 index 0000000..6e20412 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-15.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-15T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-16.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-16.csv new file mode 100644 index 0000000..5530b13 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-16.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-16T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-17.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-17.csv new file mode 100644 index 0000000..e895a92 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-17.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-17T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-18.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-18.csv new file mode 100644 index 0000000..53aa577 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-18.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-18T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-19.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-19.csv new file mode 100644 index 0000000..eaa73e7 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-19.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-19T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-21.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-21.csv new file mode 100644 index 0000000..d3ef9f4 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-21.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-21T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-22.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-22.csv new file mode 100644 index 0000000..c19ac82 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-22.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-22T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-23.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-23.csv new file mode 100644 index 0000000..9bb2ec2 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-23.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-23T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-24.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-24.csv new file mode 100644 index 0000000..e29314e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-24.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-24T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-25.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-25.csv new file mode 100644 index 0000000..c4134cd --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-25.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-25T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-27.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-27.csv new file mode 100644 index 0000000..4817f10 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-27.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-27T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-28.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-28.csv new file mode 100644 index 0000000..b8cb820 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-28.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-28T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-29.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-29.csv new file mode 100644 index 0000000..d5ac43b --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-29.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-29T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-30.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-30.csv new file mode 100644 index 0000000..33fbd82 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-09-30.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-09-30T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-01.csv new file mode 100644 index 0000000..00cdbbb --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-01.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-01T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-02.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-02.csv new file mode 100644 index 0000000..ede375a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-02.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-02T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-03.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-03.csv new file mode 100644 index 0000000..4c3d64a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-03.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-03T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-04.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-04.csv new file mode 100644 index 0000000..c28b61a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-04.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-04T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-05.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-05.csv new file mode 100644 index 0000000..d6a540b --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-05.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-05T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-06.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-06.csv new file mode 100644 index 0000000..bd3ad08 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-06.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-06T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-07.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-07.csv new file mode 100644 index 0000000..1b4bd7c --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-07.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-07T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-08.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-08.csv new file mode 100644 index 0000000..342c4b7 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-08.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-08T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-09.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-09.csv new file mode 100644 index 0000000..fd5c10d --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-09.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-09T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-10.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-10.csv new file mode 100644 index 0000000..b358964 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-10.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-10T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-11.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-11.csv new file mode 100644 index 0000000..d2f0550 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-11.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-11T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-12.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-12.csv new file mode 100644 index 0000000..f2f6b9f --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-12.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-12T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-15.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-15.csv new file mode 100644 index 0000000..9c41fa3 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-15.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-15T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-16.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-16.csv new file mode 100644 index 0000000..d88bf50 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-16.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-16T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-17.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-17.csv new file mode 100644 index 0000000..a1d2cf3 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-17.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-17T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-18.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-18.csv new file mode 100644 index 0000000..b2bb85d --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-18.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-18T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-19.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-19.csv new file mode 100644 index 0000000..4fbbfaf --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-19.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-19T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-20.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-20.csv new file mode 100644 index 0000000..add8f56 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-20.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-20T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-21.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-21.csv new file mode 100644 index 0000000..b9555dc --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-21.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-21T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-22.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-22.csv new file mode 100644 index 0000000..4b9f216 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-22.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-22T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-23.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-23.csv new file mode 100644 index 0000000..a28669c --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-23.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-23T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-24.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-24.csv new file mode 100644 index 0000000..078dcef --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-24.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-24T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-25.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-25.csv new file mode 100644 index 0000000..62d98eb --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-25.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-25T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-26.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-26.csv new file mode 100644 index 0000000..785ca18 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-26.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-26T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-27.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-27.csv new file mode 100644 index 0000000..ef3f1bf --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-27.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-27T00:00:00,13.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-28.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-28.csv new file mode 100644 index 0000000..3dbef0e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-28.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-28T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-29.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-29.csv new file mode 100644 index 0000000..384d159 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-29.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-29T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-30.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-30.csv new file mode 100644 index 0000000..28633c1 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-30.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-30T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-31.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-31.csv new file mode 100644 index 0000000..8be0d5e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-10-31.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-10-31T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-01.csv new file mode 100644 index 0000000..e82d895 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-01.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-01T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-02.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-02.csv new file mode 100644 index 0000000..0609801 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-02.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-02T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-03.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-03.csv new file mode 100644 index 0000000..7c81dec --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-03.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-03T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-04.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-04.csv new file mode 100644 index 0000000..23e274e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-04.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-04T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-05.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-05.csv new file mode 100644 index 0000000..b54ba21 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-05.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-05T00:00:00,14.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-06.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-06.csv new file mode 100644 index 0000000..0fefded --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-06.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-06T00:00:00,12.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-07.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-07.csv new file mode 100644 index 0000000..a74d34e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-07.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-07T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-08.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-08.csv new file mode 100644 index 0000000..d900032 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-08.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-08T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-09.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-09.csv new file mode 100644 index 0000000..48bc476 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-09.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-09T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-10.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-10.csv new file mode 100644 index 0000000..637a3cd --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-10.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-10T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-11.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-11.csv new file mode 100644 index 0000000..11cdb62 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-11.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-11T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-12.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-12.csv new file mode 100644 index 0000000..f877564 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-12.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-12T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-13.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-13.csv new file mode 100644 index 0000000..5bb3041 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-13.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-13T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-14.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-14.csv new file mode 100644 index 0000000..bd1526b --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-14.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-14T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-15.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-15.csv new file mode 100644 index 0000000..e92e974 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-15.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-15T00:00:00,14.0 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-16.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-16.csv new file mode 100644 index 0000000..5dea17a --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-16.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-16T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-17.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-17.csv new file mode 100644 index 0000000..f45614f --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-17.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-17T00:00:00,13.2 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-18.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-18.csv new file mode 100644 index 0000000..2ffed9c --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-18.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-18T00:00:00,14.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-19.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-19.csv new file mode 100644 index 0000000..9c1173b --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-19.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-19T00:00:00,14.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-20.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-20.csv new file mode 100644 index 0000000..e7b2bdb --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-20.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-20T00:00:00,13.4 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-21.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-21.csv new file mode 100644 index 0000000..592a126 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-21.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-21T00:00:00,13.8 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-22.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-22.csv new file mode 100644 index 0000000..a667647 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Daily Respiratory Rate Summary - 2024-11-22.csv @@ -0,0 +1,2 @@ +timestamp,daily_respiratory_rate +2024-11-22T00:00:00,13.6 \ No newline at end of file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-04-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-04-01.csv new file mode 100644 index 0000000..3f2d18e --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-04-01.csv @@ -0,0 +1,32 @@ +timestamp,full_sleep_breathing_rate,full_sleep_standard_deviation,full_sleep_signal_to_noise,deep_sleep_breathing_rate,deep_sleep_standard_deviation,deep_sleep_signal_to_noise,light_sleep_breathing_rate,light_sleep_standard_deviation,light_sleep_signal_to_noise,rem_sleep_breathing_rate,rem_sleep_standard_deviation,rem_sleep_signal_to_noise +2024-04-01T09:15:30,14.4,0.9,5.543,14.4,0.9,5.543,13.8,1.0,7.313,14.4,2.1,3.308 +2024-04-02T10:00:30,14.4,1.1,6.38,14.4,1.1,6.38,14.0,1.3,5.428,-1.0,0.0,1.603 +2024-04-03T08:44:30,14.2,0.8,4.139,14.2,0.8,4.139,13.8,0.8,4.984,-1.0,0.0,1.813 +2024-04-04T08:51:30,13.8,1.4,8.248,13.8,1.4,8.248,12.8,0.8,7.404,-1.0,0.0,1.913 +2024-04-05T09:46:30,13.8,0.9,4.904,13.8,0.9,4.904,13.8,1.2,5.903,-1.0,0.0,1.957 +2024-04-06T08:16:30,14.2,0.7,5.46,14.2,0.7,5.46,13.6,0.7,7.55,-1.0,0.0,2.182 +2024-04-07T11:28:30,13.6,0.8,5.745,13.6,0.8,5.745,13.6,1.1,4.356,4.8,0.4,2.554 +2024-04-09T09:10:30,14.4,0.6,4.717,14.4,0.6,4.717,13.4,1.0,5.797,-1.0,0.0,1.682 +2024-04-10T09:19:30,14.2,0.7,5.719,14.2,0.7,5.719,13.6,1.2,5.526,-1.0,0.0,2.343 +2024-04-10T11:15:30,14.2,0.7,5.719,14.2,0.7,5.719,13.4,1.2,6.197,-1.0,0.0,2.343 +2024-04-11T10:12:30,13.4,1.4,7.872,13.4,1.4,7.872,13.2,0.8,5.913,-1.0,0.0,1.805 +2024-04-12T10:29:00,13.6,1.2,9.401,13.6,1.2,9.401,13.0,1.1,6.878,-1.0,0.0,2.28 +2024-04-13T11:15:30,13.8,1.1,6.23,13.8,1.1,6.23,12.8,1.1,7.237,13.4,1.0,3.852 +2024-04-14T10:55:30,14.2,0.7,9.764,14.2,0.7,9.764,13.2,1.1,7.841,-1.0,0.0,1.927 +2024-04-15T08:12:30,14.0,0.8,7.217,14.0,0.8,7.217,14.0,0.8,6.128,-1.0,0.0,1.723 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diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-06-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-06-01.csv new file mode 100644 index 0000000..ff5fddb --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-06-01.csv @@ -0,0 +1,30 @@ +timestamp,full_sleep_breathing_rate,full_sleep_standard_deviation,full_sleep_signal_to_noise,deep_sleep_breathing_rate,deep_sleep_standard_deviation,deep_sleep_signal_to_noise,light_sleep_breathing_rate,light_sleep_standard_deviation,light_sleep_signal_to_noise,rem_sleep_breathing_rate,rem_sleep_standard_deviation,rem_sleep_signal_to_noise +2024-06-01T08:34:30,13.6,0.8,8.51,13.6,0.8,8.51,13.2,0.9,7.81,13.6,0.5,3.757 +2024-06-02T08:31:30,13.2,0.9,5.88,13.2,0.9,5.88,12.4,0.9,7.436,-1.0,0.0,2.005 +2024-06-03T06:39:30,13.6,0.5,6.418,13.6,0.5,6.418,13.4,0.7,7.85,-1.0,0.0,1.707 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Rate Summary - 2024-08-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-08-01.csv new file mode 100644 index 0000000..0cbac00 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-08-01.csv @@ -0,0 +1,35 @@ +timestamp,full_sleep_breathing_rate,full_sleep_standard_deviation,full_sleep_signal_to_noise,deep_sleep_breathing_rate,deep_sleep_standard_deviation,deep_sleep_signal_to_noise,light_sleep_breathing_rate,light_sleep_standard_deviation,light_sleep_signal_to_noise,rem_sleep_breathing_rate,rem_sleep_standard_deviation,rem_sleep_signal_to_noise +2024-08-01T07:58:30,13.6,0.9,4.833,13.6,0.9,4.833,13.2,0.7,7.506,-1.0,0.0,1.693 +2024-08-02T09:32:30,13.2,1.2,5.997,13.2,1.2,5.997,13.0,1.3,6.204,13.8,1.3,3.502 +2024-08-03T10:11:30,12.4,1.1,7.02,12.4,1.1,7.02,12.6,1.0,6.617,12.6,0.9,3.851 +2024-08-04T11:54:30,13.2,1.0,9.654,13.2,1.0,9.654,12.4,0.9,8.928,-1.0,0.0,2.195 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file diff --git a/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-10-01.csv b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-10-01.csv new file mode 100644 index 0000000..6051a06 --- /dev/null +++ b/data/fitness/HeartRateVariability/RespiratoryRateSummary/Respiratory Rate Summary - 2024-10-01.csv @@ -0,0 +1,35 @@ +timestamp,full_sleep_breathing_rate,full_sleep_standard_deviation,full_sleep_signal_to_noise,deep_sleep_breathing_rate,deep_sleep_standard_deviation,deep_sleep_signal_to_noise,light_sleep_breathing_rate,light_sleep_standard_deviation,light_sleep_signal_to_noise,rem_sleep_breathing_rate,rem_sleep_standard_deviation,rem_sleep_signal_to_noise +2024-10-01T09:51:30,13.4,0.7,9.899,13.4,0.7,9.899,13.4,0.9,6.293,13.2,1.2,2.963 +2024-10-02T08:37:30,13.6,0.7,9.722,13.6,0.7,9.722,13.2,1.3,10.881,13.0,1.3,2.822 +2024-10-03T08:35:30,13.8,0.7,6.543,13.8,0.7,6.543,13.8,0.9,5.037,13.8,1.3,3.582 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No newline at end of file diff --git a/data/fitness/SleepScore/Sleep Profile README.txt b/data/fitness/SleepScore/Sleep Profile README.txt new file mode 100644 index 0000000..57cd7fa --- /dev/null +++ b/data/fitness/SleepScore/Sleep Profile README.txt @@ -0,0 +1,19 @@ +By analyzing your sleep over a month, your Sleep Profile helps you understand and act on long-term sleeping behaviors. You’ll need to log at least 14 nights per month to receive a Sleep Profile on the 1st of each month. +This feature will require a Premium membership. +Your sleep animal is an illustration of your sleep behaviors. You’ll be matched with the sleep animal that most closely reflects your monthly sleep metrics, so you can get an insight into your sleep. +Your sleep animal changes with your sleep metrics, helping you understand the differences in your long-term sleep patterns and trends. +This feature provides your sleep animal, which includes: +- creation_date: The date when your sleep animal was created. +- sleep_type: One of the 6 sleep animals - Giraffe, Tortoise, Dolphin, Hedgehog, Parrot, Bear. +Monthly Sleep Analysis: Your Monthly Sleep Analysis is calculated within your Sleep Profile, and offer a more detailed look at the factors that Fitbit uses to assess your sleep: +- deep_sleep: (minutes) During deep sleep, you’re typically very still and your heart rate variability peaks. Because sleep is a time for rest and recovery, a night without much movement is likely more refreshing. However, sleeping too soundly may be an indication that you’re not getting enough quality sleep. +- rem_sleep: (percent) Rapid-eye-movement sleep, or REM, is an essential sleep stage. You’re likely to have vivid dreams during REM, as your brain is believed to be working hard on complex problems and processing emotions. When you have a lot on your mind, you might find yourself spending too much time in REM sleep. +- sleep_duration: (hours) Sleep duration measures how much sleep you get on average. This may be less than the total amount of time you spend in bed. While everyone has different sleep needs, consistently getting the right amount for your body is important to feel well rested. +- sleep_start_time: (time) This metric tracks the time you usually get to sleep. While some people prefer to sleep earlier, and others later, it’s most important to have a consistent schedule. Aligning this to your daily routine is key to good sleep hygiene. +- schedule_variability: (hours) This shows how much your sleep schedule varies from day to day. Your body clock plays a key role in your mental and physical wellbeing. An inconsistent sleep schedule can affect this natural rhythm and stop you from feeling your best. +- restorative_sleep: (score 0-100) Restorative sleep measures how much of your time asleep is spent with your heart rate lower than your usual resting rate. Many aspects of restful and restorative sleep are associated with a lower heart rate. +- time_before_sound_sleep: (minutes) This tracks how long it usually takes you to fall soundly asleep. Regularly falling asleep without significant delay is a sign your body is sleeping at a time that lines up with your schedule. +- sleep_stability: (wake counts per hour) As you change sleep stages or your sleeping position, your brain often wakes up for a moment, but it’s usually so brief you don’t notice. Stability tracks how often this happens. If you notice changes in your sleep stability, it might be time to look into your sleep environment. +- nights_with_long_awakenings: (percent of nights with long awakenings) Noise, light, or an overactive mind might wake you up and keep you up for a while. Most people have occasional long awakenings, but if you notice them becoming more common, it’s a good idea to look into the cause. +- days_with_naps: (percent of days with naps) This measures naps detected using Fitbit. Naps may be necessary after a bad night’s sleep, and can help you feel more refreshed. But if you find you’re napping often, it could be a sign of other factors affecting your long-term sleep. + diff --git a/data/fitness/SleepScore/Sleep Profile.csv b/data/fitness/SleepScore/Sleep Profile.csv new file mode 100644 index 0000000..daae358 --- /dev/null +++ b/data/fitness/SleepScore/Sleep Profile.csv @@ -0,0 +1,13 @@ +creation_date,sleep_type,deep_sleep,rem_sleep,sleep_duration,sleep_start_time,schedule_variability,restorative_sleep,time_before_sound_sleep,sleep_stability,nights_with_long_awakenings,days_with_naps +2023-12-30,Giraffe,79.71,25.77,6.34,25.91,57.27,74.86,16.91,3.48,0.00,0.00 +2024-01-01,Giraffe,79.74,23.99,6.31,27.09,66.18,78.09,21.92,3.42,3.23,3.23 +2024-02-01,Giraffe,77.30,24.97,6.15,26.78,102.25,77.96,17.40,3.33,9.68,0.00 +2024-03-01,Bear,85.38,24.72,6.19,25.69,51.65,82.92,17.83,3.80,0.00,0.00 +2024-04-01,Giraffe,81.71,25.32,6.11,26.69,55.04,81.58,17.34,3.69,3.23,0.00 +2024-05-01,Giraffe,78.72,23.00,5.84,27.11,59.37,81.95,15.90,3.45,6.67,0.00 +2024-06-01,Giraffe,79.21,24.89,6.00,25.93,83.07,76.28,20.60,3.07,6.45,3.23 +2024-07-01,Giraffe,78.97,25.30,6.10,24.76,39.35,78.18,15.98,3.58,0.00,3.45 +2024-08-01,Giraffe,82.58,23.96,6.28,25.34,55.65,76.19,19.23,3.61,0.00,3.23 +2024-09-01,Bear,86.42,22.61,6.70,26.38,53.59,72.99,23.05,3.81,9.68,3.23 +2024-10-01,Bear,85.20,22.93,6.54,25.84,52.09,76.10,23.23,3.50,6.67,3.33 +2024-11-01,Giraffe,77.28,23.06,6.23,26.45,64.53,77.00,21.93,3.39,6.90,3.45 diff --git a/data/fitness/SleepScore/sleep_score.csv b/data/fitness/SleepScore/sleep_score.csv new file mode 100644 index 0000000..8c97ec6 --- /dev/null +++ b/data/fitness/SleepScore/sleep_score.csv @@ -0,0 +1,1374 @@ +sleep_log_entry_id,timestamp,overall_score,composition_score,revitalization_score,duration_score,deep_sleep_in_minutes,resting_heart_rate,restlessness +47572319477,2024-11-24T10:38:30Z,82,22,19,41,100,59,0.0935738444193912 +47562806336,2024-11-23T10:08:30Z,89,23,21,45,108,61,0.1021759697256386 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Included: +---------- + +Stress Score.csv + +Stress Score for each of the days it was attempted to be calculated + +date - date when Stress Score was recorded +updated_at - time when score was created or update last time +stress_score - value of Stress Score +sleep_points - points from sleep components +max_sleep_points - maximal possible points from sleep components a user can get +responsiveness_points - points from responsiveness components +max_responsiveness_points - maximal points from responsiveness components a user can get +exertion_points - points from exertion components +max_exertion_points - maximal points from exertion components a user can get +status - IN_PROGRESS (calculations is in progress) / READY (Stress Score was calculated) +calculation_failed - whether stress score calculation has failed for a certain date diff --git a/data/fitness/StressScore/Stress Score.csv b/data/fitness/StressScore/Stress Score.csv new file mode 100644 index 0000000..b8fb3b2 --- /dev/null +++ b/data/fitness/StressScore/Stress Score.csv @@ -0,0 +1,1078 @@ +DATE,UPDATED_AT,STRESS_SCORE,SLEEP_POINTS,MAX_SLEEP_POINTS,RESPONSIVENESS_POINTS,MAX_RESPONSIVENESS_POINTS,EXERTION_POINTS,MAX_EXERTION_POINTS,STATUS,CALCULATION_FAILED +2021-11-10T00:00:00,2021-11-10T12:31:21.658,0,0,0,0,0,0,0,NO_DATA,true +2021-11-11T00:00:00,2021-11-11T12:42:28.114,65,23,30,18,30,24,40,READY,false +2021-11-12T00:00:00,2021-11-12T13:44:58.372,67,25,30,15,30,27,40,READY,false +2021-11-13T00:00:00,2021-11-13T16:48:56.386,72,25,30,20,30,27,40,READY,false +2021-11-14T00:00:00,2021-11-14T16:39:53.737,77,25,30,23,30,29,40,READY,false +2021-11-15T00:00:00,2021-11-15T13:46:24.739,76,27,30,17,30,32,40,READY,false +2021-11-16T00:00:00,2021-11-16T13:58:40.842,69,23,30,17,30,29,40,READY,false +2021-11-17T00:00:00,2021-11-17T15:48:34.813,73,24,30,20,30,29,40,READY,false +2021-11-18T00:00:00,2021-11-18T12:53:15.43,70,24,30,17,30,29,40,READY,false 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file diff --git a/data/fitness/Temperature/Computed Temperature - 2024-09-01.csv b/data/fitness/Temperature/Computed Temperature - 2024-09-01.csv new file mode 100644 index 0000000..dab85e3 --- /dev/null +++ b/data/fitness/Temperature/Computed Temperature - 2024-09-01.csv @@ -0,0 +1,32 @@ +type,sleep_start,sleep_end,temperature_samples,nightly_temperature,baseline_relative_sample_sum,baseline_relative_sample_sum_of_squares,baseline_relative_nightly_standard_deviation,baseline_relative_sample_standard_deviation +IDT,2024-09-01T04:05:30,2024-09-01T11:13:30,427,31.570257611241217,-135.97984096898108,1331.7755905151134,0.6596284056041919,1.281672105606576 +IDT,2024-09-02T03:01,2024-09-02T10:58:30,478,31.62531380753138,-85.67944704779802,730.0613758291584,0.6551621855840634,1.2894653903154596 +IDT,2024-09-03T03:26,2024-09-03T08:54,328,31.091158536585365,-192.7245431987376,359.70444985094946,0.6734156133799829,1.2892928554292677 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of file diff --git a/data/fitness/Temperature/Computed Temperature - 2024-10-01.csv b/data/fitness/Temperature/Computed Temperature - 2024-10-01.csv new file mode 100644 index 0000000..3aec72a --- /dev/null +++ b/data/fitness/Temperature/Computed Temperature - 2024-10-01.csv @@ -0,0 +1,31 @@ +type,sleep_start,sleep_end,temperature_samples,nightly_temperature,baseline_relative_sample_sum,baseline_relative_sample_sum_of_squares,baseline_relative_nightly_standard_deviation,baseline_relative_sample_standard_deviation +IDT,2024-10-01T03:32:30,2024-10-01T09:51:30,378,32.98677248677249,371.14146660846745,708.9812387202401,0.6090847916812169,1.303126503942556 +IDT,2024-10-02T00:41:30,2024-10-02T08:37:30,475,32.141052631578944,38.11496621194783,457.5678961039521,0.6052104410564818,1.294240740833217 +IDT,2024-10-03T02:18,2024-10-03T08:35:30,378,31.712433862433862,-131.68640583554588,1190.0680409573251,0.5823870051683918,1.315254125151479 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- 2024-11-01.csv new file mode 100644 index 0000000..c35edd3 --- /dev/null +++ b/data/fitness/Temperature/Computed Temperature - 2024-11-01.csv @@ -0,0 +1,25 @@ +type,sleep_start,sleep_end,temperature_samples,nightly_temperature,baseline_relative_sample_sum,baseline_relative_sample_sum_of_squares,baseline_relative_nightly_standard_deviation,baseline_relative_sample_standard_deviation +IDT,2024-11-01T01:57:30,2024-11-01T08:00:30,363,31.80771349862259,13.823058252428382,593.0047849572729,0.6087299767642133,1.3590009204641302 +IDT,2024-11-02T03:32,2024-11-02T09:36:30,361,30.710249307479227,-382.437674850893,807.3004851671867,0.6445906813555867,1.345944567123681 +IDT,2024-11-02T23:44:30,2024-11-03T08:55:30,551,32.32141560798548,304.03196996442784,1172.9667128140572,0.6260652852043805,1.3470067299985682 +IDT,2024-11-04T01:16:30,2024-11-04T07:41,383,30.65822454308094,-425.66961071438163,1276.4245887355646,0.6609848333571942,1.3466915143480396 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2024-01-01.csv new file mode 100644 index 0000000..688bed9 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-01-01.csv @@ -0,0 +1 @@ +recorded_time,temperature,sensor_type \ No newline at end of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-01-02.csv b/data/fitness/Temperature/Device Temperature - 2024-01-02.csv new file mode 100644 index 0000000..fa85dd6 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-01-02.csv @@ -0,0 +1,1438 @@ +recorded_time,temperature,sensor_type +2024-01-02T00:00,-3.220123203285425,UNKNOWN +2024-01-02T00:01,-3.120123203285427,UNKNOWN +2024-01-02T00:02,-3.120123203285427,UNKNOWN +2024-01-02T00:03,-3.120123203285427,UNKNOWN +2024-01-02T00:04,-3.120123203285427,UNKNOWN +2024-01-02T00:05,-3.220123203285425,UNKNOWN +2024-01-02T00:06,-3.5201232032854257,UNKNOWN +2024-01-02T00:07,-3.420123203285428,UNKNOWN +2024-01-02T00:08,-3.5201232032854257,UNKNOWN +2024-01-02T00:09,-3.620123203285427,UNKNOWN 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a/data/fitness/Temperature/Device Temperature - 2024-01-05.csv b/data/fitness/Temperature/Device Temperature - 2024-01-05.csv new file mode 100644 index 0000000..7960a09 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-01-05.csv @@ -0,0 +1,1432 @@ +recorded_time,temperature,sensor_type +2024-01-05T00:00,-4.044940667739343,UNKNOWN +2024-01-05T00:01,-4.044940667739343,UNKNOWN +2024-01-05T00:02,-4.144940667739341,UNKNOWN +2024-01-05T00:03,-4.144940667739341,UNKNOWN +2024-01-05T00:04,-4.144940667739341,UNKNOWN +2024-01-05T00:05,-4.244940667739343,UNKNOWN +2024-01-05T00:06,-4.244940667739343,UNKNOWN +2024-01-05T00:07,-4.244940667739343,UNKNOWN +2024-01-05T00:08,-4.344940667739344,UNKNOWN +2024-01-05T00:09,-4.344940667739344,UNKNOWN +2024-01-05T00:10,-4.444940667739342,UNKNOWN +2024-01-05T00:11,-4.344940667739344,UNKNOWN +2024-01-05T00:12,-4.344940667739344,UNKNOWN +2024-01-05T00:13,-4.344940667739344,UNKNOWN +2024-01-05T00:14,-4.344940667739344,UNKNOWN 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newline at end of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-01-25.csv b/data/fitness/Temperature/Device Temperature - 2024-01-25.csv new file mode 100644 index 0000000..8e33bac --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-01-25.csv @@ -0,0 +1,1437 @@ +recorded_time,temperature,sensor_type +2024-01-25T00:00,0.5270202020202,UNKNOWN +2024-01-25T00:01,0.5270202020202,UNKNOWN +2024-01-25T00:02,0.027020202020199946,UNKNOWN +2024-01-25T00:03,-0.2729797979797972,UNKNOWN +2024-01-25T00:04,-0.5729797979797979,UNKNOWN +2024-01-25T00:05,-0.6729797979797993,UNKNOWN +2024-01-25T00:06,-0.9729797979798,UNKNOWN +2024-01-25T00:07,-1.1729797979797993,UNKNOWN +2024-01-25T00:08,-1.3729797979797986,UNKNOWN +2024-01-25T00:09,-1.572979797979798,UNKNOWN +2024-01-25T00:10,-1.6729797979797993,UNKNOWN +2024-01-25T00:11,-1.6729797979797993,UNKNOWN +2024-01-25T00:12,-1.7729797979797972,UNKNOWN +2024-01-25T00:13,-1.7729797979797972,UNKNOWN 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end of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-03-05.csv b/data/fitness/Temperature/Device Temperature - 2024-03-05.csv new file mode 100644 index 0000000..23284d9 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-03-05.csv @@ -0,0 +1,1276 @@ +recorded_time,temperature,sensor_type +2024-03-05T00:00,1.1601020726196012,UNKNOWN +2024-03-05T00:01,0.9601020726196055,UNKNOWN +2024-03-05T00:02,0.6601020726196012,UNKNOWN +2024-03-05T00:03,0.7601020726196026,UNKNOWN +2024-03-05T00:04,0.7601020726196026,UNKNOWN +2024-03-05T00:05,0.860102072619604,UNKNOWN +2024-03-05T00:06,0.9601020726196055,UNKNOWN +2024-03-05T00:07,1.0601020726196069,UNKNOWN +2024-03-05T00:08,1.0601020726196069,UNKNOWN +2024-03-05T00:09,1.1601020726196012,UNKNOWN +2024-03-05T00:10,0.9601020726196055,UNKNOWN +2024-03-05T00:11,0.9601020726196055,UNKNOWN +2024-03-05T00:12,1.0601020726196069,UNKNOWN +2024-03-05T00:13,0.9601020726196055,UNKNOWN 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newline at end of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-03-20.csv b/data/fitness/Temperature/Device Temperature - 2024-03-20.csv new file mode 100644 index 0000000..fa1a27e --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-03-20.csv @@ -0,0 +1,1391 @@ +recorded_time,temperature,sensor_type +2024-03-20T00:00,0.2802052785923763,UNKNOWN +2024-03-20T00:01,0.2802052785923763,UNKNOWN +2024-03-20T00:02,0.2802052785923763,UNKNOWN +2024-03-20T00:03,0.38020527859237774,UNKNOWN +2024-03-20T00:04,0.38020527859237774,UNKNOWN +2024-03-20T00:05,0.48020527859237916,UNKNOWN +2024-03-20T00:06,0.48020527859237916,UNKNOWN +2024-03-20T00:07,0.48020527859237916,UNKNOWN +2024-03-20T00:08,0.38020527859237774,UNKNOWN +2024-03-20T00:09,0.2802052785923763,UNKNOWN +2024-03-20T00:10,0.08020527859237703,UNKNOWN +2024-03-20T00:11,-0.01979472140762084,UNKNOWN +2024-03-20T00:12,-0.01979472140762084,UNKNOWN +2024-03-20T00:14,-0.01979472140762084,UNKNOWN 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newline at end of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-03-24.csv b/data/fitness/Temperature/Device Temperature - 2024-03-24.csv new file mode 100644 index 0000000..6fa1789 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-03-24.csv @@ -0,0 +1,1438 @@ +recorded_time,temperature,sensor_type +2024-03-24T00:00,1.559179225404982,UNKNOWN +2024-03-24T00:01,1.4591792254049807,UNKNOWN +2024-03-24T00:02,1.4591792254049807,UNKNOWN +2024-03-24T00:03,1.3591792254049793,UNKNOWN +2024-03-24T00:04,1.1591792254049835,UNKNOWN +2024-03-24T00:05,1.059179225404982,UNKNOWN +2024-03-24T00:06,0.9591792254049807,UNKNOWN +2024-03-24T00:07,0.8591792254049793,UNKNOWN +2024-03-24T00:08,0.8591792254049793,UNKNOWN +2024-03-24T00:09,0.8591792254049793,UNKNOWN +2024-03-24T00:10,0.8591792254049793,UNKNOWN +2024-03-24T00:11,0.7591792254049778,UNKNOWN +2024-03-24T00:12,0.8591792254049793,UNKNOWN +2024-03-24T00:13,0.7591792254049778,UNKNOWN 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b/data/fitness/Temperature/Device Temperature - 2024-04-07.csv new file mode 100644 index 0000000..c45f5d9 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-04-07.csv @@ -0,0 +1,1441 @@ +recorded_time,temperature,sensor_type +2024-04-07T00:00,0.39351525263832343,UNKNOWN +2024-04-07T00:01,0.19351525263832414,UNKNOWN +2024-04-07T00:02,-0.10648474736167657,UNKNOWN +2024-04-07T00:03,-0.10648474736167657,UNKNOWN +2024-04-07T00:04,-0.006484747361675147,UNKNOWN +2024-04-07T00:05,0.19351525263832414,UNKNOWN +2024-04-07T00:06,0.39351525263832343,UNKNOWN +2024-04-07T00:07,0.49351525263832485,UNKNOWN +2024-04-07T00:08,0.5935152526383263,UNKNOWN +2024-04-07T00:09,0.5935152526383263,UNKNOWN +2024-04-07T00:10,0.5935152526383263,UNKNOWN +2024-04-07T00:11,0.5935152526383263,UNKNOWN +2024-04-07T00:12,0.5935152526383263,UNKNOWN +2024-04-07T00:13,0.6935152526383206,UNKNOWN +2024-04-07T00:14,0.5935152526383263,UNKNOWN +2024-04-07T00:15,0.5935152526383263,UNKNOWN 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a/data/fitness/Temperature/Device Temperature - 2024-04-27.csv b/data/fitness/Temperature/Device Temperature - 2024-04-27.csv new file mode 100644 index 0000000..7ba322f --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-04-27.csv @@ -0,0 +1,1440 @@ +recorded_time,temperature,sensor_type +2024-04-27T00:00,0.6420329670329679,UNKNOWN +2024-04-27T00:01,0.6420329670329679,UNKNOWN +2024-04-27T00:02,0.6420329670329679,UNKNOWN +2024-04-27T00:03,0.6420329670329679,UNKNOWN +2024-04-27T00:04,0.7420329670329693,UNKNOWN +2024-04-27T00:05,0.7420329670329693,UNKNOWN +2024-04-27T00:06,0.7420329670329693,UNKNOWN +2024-04-27T00:07,0.7420329670329693,UNKNOWN +2024-04-27T00:08,0.7420329670329693,UNKNOWN +2024-04-27T00:09,0.8420329670329707,UNKNOWN +2024-04-27T00:10,0.7420329670329693,UNKNOWN +2024-04-27T00:11,0.7420329670329693,UNKNOWN +2024-04-27T00:12,0.5420329670329664,UNKNOWN +2024-04-27T00:13,0.442032967032965,UNKNOWN +2024-04-27T00:14,0.24203296703296928,UNKNOWN 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of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-05-03.csv b/data/fitness/Temperature/Device Temperature - 2024-05-03.csv new file mode 100644 index 0000000..95c2661 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-05-03.csv @@ -0,0 +1,1428 @@ +recorded_time,temperature,sensor_type +2024-05-03T00:00,1.4609467455621328,UNKNOWN +2024-05-03T00:01,1.3609467455621314,UNKNOWN +2024-05-03T00:02,1.4609467455621328,UNKNOWN +2024-05-03T00:03,1.3609467455621314,UNKNOWN +2024-05-03T00:04,1.3609467455621314,UNKNOWN +2024-05-03T00:05,1.3609467455621314,UNKNOWN +2024-05-03T00:06,1.26094674556213,UNKNOWN +2024-05-03T00:07,1.3609467455621314,UNKNOWN +2024-05-03T00:08,1.26094674556213,UNKNOWN +2024-05-03T00:09,1.26094674556213,UNKNOWN +2024-05-03T00:10,1.26094674556213,UNKNOWN +2024-05-03T00:11,1.26094674556213,UNKNOWN +2024-05-03T00:12,1.1609467455621285,UNKNOWN +2024-05-03T00:13,1.1609467455621285,UNKNOWN +2024-05-03T00:14,1.26094674556213,UNKNOWN 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Temperature - 2024-05-09.csv new file mode 100644 index 0000000..fda1f0e --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-05-09.csv @@ -0,0 +1,1438 @@ +recorded_time,temperature,sensor_type +2024-05-09T00:00,0.6219548542625475,UNKNOWN +2024-05-09T00:01,0.6219548542625475,UNKNOWN +2024-05-09T00:02,0.5219548542625461,UNKNOWN +2024-05-09T00:03,0.6219548542625475,UNKNOWN +2024-05-09T00:04,0.6219548542625475,UNKNOWN +2024-05-09T00:05,0.7219548542625489,UNKNOWN +2024-05-09T00:06,0.6219548542625475,UNKNOWN +2024-05-09T00:07,0.6219548542625475,UNKNOWN +2024-05-09T00:08,0.8219548542625503,UNKNOWN +2024-05-09T00:09,0.8219548542625503,UNKNOWN +2024-05-09T00:10,0.9219548542625517,UNKNOWN +2024-05-09T00:11,0.9219548542625517,UNKNOWN +2024-05-09T00:12,0.9219548542625517,UNKNOWN +2024-05-09T00:13,0.9219548542625517,UNKNOWN +2024-05-09T00:14,0.8219548542625503,UNKNOWN +2024-05-09T00:15,0.8219548542625503,UNKNOWN +2024-05-09T00:16,0.8219548542625503,UNKNOWN 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- 2024-05-12.csv b/data/fitness/Temperature/Device Temperature - 2024-05-12.csv new file mode 100644 index 0000000..9cd462f --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-05-12.csv @@ -0,0 +1,1410 @@ +recorded_time,temperature,sensor_type +2024-05-12T00:00,-3.401402395539563,UNKNOWN +2024-05-12T00:01,-3.3014023955395615,UNKNOWN +2024-05-12T00:02,-3.0014023955395643,UNKNOWN +2024-05-12T00:03,-2.7014023955395636,UNKNOWN +2024-05-12T00:04,-2.601402395539562,UNKNOWN +2024-05-12T00:05,-2.601402395539562,UNKNOWN +2024-05-12T00:06,-2.601402395539562,UNKNOWN +2024-05-12T00:07,-3.0014023955395643,UNKNOWN +2024-05-12T00:08,-3.0014023955395643,UNKNOWN +2024-05-12T00:09,-3.0014023955395643,UNKNOWN +2024-05-12T00:10,-2.901402395539563,UNKNOWN +2024-05-12T00:11,-2.8014023955395615,UNKNOWN +2024-05-12T00:12,-2.8014023955395615,UNKNOWN +2024-05-12T00:13,-2.8014023955395615,UNKNOWN +2024-05-12T00:14,-2.8014023955395615,UNKNOWN +2024-05-12T00:15,-2.7014023955395636,UNKNOWN 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diff --git a/data/fitness/Temperature/Device Temperature - 2024-05-20.csv b/data/fitness/Temperature/Device Temperature - 2024-05-20.csv new file mode 100644 index 0000000..688bed9 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-05-20.csv @@ -0,0 +1 @@ +recorded_time,temperature,sensor_type \ No newline at end of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-05-21.csv b/data/fitness/Temperature/Device Temperature - 2024-05-21.csv new file mode 100644 index 0000000..5e59c5a --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-05-21.csv @@ -0,0 +1,1366 @@ +recorded_time,temperature,sensor_type +2024-05-21T00:00,-2.6867237687366163,UNKNOWN +2024-05-21T00:01,-2.6867237687366163,UNKNOWN +2024-05-21T00:02,-2.5867237687366185,UNKNOWN +2024-05-21T00:03,-2.486723768736617,UNKNOWN +2024-05-21T00:04,-2.5867237687366185,UNKNOWN +2024-05-21T00:05,-2.486723768736617,UNKNOWN +2024-05-21T00:06,-2.486723768736617,UNKNOWN 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Temperature - 2024-05-27.csv b/data/fitness/Temperature/Device Temperature - 2024-05-27.csv new file mode 100644 index 0000000..ce46d08 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-05-27.csv @@ -0,0 +1,1441 @@ +recorded_time,temperature,sensor_type +2024-05-27T00:00,-1.3867237687366192,UNKNOWN +2024-05-27T00:01,-1.3867237687366192,UNKNOWN +2024-05-27T00:02,-1.3867237687366192,UNKNOWN +2024-05-27T00:03,-1.5867237687366185,UNKNOWN +2024-05-27T00:04,-1.986723768736617,UNKNOWN +2024-05-27T00:05,-2.1867237687366163,UNKNOWN +2024-05-27T00:06,-2.1867237687366163,UNKNOWN +2024-05-27T00:07,-2.2867237687366178,UNKNOWN +2024-05-27T00:08,-2.386723768736619,UNKNOWN +2024-05-27T00:09,-2.386723768736619,UNKNOWN +2024-05-27T00:10,-2.2867237687366178,UNKNOWN +2024-05-27T00:11,-2.0867237687366185,UNKNOWN +2024-05-27T00:12,-1.8867237687366192,UNKNOWN +2024-05-27T00:13,-1.7867237687366178,UNKNOWN +2024-05-27T00:14,-1.5867237687366185,UNKNOWN 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a/data/fitness/Temperature/Device Temperature - 2024-05-28.csv b/data/fitness/Temperature/Device Temperature - 2024-05-28.csv new file mode 100644 index 0000000..39eb536 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-05-28.csv @@ -0,0 +1,802 @@ +recorded_time,temperature,sensor_type +2024-05-28T00:00,0.9132762312633851,UNKNOWN +2024-05-28T00:01,1.0132762312633794,UNKNOWN +2024-05-28T00:02,0.6132762312633808,UNKNOWN +2024-05-28T00:03,0.4132762312633851,UNKNOWN +2024-05-28T00:04,0.6132762312633808,UNKNOWN +2024-05-28T00:05,0.7132762312633822,UNKNOWN +2024-05-28T00:06,0.5132762312633794,UNKNOWN +2024-05-28T00:07,0.6132762312633808,UNKNOWN +2024-05-28T00:08,0.6132762312633808,UNKNOWN +2024-05-28T00:09,0.6132762312633808,UNKNOWN +2024-05-28T00:10,0.6132762312633808,UNKNOWN +2024-05-28T00:11,0.6132762312633808,UNKNOWN +2024-05-28T00:12,0.6132762312633808,UNKNOWN +2024-05-28T00:13,0.5132762312633794,UNKNOWN +2024-05-28T00:14,0.31327623126338366,UNKNOWN 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Temperature - 2024-06-01.csv new file mode 100644 index 0000000..aa4834c --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-06-01.csv @@ -0,0 +1,1438 @@ +recorded_time,temperature,sensor_type +2024-06-01T00:00,1.3313157894736847,UNKNOWN +2024-06-01T00:01,1.2313157894736833,UNKNOWN +2024-06-01T00:02,1.1313157894736818,UNKNOWN +2024-06-01T00:03,1.0313157894736804,UNKNOWN +2024-06-01T00:04,0.9313157894736861,UNKNOWN +2024-06-01T00:05,0.8313157894736847,UNKNOWN +2024-06-01T00:06,0.7313157894736833,UNKNOWN +2024-06-01T00:07,0.6313157894736818,UNKNOWN +2024-06-01T00:08,0.6313157894736818,UNKNOWN +2024-06-01T00:09,0.5313157894736804,UNKNOWN +2024-06-01T00:10,0.5313157894736804,UNKNOWN +2024-06-01T00:11,0.5313157894736804,UNKNOWN +2024-06-01T00:12,0.5313157894736804,UNKNOWN +2024-06-01T00:13,0.5313157894736804,UNKNOWN +2024-06-01T00:14,0.5313157894736804,UNKNOWN +2024-06-01T00:15,0.6313157894736818,UNKNOWN +2024-06-01T00:16,0.7313157894736833,UNKNOWN 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b/data/fitness/Temperature/Device Temperature - 2024-06-16.csv new file mode 100644 index 0000000..f7e14a0 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-06-16.csv @@ -0,0 +1,1438 @@ +recorded_time,temperature,sensor_type +2024-06-16T00:00,-1.5384393063583808,UNKNOWN +2024-06-16T00:01,-1.6384393063583786,UNKNOWN +2024-06-16T00:02,-1.73843930635838,UNKNOWN +2024-06-16T00:03,-1.73843930635838,UNKNOWN +2024-06-16T00:04,-1.9384393063583794,UNKNOWN +2024-06-16T00:05,-1.9384393063583794,UNKNOWN +2024-06-16T00:06,-2.1384393063583786,UNKNOWN +2024-06-16T00:07,-2.23843930635838,UNKNOWN +2024-06-16T00:08,-2.3384393063583815,UNKNOWN +2024-06-16T00:09,-2.4384393063583794,UNKNOWN +2024-06-16T00:10,-2.73843930635838,UNKNOWN +2024-06-16T00:11,-2.8384393063583815,UNKNOWN +2024-06-16T00:12,-2.73843930635838,UNKNOWN +2024-06-16T00:13,-2.6384393063583786,UNKNOWN +2024-06-16T00:14,-2.8384393063583815,UNKNOWN +2024-06-16T00:15,-2.8384393063583815,UNKNOWN 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--git a/data/fitness/Temperature/Device Temperature - 2024-06-26.csv b/data/fitness/Temperature/Device Temperature - 2024-06-26.csv new file mode 100644 index 0000000..09cbf90 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-06-26.csv @@ -0,0 +1,1437 @@ +recorded_time,temperature,sensor_type +2024-06-26T00:00,-0.43843930635837935,UNKNOWN +2024-06-26T00:01,-0.43843930635837935,UNKNOWN +2024-06-26T00:02,-0.43843930635837935,UNKNOWN +2024-06-26T00:03,-0.43843930635837935,UNKNOWN +2024-06-26T00:04,-0.3384393063583815,UNKNOWN +2024-06-26T00:05,-0.3384393063583815,UNKNOWN +2024-06-26T00:06,-0.23843930635838007,UNKNOWN +2024-06-26T00:07,-0.23843930635838007,UNKNOWN +2024-06-26T00:08,-0.13843930635837864,UNKNOWN +2024-06-26T00:09,-0.13843930635837864,UNKNOWN +2024-06-26T00:10,-0.038439306358380776,UNKNOWN +2024-06-26T00:11,-0.13843930635837864,UNKNOWN +2024-06-26T00:12,-0.13843930635837864,UNKNOWN +2024-06-26T00:13,-0.23843930635838007,UNKNOWN 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end of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-06-30.csv b/data/fitness/Temperature/Device Temperature - 2024-06-30.csv new file mode 100644 index 0000000..e4c43b9 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-06-30.csv @@ -0,0 +1,896 @@ +recorded_time,temperature,sensor_type +2024-06-30T00:00,1.0123919308357365,UNKNOWN +2024-06-30T00:01,1.0123919308357365,UNKNOWN +2024-06-30T00:02,1.0123919308357365,UNKNOWN +2024-06-30T00:03,1.0123919308357365,UNKNOWN +2024-06-30T00:04,0.9123919308357351,UNKNOWN +2024-06-30T00:05,0.9123919308357351,UNKNOWN +2024-06-30T00:06,1.0123919308357365,UNKNOWN +2024-06-30T00:07,1.0123919308357365,UNKNOWN +2024-06-30T00:08,1.0123919308357365,UNKNOWN +2024-06-30T00:09,1.0123919308357365,UNKNOWN +2024-06-30T00:10,1.0123919308357365,UNKNOWN +2024-06-30T00:11,1.0123919308357365,UNKNOWN +2024-06-30T00:12,1.0123919308357365,UNKNOWN +2024-06-30T00:13,1.0123919308357365,UNKNOWN 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--git a/data/fitness/Temperature/Device Temperature - 2024-07-31.csv b/data/fitness/Temperature/Device Temperature - 2024-07-31.csv new file mode 100644 index 0000000..22e9af6 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-07-31.csv @@ -0,0 +1,1351 @@ +recorded_time,temperature,sensor_type +2024-07-31T00:00,-0.1554446285556459,UNKNOWN +2024-07-31T00:01,-0.1554446285556459,UNKNOWN +2024-07-31T00:02,-0.1554446285556459,UNKNOWN +2024-07-31T00:03,-0.1554446285556459,UNKNOWN +2024-07-31T00:04,-0.05544462855564447,UNKNOWN +2024-07-31T00:05,-0.1554446285556459,UNKNOWN +2024-07-31T00:06,-0.1554446285556459,UNKNOWN +2024-07-31T00:07,-0.1554446285556459,UNKNOWN +2024-07-31T00:08,-0.3554446285556452,UNKNOWN +2024-07-31T00:09,-0.4554446285556466,UNKNOWN +2024-07-31T00:10,-0.4554446285556466,UNKNOWN +2024-07-31T00:11,-0.3554446285556452,UNKNOWN +2024-07-31T00:12,-0.25544462855564376,UNKNOWN +2024-07-31T00:13,-0.25544462855564376,UNKNOWN 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a/data/fitness/Temperature/Device Temperature - 2024-08-01.csv b/data/fitness/Temperature/Device Temperature - 2024-08-01.csv new file mode 100644 index 0000000..d2c1de6 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-08-01.csv @@ -0,0 +1,1437 @@ +recorded_time,temperature,sensor_type +2024-08-01T00:00,0.24455537144435624,UNKNOWN +2024-08-01T00:01,0.24455537144435624,UNKNOWN +2024-08-01T00:02,0.24455537144435624,UNKNOWN +2024-08-01T00:03,0.34455537144435766,UNKNOWN +2024-08-01T00:04,0.444555371444352,UNKNOWN +2024-08-01T00:05,0.444555371444352,UNKNOWN +2024-08-01T00:06,0.444555371444352,UNKNOWN +2024-08-01T00:07,0.5445553714443534,UNKNOWN +2024-08-01T00:08,0.5445553714443534,UNKNOWN +2024-08-01T00:09,0.6445553714443548,UNKNOWN +2024-08-01T00:10,0.6445553714443548,UNKNOWN +2024-08-01T00:11,0.6445553714443548,UNKNOWN +2024-08-01T00:12,0.5445553714443534,UNKNOWN +2024-08-01T00:13,0.5445553714443534,UNKNOWN +2024-08-01T00:14,0.444555371444352,UNKNOWN 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newline at end of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-08-05.csv b/data/fitness/Temperature/Device Temperature - 2024-08-05.csv new file mode 100644 index 0000000..f1ae865 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-08-05.csv @@ -0,0 +1,1437 @@ +recorded_time,temperature,sensor_type +2024-08-05T00:01,0.11364173054894877,UNKNOWN +2024-08-05T00:02,0.11364173054894877,UNKNOWN +2024-08-05T00:03,0.11364173054894877,UNKNOWN +2024-08-05T00:04,0.2136417305489502,UNKNOWN +2024-08-05T00:05,0.2136417305489502,UNKNOWN +2024-08-05T00:06,0.11364173054894877,UNKNOWN +2024-08-05T00:07,0.2136417305489502,UNKNOWN +2024-08-05T00:08,0.2136417305489502,UNKNOWN +2024-08-05T00:09,0.11364173054894877,UNKNOWN +2024-08-05T00:10,0.2136417305489502,UNKNOWN +2024-08-05T00:11,0.2136417305489502,UNKNOWN +2024-08-05T00:12,0.7136417305489502,UNKNOWN +2024-08-05T00:13,1.3136417305489516,UNKNOWN +2024-08-05T00:14,1.413641730548946,UNKNOWN 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Temperature - 2024-08-17.csv b/data/fitness/Temperature/Device Temperature - 2024-08-17.csv new file mode 100644 index 0000000..0c43f19 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-08-17.csv @@ -0,0 +1,1437 @@ +recorded_time,temperature,sensor_type +2024-08-17T00:00,-0.08635826945105052,UNKNOWN +2024-08-17T00:01,-0.08635826945105052,UNKNOWN +2024-08-17T00:02,-0.38635826945105123,UNKNOWN +2024-08-17T00:03,-0.38635826945105123,UNKNOWN +2024-08-17T00:04,0.3136417305489516,UNKNOWN +2024-08-17T00:05,0.6136417305489488,UNKNOWN +2024-08-17T00:06,0.41364173054894593,UNKNOWN +2024-08-17T00:07,0.3136417305489516,UNKNOWN +2024-08-17T00:08,0.2136417305489502,UNKNOWN +2024-08-17T00:09,0.2136417305489502,UNKNOWN +2024-08-17T00:10,0.11364173054894877,UNKNOWN +2024-08-17T00:11,0.01364173054894735,UNKNOWN +2024-08-17T00:12,0.01364173054894735,UNKNOWN +2024-08-17T00:13,0.01364173054894735,UNKNOWN +2024-08-17T00:14,0.01364173054894735,UNKNOWN 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b/data/fitness/Temperature/Device Temperature - 2024-08-18.csv new file mode 100644 index 0000000..d64f89e --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-08-18.csv @@ -0,0 +1,1382 @@ +recorded_time,temperature,sensor_type +2024-08-18T00:00,0.5112884286440718,UNKNOWN +2024-08-18T00:01,0.6112884286440732,UNKNOWN +2024-08-18T00:02,0.5112884286440718,UNKNOWN +2024-08-18T00:03,0.311288428644076,UNKNOWN +2024-08-18T00:04,0.41128842864407034,UNKNOWN +2024-08-18T00:05,1.2112884286440746,UNKNOWN +2024-08-18T00:06,1.7112884286440746,UNKNOWN +2024-08-18T00:07,1.7112884286440746,UNKNOWN +2024-08-18T00:08,1.5112884286440718,UNKNOWN +2024-08-18T00:09,1.2112884286440746,UNKNOWN +2024-08-18T00:10,1.0112884286440718,UNKNOWN +2024-08-18T00:11,0.811288428644076,UNKNOWN +2024-08-18T00:12,0.7112884286440746,UNKNOWN +2024-08-18T00:13,0.6112884286440732,UNKNOWN +2024-08-18T00:14,0.5112884286440718,UNKNOWN +2024-08-18T00:15,0.5112884286440718,UNKNOWN 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a/data/fitness/Temperature/Device Temperature - 2024-08-27.csv b/data/fitness/Temperature/Device Temperature - 2024-08-27.csv new file mode 100644 index 0000000..82903ea --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-08-27.csv @@ -0,0 +1,1440 @@ +recorded_time,temperature,sensor_type +2024-08-27T00:00,0.11128842864407318,UNKNOWN +2024-08-27T00:01,0.011288428644071757,UNKNOWN +2024-08-27T00:02,-0.08871157135592611,UNKNOWN +2024-08-27T00:03,-0.08871157135592611,UNKNOWN +2024-08-27T00:04,-0.08871157135592611,UNKNOWN +2024-08-27T00:05,-0.08871157135592611,UNKNOWN +2024-08-27T00:06,-0.08871157135592611,UNKNOWN +2024-08-27T00:07,-0.18871157135592753,UNKNOWN +2024-08-27T00:08,-0.18871157135592753,UNKNOWN +2024-08-27T00:09,-0.08871157135592611,UNKNOWN +2024-08-27T00:10,0.011288428644071757,UNKNOWN +2024-08-27T00:11,0.11128842864407318,UNKNOWN +2024-08-27T00:12,0.011288428644071757,UNKNOWN +2024-08-27T00:13,-0.08871157135592611,UNKNOWN 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newline at end of file diff --git a/data/fitness/Temperature/Device Temperature - 2024-09-06.csv b/data/fitness/Temperature/Device Temperature - 2024-09-06.csv new file mode 100644 index 0000000..6b5a242 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-09-06.csv @@ -0,0 +1,1407 @@ +recorded_time,temperature,sensor_type +2024-09-06T00:00,-0.26288542211440813,UNKNOWN +2024-09-06T00:01,-0.1628854221144067,UNKNOWN +2024-09-06T00:02,-0.06288542211440884,UNKNOWN +2024-09-06T00:03,-0.06288542211440884,UNKNOWN +2024-09-06T00:04,-0.06288542211440884,UNKNOWN +2024-09-06T00:05,-0.06288542211440884,UNKNOWN +2024-09-06T00:06,0.03711457788559258,UNKNOWN +2024-09-06T00:07,-0.06288542211440884,UNKNOWN +2024-09-06T00:08,-0.06288542211440884,UNKNOWN +2024-09-06T00:09,-0.1628854221144067,UNKNOWN +2024-09-06T00:10,-0.26288542211440813,UNKNOWN +2024-09-06T00:11,-0.5628854221144088,UNKNOWN +2024-09-06T00:12,-0.6628854221144067,UNKNOWN +2024-09-06T00:13,-0.8628854221144096,UNKNOWN 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a/data/fitness/Temperature/Device Temperature - 2024-09-24.csv b/data/fitness/Temperature/Device Temperature - 2024-09-24.csv new file mode 100644 index 0000000..0004d43 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-09-24.csv @@ -0,0 +1,1433 @@ +recorded_time,temperature,sensor_type +2024-09-24T00:00,-1.2117984794295644,UNKNOWN +2024-09-24T00:01,-1.2117984794295644,UNKNOWN +2024-09-24T00:02,-1.2117984794295644,UNKNOWN +2024-09-24T00:03,-1.111798479429563,UNKNOWN +2024-09-24T00:04,-1.111798479429563,UNKNOWN +2024-09-24T00:05,-1.111798479429563,UNKNOWN +2024-09-24T00:06,-1.111798479429563,UNKNOWN +2024-09-24T00:07,-0.8117984794295623,UNKNOWN +2024-09-24T00:08,-0.9117984794295637,UNKNOWN +2024-09-24T00:09,-1.0117984794295651,UNKNOWN +2024-09-24T00:10,-1.0117984794295651,UNKNOWN +2024-09-24T00:11,-1.111798479429563,UNKNOWN +2024-09-24T00:12,-1.111798479429563,UNKNOWN +2024-09-24T00:13,-1.2117984794295644,UNKNOWN +2024-09-24T00:14,-1.111798479429563,UNKNOWN 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b/data/fitness/Temperature/Device Temperature - 2024-10-09.csv new file mode 100644 index 0000000..e766896 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-10-09.csv @@ -0,0 +1,1410 @@ +recorded_time,temperature,sensor_type +2024-10-09T00:00,-1.0144870022010757,UNKNOWN +2024-10-09T00:01,-0.8144870022010728,UNKNOWN +2024-10-09T00:02,-0.714487002201075,UNKNOWN +2024-10-09T00:03,-0.714487002201075,UNKNOWN +2024-10-09T00:04,-0.8144870022010728,UNKNOWN +2024-10-09T00:05,-0.6144870022010736,UNKNOWN +2024-10-09T00:06,-0.714487002201075,UNKNOWN +2024-10-09T00:07,-0.714487002201075,UNKNOWN +2024-10-09T00:08,-0.9144870022010743,UNKNOWN +2024-10-09T00:09,-0.9144870022010743,UNKNOWN +2024-10-09T00:10,-1.1144870022010736,UNKNOWN +2024-10-09T00:11,-0.8144870022010728,UNKNOWN +2024-10-09T00:12,-0.714487002201075,UNKNOWN +2024-10-09T00:13,-0.6144870022010736,UNKNOWN +2024-10-09T00:14,-0.6144870022010736,UNKNOWN +2024-10-09T00:15,-0.6144870022010736,UNKNOWN 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Temperature - 2024-11-09.csv b/data/fitness/Temperature/Device Temperature - 2024-11-09.csv new file mode 100644 index 0000000..9615d49 --- /dev/null +++ b/data/fitness/Temperature/Device Temperature - 2024-11-09.csv @@ -0,0 +1,1437 @@ +recorded_time,temperature,sensor_type +2024-11-09T00:00,-1.001750725003152,UNKNOWN +2024-11-09T00:01,-0.9017507250031507,UNKNOWN +2024-11-09T00:02,-0.7017507250031514,UNKNOWN +2024-11-09T00:03,-0.5017507250031521,UNKNOWN +2024-11-09T00:04,-0.2017507250031514,UNKNOWN +2024-11-09T00:05,-0.10175072500314997,UNKNOWN +2024-11-09T00:06,-0.2017507250031514,UNKNOWN +2024-11-09T00:07,-0.4017507250031507,UNKNOWN +2024-11-09T00:08,-0.4017507250031507,UNKNOWN +2024-11-09T00:09,-0.4017507250031507,UNKNOWN +2024-11-09T00:10,-0.4017507250031507,UNKNOWN +2024-11-09T00:11,-0.60175072500315,UNKNOWN +2024-11-09T00:12,-0.60175072500315,UNKNOWN +2024-11-09T00:13,-0.4017507250031507,UNKNOWN +2024-11-09T00:14,-0.5017507250031521,UNKNOWN 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+2024-11-24T23:45,-2.572930547713156,UNKNOWN +2024-11-24T23:46,-2.4729305477131582,UNKNOWN +2024-11-24T23:47,-2.372930547713157,UNKNOWN +2024-11-24T23:48,-2.272930547713159,UNKNOWN +2024-11-24T23:49,-2.1729305477131575,UNKNOWN +2024-11-24T23:50,-1.9729305477131582,UNKNOWN +2024-11-24T23:51,-1.772930547713159,UNKNOWN +2024-11-24T23:52,-1.6729305477131575,UNKNOWN +2024-11-24T23:53,-1.4729305477131582,UNKNOWN +2024-11-24T23:54,-1.3729305477131568,UNKNOWN +2024-11-24T23:55,-1.3729305477131568,UNKNOWN +2024-11-24T23:56,-1.272930547713159,UNKNOWN +2024-11-24T23:57,-1.1729305477131575,UNKNOWN +2024-11-24T23:58,-0.9729305477131582,UNKNOWN +2024-11-24T23:59,-0.8729305477131568,UNKNOWN \ No newline at end of file From c9aeda082fc0988ff19a928322c173e5c0bfb2b8 Mon Sep 17 00:00:00 2001 From: Tony Chan <88695195+Tony911029@users.noreply.github.com> Date: Wed, 27 Nov 2024 23:35:58 -0500 Subject: [PATCH 5/6] Add simglucose for more data (#158) * Add data simulation scripts * Add data simulation scripts --------- Co-authored-by: Tony Chan Co-authored-by: Christopher <110344005+RobotPsychologist@users.noreply.github.com> --- .../data/raw/adu001_2024-11-18_2024-11-25.csv | 3362 +++++++++++++++++ .../data/raw/adu003_2024-11-18_2024-11-25.csv | 3362 +++++++++++++++++ .../meal_identification/data/sim/BG_trace.png | Bin 0 -> 107322 bytes .../meal_identification/data/sim/CVGA.png | Bin 0 -> 35107 bytes .../data/sim/CVGA_stats.csv | 2 + .../data/sim/adult#001.csv | 3362 +++++++++++++++++ .../data/sim/adult#003.csv | 3362 +++++++++++++++++ .../data/sim/performance_stats.csv | 3 + .../data/sim/risk_stats.png | Bin 0 -> 12449 bytes .../data/sim/risk_trace.csv | 7 + .../data/sim/zone_stats.png | Bin 0 -> 20407 bytes .../datasets/dataset_generator.py | 1 - .../datasets/dataset_glucose_simulator.py | 229 ++ .../datasets/dataset_operations.py | 4 +- .../notebooks/1.04-tc-simulated-date.ipynb | 102 + 15 files changed, 13792 insertions(+), 4 deletions(-) create mode 100644 0_meal_identification/meal_identification/data/raw/adu001_2024-11-18_2024-11-25.csv create mode 100644 0_meal_identification/meal_identification/data/raw/adu003_2024-11-18_2024-11-25.csv create mode 100644 0_meal_identification/meal_identification/data/sim/BG_trace.png create mode 100644 0_meal_identification/meal_identification/data/sim/CVGA.png create mode 100644 0_meal_identification/meal_identification/data/sim/CVGA_stats.csv create mode 100644 0_meal_identification/meal_identification/data/sim/adult#001.csv create mode 100644 0_meal_identification/meal_identification/data/sim/adult#003.csv create mode 100644 0_meal_identification/meal_identification/data/sim/performance_stats.csv create mode 100644 0_meal_identification/meal_identification/data/sim/risk_stats.png create mode 100644 0_meal_identification/meal_identification/data/sim/risk_trace.csv create mode 100644 0_meal_identification/meal_identification/data/sim/zone_stats.png create mode 100644 0_meal_identification/meal_identification/meal_identification/datasets/dataset_glucose_simulator.py create mode 100644 0_meal_identification/meal_identification/notebooks/1.04-tc-simulated-date.ipynb diff --git a/0_meal_identification/meal_identification/data/raw/adu001_2024-11-18_2024-11-25.csv b/0_meal_identification/meal_identification/data/raw/adu001_2024-11-18_2024-11-25.csv new file mode 100644 index 0000000..fb40522 --- /dev/null +++ b/0_meal_identification/meal_identification/data/raw/adu001_2024-11-18_2024-11-25.csv @@ -0,0 +1,3362 @@ +,date,bgl_real,bgl,msg_type,food_glycemic_index,affects_iob,affects_fob,dose_units,food_g +0,2024-01-01 00:00:00,138.56,129.89054218445446,,,,,, +1,2024-01-01 00:03:00,138.56000000000108,132.65463122491693,,,,,, +2,2024-01-01 00:06:00,138.55999999989476,134.67635307457806,,,,,, +3,2024-01-01 00:09:00,138.55999999866643,136.55224060715233,,,,,, +4,2024-01-01 00:12:00,138.5599999927699,138.1863157991345,,,,,, +5,2024-01-01 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06:39:00,81.7437623504056,86.06364838799705,,,,,, +2054,2024-01-05 06:42:00,81.9708874031992,85.84262228483641,,,,,, +2055,2024-01-05 06:45:00,82.19955823254998,85.4683303299165,,,,,, +2056,2024-01-05 06:48:00,82.42972164128255,85.02454480651888,,,,,, +2057,2024-01-05 06:51:00,82.66132533351252,84.5950389042479,,,,,, +2058,2024-01-05 06:54:00,82.89431789985602,84.26358670412581,,,,,, +2059,2024-01-05 06:57:00,83.12864880297302,84.11459019390725,,,,,, +2060,2024-01-05 07:00:00,83.36426836343357,84.23621440324764,,,,,, +2061,2024-01-05 07:03:00,83.6011277458962,84.72226847942387,,,,,, +2062,2024-01-05 07:06:00,83.839178945589,85.66781646454123,,,,,, +2063,2024-01-05 07:09:00,84.078374775083,87.16792322244608,,,,,, +2064,2024-01-05 07:12:00,84.31866885134936,89.28081197076406,,,,,, +2065,2024-01-05 07:15:00,84.5600155830906,91.84365199351788,,,,,, +2066,2024-01-05 07:18:00,84.80237015833808,94.36203126386248,,,,,, +2067,2024-01-05 07:21:00,85.0456885323067,96.26785361632632,,,,,, 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08:48:00,140.41766153915486,113.62956833759608,,,,,, +2097,2024-01-05 08:51:00,141.76926045040648,114.15262623044086,,,,,, +2098,2024-01-05 08:54:00,142.8753903531428,115.84125696693906,,,,,, +2099,2024-01-05 08:57:00,143.7604530696362,120.31138053632878,,,,,, +2100,2024-01-05 09:00:00,144.44754604243155,127.85470024510217,,,,,, +2101,2024-01-05 09:03:00,144.95813767867392,133.50148060982758,,,,,, +2102,2024-01-05 09:06:00,145.31192649110218,137.47256324692376,,,,,, +2103,2024-01-05 09:09:00,145.52682166247158,139.98700256040422,,,,,, +2104,2024-01-05 09:12:00,145.61900034586296,141.262099084494,ANNOUNCE_MEAL,,,,,2.0 +2105,2024-01-05 09:15:00,145.60460915925822,141.515086973631,,,,,, +2106,2024-01-05 09:18:00,145.62919733087764,141.04484308671073,,,,,, +2107,2024-01-05 09:21:00,146.0942380704063,140.35467409804218,,,,,, +2108,2024-01-05 09:24:00,147.18283618734603,139.93270286315058,,,,,, +2109,2024-01-05 09:27:00,148.81679832684236,139.976577962202,,,,,, +2110,2024-01-05 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11:36:00,130.834762423303,133.25696680495116,,,,,, +2153,2024-01-05 11:39:00,130.03415931225953,134.44742441961128,,,,,, +2154,2024-01-05 11:42:00,129.26489352099594,135.16423626070184,,,,,, +2155,2024-01-05 11:45:00,128.52851967652632,135.37340338262166,,,,,, +2156,2024-01-05 11:48:00,127.82686131223504,135.04117399470474,,,,,, +2157,2024-01-05 11:51:00,127.16209037681968,134.13411764735463,,,,,, +2158,2024-01-05 11:54:00,126.53682657967816,132.61921780077978,,,,,, +2159,2024-01-05 11:57:00,125.95426080455668,130.48723815887553,,,,,, +2160,2024-01-05 12:00:00,125.41830482468714,127.869119076878,,,,,, +2161,2024-01-05 12:03:00,124.93376257560332,125.10589507282164,,,,,, +2162,2024-01-05 12:06:00,124.50649991584456,122.58610791406682,,,,,, +2163,2024-01-05 12:09:00,124.14354873576798,120.699432412628,,,,,, +2164,2024-01-05 12:12:00,123.85300395798772,119.8006006451996,,,,,, +2165,2024-01-05 12:15:00,123.64346521684196,120.02877128644562,,,,,, +2166,2024-01-05 12:18:00,123.52272573420343,121.19834281742888,ANNOUNCE_MEAL,,,,,24.0 +2167,2024-01-05 12:21:00,123.49492264616148,123.04886588282125,,,,,, +2168,2024-01-05 12:24:00,123.56113519614844,125.31741485410666,,,,,, +2169,2024-01-05 12:27:00,123.7510049283844,127.76772155242008,,,,,, +2170,2024-01-05 12:30:00,124.12803768155688,130.27084250427566,,,,,, +2171,2024-01-05 12:33:00,124.7896318632764,132.83859663479922,,,,,, +2172,2024-01-05 12:36:00,125.86466050244024,135.53952013085674,,,,,, +2173,2024-01-05 12:39:00,127.49885531368338,138.46384776506673,,,,,, +2174,2024-01-05 12:42:00,129.80899870756912,141.6832696988153,,,,,, +2175,2024-01-05 12:45:00,132.837118703028,145.16651985654073,,,,,, +2176,2024-01-05 12:48:00,136.53249479259662,148.73039552368812,,,,,, +2177,2024-01-05 12:51:00,140.7567388373649,152.08727078377393,,,,,, +2178,2024-01-05 12:54:00,145.31192049893986,154.87961308135206,,,,,, +2179,2024-01-05 12:57:00,149.98214055354973,156.76942492982906,,,,,, +2180,2024-01-05 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11:00:00,140.75706374719917,140.48021545528383,,,,,, +3101,2024-01-07 11:03:00,141.38494450483643,142.66047036762802,,,,,, +3102,2024-01-07 11:06:00,141.98299099032445,144.80487205405018,,,,,, +3103,2024-01-07 11:09:00,142.53212623353787,146.8305589938434,,,,,, +3104,2024-01-07 11:12:00,143.01759008883087,148.65883768715446,,,,,, +3105,2024-01-07 11:15:00,143.4289237856392,150.21534122912885,,,,,, +3106,2024-01-07 11:18:00,143.75961073662964,151.4297769708235,,,,,, +3107,2024-01-07 11:21:00,144.0065359903751,152.2354404046797,,,,,, +3108,2024-01-07 11:24:00,144.1693793433667,152.5686262010072,,,,,, +3109,2024-01-07 11:27:00,144.2500160014225,152.38062536871848,,,,,, +3110,2024-01-07 11:30:00,144.2519674360228,151.7001662105995,,,,,, +3111,2024-01-07 11:33:00,144.17992327972152,150.670716826403,,,,,, +3112,2024-01-07 11:36:00,144.0393409158943,149.4618469322911,,,,,, +3113,2024-01-07 11:39:00,143.8361208314558,148.24367472576503,,,,,, +3114,2024-01-07 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02:06:00,147.0978339352067,144.18125551636703,,,,,, +43,2024-01-01 02:09:00,147.09769142391218,142.2731819770301,,,,,, +44,2024-01-01 02:12:00,147.0975449313014,140.30222242165445,,,,,, +45,2024-01-01 02:15:00,147.09739457958685,138.542852208088,,,,,, +46,2024-01-01 02:18:00,147.09724049420794,137.2695466975833,,,,,, +47,2024-01-01 02:21:00,147.09708280332725,136.7567812542827,,,,,, +48,2024-01-01 02:24:00,147.09692163735707,137.2790312447349,,,,,, +49,2024-01-01 02:27:00,147.09675712851632,137.27835156675735,,,,,, +50,2024-01-01 02:30:00,147.0965894104164,135.24158120675668,,,,,, +51,2024-01-01 02:33:00,147.09641861767477,134.9830779821257,,,,,, +52,2024-01-01 02:36:00,147.09624488555488,136.13373725701268,,,,,, +53,2024-01-01 02:39:00,147.0960683496322,138.32445439598828,,,,,, +54,2024-01-01 02:42:00,147.0958891454842,141.18612476372755,,,,,, +55,2024-01-01 02:45:00,147.0957074084036,144.34964372471546,,,,,, +56,2024-01-01 02:48:00,147.09552327313378,147.44590664297522,,,,,, 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03:33:00,147.09256106053795,141.34150789422097,,,,,, +72,2024-01-01 03:36:00,147.09235569055508,140.82129726002896,,,,,, +73,2024-01-01 03:39:00,147.09214995846867,141.01262260133498,,,,,, +74,2024-01-01 03:42:00,147.09194396301683,142.2294770694512,,,,,, +75,2024-01-01 03:45:00,147.09173780026603,144.60602758941536,,,,,, +76,2024-01-01 03:48:00,147.09153156359855,148.0067017481714,,,,,, +77,2024-01-01 03:51:00,147.09132534370505,152.2359850554375,,,,,, +78,2024-01-01 03:54:00,147.09111922858276,157.0983630182389,,,,,, +79,2024-01-01 03:57:00,147.09091330353874,162.39017564448133,,,,,, +80,2024-01-01 04:00:00,147.09070765119736,167.85888996081673,,,,,, +81,2024-01-01 04:03:00,147.09050235151236,173.17866352336958,,,,,, +82,2024-01-01 04:06:00,147.0902974817823,178.00736289274786,,,,,, +83,2024-01-01 04:09:00,147.09009311667018,182.00285462691727,,,,,, +84,2024-01-01 04:12:00,147.0898893282258,184.85357327486292,,,,,, +85,2024-01-01 04:15:00,147.08968618591123,186.43136134481472,,,,,, 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05:00:00,147.08675628696395,185.33734053858475,,,,,, +101,2024-01-01 05:03:00,147.0865709504037,189.5579196556591,,,,,, +102,2024-01-01 05:06:00,147.08638707945423,189.83218475482585,,,,,, +103,2024-01-01 05:09:00,147.08620470345758,187.076223085371,,,,,, +104,2024-01-01 05:12:00,147.0860238499475,182.20612189475685,,,,,, +105,2024-01-01 05:15:00,147.08584454469596,176.1379684286684,,,,,, +106,2024-01-01 05:18:00,147.08566681175952,169.7878499310597,,,,,, +107,2024-01-01 05:21:00,147.0854906735253,164.07185364420022,,,,,, +108,2024-01-01 05:24:00,147.08531615075722,159.90606680872065,,,,,, +109,2024-01-01 05:27:00,147.0851432626412,158.09256011751305,,,,,, +110,2024-01-01 05:30:00,147.0849720268307,158.7493049850473,,,,,, +111,2024-01-01 05:33:00,147.08480245949144,160.96812390897907,,,,,, +112,2024-01-01 05:36:00,147.08463457534566,163.61280629321442,,,,,, +113,2024-01-01 05:39:00,147.08446838771616,165.5471415402453,,,,,, +114,2024-01-01 05:42:00,147.0843039085696,165.74022572118298,,,,,, +115,2024-01-01 05:45:00,147.08414114855944,163.79299492575146,,,,,, +116,2024-01-01 05:48:00,147.083980117068,160.2541452722997,,,,,, +117,2024-01-01 05:51:00,147.0838208222481,155.88298621791333,,,,,, +118,2024-01-01 05:54:00,147.08366327106435,151.43882721847595,,,,,, +119,2024-01-01 05:57:00,147.0835074693333,147.63503145761638,,,,,, +120,2024-01-01 06:00:00,147.08335342176306,144.90928449128097,,,,,, +121,2024-01-01 06:03:00,147.08320113199255,143.28575543449273,,,,,, +122,2024-01-01 06:06:00,147.08305060262967,142.6967208590441,,,,,, +123,2024-01-01 06:09:00,147.08290183528896,143.0744573357215,,,,,, +124,2024-01-01 06:12:00,147.08275483062857,144.32716133150228,,,,,, +125,2024-01-01 06:15:00,147.08260958838616,146.21854869538896,,,,,, +126,2024-01-01 06:18:00,147.0824661074148,148.2956143499237,,,,,, +127,2024-01-01 06:21:00,147.0823243857173,150.0571930111062,,,,,, +128,2024-01-01 06:24:00,147.08218442048027,151.00211939410855,,,,,, +129,2024-01-01 06:27:00,147.0820462081075,150.6900659298779,,,,,, +130,2024-01-01 06:30:00,147.08190974425213,149.045731348014,,,,,, +131,2024-01-01 06:33:00,147.0817750238487,146.54135382650765,,,,,, +132,2024-01-01 06:36:00,147.0816420411439,143.77084697578982,,,,,, +133,2024-01-01 06:39:00,147.08151078972696,141.32812440562378,,,,,, +134,2024-01-01 06:42:00,147.0813812625591,139.75177406624175,,,,,, +135,2024-01-01 06:45:00,147.08125345200216,139.24842995390793,ANNOUNCE_MEAL,,,,,12.0 +136,2024-01-01 06:48:00,147.08130852104173,139.5269577148495,,,,,, +137,2024-01-01 06:51:00,147.09124753081292,140.19211699297892,,,,,, +138,2024-01-01 06:54:00,147.18262395514185,140.89032793567588,,,,,, +139,2024-01-01 06:57:00,147.5438515661356,141.39760900728115,,,,,, +140,2024-01-01 07:00:00,148.40186674112067,141.75806823873586,,,,,, +141,2024-01-01 07:03:00,149.89925441594127,142.262929431036,,,,,, +142,2024-01-01 07:06:00,152.0349340078502,143.14191712579148,,,,,, +143,2024-01-01 07:09:00,154.64430740946466,144.46723540540188,,,,,, +144,2024-01-01 07:12:00,157.46347047876765,146.18575971745256,,,,,, +145,2024-01-01 07:15:00,160.23807033982183,148.21600840324209,,,,,, +146,2024-01-01 07:18:00,162.78366132053287,150.52908117283602,,,,,, +147,2024-01-01 07:21:00,164.99169247884333,153.1720450896321,,,,,, +148,2024-01-01 07:24:00,166.81332062022372,156.2577295619456,,,,,, +149,2024-01-01 07:27:00,168.24026060399115,157.37275527173972,,,,,, +150,2024-01-01 07:30:00,169.28908621883497,153.79254093391626,,,,,, +151,2024-01-01 07:33:00,169.98997911249666,150.14482094914425,,,,,, +152,2024-01-01 07:36:00,170.3792460364329,146.51522338345836,,,,,, +153,2024-01-01 07:39:00,170.49462618225004,142.99227116771792,,,,,, +154,2024-01-01 07:42:00,170.37253278681555,139.66401087182732,,,,,, +155,2024-01-01 07:45:00,170.04657967951783,136.6161346932794,,,,,, +156,2024-01-01 07:48:00,169.5469303903753,133.9310721342763,,,,,, +157,2024-01-01 07:51:00,168.9001521089959,131.6876871286888,,,,,, +158,2024-01-01 07:54:00,168.12936173808882,129.96133437112647,,,,,, +159,2024-01-01 07:57:00,167.25452501625207,128.8214572154223,,,,,, +160,2024-01-01 08:00:00,166.29282049120053,128.31865729019532,,,,,, +161,2024-01-01 08:03:00,165.25901459805408,128.4771022488591,,,,,, +162,2024-01-01 08:06:00,164.16581710049348,129.31347507554474,,,,,, +163,2024-01-01 08:09:00,163.0242011719432,130.84261684860329,,,,,, +164,2024-01-01 08:12:00,161.8436819057165,133.05563879827292,,,,,, +165,2024-01-01 08:15:00,160.6325528017145,135.80927024073873,,,,,, +166,2024-01-01 08:18:00,159.39808302309606,138.75952726082193,,,,,, +167,2024-01-01 08:21:00,158.14667982128518,141.51715015941323,,,,,, +168,2024-01-01 08:24:00,156.88402109835476,143.69210680515295,,,,,, +169,2024-01-01 08:27:00,155.61516302744752,144.93183224031776,,,,,, +170,2024-01-01 08:30:00,154.34462726241307,145.11194180544763,,,,,, +171,2024-01-01 08:33:00,153.07647171779078,144.45067881636456,,,,,, +172,2024-01-01 08:36:00,151.81434830223623,143.2421095291023,,,,,, +173,2024-01-01 08:39:00,150.5615504086559,141.77992622905757,,,,,, +174,2024-01-01 08:42:00,149.3210524380366,140.34383405724728,,,,,, +175,2024-01-01 08:45:00,148.09554317680886,139.1313100233506,,,,,, +176,2024-01-01 08:48:00,146.88745446302426,138.2166673818785,,,,,, +177,2024-01-01 08:51:00,145.69898626117174,137.64666874801225,,,,,, +178,2024-01-01 08:54:00,144.5321290120994,137.4678598626952,,,,,, +179,2024-01-01 08:57:00,143.3886839248395,137.70703676383286,,,,,, +180,2024-01-01 09:00:00,142.2702817226721,138.27349611734326,,,,,, +181,2024-01-01 09:03:00,141.17840023865682,138.90039183484885,,,,,, +182,2024-01-01 09:06:00,140.11438116925711,139.28162857291812,,,,,, +183,2024-01-01 09:09:00,139.07944623286576,139.1109856456085,,,,,, +184,2024-01-01 09:12:00,138.07471293844182,138.12169521059403,,,,,, +185,2024-01-01 09:15:00,137.10121014460972,136.28426936652787,,,,,, +186,2024-01-01 09:18:00,136.159893578986,133.92520256443134,,,,,, +187,2024-01-01 09:21:00,135.2516614896665,131.45005114388115,,,,,, +188,2024-01-01 09:24:00,134.37737061509944,129.2643245999647,,,,,, +189,2024-01-01 09:27:00,133.53785268524115,127.73699160507562,,,,,, +190,2024-01-01 09:30:00,132.7339317071299,127.01794555129813,,,,,, +191,2024-01-01 09:33:00,131.96644234401086,126.92849167024444,,,,,, +192,2024-01-01 09:36:00,131.23624977231293,127.21693844485242,,,,,, +193,2024-01-01 09:39:00,130.5442714999672,127.63163934527068,,,,,, +194,2024-01-01 09:42:00,129.8915017594462,127.92101634128252,,,,,, +195,2024-01-01 09:45:00,129.2790392584337,127.83358650521238,,,,,, +196,2024-01-01 09:48:00,128.7081192919169,127.1179926336058,,,,,, +197,2024-01-01 09:51:00,128.1801515066077,125.52303908045668,,,,,, +198,2024-01-01 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12:21:00,139.61716420453516,145.78489495106453,,,,,, +1208,2024-01-03 12:24:00,139.21567150768192,144.90715933396535,,,,,, +1209,2024-01-03 12:27:00,138.8155153174637,143.34232646969951,,,,,, +1210,2024-01-03 12:30:00,138.41942046296916,141.1516153495329,,,,,, +1211,2024-01-03 12:33:00,138.0297110501632,138.678149484312,,,,,, +1212,2024-01-03 12:36:00,137.6483526428234,136.32741769354226,,,,,, +1213,2024-01-03 12:39:00,137.27699140793777,134.50457646521457,,,,,, +1214,2024-01-03 12:42:00,136.91699011156345,133.56821154358704,,,,,, +1215,2024-01-03 12:45:00,136.56946097369317,133.59899452169714,,,,,, +1216,2024-01-03 12:48:00,136.23529547413608,134.26088727470008,,,,,, +1217,2024-01-03 12:51:00,135.91519125578816,135.1250980573237,,,,,, +1218,2024-01-03 12:54:00,135.60967630465854,135.76265750837763,,,,,, +1219,2024-01-03 12:57:00,135.31913060351073,135.79126954637678,,,,,, +1220,2024-01-03 13:00:00,135.0438054624335,135.10947350302197,ANNOUNCE_MEAL,,,,,24.33333333333333 +1221,2024-01-03 13:03:00,134.78383826608516,134.0371447915998,,,,,, +1222,2024-01-03 13:06:00,134.5398941934883,132.98827297623626,,,,,, +1223,2024-01-03 13:09:00,134.3314108232765,132.38902421615825,,,,,, +1224,2024-01-03 13:12:00,134.25203832037948,132.66428521647694,,,,,, +1225,2024-01-03 13:15:00,134.49311145911767,134.00574246103298,,,,,, +1226,2024-01-03 13:18:00,135.31331647590358,136.19669259986117,,,,,, +1227,2024-01-03 13:21:00,137.0120035518099,138.95916614081884,,,,,, +1228,2024-01-03 13:24:00,139.83256165403668,141.9963438033618,,,,,, +1229,2024-01-03 13:27:00,143.77160234839656,144.89112667546928,,,,,, +1230,2024-01-03 13:30:00,148.51634822615074,147.2833010549144,,,,,, +1231,2024-01-03 13:33:00,153.57691621294896,149.10863533408005,,,,,, +1232,2024-01-03 13:36:00,158.4900501892106,150.37665125082611,,,,,, +1233,2024-01-03 13:39:00,162.92532579971729,151.19961159358684,,,,,, +1234,2024-01-03 13:42:00,166.69376371973866,151.8046747170656,,,,,, +1235,2024-01-03 13:45:00,169.71741820205145,152.37739123996428,,,,,, +1236,2024-01-03 13:48:00,171.99369365478074,152.94469328752376,,,,,, +1237,2024-01-03 13:51:00,173.56586008721274,153.53427598521452,,,,,, +1238,2024-01-03 13:54:00,174.5017147880311,154.20515736434663,,,,,, +1239,2024-01-03 13:57:00,174.8792725814791,155.0260416423708,,,,,, +1240,2024-01-03 14:00:00,174.7777499942953,156.03941043196627,,,,,, +1241,2024-01-03 14:03:00,174.27227703441736,157.23993266616677,,,,,, +1242,2024-01-03 14:06:00,173.4311191131924,158.60616974788155,,,,,, +1243,2024-01-03 14:09:00,172.31452388084674,160.1089915728678,,,,,, +1244,2024-01-03 14:12:00,170.97457335163313,161.71111595987855,,,,,, +1245,2024-01-03 14:15:00,169.4556194872924,163.36736493962096,,,,,, +1246,2024-01-03 14:18:00,167.79502353763888,165.02531288048897,,,,,, +1247,2024-01-03 14:21:00,166.02401936099764,166.6261149071853,,,,,, +1248,2024-01-03 14:24:00,164.1685899775607,168.10538297758887,,,,,, +1249,2024-01-03 14:27:00,162.2502934246536,169.15467877991816,,,,,, +1250,2024-01-03 14:30:00,160.2870050091752,169.32731081666603,,,,,, +1251,2024-01-03 14:33:00,158.2935629807636,168.7968352304796,,,,,, +1252,2024-01-03 14:36:00,156.2823168861123,167.60362110460505,,,,,, +1253,2024-01-03 14:39:00,154.2635848712432,165.78585708287636,,,,,, +1254,2024-01-03 14:42:00,152.24602973794762,163.37995412885712,,,,,, +1255,2024-01-03 14:45:00,150.23696488100086,160.42087441444704,,,,,, +1256,2024-01-03 14:48:00,148.2426012018528,156.94239749189728,,,,,, +1257,2024-01-03 14:51:00,146.2682453018345,152.9773343431953,,,,,, +1258,2024-01-03 14:54:00,144.31845809224365,148.55769884549022,,,,,, +1259,2024-01-03 14:57:00,142.39718166646358,143.73549484481083,ANNOUNCE_MEAL,,,,,3.333333333333333 +1260,2024-01-03 15:00:00,140.5131268567176,138.69136142022535,,,,,, +1261,2024-01-03 15:03:00,139.0926580396299,134.05544844340085,,,,,, +1262,2024-01-03 15:06:00,139.2513036876896,131.07288656461097,,,,,, +1263,2024-01-03 15:09:00,140.97475401317544,130.63353481022182,,,,,, +1264,2024-01-03 15:12:00,143.40750388830887,132.4593387301511,,,,,, +1265,2024-01-03 15:15:00,145.80928229952474,135.77939727908466,,,,,, +1266,2024-01-03 15:18:00,147.75672772242015,139.64822732366858,,,,,, +1267,2024-01-03 15:21:00,149.06907500777544,143.29242461426134,,,,,, +1268,2024-01-03 15:24:00,149.71410264682453,146.11822121132136,,,,,, +1269,2024-01-03 15:27:00,149.73970134032325,147.68032653782586,,,,,, +1270,2024-01-03 15:30:00,149.23052398101527,147.8637987128762,,,,,, +1271,2024-01-03 15:33:00,148.28252715568445,146.99295738264803,,,,,, +1272,2024-01-03 15:36:00,146.98908368510996,145.48711459630843,,,,,, +1273,2024-01-03 15:39:00,145.43419517085027,143.75822249308533,,,,,, +1274,2024-01-03 15:42:00,143.6898722063262,142.1790180966315,,,,,, +1275,2024-01-03 15:45:00,141.81583441565093,140.94226044091894,,,,,, +1276,2024-01-03 15:48:00,139.8604010015108,139.97741102633125,,,,,, +1277,2024-01-03 15:51:00,137.86190340995887,139.14755333513835,,,,,, +1278,2024-01-03 15:54:00,135.85024066909753,138.30681596541544,,,,,, +1279,2024-01-03 15:57:00,133.8483751685104,137.30821981806693,,,,,, +1280,2024-01-03 16:00:00,131.8736725776075,136.03669611461387,,,,,, +1281,2024-01-03 16:03:00,129.93905079543194,134.42927384673985,,,,,, +1282,2024-01-03 16:06:00,128.05393642883675,132.43182828787897,,,,,, +1283,2024-01-03 16:09:00,126.22504424813889,129.9872696553303,,,,,, +1284,2024-01-03 16:12:00,124.45700225706028,127.07514433971808,,,,,, +1285,2024-01-03 16:15:00,122.7528467098271,123.90680022178506,,,,,, +1286,2024-01-03 16:18:00,121.11441021425468,121.04258626962628,,,,,, +1287,2024-01-03 16:21:00,119.54262352053227,119.11970331666618,,,,,, +1288,2024-01-03 16:24:00,118.0377485972042,118.7746082432726,,,,,, +1289,2024-01-03 16:27:00,116.59955762011774,120.55880952940312,,,,,, +1290,2024-01-03 16:30:00,115.22746978571308,124.51701305760449,,,,,, +1291,2024-01-03 16:33:00,113.92065550438328,129.93403405041883,,,,,, +1292,2024-01-03 16:36:00,112.67811555131732,135.9256907740536,,,,,, +1293,2024-01-03 16:39:00,111.49874112893112,141.60767259791257,,,,,, +1294,2024-01-03 16:42:00,110.38135948585156,146.09559025813988,,,,,, +1295,2024-01-03 16:45:00,109.32476869583653,148.50501411817936,,,,,, +1296,2024-01-03 16:48:00,108.327764380472,147.95150347518734,,,,,, +1297,2024-01-03 16:51:00,107.38916052088118,143.55062926375166,,,,,, +1298,2024-01-03 16:54:00,106.50780601065355,134.41799196812337,ANNOUNCE_MEAL,,,,,27.666666666666668 +1299,2024-01-03 16:57:00,105.69980868000869,124.75897011707094,,,,,, +1300,2024-01-03 17:00:00,105.29869216990006,121.5673376576475,,,,,, +1301,2024-01-03 17:03:00,105.80681496176491,119.74031342256689,,,,,, +1302,2024-01-03 17:06:00,107.37262702717292,119.43527590012678,,,,,, +1303,2024-01-03 17:09:00,109.97121570280405,120.55033784881353,,,,,, +1304,2024-01-03 17:12:00,113.53358179434308,122.902934430967,,,,,, +1305,2024-01-03 17:15:00,118.02838669819252,126.32168512947344,,,,,, +1306,2024-01-03 17:18:00,123.41616924296888,130.64379203928937,,,,,, +1307,2024-01-03 17:21:00,129.32224161158948,135.50525510937825,,,,,, +1308,2024-01-03 17:24:00,135.22013169297102,140.2899569614586,,,,,, +1309,2024-01-03 17:27:00,140.67578721365686,144.42630213558874,,,,,, +1310,2024-01-03 17:30:00,145.41532371512943,147.64423305314747,,,,,, +1311,2024-01-03 17:33:00,149.30818291254735,150.06087562661037,,,,,, +1312,2024-01-03 17:36:00,152.3277800760107,151.9633635045339,,,,,, +1313,2024-01-03 17:39:00,154.51397060301773,153.71760812006468,,,,,, +1314,2024-01-03 17:42:00,155.94401381055783,155.6878056107737,,,,,, +1315,2024-01-03 17:45:00,156.71226648936363,157.97100030666232,,,,,, +1316,2024-01-03 17:48:00,156.916966361624,160.23623176001476,,,,,, +1317,2024-01-03 17:51:00,156.65223661909076,162.0540043637823,,,,,, +1318,2024-01-03 17:54:00,156.00372023244032,162.98766622415727,,,,,, +1319,2024-01-03 17:57:00,155.04662565130116,162.64679254119926,,,,,, +1320,2024-01-03 18:00:00,153.8453005195733,160.96676630589755,,,,,, +1321,2024-01-03 18:03:00,152.45371551999952,158.37727164205177,,,,,, +1322,2024-01-03 18:06:00,150.91643918961796,155.41044217389253,,,,,, +1323,2024-01-03 18:09:00,149.26982856642883,152.5897959142788,,,,,, +1324,2024-01-03 18:12:00,147.5432624945653,150.38450971354612,,,,,, +1325,2024-01-03 18:15:00,145.76031505621856,148.975862211459,,,,,, +1326,2024-01-03 18:18:00,143.93981434290913,148.11748029661814,,,,,, +1327,2024-01-03 18:21:00,142.09676304810478,147.4649622290697,,,,,, +1328,2024-01-03 18:24:00,140.2431169767377,146.67060522014543,,,,,, +1329,2024-01-03 18:27:00,138.38842910238762,145.42212217722914,,,,,, +1330,2024-01-03 18:30:00,136.5403728839777,143.63334569835578,,,,,, +1331,2024-01-03 18:33:00,134.70516107645125,141.55876568000048,,,,,, +1332,2024-01-03 18:36:00,132.8878765770841,139.52769151247955,,,,,, +1333,2024-01-03 18:39:00,131.09273086963157,137.86847769148045,,,,,, +1334,2024-01-03 18:42:00,129.32326398592548,136.87286721582694,,,,,, +1335,2024-01-03 18:45:00,127.58249800187632,136.61678242208112,,,,,, +1336,2024-01-03 18:48:00,125.8730541697833,136.85282724212811,,,,,, +1337,2024-01-03 18:51:00,124.19724200258412,137.2615141178092,,,,,, +1338,2024-01-03 18:54:00,122.55712703869943,137.52308970698752,,,,,, +1339,2024-01-03 18:57:00,120.95458265399505,137.3376889105075,,,,,, +1340,2024-01-03 19:00:00,119.39133014853532,136.52586418394225,,,,,, +1341,2024-01-03 19:03:00,117.86897040367816,135.08891116688082,,,,,, +1342,2024-01-03 19:06:00,116.38900965600403,133.06821666473803,,,,,, +1343,2024-01-03 19:09:00,114.95288134278856,130.50508754375645,,,,,, +1344,2024-01-03 19:12:00,113.56196551421458,127.44077089978867,,,,,, +1345,2024-01-03 19:15:00,112.2176069576928,123.91647257020148,,,,,, +1346,2024-01-03 19:18:00,110.92113292007632,119.97337495790008,,,,,, +1347,2024-01-03 19:21:00,109.67387112818808,115.65265492696,,,,,, +1348,2024-01-03 19:24:00,108.47716868424556,110.9955023854756,,,,,, +1349,2024-01-03 19:27:00,107.3324123409818,105.35874363748016,,,,,, +1350,2024-01-03 19:30:00,106.24105063501489,99.05056420910302,,,,,, +1351,2024-01-03 19:33:00,105.2046183726521,95.22672395813969,,,,,, +1352,2024-01-03 19:36:00,104.22476401892816,93.45768868756694,,,,,, +1353,2024-01-03 19:39:00,103.30328064022208,93.31405835718608,,,,,, +1354,2024-01-03 19:42:00,102.4421411983044,94.36660089812356,,,,,, +1355,2024-01-03 19:45:00,101.64353919770198,96.1862910392432,,,,,, +1356,2024-01-03 19:48:00,100.90993596163148,98.3443553270763,,,,,, +1357,2024-01-03 19:51:00,100.2441161724578,100.41232485181176,,,,,, +1358,2024-01-03 19:54:00,99.64925378516308,101.9620976262483,,,,,, +1359,2024-01-03 19:57:00,99.12899103905104,102.6059748326891,,,,,, +1360,2024-01-03 20:00:00,98.687534095382,102.1965592929788,,,,,, +1361,2024-01-03 20:03:00,98.32976986760232,100.94675016971868,,,,,, +1362,2024-01-03 20:06:00,98.06140994298818,99.15014573771796,,,,,, +1363,2024-01-03 20:09:00,97.889169170542,97.10128673772977,,,,,, +1364,2024-01-03 20:12:00,97.82098852233308,95.0790102609596,,,,,, +1365,2024-01-03 20:15:00,97.86631412073376,93.26244028621342,,,,,, +1366,2024-01-03 20:18:00,98.03644646511977,91.68075137986368,,,,,, +1367,2024-01-03 20:21:00,98.34497480611276,90.33155115511188,,,,,, +1368,2024-01-03 20:24:00,98.80830869939476,89.21508662921754,,,,,, +1369,2024-01-03 20:27:00,99.44630619303324,88.3490112549607,,,,,, +1370,2024-01-03 20:30:00,100.28296358620324,87.83991121119186,,,,,, +1371,2024-01-03 20:33:00,101.34705142429104,87.92647651175002,,,,,, +1372,2024-01-03 20:36:00,102.6724154934254,88.88072422825898,,,,,, +1373,2024-01-03 20:39:00,104.29736399526456,90.9793681641968,,,,,, +1374,2024-01-03 20:42:00,106.262146317412,94.45433453365222,,,,,, +1375,2024-01-03 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03:36:00,124.45139330530247,121.0113312672536,,,,,, +1993,2024-01-05 03:39:00,124.74739319053084,122.2516021618314,,,,,, +1994,2024-01-05 03:42:00,125.04279611693852,123.84572273336684,,,,,, +1995,2024-01-05 03:45:00,125.33743894284116,125.8385216992815,,,,,, +1996,2024-01-05 03:48:00,125.63116720383104,128.27483656481303,,,,,, +1997,2024-01-05 03:51:00,125.92383478978353,131.19951329678642,,,,,, +1998,2024-01-05 03:54:00,126.21530363141667,134.65740600699564,,,,,, +1999,2024-01-05 03:57:00,126.5054433962255,136.4601211037414,,,,,, +2000,2024-01-05 04:00:00,126.79413119360834,134.39934069588395,,,,,, +2001,2024-01-05 04:03:00,127.0812512889968,132.89368631515566,,,,,, +2002,2024-01-05 04:06:00,127.36669482679956,131.8293338350086,,,,,, +2003,2024-01-05 04:09:00,127.65035956196598,131.09246582859498,,,,,, +2004,2024-01-05 04:12:00,127.9321495999756,130.56927130595523,,,,,, +2005,2024-01-05 04:15:00,128.2119751450582,130.145945459502,,,,,, +2006,2024-01-05 04:18:00,128.48975225644912,129.70868941760398,,,,,, +2007,2024-01-05 04:21:00,128.7654026124852,129.14371000607568,,,,,, +2008,2024-01-05 04:24:00,129.03885328234702,128.33721951737812,,,,,, +2009,2024-01-05 04:27:00,129.3100365052558,127.19348163946552,,,,,, +2010,2024-01-05 04:30:00,129.57888947693328,125.7250420004691,,,,,, +2011,2024-01-05 04:33:00,129.84535414313646,124.10686641626336,,,,,, +2012,2024-01-05 04:36:00,130.10937700008012,122.55001762370644,,,,,, +2013,2024-01-05 04:39:00,130.37090890156338,121.2655627812838,,,,,, +2014,2024-01-05 04:42:00,130.6299048726192,120.45274399358404,,,,,, +2015,2024-01-05 04:45:00,130.886323929507,120.2398316941552,,,,,, +2016,2024-01-05 04:48:00,131.14012890587466,120.64863660859903,,,,,, +2017,2024-01-05 04:51:00,131.39128628491548,121.67731459377684,,,,,, +2018,2024-01-05 04:54:00,131.63976603735176,123.32402504786856,,,,,, +2019,2024-01-05 04:57:00,131.88554146507832,125.56134499936812,,,,,, +2020,2024-01-05 05:00:00,132.12858905030308,128.20834019055843,,,,,, +2021,2024-01-05 05:03:00,132.36888831002491,130.85380767191248,,,,,, +2022,2024-01-05 05:06:00,132.6064216556925,133.03537592940597,,,,,, +2023,2024-01-05 05:09:00,132.84117425789086,134.29067625306925,,,,,, +2024,2024-01-05 05:12:00,133.07313391590642,134.21400681352512,,,,,, +2025,2024-01-05 05:15:00,133.3022909320236,132.7396535776806,,,,,, +2026,2024-01-05 05:18:00,133.52863799041018,130.3118828118682,,,,,, +2027,2024-01-05 05:21:00,133.75217004045183,127.48829150354892,,,,,, +2028,2024-01-05 05:24:00,133.9728841843993,124.82647883033997,,,,,, +2029,2024-01-05 05:27:00,134.19077956919497,122.8405233838114,,,,,, +2030,2024-01-05 05:30:00,134.405857282349,121.78336973080764,,,,,, +2031,2024-01-05 05:33:00,134.61812025173822,121.51626031201782,,,,,, +2032,2024-01-05 05:36:00,134.8275731492045,121.81339401080557,,,,,, +2033,2024-01-05 05:39:00,135.034222297832,122.448971392548,,,,,, +2034,2024-01-05 05:42:00,135.2380755827856,123.21362746006535,,,,,, +2035,2024-01-05 05:45:00,135.43914236559695,123.9965957956302,,,,,, +2036,2024-01-05 05:48:00,135.63743340178632,124.83500701480222,,,,,, +2037,2024-01-05 05:51:00,135.832960761712,125.79885876579056,,,,,, +2038,2024-01-05 05:54:00,136.02573775454175,126.958149960841,,,,,, +2039,2024-01-05 05:57:00,136.2157788552439,128.37310249007453,,,,,, +2040,2024-01-05 06:00:00,136.403099634498,130.0452700896791,,,,,, +2041,2024-01-05 06:03:00,136.58771669142806,131.88820363725583,,,,,, +2042,2024-01-05 06:06:00,136.76964758906405,133.7958985714241,,,,,, +2043,2024-01-05 06:09:00,136.94891079243985,135.66235125334327,,,,,, +2044,2024-01-05 06:12:00,137.12552560923842,137.38155890651268,,,,,, +2045,2024-01-05 06:15:00,137.2995121328975,138.84751955905998,,,,,, +2046,2024-01-05 06:18:00,137.47089118809234,139.95423198843474,,,,,, +2047,2024-01-05 06:21:00,137.63968427851248,140.59569566842362,,,,,, +2048,2024-01-05 06:24:00,137.80591353685458,140.66591071840742,,,,,, +2049,2024-01-05 06:27:00,137.96960167695315,140.87592302877275,,,,,, +2050,2024-01-05 06:30:00,138.13077194797538,142.04788821049303,,,,,, +2051,2024-01-05 06:33:00,138.28944809060664,142.72721855896617,,,,,, +2052,2024-01-05 06:36:00,138.4456542951571,142.9977639015371,,,,,, +2053,2024-01-05 06:39:00,138.59941516152048,142.94337448841276,,,,,, +2054,2024-01-05 06:42:00,138.75075566091934,142.6479009538144,,,,,, +2055,2024-01-05 06:45:00,138.89970109937224,142.1951942788731,,,,,, +2056,2024-01-05 06:48:00,139.04627708282092,141.66910575620597,,,,,, +2057,2024-01-05 06:51:00,139.1905094838571,141.1534869561118,,,,,, +2058,2024-01-05 06:54:00,139.33242440999047,140.7321896943278,,,,,, +2059,2024-01-05 06:57:00,139.4720481734008,140.4896930311707,,,,,, +2060,2024-01-05 07:00:00,139.6094072621206,140.5142383916545,,,,,, +2061,2024-01-05 07:03:00,139.74452831259396,140.8997106272378,,,,,, +2062,2024-01-05 07:06:00,139.87743808356038,141.74124878009508,,,,,, +2063,2024-01-05 07:09:00,140.00816343121485,143.1339919980889,,,,,, +2064,2024-01-05 07:12:00,140.1367312855943,145.13623705582498,,,,,, +2065,2024-01-05 07:15:00,140.2631686281456,147.5852260572055,,,,,, +2066,2024-01-05 07:18:00,140.38750247042873,149.98661904898174,,,,,, +2067,2024-01-05 07:21:00,140.50975983391183,151.77239118465872,,,,,, +2068,2024-01-05 07:24:00,140.62996773081642,152.37451761484087,,,,,, +2069,2024-01-05 07:27:00,140.74815314597117,151.304281501229,,,,,, +2070,2024-01-05 07:30:00,140.86434301963558,148.54881416151173,,,,,, +2071,2024-01-05 07:33:00,140.97856423125455,144.8090191502447,,,,,, +2072,2024-01-05 07:36:00,141.09084358410806,140.94441601585797,,,,,, +2073,2024-01-05 07:39:00,141.20120779081915,137.8145242209237,ANNOUNCE_MEAL,,,,,18.666666666666668 +2074,2024-01-05 07:42:00,141.30968394579338,136.19489005695328,,,,,, +2075,2024-01-05 07:45:00,141.41722721477328,136.35796934593958,,,,,, 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08:30:00,175.48888197725284,168.03080455935398,,,,,, +2091,2024-01-05 08:33:00,176.09404992419368,165.42495939379893,,,,,, +2092,2024-01-05 08:36:00,176.27683437952302,162.06303096349023,,,,,, +2093,2024-01-05 08:39:00,176.09680450619766,158.29402533486618,,,,,, +2094,2024-01-05 08:42:00,175.60924282255394,154.4635328213771,,,,,, +2095,2024-01-05 08:45:00,174.8637879990981,150.9118984192622,,,,,, +2096,2024-01-05 08:48:00,173.90402147533277,147.97351408560272,,,,,, +2097,2024-01-05 08:51:00,172.7676157764109,145.97679358231915,,,,,, +2098,2024-01-05 08:54:00,171.4867904406542,145.24453498549886,,,,,, +2099,2024-01-05 08:57:00,170.08891120393608,147.39694525685283,,,,,, +2100,2024-01-05 09:00:00,168.597129911446,152.72670670424182,,,,,, +2101,2024-01-05 09:03:00,167.0310045222089,156.26279378655786,,,,,, +2102,2024-01-05 09:06:00,165.40706639767978,158.22327402380716,,,,,, +2103,2024-01-05 09:09:00,163.7393200544443,158.82352408831974,,,,,, +2104,2024-01-05 09:12:00,162.039671750761,158.2766822135954,ANNOUNCE_MEAL,,,,,2.0 +2105,2024-01-05 09:15:00,160.32237398134473,156.7981153015238,,,,,, +2106,2024-01-05 09:18:00,158.9647316462726,154.83294544663667,,,,,, +2107,2024-01-05 09:21:00,158.92836792350715,153.3125276630695,,,,,, +2108,2024-01-05 09:24:00,160.12930208271246,152.81865831065636,,,,,, +2109,2024-01-05 09:27:00,161.83078232777532,152.97279590238782,,,,,, +2110,2024-01-05 09:30:00,163.43426379879756,153.25750889594477,,,,,, +2111,2024-01-05 09:33:00,164.60812202445052,153.34561307623636,,,,,, +2112,2024-01-05 09:36:00,165.2162809546395,153.12016641057994,,,,,, +2113,2024-01-05 09:39:00,165.24021272527875,152.60730698391674,,,,,, +2114,2024-01-05 09:42:00,164.7240175771486,151.90034660473538,,,,,, +2115,2024-01-05 09:45:00,163.74015666953662,151.05487451299427,,,,,, +2116,2024-01-05 09:48:00,162.3695080754672,150.02592297068185,,,,,, +2117,2024-01-05 09:51:00,160.69055496500678,148.74382925199248,,,,,, +2118,2024-01-05 09:54:00,158.77412911948852,147.1328352116412,,,,,, +2119,2024-01-05 09:57:00,156.68139657704265,145.1229780002716,,,,,, +2120,2024-01-05 10:00:00,154.46364035352863,142.72384135870297,,,,,, +2121,2024-01-05 10:03:00,152.16296231840676,140.06969190919142,,,,,, +2122,2024-01-05 10:06:00,149.8133869405143,137.31614422526724,,,,,, +2123,2024-01-05 10:09:00,147.44207433348973,134.6115411057776,,,,,, +2124,2024-01-05 10:12:00,145.07048720877455,132.0973244754872,,,,,, +2125,2024-01-05 10:15:00,142.71543801877155,129.90504109147244,,,,,, +2126,2024-01-05 10:18:00,140.38998957273057,128.15484252618177,,,,,, +2127,2024-01-05 10:21:00,138.1042081098279,126.96200476465658,,,,,, +2128,2024-01-05 10:24:00,135.8657807119261,126.43922388676047,,,,,, +2129,2024-01-05 10:27:00,133.6805143603996,126.66529487504994,,,,,, +2130,2024-01-05 10:30:00,131.55273517951878,127.52624827167094,,,,,, +2131,2024-01-05 10:33:00,129.48560545945247,128.6201168565034,,,,,, +2132,2024-01-05 10:36:00,127.48137409500087,129.48022091872207,,,,,, +2133,2024-01-05 10:39:00,125.54157378455596,129.63910077146116,,,,,, +2134,2024-01-05 10:42:00,123.66717607055998,128.68192059536122,,,,,, +2135,2024-01-05 10:45:00,121.8587132459208,126.51277685123895,,,,,, +2136,2024-01-05 10:48:00,120.11637437133,123.51450813721284,,,,,, +2137,2024-01-05 10:51:00,118.44008115786887,120.17613146114375,,,,,, +2138,2024-01-05 10:54:00,116.82954824893494,116.98643723938751,,,,,, +2139,2024-01-05 10:57:00,115.28433145319298,114.39282171536934,,,,,, +2140,2024-01-05 11:00:00,113.8038666998637,112.59523040234028,,,,,, +2141,2024-01-05 11:03:00,112.38750187431182,111.4225331110813,,,,,, +2142,2024-01-05 11:06:00,111.0345232143084,110.62109000314324,,,,,, +2143,2024-01-05 11:09:00,109.7441775787644,109.93721458901533,,,,,, +2144,2024-01-05 11:12:00,108.51569161902484,109.1171938942827,,,,,, +2145,2024-01-05 11:15:00,107.34828867011328,107.90730608104104,,,,,, +2146,2024-01-05 11:18:00,106.24120402156224,106.05383623477746,,,,,, +2147,2024-01-05 11:21:00,105.19369911383104,103.30309089898611,,,,,, +2148,2024-01-05 11:24:00,104.20507512876848,99.40141185050588,,,,,, +2149,2024-01-05 11:27:00,103.2746863954493,97.05343195430837,,,,,, +2150,2024-01-05 11:30:00,102.40195401238496,99.46328565550526,,,,,, +2151,2024-01-05 11:33:00,101.58638009184304,101.53238322847336,,,,,, +2152,2024-01-05 11:36:00,100.8275630618228,103.22480708165892,,,,,, +2153,2024-01-05 11:39:00,100.12521451797518,104.50474711722272,,,,,, +2154,2024-01-05 11:42:00,99.47917820522636,105.33651898160367,,,,,, +2155,2024-01-05 11:45:00,98.88945183314132,105.68458491061756,,,,,, +2156,2024-01-05 11:48:00,98.3562125988948,105.51357798468788,,,,,, +2157,2024-01-05 11:51:00,97.87984751901416,104.7883308175098,,,,,, +2158,2024-01-05 11:54:00,97.46098997131284,103.47390997680829,,,,,, +2159,2024-01-05 11:57:00,97.100564240644,101.55890891426432,,,,,, +2160,2024-01-05 12:00:00,96.7998403673578,99.17175488357384,,,,,, +2161,2024-01-05 12:03:00,96.56050223387908,96.65052585943738,,,,,, +2162,2024-01-05 12:06:00,96.38473259497908,94.38027196038256,,,,,, +2163,2024-01-05 12:09:00,96.27531961625098,92.74661241282438,,,,,, +2164,2024-01-05 12:12:00,96.2357902696614,92.09981300918884,,,,,, +2165,2024-01-05 12:15:00,96.2705762062107,92.5747070216626,,,,,, +2166,2024-01-05 12:18:00,96.3852164362695,93.9827450709596,ANNOUNCE_MEAL,,,,,24.0 +2167,2024-01-05 12:21:00,96.5878682093442,96.06594102846464,,,,,, +2168,2024-01-05 12:24:00,96.957150083907,98.61027673037862,,,,,, +2169,2024-01-05 12:27:00,97.70588332155606,101.5261508720645,,,,,, +2170,2024-01-05 12:30:00,99.03468110294084,104.84505548554802,,,,,, +2171,2024-01-05 12:33:00,101.10062380174448,108.66428681186343,,,,,, +2172,2024-01-05 12:36:00,104.04903566499952,113.08296953309548,,,,,, +2173,2024-01-05 12:39:00,108.0433858328949,118.20075491111707,,,,,, +2174,2024-01-05 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+Risk Index,adult#001,1.5109016036945477,1.5108898399567545,1.5108416427955702,1.5107517784435078,1.5106320250870593,1.5104964084273995,1.5120172854062457,3.966519064638203,5.875401988996571,4.199676897862963,3.684969378841525,5.587669917728138,14.330097276660059,8.217927764100077,3.0445352129034786,3.3326089463795343,0.8011832819428587,0.41503691810987325,2.2326665207012684,2.562155802854853,4.079614818492648,0.8246760167080248,0.8347308542188063,0.7078151892014131,0.6708647841270446,3.8816008961437793,7.322146921789124,9.06319722967557,8.947324758212252,7.643610250932538,5.762719191021399,0.7507499367256332,0.0008737828963202765,0.013129482289312043,0.10523642304996175,0.028628057628644142,2.2532142519778358,5.478983124998892,2.5182124976719162,1.692503836540864,1.9484392689810435,0.29341036214231186,0.8343965018017035,1.8271423796294233,0.3674799689693726,0.09317959151829558,0.1388128832168797,0.22286778016086659,1.0986325636641077,1.8741924265768422,2.14228462833746,1.9661455188644699,1.54864531995544,1.0703797208527042,0.6446221725346046,0.33619938308685493,1.5125941968627594,1.1413558006651994,0.9154178042020387,1.324307449225016,0.6369697587573306,1.6917248214845508,7.422507982675226,7.4079014405908294,3.9437842620582364,3.6334226602257202,5.402801810540006,0.5211733053726378,0.2285470124204173,0.21729113859434418,4.8992477100946035,16.073499593830245,26.641947186434994,31.377092800640828,30.016047920682702,25.087064558051893,19.14687144621103,13.687306967127673,9.27735317085499,4.266901608471375,0.04590523874397753,0.0016167298746692898,0.011212181730997337,0.1524910062870844,0.04098588482454805,0.648339596343717,4.401853699988819,2.7227818224184372,1.38253701450318,1.9540316328653062,1.5005034152762675,9.302558656304962,5.767754100869313,1.722487741273823,2.375667572405182,0.6269747006460856,0.596745645428842,2.898417380551283,5.105401571579591,6.101610338819017,5.896201448800451,4.952713128809248,3.7424874973807407,2.5661139834053435,0.9605065377164508,3.1869210796235015,3.4435381480225784,1.0533023046729397,0.7458850821374448,6.611888910514528,4.368841261205953,1.3007900536397252,1.7140270627492722,0.5383338283979102,0.12421689820816338,2.4337127409619974,1.0767680198555252,0.038846338554852144,0.24725613724616977,0.13793578796831032,1.1674484926595337,1.410857004130791,1.9589613413679203,3.0767418620703095,3.5463886450723905,3.3052242445337656,2.6613161118210105,1.800313012089689,0.11653165878148669,0.07550695400272503,0.03765207872242677,0.20923739325505347,5.2194456784010175,8.033042218864797,3.803059083478991,4.2560145983349615,1.6394396125643742,0.08431469375878461,0.14245871441703278,0.9541011736022827,3.286530150765725,5.428676887325429,6.335958359352911,6.0734850385036605,5.1061547945053665,3.884526030709327,2.7080845089576338,1.7277296557333828,0.9915902495703497,0.48997116873964164,0.1868940994238645,0.16046178350320248,2.239011639405944,2.115160285807531,1.4814137633836748,2.016892781393524,5.347518607364202,7.584411969009338,3.680110217719162,2.458120799653192,2.200667534895705,3.073232758816917,5.490511813843218,1.8359626971230836,0.6342109586834317,0.8219728511211846,0.23781475409496217,0.28643394228430397 +Risk Index,adult#003,2.506251694687762,2.5061487890012555,2.5058258194385696,2.5053462172506875,2.5048289406417816,2.5043558798690766,2.5076412880957113,5.007868255149608,3.6265858679319956,1.0736089252654641,0.6213165839434511,4.048754152429768,6.126256212341117,0.6200772654096022,0.2869738125845397,1.2681636109916892,1.7971157233705917,0.29821184161442515,0.09515671036899351,5.436336389620786,2.2765908922087243,0.1626002281089238,0.388309977303275,2.679017153524911,0.8785056136896229,0.08464347437393971,0.05620363803241185,0.21090625281107994,0.5284514410435721,0.926057443698879,1.3866931104288711,3.931068556964881,2.373589599109259,0.8183268427688383,0.9617651277431245,0.5211955250504817,3.721662883260879,2.8588047016958864,0.21987292505106548,0.08938867217756179,2.22535668416912,1.394072502226468,2.6979850125594487,1.934030338564689,0.1411754250859925,0.04387813911710726,1.2179779107428683,0.8967031193537777,0.4525842695907139,0.4829279494533634,0.7233905743199021,1.0533506581623218,1.3894108250071282,1.6828748529795015,1.916514488782133,3.723859553097971,4.178970775852486,1.4005206840331994,0.48162485528719834,1.590827543322073,1.5294409313552237,3.283405013626217,4.4942155351537805,2.082598569341566,0.20669532219039458,1.9359161387391863,1.5592905632925407,0.1870421866110577,0.40494136398757274,2.720593294063124,1.7210757524372304,0.29169297056304555,0.11941073064806129,0.24787333934711628,0.5428615030315121,0.9238643377863223,1.3021104993388435,1.6259353930354628,1.8798218591025244,3.225159673884671,4.210622313681831,1.6564154023259392,0.6088419118612799,1.511983320484169,1.6432890949650099,2.99735752284627,4.258885063795319,0.7110433675482954,0.007926154295199049,1.1666847623903527,2.910153613205638,5.991008342529073,0.6687855810253673,0.45000679103517516,0.9277050844379305,1.7751766605005392,0.26919159216982597,0.02983689441814046,0.09002474576130153,0.3189067842244183,0.6852022719806903,1.0891215015269624,1.4547847934708291,1.765111580167432,5.656931397600467,4.749653523282359,1.2552722025472407,0.3156874254526221,0.6485271138058781,2.017595294847451,0.22464814868519128,0.311016751733382,0.8587633620859275,1.941099357442106,1.4016736939180987,3.5945211554068868,0.48988617572943494,0.11320324333250313,0.11469410814237041,0.00915106023497473,0.006001106659891549,0.6062981638685053,1.7470539680408017,1.5180269181325925,1.4195165251333661,1.5277807839587862,1.7117259791046155,2.049404294376772,3.8430839580292195,2.3084757135388676,1.0282293282675992,1.2316145990607705,6.818354812323024,3.0647590669738642,0.12109114671856246,0.3121371331912057,0.03448376453285279,0.10640256483095674,0.5000298373366302,1.8911298534036138,1.3364449201154405,1.076885734309526,1.162599216810766,1.3832713942125479,1.6315390927137312,1.8559921062512035,2.037824062131813,2.17581250670301,2.276276754382888,2.3474684870957807,2.3970224248953746,3.3244212123207055,4.212334624652678,0.8149711558550262,0.014983785931315672,0.2671198925282104,3.3667696313459876,1.7898126373378695,0.081498261477146,0.1345324553966079,1.2791415635978614,3.6160552880638086,2.4028096554343614,0.11740064810262617,0.18517407690271478,1.2901309330433919,0.9427984443989589,0.11616313300546979 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zX!Ixl%Jd6xN-^8^(p3xQ&u=$o#DVdkh<`$ZTKw@5JQ%t#s;Ys8isl7PMog>Nk!JO; zw~hUB)wwoCgn*jcRtptZo|t3+AduY5zcA1Ru#-qcwqr%bBs<&x;f?Vqk3!`XEfLM2 zh2ir*|CMgK!TxyNyr~2qw~7mb)?E2lznIRSP=b)q~&pU;A}pc(X6RJSqhI(Lmc-2l*t)KKhgs~{T% zVb~$NyP< 0, 'msg_type'] = 'ANNOUNCE_MEAL' + + # Rename CHO to food_g where it's > 0 + processed_df.loc[processed_df['CHO'] > 0, 'food_g'] = processed_df.loc[processed_df['CHO'] > 0, 'CHO'] + + # Drop the CHO column + processed_df = processed_df.drop(columns=['CHO']) + + + return processed_df + + +def run_glucose_simulation( + start_time=None, + simulation_days=7, + scenario_type='random', + custom_meal_schedule=None, + patient_names=None, + cgm_name="Dexcom", + insulin_pump_name="Cozmo", + global_seed=123, + animate=False, + parallel=True, +): + # Set default values + if start_time is None: + start_time = pd.Timestamp('2024-01-01 00:00:00') + if patient_names is None: + patient_names = ['adult#001'] + if custom_meal_schedule is None and scenario_type == 'custom': + custom_meal_schedule = [(1, 20)] # Default meal at hour 1 with 20g carbs + + # Create controller + controller = BBController() + + # Set up simulation time + sim_time = pd.Timedelta(days=simulation_days) + + # Scenario + if scenario_type == 'custom': + scenario = CustomScenario( + start_time=start_time, + scenario=custom_meal_schedule + ) + else: + scenario = RandomScenario( + start_time=start_time, + seed=global_seed + ) + + # Set up result directory + project_root = get_root_dir() + result_dir = os.path.join(project_root, '0_meal_identification', 'meal_identification', 'data', 'sim') + os.makedirs(result_dir, exist_ok=True) + + # Run simulation + simulate( + sim_time=sim_time, + scenario=scenario, + controller=controller, + start_time=start_time, + save_path=result_dir, + cgm_name=cgm_name, + cgm_seed=global_seed, + insulin_pump_name=insulin_pump_name, + animate=animate, + parallel=parallel, + patient_names=patient_names, + ) + + return result_dir + + +def process_sim_data(simulation_days): + """ + Process all patient CSV files in the sim directory and output them to data/raw. + + Returns: + dict: Dictionary with patient IDs as keys and processed DataFrames as values + """ + # Get the project root and construct sim directory path + project_root = get_root_dir() + sim_dir = os.path.join(project_root, '0_meal_identification', 'meal_identification', 'data', 'sim') + processed_dir = os.path.join(project_root, '0_meal_identification', 'meal_identification', 'data', 'raw') + + # Convert to Path object for easier handling + csv_files = [f for f in os.listdir(sim_dir) if f.endswith('.csv')] + + # Dictionary to store processed data for each patient + processed_data = {} + + for file in csv_files: + # Skip CVGA_stats.csv and risk_trace.csv + if ('CVGA' in file) or ('risk_trace' in file) or ('performance' in file): + continue + + file_path = os.path.join(sim_dir, file) + try: + df = pd.read_csv(file_path) + + # Convert Time column to datetime + df['date'] = pd.to_datetime(df['Time']) + + # Process the data + processed_df = process_simulated_data(df) + + # Create new filename (first 3 + last 3 characters before .csv) + base_name = file.replace('.csv', '') + short_name = f"{base_name[:3]}{base_name[-3:]}" + timestamp = datetime.today() + to = timestamp + timedelta(days=simulation_days) + start_date = timestamp.strftime('%Y-%m-%d') + end_date = to.strftime('%Y-%m-%d') + file = f"{short_name}_{start_date}_{end_date}.csv" + + # Store in dictionary + processed_data[file] = processed_df + + print(f"Successfully processed {file}") + + except Exception as e: + print(f"Error processing {file}: {str(e)}") + + # Save processed data + os.makedirs(processed_dir, exist_ok=True) + for file, df in processed_data.items(): + output_file = os.path.join(processed_dir, file) + df.to_csv(output_file) + print(f"Saved processed data for {file}") + + return processed_data + + +def generate_simulated_data( + start_time=None, + simulation_days=7, + scenario_type='random', + custom_meal_schedule=None, + patient_names=None, + cgm_name="Dexcom", + insulin_pump_name="Cozmo", + global_seed=123, + animate=False, + parallel=True, +): + """ + Run a glucose simulation with specified parameters and output to data/raw. + Animate and parallel can not be set to True at the same time for Mac. Not sure about Windows and Linux + General data flow: Sim -> Raw + + Parameters + ---------- + start_time (pd.Timestamp, optional): Start time for simulation. Defaults to '2024-01-01 00:00:00'. + simulation_days (int, optional): Duration of simulation in days. Defaults to 7. + scenario_type (str, optional): Type of scenario + - 'random' | 'custom'. Defaults to 'random'. + custom_meal_schedule (list, optional): List of tuples (hour, carbs) for custom scenario. + patient_names (list, optional): List of patient IDs to simulate. Each patient's data will look different. + cgm_name (str, optional): Name of the cgm device. + - "Dexcom" | "GuardianRT" | "Navigator". Defaults to "Dexcom". + insulin_pump_name (str, optional): Name of the insulin pump device. + - "Cozmo" | "Insulet". Defaults to "Cozmo". + global_seed (int, optional): Random seed for reproducibility. Defaults to 123. + animate (bool, optional): Whether to animate the simulation. Defaults to False. + parallel (bool, optional): Whether to run simulations in parallel. Defaults to True. + patient_names (list, optional): List of patient IDs to simulate. + - patient_names can be from adult#001 ~ adult#009 and child#001 ~ child#009. Default to ["adult#001"]. + + Returns + ------- + str: Path to the directory containing simulation results. + """ + run_glucose_simulation( + start_time=start_time, + simulation_days=simulation_days, + scenario_type=scenario_type, + custom_meal_schedule=custom_meal_schedule, + patient_names=patient_names, + cgm_name=cgm_name, + insulin_pump_name=insulin_pump_name, + global_seed=global_seed, + animate=animate, + parallel=parallel, + ) + process_sim_data(simulation_days=simulation_days) + + +default_patient_names = ['adult#001', 'adult#003'] +generate_simulated_data( + patient_names=default_patient_names +) diff --git a/0_meal_identification/meal_identification/meal_identification/datasets/dataset_operations.py b/0_meal_identification/meal_identification/meal_identification/datasets/dataset_operations.py index a1a4a1d..19b20df 100644 --- a/0_meal_identification/meal_identification/meal_identification/datasets/dataset_operations.py +++ b/0_meal_identification/meal_identification/meal_identification/datasets/dataset_operations.py @@ -101,7 +101,7 @@ def find_file_loc(output_dir, data_label, patient_id, data_gen_date, include_gen return os.path.join(full_out_path_dir, filename), filename -def save_data(data, output_dir, data_label, patient_id, data_gen_date, include_gen_date_label=True): +def save_data(data, output_dir, data_label, patient_id, data_gen_date): """ Save the data to the output directory. @@ -117,8 +117,6 @@ def save_data(data, output_dir, data_label, patient_id, data_gen_date, include_g The patient ID data_gen_date : str The date the data was generated - include_gen_date_label : bool - Whether to include the data generation date in the label Returns ------- diff --git a/0_meal_identification/meal_identification/notebooks/1.04-tc-simulated-date.ipynb b/0_meal_identification/meal_identification/notebooks/1.04-tc-simulated-date.ipynb new file mode 100644 index 0000000..462bf33 --- /dev/null +++ b/0_meal_identification/meal_identification/notebooks/1.04-tc-simulated-date.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Checkout simulated date", + "id": "6d6328076b5477ed" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-18T05:29:19.888146Z", + "start_time": "2024-11-18T05:29:19.561095Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Assuming df is your DataFrame\n", + "df = pd.read_csv('../data/interim/2024-11-18_adu001__i5mins_d4hrs_c5g_l2hrs_n3.csv')\n", + "\n", + "df[df.columns[0]] = pd.to_datetime(df[df.columns[0]]) # Second column is bgl\n", + "\n", + "# Set style\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "\n", + "# Create the plot\n", + "ax.plot(df['date'], df[\"bgl\"], 'r', linewidth=2)\n", + "\n", + "# Customize the plot\n", + "ax.set_title(f'bgl Over Time', fontsize=14, pad=20)\n", + "ax.set_xlabel(df.columns[0], fontsize=12)\n", + "ax.set_ylabel(f'bgl (mg/dL)', fontsize=12)\n", + "\n", + "# Add grid\n", + "ax.grid(True, linestyle='--', alpha=0.7)\n", + "\n", + "# Set y-axis range to include important thresholds\n", + "ax.axhline(y=70, color='r', linestyle='--', alpha=0.5, label='Hypoglycemia Threshold')\n", + "ax.axhline(y=180, color='r', linestyle='--', alpha=0.5, label='Hyperglycemia Threshold')\n", + "ax.fill_between(df[df.columns[0]], 70, 180, alpha=0.1, color='green', label='Target Range')\n", + "\n", + "# Add legend\n", + "ax.legend()\n", + "\n", + "# Rotate x-axis labels for better readability\n", + "plt.xticks(rotation=45)\n", + "\n", + "# Adjust layout to prevent label cutoff\n", + "plt.tight_layout()\n", + "\n", + "# Show plot\n", + "plt.show()" + ], + "id": "c147d429fac2a9c", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "3ea36d2d69813ebb" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 097eccfb68039355f9c5d01a6557f7297e864e8c Mon Sep 17 00:00:00 2001 From: Sneha Rao Date: Wed, 27 Nov 2024 23:36:57 -0500 Subject: [PATCH 6/6] feat: add cluster segmenter (#193) Co-authored-by: Christopher <110344005+RobotPsychologist@users.noreply.github.com> --- .../notebooks/3.10-cluster-segmenter.ipynb | 3054 +++++++++++++++++ 1 file changed, 3054 insertions(+) create mode 100644 0_meal_identification/meal_identification/notebooks/3.10-cluster-segmenter.ipynb diff --git a/0_meal_identification/meal_identification/notebooks/3.10-cluster-segmenter.ipynb b/0_meal_identification/meal_identification/notebooks/3.10-cluster-segmenter.ipynb new file mode 100644 index 0000000..9a59587 --- /dev/null +++ b/0_meal_identification/meal_identification/notebooks/3.10-cluster-segmenter.ipynb @@ -0,0 +1,3054 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "64424656-b673-4e18-a552-cf266472d6da", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting sktime\n", + " Obtaining dependency information for sktime from https://files.pythonhosted.org/packages/11/92/271c96cfa842dd2bb63f040353e76c1283c0b2613149d285be60e350455e/sktime-0.34.0-py3-none-any.whl.metadata\n", + " Downloading sktime-0.34.0-py3-none-any.whl.metadata (34 kB)\n", + "Requirement already satisfied: joblib<1.5,>=1.2.0 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from sktime) (1.4.2)\n", + "Requirement already satisfied: numpy<2.2,>=1.21 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from sktime) (1.24.4)\n", + "Requirement already satisfied: packaging in /Users/sneha/anaconda3/lib/python3.11/site-packages (from sktime) (24.1)\n", + "Requirement already satisfied: pandas<2.3.0,>=1.1 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from sktime) (2.2.2)\n", + "Collecting scikit-base<0.12.0,>=0.6.1 (from sktime)\n", + " Obtaining dependency information for scikit-base<0.12.0,>=0.6.1 from https://files.pythonhosted.org/packages/43/a0/e3c1f1297159c7d9e4afb64bc38e9532f59dec0d764be7a834df6fc9b211/scikit_base-0.11.0-py3-none-any.whl.metadata\n", + " Downloading scikit_base-0.11.0-py3-none-any.whl.metadata (8.5 kB)\n", + "Requirement already satisfied: scikit-learn<1.6.0,>=0.24 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from sktime) (1.3.0)\n", + "Requirement already satisfied: scipy<2.0.0,>=1.2 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from sktime) (1.13.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from pandas<2.3.0,>=1.1->sktime) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from pandas<2.3.0,>=1.1->sktime) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from pandas<2.3.0,>=1.1->sktime) (2023.3)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from scikit-learn<1.6.0,>=0.24->sktime) (3.5.0)\n", + "Requirement already satisfied: six>=1.5 in /Users/sneha/anaconda3/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas<2.3.0,>=1.1->sktime) (1.16.0)\n", + "Downloading sktime-0.34.0-py3-none-any.whl (36.7 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packaging 24.1\n", + " Uninstalling packaging-24.1:\n", + " Successfully uninstalled packaging-24.1\n", + " Attempting uninstall: locket\n", + " Found existing installation: locket 1.0.0\n", + " Uninstalling locket-1.0.0:\n", + " Successfully uninstalled locket-1.0.0\n", + " Attempting uninstall: fsspec\n", + " Found existing installation: fsspec 2024.6.1\n", + " Uninstalling fsspec-2024.6.1:\n", + " Successfully uninstalled fsspec-2024.6.1\n", + " Attempting uninstall: cloudpickle\n", + " Found existing installation: cloudpickle 3.0.0\n", + " Uninstalling cloudpickle-3.0.0:\n", + " Successfully uninstalled cloudpickle-3.0.0\n", + " Attempting uninstall: click\n", + " Found existing installation: click 8.1.7\n", + " Uninstalling click-8.1.7:\n", + " Successfully uninstalled click-8.1.7\n", + " Attempting uninstall: partd\n", + " Found existing installation: partd 1.4.1\n", + " Uninstalling partd-1.4.1:\n", + " Successfully uninstalled partd-1.4.1\n", + " Attempting uninstall: importlib-metadata\n", + " Found existing installation: importlib-metadata 7.0.1\n", + " Uninstalling importlib-metadata-7.0.1:\n", + " Successfully uninstalled importlib-metadata-7.0.1\n", + " Attempting uninstall: dask\n", + " Found existing installation: dask 2023.6.0\n", + " Uninstalling dask-2023.6.0:\n", + " Successfully uninstalled dask-2023.6.0\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "tables 3.8.0 requires blosc2~=2.0.0, which is not installed.\n", + "tables 3.8.0 requires cython>=0.29.21, which is not installed.\n", + "conda-repo-cli 1.0.75 requires requests_mock, which is not installed.\n", + "spyder 5.4.3 requires pyqt5<5.16, which is not installed.\n", + "spyder 5.4.3 requires pyqtwebengine<5.16, which is not installed.\n", + "langchain-core 0.2.3 requires packaging<24.0,>=23.2, but you have packaging 24.1 which is incompatible.\n", + "opentelemetry-api 1.25.0 requires importlib-metadata<=7.1,>=6.0, but you have importlib-metadata 8.5.0 which is incompatible.\n", + "s3fs 2021.10.0 requires fsspec==2021.10.0, but you have fsspec 2024.10.0 which is incompatible.\n", + "conda-repo-cli 1.0.75 requires clyent==1.2.1, but you have clyent 1.2.2 which is incompatible.\n", + "conda-repo-cli 1.0.75 requires python-dateutil==2.8.2, but you have python-dateutil 2.9.0.post0 which is incompatible.\n", + "conda-repo-cli 1.0.75 requires PyYAML==6.0.1, but you have pyyaml 6.0.2 which is incompatible.\n", + "conda-repo-cli 1.0.75 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n", + "datasets 3.0.1 requires fsspec[http]<=2024.6.1,>=2023.1.0, but you have fsspec 2024.10.0 which is incompatible.\n", + "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n", + "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n", + "fastapi 0.111.0 requires starlette<0.38.0,>=0.37.2, but you have starlette 0.27.0 which is incompatible.\n", + "python-lsp-black 1.2.1 requires black>=22.3.0, but you have black 0.0 which is incompatible.\n", + "anaconda-cloud-auth 0.1.3 requires pydantic<2.0, but you have pydantic 2.9.2 which is incompatible.\n", + "distributed 2023.6.0 requires dask==2023.6.0, but you have dask 2024.10.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed click-8.1.7 cloudpickle-3.1.0 dask-2024.10.0 fsspec-2024.10.0 importlib-metadata-8.5.0 locket-1.0.0 packaging-24.1 partd-1.4.2 pyyaml-6.0.2 toolz-1.0.0 zipp-3.20.2\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade --force-reinstall dask" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ab517bc7-c8ee-47c8-9f01-0cd6985c2309", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sneha/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n", + " from pandas.core import (\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sktime.annotation.clust import ClusterSegmenter\n", + "from sktime.clustering.k_means import TimeSeriesKMeans\n", + "from sktime.datasets import load_gunpoint\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "02ac38b1-afae-4a27-b0e5-b120215bc16c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['BaseSeriesAnnotator', 'ClusterSegmenter', 'KMeans', '__all__', '__author__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'clone', 'pd']\n" + ] + } + ], + "source": [ + "import sktime\n", + "print(dir(sktime.annotation.clust))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1df96f55-714e-4534-aed4-63763b1f37c8", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('2024-10-24_500030__timeInter5mins_dayStart4hrs_minCarb10g_2hrMealW.csv', parse_dates=['date'], index_col='date')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "90383e48-c1bc-4f7f-bc96-381f1c76a81b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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date
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2024-07-01 00:20:00-05:0094.0NaNNaNNaNNaNNaNNaNNaN2024-06-30
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" + ], + "text/plain": [ + " bgl msg_type affects_fob affects_iob dose_units \\\n", + "date \n", + "2024-07-01 00:00:00-05:00 98.0 NaN NaN NaN NaN \n", + "2024-07-01 00:05:00-05:00 100.0 NaN NaN NaN NaN \n", + "2024-07-01 00:10:00-05:00 98.0 NaN NaN NaN NaN \n", + "2024-07-01 00:15:00-05:00 94.0 NaN NaN NaN NaN \n", + "2024-07-01 00:20:00-05:00 94.0 NaN NaN NaN NaN \n", + "\n", + " food_g food_glycemic_index food_g_keep \\\n", + "date \n", + "2024-07-01 00:00:00-05:00 NaN NaN NaN \n", + "2024-07-01 00:05:00-05:00 NaN NaN NaN \n", + "2024-07-01 00:10:00-05:00 NaN NaN NaN \n", + "2024-07-01 00:15:00-05:00 NaN NaN NaN \n", + "2024-07-01 00:20:00-05:00 NaN NaN NaN \n", + "\n", + " day_start_shift \n", + "date \n", + "2024-07-01 00:00:00-05:00 2024-06-30 \n", + "2024-07-01 00:05:00-05:00 2024-06-30 \n", + "2024-07-01 00:10:00-05:00 2024-06-30 \n", + "2024-07-01 00:15:00-05:00 2024-06-30 \n", + "2024-07-01 00:20:00-05:00 2024-06-30 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "057bcaad-ffba-4161-a01a-8de30b95096b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bgl 905\n", + "msg_type 25590\n", + "affects_fob 25672\n", + "affects_iob 25672\n", + "dose_units 25886\n", + "food_g 18882\n", + "food_glycemic_index 26335\n", + "food_g_keep 25957\n", + "day_start_shift 0\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6a6fb75a-aa45-40ac-ba7a-172866bc46a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(26496, 9)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4581cb81-1368-4980-850e-ea28c702173e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25591, 1)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sneha/anaconda3/lib/python3.11/site-packages/dask/dataframe/__init__.py:42: FutureWarning: \n", + "Dask dataframe query planning is disabled because dask-expr is not installed.\n", + "\n", + "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n", + "This will raise in a future version.\n", + "\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 12\u001b[0m\n\u001b[1;32m 10\u001b[0m clusterer \u001b[38;5;241m=\u001b[39m TimeSeriesKMeans()\n\u001b[1;32m 11\u001b[0m segmenter \u001b[38;5;241m=\u001b[39m ClusterSegmenter(clusterer)\n\u001b[0;32m---> 12\u001b[0m segmenter\u001b[38;5;241m.\u001b[39mfit(X)\n\u001b[1;32m 13\u001b[0m segment_labels \u001b[38;5;241m=\u001b[39m segmenter\u001b[38;5;241m.\u001b[39mpredict(X)\n\u001b[1;32m 14\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msegment_label\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m segment_labels\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sktime/annotation/base/_base.py:148\u001b[0m, in \u001b[0;36mBaseSeriesAnnotator.fit\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_Y \u001b[38;5;241m=\u001b[39m Y\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# fkiraly: insert checks/conversions here, after PR #1012 I suggest\u001b[39;00m\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit(X\u001b[38;5;241m=\u001b[39mX, Y\u001b[38;5;241m=\u001b[39mY)\n\u001b[1;32m 150\u001b[0m \u001b[38;5;66;03m# this should happen last\u001b[39;00m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_fitted \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sktime/annotation/clust.py:79\u001b[0m, in \u001b[0;36mClusterSegmenter._fit\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m 77\u001b[0m cloned_clusterer \u001b[38;5;241m=\u001b[39m clone(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clusterer)\n\u001b[1;32m 78\u001b[0m X_flat \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m---> 79\u001b[0m cloned_clusterer\u001b[38;5;241m.\u001b[39mfit(X_flat)\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clusterer_ \u001b[38;5;241m=\u001b[39m cloned_clusterer\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sktime/clustering/base.py:142\u001b[0m, in \u001b[0;36mBaseClusterer.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 138\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself.n_jobs must be set if capability:multithreading is True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 139\u001b[0m )\n\u001b[1;32m 141\u001b[0m start \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mround\u001b[39m(time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m))\n\u001b[0;32m--> 142\u001b[0m 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\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_init):\n\u001b[0;32m--> 343\u001b[0m labels, centers, inertia, n_iters \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_one_init(X)\n\u001b[1;32m 344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inertia \u001b[38;5;241m<\u001b[39m best_inertia:\n\u001b[1;32m 345\u001b[0m best_centers \u001b[38;5;241m=\u001b[39m centers\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sktime/clustering/partitioning/_lloyds.py:413\u001b[0m, in \u001b[0;36mBaseTimeSeriesLloyds._fit_one_init\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 411\u001b[0m old_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_iter):\n\u001b[0;32m--> 413\u001b[0m labels, inertia 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442\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[np\u001b[38;5;241m.\u001b[39mndarray, \u001b[38;5;28mfloat\u001b[39m]:\n\u001b[1;32m 443\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Assign each instance to a cluster.\u001b[39;00m\n\u001b[1;32m 444\u001b[0m \n\u001b[1;32m 445\u001b[0m \u001b[38;5;124;03m This is done by computing the distance between each instance and\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[38;5;124;03m the assigned clusters.\u001b[39;00m\n\u001b[1;32m 464\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 465\u001b[0m pairwise \u001b[38;5;241m=\u001b[39m pairwise_distance(\n\u001b[1;32m 466\u001b[0m X, cluster_centres, metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetric, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distance_params\n\u001b[1;32m 467\u001b[0m )\n\u001b[1;32m 468\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pairwise\u001b[38;5;241m.\u001b[39margmin(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m), pairwise\u001b[38;5;241m.\u001b[39mmin(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39msum()\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sktime/distances/_distance.py:2155\u001b[0m, in \u001b[0;36mpairwise_distance\u001b[0;34m(x, y, metric, **kwargs)\u001b[0m\n\u001b[1;32m 2151\u001b[0m symmetric \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray_equal(_x, _y)\n\u001b[1;32m 2152\u001b[0m _metric_callable \u001b[38;5;241m=\u001b[39m _resolve_metric_to_factory(\n\u001b[1;32m 2153\u001b[0m metric, _x[\u001b[38;5;241m0\u001b[39m], _y[\u001b[38;5;241m0\u001b[39m], _METRIC_INFOS, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m 2154\u001b[0m )\n\u001b[0;32m-> 2155\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _compute_pairwise_distance(_x, _y, symmetric, _metric_callable)\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sktime/distances/_numba_utils.py:87\u001b[0m, in \u001b[0;36m_compute_pairwise_distance\u001b[0;34m(x, y, symmetric, distance_callable)\u001b[0m\n\u001b[1;32m 85\u001b[0m pairwise_matrix[i, j] \u001b[38;5;241m=\u001b[39m pairwise_matrix[j, i]\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 87\u001b[0m pairwise_matrix[i, j] \u001b[38;5;241m=\u001b[39m distance_callable(curr_x, _y[j])\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pairwise_matrix\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "X = df[['bgl']].dropna()\n", + "data = X\n", + "print(X.shape)\n", + "\n", + "# Reshape X to fit the expected input shape for `TimeSeriesKMeans` if necessary:\n", + "# If you have `n_timesteps` > 1 (like in a multivariate time series), you might reshape like below:\n", + "# shape -> (n_samples, n_timesteps, n_features)\n", + "#X_reshaped = X.reshape(len(announce_meal_data), 1, 1)\n", + "\n", + "clusterer = TimeSeriesKMeans()\n", + "segmenter = ClusterSegmenter(clusterer)\n", + "segmenter.fit(X)\n", + "segment_labels = segmenter.predict(X)\n", + "data['segment_label'] = segment_labels" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6c2c4f06-3a86-417c-b629-3d87b74925c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " bgl segment_label\n", + "2024-07-01 00:00:00-05:00 98.0 4\n", + "2024-07-01 00:05:00-05:00 100.0 6\n", + "2024-07-01 00:10:00-05:00 98.0 4\n", + "2024-07-01 00:15:00-05:00 94.0 4\n", + "2024-07-01 00:20:00-05:00 94.0 4\n", + "... ... ...\n", + "2024-09-30 23:35:00-05:00 95.0 4\n", + "2024-09-30 23:40:00-05:00 87.0 4\n", + "2024-09-30 23:45:00-05:00 86.0 4\n", + "2024-09-30 23:50:00-05:00 89.0 4\n", + "2024-09-30 23:55:00-05:00 88.0 4\n", + "\n", + "[25591 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a0dae23d-f247-449b-98a0-80371b8451be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 6, 7, 1, 3, 0])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['segment_label'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9844fb1c-f4d7-4583-b7f3-4c55d372b1ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['bgl', 'segment_label'], dtype='object')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0ded13ae-64f8-424b-8e46-071044cce1d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2024-07-01 00:00:00-05:00', '2024-07-01 00:05:00-05:00',\n", + " '2024-07-01 00:10:00-05:00', '2024-07-01 00:15:00-05:00',\n", + " '2024-07-01 00:20:00-05:00', '2024-07-01 00:25:00-05:00',\n", + " '2024-07-01 00:30:00-05:00', '2024-07-01 00:35:00-05:00',\n", + " '2024-07-01 00:40:00-05:00', '2024-07-01 00:45:00-05:00',\n", + " ...\n", + " '2024-09-30 23:10:00-05:00', '2024-09-30 23:15:00-05:00',\n", + " '2024-09-30 23:20:00-05:00', '2024-09-30 23:25:00-05:00',\n", + " '2024-09-30 23:30:00-05:00', '2024-09-30 23:35:00-05:00',\n", + " '2024-09-30 23:40:00-05:00', '2024-09-30 23:45:00-05:00',\n", + " '2024-09-30 23:50:00-05:00', '2024-09-30 23:55:00-05:00'],\n", + " dtype='datetime64[ns, UTC-05:00]', name='date', length=26496, freq=None)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.index" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "26b78b10-9224-4c12-ad93-ae2c413deb60", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", + "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", + "A typical example is when you are setting values in a column of a DataFrame, like:\n", + "\n", + "df[\"col\"][row_indexer] = value\n", + "\n", + "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['label'][i:i+24] = 1\n", + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2319773548.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "date\n", + "2024-07-01 00:00:00-05:00 0\n", + "2024-07-01 00:05:00-05:00 0\n", + "2024-07-01 00:10:00-05:00 0\n", + "2024-07-01 00:15:00-05:00 0\n", + "2024-07-01 00:20:00-05:00 0\n", + " ..\n", + "2024-09-30 23:35:00-05:00 0\n", + "2024-09-30 23:40:00-05:00 0\n", + "2024-09-30 23:45:00-05:00 0\n", + "2024-09-30 23:50:00-05:00 0\n", + "2024-09-30 23:55:00-05:00 0\n", + "Name: label, Length: 26496, dtype: int64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sneha/anaconda3/lib/python3.11/site-packages/dask/dataframe/__init__.py:42: FutureWarning: \n", + "Dask dataframe query planning is disabled because dask-expr is not installed.\n", + "\n", + "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n", + "This will raise in a future version.\n", + "\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " bgl segment_labels\n", + "2024-07-01 00:00:00-05:00 98.0 4\n", + "2024-07-01 00:05:00-05:00 100.0 4\n", + "2024-07-01 00:10:00-05:00 98.0 4\n", + "2024-07-01 00:15:00-05:00 94.0 4\n", + "2024-07-01 00:20:00-05:00 94.0 4\n" + ] + } + ], + "source": [ + "df['label'] = 0\n", + "for i in range(df.shape[0]):\n", + " if df['msg_type'][i]=='ANNOUNCE_MEAL':\n", + " # Mark the next 24 values after 'ANNOUNCE_MEAL' as label 1\n", + " if i + 24 < df.shape[0]:\n", + " df['label'][i:i+24] = 1\n", + "print(df['label'])\n", + "\n", + "# Extract the relevant features for clustering\n", + "X = df[['bgl']].dropna()\n", + "\n", + "# Initialize the clustering model and the segmenter\n", + "clusterer = TimeSeriesKMeans()\n", + "segmenter = ClusterSegmenter(clusterer)\n", + "\n", + "# Fit the model and predict clusters on the entire dataset\n", + "segmenter.fit(X, df['label'])\n", + "segment_labels = segmenter.predict(X)\n", + "\n", + "# Add the segment labels back to the DataFrame\n", + "X['segment_labels'] = segment_labels\n", + "\n", + "# Output the result\n", + "print(X.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ca57ffc6-2f65-46e9-8ca0-1ee1fceb7b81", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/k3/_3xvvcs113q2kdktg1c2q41r0000gn/T/ipykernel_72139/2976024630.py:23: UserWarning: The figure layout has changed to tight\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "segmentation = X['segment_labels']\n", + "# plot the time series\n", + "plt.plot(X['bgl'].index,X['bgl'].values.flatten(), label=\"bgl\")\n", + "\n", + "# plot segmentation\n", + "unique_segments = np.unique(segmentation)\n", + "colors = plt.cm.rainbow(np.linspace(0, 1, len(unique_segments)))\n", + "\n", + "for segment, color in zip(unique_segments, colors):\n", + " mask = segmentation == segment\n", + " plt.axvspan(X['bgl'].index[mask].min(), X['bgl'].index[mask].max(), \n", + " alpha=0.2, color=color, label=f'Segment {segment}')\n", + "\n", + "plt.title(f'bgl Over Time with Cluster Segmentation')\n", + "plt.xlabel('Time')\n", + "plt.ylabel(\"bgl\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddbf47e1-a7eb-436c-87f6-07cfe0aec616", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}