From 1d9aec229aeb5e326429e52f6ccab15b469dfd1d Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 28 Jan 2020 18:31:30 -0500 Subject: [PATCH 01/57] Add README to project --- projects/iCGM-test-matrix/README.md | 61 +++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 projects/iCGM-test-matrix/README.md diff --git a/projects/iCGM-test-matrix/README.md b/projects/iCGM-test-matrix/README.md new file mode 100644 index 00000000..b027c094 --- /dev/null +++ b/projects/iCGM-test-matrix/README.md @@ -0,0 +1,61 @@ +# iCGM Test Matrix + +This project finds snapshots of data to explore the sensitivity of the Loop Algorithm over the entire range of BG values. Each snapshot is a 48-hour window of data. In the center of this data is an "evaluation point" which falls into one of the 9 conditions detailed in the table below. + +The primary goal of this project is to find 9 snapshots for each condition from 100 datasets in the Tidepool Big Data Donation Project for a total of 900 snapshots. + +The secondary goal is to calculate the distribution of all 9 conditions within the entire TBDDP donor population. + +## Condition Table + +There are 3 value conditions and 3 rate of change conditions with a combined 9 unique iCGM states that any iCGM data point can exist within as shown in the table below. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Median BG value of the previous 6 BG values
(mg/dL)
[40-70)[70-180](180-400]
Rate of change of the
previous 3 BG values
(mg/dL/min)		< -1[40-70)
&
< -1 		[70-180]
&
< -1 		(180-400]
&
< -1
[-1 to 1][40-70)
&
[-1 to 1]		[70-180]
&
[-1 to 1]		(180-400]
&
[-1 to 1]
> 1[40-70)
&
> 1		[70-180]
&
> 1		(180-400]
&
> 1
+ + + +## Finding Snapshots Algorithm + +The algorithm for finding a snapshot is as follows + +- Fit the CGM trace to a 5-minute time series to uncover gaps +- Calculate the median mg/dL value with a 30-minute (6 cgm points) rolling window +- Calculate the slope in mg/dL/min with a 15-minute (3 cgm points) rolling window +- Calculate the max gap size of the cgm trace in a 48 hour *centered* rolling window (where the evaluation point is in the center) +- Determine whether a cgm point can be used as an evaluation point in a snapshot and label which condition it will fit into +- Randomly select one snapshot for each condition that does not overlap with any other snapshot + - This is done by —? \ No newline at end of file From 7587d8199b05e1fdf7f16e08f4656b6a12bba4b6 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 29 Jan 2020 16:58:32 -0500 Subject: [PATCH 02/57] Add iCGM Condition Finder script skeleton --- .../iCGM-test-matrix/icgm_condition_finder.py | 152 ++++++++++++++++++ 1 file changed, 152 insertions(+) create mode 100644 projects/iCGM-test-matrix/icgm_condition_finder.py diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py new file mode 100644 index 00000000..eda50e54 --- /dev/null +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +iCGM Condition Finder +===================== +:File: icgm_condition_finder.py +:Description: Finds and returns counts and locations of all 9 iCGM conditions +:Version: 0.0.1 +:Created: 2020-01-29 +:Authors: Jason Meno (jam) +:Dependencies: A .csv containing Tidepool CGM device data +:License: BSD-2-Clause +""" + +# %% Library Imports +import pandas as pd +import numpy as np + +# %% Functions + + +def import_data(file_path): + """Imports a dataset""" + + data = pd.read_csv(file_path, low_memory=False) + + return data + + +def create_5min_contiguous_df(cgm_df): + """ + Fit the CGM trace to a contiguous 5-minute time series to uncover gaps + """ + + cgm_df["rounded_time"] = pd.to_datetime(cgm_df.time).dt.ceil(freq="5min") + first_timestamp = cgm_df["rounded_time"].min() + last_timestamp = cgm_df["rounded_time"].max() + + contiguous_ts = pd.date_range(first_timestamp, last_timestamp, freq="5min") + contiguous_df = pd.DataFrame(contiguous_ts, columns=["rounded_time"]) + + contiguous_df = pd.merge(contiguous_df, + cgm_df, + how="left", + on="rounded_time" + ) + + return contiguous_df + + +def rolling_30min_median(contiguous_df): + """ + Calculate the median mg/dL value with a 30-minute (6 points) rolling window + """ + + contiguous_df["rolling_30min_median"] = \ + contiguous_df["value"].rolling(window=6, min_periods=6).median() + + return contiguous_df + + +def get_slope(y): + """ + Returns the least squares regression slope given a contiguous sequence y + """ + + # From SciPy lstsq usage Example Guide: + # Rewrite y = mx + c equation as y = Ap + # Where A = [[x 1]] and p = [[m], [c]] + x = np.arange(len(y)) + A = np.vstack([x, np.ones(len(x))]).T + m, c = np.linalg.lstsq(A, y, rcond=None)[0] + + return m + + +def rolling_15min_slope(contiguous_df): + """ + Calculate the slope in mg/dL/min with a 15-minute (3 points) rolling window + """ + + contiguous_df["rolling_15min_slope"] = \ + contiguous_df["value"].rolling(window=3, min_periods=3).apply( + lambda x: get_slope(x), raw=True) + + return contiguous_df + + +def rle(inarray): + """ run length encoding. Partial credit to R rle function. + Multi datatype arrays catered for including non Numpy + returns: tuple (runlengths, startpositions, values) + + This function is useful for finding the size of gaps in the data + + Returns 3 arrays: + - The lengths of each run + - The location of the start of each run + - The values contained in each run + """ + ia = np.asarray(inarray) # force numpy + n = len(ia) + if n == 0: + return (None, None, None) + else: + y = np.array(ia[1:] != ia[:-1]) # pairwise unequal (string safe) + i = np.append(np.where(y), n - 1) # include last element position + z = np.diff(np.append(-1, i)) # run lengths + p = np.cumsum(np.append(0, z))[:-1] # positions + + return(z, p, ia[i]) + + +def get_max_gap_size(y): + """Give a series of binaries where True = Data Gap, find the largest gap by + using run length encoding (rle) + """ + + rle_results = rle(y) + gaps = rle_results[0][rle_results[2]] + + if len(gaps) > 0: + max_gap_size = max(gaps) + else: + max_gap_size = 0 + + return max_gap_size + + +def rolling_48hour_max_gap(cgm_df): + """ + Calculate the max gap size of the cgm trace in a 48 hour centered rolling + window (where the evaluation point is in the center) + """ + + contiguous_df["rolling_48hour_max_gap"] = \ + contiguous_df["value"].rolling( + window=288*2, + min_periods=1, + center=True).apply(lambda x: + get_max_gap_size(np.isnan(x)), + raw=True) + + return rolling_max_gap_df + + +def main(): + """Main function calls" + +if __name__ == "__main__": + main() + From 9d5c09564c6ba7f7a66cf3554d40a0407bf898b6 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 29 Jan 2020 18:47:52 -0500 Subject: [PATCH 03/57] Add condition labeling feature --- .../iCGM-test-matrix/icgm_condition_finder.py | 105 +++++++++++++++++- 1 file changed, 100 insertions(+), 5 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index eda50e54..b4aca808 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -4,7 +4,9 @@ iCGM Condition Finder ===================== :File: icgm_condition_finder.py -:Description: Finds and returns counts and locations of all 9 iCGM conditions +:Description: For 9 unique iCGM conditions, the counts and distributions of + each condition is found in a dataset. One sample timestamp from + each condition is marked as an evaluation point for analysis. :Version: 0.0.1 :Created: 2020-01-29 :Authors: Jason Meno (jam) @@ -71,7 +73,7 @@ def get_slope(y): A = np.vstack([x, np.ones(len(x))]).T m, c = np.linalg.lstsq(A, y, rcond=None)[0] - return m + return m/5 def rolling_15min_slope(contiguous_df): @@ -127,7 +129,7 @@ def get_max_gap_size(y): return max_gap_size -def rolling_48hour_max_gap(cgm_df): +def rolling_48hour_max_gap(contiguous_df): """ Calculate the max gap size of the cgm trace in a 48 hour centered rolling window (where the evaluation point is in the center) @@ -141,12 +143,105 @@ def rolling_48hour_max_gap(cgm_df): get_max_gap_size(np.isnan(x)), raw=True) - return rolling_max_gap_df + return contiguous_df + + +def label_conditions(contiguous_df): + """Labels each cgm entry as one of the 9 different conditions + + Condition # || 30min Median BG (mg/dL) & 15min Rate of Change (mg/dL/min) + || + 1 || [40-70) & < -1 + 2 || [70-180] & < -1 + 3 || (180-400] & < -1 + 4 || [40-70) & [-1 to 1] + 5 || [70-180] & [-1 to 1] + 6 || (180-400] & [-1 to 1] + 7 || [40-70) & > 1 + 8 || [70-180] & > 1 + 9 || (180-400] & > 1 + """ + + # Create boolean for each range and rate type + contiguous_df["range1"] = \ + (contiguous_df["rolling_30min_median"] >= 40) & \ + (contiguous_df["rolling_30min_median"] < 70) + + contiguous_df["range2"] = \ + (contiguous_df["rolling_30min_median"] >= 70) & \ + (contiguous_df["rolling_30min_median"] <= 180) + + contiguous_df["range3"] = \ + (contiguous_df["rolling_30min_median"] > 180) & \ + (contiguous_df["rolling_30min_median"] <= 400) + + contiguous_df["rate1"] = \ + contiguous_df["rolling_15min_slope"] < -1 + + contiguous_df["rate2"] = \ + (contiguous_df["rolling_15min_slope"] >= -1) & \ + (contiguous_df["rolling_15min_slope"] <= 1) + + contiguous_df["rate3"] = \ + contiguous_df["rolling_15min_slope"] > 1 + + # Set baseline condition to 0 + contiguous_df["condition"] = 0 + + # Create boolean array for each condition + cond_1 = ((contiguous_df["range1"]) & (contiguous_df["rate1"])) + cond_2 = ((contiguous_df["range2"]) & (contiguous_df["rate1"])) + cond_3 = ((contiguous_df["range3"]) & (contiguous_df["rate1"])) + cond_4 = ((contiguous_df["range1"]) & (contiguous_df["rate2"])) + cond_5 = ((contiguous_df["range2"]) & (contiguous_df["rate2"])) + cond_6 = ((contiguous_df["range3"]) & (contiguous_df["rate2"])) + cond_7 = ((contiguous_df["range1"]) & (contiguous_df["rate3"])) + cond_8 = ((contiguous_df["range2"]) & (contiguous_df["rate3"])) + cond_9 = ((contiguous_df["range3"]) & (contiguous_df["rate3"])) + + # Set each condition value to the boolean locations + contiguous_df.loc[cond_1, "condition"] = 1 + contiguous_df.loc[cond_2, "condition"] = 2 + contiguous_df.loc[cond_3, "condition"] = 3 + contiguous_df.loc[cond_4, "condition"] = 4 + contiguous_df.loc[cond_5, "condition"] = 5 + contiguous_df.loc[cond_6, "condition"] = 6 + contiguous_df.loc[cond_7, "condition"] = 7 + contiguous_df.loc[cond_8, "condition"] = 8 + contiguous_df.loc[cond_9, "condition"] = 9 + + return contiguous_df def main(): - """Main function calls" + """Main function calls""" + file_path = "data.csv" + + data = import_data(file_path) + + # Separate CGM data + cgm_df = data[data.type == 'cbg'].copy() + + # Convert value from mmol/L to mg/dL + cgm_df["value"] = cgm_df["value"] * 18.01559 + + # Fit the CGM trace to a contiguous 5-minute time series to uncover gaps + contiguous_df = create_5min_contiguous_df(cgm_df) + + # Calculate the median BG with a 30-minute rolling window + contiguous_df = rolling_30min_median(contiguous_df) + + # Calculate the slope in mg/dL/min with a 15-minute rolling window + contiguous_df = rolling_15min_slope(contiguous_df) + + # Apply one of the 9 conditions labels to each CGM point + contiguous_df = label_conditions(contiguous_df) + + # Get the max gap size across 48-hour windows + contiguous_df = rolling_48hour_max_gap(contiguous_df) + +# %% if __name__ == "__main__": main() From a74b3a54f6ccb6626547de50323c83b968e74906 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 30 Jan 2020 17:21:20 -0500 Subject: [PATCH 04/57] Add 48-hour snapshot location finder --- .../iCGM-test-matrix/icgm_condition_finder.py | 77 ++++++++++++++++++- 1 file changed, 75 insertions(+), 2 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index b4aca808..94a38df0 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -73,7 +73,7 @@ def get_slope(y): A = np.vstack([x, np.ones(len(x))]).T m, c = np.linalg.lstsq(A, y, rcond=None)[0] - return m/5 + return m/5 # Divide by the 5-min interval to get mg/dL/min resolution def rolling_15min_slope(contiguous_df): @@ -133,6 +133,11 @@ def rolling_48hour_max_gap(contiguous_df): """ Calculate the max gap size of the cgm trace in a 48 hour centered rolling window (where the evaluation point is in the center) + + Note: Centered window behavior keeps the evaluation point on the right side + of the half-way point. + + e.g. In a window of 4: [elem1, elem2, elem3, elem4] - elem3 is the "center" """ contiguous_df["rolling_48hour_max_gap"] = \ @@ -186,6 +191,8 @@ def label_conditions(contiguous_df): contiguous_df["rolling_15min_slope"] > 1 # Set baseline condition to 0 + # Any 0's left over were cgm points + # with a rate/range that could not be calculated contiguous_df["condition"] = 0 # Create boolean array for each condition @@ -213,6 +220,65 @@ def label_conditions(contiguous_df): return contiguous_df +def get_snapshot_locations(contiguous_df): + """ + Gets a snapshot location for each condition from the dataset if available + """ + + # Set window size of snapshot + window_size = 288*2 + + # Create bool of all values with a condition and max gap <= 15min + qualified_bool = \ + (contiguous_df["condition"] > 0) & \ + (contiguous_df["rolling_48hour_max_gap"] <= 3) + + qualified_condition_list = \ + contiguous_df.loc[qualified_bool, "condition"].copy() + + snapshot_locations = np.zeros(9) + + for condition in np.arange(1, 10): + # Get the list of qualified locations for the condition + condition_locations = \ + qualified_condition_list[ + qualified_condition_list == condition + ].index + + # If no condition locations, skip loop to next condition + if(len(condition_locations) == 0): + continue + + # Randomly select one of the condition's locations + random_loc = np.random.choice(condition_locations) + + # Add location to snapshot_locations + snapshot_locations[condition - 1] = random_loc + + # Remove all locations within ± window_size-1 from qualified_locations + # to prevent overlap when selecting the next condition snapshot + overlapping_index = \ + np.arange( + random_loc - (window_size-1), + random_loc + (window_size-1)+1 + ) + + index_to_drop = \ + set(overlapping_index) & set(qualified_condition_list.index) + + qualified_condition_list.drop(index=index_to_drop, inplace=True) + + return snapshot_locations + + +def get_summary_results(contiguous_df, snapshot_locations): + """Create a summary of all results to store in a spreadsheet""" + + results = pd.DataFrame() + + return results + + def main(): """Main function calls""" file_path = "data.csv" @@ -240,8 +306,15 @@ def main(): # Get the max gap size across 48-hour windows contiguous_df = rolling_48hour_max_gap(contiguous_df) + # Get the locations of each 48-hour snapshot + snapshot_locations = get_snapshot_locations(contiguous_df) + + # Summarize results + results = get_summary_results(contiguous_df, snapshot_locations) + + return results + # %% if __name__ == "__main__": main() - From ea2d4e94f839db4eb901bab3ad4611385024932a Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 30 Jan 2020 20:14:16 -0500 Subject: [PATCH 05/57] Add results summary function --- .../iCGM-test-matrix/icgm_condition_finder.py | 74 ++++++++++++------- 1 file changed, 46 insertions(+), 28 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 94a38df0..86828719 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -17,6 +17,7 @@ # %% Library Imports import pandas as pd import numpy as np +import os # %% Functions @@ -168,26 +169,26 @@ def label_conditions(contiguous_df): """ # Create boolean for each range and rate type - contiguous_df["range1"] = \ + contiguous_df["gte40_lt70"] = \ (contiguous_df["rolling_30min_median"] >= 40) & \ (contiguous_df["rolling_30min_median"] < 70) - contiguous_df["range2"] = \ + contiguous_df["gte70_lte180"] = \ (contiguous_df["rolling_30min_median"] >= 70) & \ (contiguous_df["rolling_30min_median"] <= 180) - contiguous_df["range3"] = \ + contiguous_df["gt180_lte400"] = \ (contiguous_df["rolling_30min_median"] > 180) & \ (contiguous_df["rolling_30min_median"] <= 400) - contiguous_df["rate1"] = \ + contiguous_df["lt-1"] = \ contiguous_df["rolling_15min_slope"] < -1 - contiguous_df["rate2"] = \ + contiguous_df["gte-1_lte1"] = \ (contiguous_df["rolling_15min_slope"] >= -1) & \ (contiguous_df["rolling_15min_slope"] <= 1) - contiguous_df["rate3"] = \ + contiguous_df["gt1"] = \ contiguous_df["rolling_15min_slope"] > 1 # Set baseline condition to 0 @@ -196,15 +197,15 @@ def label_conditions(contiguous_df): contiguous_df["condition"] = 0 # Create boolean array for each condition - cond_1 = ((contiguous_df["range1"]) & (contiguous_df["rate1"])) - cond_2 = ((contiguous_df["range2"]) & (contiguous_df["rate1"])) - cond_3 = ((contiguous_df["range3"]) & (contiguous_df["rate1"])) - cond_4 = ((contiguous_df["range1"]) & (contiguous_df["rate2"])) - cond_5 = ((contiguous_df["range2"]) & (contiguous_df["rate2"])) - cond_6 = ((contiguous_df["range3"]) & (contiguous_df["rate2"])) - cond_7 = ((contiguous_df["range1"]) & (contiguous_df["rate3"])) - cond_8 = ((contiguous_df["range2"]) & (contiguous_df["rate3"])) - cond_9 = ((contiguous_df["range3"]) & (contiguous_df["rate3"])) + cond_1 = ((contiguous_df["gte40_lt70"]) & (contiguous_df["lt-1"])) + cond_2 = ((contiguous_df["gte70_lte180"]) & (contiguous_df["lt-1"])) + cond_3 = ((contiguous_df["gt180_lte400"]) & (contiguous_df["lt-1"])) + cond_4 = ((contiguous_df["gte40_lt70"]) & (contiguous_df["gte-1_lte1"])) + cond_5 = ((contiguous_df["gte70_lte180"]) & (contiguous_df["gte-1_lte1"])) + cond_6 = ((contiguous_df["gt180_lte400"]) & (contiguous_df["gte-1_lte1"])) + cond_7 = ((contiguous_df["gte40_lt70"]) & (contiguous_df["gt1"])) + cond_8 = ((contiguous_df["gte70_lte180"]) & (contiguous_df["gt1"])) + cond_9 = ((contiguous_df["gt180_lte400"]) & (contiguous_df["gt1"])) # Set each condition value to the boolean locations contiguous_df.loc[cond_1, "condition"] = 1 @@ -220,9 +221,9 @@ def label_conditions(contiguous_df): return contiguous_df -def get_snapshot_locations(contiguous_df): +def get_evaluation_points(contiguous_df): """ - Gets a snapshot location for each condition from the dataset if available + Gets a evaluation points for each condition from the dataset if available """ # Set window size of snapshot @@ -236,7 +237,7 @@ def get_snapshot_locations(contiguous_df): qualified_condition_list = \ contiguous_df.loc[qualified_bool, "condition"].copy() - snapshot_locations = np.zeros(9) + evaluation_points = np.zeros(9) for condition in np.arange(1, 10): # Get the list of qualified locations for the condition @@ -252,8 +253,8 @@ def get_snapshot_locations(contiguous_df): # Randomly select one of the condition's locations random_loc = np.random.choice(condition_locations) - # Add location to snapshot_locations - snapshot_locations[condition - 1] = random_loc + # Add location to evaluation_points + evaluation_points[condition - 1] = random_loc # Remove all locations within ± window_size-1 from qualified_locations # to prevent overlap when selecting the next condition snapshot @@ -268,25 +269,42 @@ def get_snapshot_locations(contiguous_df): qualified_condition_list.drop(index=index_to_drop, inplace=True) - return snapshot_locations + return evaluation_points -def get_summary_results(contiguous_df, snapshot_locations): +def get_summary_results(file_name, contiguous_df, evaluation_points): """Create a summary of all results to store in a spreadsheet""" - results = pd.DataFrame() + results = pd.DataFrame(index=[0]) + + # Get counts for each rate, range, and condition + results["file_name"] = file_name + results["gte40_lt70"] = contiguous_df["gte40_lt70"].sum() + results["gte70_lte180"] = contiguous_df["gte70_lte180"].sum() + results["gt180_lte400"] = contiguous_df["gt180_lte400"].sum() + results["lt-1"] = contiguous_df["lt-1"].sum() + results["gte-1_lte1"] = contiguous_df["gte-1_lte1"].sum() + results["gt1"] = contiguous_df["gt1"].sum() + + for num in range(10): + results["cond" + str(num)] = sum(contiguous_df["condition"] == num) + + for num in range(9): + # Append snapshot locations to dataframe + results["cond"+str(num)+"_eval_loc"] = evaluation_points[num] return results -def main(): +def main(file_name, file_location): """Main function calls""" - file_path = "data.csv" + + file_path = os.path.join(file_location, file_name) data = import_data(file_path) # Separate CGM data - cgm_df = data[data.type == 'cbg'].copy() + cgm_df = data[data.type == "cbg"].copy() # Convert value from mmol/L to mg/dL cgm_df["value"] = cgm_df["value"] * 18.01559 @@ -307,10 +325,10 @@ def main(): contiguous_df = rolling_48hour_max_gap(contiguous_df) # Get the locations of each 48-hour snapshot - snapshot_locations = get_snapshot_locations(contiguous_df) + evaluation_points = get_evaluation_points(contiguous_df) # Summarize results - results = get_summary_results(contiguous_df, snapshot_locations) + results = get_summary_results(file_name, contiguous_df, evaluation_points) return results From f4113bf759b4883e690f055f3d25a5b19c71d581 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 30 Jan 2020 20:18:57 -0500 Subject: [PATCH 06/57] Update README --- projects/iCGM-test-matrix/README.md | 5 ++--- projects/iCGM-test-matrix/icgm_condition_finder.py | 6 ++++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/projects/iCGM-test-matrix/README.md b/projects/iCGM-test-matrix/README.md index b027c094..74e0b77a 100644 --- a/projects/iCGM-test-matrix/README.md +++ b/projects/iCGM-test-matrix/README.md @@ -55,7 +55,6 @@ The algorithm for finding a snapshot is as follows - Fit the CGM trace to a 5-minute time series to uncover gaps - Calculate the median mg/dL value with a 30-minute (6 cgm points) rolling window - Calculate the slope in mg/dL/min with a 15-minute (3 cgm points) rolling window +- Apply one of the 9 conditions labels to each CGM point - Calculate the max gap size of the cgm trace in a 48 hour *centered* rolling window (where the evaluation point is in the center) -- Determine whether a cgm point can be used as an evaluation point in a snapshot and label which condition it will fit into -- Randomly select one snapshot for each condition that does not overlap with any other snapshot - - This is done by —? \ No newline at end of file +- Randomly select one evaluation point for each condition that does not overlap with any other 48-hour snapshot and has a max gap <= 15 minutes \ No newline at end of file diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 86828719..2f86e8e6 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -324,7 +324,7 @@ def main(file_name, file_location): # Get the max gap size across 48-hour windows contiguous_df = rolling_48hour_max_gap(contiguous_df) - # Get the locations of each 48-hour snapshot + # Get the locations of each evaluation point in a 48-hour snapshot evaluation_points = get_evaluation_points(contiguous_df) # Summarize results @@ -335,4 +335,6 @@ def main(file_name, file_location): # %% if __name__ == "__main__": - main() + file_name = "data.csv" + file_location = "." + main(file_name, file_location) From d166fb100814b40a064c4152dd66f12d88bcea5a Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 30 Jan 2020 20:27:19 -0500 Subject: [PATCH 07/57] Update main function to take in a dataframe --- projects/iCGM-test-matrix/icgm_condition_finder.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 2f86e8e6..1b9dc20f 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -296,13 +296,9 @@ def get_summary_results(file_name, contiguous_df, evaluation_points): return results -def main(file_name, file_location): +def main(data): """Main function calls""" - file_path = os.path.join(file_location, file_name) - - data = import_data(file_path) - # Separate CGM data cgm_df = data[data.type == "cbg"].copy() @@ -337,4 +333,8 @@ def main(file_name, file_location): if __name__ == "__main__": file_name = "data.csv" file_location = "." - main(file_name, file_location) + + file_path = os.path.join(file_location, file_name) + data = import_data(file_path) + + main(data) From f8585fe62547202e7e7baafba2c93126e3dd3203 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 30 Jan 2020 20:33:19 -0500 Subject: [PATCH 08/57] Add file_name to main condition finder function --- projects/iCGM-test-matrix/icgm_condition_finder.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 1b9dc20f..147009d9 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -296,7 +296,7 @@ def get_summary_results(file_name, contiguous_df, evaluation_points): return results -def main(data): +def main(data, file_name): """Main function calls""" # Separate CGM data @@ -337,4 +337,4 @@ def main(data): file_path = os.path.join(file_location, file_name) data = import_data(file_path) - main(data) + main(data, file_name) From bd1c4412611f515cf78962a1ecc843fdaa21ee40 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 30 Jan 2020 20:47:28 -0500 Subject: [PATCH 09/57] Add batch processing script for condition finder --- .../batch-icgm-condition-stats.py | 109 ++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 projects/iCGM-test-matrix/batch-icgm-condition-stats.py diff --git a/projects/iCGM-test-matrix/batch-icgm-condition-stats.py b/projects/iCGM-test-matrix/batch-icgm-condition-stats.py new file mode 100644 index 00000000..afb037e1 --- /dev/null +++ b/projects/iCGM-test-matrix/batch-icgm-condition-stats.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Batch iCGM Condition Stats +=========================== +:File: batch-icgm-condition-stats.py +:Description: A batch processing script for the icgm_condition_finder.py module + Given a folder of Tidepool datasets, get a summary of all results + using the condition finder script. +:Version: 0.0.1 +:Created: 2020-01-30 +:Authors: Jason Meno (jam) +:Dependencies: A folder of .csvs containing Tidepool CGM device data +:License: BSD-2-Clause +""" +import pandas as pd +import icgm_condition_finder +from multiprocessing.pool import ThreadPool +import time +import datetime as dt +import os +import time +from multiprocessing import Pool, cpu_count, get_context +import traceback +import sys +#%% + +data_location = "data_folder/" +file_list = os.listdir(data_location) + +# Filter only files with .csv in their name (includes .csv.gz files) +file_list = [filename for filename in file_list if '.csv' in filename] + +#%% + +def get_icgm_condition_stats(file_name, data_location, user_loc): + + file_path = data_location + file_name + print(str(user_loc) + " STARTING") + #if((user_loc % 100 == 0) & (user_loc > 99)): + #print(user_loc) + log_file = open('batch-icgm-condition-stats-log.txt', 'a') + log_file.write(str(user_loc)+"\n") + log_file.close() + + import_status = "" + try: + df = pd.read_csv(file_path, low_memory=False) + import_status = "Succesfully Imported" + + except Exception as e: + df = pd.DataFrame() + print("Failed to import: " + file_path) + import_status = "Failed to Import - " + str(e) + + results = icgm_condition_finder.main(df, file_name) + results['pandas_import_status'] = import_status + print(str(user_loc) + " COMPLETE!") + return results + + +# %% +if __name__ == "__main__": + # Start Pipeline + start_time = time.time() + + # Startup CPU multiprocessing pool + pool = Pool(int(cpu_count())) + + pool_array = [pool.apply_async( + get_icgm_condition_stats, + args=[file_list[user_loc], + data_location, + user_loc + ] + ) for user_loc in range(len(file_list))] + + pool.close() + pool.join() + + end_time = time.time() + elapsed_minutes = (end_time - start_time)/60 + elapsed_time_message = "Batch iCGM Condition Stats completed in: " + \ + str(elapsed_minutes) + " minutes\n" + print(elapsed_time_message) + log_file = open('batch-icgm-condition-stats-log.txt', 'a') + log_file.write(str(elapsed_time_message)+"\n") + log_file.close() + + # %% Append results of each pool into an array + + results_array = [] + + for result_loc in range(len(pool_array)): + try: + results_array.append(pool_array[result_loc].get()) + except Exception as e: + print('Failed to get results! ' + str(e)) + exception_text = traceback.format_exception(*sys.exc_info()) + print('\nException Text:\n') + for text_string in exception_text: + print(text_string) + + # %% + # Convert results into dataframe + icgm_condition_summary_df = pd.concat(results_array, sort=False) + today_timestamp = dt.datetime.now().strftime("%Y-%m-%d") + results_export_filename = 'PHI-batch-icgm-condition-stats-' + today_timestamp + '.csv' + icgm_condition_summary_df.to_csv(results_export_filename, index=False) From 4b88681518b1d021f41f7731522020bb82c7ce82 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 31 Jan 2020 14:12:49 -0500 Subject: [PATCH 10/57] Update evaluation points to use id instead of index --- projects/iCGM-test-matrix/icgm_condition_finder.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 147009d9..b242754f 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -237,7 +237,7 @@ def get_evaluation_points(contiguous_df): qualified_condition_list = \ contiguous_df.loc[qualified_bool, "condition"].copy() - evaluation_points = np.zeros(9) + evaluation_points = [""]*9 for condition in np.arange(1, 10): # Get the list of qualified locations for the condition @@ -253,8 +253,8 @@ def get_evaluation_points(contiguous_df): # Randomly select one of the condition's locations random_loc = np.random.choice(condition_locations) - # Add location to evaluation_points - evaluation_points[condition - 1] = random_loc + # Add location's id to evaluation_points + evaluation_points[condition - 1] = contiguous_df.loc[random_loc, "id"] # Remove all locations within ± window_size-1 from qualified_locations # to prevent overlap when selecting the next condition snapshot From 4b31b288dae880d77e198052413fb3184145257a Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 31 Jan 2020 14:13:26 -0500 Subject: [PATCH 11/57] Add rounded CGM point deduplication --- .../iCGM-test-matrix/icgm_condition_finder.py | 95 ++++++++++++++++++- 1 file changed, 92 insertions(+), 3 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index b242754f..2a66e28b 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -30,6 +30,51 @@ def import_data(file_path): return data +def add_uploadDateTime(df): + r"""Adds an "uploadTime" column to the dataframe and corrects missing + upload times to records from healthkit data + """ + + if "upload" in df.type.unique(): + uploadTimes = pd.DataFrame( + df[df.type == "upload"].groupby("uploadId").time.describe()["top"] + ) + else: + uploadTimes = pd.DataFrame(columns=["top"]) + # if an upload does not have an upload date, then add one + # NOTE: this is a new fix introduced with healthkit data...we now have + # data that does not have an upload record + unique_uploadIds = set(df["uploadId"].unique()) + unique_uploadRecords = set( + df.loc[df["type"] == "upload", "uploadId"].unique() + ) + uploadIds_missing_uploadRecords = unique_uploadIds - unique_uploadRecords + + uploadTimes.reset_index(inplace=True) + uploadTimes.rename( + columns={ + "top": "uploadTime", + "index": "uploadId" + }, + inplace=True + ) + # New method + missing_uploads_df = \ + df.loc[df['uploadId'].isin(uploadIds_missing_uploadRecords), + ['uploadId', 'time']] + last_upload_time = missing_uploads_df.groupby('uploadId').time.max() + last_upload_time = pd.DataFrame(last_upload_time).reset_index() + last_upload_time.columns = ["uploadId", "uploadTime"] + uploadTimes = pd.concat([uploadTimes, + last_upload_time]).reset_index(drop=True) + + df = pd.merge(df, uploadTimes, how='left', on='uploadId') + df["uploadTime"] = pd.to_datetime(df["uploadTime"], utc=True) + df["uploadTime"] = df["uploadTime"].dt.tz_localize(None) + + return df + + def create_5min_contiguous_df(cgm_df): """ Fit the CGM trace to a contiguous 5-minute time series to uncover gaps @@ -51,6 +96,36 @@ def create_5min_contiguous_df(cgm_df): return contiguous_df +def remove_rounded_CGM_duplicates(cgm_df): + """Removes CGM duplicates, keeping the entries with the most recent + uploadTime + """ + if "rounded_time" in cgm_df: + # Sort first by most recent rounded_time and then by newest uploadTime + cgm_df.sort_values(by=["rounded_time", "uploadTime"], + ascending=[False, False], + inplace=True) + + # Safety check for null times + dfIsNull = cgm_df[cgm_df["rounded_time"].isnull()] + dfNotNull = cgm_df[cgm_df["rounded_time"].notnull()] + + nBefore = len(dfNotNull) + + # Drop duplicates, keeping the first (most recent upload) entry + # .duplicated() defaults to keeping the first duplicate + dfNotNull = dfNotNull.loc[~(dfNotNull["rounded_time"].duplicated())] + dfNotNull = dfNotNull.reset_index(drop=True) + nRoundedTimeDuplicatesRemoved = nBefore - len(dfNotNull) + + cgm_df = pd.concat([dfIsNull, dfNotNull]) + cgm_df.sort_values(by=["rounded_time"], ascending=True, inplace=True) + else: + nRoundedTimeDuplicatesRemoved = 0 + + return cgm_df, nRoundedTimeDuplicatesRemoved + + def rolling_30min_median(contiguous_df): """ Calculate the median mg/dL value with a 30-minute (6 points) rolling window @@ -272,13 +347,17 @@ def get_evaluation_points(contiguous_df): return evaluation_points -def get_summary_results(file_name, contiguous_df, evaluation_points): +def get_summary_results(file_name, + nRoundedTimeDuplicatesRemoved, + contiguous_df, + evaluation_points): """Create a summary of all results to store in a spreadsheet""" results = pd.DataFrame(index=[0]) # Get counts for each rate, range, and condition results["file_name"] = file_name + results["nRoundedTimeDuplicatesRemoved"] = nRoundedTimeDuplicatesRemoved results["gte40_lt70"] = contiguous_df["gte40_lt70"].sum() results["gte70_lte180"] = contiguous_df["gte70_lte180"].sum() results["gt180_lte400"] = contiguous_df["gt180_lte400"].sum() @@ -299,6 +378,9 @@ def get_summary_results(file_name, contiguous_df, evaluation_points): def main(data, file_name): """Main function calls""" + # Add the upload data time to each record (used for deduplicating) + data = add_uploadDateTime(data) + # Separate CGM data cgm_df = data[data.type == "cbg"].copy() @@ -308,6 +390,10 @@ def main(data, file_name): # Fit the CGM trace to a contiguous 5-minute time series to uncover gaps contiguous_df = create_5min_contiguous_df(cgm_df) + # Remove CGM duplicates + cgm_df, nRoundedTimeDuplicatesRemoved = \ + remove_rounded_CGM_duplicates(cgm_df) + # Calculate the median BG with a 30-minute rolling window contiguous_df = rolling_30min_median(contiguous_df) @@ -324,7 +410,10 @@ def main(data, file_name): evaluation_points = get_evaluation_points(contiguous_df) # Summarize results - results = get_summary_results(file_name, contiguous_df, evaluation_points) + results = get_summary_results(file_name, + nRoundedTimeDuplicatesRemoved, + contiguous_df, + evaluation_points) return results @@ -337,4 +426,4 @@ def main(data, file_name): file_path = os.path.join(file_location, file_name) data = import_data(file_path) - main(data, file_name) + results = main(data, file_name) From 171f713c6170615388fd3f8fa33b17862d6122ba Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 31 Jan 2020 14:29:38 -0500 Subject: [PATCH 12/57] Add metric for the percent of CGM points that are duplicated --- .../iCGM-test-matrix/icgm_condition_finder.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 2a66e28b..299d9e55 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -100,6 +100,11 @@ def remove_rounded_CGM_duplicates(cgm_df): """Removes CGM duplicates, keeping the entries with the most recent uploadTime """ + + # Set Defaults + nRoundedTimeDuplicatesRemoved = 0 + cgmPercentDuplicated = 0 + if "rounded_time" in cgm_df: # Sort first by most recent rounded_time and then by newest uploadTime cgm_df.sort_values(by=["rounded_time", "uploadTime"], @@ -118,12 +123,13 @@ def remove_rounded_CGM_duplicates(cgm_df): dfNotNull = dfNotNull.reset_index(drop=True) nRoundedTimeDuplicatesRemoved = nBefore - len(dfNotNull) + if(nRoundedTimeDuplicatesRemoved > 0): + cgmPercentDuplicated = nRoundedTimeDuplicatesRemoved/nBefore + cgm_df = pd.concat([dfIsNull, dfNotNull]) cgm_df.sort_values(by=["rounded_time"], ascending=True, inplace=True) - else: - nRoundedTimeDuplicatesRemoved = 0 - return cgm_df, nRoundedTimeDuplicatesRemoved + return cgm_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated def rolling_30min_median(contiguous_df): @@ -349,6 +355,7 @@ def get_evaluation_points(contiguous_df): def get_summary_results(file_name, nRoundedTimeDuplicatesRemoved, + cgmPercentDuplicated, contiguous_df, evaluation_points): """Create a summary of all results to store in a spreadsheet""" @@ -358,6 +365,7 @@ def get_summary_results(file_name, # Get counts for each rate, range, and condition results["file_name"] = file_name results["nRoundedTimeDuplicatesRemoved"] = nRoundedTimeDuplicatesRemoved + results["cgmPercentDuplicated"] = cgmPercentDuplicated results["gte40_lt70"] = contiguous_df["gte40_lt70"].sum() results["gte70_lte180"] = contiguous_df["gte70_lte180"].sum() results["gt180_lte400"] = contiguous_df["gt180_lte400"].sum() @@ -391,7 +399,7 @@ def main(data, file_name): contiguous_df = create_5min_contiguous_df(cgm_df) # Remove CGM duplicates - cgm_df, nRoundedTimeDuplicatesRemoved = \ + cgm_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated = \ remove_rounded_CGM_duplicates(cgm_df) # Calculate the median BG with a 30-minute rolling window @@ -412,6 +420,7 @@ def main(data, file_name): # Summarize results results = get_summary_results(file_name, nRoundedTimeDuplicatesRemoved, + cgmPercentDuplicated, contiguous_df, evaluation_points) From 0f26626489cef88e16e1c0887bf310e04bdcd1e8 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 31 Jan 2020 14:52:01 -0500 Subject: [PATCH 13/57] Add an empty results frame for batch processing --- .../batch-icgm-condition-stats.py | 49 ++++++++++++------- .../iCGM-test-matrix/icgm_condition_finder.py | 26 +++++++++- 2 files changed, 55 insertions(+), 20 deletions(-) diff --git a/projects/iCGM-test-matrix/batch-icgm-condition-stats.py b/projects/iCGM-test-matrix/batch-icgm-condition-stats.py index afb037e1..bb911276 100644 --- a/projects/iCGM-test-matrix/batch-icgm-condition-stats.py +++ b/projects/iCGM-test-matrix/batch-icgm-condition-stats.py @@ -15,15 +15,13 @@ """ import pandas as pd import icgm_condition_finder -from multiprocessing.pool import ThreadPool import time import datetime as dt import os -import time -from multiprocessing import Pool, cpu_count, get_context +from multiprocessing import Pool, cpu_count import traceback import sys -#%% +# %% data_location = "data_folder/" file_list = os.listdir(data_location) @@ -31,31 +29,41 @@ # Filter only files with .csv in their name (includes .csv.gz files) file_list = [filename for filename in file_list if '.csv' in filename] -#%% +# %% + def get_icgm_condition_stats(file_name, data_location, user_loc): file_path = data_location + file_name - print(str(user_loc) + " STARTING") - #if((user_loc % 100 == 0) & (user_loc > 99)): - #print(user_loc) - log_file = open('batch-icgm-condition-stats-log.txt', 'a') - log_file.write(str(user_loc)+"\n") - log_file.close() + # print(str(user_loc) + " STARTING") + if((user_loc % 100 == 0) & (user_loc > 99)): + print(user_loc) + log_file = open('batch-icgm-condition-stats-log.txt', 'a') + log_file.write(str(user_loc)+"\n") + log_file.close() + + results = icgm_condition_finder.get_empty_results_frame() + results['file_name'] = file_name - import_status = "" try: df = pd.read_csv(file_path, low_memory=False) - import_status = "Succesfully Imported" + + if 'type' in set(df): + if 'cbg' in set(df['type']): + results = icgm_condition_finder.main(df, file_name) + results['status'] = "Complete" + else: + results['status'] = "No CGM Data" + else: + results['status'] = "Empty Dataset" except Exception as e: df = pd.DataFrame() - print("Failed to import: " + file_path) - import_status = "Failed to Import - " + str(e) + print("Processing Failed For: " + file_path) + exception_text = "Failed - " + str(e) + results['status'] = "Failed" + results['exception_text'] = exception_text - results = icgm_condition_finder.main(df, file_name) - results['pandas_import_status'] = import_status - print(str(user_loc) + " COMPLETE!") return results @@ -105,5 +113,8 @@ def get_icgm_condition_stats(file_name, data_location, user_loc): # Convert results into dataframe icgm_condition_summary_df = pd.concat(results_array, sort=False) today_timestamp = dt.datetime.now().strftime("%Y-%m-%d") - results_export_filename = 'PHI-batch-icgm-condition-stats-' + today_timestamp + '.csv' + results_export_filename = \ + 'PHI-batch-icgm-condition-stats-' + \ + today_timestamp + \ + '.csv' icgm_condition_summary_df.to_csv(results_export_filename, index=False) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 299d9e55..a254ed4b 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -353,6 +353,30 @@ def get_evaluation_points(contiguous_df): return evaluation_points +def get_empty_results_frame(): + + results_columns = ['file_name', + 'nRoundedTimeDuplicatesRemoved', + 'cgmPercentDuplicated', + 'gte40_lt70', + 'gte70_lte180', + 'gt180_lte400', + 'lt-1', + 'gte-1_lte1', + 'gt1'] + + for num in range(10): + results_columns.append("cond" + str(num)) + + for num in range(9): + # Append snapshot locations to dataframe + results_columns.append("cond"+str(num)+"_eval_loc") + + results_frame = pd.DataFrame(index=[0], columns=results_columns) + + return results_frame + + def get_summary_results(file_name, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated, @@ -360,7 +384,7 @@ def get_summary_results(file_name, evaluation_points): """Create a summary of all results to store in a spreadsheet""" - results = pd.DataFrame(index=[0]) + results = get_empty_results_frame() # Get counts for each rate, range, and condition results["file_name"] = file_name From 3f2d0d2a5f8487bd778a8c868749077a3e9064a1 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 31 Jan 2020 17:57:42 -0500 Subject: [PATCH 14/57] Add logic for counting only not-null values All timestamps without a condition are labeled with a 0, but this logic helps distinguish empty values and values which did not get a condition label due to insufficient data for calculation --- projects/iCGM-test-matrix/icgm_condition_finder.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index a254ed4b..468f1418 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -398,7 +398,10 @@ def get_summary_results(file_name, results["gt1"] = contiguous_df["gt1"].sum() for num in range(10): - results["cond" + str(num)] = sum(contiguous_df["condition"] == num) + results["cond" + str(num)] = \ + sum((contiguous_df["condition"] == num) & + (contiguous_df["value"].notnull()) + ) for num in range(9): # Append snapshot locations to dataframe From 9db160091147eaa48a886c0b1fe33826bac592ff Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Mon, 3 Feb 2020 14:54:21 -0500 Subject: [PATCH 15/57] Fix condition column names in output Needed to add a +1 to the condition string number to match the right condition index --- projects/iCGM-test-matrix/icgm_condition_finder.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 468f1418..36687929 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -370,7 +370,7 @@ def get_empty_results_frame(): for num in range(9): # Append snapshot locations to dataframe - results_columns.append("cond"+str(num)+"_eval_loc") + results_columns.append("cond"+str(num+1)+"_eval_loc") results_frame = pd.DataFrame(index=[0], columns=results_columns) @@ -405,7 +405,7 @@ def get_summary_results(file_name, for num in range(9): # Append snapshot locations to dataframe - results["cond"+str(num)+"_eval_loc"] = evaluation_points[num] + results["cond"+str(num+1)+"_eval_loc"] = evaluation_points[num] return results From 3ca481ab88f0de544f8cdbb90f588cda431daef1 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Mon, 3 Feb 2020 14:54:36 -0500 Subject: [PATCH 16/57] Update README with output information --- projects/iCGM-test-matrix/README.md | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/projects/iCGM-test-matrix/README.md b/projects/iCGM-test-matrix/README.md index 74e0b77a..88f04a25 100644 --- a/projects/iCGM-test-matrix/README.md +++ b/projects/iCGM-test-matrix/README.md @@ -57,4 +57,21 @@ The algorithm for finding a snapshot is as follows - Calculate the slope in mg/dL/min with a 15-minute (3 cgm points) rolling window - Apply one of the 9 conditions labels to each CGM point - Calculate the max gap size of the cgm trace in a 48 hour *centered* rolling window (where the evaluation point is in the center) -- Randomly select one evaluation point for each condition that does not overlap with any other 48-hour snapshot and has a max gap <= 15 minutes \ No newline at end of file +- Randomly select one evaluation point for each condition that does not overlap with any other 48-hour snapshot and has a max gap <= 15 minutes + +## Output + +The output for the icgm_condition_finder.py and batch processing script are: + +- **file_name** - The file name of the .csv analyzed +- **nRoundedTimeDuplicatesRemoved** - The number of cgm duplicates removed after rounding to the nearest 5 minutes +- **cgmPercentDuplicated** - Percent of the cgm data that was duplicated +- **gte40_lt70** - The number of cgm entries with a median BG value of the previous 6 BG values (mg/dL) in the range [40, 70) (mg/dL) +- **gte70_lte180** - The number of cgm entries with a median BG value of the previous 6 BG values in the range [70, 180] (mg/dL) +- **gt180_lte400** - The number of cgm entries with a median BG value of the previous 6 BG values in the range (180, 400] (mg/dL) +- **lt-1** - The number of cgm entries with a rate of change of the previous 3 BG values less than -1 (mg/dL/min) +- **gte-1_lte1**- The number of cgm entries with a rate of change of the previous 3 BG values in the range [-1, 1] (mg/dL/min) +- **gt1** - The number of cgm entries with a rate of change of the previous 3 BG values greater than 1 (mg/dL/min) +- **cond*[0-9]*** - The number of total evaluation points that match a given condition (note that cond0 are the number of cgm entries that could not be evaluated under a condition due to a lack of data) +- **cond*[1-9]*_eval_loc** - The id location of a randomly sampled evaluation point +- **status** - The batch processing completion status of each file \ No newline at end of file From f75713e178d22094dee46181cca576eef401bcbf Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 11 Feb 2020 12:51:24 -0500 Subject: [PATCH 17/57] Add utc=True argument to utc datetime converter --- projects/iCGM-test-matrix/icgm_condition_finder.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 36687929..ccbcc2d7 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -80,7 +80,9 @@ def create_5min_contiguous_df(cgm_df): Fit the CGM trace to a contiguous 5-minute time series to uncover gaps """ - cgm_df["rounded_time"] = pd.to_datetime(cgm_df.time).dt.ceil(freq="5min") + cgm_df["rounded_time"] = \ + pd.to_datetime(cgm_df.time, + utc=True).dt.ceil(freq="5min") first_timestamp = cgm_df["rounded_time"].min() last_timestamp = cgm_df["rounded_time"].max() From 44b48dab97cd0f06a8c90f0f555e5b9ea7da72e3 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 11 Feb 2020 12:53:26 -0500 Subject: [PATCH 18/57] Add snapshot processor --- .../iCGM-test-matrix/snapshot_processor.py | 574 ++++++++++++++++++ 1 file changed, 574 insertions(+) create mode 100644 projects/iCGM-test-matrix/snapshot_processor.py diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py new file mode 100644 index 00000000..1c1486eb --- /dev/null +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -0,0 +1,574 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Snapshot Processor +================== +:File: snapshot_processor.py +:Description: Converts a Tidepool data snapshot into a set of dataframes that + can be used by a risk simulator. +:Version: 0.0.1 +:Created: 2020-02-04 +:Authors: Jason Meno (jam) +:Dependencies: A .csv containing Tidepool CGM device data +:License: BSD-2-Clause +""" + +# %% Imports +import pandas as pd +import pickle +import numpy as np +import datetime +import ast +import os +# %% Functions + + +def get_active_schedules(data, + snapshot_df, + file_name, + evaluation_point_loc, + evaluation_time): + """Get the pumpSettinsg row of active schedules used during the snapshot""" + + # Get all schedules and their upload ids + schedules = data[data.activeSchedule.notnull()] + + # Check which schedule matches the active pump upload during the snapshot + active_schedules = \ + schedules[schedules.uploadId.isin(snapshot_df.uploadId)] + + # If more than one active schedule, pick the one closest to the time of + # the evaluation point + if(len(active_schedules) > 1): + print(file_name + + " - " + + evaluation_point_loc + + " MULTIPLE ACTIVE SCHEDULES IN ONE SNAPSHOT!") + + nearest_uploadid = \ + snapshot_df.loc[((snapshot_df.type == 'bolus') | + (snapshot_df.type == 'basal')) & + (snapshot_df.time < evaluation_time), + 'uploadId'].values[-1] + + active_schedules = \ + active_schedules[active_schedules.uploadId == nearest_uploadid] + + # If no active schedule is assigned during an upload, pick the one closest + # to the time of the evaluation point + if len(active_schedules) == 0: + nearest_schedule_time = \ + schedules.loc[schedules.time < evaluation_time, 'time'].max() + active_schedules = \ + schedules[schedules.time == nearest_schedule_time] + + return active_schedules + + +def get_relative_timestamp(timestamp_ms): + """Calculate the HH:MM:SS strftime from midnight using a relative timestamp + in milliseconds""" + + timestamp_seconds = timestamp_ms/1000 + + # To calculate relative HH:MM:SS start times, + # 2020-01-01 00:00:00 is chosen + relative_offset_seconds = 1577854800 + + full_time_seconds = timestamp_seconds + relative_offset_seconds + + relative_timestamp = \ + datetime.datetime.fromtimestamp(full_time_seconds).strftime('%H:%M:%S') + + return relative_timestamp + + +def get_basal_rates(active_schedules): + """Get the basal rates from the active schedule""" + df_columns = ['basal_rate_start_times', + 'basal_rate_minutes', + 'basal_rate_values', + 'actual_basal_rates', + 'basal_rate_units'] + + basal_rates = pd.DataFrame(columns=df_columns) + + basal_schedules = \ + ast.literal_eval(active_schedules.basalSchedules.values[0]) + + active_name = active_schedules.activeSchedule.values[0] + active_basal_rates = basal_schedules.get(active_name) + + start_times = [] + for rate_loc in range(len(active_basal_rates)): + rate_info = active_basal_rates[rate_loc] + rate = rate_info.get('rate') + start_time = rate_info.get('start') + start_times.append(start_time) + start_string = get_relative_timestamp(start_time) + + basal_rates.loc[rate_loc, 'basal_rate_start_times'] = start_string + basal_rates.loc[rate_loc, 'basal_rate_values'] = rate + + basal_rates['actual_basal_rates'] = basal_rates['basal_rate_values'] + basal_rates['basal_rate_units'] = 'U/hr' + + # Add 24 hours in ms to the start_times list + # for calculating the differential in minutes + start_times.append(24 * 60 * 60 * 1000) + basal_rates['basal_rate_minutes'] = np.diff(start_times)/1000/60 + + return basal_rates + + +def get_carb_ratios(active_schedules): + """Get the carb to insulin ratio(s) from the active schedule""" + + df_columns = ['carb_ratio_start_times', + 'carb_ratio_values', + 'actual_carb_ratios', + 'carb_ratio_value_units'] + + carb_ratios = pd.DataFrame(columns=df_columns) + + if 'carbRatios' in active_schedules: + carb_ratio_schedules = \ + ast.literal_eval(active_schedules.carbRatios.values[0]) + active_name = active_schedules.activeSchedule.values[0] + active_carb_ratios = carb_ratio_schedules.get(active_name) + elif 'carbRatio' in active_schedules: + active_carb_ratios = \ + ast.literal_eval(active_schedules.carbRatio.values[0]) + else: + print("NO CARB RATIOS!") + + for ratio_loc in range(len(active_carb_ratios)): + ratio_info = active_carb_ratios[ratio_loc] + ratio = ratio_info.get('amount') + start_time = ratio_info.get('start') + start_string = get_relative_timestamp(start_time) + + carb_ratios.loc[ratio_loc, 'carb_ratio_start_times'] = start_string + carb_ratios.loc[ratio_loc, 'carb_ratio_values'] = ratio + + carb_ratios['actual_carb_ratios'] = carb_ratios['carb_ratio_values'] + carb_ratios['carb_ratio_value_units'] = 'g/U' + + return carb_ratios + + +def get_isfs(active_schedules): + """Get the insulin sensitivity ratio(s) from the active schedule""" + + df_columns = ['sensitivity_ratio_start_times', + 'sensitivity_ratio_end_times', + 'sensitivity_ratio_values', + 'actual_sensitivity_ratios', + 'sensitivity_ratio_value_units'] + + isfs = pd.DataFrame(columns=df_columns) + + if 'insulinSensitivities' in active_schedules: + isf_schedules = \ + ast.literal_eval(active_schedules.insulinSensitivities.values[0]) + active_name = active_schedules.activeSchedule.values[0] + active_isfs = isf_schedules.get(active_name) + elif 'insulinSensitivity' in active_schedules: + active_isfs = \ + ast.literal_eval(active_schedules.insulinSensitivity.values[0]) + else: + print("NO ISFS FOUND!") + + start_times = [] + + for isf_loc in range(len(active_isfs)): + isf_info = active_isfs[isf_loc] + isf = round(isf_info.get('amount')*18.01559) + start_time = isf_info.get('start') + start_string = get_relative_timestamp(start_time) + start_times.append(start_string) + + isfs.loc[isf_loc, 'sensitivity_ratio_start_times'] = start_string + isfs.loc[isf_loc, 'sensitivity_ratio_values'] = isf + + isfs['actual_sensitivity_ratios'] = isfs['sensitivity_ratio_values'] + isfs['sensitivity_ratio_value_units'] = 'mg/dL/U' + + # Shift start times by -1 to form the end times + end_times = start_times[1:] + [start_times[0]] + isfs['sensitivity_ratio_end_times'] = end_times + + return isfs + + +def get_target_ranges(active_schedules): + """Get the target BG ranges from the active schedule""" + + df_columns = ['target_range_start_times', + 'target_range_end_times', + 'target_range_minimum_values', + 'target_range_maximum_values', + 'target_range_value_units'] + + df_target_range = pd.DataFrame(columns=df_columns) + + if 'bgTargets' in active_schedules: + target_schedules = \ + ast.literal_eval(active_schedules.bgTargets.values[0]) + + active_name = active_schedules.activeSchedule.values[0] + active_targets = target_schedules.get(active_name) + elif 'bgTarget' in active_schedules: + active_targets = ast.literal_eval(active_schedules.bgTarget.values[0]) + elif 'bgTarget.start' in active_schedules: + target_start = int(active_schedules['bgTarget.start'].values) + active_targets = dict({'start': target_start}) + + if 'bgTarget.target' in active_schedules: + bg_target = float(active_schedules['bgTarget.target'].values) + active_targets.update({'target': bg_target}) + + if 'bgTarget.range' in active_schedules: + target_range = float(active_schedules['bgTarget.range'].values) + active_targets.update({'range': target_range}) + + if 'bgTarget.high' in active_schedules: + target_high = float(active_schedules['bgTarget.high'].values) + active_targets.update({'high': target_high}) + + if 'bgTarget.low' in active_schedules: + target_low = float(active_schedules['bgTarget.low'].values) + active_targets.update({'low': target_low}) + + active_targets = [active_targets] + + else: + print("NO TARGET RANGES!") + + start_times = [] + + for target_loc in range(len(active_targets)): + target_info = active_targets[target_loc] + start_time = target_info.get('start') + start_string = get_relative_timestamp(start_time) + start_times.append(start_string) + + df_target_range.loc[target_loc, + 'target_range_start_times'] = start_string + target = round(target_info.get('target')*18.01559) + df_target_range.loc[target_loc, 'target_range_minimum_values'] = target + df_target_range.loc[target_loc, 'target_range_maximum_values'] = target + + if 'range' in target_info.keys(): + target_range = round(target_info.get('range')*18.01559) + df_target_range.loc[target_loc, 'target_range_minimum_values'] = \ + target - target_range + + df_target_range.loc[target_loc, 'target_range_maximum_values'] = \ + target + target_range + + if 'high' in target_info.keys(): + target_high = round(target_info.get('high')*18.01559) + df_target_range.loc[target_loc, + 'target_range_maximum_values'] = target_high + + if 'low' in target_info.keys(): + target_low = round(target_info.get('high')*18.01559) + df_target_range.loc[target_loc, + 'target_range_minimum_values'] = target_low + + df_target_range['target_range_value_units'] = 'mg/dL' + + # Shift start times by -1 to form the end times + end_times = start_times[1:] + [start_times[0]] + df_target_range['target_range_end_times'] = end_times + + return df_target_range + + +def get_carb_events(snapshot_df): + """Get carb events from the carbInput during boluses""" + + df_columns = ['carb_dates', + 'carb_values', + 'actual_carbs', + 'carb_absorption_times', + 'carb_value_units'] + + carb_events = pd.DataFrame(columns=df_columns) + + carb_df = snapshot_df[snapshot_df.carbInput > 0] + + carb_events['carb_dates'] = carb_df.time.dt.strftime("%Y-%m-%d %H:%M:%S") + carb_events['carb_values'] = carb_df.carbInput + carb_events['actual_carbs'] = carb_events['carb_values'] + carb_events['carb_absorption_times'] = 180 + carb_events['carb_value_units'] = 'g' + + return carb_events + + +def get_dose_events(snapshot_df): + """Create a dose_events dataframe containing boluses, extended boluses, + temp basals, and suspend events""" + + df_columns = ['dose_types', + 'dose_start_times', + 'dose_end_times', + 'dose_values', + 'actual_doses', + 'dose_value_units'] + + dose_events = pd.DataFrame(columns=df_columns) + + bolus_events = dose_events.copy() + extended_events = dose_events.copy() + temp_basal_events = dose_events.copy() + suspend_events = dose_events.copy() + + bolus_df = snapshot_df[snapshot_df.type == 'bolus'] + + if len(bolus_df) > 0: + bolus_events['dose_values'] = bolus_df['normal'] + bolus_events['dose_start_times'] = bolus_df['time'] + bolus_events['dose_end_times'] = bolus_df['time'] + bolus_events['dose_types'] = 'DoseType.bolus' + bolus_events['dose_value_units'] = 'U' + + extended_df = bolus_df[bolus_df.extended > 0] + + if len(extended_df) > 0: + extended_df = bolus_df[bolus_df.subType == 'dual/square'] + + extended_events['dose_values'] = extended_df['extended'] + extended_events['dose_start_times'] = extended_df['time'] + extended_events['dose_end_times'] = \ + extended_df.apply(lambda x: x['time'] + + datetime.timedelta(milliseconds=x['duration']), + axis=1) + extended_events['dose_types'] = 'DoseType.extended' + extended_events['dose_value_units'] = 'U' + + basal_df = snapshot_df[snapshot_df.deliveryType == 'temp'] + + if len(basal_df) > 0: + temp_basal_events['dose_values'] = basal_df['rate'] + temp_basal_events['dose_start_times'] = basal_df['time'] + temp_basal_events['dose_end_times'] = \ + basal_df.apply(lambda x: x['time'] + + datetime.timedelta(milliseconds=x['duration']), + axis=1) + temp_basal_events['dose_types'] = 'DoseType.tempbasal' + temp_basal_events['dose_value_units'] = 'U/hr' + + suspend_df = snapshot_df[snapshot_df.deliveryType == 'suspend'] + + if len(suspend_df) > 0: + suspend_events['dose_start_times'] = suspend_df['time'] + suspend_events['dose_values'] = 0 + + suspend_events['dose_end_times'] = \ + suspend_df.apply(lambda x: x['time'] + + datetime.timedelta(milliseconds=x['duration']), + axis=1) + suspend_events['dose_types'] = 'DoseType.suspend' + suspend_events['dose_value_units'] = 'U/hr' + + dose_events = pd.concat([bolus_events, + extended_events, + temp_basal_events, + suspend_events]) + + dose_events['actual_doses'] = dose_events['dose_values'] + + if len(dose_events) > 0: + dose_events['dose_start_times'] = \ + dose_events['dose_start_times'].dt.strftime("%Y-%m-%d %H:%M:%S") + + dose_events['dose_end_times'] = \ + dose_events['dose_end_times'].dt.strftime("%Y-%m-%d %H:%M:%S") + + return dose_events + + +def get_cgm_df(snapshot_df): + """Gets the CGM values from the snapshot""" + + df_columns = ['glucose_dates', + 'glucose_values', + 'actual_blood_glucose', + 'glucose_units'] + + cgm_df = pd.DataFrame(columns=df_columns) + + cgm_data = snapshot_df[snapshot_df.type == 'cbg'] + cgm_df['glucose_dates'] = cgm_data.time.dt.strftime("%Y-%m-%d %H:%M:%S") + cgm_df['glucose_values'] = round(cgm_data.value * 18.01559) + cgm_df['actual_blood_glucose'] = cgm_df['glucose_values'] + cgm_df['glucose_units'] = 'mg/dL' + + return cgm_df + + +def get_last_temp_basal(): + """Retruns a default empty dataframe for df_last_temporary_basal""" + df_last_temporary_basal = pd.DataFrame() + return df_last_temporary_basal + + +def get_time_to_calculate_at(evaluation_time): + """Creates the df_misc dataframe with the snapshot evaluation_time""" + + df_columns = [0] + df_index = ['offset_applied_to_dates', + 'time_to_calculate_at'] + + df_misc = pd.DataFrame(index=df_index, + columns=df_columns) + + df_misc.loc['offset_applied_to_dates'] = 0 + + # Get time to calculate at + time_to_calculate_at = evaluation_time.strftime("%Y-%m-%d %H:%M:%S") + df_misc.loc['time_to_calculate_at'] = time_to_calculate_at + + return df_misc + + +def get_settings(): + """Provides a default settings dataframe""" + + df_columns = ['settings'] + df_index = ['model', + 'momentum_data_interval', + 'suspend_threshold', + 'dynamic_carb_absorption_enabled', + 'retrospective_correction_integration_interval', + 'recency_interval', + 'retrospective_correction_grouping_interval', + 'rate_rounder', + 'insulin_delay', + 'carb_delay', + 'default_absorption_times', + 'max_basal_rate', + 'max_bolus', + 'retrospective_correction_enabled'] + + df_settings = pd.DataFrame(index=df_index, + columns=df_columns) + + # Create default df_settings + + df_settings.loc['model'] = [[360.0, 65]] + df_settings.loc['momentum_data_interval'] = 15 + df_settings.loc['suspend_threshold'] = 70 + df_settings.loc['dynamic_carb_absorption_enabled'] = True + df_settings.loc['retrospective_correction_integration_interval'] = 30 + df_settings.loc['recency_interval'] = 15 + df_settings.loc['retrospective_correction_grouping_interval'] = 30 + df_settings.loc['rate_rounder'] = 0.05 + df_settings.loc['insulin_delay'] = 10 + df_settings.loc['carb_delay'] = 10 + df_settings.loc['default_absorption_times'] = [[120.0, 180.0, 240.0]] + df_settings.loc['max_basal_rate'] = 35 + df_settings.loc['max_bolus'] = 30 + df_settings.loc['retrospective_correction_enabled'] = True + + return df_settings + + +def get_snapshot(data, + file_name, + evaluation_point_loc): + + """Main function wrapper to assemble snapshot dataframes""" + + # Start by getting the 48-hour window ± 24hrs around the evaluation point + evaluation_index = data.index[data.id == evaluation_point_loc] + + data['time'] = pd.to_datetime(data['time'], utc=True) + evaluation_time = pd.to_datetime(data.time[evaluation_index].values[0], + utc=True) + + df_misc = get_time_to_calculate_at(evaluation_time) + + start_time = evaluation_time - datetime.timedelta(days=1) + end_time = evaluation_time + datetime.timedelta(days=1) + + snapshot_df = data[(data.time >= start_time) & (data.time <= end_time)] + + # Get pumpSettings list of active schedules + active_schedules = get_active_schedules(data, + snapshot_df, + file_name, + evaluation_point_loc, + evaluation_time) + + active_schedules = active_schedules.dropna(axis=1) + + basal_rates = get_basal_rates(active_schedules) + carb_ratios = get_carb_ratios(active_schedules) + isfs = get_isfs(active_schedules) + df_target_range = get_target_ranges(active_schedules) + + carb_events = get_carb_events(snapshot_df) + dose_events = get_dose_events(snapshot_df) + cgm_df = get_cgm_df(snapshot_df) + + df_last_temporary_basal = get_last_temp_basal() + df_settings = get_settings() + + return (basal_rates, carb_events, carb_ratios, dose_events, cgm_df, + df_last_temporary_basal, df_misc, isfs, + df_settings, df_target_range + ) + + +def export_snapshot(snapshot, + file_name, + condition_num, + export_folder): + """Exports the snapshot (10-element tuple) into a pickle file""" + + if not os.path.exists(export_folder): + os.makedirs(export_folder) + + export_filename = file_name + "_" + str(condition_num) + ".pkl" + export_path = export_folder + "/" + export_filename + + with open(export_path, 'wb') as pickle_file: + pickle.dump(snapshot, pickle_file) + + print("Successfully completed: " + export_filename) + + return + + +# %% +if __name__ == "__main__": + + # Import results from batch-icgm-condition-stats + condition_file = "PHI-batch-icgm-condition-stats-2020-01-31.csv" + condition_df = pd.read_csv(condition_file, low_memory=False) + + file_selection = 1 + file_name = condition_df.loc[file_selection, 'file_name'] + + # Location of csvs + data_location = "train-data/" + file_path = os.path.join(data_location, file_name) + data = pd.read_csv(file_path, low_memory=False) + + # Loop through each condition, calculate, and export the snapshot + for condition_num in range(1, 10): + condition_col = 'cond' + str(condition_num) + '_eval_loc' + evaluation_point_loc = condition_df.loc[file_selection, condition_col] + + snapshot = get_snapshot(data, file_name, evaluation_point_loc) + + export_folder = "snapshot_export" + + export_snapshot(snapshot, + file_name, + condition_num, + export_folder) From d7c439c026c92c074c7994e694cfaf7ec0c5f147 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 11 Feb 2020 13:08:31 -0500 Subject: [PATCH 19/57] Add check for null evaluation points --- .../iCGM-test-matrix/snapshot_processor.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 1c1486eb..38f34be3 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -548,10 +548,10 @@ def export_snapshot(snapshot, if __name__ == "__main__": # Import results from batch-icgm-condition-stats - condition_file = "PHI-batch-icgm-condition-stats-2020-01-31.csv" + condition_file = "PHI-batch-icgm-condition-stats-2020-02-11.csv" condition_df = pd.read_csv(condition_file, low_memory=False) - file_selection = 1 + file_selection = 96 file_name = condition_df.loc[file_selection, 'file_name'] # Location of csvs @@ -564,11 +564,13 @@ def export_snapshot(snapshot, condition_col = 'cond' + str(condition_num) + '_eval_loc' evaluation_point_loc = condition_df.loc[file_selection, condition_col] - snapshot = get_snapshot(data, file_name, evaluation_point_loc) + if not pd.isnull(evaluation_point_loc): - export_folder = "snapshot_export" + snapshot = get_snapshot(data, file_name, evaluation_point_loc) - export_snapshot(snapshot, - file_name, - condition_num, - export_folder) + export_folder = "snapshot_export" + + export_snapshot(snapshot, + file_name, + condition_num, + export_folder) From f2537b9f0c7b368959b4c7c5141817497808087e Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 11 Feb 2020 13:13:15 -0500 Subject: [PATCH 20/57] Add condition text to pickle file export --- projects/iCGM-test-matrix/snapshot_processor.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 38f34be3..a9eecdd2 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -533,7 +533,7 @@ def export_snapshot(snapshot, if not os.path.exists(export_folder): os.makedirs(export_folder) - export_filename = file_name + "_" + str(condition_num) + ".pkl" + export_filename = file_name + "_condition" + str(condition_num) + ".pkl" export_path = export_folder + "/" + export_filename with open(export_path, 'wb') as pickle_file: @@ -551,7 +551,7 @@ def export_snapshot(snapshot, condition_file = "PHI-batch-icgm-condition-stats-2020-02-11.csv" condition_df = pd.read_csv(condition_file, low_memory=False) - file_selection = 96 + file_selection = 1 file_name = condition_df.loc[file_selection, 'file_name'] # Location of csvs From 8eb977bd449e6a611374e67daff8c0c7c30b943a Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 11 Feb 2020 13:38:50 -0500 Subject: [PATCH 21/57] Update README with script information and numbered condition labels --- projects/iCGM-test-matrix/README.md | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/projects/iCGM-test-matrix/README.md b/projects/iCGM-test-matrix/README.md index 88f04a25..0d95506f 100644 --- a/projects/iCGM-test-matrix/README.md +++ b/projects/iCGM-test-matrix/README.md @@ -2,10 +2,18 @@ This project finds snapshots of data to explore the sensitivity of the Loop Algorithm over the entire range of BG values. Each snapshot is a 48-hour window of data. In the center of this data is an "evaluation point" which falls into one of the 9 conditions detailed in the table below. -The primary goal of this project is to find 9 snapshots for each condition from 100 datasets in the Tidepool Big Data Donation Project for a total of 900 snapshots. +The primary goal of this project is to find 9 snapshots for each condition from 100 datasets in the Tidepool Big Data Donation Project for a total of 900 snapshots and export them for simulator use. The secondary goal is to calculate the distribution of all 9 conditions within the entire TBDDP donor population. +## Scripts + +There are 3 python scripts used in this project: + +- **icgm_condition_finder.py** - Given a Tidepool donor dataset, returns 9 locations (if available) of each condition along with some other statistics (see [Condition Finder Output][#condition-finder-ouput] below) +- **batch-icgm-condition-stats.py** - A batch script wrapper for the icgm_condition_finder. Given a folder of Tidepool datasets, creates a .csv output of condition locations and stats for every file. +- **snapshot_processor.py** - Given the output of batch-icgm-condition-stats.py, takes each 48-hour snapshot location for every dataset and converts it into a set of dataframes to be used by the pyLoopKit simulator. (Exports to a 10-element tuple pickle file) + ## Condition Table There are 3 value conditions and 3 rate of change conditions with a combined 9 unique iCGM states that any iCGM data point can exist within as shown in the table below. @@ -46,9 +54,21 @@ There are 3 value conditions and 3 rate of change conditions with a combined 9 u +The conditions are numbered 1-9 as follows: +| Condition # | 30min Median BG (mg/dL)
&
15min Rate of Change (mg/dL/min) | +| :---------: | :----------------------------------------------------------- | +| 1 | [40-70) & < -1 | +| 2 | [70-180] & < -1 | +| 3 | (180-400] & < -1 | +| 4 | [40-70) & [-1 to 1] | +| 5 | [70-180] & [-1 to 1] | +| 6 | (180-400] & [-1 to 1] | +| 7 | [40-70) & > 1 | +| 8 | [70-180] & > 1 | +| 9 | (180-400] & > 1 | -## Finding Snapshots Algorithm +## Condition Finder Algorithm The algorithm for finding a snapshot is as follows @@ -59,7 +79,7 @@ The algorithm for finding a snapshot is as follows - Calculate the max gap size of the cgm trace in a 48 hour *centered* rolling window (where the evaluation point is in the center) - Randomly select one evaluation point for each condition that does not overlap with any other 48-hour snapshot and has a max gap <= 15 minutes -## Output +## Condition Finder Output The output for the icgm_condition_finder.py and batch processing script are: From a72ec17c85bcd40c9807c6d825a7f17e7380d79e Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 11 Feb 2020 13:40:18 -0500 Subject: [PATCH 22/57] Update README formatting --- projects/iCGM-test-matrix/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/projects/iCGM-test-matrix/README.md b/projects/iCGM-test-matrix/README.md index 0d95506f..8e532358 100644 --- a/projects/iCGM-test-matrix/README.md +++ b/projects/iCGM-test-matrix/README.md @@ -92,6 +92,6 @@ The output for the icgm_condition_finder.py and batch processing script are: - **lt-1** - The number of cgm entries with a rate of change of the previous 3 BG values less than -1 (mg/dL/min) - **gte-1_lte1**- The number of cgm entries with a rate of change of the previous 3 BG values in the range [-1, 1] (mg/dL/min) - **gt1** - The number of cgm entries with a rate of change of the previous 3 BG values greater than 1 (mg/dL/min) -- **cond*[0-9]*** - The number of total evaluation points that match a given condition (note that cond0 are the number of cgm entries that could not be evaluated under a condition due to a lack of data) -- **cond*[1-9]*_eval_loc** - The id location of a randomly sampled evaluation point +- **cond[0-9]** - The number of total evaluation points that match a given condition (note that cond0 are the number of cgm entries that could not be evaluated under a condition due to a lack of data) +- **cond[1-9]_eval_loc** - The id location of a randomly sampled evaluation point - **status** - The batch processing completion status of each file \ No newline at end of file From 72792839e553e42b69771486c8a5425bedce452b Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 11 Feb 2020 13:42:07 -0500 Subject: [PATCH 23/57] Update README anchor link --- projects/iCGM-test-matrix/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/iCGM-test-matrix/README.md b/projects/iCGM-test-matrix/README.md index 8e532358..9972412d 100644 --- a/projects/iCGM-test-matrix/README.md +++ b/projects/iCGM-test-matrix/README.md @@ -10,7 +10,7 @@ The secondary goal is to calculate the distribution of all 9 conditions within t There are 3 python scripts used in this project: -- **icgm_condition_finder.py** - Given a Tidepool donor dataset, returns 9 locations (if available) of each condition along with some other statistics (see [Condition Finder Output][#condition-finder-ouput] below) +- **icgm_condition_finder.py** - Given a Tidepool donor dataset, returns 9 locations (if available) of each condition along with some other statistics (see [Condition Finder Output][#Condition-Finder-Ouput] below) - **batch-icgm-condition-stats.py** - A batch script wrapper for the icgm_condition_finder. Given a folder of Tidepool datasets, creates a .csv output of condition locations and stats for every file. - **snapshot_processor.py** - Given the output of batch-icgm-condition-stats.py, takes each 48-hour snapshot location for every dataset and converts it into a set of dataframes to be used by the pyLoopKit simulator. (Exports to a 10-element tuple pickle file) From 6d5d713550bcd8c92b51ff1016ac2a1c521b19b7 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 11 Feb 2020 13:42:45 -0500 Subject: [PATCH 24/57] Update README anchor link --- projects/iCGM-test-matrix/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/iCGM-test-matrix/README.md b/projects/iCGM-test-matrix/README.md index 9972412d..eaa01617 100644 --- a/projects/iCGM-test-matrix/README.md +++ b/projects/iCGM-test-matrix/README.md @@ -10,7 +10,7 @@ The secondary goal is to calculate the distribution of all 9 conditions within t There are 3 python scripts used in this project: -- **icgm_condition_finder.py** - Given a Tidepool donor dataset, returns 9 locations (if available) of each condition along with some other statistics (see [Condition Finder Output][#Condition-Finder-Ouput] below) +- **icgm_condition_finder.py** - Given a Tidepool donor dataset, returns 9 locations (if available) of each condition along with some other statistics (see [Condition Finder Output][#Condition-Finder-Output] below) - **batch-icgm-condition-stats.py** - A batch script wrapper for the icgm_condition_finder. Given a folder of Tidepool datasets, creates a .csv output of condition locations and stats for every file. - **snapshot_processor.py** - Given the output of batch-icgm-condition-stats.py, takes each 48-hour snapshot location for every dataset and converts it into a set of dataframes to be used by the pyLoopKit simulator. (Exports to a 10-element tuple pickle file) From aacf164359157e27ccb658de4d0b7279e2f4bab0 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 11 Feb 2020 13:47:34 -0500 Subject: [PATCH 25/57] Update README anchor link --- projects/iCGM-test-matrix/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/iCGM-test-matrix/README.md b/projects/iCGM-test-matrix/README.md index eaa01617..0c3f489f 100644 --- a/projects/iCGM-test-matrix/README.md +++ b/projects/iCGM-test-matrix/README.md @@ -10,7 +10,7 @@ The secondary goal is to calculate the distribution of all 9 conditions within t There are 3 python scripts used in this project: -- **icgm_condition_finder.py** - Given a Tidepool donor dataset, returns 9 locations (if available) of each condition along with some other statistics (see [Condition Finder Output][#Condition-Finder-Output] below) +- **icgm_condition_finder.py** - Given a Tidepool donor dataset, returns 9 locations (if available) of each condition along with some other statistics (see [Condition Finder Output](#condition-finder-output) below) - **batch-icgm-condition-stats.py** - A batch script wrapper for the icgm_condition_finder. Given a folder of Tidepool datasets, creates a .csv output of condition locations and stats for every file. - **snapshot_processor.py** - Given the output of batch-icgm-condition-stats.py, takes each 48-hour snapshot location for every dataset and converts it into a set of dataframes to be used by the pyLoopKit simulator. (Exports to a 10-element tuple pickle file) From eedd4910ce720b025a20d2acd4363c552f0033da Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 13 Feb 2020 20:04:29 -0500 Subject: [PATCH 26/57] Add new condition constraints + update time to local time --- .../iCGM-test-matrix/icgm_condition_finder.py | 191 ++++++++++++++---- 1 file changed, 148 insertions(+), 43 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index ccbcc2d7..7dc3edea 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -75,29 +75,6 @@ def add_uploadDateTime(df): return df -def create_5min_contiguous_df(cgm_df): - """ - Fit the CGM trace to a contiguous 5-minute time series to uncover gaps - """ - - cgm_df["rounded_time"] = \ - pd.to_datetime(cgm_df.time, - utc=True).dt.ceil(freq="5min") - first_timestamp = cgm_df["rounded_time"].min() - last_timestamp = cgm_df["rounded_time"].max() - - contiguous_ts = pd.date_range(first_timestamp, last_timestamp, freq="5min") - contiguous_df = pd.DataFrame(contiguous_ts, columns=["rounded_time"]) - - contiguous_df = pd.merge(contiguous_df, - cgm_df, - how="left", - on="rounded_time" - ) - - return contiguous_df - - def remove_rounded_CGM_duplicates(cgm_df): """Removes CGM duplicates, keeping the entries with the most recent uploadTime @@ -107,21 +84,23 @@ def remove_rounded_CGM_duplicates(cgm_df): nRoundedTimeDuplicatesRemoved = 0 cgmPercentDuplicated = 0 - if "rounded_time" in cgm_df: - # Sort first by most recent rounded_time and then by newest uploadTime - cgm_df.sort_values(by=["rounded_time", "uploadTime"], + if "rounded_local_time" in cgm_df: + # Sort first by most recent rounded_local_time and + # then sort by newest uploadTime + cgm_df.sort_values(by=["rounded_local_time", "uploadTime"], ascending=[False, False], inplace=True) # Safety check for null times - dfIsNull = cgm_df[cgm_df["rounded_time"].isnull()] - dfNotNull = cgm_df[cgm_df["rounded_time"].notnull()] + dfIsNull = cgm_df[cgm_df["rounded_local_time"].isnull()] + dfNotNull = cgm_df[cgm_df["rounded_local_time"].notnull()] nBefore = len(dfNotNull) # Drop duplicates, keeping the first (most recent upload) entry # .duplicated() defaults to keeping the first duplicate - dfNotNull = dfNotNull.loc[~(dfNotNull["rounded_time"].duplicated())] + dfNotNull = \ + dfNotNull.loc[~(dfNotNull["rounded_local_time"].duplicated())] dfNotNull = dfNotNull.reset_index(drop=True) nRoundedTimeDuplicatesRemoved = nBefore - len(dfNotNull) @@ -129,11 +108,80 @@ def remove_rounded_CGM_duplicates(cgm_df): cgmPercentDuplicated = nRoundedTimeDuplicatesRemoved/nBefore cgm_df = pd.concat([dfIsNull, dfNotNull]) - cgm_df.sort_values(by=["rounded_time"], ascending=True, inplace=True) + cgm_df.sort_values(by=["rounded_local_time"], + ascending=True, inplace=True) return cgm_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated +def create_5min_contiguous_df(data): + """ + Fit the CGM, Bolus, and Basal traces to a contiguous 5-minute time series + to uncover gaps + """ + # Round all data to the nearest 5 minutes (ceiling) + + data["rounded_local_time"] = \ + pd.to_datetime(data["est.localTime"], + utc=True).dt.ceil(freq="5min") + + # Separate CGM, Bolus, and Basal data + cgm_df = data[data.type == "cbg"].copy() + cgm_df.drop(columns=["normal", "duration"], inplace=True) + bolus_df = data[data.type == "bolus"].copy() + basal_df = data[data.type == "basal"].copy() + + # CGM Data Processing + # Convert value from mmol/L to mg/dL + cgm_df["value"] = cgm_df["value"] * 18.01559 + + first_timestamp = data["rounded_local_time"].min() + last_timestamp = data["rounded_local_time"].max() + + contiguous_ts = pd.date_range(first_timestamp, last_timestamp, freq="5min") + contiguous_df = pd.DataFrame(contiguous_ts, columns=["rounded_local_time"]) + + contiguous_df = pd.merge(contiguous_df, + cgm_df, + how="left", + on="rounded_local_time" + ) + + # Remove CGM duplicates from the contiguous_df + contiguous_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated = \ + remove_rounded_CGM_duplicates(contiguous_df) + + # Merge boluses together into single 5-minute timestamps + # NOTE: This is not for accuracy, this is just to count whether there + # exists any bolus in a 48-hour window later + bolus_df = pd.DataFrame( + bolus_df.groupby('rounded_local_time').normal.sum() + ).reset_index() + + contiguous_df = pd.merge(contiguous_df, + bolus_df, + how="left", + on="rounded_local_time" + ) + + # Merge basals together into single 5-minute timestamps + # NOTE: This is not for accuracy, this is just to count whether there + # exists any basal events in a 48-hour window later + basal_df = pd.DataFrame( + basal_df.groupby('rounded_local_time').duration.sum() + ).reset_index() + + basal_df["basal_duration"] = basal_df['duration'] + + contiguous_df = pd.merge(contiguous_df, + basal_df, + how="left", + on="rounded_local_time" + ) + + return contiguous_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated + + def rolling_30min_median(contiguous_df): """ Calculate the median mg/dL value with a 30-minute (6 points) rolling window @@ -235,6 +283,61 @@ def rolling_48hour_max_gap(contiguous_df): return contiguous_df +def rolling_48hour_pump_data(contiguous_df): + """ + Calculate whether pump data exists in a 48 hour centered rolling + window (where the evaluation point is in the center) + + Note: Centered window behavior keeps the evaluation point on the right side + of the half-way point. + + e.g. In a window of 4: [elem1, elem2, elem3, elem4] - elem3 is the "center" + """ + + contiguous_df["rolling_48hour_bolus_gte2"] = \ + contiguous_df['normal'].rolling( + window=288*2, + min_periods=1, + center=True).count() >= 2 + + contiguous_df["rolling_48hour_basal_gte1"] = \ + contiguous_df['basal_duration'].rolling( + window=288*2, + min_periods=1, + center=True).count() >= 1 + + contiguous_df["rolling_48hour_pump_data"] = \ + contiguous_df["rolling_48hour_bolus_gte2"] & \ + contiguous_df["rolling_48hour_basal_gte1"] + + return contiguous_df + + +def rolling_48hour_check_travel(contiguous_df): + """ + Calculate whether any travel occured in a 48 hour centered rolling + window (where the evaluation point is in the center) + + Note: Centered window behavior keeps the evaluation point on the right side + of the half-way point. + + e.g. In a window of 4: [elem1, elem2, elem3, elem4] - elem3 is the "center" + """ + + contiguous_df["is_travel"] = contiguous_df["est.annotations"] == 'travel' + + contiguous_df["rolling_48hour_has_travel"] = \ + contiguous_df['is_travel'].rolling( + window=288*2, + min_periods=1, + center=True).sum() > 0 + + contiguous_df["rolling_48hour_no_travel"] = \ + ~contiguous_df["rolling_48hour_has_travel"] + + return contiguous_df + + def label_conditions(contiguous_df): """Labels each cgm entry as one of the 9 different conditions @@ -315,7 +418,9 @@ def get_evaluation_points(contiguous_df): # Create bool of all values with a condition and max gap <= 15min qualified_bool = \ (contiguous_df["condition"] > 0) & \ - (contiguous_df["rolling_48hour_max_gap"] <= 3) + (contiguous_df["rolling_48hour_max_gap"] <= 3) & \ + (contiguous_df["rolling_48hour_pump_data"]) & \ + (contiguous_df["rolling_48hour_no_travel"]) qualified_condition_list = \ contiguous_df.loc[qualified_bool, "condition"].copy() @@ -418,18 +523,12 @@ def main(data, file_name): # Add the upload data time to each record (used for deduplicating) data = add_uploadDateTime(data) - # Separate CGM data - cgm_df = data[data.type == "cbg"].copy() - - # Convert value from mmol/L to mg/dL - cgm_df["value"] = cgm_df["value"] * 18.01559 - - # Fit the CGM trace to a contiguous 5-minute time series to uncover gaps - contiguous_df = create_5min_contiguous_df(cgm_df) - - # Remove CGM duplicates - cgm_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated = \ - remove_rounded_CGM_duplicates(cgm_df) + # Fit the cgm, bolus, and basal data traces + # to a contiguous 5-minute time series to uncover gaps + (contiguous_df, + nRoundedTimeDuplicatesRemoved, + cgmPercentDuplicated + ) = create_5min_contiguous_df(data) # Calculate the median BG with a 30-minute rolling window contiguous_df = rolling_30min_median(contiguous_df) @@ -443,6 +542,12 @@ def main(data, file_name): # Get the max gap size across 48-hour windows contiguous_df = rolling_48hour_max_gap(contiguous_df) + # Determine whether there's sufficient pump data in each 48 hour window + contiguous_df = rolling_48hour_pump_data(contiguous_df) + + # Add 48-hour window bools for travel data + contiguous_df = rolling_48hour_check_travel(contiguous_df) + # Get the locations of each evaluation point in a 48-hour snapshot evaluation_points = get_evaluation_points(contiguous_df) From 4abbec2785ac0146585aa45f8888be280220acb3 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 14 Feb 2020 13:39:05 -0500 Subject: [PATCH 27/57] Update basal 5-minute downsampling --- .../iCGM-test-matrix/icgm_condition_finder.py | 28 +++++++++++++------ 1 file changed, 20 insertions(+), 8 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 7dc3edea..6e0ab908 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -152,8 +152,6 @@ def create_5min_contiguous_df(data): remove_rounded_CGM_duplicates(contiguous_df) # Merge boluses together into single 5-minute timestamps - # NOTE: This is not for accuracy, this is just to count whether there - # exists any bolus in a 48-hour window later bolus_df = pd.DataFrame( bolus_df.groupby('rounded_local_time').normal.sum() ).reset_index() @@ -164,12 +162,21 @@ def create_5min_contiguous_df(data): on="rounded_local_time" ) - # Merge basals together into single 5-minute timestamps - # NOTE: This is not for accuracy, this is just to count whether there - # exists any basal events in a 48-hour window later - basal_df = pd.DataFrame( - basal_df.groupby('rounded_local_time').duration.sum() - ).reset_index() + # Merge basals into single 5-minute timestamps + # For multiple basals occuring in a 5-minute period, choose the last basal + # NOTE: This will result in a slight loss of accuracy of insulin delivered + # within a 5-minute period + + # TODO: Create an algorithm to upsample basals to a 1-minute interval, then + # downsample back to a 5-minute virtual "delivered" basal + + basal_df.sort_values('time', + ascending=True, + inplace=True) + + basal_df.drop_duplicates('rounded_local_time', + keep='last', + inplace=True) basal_df["basal_duration"] = basal_df['duration'] @@ -179,6 +186,11 @@ def create_5min_contiguous_df(data): on="rounded_local_time" ) + # Sort by ascending rounded_local_time + contiguous_df.sort_values('rounded_local_time', + ascending=True, + inplace=True) + return contiguous_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated From a7cec6efb5c5052a2914a1d01ac418d0c6956cb2 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 14 Feb 2020 14:04:26 -0500 Subject: [PATCH 28/57] Limit basal dataframe to necessary columns for merging --- projects/iCGM-test-matrix/icgm_condition_finder.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 6e0ab908..6c733926 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -170,7 +170,7 @@ def create_5min_contiguous_df(data): # TODO: Create an algorithm to upsample basals to a 1-minute interval, then # downsample back to a 5-minute virtual "delivered" basal - basal_df.sort_values('time', + basal_df.sort_values('est.localTime', ascending=True, inplace=True) @@ -180,6 +180,11 @@ def create_5min_contiguous_df(data): basal_df["basal_duration"] = basal_df['duration'] + # Limit basal_df to just the necessary columns + basal_df = basal_df[["rounded_local_time", + "basal_duration", + "rate"]] + contiguous_df = pd.merge(contiguous_df, basal_df, how="left", From 14a90cadd53610cc8e32fd24f9a69676d8ecd54a Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 14 Feb 2020 15:27:30 -0500 Subject: [PATCH 29/57] Update processor with rounded local times and csv output --- .../iCGM-test-matrix/snapshot_processor.py | 223 ++++++++++++++---- 1 file changed, 173 insertions(+), 50 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index a9eecdd2..b722cbe7 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -28,8 +28,7 @@ def get_active_schedules(data, file_name, evaluation_point_loc, evaluation_time): - """Get the pumpSettinsg row of active schedules used during the snapshot""" - + """Get pumpSettings row of active schedules used during the snapshot.""" # Get all schedules and their upload ids schedules = data[data.activeSchedule.notnull()] @@ -48,7 +47,7 @@ def get_active_schedules(data, nearest_uploadid = \ snapshot_df.loc[((snapshot_df.type == 'bolus') | (snapshot_df.type == 'basal')) & - (snapshot_df.time < evaluation_time), + (snapshot_df.rounded_local_time < evaluation_time), 'uploadId'].values[-1] active_schedules = \ @@ -58,9 +57,10 @@ def get_active_schedules(data, # to the time of the evaluation point if len(active_schedules) == 0: nearest_schedule_time = \ - schedules.loc[schedules.time < evaluation_time, 'time'].max() + schedules.loc[schedules.rounded_local_time < evaluation_time, + 'rounded_local_time'].max() active_schedules = \ - schedules[schedules.time == nearest_schedule_time] + schedules[schedules.rounded_local_time == nearest_schedule_time] return active_schedules @@ -298,8 +298,11 @@ def get_carb_events(snapshot_df): carb_events = pd.DataFrame(columns=df_columns) carb_df = snapshot_df[snapshot_df.carbInput > 0] + carb_df = \ + carb_df.groupby('rounded_local_time').carbInput.sum().reset_index() - carb_events['carb_dates'] = carb_df.time.dt.strftime("%Y-%m-%d %H:%M:%S") + carb_events['carb_dates'] = \ + carb_df.rounded_local_time.dt.strftime("%Y-%m-%d %H:%M:%S") carb_events['carb_values'] = carb_df.carbInput carb_events['actual_carbs'] = carb_events['carb_values'] carb_events['carb_absorption_times'] = 180 @@ -326,49 +329,90 @@ def get_dose_events(snapshot_df): temp_basal_events = dose_events.copy() suspend_events = dose_events.copy() - bolus_df = snapshot_df[snapshot_df.type == 'bolus'] + bolus_df = snapshot_df[snapshot_df.type == 'bolus'].copy() if len(bolus_df) > 0: + # Merge boluses together into single 5-minute timestamps + bolus_df = pd.DataFrame( + bolus_df.groupby('rounded_local_time').normal.sum() + ).reset_index() + bolus_events['dose_values'] = bolus_df['normal'] - bolus_events['dose_start_times'] = bolus_df['time'] - bolus_events['dose_end_times'] = bolus_df['time'] + bolus_events['dose_start_times'] = bolus_df['rounded_local_time'] + bolus_events['dose_end_times'] = bolus_df['rounded_local_time'] bolus_events['dose_types'] = 'DoseType.bolus' bolus_events['dose_value_units'] = 'U' - extended_df = bolus_df[bolus_df.extended > 0] + if('extended' in list(bolus_df)): + extended_df = bolus_df[bolus_df.extended > 0] + else: + extended_df = pd.DataFrame() + + if len(extended_df) > 0: + extended_df = bolus_df[bolus_df.subType == 'dual/square'] - if len(extended_df) > 0: - extended_df = bolus_df[bolus_df.subType == 'dual/square'] + # NOTE: Multiple extended entries may exist within the same rounded + # timestamp. - extended_events['dose_values'] = extended_df['extended'] - extended_events['dose_start_times'] = extended_df['time'] - extended_events['dose_end_times'] = \ - extended_df.apply(lambda x: x['time'] + - datetime.timedelta(milliseconds=x['duration']), - axis=1) - extended_events['dose_types'] = 'DoseType.extended' - extended_events['dose_value_units'] = 'U' + extended_events['dose_values'] = extended_df['extended'] + extended_events['dose_start_times'] = \ + extended_df['rounded_local_time'] + extended_events['dose_end_times'] = \ + extended_df.apply( + lambda x: x['rounded_local_time'] + + datetime.timedelta(milliseconds=x['duration']), + axis=1) - basal_df = snapshot_df[snapshot_df.deliveryType == 'temp'] + extended_events['dose_types'] = 'DoseType.extended' + extended_events['dose_value_units'] = 'U' + + basal_df = snapshot_df[snapshot_df.deliveryType == 'temp'].copy() if len(basal_df) > 0: + + # Merge basals into single 5-minute timestamps + # For multiple basals occuring in a 5-minute period, choose the last + # NOTE: This will result in a slight loss of accuracy of insulin + # delivered within a 5-minute period + + # TODO: Create an algorithm to upsample basals to a 1-minute interval, + # then downsample back to a 5-minute virtual "delivered" basal + + basal_df.sort_values('est.localTime', + ascending=True, + inplace=True) + + basal_df.drop_duplicates('rounded_local_time', + keep='last', + inplace=True) temp_basal_events['dose_values'] = basal_df['rate'] - temp_basal_events['dose_start_times'] = basal_df['time'] + temp_basal_events['dose_start_times'] = basal_df['rounded_local_time'] temp_basal_events['dose_end_times'] = \ - basal_df.apply(lambda x: x['time'] + + basal_df.apply(lambda x: x['rounded_local_time'] + datetime.timedelta(milliseconds=x['duration']), axis=1) temp_basal_events['dose_types'] = 'DoseType.tempbasal' temp_basal_events['dose_value_units'] = 'U/hr' - suspend_df = snapshot_df[snapshot_df.deliveryType == 'suspend'] + suspend_df = snapshot_df[snapshot_df.deliveryType == 'suspend'].copy() if len(suspend_df) > 0: - suspend_events['dose_start_times'] = suspend_df['time'] + # Merge suspends into single 5-minute timestamps + # For multiple suspends occuring in a 5-minute period, choose the last + + suspend_df.sort_values('est.localTime', + ascending=True, + inplace=True) + + suspend_df.drop_duplicates('rounded_local_time', + keep='last', + inplace=True) + + suspend_events['dose_start_times'] = suspend_df['rounded_local_time'] suspend_events['dose_values'] = 0 suspend_events['dose_end_times'] = \ - suspend_df.apply(lambda x: x['time'] + + suspend_df.apply(lambda x: x['rounded_local_time'] + datetime.timedelta(milliseconds=x['duration']), axis=1) suspend_events['dose_types'] = 'DoseType.suspend' @@ -401,8 +445,17 @@ def get_cgm_df(snapshot_df): cgm_df = pd.DataFrame(columns=df_columns) - cgm_data = snapshot_df[snapshot_df.type == 'cbg'] - cgm_df['glucose_dates'] = cgm_data.time.dt.strftime("%Y-%m-%d %H:%M:%S") + cgm_data = snapshot_df[snapshot_df.type == 'cbg'].copy() + + # Drop cgm duplicates in snapshot (if any) + cgm_data.sort_values(['uploadId', 'rounded_local_time'], + ascending=True, + inplace=True) + + cgm_data.drop_duplicates('rounded_local_time', inplace=True) + + cgm_df['glucose_dates'] = \ + cgm_data.rounded_local_time.dt.strftime("%Y-%m-%d %H:%M:%S") cgm_df['glucose_values'] = round(cgm_data.value * 18.01559) cgm_df['actual_blood_glucose'] = cgm_df['glucose_values'] cgm_df['glucose_units'] = 'mg/dL' @@ -459,20 +512,20 @@ def get_settings(): # Create default df_settings - df_settings.loc['model'] = [[360.0, 65]] - df_settings.loc['momentum_data_interval'] = 15 - df_settings.loc['suspend_threshold'] = 70 - df_settings.loc['dynamic_carb_absorption_enabled'] = True - df_settings.loc['retrospective_correction_integration_interval'] = 30 - df_settings.loc['recency_interval'] = 15 - df_settings.loc['retrospective_correction_grouping_interval'] = 30 - df_settings.loc['rate_rounder'] = 0.05 - df_settings.loc['insulin_delay'] = 10 - df_settings.loc['carb_delay'] = 10 - df_settings.loc['default_absorption_times'] = [[120.0, 180.0, 240.0]] - df_settings.loc['max_basal_rate'] = 35 - df_settings.loc['max_bolus'] = 30 - df_settings.loc['retrospective_correction_enabled'] = True + df_settings.loc['model'] = '[360.0, 65]' + df_settings.loc['momentum_data_interval'] = '15' + df_settings.loc['suspend_threshold'] = '70' + df_settings.loc['dynamic_carb_absorption_enabled'] = 'TRUE' + df_settings.loc['retrospective_correction_integration_interval'] = '30' + df_settings.loc['recency_interval'] = '15' + df_settings.loc['retrospective_correction_grouping_interval'] = '30' + df_settings.loc['rate_rounder'] = '0.05' + df_settings.loc['insulin_delay'] = '10' + df_settings.loc['carb_delay'] = '10' + df_settings.loc['default_absorption_times'] = '[120.0, 180.0, 240.0]' + df_settings.loc['max_basal_rate'] = '35' + df_settings.loc['max_bolus'] = '30' + df_settings.loc['retrospective_correction_enabled'] = 'TRUE' return df_settings @@ -480,22 +533,27 @@ def get_settings(): def get_snapshot(data, file_name, evaluation_point_loc): - """Main function wrapper to assemble snapshot dataframes""" # Start by getting the 48-hour window ± 24hrs around the evaluation point evaluation_index = data.index[data.id == evaluation_point_loc] - data['time'] = pd.to_datetime(data['time'], utc=True) - evaluation_time = pd.to_datetime(data.time[evaluation_index].values[0], - utc=True) + data["rounded_local_time"] = \ + pd.to_datetime(data["est.localTime"], + utc=True).dt.ceil(freq="5min") + + evaluation_time = \ + pd.to_datetime(data.loc[evaluation_index, + 'rounded_local_time'].values[0], + utc=True) df_misc = get_time_to_calculate_at(evaluation_time) start_time = evaluation_time - datetime.timedelta(days=1) end_time = evaluation_time + datetime.timedelta(days=1) - snapshot_df = data[(data.time >= start_time) & (data.time <= end_time)] + snapshot_df = data[(data['rounded_local_time'] >= start_time) & + (data['rounded_local_time'] <= end_time)] # Get pumpSettings list of active schedules active_schedules = get_active_schedules(data, @@ -524,6 +582,71 @@ def get_snapshot(data, ) +def dataframe_inputs_to_dict(dfs, df_misc, df_settings): + """Function from pyLoopKit's input_data_tools.py + write the dataframes back to one dictionary + """ + input_dictionary = dict() + input_dictionary = df_misc.to_dict()[0] + for df in dfs: + for col in df.columns: + if "units" not in col: + input_dictionary[col] = df[col].tolist() + else: + input_dictionary[col] = df[col].unique()[0] + + input_dictionary["settings_dictionary"] = df_settings.to_dict()["settings"] + + # set the format back for the edge cases + input_dictionary["settings_dictionary"]["model"] = np.safe_eval( + input_dictionary["settings_dictionary"]["model"] + ) + input_dictionary["settings_dictionary"]["default_absorption_times"] = ( + np.safe_eval( + input_dictionary["settings_dictionary"]["default_absorption_times"] + ) + ) + + input_dictionary["offset_applied_to_dates"] = ( + int(input_dictionary["offset_applied_to_dates"]) + ) + + return input_dictionary + + +def combine_into_one_dataframe(snapshot): + """Function from pyLoopKit's input_data_tools.py + Combines the dataframes into one big dataframe, + put glucose at end since that trace is typically long + """ + ( + df_basal_rate, df_carb, df_carb_ratio, df_dose, df_glucose, + df_last_temporary_basal, df_misc, df_sensitivity_ratio, + df_settings, df_target_range + ) = snapshot + + combined_df = pd.DataFrame() + combined_df = pd.concat([combined_df, df_settings]) + combined_df = pd.concat([combined_df, df_misc]) + + dfs = [ + df_basal_rate, df_carb, df_carb_ratio, df_dose, + df_last_temporary_basal, df_sensitivity_ratio, + df_target_range, df_glucose + ] + + for df in dfs: + df.reset_index(drop=True, inplace=True) + combined_df = pd.concat([combined_df, df.T]) + + # move settings back to the front of the dataframe + combined_df = combined_df[np.append("settings", combined_df.columns[0:-1])] + combined_df = \ + combined_df.reset_index().rename(columns={'index': 'setting_name'}) + + return combined_df + + def export_snapshot(snapshot, file_name, condition_num, @@ -533,11 +656,11 @@ def export_snapshot(snapshot, if not os.path.exists(export_folder): os.makedirs(export_folder) - export_filename = file_name + "_condition" + str(condition_num) + ".pkl" + export_filename = file_name + "_condition" + str(condition_num) + ".csv" export_path = export_folder + "/" + export_filename - with open(export_path, 'wb') as pickle_file: - pickle.dump(snapshot, pickle_file) + combined_df = combine_into_one_dataframe(snapshot) + combined_df.to_csv(export_path, index=False) print("Successfully completed: " + export_filename) From 61eaa9734afa68b51796116d2dc635fbe0599d40 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 18 Feb 2020 13:48:22 -0500 Subject: [PATCH 30/57] Add carbInput as a snapshot requirement --- .../iCGM-test-matrix/icgm_condition_finder.py | 31 ++++++++++++++++--- 1 file changed, 27 insertions(+), 4 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 6c733926..fd71f36d 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -127,9 +127,14 @@ def create_5min_contiguous_df(data): # Separate CGM, Bolus, and Basal data cgm_df = data[data.type == "cbg"].copy() - cgm_df.drop(columns=["normal", "duration"], inplace=True) + cgm_df.drop(columns=["normal", + "duration", + "rate", + "carbInput"], inplace=True) bolus_df = data[data.type == "bolus"].copy() + bolus_df.drop(columns=['rate'], inplace=True) basal_df = data[data.type == "basal"].copy() + basal_df.drop(columns=['carbInput'], inplace=True) # CGM Data Processing # Convert value from mmol/L to mg/dL @@ -152,12 +157,23 @@ def create_5min_contiguous_df(data): remove_rounded_CGM_duplicates(contiguous_df) # Merge boluses together into single 5-minute timestamps - bolus_df = pd.DataFrame( + bolus_merged_df = pd.DataFrame( bolus_df.groupby('rounded_local_time').normal.sum() ).reset_index() contiguous_df = pd.merge(contiguous_df, - bolus_df, + bolus_merged_df, + how="left", + on="rounded_local_time" + ) + + # Merge carb entires together into single 5-minute timestamps + carbs_merged_df = pd.DataFrame( + bolus_df.groupby('rounded_local_time').carbInput.sum() + ).reset_index() + + contiguous_df = pd.merge(contiguous_df, + carbs_merged_df, how="left", on="rounded_local_time" ) @@ -323,9 +339,16 @@ def rolling_48hour_pump_data(contiguous_df): min_periods=1, center=True).count() >= 1 + contiguous_df["rolling_48hour_carbs_gte1"] = \ + contiguous_df['carbInput'].rolling( + window=288*2, + min_periods=1, + center=True).count() >= 1 + contiguous_df["rolling_48hour_pump_data"] = \ contiguous_df["rolling_48hour_bolus_gte2"] & \ - contiguous_df["rolling_48hour_basal_gte1"] + contiguous_df["rolling_48hour_basal_gte1"] & \ + contiguous_df["rolling_48hour_carbs_gte1"] return contiguous_df From dc8c04f5584d8598116d896e6f3ff3c27b7a0231 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 18 Feb 2020 13:49:25 -0500 Subject: [PATCH 31/57] Remove no_travel as a snapshot requirement The est.annotations metric for finding travel is not always correct and leads to a lot of good data being thrown out. --- projects/iCGM-test-matrix/icgm_condition_finder.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index fd71f36d..88a6c0a7 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -455,12 +455,13 @@ def get_evaluation_points(contiguous_df): # Set window size of snapshot window_size = 288*2 - # Create bool of all values with a condition and max gap <= 15min + # Create bool of all values with a condition, pump data, + # and max gap <= 15min qualified_bool = \ (contiguous_df["condition"] > 0) & \ (contiguous_df["rolling_48hour_max_gap"] <= 3) & \ - (contiguous_df["rolling_48hour_pump_data"]) & \ - (contiguous_df["rolling_48hour_no_travel"]) + (contiguous_df["rolling_48hour_pump_data"]) # & \ +# (contiguous_df["rolling_48hour_no_travel"]) qualified_condition_list = \ contiguous_df.loc[qualified_bool, "condition"].copy() @@ -586,7 +587,7 @@ def main(data, file_name): contiguous_df = rolling_48hour_pump_data(contiguous_df) # Add 48-hour window bools for travel data - contiguous_df = rolling_48hour_check_travel(contiguous_df) + # contiguous_df = rolling_48hour_check_travel(contiguous_df) # Get the locations of each evaluation point in a 48-hour snapshot evaluation_points = get_evaluation_points(contiguous_df) From 5d557a836bbb60020556601c2a95c7b5fbab77a1 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 18 Feb 2020 15:33:00 -0500 Subject: [PATCH 32/57] Update activeSchedule finder and bgTarget logic --- .../iCGM-test-matrix/snapshot_processor.py | 155 +++++++++--------- 1 file changed, 75 insertions(+), 80 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index b722cbe7..a8cf90cb 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -15,7 +15,6 @@ # %% Imports import pandas as pd -import pickle import numpy as np import datetime import ast @@ -23,46 +22,37 @@ # %% Functions -def get_active_schedules(data, - snapshot_df, - file_name, - evaluation_point_loc, - evaluation_time): +def get_active_schedule(data, + snapshot_df, + file_name, + evaluation_point_loc, + evaluation_time): """Get pumpSettings row of active schedules used during the snapshot.""" # Get all schedules and their upload ids schedules = data[data.activeSchedule.notnull()] - # Check which schedule matches the active pump upload during the snapshot - active_schedules = \ - schedules[schedules.uploadId.isin(snapshot_df.uploadId)] + if len(schedules) == 0: + print("NO ACTIVE SCHEDULES FOUND IN DATA!") + active_schedule = np.nan - # If more than one active schedule, pick the one closest to the time of - # the evaluation point - if(len(active_schedules) > 1): - print(file_name + - " - " + - evaluation_point_loc + - " MULTIPLE ACTIVE SCHEDULES IN ONE SNAPSHOT!") - - nearest_uploadid = \ - snapshot_df.loc[((snapshot_df.type == 'bolus') | - (snapshot_df.type == 'basal')) & - (snapshot_df.rounded_local_time < evaluation_time), - 'uploadId'].values[-1] - - active_schedules = \ - active_schedules[active_schedules.uploadId == nearest_uploadid] + else: + # Check which schedule matches the active pump uploads in the snapshot + active_schedule = \ + schedules[schedules.uploadId.isin(snapshot_df.uploadId)] - # If no active schedule is assigned during an upload, pick the one closest - # to the time of the evaluation point - if len(active_schedules) == 0: - nearest_schedule_time = \ - schedules.loc[schedules.rounded_local_time < evaluation_time, - 'rounded_local_time'].max() - active_schedules = \ - schedules[schedules.rounded_local_time == nearest_schedule_time] + # If no matching uploadId is found OR more multiple schedule are found, + # then historical schedule records are available. + # Pick the one closest to the time of evaluation. + if (len(active_schedule) > 1) | (len(active_schedule) == 0): + nearest_schedule_time = \ + schedules.loc[schedules.rounded_local_time < evaluation_time, + 'rounded_local_time'].max() + active_schedule = \ + schedules[( + schedules.rounded_local_time == nearest_schedule_time) + ] - return active_schedules + return active_schedule def get_relative_timestamp(timestamp_ms): @@ -83,7 +73,7 @@ def get_relative_timestamp(timestamp_ms): return relative_timestamp -def get_basal_rates(active_schedules): +def get_basal_rates(active_schedule): """Get the basal rates from the active schedule""" df_columns = ['basal_rate_start_times', 'basal_rate_minutes', @@ -94,9 +84,9 @@ def get_basal_rates(active_schedules): basal_rates = pd.DataFrame(columns=df_columns) basal_schedules = \ - ast.literal_eval(active_schedules.basalSchedules.values[0]) + ast.literal_eval(active_schedule.basalSchedules.values[0]) - active_name = active_schedules.activeSchedule.values[0] + active_name = active_schedule.activeSchedule.values[0] active_basal_rates = basal_schedules.get(active_name) start_times = [] @@ -121,7 +111,7 @@ def get_basal_rates(active_schedules): return basal_rates -def get_carb_ratios(active_schedules): +def get_carb_ratios(active_schedule): """Get the carb to insulin ratio(s) from the active schedule""" df_columns = ['carb_ratio_start_times', @@ -131,14 +121,14 @@ def get_carb_ratios(active_schedules): carb_ratios = pd.DataFrame(columns=df_columns) - if 'carbRatios' in active_schedules: + if 'carbRatios' in active_schedule: carb_ratio_schedules = \ - ast.literal_eval(active_schedules.carbRatios.values[0]) - active_name = active_schedules.activeSchedule.values[0] + ast.literal_eval(active_schedule.carbRatios.values[0]) + active_name = active_schedule.activeSchedule.values[0] active_carb_ratios = carb_ratio_schedules.get(active_name) - elif 'carbRatio' in active_schedules: + elif 'carbRatio' in active_schedule: active_carb_ratios = \ - ast.literal_eval(active_schedules.carbRatio.values[0]) + ast.literal_eval(active_schedule.carbRatio.values[0]) else: print("NO CARB RATIOS!") @@ -157,7 +147,7 @@ def get_carb_ratios(active_schedules): return carb_ratios -def get_isfs(active_schedules): +def get_isfs(active_schedule): """Get the insulin sensitivity ratio(s) from the active schedule""" df_columns = ['sensitivity_ratio_start_times', @@ -168,14 +158,14 @@ def get_isfs(active_schedules): isfs = pd.DataFrame(columns=df_columns) - if 'insulinSensitivities' in active_schedules: + if 'insulinSensitivities' in active_schedule: isf_schedules = \ - ast.literal_eval(active_schedules.insulinSensitivities.values[0]) - active_name = active_schedules.activeSchedule.values[0] + ast.literal_eval(active_schedule.insulinSensitivities.values[0]) + active_name = active_schedule.activeSchedule.values[0] active_isfs = isf_schedules.get(active_name) - elif 'insulinSensitivity' in active_schedules: + elif 'insulinSensitivity' in active_schedule: active_isfs = \ - ast.literal_eval(active_schedules.insulinSensitivity.values[0]) + ast.literal_eval(active_schedule.insulinSensitivity.values[0]) else: print("NO ISFS FOUND!") @@ -201,7 +191,7 @@ def get_isfs(active_schedules): return isfs -def get_target_ranges(active_schedules): +def get_target_ranges(active_schedule): """Get the target BG ranges from the active schedule""" df_columns = ['target_range_start_times', @@ -212,32 +202,32 @@ def get_target_ranges(active_schedules): df_target_range = pd.DataFrame(columns=df_columns) - if 'bgTargets' in active_schedules: + if 'bgTargets' in active_schedule: target_schedules = \ - ast.literal_eval(active_schedules.bgTargets.values[0]) + ast.literal_eval(active_schedule.bgTargets.values[0]) - active_name = active_schedules.activeSchedule.values[0] + active_name = active_schedule.activeSchedule.values[0] active_targets = target_schedules.get(active_name) - elif 'bgTarget' in active_schedules: - active_targets = ast.literal_eval(active_schedules.bgTarget.values[0]) - elif 'bgTarget.start' in active_schedules: - target_start = int(active_schedules['bgTarget.start'].values) + elif 'bgTarget' in active_schedule: + active_targets = ast.literal_eval(active_schedule.bgTarget.values[0]) + elif 'bgTarget.start' in active_schedule: + target_start = int(active_schedule['bgTarget.start'].values) active_targets = dict({'start': target_start}) - if 'bgTarget.target' in active_schedules: - bg_target = float(active_schedules['bgTarget.target'].values) + if 'bgTarget.target' in active_schedule: + bg_target = float(active_schedule['bgTarget.target'].values) active_targets.update({'target': bg_target}) - if 'bgTarget.range' in active_schedules: - target_range = float(active_schedules['bgTarget.range'].values) + if 'bgTarget.range' in active_schedule: + target_range = float(active_schedule['bgTarget.range'].values) active_targets.update({'range': target_range}) - if 'bgTarget.high' in active_schedules: - target_high = float(active_schedules['bgTarget.high'].values) + if 'bgTarget.high' in active_schedule: + target_high = float(active_schedule['bgTarget.high'].values) active_targets.update({'high': target_high}) - if 'bgTarget.low' in active_schedules: - target_low = float(active_schedules['bgTarget.low'].values) + if 'bgTarget.low' in active_schedule: + target_low = float(active_schedule['bgTarget.low'].values) active_targets.update({'low': target_low}) active_targets = [active_targets] @@ -255,9 +245,13 @@ def get_target_ranges(active_schedules): df_target_range.loc[target_loc, 'target_range_start_times'] = start_string - target = round(target_info.get('target')*18.01559) - df_target_range.loc[target_loc, 'target_range_minimum_values'] = target - df_target_range.loc[target_loc, 'target_range_maximum_values'] = target + + if 'target' in target_info.keys(): + target = round(target_info.get('target')*18.01559) + df_target_range.loc[target_loc, 'target_range_minimum_values'] = \ + target + df_target_range.loc[target_loc, 'target_range_maximum_values'] = \ + target if 'range' in target_info.keys(): target_range = round(target_info.get('range')*18.01559) @@ -556,18 +550,19 @@ def get_snapshot(data, (data['rounded_local_time'] <= end_time)] # Get pumpSettings list of active schedules - active_schedules = get_active_schedules(data, - snapshot_df, - file_name, - evaluation_point_loc, - evaluation_time) - - active_schedules = active_schedules.dropna(axis=1) - - basal_rates = get_basal_rates(active_schedules) - carb_ratios = get_carb_ratios(active_schedules) - isfs = get_isfs(active_schedules) - df_target_range = get_target_ranges(active_schedules) + active_schedule = get_active_schedule(data, + snapshot_df, + file_name, + evaluation_point_loc, + evaluation_time) + + # Drop unused columns that may have been used in other schedule types + active_schedule = active_schedule.dropna(axis=1) + + basal_rates = get_basal_rates(active_schedule) + carb_ratios = get_carb_ratios(active_schedule) + isfs = get_isfs(active_schedule) + df_target_range = get_target_ranges(active_schedule) carb_events = get_carb_events(snapshot_df) dose_events = get_dose_events(snapshot_df) From 83167f6043fe2a5b28b646b3d1c57ecb24cb1eaf Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 18 Feb 2020 15:48:31 -0500 Subject: [PATCH 33/57] Update carb event finding for snapshots --- projects/iCGM-test-matrix/icgm_condition_finder.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 88a6c0a7..aa17528f 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -339,16 +339,16 @@ def rolling_48hour_pump_data(contiguous_df): min_periods=1, center=True).count() >= 1 - contiguous_df["rolling_48hour_carbs_gte1"] = \ + contiguous_df["rolling_48hour_carbs_gt0"] = \ contiguous_df['carbInput'].rolling( window=288*2, min_periods=1, - center=True).count() >= 1 + center=True).sum() > 0 contiguous_df["rolling_48hour_pump_data"] = \ contiguous_df["rolling_48hour_bolus_gte2"] & \ contiguous_df["rolling_48hour_basal_gte1"] & \ - contiguous_df["rolling_48hour_carbs_gte1"] + contiguous_df["rolling_48hour_carbs_gt0"] return contiguous_df From cf0506d32b0e8f074d9f17850aaa2ee4dcd5ff02 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 20 Feb 2020 10:00:03 -0500 Subject: [PATCH 34/57] Update schedule finding algorithm --- .../iCGM-test-matrix/snapshot_processor.py | 44 ++++++++++++++----- 1 file changed, 34 insertions(+), 10 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index a8cf90cb..b698678f 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -29,27 +29,51 @@ def get_active_schedule(data, evaluation_time): """Get pumpSettings row of active schedules used during the snapshot.""" # Get all schedules and their upload ids - schedules = data[data.activeSchedule.notnull()] + schedules = data[data.activeSchedule.notnull()].copy() if len(schedules) == 0: print("NO ACTIVE SCHEDULES FOUND IN DATA!") active_schedule = np.nan else: - # Check which schedule matches the active pump uploads in the snapshot + # Get the nearest pump uploadId to the evaluation_time + snapshot_uploadId = \ + snapshot_df.loc[((snapshot_df.type == 'bolus') | + (snapshot_df.type == 'basal')), + 'uploadId'].values[0] + active_schedule = \ - schedules[schedules.uploadId.isin(snapshot_df.uploadId)] + schedules[schedules.uploadId == snapshot_uploadId] # If no matching uploadId is found OR more multiple schedule are found, - # then historical schedule records are available. - # Pick the one closest to the time of evaluation. + # then historical schedule records should be available. + # Pick the one closest prior to the time of evaluation. if (len(active_schedule) > 1) | (len(active_schedule) == 0): - nearest_schedule_time = \ - schedules.loc[schedules.rounded_local_time < evaluation_time, - 'rounded_local_time'].max() + nearest_time = \ + active_schedule.loc[ + active_schedule.rounded_local_time < evaluation_time, + 'rounded_local_time'].max() + + active_schedule = \ + active_schedule[( + active_schedule.rounded_local_time == nearest_time) + ] + + # If no active schedule can STILL be found, then there is no matching + # uploadid or nearby historical record. The schedule may be able to be + # reverse-engineered from actual bolus entry settings metadata. + + # For now, select the schedule closest to the evaluation point + + if (len(active_schedule) == 0): + print("NO ACTIVE SCHEDULE MATCHED - FORCED SELECTION") + schedules['distances_from_evaluation'] = \ + abs(schedules.rounded_local_time - evaluation_time) + + min_distance = schedules['distances_from_evaluation'].min() active_schedule = \ - schedules[( - schedules.rounded_local_time == nearest_schedule_time) + schedules[ + schedules['distances_from_evaluation'] == min_distance ] return active_schedule From 95def256267aa79b67ba496d1cb735cd566a573b Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 20 Feb 2020 10:01:38 -0500 Subject: [PATCH 35/57] Add CGM Smoothing --- .../iCGM-test-matrix/snapshot_processor.py | 28 +++++++++++++++---- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index b698678f..4aba3593 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -453,7 +453,7 @@ def get_dose_events(snapshot_df): return dose_events -def get_cgm_df(snapshot_df): +def get_cgm_df(snapshot_df, smooth_cgm): """Gets the CGM values from the snapshot""" df_columns = ['glucose_dates', @@ -475,6 +475,14 @@ def get_cgm_df(snapshot_df): cgm_df['glucose_dates'] = \ cgm_data.rounded_local_time.dt.strftime("%Y-%m-%d %H:%M:%S") cgm_df['glucose_values'] = round(cgm_data.value * 18.01559) + + if(smooth_cgm): + cgm_rolling = cgm_df.actual_blood_glucose.rolling(window=12, + min_periods=1, + center=True) + + cgm_df['glucose_values'] = cgm_rolling.mean().round().astype(int) + cgm_df['actual_blood_glucose'] = cgm_df['glucose_values'] cgm_df['glucose_units'] = 'mg/dL' @@ -550,7 +558,9 @@ def get_settings(): def get_snapshot(data, file_name, - evaluation_point_loc): + evaluation_point_loc, + smooth_cgm, + simplify_settings): """Main function wrapper to assemble snapshot dataframes""" # Start by getting the 48-hour window ± 24hrs around the evaluation point @@ -590,7 +600,7 @@ def get_snapshot(data, carb_events = get_carb_events(snapshot_df) dose_events = get_dose_events(snapshot_df) - cgm_df = get_cgm_df(snapshot_df) + cgm_df = get_cgm_df(snapshot_df, smooth_cgm) df_last_temporary_basal = get_last_temp_basal() df_settings = get_settings() @@ -693,7 +703,10 @@ def export_snapshot(snapshot, condition_file = "PHI-batch-icgm-condition-stats-2020-02-11.csv" condition_df = pd.read_csv(condition_file, low_memory=False) - file_selection = 1 + # Snapshot processing parameters + smooth_cgm = True + simplify_settings = True + file_name = condition_df.loc[file_selection, 'file_name'] # Location of csvs @@ -708,7 +721,12 @@ def export_snapshot(snapshot, if not pd.isnull(evaluation_point_loc): - snapshot = get_snapshot(data, file_name, evaluation_point_loc) + snapshot = get_snapshot(data, + file_name, + evaluation_point_loc, + smooth_cgm, + simplify_settings + ) export_folder = "snapshot_export" From f924a1f5c08ca0c5f97acf58e7e7bd10727a501a Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 20 Feb 2020 10:38:26 -0500 Subject: [PATCH 36/57] Add functions to simplify settings and add empty events --- .../iCGM-test-matrix/snapshot_processor.py | 119 +++++++++++++++++- 1 file changed, 117 insertions(+), 2 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 4aba3593..161eb746 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -556,11 +556,110 @@ def get_settings(): return df_settings +def get_simple_settings(basal_rates, + carb_ratios, + isfs, + df_target_range): + + # Get the weighted average basal_rates + simple_basal_rate = pd.DataFrame(columns=basal_rates.columns, index=[0]) + + weighted_avg_basal_rate = np.round( + (np.sum(basal_rates["basal_rate_minutes"] * + basal_rates["actual_basal_rates"]) / + basal_rates["basal_rate_minutes"].sum()), 2) + + simple_basal_rate.loc[0, :] = ( + datetime.time(0, 0).strftime('%H:%M:%S'), + 60*24, + weighted_avg_basal_rate, + weighted_avg_basal_rate, + "U/hr" + ) + + # Get average carb ratio + simple_carb_ratio = pd.DataFrame(columns=carb_ratios.columns, index=[0]) + + average_cir = carb_ratios["carb_ratio_values"].mean().round().astype(int) + + simple_carb_ratio.loc[0, :] = ( + datetime.time(0, 0).strftime('%H:%M:%S'), + average_cir, + average_cir, + "g/U" + ) + + # Get average isf + simple_isf = pd.DataFrame(columns=isfs.columns, index=[0]) + + average_isf = isfs["sensitivity_ratio_values"].mean().round().astype(int) + + simple_isf.loc[0, :] = ( + datetime.time(0, 0).strftime('%H:%M:%S'), + datetime.time(0, 0).strftime('%H:%M:%S'), + average_isf, + average_isf, + "mg/dL/U" + ) + + # Set bg targets to 100-120 + simple_targets = pd.DataFrame(columns=df_target_range.columns, index=[0]) + + simple_isf.loc[0, :] = ( + datetime.time(0, 0).strftime('%H:%M:%S'), + datetime.time(0, 0).strftime('%H:%M:%S'), + 100, + 120, + "mg/dL" + ) + + return simple_basal_rate, simple_carb_ratio, simple_isf, simple_targets + + +def add_empty_start_events(carb_events, + dose_events, + cgm_df): + + first_cgm_time = cgm_df["glucose_dates"].min() + + # Create empty carb event + empty_carb_event = pd.DataFrame(columns=carb_events.columns, index=[0]) + empty_carb_event.loc[0, :] = ( + first_cgm_time, + 0, + 0, + 180, + "g" + ) + + # Create empty dose event + empty_dose_event = pd.DataFrame(columns=dose_events.columns, index=[0]) + empty_dose_event.loc[0, :] = ( + "DoseType.bolus", + first_cgm_time, + first_cgm_time, + 0, + 0, + "U" + ) + + if(first_cgm_time not in carb_events["carb_dates"]): + carb_events = pd.concat([empty_carb_event, + carb_events]).reset_index(drop=True) + + if(first_cgm_time not in dose_events["dose_start_times"]): + dose_events = pd.concat([empty_dose_event, + dose_events]).reset_index(drop=True) + + return carb_events, dose_events + + def get_snapshot(data, file_name, evaluation_point_loc, smooth_cgm, - simplify_settings): + simplify_settings, + empty_start_events): """Main function wrapper to assemble snapshot dataframes""" # Start by getting the 48-hour window ± 24hrs around the evaluation point @@ -605,6 +704,20 @@ def get_snapshot(data, df_last_temporary_basal = get_last_temp_basal() df_settings = get_settings() + if(simplify_settings): + (basal_rates, + carb_ratios, + isfs, + df_target_range) = get_simple_settings(basal_rates, + carb_ratios, + isfs, + df_target_range) + + if(empty_start_events): + carb_events, dose_events = add_empty_start_events(carb_events, + dose_events, + cgm_df) + return (basal_rates, carb_events, carb_ratios, dose_events, cgm_df, df_last_temporary_basal, df_misc, isfs, df_settings, df_target_range @@ -706,6 +819,7 @@ def export_snapshot(snapshot, # Snapshot processing parameters smooth_cgm = True simplify_settings = True + empty_start_events = True file_name = condition_df.loc[file_selection, 'file_name'] @@ -725,7 +839,8 @@ def export_snapshot(snapshot, file_name, evaluation_point_loc, smooth_cgm, - simplify_settings + simplify_settings, + empty_start_events ) export_folder = "snapshot_export" From d0fd8e9c95a1cd0092d4fecdfb36a7f3b7d45c7a Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 20 Feb 2020 10:57:40 -0500 Subject: [PATCH 37/57] Fix CGM rolling error and target bg bug --- projects/iCGM-test-matrix/snapshot_processor.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 161eb746..33fa51a5 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -477,9 +477,9 @@ def get_cgm_df(snapshot_df, smooth_cgm): cgm_df['glucose_values'] = round(cgm_data.value * 18.01559) if(smooth_cgm): - cgm_rolling = cgm_df.actual_blood_glucose.rolling(window=12, - min_periods=1, - center=True) + cgm_rolling = cgm_df['glucose_values'].rolling(window=12, + min_periods=1, + center=True) cgm_df['glucose_values'] = cgm_rolling.mean().round().astype(int) @@ -605,7 +605,7 @@ def get_simple_settings(basal_rates, # Set bg targets to 100-120 simple_targets = pd.DataFrame(columns=df_target_range.columns, index=[0]) - simple_isf.loc[0, :] = ( + simple_targets.loc[0, :] = ( datetime.time(0, 0).strftime('%H:%M:%S'), datetime.time(0, 0).strftime('%H:%M:%S'), 100, From 69ccdea7db138532eb03fe9764ac06fbdd2f5fed Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 20 Feb 2020 17:00:08 -0500 Subject: [PATCH 38/57] Update empty carb/dose events to remove all other events from dataframe --- .../iCGM-test-matrix/snapshot_processor.py | 30 +++++++------------ 1 file changed, 11 insertions(+), 19 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 33fa51a5..91a36454 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -616,9 +616,9 @@ def get_simple_settings(basal_rates, return simple_basal_rate, simple_carb_ratio, simple_isf, simple_targets -def add_empty_start_events(carb_events, - dose_events, - cgm_df): +def create_empty_events(carb_events, + dose_events, + cgm_df): first_cgm_time = cgm_df["glucose_dates"].min() @@ -643,15 +643,7 @@ def add_empty_start_events(carb_events, "U" ) - if(first_cgm_time not in carb_events["carb_dates"]): - carb_events = pd.concat([empty_carb_event, - carb_events]).reset_index(drop=True) - - if(first_cgm_time not in dose_events["dose_start_times"]): - dose_events = pd.concat([empty_dose_event, - dose_events]).reset_index(drop=True) - - return carb_events, dose_events + return empty_carb_event, empty_dose_event def get_snapshot(data, @@ -659,7 +651,7 @@ def get_snapshot(data, evaluation_point_loc, smooth_cgm, simplify_settings, - empty_start_events): + empty_events): """Main function wrapper to assemble snapshot dataframes""" # Start by getting the 48-hour window ± 24hrs around the evaluation point @@ -713,10 +705,10 @@ def get_snapshot(data, isfs, df_target_range) - if(empty_start_events): - carb_events, dose_events = add_empty_start_events(carb_events, - dose_events, - cgm_df) + if(empty_events): + carb_events, dose_events = create_empty_events(carb_events, + dose_events, + cgm_df) return (basal_rates, carb_events, carb_ratios, dose_events, cgm_df, df_last_temporary_basal, df_misc, isfs, @@ -819,7 +811,7 @@ def export_snapshot(snapshot, # Snapshot processing parameters smooth_cgm = True simplify_settings = True - empty_start_events = True + empty_events = True file_name = condition_df.loc[file_selection, 'file_name'] @@ -840,7 +832,7 @@ def export_snapshot(snapshot, evaluation_point_loc, smooth_cgm, simplify_settings, - empty_start_events + empty_events ) export_folder = "snapshot_export" From 1e310a9b535f645f5472f4dba226a78fa42b8b04 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 20 Feb 2020 17:08:25 -0500 Subject: [PATCH 39/57] Add placeholder for age and ylw --- .../iCGM-test-matrix/snapshot_processor.py | 23 +++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 91a36454..5416896c 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -514,11 +514,15 @@ def get_time_to_calculate_at(evaluation_time): return df_misc -def get_settings(): +def get_settings(birthdate, + diagnosis_date, + evaluation_time): """Provides a default settings dataframe""" df_columns = ['settings'] - df_index = ['model', + df_index = ['age', + 'ylw', + 'model', 'momentum_data_interval', 'suspend_threshold', 'dynamic_carb_absorption_enabled', @@ -537,7 +541,8 @@ def get_settings(): columns=df_columns) # Create default df_settings - + df_settings.loc['age'] = 'placeholder_age' + df_settings.loc['ylw'] = 'placeholder_ylw' df_settings.loc['model'] = '[360.0, 65]' df_settings.loc['momentum_data_interval'] = '15' df_settings.loc['suspend_threshold'] = '70' @@ -649,6 +654,8 @@ def create_empty_events(carb_events, def get_snapshot(data, file_name, evaluation_point_loc, + birthdate, + diagnosis_date, smooth_cgm, simplify_settings, empty_events): @@ -694,7 +701,9 @@ def get_snapshot(data, cgm_df = get_cgm_df(snapshot_df, smooth_cgm) df_last_temporary_basal = get_last_temp_basal() - df_settings = get_settings() + df_settings = get_settings(birthdate, + diagnosis_date, + evaluation_time) if(simplify_settings): (basal_rates, @@ -815,6 +824,10 @@ def export_snapshot(snapshot, file_name = condition_df.loc[file_selection, 'file_name'] + # TODO: Bring in birthdate/diagnosis_date metadata for each person + birthdate = "placeholder_age" + diagnosis_date = "placeholder_ylw" + # Location of csvs data_location = "train-data/" file_path = os.path.join(data_location, file_name) @@ -830,6 +843,8 @@ def export_snapshot(snapshot, snapshot = get_snapshot(data, file_name, evaluation_point_loc, + birthdate, + diagnosis_date, smooth_cgm, simplify_settings, empty_events From e09eb97d51cd98112e7353e584ce47423696dc1d Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 21 Feb 2020 18:56:17 -0500 Subject: [PATCH 40/57] Update active schedule algorithm --- projects/iCGM-test-matrix/snapshot_processor.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 5416896c..b8426be8 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -50,15 +50,20 @@ def get_active_schedule(data, # Pick the one closest prior to the time of evaluation. if (len(active_schedule) > 1) | (len(active_schedule) == 0): nearest_time = \ - active_schedule.loc[ - active_schedule.rounded_local_time < evaluation_time, + schedules.loc[ + schedules.rounded_local_time < evaluation_time, 'rounded_local_time'].max() active_schedule = \ - active_schedule[( - active_schedule.rounded_local_time == nearest_time) + schedules[( + schedules.rounded_local_time == nearest_time) ] + # If two active schedules are set within 5 minutes of each other + # Select the last one + if(len(active_schedule) > 1): + active_schedule = active_schedule.head(1) + # If no active schedule can STILL be found, then there is no matching # uploadid or nearby historical record. The schedule may be able to be # reverse-engineered from actual bolus entry settings metadata. From 6680bde9eb91f384b75d283e0b1ad125872daa87 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Fri, 21 Feb 2020 18:57:10 -0500 Subject: [PATCH 41/57] Add a cgm data validation for 576 points --- .../iCGM-test-matrix/snapshot_processor.py | 37 +++++++++++++++++-- 1 file changed, 33 insertions(+), 4 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index b8426be8..4390e19b 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -470,6 +470,27 @@ def get_cgm_df(snapshot_df, smooth_cgm): cgm_data = snapshot_df[snapshot_df.type == 'cbg'].copy() + first_timestamp = cgm_data["rounded_local_time"].min() + last_timestamp = cgm_data["rounded_local_time"].max() + + ts_len = len(pd.date_range(first_timestamp, last_timestamp, freq="5min")) + + # Force cgm dataframe length to 576 + if ts_len < 576: + delta_to_add = 576 - ts_len + last_timestamp = \ + last_timestamp + datetime.timedelta(minutes=5*delta_to_add) + + contiguous_ts = pd.date_range(first_timestamp, last_timestamp, freq="5min") + + contiguous_df = pd.DataFrame(contiguous_ts, columns=["rounded_local_time"]) + + cgm_data = pd.merge(contiguous_df, + cgm_data, + how="left", + on="rounded_local_time" + ) + # Drop cgm duplicates in snapshot (if any) cgm_data.sort_values(['uploadId', 'rounded_local_time'], ascending=True, @@ -477,6 +498,10 @@ def get_cgm_df(snapshot_df, smooth_cgm): cgm_data.drop_duplicates('rounded_local_time', inplace=True) + cgm_data.sort_values(['rounded_local_time'], + ascending=True, + inplace=True) + cgm_df['glucose_dates'] = \ cgm_data.rounded_local_time.dt.strftime("%Y-%m-%d %H:%M:%S") cgm_df['glucose_values'] = round(cgm_data.value * 18.01559) @@ -546,8 +571,8 @@ def get_settings(birthdate, columns=df_columns) # Create default df_settings - df_settings.loc['age'] = 'placeholder_age' - df_settings.loc['ylw'] = 'placeholder_ylw' + df_settings.loc['age'] = '0' + df_settings.loc['ylw'] = '0' df_settings.loc['model'] = '[360.0, 65]' df_settings.loc['momentum_data_interval'] = '15' df_settings.loc['suspend_threshold'] = '70' @@ -684,7 +709,7 @@ def get_snapshot(data, end_time = evaluation_time + datetime.timedelta(days=1) snapshot_df = data[(data['rounded_local_time'] >= start_time) & - (data['rounded_local_time'] <= end_time)] + (data['rounded_local_time'] < end_time)] # Get pumpSettings list of active schedules active_schedule = get_active_schedule(data, @@ -694,7 +719,11 @@ def get_snapshot(data, evaluation_time) # Drop unused columns that may have been used in other schedule types - active_schedule = active_schedule.dropna(axis=1) + active_schedule = active_schedule.dropna(axis=1).reset_index(drop=True) + + # schedule_distance = \ + # int((active_schedule.rounded_local_time - + # evaluation_time).values[0])/1e9/60/60/24 basal_rates = get_basal_rates(active_schedule) carb_ratios = get_carb_ratios(active_schedule) From caf605f496ce1b08b907b2a62e33b060aee34e3c Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 4 Mar 2020 11:18:25 -0500 Subject: [PATCH 42/57] Update CGM deduplication Contiguous dataframe does not have null times, so a fast and simple deduplication method can be used --- .../iCGM-test-matrix/icgm_condition_finder.py | 35 +++++++++---------- 1 file changed, 16 insertions(+), 19 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index aa17528f..5efe1cd1 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -75,7 +75,7 @@ def add_uploadDateTime(df): return df -def remove_rounded_CGM_duplicates(cgm_df): +def remove_contiguous_rounded_CGM_duplicates(contiguous_df): """Removes CGM duplicates, keeping the entries with the most recent uploadTime """ @@ -84,34 +84,31 @@ def remove_rounded_CGM_duplicates(cgm_df): nRoundedTimeDuplicatesRemoved = 0 cgmPercentDuplicated = 0 - if "rounded_local_time" in cgm_df: + if "rounded_local_time" in contiguous_df: # Sort first by most recent rounded_local_time and # then sort by newest uploadTime - cgm_df.sort_values(by=["rounded_local_time", "uploadTime"], - ascending=[False, False], - inplace=True) + contiguous_df.sort_values(by=["rounded_local_time", "uploadTime"], + ascending=[False, False], + inplace=True) - # Safety check for null times - dfIsNull = cgm_df[cgm_df["rounded_local_time"].isnull()] - dfNotNull = cgm_df[cgm_df["rounded_local_time"].notnull()] - - nBefore = len(dfNotNull) + nBefore = len(contiguous_df) # Drop duplicates, keeping the first (most recent upload) entry # .duplicated() defaults to keeping the first duplicate - dfNotNull = \ - dfNotNull.loc[~(dfNotNull["rounded_local_time"].duplicated())] - dfNotNull = dfNotNull.reset_index(drop=True) - nRoundedTimeDuplicatesRemoved = nBefore - len(dfNotNull) + contiguous_df = \ + contiguous_df.loc[ + ~(contiguous_df["rounded_local_time"].duplicated()) + ] + contiguous_df = contiguous_df.reset_index(drop=True) + nRoundedTimeDuplicatesRemoved = nBefore - len(contiguous_df) if(nRoundedTimeDuplicatesRemoved > 0): cgmPercentDuplicated = nRoundedTimeDuplicatesRemoved/nBefore - cgm_df = pd.concat([dfIsNull, dfNotNull]) - cgm_df.sort_values(by=["rounded_local_time"], - ascending=True, inplace=True) + contiguous_df.sort_values(by=["rounded_local_time"], + ascending=True, inplace=True) - return cgm_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated + return contiguous_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated def create_5min_contiguous_df(data): @@ -154,7 +151,7 @@ def create_5min_contiguous_df(data): # Remove CGM duplicates from the contiguous_df contiguous_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated = \ - remove_rounded_CGM_duplicates(contiguous_df) + remove_contiguous_rounded_CGM_duplicates(contiguous_df) # Merge boluses together into single 5-minute timestamps bolus_merged_df = pd.DataFrame( From 5538d3145e7d3067711a869db635c51504dc59b1 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 4 Mar 2020 11:19:42 -0500 Subject: [PATCH 43/57] Change condition finding to use 10 days of data --- .../iCGM-test-matrix/icgm_condition_finder.py | 99 ++++++++++++++++--- 1 file changed, 83 insertions(+), 16 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 5efe1cd1..4d3a6921 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -129,13 +129,13 @@ def create_5min_contiguous_df(data): "rate", "carbInput"], inplace=True) bolus_df = data[data.type == "bolus"].copy() - bolus_df.drop(columns=['rate'], inplace=True) + bolus_df.drop(columns=['rate', 'id', 'type'], inplace=True) basal_df = data[data.type == "basal"].copy() - basal_df.drop(columns=['carbInput'], inplace=True) + basal_df.drop(columns=['carbInput', 'id', 'type'], inplace=True) # CGM Data Processing # Convert value from mmol/L to mg/dL - cgm_df["value"] = cgm_df["value"] * 18.01559 + cgm_df["value"] = round(cgm_df["value"] * 18.01559) first_timestamp = data["rounded_local_time"].min() last_timestamp = data["rounded_local_time"].max() @@ -153,6 +153,17 @@ def create_5min_contiguous_df(data): contiguous_df, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated = \ remove_contiguous_rounded_CGM_duplicates(contiguous_df) + # Use centered rolling average ± 3 points (15 min) + cgm_rolling = contiguous_df['value'].rolling(window=7, + min_periods=1, + center=True) + + contiguous_df['value'] = cgm_rolling.mean().round(2).round() + + contiguous_df.loc[contiguous_df['value'].notnull(), 'value'] = \ + contiguous_df.loc[contiguous_df['value'].notnull(), + 'value'].astype(int) + # Merge boluses together into single 5-minute timestamps bolus_merged_df = pd.DataFrame( bolus_df.groupby('rounded_local_time').normal.sum() @@ -313,6 +324,23 @@ def rolling_48hour_max_gap(contiguous_df): return contiguous_df +def rolling_24hour_max_gap(contiguous_df): + """ + Calculate the max gap size of the cgm trace in a 24 hour normal rolling + window (where the evaluation point is at the far right edge) + + """ + + contiguous_df["rolling_24hour_max_gap"] = \ + contiguous_df["value"].rolling( + window=288, + min_periods=1).apply(lambda x: + get_max_gap_size(np.isnan(x)), + raw=True) + + return contiguous_df + + def rolling_48hour_pump_data(contiguous_df): """ Calculate whether pump data exists in a 48 hour centered rolling @@ -350,6 +378,36 @@ def rolling_48hour_pump_data(contiguous_df): return contiguous_df +def rolling_24hour_pump_data(contiguous_df): + """ + Calculate whether pump data exists in a 24 hour normal rolling + window (where the evaluation point is in the center) + + """ + + contiguous_df["rolling_24hour_bolus_gte2"] = \ + contiguous_df['normal'].rolling( + window=288, + min_periods=1).count() >= 2 + + contiguous_df["rolling_24hour_basal_gte1"] = \ + contiguous_df['basal_duration'].rolling( + window=288, + min_periods=1).count() >= 1 + + contiguous_df["rolling_24hour_carbs_gt0"] = \ + contiguous_df['carbInput'].rolling( + window=288, + min_periods=1).sum() > 0 + + contiguous_df["rolling_24hour_pump_data"] = \ + contiguous_df["rolling_24hour_bolus_gte2"] & \ + contiguous_df["rolling_24hour_basal_gte1"] & \ + contiguous_df["rolling_24hour_carbs_gt0"] + + return contiguous_df + + def rolling_48hour_check_travel(contiguous_df): """ Calculate whether any travel occured in a 48 hour centered rolling @@ -449,15 +507,16 @@ def get_evaluation_points(contiguous_df): Gets a evaluation points for each condition from the dataset if available """ - # Set window size of snapshot - window_size = 288*2 + # Set size of snapshot (10 days) + window_size = 288*10 # Create bool of all values with a condition, pump data, # and max gap <= 15min qualified_bool = \ (contiguous_df["condition"] > 0) & \ - (contiguous_df["rolling_48hour_max_gap"] <= 3) & \ - (contiguous_df["rolling_48hour_pump_data"]) # & \ + (contiguous_df["rolling_24hour_max_gap"] <= 3) & \ + (contiguous_df["rolling_24hour_basal_gte1"]) +# (contiguous_df["rolling_48hour_pump_data"]) # & \ # (contiguous_df["rolling_48hour_no_travel"]) qualified_condition_list = \ @@ -479,15 +538,17 @@ def get_evaluation_points(contiguous_df): # Randomly select one of the condition's locations random_loc = np.random.choice(condition_locations) - # Add location's id to evaluation_points - evaluation_points[condition - 1] = contiguous_df.loc[random_loc, "id"] + # Add location's rounded_local_time to evaluation_points + evaluation_point = contiguous_df.loc[random_loc, "rounded_local_time"] + evaluation_point = evaluation_point.strftime('%Y-%m-%dT%H:%M:%S') + evaluation_points[condition - 1] = evaluation_point - # Remove all locations within ± window_size-1 from qualified_locations + # Remove all locations 10 days before from qualified_locations # to prevent overlap when selecting the next condition snapshot overlapping_index = \ np.arange( random_loc - (window_size-1), - random_loc + (window_size-1)+1 + random_loc + 1 ) index_to_drop = \ @@ -515,7 +576,7 @@ def get_empty_results_frame(): for num in range(9): # Append snapshot locations to dataframe - results_columns.append("cond"+str(num+1)+"_eval_loc") + results_columns.append("cond"+str(num+1)+"_eval_time") results_frame = pd.DataFrame(index=[0], columns=results_columns) @@ -550,7 +611,7 @@ def get_summary_results(file_name, for num in range(9): # Append snapshot locations to dataframe - results["cond"+str(num+1)+"_eval_loc"] = evaluation_points[num] + results["cond"+str(num+1)+"_eval_time"] = evaluation_points[num] return results @@ -578,15 +639,21 @@ def main(data, file_name): contiguous_df = label_conditions(contiguous_df) # Get the max gap size across 48-hour windows - contiguous_df = rolling_48hour_max_gap(contiguous_df) + # contiguous_df = rolling_48hour_max_gap(contiguous_df) + + # Get the max gap size across a 24-hour window + contiguous_df = rolling_24hour_max_gap(contiguous_df) # Determine whether there's sufficient pump data in each 48 hour window - contiguous_df = rolling_48hour_pump_data(contiguous_df) + # contiguous_df = rolling_48hour_pump_data(contiguous_df) + + # Determine whether there's sufficient pump data in each 24 hour window + contiguous_df = rolling_24hour_pump_data(contiguous_df) # Add 48-hour window bools for travel data # contiguous_df = rolling_48hour_check_travel(contiguous_df) - # Get the locations of each evaluation point in a 48-hour snapshot + # Get the locations of each evaluation point in a 10-day snapshot evaluation_points = get_evaluation_points(contiguous_df) # Summarize results From ce183c81ce95a912c843739f3b171d65a295bd32 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 4 Mar 2020 11:21:53 -0500 Subject: [PATCH 44/57] Update snapshot settings to use bolus calculator entries if needed --- .../iCGM-test-matrix/snapshot_processor.py | 195 +++++++++++++----- 1 file changed, 141 insertions(+), 54 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 4390e19b..c722537c 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -16,16 +16,17 @@ # %% Imports import pandas as pd import numpy as np +import time import datetime import ast import os +import icgm_condition_finder as cond_finder # %% Functions def get_active_schedule(data, snapshot_df, file_name, - evaluation_point_loc, evaluation_time): """Get pumpSettings row of active schedules used during the snapshot.""" # Get all schedules and their upload ids @@ -102,7 +103,7 @@ def get_relative_timestamp(timestamp_ms): return relative_timestamp -def get_basal_rates(active_schedule): +def get_basal_rates(active_schedule, basal_df): """Get the basal rates from the active schedule""" df_columns = ['basal_rate_start_times', 'basal_rate_minutes', @@ -119,15 +120,33 @@ def get_basal_rates(active_schedule): active_basal_rates = basal_schedules.get(active_name) start_times = [] - for rate_loc in range(len(active_basal_rates)): - rate_info = active_basal_rates[rate_loc] - rate = rate_info.get('rate') - start_time = rate_info.get('start') - start_times.append(start_time) - start_string = get_relative_timestamp(start_time) - basal_rates.loc[rate_loc, 'basal_rate_start_times'] = start_string - basal_rates.loc[rate_loc, 'basal_rate_values'] = rate + # If no basal rates were found, the schedule's entry is blank + # Use the weighted average used from data's basal rates + if len(active_basal_rates) == 0: + print("\nUSING WEIGHTED BASAL RATE HISTORY FOR BR\n") + + basal_df["duration_hours"] = basal_df["duration"]/1000/60/60 + + weighted_avg_basal_rate = \ + np.round((np.sum(basal_df["rate"] * basal_df["duration_hours"]) / + basal_df["duration_hours"].sum()), 2) + + start_string = datetime.time(0, 0).strftime('%H:%M:%S') + start_times.append(0) + basal_rates.loc[0, 'basal_rate_start_times'] = start_string + basal_rates.loc[0, 'basal_rate_values'] = weighted_avg_basal_rate + + else: + for rate_loc in range(len(active_basal_rates)): + rate_info = active_basal_rates[rate_loc] + rate = rate_info.get('rate') + start_time = rate_info.get('start') + start_times.append(start_time) + start_string = get_relative_timestamp(start_time) + + basal_rates.loc[rate_loc, 'basal_rate_start_times'] = start_string + basal_rates.loc[rate_loc, 'basal_rate_values'] = rate basal_rates['actual_basal_rates'] = basal_rates['basal_rate_values'] basal_rates['basal_rate_units'] = 'U/hr' @@ -140,7 +159,7 @@ def get_basal_rates(active_schedule): return basal_rates -def get_carb_ratios(active_schedule): +def get_carb_ratios(active_schedule, bolus_df): """Get the carb to insulin ratio(s) from the active schedule""" df_columns = ['carb_ratio_start_times', @@ -161,14 +180,26 @@ def get_carb_ratios(active_schedule): else: print("NO CARB RATIOS!") - for ratio_loc in range(len(active_carb_ratios)): - ratio_info = active_carb_ratios[ratio_loc] - ratio = ratio_info.get('amount') - start_time = ratio_info.get('start') - start_string = get_relative_timestamp(start_time) + # If no carb ratios were found, the schedule's entry is blank + # Use the average ratio used from data's bolus calculations + if len(active_carb_ratios) == 0: + print("\nUSING BOLUS CALCULATOR ENTRIES FOR CIR\n") - carb_ratios.loc[ratio_loc, 'carb_ratio_start_times'] = start_string - carb_ratios.loc[ratio_loc, 'carb_ratio_values'] = ratio + ratio = bolus_df.insulinCarbRatio.astype(float).mean().round() + start_string = datetime.time(0, 0).strftime('%H:%M:%S') + + carb_ratios.loc[0, 'carb_ratio_start_times'] = start_string + carb_ratios.loc[0, 'carb_ratio_values'] = ratio + + else: + for ratio_loc in range(len(active_carb_ratios)): + ratio_info = active_carb_ratios[ratio_loc] + ratio = ratio_info.get('amount') + start_time = ratio_info.get('start') + start_string = get_relative_timestamp(start_time) + + carb_ratios.loc[ratio_loc, 'carb_ratio_start_times'] = start_string + carb_ratios.loc[ratio_loc, 'carb_ratio_values'] = ratio carb_ratios['actual_carb_ratios'] = carb_ratios['carb_ratio_values'] carb_ratios['carb_ratio_value_units'] = 'g/U' @@ -176,7 +207,7 @@ def get_carb_ratios(active_schedule): return carb_ratios -def get_isfs(active_schedule): +def get_isfs(active_schedule, bolus_df): """Get the insulin sensitivity ratio(s) from the active schedule""" df_columns = ['sensitivity_ratio_start_times', @@ -200,15 +231,35 @@ def get_isfs(active_schedule): start_times = [] - for isf_loc in range(len(active_isfs)): - isf_info = active_isfs[isf_loc] - isf = round(isf_info.get('amount')*18.01559) - start_time = isf_info.get('start') - start_string = get_relative_timestamp(start_time) + # If no isfs were found, the schedule's entry is blank + # Use the average isf used from data's bolus calculations + if len(active_isfs) == 0: + print("\nUSING BOLUS CALCULATOR ENTRIES FOR ISF\n") + + isf = bolus_df.insulinSensitivity.astype(float).mean() + isf = round(isf*18.01559) + start_string = datetime.time(0, 0).strftime('%H:%M:%S') start_times.append(start_string) - isfs.loc[isf_loc, 'sensitivity_ratio_start_times'] = start_string - isfs.loc[isf_loc, 'sensitivity_ratio_values'] = isf + isfs.loc[0, 'sensitivity_ratio_start_times'] = start_string + isfs.loc[0, 'sensitivity_ratio_values'] = isf + + else: + for isf_loc in range(len(active_isfs)): + isf_info = active_isfs[isf_loc] + isf = round(isf_info.get('amount')*18.01559) + + start_time = isf_info.get('start') + start_string = get_relative_timestamp(start_time) + start_times.append(start_string) + + isfs.loc[isf_loc, 'sensitivity_ratio_start_times'] = start_string + isfs.loc[isf_loc, 'sensitivity_ratio_values'] = isf + + # Check if sensitivity ratios were improperly converted from mg/dL + if any(isfs['sensitivity_ratio_values'] > 500): + isfs['sensitivity_ratio_values'] = \ + (isfs['sensitivity_ratio_values'].astype(float)/18.01559).round() isfs['actual_sensitivity_ratios'] = isfs['sensitivity_ratio_values'] isfs['sensitivity_ratio_value_units'] = 'mg/dL/U' @@ -220,7 +271,7 @@ def get_isfs(active_schedule): return isfs -def get_target_ranges(active_schedule): +def get_target_ranges(active_schedule, bolus_df): """Get the target BG ranges from the active schedule""" df_columns = ['target_range_start_times', @@ -266,39 +317,64 @@ def get_target_ranges(active_schedule): start_times = [] - for target_loc in range(len(active_targets)): - target_info = active_targets[target_loc] - start_time = target_info.get('start') - start_string = get_relative_timestamp(start_time) - start_times.append(start_string) + # If no targets were found, the schedule's entry is blank + # Use the min/max bgTarget.target used from data's bolus calculations + if len(active_targets) == 0: + print("\nUSING BOLUS CALCULATOR ENTRIES FOR TARGETS\n") - df_target_range.loc[target_loc, - 'target_range_start_times'] = start_string + min_target = bolus_df['bgTarget.target'].astype(float).min() + min_target = round(min_target*18.01559) - if 'target' in target_info.keys(): - target = round(target_info.get('target')*18.01559) - df_target_range.loc[target_loc, 'target_range_minimum_values'] = \ - target - df_target_range.loc[target_loc, 'target_range_maximum_values'] = \ - target + max_target = bolus_df['bgTarget.target'].astype(float).max() + max_target = round(max_target*18.01559) - if 'range' in target_info.keys(): - target_range = round(target_info.get('range')*18.01559) - df_target_range.loc[target_loc, 'target_range_minimum_values'] = \ - target - target_range + start_string = datetime.time(0, 0).strftime('%H:%M:%S') + start_times.append(start_string) - df_target_range.loc[target_loc, 'target_range_maximum_values'] = \ - target + target_range + df_target_range.loc[0, 'target_range_start_times'] = start_string + df_target_range.loc[0, 'target_range_minimum_values'] = min_target + df_target_range.loc[0, 'target_range_maximum_values'] = max_target - if 'high' in target_info.keys(): - target_high = round(target_info.get('high')*18.01559) - df_target_range.loc[target_loc, - 'target_range_maximum_values'] = target_high + else: + for target_loc in range(len(active_targets)): + target_info = active_targets[target_loc] + start_time = target_info.get('start') + start_string = get_relative_timestamp(start_time) + start_times.append(start_string) - if 'low' in target_info.keys(): - target_low = round(target_info.get('high')*18.01559) df_target_range.loc[target_loc, - 'target_range_minimum_values'] = target_low + 'target_range_start_times'] = start_string + + if 'target' in target_info.keys(): + target = round(target_info.get('target')*18.01559) + df_target_range.loc[target_loc, + 'target_range_minimum_values'] = \ + target + df_target_range.loc[target_loc, + 'target_range_maximum_values'] = \ + target + + if 'range' in target_info.keys(): + target_range = round(target_info.get('range')*18.01559) + df_target_range.loc[target_loc, + 'target_range_minimum_values'] = \ + target - target_range + + df_target_range.loc[target_loc, + 'target_range_maximum_values'] = \ + target + target_range + + if 'high' in target_info.keys(): + target_high = round(target_info.get('high')*18.01559) + df_target_range.loc[target_loc, + 'target_range_maximum_values'] = \ + target_high + + if 'low' in target_info.keys(): + target_low = round(target_info.get('high')*18.01559) + df_target_range.loc[target_loc, + 'target_range_minimum_values'] = \ + target_low df_target_range['target_range_value_units'] = 'mg/dL' @@ -306,6 +382,17 @@ def get_target_ranges(active_schedule): end_times = start_times[1:] + [start_times[0]] df_target_range['target_range_end_times'] = end_times + # Check if bg targets were improperly converted from mg/dL + if any(df_target_range['target_range_minimum_values'] > 500): + df_target_range['target_range_minimum_values'] = \ + (df_target_range['target_range_minimum_values'].astype(float) / + 18.01559).round() + + if any(df_target_range['target_range_maximum_values'] > 500): + df_target_range['target_range_maximum_values'] = \ + (df_target_range['target_range_maximum_values'].astype(float) / + 18.01559).round() + return df_target_range @@ -444,7 +531,7 @@ def get_dose_events(snapshot_df): dose_events = pd.concat([bolus_events, extended_events, temp_basal_events, - suspend_events]) + suspend_events], sort=False) dose_events['actual_doses'] = dose_events['dose_values'] From d00f9bac64ad3a8d02b4fbeeb625de5bef5d803c Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 4 Mar 2020 11:22:21 -0500 Subject: [PATCH 45/57] Add smoothing and interpolation to CGM values --- .../iCGM-test-matrix/snapshot_processor.py | 100 ++++++++++++++---- 1 file changed, 77 insertions(+), 23 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index c722537c..8d93e8c6 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -545,7 +545,54 @@ def get_dose_events(snapshot_df): return dose_events -def get_cgm_df(snapshot_df, smooth_cgm): +def interpolate_and_smooth_cgm(cgm_df, + condition_num, + add_cgm_trail, + evaluation_time): + """Add 30-minute trailing cgm trace by applying the condition rate to + evaluation point""" + + # Get the start of the condition's calculation area + # WARNING: Avoid interpolating or backfilling data after this point + # Else you might risk filling in gaps that were necessary in the condition + condition_calc_start = evaluation_time - datetime.timedelta(days=1) + before_condition = cgm_df['glucose_dates'] < condition_calc_start + + # Interpolate forward NaNs (using linear default) + cgm_df.loc[before_condition, + 'glucose_values'] = cgm_df.loc[before_condition, + 'glucose_values'].interpolate() + + # Backfill NaNs to the start of the dataframe + cgm_df.loc[before_condition, + 'glucose_values'] = \ + cgm_df.loc[before_condition, + 'glucose_values'].fillna(method='backfill') + + # Use centered rolling average ± 3 points (15 min) + cgm_rolling = cgm_df['glucose_values'].rolling(window=7, + min_periods=1, + center=True) + + cgm_df['glucose_values'] = cgm_rolling.mean().round(2).round() + + cgm_df.loc[cgm_df['glucose_values'].notnull(), 'glucose_values'] = \ + cgm_df.loc[cgm_df['glucose_values'].notnull(), + 'glucose_values'].astype(int) + + # Drop the trailing values after the evaluation point + cgm_df = cgm_df[cgm_df.glucose_dates <= evaluation_time] + + return cgm_df + + +def get_cgm_df(snapshot_df, + smooth_cgm, + condition_num, + start_time, + end_time, + add_cgm_trail, + evaluation_time): """Gets the CGM values from the snapshot""" df_columns = ['glucose_dates', @@ -557,18 +604,20 @@ def get_cgm_df(snapshot_df, smooth_cgm): cgm_data = snapshot_df[snapshot_df.type == 'cbg'].copy() - first_timestamp = cgm_data["rounded_local_time"].min() - last_timestamp = cgm_data["rounded_local_time"].max() + # first_timestamp = cgm_data["rounded_local_time"].min() + # last_timestamp = cgm_data["rounded_local_time"].max() - ts_len = len(pd.date_range(first_timestamp, last_timestamp, freq="5min")) + # ts_len = len(pd.date_range(first_timestamp, last_timestamp, freq="5min")) + # print("CGM Data Length: " + str(ts_len)) - # Force cgm dataframe length to 576 - if ts_len < 576: - delta_to_add = 576 - ts_len - last_timestamp = \ - last_timestamp + datetime.timedelta(minutes=5*delta_to_add) + # Force cgm dataframe length to 2880 + # cgm_len = 288*10 + # if ts_len < cgm_len: + # delta_to_add = cgm_len - ts_len + # last_timestamp = \ + # last_timestamp + datetime.timedelta(minutes=5*delta_to_add) - contiguous_ts = pd.date_range(first_timestamp, last_timestamp, freq="5min") + contiguous_ts = pd.date_range(start_time, end_time, freq="5min") contiguous_df = pd.DataFrame(contiguous_ts, columns=["rounded_local_time"]) @@ -577,28 +626,33 @@ def get_cgm_df(snapshot_df, smooth_cgm): how="left", on="rounded_local_time" ) + # Remove CGM duplicates from the contiguous_df + cgm_data, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated = \ + cond_finder.remove_contiguous_rounded_CGM_duplicates(cgm_data) # Drop cgm duplicates in snapshot (if any) - cgm_data.sort_values(['uploadId', 'rounded_local_time'], - ascending=True, - inplace=True) + # cgm_data.sort_values(['uploadId', 'rounded_local_time'], + # ascending=True, + # inplace=True) - cgm_data.drop_duplicates('rounded_local_time', inplace=True) + # cgm_data.drop_duplicates('rounded_local_time', inplace=True) - cgm_data.sort_values(['rounded_local_time'], - ascending=True, - inplace=True) + # cgm_data.sort_values(['rounded_local_time'], + # ascending=True, + # inplace=True) - cgm_df['glucose_dates'] = \ - cgm_data.rounded_local_time.dt.strftime("%Y-%m-%d %H:%M:%S") + cgm_df['glucose_dates'] = cgm_data.rounded_local_time cgm_df['glucose_values'] = round(cgm_data.value * 18.01559) if(smooth_cgm): - cgm_rolling = cgm_df['glucose_values'].rolling(window=12, - min_periods=1, - center=True) + # Interpolate and smooth the data + cgm_df = interpolate_and_smooth_cgm(cgm_df, + condition_num, + add_cgm_trail, + evaluation_time) - cgm_df['glucose_values'] = cgm_rolling.mean().round().astype(int) + cgm_df['glucose_dates'] = \ + cgm_df['glucose_dates'].dt.strftime("%Y-%m-%d %H:%M:%S") cgm_df['actual_blood_glucose'] = cgm_df['glucose_values'] cgm_df['glucose_units'] = 'mg/dL' From fb11ca18c512a853ba682ecf6d089a40fced1ac4 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 4 Mar 2020 11:24:00 -0500 Subject: [PATCH 46/57] Add handling of birthdate/diagnosisDate metadata from file --- .../iCGM-test-matrix/snapshot_processor.py | 36 +++++++++++++++---- 1 file changed, 30 insertions(+), 6 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 8d93e8c6..73b5b824 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -711,9 +711,33 @@ def get_settings(birthdate, df_settings = pd.DataFrame(index=df_index, columns=df_columns) + # Calculate age and ylw + if type(birthdate) == float: + age = '-1' + else: + birthdate = pd.to_datetime(birthdate, utc=True) + + if birthdate.year > datetime.datetime.now().year: + birthdate = birthdate.replace(year=birthdate.year-100) + + age = \ + int(np.floor((evaluation_time - birthdate).days/365)) + + if type(diagnosis_date) == float: + ylw = '-1' + else: + diagnosis_date = pd.to_datetime(diagnosis_date, utc=True) + + if diagnosis_date.year > datetime.datetime.now().year: + diagnosis_date = \ + diagnosis_date.replace(year=diagnosis_date.year-100) + + ylw = \ + int(np.floor((evaluation_time - diagnosis_date).days/365)) + # Create default df_settings - df_settings.loc['age'] = '0' - df_settings.loc['ylw'] = '0' + df_settings.loc['age'] = age + df_settings.loc['ylw'] = ylw df_settings.loc['model'] = '[360.0, 65]' df_settings.loc['momentum_data_interval'] = '15' df_settings.loc['suspend_threshold'] = '70' @@ -989,7 +1013,7 @@ def export_snapshot(snapshot, if __name__ == "__main__": # Import results from batch-icgm-condition-stats - condition_file = "PHI-batch-icgm-condition-stats-2020-02-11.csv" + condition_file = "PHI-batch-icgm-condition-stats-2020-02-27-with-metadata.csv" condition_df = pd.read_csv(condition_file, low_memory=False) # Snapshot processing parameters @@ -999,9 +1023,9 @@ def export_snapshot(snapshot, file_name = condition_df.loc[file_selection, 'file_name'] - # TODO: Bring in birthdate/diagnosis_date metadata for each person - birthdate = "placeholder_age" - diagnosis_date = "placeholder_ylw" + # Get birthdate/diagnosis_date metadata for each person + birthdate = condition_df.loc[file_selection, 'birthday'] + diagnosis_date = condition_df.loc[file_selection, 'diagnosisDate'] # Location of csvs data_location = "train-data/" From 6b4e585d52d3ec6d46e63505402135ab30bfcf1d Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 4 Mar 2020 11:24:44 -0500 Subject: [PATCH 47/57] Use 10 days from evaluation point instead of 48 hours --- .../iCGM-test-matrix/snapshot_processor.py | 38 +++++++++++-------- 1 file changed, 22 insertions(+), 16 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 73b5b824..a49b8597 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -818,14 +818,14 @@ def get_simple_settings(basal_rates, def create_empty_events(carb_events, dose_events, - cgm_df): + df_misc): - first_cgm_time = cgm_df["glucose_dates"].min() + time_to_calculate_at = df_misc.loc['time_to_calculate_at', 0] # Create empty carb event empty_carb_event = pd.DataFrame(columns=carb_events.columns, index=[0]) empty_carb_event.loc[0, :] = ( - first_cgm_time, + time_to_calculate_at, 0, 0, 180, @@ -836,8 +836,8 @@ def create_empty_events(carb_events, empty_dose_event = pd.DataFrame(columns=dose_events.columns, index=[0]) empty_dose_event.loc[0, :] = ( "DoseType.bolus", - first_cgm_time, - first_cgm_time, + time_to_calculate_at, + time_to_calculate_at, 0, 0, "U" @@ -848,39 +848,45 @@ def create_empty_events(carb_events, def get_snapshot(data, file_name, - evaluation_point_loc, + evaluation_time, birthdate, diagnosis_date, smooth_cgm, simplify_settings, - empty_events): + empty_events, + condition_num, + add_cgm_trail): """Main function wrapper to assemble snapshot dataframes""" # Start by getting the 48-hour window ± 24hrs around the evaluation point - evaluation_index = data.index[data.id == evaluation_point_loc] + # evaluation_index = data.index[data.id == evaluation_point_loc] data["rounded_local_time"] = \ pd.to_datetime(data["est.localTime"], utc=True).dt.ceil(freq="5min") - evaluation_time = \ - pd.to_datetime(data.loc[evaluation_index, - 'rounded_local_time'].values[0], - utc=True) + # evaluation_time = \ + # pd.to_datetime(data.loc[evaluation_index, + # 'rounded_local_time'].values[0], + # utc=True) df_misc = get_time_to_calculate_at(evaluation_time) - start_time = evaluation_time - datetime.timedelta(days=1) - end_time = evaluation_time + datetime.timedelta(days=1) + # Start time is evaluation_time - 10 days with a non-inclusive first value, + # add 5 minutes to offset to a timestamp of 9 days, 23 hours, 55 minutes + start_time = evaluation_time - datetime.timedelta(days=9, + hours=23, + minutes=55) + + end_time = evaluation_time + datetime.timedelta(minutes=15) snapshot_df = data[(data['rounded_local_time'] >= start_time) & - (data['rounded_local_time'] < end_time)] + (data['rounded_local_time'] <= end_time)] # Get pumpSettings list of active schedules active_schedule = get_active_schedule(data, snapshot_df, file_name, - evaluation_point_loc, evaluation_time) # Drop unused columns that may have been used in other schedule types From 70ac504ed636bec9b8a489c9a6ff091429045e3d Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 4 Mar 2020 11:26:32 -0500 Subject: [PATCH 48/57] Add condition validation check --- .../iCGM-test-matrix/snapshot_processor.py | 203 +++++++++++++++--- 1 file changed, 176 insertions(+), 27 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index a49b8597..7ecc8fb8 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -896,14 +896,25 @@ def get_snapshot(data, # int((active_schedule.rounded_local_time - # evaluation_time).values[0])/1e9/60/60/24 - basal_rates = get_basal_rates(active_schedule) - carb_ratios = get_carb_ratios(active_schedule) - isfs = get_isfs(active_schedule) - df_target_range = get_target_ranges(active_schedule) + # Get entire dataframe's bolus and basal entries as a last effort + # to manually extract pump settings if not available + bolus_df = data[data.type == 'bolus'].copy() + basal_df = data[data.type == 'basal'].copy() + + basal_rates = get_basal_rates(active_schedule, basal_df) + carb_ratios = get_carb_ratios(active_schedule, bolus_df) + isfs = get_isfs(active_schedule, bolus_df) + df_target_range = get_target_ranges(active_schedule, bolus_df) carb_events = get_carb_events(snapshot_df) dose_events = get_dose_events(snapshot_df) - cgm_df = get_cgm_df(snapshot_df, smooth_cgm) + cgm_df = get_cgm_df(snapshot_df, + smooth_cgm, + condition_num, + start_time, + end_time, + add_cgm_trail, + evaluation_time) df_last_temporary_basal = get_last_temp_basal() df_settings = get_settings(birthdate, @@ -922,7 +933,7 @@ def get_snapshot(data, if(empty_events): carb_events, dose_events = create_empty_events(carb_events, dose_events, - cgm_df) + df_misc) return (basal_rates, carb_events, carb_ratios, dose_events, cgm_df, df_last_temporary_basal, df_misc, isfs, @@ -974,8 +985,8 @@ def combine_into_one_dataframe(snapshot): ) = snapshot combined_df = pd.DataFrame() - combined_df = pd.concat([combined_df, df_settings]) - combined_df = pd.concat([combined_df, df_misc]) + combined_df = pd.concat([combined_df, df_settings], sort=False) + combined_df = pd.concat([combined_df, df_misc], sort=False) dfs = [ df_basal_rate, df_carb, df_carb_ratio, df_dose, @@ -985,7 +996,7 @@ def combine_into_one_dataframe(snapshot): for df in dfs: df.reset_index(drop=True, inplace=True) - combined_df = pd.concat([combined_df, df.T]) + combined_df = pd.concat([combined_df, df.T], sort=False) # move settings back to the front of the dataframe combined_df = combined_df[np.append("settings", combined_df.columns[0:-1])] @@ -1015,49 +1026,187 @@ def export_snapshot(snapshot, return +def get_settings_summary(file_name, + condition_num, + snapshot, + condition_check): + """Get a table of settings to be used in a cumulative summary file""" + + summary_columns = ['file_name', + 'condition_num', + 'condition_check', + 'age', + 'ylw', + 'CIR', + 'ISF', + 'BR'] + + settings_summary = pd.DataFrame(index=[0], columns=summary_columns) + + settings_summary['file_name'] = file_name + settings_summary['condition_num'] = condition_num + settings_summary['condition_check'] = condition_check + settings_summary['age'] = snapshot[8].loc['age']['settings'] + settings_summary['ylw'] = snapshot[8].loc['ylw']['settings'] + settings_summary['CIR'] = snapshot[2]['actual_carb_ratios'][0] + settings_summary['ISF'] = snapshot[7]['actual_sensitivity_ratios'][0] + settings_summary['BR'] = snapshot[0]['actual_basal_rates'][0] + + return settings_summary + + +def validate_condition(snapshot, + condition_num, + contiguous_df, + evaluation_time): + """Validate the snapshot cgm data condition with the expected condition""" + + cgm_df = snapshot[4].copy() + cgm_df["rounded_local_time"] = \ + pd.to_datetime(cgm_df["glucose_dates"], utc=True) + cgm_df["value"] = cgm_df["glucose_values"] + cgm_df = cond_finder.rolling_30min_median(cgm_df) + cgm_df = cond_finder.rolling_15min_slope(cgm_df) + cgm_df = cond_finder.label_conditions(cgm_df) + + condition_start = evaluation_time - datetime.timedelta(minutes=30) + condition_end = evaluation_time + + original_values = \ + contiguous_df.loc[ + (contiguous_df['rounded_local_time'] > condition_start) & + (contiguous_df['rounded_local_time'] <= condition_end), + "value"].values + + new_values = \ + cgm_df.loc[ + (cgm_df['rounded_local_time'] > condition_start) & + (cgm_df['rounded_local_time'] <= condition_end), + "value"].values + + original_eval = \ + contiguous_df[contiguous_df['rounded_local_time'] == evaluation_time] + + original_median = original_eval['rolling_30min_median'] + original_rate = original_eval['rolling_15min_slope'] + original_condition = original_eval['condition'].values[0] + + new_eval = cgm_df.loc[cgm_df['rounded_local_time'] == evaluation_time] + + new_median = new_eval['rolling_30min_median'] + new_rate = new_eval['rolling_15min_slope'] + new_condition = new_eval['condition'].values[0] + + if any(original_values != new_values): + print("\nOriginal and new cgm values are different!\n") + + condition_check = (original_condition == new_condition == condition_num) + + return condition_check + + # %% if __name__ == "__main__": + start_time = time.time() + # Import results from batch-icgm-condition-stats condition_file = "PHI-batch-icgm-condition-stats-2020-02-27-with-metadata.csv" condition_df = pd.read_csv(condition_file, low_memory=False) # Snapshot processing parameters - smooth_cgm = True simplify_settings = True empty_events = True + smooth_cgm = True + add_cgm_trail = False + + settings_summaries = [] - file_name = condition_df.loc[file_selection, 'file_name'] + for file_selection in range(int(len(condition_df))): + #file_selection = 1 + print("STARTING FILE #: " + str(file_selection)) + file_name = condition_df.loc[file_selection, 'file_name'] # Get birthdate/diagnosis_date metadata for each person birthdate = condition_df.loc[file_selection, 'birthday'] diagnosis_date = condition_df.loc[file_selection, 'diagnosisDate'] - # Location of csvs + # Location of csvs data_location = "train-data/" - file_path = os.path.join(data_location, file_name) - data = pd.read_csv(file_path, low_memory=False) + file_path = os.path.join(data_location, file_name) + data = pd.read_csv(file_path, low_memory=False) + data = cond_finder.add_uploadDateTime(data) + + (contiguous_df, + nRoundedTimeDuplicatesRemoved, + cgmPercentDuplicated + ) = cond_finder.create_5min_contiguous_df(data) + + # Calculate the median BG with a 30-minute rolling window + contiguous_df = \ + cond_finder.rolling_30min_median(contiguous_df) - # Loop through each condition, calculate, and export the snapshot - for condition_num in range(1, 10): - condition_col = 'cond' + str(condition_num) + '_eval_loc' - evaluation_point_loc = condition_df.loc[file_selection, condition_col] + # Calculate the slope in mg/dL/min with a 15-minute rolling window + contiguous_df = \ + cond_finder.rolling_15min_slope(contiguous_df) - if not pd.isnull(evaluation_point_loc): + # Apply one of the 9 conditions labels to each CGM point + contiguous_df = \ + cond_finder.label_conditions(contiguous_df) + + # Loop through each condition, calculate, and export the snapshot + for condition_num in range(1, 10): + condition_col = 'cond' + str(condition_num) + '_eval_time' + evaluation_time = condition_df.loc[file_selection, + condition_col] + + if not pd.isnull(evaluation_time): + evaluation_time = pd.to_datetime(evaluation_time, utc=True) snapshot = get_snapshot(data, file_name, - evaluation_point_loc, + evaluation_time, birthdate, diagnosis_date, smooth_cgm, simplify_settings, - empty_events + empty_events, + condition_num, + add_cgm_trail ) - export_folder = "snapshot_export" - - export_snapshot(snapshot, - file_name, - condition_num, - export_folder) + condition_check = \ + validate_condition(snapshot, + condition_num, + contiguous_df, + evaluation_time) + + settings_summary = get_settings_summary(file_name, + condition_num, + snapshot, + condition_check) + + settings_summaries.append(settings_summary) + + export_folder = "snapshot_export" + + export_snapshot(snapshot, + file_name, + condition_num, + export_folder) + + # Create settings summaries for export + settings_summaries = pd.concat(settings_summaries).reset_index(drop=True) + today_timestamp = datetime.datetime.now().strftime("%Y-%m-%d") + settings_summary_export_filename = \ + 'snapshot-processor-settings-summary-table-' + \ + today_timestamp + \ + '.csv' + + settings_summaries.to_csv(settings_summary_export_filename, index=False) + + end_time = time.time() + elapsed_minutes = (end_time - start_time)/60 + elapsed_time_message = "Processed all snapshots in: " + \ + str(elapsed_minutes) + " minutes\n" + print(elapsed_time_message) From c06415c8249acf1e8749e90d60917cc35c60a976 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 26 Mar 2020 13:02:13 -0400 Subject: [PATCH 49/57] Round CGM values to 1 decimal --- projects/iCGM-test-matrix/icgm_condition_finder.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 4d3a6921..3e31e6bd 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -135,7 +135,7 @@ def create_5min_contiguous_df(data): # CGM Data Processing # Convert value from mmol/L to mg/dL - cgm_df["value"] = round(cgm_df["value"] * 18.01559) + cgm_df["value"] = round(cgm_df["value"] * 18.01559, 1) first_timestamp = data["rounded_local_time"].min() last_timestamp = data["rounded_local_time"].max() @@ -158,11 +158,11 @@ def create_5min_contiguous_df(data): min_periods=1, center=True) - contiguous_df['value'] = cgm_rolling.mean().round(2).round() + contiguous_df['value'] = cgm_rolling.mean().round(2).round(1) contiguous_df.loc[contiguous_df['value'].notnull(), 'value'] = \ contiguous_df.loc[contiguous_df['value'].notnull(), - 'value'].astype(int) + 'value'] # Merge boluses together into single 5-minute timestamps bolus_merged_df = pd.DataFrame( From f28c406887ce2611424c687822971b5493c5260b Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 26 Mar 2020 13:04:28 -0400 Subject: [PATCH 50/57] Round basal rates to nearest 0.05u --- .../iCGM-test-matrix/snapshot_processor.py | 28 ++++++++++++++----- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 7ecc8fb8..2c2fbbfc 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -128,9 +128,15 @@ def get_basal_rates(active_schedule, basal_df): basal_df["duration_hours"] = basal_df["duration"]/1000/60/60 - weighted_avg_basal_rate = \ - np.round((np.sum(basal_df["rate"] * basal_df["duration_hours"]) / - basal_df["duration_hours"].sum()), 2) + weighted_avg_basal_rate = ( + np.sum(basal_df["rate"] * basal_df["duration_hours"]) + / basal_df["duration_hours"].sum() + ) + + # Round to nearest 0.05 U/hr + weighted_avg_basal_rate = ( + round(round(weighted_avg_basal_rate/0.05)*0.05, 2) + ) start_string = datetime.time(0, 0).strftime('%H:%M:%S') start_times.append(0) @@ -764,10 +770,18 @@ def get_simple_settings(basal_rates, # Get the weighted average basal_rates simple_basal_rate = pd.DataFrame(columns=basal_rates.columns, index=[0]) - weighted_avg_basal_rate = np.round( - (np.sum(basal_rates["basal_rate_minutes"] * - basal_rates["actual_basal_rates"]) / - basal_rates["basal_rate_minutes"].sum()), 2) + weighted_avg_basal_rate = ( + np.sum( + basal_rates["basal_rate_minutes"] + * basal_rates["actual_basal_rates"] + ) + / basal_rates["basal_rate_minutes"].sum() + ) + + # Round to nearest 0.05 U/hr + weighted_avg_basal_rate = ( + round(round(weighted_avg_basal_rate/0.05)*0.05, 2) + ) simple_basal_rate.loc[0, :] = ( datetime.time(0, 0).strftime('%H:%M:%S'), From 88bc53485bd8cf7f4ec235f875275cabe0d441db Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 26 Mar 2020 13:06:10 -0400 Subject: [PATCH 51/57] Update interpolation logic --- .../iCGM-test-matrix/snapshot_processor.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 2c2fbbfc..9c1a0eda 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -564,7 +564,8 @@ def interpolate_and_smooth_cgm(cgm_df, condition_calc_start = evaluation_time - datetime.timedelta(days=1) before_condition = cgm_df['glucose_dates'] < condition_calc_start - # Interpolate forward NaNs (using linear default) + # Interpolate forward NaNs before condition area (using linear default) + # This is to prevent the rolling average from disrupting the condition cgm_df.loc[before_condition, 'glucose_values'] = cgm_df.loc[before_condition, 'glucose_values'].interpolate() @@ -580,15 +581,21 @@ def interpolate_and_smooth_cgm(cgm_df, min_periods=1, center=True) - cgm_df['glucose_values'] = cgm_rolling.mean().round(2).round() + cgm_df['glucose_values'] = cgm_rolling.mean().round(2) - cgm_df.loc[cgm_df['glucose_values'].notnull(), 'glucose_values'] = \ - cgm_df.loc[cgm_df['glucose_values'].notnull(), - 'glucose_values'].astype(int) + # Interpolate forward NaNs on entire dataframe (using linear default) + cgm_df['glucose_values'] = cgm_df['glucose_values'].interpolate().round(1) + + # cgm_df.loc[cgm_df['glucose_values'].notnull(), 'glucose_values'] = \ + # cgm_df.loc[cgm_df['glucose_values'].notnull(), + # 'glucose_values'].astype(int) # Drop the trailing values after the evaluation point cgm_df = cgm_df[cgm_df.glucose_dates <= evaluation_time] + if cgm_df.glucose_values.isnull().sum() > 0: + print("NULL VALUES STILL IN CGM_DF!!") + return cgm_df @@ -648,7 +655,7 @@ def get_cgm_df(snapshot_df, # inplace=True) cgm_df['glucose_dates'] = cgm_data.rounded_local_time - cgm_df['glucose_values'] = round(cgm_data.value * 18.01559) + cgm_df['glucose_values'] = round(cgm_data.value * 18.01559, 1) if(smooth_cgm): # Interpolate and smooth the data From 1b765e5cfe64ac355825030af96c6443f4c2234d Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 26 Mar 2020 13:07:08 -0400 Subject: [PATCH 52/57] Update max basal & bolus settings defaults Max basal rate - use 3x the max scheduled basal rate Max bolus - use 20u. --- .../iCGM-test-matrix/snapshot_processor.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 9c1a0eda..2d600b34 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -700,7 +700,8 @@ def get_time_to_calculate_at(evaluation_time): def get_settings(birthdate, diagnosis_date, - evaluation_time): + evaluation_time, + basal_rates): """Provides a default settings dataframe""" df_columns = ['settings'] @@ -752,6 +753,8 @@ def get_settings(birthdate, df_settings.loc['age'] = age df_settings.loc['ylw'] = ylw df_settings.loc['model'] = '[360.0, 65]' + if age < 13: + df_settings.loc['model'] = '[360.0, 55]' df_settings.loc['momentum_data_interval'] = '15' df_settings.loc['suspend_threshold'] = '70' df_settings.loc['dynamic_carb_absorption_enabled'] = 'TRUE' @@ -762,8 +765,10 @@ def get_settings(birthdate, df_settings.loc['insulin_delay'] = '10' df_settings.loc['carb_delay'] = '10' df_settings.loc['default_absorption_times'] = '[120.0, 180.0, 240.0]' - df_settings.loc['max_basal_rate'] = '35' - df_settings.loc['max_bolus'] = '30' + + max_sched_basal_rate = basal_rates['actual_basal_rates'].max() + df_settings.loc['max_basal_rate'] = str(max_sched_basal_rate * 3) + df_settings.loc['max_bolus'] = '20' df_settings.loc['retrospective_correction_enabled'] = 'TRUE' return df_settings @@ -938,9 +943,6 @@ def get_snapshot(data, evaluation_time) df_last_temporary_basal = get_last_temp_basal() - df_settings = get_settings(birthdate, - diagnosis_date, - evaluation_time) if(simplify_settings): (basal_rates, @@ -951,6 +953,11 @@ def get_snapshot(data, isfs, df_target_range) + df_settings = get_settings(birthdate, + diagnosis_date, + evaluation_time, + basal_rates) + if(empty_events): carb_events, dose_events = create_empty_events(carb_events, dose_events, From d2b23085633facb93b29ebcdb9c1706e67f82bd0 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 26 Mar 2020 14:45:48 -0400 Subject: [PATCH 53/57] Add a final NaN CGM backfill after interpolating --- projects/iCGM-test-matrix/snapshot_processor.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 2d600b34..badaba31 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -586,6 +586,11 @@ def interpolate_and_smooth_cgm(cgm_df, # Interpolate forward NaNs on entire dataframe (using linear default) cgm_df['glucose_values'] = cgm_df['glucose_values'].interpolate().round(1) + # Backfill any remaining NaNs on entire dataframe + cgm_df['glucose_values'] = ( + cgm_df['glucose_values'].fillna(method='backfill') + ) + # cgm_df.loc[cgm_df['glucose_values'].notnull(), 'glucose_values'] = \ # cgm_df.loc[cgm_df['glucose_values'].notnull(), # 'glucose_values'].astype(int) From 0870a71453bafceb31a0f5b65a5e9aa99b37560e Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Thu, 26 Mar 2020 15:26:55 -0400 Subject: [PATCH 54/57] Add logic to handle carb to insulin ratios of 0 --- projects/iCGM-test-matrix/snapshot_processor.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index badaba31..985fc7d2 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -207,6 +207,11 @@ def get_carb_ratios(active_schedule, bolus_df): carb_ratios.loc[ratio_loc, 'carb_ratio_start_times'] = start_string carb_ratios.loc[ratio_loc, 'carb_ratio_values'] = ratio + # If all carb ratios are 0 use average ratio from data bolus calculations + if carb_ratios['carb_ratio_values'].sum() == 0: + ratio = bolus_df.insulinCarbRatio.astype(float).mean().round() + carb_ratios['carb_ratio_values'] = ratio + carb_ratios['actual_carb_ratios'] = carb_ratios['carb_ratio_values'] carb_ratios['carb_ratio_value_units'] = 'g/U' From 008867e108f8e4bd952c2f2265ec146d72ad0b90 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Tue, 7 Apr 2020 18:03:43 -0400 Subject: [PATCH 55/57] Add weighted cgm gap blending to snapshot processor --- .../iCGM-test-matrix/snapshot_processor.py | 123 ++++++++++++++++-- 1 file changed, 112 insertions(+), 11 deletions(-) diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index 985fc7d2..fbd62692 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -556,6 +556,74 @@ def get_dose_events(snapshot_df): return dose_events +def blend_cgm_gaps( + df, + time_interval_minutes=5, + min_gap_size=15, + min_snippet_minutes=75, +): + + df = df[df['value'].notnull()].reset_index().copy() + + df["timeBetweenRecords"] = round( + df['rounded_local_time'].diff().dt.days*1440 + + df['rounded_local_time'].diff().dt.seconds/60 + ) + + snippet_index = list(df[df.timeBetweenRecords.abs() > min_gap_size].index) + snippet_index.insert(0, 0) + + # create a dataframe of just the snippet starts and sizes + just_snippets = pd.DataFrame(snippet_index, columns=["snippet_index"]) + just_snippets["snippet_size"] = np.diff(snippet_index + [len(df)]) + + # get rid of all snippets less than (user defined length) + min_snippet_size = min_snippet_minutes / time_interval_minutes + qualified_snippets = just_snippets["snippet_size"] >= min_snippet_size + #keep_snippets_array = just_snippets.loc[qualified_snippets, :].values + keep_snippets_array = just_snippets.loc[qualified_snippets, :] + keep_snippets_array.reset_index(drop=True, inplace=True) + + n_smoothing_points = int(min_snippet_minutes / time_interval_minutes) + + # Get the starting trace + snip_start = keep_snippets_array.loc[0, "snippet_index"] + snip_end = snip_start + keep_snippets_array.loc[0, "snippet_size"] - 1 + previous_trace = df.loc[snip_start:snip_end, "value"].values + + # Apply weighted smoothing to each traces + for s in range(1, len(keep_snippets_array)): + snip_start = keep_snippets_array.loc[s, "snippet_index"] + snip_end = snip_start + keep_snippets_array.loc[s, "snippet_size"] - 1 + current_trace = df.loc[snip_start:snip_end, "value"].values + + # weight the last n_smoothing_points of the previous trace + weighted_prev = ( + previous_trace[-n_smoothing_points:] + * np.linspace(1, 0, n_smoothing_points) + ) + # weight the first n_smoothing_points of the current trace + weighted_curr = ( + current_trace[:n_smoothing_points] + * np.linspace(0, 1, n_smoothing_points) + ) + # combine the weighted traces to make a smooth transition + blended_section = weighted_prev + weighted_curr + + # insert the blended section into the previous and current traces + previous_trace = np.concatenate( + ( + previous_trace[:-n_smoothing_points], + blended_section, + current_trace[n_smoothing_points:] + ) + ) + + blended_cgm_trace = previous_trace + + return blended_cgm_trace + + def interpolate_and_smooth_cgm(cgm_df, condition_num, add_cgm_trail, @@ -609,7 +677,8 @@ def interpolate_and_smooth_cgm(cgm_df, return cgm_df -def get_cgm_df(snapshot_df, +def get_cgm_df(data, + snapshot_df, smooth_cgm, condition_num, start_time, @@ -625,6 +694,12 @@ def get_cgm_df(snapshot_df, cgm_df = pd.DataFrame(columns=df_columns) + blended_cgm_start = start_time - datetime.timedelta(days=10) + blended_cgm_end = evaluation_time - datetime.timedelta(days=1) + + snapshot_df = data[(data['rounded_local_time'] >= blended_cgm_start) & + (data['rounded_local_time'] <= end_time)] + cgm_data = snapshot_df[snapshot_df.type == 'cbg'].copy() # first_timestamp = cgm_data["rounded_local_time"].min() @@ -644,14 +719,15 @@ def get_cgm_df(snapshot_df, contiguous_df = pd.DataFrame(contiguous_ts, columns=["rounded_local_time"]) - cgm_data = pd.merge(contiguous_df, - cgm_data, - how="left", - on="rounded_local_time" - ) + ten_day_cgm_data = pd.merge( + contiguous_df, + cgm_data, + how="left", + on="rounded_local_time" + ) # Remove CGM duplicates from the contiguous_df - cgm_data, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated = \ - cond_finder.remove_contiguous_rounded_CGM_duplicates(cgm_data) + ten_day_cgm_data, nRoundedTimeDuplicatesRemoved, cgmPercentDuplicated = \ + cond_finder.remove_contiguous_rounded_CGM_duplicates(ten_day_cgm_data) # Drop cgm duplicates in snapshot (if any) # cgm_data.sort_values(['uploadId', 'rounded_local_time'], @@ -664,8 +740,32 @@ def get_cgm_df(snapshot_df, # ascending=True, # inplace=True) - cgm_df['glucose_dates'] = cgm_data.rounded_local_time - cgm_df['glucose_values'] = round(cgm_data.value * 18.01559, 1) + cgm_df['glucose_dates'] = ten_day_cgm_data.rounded_local_time + cgm_df['glucose_values'] = round(ten_day_cgm_data.value * 18.01559, 1) + cgm_df.reset_index(drop=True, inplace=True) + + # Take only the days before the 24-hour scenario for blending + cgm_values_to_blend = ( + cgm_data.loc[ + cgm_data['rounded_local_time'] <= blended_cgm_end, + ['rounded_local_time', 'value'] + ].copy() + ) + + blended_cgm_values = blend_cgm_gaps(df=cgm_values_to_blend) + blended_length = len(blended_cgm_values) + + if blended_length >= 288*9: + blended_cgm_values = blended_cgm_values[-(288*9):] + else: + padding_size = 288*9 - blended_length + print("Adding " + str(padding_size) + " NaNs to pad blended CGMs.") + padding = [np.nan]*padding_size + blended_cgm_values = np.concatenate((padding, blended_cgm_values)) + + blended_cgm_values = np.round(blended_cgm_values * 18.01559, 1) + blended_glucose_dates = cgm_df['glucose_dates'] <= blended_cgm_end + cgm_df.loc[blended_glucose_dates, 'glucose_values'] = blended_cgm_values if(smooth_cgm): # Interpolate and smooth the data @@ -944,7 +1044,8 @@ def get_snapshot(data, carb_events = get_carb_events(snapshot_df) dose_events = get_dose_events(snapshot_df) - cgm_df = get_cgm_df(snapshot_df, + cgm_df = get_cgm_df(data, + snapshot_df, smooth_cgm, condition_num, start_time, From 255f0b66a89d38336435e7934553bc2d383c1907 Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 8 Apr 2020 14:18:40 -0400 Subject: [PATCH 56/57] Add sample data for testing --- .../batch-icgm-condition-stats.py | 4 +- .../iCGM-test-matrix/icgm_condition_finder.py | 5 +- .../sample-batch-icgm-condition-stats.csv | 2 + .../iCGM-test-matrix/sample_data/sample.csv | 37733 ++++++++++++++++ .../iCGM-test-matrix/snapshot_processor.py | 7 +- 5 files changed, 37743 insertions(+), 8 deletions(-) create mode 100644 projects/iCGM-test-matrix/sample_data/sample-batch-icgm-condition-stats.csv create mode 100644 projects/iCGM-test-matrix/sample_data/sample.csv diff --git a/projects/iCGM-test-matrix/batch-icgm-condition-stats.py b/projects/iCGM-test-matrix/batch-icgm-condition-stats.py index bb911276..30a18895 100644 --- a/projects/iCGM-test-matrix/batch-icgm-condition-stats.py +++ b/projects/iCGM-test-matrix/batch-icgm-condition-stats.py @@ -23,7 +23,7 @@ import sys # %% -data_location = "data_folder/" +data_location = "sample_data/" file_list = os.listdir(data_location) # Filter only files with .csv in their name (includes .csv.gz files) @@ -114,7 +114,7 @@ def get_icgm_condition_stats(file_name, data_location, user_loc): icgm_condition_summary_df = pd.concat(results_array, sort=False) today_timestamp = dt.datetime.now().strftime("%Y-%m-%d") results_export_filename = \ - 'PHI-batch-icgm-condition-stats-' + \ + 'batch-icgm-condition-stats-' + \ today_timestamp + \ '.csv' icgm_condition_summary_df.to_csv(results_export_filename, index=False) diff --git a/projects/iCGM-test-matrix/icgm_condition_finder.py b/projects/iCGM-test-matrix/icgm_condition_finder.py index 3e31e6bd..d37a524e 100644 --- a/projects/iCGM-test-matrix/icgm_condition_finder.py +++ b/projects/iCGM-test-matrix/icgm_condition_finder.py @@ -668,10 +668,11 @@ def main(data, file_name): # %% if __name__ == "__main__": - file_name = "data.csv" - file_location = "." + file_name = "sample.csv" + file_location = "sample_data/" file_path = os.path.join(file_location, file_name) + data = import_data(file_path) results = main(data, file_name) diff --git a/projects/iCGM-test-matrix/sample_data/sample-batch-icgm-condition-stats.csv b/projects/iCGM-test-matrix/sample_data/sample-batch-icgm-condition-stats.csv new file mode 100644 index 00000000..88566d13 --- /dev/null +++ b/projects/iCGM-test-matrix/sample_data/sample-batch-icgm-condition-stats.csv @@ -0,0 +1,2 @@ +file_name,nRoundedTimeDuplicatesRemoved,cgmPercentDuplicated,gte40_lt70,gte70_lte180,gt180_lte400,lt-1,gte-1_lte1,gt1,cond0,cond1,cond2,cond3,cond4,cond5,cond6,cond7,cond8,cond9,cond1_eval_time,cond2_eval_time,cond3_eval_time,cond4_eval_time,cond5_eval_time,cond6_eval_time,cond7_eval_time,cond8_eval_time,cond9_eval_time,status,birthday,diagnosisDate +sample.csv,35,0.0012317,14,15064,12567,756,25915,1091,195,0,233,520,12,14247,11542,2,584,505,,2020-02-20T11:40:00,2020-04-03T01:05:00,,2020-01-31T07:50:00,2020-02-25T23:30:00,,2020-01-31T16:15:00,2020-02-02T13:25:00,Complete,1/1/80,1/1/90 \ No newline at end of file diff --git a/projects/iCGM-test-matrix/sample_data/sample.csv b/projects/iCGM-test-matrix/sample_data/sample.csv new file mode 100644 index 00000000..fea74882 --- /dev/null +++ b/projects/iCGM-test-matrix/sample_data/sample.csv @@ -0,0 +1,37733 @@ +activeSchedule,activityDuration.units,activityDuration.value,activityName,alarmType,basalSchedules,bgInput,bgTarget,bgTarget.range,bgTarget.start,bgTarget.target,bolusSchedules,carbInput,carbRatio,date,defaultAlerts,deliveryType,deviceId,deviceManufacturers,deviceModel,deviceSerialNumber,deviceTags,deviceTime,distance.units,distance.value,dose.total,dose.units,duration,energy.units,energy.value,est.annotations,est.gapSize,est.localTime,est.timeProcessing,est.timezoneOffset,est.type,est.version,expectedNormal,formulation.simple.actingType,highAlerts.enabled,highAlerts.level,highAlerts.snooze,id,insulinCarbRatio,insulinOnBoard,insulinSensitivity,lowAlerts.enabled,lowAlerts.level,lowAlerts.snooze,normal,nutrition.carbohydrate.net,nutrition.carbohydrate.units,outOfRangeAlerts.enabled,outOfRangeAlerts.snooze,primeTarget,rate,reason.resumed,reason.suspended,recommended.carb,recommended.correction,recommended.net,reportedIntensity,states,status,subType,time,timeProcessing,timezoneOffset,transmitterId,type,units,units.bg,units.carb,uploadId,value,version,volume +,,,,,,,,,,,,,,2020-02-28,"{'enabled': False, 'fall': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'high': {'enabled': True, 'level': 180, 'snooze': {'duration': 0, 'units': 'minutes'}, 'units': 'mg/dL'}, 'low': {'enabled': True, 'level': 70, 'snooze': {'duration': 15, 'units': 'minutes'}, 'units': 'mg/dL'}, 'outOfRange': {'duration': 60, 'enabled': False, 'units': 'minutes'}, 'rise': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'urgentLow': {'enabled': True, 'level': 55, 'snooze': {'duration': 30, 'units': 'minutes'}, 'units': 'mg/dL'}}",,,,,,,,,,,,,,,,,2020-02-28T22:34:01,,-300.0,,0.0.4,,,True,9.99135,0.0,38f9e7caf856d8f3f3782d9422d38c41,,,,True,3.88552,900000.0,,,,False,3600000.0,,,,,,,,,,,,2020-02-29T03:34:00.985Z,,,322edd38,cgmSettings,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,,, +,,,,,,,,,,,,,,2020-03-12,"{'enabled': False, 'fall': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'high': {'enabled': True, 'level': 180, 'snooze': {'duration': 0, 'units': 'minutes'}, 'units': 'mg/dL'}, 'low': {'enabled': True, 'level': 70, 'snooze': {'duration': 15, 'units': 'minutes'}, 'units': 'mg/dL'}, 'noData': {'duration': 20, 'enabled': True, 'units': 'minutes'}, 'outOfRange': {'duration': 60, 'enabled': False, 'units': 'minutes'}, 'rise': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'urgentLow': {'enabled': True, 'level': 55, 'snooze': {'duration': 30, 'units': 'minutes'}, 'units': 'mg/dL'}, 'urgentLowPredicted': {'enabled': True, 'level': 55, 'snooze': {'duration': 30, 'units': 'minutes'}, 'units': 'mg/dL'}}",,,,,,,,,,,,,,,,,2020-03-12T17:46:43,,-240.0,UPLOAD,0.0.4,,,True,9.99135,0.0,c254555f237e1270fe60e5b2fc40dd21,,,,True,3.88552,900000.0,,,,False,3600000.0,,,,,,,,,,,,2020-03-12T21:46:42.583Z,,,24b1b186,cgmSettings,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,,, +,,,,,,,,,,,,,,2020-03-16,"{'enabled': False, 'fall': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'high': {'enabled': True, 'level': 180, 'snooze': {'duration': 0, 'units': 'minutes'}, 'units': 'mg/dL'}, 'low': {'enabled': True, 'level': 70, 'snooze': {'duration': 15, 'units': 'minutes'}, 'units': 'mg/dL'}, 'noData': {'duration': 20, 'enabled': True, 'units': 'minutes'}, 'outOfRange': {'duration': 60, 'enabled': False, 'units': 'minutes'}, 'rise': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'urgentLow': {'enabled': True, 'level': 55, 'snooze': {'duration': 30, 'units': 'minutes'}, 'units': 'mg/dL'}, 'urgentLowPredicted': {'enabled': True, 'level': 55, 'snooze': {'duration': 30, 'units': 'minutes'}, 'units': 'mg/dL'}}",,,,,,,,,,,,,,,,,2020-03-16T12:05:12,,-240.0,UPLOAD,0.0.4,,,True,9.99135,0.0,9e54ee4909badb4bd7c609e5663f3911,,,,True,3.88552,900000.0,,,,False,3600000.0,,,,,,,,,,,,2020-03-16T16:05:12.335Z,,,24b1b186,cgmSettings,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,,, +,,,,,,,,,,,,,,2020-03-19,"{'enabled': False, 'fall': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'high': {'enabled': True, 'level': 180, 'snooze': {'duration': 0, 'units': 'minutes'}, 'units': 'mg/dL'}, 'low': {'enabled': True, 'level': 70, 'snooze': {'duration': 15, 'units': 'minutes'}, 'units': 'mg/dL'}, 'outOfRange': {'duration': 60, 'enabled': False, 'units': 'minutes'}, 'rise': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'urgentLow': {'enabled': True, 'level': 55, 'snooze': {'duration': 30, 'units': 'minutes'}, 'units': 'mg/dL'}}",,,,,,,,,,,,,,,,,2020-03-19T02:43:06,,-240.0,UPLOAD,0.0.4,,,True,9.99135,0.0,267cc0995b24e42ea44fa48003b08895,,,,True,3.88552,900000.0,,,,False,3600000.0,,,,,,,,,,,,2020-03-19T06:43:05.672Z,,,322edd38,cgmSettings,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,,, +,,,,,,,,,,,,,,2020-03-22,"{'enabled': False, 'fall': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'high': {'enabled': True, 'level': 180, 'snooze': {'duration': 0, 'units': 'minutes'}, 'units': 'mg/dL'}, 'low': {'enabled': True, 'level': 70, 'snooze': {'duration': 15, 'units': 'minutes'}, 'units': 'mg/dL'}, 'noData': {'duration': 20, 'enabled': False, 'units': 'minutes'}, 'outOfRange': {'duration': 60, 'enabled': False, 'units': 'minutes'}, 'rise': {'enabled': False, 'rate': 3, 'units': 'mg/dL/minute'}, 'urgentLow': {'enabled': True, 'level': 55, 'snooze': {'duration': 30, 'units': 'minutes'}, 'units': 'mg/dL'}, 'urgentLowPredicted': {'enabled': True, 'level': 55, 'snooze': {'duration': 30, 'units': 'minutes'}, 'units': 'mg/dL'}}",,,,,,,,,,,,,,,,,2020-03-22T05:17:17,,-240.0,UPLOAD,0.0.4,,,True,9.99135,0.0,07526988f04f93271d4a114909dea954,,,,True,3.88552,900000.0,,,,False,3600000.0,,,,,,,,,,,,2020-03-22T09:17:16.712Z,,,24b1b186,cgmSettings,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T00:00:27,,-300.0,,0.0.4,,,,,,feb3888bb0ace3023f466df579ac279f,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T05:00:27.069Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.20991,, 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+,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T00:22:14,,-300.0,,0.0.4,,,,,,f55081c774cfe6750dac969701300c08,,,,,,,3.0,,,,,,,,,,,,,,,normal,2020-01-01T05:22:13.809Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T00:25:27,,-300.0,,0.0.4,,,,,,7ce27794108c139230a254a4705e7a0a,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T05:25:26.916Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.20991,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T00:30:27,,-300.0,,0.0.4,,,,,,b2df6c44b1021e2c827dfe5b46025ec3,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T05:30:27.327Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.32093,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T00:35:27,,-300.0,,0.0.4,,,,,,290edfc52731a07d0c45eef73994a34c,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T05:35:26.936Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.8205,, 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+,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T01:20:27,,-300.0,,0.0.4,,,,,,192881c03f7f87eb5e27bd3c276c97b7,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T06:20:27.086Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,15.819629999999998,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T01:25:27,,-300.0,,0.0.4,,,,,,829fd99ff33542a78b6e0ede49786b21,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T06:25:27.086Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,16.3192,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T01:30:27,,-300.0,,0.0.4,,,,,,85ec02a2edb361bae1b9f6754249cef8,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T06:30:27.086Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,16.59674,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T01:35:27,,-300.0,,0.0.4,,,,,,6d7f291b4c376b0ce9c1e38b2f6766e8,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T06:35:26.900Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,16.43021,, 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+,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T18:45:25,,-300.0,,0.0.4,,,,,,9b4aeebe8893026ae01de97564da8668,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T23:45:25.131Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.65744,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T18:50:25,,-300.0,,0.0.4,,,,,,c1d43997582b8d9c0232953fcb4e0009,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T23:50:24.643Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.65744,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T18:55:25,,-300.0,,0.0.4,,,,,,900802ea63d35e6c1b908e04189c1ae2,,,,,,,,,,,,,,,,,,,,,,,2020-01-01T23:55:24.807Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.54642,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T19:00:25,,-300.0,,0.0.4,,,,,,a74d438a1ef5f547807553812b7a4cb4,,,,,,,,,,,,,,,,,,,,,,,2020-01-02T00:00:25.005Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.49091,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T19:05:25,,-300.0,,0.0.4,,,,,,b7e2ac904cb0161b91d0c86fc2a47bfb,,,,,,,,,,,,,,,,,,,,,,,2020-01-02T00:05:24.791Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.43541,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T19:10:25,,-300.0,,0.0.4,,,,,,a7c1618505d0a325833e908df40f692f,,,,,,,,,,,,,,,,,,,,,,,2020-01-02T00:10:25.011Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.3799,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T19:15:01,,-300.0,,0.0.4,,,,,,4b5a9ac609286306c39a205bebbde107,,,,,,,,40.0,grams,,,,,,,,,,,,,,2020-01-02T00:15:00.619Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T19:15:08,,-300.0,,0.0.4,,,,,,811d24103dfdb60ceaf0f5afa25890a3,,,,,,,4.0,,,,,,,,,,,,,,,normal,2020-01-02T00:15:08.206Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, 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+,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T20:35:25,,-300.0,,0.0.4,,,,,,ceb7c837bbccd834453cbbccf08b9951,,,,,,,,,,,,,,,,,,,,,,,2020-01-02T01:35:25.353Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,22.09198,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T20:40:25,,-300.0,,0.0.4,,,,,,e9b45549507eaecc63a4b07e98d06500,,,,,,,,,,,,,,,,,,,,,,,2020-01-02T01:40:24.533Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,22.03647,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T20:45:25,,-300.0,,0.0.4,,,,,,07649ad0e137a89392371d26e4dc6879,,,,,,,,,,,,,,,,,,,,,,,2020-01-02T01:45:24.625Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,21.98096,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T20:50:25,,-300.0,,0.0.4,,,,,,b70e79b72d12868bc320d9742e7146c3,,,,,,,,,,,,,,,,,,,,,,,2020-01-02T01:50:25.078Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,21.92545,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T20:55:25,,-300.0,,0.0.4,,,,,,b28603dd371616dad7d74e7275b43653,,,,,,,,,,,,,,,,,,,,,,,2020-01-02T01:55:24.990Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,21.86995,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T20:57:48,,-300.0,,0.0.4,,,,,,fe8a54bc07ba88d6be38a1afbcd9c234,,,,,,,4.0,,,,,,,,,,,,,,,normal,2020-01-02T01:57:48.475Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T20:57:50,,-300.0,,0.0.4,,,,,,042172a9d14b2bf7d327939a24cbc6bd,,,,,,,,10.0,grams,,,,,,,,,,,,,,2020-01-02T01:57:50.056Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-01,,,,,,,,,,,,,,,,,,2020-01-01T21:00:25,,-300.0,,0.0.4,,,,,,79e3799497ce531bb6cf6472d8efe89b,,,,,,,,,,,,,,,,,,,,,,,2020-01-02T02:00:25.011Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,21.81444,, 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+,,,,,,,,,,,,,,2020-01-07,,,,,,,,,,,,,,,,,,2020-01-07T08:25:13,,-300.0,,0.0.4,,,,,,ac22c379925625dcd828072ee8116f7a,,,,,,,,,,,,,,,,,,,,,,,2020-01-07T13:25:12.750Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.82569,, +,,,,,,,,,,,,,,2020-01-07,,,,,,,,,,,,,,,,,,2020-01-07T08:30:12,,-300.0,,0.0.4,,,,,,48c539bef67f4307e17e6012298d6245,,,,,,,,,,,,,,,,,,,,,,,2020-01-07T13:30:12.482Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.77018,, +,,,,,,,,,,,,,,2020-01-07,,,,,,,,,,,,,,,,,,2020-01-07T08:35:12,,-300.0,,0.0.4,,,,,,57ada50b028c6cf163d213a9969a3977,,,,,,,,,,,,,,,,,,,,,,,2020-01-07T13:35:12.471Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.71467,, +,,,,,,,,,,,,,,2020-01-07,,,,,,,,2020-01-07T08:38:24,,,,,,,,,,2020-01-07T08:38:24,,-300.0,,0.0.4,,,,,,3ed005e7597fcb16da9b7cb17b8d5d00,,,,,,,,,,,,,,,,,,,,,,calibration,2020-01-07T13:38:24Z,,-300.0,,deviceEvent,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,8.65917,, 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+,,,,,,,,,,,,,,2020-01-14,,,,,,,,,,,,,,,,,,2020-01-14T19:29:54,,-300.0,,0.0.4,,,,,,d4f1bb688077dbb3d16ffe5ec9e3ecd0,,,,,,,,,,,,,,,,,,,,,,,2020-01-15T00:29:54.467Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.215110000000001,, +,,,,,,,,,,,,,,2020-01-14,,,,,,,,,,,,,,,,,,2020-01-14T19:34:55,,-300.0,,0.0.4,,,,,,5ca3e41c34d637fded7cbf6bfb2ec2ef,,,,,,,,,,,,,,,,,,,,,,,2020-01-15T00:34:54.919Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.38249,, +,,,,,,,,,,,,,,2020-01-14,,,,,,,,,,,,,,,,,,2020-01-14T19:39:55,,-300.0,,0.0.4,,,,,,4a48e5f08b6189dac3752a22c72dff20,,,,,,,,,,,,,,,,,,,,,,,2020-01-15T00:39:54.713Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.605389999999999,, +,,,,,,,,,,,,,,2020-01-14,,,,,,,,,,,,,,,,,,2020-01-14T19:44:55,,-300.0,,0.0.4,,,,,,45cd420a09da30d70ff48c2705131c53,,,,,,,,,,,,,,,,,,,,,,,2020-01-15T00:44:55.344Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.38336,, 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+,,,,,,,,,,,,,,2020-01-14,,,,,,,,,,,,,,,,,,2020-01-14T22:39:54,,-300.0,,0.0.4,,,,,,1a3220a9383302a33f3f61d756940459,,,,,,,,,,,,,,,,,,,,,,,2020-01-15T03:39:54.385Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.270610000000001,, +,,,,,,,,,,,,,,2020-01-14,,,,,,,,,,,,,,,,,,2020-01-14T22:44:55,,-300.0,,0.0.4,,,,,,9a1566f962b69b78dafd354b882694c4,,,,,,,,,,,,,,,,,,,,,,,2020-01-15T03:44:55.316Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.270610000000001,, +,,,,,,,,,,,,,,2020-01-14,,,,,,,,,,,,,,,,,,2020-01-14T22:49:55,,-300.0,,0.0.4,,,,,,394c93df591958090d395eddac2b259d,,,,,,,,,,,,,,,,,,,,,,,2020-01-15T03:49:54.978Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.1596,, +,,,,,,,,,,,,,,2020-01-14,,,,,,,,,,,,,,,,,,2020-01-14T22:54:55,,-300.0,,0.0.4,,,,,,351f11d65af0f3b6487e46ac9e1ef297,,,,,,,,,,,,,,,,,,,,,,,2020-01-15T03:54:54.759Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.1596,, 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+,,,,,,,,,,,,,,2020-01-26,,,,,,,,,,,,,,,,travel,,2020-01-26T00:34:29,,-360.0,,0.0.4,,,,,,c0cc1b85147f77e81c935ec29a262e22,,,,,,,,,,,,,,,,,,,,,,,2020-01-26T06:34:28.912Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.54469,, +,,,,,,,,,,,,,,2020-01-26,,,,,,,,,,,,,,,,travel,,2020-01-26T00:39:29,,-360.0,,0.0.4,,,,,,5304ef1dd80fce142cac48d412386a24,,,,,,,,,,,,,,,,,,,,,,,2020-01-26T06:39:28.990Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.43368,, +,,,,,,,,,,,,,,2020-01-26,,,,,,,,2020-01-26T00:41:38,,,,,,,,travel,,2020-01-26T00:41:38,,-360.0,,0.0.4,,,,,,fbc26533c11e15226139a3c42db6c7c0,,,,,,,,,,,,,,,,,,,,,,calibration,2020-01-26T06:41:38Z,,-360.0,,deviceEvent,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,13.876870000000002,, +,,,,,,,,,,,,,,2020-01-26,,,,,,,,,,,,,,,,travel,,2020-01-26T00:44:33,,-360.0,,0.0.4,,,,,,aaf8abf4dadda171a06e9257ee519c04,,,,,,,,,,,,,,,,,,,,,,,2020-01-26T06:44:33.185Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.3773,, 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+,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T18:54:25,,-300.0,,0.0.4,,,,,,d81196c839499a17e06c060e9f5e19be,,,,,,,,,,,,,,,,,,,,,,,2020-01-27T23:54:25.082Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.43541,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T18:59:25,,-300.0,,0.0.4,,,,,,c2a9419084d7d030982b7be8532ec211,,,,,,,,,,,,,,,,,,,,,,,2020-01-27T23:59:24.955Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.99135,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T19:04:24,,-300.0,,0.0.4,,,,,,573b000dae3c369b5b800cd555ad255b,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T00:04:24.452Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.26888,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T19:09:25,,-300.0,,0.0.4,,,,,,37e928850b32ea4bdbc05eabbd809d9b,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T00:09:24.677Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.49091,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,2020-01-27T21:57:55,,,,,,,,,,2020-01-27T21:57:55,,-300.0,,0.0.4,,,,,,0a66f81f151eb52d54db082fd29aabda,,,,,,,,,,,,,,,,,,,,,,calibration,2020-01-28T02:57:55Z,,-300.0,,deviceEvent,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,16.097170000000002,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,2020-01-27T21:57:58,,,,,,,,,,2020-01-27T21:57:58,,-300.0,,0.0.4,,,,,,8d55b17992c0a6f2b41ef37795c1ab4c,,,,,,,,,,,,,,,,,,,,,,calibration,2020-01-28T02:57:58Z,,-300.0,,deviceEvent,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,17.59587,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T21:59:32,,-300.0,,0.0.4,,,,,,0ba11f780a5b4c62a1dedccc610e0bce,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T02:59:32.351Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,16.59674,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T21:59:58,,-300.0,,0.0.4,,,,,,92e6b0fe75bb5ff703caa2de7b53402a,,,,,,,2.5,,,,,,,,,,,,,,,normal,2020-01-28T02:59:58.408Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:01:48,,-300.0,,0.0.4,,,,,,6f08e7129346c584d18347b6ab212cc8,,,,,,,1.3,,,,,,,,,,,,,,,normal,2020-01-28T03:01:48.268Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:04:25,,-300.0,,0.0.4,,,,,,2053252f3ba30634e1eab9e905d0617f,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:04:25.034Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,16.59674,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:09:24,,-300.0,,0.0.4,,,,,,25308c9740e483ea935ec1048463d54c,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:09:24.356Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,16.54123,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:14:25,,-300.0,,0.0.4,,,,,,1290a8669bc240b884c76fae946dca72,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:14:25.219Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,16.43021,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:19:25,,-300.0,,0.0.4,,,,,,e396a4539bc42ef10a4a692cfaaf7649,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:19:25.093Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,16.43021,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:24:24,,-300.0,,0.0.4,,,,,,77969de8808b0e7b75be06caa835341a,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:24:24.484Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,16.43021,, +,,,,,,,,,,,,,,2020-01-27,,temp,,,,,,,,,,,298082.0,,,,,2020-01-27T22:24:34,,-300.0,,0.0.4,,,,,,40b085dba4f06d4bd4a29c3b4411d551,,,,,,,,,,,,,1.2077195621810963,,,,,,,,,,2020-01-28T03:24:34.271Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:29:25,,-300.0,,0.0.4,,,,,,bd60732e6f4812d6e6788b264a6f1c35,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:29:24.927Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,15.87514,, +,,,,,,,,,,,,,,2020-01-27,,temp,,,,,,,,,,,906244.0,,,,,2020-01-27T22:29:32,,-300.0,,0.0.4,,,,,,9fa36f43ba1f5e39099c6a360d104c32,,,,,,,,,,,,,0.5958655280721578,,,,,,,,,,2020-01-28T03:29:32.353Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:34:25,,-300.0,,0.0.4,,,,,,def419520d58c74db6ba4f06e6e2dc92,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:34:25.010Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,15.43108,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:39:25,,-300.0,,0.0.4,,,,,,7c2c498b1922a9d318b57d9540522f27,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:39:24.501Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,15.375570000000002,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:44:25,,-300.0,,0.0.4,,,,,,4ad14a830305efc2f1441782549654a7,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:44:24.955Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,15.43108,, +,,,,,,,,,,,,,,2020-01-27,,temp,,,,,,,,,,,292803.0,,,,,2020-01-27T22:44:39,,-300.0,,0.0.4,,,,,,bcc05e12566cbe4da1ae8a4ac37c2a25,,,,,,,,,,,,,1.2294921816856712,,,,,,,,,,2020-01-28T03:44:38.598Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T22:49:25,,-300.0,,0.0.4,,,,,,569de858286104aac9ce14959d957b02,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T03:49:24.839Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,15.32006,, +,,,,,,,,,,,,,,2020-01-27,,temp,,,,,,,,,,,2368.0,,,,,2020-01-27T22:49:31,,-300.0,,0.0.4,,,,,,8c9c8417ef060c9391321d84c6abff78,,,,,,,,,,,,,0.0,,,,,,,,,,2020-01-28T03:49:31.401Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-27,,temp,,,,,,,,,,,297484.0,,,,,2020-01-27T22:49:34,,-300.0,,0.0.4,,,,,,03e3d4d7bdfe314bfbc65bd0eb327e5d,,,,,,,,,,,,,1.2101482304461186,,,,,,,,,,2020-01-28T03:49:33.770Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, 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+,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T23:19:25,,-300.0,,0.0.4,,,,,,eaa763da409463e2fecb85cce47a4b3c,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T04:19:25.053Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.26629,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T23:24:25,,-300.0,,0.0.4,,,,,,847f23cfa22d715201ef8a061d331460,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T04:24:24.854Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.98875,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T23:29:25,,-300.0,,0.0.4,,,,,,e010260e55e7b05a05e2ada2f82fda12,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T04:29:24.568Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.71121,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T23:34:25,,-300.0,,0.0.4,,,,,,30ab074f4d3c58406bfcb909ce38bc25,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T04:34:24.608Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.48918,, 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+,,,,,,,,,,,,,,2020-01-27,,temp,,,,,,,,,,,293935.0,,,,,2020-01-27T23:44:39,,-300.0,,0.0.4,,,,,,b96cba799462e32ac03b95177c5f1a42,,,,,,,,,,,,,1.837135189599221,,,,,,,,,,2020-01-28T04:44:38.534Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-27,,,,,,,,,,,,,,,,,,2020-01-27T23:49:25,,-300.0,,0.0.4,,,,,,9fd875cb4267f1f9189d3d5cc49f256f,,,,,,,,,,,,,,,,,,,,,,,2020-01-28T04:49:24.608Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.21165,, +,,,,,,,,,,,,,,2020-01-27,,temp,,,,,,,,,,,2789.0,,,,,2020-01-27T23:49:32,,-300.0,,0.0.4,,,,,,60b79342cf4fb51552a0a790c6211e14,,,,,,,,,,,,,0.0,,,,,,,,,,2020-01-28T04:49:32.470Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-27,,temp,,,,,,,,,,,298106.0,,,,,2020-01-27T23:49:35,,-300.0,,0.0.4,,,,,,bde9789de36af3118557c8d1c9a86dff,,,,,,,,,,,,,1.8114303124593385,,,,,,,,,,2020-01-28T04:49:35.259Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, 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+,,,,,,,,,,,,,,2020-01-29,,temp,,,,,,,,,,,4507720.0,,,,,2020-01-29T13:04:27,,-300.0,,0.0.4,,,,,,1c7c1f652876bffdf8319a771a33d1a2,,,,,,,,,,,,,0.638903878894415,,,,,,,,,,2020-01-29T18:04:27.267Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-29,,,,,,,,,,,,,,,,,,2020-01-29T13:09:21,,-300.0,,0.0.4,,,,,,75197d12fa10a5de2553ddf356b14c4d,,,,,,,,,,,,,,,,,,,,,,,2020-01-29T18:09:20.552Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.99221,, +,,,,,,,,,,,,,,2020-01-29,,,,,,,,,,,,,,,,,,2020-01-29T13:10:26,,-300.0,,0.0.4,,,,,,5e9bd9aae1e12b6828184f11822fab3a,,,,,,,,5.0,grams,,,,,,,,,,,,,,2020-01-29T18:10:25.946Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-29,,,,,,,,,,,,,,,,,,2020-01-29T13:10:41,,-300.0,,0.0.4,,,,,,d2a7f88c0da1340950b9ec6f7642bf59,,,,,,,0.65,,,,,,,,,,,,,,,normal,2020-01-29T18:10:40.920Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, 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+,,,,,,,,,,,,,,2020-01-30,,,,,,,,,,,,,,,,,,2020-01-30T04:24:19,,-300.0,,0.0.4,,,,,,6a3c7b6b1f06e44070d2adb261b97917,,,,,,,,,,,,,,,,,,,,,,,2020-01-30T09:24:18.592Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.88293,, +,,,,,,,,,,,,,,2020-01-30,,,,,,,,,,,,,,,,,,2020-01-30T04:29:18,,-300.0,,0.0.4,,,,,,1253a53ad2168e8d53e99c5b377aad25,,,,,,,,,,,,,,,,,,,,,,,2020-01-30T09:29:18.459Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.54988,, +,,,,,,,,,,,,,,2020-01-30,,,,,,,,,,,,,,,,,,2020-01-30T04:34:18,,-300.0,,0.0.4,,,,,,ccf06b768c5fac20adc040bb4a211fa5,,,,,,,,,,,,,,,,,,,,,,,2020-01-30T09:34:18.426Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.43887,, +,,,,,,,,,,,,,,2020-01-30,,,,,,,,,,,,,,,,,,2020-01-30T04:39:19,,-300.0,,0.0.4,,,,,,36230ba75cbe9f104314da64d695eadb,,,,,,,,,,,,,,,,,,,,,,,2020-01-30T09:39:19.063Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.32785,, 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+,,,,,,,,,,,,,,2020-01-31,,,,,,,,,,,,,,,,,,2020-01-31T14:44:16,,-300.0,,0.0.4,,,,,,b0d93bbb02e9c829a8f02208afd9ecc7,,,,,,,,,,,,,,,,,,,,,,,2020-01-31T19:44:15.993Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.438,, +,,,,,,,,,,,,,,2020-01-31,,,,,,,,,,,,,,,,,,2020-01-31T14:49:16,,-300.0,,0.0.4,,,,,,5397af7260178edb4c5c24de72e60ef6,,,,,,,,,,,,,,,,,,,,,,,2020-01-31T19:49:15.695Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.54902,, +,,,,,,,,,,,,,,2020-01-31,,,,,,,,,,,,,,,,,,2020-01-31T14:54:16,,-300.0,,0.0.4,,,,,,23f23d1413d8475bae4c62c1f36c0902,,,,,,,,,,,,,,,,,,,,,,,2020-01-31T19:54:15.818Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.66003,, +,,,,,,,,,,,,,,2020-01-31,,,,,,,,,,,,,,,,,,2020-01-31T14:59:16,,-300.0,,0.0.4,,,,,,86e56a03a0a2afcb74172b4643034645,,,,,,,,,,,,,,,,,,,,,,,2020-01-31T19:59:16.295Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.71554,, +,,,,,,,,,,,,,,2020-01-31,,,,,,,,,,,,,,,,,,2020-01-31T14:59:56,,-300.0,,0.0.4,,,,,,38825dfe35faa74a74a8fc68209dbe06,,,,,,,2.5,,,,,,,,,,,,,,,normal,2020-01-31T19:59:55.853Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-31,,,,,,,,,,,,,,,,,,2020-01-31T15:04:16,,-300.0,,0.0.4,,,,,,e2f4f51b2015ba63a4578663ca9b0a3f,,,,,,,,,,,,,,,,,,,,,,,2020-01-31T20:04:15.694Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.71554,, +,,,,,,,,,,,,,,2020-01-31,,,,,,,,,,,,,,,,,,2020-01-31T15:05:25,,-300.0,,0.0.4,,,,,,87f2badaa58b6bf6410be4d73fa706be,,,,,,,,30.0,grams,,,,,,,,,,,,,,2020-01-31T20:05:25.000Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-01-31,,,,,,,,,,,,,,,,,,2020-01-31T15:09:16,,-300.0,,0.0.4,,,,,,f13ef1ec3a48c3ba77331727f58513a4,,,,,,,,,,,,,,,,,,,,,,,2020-01-31T20:09:15.859Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.71554,, 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+,,,,,,,,,,,,,,2020-02-03,,,,,,,,,,,,,,,,,,2020-02-03T19:24:37,,-300.0,,0.0.4,,,,,,cf7e36827fa18e741d4fa652d16eb210,,,,,,,2.4,,,,,,,,,,,,,,,normal,2020-02-04T00:24:37.265Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-03,,,,,,,,,,,,,,,,,,2020-02-03T19:29:08,,-300.0,,0.0.4,,,,,,6ddcf819800747e52edbe6797497a442,,,,,,,,,,,,,,,,,,,,,,,2020-02-04T00:29:07.953Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.215969999999999,, +,,,,,,,,,,,,,,2020-02-03,,temp,,,,,,,,,,,10206503.0,,,,,2020-02-03T19:29:14,,-300.0,,0.0.4,,,,,,536fcc2e754ec7f401811f4e16484f32,,,,,,,,,,,,,0.6525251090573451,,,,,,,,,,2020-02-04T00:29:14.437Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-03,,,,,,,,,,,,,,,,,,2020-02-03T19:34:08,,-300.0,,0.0.4,,,,,,07e694f97156a7197f6fea94b80166c4,,,,,,,,,,,,,,,,,,,,,,,2020-02-04T00:34:07.971Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.326989999999999,, 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+,,,,,,,,,,,,,,2020-02-03,,temp,,,,,,,,,,,295329.0,,,,,2020-02-03T22:19:21,,-300.0,,0.0.4,,,,,,886a7e3117e97e9981f87ded18c97d78,,,,,,,,,,,,,4.266418522272687,,,,,,,,,,2020-02-04T03:19:20.940Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-03,,,,,,,,,,,,,,,,,,2020-02-03T22:19:25,,-300.0,,0.0.4,,,,,,0e7e20cb5d4383b0743787406cef4ae7,,,,,,,,15.0,grams,,,,,,,,,,,,,,2020-02-04T03:19:24.907Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-03,,,,,,,,,,,,,,,,,,2020-02-03T22:19:44,,-300.0,,0.0.4,,,,,,844769d70956152eb344301bb44619f2,,,,,,,1.25,,,,,,,,,,,,,,,normal,2020-02-04T03:19:43.531Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-03,,,,,,,,,,,,,,,,,,2020-02-03T22:24:08,,-300.0,,0.0.4,,,,,,0db793f6d6121c104716ddb460573008,,,,,,,,,,,,,,,,,,,,,,,2020-02-04T03:24:07.971Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.04685,, +,,,,,,,,,,,,,,2020-02-03,,temp,,,,,,,,,,,2340.0,,,,,2020-02-03T22:24:16,,-300.0,,0.0.4,,,,,,227bcc36b8f259addb0df1c099f97017,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-04T03:24:16.270Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-03,,temp,,,,,,,,,,,299373.0,,,,,2020-02-03T22:24:19,,-300.0,,0.0.4,,,,,,2329750721c99f95ab273d2e7da424b5,,,,,,,,,,,,,3.607529442236678,,,,,,,,,,2020-02-04T03:24:18.610Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-03,,,,,,,,,,,,,,,,,,2020-02-03T22:29:09,,-300.0,,0.0.4,,,,,,ae29d9844c0d542439d862ff5f0fa9e9,,,,,,,,,,,,,,,,,,,,,,,2020-02-04T03:29:08.608Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.76932,, +,,,,,,,,,,,,,,2020-02-03,,temp,,,,,,,,,,,3299.0,,,,,2020-02-03T22:29:18,,-300.0,,0.0.4,,,,,,dc3c4bbe410f15b2ecd6c78d0cee6595,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-04T03:29:17.984Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, 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+,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T11:54:00,,-300.0,,0.0.4,,,,,,62346f409f79508041d5840b34666b18,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T16:53:59.743Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.215110000000001,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T11:58:59,,-300.0,,0.0.4,,,,,,cfd2bfa029e1ae2f6711cb293db96091,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T16:58:59.238Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.66003,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,2020-02-07T12:01:26,,,,,,,,,,2020-02-07T12:01:26,,-300.0,,0.0.4,,,,,,9edb7af13f1c31eeff2ddc7b32c3cedf,,,,,,,,,,,,,,,,,,,,,,calibration,2020-02-07T17:01:26Z,,-300.0,,deviceEvent,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,8.71467,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:04:04,,-300.0,,0.0.4,,,,,,51e09e4b1687b338f13a8bd17ca5c0a1,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:04:03.938Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.99308,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,2289096.0,,,,,2020-02-07T12:06:12,,-300.0,,0.0.4,,,,,,0408f6b94a92e9b3d6ebd57b801a81dd,,,,,,,,,,,,,0.6290690561878036,,,,,,,,,,2020-02-07T17:06:12.345Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:09:00,,-300.0,,0.0.4,,,,,,7425fec27b9bfe97cdf8345ff0a425fc,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:08:59.752Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.66003,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:13:59,,-300.0,,0.0.4,,,,,,d45584ddec8f527439c7ae9e8c3dfca1,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:13:59.335Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.54902,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:14:20,,-300.0,,0.0.4,,,,,,6218efd19694d9fac89b903d3226a020,,,,,,,,15.0,grams,,,,,,,,,,,,,,2020-02-07T17:14:20.302Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:14:42,,-300.0,,0.0.4,,,,,,71a636e41dfa4315abd87df3d51af1f5,,,,,,,1.4,,,,,,,,,,,,,,,normal,2020-02-07T17:14:41.778Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:19:00,,-300.0,,0.0.4,,,,,,230499be1f979aa581a38e107944e724,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:18:59.792Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.438,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:23:59,,-300.0,,0.0.4,,,,,,2708c782bcaeed37e851545bc18cc7b8,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:23:59.158Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.438,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:28:59,,-300.0,,0.0.4,,,,,,968044aa3b37d97480ad02e0042b5633,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:28:59.050Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.4935100000000014,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:34:00,,-300.0,,0.0.4,,,,,,d7f55bb91cb796afa82bf5ded8fbe8b1,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:33:59.805Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.60452,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:38:59,,-300.0,,0.0.4,,,,,,ea32819ec831e33e02277b168dcdfd2e,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:38:59.205Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.82655,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:44:00,,-300.0,,0.0.4,,,,,,31dfa0cc4af5674a8ceab5ca459d61f0,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:43:59.571Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.048580000000001,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,586957.0,,,,,2020-02-07T12:44:21,,-300.0,,0.0.4,,,,,,0134e939067b139dc3a439cadb8e18d5,,,,,,,,,,,,,1.2266637485606535,,,,,,,,,,2020-02-07T17:44:21.441Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:49:00,,-300.0,,0.0.4,,,,,,9c4ce5f5468a165de9cbb894ed46fd9e,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:48:59.704Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.270610000000001,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:53:59,,-300.0,,0.0.4,,,,,,ae89cdae57ffe581ed74518a037adebf,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:53:59.069Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.54815,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,1801300.0,,,,,2020-02-07T12:54:08,,-300.0,,0.0.4,,,,,,d72b5ee5b0c4f2e329abfaf12ce2f42f,,,,,,,,,,,,,0.6994944505116502,,,,,,,,,,2020-02-07T17:54:08.399Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T12:59:00,,-300.0,,0.0.4,,,,,,d13d71d137dd22961cd3c4a1315c5852,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T17:58:59.701Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.77018,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T13:03:59,,-300.0,,0.0.4,,,,,,cfe665b137b7a2ba6751d9dfedd20a6b,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T18:03:58.952Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.04772,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T13:08:59,,-300.0,,0.0.4,,,,,,714a80b66931e5f2e5c6984b54cb4500,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T18:08:59.109Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.04772,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T13:14:00,,-300.0,,0.0.4,,,,,,c119dad833d57111d1469ab1724d5508,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T18:13:59.581Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.10323,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T13:18:59,,-300.0,,0.0.4,,,,,,c70fc4db304c769f1e1976f2768c644a,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T18:18:59.314Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.26975,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T13:23:59,,-300.0,,0.0.4,,,,,,d6d610fad97d90c7346e17c782ba8c35,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T18:23:59.019Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.49178,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,605316.0,,,,,2020-02-07T13:24:10,,-300.0,,0.0.4,,,,,,4483fc693469161e5f5ee69e5c26db84,,,,,,,,,,,,,1.4868259856537105,,,,,,,,,,2020-02-07T18:24:09.700Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T13:28:59,,-300.0,,0.0.4,,,,,,418a4b246c5c14ad33fa088f0da6205a,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T18:28:59.333Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.6583,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T13:33:59,,-300.0,,0.0.4,,,,,,9ed029c3c8575f8a90565a0dc203604f,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T18:33:59.333Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.88033,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,3509.0,,,,,2020-02-07T13:34:15,,-300.0,,0.0.4,,,,,,9e02f315a0691abcdcb7ea97aa98dd17,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-07T18:34:15.017Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,1951.0,,,,,2020-02-07T13:34:19,,-300.0,,0.0.4,,,,,,d92293a1d8d8c3d20464b8add2fd3a12,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-07T18:34:18.526Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,3329.0,,,,,2020-02-07T13:34:20,,-300.0,,0.0.4,,,,,,0bb61319c98737bf328dc60ace3680ca,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-07T18:34:20.478Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,281853.0,,,,,2020-02-07T13:34:24,,-300.0,,0.0.4,,,,,,dc4bc33c0d68ba948108cb4f642c7f8d,,,,,,,,,,,,,1.915886834998641,,,,,,,,,,2020-02-07T18:34:23.807Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, 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+,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,295052.0,,,,,2020-02-07T14:44:11,,-300.0,,0.0.4,,,,,,19ba21c0f69fc762bdce49123653792e,,,,,,,,,,,,,1.2201225210004318,,,,,,,,,,2020-02-07T19:44:11.116Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T14:49:00,,-300.0,,0.0.4,,,,,,030c9a506f6f4719b6ad09bff4444a4b,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T19:48:59.847Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.21165,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,2339.0,,,,,2020-02-07T14:49:06,,-300.0,,0.0.4,,,,,,ab77d4149d47480f500917464015f5b6,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-07T19:49:06.168Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,300385.0,,,,,2020-02-07T14:49:09,,-300.0,,0.0.4,,,,,,baf30d1c8655b56404390a550471a1d4,,,,,,,,,,,,,1.1984582031695026,,,,,,,,,,2020-02-07T19:49:08.508Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T14:53:59,,-300.0,,0.0.4,,,,,,de586e459a728cb74b84b84c9b2fbabd,,,,,,,,,,,,,,,,,,,,,,,2020-02-07T19:53:59.033Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.21165,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,2400.0,,,,,2020-02-07T14:54:09,,-300.0,,0.0.4,,,,,,a3457da85cd0ee7adba9f8dfbc134596,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-07T19:54:08.894Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,temp,,,,,,,,,,,294034.0,,,,,2020-02-07T14:54:11,,-300.0,,0.0.4,,,,,,979bc5711b999e6458c0607c4ce80dd3,,,,,,,,,,,,,1.2243464264228154,,,,,,,,,,2020-02-07T19:54:11.294Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-07,,,,,,,,,,,,,,,,,,2020-02-07T14:54:26,,-300.0,,0.0.4,,,,,,2e40f9b05e50d48fe3b12cf09cd48a66,,,,,,,2.45,,,,,,,,,,,,,,,normal,2020-02-07T19:54:25.695Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, 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+,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T11:48:38,,-300.0,UPLOAD,0.0.4,,,,,,3299f8821764fc0a16c4276eed3be492,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T16:48:38.427Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.60626,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T11:53:38,,-300.0,UPLOAD,0.0.4,,,,,,c256eb23db25fa78c139eec9d756c7a5,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T16:53:38.468Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.71727,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T11:58:39,,-300.0,UPLOAD,0.0.4,,,,,,365c3323e10646c2b8c78bdcc8dcdea1,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T16:58:38.899Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.82829,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:03:39,,-300.0,UPLOAD,0.0.4,,,,,,acf31484c8357b9efe8f7e3240bc903d,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:03:38.990Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.82829,, 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+,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,298531.0,,,,,2020-02-16T12:13:47,,-300.0,UPLOAD,0.0.4,,,,,,cab756b275d1ea53daa0002f27e6476b,,,,,,,,,,,,,1.2059046961860416,,,,,,,,,,2020-02-16T17:13:47.415Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:18:38,,-300.0,UPLOAD,0.0.4,,,,,,8ba056674e8ff2b2c90adefbf536e3f4,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:18:38.478Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.32785,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,3008178.0,,,,,2020-02-16T12:18:46,,-300.0,UPLOAD,0.0.4,,,,,,ddd3550be5b021346d3fec5645c7574c,,,,,,,,,,,,,0.658205592443793,,,,,,,,,,2020-02-16T17:18:45.946Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:23:38,,-300.0,UPLOAD,0.0.4,,,,,,1de011531ede0891318f6453da5e7b51,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:23:38.355Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.4943800000000005,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:28:39,,-300.0,UPLOAD,0.0.4,,,,,,bf73f405396d4d50960f4f4b3658bd25,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:28:39.177Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.605389999999999,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:33:39,,-300.0,UPLOAD,0.0.4,,,,,,22eed6d694f1a92ace69cffe5a4bdbfd,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:33:39.017Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.54988,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:38:39,,-300.0,UPLOAD,0.0.4,,,,,,c20f34a1c32b8935b76a722f181911f2,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:38:38.797Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.4943800000000005,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:43:39,,-300.0,UPLOAD,0.0.4,,,,,,9a79e4dcac0f77ab7f656497c3ff5c91,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:43:38.887Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.605389999999999,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:48:39,,-300.0,UPLOAD,0.0.4,,,,,,3bd1f7ab25a614829221da5df762bd4a,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:48:39.181Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.43887,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:53:39,,-300.0,UPLOAD,0.0.4,,,,,,4c92b49f676113247d3ba77e63855655,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:53:38.663Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.4943800000000005,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T12:58:39,,-300.0,UPLOAD,0.0.4,,,,,,848f71eb2c062640ac9f6136991074bc,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T17:58:38.746Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.4943800000000005,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T13:03:38,,-300.0,UPLOAD,0.0.4,,,,,,a02c3680e328c3baa70b75dd7cee1d61,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T18:03:38.476Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.54988,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,2020-02-16T13:08:10,,,,,,,,,,2020-02-16T13:08:10,,-300.0,UPLOAD,0.0.4,,,,,,b0a8f86ae8fb802158c1bea0e91456ad,,,,,,,,,,,,,,,,,,,,,,calibration,2020-02-16T18:08:10Z,,-300.0,,deviceEvent,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,7.82655,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T13:08:39,,-300.0,UPLOAD,0.0.4,,,,,,1274f2895913cbbe2836bc6124da3b49,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T18:08:39.237Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.6609,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,2431.0,,,,,2020-02-16T13:08:54,,-300.0,UPLOAD,0.0.4,,,,,,6b39312e08fb18418df27eb03c855932,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-16T18:08:54.125Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,3810.0,,,,,2020-02-16T13:08:57,,-300.0,UPLOAD,0.0.4,,,,,,948d2c66b0a23e6ef334fa830bc16ef0,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-16T18:08:56.557Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,285451.0,,,,,2020-02-16T13:09:00,,-300.0,UPLOAD,0.0.4,,,,,,7d2e3fe1f02e78b86b2297ba0cd9bfe1,,,,,,,,,,,,,1.2611591120791927,,,,,,,,,,2020-02-16T18:09:00.367Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T13:13:39,,-300.0,UPLOAD,0.0.4,,,,,,7d9e2c81baeb447274e0d077cf14fcc8,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T18:13:38.929Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.1604600000000005,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,2342378.0,,,,,2020-02-16T13:13:46,,-300.0,UPLOAD,0.0.4,,,,,,738f69bdf738bac72b944143953401cd,,,,,,,,,,,,,0.6147598562394383,,,,,,,,,,2020-02-16T18:13:45.819Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T13:18:39,,-300.0,UPLOAD,0.0.4,,,,,,6c5f0e934ac32b967a0eede47c16d0ad,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T18:18:38.513Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.10496,, 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+,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T18:23:37,,-300.0,UPLOAD,0.0.4,,,,,,01177b63387fc794c745e158ded5d786,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T23:23:37.353Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.60452,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T18:28:37,,-300.0,UPLOAD,0.0.4,,,,,,cbeb33ba5d1e6f0b8c15ac080f7684b3,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T23:28:37.451Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.27148,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,370397.0,,,,,2020-02-16T18:32:48,,-300.0,UPLOAD,0.0.4,,,,,,4e8aa7fe849ebea5d7e80c049142cb97,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-16T23:32:47.710Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T18:33:38,,-300.0,UPLOAD,0.0.4,,,,,,3108940f9d4e1b4f14a7625dad3f19b0,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T23:33:37.723Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.88293,, +,seconds,473.0,High Intensity Interval Training,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,kilocalories,104.0,,,2020-02-16T18:36:56,,-300.0,UPLOAD,0.0.4,,,,,,614decfefd70fc54ef7560ece0e5fc5e,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T23:36:56.000Z,,,,physicalActivity,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T18:38:38,,-300.0,UPLOAD,0.0.4,,,,,,a8b151117eeef5388b1badfee4aa6dd1,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T23:38:37.521Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.43887,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,4561.0,,,,,2020-02-16T18:38:58,,-300.0,UPLOAD,0.0.4,,,,,,05bd52cbf1e531630b3c0d2730dcb827,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-16T23:38:58.107Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,1187585.0,,,,,2020-02-16T18:39:03,,-300.0,UPLOAD,0.0.4,,,,,,6ba9b28c1836abb9a791579a725ffcbc,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-16T23:39:02.669Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T18:43:37,,-300.0,UPLOAD,0.0.4,,,,,,755164986f462cf1f76281148114cf59,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T23:43:37.490Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.9393,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T18:47:18,,-300.0,UPLOAD,0.0.4,,,,,,21104c23a27b0faefadc8277b628ea40,,,,,,,,8.0,grams,,,,,,,,,,,,,,2020-02-16T23:47:18.364Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T18:48:38,,-300.0,UPLOAD,0.0.4,,,,,,478df682967faf5b0794bcc5ba7f414b,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T23:48:37.781Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.60626,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T18:53:38,,-300.0,UPLOAD,0.0.4,,,,,,ac4e6391e47df7621177397fc462efdd,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T23:53:37.681Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.27321,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T18:58:37,,-300.0,UPLOAD,0.0.4,,,,,,01c01fe04502ee79330dc1a1bff6800b,,,,,,,,,,,,,,,,,,,,,,,2020-02-16T23:58:37.454Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.10669,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,3779.0,,,,,2020-02-16T18:58:50,,-300.0,UPLOAD,0.0.4,,,,,,952f01ad76b02abd3ed70abf28f38633,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-16T23:58:50.254Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,295926.0,,,,,2020-02-16T18:58:54,,-300.0,UPLOAD,0.0.4,,,,,,3a8feaa06a1cc7470705568f0788bf80,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-16T23:58:54.033Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:03:38,,-300.0,,0.0.4,,,,,,509469455729b6b590f02b276fffdad6,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:03:37.550Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.2177,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,1207361.0,,,,,2020-02-16T19:03:50,,-300.0,,0.0.4,,,,,,c2e63310eac5158435f252a40f640082,,,,,,,,,,,,,0.5963416319475927,,,,,,,,,,2020-02-17T00:03:49.959Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:08:38,,-300.0,,0.0.4,,,,,,148cb4397c27082333b77bc05b7e37e1,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:08:38.181Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.3842300000000005,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:13:37,,-300.0,,0.0.4,,,,,,31076f0b9e9462d417bf558de97facf3,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:13:37.430Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.55075,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:18:37,,-300.0,,0.0.4,,,,,,418947f9e18a099db68c32ea5a8fe725,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:18:37.361Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.88379,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:23:38,,-300.0,,0.0.4,,,,,,19f848db78003776410f2e1872dff0a3,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:23:37.632Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.43887,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,291959.0,,,,,2020-02-16T19:23:57,,-300.0,,0.0.4,,,,,,fc5546d864de87a621444ddf94657de2,,,,,,,,,,,,,1.2330458843267416,,,,,,,,,,2020-02-17T00:23:57.321Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:27:26,,-300.0,,0.0.4,,,,,,f1209f48f79c5290903befe3c5fb5bb2,,,,,,,,15.0,grams,,,,,,,,,,,,,,2020-02-17T00:27:26.410Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:27:38,,-300.0,,0.0.4,,,,,,3d58b35b04780cfd7a2c3cfc4bafaf72,,,,,,,1.25,,,,,,,,,,,,,,,normal,2020-02-17T00:27:38.301Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:28:37,,-300.0,,0.0.4,,,,,,20bc5bd34651027e71b87b2d00b087a6,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:28:37.433Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.9939399999999985,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,5880.0,,,,,2020-02-16T19:28:49,,-300.0,,0.0.4,,,,,,1f73eeb3c169b9e0b1fd41799b8f1820,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-17T00:28:49.281Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,592984.0,,,,,2020-02-16T19:28:55,,-300.0,,0.0.4,,,,,,89d5850fc0a50f30fde62be656a39ad5,,,,,,,,,,,,,2.4283950740291216,,,,,,,,,,2020-02-17T00:28:55.161Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:33:38,,-300.0,,0.0.4,,,,,,e1552954d3bbd05cbd72d12d2c6bdc29,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:33:37.994Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.438,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:38:38,,-300.0,,0.0.4,,,,,,8244b08caeb1cb6f965958fe78707a17,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:38:37.699Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.66003,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,3690.0,,,,,2020-02-16T19:38:48,,-300.0,,0.0.4,,,,,,a82afbd3582052305235cf5ff3517611,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-17T00:38:48.145Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,292621.0,,,,,2020-02-16T19:38:52,,-300.0,,0.0.4,,,,,,17c417958e12bdef2a4f8f5b297f9ec2,,,,,,,,,,,,,1.2302598482820128,,,,,,,,,,2020-02-17T00:38:51.835Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:43:38,,-300.0,,0.0.4,,,,,,13cd07295d2a7f4cb9392a228c55971d,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:43:38.168Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.882060000000001,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,6630.0,,,,,2020-02-16T19:43:44,,-300.0,,0.0.4,,,,,,9cf9f8c6a3099e1498642857142c5827,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-17T00:43:44.456Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,299190.0,,,,,2020-02-16T19:43:51,,-300.0,,0.0.4,,,,,,791c4f47be869b3ad67d1a8facc9601c,,,,,,,,,,,,,1.2032468413013535,,,,,,,,,,2020-02-17T00:43:51.087Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:48:37,,-300.0,,0.0.4,,,,,,62317bd977fd5d88580e2e73ae898906,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:48:37.320Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.1596,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,6897.0,,,,,2020-02-16T19:48:50,,-300.0,,0.0.4,,,,,,75c6ec13879e1bcc9917f153f092046e,,,,,,,,,,,,,0.0,,,,,,,,,,2020-02-17T00:48:50.277Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,287431.0,,,,,2020-02-16T19:48:57,,-300.0,,0.0.4,,,,,,85e10bb678af7858b076118be1fbe880,,,,,,,,,,,,,1.2524702801837442,,,,,,,,,,2020-02-17T00:48:57.175Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:53:38,,-300.0,,0.0.4,,,,,,9a403463173880bbb24fafd9851bd79d,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:53:38.190Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.1596,, +,,,,,,,,,,,,,,2020-02-16,,temp,,,,,,,,,,,2716739.0,,,,,2020-02-16T19:53:45,,-300.0,,0.0.4,,,,,,0e72c0f3d979d2c09400827587a8d3aa,,,,,,,,,,,,,0.6625589498973685,,,,,,,,,,2020-02-17T00:53:44.607Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T19:58:38,,-300.0,,0.0.4,,,,,,e55e2f8a09ddbdde8937db7da1ea7ad6,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T00:58:37.712Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.048580000000001,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T20:03:37,,-300.0,,0.0.4,,,,,,051721e2a7e2e4a0adcd1ddb77eaf757,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T01:03:37.412Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.10409,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T20:08:38,,-300.0,,0.0.4,,,,,,6411b11875bc90df7531991893963ca1,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T01:08:37.854Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.270610000000001,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T20:13:37,,-300.0,,0.0.4,,,,,,6b7b0e116af1f610fd94ef5c2a261222,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T01:13:37.384Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.54815,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T20:18:38,,-300.0,,0.0.4,,,,,,1a1822c883ad21a62a4299887f8c069b,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T01:18:38.247Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.603660000000001,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T20:23:38,,-300.0,,0.0.4,,,,,,f125b90c2ac60fd45106609845984518,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T01:23:38.247Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.9367,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T20:28:38,,-300.0,,0.0.4,,,,,,883d796ffbb13a0d94e079adf22e56d3,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T01:28:38.286Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.99221,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T20:33:38,,-300.0,,0.0.4,,,,,,e8d641f17bb8272bc4b3786099279410,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T01:33:37.938Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.15873,, +,,,,,,,,,,,,,,2020-02-16,,,,,,,,,,,,,,,,,,2020-02-16T20:38:38,,-300.0,,0.0.4,,,,,,44610cda05c6fe7a88be74f962f0dedb,,,,,,,,,,,,,,,,,,,,,,,2020-02-17T01:38:37.978Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.6583,, 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+,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T12:33:13,,-240.0,UPLOAD,0.0.4,,,,,,c7c7ea5d7c5764e259a0623785d98d97,,,,,,,2.0,,,,,,,,,,,,,,,normal,2020-03-16T16:33:12.893Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T12:36:45,,-240.0,UPLOAD,0.0.4,,,,,,e9ffa5b0c87c0b5488c710bc6f8211a4,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T16:36:45.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.32612,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,313561.0,,,,,2020-03-16T12:37:14,,-240.0,UPLOAD,0.0.4,,,,,,4efe515deeddd406d93db4accd58c557,,,,,,,,,,,,,2.870254893164435,,,,,,,,,,2020-03-16T16:37:14.115Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T12:41:46,,-240.0,UPLOAD,0.0.4,,,,,,8b0a7685d3469868f82e5a747a5cfa3d,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T16:41:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.71467,, 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+,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,284353.0,,,,,2020-03-16T12:52:28,,-240.0,UPLOAD,0.0.4,,,,,,a48bc2d6783798c69dbe86d5b569b79b,,,,,,,,,,,,,1.884997265599236,,,,,,,,,,2020-03-16T16:52:27.646Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T12:56:45,,-240.0,UPLOAD,0.0.4,,,,,,291f88e327a531a254c1c8b780071a04,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T16:56:45.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.71381,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,917737.0,,,,,2020-03-16T12:57:12,,-240.0,UPLOAD,0.0.4,,,,,,8f4fe4f2c89a90e84835af4115306e87,,,,,,,,,,,,,0.5884033332495565,,,,,,,,,,2020-03-16T16:57:12.000Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:01:46,,-240.0,UPLOAD,0.0.4,,,,,,8df8e575c4b9e854ec74d7be09f5d45c,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:01:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.88033,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:06:46,,-240.0,UPLOAD,0.0.4,,,,,,c5ee488b5c318f756a92a970cdf5eacb,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:06:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.82482,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:11:46,,-240.0,UPLOAD,0.0.4,,,,,,800bcc7a7e7f393b7ef60a5091ae7226,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:11:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.32526,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,1197952.0,,,,,2020-03-16T13:12:30,,-240.0,UPLOAD,0.0.4,,,,,,d6c29728a09bdba7ea428df145f7b289,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-16T17:12:29.738Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:16:46,,-240.0,UPLOAD,0.0.4,,,,,,843e706976403464aec4e08391e4d252,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:16:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.26975,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:21:46,,-240.0,UPLOAD,0.0.4,,,,,,2b3fd4950e78512de5d5d7f0e2250ef0,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:21:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.21424,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:26:46,,-240.0,UPLOAD,0.0.4,,,,,,56ea64c00cfddda590ee49bc445d77bc,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:26:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.49178,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:31:45,,-240.0,UPLOAD,0.0.4,,,,,,4d7783632732572eab87531bd847c39b,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:31:45.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.43627,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,1200032.0,,,,,2020-03-16T13:32:28,,-240.0,UPLOAD,0.0.4,,,,,,0a85bf1c0a960901856fba6ca925edcf,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-16T17:32:27.691Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:36:46,,-240.0,UPLOAD,0.0.4,,,,,,f3ba7de746bf4ce3e9e814166162e0cd,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:36:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.10323,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:41:46,,-240.0,UPLOAD,0.0.4,,,,,,9d796f3ba3d6bc23659a6f54a25a2577,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:41:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,8.10409,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T13:46:45,,-240.0,UPLOAD,0.0.4,,,,,,327bf37b77ce96b30d897caa91b7f16f,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T17:46:45.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.4943800000000005,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,1800000.0,,,,,2020-03-16T13:52:28,,-240.0,UPLOAD,0.0.4,,,,,,827cb63f5909dddc2d91f7a7dbf41075,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-16T17:52:27.723Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,601967.0,,,,,2020-03-16T14:22:28,,-240.0,UPLOAD,0.0.4,,,,,,0cb1e6282a5b282e4fcf4d6343148948,,,,,,,,,,,,,0.5980385680651578,,,,,,,,,,2020-03-16T18:22:27.723Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T14:31:46,,-240.0,UPLOAD,0.0.4,,,,,,91def4b352eae4e29442266fac21d7c4,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T18:31:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.99135,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,297961.0,,,,,2020-03-16T14:32:30,,-240.0,UPLOAD,0.0.4,,,,,,cbb35422b28df19208c3804dff779091,,,,,,,,,,,,,1.8123176710616464,,,,,,,,,,2020-03-16T18:32:29.691Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T14:36:46,,-240.0,UPLOAD,0.0.4,,,,,,427437dfeb1b2128c6a01093b82b8109,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T18:36:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.04685,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,300062.0,,,,,2020-03-16T14:37:28,,-240.0,UPLOAD,0.0.4,,,,,,2c473671a96f6642ca21cee5992d6e7d,,,,,,,,,,,,,1.7996247063307445,,,,,,,,,,2020-03-16T18:37:27.652Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T14:41:46,,-240.0,UPLOAD,0.0.4,,,,,,eb0f6da3dd6d3659d1aaf6d1ae7753cc,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T18:41:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.21338,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,299972.0,,,,,2020-03-16T14:42:28,,-240.0,UPLOAD,0.0.4,,,,,,bd6f42fa9ad3532cd4619b4043d3ab88,,,,,,,,,,,,,2.4002175795380216,,,,,,,,,,2020-03-16T18:42:27.715Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T14:46:46,,-240.0,UPLOAD,0.0.4,,,,,,f07352fa2ba67116183066ba1e70e2b9,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T18:46:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.43541,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,616808.0,,,,,2020-03-16T14:47:28,,-240.0,UPLOAD,0.0.4,,,,,,700eac3e060ce9fac63eab821646113d,,,,,,,,,,,,,2.6264222170579807,,,,,,,,,,2020-03-16T18:47:27.687Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T14:51:46,,-240.0,UPLOAD,0.0.4,,,,,,13e9037641ec3b3152be82acb673d34f,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T18:51:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,10.43541,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T14:56:46,,-240.0,UPLOAD,0.0.4,,,,,,9d329dff14347217dd9bd7d8e3268567,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T18:56:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.93584,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,2130.0,,,,,2020-03-16T14:57:44,,-240.0,UPLOAD,0.0.4,,,,,,d7ddcdb88fd43db3c98e470d7d589b87,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-16T18:57:44.496Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,581642.0,,,,,2020-03-16T14:57:47,,-240.0,UPLOAD,0.0.4,,,,,,fa78a5aef773f01805419129b87e8317,,,,,,,,,,,,,1.856809536724884,,,,,,,,,,2020-03-16T18:57:46.626Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T15:00:50,,-240.0,UPLOAD,0.0.4,,,,,,6cb2b193b562e332a7b83c42fa91cddf,,,,,,,,30.0,grams,,,,,,,,,,,,,,2020-03-16T19:00:49.547Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T15:01:11,,-240.0,UPLOAD,0.0.4,,,,,,17089c33c3f8c285d1a7ebfc6ab662d1,,,,,,,2.5,,,,,,,,,,,,,,,normal,2020-03-16T19:01:10.665Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T15:01:46,,-240.0,UPLOAD,0.0.4,,,,,,2fb71f645439eaf2ac307c58075d189f,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T19:01:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.60279,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T15:06:46,,-240.0,UPLOAD,0.0.4,,,,,,a7ccc51ec20483af30f5e6874695f639,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T19:06:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.38076,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,300156.0,,,,,2020-03-16T15:07:28,,-240.0,UPLOAD,0.0.4,,,,,,fec8e6a65b235111c7e693125d10c7c7,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-16T19:07:28.269Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-16,,,,,,,,,,,,,,,,,,2020-03-16T15:11:46,,-240.0,UPLOAD,0.0.4,,,,,,4377e65c888071b3d8baa0cd7079f151,,,,,,,,,,,,,,,,,,,,,,,2020-03-16T19:11:46.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,9.6583,, +,,,,,,,,,,,,,,2020-03-16,,temp,,,,,,,,,,,301293.0,,,,,2020-03-16T15:12:28,,-240.0,UPLOAD,0.0.4,,,,,,fd4915eb3bf06a05d72282920989aac3,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-16T19:12:28.426Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, 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+,,,,,,,,,,,,,,2020-03-27,,,,,,,,,,,,,,,,,,2020-03-27T10:50:22,,-240.0,UPLOAD,0.0.4,,,,,,2b81c3e9c203e5e721b871d6faab19e3,,,,,,,1.9,,,,,,,,,,,,,,,normal,2020-03-27T14:50:21.708Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-27,,,,,,,,,,,,,,,,,,2020-03-27T10:52:22,,-240.0,UPLOAD,0.0.4,,,,,,258f0ec43c95d4b708e300da041e64c2,,,,,,,,,,,,,,,,,,,,,,,2020-03-27T14:52:22.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.38249,, +,,,,,,,,,,,,,,2020-03-27,,,,,,,,,,,,,,,,,,2020-03-27T10:57:21,,-240.0,UPLOAD,0.0.4,,,,,,fee364705b08435b5e9764528549b8df,,,,,,,,,,,,,,,,,,,,,,,2020-03-27T14:57:21.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.82655,, +,,,,,,,,,,,,,,2020-03-27,,temp,,,,,,,,,,,600093.0,,,,,2020-03-27T10:58:03,,-240.0,UPLOAD,0.0.4,,,,,,787141c97336c1e145cf0f5809f62285,,,,,,,,,,,,,1.49976707380544,,,,,,,,,,2020-03-27T14:58:02.682Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, 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+,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:12:36,,-240.0,UPLOAD,0.0.4,,,,,,cd17af5f8fb11fa84e616ebc4992bb00,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:12:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.93757,, +,,,,,,,,,,,,,,2020-03-31,,temp,,,,,,,,,,,296728.0,,,,,2020-03-31T09:13:15,,-240.0,UPLOAD,0.0.4,,,,,,64d2724f6673072ae6beed638e44e941,,,,,,,,,,,,,1.2132294173113425,,,,,,,,,,2020-03-31T13:13:15.271Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:17:35,,-240.0,UPLOAD,0.0.4,,,,,,e1626566527b24711dcc5ae13a9942d7,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:17:35.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.99308,, +,,,,,,,,,,,,,,2020-03-31,,temp,,,,,,,,,,,903283.0,,,,,2020-03-31T09:18:12,,-240.0,UPLOAD,0.0.4,,,,,,c68a4488dba333c6f5229854b80ebab8,,,,,,,,,,,,,0.5978191239867543,,,,,,,,,,2020-03-31T13:18:12.000Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:22:36,,-240.0,UPLOAD,0.0.4,,,,,,976c4f465260dcc8952ee674583b4dfa,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:22:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.99308,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:24:53,,-240.0,UPLOAD,0.0.4,,,,,,14ae344e5c6d5bf8274cd20f21b998cb,,,,,,,,30.0,grams,,,,,,,,,,,,,,2020-03-31T13:24:53.093Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:25:18,,-240.0,UPLOAD,0.0.4,,,,,,9cd08e85e4825b12efcd2ff6ec90641a,,,,,,,3.0,,,,,,,,,,,,,,,normal,2020-03-31T13:25:18.222Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:27:36,,-240.0,UPLOAD,0.0.4,,,,,,f07e8dc7af9671cd1dfcf791a0e607fe,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:27:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.82655,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:32:35,,-240.0,UPLOAD,0.0.4,,,,,,62b100b00e11e3a14aee5056f328e810,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:32:35.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.71554,, +,,,,,,,,,,,,,,2020-03-31,,temp,,,,,,,,,,,1500913.0,,,,,2020-03-31T09:33:15,,-240.0,UPLOAD,0.0.4,,,,,,9cf1ed8e862a436f64544084234f4c7a,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-31T13:33:15.283Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:37:36,,-240.0,UPLOAD,0.0.4,,,,,,15876f1cbf78624db1f924274f2d450d,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:37:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.4935100000000014,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:42:36,,-240.0,UPLOAD,0.0.4,,,,,,1c519be7574795c5be0a5549d13a0897,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:42:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,7.1604600000000005,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:47:36,,-240.0,UPLOAD,0.0.4,,,,,,9d4428cec7fc3550cd165505ca59f37d,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:47:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.605389999999999,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:52:36,,-240.0,UPLOAD,0.0.4,,,,,,ab680b8a8e369ec86e41f5cf83acd4ff,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:52:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,5.71727,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T09:57:35,,-240.0,UPLOAD,0.0.4,,,,,,267f72c7768ad4fc662c7c6d3d8bc8de,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T13:57:35.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,4.99567,, +,,,,,,,,,,,,,,2020-03-31,,temp,,,,,,,,,,,1215466.0,,,,,2020-03-31T09:58:16,,-240.0,UPLOAD,0.0.4,,,,,,62c5439cf8a3482d301c4d1d637ae7b3,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-31T13:58:16.197Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T10:02:36,,-240.0,UPLOAD,0.0.4,,,,,,9119d90db97a52807d24bb38ca66180e,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T14:02:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,4.71814,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,2020-03-31T10:05:02,,,,,,,,,,2020-03-31T10:05:02,,-240.0,UPLOAD,0.0.4,,,,,,216d1ac86a91732327c8d02b11fc762d,,,,,,,,,,,,,,,,,,,,,,calibration,2020-03-31T14:05:02Z,,-240.0,,deviceEvent,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,8.8812,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T10:07:36,,-240.0,UPLOAD,0.0.4,,,,,,455f50ecaba4cc1006b25cdf9f4069d2,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T14:07:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,6.10582,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,2020-03-31T10:07:57,,,,,,,,,,2020-03-31T10:07:57,,-240.0,UPLOAD,0.0.4,,,,,,241a91b15272a528ead0ef8aa3f840cb,,,,,,,,,,,,,,,,,,,,,,calibration,2020-03-31T14:07:57Z,,-240.0,,deviceEvent,mmol/L,,,3f434e5abc1e456f807edf5c7c25f738,8.8812,, 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+,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T13:13:00,,-240.0,UPLOAD,0.0.4,,,,,,6488de6cbf38b12098bafcb2e6b8ccd3,,,,,,,,,,,,,,,,,,,,,,manual,2020-03-31T17:13:00.000Z,,,,smbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,15.09803,, +,,,,,,,,,,,,,,2020-03-31,,temp,,,,,,,,,,,2160.0,,,,,2020-03-31T13:13:51,,-240.0,UPLOAD,0.0.4,,,,,,39b1aa8ff4937e2cefc11195f2866fb4,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-31T17:13:51.379Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,temp,,,,,,,,,,,460.0,,,,,2020-03-31T13:13:54,,-240.0,UPLOAD,0.0.4,,,,,,590e08d86e1f58e81adf04b73d98cf6c,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-31T17:13:53.539Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,temp,,,,,,,,,,,1772255.0,,,,,2020-03-31T13:13:54,,-240.0,UPLOAD,0.0.4,,,,,,28a60c3e85e1a514d457d33b6b8ca126,,,,,,,,,,,,,0.6093930806719402,,,,,,,,,,2020-03-31T17:13:54.000Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T13:14:33,,-240.0,UPLOAD,0.0.4,,,,,,26345bc42b56d9798416ef82af465b7b,,,,,,,,20.0,grams,,,,,,,,,,,,,,2020-03-31T17:14:32.720Z,,,,food,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T13:14:40,,-240.0,UPLOAD,0.0.4,,,,,,3b0cd91cc3a8c941bef0f1c61bf68a9b,,,,,,,1.7,,,,,,,,,,,,,,,normal,2020-03-31T17:14:40.288Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T13:32:36,,-240.0,UPLOAD,0.0.4,,,,,,b312118b9d54ddc840159d11e73c3954,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T17:32:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.0989,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T13:37:36,,-240.0,UPLOAD,0.0.4,,,,,,a1423f33f969e77940f0930a52c41a97,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T17:37:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.43281,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T13:42:36,,-240.0,UPLOAD,0.0.4,,,,,,7cc91a840edd4a7021e68193fffbcb26,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T17:42:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.82223,, +,,,,,,,,,,,,,,2020-03-31,,temp,,,,,,,,,,,887744.0,,,,,2020-03-31T13:43:26,,-240.0,UPLOAD,0.0.4,,,,,,55289c7066186a3a0dd36e6b091eecb9,,,,,,,,,,,,,0.0,,,,,,,,,,2020-03-31T17:43:26.255Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T13:47:36,,-240.0,UPLOAD,0.0.4,,,,,,7370f90ffb32b66ba59f512b431b73ed,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T17:47:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.43368,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T13:52:36,,-240.0,UPLOAD,0.0.4,,,,,,434c205c812146b5f999ccea9debe1f4,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T17:52:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.32266,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T13:57:36,,-240.0,UPLOAD,0.0.4,,,,,,1c3519eced3efa68821488f4f2c9074a,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T17:57:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.32266,, +,,,,,,,,,,,,,,2020-03-31,,temp,,,,,,,,,,,902268.0,,,,,2020-03-31T13:58:14,,-240.0,UPLOAD,0.0.4,,,,,,2bbd35153890d9d14de755bd07e47994,,,,,,,,,,,,,0.5984912699939305,,,,,,,,,,2020-03-31T17:58:14.000Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T14:02:36,,-240.0,UPLOAD,0.0.4,,,,,,1dea8f5ca8c23f10b0de2c227ed2714f,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T18:02:36.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.26715,, +,,,,,,,,,,,,,,2020-03-31,,,,,,,,,,,,,,,,,,2020-03-31T14:07:37,,-240.0,UPLOAD,0.0.4,,,,,,f93adbbf8a7f9a1c21754dd9f5575d50,,,,,,,,,,,,,,,,,,,,,,,2020-03-31T18:07:37.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.26715,, 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+,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T12:32:40,,-240.0,UPLOAD,0.0.4,,,,,,9166783b8ef3d113344c2e16edef572a,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T16:32:40.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.37644,, +,,,,,,,,,,,,,,2020-04-01,,temp,,,,,,,,,,,399182.0,,,,,2020-04-01T12:33:19,,-240.0,UPLOAD,0.0.4,,,,,,23f05a7dbaab2a409726933c06d11269,,,,,,,,,,,,,2.7055319336475674,,,,,,,,,,2020-04-01T16:33:18.605Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T12:37:39,,-240.0,UPLOAD,0.0.4,,,,,,489cd5a9f5456d9c4f07617ffc218198,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T16:37:39.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.43194,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T12:38:53,,-240.0,UPLOAD,0.0.4,,,,,,77e7ae601e24ad937a7b6a0b41c208c9,,,,,,,1.5,,,,,,,,,,,,,,,normal,2020-04-01T16:38:52.665Z,,,,bolus,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-04-01,,temp,,,,,,,,,,,1523368.0,,,,,2020-04-01T12:39:58,,-240.0,UPLOAD,0.0.4,,,,,,4686111441b35a9b0c94f78ed5789c71,,,,,,,,,,,,,0.0,,,,,,,,,,2020-04-01T16:39:57.787Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T12:42:40,,-240.0,UPLOAD,0.0.4,,,,,,1e58d4036740780296af172a0f316208,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T16:42:40.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.43194,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T12:47:40,,-240.0,UPLOAD,0.0.4,,,,,,7e69ac74baf257d77244679a82fb06ae,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T16:47:40.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.26542,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T12:52:40,,-240.0,UPLOAD,0.0.4,,,,,,6c61845fd14a7e4a3f5991753345a100,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T16:52:40.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,14.0989,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T12:57:39,,-240.0,UPLOAD,0.0.4,,,,,,541f03e7e5775c7a4992cc703b3a2aaa,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T16:57:39.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.932379999999998,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T13:02:39,,-240.0,UPLOAD,0.0.4,,,,,,0769b06117f985d7d15a9289cabd6a30,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T17:02:39.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.876870000000002,, +,,,,,,,,,,,,,,2020-04-01,,temp,,,,,,,,,,,477844.0,,,,,2020-04-01T13:05:21,,-240.0,UPLOAD,0.0.4,,,,,,66180bf70450402048ed446ac2c1bf2e,,,,,,,,,,,,,0.0,,,,,,,,,,2020-04-01T17:05:21.156Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T13:07:39,,-240.0,UPLOAD,0.0.4,,,,,,5649ac8618ccc1fde1fff50d373affed,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T17:07:39.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.76586,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T13:12:40,,-240.0,UPLOAD,0.0.4,,,,,,acc5907c6231aa3239effaf1c7e3e63b,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T17:12:40.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.71035,, +,,,,,,,,,,,,,,2020-04-01,,temp,,,,,,,,,,,2719912.0,,,,,2020-04-01T13:13:19,,-240.0,UPLOAD,0.0.4,,,,,,089f34d8b1e33b91070555e6e7045cb0,,,,,,,,,,,,,0.5956073958970491,,,,,,,,,,2020-04-01T17:13:19.000Z,,,,basal,,,,18e025c0eb0508163dac1f53706aa925,,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T13:17:39,,-240.0,UPLOAD,0.0.4,,,,,,fb1e7e601870038b2385cb49c1754087,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T17:17:39.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.599329999999998,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T13:22:40,,-240.0,UPLOAD,0.0.4,,,,,,8d3a3bbd7813136639b2226985a72244,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T17:22:40.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.488320000000002,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T13:27:39,,-240.0,UPLOAD,0.0.4,,,,,,5a05170557db0183ef7dd9f778086f63,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T17:27:39.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.3218,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T13:32:40,,-240.0,UPLOAD,0.0.4,,,,,,3773bab8f19a2cbc7c1b17e0aa52435d,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T17:32:40.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,13.09977,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T13:37:40,,-240.0,UPLOAD,0.0.4,,,,,,d95294ba57b6ba38839afe5254e368a7,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T17:37:40.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.82223,, +,,,,,,,,,,,,,,2020-04-01,,,,,,,,,,,,,,,,,,2020-04-01T13:42:40,,-240.0,UPLOAD,0.0.4,,,,,,930bbc25d469af09b01a79f103c025bb,,,,,,,,,,,,,,,,,,,,,,,2020-04-01T17:42:40.000Z,,,,cbg,mmol/L,,,18e025c0eb0508163dac1f53706aa925,12.6002,, 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0}]",,,,253587d9,,,,,2018-12-14T12:17:00,,,,,,,,,,2018-12-14T12:17:00,,-300.0,UPLOAD,0.0.4,,,,,,ddf050a8de306250da015673a1876397,,,"[{'amount': 2.2203, 'start': 0}, {'amount': 2.2203, 'start': 82800000}]",,,,,,,,,,,,,,,,,,,,2018-12-14T17:17:00.000Z,,-300.0,,pumpSettings,,mmol/L,grams,79cf19de80b18813f655f38be389f760,,, diff --git a/projects/iCGM-test-matrix/snapshot_processor.py b/projects/iCGM-test-matrix/snapshot_processor.py index fbd62692..899b4cc0 100644 --- a/projects/iCGM-test-matrix/snapshot_processor.py +++ b/projects/iCGM-test-matrix/snapshot_processor.py @@ -1250,8 +1250,8 @@ def validate_condition(snapshot, start_time = time.time() # Import results from batch-icgm-condition-stats - condition_file = "PHI-batch-icgm-condition-stats-2020-02-27-with-metadata.csv" - condition_df = pd.read_csv(condition_file, low_memory=False) + batch_condition_file = "sample_data/sample-batch-icgm-condition-stats.csv" + condition_df = pd.read_csv(batch_condition_file, low_memory=False) # Snapshot processing parameters simplify_settings = True @@ -1262,7 +1262,6 @@ def validate_condition(snapshot, settings_summaries = [] for file_selection in range(int(len(condition_df))): - #file_selection = 1 print("STARTING FILE #: " + str(file_selection)) file_name = condition_df.loc[file_selection, 'file_name'] @@ -1271,7 +1270,7 @@ def validate_condition(snapshot, diagnosis_date = condition_df.loc[file_selection, 'diagnosisDate'] # Location of csvs - data_location = "train-data/" + data_location = "sample_data/" file_path = os.path.join(data_location, file_name) data = pd.read_csv(file_path, low_memory=False) data = cond_finder.add_uploadDateTime(data) From 081cb706c8cf7d294640572dfd5e69eb3c6d315e Mon Sep 17 00:00:00 2001 From: Jason Meno Date: Wed, 8 Apr 2020 14:28:20 -0400 Subject: [PATCH 57/57] Update README --- projects/iCGM-test-matrix/README.md | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/projects/iCGM-test-matrix/README.md b/projects/iCGM-test-matrix/README.md index 0c3f489f..ed80b753 100644 --- a/projects/iCGM-test-matrix/README.md +++ b/projects/iCGM-test-matrix/README.md @@ -1,8 +1,8 @@ # iCGM Test Matrix -This project finds snapshots of data to explore the sensitivity of the Loop Algorithm over the entire range of BG values. Each snapshot is a 48-hour window of data. In the center of this data is an "evaluation point" which falls into one of the 9 conditions detailed in the table below. +This project finds snapshots of data to explore the sensitivity of the Loop Algorithm over the entire range of BG values. Each snapshot is a 10-day window of data. At the end of this data is an "evaluation point" which falls into one of the 9 conditions detailed in the table below. -The primary goal of this project is to find 9 snapshots for each condition from 100 datasets in the Tidepool Big Data Donation Project for a total of 900 snapshots and export them for simulator use. +The primary goal of this project is to find 9 snapshots for each condition from 100 datasets in the Tidepool Big Data Donation Project (TBDDP). The secondary goal is to calculate the distribution of all 9 conditions within the entire TBDDP donor population. @@ -12,7 +12,7 @@ There are 3 python scripts used in this project: - **icgm_condition_finder.py** - Given a Tidepool donor dataset, returns 9 locations (if available) of each condition along with some other statistics (see [Condition Finder Output](#condition-finder-output) below) - **batch-icgm-condition-stats.py** - A batch script wrapper for the icgm_condition_finder. Given a folder of Tidepool datasets, creates a .csv output of condition locations and stats for every file. -- **snapshot_processor.py** - Given the output of batch-icgm-condition-stats.py, takes each 48-hour snapshot location for every dataset and converts it into a set of dataframes to be used by the pyLoopKit simulator. (Exports to a 10-element tuple pickle file) +- **snapshot_processor.py** - Given the output of batch-icgm-condition-stats.py, takes each snapshot location for every dataset and converts it into a formatted .csv of input data tables used by the pyLoopKit simulator. ## Condition Table @@ -76,7 +76,7 @@ The algorithm for finding a snapshot is as follows - Calculate the median mg/dL value with a 30-minute (6 cgm points) rolling window - Calculate the slope in mg/dL/min with a 15-minute (3 cgm points) rolling window - Apply one of the 9 conditions labels to each CGM point -- Calculate the max gap size of the cgm trace in a 48 hour *centered* rolling window (where the evaluation point is in the center) +- Calculate the max gap size of the cgm trace in a 24 hour *centered* rolling window (where the evaluation point is in the center) - Randomly select one evaluation point for each condition that does not overlap with any other 48-hour snapshot and has a max gap <= 15 minutes ## Condition Finder Output @@ -93,5 +93,9 @@ The output for the icgm_condition_finder.py and batch processing script are: - **gte-1_lte1**- The number of cgm entries with a rate of change of the previous 3 BG values in the range [-1, 1] (mg/dL/min) - **gt1** - The number of cgm entries with a rate of change of the previous 3 BG values greater than 1 (mg/dL/min) - **cond[0-9]** - The number of total evaluation points that match a given condition (note that cond0 are the number of cgm entries that could not be evaluated under a condition due to a lack of data) -- **cond[1-9]_eval_loc** - The id location of a randomly sampled evaluation point -- **status** - The batch processing completion status of each file \ No newline at end of file +- **cond[1-9]_eval_time** - The rounded local timestamp of a randomly sampled evaluation point +- **status** - The batch processing completion status of each file + +## Snapshot Processor Output + +The output for **snapshot_processor.py** is a "snapshot_export" folder containing the pyLoopKit-formatted .csv tables. These .csvs will can also be used in the risk simulation pipeline (public repository coming soon). \ No newline at end of file