Skip to content

In insulearner added optional plotting of dates instead of avg cgm #4

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
kireffoh wants to merge 1 commit into
base: main
Choose a base branch
from
Open

In insulearner added optional plotting of dates instead of avg cgm #4

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
29 changes: 20 additions & 9 deletions InsuLearner/insulearner.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,8 @@ def estimate_therapy_settings_from_window_stats_lr(aggregated_df,
y="total_insulin",
do_plots=True,
trained_model=None,
weight_scheme=None):
weight_scheme=None,
dates=False):
"""
Fit the linear model and estimate settings
"""
Expand Down Expand Up @@ -133,12 +134,13 @@ def estimate_therapy_settings_from_window_stats_lr(aggregated_df,
lr_model=lm_carb_to_insulin,
settings=settings,
plot_aace=True,
weight_scheme=weight_scheme)
weight_scheme=weight_scheme,
dates=dates)

return settings


def plot_aggregated_scatter(aggregated_df, period_window_size_hours, lr_model=None, settings=None, plot_aace=True, weight_scheme=None):
def plot_aggregated_scatter(aggregated_df, period_window_size_hours, lr_model=None, settings=None, plot_aace=True, weight_scheme=None,dates=False):
"""
Plot the linear model on the data.
"""
Expand All @@ -149,10 +151,13 @@ def plot_aggregated_scatter(aggregated_df, period_window_size_hours, lr_model=No
win_end_dates_str = aggregated_df["end_date"].dt.strftime("%Y-%m-%d")
plt.title(f"Insulin Prediction Modeling, Aggr Period={period_window_size_days} Days, {win_start_dates_str.values[0]} to {win_end_dates_str.values[-1]}, {len(aggregated_df)} data points")

hue_col = "cgm_geo_mean"
# hue_col = None
if dates:
hue_col = "start_date"
else:
hue_col = "cgm_geo_mean"
# hue_col = None

vars_to_plot = ["total_carbs", "total_insulin", "cgm_geo_mean"]
vars_to_plot = ["total_carbs", "total_insulin", "cgm_geo_mean", "start_date"]
scatter_df = aggregated_df[vars_to_plot]

sns.scatterplot(data=scatter_df, x="total_carbs", y="total_insulin", hue=hue_col, ax=ax)
Expand Down Expand Up @@ -221,7 +226,8 @@ def analyze_settings_lr(user, data_start_date, data_end_date,
use_circadian_hour_estimate=True,
agg_period_window_size_hours=24,
agg_period_hop_size_hours=24,
weight_scheme=None):
weight_scheme=None,
dates=False):
"""
Aggregate the data and fit a linear model
"""
Expand All @@ -244,7 +250,8 @@ def analyze_settings_lr(user, data_start_date, data_end_date,
x="total_carbs",
y="total_insulin",
do_plots=do_plots,
weight_scheme=weight_scheme)
weight_scheme=weight_scheme,
dates=dates)

cir_estimate, isf_estimate, basal_insulin_estimate, lr_model, lr_score, K = settings

Expand Down Expand Up @@ -304,7 +311,8 @@ def main():
help="The size in hours of each period to aggregate for fitting the model.", default=24)
parser.add_argument("-ah", "--agg_period_hop_size_hours", "-ah", type=int,
help="The size in hours to hop each period for aggregation.", default=24)

parser.add_argument("--dates", action="store_true",
help="Adds dates in legend", default=False)
args = parser.parse_args()

tp_username = args.tp_username
Expand All @@ -317,6 +325,7 @@ def main():
estimate_agg_boundaries = args.estimate_agg_boundaries
agg_period_window_size_hours = args.agg_period_window_size_hours
agg_period_hop_size_hours = args.agg_period_hop_size_hours
dates = args.dates

logger.debug(f"Args:")
logger.debug(f"estimation_window_size_days: {estimation_window_size_days}")
Expand All @@ -327,6 +336,7 @@ def main():
logger.debug(f"estimate_agg_boundaries: {estimate_agg_boundaries}")
logger.debug(f"agg_period_window_size_hours: {agg_period_window_size_hours}")
logger.debug(f"agg_period_hop_size_hours: {agg_period_hop_size_hours}")
logger.debug(f"dates: {dates}")

K = CSF
if CSF is None:
Expand Down Expand Up @@ -359,6 +369,7 @@ def main():
use_circadian_hour_estimate=estimate_agg_boundaries,
agg_period_window_size_hours=agg_period_window_size_hours,
agg_period_hop_size_hours=agg_period_hop_size_hours,
dates=dates,
)


Expand Down
2 changes: 2 additions & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -86,6 +86,8 @@ be days 3-5, and so on.
estimate the hour of the day when blood glucose movement is the least active, ie
isolating associated insulin and carb effects.

`--dates` If set this will plot the dates of the measurement points instead of average CGM value.
This can be useful to observe for example menstrual cycle influence on ISF.

## Usage (code)

Expand Down