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Merged
staskh merged 5 commits into from
Sep 9, 2025
Merged

Synchronize with IGLU-R v4.3.0 and improve compatibility #10

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5df5730
fix: relax numpy and pandas version requirements to play better with …
staskh Sep 9, 2025
010fab4
IGLU-R updated to v 4.3.0
staskh Sep 9, 2025
8e420ab
Update MAG function to match IGLU-R v4.3.0 behavior
staskh Sep 9, 2025
ea139b5
fix: making ruff happy
staskh Sep 9, 2025
74174d1
fix: making ruff happy
staskh Sep 9, 2025
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -39,7 +39,7 @@ result_float = iglu.cv_glu(glucose_list) # list of glucose values
```

## IGLU-R Compatibility Test Status
The current version of IGLU-PYTHON is test-compatible with IGLU-R v4.2.2
The current version of IGLU-PYTHON is test-compatible with IGLU-R v4.3.0 (2025-07-12)

Unless noted, IGLU-R test compatability is considered successful if it achieves precision of 0.001

Expand Down Expand Up @@ -73,7 +73,7 @@ Unless noted, IGLU-R test compatability is considered successful if it achieves
| lbgi | Low Blood Glucose Index| ✅ |✅ returns float |
| m_value | M-value of Schlichtkrull et al | ✅ |✅ returns float |
| mad_glu | Median Absolute Deviation | ✅ |✅ returns float |
| mag | Mean Absolute Glucose| ✅ | ✅ only Series(DatetimeIndex) returns float ||| IMHO, Original R implementation has an error |
| mag | Mean Absolute Glucose| ✅ | ✅ only Series(DatetimeIndex) returns float ||| IMHO, Original R bug fixed in v4.3.0 |
| mage | Mean Amplitude of Glycemic Excursions| ✅ |✅ only Series(DatetimeIndex) returns float || See algorithm at [MAGE](https://irinagain.github.io/iglu/articles/MAGE.html) |
| mean_glu | Mean glucose value | ✅ | ✅ returns float|
| median_glu |Median glucose value| ✅ |✅ returns float |
Expand Down
50 changes: 22 additions & 28 deletions iglu_python/mag.py
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Expand Up @@ -3,12 +3,12 @@
import numpy as np
import pandas as pd

from .utils import CGMS2DayByDay, check_data_columns, is_iglu_r_compatible
from .utils import CGMS2DayByDay, check_data_columns


def mag(
data: Union[pd.DataFrame, pd.Series],
n: int = 60,
n: int | None = None, # to match a new IGLU-R behavior
dt0: Optional[int] = None,
inter_gap: int = 45,
tz: str = "",
Expand All @@ -26,9 +26,10 @@ def mag(
----------
data : Union[pd.DataFrame, pd.Series]
DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values
n : int, default=60
n : int|None, default=None
Integer giving the desired interval in minutes over which to calculate
the change in glucose. Default is 60 for hourly intervals.
the change in glucose. Default is None - will be automatically set to dt0
(from data collection frequency).
dt0 : Optional[int], default=None
Time interval between measurements in minutes. If None, it will be automatically
determined from the data.
Expand Down Expand Up @@ -85,14 +86,20 @@ def mag(
return out


def mag_single(gl: pd.Series, n: int = 60, dt0: Optional[int] = None, inter_gap: int = 45, tz: str = "") -> float:
def mag_single(
gl: pd.Series,
n: int | None = None, # to match a new IGLU-R behavior
dt0: Optional[int] = None,
inter_gap: int = 45,
tz: str = "",
) -> float:
"""Calculate MAG for a single subject"""
# Convert data to day-by-day format
data_ip = CGMS2DayByDay(gl, dt0=dt0, inter_gap=inter_gap, tz=tz)
dt0_actual = data_ip[2] # Time between measurements in minutes

# Ensure n is not less than data collection frequency
if n < dt0_actual:
if n is None or n < dt0_actual:
n = dt0_actual

# Calculate number of readings per interval
Expand All @@ -108,27 +115,14 @@ def mag_single(gl: pd.Series, n: int = 60, dt0: Optional[int] = None, inter_gap:
# Calculate absolute differences between readings n minutes apart
lag = readings_per_interval

if is_iglu_r_compatible():
idx = np.arange(0, len(gl_values), lag)
gl_values_idx = gl_values[idx]
diffs = gl_values_idx[1:] - gl_values_idx[:-1]
diffs = np.abs(diffs)
diffs = diffs[~np.isnan(diffs)]
# to be IGLU-R test compatible, imho they made error.
# has to be total_time_hours = ((len(diffs)) * n) / 60
total_time_hours = ((len(gl_values_idx[~np.isnan(gl_values_idx)])) * n) / 60
if total_time_hours == 0:
return 0.0
mag = float(np.sum(diffs) / total_time_hours)
else:
diffs = gl_values[lag:] - gl_values[:-lag]
diffs = np.abs(diffs)
diffs = diffs[~np.isnan(diffs)]

# Calculate MAG: sum of absolute differences divided by total time in hours
total_time_hours = ((len(diffs)) * n) / 60
if total_time_hours == 0:
return 0.0
mag = float(np.sum(diffs) / total_time_hours)
diffs = gl_values[lag:] - gl_values[:-lag]
diffs = np.abs(diffs)
diffs = diffs[~np.isnan(diffs)]

# Calculate MAG: sum of absolute differences divided by total time in hours
total_time_hours = ((len(diffs)) * n) / 60
if total_time_hours == 0:
return 0.0
mag = float(np.sum(diffs) / total_time_hours)

return mag
40 changes: 20 additions & 20 deletions iglu_r_discrepancies.ipynb
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Expand Up @@ -10,7 +10,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -25,7 +25,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 29,
"metadata": {},
"outputs": [
{
Expand All @@ -35,7 +35,7 @@
"Python version: 3.11.10 (main, Oct 3 2024, 02:26:51) [Clang 14.0.6 ]\n",
"R version: [1] \"R version 4.4.3 (2025-02-28)\"\n",
"\n",
"iglu version: [1] ‘4.2.2’\n",
"iglu version: [1] ‘4.3.0’\n",
"\n",
"iglu_py version: 1.1.1\n",
"rpy2 version: 3.6.0\n"
Expand All @@ -61,7 +61,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -93,7 +93,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 31,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -190,7 +190,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 32,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -244,7 +244,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 33,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -287,7 +287,7 @@
"1 subject1 102.222222"
]
},
"execution_count": 7,
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -315,7 +315,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 34,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -361,7 +361,7 @@
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{
"cell_type": "code",
"execution_count": 9,
"execution_count": 35,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -451,7 +451,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 36,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -504,7 +504,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 37,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -626,7 +626,7 @@
},
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"cell_type": "code",
"execution_count": 22,
"execution_count": 38,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -677,7 +677,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 39,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -799,7 +799,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 40,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -847,7 +847,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -858,7 +858,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 42,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1026,7 +1026,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 43,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1196,7 +1196,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 44,
"metadata": {},
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{
Expand Down Expand Up @@ -1366,7 +1366,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 45,
"metadata": {},
"outputs": [
{
Expand Down
24 changes: 12 additions & 12 deletions notebooks/auc_evaluation.ipynb
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Expand Up @@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -51,7 +51,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 11,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -153,7 +153,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -167,7 +167,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 13,
"metadata": {},
"outputs": [
{
Expand All @@ -177,7 +177,7 @@
"Python version: 3.11.10 (main, Oct 3 2024, 02:26:51) [Clang 14.0.6 ]\n",
"R version: [1] \"R version 4.4.3 (2025-02-28)\"\n",
"\n",
"iglu version: [1] ‘4.2.2’\n",
"iglu version: [1] ‘4.3.0’\n",
"\n",
"iglu_py version: 1.1.1\n",
"rpy2 version: 3.6.0\n"
Expand Down Expand Up @@ -209,7 +209,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 14,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -252,7 +252,7 @@
"1 subject1 102.222222"
]
},
"execution_count": 5,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -294,7 +294,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 15,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -410,7 +410,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -433,7 +433,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 17,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -476,7 +476,7 @@
"0 subject1 100.0"
]
},
"execution_count": 8,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -502,7 +502,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 18,
"metadata": {},
"outputs": [
{
Expand Down
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