change name of variable target_range -> interval

glucopy/classes/Gframe.py

@@ -8,7 +8,8 @@
 
 # Local
 from glucopy.utils import (disjoin_days_and_hours,
-                           mgdl_to_mmoll)
+                           mgdl_to_mmoll,
+                           mmoll_to_mgdl)
 import glucopy.metrics as metrics
 
 class Gframe:
@@ -165,7 +166,7 @@ def convert_unit(self,
                 raise ValueError('The data is already in mmol/L')
         else:
             if self.unit == 'mmol/L':
-                self.data['CGM'] = mgdl_to_mmoll(self.data['CGM'], reverse=True)
+                self.data['CGM'] = mmoll_to_mgdl(self.data['CGM'])
                 self.unit = new_unit
             else:
                 raise ValueError('The data is already in mg/dL')
@@ -501,7 +502,7 @@ def modd(self,
     # Time in Range
     def tir(self, 
             per_day: bool = False,
-            target_range:list= [0,70,180],
+            interval:list= [0,70,180],
             percentage: bool = True,
             decimals: int = 2):
         '''
@@ -518,7 +519,7 @@ def tir(self,
         ----------
         per_day : bool, default False
             If True, returns a pandas Series with the TIR for each day. If False, returns the TIR for all days combined.
-        target_range : list of int|float, default [0,70,180]
+        interval : list of int|float, default [0,70,180]
             Interval of glucose concentration to calculate :math:`\\tau`. Can be a list of 1 number, in that case the 
             time will be calculated below and above that number. It will always try to calculate the time below the first 
             number and above the last number.
@@ -547,7 +548,7 @@ def tir(self,
 
         .. ipython:: python
 
-            gf.tir(target_range=[0,70,150,180,230])
+            gf.tir(interval=[0,70,150,180,230])
 
         Calculating the TIR for each day and the default range (0,70,180):
 
@@ -565,10 +566,10 @@ def tir(self,
 
             # Calculate TIR for each day
             for day, day_data in day_groups:
-                tir[str(day)] = metrics.tir(df=day_data, target_range=target_range, percentage=percentage, decimals=decimals).tolist()
+                tir[str(day)] = metrics.tir(df=day_data, interval=interval, percentage=percentage, decimals=decimals).tolist()
 
         else: # Calculate TIR for all data
-            tir = metrics.tir(df=self.data, target_range=target_range, percentage=percentage, decimals=decimals)
+            tir = metrics.tir(df=self.data, interval=interval, percentage=percentage, decimals=decimals)
 
         return tir
 
@@ -577,7 +578,7 @@ def tir(self,
     # Frecuency distribution : counts the amount of observations given certain intervals of CGM
     def fd(self,
            per_day: bool = False,
-           target_range: list = [0,70,180],
+           interval: list = [0,70,180],
            decimals: int = 2,
            count: bool = False):
         '''
@@ -594,7 +595,7 @@ def fd(self,
         ----------
         per_day : bool, default False
             If True, returns a pandas Series with the FD for each day. If False, returns the FD for all days combined.
-        target_range : list of int|float, default [0,70,180]
+        interval : list of int|float, default [0,70,180]
             Interval of glucose concentration to calculate `FD`. Can be a list of 1 number, in that case the time will
             be calculated below and above that number. It will always try to calculate the time below the first number
             and above the last number.
@@ -622,7 +623,7 @@ def fd(self,
 
         .. ipython:: python
 
-            gf.fd(target_range=[0,70,150,180,230])
+            gf.fd(interval=[0,70,150,180,230])
         
         Calculating the FD for each day and the default range (0,70,180):
 
@@ -638,10 +639,10 @@ def fd(self,
             fd.index.name = 'Day'
 
             for day, day_data in day_groups:
-                fd[str(day)] = metrics.fd(df=day_data, target_range=target_range, decimals=decimals, count=count).values
+                fd[str(day)] = metrics.fd(df=day_data, interval=interval, decimals=decimals, count=count).values
 
         else:
-            fd = metrics.fd(df=self.data, target_range=target_range, decimals=decimals, count=count)
+            fd = metrics.fd(df=self.data, interval=interval, decimals=decimals, count=count)
 
         return fd
 
@@ -1060,11 +1061,11 @@ def qscore(self,
         '''
         # Time in range [70.2,160.2] mg/dL = [3.9,8.9] mmol/L
         if self.unit == 'mmol/L':
-            target_range = [3.9,8.9]
+            interval = [3.9,8.9]
         elif self.unit == 'mg/dL':
-            target_range = [70.2,160.2]
+            interval = [70.2,160.2]
 
-        tir_per_day = self.tir(per_day=True, target_range=target_range, percentage=False)
+        tir_per_day = self.tir(per_day=True, interval=interval, percentage=False)
 
         # List with the Timedelta corresponding to the time spent under 3.9 mmol/L for each day
         tir_per_day_minus_3_9 = [time[0].total_seconds() for time in tir_per_day] 
@@ -1604,8 +1605,13 @@ def summary(self,
             gf = gp.data('prueba_1')
             gf.summary()
         '''
+        # Time in range [70,180] mg/dL
+        tir_interval = np.array([0, 70, 180])
+        if self.unit == 'mmol/L':
+            tir_interval = mgdl_to_mmoll(tir_interval)
+
         # Metrics that return Series
-        tir = self.tir()
+        tir = self.tir(interval=tir_interval)
         grade = self.grade()
 
         # Summary

glucopy/metrics/fd.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 def fd(df: pd.DataFrame,
-       target_range: list = [0,70,180],
+       interval: list = [0,70,180],
        decimals: int = 2,
        count: bool = False
        ):
@@ -22,7 +22,7 @@ def fd(df: pd.DataFrame,
     df : pandas.DataFrame
         DataFrame containing the CGM values. The dataframe must contain 'CGM' column present in
         :attr:`glucopy.Gframe.data`.
-    target_range : list of int|float, default [0,70,180]
+    interval : list of int|float, default [0,70,180]
         Target range in CGM unit. It must have at least 2 values, for the "normal"
         range, low and high values will be values outside that range.
     decimals : int, default 2
@@ -39,22 +39,26 @@ def fd(df: pd.DataFrame,
     -----
     This function is meant to be used by :meth:`glucopy.Gframe.fd`
     '''
-    # Check input, Ensure target_range is a list with 0 and the max value of the data
-    if not isinstance(target_range, list) or not all(isinstance(i, (int, float)) for i in target_range):
-        raise ValueError("target_range must be a list of numbers")
+    # Check input, Ensure interval is a list or numpy array of numbers
+    if not isinstance(interval, (list, np.ndarray)):
+        raise ValueError("interval must be a list or numpy array of numbers")
+
+    # Convert interval to a list if it's a numpy array
+    if isinstance(interval, np.ndarray):
+        interval = interval.tolist()
 
     # Add 0 to the target range if it is not present to count the time below the target range
-    if 0 not in target_range:
-        target_range = [0] + target_range
+    if 0 not in interval:
+        interval = [0] + interval
 
     # Add the max value of the data to the target range if it is not present to count the time above the target range
     max_value = max(df['CGM'])
-    if max_value <= target_range[-1]:
-        max_value = target_range[-1] + 1
-    if max_value > target_range[-1]:
-        target_range = target_range + [max_value]
+    if max_value <= interval[-1]:
+        max_value = interval[-1] + 1
+    if max_value > interval[-1]:
+        interval = interval + [max_value]
 
-    result = pd.cut(df['CGM'], bins=target_range).groupby(pd.cut(df['CGM'], bins=target_range), observed=False).count()
+    result = pd.cut(df['CGM'], bins=interval).groupby(pd.cut(df['CGM'], bins=interval), observed=False).count()
 
     if not count:
         result = result / result.sum()

glucopy/metrics/tir.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 def tir(df: pd.DataFrame, 
-        target_range:list= [0,70,180],
+        interval:list= [0,70,180],
         percentage: bool = True,
         decimals: int = 2
         ):
@@ -21,7 +21,7 @@ def tir(df: pd.DataFrame,
     ----------
     per_day : bool, default False
         If True, returns a pandas Series with the TIR for each day. If False, returns the TIR for all days combined.
-    target_range : list of int|float, default [0,70,180]
+    interval : list of int|float, default [0,70,180]
         Interval of glucose concentration to calculate :math:`\\tau`. Can be a list of 1 number, in that case the 
         time will be calculated below and above that number. It will always try to calculate the time below the first 
         number and above the last number.
@@ -39,26 +39,30 @@ def tir(df: pd.DataFrame,
     -----
     This function is meant to be used by :meth:`glucopy.Gframe.tir`
     '''
-    # Check input, Ensure target_range is a list with 0 and the max value of the data
-    if not isinstance(target_range, list) or not all(isinstance(i, (int, float)) for i in target_range):
-        raise ValueError("target_range must be a list of numbers")
+    # Check input, Ensure interval is a list or numpy array of numbers
+    if not isinstance(interval, (list, np.ndarray)):
+        raise ValueError("interval must be a list or numpy array of numbers")
+
+    # Convert interval to a list if it's a numpy array
+    if isinstance(interval, np.ndarray):
+        interval = interval.tolist()
 
     # Add 0 to the target range if it is not present to count the time below the target range
-    if 0 not in target_range:
-        target_range = [0] + target_range
+    if 0 not in interval:
+        interval = [0] + interval
 
     # Add the max value of the data to the target range if it is not present to count the time above the target range
     max_value = max(df['CGM'])
-    if max_value <= target_range[-1]:
-        max_value = target_range[-1] + 1
-    if max_value > target_range[-1]:
-        target_range = target_range + [max_value]
+    if max_value <= interval[-1]:
+        max_value = interval[-1] + 1
+    if max_value > interval[-1]:
+        interval = interval + [max_value]
 
     data_copy = df.copy()
     # Calculate time difference between consecutive timestamps
     data_copy['Time_Diff'] = data_copy['Timestamp'].diff().dt.total_seconds()
     # Create a column with the range that each CGM value belongs to
-    data_copy['ranges'] = pd.cut(data_copy['CGM'], bins=target_range)
+    data_copy['ranges'] = pd.cut(data_copy['CGM'], bins=interval)
     # Group data by range and sum the time difference
     time_count = data_copy.groupby('ranges', observed=False)['Time_Diff'].sum()
 

glucopy/plot/hist/freq.py

@@ -1,12 +1,13 @@
 # 3rd party
 import plotly.graph_objects as go
+import numpy as np
 
 # Local
 from ...classes import Gframe
 
 def freq(gf: Gframe,
          per_day: bool = True,
-         target_range: list = [0,70,180],
+         interval: list = [0,70,180],
          count: bool = False,
          height: float = None,
          width: float = None):
@@ -19,7 +20,7 @@ def freq(gf: Gframe,
         Gframe object to plot
     per_day : bool, default True
         If True, the plot will be separated by days
-    target_range : list, default [0,70,180]
+    interval : list, default [0,70,180]
         Target range for the glucose
     count : bool, default False
         If True, the y axis will be the count of the glucose in the target range. If False, the y axis will be the
@@ -56,7 +57,7 @@ def freq(gf: Gframe,
 
     .. ipython:: python
 
-        fig = gp.plot.freq(gf, per_day=False, target_range=[0,100,200,300])
+        fig = gp.plot.freq(gf, per_day=False, interval=[0,100,200,300])
 
     .. image:: /../img/freq_plot_2.png
         :alt: Frequency plot
@@ -66,17 +67,28 @@ def freq(gf: Gframe,
     if not isinstance(gf, Gframe):
         raise TypeError('gf must be a Gframe object')
 
-    # Check input, Ensure target_range is a list with 0 and the max value of the data
-    if not isinstance(target_range, list) or not all(isinstance(i, (int, float)) for i in target_range):
-        raise ValueError("target_range must be a list of numbers")
-    if 0 not in target_range:
-        target_range = [0] + target_range
-    if max(gf.data['CGM']) > target_range[-1]:
-        target_range = target_range + [max(gf.data['CGM'])]
+    # Check input, Ensure interval is a list or numpy array of numbers
+    if not isinstance(interval, (list, np.ndarray)):
+        raise ValueError("interval must be a list or numpy array of numbers")
+
+    # Convert interval to a list if it's a numpy array
+    if isinstance(interval, np.ndarray):
+        interval = interval.tolist()
+
+    # Add 0 to the target range if it is not present to count the time below the target range
+    if 0 not in interval:
+        interval = [0] + interval
+
+    # Add the max value of the data to the target range if it is not present to count the time above the target range
+    max_value = max(gf.data['CGM'])
+    if max_value <= interval[-1]:
+        max_value = interval[-1] + 1
+    if max_value > interval[-1]:
+        interval = interval + [max_value]
 
     # Get frequencies
-    frequencies = gf.fd(per_day = per_day, target_range = target_range, count = count)
-    range_labels = [f'{target_range[i]}-{target_range[i+1]}' for i in range(len(target_range)-1)]
+    frequencies = gf.fd(per_day = per_day, interval = interval, count = count)
+    range_labels = [f'{interval[i]}-{interval[i+1]}' for i in range(len(interval)-1)]
 
     fig = go.Figure()
     first_day = ''