Merge pull request #1 from stadmill/electromagnetic_property

Separated R1->C , independent of S->C conversion.

src/osipi/_electromagnetic_property.py

@@ -0,0 +1,40 @@
+import numpy as np
+from numpy.typing import NDArray
+
+
+def R1_to_C_linear_relaxivity(
+    R1: NDArray[np.float64], R10: np.float64, r1: np.float64
+) -> NDArray[np.float64]:
+    """
+    Electromagnetic property inverse model:
+    - longitudinal relaxation rate, linear with relaxivity
+
+    Converts R1 to tissue concentration
+
+    Args:
+        R1 (1D array of np.float64):
+            Vector of longitudinal relaxation rate in units of /s. [OSIPI code Q.EL1.001]
+        R10 (np.float64):
+            Native longitudinal relaxation rate in units of /s. [OSIPI code Q.EL1.002]
+        r1 (np.float64):
+            Longitudinal relaxivity in units of /s/mM. [OSIPI code Q.EL1.015]
+
+    Returns:
+        NDArray[np.float64]:
+            Vector of indicator concentration in units of mM. [OSIPI code Q.IC1.001]
+
+    References:
+        - Lexicon URL: https://osipi.github.io/OSIPI_CAPLEX/perfusionProcesses/#
+        - Lexicon code: P.EC1.001
+        - OSIPI name: model-based
+          - Inversion method: analytical inversion [OSIPI code G.MI1.001]
+          - Forward model:
+            longitudinal relaxation rate, linear with relaxivity model [OSIPI code M.EL1.003]
+        - Adapted from equation given in lexicon
+    """
+    # Check R1 is a 1D array of floats
+    if not (isinstance(R1, np.ndarray) and R1.ndim == 1 and R1.dtype == np.float64):
+        raise TypeError("R1 must be a 1D NumPy array of np.float64")
+    elif not (r1 >= 0):
+        raise ValueError("r1 must be positive")
+    return (R1 - R10) / r1  # C

src/osipi/_signal_to_concentration.py

@@ -1,6 +1,8 @@
 import numpy as np
 from numpy.typing import NDArray
 
+from ._electromagnetic_property import R1_to_C_linear_relaxivity
+
 
 def S_to_C_via_R1_SPGR(
     S: NDArray[np.float64],
@@ -88,41 +90,3 @@ def S_to_R1_SPGR(
     cos_a = np.cos(a_rad)
     S0 = S_baseline * (1 - cos_a * exp_TR_R10) / (sin_a * (1 - exp_TR_R10))
     return np.log(((S0 * sin_a) - S) / (S0 * sin_a - (S * cos_a))) * (-1 / TR)  # R1
-
-
-def R1_to_C_linear_relaxivity(
-    R1: NDArray[np.float64], R10: np.float64, r1: np.float64
-) -> NDArray[np.float64]:
-    """
-    Electromagnetic property inverse model:
-    - longitudinal relaxation rate, linear with relaxivity
-
-    Converts R1 to tissue concentration
-
-    Args:
-        R1 (1D array of np.float64):
-            Vector of longitudinal relaxation rate in units of /s. [OSIPI code Q.EL1.001]
-        R10 (np.float64):
-            Native longitudinal relaxation rate in units of /s. [OSIPI code Q.EL1.002]
-        r1 (np.float64):
-            Longitudinal relaxivity in units of /s/mM. [OSIPI code Q.EL1.015]
-
-    Returns:
-        NDArray[np.float64]:
-            Vector of indicator concentration in units of mM. [OSIPI code Q.IC1.001]
-
-    References:
-        - Lexicon URL: https://osipi.github.io/OSIPI_CAPLEX/perfusionProcesses/#
-        - Lexicon code: P.EC1.001
-        - OSIPI name: model-based
-          - Inversion method: analytical inversion [OSIPI code G.MI1.001]
-          - Forward model:
-            longitudinal relaxation rate, linear with relaxivity model [OSIPI code M.EL1.003]
-        - Adapted from equation given in lexicon
-    """
-    # Check R1 is a 1D array of floats
-    if not (isinstance(R1, np.ndarray) and R1.ndim == 1 and R1.dtype == np.float64):
-        raise TypeError("R1 must be a 1D NumPy array of np.float64")
-    elif not (r1 >= 0):
-        raise ValueError("r1 must be positive")
-    return (R1 - R10) / r1  # C