refactored

chromatopy/__init__.py

@@ -1,8 +1 @@
 import os
-
-from .core.chromatogram import Chromatogram
-from .core.measurement import Measurement
-from .core.peak import Peak
-from .core.role import Role
-from .core.signaltype import SignalType
-from .core.standard import Standard

chromatopy/core/chromatogram.py

@@ -21,7 +21,7 @@
 from .signaltype import SignalType
 
 if TYPE_CHECKING:
-    from chromatopy.tools.species import Species
+    from chromatopy.tools.molecule import Species
 
 
 class Chromatogram(
@@ -231,6 +231,8 @@ def fit(
         # self.peaks = self._map_hplcpy_peaks(fitter.peaks)
         self.processed_signal = np.sum(fitter.unmixed_chromatograms, axis=1)
 
+        print(fitter.peaks.to_dict(orient="records"))
+
         for record in fitter.peaks.to_dict(orient="records"):
             for species in molecules:
                 tol_interval = (
@@ -249,13 +251,17 @@ def fit(
                         height=record["amplitude"],
                     )
                     self.peaks.append(peak)
-                else:
-                    continue
 
-                print(
-                    f"Assigned peak at [bold]{record['retention_time']} min[/bold] to"
-                    f" [green]{species.name}[/green]"
-                )
+                    print(
+                        f"Assigned peak at [bold]{record['retention_time']} min[/bold] to"
+                        f" [green]{species.name}[/green]"
+                    )
+
+            self.add_to_peaks(
+                retention_time=record["retention_time"],
+                area=record["area"],
+                height=record["amplitude"],
+            )
 
     def _map_hplcpy_peaks(self, fitter_peaks: pd.DataFrame) -> List[Peak]:
         peaks = fitter_peaks.to_dict(orient="records")
@@ -278,7 +284,7 @@ def to_dataframe(self) -> pd.DataFrame:
                 "time": self.times,
                 "signal": self.signals,
             }
-        )
+        ).dropna()
 
     def visualize(self) -> go.Figure:
         """

chromatopy/readers/__init__.py

@@ -1 +0,0 @@
-from .chromreader import ChromReader

chromatopy/readers/chromeleon.py

@@ -1,9 +1,12 @@
+import re
+
 import pandas as pd
 
-from chromatopy.core import Chromatogram, Measurement
+from chromatopy.model import Chromatogram, Measurement
+from chromatopy.units import h, min, ul
 
 
-def read_chromeleon_file(path: str):
+def read_chromeleon_file(file_path: str):
     with open(file_path, "r", encoding="ISO-8859-1") as file:
         content = file.read()
 
@@ -22,27 +25,54 @@ def read_chromeleon_file(path: str):
 
     content_dict["Raw Data"] = transpose_data(content_dict["Raw Data"])
 
-    return content_dict
+    measurement = map_measurement(content_dict, file_path)
+
+    return measurement
 
 
-def map_measurement(content: dict) -> Measurement:
+def map_measurement(content: dict, file_name: str) -> Measurement:
     chromatogram = Chromatogram(
+        id=file_name,
         wavelength=int(content["Signal Parameter Information"][1][1].split(" ")[0]),
         times=content["Raw Data"]["time"],
         signals=content["Raw Data"]["value"],
-        time_unit="min",
+        time_unit=min,
     )
 
+    reaction_time, unit = extract_reaction_time(file_name)
+
     return Measurement(
         id=content["Sample Information"][2][1],
         chromatograms=[chromatogram],
         injection_volume=float(content["Sample Information"][13][1].replace(",", ".")),
-        injection_volume_unit="µL",
+        injection_volume_unit=ul,
         signal_parameter_information=content["Signal Parameter Information"],
         dilution_factor=float(content["Sample Information"][14][1].replace(",", ".")),
+        reaction_time=reaction_time,
+        time_unit=unit,
     )
 
 
+def extract_reaction_time(file_name: str) -> tuple[float, str]:
+    pattern = r"\b(\d+(?:\.\d+)?)\s*(h|min)\b"
+
+    matches = re.findall(pattern, file_name)
+
+    if len(matches) == 0:
+        return None, None
+
+    reaction_time, unit_str = matches[0]
+
+    if unit_str == "h":
+        unit = h
+    elif unit_str == "min":
+        unit = min
+    else:
+        raise ValueError(f"Unit '{unit_str}' not recognized")
+
+    return reaction_time, unit
+
+
 def transpose_data(data: list) -> pd.DataFrame:
     data = data
     df = pd.DataFrame(data[1:], columns=["time", "step", "value"])
@@ -54,23 +84,8 @@ def transpose_data(data: list) -> pd.DataFrame:
     return df
 
 
-if __name__ == "__main__":
-    dir_path = "/Users/max/Documents/jan-niklas/MjNK/Standards"
-    file_path = (
-        "/Users/max/Documents/jan-niklas/MjNK/Standards/Adenosine Stadards_ 0.5 mM.txt"
-    )
-
-    import matplotlib.pyplot as plt
-
-    content = read_chromeleon_file(file_path)
-    print(content.keys())
-    print(content["Raw Data"][0:3])
-
-    measurement = map_measurement(content)
+# if __name__ == "__main__":
+#     dir_path = "/Users/max/Documents/jan-niklas/MjNK/Standards"
+#     file_path = "/Users/max/Documents/jan-niklas/MjNK/adenosine_std/Adenosine Stadards_ 0.5 mM.txt"
 
-    print(measurement)
-
-    plt.scatter(
-        measurement.chromatograms[0].times, measurement.chromatograms[0].signals
-    )
-    plt.show()
+#     content = read_chromeleon_file(file_path)