this was an old attempt to provide API for calibration

not used at the moment
removed

src/rcapi/services/process_service.py

@@ -1,45 +1,21 @@
 import os
-import uuid
 import time
-import shutil
 from rcapi.models.models import Task  # Import your data models
-import h5py
 from ..config.app_config import load_config
-from pyambit.nexus_parser import SpectrumParser
 
-config = load_config()
-async def process(task : Task,process_config : dict, nexus_dataset_url: str,base_url: str):
-    try:
-        process_class = process_config["class"]
-        process_class.process(task,nexus_dataset_url,base_url)
 
-        task.status = "Completed"
-    except (ImportError, AttributeError) as e:
-        task.status = "Error"
-        task.error = f"Failed to load plugin or class: {e}"
-    except Exception as e:
-        task.status = "Error"
-        task.error = f"{e}"   
-    task.completed=int(time.time() * 1000)  
+config = load_config()
 UPLOAD_DIR = config.upload_dir
 os.makedirs(UPLOAD_DIR, exist_ok=True)
 
-async def process_new(task : Task,nexus_dataset_url: str,base_url: str):
-    open_dataset(nexus_dataset_url,base_url)
-    task.status = "Completed"
-    task.completed=int(time.time() * 1000)
-
 
-def open_dataset(nexus_dataset_url: str,base_url: str):
-    if nexus_dataset_url.startswith(base_url):
-        uuid = nexus_dataset_url.split("/")[-1]
-        spectrum_parser = SpectrumParser()
-        spectrum_parser.parse(os.path.join(UPLOAD_DIR,f"{uuid}.nxs"))
-        # Access the spectrum data
-        for key in spectrum_parser.parsed_objects:
-            spe = spectrum_parser.parsed_objects[key]
-            print("Spectrum data", key, spe)
-            #spe.plot()     
-
-    else:
-        pass
+async def process(task: Task, process_config: dict,
+                  nexus_dataset_url: str, base_url: str):
+    task.status = "Error"
+    task.error = "Not implemented"
+    task.completed = int(time.time() * 1000)
+
+
+async def process_new(task: Task, nexus_dataset_url: str, base_url: str):
+    task.status = "Error"
+    task.completed = int(time.time() * 1000)

src/rcapi/services/processing_spectra.py

@@ -1,45 +1,20 @@
-import os
 from rcapi.config.app_config import initialize_dirs
 from rcapi.models.models import Task
-from pyambit.nexus_parser import SpectrumParser
-from pyambit.nexus_spectra import peaks2nxdata
 
-#from ramanchada2.protocols.calibration import CalibrationModel
 
 config, UPLOAD_DIR, NEXUS_DIR, TEMPLATE_DIR = initialize_dirs()
 
-def open_dataset(nexus_dataset_url: str,base_url: str):
-    print(nexus_dataset_url,base_url)
-    if nexus_dataset_url.startswith(base_url):
-        uuid = nexus_dataset_url.split("/")[-1]
-        spectrum_parser = SpectrumParser()
-        spectrum_parser.parse(os.path.join(NEXUS_DIR,f"{uuid}.nxs"))
-        return spectrum_parser
-    else:
-        return None
 
 class ProcessMock:
-    def process(task : Task,nexus_dataset_url: str,base_url: str):
-        spectrum_parser : SpectrumParser = open_dataset(nexus_dataset_url,base_url)
-        for key in spectrum_parser.parsed_objects:
-            spe = spectrum_parser.parsed_objects[key]
-            print("Spectrum data", key, spe)
-            #spe.plot()     
+    def process(task: Task, nexus_dataset_url: str, base_url: str):
+        pass
 
 
 class ProcessCalibrate:
-    def process(task : Task,nexus_dataset_url: str,base_url: str):
-        #calmodel = CalibrationModel(laser_wl)
-        #calmodel.derive_model_x(spe_neon,spe_neon_units="cm-1",ref_neon=None,ref_neon_units="nm",spe_sil=None,spe_sil_units="cm-1",ref_sil=None,ref_sil_units="cm-1")
+    def process(task: Task, nexus_dataset_url: str, base_url: str):
         pass
-       
+
 
 class ProcessFindPeak:
-    def process(task : Task,nexus_dataset_url: str,base_url: str):
-        spectrum_parser : SpectrumParser = open_dataset(nexus_dataset_url,base_url)
-        for key in spectrum_parser.parsed_objects:
-            spe = spectrum_parser.parsed_objects[key]
-            print("Spectrum data", key, spe)
-            peak_candidates = spe.find_peak_multipeak(sharpening='hht', strategy='topo')
-            fitres = spe.fit_peak_multimodel(profile='Moffat', candidates=peak_candidates, no_fit=True)
-            print(fitres.to_dataframe_peaks())
+    def process(task: Task, nexus_dataset_url: str, base_url: str):
+        pass