Merge branch 'develop'

autodeer/DEER_analysis.py

@@ -1106,7 +1106,10 @@ def calc_DEER_settings(relaxation_data,mode='auto', target_time=2,
         raise ValueError("No signal at either optimal")
 
     # Select between five-pulse and four-pulse DEER
-    if (V_4p > V_5p * 0.9) or (tau1_5p < 1.5) or mode =='4pDEER':
+    if (tau2_4p >= 2*tau1_5p) and (V_4p > V_5p * 0.9):
+        mode = '4pDEER'
+
+    if (tau1_5p < 1.5) or mode =='4pDEER':
         # Use four-pulse DEER
         deer_settings = {
             'ExpType': '4pDEER',

autodeer/gui/main.py

@@ -871,8 +871,11 @@ def initialise_deer_settings(self):
         if 'T2_relax' in self.current_results:
             relax_data['Tm'] = self.current_results['T2_relax']
         if 'relax2D' in self.current_results:
-            relax_data['Ref2D'] = self.current_results['relax2D']   
-
+            relax_data['Ref2D'] = self.current_results['relax2D']    
+
+        #debug only, remove later
+        store_pickle(relax_data,os.path.join(self.data_folder,'relax_data.pkl'))
+
         self.deer_settings = ad.calc_DEER_settings(relax_data,exp,self.aim_time,aim_SNR,self.waveform_precision)
 
         self.deer_settings['criteria'] = self.aim_MNR
@@ -949,12 +952,12 @@ def update_tau_delays_figure(self, SNRs, MeasTimes, labels=None):
         axs.cla()
         # Only supports 5pDEER expand to 4pDEER
         CP_analysis = self.current_results['relax']
-        ad.plot_optimal_tau(CP_analysis,SNRs,MeasTimes,MaxMeasTime=36, labels=['5pDEER'],fig=fig,axs=axs,cmap=[ad.primary_colors[0]])
+        ad.plot_optimal_tau(CP_analysis,SNRs,MeasTimes,MaxMeasTime=36, labels=['5pDEER'],fig=fig,axs=axs,cmap=[ad.primary_colors[0]],corr_factor=self.correction_factor)
 
         if 'relax2D' in self.current_results:
-            ad.plot_optimal_tau(self.current_results['relax2D'],SNRs,MeasTimes,MaxMeasTime=36, labels=['4pDEER'],fig=fig,axs=axs,cmap=[ad.primary_colors[1]])
+            ad.plot_optimal_tau(self.current_results['relax2D'],SNRs,MeasTimes,MaxMeasTime=36, labels=['4pDEER'],fig=fig,axs=axs,cmap=[ad.primary_colors[1]],corr_factor=self.correction_factor)
         elif 'T2_relax' in self.current_results:
-            ad.plot_optimal_tau(self.current_results['T2_relax'],SNRs,MeasTimes,MaxMeasTime=36, labels=['4pDEER'],fig=fig,axs=axs,cmap=[ad.primary_colors[1]])
+            ad.plot_optimal_tau(self.current_results['T2_relax'],SNRs,MeasTimes,MaxMeasTime=36, labels=['4pDEER'],fig=fig,axs=axs,cmap=[ad.primary_colors[1]],corr_factor=self.correction_factor)
 
         axs.set_title(labels[0])
 

autodeer/relaxation_autodeer.py

@@ -225,7 +225,8 @@ def calc_correction_factor(CPanalysis,DEER_fit_result):
 
     actual_time = DEER_sequence._estimate_time() *(DEER_fit_result.dataset.nAvgs/DEER_fit_result.dataset.averages)
 
-    actual_MNR = DEER_fit_result.MNR
+    # actual_MNR = DEER_fit_result.MNR
+    actual_SNR = 1/DEER_fit_result.noiselvl
 
     if DEER_sequence.name == '5pDEER':
         dataset = CPanalysis.dataset
@@ -238,7 +239,8 @@ def calc_correction_factor(CPanalysis,DEER_fit_result):
         est_SNR = V(tau)/(noise*np.sqrt(CP_averages))
         est_SNR *= np.sqrt(nPointsInTime(actual_time)/t_axis.shape[0])
 
-        correction_factor = actual_MNR/est_SNR
+        correction_factor = actual_SNR/est_SNR
+
     elif DEER_sequence.name == '4pDEER':
         dataset = CPanalysis.dataset
         CP_averages = dataset.nAvgs * dataset.shots * dataset.nPcyc
@@ -250,7 +252,7 @@ def calc_correction_factor(CPanalysis,DEER_fit_result):
         est_SNR = V(tau)/(noise*np.sqrt(CP_averages))
         est_SNR *= np.sqrt(nPointsInTime(actual_time)/t_axis.shape[0])
 
-        correction_factor = actual_MNR/est_SNR
+        correction_factor = actual_SNR/est_SNR
     else:
         correction_factor = 1
 

docsrc/source/releasenotes.rst

@@ -1,8 +1,24 @@
 Release Notes
 =============
 
+Version 0.9.0 (2024-11-11):
++++++++++++++++++++++++++++
+
+- Faster implmenetation of excite profile
+- Implementation of DeerLab based fitting into relaxation traces
+- New implementation of correction factor
+- Updated MNR and SNR parameters
+- Improved versioning (including git branch for dev versions)
+- Added a button for keep running (not to automatically stop)
+- Implementation of a global waveform precision
+- Added autoextending relxation data traces
+- New save folder for raw data folder
+- Improved calc_DEER_delays function, with new plot
+
+
 Version 0.8.0 (TBA):
 +++++++++++++++++++++++++++
+
 - Major Support Update for 2D Decoherence
   - `RefocusedEcho2DSequence` reforumlated to support match normal style
   - Added 2D Decoherence to the GUI

setup.py

@@ -6,9 +6,9 @@
     "Matlab": ["matlabengine"],
     "Docs": ["sphinx", "furo", "sphinx-gallery", "sphinx-design","myst-parser","sphinx-copybutton","sphinx-toolbox","sphinx-autoapi","sphinxcontrib-bibtex","numpydoc"],
     "GUI": ["PyQt6","threadpoolctl", "pyinstaller"],
-    "test": ["pytest", "pytest-cov", "pytest-qt", "pytest-xdist"],
+    "test": ["pytest", "pytest-cov", "pytest-qt", "pytest-xdist", "GitPython"],
 }
-extras["Dev"] = extras["Docs"] + extras["test"] + ['GitPython']
+extras["Dev"] = extras["Docs"] + extras["test"]
 setup(
     name='autoDEER',
     version=str(__version__),
@@ -19,10 +19,10 @@
     url = "https://github.com/HKaras/autoDeer",
     python_requires=">=3.8",
     install_requires=[
-        'numpy',
+        'numpy >= 2.0',
         'scipy',
         'matplotlib',
-        'deerlab >= 1.0',
+        'deerlab >= 1.1.4',
         'pyyaml',
         'reportlab',
         'svglib',