minor format changes

libeos/header.py

@@ -70,3 +70,81 @@ class Header:
         if '-Y' not in callString:
             callString += f' -Y {datetime.now().year}'
         return callString
+    #-------------------------------------------------------------------------------------------------
+    def call_string():
+        base = 'python eos.py '
+
+        inpt = ''
+        if clas.year:
+            inpt += f' --year {clas.year}'
+        else:
+            inpt += f' --year {datetime.now().year}'
+        if clas.dataPath:
+            inpt += f' --dataPath {clas.dataPath}'
+        if clas.subtract:
+            inpt += f' -subtract {clas.subtract}'
+        if clas.normalisationFileIdentifier:
+            inpt += f' -r {clas.normalisationFileIdentifier}'
+        # get file list somehow
+        if ...:
+            inpt += f' -n {file_list}'
+        else:
+            inpt += f' -n {clas.fileIdentifier}'
+
+        otpt = ''
+        if outputFormats != 'Rqz.ort':
+            otpt =  f" -of  '{outputFormats}'"
+        if clas.outputName:
+            otpt += f' -o {clas.outputName}'
+        else:
+            pass
+            # default name
+            
+        mask = ''    
+        if clas.yRange:
+            mask += f' -y {clas.yRange}'
+        if clas.lambdaRange:
+            mask += f' -l {clas.lambdaRange}'
+        if clas.thetaRange:
+            mask += f' -- thetaRange {clas.thetaRange}'
+        elif clas.thetaRangeR:
+            mask += f' -t {clas.thetaRangeR}'
+        if clas.qzRange:
+            mask += f' -q {clas.qzRange}'
+        if clas.qResolution:
+            mask += f' -a {clas.qResolution}'
+
+        para = ''
+        if clas.chopperPhase:
+            para += f' --chopperPhase {clas.chopperPhase}'
+        if clas.chopperPhaseOffset:
+            para += f' --chopperPhaseOffset {clas.chopperPhaseOffset}'
+        if clas.mu:
+            para += f' --mu {clas.mu}'
+        elif clas.muOffset:
+            para += f' --muOffset {clas.muOffset}'
+        if clas.nu:
+            para += f' --nu {clas.nu}'
+
+        if clas.sampleModel:
+            modl =  f" --sampleModel '{clas.sampleModel}'"
+
+        acts = ''
+        if clas.autoscale:
+            acts += f' --autoscale {clas.autoscale}'
+        if clas.scale:
+            acts += f' --scale {clas.scale}'
+        if clas.timeSlize:
+            acts += f' --timeSlize {clas.timeSlize}'
+
+        mlst = base + '\n' + inpt + '\n' + outp 
+        if mask:
+            mlst += '\n' + mask
+        if para:
+            mlst += '\n' + para
+        if acts:
+            mlst += '\n' + acts
+        if mask:
+            mlst += '\n' + modl
+
+        return  mlst

libeos/reduction.py

@@ -229,7 +229,7 @@ class AmorReduction:
             if len(filter_q[filter_q]) > 0:
                 scale = np.sum(R_q[filter_q]**2/dR_q[filter_q]) / np.sum(R_q[filter_q]/dR_q[filter_q])
             else:
-                logging.warning(f'#     automatic scaling not possible')
+                logging.warning('#     automatic scaling not possible')
                 scale = 1.
         else:
             filter_q  = np.where(np.isnan(pR_q*R_q), False, True)
@@ -239,7 +239,7 @@ class AmorReduction:
                 scale = np.sum(R_q[filter_q]**3 * pR_q[filter_q] / (dR_q[filter_q]**2 * pdR_q[filter_q]**2)) \
                       / np.sum(R_q[filter_q]**2 * pR_q[filter_q]**2 / (dR_q[filter_q]**2  * pdR_q[filter_q]**2))
             else:
-                logging.warning(f'#     automatic scaling not possible')
+                logging.warning('#     automatic scaling not possible')
                 scale = 1.
         R_q  /= scale
         dR_q /= scale
@@ -378,7 +378,7 @@ class AmorReduction:
         #theta_lz += np.rad2deg( np.arctan( 3.07e-10 * (fromHDF.detectorDistance + detXdist_e) * lamda_lz**2 ) )
         theta_lz += np.rad2deg( np.arctan( 3.07e-10 * fromHDF.detectorDistance * lamda_lz**2 ) )
 
-        z_z       = enumerate(theta_z)
+        #z_z       = enumerate(theta_z)
         qz_lz     = 4.0*np.pi * np.sin(np.deg2rad(theta_lz)) / lamda_lz
         int_lz, bins_l, bins_z  = np.histogram2d(lamda_e, detZ_e, bins = (lamda_l, self.grid.z()))
         #           cut normalisation sample horizon