[added] computing mean, std, cov, corr from iterations

analysis/etc/detectors_jitters.yaml

 MC config:
-  UUID: '000000'
-  style: center
-  when: '2024-02-06 12:32:02.500951'
-U3O8: 0.0
-UF4: 0.0
-bleu: 0.0
-gris: 0.0
+  UUID: 3d3158
+  style: mc
+  when: '2024-02-07 22:02:44.413645'
+U3O8: -0.046884891000881314
+UF4: -0.043821489126609214
+bleu: -7.885436293326389
+gris: -5.753129843290958
 pm_U3O8: 0.1
 pm_UF4: 0.1
 pm_bleu: 10.0
 pm_gris: 10.0
 pm_rouge: 10.0
 pm_vert: 10.0
-rouge: 0.0
+rouge: -3.9298816472593128
 small_time_jitter: 0.1
+src_U3O8: 0.0
+src_UF4: 0.0
+src_bleu: 0.0
+src_gris: 0.0
+src_rouge: 0.0
+src_vert: 0.0
 time_jitter: 10.0
-vert: 0.0
+vert: 6.880420658582953

analysis/etc/fc_dof.yaml

 FC_DOF:
-  U3O8: 27.3981
-  UF4: 27.285
+  U3O8: 27.39128756004084
+  UF4: 27.291101384481905
+  src_U3O8: 27.3981
+  src_UF4: 27.285
   u_U3O8: 0.005
   u_UF4: 0.005
 MC config:
-  UUID: '000000'
-  style: center
-  when: '2024-02-06 12:32:02.500951'
+  UUID: 3d3158
+  style: mc
+  when: '2024-02-07 22:02:44.413645'
 SPEED_OF_LIGHT: 0.299792458
 TNT_TS_STEP: 10.0

analysis/etc/fc_flux_factors.yaml

@@ -3,11 +3,13 @@ FC_DENSITY:
   U3O8: 9.912e-07
   UF4: 8.2978e-07
 FC_EFF:
-  U3O8: 0.776
-  UF4: 0.944
+  U3O8: 0.7399088773988158
+  UF4: 0.9595920484031608
+  src_U3O8: 0.776
+  src_UF4: 0.944
   u_U3O8: 0.04
   u_UF4: 0.021
 MC config:
-  UUID: '000000'
-  style: center
-  when: '2024-02-06 12:32:02.500951'
+  UUID: 3d3158
+  style: mc
+  when: '2024-02-07 22:02:44.413645'

analysis/etc/ge_dof.yaml

-GE_DOF: 28.8168
+GE_DOF: 28.815740545672163
 MC config:
-  UUID: '000000'
-  style: center
-  when: '2024-02-06 12:32:02.500951'
+  UUID: 3d3158
+  style: mc
+  when: '2024-02-07 22:02:44.413645'
+src_GE_DOF: 28.8168
 u_GE_DOF: 0.005

analysis/etc/neutron_loss.yaml

 MC config:
-  UUID: '000000'
-  style: center
-  when: '2024-02-06 12:32:02.500951'
-neutron loss: 0.018
+  UUID: 3d3158
+  style: mc
+  when: '2024-02-07 22:02:44.413645'
+neutron loss: 0.017387749600860196
 neutron loss uncertainty: 0.001
+src_neutron loss: 0.018
 u_neutron loss: 0.001

analysis/etc/target.yaml

 MC config:
-  UUID: '000000'
-  style: center
-  when: '2024-02-06 12:32:02.500951'
+  UUID: 3d3158
+  style: mc
+  when: '2024-02-07 22:02:44.413645'
 isotope of interest: 183W
-nuclei density: 0.00312079131
+nuclei density: 0.0029994112767346742
 nuclei density uncertainty: 0.00031066
 nuclei density unit: nuclei par barn
+src_nuclei density: 0.00312079131
 u_nuclei density: 0.00031066

analysis/scripts/convergence_cv.py

@@ -20,7 +20,7 @@ import matplotlib.pyplot as plt
 
 
 def convergence_cv(the_files: list[str],
-                   nCV: int = 4,
+                   nCV: int = 16,
                    output_prefix: str = './_') -> None:
     # loading data
     data_cols = []
@@ -88,7 +88,7 @@ def convergence_cv(the_files: list[str],
 if __name__ == "__main__":
     arg_parser = argparse.ArgumentParser(description=__doc__)
     arg_parser.add_argument('--nCV', type=int,
-                            default=5, nargs='?',
+                            default=16, nargs='?',
                             help='number of CV loops')
     arg_parser.add_argument('--output_prefix', type=str,
                             default='./_',

analysis/scripts/iters2cov_corr.py

+#!/usr/env python
+# -*- coding: utf-8 -*-
+# -*- format: python -*-
+# -*- author: G. Henning -*-
+# -*- created: feb 2024 -*-
+
+'''
+Process files from iterations to mean and std
+'''
+
+import argparse
+from random import shuffle as rand_shuffle
+import yaml 
+
+import numpy as np
+
+import matplotlib
+matplotlib.use('agg')
+import matplotlib.pyplot as plt
+
+
+def iters2cov_corr(the_files: list[str],
+                   data_reduction: int = 1,
+                   output_prefix: str = './_') -> None:
+    # loading data
+    data_cols = []
+    x, dx = None, None
+    
+    for this_file in the_files:
+        x, dx, y = np.loadtxt(this_file, unpack=True)
+        data_cols.append(y)
+    the_data = np.column_stack(data_cols)
+    #print("#data loaded")
+    reduced_data = the_data[::data_reduction,]
+    # computing cov, corr
+    the_cov = np.cov(reduced_data)
+    the_corr = np.corrcoef(reduced_data)
+    
+    grid_limits = list(x[::data_reduction])
+    grid_limits.append(x[-1]+dx[-1])
+    # exporting covariance.
+    plt.figure(figsize=(12, 10))
+    plt.pcolormesh(grid_limits, grid_limits,
+                   the_cov,
+                   cmap='viridis',
+                   shading='flat')
+    plt.colorbar()
+    plt.savefig(f"{output_prefix}_cov.png")
+    np.savetxt(f"{output_prefix}_cov.npy.txt", 
+               the_cov)
+
+    # exporting correlation.
+    plt.figure(figsize=(12, 10))
+    plt.pcolormesh(grid_limits, grid_limits,
+                   the_corr,
+                   vmin=-1, vmax=1, 
+                   cmap='seismic',
+                   shading='flat')
+    plt.colorbar()
+    plt.savefig(f"{output_prefix}_corr.png")
+    np.savetxt(f"{output_prefix}_corr.npy.txt", 
+               the_corr)
+    
+# Now, execution part
+
+if __name__ == "__main__":
+    arg_parser = argparse.ArgumentParser(description=__doc__)
+    arg_parser.add_argument('--data_reduction', type=int,
+                           default=1, 
+                           nargs='?', 
+                           help='Factor by which to reduce the data (in case it\'s too big)')
+    arg_parser.add_argument('--output_prefix', type=str,
+                            default='./_',
+                            nargs='?', help="prefix for output files")
+    arg_parser.add_argument('the_files', type=str,
+                        nargs='*', help="Files to read and process")
+    the_arguments = arg_parser.parse_args()
+    try:
+        iters2cov_corr(**vars(the_arguments))
+    except Exception as err_:
+        print("error")
+        print(err_)
+

analysis/scripts/iters2mean_n_std.py

+#!/usr/env python
+# -*- coding: utf-8 -*-
+# -*- format: python -*-
+# -*- author: G. Henning -*-
+# -*- created: feb 2024 -*-
+
+'''
+Process files from iterations to mean and std
+'''
+
+import argparse
+from random import shuffle as rand_shuffle
+import yaml 
+
+import numpy as np
+
+import matplotlib
+matplotlib.use('agg')
+import matplotlib.pyplot as plt
+
+
+def iters2mean_n_std(the_files: list[str],
+                     output_prefix: str = './_') -> None:
+    # loading data
+    data_cols = []
+    x, dx = None, None
+    plt.figure(figsize=(8,6))
+    
+    for this_file in the_files:
+        x, dx, y = np.loadtxt(this_file, unpack=True)
+        plt.plot(x, y, color='gray')
+        data_cols.append(y)
+    the_data = np.column_stack(data_cols)
+    #print("#data loaded")
+    # computing whole set mean and std
+    the_mean = the_data.mean(axis=1)
+    the_std = the_data.std(axis=1)
+    
+    plt.ylim(bottom=0.)
+    plt.plot(x, the_mean, color="red", label='mean')
+    plt.plot(x, the_mean+the_std, color='orange', label='mean+std')
+    plt.plot(x, the_mean-the_std, color='orange', label='mean-std')
+    plt.savefig(f"{output_prefix}.png")
+    the_output = np.column_stack((x, dx, the_mean, the_std))
+    np.savetxt(f"{output_prefix}.txt", 
+               the_output,
+               fmt="%12.5f",
+               header=f"""
+Mean and Std values computed from {len(data_cols)} input files.
+writen to {output_prefix}
+Columns: x_low, w_width, mean, std
+               """,
+              footer="end of file")
+
+# Now, execution part
+
+if __name__ == "__main__":
+    arg_parser = argparse.ArgumentParser(description=__doc__)
+    arg_parser.add_argument('--output_prefix', type=str,
+                            default='./_',
+                            nargs='?', help="prefix for output files")
+    arg_parser.add_argument('the_files', type=str,
+                        nargs='*', help="Files to read and process")
+    the_arguments = arg_parser.parse_args()
+    try:
+        iters2mean_n_std(**vars(the_arguments))
+    except Exception as err_:
+        print("error")
+        print(err_)
+