add an option to save all important files to path_out

params.yml

@@ -75,6 +75,7 @@ input: # Parameters on how to look for input files (= output from Ramses)
     ramses_ism     : True              # If ramses-ism is used
 
 out: # Parameters for post processing
+    copy_info      : True                 # Copy logs, nml and info files to the outdir
     tag            : ""                   # Tag for the image
     interactive    : False                # Interactive mode (keep figures open)
     save           : True                 # Save the plots on the disk

run_selector.py

@@ -205,8 +205,7 @@ class RunSelector:
         return selected_runs, selected_nums
 
     def load_namelist(self, run):
-        path_run = self.path_in + "/" + run
-        path_nml = path_run + "/" + self.nml_filename
+        path_nml = f"{self.path_in}/{run}/{self.nml_filename}"
         return NamelistRecursive(f90nml.read(path_nml))
 
     def get_nml_value(self, nml_key, run):
@@ -281,8 +280,12 @@ class RunSelector:
         return runs
 
     def load_info(self, run, num):
-        info_filename = f"{self.path_in}/{run}/output_{num:05}/info_{num:05}.txt"
-        info = read_ramses_info_file(info_filename)
+        info_filename_output = f"{self.path_in}/{run}/output_{num:05}/info_{num:05}.txt"
+        info_filename_folder = f"{self.path_in}/{run}/info/info_{num:05}.txt"
+        if os.path.exists(info_filename_output):
+            info = read_ramses_info_file(info_filename_output)
+        else:
+            info = read_ramses_info_file(info_filename_folder)
         return info
 
     def get_nums(

snapshotprocessor.py

@@ -9,6 +9,8 @@ import pandas as pd
 from skimage.morphology import medial_axis
 
 import os
+from distutils.file_util import copy_file
+
 from functools import partial
 from scipy.stats import linregress
 
@@ -343,8 +345,17 @@ class SnapshotProcessor(HDF5Container):
 
         self.info = selector.info[self.run][self.num]
         self.namelist = selector.namelist[self.run]
-        self.lbox = self.info["boxlen"]
 
+        # Save important info files
+        if self.params.out.copy_info:
+            info_src = f"{self.path}/output_{self.num:05}/info_{self.num:05}.txt"
+            info_dest = f"{self.path_out}/info/info_{self.num:05}.txt"
+            if os.path.exists(info_src):
+                os.makedirs(os.path.dirname(info_dest), exist_ok=True)
+                copy_file(info_src, info_dest, update=1)
+
+        # Get box length
+        self.lbox = self.info["boxlen"]
         # Get time
         self.time = self.info["time"]
 
@@ -352,7 +363,7 @@ class SnapshotProcessor(HDF5Container):
         self.open()
         self.save.root._v_attrs.dir = os.path.dirname(path)
         self.save.root._v_attrs.run = os.path.basename(path)
-        self.save.root._v_attrs.num = num
+        self.save.root._v_attrs.num = self.num
         self.save.root._v_attrs.lbox = self.lbox
         self.save.root._v_attrs.unit_length = self.info["unit_length"]
         self.save.root._v_attrs.time = self.time

studyprocessor.py

@@ -1,6 +1,9 @@
 # coding: utf-8
 
+
 import os
+from distutils.file_util import copy_file
+
 import glob
 import numpy as np
 from functools import partial
@@ -82,6 +85,20 @@ class StudyProcessor(Aggregator, HDF5Container):
         run0 = self.runs[0]
         self.info = selector.info[run0][self.nums[run0][0]]
         self.namelist = selector.namelist
+
+        # Save namelist and logs
+        if self.params.out.copy_info:
+            for run in self.runs:
+                nml_src = f"{self.path}/{run}/{self.params.input.nml_filename}"
+                nml_dest = f"{self.path_out}/{run}/{self.params.input.nml_filename}"
+                copy_file(nml_src, nml_dest, update=1)
+
+                logs = self.get_logs(run)
+                os.makedirs(f"{self.path_out}/{run}/logs", exist_ok=True)
+                for log in logs:
+                    dest = f"{self.path_out}/{run}/logs/{os.path.basename(log)}"
+                    copy_file(log, dest, update=1)
+
         # log info
         self.log_id = "[study {}] ".format(self.params.out.tag)
 
@@ -334,6 +351,14 @@ class StudyProcessor(Aggregator, HDF5Container):
                 series["turb_energy"][run].append(np.nan)
         return series
 
+    def get_logs(self, run):
+        glob_str = f"{self.path}/{run}/{self.params.input.log_prefix}*"
+        logs = glob.glob(glob_str)
+        if len(logs) == 0:
+            glob_str = f"{self.path}/{run}/logs/{self.params.input.log_prefix}*"
+            logs = glob.glob(glob_str)
+        return logs
+
     def _from_log(self, keys, extractor):
 
         # Initialize series
@@ -346,14 +371,11 @@ class StudyProcessor(Aggregator, HDF5Container):
             for key in keys:
                 series[key][run] = []
 
-            # get one preprocessor
-            path_run = self.path + "/" + run
-
-            # Get list of run files
-            log_files = path_run + "/" + self.params.input.log_prefix + "*"
+            # Get list of log files
+            log_files = self.get_logs(run)
 
             # Parse files
-            for log_filename in glob.glob(log_files):
+            for log_filename in log_files:
                 series = extractor(series, log_filename, run)
 
             # Numpify the lists