[backend/util] progress bar, some docstrings

fishbook/backend/util.py

@@ -3,6 +3,7 @@ import numpy as np
 import nixio as nix
 import re
 import os
+import sys
 import glob
 import datetime as dt
 import subprocess
@@ -14,6 +15,18 @@ except AttributeError:
     MonkeyPatch.patch_fromisoformat()
 
 
+def progress(count, total, status='', bar_len=60):
+    """
+    modified after https://gist.github.com/vladignatyev/06860ec2040cb497f0f3
+    by Vladimir Ignatev published under MIT License
+    """
+    percents = count / total
+    filled_len = int(percents * bar_len)
+    bar = '=' * filled_len + '-' * (bar_len - filled_len)
+
+    sys.stderr.write('[%s] %.2f%s ...%s\r' % (bar, percents * 100, '%', status))
+    sys.stderr.flush()
+
 
 def read_info_file(file_name):
     """
@@ -220,6 +233,15 @@ def read_dataset_info(info_file):
 
 
 def nix_metadata_to_dict(section):
+    """Converts a nix.Section to a dictionary. Keys are the property names, values are 
+    always a list of values. Additional information such as unit, definition etc are discarded.
+
+    Args:
+        section (nix.Section): The section
+
+    Returns:
+        dict: the dictionary containing the section info.
+    """
     info = {}
     for p in section.props:
         info[p.name] = [v.value for v in p.values]
@@ -229,6 +251,16 @@ def nix_metadata_to_dict(section):
 
 
 def nix_metadata_to_yaml(section, cur_depth=0, val_count=1):
+    """Convert a section to yaml
+
+    Args:
+        section ([type]): [description]
+        cur_depth (int, optional): [description]. Defaults to 0.
+        val_count (int, optional): [description]. Defaults to 1.
+
+    Returns:
+        [type]: [description]
+    """
     assert(isinstance(section, nix.section.SectionMixin))
     yaml = "%s%s:\n" % ("\t" * cur_depth, section.name)
     for p in section.props:
@@ -306,6 +338,6 @@ if __name__ == "__main__":
     embed()
     f.close()
     """
-    dataset = "/Users/jan/zwischenlager/2012-03-23-ad"
+    dataset = "/data/apteronotus/2012-03-23-ad"
-    settings = read_stimuli_file(os.path.join(dataset, "stimuli.dat"))
+    settings = read_stimuli_file(dataset)
     embed()