forked from jgrewe/fishbook
271 lines
8.7 KiB
Python
271 lines
8.7 KiB
Python
from functools import reduce
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import numpy as np
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import nixio as nix
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import re
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import os
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import glob
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import datetime as dt
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from IPython import embed
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def read_info_file(file_name):
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"""
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Reads the info file and returns the stored metadata in a dictionary. The dictionary may be nested.
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@param file_name: The name of the info file.
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@return: dictionary, the stored information.
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"""
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root = {}
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with open(file_name, 'r') as f:
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lines = f.readlines()
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for l in lines:
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if not l.startswith("#"):
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continue
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l = l.strip("#").strip()
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if len(l) == 0:
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continue
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if not ": " in l: # subsection
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sec = {}
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root[l[:-1] if l.endswith(":") else l] = sec
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else:
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parts = l.split(': ')
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sec[parts[0].strip()] = parts[1].strip('"').strip()
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return root
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def parse_metadata_line(line):
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if not line.startswith("#"):
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return None, None
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line = line.strip("#").strip()
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parts = line.split(":")
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if len(parts) == 0:
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return None, None
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if len(parts) == 1 or len(parts[-1].strip()) == 0:
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return parts[0].strip(), None
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else:
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return parts[0].strip(), parts[-1].strip()
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def has_signal(line, col_names):
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"""
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Checks whether a signal/stimulus was given in the line.
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:param line: the current line of the data table
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:param col_names: The names of the table header columns
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:return: whether or not any of the signal entries is not empty ("-")
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"""
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values = line.split()
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for i, n in enumerate(col_names):
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if n.lower() == "signal" and i < len(values):
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if len(values[i].strip()) > 0 and values[i].strip()[0] != "-":
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return True
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else:
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print(i, values[i])
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return False
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def parse_table(lines, start_index):
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"""
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:param lines:
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:param start_index:
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:return:
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"""
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data_indices = {}
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stim_count = 0
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names = re.split(r'\s{2,}', lines[start_index + 3][1:].strip())
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while start_index < len(lines):
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l = lines[start_index].strip()
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if l.startswith("#"): # ignore
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start_index += 1
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elif len(l) > 0:
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if stim_count == 0 and (has_signal(l, names)):
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data_indices[stim_count] = l.split()[0]
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stim_count += 1
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elif stim_count > 0:
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data_indices[stim_count] = l.split()[0]
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stim_count += 1
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start_index += 1
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else:
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start_index += 1
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break
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return data_indices, start_index
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def read_stimuli_file(file_name):
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repro_settings = []
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settings = {}
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with open(file_name, 'r') as f:
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lines = f.readlines()
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index = 0
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current_section = None
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current_section_name = ""
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while index < len(lines):
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l = lines[index].strip()
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if len(l) == 0:
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index += 1
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elif l.startswith("#") and "key" not in l.lower():
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name, value = parse_metadata_line(l)
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if not name:
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continue
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if name and not value:
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if current_section:
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settings[current_section_name] = current_section.copy()
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current_section = {}
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current_section_name = name
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else:
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current_section[name] = value
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index += 1
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elif l.lower().startswith("#key"): # table data coming
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data, index = parse_table(lines, index)
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settings["stim_data"] = data
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# we are done with this repro run
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settings[current_section_name] = current_section.copy()
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repro_settings.append(settings.copy())
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current_section = None
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settings = {}
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else:
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# data lines, ignore them here
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index += 1
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return repro_settings
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def find_key_recursive(dictionary, key, path=[]):
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assert(isinstance(dictionary, dict))
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if key in dictionary.keys():
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path.append(key)
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return True
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for k in dictionary.keys():
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if isinstance(dictionary[k], dict):
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if find_key_recursive(dictionary[k], key, path):
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path.insert(-1, k)
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break
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return len(path) > 0
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def deep_get(dictionary, keys, default=None):
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assert(isinstance(dictionary, dict))
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assert(isinstance(keys, list))
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return reduce(lambda d, key: d.get(key, default) if isinstance(d, dict) else default, keys, dictionary)
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def read_dataset_info(info_file):
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exp = ""
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quality = ""
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comment = ""
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rec_date = None
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has_nix = False
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if not os.path.exists(info_file):
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return exp, rec_date, quality, comment, has_nix
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has_nix = len(glob.glob(os.path.sep.join(info_file.split(os.path.sep)[:-1]) + os.path.sep + "*.nix")) > 0
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info = read_info_file(info_file)
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p = []
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find_key_recursive(info, "Experimenter", p)
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if len(p) > 0:
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exp = deep_get(info, p)
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p = []
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find_key_recursive(info, "Date", p)
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if len(p) > 0:
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rec_date = dt.date.fromisoformat(deep_get(info, p))
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p = []
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find_key_recursive(info, "Recording quality", p)
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if len(p) > 0:
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quality = deep_get(info, p)
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find_key_recursive(info, "Comment", p)
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if len(p) > 0:
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comment = deep_get(info, p, default="")
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return exp, rec_date, quality, comment, has_nix
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def nix_metadata_to_dict(section):
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info = {}
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for p in section.props:
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info[p.name] = [v.value for v in p.values]
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for s in section.sections:
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info[s.name] = nix_metadata_to_dict(s)
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return info
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def nix_metadata_to_yaml(section, cur_depth=0, val_count=1):
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assert(isinstance(section, nix.section.SectionMixin))
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yaml = "%s%s:\n" % ("\t" * cur_depth, section.name)
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for p in section.props:
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val_str = ""
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if val_count > 1 and len(p.values) > 1:
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val_str = "[" + ', '.join([v.to_string() for v in p.values]) + "]"
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elif len(p.values) == 1:
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val_str = p.values[0].to_string()
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yaml += "%s%s: %s\n" % ("\t" * (cur_depth+1), p.name, val_str)
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for s in section.sections:
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yaml += nix_metadata_to_yaml(s, cur_depth+1)
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return yaml
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def find_mtags_for_tag(block, tag):
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"""
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Finds those multi tags and the respective positions within that match to a certain
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repro run.
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@:returns list of mtags, list of mtag positions
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"""
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assert(isinstance(block, nix.pycore.block.Block))
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assert(isinstance(tag, nix.pycore.tag.Tag))
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mtags = []
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indices = []
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tag_start = np.atleast_1d(tag.position)
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tag_end = tag_start + np.atleast_1d(tag.extent)
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for mt in block.multi_tags:
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position_count = mt.positions.shape[0]
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in_tag_positions = []
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for i in range(position_count):
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mt_start = np.atleast_1d(mt.positions[i, :])
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mt_end = mt_start + np.atleast_1d(mt.extents[i, :])
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for j in range(len(tag_start)):
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if mt_start[j] >= tag_start[j] and mt_end[j] <= tag_end[j]:
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in_tag_positions.append(i)
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if len(in_tag_positions) > 0:
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mtags.append(mt)
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indices.append(in_tag_positions)
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return mtags, indices
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def mtag_settings_to_yaml(mtag, pos_index):
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assert(isinstance(mtag, nix.pycore.multi_tag.MultiTag))
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assert(0 <= pos_index < mtag.positions.shape[0])
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yaml = ""
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if mtag.metadata is not None:
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yaml = nix_metadata_to_yaml(mtag.metadata)
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for i in range(len(mtag.features)):
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feat = mtag.features[i]
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feat_data = mtag.retrieve_feature_data(pos_index, i)
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if len(feat_data.shape) == 1:
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feat_name = feat.data.label if feat.data.label and len(feat.data.label) > 0 else feat.data.name
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feat_unit = feat.data.unit if feat.data.unit and len(feat.data.unit) > 0 else ""
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if feat_data.shape[0] == 1:
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feat_content = "%s %s" % (feat_data[0], feat_unit)
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else:
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feat_content = "[" + ','.join(map(str, feat_data[:])) + "] %s" % feat_unit
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yaml += "\t%s: %s\n" % (feat_name, feat_content)
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return yaml
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if __name__ == "__main__":
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"""
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nix_file = "../../science/high_freq_chirps/data/2018-11-09-aa-invivo-1/2018-11-09-aa-invivo-1.nix"
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f = nix.File.open(nix_file, nix.FileMode.ReadOnly)
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b = f.blocks[0]
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yml = nix_metadata_to_yaml(b.tags[0].metadata)
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print(yml)
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print("-"* 80)
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print(nix_metadata_to_yaml(b.metadata))
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embed()
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f.close()
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"""
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dataset = "/Users/jan/zwischenlager/2012-03-23-ad"
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settings = read_stimuli_file(os.path.join(dataset, "stimuli.dat"))
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embed()
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