Module bvmpc.bv_session_info
Session information is held here.
Expand source code
"""Session information is held here."""
import numpy as np
class SessionInfo:
"""Holds mappings of information in MEDPC sessions."""
def __init__(self):
"""Init and setup the session information map."""
self._init_metadata_info()
self._init_session_info()
self._init_experiment_vars()
self._init_io_channels()
self._init_axona_metadata_map()
def _init_metadata_info(self):
self.metadata_info = [
"start_date", "end_date", "subject",
"experiment", "group",
"box", "start_time", "end_time", "name"]
self.metadata_start_idx = [
len("Start Date: "),
len("End Date: "),
len("Subject: "),
len("Experiment: "),
len("Group: "),
len("Box: "),
len("Start Time: "),
len("End Time: "),
len("MSN: ")
]
def _init_session_info(self):
self.session_info_dict = {}
# For Fion
# self.session_info_dict['DNMTS_0_delay'] = (
# np.array([
# ['C:', 'D:', 'Counters'],
# ['E:', 'F:', 'Results']
# ]))
# self.session_info_dict['Match_to_sample_0_delay'] = (
# self.session_info_dict['DNMTS_0_delay'])
self.session_info_dict['2_MagazineHabituation_p'] = (
np.array([
['A:', 'B:', 'Experiment Variables'],
['D:', 'E:', 'Reward'],
['E:', 'END', 'Nosepoke']
]))
self.session_info_dict['3_LeverHabituation_p'] = (
np.array([
['A:', 'B:', 'Experiment Variables'],
['D:', 'E:', 'Reward'],
['E:', 'L:', 'Nosepoke'],
['L:', 'M:', 'L'],
['M:', 'N:', 'Un_L'],
['N:', 'O:', 'Un_R'],
['O:', 'R:', 'Un_Nosepoke'],
['R:', 'END', 'R']
]))
self.session_info_dict['4_LeverTraining_p'] = (
np.array([
['A:', 'B:', 'Experiment Variables'],
['D:', 'E:', 'Reward'],
['E:', 'L:', 'Nosepoke'],
['L:', 'M:', 'L'],
['M:', 'N:', 'Un_L'],
['N:', 'O:', 'Un_R'],
['O:', 'R:', 'Un_Nosepoke'],
['R:', 'END', 'R']
]))
self.session_info_dict['5a_FixedRatio_p'] = (
np.array([
['A:', 'B:', 'Experiment Variables'],
['D:', 'E:', 'Reward'],
['E:', 'M:', 'Nosepoke'],
['M:', 'N:', 'FR Changes'],
['N:', 'O:', 'Un_R'],
['O:', 'R:', 'Un_Nosepoke'],
['R:', 'END', 'R']
]))
self.session_info_dict['5b_FixedInterval_p'] = (
np.array([
['A:', 'B:', 'Experiment Variables'],
['D:', 'E:', 'Reward'],
['E:', 'N:', 'Nosepoke'],
['N:', 'O:', 'Un_L'],
['O:', 'R:', 'Un_Nosepoke'],
['R:', 'END', 'L']
]))
self.session_info_dict['6_RandomisedBlocks_p'] = (
np.array([
['A:', 'B:', 'Experiment Variables'],
['D:', 'E:', 'Reward'],
['E:', 'L:', 'Nosepoke'],
['L:', 'M:', 'L'],
['M:', 'N:', 'Un_L'],
['N:', 'O:', 'Un_R'],
['O:', 'R:', 'Un_Nosepoke'],
['R:', 'U:', 'R'],
['U:', 'V:', 'Trial Type'], # 1 is FR, 0 is FI
# ['V:', 'END', 'Per Trial Pellets']
]))
self.session_info_dict['7_RandomisedBlocksExtended_p'] = (
np.array([
['A:', 'B:', 'Experiment Variables'],
['D:', 'E:', 'Reward'],
['E:', 'F:', 'Nosepoke'],
# L during FR Trial; L when R available
['F:', 'G:', 'FR_Err'],
# R during FI Trial; R when L available
['G:', 'H:', 'FI_Err'],
# L during FR Trial_Un; L before reward collection
['H:', 'I:', 'Un_FR_Err'],
# R during FI Trial_Un; R during waiting time
['I:', 'L:', 'Un_FI_Err'],
['L:', 'M:', 'L'],
['M:', 'N:', 'Un_L'],
['N:', 'O:', 'Un_R'],
['O:', 'R:', 'Un_Nosepoke'],
['R:', 'U:', 'R'],
['U:', 'V:', 'Trial Type'],
# ['V:', 'END', 'Per Trial Pellets']
]))
def _init_experiment_vars(self):
# Note, ticks (10ms) are converted to seconds when parsing the file.
base_exp_list = [
"trial_length (mins)", "max_pellets", "advancement_pellets",
"fixed_ratio", "fast_inter_response_time (secs)",
"fixed_interval (ticks)", "double_reward_window (ticks)"
]
self.experiment_var_dict = {}
self.experiment_var_dict['2_MagazineHabituation_p'] = [
base_exp_list[0], "drop_rate (secs)"]
self.experiment_var_dict['3_LeverHabituation_p'] = [
base_exp_list[0], "drop_rate (secs)", base_exp_list[1],
"lever_presses_required"]
self.experiment_var_dict['4_LeverTraining_p'] = (
self.experiment_var_dict['3_LeverHabituation_p'])
self.experiment_var_dict['5a_FixedRatio_p'] = (
base_exp_list[:3] +
["fixed_ratio", "ending_ratio", "ratio_increment",
"max_ratio", base_exp_list[4], "fast_trials_to_advance"])
self.experiment_var_dict['5b_FixedInterval_p'] = (
base_exp_list[:3] + [base_exp_list[5]])
self.experiment_var_dict['6_RandomisedBlocks_p'] = base_exp_list
self.experiment_var_dict['7_RandomisedBlocksExtended_p'] = (
base_exp_list)
def _init_io_channels(self):
self.io_channel_map = {}
# Each tuple is the pin number and True if the value is inverted
input_dict = {
"left_lever": (1, True),
"right_lever": (3, True),
"all_nosepokes": (7, True)}
output_dict = {
"Reward": (9, False),
"left_out": (1, False),
"right_out": (2, False),
"left_light": (4, False),
"right_light": (5, False),
"sound": (8, True)} # ts when sound is turned OFF
self.io_channel_map["6"] = {
"i": input_dict, "o": output_dict}
self.io_channel_map["7"] = self.io_channel_map["6"]
def _init_axona_metadata_map(self):
self.axona_metadata_map = {
"start_date": "trial_date",
"end_date": "trial_date",
"start_time": "trial_time",
"end_time": "end_time",
"name": "script"}
def get_session_type_info(self, key=None):
"""
Return the mapping for a given session type.
If key is passed as None, return a Dict of all possible maps.
Parameters
----------
key : str
Which session type info to get, default None.
Returns
-------
np.ndarray : an array of mappings.
Dict: if key is None, all the mappings.
"""
if key:
return self.session_info_dict.get(key, None)
return self.session_info_dict
def get_session_variable_list(self, key=None):
"""
Return the mapping of Experiment variables to keys.
If key is passed as None, return a Dict of all maps.
Parameters
----------
key: str
Which session type info to get, default None.
Returns
-------
np.ndarray : an array of mappings.
Dict: if key is None, all the mappings.
"""
if key:
return self.experiment_var_dict.get(key, None)
return self.experiment_var_dict
def get_metadata(self, key=None):
"""
Return the metadata index of the session.
If key is passed as None, return a list of metadata keys.
Parameters
----------
key : str - Default None
Which metadata item to get.
Returns
-------
int : the index for key.
List : if key is None, all the keys.
"""
if key:
return self.metadata_info.index(key)
return self.metadata_info
def get_metadata_start(self, idx=None):
"""
Return the start index of the metadata information.
Parameters
----------
idx : int
The index for which to return the start point.
Returns
-------
int : The start index for the information
List : If idx is None, all the start indices.
"""
if idx is not None:
return self.metadata_start_idx[idx]
return self.metadata_start_idx
def get_input_channel(self, s_type, key=None):
"""
Return the input channel for the given key.
If key is None, return the full input channel dictionary.
Parameters
----------
key: str, default None
Returns
-------
int: The channel for the given key
Dict: If key is None, the full dictionary of channels.
"""
return self._get_channel("i", s_type, key=key)
def get_output_channel(self, s_type, key=None):
"""
Return the output channel for the given key.
If key is None, return the full output channel dictionary.
Parameters
----------
key: str, default None
Returns
-------
int: The channel for the given date
Dict: If key is None, the full dictionary of channels.
"""
return self._get_channel("o", s_type, key=key)
def get_axona_metadata_map(self, key=None):
if key is None:
return self.axona_metadata_map
return self.axona_metadata_map.get(key, None)
def _get_channel(self, io, s_type, key=None):
"""Intended private function to help channel retrieval."""
if key is not None:
return self.io_channel_map.get(
s_type, None).get(io, None).get(key, None)
return self.io_channel_map.get(
s_type, None).get(io, None)Classes
class SessionInfo-
Holds mappings of information in MEDPC sessions.
Init and setup the session information map.
Expand source code
class SessionInfo: """Holds mappings of information in MEDPC sessions.""" def __init__(self): """Init and setup the session information map.""" self._init_metadata_info() self._init_session_info() self._init_experiment_vars() self._init_io_channels() self._init_axona_metadata_map() def _init_metadata_info(self): self.metadata_info = [ "start_date", "end_date", "subject", "experiment", "group", "box", "start_time", "end_time", "name"] self.metadata_start_idx = [ len("Start Date: "), len("End Date: "), len("Subject: "), len("Experiment: "), len("Group: "), len("Box: "), len("Start Time: "), len("End Time: "), len("MSN: ") ] def _init_session_info(self): self.session_info_dict = {} # For Fion # self.session_info_dict['DNMTS_0_delay'] = ( # np.array([ # ['C:', 'D:', 'Counters'], # ['E:', 'F:', 'Results'] # ])) # self.session_info_dict['Match_to_sample_0_delay'] = ( # self.session_info_dict['DNMTS_0_delay']) self.session_info_dict['2_MagazineHabituation_p'] = ( np.array([ ['A:', 'B:', 'Experiment Variables'], ['D:', 'E:', 'Reward'], ['E:', 'END', 'Nosepoke'] ])) self.session_info_dict['3_LeverHabituation_p'] = ( np.array([ ['A:', 'B:', 'Experiment Variables'], ['D:', 'E:', 'Reward'], ['E:', 'L:', 'Nosepoke'], ['L:', 'M:', 'L'], ['M:', 'N:', 'Un_L'], ['N:', 'O:', 'Un_R'], ['O:', 'R:', 'Un_Nosepoke'], ['R:', 'END', 'R'] ])) self.session_info_dict['4_LeverTraining_p'] = ( np.array([ ['A:', 'B:', 'Experiment Variables'], ['D:', 'E:', 'Reward'], ['E:', 'L:', 'Nosepoke'], ['L:', 'M:', 'L'], ['M:', 'N:', 'Un_L'], ['N:', 'O:', 'Un_R'], ['O:', 'R:', 'Un_Nosepoke'], ['R:', 'END', 'R'] ])) self.session_info_dict['5a_FixedRatio_p'] = ( np.array([ ['A:', 'B:', 'Experiment Variables'], ['D:', 'E:', 'Reward'], ['E:', 'M:', 'Nosepoke'], ['M:', 'N:', 'FR Changes'], ['N:', 'O:', 'Un_R'], ['O:', 'R:', 'Un_Nosepoke'], ['R:', 'END', 'R'] ])) self.session_info_dict['5b_FixedInterval_p'] = ( np.array([ ['A:', 'B:', 'Experiment Variables'], ['D:', 'E:', 'Reward'], ['E:', 'N:', 'Nosepoke'], ['N:', 'O:', 'Un_L'], ['O:', 'R:', 'Un_Nosepoke'], ['R:', 'END', 'L'] ])) self.session_info_dict['6_RandomisedBlocks_p'] = ( np.array([ ['A:', 'B:', 'Experiment Variables'], ['D:', 'E:', 'Reward'], ['E:', 'L:', 'Nosepoke'], ['L:', 'M:', 'L'], ['M:', 'N:', 'Un_L'], ['N:', 'O:', 'Un_R'], ['O:', 'R:', 'Un_Nosepoke'], ['R:', 'U:', 'R'], ['U:', 'V:', 'Trial Type'], # 1 is FR, 0 is FI # ['V:', 'END', 'Per Trial Pellets'] ])) self.session_info_dict['7_RandomisedBlocksExtended_p'] = ( np.array([ ['A:', 'B:', 'Experiment Variables'], ['D:', 'E:', 'Reward'], ['E:', 'F:', 'Nosepoke'], # L during FR Trial; L when R available ['F:', 'G:', 'FR_Err'], # R during FI Trial; R when L available ['G:', 'H:', 'FI_Err'], # L during FR Trial_Un; L before reward collection ['H:', 'I:', 'Un_FR_Err'], # R during FI Trial_Un; R during waiting time ['I:', 'L:', 'Un_FI_Err'], ['L:', 'M:', 'L'], ['M:', 'N:', 'Un_L'], ['N:', 'O:', 'Un_R'], ['O:', 'R:', 'Un_Nosepoke'], ['R:', 'U:', 'R'], ['U:', 'V:', 'Trial Type'], # ['V:', 'END', 'Per Trial Pellets'] ])) def _init_experiment_vars(self): # Note, ticks (10ms) are converted to seconds when parsing the file. base_exp_list = [ "trial_length (mins)", "max_pellets", "advancement_pellets", "fixed_ratio", "fast_inter_response_time (secs)", "fixed_interval (ticks)", "double_reward_window (ticks)" ] self.experiment_var_dict = {} self.experiment_var_dict['2_MagazineHabituation_p'] = [ base_exp_list[0], "drop_rate (secs)"] self.experiment_var_dict['3_LeverHabituation_p'] = [ base_exp_list[0], "drop_rate (secs)", base_exp_list[1], "lever_presses_required"] self.experiment_var_dict['4_LeverTraining_p'] = ( self.experiment_var_dict['3_LeverHabituation_p']) self.experiment_var_dict['5a_FixedRatio_p'] = ( base_exp_list[:3] + ["fixed_ratio", "ending_ratio", "ratio_increment", "max_ratio", base_exp_list[4], "fast_trials_to_advance"]) self.experiment_var_dict['5b_FixedInterval_p'] = ( base_exp_list[:3] + [base_exp_list[5]]) self.experiment_var_dict['6_RandomisedBlocks_p'] = base_exp_list self.experiment_var_dict['7_RandomisedBlocksExtended_p'] = ( base_exp_list) def _init_io_channels(self): self.io_channel_map = {} # Each tuple is the pin number and True if the value is inverted input_dict = { "left_lever": (1, True), "right_lever": (3, True), "all_nosepokes": (7, True)} output_dict = { "Reward": (9, False), "left_out": (1, False), "right_out": (2, False), "left_light": (4, False), "right_light": (5, False), "sound": (8, True)} # ts when sound is turned OFF self.io_channel_map["6"] = { "i": input_dict, "o": output_dict} self.io_channel_map["7"] = self.io_channel_map["6"] def _init_axona_metadata_map(self): self.axona_metadata_map = { "start_date": "trial_date", "end_date": "trial_date", "start_time": "trial_time", "end_time": "end_time", "name": "script"} def get_session_type_info(self, key=None): """ Return the mapping for a given session type. If key is passed as None, return a Dict of all possible maps. Parameters ---------- key : str Which session type info to get, default None. Returns ------- np.ndarray : an array of mappings. Dict: if key is None, all the mappings. """ if key: return self.session_info_dict.get(key, None) return self.session_info_dict def get_session_variable_list(self, key=None): """ Return the mapping of Experiment variables to keys. If key is passed as None, return a Dict of all maps. Parameters ---------- key: str Which session type info to get, default None. Returns ------- np.ndarray : an array of mappings. Dict: if key is None, all the mappings. """ if key: return self.experiment_var_dict.get(key, None) return self.experiment_var_dict def get_metadata(self, key=None): """ Return the metadata index of the session. If key is passed as None, return a list of metadata keys. Parameters ---------- key : str - Default None Which metadata item to get. Returns ------- int : the index for key. List : if key is None, all the keys. """ if key: return self.metadata_info.index(key) return self.metadata_info def get_metadata_start(self, idx=None): """ Return the start index of the metadata information. Parameters ---------- idx : int The index for which to return the start point. Returns ------- int : The start index for the information List : If idx is None, all the start indices. """ if idx is not None: return self.metadata_start_idx[idx] return self.metadata_start_idx def get_input_channel(self, s_type, key=None): """ Return the input channel for the given key. If key is None, return the full input channel dictionary. Parameters ---------- key: str, default None Returns ------- int: The channel for the given key Dict: If key is None, the full dictionary of channels. """ return self._get_channel("i", s_type, key=key) def get_output_channel(self, s_type, key=None): """ Return the output channel for the given key. If key is None, return the full output channel dictionary. Parameters ---------- key: str, default None Returns ------- int: The channel for the given date Dict: If key is None, the full dictionary of channels. """ return self._get_channel("o", s_type, key=key) def get_axona_metadata_map(self, key=None): if key is None: return self.axona_metadata_map return self.axona_metadata_map.get(key, None) def _get_channel(self, io, s_type, key=None): """Intended private function to help channel retrieval.""" if key is not None: return self.io_channel_map.get( s_type, None).get(io, None).get(key, None) return self.io_channel_map.get( s_type, None).get(io, None)Methods
def get_axona_metadata_map(self, key=None)-
Expand source code
def get_axona_metadata_map(self, key=None): if key is None: return self.axona_metadata_map return self.axona_metadata_map.get(key, None) def get_input_channel(self, s_type, key=None)-
Return the input channel for the given key.
If key is None, return the full input channel dictionary.
Parameters
key:str, defaultNone
Returns
int:The channel for the given key
Dict: If key is None, the full dictionary of channels.
Expand source code
def get_input_channel(self, s_type, key=None): """ Return the input channel for the given key. If key is None, return the full input channel dictionary. Parameters ---------- key: str, default None Returns ------- int: The channel for the given key Dict: If key is None, the full dictionary of channels. """ return self._get_channel("i", s_type, key=key) def get_metadata(self, key=None)-
Return the metadata index of the session.
If key is passed as None, return a list of metadata keys.
Parameters
key:str - Default None- Which metadata item to get.
Returns
int : the index for key. List : if key is None, all the keys.
Expand source code
def get_metadata(self, key=None): """ Return the metadata index of the session. If key is passed as None, return a list of metadata keys. Parameters ---------- key : str - Default None Which metadata item to get. Returns ------- int : the index for key. List : if key is None, all the keys. """ if key: return self.metadata_info.index(key) return self.metadata_info def get_metadata_start(self, idx=None)-
Return the start index of the metadata information.
Parameters
idx:int- The index for which to return the start point.
Returns
int:The start index for the information
List : If idx is None, all the start indices.
Expand source code
def get_metadata_start(self, idx=None): """ Return the start index of the metadata information. Parameters ---------- idx : int The index for which to return the start point. Returns ------- int : The start index for the information List : If idx is None, all the start indices. """ if idx is not None: return self.metadata_start_idx[idx] return self.metadata_start_idx def get_output_channel(self, s_type, key=None)-
Return the output channel for the given key.
If key is None, return the full output channel dictionary.
Parameters
key:str, defaultNone
Returns
int:The channel for the given date
Dict: If key is None, the full dictionary of channels.
Expand source code
def get_output_channel(self, s_type, key=None): """ Return the output channel for the given key. If key is None, return the full output channel dictionary. Parameters ---------- key: str, default None Returns ------- int: The channel for the given date Dict: If key is None, the full dictionary of channels. """ return self._get_channel("o", s_type, key=key) def get_session_type_info(self, key=None)-
Return the mapping for a given session type.
If key is passed as None, return a Dict of all possible maps.
Parameters
key:str- Which session type info to get, default None.
Returns
np.ndarray : an array of mappings. Dict: if key is None, all the mappings.
Expand source code
def get_session_type_info(self, key=None): """ Return the mapping for a given session type. If key is passed as None, return a Dict of all possible maps. Parameters ---------- key : str Which session type info to get, default None. Returns ------- np.ndarray : an array of mappings. Dict: if key is None, all the mappings. """ if key: return self.session_info_dict.get(key, None) return self.session_info_dict def get_session_variable_list(self, key=None)-
Return the mapping of Experiment variables to keys.
If key is passed as None, return a Dict of all maps.
Parameters
key:str- Which session type info to get, default None.
Returns
np.ndarray : an array of mappings. Dict: if key is None, all the mappings.
Expand source code
def get_session_variable_list(self, key=None): """ Return the mapping of Experiment variables to keys. If key is passed as None, return a Dict of all maps. Parameters ---------- key: str Which session type info to get, default None. Returns ------- np.ndarray : an array of mappings. Dict: if key is None, all the mappings. """ if key: return self.experiment_var_dict.get(key, None) return self.experiment_var_dict