Source code for expyriment.misc.data_preprocessing
"""Data Preprocessing Module.
This module contains several classes and functions that help
to handle, peprocessing and aggregate expyriment data files.
"""
__author__ = 'Florian Krause <florian@expyriment.org>, \
Oliver Lindemann <oliver@expyriment.org>'
__version__ = '0.6.3+'
__revision__ = '0a47722d7fb0'
__date__ = 'Fri Jul 26 11:31:53 2013 +0200'
import os as _os
import sys as _sys
import types as _types
from copy import copy as _copy
try:
import numpy as _np
except:
_np = None
[docs]def read_datafile(filename, only_header_and_variable_names=False):
"""Read an Expyriment data file.
Returns the data, the variable names, the subject info & the comments:
Parameters
----------
filename : str
name (fullpath) of the expyriment data file
only_header_and_variable_names : bool, optional
if True the function reads only the header and variable names
(default=False)
Returns
-------
data : list of list
data array
variables : list of str
variable names list
subject_info : dict
dictionary with subject information (incl. date and between
subject factors)
comments : str
string with remaining comments
"""
delimiter = ","
variables = None
subject_info = {}
comments = ""
data = []
fl = open(filename)
for ln in fl:
# parse infos
ln = ln.strip()
if not(ln.startswith("#")):
if variables is None:
variables = ln.split(delimiter)
if only_header_and_variable_names:
break
else:
data.append(ln.split(delimiter))
else:
if ln.startswith("#s"):
ln = ln.replace("#s", "")
tmp = ln.replace("=", ":")
tmp = tmp.split(":")
if len(tmp) == 2:
subject_info[tmp[0].strip()] = tmp[1].strip()
else:
subject_info["#s{0}".format(len(subject_info))] = ln.strip()
elif ln.startswith("#date:"):
ln = ln.replace("#date:", "")
subject_info["date"] = ln.strip()
else:
comments = comments + "\n" + ln
fl.close()
# strip variables
for x in range(len(variables)):
variables[x] = variables[x].strip()
return data, variables, subject_info, comments
[docs]def write_csv_file(filename, data, varnames=None, delimiter=','):
"""Write 2D data array to csv file.
Parameters
----------
filename : str
name (fullpath) of the data file
data : list of list
2D array with data (list of list)
variables : list of str, optional
array of strings representing variable names
delimiter : str, optional
delimiter character (default=",")
"""
_sys.stdout.write("write file: {0}".format(filename))
with open(filename, 'w') as f:
if varnames is not None:
for c, v in enumerate(varnames):
if c > 0:
f.write(delimiter)
f.write(v)
f.write("\n")
cnt = 0
for row in data:
for c, v in enumerate(row):
if c > 0:
f.write(delimiter)
f.write(v)
cnt += 1
f.write("\n")
print " ({0} cells in {1} rows)".format(cnt, len(data))
[docs]def write_concatenated_data(data_folder, file_name, output_file=None,
delimiter=','):
"""Concatenate data and write it to a csv file.
All files that start with this name will be considered for the
analysis (cf. aggregator.data_files)
Notes
-----
The function is useful to combine the experimental data and prepare for
further processing with other software.
It basically wraps Aggregator.write_concatinated_data.
Parameters
----------
data_folder : str
folder which contains of data of the subjects (str)
file_name : str
name of the files
output_file : str, optional
name of data output file. If no specified data will the save
to {file_name}.csv
delimiter : str, optional
delimiter character (default=",")
"""
return Aggregator(data_folder=data_folder, file_name=file_name)\
.write_concatenated_data(output_file=output_file, delimiter=delimiter)
[docs]class Aggregator(object):
"""A class implementing a tool to aggregate expyriment data.
This class is used to handle the multiple data files of a experiment
and preprocess (i.e, aggregate) the data for further analysis
Examples
--------
This tool helps, for instance, to aggregate your data for certain combinations
of independent variables. E.g., data of a numerical magnitude judgement
experiment. The code below makes a file with mean and median RTs and a
second file with the errors and the number of trials::
from expyriment.misc import data_preprocessing
agg = data_preprocessing.Aggregator(data_folder= "./mydata/",
file_name = "MagnitudeJudgements")
agg.set_computed_variables(["parity = target_number % 2",
"size = target_number > 65"])
agg.set_independent_variables(["hand", "size" , "parity"])
agg.set_exclusions(["trial_counter < 0", "error != 0"])
agg.set_dependent_variables(["mean(RT)", "median(RT)"])
agg.aggregate(output_file="rts.csv")
agg.set_exclusions(["trial_counter < 0"])
agg.set_dependent_variables(["sum(error)", "n_trials"])
agg.aggregate(output_file="errors.csv")
"""
_relations = ["==", "!=", ">", "<", ">=", "<=", "=>", "<="]
_operations = [ "+", "-", "*", "/", "%"]
_dv_functions = ["mean", "median", "sum", "std", "n_trials"]
_default_suffix = ".xpd"
[docs] def __init__(self, data_folder, file_name, suffix=_default_suffix):
"""Create an aggregator.
Parameters
----------
data_folder :str
folder which contains of data of the subjects
file_name : str
name of the files. All files that start with this name will
be considered for the analysis (cf. aggregator.data_files)
suffix : str, optional
if specified only files that end with this particular
suffix will be considered (default=.xpd)
"""
if type(_np) is not _types.ModuleType:
message = """Aggregator can not be initialized.
The Python package 'numpy' is not installed."""
raise ImportError(message)
_version = _np.version.version.split(".")
if not _version[0] == 1 and _version[1] < 6:
raise ImportError("Expyriment {0} ".format(__version__) +
"is not compatible with Numpy {0}.".format(
_np.version.version) +
"\nPlease install Numpy 1.6 or higher.")
print "** Expyriment Data Preprocessor **"
self.reset(data_folder, file_name, suffix)
def __str__(self):
"""Getter for the current design as text string."""
design_str = "Data\n"
design_str = design_str + "- file name: " + self._file_name + "\n"
design_str = design_str + "- folder: " + self._data_folder + "\n"
design_str = design_str + "- {0} subject_data sets\n".format(
len(self._data_files))
design_str = design_str + "- {0} variables: {1}\n".format(
len(self.variables), self.variables)
design_str = design_str + "- recoded variables: {0}\n".format(
self._recode_txt)
design_str = design_str + "- computed variables: {0}\n".format(
self._computes_txt)
design_str = design_str + "Design\n"
design_str = design_str + "- independent Variables: {0}\n".format(
self._iv_txt)
design_str = design_str + "- dependent Variables: {0}\n".format(
self._dv_txt)
design_str = design_str + "- exclude: {0}\n".format(
self._exclusions_txt)
return design_str
def _parse_syntax(self, syntax, throw_exception):
"""Preprocess relation and operation syntax.
Returns relation array.
"""
rels_ops = _copy(self._relations)
rels_ops.extend(self._operations)
found = None
for ro in rels_ops:
if syntax.find(ro) > 0:
found = ro
break
if found is None:
if throw_exception:
raise RuntimeError("Incorrect syntax: '{0}'".format(syntax))
else:
return None
else:
syntax = syntax.split(found)
var_id = self._get_variable_id(syntax[0].strip(), True)
return [var_id, found, syntax[1].strip()]
def _get_variable_id(self, variables, throw_exception=False):
for cnt, v in enumerate(self.variables):
if variables == v:
return cnt
if (throw_exception):
raise RuntimeError("Unknown variable name '{0}'".format(variables))
return None
def _add_independent_variable(self, variable):
var_id = self._get_variable_id(variable, True)
self._iv.append(var_id)
def _add_dependent_variable(self, variable):
if variable == "n_trials":
self._dv.append([variable, 0])
else:
tmp = variable.replace(")", "").split("(")
dv_fnc = tmp[0].strip()
try:
dv_txt = tmp[1].strip()
except:
raise RuntimeError("Incorrect syntax for DV: '{0}'".format(
variable))
var_id = self._get_variable_id(dv_txt, True)
if dv_fnc in self._dv_functions:
self._dv.append([dv_fnc, var_id])
else:
raise RuntimeError("Unknown function for dependent variable:" +
" '{0}'".format(dv_fnc))
def _add_compute_variable(self, compute_syntax):
"""Add a new variable to be computed."""
tmp = compute_syntax.replace("==", "@@") # avoid confusion = & ==
tmp = tmp.replace("!=", "##") # avoid confusion = & ==
tmp = tmp.split("=")
variable_name = tmp[0].strip()
try:
syntax = tmp[1].strip()
syntax = syntax.replace("@@", "==")
syntax = syntax.replace("##", "==")
except:
raise RuntimeError("Incorrect compute syntax: '{0}'".format(
compute_syntax))
variable_def = self._parse_syntax(syntax, throw_exception=True)
if variable_def is None:
variable_def = self._parse_operation(syntax, throw_exception=True)
if self._get_variable_id(variable_name) is not None:
raise RuntimeError("Variable already defined '{0}'".format(
variable_name))
else:
self._variables.append(variable_name)
self._computes.append([variable_name, variable_def])
def _add_exclusion(self, relation_syntax):
"""Add an exclusion."""
relation = self._parse_syntax(relation_syntax, throw_exception=True)
if relation[1] in self._relations:
self._exclusions.append(relation)
else:
raise RuntimeError("Incorrect exclusion syntax: '{0}'".format(
relation_syntax))
def _add_variable_recoding(self, recode_syntax):
"""Add a new variable recoding rule."""
error = False
tmp = recode_syntax.split(":")
if len(tmp) == 2:
var_id = self._get_variable_id(tmp[0].strip(), True)
excl_array = []
for rule in tmp[1].split(","):
rule = rule.split("=")
if len(rule) == 2:
excl_array.append([rule[0].strip(), rule[1].strip()])
else:
error = True
else:
error = True
if error:
raise RuntimeError("Incorrect recoding syntax: '{0}'".format(
recode_syntax))
else:
self._recode.append([var_id, excl_array])
def _find_idx(self, data, column_id, relation, value):
"""Find the indices of elements in a data column.
Notes
-----
It compares of column elements with a value or the elements of a second
column, if value is a name of variable.
The method deals with numerical and string comparisons and throws an
exception for invalied string comparisions.
Parameters
----------
data : numpy.array
the data
column_id : int
id of column to compare
relation : str
relation as string. possible relations:
"==", "!=", ">", "<", ">=", "<=", "=>", "<="
value : numeric or string
value to find or a variable name
"""
# is value a variable name
second_var_id = self._get_variable_id(value, False)
# _add_exclusion
try:
col = _np.float64(data[:, column_id])
if second_var_id is not None:
val = _np.float64(data[:, second_var_id])
else:
val = _np.float64(value)
except:
# handling strings
col = data[:, column_id]
if second_var_id is not None:
val = data[:, second_var_id]
else:
val = value
if relation == "!=":
comp = (col != val)
elif relation == "==":
comp = (col == val)
elif relation == "<":
comp = (col < val)
elif relation == ">":
comp = (col > val)
elif relation == "=<" or relation == "<=":
comp = (col <= val)
elif relation == "=>" or relation == ">=":
comp = (col >= val)
else:
comp = None # should never occur
return _np.flatnonzero(comp)
def _get_new_variables(self, iv_values):
"""Return the new variables names and factor_combinations.
Requires the values for all independent variables iv_values: 2d array.
Adds furthermore the defined the subject variables.
"""
def increase_combination(comb, maxima, pos=None):
"""Recursive helper function.
Returns None if end reached.
"""
if pos is None:
pos = len(comb) - 1
comb[pos] += 1 # increase last position
if comb[pos] > maxima[pos]:
if pos <= 0: # end reached
return None
else:
for x in range(pos, len(comb)): # set to zero & all pos. behind
comb[x] = 0
return increase_combination(comb, maxima, pos - 1) # increase position before
else:
return comb
# calc n levels
n_levels = []
for x in iv_values:
n_levels.append(len(x) - 1)
# build new variables names
factor_combinations = []
names = []
if len(iv_values) > 0:
tmp_comb = _np.zeros(len(self._iv), dtype=int)
while tmp_comb is not None:
txt = ""
comb_values = []
for c, x in enumerate(tmp_comb):
comb_values.append(iv_values[c][x])
if len(txt) > 0:
txt = txt + "_"
txt = txt + "{0}{1}".format(self.variables[self._iv[c]],
comb_values[-1])
names.append(txt)
factor_combinations.append(comb_values)
tmp_comb = increase_combination(tmp_comb, n_levels)
new_variable_names = ["subject_id"]
for sv in self.subject_variables:
new_variable_names.append("{0}".format(sv))
for dv in self._dv:
if dv[0] == "n_trials":
dv_txt = "ntr"
else:
dv_txt = self.variables[dv[1]]
if len(names) > 0:
for n in names:
new_variable_names.append("{0}_{1}".format(dv_txt, n))
else:
new_variable_names.append("{0}_total".format(dv_txt))
return new_variable_names, factor_combinations
[docs] def reset(self, data_folder, file_name, suffix=_default_suffix):
"""Reset the aggregator class and clear design.
Parameters
----------
data_folder : str
folder which contains of data of the subjects
file_name : str
name of the files. All files that start with this name
will be considered for the analysis (cf. aggregator.data_files)
suffix : str, optional
if specified only files that end with this particular suffix
will be considered (default=.xpd)
"""
self._data_folder = data_folder
self._file_name = file_name
self._data_files = []
self._variables = []
self._dv = []
self._dv_txt = []
self._iv = []
self._iv_txt = []
self._exclusions = []
self._exclusions_txt = []
self._computes = []
self._computes_txt = []
self._recode_txt = []
self._recode = []
self._subject_variables = []
self._last_data = []
self._added_data = []
self._added_variables = []
self._suffix = suffix
for flname in _os.listdir(_os.path.dirname(self._data_folder + "/")):
if flname.endswith(self._suffix) and flname.startswith(
self._file_name):
_data, vnames, _subject_info, _comments = \
read_datafile(self._data_folder + "/" + flname)
if len(self._variables) < 1:
self._variables = vnames
else:
if vnames != self._variables:
message = "Different variables in ".format(flname)
message = message + "\n{0}".format(vnames)
message = message + "\ninstead of\n{0}".format(
self._variables)
raise RuntimeError(message)
self._data_files.append(flname)
if len(self._data_files) < 1:
raise Exception("No data files found in {0}".format(
self._data_folder))
print "found {0} subject_data sets".format(len(self._data_files))
print "found {0} variables: {1}".format(len(self._variables),
self._variables)
@property
@property
[docs] def data_files (self):
"""Getter for data_files.
The list of the data files considered for the analysis.
"""
return self._data_files
@property
@property
[docs] def variables (self):
"""Getter for variables.
The specified variables including the new computer variables and
between subject variables and added variables.
"""
variables = _copy(self._variables)
variables.extend(self._subject_variables)
variables.extend(self._added_variables)
return variables
@property
@property
@property
@property
[docs] def subject_variables(self):
"""Getter for subject variable."""
return self._subject_variables
@property
@property
@property
[docs] def get_data(self, filename, recode_variables=True,
compute_new_variables=True, exclude_trials=True):
"""Read data from from Expyriment data file.
Notes
-----
The function can be only applied on data that in aggregator.data_files,
that is, on the files that in defined data folder and the start with
the experiment name. According to the defined design, the result
contains recoded data together with the new computed variables, and the
subject variables from the headers of the Expyriment data files.
Parameters
----------
filename : str
name of the Expyriment data file
recode_variables : bool, optional
set to False if defined variable recodings should not be applied
(default=False)
compute_new_variables : bool, optional
set to False if new defined variables should not be computed
(default=False)
exclude_trials : bool, optional
set to False if exclusion rules should not be applied
(default=False)
Returns
-------
data : numpy. arry
var_names : list
list of variable names
info : str
subject info
comment : str
comments in data
"""
# check filename
if filename not in self._data_files:
raise RuntimeError("'{0}' is not in the data list\n".format(\
filename, self._data_files))
data, _vnames, subject_info, comments = \
read_datafile(self._data_folder + "/" + filename)
print " reading {0}".format(filename)
if recode_variables:
for var_id, recoding in self._recode:
for old, new in recoding:
for row in range(len(data)):
if data[row][var_id] == old:
data[row][var_id] = new
data = _np.array(data, dtype='|S99')
# compute new defined variables and append
if compute_new_variables:
for new_var_name, var_def in self._computes:
if var_def[1] in self._relations:
# relations are true or false
col = _np.zeros([data.shape[0], 1], dtype=int)
idx = self._find_idx(data, var_def[0], var_def[1], var_def[2])
col[idx, 0] = 1
else:
# operations
try:
a = _np.float64([data[:, var_def[0]]]).transpose()
second_var_id = self._get_variable_id(var_def[2], False)
if second_var_id is not None:
b = _np.float64([data[:, second_var_id ]]).transpose()
else:
b = _np.float64(var_def[2])
except:
msg = "Error while computing new variable {0}. " + \
"Non-number in variables of {1}"
msg.format(new_var_name, filename)
raise RuntimeError(msg)
if var_def[1] == "+":
col = a + b
elif var_def[1] == "-":
col = a - b
elif var_def[1] == "*":
col = a * b
elif var_def[1] == "/":
col = a / b
elif var_def[1] == "%":
col = a % b
data = _np.concatenate((data, col), axis=1)
# add subject information
for sv in self.subject_variables:
try:
info = subject_info[sv]
except:
info = "nan"
col = _np.array([[info for _x in range(data.shape[0])]])
data = _np.c_[data, col.transpose()]
# _add_exclusion trials
if exclude_trials:
for exl in self._exclusions:
idx = self._find_idx(data, exl[0], exl[1], exl[2])
if len(idx) > 0:
data = _np.delete(data, idx, axis=0)
var = _copy(self._variables)
var.extend(self._subject_variables)
return [data, var, subject_info, comments]
@property
[docs] def concatenated_data(self):
"""Getter for concatenated_data.
Notes
-----
Returns all data of all subjects as numpy.array and all variables
names (including added variables). According to the defined design, the
result contains the new computed variables and the subject variables
from the headers of the Expyriment data files.
If data have been loaded and no new variable or exclusion has been defined
the concatenated_data will merely return the previous data without
re-processing.
Returns
-------
data : numpy.array
variables : list of str
"""
if len(self._last_data) > 0: # data are already loaded and unchanged
cdata = self._last_data
else:
cdata = None
for flname in self._data_files:
tmp = self.get_data(flname)[0]
if cdata is None:
cdata = tmp
else:
cdata = _np.concatenate((cdata, tmp), axis=0)
self._last_data = cdata
# append added data
if len(self._added_variables) > 0:
if cdata is not None:
cdata = _np.concatenate((cdata, self._added_data), axis=1)
else:
cdata = self._added_data
return [cdata, self.variables]
[docs] def get_variable_data(self, variables):
"""Returns the column of data as numpy array.
Parameters
----------
variables : list of str
names of the variables to be extracted
Returns
-------
data : numpy.array
"""
if type(variables) != _types.ListType:
variables = [variables]
cols = []
for v in variables:
cols.append(self._get_variable_id(v, throw_exception=True))
data = self.concatenated_data[0]
try:
data = _np.float64(data[:, cols])
except:
data = data[:, cols]
return data
[docs] def add_variables(self, variable_names, data_columns):
"""Adds a new variable to the data.
Notes
-----
The amount of variables and added columns must match. The added data
must also match the number of rows. Note, manually added variables
will be lost if cases will be excluded afterwards via a call of
the method `set_exclusions`.
Parameters
----------
variable_names : str
name of the new variable(s)
data_columns : numpy.array
the new data columns as numpy array
"""
d = _np.array(data_columns)
data_shape = _np.shape(d)
if len(data_shape) < 2:
d = _np.transpose([d])
data_shape = (data_shape[0], 1)
if type(variable_names) != _types.ListType:
variable_names = [variable_names]
if len(variable_names) != data_shape[1]:
raise RuntimeError("Amount of variables and added colums doesn't fit.")
if data_shape[0] != _np.shape(self.concatenated_data[0])[0]:
raise RuntimeError("Number of rows doesn't match.")
self._added_variables.extend(variable_names)
if len(self._added_data) == 0:
self._added_data = d
else:
self._added_data = _np.concatenate((self._added_data, d), axis=1)
self._last_data = []
[docs] def write_concatenated_data(self, output_file=None, delimiter=','):
"""Concatenate data and write it to a csv file.
Parameters
----------
output_file : str, optional
name of data output file
If no specified data will the save to {file_name}.csv
delimiter : str
delimiter character (default=",")
"""
if output_file is None:
output_file = "{0}.csv".format(self.file_name)
data = self.concatenated_data
write_csv_file(filename=output_file, data=data[0], varnames=data[1],
delimiter=delimiter)
[docs] def set_independent_variables(self, variables):
"""Set the independent variables.
Parameters
----------
variables : str or list
the name(s) of one or more data variables (aggregator.variables)
"""
if type(variables) != _types.ListType:
self._iv_txt = [variables]
else:
self._iv_txt = variables
self._iv = []
for v in self._iv_txt:
self._add_independent_variable(v)
self._last_data = []
[docs] def set_dependent_variables(self, dv_syntax):
"""Set dependent variables.
Parameters
----------
dv_syntax : str or list
syntax describing the dependent variable by a function and variable,
e.g. mean(RT)
Notes
-----
Syntax::
{function}({variable})
{function} -- mean, median, sum, std or n_trials
Note: n_trials counts the number of trials
and does not require a variable as argument
{variable} -- a defined data variable
"""
if type(dv_syntax) != _types.ListType:
self._dv_txt = [dv_syntax]
else:
self._dv_txt = dv_syntax
self._dv = []
for v in self._dv_txt:
self._add_dependent_variable(v)
self._last_data = []
[docs] def set_exclusions(self, rule_syntax):
"""Set rules to exclude trials from the analysis.
The method indicates the rows, which are ignored while reading
the data files. It can therefore not be applied on variables that have
been added later via `add_variables` and results in a loss of all
manually added variables. Setting exclusions requires re-reading of
the data files and might be therefore time consuming. Thus, call this
mathod always at the beginning of your analysis script.
Parameters
----------
rule_syntax : str or list
A string or a list of strings that represent the rules
to exclude trials
Notes
-----
Rule syntax::
{variable} {relation} {variable/value}
{variable} -- a defined data variable
{relation} -- ==, !=, >, <, >=, <=, => or <=
{value} -- string or numeric
"""
if type(rule_syntax) != _types.ListType:
self._exclusions_txt = [rule_syntax]
else:
self._exclusions_txt = rule_syntax
self._exclusions = []
for r in self._exclusions_txt:
self._add_exclusion(r)
self._last_data = []
self._added_data = []
self._added_variables = []
[docs] def set_variable_recoding(self, recoding_syntax):
"""Set syntax to recode variables.
The method defines the variables, which will recoded. It can not
be applied on variables that have been added later via
`add_variables`. Recoding variables requires re-reading of the data
files and might be therefore time consuming.
Parameters
----------
rule_syntax : str or list
A string or a list of strings that represent the variable
recoding syntax
Notes
-----
Recoding syntax::
{variable}: {old_value1} = {new_value1}, {old_value2} = {new_value2},...
"""
if type(recoding_syntax) != _types.ListType:
self._recode_txt = [recoding_syntax]
else:
self._recode_txt = recoding_syntax
self._recode = []
for syntax in self._recode_txt:
self._add_variable_recoding(syntax)
self._last_data = []
[docs] def set_subject_variables(self, variables):
"""Set subject variables to be considered for the analysis.
The method sets the subject variables. Subject variables are between
subject factors or other variables defines in the subject information
section (#s) of the Expyriment data file. The method requires a
re-reading of the data files and might be therefore time consuming.
Parameters
----------
variables : str or list
A string or a list of strings that represent the subject
variables
"""
if type(variables) != _types.ListType:
self._subject_variables = [variables]
else:
self._subject_variables = variables
self._last_data = []
[docs] def set_computed_variables(self, compute_syntax):
"""Set syntax to compute new variables.
The method defines the variables, which will be computed. It can not
be applied on variables that have been added manually via
`add_variables`. The method requires a re-reading of the data files
and might be therefore time consuming.
Parameters
----------
compute_syntax : str or list
A string or a list of strings that represent the syntax to
compute the new variables
Notes
-----
Compute Syntax::
{new-variable} = {variable} {relation/operation} {variable/value}
{new-variable} -- a new not yet defined variable name
{variable} -- a defined data variable
{relation} -- ==, !=, >, <, >=, <=, => or <=
{operation} -- +, -, *, / or %
{value} -- string or numeric
"""
if type(compute_syntax) != _types.ListType:
self._computes_txt = [compute_syntax]
else:
self._computes_txt = compute_syntax
self._computes = []
self._variables = read_datafile(self._data_folder + "/" +
self._data_files[0],
only_header_and_variable_names=True)[1] # original variables
for syntax in self._computes_txt:
self._add_compute_variable(syntax)
self._last_data = []
[docs] def print_n_trials(self, variables):
"""Print the number of trials in the combinations of the independent
variables.
Notes
-----
The functions is for instance useful to quickly check the experimental
design.
Parameters
----------
variables : str or list
A string or a list of strings that represent the names of one or
more data variables (aggregator.variables)
"""
old_iv = self._iv
old_dv = self._dv
self.set_dependent_variables("n_trials")
self.set_independent_variables(variables)
result, varnames = self.aggregate()
for row in result:
print "Subject {0}".format(row[0])
for cnt, var in enumerate(varnames):
if cnt > 0:
print "\t{0}:\t{1}".format(var[4:], row[cnt])
print "\n"
self._dv = old_dv
self._iv = old_iv
[docs] def aggregate(self, output_file=None, column_subject_id=0):
"""Aggregate the data as defined by the design.
The design will be printed and the resulting data will be return as
numpy.array together with the variable names.
Parameters
----------
output_file : str, optional
name of data output file. If this output_file is defined the
function write the results as csv data file
subject variable : int, optional
data column containing the subject id (odefault=0)
Returns
-------
result : numpy.array
new_variable_names : list of strings
"""
data, _variables = self.concatenated_data
subjects = list(set(data[:, column_subject_id]))
subjects.sort()
# get all iv values
iv_values = []
for iv in self._iv:
tmp = list(set(data[:, iv]))
tmp.sort()
iv_values.append(tmp)
new_variable_names, combinations = self._get_new_variables(iv_values)
if len(combinations) == 0:
combinations = ["total"]
# calculate subject wise
result = None
for sub in subjects:
mtx = data[data[:, column_subject_id] == sub, :]
row = [sub]
# subject info
for sv in self.subject_variables:
row.append(mtx[0, self._get_variable_id(sv)])
for dv in self._dv:
for fac_cmb in combinations:
if fac_cmb == "total":
idx = range(0, mtx.shape[0])
else:
# find idx of combinations
idx = None
for c, iv in enumerate(self._iv):
tmp = _np.array(mtx[:, iv] == fac_cmb[c])
if idx is None:
idx = tmp.copy()
else:
idx = idx & tmp
# calc mean over idx
if len(idx) > 0:
values = mtx[idx, dv[1]]
if dv[0] == "median":
row.append(_np.median(_np.float64(values)))
elif dv[0] == "mean":
row.append(_np.mean(_np.float64(values)))
elif dv[0] == "sum":
row.append(_np.sum(_np.float64(values)))
elif dv[0] == "std":
row.append(_np.std(_np.float64(values)))
elif dv[0] == "n_trials":
row.append(values.shape[0])
else:
row.append(_np.NaN)
else:
row.append(_np.NaN)
if result is None:
result = _np.array([row], dtype='|S99')
else:
result = _np.r_[result, [row]]
if output_file is not None:
write_csv_file(output_file, result, new_variable_names)
return result, new_variable_names
