topo :: command :: pylabplots :: measure_or_tuning_fullfield :: Class measure_or_tuning_fullfield

Class measure_or_tuning_fullfield

                                    object --+                    
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             param.parameterized.Parameterized --+                
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         param.parameterized.ParameterizedFunction --+            
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      analysis.featureresponses.MeasureResponseCommand --+        
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analysis.featureresponses.SinusoidalMeasureResponseCommand --+    
                                                             |    
                 analysis.featureresponses.FeatureCurveCommand --+
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                                                                measure_or_tuning_fullfield

Measures orientation tuning curve(s) of a particular unit using a full-field sine grating stimulus.

The curve can be plotted at various different values of the contrast (or actually any other parameter) of the stimulus. If using contrast and the network contains an LGN layer, then one would usually specify michelson_contrast as the contrast_parameter. If there is no explicit LGN, then scale (offset=0.0) can be used to define the contrast. Other relevant contrast definitions (or other parameters) can also be used, provided they are defined in PatternPresenter and the units parameter is changed as appropriate.

Nested Classes

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Inherited from param.parameterized.Parameterized: __metaclass__

Instance Methods

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Inherited from analysis.featureresponses.FeatureCurveCommand: __call__

Inherited from analysis.featureresponses.FeatureCurveCommand (private): _compute_curves, _feature_list, _sheetview_unit

Inherited from param.parameterized.ParameterizedFunction: __reduce__, script_repr

Inherited from param.parameterized.Parameterized: __getstate__, __init__, __repr__, __setstate__, __str__, debug, defaults, force_new_dynamic_value, get_param_values, get_value_generator, inspect_value, message, print_param_values, set_default, set_dynamic_time_fn, set_param, state_pop, state_push, verbose, warning

Inherited from param.parameterized.Parameterized (private): _add_parameter, _instantiate_param, _set_name, _setup_params

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Class Methods

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Inherited from param.parameterized.ParameterizedFunction: instance

Inherited from param.parameterized.Parameterized: params, print_param_defaults

Static Methods

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Inherited from param.parameterized.ParameterizedFunction: __new__

Class Variables

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coords = param.Parameter(default= None, doc= """Ignored; here ...
Ignored; here just to suppress warning.

pattern_presenter = param.Callable(default= PatternPresenter(p...
Callable object that will present a parameter-controlled pattern to a set of Sheets.

__params = {'apply_output_fns': <param.Boolean object at 0xb22...
dict() -> new empty dictionary.

name = <param.parameterized.String object at 0xb23b4c4>
String identifier for this object.

Inherited from analysis.featureresponses.FeatureCurveCommand: curve_parameters, num_orientation, sheet, static_parameters, units, x_axis

Inherited from analysis.featureresponses.SinusoidalMeasureResponseCommand: frequencies, num_phase, scale

Inherited from analysis.featureresponses.MeasureResponseCommand: apply_output_fns, display, duration, generator_sheets, offset, sheet_views_prefix, subplot, weighted_average

Inherited from param.parameterized.Parameterized: print_level

Properties

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Inherited from object: __class__

Class Variable Details

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coords

Ignored; here just to suppress warning.

Value:

param.Parameter(default= None, doc= """Ignored; here just to suppress warning.""")

pattern_presenter

Callable object that will present a parameter-controlled pattern to a set of Sheets. By default, uses a SineGrating presented for a short duration. By convention, most Topographica example files are designed to have a suitable activity pattern computed by that time, but the duration will need to be changed for other models that do not follow that convention.

Value:

param.Callable(default= PatternPresenter(pattern_generator= SineGrating(), contras
t_parameter= "michelson_contrast"))

__params

dict() -> new empty dictionary.
dict(mapping) -> new dictionary initialized from a mapping object's
    (key, value) pairs.
dict(seq) -> new dictionary initialized as if via:
    d = {}
    for k, v in seq:
        d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs
    in the keyword argument list.  For example:  dict(one=1, two=2)

Value:

{'apply_output_fns': <param.Boolean object at 0xb22c56c>,
 'coords': <param.parameterized.Parameter object at 0xb2456bc>,
 'curve_parameters': <param.parameterized.Parameter object at 0xb233374>,
 'display': <param.Boolean object at 0xb22cbac>,
 'duration': <param.Number object at 0xb235734>,
 'frequencies': <param.List object at 0xb22c0ac>,
 'generator_sheets': <param.List object at 0xb22c06c>,
 'name': <param.parameterized.String object at 0xb23b4c4>,
...