featuremapper.analysis Package

analysis Package

class featuremapper.analysis.cyclic_difference(**params)

Bases: holoviews.core.operation.ElementOperation

params(value=String, input_ranges=ClassSelector, group=String, name=String)

The cyclic difference between any two cyclic Image quantities normalized such that maximum possible cyclic difference is 0.5.

Although this operation may be applied to any Image data defined over some cyclic quantity, in practice it is rarely used outside some fairly special uses; namely the computation of cyclic similarity (e.g. the similarity of two orientation of direction maps).

The cyclic similarity is the normalized inverse of the cyclic difference:

cyclic_similarity = 1 - (2.0 * cyclic_difference)

This may be computed in conjunction with the transform operator over the input overlay ‘input’:

transform(cyclic_difference(input), operator=lambda x: 1-2*x)

This quantity varies between 0.0 and 1.0 where 0.0 corresponds to the maximum possible difference (i.e the difference between two anti-correlated quanitities). A cyclic similarity of 0.5 indicates two uncorrelated inputs and a value of 1.0 indicates perfectly matching cyclic values.

Often a more useful measure is a rescaled cyclic similarity such that -1.0 indicates anti-correlation, 0.0 indicates zero-correlation and 1.0 indicates perfect correlation:

nominal_cyclic_similarity = (2 * (cyclic_similarity - 0.5))

Or alternatively:

nominal_cyclic_similarity = 1 - (4.0 * cyclic_difference)

This may be expressed as the following operation on the input:

transform(cyclic_difference(input), operator=lambda x: 1-4*x)

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

group ‘Operation’ String V RW input_ranges {} ClassSelector V RW value ‘CyclicDifference’ String V RW

Parameter docstrings: =====================

group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  value: The value assigned to the result after computing the cyclic  difference.

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param String value (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=CyclicDifference, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The value assigned to the result after computing the cyclic difference.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

classmethod difference(arr1, arr2)

Computes the cyclic difference between two arrays assumed to be normalized in the range 0.0-1.0.

The minimum possible cyclic difference between two such quantities is 0.0 and the maximum possible difference is 0.5.

force_new_dynamic_value = <functools.partial object at 0x2ad5f5f55940>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f5f556d8>

inspect_value = <functools.partial object at 0x2ad5f5f559f0>

instance = <functools.partial object at 0x2ad5f5f55a48>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f5f55998>

set_param = <functools.partial object at 0x2ad5f5f55b50>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.decode_feature(**params)

Bases: holoviews.core.operation.ElementOperation

params(reference_value=Number, weighted_average=Boolean, input_ranges=ClassSelector, group=String, name=String)

Estimate the value of a feature from the current activity pattern on a sheet and a preference map of the sheet. The activity and preference should be supplied as an Overlay or a ViewMap of Overlay objects.

If weighted_average is False, the feature value returned is the value of the preference_map at the maximally active location.

If weighted_average is True, the feature value is estimated by weighting the preference_map by the current activity level, and averaging the result across all units in the sheet.

For instance, if preference_map is OrientationPreference (a cyclic quantity), then the result will be the vector average of the activated orientations. For an orientation map this value should be an estimate of the orientation present on the input.

Record the V1 activity in response to an oriented Gaussian and the orientation preference as follows and overlay them:

act = measure_response(pattern_generator=imagen.Gaussian(),
                       durations=[0.1, 0.5, 1.0])['GaussianResponse']['V1']
pref = measure_or_pref()['OrientationPreference']['V1'].last
ActivityPreferenceStack = act * pref

decode_feature(ActivityPreferenceStack)

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Bounds Mode 

group ‘Operation’ String V RW input_ranges {} ClassSelector V RW reference_value None Number V RW AN weighted_average True Boolean (0, 1) V RW

Parameter docstrings: =====================

group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  reference_value: Allows specifying a reference value, to compute the error of the  decoded value. weighted_average: Decode as vector average if True or by maximal responding unit.

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param Number reference_value (allow_None=True, bounds=None, constant=False, default=None, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Allows specifying a reference value, to compute the error of the decoded value.

param Boolean weighted_average (allow_None=False, bounds=(0, 1), constant=False, default=True, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

Decode as vector average if True or by maximal responding unit.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f5f55680>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f5f55940>

inspect_value = <functools.partial object at 0x2ad5f5f557e0>

instance = <functools.partial object at 0x2ad5f5f55838>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f5f55af8>

set_param = <functools.partial object at 0x2ad5f5f55ba8>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.toHCS(**params)

Bases: holoviews.core.operation.ElementOperation

params(C_multiplier=Number, S_multiplier=Number, flipSC=Boolean, input_ranges=ClassSelector, group=String, name=String)

Hue-Confidence-Strength plot.

Accepts an overlay containing either 2 or 3 layers. The first two layers are hue and confidence and the third layer (if available) is the strength channel.

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Bounds Mode 

C_multiplier 1.0 Number (0.0, None) V RW S_multiplier 1.0 Number (0.0, None) V RW flipSC False Boolean (0, 1) V RW group ‘HCS’ String V RW input_ranges {} ClassSelector V RW

Parameter docstrings: =====================

C_multiplier: Post-normalization multiplier for the confidence value.    Note that if the result is outside the bounds 0.0-1.0, it will  be clipped. S_multiplier: Post-normalization multiplier for the strength value.    Note that if the result is outside the bounds 0.0-1.0, it will  be clipped.  flipSC: Whether to flip the strength and confidence channels group: The group string for the output (an RGB element). input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys. 

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=HCS, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string for the output (an RGB element).

param Number C_multiplier (allow_None=False, bounds=(0.0, None), constant=False, default=1.0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Post-normalization multiplier for the confidence value. Note that if the result is outside the bounds 0.0-1.0, it will be clipped.

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param Number S_multiplier (allow_None=False, bounds=(0.0, None), constant=False, default=1.0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Post-normalization multiplier for the strength value. Note that if the result is outside the bounds 0.0-1.0, it will be clipped.

param Boolean flipSC (allow_None=False, bounds=(0, 1), constant=False, default=False, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

Whether to flip the strength and confidence channels

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f5f557e0>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f5f55a48>

inspect_value = <functools.partial object at 0x2ad5f5f55998>

instance = <functools.partial object at 0x2ad5f5f558e8>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

output_type

alias of RGB

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f5f55b50>

set_param = <functools.partial object at 0x2ad5f5f55c00>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

hypercolumns Module

This module is used to analyse the hypercolumn structure of preference maps. Currently this file offers a means to estimate the hypercolumn distance from the Fourier power spectrum but different types of analysis (eg. wavelet analysis) may be introduced in future.

class featuremapper.analysis.hypercolumns.PowerSpectrumAnalysis(**params)

Bases: holoviews.core.operation.TreeOperation

params(averaging_fn=Callable, fit_table=Boolean, gamma_k=Number, init_fit=Dict, label=String, group=String, name=String)

Estimation of hypercolumn distance in a cyclic preference map from the size of the ring in the fourier power spectrum, following the methods described in the in the supplementary materials of ``Universality in the Evolution of Orientation Columns in the Visual Cortex’‘, Kaschube et al. 2010.

If supplied with a preference overlayed with pinwheels, the pinwheel_density is computed from kmax (the wavenumber of highest power) using the equation:

rho = pinwheel count/(kmax**2)

This is then used to generate a map quality estimate (with unit range) based on the pi-pinwheel density criterion.

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Bounds Mode 

averaging_fn <function mean at 0x2ad5d87c40c8> Callable V RW fit_table False Boolean (0, 1) V RW gamma_k 1.8 Number V RW group ‘Operation’ String V RW init_fit None Dict V RW AN label None String C RW AN

Parameter docstrings: =====================

averaging_fn: The averaging function used to collapse the power spectrum at each  wavenumber down to a scalar value. By default, finds the mean  power for each wavenumber. fit_table: Whether or not to add table listing the fit coefficients at the  end of the output layout. gamma_k: The degree to which the gamma kernel is heavily tailed when  squashing the pinwheel density into a unit map metric. group: The group string used to identify the output of the  Operation. By default this should match the operation name. init_fit: If set to None, an initial fit is automatically selected for  the curve fitting procedure. Otherwise, this is a dictionary of  the initial coefficients for equation (7) from the 2010 Science  paper mentioned above (supplementary materials). For instance,  the values used in the GCAL (Stevens et al. 2013):    init_fit = dict(a0=0.35, a1=3.8, a2=1.3, a3=0.15, a4=-0.003, a5=0)    These coefficients may be understood as follows:    a0 => Gaussian height.  a1 => Peak x-axis position.  a2 => Gaussian spread (ie. variance).  a3 => Baseline value (without falloff).  a4 => Linear falloff.  a5 => Quadratic falloff. label: Label suffixes are fixed as there are too many labels to specify.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param Number gamma_k (allow_None=False, bounds=None, constant=False, default=1.8, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

The degree to which the gamma kernel is heavily tailed when squashing the pinwheel density into a unit map metric.

param String label (allow_None=True, basestring=<type ‘basestring’>, constant=True, default=None, instantiate=True, pickle_default_value=True, precedence=-1, readonly=False)

Label suffixes are fixed as there are too many labels to specify.

param Dict init_fit (allow_None=True, constant=False, default=None, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

If set to None, an initial fit is automatically selected for the curve fitting procedure. Otherwise, this is a dictionary of the initial coefficients for equation (7) from the 2010 Science paper mentioned above (supplementary materials). For instance, the values used in the GCAL (Stevens et al. 2013): init_fit = dict(a0=0.35, a1=3.8, a2=1.3, a3=0.15, a4=-0.003, a5=0) These coefficients may be understood as follows: a0 => Gaussian height. a1 => Peak x-axis position. a2 => Gaussian spread (ie. variance). a3 => Baseline value (without falloff). a4 => Linear falloff. a5 => Quadratic falloff.

param Callable averaging_fn (allow_None=False, constant=False, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The averaging function used to collapse the power spectrum at each wavenumber down to a scalar value. By default, finds the mean power for each wavenumber.

param Boolean fit_table (allow_None=False, bounds=(0, 1), constant=False, default=False, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

Whether or not to add table listing the fit coefficients at the end of the output layout.

KaschubeFit(k, a0, a1, a2, a3, a4, a5)

Fitting function used by Kaschube for finding the hypercolumn distance from the Fourier power spectrum. These values should match the init_fit defaults of pinwheel_analysis below.

static averaging_fn(a, axis=None, dtype=None, out=None, keepdims=False)

Compute the arithmetic mean along the specified axis.

Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.

a : array_like

Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.

axis : int, optional

Axis along which the means are computed. The default is to compute the mean of the flattened array.

dtype : data-type, optional

Type to use in computing the mean. For integer inputs, the default is float64; for floating point inputs, it is the same as the input dtype.

out : ndarray, optional

Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See doc.ufuncs for details.

keepdims : bool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original arr.

m : ndarray, see dtype parameter above

If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned.

average : Weighted average std, var, nanmean, nanstd, nanvar

The arithmetic mean is the sum of the elements along the axis divided by the number of elements.

Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.

>>> a = np.array([[1, 2], [3, 4]])
>>> np.mean(a)
2.5
>>> np.mean(a, axis=0)
array([ 2.,  3.])
>>> np.mean(a, axis=1)
array([ 1.5,  3.5])

In single precision, mean can be inaccurate:

>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.mean(a)
0.546875

Computing the mean in float64 is more accurate:

>>> np.mean(a, dtype=np.float64)
0.55000000074505806

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

estimate_hypercolumn_distance(power_spectrum)

Estimating the hypercolumn distance by fitting Equation 7 of Kaschube et al. 2010 Equation 7 (supplementary material). Returns the analysed values as a dictionary.

fit_samples(max_k, samples, fit)

Compute a curve based from the fit coefficients

force_new_dynamic_value = <functools.partial object at 0x2ad5f5a2ef18>

gamma_dist(x, k, theta)

The gamma distribution used for the gamma metric

gamma_metric(pwd)

The heavily-tailed gamma kernel used to squash the pinwheel density into unit range. The maximum value of unity is attained when the input pinwheel density is pi.

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f596c208>

inspect_value = <functools.partial object at 0x2ad5f596c260>

instance = <functools.partial object at 0x2ad5f596c310>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f596c788>

set_param = <functools.partial object at 0x2ad5f596c7e0>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

wavenumber_spectrum(spectrum)

Bins the power values in the 2D FFT power spectrum as a function of wavenumber (1D). Requires square FFT spectra with an odd dimension to work to ensure there is a central sample corresponding to the DC component (wavenumber zero).

pinwheels Module

class featuremapper.analysis.pinwheels.PinwheelAnalysis(**params)

Bases: holoviews.core.operation.ElementOperation

params(include_contours=Boolean, label=String, silence_warnings=Boolean, input_ranges=ClassSelector, group=String, name=String)

Given a Matrix or HoloMap of a cyclic feature preference, compute the position of all pinwheel singularities in the map. Optionally includes the contours for the real and imaginary components of the preference map used to determine the pinwheel locations.

Returns the original Matrix input overlayed with a Points object containing the computed pinwheel locations and (optionally) Contours overlays including the real and imaginary contour lines respectively.

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Bounds Mode 

group ‘Operation’ String V RW include_contours True Boolean (0, 1) V RW input_ranges {} ClassSelector V RW label None String C RW AN silence_warnings True Boolean (0, 1) V RW

Parameter docstrings: =====================

group: The group string used to identify the output of the  Operation. By default this should match the operation name. include_contours: Whether or not to include the computed contours for the real and  imaginary components of the map. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  label: Label suffixes are fixed as there are too many labels to specify. silence_warnings: Whether or not to show warnings about invalid intersection  events when locating pinwheels.

param Boolean silence_warnings (allow_None=False, bounds=(0, 1), constant=False, default=True, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

Whether or not to show warnings about invalid intersection events when locating pinwheels.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param Boolean include_contours (allow_None=False, bounds=(0, 1), constant=False, default=True, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

Whether or not to include the computed contours for the real and imaginary components of the map.

param String label (allow_None=True, basestring=<type ‘basestring’>, constant=True, default=None, instantiate=True, pickle_default_value=True, precedence=-1, readonly=False)

Label suffixes are fixed as there are too many labels to specify.

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

find_intersections(contour1, contour2)

Vectorized code to find intersections between contours. All successive duplicate vertices along the input contours must be removed to help avoid division-by-zero errors.

There are cases were no intersection exists (eg. parallel lines) where division by zero and invalid value exceptions occur. These exceptions should be caught as warnings: these edge cases are unavoidable with this algorithm and do not indicate that the output is erroneous.

force_new_dynamic_value = <functools.partial object at 0x2ad5f5aae8e8>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f5aae9f0>

identify_pinwheels(re_contours, im_contours, intersections, silence_warnings=True)

Locates the pinwheels from the intersection of the real and imaginary contours of of polar OR map.

inspect_value = <functools.partial object at 0x2ad5f5aaea48>

instance = <functools.partial object at 0x2ad5f5aaeaa0>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

normalize_polar_channel(polar_channel)

This functions normalizes an OR map (polar_channel) taking into account the region of interest (ROI). The ROI is specified by values set to 99. Note that this functionality is implemented to reproduce the experimental approach and has not been tested (not required for Topographica simulations)

output_type

alias of Overlay

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

polar_preference(pref)

Turns hue representation to polar representation. Hue representation uses values expected in the range 0-1.0

polarmap_contours(polarmap, bounds)

Identifies the real and imaginary contours in a polar map. Returns the real and imaginary contours as 2D vertex arrays together with the pairs of contours known to intersect. The coordinate system used is specified by the supplied bounds.

Contour plotting requires origin=’upper’ for consistency with image coordinate system.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

remove_path_duplicates(vertices)

Removes successive duplicates along a path of vertices.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f5aae940>

set_param = <functools.partial object at 0x2ad5f5aaeba8>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.pinwheels.WarningCounter

Bases: object

A simple class to count ‘divide by zero’ and ‘invalid value’ exceptions to allow a suitable warning message to be generated.

raster Module

Useful ElementOperations over Raster elements.

class featuremapper.analysis.raster.fft_power(**params)

Bases: holoviews.core.operation.ElementOperation

params(max_power=Number, input_ranges=ClassSelector, group=String, name=String)

Given a Image element, compute the power of the 2D Fast Fourier Transform (FFT).

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

group ‘FFT Power’ String V RW input_ranges {} ClassSelector V RW max_power 1.0 Number V RW

Parameter docstrings: =====================

group: The group assigned to the output power spectrum. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  max_power: The maximum power value of the output power spectrum.

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=FFT Power, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group assigned to the output power spectrum.

param Number max_power (allow_None=False, bounds=None, constant=False, default=1.0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

The maximum power value of the output power spectrum.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f5c51680>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f5c51628>

inspect_value = <functools.partial object at 0x2ad5f5c51730>

instance = <functools.partial object at 0x2ad5f5c51788>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

output_type

alias of Curve

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f5c516d8>

set_param = <functools.partial object at 0x2ad5f5c51890>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.raster.vectorfield(**params)

Bases: holoviews.core.operation.ElementOperation

params(cols=Integer, rows=Integer, input_ranges=ClassSelector, group=String, name=String)

Given a Image with a single channel, convert it to a VectorField object at a given spatial sampling interval. The values in the Image are assumed to correspond to the vector angle in radians and the value is assumed to be cyclic.

If supplied with an Overlay, the second sheetview in the overlay will be interpreted as the third vector dimension.

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

cols 10 Integer V RW group ‘Vectors’ String V RW input_ranges {} ClassSelector V RW rows 10 Integer V RW

Parameter docstrings: =====================

cols: The number of columns in the vector field. group: The group assigned to the output vector field. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  rows: The number of rows in the vector field.

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param Integer rows (allow_None=False, bounds=None, constant=False, default=10, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

The number of rows in the vector field.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Vectors, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group assigned to the output vector field.

param Integer cols (allow_None=False, bounds=None, constant=False, default=10, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

The number of columns in the vector field.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f5c514c8>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f5c51788>

inspect_value = <functools.partial object at 0x2ad5f5c51418>

instance = <functools.partial object at 0x2ad5f5c51520>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

output_type

alias of VectorField

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f5c515d0>

set_param = <functools.partial object at 0x2ad5f5c517e0>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

spatialtuning Module

class featuremapper.analysis.spatialtuning.DoGModelFit(**params)

Bases: featuremapper.analysis.spatialtuning.TuningCurveAnalysis

params(K_c=Number, K_s=Number, a=Number, b=Number, max_iterations=Number, feature=String, input_ranges=ClassSelector, group=String, name=String)

Baseclass to implement basic size tuning curve fitting procedures. Subclasses have to implement the _function method with the function that is to be fit to the supplied curve.

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

K_c 0 Number V RW K_s 0 Number V RW a 0 Number V RW b 0 Number V RW feature ‘Size’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW max_iterations 100000 Number V RW

Parameter docstrings: =====================

K_c: Center excitatory kernel strength. K_s: Surround inhibitory kernel strength. a: Center excitatory space constant. b: Surround inhibitory space constant. feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  max_iterations: Number of iterations to optimize the fit.

param Number a (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory space constant.

param Number b (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory space constant.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param Number K_s (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory kernel strength.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Size, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param Number K_c (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory kernel strength.

param Number max_iterations (allow_None=False, bounds=None, constant=False, default=100000, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Number of iterations to optimize the fit.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b520>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632b368>

inspect_value = <functools.partial object at 0x2ad5f632b6d8>

instance = <functools.partial object at 0x2ad5f632b5d0>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b578>

set_param = <functools.partial object at 0x2ad5f632b680>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.spatialtuning.FrequencyTuningAnalysis(**params)

Bases: featuremapper.analysis.spatialtuning.TuningCurveAnalysis

params(feature=String, input_ranges=ClassSelector, group=String, name=String)

Analyzes frequency-tuning curve to find the preferred frequency, lower and upper cutoff frequencies as well as the Q-Factor and bandwidth.

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

feature ‘Frequency’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW

Parameter docstrings: =====================

feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys. 

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Frequency, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b3c0>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632b520>

inspect_value = <functools.partial object at 0x2ad5f632b418>

instance = <functools.partial object at 0x2ad5f632b470>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b2b8>

set_param = <functools.partial object at 0x2ad5f632b7e0>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.spatialtuning.NormalizationDoGModel(**params)

Bases: featuremapper.analysis.spatialtuning.DoGModelFit

params(beta=Number, default_contrast=Number, label=String, K_c=Number, K_s=Number, a=Number, b=Number, max_iterations=Number, feature=String, input_ranges=ClassSelector, group=String, name=String)

Normalization model describing response of V1 neurons to sine grating disk stimuli of varying sizes. Ref: Sceniak et al. (200q1) - page 1875 Fitting parameters: K_c - Center strength

a - Center spatial constant K_s - Surround Strength b - Surround spatial constant beta - Arbitrary exponent

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

K_c 0 Number V RW K_s 0 Number V RW a 0 Number V RW b 0 Number V RW beta 0 Number V RW default_contrast 1.0 Number V RW feature ‘Size’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW label ‘Normalization DoG Model Fit’ String V RW max_iterations 100000 Number V RW

Parameter docstrings: =====================

K_c: Center excitatory kernel strength. K_s: Surround inhibitory kernel strength. a: Center excitatory space constant. b: Surround inhibitory space constant. beta: Baseline response. default_contrast: Default contrast to use if supplied curve doesn’t provide contrast. feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  label: < No docstring available > max_iterations: Number of iterations to optimize the fit.

param Number a (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory space constant.

param Number b (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory space constant.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param Number K_s (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory kernel strength.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Size, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param String label (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Normalization DoG Model Fit, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param Number beta (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Baseline response.

param Number K_c (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory kernel strength.

param Number default_contrast (allow_None=False, bounds=None, constant=False, default=1.0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Default contrast to use if supplied curve doesn’t provide contrast.

param Number max_iterations (allow_None=False, bounds=None, constant=False, default=100000, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Number of iterations to optimize the fit.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b680>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632b5d0>

inspect_value = <functools.partial object at 0x2ad5f632b890>

instance = <functools.partial object at 0x2ad5f632b940>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632bdb8>

set_param = <functools.partial object at 0x2ad5f632be10>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.spatialtuning.OrientationContrastAnalysis(**params)

Bases: featuremapper.analysis.spatialtuning.TuningCurveAnalysis

params(feature=String, input_ranges=ClassSelector, group=String, name=String) Parameters of ‘OrientationContrastAnalysis’ ===========================================  Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

feature ‘OrientationSurround’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW

Parameter docstrings: =====================

feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys. 

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=OrientationSurround, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b4c8>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632bf70>

inspect_value = <functools.partial object at 0x2ad5f632b838>

instance = <functools.partial object at 0x2ad5f632b890>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b730>

set_param = <functools.partial object at 0x2ad5f632b998>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.spatialtuning.SF_DoGModel(**params)

Bases: featuremapper.analysis.spatialtuning.DoGModelFit

params(R_0=Number, label=String, K_c=Number, K_s=Number, a=Number, b=Number, max_iterations=Number, feature=String, input_ranges=ClassSelector, group=String, name=String)

DoG model response function to sine grating disk stimulus with varying spatial frequency (f). Ref: Sceniak et al. (2006) - page 3476 Fitting parameters: R_0 - Steady-state response

K_c - Center strength a - Center spatial constant K_s - Surround Strength b - Surround spatial constant

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

K_c 0 Number V RW K_s 0 Number V RW R_0 0 Number V RW a 0 Number V RW b 0 Number V RW feature ‘Frequency’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW label ‘DoG Model Fit’ String V RW max_iterations 100000 Number V RW

Parameter docstrings: =====================

K_c: Center excitatory kernel strength. K_s: Surround inhibitory kernel strength. R_0: Baseline response. a: Center excitatory space constant. b: Surround inhibitory space constant. feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  label: < No docstring available > max_iterations: Number of iterations to optimize the fit.

param Number a (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory space constant.

param Number b (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory space constant.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param Number R_0 (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Baseline response.

param Number K_s (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory kernel strength.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Frequency, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param String label (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=DoG Model Fit, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param Number K_c (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory kernel strength.

param Number max_iterations (allow_None=False, bounds=None, constant=False, default=100000, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Number of iterations to optimize the fit.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b628>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632b6d8>

inspect_value = <functools.partial object at 0x2ad5f632b578>

instance = <functools.partial object at 0x2ad5f632b4c8>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b788>

set_param = <functools.partial object at 0x2ad5f632b520>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.spatialtuning.SizeTuningPeaks(**params)

Bases: featuremapper.analysis.spatialtuning.TuningCurveAnalysis

params(feature=String, input_ranges=ClassSelector, group=String, name=String)

Analysis size-tuning curve to find peak facilitation, peak suppression and peak counter-suppression values, which can be used to derive metrics like contrast dependent size tuning shifts and counter suppression indices.

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

feature ‘Size’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW

Parameter docstrings: =====================

feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys. 

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Size, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b838>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632b628>

inspect_value = <functools.partial object at 0x2ad5f632b5d0>

instance = <functools.partial object at 0x2ad5f632b730>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b890>

set_param = <functools.partial object at 0x2ad5f632b940>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.spatialtuning.SizeTuningShift(**params)

Bases: holoviews.core.operation.ElementOperation

params(input_ranges=ClassSelector, group=String, name=String)

Takes an overlay of two curves as input and computes the contrast-dependent size tuning shift. Assumes the first curve is low contrast and the second high contrast.

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

group ‘Operation’ String V RW input_ranges {} ClassSelector V RW

Parameter docstrings: =====================

group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys. 

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b680>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632b628>

inspect_value = <functools.partial object at 0x2ad5f632b788>

instance = <functools.partial object at 0x2ad5f632b520>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b4c8>

set_param = <functools.partial object at 0x2ad5f632b8e8>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.spatialtuning.Size_DivDoGModel(**params)

Bases: featuremapper.analysis.spatialtuning.DoGModelFit

params(R_0=Number, label=String, K_c=Number, K_s=Number, a=Number, b=Number, max_iterations=Number, feature=String, input_ranges=ClassSelector, group=String, name=String)

iDoG model response function to sine grating disk stimulus with optimal spatial frequency and varying disk radius (r). Ref: Sceniak et al. (2006) - page 3476 Fitting parameters: R_0 - Steady-state response

K_c - Center strength a - Center spatial constant K_s - Surround Strength b - Surround spatial constant

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

K_c 0 Number V RW K_s 0 Number V RW R_0 0 Number V RW a 0 Number V RW b 0 Number V RW feature ‘Size’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW label ‘IDoG Model Fit’ String V RW max_iterations 100000 Number V RW

Parameter docstrings: =====================

K_c: Center excitatory kernel strength. K_s: Surround inhibitory kernel strength. R_0: Baseline response. a: Center excitatory space constant. b: Surround inhibitory space constant. feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  label: < No docstring available > max_iterations: Number of iterations to optimize the fit.

param Number a (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory space constant.

param Number b (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory space constant.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param Number R_0 (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Baseline response.

param Number K_s (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory kernel strength.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Size, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param String label (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=IDoG Model Fit, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param Number K_c (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory kernel strength.

param Number max_iterations (allow_None=False, bounds=None, constant=False, default=100000, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Number of iterations to optimize the fit.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b890>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632bf70>

inspect_value = <functools.partial object at 0x2ad5f632b730>

instance = <functools.partial object at 0x2ad5f632b6d8>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b680>

set_param = <functools.partial object at 0x2ad5f632b940>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.spatialtuning.Size_iDoGModel(**params)

Bases: featuremapper.analysis.spatialtuning.DoGModelFit

params(R_0=Number, label=String, K_c=Number, K_s=Number, a=Number, b=Number, max_iterations=Number, feature=String, input_ranges=ClassSelector, group=String, name=String)

iDoG model response function to sine grating disk stimulus with optimal spatial frequency and varying disk radius (r). Ref: Sceniak et al. (2006) - page 3476 Fitting parameters: R_0 - Steady-state response

K_c - Center strength a - Center spatial constant K_s - Surround Strength b - Surround spatial constant

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

K_c 0 Number V RW K_s 0 Number V RW R_0 0 Number V RW a 0 Number V RW b 0 Number V RW feature ‘Size’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW label ‘IDoG Model Fit’ String V RW max_iterations 100000 Number V RW

Parameter docstrings: =====================

K_c: Center excitatory kernel strength. K_s: Surround inhibitory kernel strength. R_0: Baseline response. a: Center excitatory space constant. b: Surround inhibitory space constant. feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  label: < No docstring available > max_iterations: Number of iterations to optimize the fit.

param Number a (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory space constant.

param Number b (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory space constant.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param Number R_0 (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Baseline response.

param Number K_s (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory kernel strength.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Size, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param String label (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=IDoG Model Fit, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param Number K_c (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory kernel strength.

param Number max_iterations (allow_None=False, bounds=None, constant=False, default=100000, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Number of iterations to optimize the fit.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b578>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632b520>

inspect_value = <functools.partial object at 0x2ad5f632b4c8>

instance = <functools.partial object at 0x2ad5f632b8e8>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b890>

set_param = <functools.partial object at 0x2ad5f632baf8>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

class featuremapper.analysis.spatialtuning.TuningCurveAnalysis(**params)

Bases: holoviews.core.operation.ElementOperation

params(feature=String, input_ranges=ClassSelector, group=String, name=String) Parameters of ‘TuningCurveAnalysis’ ===================================  Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

feature ‘’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW

Parameter docstrings: =====================

feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys. 

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b578>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632b730>

inspect_value = <functools.partial object at 0x2ad5f632b5d0>

instance = <functools.partial object at 0x2ad5f632b680>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b6d8>

set_param = <functools.partial object at 0x2ad5f632b628>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

featuremapper.analysis.spatialtuning.fr2sp(fr)

Convert spatial frequency to spatial constant.

class featuremapper.analysis.spatialtuning.iDoG_DeAngelisModel(**params)

Bases: featuremapper.analysis.spatialtuning.DoGModelFit

params(R_0=Number, label=String, K_c=Number, K_s=Number, a=Number, b=Number, max_iterations=Number, feature=String, input_ranges=ClassSelector, group=String, name=String)

Basic integrated difference of Gaussian response function for area summation curves. Ref: DeAngelis et al. 1994 Fitting parameters: K_c - Center strength

a - Center spatial constant K_s - Surround Strength b - Surround spatial constant R_0 - Steady-state response

 Parameters changed from their default values are marked in red. Soft bound values are marked in cyan. C/V= Constant/Variable, RO/RW = ReadOnly/ReadWrite, AN=Allow None

Name Value Type Mode 

K_c 0 Number V RW K_s 0 Number V RW R_0 0 Number V RW a 0 Number V RW b 0 Number V RW feature ‘Size’ String V RW group ‘Operation’ String V RW input_ranges {} ClassSelector V RW label ‘IDoG Model Fit’ String V RW max_iterations 100000 Number V RW

Parameter docstrings: =====================

K_c: Center excitatory kernel strength. K_s: Surround inhibitory kernel strength. R_0: Baseline response. a: Center excitatory space constant. b: Surround inhibitory space constant. feature: < No docstring available > group: The group string used to identify the output of the  Operation. By default this should match the operation name. input_ranges: Ranges to be used for input normalization (if applicable) in a  format appropriate for the Normalization.ranges parameter.    By default, no normalization is applied. If key-wise  normalization is required, a 2-tuple may be supplied where the  first component is a Normalization.ranges list and the second  component is Normalization.keys.  label: < No docstring available > max_iterations: Number of iterations to optimize the fit.

param Number a (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory space constant.

param Number b (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory space constant.

param String group (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Operation, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

The group string used to identify the output of the Operation. By default this should match the operation name.

param Number R_0 (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Baseline response.

param Number K_s (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Surround inhibitory kernel strength.

param String feature (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=Size, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param String label (allow_None=False, basestring=<type ‘basestring’>, constant=False, default=IDoG Model Fit, instantiate=False, pickle_default_value=True, precedence=None, readonly=False)

param ClassSelector input_ranges (allow_None=False, constant=False, default={}, instantiate=True, is_instance=True, pickle_default_value=True, precedence=None, readonly=False)

Ranges to be used for input normalization (if applicable) in a format appropriate for the Normalization.ranges parameter. By default, no normalization is applied. If key-wise normalization is required, a 2-tuple may be supplied where the first component is a Normalization.ranges list and the second component is Normalization.keys.

param Number K_c (allow_None=False, bounds=None, constant=False, default=0, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Center excitatory kernel strength.

param Number max_iterations (allow_None=False, bounds=None, constant=False, default=100000, inclusive_bounds=(True, True), instantiate=False, pickle_default_value=True, precedence=None, readonly=False, time_dependent=False, time_fn=<Time Time00001>)

Number of iterations to optimize the fit.

debug(msg, *args, **kw)

Print msg merged with args as a debugging statement.

See Python’s logging module for details of message formatting.

defaults()

Return {parameter_name:parameter.default} for all non-constant Parameters.

Note that a Parameter for which instantiate==True has its default instantiated.

force_new_dynamic_value = <functools.partial object at 0x2ad5f632b578>

classmethod get_overlay_bounds(overlay)

Returns the extents if all the elements of an overlay agree on a consistent extents, otherwise raises an exception.

classmethod get_overlay_label(overlay, default_label='')

Returns a label if all the elements of an overlay agree on a consistent label, otherwise returns the default label.

get_param_values(onlychanged=False)

Return a list of name,value pairs for all Parameters of this object.

If onlychanged is True, will only return values that are not equal to the default value.

get_value_generator = <functools.partial object at 0x2ad5f632b890>

inspect_value = <functools.partial object at 0x2ad5f632baf8>

instance = <functools.partial object at 0x2ad5f632b520>

message(msg, *args, **kw)

Print msg merged with args as a message.

See Python’s logging module for details of message formatting.

classmethod params(parameter_name=None)

Return the Parameters of this class as the dictionary {name: parameter_object}

Includes Parameters from this class and its superclasses.

pprint(imports=None, prefix='n ', unknown_value='<?>', qualify=False, separator='')

Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod print_param_defaults()

Print the default values of all cls’s Parameters.

print_param_values()

Print the values of all this object’s Parameters.

process_element(element, key, **params)

The process_element method allows a single element to be operated on given an externally supplied key.

script_repr(imports=, []prefix=' ')

Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y

classmethod search(element, pattern)

Helper method that returns a list of elements that match the given path pattern of form {type}.{group}.{label}.

The input may be a Layout, an Overlay type or a single Element.

classmethod set_default(param_name, value)

Set the default value of param_name.

Equivalent to setting param_name on the class.

set_dynamic_time_fn = <functools.partial object at 0x2ad5f632b788>

set_param = <functools.partial object at 0x2ad5f632ba48>

state_pop()

Restore the most recently saved state.

See state_push() for more details.

state_push()

Save this instance’s state.

For Parameterized instances, this includes the state of dynamically generated values.

Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop().

Generally, this method is used by operations that need to test something without permanently altering the objects’ state.

verbose(msg, *args, **kw)

Print msg merged with args as a verbose message.

See Python’s logging module for details of message formatting.

warning(msg, *args, **kw)

Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments.

See Python’s logging module for details of message formatting.

featuremapper.analysis.spatialtuning.idog_conv(sc)

Conversion of iDoG spatial constants to extents.