CompoundPye  0.93
Modelling and Simulation Framework for Neural Networks of Arthropod Compound Eyes
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CompoundPye.src.Analyzer.analyze_compare.AnalyzeCompare Class Reference

This class is designed to analyze several simulations, with exact same parameters but stimulus speed. More...

Public Member Functions

def __init__
 Initializes and AnalyzeCompare object. More...
 
def get_max_min_lines
 Reads maximum, minimum, and mean response of one cell for each simulation. More...
 
def plot_individuals
 NOT IMPLEMENTED YET. More...
 
def plot_max_resp
 Plots a tuning curve using maximum responses (AnalyzeCompare.max_lines). More...
 
def plot_min_resp
 Plots a tuning curve using minimum responses (AnalyzeCompare.min_lines). More...
 

Detailed Description

This class is designed to analyze several simulations, with exact same parameters but stimulus speed.

It expects data to be created with the file cp_non_GUI_wrapper.py, or to be arranged in the same way as the non-GUI wrapper would do.

Constructor & Destructor Documentation

def CompoundPye.src.Analyzer.analyze_compare.AnalyzeCompare.__init__ (   self,
  path,
  memory_friendly = False 
)

Initializes and AnalyzeCompare object.

Parameters
pathPath to folder, in which the output folders of the different simulations belonging to the set to be analyzed lie.
memory_friendlyIf True, Analyze objects of individual simulations will be deleted after necessary values have been read.

Member Function Documentation

def CompoundPye.src.Analyzer.analyze_compare.AnalyzeCompare.get_max_min_lines (   self,
  neuron_name = 'tangential HS',
  skip = 0.4 
)

Reads maximum, minimum, and mean response of one cell for each simulation.

These values (usually only mean) can be used to plot tuning curves. The default parameter changing among simulations is stimulus speed. Thus, speed tuning curves can be generated this way.

def CompoundPye.src.Analyzer.analyze_compare.AnalyzeCompare.plot_individuals (   self,
  ax,
  plot_dict,
  plot_kwargs_excluding_color = {},
  colors = {} 
)

NOT IMPLEMENTED YET.

plot neurons' responses for simulations with different movement speeds idea: plot_dict={'one neurons name':[list,of,speed,indices],'another neurons name':[another,list,of,indices],...}

def CompoundPye.src.Analyzer.analyze_compare.AnalyzeCompare.plot_max_resp (   self,
  ax_max,
  neuron_name,
  scale = 'normal',
  plot_kwargs = {'linestyle':'dashed',
  color 
)

Plots a tuning curve using maximum responses (AnalyzeCompare.max_lines).

Parameters
ax_maxAxis object in which to plot.
neuron_nameName of neuron to read the response from.
scale'normal' for normal scale, 'log' for logarithmic scale.
plot_kwargsDictionary of keyword parameters to be passed on to the plot function.
normalize_0_to_1If True, data will be linearly stretched and shifted to cover values between 0 and 1.
min_max_speedsList of lower and upper speed boundaries, if you wish to inlcude only stimuli in a certain interval.
def CompoundPye.src.Analyzer.analyze_compare.AnalyzeCompare.plot_min_resp (   self,
  ax_min,
  neuron_name,
  scale = 'normal',
  plot_kwargs = {'linestyle':'dashed',
  color 
)

Plots a tuning curve using minimum responses (AnalyzeCompare.min_lines).

Parameters
ax_minAxis object in which to plot.
neuron_nameName of neuron to read the response from.
scale'normal' for normal scale, 'log' for logarithmic scale.
plot_kwargsDictionary of keyword parameters to be passed on to the plot function.
normalize_0_to_1If True, data will be linearly stretched and shifted to cover values between 0 and 1.
min_max_speedsList of lower and upper speed boundaries, if you wish to inlcude only stimuli in a certain interval.

The documentation for this class was generated from the following file: