Added data from simulation run. changing number of stimuli and number of responses per stimuli. nh was set to 10
Added script mi_calculation.py that runs the algorithm for changing values of both the number of stimuli and the number of responses to stimuli, the number of nh nearest neighbours can also be set. in the future it might be better to use sumatra or something like it to manage the simulations
Added the make_distance_matrix function the file spike_train_object.py and commented out the DistMatrix class
changed notebook names, the one where my mistake was made is now noted, and the new one is called KDE MI estimation (correct version). NOTE: the one with the mistake is correct until when stated
see previous commit message
Modified SpikeTrain to accept spike trains with no spikes. It plays nicely with the distance function
Added split_into_stimuli function to parsing.py, this function splits experiment runs into sections corresponding to stimuli. If no spike occurs within a stimuli time frame it makes the spiking_times an empty list. SpikeTrain object has been modified to accept empty lists as spiking times when there are no spikes
Revert "Added split_into_stimuli function to parsing.py, this function splits experiment runs into sections corresponding to stimuli. If no spike occurs within a stimuli time frame it makes the spiking_times an empty list. SpikeTrain object has been modified to accept empty lists as spiking times when there are no spikes"

This reverts commit 65c576572522f31552709c6bced573a7585b7763.
Added split_into_stimuli function to parsing.py, this function splits experiment runs into sections corresponding to stimuli. If no spike occurs within a stimuli time frame it makes the spiking_times an empty list. SpikeTrain object has been modified to accept empty lists as spiking times when there are no spikes. also changed a variable name in convert_to_words function to make things clearer
Added structure of report
Added script calcuate_distance_matrices which claculates distance matrices for spike trains given the number of trials to consider and the number of stimuli into which the data is split. these matrices will be used by other scripts to actually calculate the mutual information when given the ammount of neighbours to consider
Added script that calculates MI using both methods such that they are equivalent and can be easily compared