#Projectome_finder This project includes different notebooks which all were combine to "Notebook_combined" and two different datasets: artifact data and robins data

Notebook_combined

combined_NB_31.01.ipynb

-Load data (muscles) and simulation (roots)
-plot recruitment curves

-process recruitment
	- Heatmap
	- Sigmoid fit

-Evaluate: combine roots and muscles
	- Combine activation pattern (muscles, roots)
	- Compare projectome (from paper) with generated
	- Plot Heatmap (Spinalcord, projectome)

Notebook evaluation

-Evaluate: combine roots and muscles

Notebook_muscles

Notebook_6_Notebook_6_Loading_Mat_from_RD

-Load the data from matlab format
-Restructure it to a class
-Save it as pickle file

Notebook_7_Create_tensor_filteredData

-Creates a 5D tensor with filtered data "filtered_data_to_tensor"
	Dim 0: all electrodes, (0 to 15)
	Dim 1: all muscles (0 to 13)
	Dim 2: all amplitudes (0 to 40)
	Dim 3: all repeats (0 to 39) (since trials are repeated with the same amplitude, max 40x)
	Dim 4: EMG Raw (time array of 1404 EMG values)
	size of tensor: torch.Size([16, 16, 40, 39, 1000])

-Creates a 5D tensor with peak to peak value per trial "peak2peak_data_to_tensor"
	Dim 0: all electrodes, (0 to 15)
	Dim 1: all muscles (0 to 13)
	Dim 2: all amplitudes (0 to 40)
	Dim 3: all repeats (0 to 39) 
	Dim 4: peak to peak value (1 value)
	size of tensor: torch.Size([16, 16, 40, 39, 1])

-Creates a 3D tensor: "amplitude_filtereddata_to_tensor"
	Dim 0: all electrodes, (0 to 15)
	Dim 1: all muscles (0 to 13)
	Dim 2: all amplitudes (40 amplitude values starting from 0.2 to 4.9, if for specific combination no EMG data for specific amplitude, space filled with "nan")

Notebook_8_Analysis

-Plot Filtered EMG
-EMG Processing: plot peak to peak values = recruitment curves
	
-Muscle recruitment processing
	-A: Heat map
		-Normalisation of data
		-build heat map ( with and without interpolation)
		-plot heatmap
	-B: Fit recruitment to sigmoids
		-with max value set by data
		-with interpolated max value (higher than data)

Notebook_roots

Notebook_9_Loading_Neuron_Sim_Results -Load the simulations -Restructure it to a df -Save it as pickle file

Notebook_10_Load_roots_simulation -Plot the roots recruitment curve -build and plot the heat map -build and plot the sigmoid curve

Notebooks on hold

Notebook_x_Cleaning_data

This notebook is put on hold for now, since we don't focus on artifact removal anymore
-Section D: For each artifact:
	-load the data
	-create an artefact-free series (new attribute of object)
	-save the data

Notebook_artifacts

-Notebook_1__Load_data_transform_object

-Section A: Load the data from the original data

-Section B: Define a way so that it is simple to:	
		-save part or all the data
		-load part or all the data

Notebook_2_Building_artifact_library

-Section C: Building the library of artifacts
		-load data
		-find artifacts
		-save artifacts

Notebook_3_Making_tensor

-Creates a 5D tensor: "raw_data_to_tensor"
	Dim 0: all electrodes, (0 to 15)
	Dim 1: all muscles (0 to 13)
	Dim 2: all amplitudes (0 to 32)
	Dim 3: all repeats (0 to 11) (since trials are repeated with the same amplitude, max 11x)
	Dim 4: EMG Raw (time array of 1404 EMG values)
	size of tensor: torch.Size([16, 14, 32, 11, 1404])

-Creates a 3D tensor: "amplitude_data_to_tensor"
	Dim 0: all electrodes, (0 to 15)
	Dim 1: all muscles (0 to 13)
	Dim 2: all amplitudes (32 amplitude values starting from 0.5 to 3.7, if for specific combination no EMG data for specific amplitude, space filled with "nan")

-df_muscles: list of muscles with indices
-df_elecs: list of electrodes with indices
All data is saved as pickle file /csv file

Notebook_4_Plot_tensor

-plots max, min and middle amplitude of an elec-muscle combination

Notebook_5_Peak_to_peak_analysis

-get muscle number from name
-get data tensor from muscle name
-get amplitude tensor from muscle name
-get max EMG for normalisation
-get peak to peak value
-get histogram
-analysing data by plotting histograms