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Merge pull request #35 from pgleeson/main
Updated Britten data set; restructured connectome dataset page
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,143 @@ | ||
| import sys | ||
| import numpy as np | ||
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| from cect.Cells import get_contralateral_neuron | ||
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| def test_bilaterals(): | ||
| from cect.White_whole import get_instance | ||
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| """ | ||
| from cect.Cook2019MaleReader import get_instance | ||
| from cect.Cook2019HermReader import get_instance | ||
| from cect.WitvlietDataReader1 import get_instance | ||
| from cect.WitvlietDataReader8 import get_instance | ||
| from cect.TestDataReader import get_instance | ||
| from cect.SpreadsheetDataReader import get_instance | ||
| from cect.Cook2020DataReader import get_instance | ||
| from cect.BrittinDataReader import get_instance | ||
| from cect.WormNeuroAtlasMAReader import get_instance | ||
| from cect.WormNeuroAtlasFuncReader import get_instance | ||
| from cect.RipollSanchezDataReader import get_instance | ||
| from cect.ConnectomeView import PHARYNX_VIEW as view | ||
| from cect.ConnectomeView import SOCIAL_VIEW as view | ||
| from cect.ConnectomeView import NEURONS_VIEW as view | ||
| from cect.ConnectomeView import RAW_VIEW as view""" | ||
| from cect.ConnectomeView import NONPHARYNGEAL_NEURONS_VIEW as view | ||
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| print( | ||
| "NOTE: For the sake of this paper, we excluded the pharyngeal neurons from the connectome data for both genders due to their distinction from the somatic nervous system." | ||
| ) | ||
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| cds = get_instance() | ||
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| cds2 = cds.get_connectome_view(view) | ||
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| synclass = "Chemical Inh" | ||
| synclass = "Chemical Exc" if "Raw" not in view.name else "Chemical" | ||
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| # synclass = "Acetylcholine" | ||
| # synclass = "Chemical" | ||
| # synclass = "Electrical" | ||
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| print(cds2.summary()) | ||
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| print("Keys: %s, plotting: %s" % (view.synclass_sets.keys(), synclass)) | ||
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| def array_info(conn_array): | ||
| nonzero = np.count_nonzero(conn_array) | ||
| print( | ||
| "- Connection - shape: %s, %i non-zero entries, %i total\n%s\n" | ||
| % ( | ||
| conn_array.shape, | ||
| nonzero, | ||
| np.sum(conn_array), | ||
| conn_array, | ||
| ) | ||
| ) | ||
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| dim = cds2.connections[synclass].shape[0] | ||
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| new_conn_array = np.zeros([dim, dim], dtype=np.float64) | ||
| array_info(new_conn_array) | ||
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| for synclass in [ | ||
| "Chemical", | ||
| "Chemical Exc", | ||
| "Chemical Inh", | ||
| "Electrical", | ||
| "Contact", | ||
| "Functional", | ||
| "Extrasynaptic", | ||
| ]: | ||
| print(" Adding conns of type: %s" % synclass) | ||
| if synclass in cds2.connections: | ||
| conns_cs = cds2.connections[synclass] | ||
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| array_info(conns_cs) | ||
| for i in range(len(conns_cs)): | ||
| for j in range(len(conns_cs[i])): | ||
| if ( | ||
| i != j | ||
| ): # Kim et al 2024: Self-connections are treated as nonexistent (∀𝐴𝑖𝑖 = 0). | ||
| new_conn_array[i, j] = ( | ||
| new_conn_array[i, j] or conns_cs[i, j] > 0 | ||
| ) | ||
|
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| amal = "CS+GJ" | ||
| print("Amalgamated array:") | ||
| array_info(new_conn_array) | ||
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| # fig = cds2.to_plotly_hive_plot_fig(list(view.synclass_sets.keys())[0], view) | ||
| # fig = cds2.to_plotly_graph_fig(synclass, view) | ||
| # fig = cds2.to_plotly_matrix_fig(list(view.synclass_sets.keys())[0], view) | ||
|
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| conn_count = 0 | ||
| symm_conn_count = 0 | ||
|
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| POS_ZERO_NEG_COLORMAP2 = ["red", "pink", "white", "lightblue", "blue"] | ||
| POS_ZERO_COLORMAP2 = ["white", "lightblue", "blue"] | ||
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| colormap = POS_ZERO_COLORMAP2 | ||
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| scaled_conn_array = np.array(new_conn_array) | ||
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| for i in range(len(new_conn_array)): | ||
| pre = cds2.nodes[i] | ||
| pre_ = get_contralateral_neuron(pre) | ||
| for j in range(len(new_conn_array[i])): | ||
| post = cds2.nodes[j] | ||
| post_ = get_contralateral_neuron(post) | ||
| w = new_conn_array[i][j] | ||
| if w != 0: | ||
| print(f"Connection {conn_count}:\t{pre}->{post} ({w})") | ||
| assert pre is not post | ||
| w_ = new_conn_array[cds2.nodes.index(pre_), cds2.nodes.index(post_)] | ||
| print(f" - Mirror:\t{pre_}->{post_} ({w_})\n") | ||
| symm_conn_count += w_ | ||
| if w_ == 0: | ||
| scaled_conn_array[i][j] = 0.5 | ||
| scaled_conn_array[ | ||
| cds2.nodes.index(pre_), cds2.nodes.index(post_) | ||
| ] = -1 | ||
| colormap = POS_ZERO_NEG_COLORMAP2 | ||
| conn_count += 1 | ||
|
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| print( | ||
| f"Of {(len(new_conn_array)**2)} possible edges, {conn_count} are connected, {symm_conn_count} are mirrored {'%.2f'%(100*symm_conn_count/conn_count)}% " | ||
| ) | ||
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| cds2.connections[amal] = scaled_conn_array | ||
|
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| # import plotly.io as pio | ||
| # pio.renderers.default = "browser" | ||
| if "-nogui" not in sys.argv: | ||
| # cds2.connection_number_plot | ||
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| fig = cds2.to_plotly_matrix_fig(amal, view, colormap, bold_bilaterals=True) | ||
|
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| fig.show() | ||
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| if __name__ == "__main__": | ||
| test_bilaterals() |
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