Skip to content

BMPlot API Reference

This page provides API reference documentation for the BMPlot module which contains functions for plotting and visualizing BMTK network models and simulation results.

Connections Module

bmtool.bmplot.connections.is_notebook()

Detect if code is running in a Jupyter notebook environment.

Returns:

bool True if running in a Jupyter notebook, False otherwise.

Notes:

This is used to determine whether to call plt.show() explicitly or rely on Jupyter's automatic display functionality.

Source code in bmtool/bmplot/connections.py 
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
def is_notebook() -> bool:
    """
    Detect if code is running in a Jupyter notebook environment.

    Returns:
    --------
    bool
        True if running in a Jupyter notebook, False otherwise.

    Notes:
    ------
    This is used to determine whether to call plt.show() explicitly or
    rely on Jupyter's automatic display functionality.
    """
    try:
        shell = get_ipython().__class__.__name__
        if shell == 'ZMQInteractiveShell':
            return True   # Jupyter notebook or qtconsole
        elif shell == 'TerminalInteractiveShell':
            return False  # Terminal running IPython
        else:
            return False  # Other type (?)
    except NameError:
        return False      # Probably standard Python interpreter

bmtool.bmplot.connections.total_connection_matrix(config=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, synaptic_info='0', include_gap=True)

Generate a plot displaying total connections or other synaptic statistics.

Parameters:

config : str Path to a BMTK simulation config file. title : str, optional Title for the plot. If None, a default title will be used. sources : str Comma-separated string of network names to use as sources. targets : str Comma-separated string of network names to use as targets. sids : str, optional Comma-separated string of source node identifiers to filter. tids : str, optional Comma-separated string of target node identifiers to filter. no_prepend_pop : bool, optional If True, don't display population name before sid or tid in the plot. save_file : str, optional Path to save the plot. If None, plot is not saved. synaptic_info : str, optional Type of information to display: - '0': Total connections (default) - '1': Mean and standard deviation of connections - '2': All synapse .mod files used - '3': All synapse .json files used include_gap : bool, optional If True, include gap junctions and chemical synapses in the analysis. If False, only include chemical synapses.

Returns:

None The function generates and displays a plot.

Source code in bmtool/bmplot/connections.py 
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
def total_connection_matrix(config=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, synaptic_info='0', include_gap=True):
    """
    Generate a plot displaying total connections or other synaptic statistics.

    Parameters:
    -----------
    config : str
        Path to a BMTK simulation config file.
    title : str, optional
        Title for the plot. If None, a default title will be used.
    sources : str
        Comma-separated string of network names to use as sources.
    targets : str
        Comma-separated string of network names to use as targets.
    sids : str, optional
        Comma-separated string of source node identifiers to filter.
    tids : str, optional
        Comma-separated string of target node identifiers to filter.
    no_prepend_pop : bool, optional
        If True, don't display population name before sid or tid in the plot.
    save_file : str, optional
        Path to save the plot. If None, plot is not saved.
    synaptic_info : str, optional
        Type of information to display:
        - '0': Total connections (default)
        - '1': Mean and standard deviation of connections
        - '2': All synapse .mod files used
        - '3': All synapse .json files used
    include_gap : bool, optional
        If True, include gap junctions and chemical synapses in the analysis.
        If False, only include chemical synapses.

    Returns:
    --------
    None
        The function generates and displays a plot.
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []
    text,num, source_labels, target_labels = util.connection_totals(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,synaptic_info=synaptic_info,include_gap=include_gap)

    if title == None or title=="":
        title = "Total Connections"
    if synaptic_info=='1':
        title = "Mean and Stdev # of Conn on Target"    
    if synaptic_info=='2':
        title = "All Synapse .mod Files Used"
    if synaptic_info=='3':
        title = "All Synapse .json Files Used"
    plot_connection_info(text,num,source_labels,target_labels,title, syn_info=synaptic_info, save_file=save_file)
    return

bmtool.bmplot.connections.percent_connection_matrix(config=None, nodes=None, edges=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, method='total', include_gap=True)

Generates a plot showing the percent connectivity of a network config: A BMTK simulation config sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph method: what percent to displace on the graph 'total','uni',or 'bi' for total connections, unidirectional connections or bidirectional connections save_file: If plot should be saved include_gap: Determines if connectivity shown should include gap junctions + chemical synapses. False will only include chemical

Source code in bmtool/bmplot/connections.py 
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
def percent_connection_matrix(config=None,nodes=None,edges=None,title=None,sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False,save_file=None,method = 'total',include_gap=True):
    """
    Generates a plot showing the percent connectivity of a network
    config: A BMTK simulation config 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    method: what percent to displace on the graph 'total','uni',or 'bi' for total connections, unidirectional connections or bidirectional connections
    save_file: If plot should be saved
    include_gap: Determines if connectivity shown should include gap junctions + chemical synapses. False will only include chemical
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")

    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []
    text,num, source_labels, target_labels = util.percent_connections(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,method=method,include_gap=include_gap)
    if title == None or title=="":
        title = "Percent Connectivity"


    plot_connection_info(text,num,source_labels,target_labels,title, save_file=save_file)
    return

bmtool.bmplot.connections.probability_connection_matrix(config=None, nodes=None, edges=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, dist_X=True, dist_Y=True, dist_Z=True, bins=8, line_plot=False, verbose=False, include_gap=True)

Generates probability graphs need to look into this more to see what it does needs model_template to be defined to work

Source code in bmtool/bmplot/connections.py 
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
def probability_connection_matrix(config=None,nodes=None,edges=None,title=None,sources=None, targets=None, sids=None, tids=None, 
                            no_prepend_pop=False,save_file=None, dist_X=True,dist_Y=True,dist_Z=True,bins=8,line_plot=False,verbose=False,include_gap=True):
    """
    Generates probability graphs
    need to look into this more to see what it does
    needs model_template to be defined to work
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []

    throwaway, data, source_labels, target_labels = util.connection_probabilities(config=config,nodes=None,
        edges=None,sources=sources,targets=targets,sids=sids,tids=tids,
        prepend_pop=not no_prepend_pop,dist_X=dist_X,dist_Y=dist_Y,dist_Z=dist_Z,num_bins=bins,include_gap=include_gap)
    if not data.any():
        return
    if data[0][0]==-1:
        return
    #plot_connection_info(data,source_labels,target_labels,title, save_file=save_file)

    #plt.clf()# clears previous plots
    np.seterr(divide='ignore', invalid='ignore')
    num_src, num_tar = data.shape
    fig, axes = plt.subplots(nrows=num_src, ncols=num_tar, figsize=(12,12))
    fig.subplots_adjust(hspace=0.5, wspace=0.5)

    for x in range(num_src):
        for y in range(num_tar):
            ns = data[x][y]["ns"]
            bins = data[x][y]["bins"]

            XX = bins[:-1]
            YY = ns[0]/ns[1]

            if line_plot:
                axes[x,y].plot(XX,YY)
            else:
                axes[x,y].bar(XX,YY)

            if x == num_src-1:
                axes[x,y].set_xlabel(target_labels[y])
            if y == 0:
                axes[x,y].set_ylabel(source_labels[x])

            if verbose:
                print("Source: [" + source_labels[x] + "] | Target: ["+ target_labels[y] +"]")
                print("X:")
                print(XX)
                print("Y:")
                print(YY)

    tt = "Distance Probability Matrix"
    if title:
        tt = title
    st = fig.suptitle(tt, fontsize=14)
    fig.text(0.5, 0.04, 'Target', ha='center')
    fig.text(0.04, 0.5, 'Source', va='center', rotation='vertical')
    notebook = is_notebook
    if notebook == False:
        fig.show()

    return

bmtool.bmplot.connections.convergence_connection_matrix(config=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, convergence=True, method='mean+std', include_gap=True, return_dict=None)

Generates connection plot displaying convergence data config: A BMTK simulation config sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph save_file: If plot should be saved method: 'mean','min','max','stdev' or 'mean+std' connvergence plot

Source code in bmtool/bmplot/connections.py 
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
def convergence_connection_matrix(config=None,title=None,sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False,save_file=None,convergence=True,method='mean+std',include_gap=True,return_dict=None):
    """
    Generates connection plot displaying convergence data
    config: A BMTK simulation config 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    save_file: If plot should be saved
    method: 'mean','min','max','stdev' or 'mean+std' connvergence plot 
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    return divergence_connection_matrix(config,title ,sources, targets, sids, tids, no_prepend_pop, save_file ,convergence, method,include_gap=include_gap,return_dict=return_dict)

bmtool.bmplot.connections.divergence_connection_matrix(config=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, convergence=False, method='mean+std', include_gap=True, return_dict=None)

Generates connection plot displaying divergence data config: A BMTK simulation config sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph save_file: If plot should be saved method: 'mean','min','max','stdev', and 'mean+std' for divergence plot

Source code in bmtool/bmplot/connections.py 
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
def divergence_connection_matrix(config=None,title=None,sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False,save_file=None,convergence=False,method='mean+std',include_gap=True,return_dict=None):
    """
    Generates connection plot displaying divergence data
    config: A BMTK simulation config 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    save_file: If plot should be saved
    method: 'mean','min','max','stdev', and 'mean+std' for divergence plot
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []

    syn_info, data, source_labels, target_labels = util.connection_divergence(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,convergence=convergence,method=method,include_gap=include_gap)


    #data, labels = util.connection_divergence_average(config=config,nodes=nodes,edges=edges,populations=populations)

    if title == None or title=="":

        if method == 'min':
            title = "Minimum "
        elif method == 'max':
            title = "Maximum "
        elif method == 'std':
            title = "Standard Deviation "
        elif method == 'mean':
            title = "Mean "
        else: 
            title = 'Mean + Std '

        if convergence:
            title = title + "Synaptic Convergence"
        else:
            title = title + "Synaptic Divergence"
    if return_dict:
        dict = plot_connection_info(syn_info,data,source_labels,target_labels,title, save_file=save_file,return_dict=return_dict)
        return dict
    else:
        plot_connection_info(syn_info,data,source_labels,target_labels,title, save_file=save_file)
        return

bmtool.bmplot.connections.gap_junction_matrix(config=None, title=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=False, save_file=None, method='convergence')

Generates connection plot displaying gap junction data. config: A BMTK simulation config sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph save_file: If plot should be saved type:'convergence' or 'percent' connections

Source code in bmtool/bmplot/connections.py 
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
def gap_junction_matrix(config=None,title=None,sources=None, targets=None, sids=None,tids=None, no_prepend_pop=False,save_file=None,method='convergence'):
    """
    Generates connection plot displaying gap junction data.
    config: A BMTK simulation config 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    save_file: If plot should be saved
    type:'convergence' or 'percent' connections
    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    if method !='convergence' and method!='percent':
        raise Exception("type must be 'convergence' or 'percent'")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []
    syn_info, data, source_labels, target_labels = util.gap_junction_connections(config=config,nodes=None,edges=None,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,method=method)


    def filter_rows(syn_info, data, source_labels, target_labels):
        """
        Filters out rows in a connectivity matrix that contain only NaN or zero values.

        This function is used to clean up connection matrices by removing rows that have no meaningful data,
        which helps create more informative visualizations of network connectivity.

        Parameters:
        -----------
        syn_info : numpy.ndarray
            Array containing synaptic information corresponding to the data matrix.
        data : numpy.ndarray
            2D matrix containing connectivity data with rows representing sources and columns representing targets.
        source_labels : list
            List of labels for the source populations corresponding to rows in the data matrix.
        target_labels : list
            List of labels for the target populations corresponding to columns in the data matrix.

        Returns:
        --------
        tuple
            A tuple containing the filtered (syn_info, data, source_labels, target_labels) with invalid rows removed.
        """
        # Identify rows with all NaN or all zeros
        valid_rows = ~np.all(np.isnan(data), axis=1) & ~np.all(data == 0, axis=1)

        # Filter rows based on valid_rows mask
        new_syn_info = syn_info[valid_rows]
        new_data = data[valid_rows]
        new_source_labels = np.array(source_labels)[valid_rows]

        return new_syn_info, new_data, new_source_labels, target_labels

    def filter_rows_and_columns(syn_info, data, source_labels, target_labels):
        """
        Filters out both rows and columns in a connectivity matrix that contain only NaN or zero values.

        This function performs a two-step filtering process: first removing rows with no data,
        then transposing the matrix and removing columns with no data (by treating them as rows).
        This creates a cleaner, more informative connectivity matrix visualization.

        Parameters:
        -----------
        syn_info : numpy.ndarray
            Array containing synaptic information corresponding to the data matrix.
        data : numpy.ndarray
            2D matrix containing connectivity data with rows representing sources and columns representing targets.
        source_labels : list
            List of labels for the source populations corresponding to rows in the data matrix.
        target_labels : list
            List of labels for the target populations corresponding to columns in the data matrix.

        Returns:
        --------
        tuple
            A tuple containing the filtered (syn_info, data, source_labels, target_labels) with both
            invalid rows and columns removed.
        """
        # Filter rows first
        syn_info, data, source_labels, target_labels = filter_rows(syn_info, data, source_labels, target_labels)

        # Transpose data to filter columns
        transposed_syn_info = np.transpose(syn_info)
        transposed_data = np.transpose(data)
        transposed_source_labels = target_labels
        transposed_target_labels = source_labels

        # Filter columns (by treating them as rows in transposed data)
        transposed_syn_info, transposed_data, transposed_source_labels, transposed_target_labels = filter_rows(
            transposed_syn_info, transposed_data, transposed_source_labels, transposed_target_labels
        )

        # Transpose back to original orientation
        filtered_syn_info = np.transpose(transposed_syn_info)
        filtered_data = np.transpose(transposed_data)
        filtered_source_labels = transposed_target_labels  # Back to original source_labels
        filtered_target_labels = transposed_source_labels  # Back to original target_labels

        return filtered_syn_info, filtered_data, filtered_source_labels, filtered_target_labels


    syn_info, data, source_labels, target_labels = filter_rows_and_columns(syn_info, data, source_labels, target_labels)

    if title == None or title=="":
        title = 'Gap Junction'
        if method == 'convergence':
            title+=' Syn Convergence'
        elif method == 'percent':
            title+=' Percent Connectivity'
    plot_connection_info(syn_info,data,source_labels,target_labels,title, save_file=save_file)
    return

bmtool.bmplot.connections.connection_histogram(config=None, nodes=None, edges=None, sources=[], targets=[], sids=[], tids=[], no_prepend_pop=True, synaptic_info='0', source_cell=None, target_cell=None, include_gap=True)

Generates histogram of number of connections individual cells in a population receieve from another population config: A BMTK simulation config sources: network name(s) to plot targets: network name(s) to plot sids: source node identifier tids: target node identifier no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph source_cell: where connections are coming from target_cell: where connections on coming onto save_file: If plot should be saved

Source code in bmtool/bmplot/connections.py 
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
def connection_histogram(config=None,nodes=None,edges=None,sources=[],targets=[],sids=[],tids=[],no_prepend_pop=True,synaptic_info='0',
                      source_cell = None,target_cell = None,include_gap=True):
    """
    Generates histogram of number of connections individual cells in a population receieve from another population
    config: A BMTK simulation config 
    sources: network name(s) to plot
    targets: network name(s) to plot
    sids: source node identifier 
    tids: target node identifier
    no_prepend_pop: dictates if population name is displayed before sid or tid when displaying graph
    source_cell: where connections are coming from
    target_cell: where connections on coming onto
    save_file: If plot should be saved
    """
    def connection_pair_histogram(**kwargs):
        """
        Creates a histogram showing the distribution of connection counts between a specific source and target cell type.

        This function is designed to be used with the relation_matrix utility and will only create histograms
        for the specified source and target cell types, ignoring all other combinations.

        Parameters:
        -----------
        kwargs : dict
            Dictionary containing the following keys:
            - edges: DataFrame containing edge information
            - sid: Column name for source ID type in the edges DataFrame
            - tid: Column name for target ID type in the edges DataFrame
            - source_id: Value to filter edges by source ID type
            - target_id: Value to filter edges by target ID type

        Global parameters used:
        ---------------------
        source_cell : str
            The source cell type to plot.
        target_cell : str
            The target cell type to plot.
        include_gap : bool
            Whether to include gap junctions in the analysis. If False, gap junctions are excluded.

        Returns:
        --------
        None
            Displays a histogram showing the distribution of connection counts.
        """
        edges = kwargs["edges"] 
        source_id_type = kwargs["sid"]
        target_id_type = kwargs["tid"]
        source_id = kwargs["source_id"]
        target_id = kwargs["target_id"]
        if source_id == source_cell and target_id == target_cell:
            temp = edges[(edges[source_id_type] == source_id) & (edges[target_id_type]==target_id)]
            if include_gap == False:
                temp = temp[temp['is_gap_junction'] != True]
            node_pairs = temp.groupby('target_node_id')['source_node_id'].count()
            try:
                conn_mean = statistics.mean(node_pairs.values)
                conn_std = statistics.stdev(node_pairs.values)
                conn_median = statistics.median(node_pairs.values)
                label = "mean {:.2f} std {:.2f} median {:.2f}".format(conn_mean,conn_std,conn_median)
            except: # lazy fix for std not calculated with 1 node
                conn_mean = statistics.mean(node_pairs.values)
                conn_median = statistics.median(node_pairs.values)
                label = "mean {:.2f} median {:.2f}".format(conn_mean,conn_median)
            plt.hist(node_pairs.values,density=False,bins='auto',stacked=True,label=label)
            plt.legend()
            plt.xlabel("# of conns from {} to {}".format(source_cell,target_cell))
            plt.ylabel("# of cells")
            plt.show()
        else: # dont care about other cell pairs so pass
            pass

    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    sources = sources.split(",")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []
    util.relation_matrix(config,nodes,edges,sources,targets,sids,tids,not no_prepend_pop,relation_func=connection_pair_histogram,synaptic_info=synaptic_info)

bmtool.bmplot.connections.connection_distance(config, sources, targets, source_cell_id, target_id_type, ignore_z=False)

Plots the 3D spatial distribution of target nodes relative to a source node and a histogram of distances from the source node to each target node.

Parameters:

config: (str) A BMTK simulation config sources: (str) network name(s) to plot targets: (str) network name(s) to plot source_cell_id : (int) ID of the source cell for calculating distances to target nodes. target_id_type : (str) A string to filter target nodes based off the target_query. ignore_z : (bool) A bool to ignore_z axis or not for when calculating distance default is False

Source code in bmtool/bmplot/connections.py 
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
def connection_distance(config: str,sources: str,targets: str,
                        source_cell_id: int,target_id_type: str,ignore_z:bool=False) -> None:
    """
    Plots the 3D spatial distribution of target nodes relative to a source node
    and a histogram of distances from the source node to each target node.

    Parameters:
    ----------
    config: (str) A BMTK simulation config 
    sources: (str) network name(s) to plot
    targets: (str) network name(s) to plot
    source_cell_id : (int) ID of the source cell for calculating distances to target nodes.
    target_id_type : (str) A string to filter target nodes based off the target_query.
    ignore_z : (bool) A bool to ignore_z axis or not for when calculating distance default is False

    """
    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    #if source != target:
        #raise Exception("Code is setup for source and target to be the same! Look at source code for function to add feature")

    # Load nodes and edges based on config file
    nodes, edges = util.load_nodes_edges_from_config(config)

    edge_network = sources + "_to_" + targets
    node_network = sources

    # Filter edges to obtain connections originating from the source node
    edge = edges[edge_network]
    edge = edge[edge['source_node_id'] == source_cell_id]
    if target_id_type:
        edge = edge[edge['target_query'].str.contains(target_id_type, na=False)]

    target_node_ids = edge['target_node_id']

    # Filter nodes to obtain only the target and source nodes
    node = nodes[node_network]
    target_nodes = node.loc[node.index.isin(target_node_ids)]
    source_node = node.loc[node.index == source_cell_id]

    # Calculate distances between source node and each target node
    if ignore_z:
        target_positions = target_nodes[['pos_x', 'pos_y']].values
        source_position = np.array([source_node['pos_x'], source_node['pos_y']]).ravel()  # Ensure 1D shape
    else:
        target_positions = target_nodes[['pos_x', 'pos_y', 'pos_z']].values
        source_position = np.array([source_node['pos_x'], source_node['pos_y'], source_node['pos_z']]).ravel()  # Ensure 1D shape
    distances = np.linalg.norm(target_positions - source_position, axis=1)

    # Plot positions of source and target nodes in 3D space or 2D
    if ignore_z:
        fig = plt.figure(figsize=(8, 6)) 
        ax = fig.add_subplot(111)
        ax.scatter(target_nodes['pos_x'], target_nodes['pos_y'], c='blue', label="target cells")
        ax.scatter(source_node['pos_x'], source_node['pos_y'], c='red', label="source cell")
    else:
        fig = plt.figure(figsize=(8, 6)) 
        ax = fig.add_subplot(111, projection='3d')
        ax.scatter(target_nodes['pos_x'], target_nodes['pos_y'], target_nodes['pos_z'], c='blue', label="target cells")
        ax.scatter(source_node['pos_x'], source_node['pos_y'], source_node['pos_z'], c='red', label="source cell")

    # Optional: Add text annotations for distances
    # for i, distance in enumerate(distances):
    #     ax.text(target_nodes['pos_x'].iloc[i], target_nodes['pos_y'].iloc[i], target_nodes['pos_z'].iloc[i],
    #             f'{distance:.2f}', color='black', fontsize=8, ha='center')

    plt.legend()
    plt.show()

    # Plot distances in a separate 2D plot
    plt.figure(figsize=(8, 6)) 
    plt.hist(distances, bins=20, color='blue', edgecolor='black')
    plt.xlabel("Distance")
    plt.ylabel("Count")
    plt.title(f"Distance from Source Node to Each Target Node")
    plt.grid(True)
    plt.show()

bmtool.bmplot.connections.edge_histogram_matrix(config=None, sources=None, targets=None, sids=None, tids=None, no_prepend_pop=None, edge_property=None, time=None, time_compare=None, report=None, title=None, save_file=None)

Generates a matrix of histograms showing the distribution of edge properties between different populations.

This function creates a grid of histograms where each cell in the grid represents the distribution of a specific edge property (e.g., synaptic weights, delays) between a source population (row) and target population (column).

Parameters:

config : str Path to a BMTK simulation config file. sources : str Comma-separated list of source network names. targets : str Comma-separated list of target network names. sids : str, optional Comma-separated list of source node identifiers to filter by. tids : str, optional Comma-separated list of target node identifiers to filter by. no_prepend_pop : bool, optional If True, population names are not prepended to node identifiers in the display. edge_property : str The edge property to analyze and display in the histograms (e.g., 'syn_weight', 'delay'). time : int, optional Time point to analyze from a time series report. time_compare : int, optional Second time point for comparison with 'time'. report : str, optional Name of the report to analyze. title : str, optional Custom title for the plot. save_file : str, optional Path to save the generated plot.

Returns:

None Displays a matrix of histograms.

Source code in bmtool/bmplot/connections.py 
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
def edge_histogram_matrix(config=None,sources = None,targets=None,sids=None,tids=None,no_prepend_pop=None,edge_property = None,time = None,time_compare = None,report=None,title=None,save_file=None):
    """
    Generates a matrix of histograms showing the distribution of edge properties between different populations.

    This function creates a grid of histograms where each cell in the grid represents the distribution of a 
    specific edge property (e.g., synaptic weights, delays) between a source population (row) and 
    target population (column).

    Parameters:
    -----------
    config : str
        Path to a BMTK simulation config file.
    sources : str
        Comma-separated list of source network names.
    targets : str
        Comma-separated list of target network names.
    sids : str, optional
        Comma-separated list of source node identifiers to filter by.
    tids : str, optional
        Comma-separated list of target node identifiers to filter by.
    no_prepend_pop : bool, optional
        If True, population names are not prepended to node identifiers in the display.
    edge_property : str
        The edge property to analyze and display in the histograms (e.g., 'syn_weight', 'delay').
    time : int, optional
        Time point to analyze from a time series report.
    time_compare : int, optional
        Second time point for comparison with 'time'.
    report : str, optional
        Name of the report to analyze.
    title : str, optional
        Custom title for the plot.
    save_file : str, optional
        Path to save the generated plot.

    Returns:
    --------
    None
        Displays a matrix of histograms.
    """

    if not config:
        raise Exception("config not defined")
    if not sources or not targets:
        raise Exception("Sources or targets not defined")
    targets = targets.split(",")
    if sids:
        sids = sids.split(",")
    else:
        sids = []
    if tids:
        tids = tids.split(",")
    else:
        tids = []

    if time_compare:
        time_compare = int(time_compare)

    data, source_labels, target_labels = util.edge_property_matrix(edge_property,nodes=None,edges=None,config=config,sources=sources,targets=targets,sids=sids,tids=tids,prepend_pop=not no_prepend_pop,report=report,time=time,time_compare=time_compare)

    # Fantastic resource
    # https://stackoverflow.com/questions/7941207/is-there-a-function-to-make-scatterplot-matrices-in-matplotlib 
    num_src, num_tar = data.shape
    fig, axes = plt.subplots(nrows=num_src, ncols=num_tar, figsize=(12,12))
    fig.subplots_adjust(hspace=0.5, wspace=0.5)

    for x in range(num_src):
        for y in range(num_tar):
            axes[x,y].hist(data[x][y])

            if x == num_src-1:
                axes[x,y].set_xlabel(target_labels[y])
            if y == 0:
                axes[x,y].set_ylabel(source_labels[x])

    tt = edge_property + " Histogram Matrix"
    if title:
        tt = title
    st = fig.suptitle(tt, fontsize=14)
    fig.text(0.5, 0.04, 'Target', ha='center')
    fig.text(0.04, 0.5, 'Source', va='center', rotation='vertical')
    plt.draw()

bmtool.bmplot.connections.plot_connection_info(text, num, source_labels, target_labels, title, syn_info='0', save_file=None, return_dict=None)

Function to plot connection information as a heatmap, including handling missing source and target values. If there is no source or target, set the value to 0.

Source code in bmtool/bmplot/connections.py 
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
def plot_connection_info(text, num, source_labels, target_labels, title, syn_info='0', save_file=None, return_dict=None):
    """
    Function to plot connection information as a heatmap, including handling missing source and target values.
    If there is no source or target, set the value to 0.
    """

    # Ensure text dimensions match num dimensions
    num_source = len(source_labels)
    num_target = len(target_labels)

    # Set color map
    matplotlib.rc('image', cmap='viridis')

    # Create figure and axis for the plot
    fig1, ax1 = plt.subplots(figsize=(num_source, num_target))
    num = np.nan_to_num(num, nan=0) # replace NaN with 0
    im1 = ax1.imshow(num)

    # Set ticks and labels for source and target
    ax1.set_xticks(list(np.arange(len(target_labels))))
    ax1.set_yticks(list(np.arange(len(source_labels))))
    ax1.set_xticklabels(target_labels)
    ax1.set_yticklabels(source_labels, size=12, weight='semibold')

    # Rotate the tick labels for better visibility
    plt.setp(ax1.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor", size=12, weight='semibold')

    # Dictionary to store connection information
    graph_dict = {}

    # Loop over data dimensions and create text annotations
    for i in range(num_source):
        for j in range(num_target):
            # Get the edge info, or set it to '0' if it's missing
            edge_info = text[i, j] if text[i, j] is not None else 0

            # Initialize the dictionary for the source node if not already done
            if source_labels[i] not in graph_dict:
                graph_dict[source_labels[i]] = {}

            # Add edge info for the target node
            graph_dict[source_labels[i]][target_labels[j]] = edge_info

            # Set text annotations based on syn_info type
            if syn_info == '2' or syn_info == '3':
                if num_source > 8 and num_source < 20:
                    fig_text = ax1.text(j, i, edge_info,
                                        ha="center", va="center", color="w", rotation=37.5, size=8, weight='semibold')
                elif num_source > 20:
                    fig_text = ax1.text(j, i, edge_info,
                                        ha="center", va="center", color="w", rotation=37.5, size=7, weight='semibold')
                else:
                    fig_text = ax1.text(j, i, edge_info,
                                        ha="center", va="center", color="w", rotation=37.5, size=11, weight='semibold')
            else:
                fig_text = ax1.text(j, i, edge_info,
                                    ha="center", va="center", color="w", size=11, weight='semibold')

    # Set labels and title for the plot
    ax1.set_ylabel('Source', size=11, weight='semibold')
    ax1.set_xlabel('Target', size=11, weight='semibold')
    ax1.set_title(title, size=20, weight='semibold')

    # Display the plot or save it based on the environment and arguments
    notebook = is_notebook()  # Check if running in a Jupyter notebook
    if notebook == False:
        fig1.show()

    if save_file:
        plt.savefig(save_file)

    if return_dict:
        return graph_dict
    else:
        return

bmtool.bmplot.connections.connector_percent_matrix(csv_path=None, exclude_strings=None, assemb_key=None, title='Percent connection matrix', pop_order=None)

Generates and plots a connection matrix based on connection probabilities from a CSV file produced by bmtool.connector.

This function is useful for visualizing percent connectivity while factoring in population distance and other parameters. It processes the connection data by filtering the 'Source' and 'Target' columns in the CSV, and displays the percentage of connected pairs for each population combination in a matrix.

Parameters:

csv_path : str Path to the CSV file containing the connection data. The CSV should be an output from the bmtool.connector classes, specifically generated by the save_connection_report() function. exclude_strings : list of str, optional List of strings to exclude rows where 'Source' or 'Target' contain these strings. title : str, optional, default='Percent connection matrix' Title for the generated plot. pop_order : list of str, optional List of population labels to specify the order for the x- and y-ticks in the plot.

Returns:

None Displays a heatmap plot of the connection matrix, showing the percentage of connected pairs between populations.

Source code in bmtool/bmplot/connections.py 
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
def connector_percent_matrix(csv_path: str = None, exclude_strings=None, assemb_key=None, title: str = 'Percent connection matrix', pop_order=None) -> None:
    """
    Generates and plots a connection matrix based on connection probabilities from a CSV file produced by bmtool.connector.

    This function is useful for visualizing percent connectivity while factoring in population distance and other parameters. 
    It processes the connection data by filtering the 'Source' and 'Target' columns in the CSV, and displays the percentage of 
    connected pairs for each population combination in a matrix.

    Parameters:
    -----------
    csv_path : str
        Path to the CSV file containing the connection data. The CSV should be an output from the bmtool.connector 
        classes, specifically generated by the `save_connection_report()` function.
    exclude_strings : list of str, optional
        List of strings to exclude rows where 'Source' or 'Target' contain these strings.
    title : str, optional, default='Percent connection matrix'
        Title for the generated plot.
    pop_order : list of str, optional
        List of population labels to specify the order for the x- and y-ticks in the plot.

    Returns:
    --------
    None
        Displays a heatmap plot of the connection matrix, showing the percentage of connected pairs between populations.
    """
    # Read the CSV data
    df = pd.read_csv(csv_path)

    # Choose the column to display
    selected_column = "Percent connectionivity within possible connections"

    # Filter the DataFrame based on exclude_strings
    def filter_dataframe(df, column_name, exclude_strings):
        def process_string(string):

            match = re.search(r"\[\'(.*?)\'\]", string)
            if exclude_strings and any(ex_string in string for ex_string in exclude_strings):
                return None
            elif match:
                filtered_string = match.group(1)
                if 'Gap' in string:
                        filtered_string = filtered_string + "-Gap"

                if assemb_key:
                    if assemb_key in string:
                        filtered_string = filtered_string + assemb_key

                return filtered_string  # Return matched string

            return string  # If no match, return the original string

        df[column_name] = df[column_name].apply(process_string)
        df = df.dropna(subset=[column_name])

        return df

    df = filter_dataframe(df, 'Source', exclude_strings)
    df = filter_dataframe(df, 'Target', exclude_strings)

    #process assem rows and combine them into one prob per assem type
    if assemb_key:
        assems = df[df['Source'].str.contains(assemb_key)]
        unique_sources = assems['Source'].unique()

        for source in unique_sources:
            source_assems = assems[assems['Source'] == source]
            unique_targets = source_assems['Target'].unique()  # Filter targets for the current source

            for target in unique_targets:
                # Filter the assemblies with the current source and target
                unique_assems = source_assems[source_assems['Target'] == target]

                # find the prob of a conn
                forward_probs = []
                for _,row in unique_assems.iterrows():
                    selected_percentage = row[selected_column]
                    selected_percentage = [float(p) for p in selected_percentage.strip('[]').split()]
                    if len(selected_percentage) == 1 or len(selected_percentage) == 2:
                        forward_probs.append(selected_percentage[0])
                    if len(selected_percentage) == 3:
                        forward_probs.append(selected_percentage[0])
                        forward_probs.append(selected_percentage[1])

                mean_probs = np.mean(forward_probs)
                source = source.replace(assemb_key, "")
                target = target.replace(assemb_key, "")
                new_row = pd.DataFrame({
                    'Source': [source],
                    'Target': [target],
                    'Percent connectionivity within possible connections': [mean_probs],
                    'Percent connectionivity within all connections': [0]
                })

                df = pd.concat([df, new_row], ignore_index=False)

    # Prepare connection data
    connection_data = {}
    for _, row in df.iterrows():
        source, target, selected_percentage = row['Source'], row['Target'], row[selected_column]
        if isinstance(selected_percentage, str):
            selected_percentage = [float(p) for p in selected_percentage.strip('[]').split()]
        connection_data[(source, target)] = selected_percentage

    # Determine population order
    populations = sorted(list(set(df['Source'].unique()) | set(df['Target'].unique())))
    if pop_order:
        populations = [pop for pop in pop_order if pop in populations]  # Order according to pop_order, if provided
    num_populations = len(populations)

    # Create an empty matrix and populate it
    connection_matrix = np.zeros((num_populations, num_populations), dtype=float)
    for (source, target), probabilities in connection_data.items():
        if source in populations and target in populations:
            source_idx = populations.index(source)
            target_idx = populations.index(target)

            if type(probabilities) == float:
                connection_matrix[source_idx][target_idx] = probabilities
            elif len(probabilities) == 1:
                connection_matrix[source_idx][target_idx] = probabilities[0]
            elif len(probabilities) == 2:
                connection_matrix[source_idx][target_idx] = probabilities[0]
            elif len(probabilities) == 3:
                connection_matrix[source_idx][target_idx] = probabilities[0] 
                connection_matrix[target_idx][source_idx] = probabilities[1]
            else:
                raise Exception("unsupported format")

    # Plotting
    fig, ax = plt.subplots(figsize=(10, 8))
    im = ax.imshow(connection_matrix, cmap='viridis', interpolation='nearest')

    # Add annotations
    for i in range(num_populations):
        for j in range(num_populations):
            text = ax.text(j, i, f"{connection_matrix[i, j]:.2f}%", ha="center", va="center", color="w", size=10, weight='semibold')

    # Add colorbar
    plt.colorbar(im, label=f'{selected_column}')

    # Set title and axis labels
    ax.set_title(title)
    ax.set_xlabel('Target Population')
    ax.set_ylabel('Source Population')

    # Set ticks and labels based on populations in specified order
    ax.set_xticks(np.arange(num_populations))
    ax.set_yticks(np.arange(num_populations))
    ax.set_xticklabels(populations, rotation=45, ha="right", size=12, weight='semibold')
    ax.set_yticklabels(populations, size=12, weight='semibold')

    plt.tight_layout()
    plt.show()

bmtool.bmplot.connections.plot_3d_positions(config=None, sources=None, sid=None, title=None, save_file=None, subset=None)

Plots a 3D graph of all cells with x, y, z location.

Parameters: - config: A BMTK simulation config - sources: Which network(s) to plot - sid: How to name cell groups - title: Plot title - save_file: If plot should be saved - subset: Take every Nth row. This will make plotting large network graphs easier to see.

Source code in bmtool/bmplot/connections.py 
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
def plot_3d_positions(config=None, sources=None, sid=None, title=None, save_file=None, subset=None):
    """
    Plots a 3D graph of all cells with x, y, z location.

    Parameters:
    - config: A BMTK simulation config 
    - sources: Which network(s) to plot 
    - sid: How to name cell groups
    - title: Plot title
    - save_file: If plot should be saved
    - subset: Take every Nth row. This will make plotting large network graphs easier to see.
    """

    if not config:
        raise Exception("config not defined")

    if sources is None:
        sources = "all"

    # Set group keys (e.g., node types)
    group_keys = sid
    if title is None:
        title = "3D positions"

    # Load nodes from the configuration
    nodes = util.load_nodes_from_config(config)

    # Get the list of populations to plot
    if 'all' in sources:
        populations = list(nodes)
    else:
        populations = sources.split(",")

    # Split group_by into list 
    group_keys = group_keys.split(",")
    group_keys += (len(populations) - len(group_keys)) * ["node_type_id"]  # Extend the array to default values if not enough given
    if len(group_keys) > 1:
        raise Exception("Only one group by is supported currently!")

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(projection='3d')
    handles = []

    for pop in (list(nodes)):

        if 'all' not in populations and pop not in populations:
            continue

        nodes_df = nodes[pop]
        group_key = group_keys[0] 

        # If group_key is provided, ensure the column exists in the dataframe
        if group_key is not None:
            if group_key not in nodes_df:
                raise Exception(f"Could not find column '{group_key}' in {pop}")

            groupings = nodes_df.groupby(group_key)
            n_colors = nodes_df[group_key].nunique()
            color_norm = colors.Normalize(vmin=0, vmax=(n_colors - 1))
            scalar_map = cmx.ScalarMappable(norm=color_norm, cmap='hsv')
            color_map = [scalar_map.to_rgba(i) for i in range(n_colors)]
        else:
            groupings = [(None, nodes_df)]
            color_map = ['blue']

        # Loop over groupings and plot
        for color, (group_name, group_df) in zip(color_map, groupings):
            if "pos_x" not in group_df or "pos_y" not in group_df or "pos_z" not in group_df:
                print(f"Warning: Missing position columns in group '{group_name}' for {pop}. Skipping this group.")
                continue  # Skip if position columns are missing

            # Subset the dataframe by taking every Nth row if subset is provided
            if subset is not None:
                group_df = group_df.iloc[::subset]

            h = ax.scatter(group_df["pos_x"], group_df["pos_y"], group_df["pos_z"], color=color, label=group_name)
            handles.append(h)

    if not handles:
        print("No data to plot.")
        return

    # Set plot title and legend
    plt.title(title)
    plt.legend(handles=handles)

    # Draw the plot
    plt.draw()

    # Save the plot if save_file is provided
    if save_file:
        plt.savefig(save_file)

    # Show the plot if running outside of a notebook
    if not is_notebook:
        plt.show()

    return ax

bmtool.bmplot.connections.plot_3d_cell_rotation(config=None, sources=None, sids=None, title=None, save_file=None, quiver_length=None, arrow_length_ratio=None, group=None, subset=None)

Source code in bmtool/bmplot/connections.py 
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
def plot_3d_cell_rotation(config=None, sources=None, sids=None, title=None, save_file=None, quiver_length=None, arrow_length_ratio=None, group=None, subset=None):
    from scipy.spatial.transform import Rotation as R
    if not config:
        raise Exception("config not defined")

    if sources is None:
        sources = ["all"]

    group_keys = sids.split(",") if sids else []

    if title is None:
        title = "Cell rotations"

    nodes = util.load_nodes_from_config(config)

    if 'all' in sources:
        populations = list(nodes)
    else:
        populations = sources

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(111, projection='3d')
    handles = []

    for nodes_key, group_key in zip(list(nodes), group_keys):
        if 'all' not in populations and nodes_key not in populations:
            continue

        nodes_df = nodes[nodes_key]

        if group_key is not None:
            if group_key not in nodes_df.columns:
                raise Exception(f'Could not find column {group_key}')
            groupings = nodes_df.groupby(group_key)

            n_colors = nodes_df[group_key].nunique()
            color_norm = colors.Normalize(vmin=0, vmax=(n_colors - 1))
            scalar_map = cmx.ScalarMappable(norm=color_norm, cmap='hsv')
            color_map = [scalar_map.to_rgba(i) for i in range(n_colors)]
        else:
            groupings = [(None, nodes_df)]
            color_map = ['blue']

        for color, (group_name, group_df) in zip(color_map, groupings):
            if subset is not None:
                group_df = group_df.iloc[::subset]

            if group and group_name not in group.split(","):
                continue

            if "pos_x" not in group_df or "rotation_angle_xaxis" not in group_df:
                continue

            X = group_df["pos_x"]
            Y = group_df["pos_y"]
            Z = group_df["pos_z"]
            U = group_df["rotation_angle_xaxis"].values
            V = group_df["rotation_angle_yaxis"].values
            W = group_df["rotation_angle_zaxis"].values

            if U is None:
                U = np.zeros(len(X))
            if V is None:
                V = np.zeros(len(Y))
            if W is None:
                W = np.zeros(len(Z))

            # Create rotation matrices from Euler angles
            rotations = R.from_euler('xyz', np.column_stack((U, V, W)), degrees=False)

            # Define initial vectors 
            init_vectors = np.column_stack((np.ones(len(X)), np.zeros(len(Y)), np.zeros(len(Z))))

            # Apply rotations to initial vectors
            rots = np.dot(rotations.as_matrix(), init_vectors.T).T

            # Extract x, y, and z components of the rotated vectors
            rot_x = rots[:, 0]
            rot_y = rots[:, 1]
            rot_z = rots[:, 2]

            h = ax.quiver(X, Y, Z, rot_x, rot_y, rot_z, color=color, label=group_name, arrow_length_ratio=arrow_length_ratio, length=quiver_length)
            ax.scatter(X, Y, Z, color=color, label=group_name)
            ax.set_xlim([min(X), max(X)])
            ax.set_ylim([min(Y), max(Y)])
            ax.set_zlim([min(Z), max(Z)])
            handles.append(h)

    if not handles:
        return

    plt.title(title)
    plt.legend(handles=handles)
    plt.draw()

    if save_file:
        plt.savefig(save_file)
    notebook = is_notebook
    if notebook == False:
        plt.show()

Spikes Module

bmtool.bmplot.spikes.raster(spikes_df=None, config=None, network_name=None, groupby='pop_name', ax=None, tstart=None, tstop=None, color_map=None)

Plots a raster plot of neural spikes, with different colors for each population.

Parameters:

spikes_df : pd.DataFrame, optional DataFrame containing spike data with columns 'timestamps', 'node_ids', and optional 'pop_name'. config : str, optional Path to the configuration file used to load node data. network_name : str, optional Specific network name to select from the configuration; if not provided, uses the first network. ax : matplotlib.axes.Axes, optional Axes on which to plot the raster; if None, a new figure and axes are created. tstart : float, optional Start time for filtering spikes; only spikes with timestamps greater than tstart will be plotted. tstop : float, optional Stop time for filtering spikes; only spikes with timestamps less than tstop will be plotted. color_map : dict, optional Dictionary specifying colors for each population. Keys should be population names, and values should be color values.

Returns:

matplotlib.axes.Axes Axes with the raster plot.

Notes:
  • If config is provided, the function merges population names from the node data with spikes_df.
  • Each unique population from groupby in spikes_df will be represented by a different color if color_map is not specified.
  • If color_map is provided, it should contain colors for all unique pop_name values in spikes_df.
Source code in bmtool/bmplot/spikes.py 
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
def raster(spikes_df: Optional[pd.DataFrame] = None, config: Optional[str] = None, network_name: Optional[str] = None, groupby:Optional[str] = 'pop_name',
           ax: Optional[Axes] = None,tstart: Optional[float] = None,tstop: Optional[float] = None,
           color_map: Optional[Dict[str, str]] = None) -> Axes:
    """
    Plots a raster plot of neural spikes, with different colors for each population.

    Parameters:
    ----------
    spikes_df : pd.DataFrame, optional
        DataFrame containing spike data with columns 'timestamps', 'node_ids', and optional 'pop_name'.
    config : str, optional
        Path to the configuration file used to load node data.
    network_name : str, optional
        Specific network name to select from the configuration; if not provided, uses the first network.
    ax : matplotlib.axes.Axes, optional
        Axes on which to plot the raster; if None, a new figure and axes are created.
    tstart : float, optional
        Start time for filtering spikes; only spikes with timestamps greater than `tstart` will be plotted.
    tstop : float, optional
        Stop time for filtering spikes; only spikes with timestamps less than `tstop` will be plotted.
    color_map : dict, optional
        Dictionary specifying colors for each population. Keys should be population names, and values should be color values.

    Returns:
    -------
    matplotlib.axes.Axes
        Axes with the raster plot.

    Notes:
    -----
    - If `config` is provided, the function merges population names from the node data with `spikes_df`.
    - Each unique population from groupby in `spikes_df` will be represented by a different color if `color_map` is not specified.
    - If `color_map` is provided, it should contain colors for all unique `pop_name` values in `spikes_df`.
    """
    # Initialize axes if none provided
    if ax is None:
        _, ax = plt.subplots(1, 1)

    # Filter spikes by time range if specified
    if tstart is not None:
        spikes_df = spikes_df[spikes_df['timestamps'] > tstart]
    if tstop is not None:
        spikes_df = spikes_df[spikes_df['timestamps'] < tstop]

    # Load and merge node population data if config is provided
    if config:
        nodes = load_nodes_from_config(config)
        if network_name:
            nodes = nodes.get(network_name, {})
        else:
            nodes = list(nodes.values())[0] if nodes else {}
            print("Grabbing first network; specify a network name to ensure correct node population is selected.")

        # Find common columns, but exclude the join key from the list
        common_columns = spikes_df.columns.intersection(nodes.columns).tolist()
        common_columns = [col for col in common_columns if col != 'node_ids']  # Remove our join key from the common list

        # Drop all intersecting columns except the join key column from df2
        spikes_df = spikes_df.drop(columns=common_columns)
        # merge nodes and spikes df
        spikes_df = spikes_df.merge(nodes[groupby], left_on='node_ids', right_index=True, how='left')


    # Get unique population names
    unique_pop_names = spikes_df[groupby].unique()

    # Generate colors if no color_map is provided
    if color_map is None:
        cmap = plt.get_cmap('tab10')  # Default colormap
        color_map = {pop_name: cmap(i / len(unique_pop_names)) for i, pop_name in enumerate(unique_pop_names)}
    else:
        # Ensure color_map contains all population names
        missing_colors = [pop for pop in unique_pop_names if pop not in color_map]
        if missing_colors:
            raise ValueError(f"color_map is missing colors for populations: {missing_colors}")

    # Plot each population with its specified or generated color
    for pop_name, group in spikes_df.groupby(groupby):
        ax.scatter(group['timestamps'], group['node_ids'], label=pop_name, color=color_map[pop_name], s=0.5)

    # Label axes
    ax.set_xlabel("Time")
    ax.set_ylabel("Node ID")
    ax.legend(title="Population", loc='upper right', framealpha=0.9, markerfirst=False)

    return ax

bmtool.bmplot.spikes.plot_firing_rate_pop_stats(firing_stats, groupby, ax=None, color_map=None)

Plots a bar graph of mean firing rates with error bars (standard deviation).

Parameters:

firing_stats : pd.DataFrame Dataframe containing 'firing_rate_mean' and 'firing_rate_std'. groupby : str or list of str Column(s) used for grouping. ax : matplotlib.axes.Axes, optional Axes on which to plot the bar chart; if None, a new figure and axes are created. color_map : dict, optional Dictionary specifying colors for each group. Keys should be group names, and values should be color values.

Returns:

matplotlib.axes.Axes Axes with the bar plot.

Source code in bmtool/bmplot/spikes.py 
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
def plot_firing_rate_pop_stats(firing_stats: pd.DataFrame, groupby: Union[str, List[str]], ax: Optional[Axes] = None, 
                               color_map: Optional[Dict[str, str]] = None) -> Axes:
    """
    Plots a bar graph of mean firing rates with error bars (standard deviation).

    Parameters:
    ----------
    firing_stats : pd.DataFrame
        Dataframe containing 'firing_rate_mean' and 'firing_rate_std'.
    groupby : str or list of str
        Column(s) used for grouping.
    ax : matplotlib.axes.Axes, optional
        Axes on which to plot the bar chart; if None, a new figure and axes are created.
    color_map : dict, optional
        Dictionary specifying colors for each group. Keys should be group names, and values should be color values.

    Returns:
    -------
    matplotlib.axes.Axes
        Axes with the bar plot.
    """
    # Ensure groupby is a list for consistent handling
    if isinstance(groupby, str):
        groupby = [groupby]

    # Create a categorical column for grouping
    firing_stats["group"] = firing_stats[groupby].astype(str).agg("_".join, axis=1)

    # Get unique group names
    unique_groups = firing_stats["group"].unique()

    # Generate colors if no color_map is provided
    if color_map is None:
        cmap = plt.get_cmap('viridis')
        color_map = {group: cmap(i / len(unique_groups)) for i, group in enumerate(unique_groups)}
    else:
        # Ensure color_map contains all groups
        missing_colors = [group for group in unique_groups if group not in color_map]
        if missing_colors:
            raise ValueError(f"color_map is missing colors for groups: {missing_colors}")

    # Create new figure and axes if ax is not provided
    if ax is None:
        fig, ax = plt.subplots(figsize=(10, 6))

    # Sort data for consistent plotting
    firing_stats = firing_stats.sort_values(by="group")

    # Extract values for plotting
    x_labels = firing_stats["group"]
    means = firing_stats["firing_rate_mean"]
    std_devs = firing_stats["firing_rate_std"]

    # Get colors for each group
    colors = [color_map[group] for group in x_labels]

    # Create bar plot
    bars = ax.bar(x_labels, means, yerr=std_devs, capsize=5, color=colors, edgecolor="black")

    # Add error bars manually with caps
    _, caps, _ = ax.errorbar(
        x=np.arange(len(x_labels)), 
        y=means, 
        yerr=std_devs, 
        fmt='none', 
        capsize=5, 
        capthick=2, 
        color="black"
    )

    # Formatting
    ax.set_xticks(np.arange(len(x_labels)))
    ax.set_xticklabels(x_labels, rotation=45, ha="right")
    ax.set_xlabel("Population Group")
    ax.set_ylabel("Mean Firing Rate (spikes/s)")
    ax.set_title("Firing Rate Statistics by Population")
    ax.grid(axis='y', linestyle='--', alpha=0.7)

    return ax

bmtool.bmplot.spikes.plot_firing_rate_distribution(individual_stats, groupby, ax=None, color_map=None, plot_type='box', swarm_alpha=0.6)

Plots a distribution of individual firing rates using one or more plot types (box plot, violin plot, or swarm plot), overlaying them on top of each other.

Parameters:

individual_stats : pd.DataFrame Dataframe containing individual firing rates and corresponding group labels. groupby : str or list of str Column(s) used for grouping. ax : matplotlib.axes.Axes, optional Axes on which to plot the graph; if None, a new figure and axes are created. color_map : dict, optional Dictionary specifying colors for each group. Keys should be group names, and values should be color values. plot_type : str or list of str, optional List of plot types to generate. Options: "box", "violin", "swarm". Default is "box". swarm_alpha : float, optional Transparency of swarm plot points. Default is 0.6.

Returns:

matplotlib.axes.Axes Axes with the selected plot type(s) overlayed.

Source code in bmtool/bmplot/spikes.py 
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
def plot_firing_rate_distribution(individual_stats: pd.DataFrame, groupby: Union[str, list], ax: Optional[Axes] = None, 
                                  color_map: Optional[Dict[str, str]] = None, 
                                  plot_type: Union[str, list] = "box", swarm_alpha: float = 0.6) -> Axes:
    """
    Plots a distribution of individual firing rates using one or more plot types
    (box plot, violin plot, or swarm plot), overlaying them on top of each other.

    Parameters:
    ----------
    individual_stats : pd.DataFrame
        Dataframe containing individual firing rates and corresponding group labels.
    groupby : str or list of str
        Column(s) used for grouping.
    ax : matplotlib.axes.Axes, optional
        Axes on which to plot the graph; if None, a new figure and axes are created.
    color_map : dict, optional
        Dictionary specifying colors for each group. Keys should be group names, and values should be color values.
    plot_type : str or list of str, optional
        List of plot types to generate. Options: "box", "violin", "swarm". Default is "box".
    swarm_alpha : float, optional
        Transparency of swarm plot points. Default is 0.6.

    Returns:
    -------
    matplotlib.axes.Axes
        Axes with the selected plot type(s) overlayed.
    """
    # Ensure groupby is a list for consistent handling
    if isinstance(groupby, str):
        groupby = [groupby]

    # Create a categorical column for grouping
    individual_stats["group"] = individual_stats[groupby].astype(str).agg("_".join, axis=1)

    # Validate plot_type (it can be a list or a single type)
    if isinstance(plot_type, str):
        plot_type = [plot_type]

    for pt in plot_type:
        if pt not in ["box", "violin", "swarm"]:
            raise ValueError("plot_type must be one of: 'box', 'violin', 'swarm'.")

    # Get unique groups for coloring
    unique_groups = individual_stats["group"].unique()

    # Generate colors if no color_map is provided
    if color_map is None:
        cmap = plt.get_cmap('viridis')
        color_map = {group: cmap(i / len(unique_groups)) for i, group in enumerate(unique_groups)}

    # Ensure color_map contains all groups
    missing_colors = [group for group in unique_groups if group not in color_map]
    if missing_colors:
        raise ValueError(f"color_map is missing colors for groups: {missing_colors}")

    # Create new figure and axes if ax is not provided
    if ax is None:
        fig, ax = plt.subplots(figsize=(10, 6))

    # Sort data for consistent plotting
    individual_stats = individual_stats.sort_values(by="group")

    # Loop over each plot type and overlay them
    for pt in plot_type:
        if pt == "box":
            sns.boxplot(data=individual_stats, x="group", y="firing_rate", ax=ax, palette=color_map, width=0.5)
        elif pt == "violin":
            sns.violinplot(data=individual_stats, x="group", y="firing_rate", ax=ax, palette=color_map, inner="quartile", alpha=0.4)
        elif pt == "swarm":
            sns.swarmplot(data=individual_stats, x="group", y="firing_rate", ax=ax, palette=color_map, alpha=swarm_alpha)

    # Formatting
    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
    ax.set_xlabel("Population Group")
    ax.set_ylabel("Firing Rate (spikes/s)")
    ax.set_title("Firing Rate Distribution for individual cells")
    ax.grid(axis='y', linestyle='--', alpha=0.7)

    return ax

Entrainment Module

bmtool.bmplot.entrainment.plot_spike_power_correlation(correlation_results, frequencies, pop_names)

Plot the correlation between population spike rates and LFP power.

Parameters:

correlation_results : dict Dictionary with correlation results for calculate_spike_rate_power_correlation frequencies : array Array of frequencies analyzed pop_names : list List of population names

Source code in bmtool/bmplot/entrainment.py 
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
def plot_spike_power_correlation(correlation_results, frequencies, pop_names):
    """
    Plot the correlation between population spike rates and LFP power.

    Parameters:
    -----------
    correlation_results : dict
        Dictionary with correlation results for calculate_spike_rate_power_correlation
    frequencies : array
        Array of frequencies analyzed
    pop_names : list
        List of population names
    """
    sns.set_style("whitegrid")
    plt.figure(figsize=(10, 6))

    for pop in pop_names:
        # Extract correlation values for each frequency
        corr_values = []
        valid_freqs = []

        for freq in frequencies:
            if freq in correlation_results[pop]:
                corr_values.append(correlation_results[pop][freq]['correlation'])
                valid_freqs.append(freq)

        # Plot correlation line
        plt.plot(valid_freqs, corr_values, marker='o', label=pop, 
                linewidth=2, markersize=6)

    plt.xlabel('Frequency (Hz)', fontsize=12)
    plt.ylabel('Spike Rate-Power Correlation', fontsize=12)
    plt.title('Spike rate LFP power correlation during stimulus', fontsize=14)
    plt.grid(True, alpha=0.3)
    plt.legend(fontsize=12)
    plt.xticks(frequencies[::2])  # Display every other frequency on x-axis

    # Add horizontal line at zero for reference
    plt.axhline(y=0, color='gray', linestyle='-', alpha=0.5)

    # Set y-axis limits to make zero visible
    y_min, y_max = plt.ylim()
    plt.ylim(min(y_min, -0.1), max(y_max, 0.1))

    plt.tight_layout()

    plt.show()

LFP Module

bmtool.bmplot.lfp.plot_spectrogram(sxx_xarray, remove_aperiodic=None, log_power=False, plt_range=None, clr_freq_range=None, pad=0.03, ax=None)

Plot spectrogram. Determine color limits using value in frequency band clr_freq_range

Source code in bmtool/bmplot/lfp.py 
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
def plot_spectrogram(sxx_xarray, remove_aperiodic=None, log_power=False,
                     plt_range=None, clr_freq_range=None, pad=0.03, ax=None):
    """Plot spectrogram. Determine color limits using value in frequency band clr_freq_range"""
    sxx = sxx_xarray.PSD.values.copy()
    t = sxx_xarray.time.values.copy()
    f = sxx_xarray.frequency.values.copy()

    cbar_label = 'PSD' if remove_aperiodic is None else 'PSD Residual'
    if log_power:
        with np.errstate(divide='ignore'):
            sxx = np.log10(sxx)
        cbar_label += ' dB' if log_power == 'dB' else ' log(power)'

    if remove_aperiodic is not None:
        f1_idx = 0 if f[0] else 1
        ap_fit = gen_aperiodic(f[f1_idx:], remove_aperiodic.aperiodic_params)
        sxx[f1_idx:, :] -= (ap_fit if log_power else 10 ** ap_fit)[:, None]
        sxx[:f1_idx, :] = 0.

    if log_power == 'dB':
        sxx *= 10

    if ax is None:
        _, ax = plt.subplots(1, 1)
    plt_range = np.array(f[-1]) if plt_range is None else np.array(plt_range)
    if plt_range.size == 1:
        plt_range = [f[0 if f[0] else 1] if log_power else 0., plt_range.item()]
    f_idx = (f >= plt_range[0]) & (f <= plt_range[1])
    if clr_freq_range is None:
        vmin, vmax = None, None
    else:
        c_idx = (f >= clr_freq_range[0]) & (f <= clr_freq_range[1])
        vmin, vmax = sxx[c_idx, :].min(), sxx[c_idx, :].max()

    f = f[f_idx]
    pcm = ax.pcolormesh(t, f, sxx[f_idx, :], shading='gouraud', vmin=vmin, vmax=vmax)
    if 'cone_of_influence_frequency' in sxx_xarray:
        coif = sxx_xarray.cone_of_influence_frequency
        ax.plot(t, coif)
        ax.fill_between(t, coif, step='mid', alpha=0.2)
    ax.set_xlim(t[0], t[-1])
    #ax.set_xlim(t[0],0.2)
    ax.set_ylim(f[0], f[-1])
    plt.colorbar(mappable=pcm, ax=ax, label=cbar_label, pad=pad)
    ax.set_xlabel('Time (sec)')
    ax.set_ylabel('Frequency (Hz)')
    return sxx