Skip to content

Entrainment Plotting API

bmtool.bmplot.entrainment.calculate_trial_statistics(data, error_type='ci')

Calculate mean and error statistics across trials.

Computes trial-averaged statistics with proper handling of NaN values. Supports three error types: 95% confidence intervals (via t-distribution), standard error of the mean (SEM), and standard deviation (SD).

Parameters:

Name Type Description Default
data ndarray

2D array of shape (n_trials, n_values) containing trial-wise data. Can contain NaN values which are ignored in calculations.

 required
error_type str

Type of error to compute: "ci" for 95% confidence interval, "sem" for standard error of the mean, or "std" for standard deviation (default: "ci").

 'ci'

Returns:

Type Description
Tuple[ndarray, ndarray]
  • mean_values : 1D array of mean values across trials (one per value).
  • error_values : 1D array of error bounds/bars corresponding to each mean. For "ci", represents the half-width of the 95% confidence interval. For "sem" and "std", represents the error magnitude.

Raises:

Type Description
ValueError

If error_type is not 'ci', 'sem', or 'std'. If data is not 2D or is empty.
Notes
  • NaN values are ignored using numpy's nanmean and nanstd functions.
  • For "ci", uses the t-distribution with degrees of freedom = min(valid_counts) - 1.
  • Confidence intervals are computed at 95% (α = 0.05, two-tailed).
  • If fewer than 2 valid trials exist for a value, error is set to NaN.

Examples:

>>> data = np.array([[1, 2, 3], [1.1, 2.2, 3.1], [0.9, 1.9, 3.2]])  # 3 trials, 3 values
>>> mean, error = calculate_trial_statistics(data, error_type='ci')
>>> print(mean, error)
Source code in bmtool/bmplot/entrainment.py 
 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
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
def calculate_trial_statistics(
    data: np.ndarray,
    error_type: str = "ci",
) -> Tuple[np.ndarray, np.ndarray]:
    """
    Calculate mean and error statistics across trials.

    Computes trial-averaged statistics with proper handling of NaN values. Supports
    three error types: 95% confidence intervals (via t-distribution), standard error
    of the mean (SEM), and standard deviation (SD).

    Parameters
    ----------
    data : np.ndarray
        2D array of shape (n_trials, n_values) containing trial-wise data.
        Can contain NaN values which are ignored in calculations.
    error_type : str, optional
        Type of error to compute: "ci" for 95% confidence interval, "sem" for
        standard error of the mean, or "std" for standard deviation (default: "ci").

    Returns
    -------
    Tuple[np.ndarray, np.ndarray]
        - mean_values : 1D array of mean values across trials (one per value).
        - error_values : 1D array of error bounds/bars corresponding to each mean.
          For "ci", represents the half-width of the 95% confidence interval.
          For "sem" and "std", represents the error magnitude.

    Raises
    ------
    ValueError
        If error_type is not 'ci', 'sem', or 'std'.
        If data is not 2D or is empty.

    Notes
    -----
    - NaN values are ignored using numpy's nanmean and nanstd functions.
    - For "ci", uses the t-distribution with degrees of freedom = min(valid_counts) - 1.
    - Confidence intervals are computed at 95% (α = 0.05, two-tailed).
    - If fewer than 2 valid trials exist for a value, error is set to NaN.

    Examples
    --------
    >>> data = np.array([[1, 2, 3], [1.1, 2.2, 3.1], [0.9, 1.9, 3.2]])  # 3 trials, 3 values
    >>> mean, error = calculate_trial_statistics(data, error_type='ci')
    >>> print(mean, error)
    """
    if error_type not in ["ci", "sem", "std"]:
        raise ValueError(
            "error_type must be 'ci' for confidence interval, 'sem' for standard error, "
            "or 'std' for standard deviation."
        )

    if data.ndim != 2 or data.size == 0:
        raise ValueError("data must be a non-empty 2D array of shape (n_trials, n_values).")

    # Calculate mean across trials, ignoring NaNs
    with np.errstate(invalid="ignore", divide="ignore"):
        mean_values = np.nanmean(data, axis=0)

        # Count valid trials per value (trials without NaN)
        valid_counts = np.sum(~np.isnan(data), axis=0)

        # Calculate standard deviation across trials, ignoring NaNs
        std_values = np.nanstd(data, axis=0, ddof=1)

        if error_type == "ci":
            # Calculate 95% confidence interval using t-distribution
            # SEM = std / sqrt(n_valid)
            sem_values = std_values / np.sqrt(np.maximum(valid_counts, 1))

            # Find the minimum valid count to use a conservative t-value
            # (all values use the same t-value for consistency)
            min_valid = np.min(valid_counts[valid_counts > 1])

            if min_valid > 1:
                # Two-tailed t-distribution at 95% confidence level
                t_value = stats.t.ppf(0.975, min_valid - 1)
                error_values = t_value * sem_values
            else:
                # Insufficient data for meaningful CI
                error_values = np.full_like(mean_values, np.nan)

        elif error_type == "sem":
            # Standard error of the mean
            sem_values = std_values / np.sqrt(np.maximum(valid_counts, 1))
            error_values = sem_values

        else:  # error_type == "std"
            # Standard deviation (no division by sqrt(n))
            error_values = std_values

    return mean_values, error_values

bmtool.bmplot.entrainment.plot_spike_power_correlation(spike_df, lfp_data, fs, pop_names, filter_method='wavelet', bandwidth=2.0, lowcut=None, highcut=None, freq_range=(10, 100), freq_step=5, type_name='raw', figsize=(12, 8))

Calculate and plot spike rate-LFP power correlation across frequencies for full signal.

Analyzes the relationship between population spike rates and LFP power across a range of frequencies, using Spearman correlation for the entire signal duration.

Parameters:

Name Type Description Default
spike_df DataFrame

DataFrame containing spike data with columns 'timestamps', 'node_ids', and 'pop_name'.

 required
lfp_data DataArray

LFP data with time dimension.

 required
fs float

Sampling frequency in Hz.

 required
pop_names List[str]

List of population names to analyze.

 required
filter_method str

Filtering method: 'wavelet' or 'butter' (default: 'wavelet').

 'wavelet'
bandwidth float

Bandwidth parameter for wavelet filter (default: 2.0).

 2.0
lowcut float

Lower frequency bound (Hz) for butterworth filter. Required if filter_method='butter'.

 None
highcut float

Upper frequency bound (Hz) for butterworth filter. Required if filter_method='butter'.

 None
freq_range Tuple[float, float]

Min and max frequency to analyze in Hz (default: (10, 100)).

 (10, 100)
freq_step float

Step size for frequency analysis in Hz (default: 5).

 5
type_name str

Which type of spike rate to use (default: 'raw').

 'raw'
figsize Tuple[float, float]

Figure size (width, height) in inches (default: (12, 8)).

 (12, 8)

Returns:

Type Description
Figure

Figure containing the correlation plot.
Notes
  • Uses Spearman correlation (rank-based, robust to outliers).
  • Pre-computes LFP power at all frequencies for efficiency.

Examples:

>>> fig = plot_spike_power_correlation(
...     spike_df=spike_df,
...     lfp_data=lfp,
...     fs=400,
...     pop_names=['PV', 'SST'],
...     freq_range=(10, 100),
...     freq_step=5
... )
Source code in bmtool/bmplot/entrainment.py 
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
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
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
259
260
261
262
263
264
265
266
def plot_spike_power_correlation(
    spike_df: pd.DataFrame,
    lfp_data: xr.DataArray,
    fs: float,
    pop_names: List[str],
    filter_method: str = "wavelet",
    bandwidth: float = 2.0,
    lowcut: Optional[float] = None,
    highcut: Optional[float] = None,
    freq_range: Tuple[float, float] = (10, 100),
    freq_step: float = 5,
    type_name: str = "raw",
    figsize: Tuple[float, float] = (12, 8),
) -> Figure:
    """
    Calculate and plot spike rate-LFP power correlation across frequencies for full signal.

    Analyzes the relationship between population spike rates and LFP power across a range
    of frequencies, using Spearman correlation for the entire signal duration.

    Parameters
    ----------
    spike_df : pd.DataFrame
        DataFrame containing spike data with columns 'timestamps', 'node_ids', and 'pop_name'.
    lfp_data : xr.DataArray
        LFP data with time dimension.
    fs : float
        Sampling frequency in Hz.
    pop_names : List[str]
        List of population names to analyze.
    filter_method : str, optional
        Filtering method: 'wavelet' or 'butter' (default: 'wavelet').
    bandwidth : float, optional
        Bandwidth parameter for wavelet filter (default: 2.0).
    lowcut : float, optional
        Lower frequency bound (Hz) for butterworth filter. Required if filter_method='butter'.
    highcut : float, optional
        Upper frequency bound (Hz) for butterworth filter. Required if filter_method='butter'.
    freq_range : Tuple[float, float], optional
        Min and max frequency to analyze in Hz (default: (10, 100)).
    freq_step : float, optional
        Step size for frequency analysis in Hz (default: 5).
    type_name : str, optional
        Which type of spike rate to use (default: 'raw').
    figsize : Tuple[float, float], optional
        Figure size (width, height) in inches (default: (12, 8)).

    Returns
    -------
    matplotlib.figure.Figure
        Figure containing the correlation plot.

    Notes
    -----
    - Uses Spearman correlation (rank-based, robust to outliers).
    - Pre-computes LFP power at all frequencies for efficiency.

    Examples
    --------
    >>> fig = plot_spike_power_correlation(
    ...     spike_df=spike_df,
    ...     lfp_data=lfp,
    ...     fs=400,
    ...     pop_names=['PV', 'SST'],
    ...     freq_range=(10, 100),
    ...     freq_step=5
    ... )
    """
    # Compute spike rate for all spikes
    spike_rate = bmspikes.get_population_spike_rate(spike_df, fs=fs)

    # Setup frequencies for analysis
    frequencies = np.arange(freq_range[0], freq_range[1] + 1, freq_step)

    # Pre-calculate LFP power for all frequencies
    power_by_freq = {}
    for freq in frequencies:
        power_by_freq[freq] = get_lfp_power(
            lfp_data, freq, fs, filter_method, lowcut=lowcut, highcut=highcut, bandwidth=bandwidth
        )

    # Calculate correlations for each population and frequency
    results = {}
    for pop in pop_names:
        results[pop] = {}
        pop_spike_rate = spike_rate.sel(population=pop, type=type_name)

        for freq in frequencies:
            lfp_power = power_by_freq[freq]

            if len(pop_spike_rate) != len(lfp_power):
                print(f"Warning: Length mismatch for {pop} at {freq} Hz")
                print(f"{len(pop_spike_rate)} {len(lfp_power)}")
                continue

            corr, p_val = stats.spearmanr(pop_spike_rate.values, lfp_power.values)
            results[pop][freq] = {"correlation": corr, "p_value": p_val}

    # Create plot
    sns.set_style("whitegrid")
    fig = plt.figure(figsize=figsize)

    colors = plt.get_cmap("tab10")
    for i, pop in enumerate(pop_names):
        plot_freqs = []
        plot_corrs = []

        for freq in frequencies:
            if freq in results[pop] and not np.isnan(results[pop][freq]["correlation"]):
                plot_freqs.append(freq)
                plot_corrs.append(results[pop][freq]["correlation"])

        if len(plot_freqs) == 0:
            continue

        plot_freqs = np.array(plot_freqs)
        plot_corrs = np.array(plot_corrs)
        color = colors(i)

        plt.plot(
            plot_freqs, plot_corrs, marker="o", label=pop, linewidth=2, markersize=6, color=color
        )

    # Formatting
    plt.xlabel("Frequency (Hz)", fontsize=12)
    plt.ylabel("Spike Rate-Power Correlation", fontsize=12)

    plt.title(
        "Spike Rate-LFP Power Correlation",
        fontsize=14,
    )
    plt.grid(True, alpha=0.3)
    plt.axhline(y=0, color="gray", linestyle="-", alpha=0.5)

    # Setup legend
    from matplotlib.lines import Line2D

    legend_elements = [
        Line2D([0], [0], color=colors(i), marker="o", linestyle="-", label=pop)
        for i, pop in enumerate(pop_names)
    ]
    plt.legend(handles=legend_elements, fontsize=10, loc="best")

    # Axis formatting
    if len(frequencies) > 10:
        plt.xticks(frequencies[::2])
    else:
        plt.xticks(frequencies)
    plt.xlim(frequencies[0], frequencies[-1])

    y_min, y_max = plt.ylim()
    plt.ylim(min(y_min, -0.1), max(y_max, 0.1))

    plt.tight_layout()
    return fig

bmtool.bmplot.entrainment.plot_trial_avg_spike_power_correlation(spike_df, lfp_data, time_windows, fs, pop_names, filter_method='wavelet', bandwidth=2.0, lowcut=None, highcut=None, freq_range=(10, 100), freq_step=5, type_name='raw', error_type='ci', figsize=(12, 8))

Calculate and plot trial-averaged spike rate-LFP power correlation across frequencies.

Computes spike rate-LFP power correlation for each trial separately, then averages results across trials with optional error bands.

Parameters:

Name Type Description Default
spike_df DataFrame

DataFrame containing spike data with columns 'timestamps', 'node_ids', and 'pop_name'.

 required
lfp_data DataArray

LFP data with time dimension.

 required
time_windows List[Tuple[float, float]]

List of (start, end) time tuples in milliseconds for each trial.

 required
fs float

Sampling frequency in Hz.

 required
pop_names List[str]

List of population names to analyze.

 required
filter_method str

Filtering method: 'wavelet' or 'butter' (default: 'wavelet').

 'wavelet'
bandwidth float

Bandwidth parameter for wavelet filter (default: 2.0).

 2.0
lowcut float

Lower frequency bound (Hz) for butterworth filter.

 None
highcut float

Upper frequency bound (Hz) for butterworth filter.

 None
freq_range Tuple[float, float]

Min and max frequency to analyze in Hz (default: (10, 100)).

 (10, 100)
freq_step float

Step size for frequency analysis in Hz (default: 5).

 5
type_name str

Which type of spike rate to use (default: 'raw').

 'raw'
error_type str

Type of error bars: "ci" for 95% CI, "sem" for SEM, or "std" for SD (default: "ci").

 'ci'
figsize Tuple[float, float]

Figure size (width, height) in inches (default: (12, 8)).

 (12, 8)

Returns:

Type Description
Figure

Figure containing the trial-averaged correlation plot.

Raises:

Type Description
ValueError

If error_type is invalid.
Notes
  • Uses calculate_trial_statistics helper for consistent error computation.
  • NaN values are handled gracefully with warnings for problematic trials.

Examples:

>>> time_windows = [(1000, 2000), (2500, 3500), (4000, 5000)]
>>> fig = plot_trial_avg_spike_power_correlation(
...     spike_df=spike_df,
...     lfp_data=lfp,
...     time_windows=time_windows,
...     fs=400,
...     pop_names=['PV', 'SST'],
...     error_type='ci'
... )
Source code in bmtool/bmplot/entrainment.py 
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
309
310
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
433
434
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
def plot_trial_avg_spike_power_correlation(
    spike_df: pd.DataFrame,
    lfp_data: xr.DataArray,
    time_windows: List[Tuple[float, float]],
    fs: float,
    pop_names: List[str],
    filter_method: str = "wavelet",
    bandwidth: float = 2.0,
    lowcut: Optional[float] = None,
    highcut: Optional[float] = None,
    freq_range: Tuple[float, float] = (10, 100),
    freq_step: float = 5,
    type_name: str = "raw",
    error_type: str = "ci",
    figsize: Tuple[float, float] = (12, 8),
) -> Figure:
    """
    Calculate and plot trial-averaged spike rate-LFP power correlation across frequencies.

    Computes spike rate-LFP power correlation for each trial separately, then averages
    results across trials with optional error bands.

    Parameters
    ----------
    spike_df : pd.DataFrame
        DataFrame containing spike data with columns 'timestamps', 'node_ids', and 'pop_name'.
    lfp_data : xr.DataArray
        LFP data with time dimension.
    time_windows : List[Tuple[float, float]]
        List of (start, end) time tuples in milliseconds for each trial.
    fs : float
        Sampling frequency in Hz.
    pop_names : List[str]
        List of population names to analyze.
    filter_method : str, optional
        Filtering method: 'wavelet' or 'butter' (default: 'wavelet').
    bandwidth : float, optional
        Bandwidth parameter for wavelet filter (default: 2.0).
    lowcut : float, optional
        Lower frequency bound (Hz) for butterworth filter.
    highcut : float, optional
        Upper frequency bound (Hz) for butterworth filter.
    freq_range : Tuple[float, float], optional
        Min and max frequency to analyze in Hz (default: (10, 100)).
    freq_step : float, optional
        Step size for frequency analysis in Hz (default: 5).
    type_name : str, optional
        Which type of spike rate to use (default: 'raw').
    error_type : str, optional
        Type of error bars: "ci" for 95% CI, "sem" for SEM, or "std" for SD (default: "ci").
    figsize : Tuple[float, float], optional
        Figure size (width, height) in inches (default: (12, 8)).

    Returns
    -------
    matplotlib.figure.Figure
        Figure containing the trial-averaged correlation plot.

    Raises
    ------
    ValueError
        If error_type is invalid.

    Notes
    -----
    - Uses calculate_trial_statistics helper for consistent error computation.
    - NaN values are handled gracefully with warnings for problematic trials.

    Examples
    --------
    >>> time_windows = [(1000, 2000), (2500, 3500), (4000, 5000)]
    >>> fig = plot_trial_avg_spike_power_correlation(
    ...     spike_df=spike_df,
    ...     lfp_data=lfp,
    ...     time_windows=time_windows,
    ...     fs=400,
    ...     pop_names=['PV', 'SST'],
    ...     error_type='ci'
    ... )
    """
    if error_type not in ["ci", "sem", "std"]:
        raise ValueError(
            "error_type must be 'ci' for confidence interval, 'sem' for standard error, "
            "or 'std' for standard deviation"
        )

    # Validate that fs matches LFP data sampling rate if available
    if hasattr(lfp_data, 'fs') and lfp_data.fs != fs:
        raise ValueError(
            f"Provided fs ({fs} Hz) does not match LFP data sampling rate ({lfp_data.fs} Hz). "
        )

    # Setup frequencies for analysis
    frequencies = np.arange(freq_range[0], freq_range[1] + 1, freq_step)

    # Pre-calculate LFP power for all frequencies (same for all trials)
    power_by_freq = {}
    for freq in frequencies:
        power_by_freq[freq] = get_lfp_power(
            lfp_data, freq, fs, filter_method, lowcut=lowcut, highcut=highcut, bandwidth=bandwidth
        )

    # Storage: dict of pop_name -> list of trial_correlations per frequency
    all_correlations = {pop: {freq: [] for freq in frequencies} for pop in pop_names}

    # Process each trial
    for trial_idx, (start_time, end_time) in enumerate(time_windows):
        # Extract spikes for this trial
        trial_spikes = spike_df[
            (spike_df["timestamps"] >= start_time) & (spike_df["timestamps"] <= end_time)
        ].copy()

        if len(trial_spikes) == 0:
            print(f"Warning: No spikes found in trial {trial_idx} ({start_time}-{end_time} ms)")
            continue

        # Compute spike rate for this trial
        trial_spike_rate = bmspikes.get_population_spike_rate(
            trial_spikes, fs=fs, t_start=start_time, t_stop=end_time
        )

        # Calculate correlations for each population and frequency
        for pop in pop_names:
            if pop not in trial_spike_rate.population.values:
                print(f"Warning: Population {pop} not found in trial {trial_idx}")
                continue

            pop_spike_rate = trial_spike_rate.sel(population=pop, type=type_name)

            for freq in frequencies:
                try:
                    lfp_power = power_by_freq[freq]
                    trial_lfp_power = lfp_power.sel(time=slice(start_time, end_time))

                    if len(trial_lfp_power) < 2 or len(pop_spike_rate) < 2:
                        continue

                    # Align time coordinates
                    common_times = np.intersect1d(pop_spike_rate.time.values,
                                                 trial_lfp_power.time.values)
                    if len(common_times) < 2:
                        continue

                    trial_sr = pop_spike_rate.sel(time=common_times).values
                    trial_lfp = trial_lfp_power.sel(time=common_times).values

                    # Compute correlation
                    corr, _ = stats.spearmanr(trial_sr, trial_lfp)
                    if not np.isnan(corr):
                        all_correlations[pop][freq].append(corr)

                except Exception as e:
                    print(
                        f"Warning: Error computing correlation for {pop} at {freq} Hz "
                        f"in trial {trial_idx}: {e}"
                    )
                    continue

    # Calculate trial statistics for each population/frequency
    results = {pop: {} for pop in pop_names}
    for pop in pop_names:
        for freq in frequencies:
            if len(all_correlations[pop][freq]) > 0:
                freq_data = np.array(all_correlations[pop][freq])
                mean_corr, error_corr = calculate_trial_statistics(
                    freq_data.reshape(-1, 1), error_type=error_type
                )
                results[pop][freq] = {
                    "mean": mean_corr[0],
                    "error": error_corr[0],
                    "n_trials": len(freq_data),
                }

    # Create plot
    sns.set_style("whitegrid")
    fig = plt.figure(figsize=figsize)

    colors = plt.get_cmap("tab10")
    for i, pop in enumerate(pop_names):
        plot_freqs = []
        plot_means = []
        plot_errors = []

        for freq in frequencies:
            if freq in results[pop] and "mean" in results[pop][freq]:
                plot_freqs.append(freq)
                plot_means.append(results[pop][freq]["mean"])
                plot_errors.append(results[pop][freq]["error"])

        if len(plot_freqs) == 0:
            continue

        plot_freqs = np.array(plot_freqs)
        plot_means = np.array(plot_means)
        plot_errors = np.array(plot_errors)
        color = colors(i)

        # Plot line
        plt.plot(
            plot_freqs, plot_means, marker="o", label=pop, linewidth=2, markersize=6, color=color
        )

        # Plot error band
        plt.fill_between(
            plot_freqs,
            plot_means - plot_errors,
            plot_means + plot_errors,
            alpha=0.2,
            color=color,
        )

    # Formatting
    plt.xlabel("Frequency (Hz)", fontsize=12)
    plt.ylabel("Spike Rate-Power Correlation", fontsize=12)

    error_labels = {"ci": "95% CI", "sem": "±SEM", "std": "±1 SD"}
    error_label = error_labels[error_type]
    plt.title(
        f"Trial-Averaged Spike Rate-LFP Power Correlation ({error_label})",
        fontsize=14,
    )
    plt.grid(True, alpha=0.3)
    plt.axhline(y=0, color="gray", linestyle="-", alpha=0.5)

    # Setup legend
    from matplotlib.lines import Line2D

    legend_elements = [
        Line2D([0], [0], color=colors(i), marker="o", linestyle="-", label=pop)
        for i, pop in enumerate(pop_names)
    ]
    legend_elements.append(Line2D([0], [0], color="gray", alpha=0.3, linewidth=10, label=error_label))
    plt.legend(handles=legend_elements, fontsize=10, loc="best")

    # Axis formatting
    if len(frequencies) > 10:
        plt.xticks(frequencies[::2])
    else:
        plt.xticks(frequencies)
    plt.xlim(frequencies[0], frequencies[-1])

    y_min, y_max = plt.ylim()
    plt.ylim(min(y_min, -0.1), max(y_max, 0.1))

    plt.tight_layout()
    return fig

bmtool.bmplot.entrainment.plot_cycle_with_spike_histograms(phase_data, pop_names, bins=36)

Plot an idealized cycle with spike histograms for different neuron populations.

Parameters:

Name Type Description Default
phase_data dict

Dictionary containing phase values for each spike and neuron population

 required
pop_names List[str]

List of population names to be plotted

 required
bins int

Number of bins for the phase histogram (default 36 gives 10-degree bins)

 36

Returns:

Type Description
Figure

The figure containing the cycle and histograms
Source code in bmtool/bmplot/entrainment.py 
517
518
519
520
521
522
523
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
def plot_cycle_with_spike_histograms(phase_data, pop_names: List[str], bins: int = 36):
    """
    Plot an idealized cycle with spike histograms for different neuron populations.

    Parameters
    -----------
    phase_data : dict
        Dictionary containing phase values for each spike and neuron population
    pop_names : List[str]
        List of population names to be plotted
    bins : int, optional
        Number of bins for the phase histogram (default 36 gives 10-degree bins)

    Returns
    -------
    matplotlib.figure.Figure
        The figure containing the cycle and histograms
    """
    sns.set_style("whitegrid")
    # Create a figure with subplots
    fig = plt.figure(figsize=(12, 8))
    gs = GridSpec(len(pop_names) + 1, 1, height_ratios=[1.5] + [1] * len(pop_names))

    # Top subplot: Idealized gamma cycle
    ax_gamma = fig.add_subplot(gs[0])

    # Create an idealized gamma cycle
    x = np.linspace(-np.pi, np.pi, 1000)
    y = np.sin(x)

    ax_gamma.plot(x, y, "b-", linewidth=2)
    ax_gamma.set_title("Cycle with Neuron Population Spike Distributions", fontsize=14)
    ax_gamma.set_ylabel("Amplitude", fontsize=12)
    ax_gamma.set_xlim(-np.pi, np.pi)
    ax_gamma.set_xticks(np.linspace(-np.pi, np.pi, 9))
    ax_gamma.set_xticklabels(["-180°", "-135°", "-90°", "-45°", "0°", "45°", "90°", "135°", "180°"])
    ax_gamma.grid(True)
    ax_gamma.axhline(y=0, color="k", linestyle="-", alpha=0.3)
    ax_gamma.axvline(x=0, color="k", linestyle="--", alpha=0.3)

    # Generate a color map for the different populations
    colors = plt.cm.tab10(np.linspace(0, 1, len(pop_names)))

    # Add histograms for each neuron population
    for i, pop_name in enumerate(pop_names):
        ax_hist = fig.add_subplot(gs[i + 1], sharex=ax_gamma)

        # Compute histogram
        hist, bin_edges = np.histogram(phase_data[pop_name], bins=bins, range=(-np.pi, np.pi))
        bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2

        # Normalize histogram
        if np.sum(hist) > 0:
            hist = hist / np.sum(hist) * 100  # Convert to percentage

        # Plot histogram
        ax_hist.bar(bin_centers, hist, width=2 * np.pi / bins, alpha=0.7, color=colors[i])
        ax_hist.set_ylabel(f"{pop_name}\nSpikes (%)", fontsize=10)

        # Add grid to align with gamma cycle
        ax_hist.grid(True, alpha=0.3)
        ax_hist.set_ylim(0, max(hist) * 1.2)  # Add some headroom

    # Set x-label for the last subplot
    ax_hist.set_xlabel("Phase (degrees)", fontsize=12)

    plt.tight_layout()
    return fig

bmtool.bmplot.entrainment.plot_entrainment_by_population(ppc_dict, pop_names, freqs, figsize=(15, 8), title=None)

Plot PPC for all node populations on one graph with mean and standard error.

Parameters:

ppc_dict : Dict[str, Dict[str, Dict[float, float]]] Dictionary containing PPC data organized by population, node, and frequency pop_names : List[str] List of population names to plot data for freqs : List[float] List of frequencies to plot figsize : Tuple[float, float], optional Figure size for the plot title : str, optional Title for the plot

Returns:

Type Description
Figure

The figure containing the bar plot
Source code in bmtool/bmplot/entrainment.py 
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
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
def plot_entrainment_by_population(ppc_dict: Dict[str, Dict[str, Dict[float, float]]], pop_names: List[str], freqs: List[float], figsize: Tuple[float, float] = (15, 8), title: Optional[str] = None):
    """
    Plot PPC for all node populations on one graph with mean and standard error.

    Parameters:
    -----------
    ppc_dict : Dict[str, Dict[str, Dict[float, float]]]
        Dictionary containing PPC data organized by population, node, and frequency
    pop_names : List[str]
        List of population names to plot data for
    freqs : List[float]
        List of frequencies to plot
    figsize : Tuple[float, float], optional
        Figure size for the plot
    title : str, optional
        Title for the plot

    Returns
    -------
    matplotlib.figure.Figure
        The figure containing the bar plot
    """
    # Set up the visualization style
    sns.set_style("whitegrid")
    fig = plt.figure(figsize=figsize)

    # Calculate the width of each group of bars
    n_groups = len(freqs)
    n_populations = len(pop_names)
    group_width = 0.8
    bar_width = group_width / n_populations

    # Color palette for different populations
    pop_colors = sns.color_palette(n_colors=n_populations)

    # For tracking x-axis positions and labels
    x_centers = np.arange(n_groups)
    tick_labels = [str(freq) for freq in freqs]

    # Process and plot data for each population
    for i, pop in enumerate(pop_names):
        # Store mean and SE for each frequency in this population
        means = []
        errors = []
        valid_freqs_idx = []

        # Collect and process data for all frequencies in this population
        for freq_idx, freq in enumerate(freqs):
            freq_values = []

            # Collect values across all nodes for this frequency
            for node in ppc_dict[pop]:
                try:
                    ppc_value = ppc_dict[pop][node][freq]
                    freq_values.append(ppc_value)
                except KeyError:
                    continue

            # If we have data for this frequency
            if freq_values:
                mean_val = np.mean(freq_values)
                se_val = stats.sem(freq_values)
                means.append(mean_val)
                errors.append(se_val)
                valid_freqs_idx.append(freq_idx)

        # Calculate x positions for this population's bars
        # Each population's bars are offset within their frequency group
        x_positions = x_centers[valid_freqs_idx] + (i - n_populations / 2 + 0.5) * bar_width

        # Plot bars with error bars
        plt.bar(
            x_positions, means, width=bar_width * 0.9, color=pop_colors[i], alpha=0.7, label=pop
        )
        plt.errorbar(x_positions, means, yerr=errors, fmt="none", ecolor="black", capsize=4)

    # Set up the plot labels and legend
    plt.xlabel("Frequency")
    plt.ylabel("PPC Value")
    if title:
        plt.title(title)
    plt.xticks(x_centers, tick_labels)
    plt.legend(title="Population")

    # Adjust layout and save
    plt.tight_layout()
    return fig

bmtool.bmplot.entrainment.plot_entrainment_swarm_plot(ppc_dict, pop_names, freq, save_path=None, title=None)

Plot a swarm plot of the entrainment for different populations at a single frequency.

Parameters:

ppc_dict : Dict[str, Dict[str, Dict[float, float]]] Dictionary containing PPC values organized by population, node, and frequency pop_names : List[str] List of population names to include in the plot freq : Union[float, int] The specific frequency to plot save_path : str, optional Path to save the figure. If None, figure is just displayed. title : str, optional Title for the plot

Returns:

matplotlib.figure.Figure The figure object for further customization if needed

Source code in bmtool/bmplot/entrainment.py 
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
def plot_entrainment_swarm_plot(ppc_dict: Dict[str, Dict[str, Dict[float, float]]], pop_names: List[str], freq: Union[float, int], save_path: Optional[str] = None, title: Optional[str] = None):
    """
    Plot a swarm plot of the entrainment for different populations at a single frequency.

    Parameters:
    -----------
    ppc_dict : Dict[str, Dict[str, Dict[float, float]]]
        Dictionary containing PPC values organized by population, node, and frequency
    pop_names : List[str]
        List of population names to include in the plot
    freq : Union[float, int]
        The specific frequency to plot
    save_path : str, optional
        Path to save the figure. If None, figure is just displayed.
    title : str, optional
        Title for the plot

    Returns:
    --------
    matplotlib.figure.Figure
        The figure object for further customization if needed
    """
    # Set the style
    sns.set_style("whitegrid")

    # Prepare data for the swarm plot
    data_list = []

    for pop in pop_names:
        values = []
        node_ids = []

        for node in ppc_dict[pop]:
            if freq in ppc_dict[pop][node] and ppc_dict[pop][node][freq] is not None:
                data_list.append(
                    {"Population": pop, "Node": node, "PPC Difference": ppc_dict[pop][node][freq]}
                )

    # Create DataFrame in long format
    df = pd.DataFrame(data_list)

    if df.empty:
        print(f"No data available for frequency {freq}.")
        return None

    # Print mean PPC change for each population)
    for pop in pop_names:
        subset = df[df["Population"] == pop]
        if not subset.empty:
            mean_val = subset["PPC Difference"].mean()
            std_val = subset["PPC Difference"].std()
            n = len(subset)
            sem_val = std_val / np.sqrt(n)  # Standard error of the mean
            print(f"{pop}: {mean_val:.4f} ± {sem_val:.4f} (n={n})")

    # Create figure
    fig = plt.figure(figsize=(max(8, len(pop_names) * 1.5), 8))

    # Create swarm plot
    ax = sns.swarmplot(
        x="Population",
        y="PPC Difference",
        data=df,
        size=3,
        # palette='Set2'
    )

    # Add sample size annotations
    for i, pop in enumerate(pop_names):
        subset = df[df["Population"] == pop]
        if not subset.empty:
            n = len(subset)
            y_min = subset["PPC Difference"].min()
            y_max = subset["PPC Difference"].max()

            # Position annotation below the lowest point
            plt.annotate(
                f"n={n}", (i, y_min - 0.05 * (y_max - y_min) - 0.05), ha="center", fontsize=10
            )

    # Add reference line at y=0
    plt.axhline(y=0, color="black", linestyle="-", linewidth=0.5, alpha=0.7)

    # Add horizontal lines for mean values
    for i, pop in enumerate(pop_names):
        subset = df[df["Population"] == pop]
        if not subset.empty:
            mean_val = subset["PPC Difference"].mean()
            plt.plot([i - 0.25, i + 0.25], [mean_val, mean_val], "r-", linewidth=2)

    # Calculate and display statistics
    if len(pop_names) > 1:
        # Print statistical test results
        print(f"\nMann-Whitney U Test Results at {freq} Hz:")
        print("-" * 60)

        # Add p-values for pairwise comparisons
        y_max = df["PPC Difference"].max()
        y_min = df["PPC Difference"].min()
        y_range = y_max - y_min

        # Perform t-tests between populations if there are at least 2
        for i in range(len(pop_names)):
            for j in range(i + 1, len(pop_names)):
                pop1 = pop_names[i]
                pop2 = pop_names[j]

                vals1 = df[df["Population"] == pop1]["PPC Difference"].values
                vals2 = df[df["Population"] == pop2]["PPC Difference"].values

                if len(vals1) > 1 and len(vals2) > 1:
                    # Perform Mann-Whitney U test (non-parametric)
                    u_stat, p_val = stats.mannwhitneyu(vals1, vals2, alternative="two-sided")

                    # Add significance markers
                    sig_str = "ns"
                    if p_val < 0.05:
                        sig_str = "*"
                    if p_val < 0.01:
                        sig_str = "**"
                    if p_val < 0.001:
                        sig_str = "***"

                    # Position the significance bar
                    bar_height = y_max + 0.1 * y_range * (1 + (j - i - 1) * 0.5)

                    # Draw the bar
                    plt.plot([i, j], [bar_height, bar_height], "k-")
                    plt.plot([i, i], [bar_height - 0.02 * y_range, bar_height], "k-")
                    plt.plot([j, j], [bar_height - 0.02 * y_range, bar_height], "k-")

                    # Add significance marker
                    plt.text(
                        (i + j) / 2,
                        bar_height + 0.01 * y_range,
                        sig_str,
                        ha="center",
                        va="bottom",
                        fontsize=12,
                    )

                    # Print the statistical comparison
                    print(f"{pop1} vs {pop2}: U={u_stat:.1f}, p={p_val:.4f} {sig_str}")

    # Add labels and title
    plt.xlabel("Population", fontsize=14)
    plt.ylabel("PPC", fontsize=14)
    if title:
        plt.title(title, fontsize=16)

    # Adjust y-axis limits to make room for annotations
    y_min, y_max = plt.ylim()
    plt.ylim(y_min - 0.15 * (y_max - y_min), y_max + 0.25 * (y_max - y_min))

    # Add gridlines
    plt.grid(True, linestyle="--", alpha=0.7, axis="y")

    # Adjust layout
    plt.tight_layout()

    # Save figure if path is provided
    if save_path:
        plt.savefig(f"{save_path}/ppc_change_swarm_plot_{freq}Hz.png", dpi=300, bbox_inches="tight")

    return fig

bmtool.bmplot.entrainment.plot_trial_avg_entrainment(spike_df, lfp, time_windows, entrainment_method, pop_names, freqs, firing_quantile, spike_fs=1000, error_type='ci')

Plot trial-averaged entrainment for specified population names. Only supports wavelet filter current, could easily add other support

Parameters:

spike_df : pd.DataFrame Spike data containing timestamps, node_ids, and pop_name columns lfp : xr.DataArray Xarray for a channel of the lfp data time_windows : List[Tuple[float, float]] List of windows to analysis with start and stp time [(start_time, end_time), ...] for each trial entrainment_method : str Method for entrainment calculation ('ppc', 'ppc2' or 'plv') pop_names : List[str] List of population names to process (e.g., ['FSI', 'LTS']) freqs : Union[List[float], np.ndarray] Array of frequencies to analyze (Hz) firing_quantile : float Upper quantile threshold for selecting high-firing cells (e.g., 0.8 for top 20%) spike_fs : float, optional fs for spike data. Default is 1000 error_type : str, optional Type of error bars to plot: "ci" for 95% confidence interval, "sem" for standard error, "std" for standard deviation

Raises:

ValueError If entrainment_method is not 'ppc', 'ppc2' or 'plv' If error_type is not 'ci', 'sem', or 'std' If no spikes found for a population in a trial

Returns:

matplotlib.figure.Figure The figure containing the plot

Source code in bmtool/bmplot/entrainment.py 
 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
 916
 917
 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 plot_trial_avg_entrainment(
    spike_df: pd.DataFrame,
    lfp: xr.DataArray,
    time_windows: List[Tuple[float, float]],
    entrainment_method: str,
    pop_names: List[str],
    freqs: Union[List[float], np.ndarray],
    firing_quantile: float,
    spike_fs: float = 1000,
    error_type: str = "ci",
) -> Figure:
    """
    Plot trial-averaged entrainment for specified population names. Only supports wavelet filter current, could easily add other support

    Parameters:
    -----------
    spike_df : pd.DataFrame
        Spike data containing timestamps, node_ids, and pop_name columns
    lfp : xr.DataArray
        Xarray for a channel of the lfp data
    time_windows : List[Tuple[float, float]]
        List of windows to analysis with start and stp time [(start_time, end_time), ...] for each trial
    entrainment_method : str
        Method for entrainment calculation ('ppc', 'ppc2' or 'plv')
    pop_names : List[str]
        List of population names to process (e.g., ['FSI', 'LTS'])
    freqs : Union[List[float], np.ndarray]
        Array of frequencies to analyze (Hz)
    firing_quantile : float
        Upper quantile threshold for selecting high-firing cells (e.g., 0.8 for top 20%)
    spike_fs : float, optional
        fs for spike data. Default is 1000
    error_type : str, optional
        Type of error bars to plot: "ci" for 95% confidence interval, "sem" for standard error, "std" for standard deviation

    Raises:
    -------
    ValueError
        If entrainment_method is not 'ppc', 'ppc2' or 'plv'
        If error_type is not 'ci', 'sem', or 'std'
        If no spikes found for a population in a trial

    Returns:
    --------
    matplotlib.figure.Figure
        The figure containing the plot
    """
    sns.set_style("whitegrid")
    # Validate inputs
    if entrainment_method not in ["ppc", "plv", "ppc2"]:
        raise ValueError("entrainment_method must be 'ppc', ppc2 or 'plv'")

    if error_type not in ["ci", "sem", "std"]:
        raise ValueError(
            "error_type must be 'ci' for confidence interval, 'sem' for standard error, or 'std' for standard deviation"
        )

    if not (0 <= firing_quantile < 1):
        raise ValueError("firing_quantile must be between 0 and 1")

    # Convert freqs to numpy array for easier indexing
    freqs = np.array(freqs)

    # Collect all PPC/PLV values across trials for each population
    all_plv_data = {}  # Dictionary to store results for each population

    # Initialize storage for each population
    for pop_name in pop_names:
        all_plv_data[pop_name] = []  # Will be shape (n_trials, n_freqs)

    # Loop through all pulse groups to collect data
    for trial_idx in range(len(time_windows)):
        plv_lists = {}  # Store PLV lists for this trial

        # Initialize PLV lists for each population
        for pop_name in pop_names:
            plv_lists[pop_name] = []

        # Filter spikes for this trial
        network_spikes = spike_df[
            (spike_df["timestamps"] >= time_windows[trial_idx][0])
            & (spike_df["timestamps"] <= time_windows[trial_idx][1])
        ].copy()

        # Process each population
        pop_spike_data = {}
        for pop_name in pop_names:
            # Get spikes for this population
            pop_spikes = network_spikes[network_spikes["pop_name"] == pop_name]

            if len(pop_spikes) == 0:
                print(f"Warning: No spikes found for population {pop_name} in trial {trial_idx}")
                # Add NaN values for this trial/population
                plv_lists[pop_name] = [np.nan] * len(freqs)
                continue

            # Filter to get the top firing cells
            # firing_quantile of 0.8 gets the top 20% of firing cells to use
            pop_spikes = bmspikes.find_highest_firing_cells(
                pop_spikes, upper_quantile=firing_quantile
            )

            if len(pop_spikes) == 0:
                print(
                    f"Warning: No high-firing spikes found for population {pop_name} in trial {trial_idx}"
                )
                plv_lists[pop_name] = [np.nan] * len(freqs)
                continue

            pop_spike_data[pop_name] = pop_spikes

        # Calculate PPC/PLV for each frequency and each population
        for freq_idx, freq in enumerate(freqs):
            for pop_name in pop_names:
                if pop_name not in pop_spike_data:
                    continue  # Skip if no data for this population

                pop_spikes = pop_spike_data[pop_name]

                try:
                    if entrainment_method == "ppc":
                        result = bmentr.calculate_ppc(
                            pop_spikes["timestamps"].values,
                            lfp,
                            spike_fs=spike_fs,
                            lfp_fs=lfp.fs,
                            freq_of_interest=freq,
                            filter_method="wavelet",
                            ppc_method="gpu",
                        )
                    elif entrainment_method == "plv":
                        result = bmentr.calculate_spike_lfp_plv(
                            pop_spikes["timestamps"].values,
                            lfp,
                            spike_fs=spike_fs,
                            lfp_fs=lfp.fs,
                            freq_of_interest=freq,
                            filter_method="wavelet",
                        )
                    elif entrainment_method == "ppc2":
                        result = bmentr.calculate_ppc2(
                            pop_spikes["timestamps"].values,
                            lfp,
                            spike_fs=spike_fs,
                            lfp_fs=lfp.fs,
                            freq_of_interest=freq,
                            filter_method="wavelet",
                        )

                    plv_lists[pop_name].append(result)

                except Exception as e:
                    print(
                        f"Warning: Error calculating {entrainment_method} for {pop_name} at {freq}Hz in trial {trial_idx}: {e}"
                    )
                    plv_lists[pop_name].append(np.nan)

        # Store this trial's results for each population
        for pop_name in pop_names:
            if pop_name in plv_lists and len(plv_lists[pop_name]) == len(freqs):
                all_plv_data[pop_name].append(plv_lists[pop_name])
            else:
                # Fill with NaNs if data is missing
                all_plv_data[pop_name].append([np.nan] * len(freqs))

    # Convert to numpy arrays and calculate statistics
    mean_plv = {}
    error_plv = {}

    for pop_name in pop_names:
        all_plv_data[pop_name] = np.array(all_plv_data[pop_name])  # Shape: (n_trials, n_freqs)

        # Use helper function to calculate trial statistics
        mean_plv[pop_name], error_plv[pop_name] = calculate_trial_statistics(
            all_plv_data[pop_name], error_type=error_type
        )

    # Create the combined plot
    fig = plt.figure(figsize=(12, 8))

    # Define markers and colors for different populations
    markers = ["o-", "s-", "^-", "D-", "v-", "<-", ">-", "p-"]
    colors = sns.color_palette(n_colors=len(pop_names))

    # Plot each population
    for i, pop_name in enumerate(pop_names):
        marker = markers[i % len(markers)]  # Cycle through markers if more populations than markers
        color = colors[i]

        # Only plot if we have valid data
        valid_mask = ~np.isnan(mean_plv[pop_name])
        if np.any(valid_mask):
            plt.plot(
                freqs[valid_mask],
                mean_plv[pop_name][valid_mask],
                marker,
                linewidth=2,
                label=pop_name,
                color=color,
                markersize=6,
            )

            # Add error bars/shading if available
            if not np.all(np.isnan(error_plv[pop_name])):
                plt.fill_between(
                    freqs[valid_mask],
                    (mean_plv[pop_name] - error_plv[pop_name])[valid_mask],
                    (mean_plv[pop_name] + error_plv[pop_name])[valid_mask],
                    alpha=0.3,
                    color=color,
                )

    plt.xlabel("Frequency (Hz)", fontsize=12)
    plt.ylabel(f"{entrainment_method.upper()}", fontsize=12)

    # Calculate percentage for title and update title based on error type
    firing_percentage = round(float((1 - firing_quantile) * 100), 1)
    error_labels = {"ci": "95% CI", "sem": "±SEM", "std": "±1 SD"}
    error_label = error_labels[error_type]
    plt.title(
        f"{entrainment_method.upper()} Across Trials for Top {firing_percentage}% Firing Cells ({error_label})",
        fontsize=14,
    )

    plt.legend(fontsize=10)
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    return fig

bmtool.bmplot.entrainment.plot_fr_hist_phase_amplitude(fr_hist, pop_names, freq_labels, nbins_pha=16, nbins_amp=16, common_clim=True, figsize=(3, 2), cmap='viridis', title=None)

Plot firing rate histograms binned by LFP phase and amplitude. Check out the bmtool/bmtool/analysis/entrainment.py function compute_fr_hist_phase_amplitude

Parameters:

Name Type Description Default
fr_hist ndarray

Firing rate histogram of shape (n_pop, n_freq, nbins_pha, nbins_amp)

 required
pop_names List[str]

List of population names

 required
freq_labels List[str]

List of frequency labels for subplot titles (e.g., ['Beta', 'Gamma'])

 required
nbins_pha int

Number of phase bins

 16
nbins_amp int

Number of amplitude bins

 16
common_clim bool

Whether to use common color limits across all subplots

 True
figsize Tuple[float, float]

Size of each subplot

 (3, 2)
cmap str

Colormap to use

 'RdBu_r'
title Optional[str]

Overall title for the figure

 None

Returns:

Type Description
Tuple[Figure, ndarray]

Figure and axes objects

Examples:

>>> fig, axs = plot_fr_hist_phase_amplitude(
...     fr_hist, ['PV', 'SST'], ['Beta', 'Gamma'], 
...     common_clim=True, cmap='RdBu_r', title='LFP Phase-Amplitude Coupling'
... )
Source code in bmtool/bmplot/entrainment.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
def plot_fr_hist_phase_amplitude(
    fr_hist: np.ndarray, 
    pop_names: List[str], 
    freq_labels: List[str], 
    nbins_pha: int = 16, 
    nbins_amp: int = 16,
    common_clim: bool = True, 
    figsize: Tuple[float, float] = (3, 2),
    cmap: str = 'viridis',
    title: Optional[str] = None
) -> Tuple[plt.Figure, np.ndarray]:
    """
    Plot firing rate histograms binned by LFP phase and amplitude. 
    Check out the bmtool/bmtool/analysis/entrainment.py function
    compute_fr_hist_phase_amplitude

    Parameters
    ----------
    fr_hist : np.ndarray
        Firing rate histogram of shape (n_pop, n_freq, nbins_pha, nbins_amp)
    pop_names : List[str]
        List of population names
    freq_labels : List[str]
        List of frequency labels for subplot titles (e.g., ['Beta', 'Gamma'])
    nbins_pha : int, default=16
        Number of phase bins
    nbins_amp : int, default=16
        Number of amplitude bins
    common_clim : bool, default=True
        Whether to use common color limits across all subplots
    figsize : Tuple[float, float], default=(3, 2)
        Size of each subplot
    cmap : str, default='RdBu_r'
        Colormap to use
    title : Optional[str], default=None
        Overall title for the figure

    Returns
    -------
    Tuple[plt.Figure, np.ndarray]
        Figure and axes objects

    Examples
    --------
    >>> fig, axs = plot_fr_hist_phase_amplitude(
    ...     fr_hist, ['PV', 'SST'], ['Beta', 'Gamma'], 
    ...     common_clim=True, cmap='RdBu_r', title='LFP Phase-Amplitude Coupling'
    ... )
    """
    pha_bins = np.linspace(-np.pi, np.pi, nbins_pha + 1)
    quantiles = np.linspace(0, 1, nbins_amp + 1)

    n_pop = len(pop_names)
    n_freq = len(freq_labels)

    fig, axs = plt.subplots(n_pop, n_freq, 
                           figsize=(figsize[0] * n_freq, figsize[1] * n_pop),
                           squeeze=False)


    # Add overall title if provided
    if title:
        fig.suptitle(title, fontsize=14, y=0.98)

    for i, p in enumerate(pop_names):
        if common_clim:
            vmin, vmax = fr_hist.min(), fr_hist.max()
        else:
            # Calculate min/max for this population across all frequencies
            vmin, vmax = fr_hist[i].min(), fr_hist[i].max()

        for j, freq_label in enumerate(freq_labels):
            ax = axs[i, j]
            pcm = ax.pcolormesh(pha_bins, quantiles, fr_hist[i, j].T, 
                               vmin=vmin, vmax=vmax, cmap=cmap)
            ax.set_title(p)

            if i < n_pop - 1:
                ax.get_xaxis().set_visible(False)
            else:
                ax.set_xlabel(freq_label.title() + ' Phase')
                ax.set_xticks((-np.pi, 0, np.pi))
                ax.set_xticklabels([r'$-\pi$', '0', r'$\pi$'])

            if j > 0:
                ax.get_yaxis().set_visible(False)
            else:
                ax.set_ylabel('Amplitude (quantile)')

        # Add single colorbar for the entire population (all frequencies)
        plt.colorbar(mappable=pcm, ax=axs[i], 
                    label='Firing rate (% Change)', pad=0.02)

    return fig, axs

bmtool.bmplot.entrainment.plot_trial_avg_spike_rate_plv(spike_rate, time_windows, pop_names, freqs, pop_pairs=None, error_type='ci', figsize=(12, 8), filter_method='wavelet', bandwidth=1.0)

Plot trial-averaged Phase Locking Value (PLV) between spike rate pairs across frequencies.

This function computes PLV between spike rates of different population pairs for each trial, then averages the results across trials with optional error bars/bands.

Parameters:

Name Type Description Default
spike_rate DataArray

Pre-computed spike rate data with dimensions (time, population, type). Must have an 'fs' attribute (sampling frequency in Hz). Time dimension should be in milliseconds.

 required
time_windows List[Tuple[float, float]]

List of (start, end) time tuples in milliseconds for each trial window.

 required
pop_names List[str]

List of population names to consider (must match 'population' coords in spike_rate).

 required
freqs Union[List[float], ndarray]

Array of frequencies (Hz) to analyze.

 required
pop_pairs Optional[List[Tuple[str, str]]]

List of (pop1, pop2) tuples specifying population pairs to analyze. If None, all unique pairs from pop_names are generated (default: None).

 None
error_type str

Type of error bars to plot: "ci" for 95% confidence interval, "sem" for standard error, or "std" for standard deviation (default: "ci").

 'ci'
figsize Tuple[float, float]

Figure size (width, height) in inches (default: (12, 8)).

 (12, 8)
filter_method str

Filtering method for PLV calculation: 'wavelet' or 'butter' (default: 'wavelet').

 'wavelet'
bandwidth float

Bandwidth parameter for wavelet filter (default: 1.0).

 1.0

Returns:

Type Description
Figure

The figure containing the trial-averaged PLV plot.

Raises:

Type Description
ValueError

If error_type is not 'ci', 'sem', or 'std'. If pop_pairs contains invalid population names. If spike_rate does not have required dimensions/attributes.
Notes
  • Uses 'raw' type from spike_rate dimension (hardcoded for spike rate analysis).
  • Time units in time_windows must match spike_rate.time units (milliseconds).
  • Generates all unique population pairs if pop_pairs is None.

Examples:

>>> fig = plot_trial_avg_spike_rate_plv(
...     spike_rate=spike_rate,
...     time_windows=[(1000, 2000), (2500, 3500), (4000, 5000)],
...     pop_names=['ET', 'IT', 'PV', 'SST'],
...     freqs=[10, 20, 30, 40, 50],
...     error_type='ci'
... )
Source code in bmtool/bmplot/entrainment.py 
1169
1170
1171
1172
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
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
def plot_trial_avg_spike_rate_plv(
    spike_rate: xr.DataArray,
    time_windows: List[Tuple[float, float]],
    pop_names: List[str],
    freqs: Union[List[float], np.ndarray],
    pop_pairs: Optional[List[Tuple[str, str]]] = None,
    error_type: str = "ci",
    figsize: Tuple[float, float] = (12, 8),
    filter_method: str = "wavelet",
    bandwidth: float = 1.0,
) -> Figure:
    """
    Plot trial-averaged Phase Locking Value (PLV) between spike rate pairs across frequencies.

    This function computes PLV between spike rates of different population pairs for each trial,
    then averages the results across trials with optional error bars/bands.

    Parameters
    ----------
    spike_rate : xr.DataArray
        Pre-computed spike rate data with dimensions (time, population, type).
        Must have an 'fs' attribute (sampling frequency in Hz).
        Time dimension should be in milliseconds.
    time_windows : List[Tuple[float, float]]
        List of (start, end) time tuples in milliseconds for each trial window.
    pop_names : List[str]
        List of population names to consider (must match 'population' coords in spike_rate).
    freqs : Union[List[float], np.ndarray]
        Array of frequencies (Hz) to analyze.
    pop_pairs : Optional[List[Tuple[str, str]]], optional
        List of (pop1, pop2) tuples specifying population pairs to analyze.
        If None, all unique pairs from pop_names are generated (default: None).
    error_type : str, optional
        Type of error bars to plot: "ci" for 95% confidence interval, "sem" for standard error,
        or "std" for standard deviation (default: "ci").
    figsize : Tuple[float, float], optional
        Figure size (width, height) in inches (default: (12, 8)).
    filter_method : str, optional
        Filtering method for PLV calculation: 'wavelet' or 'butter' (default: 'wavelet').
    bandwidth : float, optional
        Bandwidth parameter for wavelet filter (default: 1.0).

    Returns
    -------
    matplotlib.figure.Figure
        The figure containing the trial-averaged PLV plot.

    Raises
    ------
    ValueError
        If error_type is not 'ci', 'sem', or 'std'.
        If pop_pairs contains invalid population names.
        If spike_rate does not have required dimensions/attributes.

    Notes
    -----
    - Uses 'raw' type from spike_rate dimension (hardcoded for spike rate analysis).
    - Time units in time_windows must match spike_rate.time units (milliseconds).
    - Generates all unique population pairs if pop_pairs is None.

    Examples
    --------
    >>> fig = plot_trial_avg_spike_rate_plv(
    ...     spike_rate=spike_rate,
    ...     time_windows=[(1000, 2000), (2500, 3500), (4000, 5000)],
    ...     pop_names=['ET', 'IT', 'PV', 'SST'],
    ...     freqs=[10, 20, 30, 40, 50],
    ...     error_type='ci'
    ... )
    """
    sns.set_style("whitegrid")

    # Validate inputs
    if error_type not in ["ci", "sem", "std"]:
        raise ValueError(
            "error_type must be 'ci' for confidence interval, 'sem' for standard error, or 'std' for standard deviation"
        )

    if "fs" not in spike_rate.attrs:
        raise ValueError("spike_rate must have 'fs' attribute (sampling frequency).")

    if "time" not in spike_rate.dims or "population" not in spike_rate.dims:
        raise ValueError("spike_rate must have 'time' and 'population' dimensions.")

    # Convert freqs to numpy array
    freqs = np.array(freqs)

    # Generate population pairs if not provided
    if pop_pairs is None:
        n_pops = len(pop_names)
        pop_pairs = [
            (pop_names[i], pop_names[j]) for i in range(n_pops) for j in range(i + 1, n_pops)
        ]

    # Validate population pairs
    available_pops = set(spike_rate.population.values)
    for pop1, pop2 in pop_pairs:
        if pop1 not in available_pops or pop2 not in available_pops:
            raise ValueError(
                f"Population pair ({pop1}, {pop2}) contains names not in spike_rate. "
                f"Available: {available_pops}"
            )

    # Get sampling frequency
    fs = spike_rate.fs

    # Storage for PLV data: dict of pair -> list of trials -> list of freqs
    all_plv_data = {f"{p1}-{p2}": [] for p1, p2 in pop_pairs}

    # Loop through each trial window
    for trial_idx, (start_time, end_time) in enumerate(time_windows):
        try:
            # Slice spike rate data for this trial window
            trial_data = spike_rate.sel(time=slice(start_time, end_time), type="raw")

            if trial_data.time.size == 0:
                print(f"Warning: No data in trial {trial_idx} for window ({start_time}, {end_time})")
                for pair_key in all_plv_data.keys():
                    all_plv_data[pair_key].append([np.nan] * len(freqs))
                continue

            # Calculate PLV for each population pair at each frequency
            trial_plv_values = {}
            for pop1, pop2 in pop_pairs:
                pair_key = f"{pop1}-{pop2}"
                trial_plv_values[pair_key] = []

                try:
                    # Extract spike rate signals for this pair
                    sr1 = trial_data.sel(population=pop1).values
                    sr2 = trial_data.sel(population=pop2).values

                    if len(sr1) < 2 or len(sr2) < 2:
                        print(
                            f"Warning: Insufficient data for {pair_key} in trial {trial_idx}"
                        )
                        trial_plv_values[pair_key] = [np.nan] * len(freqs)
                        continue

                    # Calculate PLV for each frequency
                    for freq in freqs:
                        try:
                            plv = bmentr.calculate_signal_signal_plv(
                                sr1,
                                sr2,
                                fs=fs,
                                freq_of_interest=freq,
                                filter_method=filter_method,
                                bandwidth=bandwidth,
                            )
                            trial_plv_values[pair_key].append(plv)
                        except Exception as e:
                            print(
                                f"Warning: Error calculating PLV for {pair_key} at {freq} Hz in trial {trial_idx}: {e}"
                            )
                            trial_plv_values[pair_key].append(np.nan)

                except Exception as e:
                    print(f"Warning: Error processing {pair_key} in trial {trial_idx}: {e}")
                    trial_plv_values[pair_key] = [np.nan] * len(freqs)

            # Store trial results
            for pair_key in all_plv_data.keys():
                if pair_key in trial_plv_values and len(trial_plv_values[pair_key]) == len(freqs):
                    all_plv_data[pair_key].append(trial_plv_values[pair_key])
                else:
                    all_plv_data[pair_key].append([np.nan] * len(freqs))

        except Exception as e:
            print(f"Warning: Error processing trial {trial_idx}: {e}")
            for pair_key in all_plv_data.keys():
                all_plv_data[pair_key].append([np.nan] * len(freqs))

    # Convert to numpy arrays and calculate statistics
    mean_plv = {}
    error_plv = {}

    for pair_key in all_plv_data.keys():
        all_plv_data[pair_key] = np.array(all_plv_data[pair_key])  # Shape: (n_trials, n_freqs)

        # Use helper function to calculate trial statistics
        mean_plv[pair_key], error_plv[pair_key] = calculate_trial_statistics(
            all_plv_data[pair_key], error_type=error_type
        )

    # Create plot
    fig = plt.figure(figsize=figsize)

    # Determine subplot layout if many pairs
    n_pairs = len(pop_pairs)
    if n_pairs <= 3:
        # Single row for up to 3 pairs
        n_rows, n_cols = 1, n_pairs
    else:
        # Multiple rows for more pairs
        n_cols = min(n_pairs, 3)
        n_rows = (n_pairs + n_cols - 1) // n_cols

    # Define markers and colors for different pairs
    markers = ["o-", "s-", "^-", "D-", "v-", "<-", ">-", "p-"]
    colors = sns.color_palette(n_colors=n_pairs)

    # Plot each population pair
    if n_rows == 1 and n_cols == 1:
        axes = [plt.subplot(n_rows, n_cols, 1)]
    elif n_rows == 1:
        axes = [plt.subplot(n_rows, n_cols, i + 1) for i in range(n_cols)]
    else:
        axes = [plt.subplot(n_rows, n_cols, i + 1) for i in range(n_pairs)]

    for pair_idx, (pop1, pop2) in enumerate(pop_pairs):
        pair_key = f"{pop1}-{pop2}"
        marker = markers[pair_idx % len(markers)]
        color = colors[pair_idx]
        ax = axes[pair_idx] if isinstance(axes, list) else axes

        # Only plot if we have valid data
        valid_mask = ~np.isnan(mean_plv[pair_key])
        if np.any(valid_mask):
            ax.plot(
                freqs[valid_mask],
                mean_plv[pair_key][valid_mask],
                marker,
                linewidth=2,
                label=pair_key,
                color=color,
                markersize=6,
            )

            # Add error bars/shading if available
            if not np.all(np.isnan(error_plv[pair_key])):
                ax.fill_between(
                    freqs[valid_mask],
                    (mean_plv[pair_key] - error_plv[pair_key])[valid_mask],
                    (mean_plv[pair_key] + error_plv[pair_key])[valid_mask],
                    alpha=0.3,
                    color=color,
                )

        ax.set_xlabel("Frequency (Hz)", fontsize=11)
        ax.set_ylabel("PLV", fontsize=11)
        ax.set_title(f"{pair_key}", fontsize=12)
        ax.grid(True, alpha=0.3)

    # Add overall title
    error_labels = {"ci": "95% CI", "sem": "±SEM", "std": "±1 SD"}
    error_label = error_labels[error_type]
    fig.suptitle(f"Trial-Averaged Spike Rate PLV ({error_label})", fontsize=14, y=0.995)

    plt.tight_layout()
    return fig