From a5998daefdb7155a280667870f391ae3659a3602 Mon Sep 17 00:00:00 2001 From: Seung Hyun Kim Date: Thu, 14 Nov 2024 17:07:16 -0600 Subject: [PATCH] remove temporary module --- miv/visualization/activity.py | 767 ---------------------------------- miv/visualization/event.py | 3 + pyproject.toml | 3 +- 3 files changed, 5 insertions(+), 768 deletions(-) diff --git a/miv/visualization/activity.py b/miv/visualization/activity.py index 620c3629..e223fb27 100644 --- a/miv/visualization/activity.py +++ b/miv/visualization/activity.py @@ -132,770 +132,3 @@ def __call__(self, spikestamps: Spikestamps, mea: MEAGeometryProtocol): plt.close(plt.gcf()) return - - -# DEBUG: Temporary activity visualization for 512 electrode debugging -@dataclass -class NeuralActivity512(OperatorMixin): # pragma: no cover - bin_size: float = 0.001 # sec - firing_rate_interval: float = 1.0 # sec - skip_interval: int = 50 # frames to skip. TODO: refactor - - tag: str = "neural activity render" - - progress_bar: bool = False - - fps: int = 25 - minimum_split_size: int = 60 # sec. Each video is at least 1 min - - def __post_init__(self): # pragma: no cover - super().__init__() - self.runner = StrictMPIRunner() - - def __call__( - self, spikestamps: Spikestamps, mea: MEAGeometryProtocol - ): # pragma: no cover - comm = self.runner.comm - rank = self.runner.get_rank() - size = self.runner.get_size() - - # Binning in first rank - if self.runner.is_root(): - binned = spikestamps.binning(self.bin_size, return_count=True) - probe_times = binned.timestamps[:: self.skip_interval] - else: - probe_times = None - probe_times = comm.bcast(probe_times, root=self.runner.get_root()) - - # Split Tasks - num_frames = probe_times.shape[0] - if size * self.fps * self.minimum_split_size > num_frames: - size = np.ceil(num_frames / (self.fps * self.minimum_split_size)).astype( - np.int_ - ) - if rank == self.runner.get_root(): - logging.warning( - f"Too many ranks. Splitting into {size} tasks for total {num_frames} frames." - ) - if rank >= size: - return - - # Get spikestamps view - probe_times = np.array_split(probe_times, size)[rank] - start_time = probe_times[0] - end_time = probe_times[-1] - spiketrains_bins = spikestamps.get_view(start_time, end_time).binning( - self.bin_size, return_count=True - ) - logging.info( - f"{rank=} | rendering from {start_time=:.03f} to {end_time=:.03f}: {probe_times.shape[0]} frames." - ) - - # Find firing rate - xs = [] - for i in range(spiketrains_bins.number_of_channels): - x = spike_counts_with_kernel( - spikestamps[i], - probe_times, - lambda x: np.logical_and(x > 0, x < self.firing_rate_interval).astype( - np.float_ - ), - ) - xs.append(x) - xs = np.asarray(xs) - - xmax = np.max(xs) - xmin = np.min(xs) - - # DEBUG - electrode_map_512 = ( - np.array( - [ - [ - 48, - 211, - 214, - 217, - 220, - 223, - 226, - 229, - 232, - 235, - 238, - 16, - 245, - 248, - 251, - 254, - 193, - 196, - 197, - 200, - 203, - 206, - 113, - 179, - 182, - 185, - 188, - 191, - 130, - 133, - 136, - 139, - 142, - 81, - 147, - 150, - 153, - 156, - 159, - 162, - 165, - 168, - 171, - 174, - 467, - 470, - 473, - 476, - 479, - 482, - 485, - 488, - 491, - 494, - 304, - 499, - 502, - 505, - 508, - 511, - 450, - 453, - 456, - 459, - 462, - 272, - 435, - 438, - 441, - 444, - 445, - 448, - 387, - 390, - 393, - 396, - 369, - 403, - 406, - 409, - 412, - 415, - 418, - 421, - 424, - 427, - 430, - 337, - ], - [ - 209, - 45, - 42, - 39, - 36, - 33, - 30, - 27, - 24, - 21, - 18, - 241, - 13, - 10, - 7, - 4, - 1, - 62, - 59, - 56, - 53, - 50, - 177, - 116, - 119, - 122, - 125, - 128, - 67, - 70, - 73, - 76, - 79, - 145, - 84, - 87, - 90, - 93, - 96, - 99, - 102, - 105, - 108, - 111, - 274, - 277, - 280, - 283, - 286, - 289, - 292, - 295, - 298, - 301, - 496, - 306, - 309, - 312, - 315, - 318, - 257, - 260, - 263, - 266, - 269, - 464, - 335, - 332, - 329, - 326, - 323, - 384, - 381, - 378, - 375, - 372, - 400, - 367, - 364, - 361, - 358, - 355, - 352, - 349, - 346, - 343, - 340, - 432, - ], - [ - 47, - 212, - 215, - 218, - 221, - 224, - 227, - 230, - 233, - 236, - 239, - 15, - 244, - 247, - 250, - 253, - 256, - 195, - 198, - 201, - 204, - 207, - 114, - 180, - 183, - 186, - 189, - 192, - 131, - 134, - 137, - 140, - 143, - 82, - 148, - 151, - 154, - 157, - 160, - 163, - 166, - 169, - 172, - 175, - 466, - 469, - 472, - 475, - 478, - 481, - 484, - 487, - 490, - 493, - 303, - 498, - 501, - 504, - 507, - 510, - 449, - 452, - 455, - 458, - 461, - 271, - 434, - 437, - 440, - 443, - 446, - 385, - 388, - 391, - 394, - 397, - 370, - 402, - 405, - 408, - 411, - 414, - 417, - 420, - 423, - 426, - 429, - 338, - ], - [ - 210, - 44, - 41, - 38, - 35, - 32, - 29, - 26, - 23, - 20, - 17, - 242, - 12, - 9, - 6, - 3, - 64, - 61, - 58, - 55, - 52, - 49, - 178, - 117, - 120, - 123, - 126, - 65, - 68, - 71, - 74, - 77, - 80, - 146, - 85, - 88, - 91, - 94, - 97, - 100, - 103, - 106, - 109, - 112, - 273, - 276, - 279, - 282, - 285, - 288, - 291, - 294, - 297, - 300, - 495, - 305, - 308, - 311, - 314, - 317, - 320, - 259, - 262, - 265, - 268, - 463, - 336, - 333, - 330, - 327, - 324, - 321, - 382, - 379, - 376, - 373, - 399, - 368, - 365, - 362, - 359, - 356, - 353, - 350, - 347, - 344, - 341, - 431, - ], - [ - 46, - 213, - 216, - 219, - 222, - 225, - 228, - 231, - 234, - 237, - 240, - 14, - 243, - 246, - 249, - 252, - 255, - 194, - 199, - 202, - 205, - 208, - 115, - 181, - 184, - 187, - 190, - 129, - 132, - 135, - 138, - 141, - 144, - 83, - 149, - 152, - 155, - 158, - 161, - 164, - 167, - 170, - 173, - 176, - 465, - 468, - 471, - 474, - 477, - 480, - 483, - 486, - 489, - 492, - 302, - 497, - 500, - 503, - 506, - 509, - 512, - 451, - 454, - 457, - 460, - 270, - 433, - 436, - 439, - 442, - 447, - 386, - 389, - 392, - 395, - 398, - 371, - 401, - 404, - 407, - 410, - 413, - 416, - 419, - 422, - 425, - 428, - 339, - ], - [ - 0, - 43, - 40, - 37, - 34, - 31, - 28, - 25, - 22, - 19, - 0, - 0, - 11, - 8, - 5, - 2, - 63, - 60, - 57, - 54, - 51, - 0, - 0, - 118, - 121, - 124, - 127, - 66, - 69, - 72, - 75, - 78, - 0, - 0, - 86, - 89, - 92, - 95, - 98, - 101, - 104, - 107, - 110, - 0, - 0, - 275, - 278, - 281, - 284, - 287, - 290, - 293, - 296, - 299, - 0, - 0, - 307, - 310, - 313, - 316, - 319, - 258, - 261, - 264, - 267, - 0, - 0, - 334, - 331, - 328, - 325, - 322, - 383, - 380, - 377, - 374, - 0, - 0, - 366, - 363, - 360, - 357, - 354, - 351, - 348, - 345, - 342, - 0, - ], - ], - dtype=np.int_, - ) - - 1 - ) - for a, b in zip(range(288, 319 + 1), range(287, 256 + 1)): - ma = electrode_map_512 == a - mb = electrode_map_512 == b - electrode_map_512[ma] = b - electrode_map_512[mb] = a - for a, b in zip(range(96, 127 + 1), range(95, 64 + 1)): - ma = electrode_map_512 == a - mb = electrode_map_512 == b - electrode_map_512[ma] = b - electrode_map_512[mb] = a - nr, nt = electrode_map_512.shape - it = np.linspace(np.pi / 2, np.pi / 2 - 2 * np.pi, nt + 1)[::-1] # pcb flipped - it = (it[1:] + it[:-1]) / 2.0 - ir = np.linspace(4950, 3300, nr) - tv, rv = np.meshgrid(it, ir) - xv = rv * np.cos(tv) + 1650 - yv = rv * np.sin(tv) - 1650 - - def map_electrode_data(vector, missing_value=0.0): - grid = electrode_map_512 - value_grid = np.full_like(grid, missing_value, dtype=np.float_) - for idx, value in enumerate(vector): - if idx not in grid: - continue - value_grid[grid == idx] = value - return xv, yv, value_grid - - # DEBUG - if self.runner.is_root(): - plt.figure() - plt.plot(xv.ravel(), yv.ravel(), "k.", ms=1) - plt.axvline(0, color="red", linestyle=":") - plt.axhline(0, color="red", linestyle=":") - plt.savefig(os.path.join(self.analysis_path, "grid_electrodes.png")) - plt.close("all") - - # Test - index = np.arange(xs.shape[0]) - - X, Y, Z = mea.map_data(index) - fig, ax = plt.subplots(1, 1, figsize=(16, 16)) - ax.scatter(X.ravel(), Y.ravel()) - for i, txt in enumerate(Z.ravel()): - ax.annotate(str(int(txt)), (X.ravel()[i], Y.ravel()[i])) - X, Y, Z = map_electrode_data(index) - ax.scatter(X.ravel(), Y.ravel()) - for i, txt in enumerate(Z.ravel()): - ax.annotate(str(int(txt)), (X.ravel()[i], Y.ravel()[i])) - plt.axvline(0, color="red", linestyle=":") - plt.axhline(0, color="red", linestyle=":") - ax.set_aspect("equal") - plt.savefig(os.path.join(self.analysis_path, "grid.png")) - plt.close("all") - - X, Y, Z = mea.map_data(index) - eX, eY, eZ = map_electrode_data(index) - fig = plt.figure(figsize=(8, 6)) - ax = fig.add_subplot(111) - ax.plot(mea.coordinates[:, 0], mea.coordinates[:, 1], "k.", ms=1) - pcm = ax.pcolormesh( - X, - Y, - Z, - cmap="twilight", - vmin=index.min(), - vmax=index.max(), - shading="gouraud", - ) - ax.scatter( - eX.ravel(), - eY.ravel(), - c=eZ.ravel(), - cmap="twilight", - vmin=index.min(), - vmax=index.max(), - ) - cbar = fig.colorbar(pcm, ax=ax) - cbar.ax.set_ylabel( - f"activity per {self.firing_rate_interval:.03f} sec", rotation=270 - ) - - ax.set_aspect("equal") - ax.set_xlabel("channels x-axis") - ax.set_ylabel("channels y-axis") - ax.set_title("Spatial Neural Activity (--)") - plt.savefig(os.path.join(self.analysis_path, "grid_test.png")) - plt.close("all") - # DEBUG - - # Output Images - FFMpegWriter = manimation.writers["ffmpeg"] - metadata = dict( - title="Movie Test", artist="Matplotlib", comment="Movie support!" - ) - writer = FFMpegWriter(fps=self.fps, metadata=metadata) - - os.makedirs(self.analysis_path, exist_ok=True) - video_name = os.path.join( - self.analysis_path, f"output_from_{start_time:.03f}_to_{end_time:.03f}.mp4" - ) - - fig = plt.figure(figsize=(8, 6)) - with writer.saving(fig, video_name, dpi=200): - for timestep, time in tqdm( - enumerate(probe_times), - desc=f"Rendering {rank}/{size}", - position=rank, - total=probe_times.shape[0], - disable=not self.progress_bar, - ): - data = xs[:, timestep] - X, Y, Z = mea.map_data(data) - eX, eY, eZ = map_electrode_data(data) - - fig.clf() - ax = fig.add_subplot(111) - # X, Y, Z = interp_2d(Z) - ax.plot(mea.coordinates[:, 0], mea.coordinates[:, 1], "k.", ms=1) - pcm = ax.pcolormesh( - X, Y, Z, cmap="Oranges", vmin=xmin, vmax=xmax, shading="gouraud" - ) - ax.scatter( - eX.ravel(), - eY.ravel(), - c=eZ.ravel(), - cmap="Oranges", - vmin=xmin, - vmax=xmax, - ) - cbar = fig.colorbar(pcm, ax=ax) - cbar.ax.set_ylabel( - f"activity per {self.firing_rate_interval:.03f} sec", rotation=270 - ) - - ax.set_aspect("equal") - ax.set_xlabel("channels x-axis") - ax.set_ylabel("channels y-axis") - ax.set_title(f"Spatial Neural Activity ({time:.03f} sec)") - - writer.grab_frame() - plt.close(plt.gcf()) - - return diff --git a/miv/visualization/event.py b/miv/visualization/event.py index ea351e62..e40595a8 100644 --- a/miv/visualization/event.py +++ b/miv/visualization/event.py @@ -25,6 +25,9 @@ def plot_spiketrain_raster( """ fig, ax = plt.subplots(figsize=(16, 6)) spikes = spikestamps.get_view(t_start, t_stop) + print( + f"Plotting raster plot from {t_start} to {t_stop}: {sum(spikes.get_count())} spikes" + ) ax.eventplot(spikes) ax.set_xlabel("Time (s)") ax.set_ylabel("Channel") diff --git a/pyproject.toml b/pyproject.toml index 5a0955fa..69006383 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "MiV-OS" -version = "0.3.1.pre1" +version = "0.3.1.pre2" description = "Python software for analysis and computing framework used in MiV project." readme = "README.md" authors = ["GazzolaLab "] @@ -246,4 +246,5 @@ omit = [ "miv/io/intan/rhs.py",# not part of the dev target "miv/io/asdf/asdf.py",# not part of the dev target "miv/visualization/colors.py",# not part of the dev target + "miv/visualization/format.py", ]