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"searchText": "class\nali\ntorch_volpy.extraction.ali.ali\n(fs: int = 2000, hp_window_ms: float = 10.0, nsvd: int = 25, factor: int = 4, coarse_sigma: float = 1.8, coarse_gaussian_radius: int = 2, coarse_threshold_std: float = 5.0, min_component_size: int = 4, fine_npix: int = 15, fine_radius: float = 4.0, cluster_threshold: float = 2.0, peak_kernel_size: int = 3, assign_radius: float = 1.5, alimap_sigma: float = 0.7, alimap_gaussian_radius: int = 2, footprint_radius: float = 10.0, solve_eps: float = 1e-06, device: optional[str] = none, cc_max_iter: int = 2048, verbose: bool = false) -> none\nactivity localization imaging (ali) pipeline in pure pytorch.\nactivity localization imaging (ali) pipeline in pure pytorch.\n\ndevice policy:\n - if `device` is passed at init, the whole pipeline runs there.\n - otherwise, tensors run on the input tensor's device.\n - non-tensor inputs default to cpu.\n - outputs stay on the working device.\n\ninput:\n data: array-like or torch.tensor of shape [h, w, t]\n\noutput dict keys:\n data, df, f0, spk, df_ap, df_ap_denoised, ucomps, vcomps, spk_fine,\n brightness, roi, cnt, cen, alimap, peaks, clust_cen,\n clust_idx, sort_idx, footprint, fp, support, traces_ls, traces",
"signature": "(fs: int = 2000, hp_window_ms: float = 10.0, nsvd: int = 25, factor: int = 4, coarse_sigma: float = 1.8, coarse_gaussian_radius: int = 2, coarse_threshold_std: float = 5.0, min_component_size: int = 4, fine_npix: int = 15, fine_radius: float = 4.0, cluster_threshold: float = 2.0, peak_kernel_size: int = 3, assign_radius: float = 1.5, alimap_sigma: float = 0.7, alimap_gaussian_radius: int = 2, footprint_radius: float = 10.0, solve_eps: float = 1e-06, device: Optional[str] = None, cc_max_iter: int = 2048, verbose: bool = False) -> None",
"source": {
"endLine": 833,
"line": 37,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Activity Localization Imaging (ALI) pipeline in pure PyTorch."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "__init__",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": false,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.__init__",
"searchText": "method\n__init__\ntorch_volpy.extraction.ali.ali.__init__\n(self, fs: int = 2000, hp_window_ms: float = 10.0, nsvd: int = 25, factor: int = 4, coarse_sigma: float = 1.8, coarse_gaussian_radius: int = 2, coarse_threshold_std: float = 5.0, min_component_size: int = 4, fine_npix: int = 15, fine_radius: float = 4.0, cluster_threshold: float = 2.0, peak_kernel_size: int = 3, assign_radius: float = 1.5, alimap_sigma: float = 0.7, alimap_gaussian_radius: int = 2, footprint_radius: float = 10.0, solve_eps: float = 1e-06, device: optional[str] = none, cc_max_iter: int = 2048, verbose: bool = false) -> none",
"signature": "(self, fs: int = 2000, hp_window_ms: float = 10.0, nsvd: int = 25, factor: int = 4, coarse_sigma: float = 1.8, coarse_gaussian_radius: int = 2, coarse_threshold_std: float = 5.0, min_component_size: int = 4, fine_npix: int = 15, fine_radius: float = 4.0, cluster_threshold: float = 2.0, peak_kernel_size: int = 3, assign_radius: float = 1.5, alimap_sigma: float = 0.7, alimap_gaussian_radius: int = 2, footprint_radius: float = 10.0, solve_eps: float = 1e-06, device: Optional[str] = None, cc_max_iter: int = 2048, verbose: bool = False) -> None",
"source": {
"endLine": 107,
"line": 56,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Run the ALI pipeline on an input movie crop.",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "__call__",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.__call__",
"searchText": "method\n__call__\ntorch_volpy.extraction.ali.ali.__call__\n(self, data: any) -> aliresult\nrun the ali pipeline on an input movie crop.\nrun the ali pipeline on an input movie crop.",
"signature": "(self, data: Any) -> ALIResult",
"source": {
"endLine": 111,
"line": 109,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Run the ALI pipeline on an input movie crop."
},
{
"decorators": [],
"docstring": "Set the default torch device used by subsequent ALI runs.",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "to",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.to",
"searchText": "method\nto\ntorch_volpy.extraction.ali.ali.to\n(self, device: str) -> 'ali'\nset the default torch device used by subsequent ali runs.\nset the default torch device used by subsequent ali runs.",
"signature": "(self, device: str) -> 'ALI'",
"source": {
"endLine": 118,
"line": 113,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Set the default torch device used by subsequent ALI runs."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "_resolve_device",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": false,
"qualifiedName": "torch_volpy.extraction.ALI.ALI._resolve_device",
"searchText": "method\n_resolve_device\ntorch_volpy.extraction.ali.ali._resolve_device\n(self, x: any) -> torch.device",
"signature": "(self, x: Any) -> torch.device",
"source": {
"endLine": 125,
"line": 120,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "_as_tensor",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": false,
"qualifiedName": "torch_volpy.extraction.ALI.ALI._as_tensor",
"searchText": "method\n_as_tensor\ntorch_volpy.extraction.ali.ali._as_tensor\n(self, x: any, device: torch.device) -> torch.tensor",
"signature": "(self, x: Any, device: torch.device) -> torch.Tensor",
"source": {
"endLine": 130,
"line": 127,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.extraction.ALI",
"name": "_ensure_odd",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": false,
"qualifiedName": "torch_volpy.extraction.ALI.ALI._ensure_odd",
"searchText": "staticmethod\n_ensure_odd\ntorch_volpy.extraction.ali.ali._ensure_odd\n(k: int) -> int\nstaticmethod",
"signature": "(k: int) -> int",
"source": {
"endLine": 134,
"line": 133,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "_hp_window_samples",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": false,
"qualifiedName": "torch_volpy.extraction.ALI.ALI._hp_window_samples",
"searchText": "method\n_hp_window_samples\ntorch_volpy.extraction.ali.ali._hp_window_samples\n(self, t: int) -> int",
"signature": "(self, T: int) -> int",
"source": {
"endLine": 141,
"line": 136,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.extraction.ALI",
"name": "_gaussian_kernel2d",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": false,
"qualifiedName": "torch_volpy.extraction.ALI.ALI._gaussian_kernel2d",
"searchText": "staticmethod\n_gaussian_kernel2d\ntorch_volpy.extraction.ali.ali._gaussian_kernel2d\n(sigma: float, radius: int, device: torch.device, dtype: torch.dtype) -> torch.tensor\nstaticmethod",
"signature": "(sigma: float, radius: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor",
"source": {
"endLine": 154,
"line": 144,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "stack: [H, W, T]\nreturns: [H, W, T]",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "_gaussian_blur_frames",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": false,
"qualifiedName": "torch_volpy.extraction.ALI.ALI._gaussian_blur_frames",
"searchText": "method\n_gaussian_blur_frames\ntorch_volpy.extraction.ali.ali._gaussian_blur_frames\n(self, stack: torch.tensor, sigma: float, radius: int) -> torch.tensor\nstack: [h, w, t]\nstack: [h, w, t]\nreturns: [h, w, t]",
"signature": "(self, stack: torch.Tensor, sigma: float, radius: int) -> torch.Tensor",
"source": {
"endLine": 175,
"line": 156,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "stack: [H, W, T]"
},
{
"decorators": [],
"docstring": "image: [H, W]\nreturns: [H, W]",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "_gaussian_blur_image",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": false,
"qualifiedName": "torch_volpy.extraction.ALI.ALI._gaussian_blur_image",
"searchText": "method\n_gaussian_blur_image\ntorch_volpy.extraction.ali.ali._gaussian_blur_image\n(self, image: torch.tensor, sigma: float, radius: int) -> torch.tensor\nimage: [h, w]\nimage: [h, w]\nreturns: [h, w]",
"signature": "(self, image: torch.Tensor, sigma: float, radius: int) -> torch.Tensor",
"source": {
"endLine": 196,
"line": 177,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "image: [H, W]"
},
{
"decorators": [],
"docstring": "Median high-pass filter along time for each pixel trace.\n\nstack: [H, W, T]\nreturns: [H, W, T]",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "hp_filter",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.hp_filter",
"searchText": "method\nhp_filter\ntorch_volpy.extraction.ali.ali.hp_filter\n(self, stack: torch.tensor) -> torch.tensor\nmedian high-pass filter along time for each pixel trace.\nmedian high-pass filter along time for each pixel trace.\n\nstack: [h, w, t]\nreturns: [h, w, t]",
"signature": "(self, stack: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 220,
"line": 198,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Median high-pass filter along time for each pixel trace."
},
{
"decorators": [],
"docstring": "26-connected components for [H, W, T] bool tensor using iterative max-pooling.\nWorks on CPU and CUDA.\n\nReturns:\n labeled: [H, W, T] long tensor\n num_features: int",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "_connected_components_3d_torch",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": false,
"qualifiedName": "torch_volpy.extraction.ALI.ALI._connected_components_3d_torch",
"searchText": "method\n_connected_components_3d_torch\ntorch_volpy.extraction.ali.ali._connected_components_3d_torch\n(self, bw: torch.tensor) -> tuple[torch.tensor, int]\n26-connected components for [h, w, t] bool tensor using iterative max-pooling.\n26-connected components for [h, w, t] bool tensor using iterative max-pooling.\nworks on cpu and cuda.\n\nreturns:\n labeled: [h, w, t] long tensor\n num_features: int",
"signature": "(self, bw: torch.Tensor) -> Tuple[torch.Tensor, int]",
"source": {
"endLine": 262,
"line": 222,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "26-connected components for [H, W, T] bool tensor using iterative max-pooling."
},
{
"decorators": [],
"docstring": "Detect coarse spike locations from positive-going events.\n\ndf_pos: [H, W, T]\nreturns:\n spk: [N, 3] int64, columns are [row, col, frame]",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "spk_coarse",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.spk_coarse",
"searchText": "method\nspk_coarse\ntorch_volpy.extraction.ali.ali.spk_coarse\n(self, df_pos: torch.tensor) -> torch.tensor\ndetect coarse spike locations from positive-going events.\ndetect coarse spike locations from positive-going events.\n\ndf_pos: [h, w, t]\nreturns:\n spk: [n, 3] int64, columns are [row, col, frame]",
"signature": "(self, df_pos: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 303,
"line": 264,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Detect coarse spike locations from positive-going events."
},
{
"decorators": [],
"docstring": "Truncated SVD denoising.\n\ndf_ap: [H, W, N]\nreturns:\n df_denoised: [H, W, N]\n ucomps: [H, W, K]\n vcomps: [N, K]",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "denoising",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.denoising",
"searchText": "method\ndenoising\ntorch_volpy.extraction.ali.ali.denoising\n(self, df_ap: torch.tensor, nsvd: optional[int] = none) -> tuple[torch.tensor, torch.tensor, torch.tensor]\ntruncated svd denoising.\ntruncated svd denoising.\n\ndf_ap: [h, w, n]\nreturns:\n df_denoised: [h, w, n]\n ucomps: [h, w, k]\n vcomps: [n, k]",
"signature": "(self, df_ap: torch.Tensor, nsvd: Optional[int] = None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]",
"source": {
"endLine": 336,
"line": 305,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Truncated SVD denoising."
},
{
"decorators": [],
"docstring": "Return up to N brightest connected pixels within radius of startpix.\n\nInputs:\n im: [H, W]\n startpix: [2] long tensor [row, col]\n\nReturns:\n pixel_list: [M] linear indices\n roimap: [H, W] bool\n indexIJ: [M, 2] long, rows then cols",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "select_connected",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.select_connected",
"searchText": "method\nselect_connected\ntorch_volpy.extraction.ali.ali.select_connected\n(self, im: torch.tensor, startpix: torch.tensor, n: int, radius: float) -> tuple[torch.tensor, torch.tensor, torch.tensor]\nreturn up to n brightest connected pixels within radius of startpix.\nreturn up to n brightest connected pixels within radius of startpix.\n\ninputs:\n im: [h, w]\n startpix: [2] long tensor [row, col]\n\nreturns:\n pixel_list: [m] linear indices\n roimap: [h, w] bool\n indexij: [m, 2] long, rows then cols",
"signature": "(self, im: torch.Tensor, startpix: torch.Tensor, N: int, radius: float) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]",
"source": {
"endLine": 393,
"line": 338,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Return up to N brightest connected pixels within radius of startpix."
},
{
"decorators": [],
"docstring": "Sub-pixel spike localization.\n\ndf_pos: [H, W, N]\ninitloc: [N, 2] integer coarse locations in 0-based coords\n\nreturns:\n sloc: [N, 2] float\n bns: [N]\n roi: [H, W, N] bool",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "spk_fine",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.spk_fine",
"searchText": "method\nspk_fine\ntorch_volpy.extraction.ali.ali.spk_fine\n(self, df_pos: torch.tensor, npix: optional[int] = none, radius: optional[float] = none, initloc: optional[torch.tensor] = none) -> tuple[torch.tensor, torch.tensor, torch.tensor]\nsub-pixel spike localization.\nsub-pixel spike localization.\n\ndf_pos: [h, w, n]\ninitloc: [n, 2] integer coarse locations in 0-based coords\n\nreturns:\n sloc: [n, 2] float\n bns: [n]\n roi: [h, w, n] bool",
"signature": "(self, df_pos: torch.Tensor, npix: Optional[int] = None, radius: Optional[float] = None, initloc: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]",
"source": {
"endLine": 455,
"line": 395,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Sub-pixel spike localization."
},
{
"decorators": [],
"docstring": "Build a high-resolution spike density map using 0-based coordinates.\n\nsloc: [N, 2] with row/col in 0-based pixel coordinates\nsz: (H, W)\n\nreturns:\n cnt: [H*factor, W*factor]\n cen: (cen_row, cen_col)",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "density_map",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.density_map",
"searchText": "method\ndensity_map\ntorch_volpy.extraction.ali.ali.density_map\n(self, sloc: torch.tensor, sz: tuple[int, int], factor: optional[int] = none) -> tuple[torch.tensor, tuple[torch.tensor, torch.tensor]]\nbuild a high-resolution spike density map using 0-based coordinates.\nbuild a high-resolution spike density map using 0-based coordinates.\n\nsloc: [n, 2] with row/col in 0-based pixel coordinates\nsz: (h, w)\n\nreturns:\n cnt: [h*factor, w*factor]\n cen: (cen_row, cen_col)",
"signature": "(self, sloc: torch.Tensor, sz: Tuple[int, int], factor: Optional[int] = None) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]",
"source": {
"endLine": 498,
"line": 457,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Build a high-resolution spike density map using 0-based coordinates."
},
{
"decorators": [],
"docstring": "Local-max peak detection in torch.\n\nalimap: [H, W]\nreturns:\n peaks: [N, 2] int64 [row_idx, col_idx]",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "detect_peaks",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.detect_peaks",
"searchText": "method\ndetect_peaks\ntorch_volpy.extraction.ali.ali.detect_peaks\n(self, alimap: torch.tensor, threshold_abs: optional[float] = none, kernel_size: optional[int] = none) -> torch.tensor\nlocal-max peak detection in torch.\nlocal-max peak detection in torch.\n\nalimap: [h, w]\nreturns:\n peaks: [n, 2] int64 [row_idx, col_idx]",
"signature": "(self, alimap: torch.Tensor, threshold_abs: Optional[float] = None, kernel_size: Optional[int] = None) -> torch.Tensor",
"source": {
"endLine": 528,
"line": 500,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Local-max peak detection in torch."
},
{
"decorators": [],
"docstring": "Assign spikes to nearest cluster center.\n\nsloc: [N, 2]\nclust_cen: [2, C]\nreturns:\n clust_idx: [N], values in {0,1,...,C}; 0 = unassigned",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "assign_cluster",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.assign_cluster",
"searchText": "method\nassign_cluster\ntorch_volpy.extraction.ali.ali.assign_cluster\n(self, sloc: torch.tensor, clust_cen: torch.tensor, radius: optional[float] = none) -> torch.tensor\nassign spikes to nearest cluster center.\nassign spikes to nearest cluster center.\n\nsloc: [n, 2]\nclust_cen: [2, c]\nreturns:\n clust_idx: [n], values in {0,1,...,c}; 0 = unassigned",
"signature": "(self, sloc: torch.Tensor, clust_cen: torch.Tensor, radius: Optional[float] = None) -> torch.Tensor",
"source": {
"endLine": 557,
"line": 530,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Assign spikes to nearest cluster center."
},
{
"decorators": [],
"docstring": "Limit footprint support to an r-pixel disk around the cluster center.\n\nfootprint: [H, W, C]\nclust_cen: [2, C]",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "fp_support",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.fp_support",
"searchText": "method\nfp_support\ntorch_volpy.extraction.ali.ali.fp_support\n(self, footprint: torch.tensor, clust_cen: torch.tensor, r: optional[float] = none) -> tuple[torch.tensor, torch.tensor]\nlimit footprint support to an r-pixel disk around the cluster center.\nlimit footprint support to an r-pixel disk around the cluster center.\n\nfootprint: [h, w, c]\nclust_cen: [2, c]",
"signature": "(self, footprint: torch.Tensor, clust_cen: torch.Tensor, r: Optional[float] = None) -> Tuple[torch.Tensor, torch.Tensor]",
"source": {
"endLine": 594,
"line": 559,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Limit footprint support to an r-pixel disk around the cluster center."
},
{
"decorators": [],
"docstring": "Extract temporal traces from df movie given footprints.\n\ndf: [P, T]\nfp: [P, C]\n\nreturns:\n trace_ls: [T, C]\n trace_new: [T, C]",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "t_decompose",
"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.t_decompose",
"searchText": "method\nt_decompose\ntorch_volpy.extraction.ali.ali.t_decompose\n(self, df: torch.tensor, fp: torch.tensor) -> tuple[torch.tensor, torch.tensor]\nextract temporal traces from df movie given footprints.\nextract temporal traces from df movie given footprints.\n\ndf: [p, t]\nfp: [p, c]\n\nreturns:\n trace_ls: [t, c]\n trace_new: [t, c]",
"signature": "(self, df: torch.Tensor, fp: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]",
"source": {
"endLine": 637,
"line": 596,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Extract temporal traces from df movie given footprints."
},
{
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"docstring": "Average df frames for spikes assigned to each cluster.\n\ndf: [H, W, T]\nspike_frames: [N]\nclust_idx: [N]\nreturns:\n footprint: [H, W, C]",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
"name": "compute_footprints",
"parent": "torch_volpy.extraction.ALI.ALI",
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"searchText": "method\ncompute_footprints\ntorch_volpy.extraction.ali.ali.compute_footprints\n(self, df: torch.tensor, spike_frames: torch.tensor, clust_idx: torch.tensor, nclust: int) -> torch.tensor\naverage df frames for spikes assigned to each cluster.\naverage df frames for spikes assigned to each cluster.\n\ndf: [h, w, t]\nspike_frames: [n]\nclust_idx: [n]\nreturns:\n footprint: [h, w, c]",
"signature": "(self, df: torch.Tensor, spike_frames: torch.Tensor, clust_idx: torch.Tensor, nclust: int) -> torch.Tensor",
"source": {
"endLine": 664,
"line": 639,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Average df frames for spikes assigned to each cluster."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Compute ALI spike localization, clusters, footprints, and traces.",
"kind": "method",
"module": "torch_volpy.extraction.ALI",
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"parent": "torch_volpy.extraction.ALI.ALI",
"public": true,
"qualifiedName": "torch_volpy.extraction.ALI.ALI.forward",
"searchText": "method\nforward\ntorch_volpy.extraction.ali.ali.forward\n(self, data: any) -> aliresult\ncompute ali spike localization, clusters, footprints, and traces.\ncompute ali spike localization, clusters, footprints, and traces.\ntorch.inference_mode",
"signature": "(self, data: Any) -> ALIResult",
"source": {
"endLine": 833,
"line": 667,
"path": "src/torch_volpy/extraction/ALI.py"
},
"summary": "Compute ALI spike localization, clusters, footprints, and traces."
},
{
"decorators": [],
"docstring": "",
"exports": [],
"kind": "module",
"module": "torch_volpy.extraction.Spikepursuit",
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"searchText": "module\ntorch_volpy.extraction.spikepursuit\ntorch_volpy.extraction.spikepursuit",
"signature": "",
"source": {
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"line": 1,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"default": "{'sos32'}",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_SIGNAL_FILTER_METHODS",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit._SIGNAL_FILTER_METHODS",
"searchText": "constant\n_signal_filter_methods\ntorch_volpy.extraction.spikepursuit._signal_filter_methods\n{'sos32'}",
"signature": "",
"source": {
"endLine": 14,
"line": 14,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"bases": [],
"dataclass": true,
"decorators": [
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],
"docstring": "Container for one fitted SpikePursuit ROI result.",
"kind": "class",
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"name": "SpikePursuitResult",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult",
"searchText": "class\nspikepursuitresult\ntorch_volpy.extraction.spikepursuit.spikepursuitresult\n()\ncontainer for one fitted spikepursuit roi result.\ncontainer for one fitted spikepursuit roi result.\ndataclass",
"signature": "()",
"source": {
"endLine": 37,
"line": 18,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Container for one fitted SpikePursuit ROI result."
},
{
"annotation": "int",
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"docstring": "Dataclass field `roi_id`. Type: `int`.",
"kind": "field",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "roi_id",
"parent": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult.roi_id",
"searchText": "field\nroi_id\ntorch_volpy.extraction.spikepursuit.spikepursuitresult.roi_id\n: int\ndataclass field `roi_id`. type: `int`.\ndataclass field `roi_id`. type: `int`.\nint",
"signature": ": int",
"source": {
"endLine": 20,
"line": 20,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `roi_id`. Type: `int`."
},
{
"annotation": "torch.Tensor",
"decorators": [],
"docstring": "Dataclass field `t`. Type: `torch.Tensor`.",
"kind": "field",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "t",
"parent": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult.t",
"searchText": "field\nt\ntorch_volpy.extraction.spikepursuit.spikepursuitresult.t\n: torch.tensor\ndataclass field `t`. type: `torch.tensor`.\ndataclass field `t`. type: `torch.tensor`.\ntorch.tensor",
"signature": ": torch.Tensor",
"source": {
"endLine": 21,
"line": 21,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `t`. Type: `torch.Tensor`."
},
{
"annotation": "torch.Tensor",
"decorators": [],
"docstring": "Dataclass field `ts`. Type: `torch.Tensor`.",
"kind": "field",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "ts",
"parent": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult.ts",
"searchText": "field\nts\ntorch_volpy.extraction.spikepursuit.spikepursuitresult.ts\n: torch.tensor\ndataclass field `ts`. type: `torch.tensor`.\ndataclass field `ts`. type: `torch.tensor`.\ntorch.tensor",
"signature": ": torch.Tensor",
"source": {
"endLine": 22,
"line": 22,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `ts`. Type: `torch.Tensor`."
},
{
"annotation": "torch.Tensor",
"decorators": [],
"docstring": "Dataclass field `t_rec`. Type: `torch.Tensor`.",
"kind": "field",
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"parent": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult.t_rec",
"searchText": "field\nt_rec\ntorch_volpy.extraction.spikepursuit.spikepursuitresult.t_rec\n: torch.tensor\ndataclass field `t_rec`. type: `torch.tensor`.\ndataclass field `t_rec`. type: `torch.tensor`.\ntorch.tensor",
"signature": ": torch.Tensor",
"source": {
"endLine": 23,
"line": 23,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `t_rec`. Type: `torch.Tensor`."
},
{
"annotation": "torch.Tensor",
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"docstring": "Dataclass field `t_sub`. Type: `torch.Tensor`.",
"kind": "field",
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"name": "t_sub",
"parent": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult",
"public": true,
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"searchText": "field\nt_sub\ntorch_volpy.extraction.spikepursuit.spikepursuitresult.t_sub\n: torch.tensor\ndataclass field `t_sub`. type: `torch.tensor`.\ndataclass field `t_sub`. type: `torch.tensor`.\ntorch.tensor",
"signature": ": torch.Tensor",
"source": {
"endLine": 24,
"line": 24,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `t_sub`. Type: `torch.Tensor`."
},
{
"annotation": "torch.Tensor",
"decorators": [],
"docstring": "Dataclass field `spikes`. Type: `torch.Tensor`.",
"kind": "field",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "spikes",
"parent": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult.spikes",
"searchText": "field\nspikes\ntorch_volpy.extraction.spikepursuit.spikepursuitresult.spikes\n: torch.tensor\ndataclass field `spikes`. type: `torch.tensor`.\ndataclass field `spikes`. type: `torch.tensor`.\ntorch.tensor",
"signature": ": torch.Tensor",
"source": {
"endLine": 25,
"line": 25,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `spikes`. Type: `torch.Tensor`."
},
{
"annotation": "List[int]",
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"docstring": "Dataclass field `num_spikes`. Type: `List[int]`.",
"kind": "field",
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"signature": ": List[int]",
"source": {
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"line": 26,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `num_spikes`. Type: `List[int]`."
},
{
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"signature": ": bool",
"source": {
"endLine": 27,
"line": 27,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `low_spikes`. Type: `bool`."
},
{
"annotation": "torch.Tensor",
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"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult.templates",
"searchText": "field\ntemplates\ntorch_volpy.extraction.spikepursuit.spikepursuitresult.templates\n: torch.tensor\ndataclass field `templates`. type: `torch.tensor`.\ndataclass field `templates`. type: `torch.tensor`.\ntorch.tensor",
"signature": ": torch.Tensor",
"source": {
"endLine": 28,
"line": 28,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `templates`. Type: `torch.Tensor`."
},
{
"annotation": "float",
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"docstring": "Dataclass field `snr`. Type: `float`.",
"kind": "field",
"module": "torch_volpy.extraction.Spikepursuit",
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"parent": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult.snr",
"searchText": "field\nsnr\ntorch_volpy.extraction.spikepursuit.spikepursuitresult.snr\n: float\ndataclass field `snr`. type: `float`.\ndataclass field `snr`. type: `float`.\nfloat",
"signature": ": float",
"source": {
"endLine": 29,
"line": 29,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `snr`. Type: `float`."
},
{
"annotation": "float",
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"docstring": "Dataclass field `thresh`. Type: `float`.",
"kind": "field",
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"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.SpikePursuitResult.thresh",
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"signature": ": float",
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"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `thresh`. Type: `float`."
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{
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{
"annotation": "bool",
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"docstring": "Dataclass field `locality`. Type: `bool`.",
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"line": 32,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `locality`. Type: `bool`."
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{
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"path": "src/torch_volpy/extraction/Spikepursuit.py"
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{
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"path": "src/torch_volpy/extraction/Spikepursuit.py"
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{
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"path": "src/torch_volpy/extraction/Spikepursuit.py"
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{
"annotation": "torch.Tensor",
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},
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{
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"line": 37,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `rawROI`. Type: `Dict[str, torch.Tensor]`."
},
{
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"searchText": "class\nspikepursuitpreparedroi\ntorch_volpy.extraction.spikepursuit.spikepursuitpreparedroi\n()\nreusable prepared state for fitting one spikepursuit roi.\nreusable prepared state for fitting one spikepursuit roi.\ndataclass",
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"line": 41,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
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},
{
"annotation": "int",
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"parent": "torch_volpy.extraction.Spikepursuit.SpikePursuitPreparedROI",
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"signature": ": int",
"source": {
"endLine": 43,
"line": 43,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `roi_id`. Type: `int`."
},
{
"annotation": "torch.Tensor",
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"signature": ": torch.Tensor",
"source": {
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"line": 44,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Dataclass field `t0`. Type: `torch.Tensor`."
},
{
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"searchText": "class\nspikepursuit\ntorch_volpy.extraction.spikepursuit.spikepursuit\n(movie, roi_mask: torch.tensor, *, channel: optional[int] = none, fr: float = 400.0, device: optional[union[str, torch.device]] = none, dtype: torch.dtype = torch.float32, cache_movie: bool = false, cache_movie_device: union[str, torch.device] = 'cpu', template_size: float = 0.02, context_size: int = 35, censor_size: int = 12, visualize_roi: bool = false, flip_signal: bool = true, hp_freq_pb: float = 10.0, npc_bg: int = 8, ridge_bg: float = 0.01, hp_freq: float = 1.0, clip: int = 100, threshold_method: str = 'adaptive_threshold', min_spikes: int = 5, pnorm: float = 0.5, threshold: float = 2.0, sigmas: sequence[float] = (1.0, 1.5, 2.0), n_iter: int = 2, weight_update: str = 'ridge', do_plot: bool = false, do_cross_val: bool = false, sub_freq: float = 20.0, batch_filter_workspace_bytes: optional[int] = none, prefetch_next_batch_patch: bool = false, keep_cuda_cache_between_batches: bool = false, cuda_batch_safety_margin: float = 1.0, signal_filter_method: str = 'sos32', eps: float = 1e-08) -> none\npytorch port of the original volspike/spikepursuit-style algorithm.\npytorch port of the original volspike/spikepursuit-style algorithm.\n\nassumptions\n-----------\n- movie is a movie instance backed by hdf5 with shape (t, y, x) or (t, y, x, c)\n- roi_mask is either a 2d label tensor where:\n * background is 0\n * each roi has a unique non-zero integer/label value\n or a 3d mask-rcnn-style instance stack with shape (n, y, x)\n- all numeric work is performed with pytorch tensors\n\nnotes\n-----\nthis is algorithmically faithful to the original implementation, but a few\nscipy/sklearn operations are replaced with native torch equivalents:\n- spike denoising uses a torch-native volpy-compatible butterworth filtfilt\n- peak finding is implemented with tensor comparisons\n- pca/background extraction uses torch.pca_lowrank\n- ridge regression follows volpy/scikit-learn lsqr semantics for parity\n- morphology uses conv2d-based binary dilation",
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"searchText": "method\n__init__\ntorch_volpy.extraction.spikepursuit.spikepursuit.__init__\n(self, movie, roi_mask: torch.tensor, *, channel: optional[int] = none, fr: float = 400.0, device: optional[union[str, torch.device]] = none, dtype: torch.dtype = torch.float32, cache_movie: bool = false, cache_movie_device: union[str, torch.device] = 'cpu', template_size: float = 0.02, context_size: int = 35, censor_size: int = 12, visualize_roi: bool = false, flip_signal: bool = true, hp_freq_pb: float = 10.0, npc_bg: int = 8, ridge_bg: float = 0.01, hp_freq: float = 1.0, clip: int = 100, threshold_method: str = 'adaptive_threshold', min_spikes: int = 5, pnorm: float = 0.5, threshold: float = 2.0, sigmas: sequence[float] = (1.0, 1.5, 2.0), n_iter: int = 2, weight_update: str = 'ridge', do_plot: bool = false, do_cross_val: bool = false, sub_freq: float = 20.0, batch_filter_workspace_bytes: optional[int] = none, prefetch_next_batch_patch: bool = false, keep_cuda_cache_between_batches: bool = false, cuda_batch_safety_margin: float = 1.0, signal_filter_method: str = 'sos32', eps: float = 1e-08) -> none",
"signature": "(self, movie, roi_mask: torch.Tensor, *, channel: Optional[int] = None, fr: float = 400.0, device: Optional[Union[str, torch.device]] = None, dtype: torch.dtype = torch.float32, cache_movie: bool = False, cache_movie_device: Union[str, torch.device] = 'cpu', template_size: float = 0.02, context_size: int = 35, censor_size: int = 12, visualize_roi: bool = False, flip_signal: bool = True, hp_freq_pb: float = 10.0, nPC_bg: int = 8, ridge_bg: float = 0.01, hp_freq: float = 1.0, clip: int = 100, threshold_method: str = 'adaptive_threshold', min_spikes: int = 5, pnorm: float = 0.5, threshold: float = 2.0, sigmas: Sequence[float] = (1.0, 1.5, 2.0), n_iter: int = 2, weight_update: str = 'ridge', do_plot: bool = False, do_cross_val: bool = False, sub_freq: float = 20.0, batch_filter_workspace_bytes: Optional[int] = None, prefetch_next_batch_patch: bool = False, keep_cuda_cache_between_batches: bool = False, cuda_batch_safety_margin: float = 1.0, signal_filter_method: str = 'sos32', eps: float = 1e-08) -> None",
"source": {
"endLine": 217,
"line": 93,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Fit SpikePursuit traces for the selected ROIs and return them by ROI id.",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "fit",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit.fit",
"searchText": "method\nfit\ntorch_volpy.extraction.spikepursuit.spikepursuit.fit\n(self, roi_ids: optional[iterable[int]] = none, *, batch_patch_bytes: optional[int] = none, max_rois_per_batch: optional[int] = none) -> dict[int, spikepursuitresult]\nfit spikepursuit traces for the selected rois and return them by roi id.\nfit spikepursuit traces for the selected rois and return them by roi id.",
"signature": "(self, roi_ids: Optional[Iterable[int]] = None, *, batch_patch_bytes: Optional[int] = None, max_rois_per_batch: Optional[int] = None) -> Dict[int, SpikePursuitResult]",
"source": {
"endLine": 238,
"line": 223,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Fit SpikePursuit traces for the selected ROIs and return them by ROI id."
},
{
"decorators": [],
"docstring": "Yield ROI fits while batching movie reads by spatial union patch.\n\nEach batch reads a shared movie patch once, high-pass filters the union\npatch once, then fits the ROIs whose context windows are contained in\nthat patch. This keeps peak memory bounded by ``batch_patch_bytes``\ninstead of loading/filtering all ROI patches independently.",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "iter_fit",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit.iter_fit",
"searchText": "method\niter_fit\ntorch_volpy.extraction.spikepursuit.spikepursuit.iter_fit\n(self, roi_ids: optional[iterable[int]] = none, *, batch_patch_bytes: optional[int] = none, max_rois_per_batch: optional[int] = none) -> iterator[spikepursuitresult]\nyield roi fits while batching movie reads by spatial union patch.\nyield roi fits while batching movie reads by spatial union patch.\n\neach batch reads a shared movie patch once, high-pass filters the union\npatch once, then fits the rois whose context windows are contained in\nthat patch. this keeps peak memory bounded by ``batch_patch_bytes``\ninstead of loading/filtering all roi patches independently.",
"signature": "(self, roi_ids: Optional[Iterable[int]] = None, *, batch_patch_bytes: Optional[int] = None, max_rois_per_batch: Optional[int] = None) -> Iterator[SpikePursuitResult]",
"source": {
"endLine": 260,
"line": 240,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Yield ROI fits while batching movie reads by spatial union patch."
},
{
"decorators": [],
"docstring": "Yield prepared ROI state while batching movie reads by spatial union patch.\n\nThe prepared state contains the expensive movie/background/predictor\nterms and can be passed to ``fit_prepared_roi`` for multiple\nthreshold-parameter sweeps.",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "iter_prepare",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit.iter_prepare",
"searchText": "method\niter_prepare\ntorch_volpy.extraction.spikepursuit.spikepursuit.iter_prepare\n(self, roi_ids: optional[iterable[int]] = none, *, batch_patch_bytes: optional[int] = none, max_rois_per_batch: optional[int] = none) -> iterator[spikepursuitpreparedroi]\nyield prepared roi state while batching movie reads by spatial union patch.\nyield prepared roi state while batching movie reads by spatial union patch.\n\nthe prepared state contains the expensive movie/background/predictor\nterms and can be passed to ``fit_prepared_roi`` for multiple\nthreshold-parameter sweeps.",
"signature": "(self, roi_ids: Optional[Iterable[int]] = None, *, batch_patch_bytes: Optional[int] = None, max_rois_per_batch: Optional[int] = None) -> Iterator[SpikePursuitPreparedROI]",
"source": {
"endLine": 355,
"line": 262,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Yield prepared ROI state while batching movie reads by spatial union patch."
},
{
"decorators": [],
"docstring": "Cache the full movie on CPU or GPU so ROI extraction does not reread\nfull-frame HDF5 chunks for every spatial crop.",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "preload_movie",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit.preload_movie",
"searchText": "method\npreload_movie\ntorch_volpy.extraction.spikepursuit.spikepursuit.preload_movie\n(self, device: union[str, torch.device] = 'cpu') -> 'spikepursuit'\ncache the full movie on cpu or gpu so roi extraction does not reread\ncache the full movie on cpu or gpu so roi extraction does not reread\nfull-frame hdf5 chunks for every spatial crop.",
"signature": "(self, device: Union[str, torch.device] = 'cpu') -> 'Spikepursuit'",
"source": {
"endLine": 378,
"line": 357,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Cache the full movie on CPU or GPU so ROI extraction does not reread"
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Prepare and fit a single ROI.",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "fit_roi",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit.fit_roi",
"searchText": "method\nfit_roi\ntorch_volpy.extraction.spikepursuit.spikepursuit.fit_roi\n(self, roi_id: int, weights_init: optional[torch.tensor] = none) -> spikepursuitresult\nprepare and fit a single roi.\nprepare and fit a single roi.\ntorch.inference_mode",
"signature": "(self, roi_id: int, weights_init: Optional[torch.Tensor] = None) -> SpikePursuitResult",
"source": {
"endLine": 388,
"line": 381,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Prepare and fit a single ROI."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Build reusable background, predictor, and initial trace state for one ROI.",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "prepare_roi",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit.prepare_roi",
"searchText": "method\nprepare_roi\ntorch_volpy.extraction.spikepursuit.spikepursuit.prepare_roi\n(self, roi_id: int, weights_init: optional[torch.tensor] = none) -> spikepursuitpreparedroi\nbuild reusable background, predictor, and initial trace state for one roi.\nbuild reusable background, predictor, and initial trace state for one roi.\ntorch.inference_mode",
"signature": "(self, roi_id: int, weights_init: Optional[torch.Tensor] = None) -> SpikePursuitPreparedROI",
"source": {
"endLine": 487,
"line": 391,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Build reusable background, predictor, and initial trace state for one ROI."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Fit a previously prepared ROI state into a SpikePursuit result.",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "fit_prepared_roi",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit.fit_prepared_roi",
"searchText": "method\nfit_prepared_roi\ntorch_volpy.extraction.spikepursuit.spikepursuit.fit_prepared_roi\n(self, prepared: spikepursuitpreparedroi) -> spikepursuitresult\nfit a previously prepared roi state into a spikepursuit result.\nfit a previously prepared roi state into a spikepursuit result.\ntorch.inference_mode",
"signature": "(self, prepared: SpikePursuitPreparedROI) -> SpikePursuitResult",
"source": {
"endLine": 638,
"line": 490,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": "Fit a previously prepared ROI state into a SpikePursuit result."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_get_roi_ids",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._get_roi_ids",
"searchText": "method\n_get_roi_ids\ntorch_volpy.extraction.spikepursuit.spikepursuit._get_roi_ids\n(self, mask: torch.tensor) -> torch.tensor",
"signature": "(self, mask: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 653,
"line": 644,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_roi_binary_mask",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._roi_binary_mask",
"searchText": "method\n_roi_binary_mask\ntorch_volpy.extraction.spikepursuit.spikepursuit._roi_binary_mask\n(self, roi_id: int) -> torch.tensor",
"signature": "(self, roi_id: int) -> torch.Tensor",
"source": {
"endLine": 662,
"line": 655,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_extract_context_patch",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._extract_context_patch",
"searchText": "method\n_extract_context_patch\ntorch_volpy.extraction.spikepursuit.spikepursuit._extract_context_patch\n(self, bw_full: torch.tensor) -> tuple[torch.tensor, torch.tensor, torch.tensor, torch.tensor]",
"signature": "(self, bw_full: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]",
"source": {
"endLine": 676,
"line": 664,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_prepare_context_batch_highpass",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._prepare_context_batch_highpass",
"searchText": "method\n_prepare_context_batch_highpass\ntorch_volpy.extraction.spikepursuit.spikepursuit._prepare_context_batch_highpass\n(self, patch: torch.tensor, *, flip_signal: optional[bool] = none) -> torch.tensor",
"signature": "(self, patch: torch.Tensor, *, flip_signal: Optional[bool] = None) -> torch.Tensor",
"source": {
"endLine": 725,
"line": 678,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_extract_prepared_context_patch_data",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._extract_prepared_context_patch_data",
"searchText": "method\n_extract_prepared_context_patch_data\ntorch_volpy.extraction.spikepursuit.spikepursuit._extract_prepared_context_patch_data\n(self, bw_full: torch.tensor) -> optional[tuple[torch.tensor, torch.tensor, torch.tensor, torch.tensor, torch.tensor, torch.tensor]]",
"signature": "(self, bw_full: torch.Tensor) -> Optional[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]]",
"source": {
"endLine": 777,
"line": 727,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_context_patch_spec",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._context_patch_spec",
"searchText": "method\n_context_patch_spec\ntorch_volpy.extraction.spikepursuit.spikepursuit._context_patch_spec\n(self, roi_id: int) -> _contextpatchspec",
"signature": "(self, roi_id: int) -> _ContextPatchSpec",
"source": {
"endLine": 786,
"line": 779,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_context_patch_spec_for_mask",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._context_patch_spec_for_mask",
"searchText": "method\n_context_patch_spec_for_mask\ntorch_volpy.extraction.spikepursuit.spikepursuit._context_patch_spec_for_mask\n(self, bw_full: torch.tensor, roi_id: int) -> _contextpatchspec",
"signature": "(self, bw_full: torch.Tensor, roi_id: int) -> _ContextPatchSpec",
"source": {
"endLine": 802,
"line": 788,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_context_patch_batches",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._context_patch_batches",
"searchText": "method\n_context_patch_batches\ntorch_volpy.extraction.spikepursuit.spikepursuit._context_patch_batches\n(self, specs: sequence[_contextpatchspec], *, max_patch_bytes: int, max_rois_per_batch: optional[int]) -> list[list[_contextpatchspec]]",
"signature": "(self, specs: Sequence[_ContextPatchSpec], *, max_patch_bytes: int, max_rois_per_batch: Optional[int]) -> List[List[_ContextPatchSpec]]",
"source": {
"endLine": 843,
"line": 804,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_context_batch_bounds",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._context_batch_bounds",
"searchText": "staticmethod\n_context_batch_bounds\ntorch_volpy.extraction.spikepursuit.spikepursuit._context_batch_bounds\n(batch: sequence[_contextpatchspec]) -> tuple[int, int, int, int]\nstaticmethod",
"signature": "(batch: Sequence[_ContextPatchSpec]) -> Tuple[int, int, int, int]",
"source": {
"endLine": 851,
"line": 846,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_normalize_batch_patch_bytes",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._normalize_batch_patch_bytes",
"searchText": "method\n_normalize_batch_patch_bytes\ntorch_volpy.extraction.spikepursuit.spikepursuit._normalize_batch_patch_bytes\n(self, batch_patch_bytes: optional[int]) -> int",
"signature": "(self, batch_patch_bytes: Optional[int]) -> int",
"source": {
"endLine": 856,
"line": 853,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_default_batch_patch_bytes",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._default_batch_patch_bytes",
"searchText": "method\n_default_batch_patch_bytes\ntorch_volpy.extraction.spikepursuit.spikepursuit._default_batch_patch_bytes\n(self) -> int",
"signature": "(self) -> int",
"source": {
"endLine": 867,
"line": 858,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_estimate_patch_bytes",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._estimate_patch_bytes",
"searchText": "method\n_estimate_patch_bytes\ntorch_volpy.extraction.spikepursuit.spikepursuit._estimate_patch_bytes\n(self, x0: int, x1: int, y0: int, y1: int) -> int",
"signature": "(self, x0: int, x1: int, y0: int, y1: int) -> int",
"source": {
"endLine": 870,
"line": 869,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_estimate_context_batch_bytes",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._estimate_context_batch_bytes",
"searchText": "method\n_estimate_context_batch_bytes\ntorch_volpy.extraction.spikepursuit.spikepursuit._estimate_context_batch_bytes\n(self, specs: sequence[_contextpatchspec]) -> int",
"signature": "(self, specs: Sequence[_ContextPatchSpec]) -> int",
"source": {
"endLine": 889,
"line": 872,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_estimate_roi_fit_peak_bytes",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._estimate_roi_fit_peak_bytes",
"searchText": "method\n_estimate_roi_fit_peak_bytes\ntorch_volpy.extraction.spikepursuit.spikepursuit._estimate_roi_fit_peak_bytes\n(self, roi_patch_bytes: int) -> int",
"signature": "(self, roi_patch_bytes: int) -> int",
"source": {
"endLine": 901,
"line": 891,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_estimate_context_prepare_peak_bytes",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._estimate_context_prepare_peak_bytes",
"searchText": "method\n_estimate_context_prepare_peak_bytes\ntorch_volpy.extraction.spikepursuit.spikepursuit._estimate_context_prepare_peak_bytes\n(self, x0: int, x1: int, y0: int, y1: int) -> int",
"signature": "(self, x0: int, x1: int, y0: int, y1: int) -> int",
"source": {
"endLine": 925,
"line": 903,
"path": "src/torch_volpy/extraction/Spikepursuit.py"
},
"summary": ""
},
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"signature": "(self, x0: int, x1: int, y0: int, y1: int, *, prefetch_future: Optional[Future[torch.Tensor]], prefetch_bounds: Optional[Tuple[int, int, int, int]]) -> torch.Tensor",
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"searchText": "method\n_move_cpu_patch_to_device\ntorch_volpy.extraction.spikepursuit.spikepursuit._move_cpu_patch_to_device\n(self, patch: torch.tensor) -> torch.tensor",
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"docstring": "Denoise a trace and detect spikes with VolPy-compatible thresholding.",
"kind": "method",
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"name": "denoise_spikes",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit.denoise_spikes",
"searchText": "method\ndenoise_spikes\ntorch_volpy.extraction.spikepursuit.spikepursuit.denoise_spikes\n(self, data: torch.tensor, *, window_length: int, fr: float, hp_freq: float = 1.0, clip: int = 100, threshold_method: str = 'adaptive_threshold', min_spikes: int = 5, pnorm: float = 0.5, threshold: float = 2.0, do_plot: bool = false) -> tuple[torch.tensor, torch.tensor, torch.tensor, torch.tensor, bool, float]\ndenoise a trace and detect spikes with volpy-compatible thresholding.\ndenoise a trace and detect spikes with volpy-compatible thresholding.",
"signature": "(self, data: torch.Tensor, *, window_length: int, fr: float, hp_freq: float = 1.0, clip: int = 100, threshold_method: str = 'adaptive_threshold', min_spikes: int = 5, pnorm: float = 0.5, threshold: float = 2.0, do_plot: bool = False) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, bool, float]",
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},
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"docstring": "Estimate an adaptive spike threshold from candidate peak amplitudes.",
"kind": "method",
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"name": "adaptive_thresh",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": true,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit.adaptive_thresh",
"searchText": "method\nadaptive_thresh\ntorch_volpy.extraction.spikepursuit.spikepursuit.adaptive_thresh\n(self, pks: torch.tensor, clip: int, pnorm: float = 0.5, min_spikes: int = 5) -> tuple[float, float, float, bool]\nestimate an adaptive spike threshold from candidate peak amplitudes.\nestimate an adaptive spike threshold from candidate peak amplitudes.",
"signature": "(self, pks: torch.Tensor, clip: int, pnorm: float = 0.5, min_spikes: int = 5) -> Tuple[float, float, float, bool]",
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"docstring": "Compute a noise-scaled spike threshold with low-spike fallback.",
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"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
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"signature": "(self, data: torch.Tensor, pks: torch.Tensor, clip: int, threshold: float = 2.0, min_spikes: int = 5) -> Tuple[float, bool]",
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"summary": "Compute a noise-scaled spike threshold with low-spike fallback."
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"docstring": "Apply a whitened matched filter around candidate spike locations.",
"kind": "method",
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"name": "whitened_matched_filter",
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"searchText": "method\nwhitened_matched_filter\ntorch_volpy.extraction.spikepursuit.spikepursuit.whitened_matched_filter\n(self, data: torch.tensor, locs: torch.tensor, window: torch.tensor) -> torch.tensor\napply a whitened matched filter around candidate spike locations.\napply a whitened matched filter around candidate spike locations.",
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"path": "src/torch_volpy/extraction/Spikepursuit.py"
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"kind": "method",
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"name": "_volpy_signal_filter_1d",
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"searchText": "method\n_volpy_signal_filter_1d\ntorch_volpy.extraction.spikepursuit.spikepursuit._volpy_signal_filter_1d\n(self, sg: torch.tensor, freq: float, fr: float, order: int = 3, mode: str = 'high') -> torch.tensor",
"signature": "(self, sg: torch.Tensor, freq: float, fr: float, order: int = 3, mode: str = 'high') -> torch.Tensor",
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"kind": "method",
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"name": "_volpy_signal_filter_nd",
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"searchText": "method\n_volpy_signal_filter_nd\ntorch_volpy.extraction.spikepursuit.spikepursuit._volpy_signal_filter_nd\n(self, sg: torch.tensor, freq: float, fr: float, order: int = 3, mode: str = 'high', dim: int = -1, method_override: optional[str] = none) -> torch.tensor",
"signature": "(self, sg: torch.Tensor, freq: float, fr: float, order: int = 3, mode: str = 'high', dim: int = -1, method_override: Optional[str] = None) -> torch.Tensor",
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"kind": "method",
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"name": "_butter_sos_coefficients",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
"qualifiedName": "torch_volpy.extraction.Spikepursuit.Spikepursuit._butter_sos_coefficients",
"searchText": "method\n_butter_sos_coefficients\ntorch_volpy.extraction.spikepursuit.spikepursuit._butter_sos_coefficients\n(self, order: int, norm_freq: float, mode: str, *, device: torch.device, dtype: torch.dtype) -> torch.tensor",
"signature": "(self, order: int, norm_freq: float, mode: str, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor",
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"kind": "method",
"module": "torch_volpy.extraction.Spikepursuit",
"name": "_zpk2sos_butter",
"parent": "torch_volpy.extraction.Spikepursuit.Spikepursuit",
"public": false,
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"searchText": "method\nread_frames\ntorch_volpy.gui.core.tiffmovie.read_frames\n(self, start: int, stop: optional[int] = none, step: int = 1, as_tensor: bool = true, dtype: optional[union[np.dtype, str]] = none, device: optional[union[str, torch.device]] = none)\nread a contiguous frame range from the tiff stack.\nread a contiguous frame range from the tiff stack.",
"signature": "(self, start: int, stop: Optional[int] = None, step: int = 1, as_tensor: bool = True, dtype: Optional[Union[np.dtype, str]] = None, device: Optional[Union[str, torch.device]] = None)",
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"path": "src/torch_volpy/gui/core.py"
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"summary": "Read a contiguous frame range from the TIFF stack."
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"docstring": "Expose one channel of a movie as a grayscale `(T, Y, X)` movie.",
"kind": "class",
"module": "torch_volpy.gui.core",
"name": "ChannelMovie",
"public": true,
"qualifiedName": "torch_volpy.gui.core.ChannelMovie",
"searchText": "class\nchannelmovie\ntorch_volpy.gui.core.channelmovie\n(movie, channel: optional[int] = none) -> none\nexpose one channel of a movie as a grayscale `(t, y, x)` movie.\nexpose one channel of a movie as a grayscale `(t, y, x)` movie.",
"signature": "(movie, channel: Optional[int] = None) -> None",
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"endLine": 228,
"line": 197,
"path": "src/torch_volpy/gui/core.py"
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"summary": "Expose one channel of a movie as a grayscale `(T, Y, X)` movie."
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"kind": "method",
"module": "torch_volpy.gui.core",
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"searchText": "method\n__init__\ntorch_volpy.gui.core.channelmovie.__init__\n(self, movie, channel: optional[int] = none) -> none",
"signature": "(self, movie, channel: Optional[int] = None) -> None",
"source": {
"endLine": 212,
"line": 200,
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"docstring": "Read grayscale frames from the selected channel.",
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"parent": "torch_volpy.gui.core.ChannelMovie",
"public": true,
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"searchText": "method\nread_frames\ntorch_volpy.gui.core.channelmovie.read_frames\n(self, start: int, stop: optional[int] = none, step: int = 1, as_tensor: bool = true, dtype: optional[union[np.dtype, str]] = none, device: optional[union[str, torch.device]] = none)\nread grayscale frames from the selected channel.\nread grayscale frames from the selected channel.",
"signature": "(self, start: int, stop: Optional[int] = None, step: int = 1, as_tensor: bool = True, dtype: Optional[Union[np.dtype, str]] = None, device: Optional[Union[str, torch.device]] = None)",
"source": {
"endLine": 228,
"line": 214,
"path": "src/torch_volpy/gui/core.py"
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"summary": "Read grayscale frames from the selected channel."
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{
"decorators": [],
"docstring": "Open an HDF5 Movie or TIFF stack with a Movie-compatible interface.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "open_movie",
"public": true,
"qualifiedName": "torch_volpy.gui.core.open_movie",
"searchText": "function\nopen_movie\ntorch_volpy.gui.core.open_movie\n(path: pathlike, dataset: str = 'movie')\nopen an hdf5 movie or tiff stack with a movie-compatible interface.\nopen an hdf5 movie or tiff stack with a movie-compatible interface.",
"signature": "(path: PathLike, dataset: str = 'movie')",
"source": {
"endLine": 239,
"line": 231,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Open an HDF5 Movie or TIFF stack with a Movie-compatible interface."
},
{
"decorators": [],
"docstring": "Return the default HDF5 path corresponding to a source movie path.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "default_h5_path_for_movie",
"public": true,
"qualifiedName": "torch_volpy.gui.core.default_h5_path_for_movie",
"searchText": "function\ndefault_h5_path_for_movie\ntorch_volpy.gui.core.default_h5_path_for_movie\n(path: pathlike) -> path\nreturn the default hdf5 path corresponding to a source movie path.\nreturn the default hdf5 path corresponding to a source movie path.",
"signature": "(path: PathLike) -> Path",
"source": {
"endLine": 247,
"line": 242,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Return the default HDF5 path corresponding to a source movie path."
},
{
"decorators": [],
"docstring": "Return the default corrected HDF5 output path for a source movie.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "default_corrected_h5_path",
"public": true,
"qualifiedName": "torch_volpy.gui.core.default_corrected_h5_path",
"searchText": "function\ndefault_corrected_h5_path\ntorch_volpy.gui.core.default_corrected_h5_path\n(path: pathlike) -> path\nreturn the default corrected hdf5 output path for a source movie.\nreturn the default corrected hdf5 output path for a source movie.",
"signature": "(path: PathLike) -> Path",
"source": {
"endLine": 255,
"line": 250,
"path": "src/torch_volpy/gui/core.py"
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"summary": "Return the default corrected HDF5 output path for a source movie."
},
{
"decorators": [],
"docstring": "Convert a TIFF stack to HDF5 with optional percent progress callback.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "convert_tiff_to_h5",
"public": true,
"qualifiedName": "torch_volpy.gui.core.convert_tiff_to_h5",
"searchText": "function\nconvert_tiff_to_h5\ntorch_volpy.gui.core.convert_tiff_to_h5\n(tiff_path: pathlike, h5_path: optional[pathlike] = none, *, dataset: str = 'movie', overwrite: bool = false, chunk_frames: int = 16, compression: optional[str] = none, compression_opts: optional[int] = none, progress_callback = none) -> path\nconvert a tiff stack to hdf5 with optional percent progress callback.\nconvert a tiff stack to hdf5 with optional percent progress callback.",
"signature": "(tiff_path: PathLike, h5_path: Optional[PathLike] = None, *, dataset: str = 'movie', overwrite: bool = False, chunk_frames: int = 16, compression: Optional[str] = None, compression_opts: Optional[int] = None, progress_callback = None) -> Path",
"source": {
"endLine": 327,
"line": 258,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Convert a TIFF stack to HDF5 with optional percent progress callback."
},
{
"decorators": [],
"docstring": "Return whether an HDF5 dataset is marked as motion corrected.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "is_motion_corrected_h5",
"public": true,
"qualifiedName": "torch_volpy.gui.core.is_motion_corrected_h5",
"searchText": "function\nis_motion_corrected_h5\ntorch_volpy.gui.core.is_motion_corrected_h5\n(path: pathlike, dataset: str = 'movie') -> bool\nreturn whether an hdf5 dataset is marked as motion corrected.\nreturn whether an hdf5 dataset is marked as motion corrected.",
"signature": "(path: PathLike, dataset: str = 'movie') -> bool",
"source": {
"endLine": 341,
"line": 330,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Return whether an HDF5 dataset is marked as motion corrected."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Motion-correct a Movie-like object into a corrected HDF5 path.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "motion_correct_movie",
"public": true,
"qualifiedName": "torch_volpy.gui.core.motion_correct_movie",
"searchText": "function\nmotion_correct_movie\ntorch_volpy.gui.core.motion_correct_movie\n(movie, out_h5_path: optional[pathlike] = none, *, dataset: str = 'movie', overwrite: bool = false, max_shifts: union[int, tuple[int, int]] = (15, 15), frames_per_chunk: int = 256, device: union[str, torch.device] = 'cpu', progress_callback = none, source_path: optional[pathlike] = none, source_dataset: optional[str] = none, close_movie: bool = false, compression: optional[str] = none, compression_opts: optional[int] = none) -> path\nmotion-correct a movie-like object into a corrected hdf5 path.\nmotion-correct a movie-like object into a corrected hdf5 path.\ntorch.inference_mode",
"signature": "(movie, out_h5_path: Optional[PathLike] = None, *, dataset: str = 'movie', overwrite: bool = False, max_shifts: Union[int, Tuple[int, int]] = (15, 15), frames_per_chunk: int = 256, device: Union[str, torch.device] = 'cpu', progress_callback = None, source_path: Optional[PathLike] = None, source_dataset: Optional[str] = None, close_movie: bool = False, compression: Optional[str] = None, compression_opts: Optional[int] = None) -> Path",
"source": {
"endLine": 474,
"line": 345,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Motion-correct a Movie-like object into a corrected HDF5 path."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Motion-correct an HDF5 movie into a corrected HDF5 path.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "motion_correct_h5",
"public": true,
"qualifiedName": "torch_volpy.gui.core.motion_correct_h5",
"searchText": "function\nmotion_correct_h5\ntorch_volpy.gui.core.motion_correct_h5\n(h5_path: pathlike, out_h5_path: optional[pathlike] = none, *, dataset: str = 'movie', overwrite: bool = false, max_shifts: union[int, tuple[int, int]] = (15, 15), frames_per_chunk: int = 256, device: union[str, torch.device] = 'cpu', progress_callback = none, compression: optional[str] = none, compression_opts: optional[int] = none) -> path\nmotion-correct an hdf5 movie into a corrected hdf5 path.\nmotion-correct an hdf5 movie into a corrected hdf5 path.\ntorch.inference_mode",
"signature": "(h5_path: PathLike, out_h5_path: Optional[PathLike] = None, *, dataset: str = 'movie', overwrite: bool = False, max_shifts: Union[int, Tuple[int, int]] = (15, 15), frames_per_chunk: int = 256, device: Union[str, torch.device] = 'cpu', progress_callback = None, compression: Optional[str] = None, compression_opts: Optional[int] = None) -> Path",
"source": {
"endLine": 509,
"line": 478,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Motion-correct an HDF5 movie into a corrected HDF5 path."
},
{
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"kind": "function",
"module": "torch_volpy.gui.core",
"name": "_frame_index",
"public": false,
"qualifiedName": "torch_volpy.gui.core._frame_index",
"searchText": "function\n_frame_index\ntorch_volpy.gui.core._frame_index\n(movie, frame_index: int, channel: optional[int])",
"signature": "(movie, frame_index: int, channel: Optional[int])",
"source": {
"endLine": 520,
"line": 512,
"path": "src/torch_volpy/gui/core.py"
},
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},
{
"decorators": [],
"docstring": "Read one movie frame as a 2D numpy array for display.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "read_display_frame",
"public": true,
"qualifiedName": "torch_volpy.gui.core.read_display_frame",
"searchText": "function\nread_display_frame\ntorch_volpy.gui.core.read_display_frame\n(movie, frame_index: int, channel: optional[int] = none) -> np.ndarray\nread one movie frame as a 2d numpy array for display.\nread one movie frame as a 2d numpy array for display.",
"signature": "(movie, frame_index: int, channel: Optional[int] = None) -> np.ndarray",
"source": {
"endLine": 532,
"line": 523,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Read one movie frame as a 2D numpy array for display."
},
{
"decorators": [],
"docstring": "Robustly normalize a 2D image into uint8 for Qt display.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "normalize_to_uint8",
"public": true,
"qualifiedName": "torch_volpy.gui.core.normalize_to_uint8",
"searchText": "function\nnormalize_to_uint8\ntorch_volpy.gui.core.normalize_to_uint8\n(image: np.ndarray, low_percentile: float = 1.0, high_percentile: float = 99.5) -> np.ndarray\nrobustly normalize a 2d image into uint8 for qt display.\nrobustly normalize a 2d image into uint8 for qt display.",
"signature": "(image: np.ndarray, low_percentile: float = 1.0, high_percentile: float = 99.5) -> np.ndarray",
"source": {
"endLine": 559,
"line": 535,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Robustly normalize a 2D image into uint8 for Qt display."
},
{
"decorators": [],
"docstring": "Release Python references and flush CUDA allocator caches when available.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "release_torch_memory",
"public": true,
"qualifiedName": "torch_volpy.gui.core.release_torch_memory",
"searchText": "function\nrelease_torch_memory\ntorch_volpy.gui.core.release_torch_memory\n(device: optional[union[str, torch.device]] = none, *, flush_cuda_cache: bool = true) -> none\nrelease python references and flush cuda allocator caches when available.\nrelease python references and flush cuda allocator caches when available.",
"signature": "(device: Optional[Union[str, torch.device]] = None, *, flush_cuda_cache: bool = True) -> None",
"source": {
"endLine": 584,
"line": 562,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Release Python references and flush CUDA allocator caches when available."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Build the Cellpose input image used in the project tests: `[mean, mean, corr]`.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "build_summary_image",
"public": true,
"qualifiedName": "torch_volpy.gui.core.build_summary_image",
"searchText": "function\nbuild_summary_image\ntorch_volpy.gui.core.build_summary_image\n(movie, *, channel: optional[int] = none, window_size: int = 1000, baseline_percentile: float = 8.0, device: union[str, torch.device] = 'cpu') -> torch.tensor\nbuild the cellpose input image used in the project tests: `[mean, mean, corr]`.\nbuild the cellpose input image used in the project tests: `[mean, mean, corr]`.\ntorch.inference_mode",
"signature": "(movie, *, channel: Optional[int] = None, window_size: int = 1000, baseline_percentile: float = 8.0, device: Union[str, torch.device] = 'cpu') -> torch.Tensor",
"source": {
"endLine": 606,
"line": 588,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Build the Cellpose input image used in the project tests: `[mean, mean, corr]`."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Build a summary image and run Cellpose segmentation to produce labeled ROIs.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "build_cellpose_rois",
"public": true,
"qualifiedName": "torch_volpy.gui.core.build_cellpose_rois",
"searchText": "function\nbuild_cellpose_rois\ntorch_volpy.gui.core.build_cellpose_rois\n(movie, *, model_path: pathlike, channel: optional[int] = none, summary_window_size: int = 1000, baseline_percentile: float = 8.0, device: union[str, torch.device] = 'cpu', gpu: bool = true, save_to_disk: bool = false, save_dir: optional[pathlike] = none) -> tuple[np.ndarray, torch.tensor]\nbuild a summary image and run cellpose segmentation to produce labeled rois.\nbuild a summary image and run cellpose segmentation to produce labeled rois.\ntorch.inference_mode",
"signature": "(movie, *, model_path: PathLike, channel: Optional[int] = None, summary_window_size: int = 1000, baseline_percentile: float = 8.0, device: Union[str, torch.device] = 'cpu', gpu: bool = True, save_to_disk: bool = False, save_dir: Optional[PathLike] = None) -> Tuple[np.ndarray, torch.Tensor]",
"source": {
"endLine": 655,
"line": 610,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Build a summary image and run Cellpose segmentation to produce labeled ROIs."
},
{
"decorators": [],
"docstring": "Convert an image-space rectangle `(x0, y0, x1, y1)` to a labeled mask.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "rectangle_to_mask",
"public": true,
"qualifiedName": "torch_volpy.gui.core.rectangle_to_mask",
"searchText": "function\nrectangle_to_mask\ntorch_volpy.gui.core.rectangle_to_mask\n(rect: rect, shape: tuple[int, int], label: int = 1) -> np.ndarray\nconvert an image-space rectangle `(x0, y0, x1, y1)` to a labeled mask.\nconvert an image-space rectangle `(x0, y0, x1, y1)` to a labeled mask.",
"signature": "(rect: Rect, shape: Tuple[int, int], label: int = 1) -> np.ndarray",
"source": {
"endLine": 677,
"line": 658,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Convert an image-space rectangle `(x0, y0, x1, y1)` to a labeled mask."
},
{
"decorators": [],
"docstring": "Rasterize a polygon into a labeled mask using pixel-center tests.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "polygon_to_mask",
"public": true,
"qualifiedName": "torch_volpy.gui.core.polygon_to_mask",
"searchText": "function\npolygon_to_mask\ntorch_volpy.gui.core.polygon_to_mask\n(points: sequence[point], shape: tuple[int, int], label: int = 1) -> np.ndarray\nrasterize a polygon into a labeled mask using pixel-center tests.\nrasterize a polygon into a labeled mask using pixel-center tests.",
"signature": "(points: Sequence[Point], shape: Tuple[int, int], label: int = 1) -> np.ndarray",
"source": {
"endLine": 708,
"line": 680,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Rasterize a polygon into a labeled mask using pixel-center tests."
},
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"decorators": [],
"docstring": "Rasterize a freehand stroke into a labeled mask.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "freehand_to_mask",
"public": true,
"qualifiedName": "torch_volpy.gui.core.freehand_to_mask",
"searchText": "function\nfreehand_to_mask\ntorch_volpy.gui.core.freehand_to_mask\n(points: sequence[point], shape: tuple[int, int], radius: float = 2.5, label: int = 1, fill_closed: bool = true) -> np.ndarray\nrasterize a freehand stroke into a labeled mask.\nrasterize a freehand stroke into a labeled mask.",
"signature": "(points: Sequence[Point], shape: Tuple[int, int], radius: float = 2.5, label: int = 1, fill_closed: bool = True) -> np.ndarray",
"source": {
"endLine": 764,
"line": 711,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Rasterize a freehand stroke into a labeled mask."
},
{
"decorators": [],
"docstring": "Return non-background ROI ids present in a label mask or instance stack.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "available_roi_ids",
"public": true,
"qualifiedName": "torch_volpy.gui.core.available_roi_ids",
"searchText": "function\navailable_roi_ids\ntorch_volpy.gui.core.available_roi_ids\n(mask: np.ndarray) -> np.ndarray\nreturn non-background roi ids present in a label mask or instance stack.\nreturn non-background roi ids present in a label mask or instance stack.",
"signature": "(mask: np.ndarray) -> np.ndarray",
"source": {
"endLine": 776,
"line": 767,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Return non-background ROI ids present in a label mask or instance stack."
},
{
"decorators": [],
"docstring": "Return `(x0, y0, x1, y1)` bounds for a labeled ROI.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "bounding_box_from_mask",
"public": true,
"qualifiedName": "torch_volpy.gui.core.bounding_box_from_mask",
"searchText": "function\nbounding_box_from_mask\ntorch_volpy.gui.core.bounding_box_from_mask\n(mask: np.ndarray, roi_id: int = 1, padding: int = 0) -> tuple[int, int, int, int]\nreturn `(x0, y0, x1, y1)` bounds for a labeled roi.\nreturn `(x0, y0, x1, y1)` bounds for a labeled roi.",
"signature": "(mask: np.ndarray, roi_id: int = 1, padding: int = 0) -> Tuple[int, int, int, int]",
"source": {
"endLine": 799,
"line": 779,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Return `(x0, y0, x1, y1)` bounds for a labeled ROI."
},
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"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "_movie_read_batch",
"public": false,
"qualifiedName": "torch_volpy.gui.core._movie_read_batch",
"searchText": "function\n_movie_read_batch\ntorch_volpy.gui.core._movie_read_batch\n(movie, start: int, stop: int, channel: optional[int], device: torch.device)",
"signature": "(movie, start: int, stop: int, channel: Optional[int], device: torch.device)",
"source": {
"endLine": 813,
"line": 802,
"path": "src/torch_volpy/gui/core.py"
},
"summary": ""
},
{
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],
"docstring": "Extract mean intensity traces for multiple labeled ROIs in one pass.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "extract_mean_traces",
"public": true,
"qualifiedName": "torch_volpy.gui.core.extract_mean_traces",
"searchText": "function\nextract_mean_traces\ntorch_volpy.gui.core.extract_mean_traces\n(movie, roi_mask: np.ndarray, roi_ids: optional[iterable[int]] = none, *, channel: optional[int] = none, batch_size: int = 256, device: union[str, torch.device] = 'cpu') -> dict[int, torch.tensor]\nextract mean intensity traces for multiple labeled rois in one pass.\nextract mean intensity traces for multiple labeled rois in one pass.\ntorch.inference_mode",
"signature": "(movie, roi_mask: np.ndarray, roi_ids: Optional[Iterable[int]] = None, *, channel: Optional[int] = None, batch_size: int = 256, device: Union[str, torch.device] = 'cpu') -> dict[int, torch.Tensor]",
"source": {
"endLine": 897,
"line": 817,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Extract mean intensity traces for multiple labeled ROIs in one pass."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Extract a simple mean intensity trace from one labeled ROI.",
"kind": "function",
"module": "torch_volpy.gui.core",
"name": "extract_mean_trace",
"public": true,
"qualifiedName": "torch_volpy.gui.core.extract_mean_trace",
"searchText": "function\nextract_mean_trace\ntorch_volpy.gui.core.extract_mean_trace\n(movie, roi_mask: np.ndarray, roi_id: int = 1, *, channel: optional[int] = none, batch_size: int = 256, device: union[str, torch.device] = 'cpu') -> torch.tensor\nextract a simple mean intensity trace from one labeled roi.\nextract a simple mean intensity trace from one labeled roi.\ntorch.inference_mode",
"signature": "(movie, roi_mask: np.ndarray, roi_id: int = 1, *, channel: Optional[int] = None, batch_size: int = 256, device: Union[str, torch.device] = 'cpu') -> torch.Tensor",
"source": {
"endLine": 919,
"line": 901,
"path": "src/torch_volpy/gui/core.py"
},
"summary": "Extract a simple mean intensity trace from one labeled ROI."
},
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"signature": "(movie, roi_mask: np.ndarray, roi_id: int = 1, *, channel: Optional[int] = None, padding: int = 0, device: Union[str, torch.device] = 'cpu') -> Tuple[torch.Tensor, Tuple[int, int, int, int]]",
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"path": "src/torch_volpy/gui/core.py"
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"summary": "Read the ROI bounding crop and return it as `[H, W, T]` for ALI."
},
{
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"docstring": "Run SpikePursuit extraction for one ROI from the GUI pipeline.",
"kind": "function",
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"signature": "(movie, roi_mask: np.ndarray, roi_id: int, *, frame_rate: float, channel: Optional[int] = None, device: Union[str, torch.device] = 'cpu', flip_signal: bool = True, **spikepursuit_options)",
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"kind": "function",
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"kind": "constant",
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"source": {
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"searchText": "function\n_float_mask_threshold\ntorch_volpy.gui.core._float_mask_threshold\n(arr: np.ndarray) -> float",
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"signature": "(path: PathLike, dataset: Optional[str] = None, *, preserve_instances: bool = False) -> np.ndarray",
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"kind": "constant",
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},
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"kind": "constant",
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{
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"kind": "constant",
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{
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"kind": "constant",
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"searchText": "constant\nalign_top\ntorch_volpy.gui.qt_app.align_top\n_qt_enum('alignmentflag', 'aligntop')",
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{
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"kind": "constant",
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"searchText": "constant\nevent_mouse_button_press\ntorch_volpy.gui.qt_app.event_mouse_button_press\n_event_enum('mousebuttonpress')",
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"searchText": "constant\nevent_mouse_button_release\ntorch_volpy.gui.qt_app.event_mouse_button_release\n_event_enum('mousebuttonrelease')",
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"searchText": "constant\nevent_window_deactivate\ntorch_volpy.gui.qt_app.event_window_deactivate\n_event_enum('windowdeactivate')",
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"searchText": "constant\nspin_down_indicator\ntorch_volpy.gui.qt_app.spin_down_indicator\n_style_enum('primitiveelement', 'pe_indicatorspindown')",
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"searchText": "constant\nstate_enabled\ntorch_volpy.gui.qt_app.state_enabled\n_style_enum('stateflag', 'state_enabled')",
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"searchText": "constant\ndefault_window_width\ntorch_volpy.gui.qt_app.default_window_width\n1560",
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"module": "torch_volpy.gui.qt_app",
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"searchText": "constant\nmin_window_width\ntorch_volpy.gui.qt_app.min_window_width\n820",
"signature": "",
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"module": "torch_volpy.gui.qt_app",
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"searchText": "constant\nmin_window_height\ntorch_volpy.gui.qt_app.min_window_height\n520",
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"public": true,
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"searchText": "constant\ninitial_screen_width_fraction\ntorch_volpy.gui.qt_app.initial_screen_width_fraction\n0.92",
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"searchText": "constant\ninitial_screen_height_fraction\ntorch_volpy.gui.qt_app.initial_screen_height_fraction\n0.9",
"signature": "",
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{
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"name": "MOVIE_VIEW_MIN_HEIGHT",
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"searchText": "constant\nmovie_view_min_height\ntorch_volpy.gui.qt_app.movie_view_min_height\n120",
"signature": "",
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"name": "ROI_MASK_HISTORY_LIMIT",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ROI_MASK_HISTORY_LIMIT",
"searchText": "constant\nroi_mask_history_limit\ntorch_volpy.gui.qt_app.roi_mask_history_limit\n32",
"signature": "",
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"module": "torch_volpy.gui.qt_app",
"name": "MOVIE_ZOOM_MIN_PERCENT",
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"qualifiedName": "torch_volpy.gui.qt_app.MOVIE_ZOOM_MIN_PERCENT",
"searchText": "constant\nmovie_zoom_min_percent\ntorch_volpy.gui.qt_app.movie_zoom_min_percent\n10.0",
"signature": "",
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"kind": "constant",
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"name": "MOVIE_ZOOM_MAX_PERCENT",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MOVIE_ZOOM_MAX_PERCENT",
"searchText": "constant\nmovie_zoom_max_percent\ntorch_volpy.gui.qt_app.movie_zoom_max_percent\n100000.0",
"signature": "",
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{
"decorators": [],
"default": "'\\nQWidget {\\n color: #0f172a;\\n font-family: \"Segoe UI\";\\n font-size: 13px;\\n}\\n\\nQMainWindow,\\nQDialog#advancedOptionsDialog,\\nQDialog#traceWindow,\\nQWidget#appRoot,\\nQWidget#workspaceBody,\\nQWidget#centerWorkspace,\\nQWidget#controlPanel,\\nQWidget#inspectorPanel,\\nQScrollArea#controlsScroll,\\nQScrollArea#controlsScroll > QWidget > QWidget {\\n background-color: #f3f6fb;\\n}\\n\\nQWidget#topBar {\\n background-color: #ffffff;\\n border-bottom: 1px solid #dfe7f2;\\n}\\n\\nQLabel#topAppTitle {\\n color: #0f172a;\\n font-size: 18px;\\n font-weight: 800;\\n}\\n\\nQLabel#versionBadge,\\nQLabel#footerMetric,\\nQLabel[role=\"muted\"] {\\n color: #64748b;\\n}\\n\\nQLabel#versionBadge {\\n padding: 1px 10px;\\n border-left: 1px solid #e2e8f0;\\n}\\n\\nQWidget#footerBar {\\n background-color: #ffffff;\\n border-top: 1px solid #dfe7f2;\\n}\\n\\nQLabel#readyDot {\\n background-color: #10b981;\\n border-radius: 5px;\\n}\\n\\nQLabel#footerMetric {\\n padding: 0 12px;\\n border-left: 1px solid #e2e8f0;\\n}\\n\\nQWidget#tracePanel,\\nQDialog#traceWindow,\\nQFrame#inspectorCard,\\nQFrame#metricTile {\\n background-color: #ffffff;\\n border: 1px solid #dfe7f2;\\n border-radius: 8px;\\n}\\n\\nQFrame#viewerCard {\\n background-color: #ffffff;\\n border: none;\\n border-radius: 8px;\\n}\\n\\nQWidget#viewerToolbar,\\nQWidget#traceTabs,\\nQWidget#inspectorTabs {\\n background-color: #ffffff;\\n border-bottom: 1px solid #e2e8f0;\\n}\\n\\nQWidget#tracePlotArea {\\n background-color: #ffffff;\\n}\\n\\nQLabel[role=\"eyebrow\"] {\\n color: #334155;\\n font-size: 11px;\\n font-weight: 800;\\n letter-spacing: 0.4px;\\n text-transform: uppercase;\\n}\\n\\nQLabel[role=\"hero\"] {\\n color: #0f172a;\\n font-size: 20px;\\n font-weight: 700;\\n}\\n\\nQLabel[role=\"metricLabel\"] {\\n color: #475569;\\n font-size: 11px;\\n}\\n\\nQLabel[role=\"metricValue\"] {\\n color: #0f172a;\\n font-size: 18px;\\n font-weight: 700;\\n}\\n\\nQLabel[role=\"successValue\"] {\\n color: #059669;\\n font-size: 18px;\\n font-weight: 700;\\n}\\n\\nQLabel[role=\"pill\"],\\nQLabel[role=\"okPill\"] {\\n background-color: #eff6ff;\\n color: #1d4ed8;\\n border: 1px solid #bfdbfe;\\n border-radius: 5px;\\n padding: 4px 8px;\\n font-weight: 600;\\n}\\n\\nQLabel[role=\"okPill\"] {\\n background-color: #dcfce7;\\n color: #047857;\\n border-color: #a7f3d0;\\n}\\n\\nQLabel[role=\"warningDot\"] {\\n background-color: #facc15;\\n border-radius: 7px;\\n}\\n\\nQFrame#verticalDivider {\\n background-color: #e2e8f0;\\n border: none;\\n}\\n\\nQScrollArea#controlsScroll {\\n border: none;\\n background-color: transparent;\\n}\\n\\nQScrollArea#advancedOptionsScroll,\\nQWidget#advancedOptionsViewport,\\nQWidget#advancedOptionsForm {\\n background-color: #f3f6fb;\\n}\\n\\nQFrame#advancedOptionsPanel {\\n background-color: #f3f6fb;\\n border: 1px solid #cbd5e1;\\n border-radius: 8px;\\n}\\n\\nQScrollArea#advancedOptionsScroll {\\n border: none;\\n}\\n\\nQWidget#sidebarHeader {\\n background-color: transparent;\\n margin-bottom: 2px;\\n}\\n\\nQLabel#appMark {\\n background-color: #0f766e;\\n border-radius: 6px;\\n}\\n\\nQLabel#appTitle {\\n color: #0f172a;\\n font-size: 18px;\\n font-weight: 700;\\n}\\n\\nQLabel#appSubtitle {\\n color: #64748b;\\n}\\n\\nQLabel#frameCounter {\\n color: #334155;\\n background-color: #f8fafc;\\n border: 1px solid #e2e8f0;\\n border-radius: 6px;\\n padding: 6px 8px;\\n font-weight: 600;\\n}\\n\\nQLabel[role=\"status\"] {\\n color: #334155;\\n background-color: #f8fafc;\\n border: 1px solid #e2e8f0;\\n border-radius: 6px;\\n padding: 7px 8px;\\n}\\n\\nQGroupBox {\\n background-color: #ffffff;\\n border: 1px solid #dbe3ef;\\n border-radius: 8px;\\n margin-top: 0;\\n}\\n\\nQLineEdit,\\nQComboBox,\\nQSpinBox,\\nQDoubleSpinBox {\\n background-color: #ffffff;\\n border: 1px solid #cbd5e1;\\n border-radius: 6px;\\n padding: 5px 8px;\\n padding-right: 26px;\\n min-height: 24px;\\n selection-background-color: #2563eb;\\n}\\n\\nQLabel#sectionTitle {\\n color: #0f172a;\\n font-size: 14px;\\n font-weight: 700;\\n padding: 0 0 6px 0;\\n}\\n\\nQLineEdit:hover,\\nQComboBox:hover,\\nQSpinBox:hover,\\nQDoubleSpinBox:hover {\\n border-color: #94a3b8;\\n}\\n\\nQLineEdit:focus,\\nQComboBox:focus,\\nQSpinBox:focus,\\nQDoubleSpinBox:focus {\\n border: 1px solid #2563eb;\\n}\\n\\nQLineEdit:disabled,\\nQComboBox:disabled,\\nQSpinBox:disabled,\\nQDoubleSpinBox:disabled {\\n background-color: #f1f5f9;\\n border-color: #dbe3ef;\\n color: #94a3b8;\\n}\\n\\nQComboBox::drop-down {\\n border: none;\\n width: 26px;\\n}\\n\\nQComboBox::down-arrow {\\n image: url(\"__COMBOBOX_DOWN_ARROW__\");\\n width: 11px;\\n height: 11px;\\n}\\n\\nQComboBox::down-arrow:disabled {\\n image: url(\"__COMBOBOX_DOWN_ARROW_DISABLED__\");\\n}\\n\\nQComboBox QAbstractItemView {\\n background-color: #ffffff;\\n border: 1px solid #cbd5e1;\\n border-radius: 0;\\n color: #0f172a;\\n outline: 0;\\n padding: 3px;\\n selection-background-color: #dbeafe;\\n selection-color: #1d4ed8;\\n}\\n\\nQFrame#comboPopup {\\n background-color: #ffffff;\\n border: 1px solid #cbd5e1;\\n border-radius: 6px;\\n}\\n\\nQFrame#comboPopup QListView {\\n background-color: #ffffff;\\n border: none;\\n border-radius: 5px;\\n color: #0f172a;\\n outline: 0;\\n padding: 3px;\\n selection-background-color: #dbeafe;\\n selection-color: #1d4ed8;\\n}\\n\\nQComboBox QAbstractItemView::item,\\nQListView::item {\\n min-height: 28px;\\n padding: 6px 10px;\\n border-radius: 4px;\\n}\\n\\nQComboBox QAbstractItemView::item:hover,\\nQListView::item:hover {\\n background-color: #f1f5f9;\\n}\\n\\nQComboBox QAbstractItemView::item:selected,\\nQListView::item:selected {\\n background-color: #dbeafe;\\n color: #1d4ed8;\\n}\\n\\nQPushButton {\\n background-color: #ffffff;\\n border: 1px solid #cbd5e1;\\n border-radius: 6px;\\n color: #1e293b;\\n font-weight: 600;\\n min-height: 30px;\\n padding: 6px 10px;\\n}\\n\\nQPushButton:hover {\\n background-color: #f8fafc;\\n border-color: #94a3b8;\\n}\\n\\nQPushButton:pressed {\\n background-color: #e2e8f0;\\n}\\n\\nQPushButton:disabled {\\n background-color: #f1f5f9;\\n border-color: #dbe3ef;\\n color: #94a3b8;\\n}\\n\\nQPushButton[variant=\"primary\"] {\\n background-color: #2563eb;\\n border-color: #2563eb;\\n color: #ffffff;\\n}\\n\\nQPushButton[variant=\"primary\"]:hover {\\n background-color: #1d4ed8;\\n border-color: #1d4ed8;\\n}\\n\\nQPushButton[variant=\"primary\"]:pressed {\\n background-color: #1e40af;\\n border-color: #1e40af;\\n}\\n\\nQPushButton[variant=\"primary\"]:disabled {\\n background-color: #cbd5e1;\\n border-color: #cbd5e1;\\n color: #f8fafc;\\n}\\n\\nQPushButton[variant=\"secondary\"] {\\n background-color: #f8fafc;\\n}\\n\\nQPushButton[variant=\"toolbar\"],\\nQPushButton[variant=\"toggle\"],\\nQPushButton[variant=\"tab\"],\\nQPushButton[variant=\"link\"] {\\n background-color: transparent;\\n border-color: transparent;\\n min-height: 26px;\\n padding: 4px 8px;\\n color: #1e293b;\\n}\\n\\nQPushButton[variant=\"toolbar\"]:hover,\\nQPushButton[variant=\"toggle\"]:hover,\\nQPushButton[variant=\"tab\"]:hover {\\n background-color: #f1f5f9;\\n border-color: #e2e8f0;\\n}\\n\\nQPushButton[variant=\"toggle\"]:checked {\\n background-color: #2563eb;\\n border-color: #2563eb;\\n color: #ffffff;\\n}\\n\\nQPushButton[variant=\"tab\"] {\\n border-radius: 0;\\n border-bottom: 2px solid transparent;\\n font-weight: 700;\\n color: #334155;\\n}\\n\\nQPushButton[variant=\"tab\"]:checked {\\n color: #2563eb;\\n border-bottom-color: #2563eb;\\n}\\n\\nQPushButton[variant=\"link\"] {\\n color: #2563eb;\\n font-weight: 700;\\n}\\n\\nQPushButton[variant=\"quiet\"] {\\n background-color: transparent;\\n border-color: transparent;\\n}\\n\\nQPushButton[variant=\"icon\"] {\\n background-color: rgba(255, 255, 255, 210);\\n border: 1px solid #cbd5e1;\\n border-radius: 6px;\\n min-height: 24px;\\n min-width: 26px;\\n padding: 0;\\n}\\n\\nQPushButton[variant=\"iconQuiet\"] {\\n background-color: transparent;\\n border: none;\\n border-radius: 6px;\\n min-height: 24px;\\n min-width: 26px;\\n padding: 0;\\n}\\n\\nQPushButton[variant=\"iconQuiet\"]:hover {\\n background-color: #f1f5f9;\\n}\\n\\nQPushButton[variant=\"iconQuiet\"]:pressed {\\n background-color: #e2e8f0;\\n}\\n\\nQPushButton[variant=\"iconQuiet\"]:disabled {\\n background-color: transparent;\\n color: #94a3b8;\\n}\\n\\nQProgressBar {\\n background-color: #e2e8f0;\\n border: none;\\n border-radius: 4px;\\n height: 8px;\\n text-align: center;\\n color: transparent;\\n}\\n\\nQProgressBar::chunk {\\n background-color: #0f766e;\\n border-radius: 4px;\\n}\\n\\nQSlider {\\n min-height: 28px;\\n}\\n\\nQSlider::groove:horizontal {\\n height: 4px;\\n background-color: #dbe3ef;\\n border-radius: 2px;\\n margin: 0 8px;\\n}\\n\\nQSlider::sub-page:horizontal {\\n background-color: #2563eb;\\n border-radius: 2px;\\n margin: 0 8px;\\n}\\n\\nQSlider::add-page:horizontal {\\n background-color: #dbe3ef;\\n border-radius: 2px;\\n margin: 0 8px;\\n}\\n\\nQSlider::handle:horizontal {\\n background-color: #2563eb;\\n border: 2px solid #ffffff;\\n width: 16px;\\n height: 16px;\\n margin: -6px 0;\\n border-radius: 8px;\\n}\\n\\nQSlider::groove:horizontal:disabled {\\n background-color: #e2e8f0;\\n}\\n\\nQSlider::sub-page:horizontal:disabled,\\nQSlider::add-page:horizontal:disabled {\\n background-color: #e2e8f0;\\n}\\n\\nQSlider::handle:horizontal:disabled {\\n background-color: #cbd5e1;\\n border-color: #f8fafc;\\n}\\n\\nQCheckBox {\\n spacing: 8px;\\n color: #334155;\\n}\\n\\nQGraphicsView#movieView {\\n background-color: #f8fafc;\\n border: none;\\n}\\n\\nQFrame#roiToolIsland {\\n background-color: rgba(255, 255, 255, 236);\\n border: 1px solid #dfe7f2;\\n border-radius: 8px;\\n}\\n\\nQPushButton[variant=\"roiTool\"] {\\n background-color: transparent;\\n border: 1px solid transparent;\\n border-radius: 6px;\\n min-height: 28px;\\n min-width: 30px;\\n padding: 0;\\n}\\n\\nQPushButton[variant=\"roiTool\"]:hover {\\n background-color: #f1f5f9;\\n border-color: #e2e8f0;\\n}\\n\\nQPushButton[variant=\"roiTool\"]:checked {\\n background-color: #dbeafe;\\n border-color: #60a5fa;\\n}\\n\\nQWidget#tracePanel,\\nQWidget#traceContent {\\n background-color: #ffffff;\\n}\\n\\nQWidget#traceControls {\\n background-color: #ffffff;\\n border-top: 1px solid #e2e8f0;\\n}\\n\\nQWidget#traceRestoreBar {\\n background-color: #f8fafc;\\n border-top: 1px solid #cbd5e1;\\n}\\n\\nQScrollBar:vertical {\\n background-color: transparent;\\n width: 14px;\\n margin: 2px 0;\\n}\\n\\nQScrollBar::handle:vertical {\\n background-color: #cbd5e1;\\n border-radius: 3px;\\n min-height: 28px;\\n margin: 0 4px;\\n}\\n\\nQScrollBar::handle:vertical:hover {\\n background-color: #94a3b8;\\n}\\n\\nQScrollBar::add-page:vertical,\\nQScrollBar::sub-page:vertical {\\n background-color: transparent;\\n}\\n\\nQScrollBar::add-line:vertical,\\nQScrollBar::sub-line:vertical {\\n height: 0;\\n}\\n\\nQWidget#roundedTooltip QLabel {\\n background-color: transparent;\\n color: #f8fafc;\\n}\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "APP_STYLESHEET",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.APP_STYLESHEET",
"searchText": "constant\napp_stylesheet\ntorch_volpy.gui.qt_app.app_stylesheet\n'\\nqwidget {\\n color: #0f172a;\\n font-family: \"segoe ui\";\\n font-size: 13px;\\n}\\n\\nqmainwindow,\\nqdialog#advancedoptionsdialog,\\nqdialog#tracewindow,\\nqwidget#approot,\\nqwidget#workspacebody,\\nqwidget#centerworkspace,\\nqwidget#controlpanel,\\nqwidget#inspectorpanel,\\nqscrollarea#controlsscroll,\\nqscrollarea#controlsscroll > qwidget > qwidget {\\n background-color: #f3f6fb;\\n}\\n\\nqwidget#topbar {\\n background-color: #ffffff;\\n border-bottom: 1px solid #dfe7f2;\\n}\\n\\nqlabel#topapptitle {\\n color: #0f172a;\\n font-size: 18px;\\n font-weight: 800;\\n}\\n\\nqlabel#versionbadge,\\nqlabel#footermetric,\\nqlabel[role=\"muted\"] {\\n color: #64748b;\\n}\\n\\nqlabel#versionbadge {\\n padding: 1px 10px;\\n border-left: 1px solid #e2e8f0;\\n}\\n\\nqwidget#footerbar {\\n background-color: #ffffff;\\n border-top: 1px solid #dfe7f2;\\n}\\n\\nqlabel#readydot {\\n background-color: #10b981;\\n border-radius: 5px;\\n}\\n\\nqlabel#footermetric {\\n padding: 0 12px;\\n border-left: 1px solid #e2e8f0;\\n}\\n\\nqwidget#tracepanel,\\nqdialog#tracewindow,\\nqframe#inspectorcard,\\nqframe#metrictile {\\n background-color: #ffffff;\\n border: 1px solid #dfe7f2;\\n border-radius: 8px;\\n}\\n\\nqframe#viewercard {\\n background-color: #ffffff;\\n border: none;\\n border-radius: 8px;\\n}\\n\\nqwidget#viewertoolbar,\\nqwidget#tracetabs,\\nqwidget#inspectortabs {\\n background-color: #ffffff;\\n border-bottom: 1px solid #e2e8f0;\\n}\\n\\nqwidget#traceplotarea {\\n background-color: #ffffff;\\n}\\n\\nqlabel[role=\"eyebrow\"] {\\n color: #334155;\\n font-size: 11px;\\n font-weight: 800;\\n letter-spacing: 0.4px;\\n text-transform: uppercase;\\n}\\n\\nqlabel[role=\"hero\"] {\\n color: #0f172a;\\n font-size: 20px;\\n font-weight: 700;\\n}\\n\\nqlabel[role=\"metriclabel\"] {\\n color: #475569;\\n font-size: 11px;\\n}\\n\\nqlabel[role=\"metricvalue\"] {\\n color: #0f172a;\\n font-size: 18px;\\n font-weight: 700;\\n}\\n\\nqlabel[role=\"successvalue\"] {\\n color: #059669;\\n font-size: 18px;\\n font-weight: 700;\\n}\\n\\nqlabel[role=\"pill\"],\\nqlabel[role=\"okpill\"] {\\n background-color: #eff6ff;\\n color: #1d4ed8;\\n border: 1px solid #bfdbfe;\\n border-radius: 5px;\\n padding: 4px 8px;\\n font-weight: 600;\\n}\\n\\nqlabel[role=\"okpill\"] {\\n background-color: #dcfce7;\\n color: #047857;\\n border-color: #a7f3d0;\\n}\\n\\nqlabel[role=\"warningdot\"] {\\n background-color: #facc15;\\n border-radius: 7px;\\n}\\n\\nqframe#verticaldivider {\\n background-color: #e2e8f0;\\n border: none;\\n}\\n\\nqscrollarea#controlsscroll {\\n border: none;\\n background-color: transparent;\\n}\\n\\nqscrollarea#advancedoptionsscroll,\\nqwidget#advancedoptionsviewport,\\nqwidget#advancedoptionsform {\\n background-color: #f3f6fb;\\n}\\n\\nqframe#advancedoptionspanel {\\n background-color: #f3f6fb;\\n border: 1px solid #cbd5e1;\\n border-radius: 8px;\\n}\\n\\nqscrollarea#advancedoptionsscroll {\\n border: none;\\n}\\n\\nqwidget#sidebarheader {\\n background-color: transparent;\\n margin-bottom: 2px;\\n}\\n\\nqlabel#appmark {\\n background-color: #0f766e;\\n border-radius: 6px;\\n}\\n\\nqlabel#apptitle {\\n color: #0f172a;\\n font-size: 18px;\\n font-weight: 700;\\n}\\n\\nqlabel#appsubtitle {\\n color: #64748b;\\n}\\n\\nqlabel#framecounter {\\n color: #334155;\\n background-color: #f8fafc;\\n border: 1px solid #e2e8f0;\\n border-radius: 6px;\\n padding: 6px 8px;\\n font-weight: 600;\\n}\\n\\nqlabel[role=\"status\"] {\\n color: #334155;\\n background-color: #f8fafc;\\n border: 1px solid #e2e8f0;\\n border-radius: 6px;\\n padding: 7px 8px;\\n}\\n\\nqgroupbox {\\n background-color: #ffffff;\\n border: 1px solid #dbe3ef;\\n border-radius: 8px;\\n margin-top: 0;\\n}\\n\\nqlineedit,\\nqcombobox,\\nqspinbox,\\nqdoublespinbox {\\n background-color: #ffffff;\\n border: 1px solid #cbd5e1;\\n border-radius: 6px;\\n padding: 5px 8px;\\n padding-right: 26px;\\n min-height: 24px;\\n selection-background-color: #2563eb;\\n}\\n\\nqlabel#sectiontitle {\\n color: #0f172a;\\n font-size: 14px;\\n font-weight: 700;\\n padding: 0 0 6px 0;\\n}\\n\\nqlineedit:hover,\\nqcombobox:hover,\\nqspinbox:hover,\\nqdoublespinbox:hover {\\n border-color: #94a3b8;\\n}\\n\\nqlineedit:focus,\\nqcombobox:focus,\\nqspinbox:focus,\\nqdoublespinbox:focus {\\n border: 1px solid #2563eb;\\n}\\n\\nqlineedit:disabled,\\nqcombobox:disabled,\\nqspinbox:disabled,\\nqdoublespinbox:disabled {\\n background-color: #f1f5f9;\\n border-color: #dbe3ef;\\n color: #94a3b8;\\n}\\n\\nqcombobox::drop-down {\\n border: none;\\n width: 26px;\\n}\\n\\nqcombobox::down-arrow {\\n image: url(\"__combobox_down_arrow__\");\\n width: 11px;\\n height: 11px;\\n}\\n\\nqcombobox::down-arrow:disabled {\\n image: url(\"__combobox_down_arrow_disabled__\");\\n}\\n\\nqcombobox qabstractitemview {\\n background-color: #ffffff;\\n border: 1px solid #cbd5e1;\\n border-radius: 0;\\n color: #0f172a;\\n outline: 0;\\n padding: 3px;\\n selection-background-color: #dbeafe;\\n selection-color: #1d4ed8;\\n}\\n\\nqframe#combopopup {\\n background-color: #ffffff;\\n border: 1px solid #cbd5e1;\\n border-radius: 6px;\\n}\\n\\nqframe#combopopup qlistview {\\n background-color: #ffffff;\\n border: none;\\n border-radius: 5px;\\n color: #0f172a;\\n outline: 0;\\n padding: 3px;\\n selection-background-color: #dbeafe;\\n selection-color: #1d4ed8;\\n}\\n\\nqcombobox qabstractitemview::item,\\nqlistview::item {\\n min-height: 28px;\\n padding: 6px 10px;\\n border-radius: 4px;\\n}\\n\\nqcombobox qabstractitemview::item:hover,\\nqlistview::item:hover {\\n background-color: #f1f5f9;\\n}\\n\\nqcombobox qabstractitemview::item:selected,\\nqlistview::item:selected {\\n background-color: #dbeafe;\\n color: #1d4ed8;\\n}\\n\\nqpushbutton {\\n background-color: #ffffff;\\n border: 1px solid #cbd5e1;\\n border-radius: 6px;\\n color: #1e293b;\\n font-weight: 600;\\n min-height: 30px;\\n padding: 6px 10px;\\n}\\n\\nqpushbutton:hover {\\n background-color: #f8fafc;\\n border-color: #94a3b8;\\n}\\n\\nqpushbutton:pressed {\\n background-color: #e2e8f0;\\n}\\n\\nqpushbutton:disabled {\\n background-color: #f1f5f9;\\n border-color: #dbe3ef;\\n color: #94a3b8;\\n}\\n\\nqpushbutton[variant=\"primary\"] {\\n background-color: #2563eb;\\n border-color: #2563eb;\\n color: #ffffff;\\n}\\n\\nqpushbutton[variant=\"primary\"]:hover {\\n background-color: #1d4ed8;\\n border-color: #1d4ed8;\\n}\\n\\nqpushbutton[variant=\"primary\"]:pressed {\\n background-color: #1e40af;\\n border-color: #1e40af;\\n}\\n\\nqpushbutton[variant=\"primary\"]:disabled {\\n background-color: #cbd5e1;\\n border-color: #cbd5e1;\\n color: #f8fafc;\\n}\\n\\nqpushbutton[variant=\"secondary\"] {\\n background-color: #f8fafc;\\n}\\n\\nqpushbutton[variant=\"toolbar\"],\\nqpushbutton[variant=\"toggle\"],\\nqpushbutton[variant=\"tab\"],\\nqpushbutton[variant=\"link\"] {\\n background-color: transparent;\\n border-color: transparent;\\n min-height: 26px;\\n padding: 4px 8px;\\n color: #1e293b;\\n}\\n\\nqpushbutton[variant=\"toolbar\"]:hover,\\nqpushbutton[variant=\"toggle\"]:hover,\\nqpushbutton[variant=\"tab\"]:hover {\\n background-color: #f1f5f9;\\n border-color: #e2e8f0;\\n}\\n\\nqpushbutton[variant=\"toggle\"]:checked {\\n background-color: #2563eb;\\n border-color: #2563eb;\\n color: #ffffff;\\n}\\n\\nqpushbutton[variant=\"tab\"] {\\n border-radius: 0;\\n border-bottom: 2px solid transparent;\\n font-weight: 700;\\n color: #334155;\\n}\\n\\nqpushbutton[variant=\"tab\"]:checked {\\n color: #2563eb;\\n border-bottom-color: #2563eb;\\n}\\n\\nqpushbutton[variant=\"link\"] {\\n color: #2563eb;\\n font-weight: 700;\\n}\\n\\nqpushbutton[variant=\"quiet\"] {\\n background-color: transparent;\\n border-color: transparent;\\n}\\n\\nqpushbutton[variant=\"icon\"] {\\n background-color: rgba(255, 255, 255, 210);\\n border: 1px solid #cbd5e1;\\n border-radius: 6px;\\n min-height: 24px;\\n min-width: 26px;\\n padding: 0;\\n}\\n\\nqpushbutton[variant=\"iconquiet\"] {\\n background-color: transparent;\\n border: none;\\n border-radius: 6px;\\n min-height: 24px;\\n min-width: 26px;\\n padding: 0;\\n}\\n\\nqpushbutton[variant=\"iconquiet\"]:hover {\\n background-color: #f1f5f9;\\n}\\n\\nqpushbutton[variant=\"iconquiet\"]:pressed {\\n background-color: #e2e8f0;\\n}\\n\\nqpushbutton[variant=\"iconquiet\"]:disabled {\\n background-color: transparent;\\n color: #94a3b8;\\n}\\n\\nqprogressbar {\\n background-color: #e2e8f0;\\n border: none;\\n border-radius: 4px;\\n height: 8px;\\n text-align: center;\\n color: transparent;\\n}\\n\\nqprogressbar::chunk {\\n background-color: #0f766e;\\n border-radius: 4px;\\n}\\n\\nqslider {\\n min-height: 28px;\\n}\\n\\nqslider::groove:horizontal {\\n height: 4px;\\n background-color: #dbe3ef;\\n border-radius: 2px;\\n margin: 0 8px;\\n}\\n\\nqslider::sub-page:horizontal {\\n background-color: #2563eb;\\n border-radius: 2px;\\n margin: 0 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"name": "PLAY_SVG",
"public": true,
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"searchText": "constant\nplay_svg\ntorch_volpy.gui.qt_app.play_svg\n'\\n\\n'",
"signature": "",
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"line": 950,
"path": "src/torch_volpy/gui/qt_app.py"
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"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "PAUSE_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.PAUSE_SVG",
"searchText": "constant\npause_svg\ntorch_volpy.gui.qt_app.pause_svg\n'\\n\\n'",
"signature": "",
"source": {
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"line": 956,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": ""
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"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "SLIDERS_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SLIDERS_SVG",
"searchText": "constant\nsliders_svg\ntorch_volpy.gui.qt_app.sliders_svg\n'\\n\\n'",
"signature": "",
"source": {
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"line": 962,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": ""
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"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "TARGET_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TARGET_SVG",
"searchText": "constant\ntarget_svg\ntorch_volpy.gui.qt_app.target_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 977,
"line": 971,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
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{
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"default": "'\\n\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "DOWNLOAD_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.DOWNLOAD_SVG",
"searchText": "constant\ndownload_svg\ntorch_volpy.gui.qt_app.download_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 983,
"line": 979,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"default": "'\\n\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "SPARK_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SPARK_SVG",
"searchText": "constant\nspark_svg\ntorch_volpy.gui.qt_app.spark_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 989,
"line": 985,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"default": "'\\n\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "ZOOM_OUT_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ZOOM_OUT_SVG",
"searchText": "constant\nzoom_out_svg\ntorch_volpy.gui.qt_app.zoom_out_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 996,
"line": 991,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"default": "'\\n\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "ZOOM_IN_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ZOOM_IN_SVG",
"searchText": "constant\nzoom_in_svg\ntorch_volpy.gui.qt_app.zoom_in_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 1003,
"line": 998,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"default": "'\\n\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "RESET_VIEW_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RESET_VIEW_SVG",
"searchText": "constant\nreset_view_svg\ntorch_volpy.gui.qt_app.reset_view_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 1010,
"line": 1005,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"default": "'\\n\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "BRUSH_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.BRUSH_SVG",
"searchText": "constant\nbrush_svg\ntorch_volpy.gui.qt_app.brush_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 1017,
"line": 1012,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"default": "'\\n\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "RECTANGLE_ROI_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RECTANGLE_ROI_SVG",
"searchText": "constant\nrectangle_roi_svg\ntorch_volpy.gui.qt_app.rectangle_roi_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 1023,
"line": 1019,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"default": "'\\n\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "ERASER_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ERASER_SVG",
"searchText": "constant\neraser_svg\ntorch_volpy.gui.qt_app.eraser_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 1031,
"line": 1025,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"default": "'\\n\\n'",
"docstring": "",
"kind": "constant",
"module": "torch_volpy.gui.qt_app",
"name": "UNDO_SVG",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.UNDO_SVG",
"searchText": "constant\nundo_svg\ntorch_volpy.gui.qt_app.undo_svg\n'\\n\\n'",
"signature": "",
"source": {
"endLine": 1038,
"line": 1033,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_icon_from_svg",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._icon_from_svg",
"searchText": "function\n_icon_from_svg\ntorch_volpy.gui.qt_app._icon_from_svg\n(svg: str) -> qicon",
"signature": "(svg: str) -> QIcon",
"source": {
"endLine": 1044,
"line": 1041,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_icon_from_template",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._icon_from_template",
"searchText": "function\n_icon_from_template\ntorch_volpy.gui.qt_app._icon_from_template\n(svg: str, color: str = '#334155') -> qicon",
"signature": "(svg: str, color: str = '#334155') -> QIcon",
"source": {
"endLine": 1048,
"line": 1047,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_set_button_icon",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._set_button_icon",
"searchText": "function\n_set_button_icon\ntorch_volpy.gui.qt_app._set_button_icon\n(button: qpushbutton, svg: str, color: str = '#334155') -> none",
"signature": "(button: QPushButton, svg: str, color: str = '#334155') -> None",
"source": {
"endLine": 1053,
"line": 1051,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_set_button_role",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._set_button_role",
"searchText": "function\n_set_button_role\ntorch_volpy.gui.qt_app._set_button_role\n(button: qpushbutton, role: str) -> none",
"signature": "(button: QPushButton, role: str) -> None",
"source": {
"endLine": 1057,
"line": 1056,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"bases": [],
"dataclass": false,
"decorators": [],
"docstring": "",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "_ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"searchText": "class\n_modernspinboxmixin\ntorch_volpy.gui.qt_app._modernspinboxmixin\n()",
"signature": "()",
"source": {
"endLine": 1206,
"line": 1060,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_init_modern_spinbox",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin._init_modern_spinbox",
"searchText": "method\n_init_modern_spinbox\ntorch_volpy.gui.qt_app._modernspinboxmixin._init_modern_spinbox\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 1068,
"line": 1064,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_spin_button_rect",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin._spin_button_rect",
"searchText": "method\n_spin_button_rect\ntorch_volpy.gui.qt_app._modernspinboxmixin._spin_button_rect\n(self) -> qrect",
"signature": "(self) -> QRect",
"source": {
"endLine": 1073,
"line": 1070,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_spin_button_at",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin._spin_button_at",
"searchText": "method\n_spin_button_at\ntorch_volpy.gui.qt_app._modernspinboxmixin._spin_button_at\n(self, point) -> optional[str]",
"signature": "(self, point) -> Optional[str]",
"source": {
"endLine": 1079,
"line": 1075,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_can_step_spin_button",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin._can_step_spin_button",
"searchText": "method\n_can_step_spin_button\ntorch_volpy.gui.qt_app._modernspinboxmixin._can_step_spin_button\n(self, button: str) -> bool",
"signature": "(self, button: str) -> bool",
"source": {
"endLine": 1084,
"line": 1081,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_step_spin_button",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin._step_spin_button",
"searchText": "method\n_step_spin_button\ntorch_volpy.gui.qt_app._modernspinboxmixin._step_spin_button\n(self, button: str) -> none",
"signature": "(self, button: str) -> None",
"source": {
"endLine": 1088,
"line": 1086,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_event_point",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin._event_point",
"searchText": "method\n_event_point\ntorch_volpy.gui.qt_app._modernspinboxmixin._event_point\n(self, event)",
"signature": "(self, event)",
"source": {
"endLine": 1093,
"line": 1090,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_draw_modern_spin_arrow",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin._draw_modern_spin_arrow",
"searchText": "method\n_draw_modern_spin_arrow\ntorch_volpy.gui.qt_app._modernspinboxmixin._draw_modern_spin_arrow\n(self, painter: qpainter, center: qpointf, *, up: bool, enabled: bool) -> none",
"signature": "(self, painter: QPainter, center: QPointF, *, up: bool, enabled: bool) -> None",
"source": {
"endLine": 1114,
"line": 1095,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_draw_modern_spin_buttons",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin._draw_modern_spin_buttons",
"searchText": "method\n_draw_modern_spin_buttons\ntorch_volpy.gui.qt_app._modernspinboxmixin._draw_modern_spin_buttons\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 1164,
"line": 1116,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Paint the custom spinbox frame and arrow controls.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "paintEvent",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin.paintEvent",
"searchText": "method\npaintevent\ntorch_volpy.gui.qt_app._modernspinboxmixin.paintevent\n(self, event) -> none\npaint the custom spinbox frame and arrow controls.\npaint the custom spinbox frame and arrow controls.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1169,
"line": 1166,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Paint the custom spinbox frame and arrow controls."
},
{
"decorators": [],
"docstring": "Track pointer presses on custom arrow controls.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "mousePressEvent",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin.mousePressEvent",
"searchText": "method\nmousepressevent\ntorch_volpy.gui.qt_app._modernspinboxmixin.mousepressevent\n(self, event) -> none\ntrack pointer presses on custom arrow controls.\ntrack pointer presses on custom arrow controls.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1181,
"line": 1171,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Track pointer presses on custom arrow controls."
},
{
"decorators": [],
"docstring": "Complete a custom arrow press and trigger the step action.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "mouseReleaseEvent",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin.mouseReleaseEvent",
"searchText": "method\nmousereleaseevent\ntorch_volpy.gui.qt_app._modernspinboxmixin.mousereleaseevent\n(self, event) -> none\ncomplete a custom arrow press and trigger the step action.\ncomplete a custom arrow press and trigger the step action.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1190,
"line": 1183,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Complete a custom arrow press and trigger the step action."
},
{
"decorators": [],
"docstring": "Update hover state for custom arrow controls.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "mouseMoveEvent",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin.mouseMoveEvent",
"searchText": "method\nmousemoveevent\ntorch_volpy.gui.qt_app._modernspinboxmixin.mousemoveevent\n(self, event) -> none\nupdate hover state for custom arrow controls.\nupdate hover state for custom arrow controls.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1199,
"line": 1192,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Update hover state for custom arrow controls."
},
{
"decorators": [],
"docstring": "Clear hover state when the pointer leaves the spinbox.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "leaveEvent",
"parent": "torch_volpy.gui.qt_app._ModernSpinBoxMixin",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ModernSpinBoxMixin.leaveEvent",
"searchText": "method\nleaveevent\ntorch_volpy.gui.qt_app._modernspinboxmixin.leaveevent\n(self, event) -> none\nclear hover state when the pointer leaves the spinbox.\nclear hover state when the pointer leaves the spinbox.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1206,
"line": 1201,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Clear hover state when the pointer leaves the spinbox."
},
{
"bases": [
"_ModernSpinBoxMixin",
"QSpinBox"
],
"dataclass": false,
"decorators": [],
"docstring": "Integer spinbox with custom modern arrow rendering.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "ModernSpinBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ModernSpinBox",
"searchText": "class\nmodernspinbox\ntorch_volpy.gui.qt_app.modernspinbox\n(parent: optional[qwidget] = none) -> none\ninteger spinbox with custom modern arrow rendering.\ninteger spinbox with custom modern arrow rendering.\n_modernspinboxmixin qspinbox",
"signature": "(parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1213,
"line": 1209,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Integer spinbox with custom modern arrow rendering."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.ModernSpinBox",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.ModernSpinBox.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.modernspinbox.__init__\n(self, parent: optional[qwidget] = none) -> none",
"signature": "(self, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1213,
"line": 1211,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"bases": [
"ModernSpinBox"
],
"dataclass": false,
"decorators": [],
"docstring": "Spinbox for controlling ROI brush size in the movie view.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "RoiBrushSizeSpinBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"searchText": "class\nroibrushsizespinbox\ntorch_volpy.gui.qt_app.roibrushsizespinbox\n(parent: optional[qwidget] = none) -> none\nspinbox for controlling roi brush size in the movie view.\nspinbox for controlling roi brush size in the movie view.\nmodernspinbox",
"signature": "(parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1329,
"line": 1216,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Spinbox for controlling ROI brush size in the movie view."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.roibrushsizespinbox.__init__\n(self, parent: optional[qwidget] = none) -> none",
"signature": "(self, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1220,
"line": 1218,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Return the preferred size for the ROI brush spinbox.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "sizeHint",
"parent": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox.sizeHint",
"searchText": "method\nsizehint\ntorch_volpy.gui.qt_app.roibrushsizespinbox.sizehint\n(self) -> qsize\nreturn the preferred size for the roi brush spinbox.\nreturn the preferred size for the roi brush spinbox.",
"signature": "(self) -> QSize",
"source": {
"endLine": 1226,
"line": 1222,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Return the preferred size for the ROI brush spinbox."
},
{
"decorators": [],
"docstring": "Return the minimum usable size for the ROI brush spinbox.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "minimumSizeHint",
"parent": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox.minimumSizeHint",
"searchText": "method\nminimumsizehint\ntorch_volpy.gui.qt_app.roibrushsizespinbox.minimumsizehint\n(self) -> qsize\nreturn the minimum usable size for the roi brush spinbox.\nreturn the minimum usable size for the roi brush spinbox.",
"signature": "(self) -> QSize",
"source": {
"endLine": 1232,
"line": 1228,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Return the minimum usable size for the ROI brush spinbox."
},
{
"decorators": [],
"docstring": "Switch the brush-size control between vertical and horizontal display.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_vertical_display",
"parent": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox.set_vertical_display",
"searchText": "method\nset_vertical_display\ntorch_volpy.gui.qt_app.roibrushsizespinbox.set_vertical_display\n(self, vertical: bool) -> none\nswitch the brush-size control between vertical and horizontal display.\nswitch the brush-size control between vertical and horizontal display.",
"signature": "(self, vertical: bool) -> None",
"source": {
"endLine": 1248,
"line": 1234,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Switch the brush-size control between vertical and horizontal display."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_sync_line_edit_visibility",
"parent": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox._sync_line_edit_visibility",
"searchText": "method\n_sync_line_edit_visibility\ntorch_volpy.gui.qt_app.roibrushsizespinbox._sync_line_edit_visibility\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 1251,
"line": 1250,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_spin_button_at",
"parent": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox._spin_button_at",
"searchText": "method\n_spin_button_at\ntorch_volpy.gui.qt_app.roibrushsizespinbox._spin_button_at\n(self, point) -> optional[str]",
"signature": "(self, point) -> Optional[str]",
"source": {
"endLine": 1262,
"line": 1253,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Paint the ROI brush-size value and custom arrow controls.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "paintEvent",
"parent": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox.paintEvent",
"searchText": "method\npaintevent\ntorch_volpy.gui.qt_app.roibrushsizespinbox.paintevent\n(self, event) -> none\npaint the roi brush-size value and custom arrow controls.\npaint the roi brush-size value and custom arrow controls.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1316,
"line": 1264,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Paint the ROI brush-size value and custom arrow controls."
},
{
"decorators": [],
"docstring": "Recalculate arrow hit regions after resizing.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "resizeEvent",
"parent": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox.resizeEvent",
"searchText": "method\nresizeevent\ntorch_volpy.gui.qt_app.roibrushsizespinbox.resizeevent\n(self, event) -> none\nrecalculate arrow hit regions after resizing.\nrecalculate arrow hit regions after resizing.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1322,
"line": 1318,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Recalculate arrow hit regions after resizing."
},
{
"decorators": [],
"docstring": "Initialize arrow hit regions when the widget is shown.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "showEvent",
"parent": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiBrushSizeSpinBox.showEvent",
"searchText": "method\nshowevent\ntorch_volpy.gui.qt_app.roibrushsizespinbox.showevent\n(self, event) -> none\ninitialize arrow hit regions when the widget is shown.\ninitialize arrow hit regions when the widget is shown.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1329,
"line": 1324,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Initialize arrow hit regions when the widget is shown."
},
{
"bases": [
"_ModernSpinBoxMixin",
"QDoubleSpinBox"
],
"dataclass": false,
"decorators": [],
"docstring": "Floating-point spinbox with custom modern arrow rendering.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "ModernDoubleSpinBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ModernDoubleSpinBox",
"searchText": "class\nmoderndoublespinbox\ntorch_volpy.gui.qt_app.moderndoublespinbox\n(parent: optional[qwidget] = none) -> none\nfloating-point spinbox with custom modern arrow rendering.\nfloating-point spinbox with custom modern arrow rendering.\n_modernspinboxmixin qdoublespinbox",
"signature": "(parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1336,
"line": 1332,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Floating-point spinbox with custom modern arrow rendering."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.ModernDoubleSpinBox",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.ModernDoubleSpinBox.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.moderndoublespinbox.__init__\n(self, parent: optional[qwidget] = none) -> none",
"signature": "(self, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1336,
"line": 1334,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_show_spinbox_buttons",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._show_spinbox_buttons",
"searchText": "function\n_show_spinbox_buttons\ntorch_volpy.gui.qt_app._show_spinbox_buttons\n(spinbox: qabstractspinbox) -> none",
"signature": "(spinbox: QAbstractSpinBox) -> None",
"source": {
"endLine": 1343,
"line": 1339,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_section_title",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._section_title",
"searchText": "function\n_section_title\ntorch_volpy.gui.qt_app._section_title\n(text: str) -> qlabel",
"signature": "(text: str) -> QLabel",
"source": {
"endLine": 1349,
"line": 1346,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_styled_frame",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._styled_frame",
"searchText": "function\n_styled_frame\ntorch_volpy.gui.qt_app._styled_frame\n(name: str) -> qframe",
"signature": "(name: str) -> QFrame",
"source": {
"endLine": 1356,
"line": 1352,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_role_label",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._role_label",
"searchText": "function\n_role_label\ntorch_volpy.gui.qt_app._role_label\n(text: str = '', role: str = 'muted') -> qlabel",
"signature": "(text: str = '', role: str = 'muted') -> QLabel",
"source": {
"endLine": 1362,
"line": 1359,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_make_tab_button",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._make_tab_button",
"searchText": "function\n_make_tab_button\ntorch_volpy.gui.qt_app._make_tab_button\n(text: str, checked: bool = false) -> qpushbutton",
"signature": "(text: str, checked: bool = False) -> QPushButton",
"source": {
"endLine": 1370,
"line": 1365,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_make_metric_tile",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._make_metric_tile",
"searchText": "function\n_make_metric_tile\ntorch_volpy.gui.qt_app._make_metric_tile\n(title: str, value: str = '--', role: str = 'metricvalue') -> tuple[qframe, qlabel]",
"signature": "(title: str, value: str = '--', role: str = 'metricValue') -> tuple[QFrame, QLabel]",
"source": {
"endLine": 1382,
"line": 1373,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_set_metric_value_color",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._set_metric_value_color",
"searchText": "function\n_set_metric_value_color\ntorch_volpy.gui.qt_app._set_metric_value_color\n(label: qlabel, color: str) -> none",
"signature": "(label: QLabel, color: str) -> None",
"source": {
"endLine": 1386,
"line": 1385,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_normalize_rgb_to_uint8",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._normalize_rgb_to_uint8",
"searchText": "function\n_normalize_rgb_to_uint8\ntorch_volpy.gui.qt_app._normalize_rgb_to_uint8\n(frame: np.ndarray) -> np.ndarray",
"signature": "(frame: np.ndarray) -> np.ndarray",
"source": {
"endLine": 1404,
"line": 1389,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_make_toolbar_button",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._make_toolbar_button",
"searchText": "function\n_make_toolbar_button\ntorch_volpy.gui.qt_app._make_toolbar_button\n(text: str, svg: optional[str] = none, *, checkable: bool = false) -> qpushbutton",
"signature": "(text: str, svg: Optional[str] = None, *, checkable: bool = False) -> QPushButton",
"source": {
"endLine": 1413,
"line": 1407,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_make_roi_tool_button",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._make_roi_tool_button",
"searchText": "function\n_make_roi_tool_button\ntorch_volpy.gui.qt_app._make_roi_tool_button\n(svg: str, tooltip: str, *, checkable: bool = true) -> qpushbutton",
"signature": "(svg: str, tooltip: str, *, checkable: bool = True) -> QPushButton",
"source": {
"endLine": 1424,
"line": 1416,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_make_divider",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._make_divider",
"searchText": "function\n_make_divider\ntorch_volpy.gui.qt_app._make_divider\n() -> qframe",
"signature": "() -> QFrame",
"source": {
"endLine": 1432,
"line": 1427,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"bases": [
"QFrame"
],
"dataclass": false,
"decorators": [],
"docstring": "Floating toolbar that hosts ROI drawing and selection controls.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "RoiToolIsland",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland",
"searchText": "class\nroitoolisland\ntorch_volpy.gui.qt_app.roitoolisland\n(parent: optional[qwidget] = none) -> none\nfloating toolbar that hosts roi drawing and selection controls.\nfloating toolbar that hosts roi drawing and selection controls.\nqframe",
"signature": "(parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1572,
"line": 1435,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Floating toolbar that hosts ROI drawing and selection controls."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.RoiToolIsland",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.roitoolisland.__init__\n(self, parent: optional[qwidget] = none) -> none",
"signature": "(self, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1457,
"line": 1437,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Handle drag interactions for the floating ROI tool island.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "eventFilter",
"parent": "torch_volpy.gui.qt_app.RoiToolIsland",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland.eventFilter",
"searchText": "method\neventfilter\ntorch_volpy.gui.qt_app.roitoolisland.eventfilter\n(self, obj, event) -> bool\nhandle drag interactions for the floating roi tool island.\nhandle drag interactions for the floating roi tool island.",
"signature": "(self, obj, event) -> bool",
"source": {
"endLine": 1499,
"line": 1459,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Handle drag interactions for the floating ROI tool island."
},
{
"decorators": [],
"docstring": "Add a tool control to the ROI tool island.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "add_tool_widget",
"parent": "torch_volpy.gui.qt_app.RoiToolIsland",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland.add_tool_widget",
"searchText": "method\nadd_tool_widget\ntorch_volpy.gui.qt_app.roitoolisland.add_tool_widget\n(self, widget: qwidget) -> none\nadd a tool control to the roi tool island.\nadd a tool control to the roi tool island.",
"signature": "(self, widget: QWidget) -> None",
"source": {
"endLine": 1504,
"line": 1501,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Add a tool control to the ROI tool island."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_install_drag_filter",
"parent": "torch_volpy.gui.qt_app.RoiToolIsland",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland._install_drag_filter",
"searchText": "method\n_install_drag_filter\ntorch_volpy.gui.qt_app.roitoolisland._install_drag_filter\n(self, widget: qwidget) -> none",
"signature": "(self, widget: QWidget) -> None",
"source": {
"endLine": 1509,
"line": 1506,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Set the side of the movie stage where the island is docked.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_dock_side",
"parent": "torch_volpy.gui.qt_app.RoiToolIsland",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland.set_dock_side",
"searchText": "method\nset_dock_side\ntorch_volpy.gui.qt_app.roitoolisland.set_dock_side\n(self, side: str) -> none\nset the side of the movie stage where the island is docked.\nset the side of the movie stage where the island is docked.",
"signature": "(self, side: str) -> None",
"source": {
"endLine": 1517,
"line": 1511,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Set the side of the movie stage where the island is docked."
},
{
"decorators": [],
"docstring": "Dock the island to the nearest side of the target widget.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "snap_to_nearest_side",
"parent": "torch_volpy.gui.qt_app.RoiToolIsland",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland.snap_to_nearest_side",
"searchText": "method\nsnap_to_nearest_side\ntorch_volpy.gui.qt_app.roitoolisland.snap_to_nearest_side\n(self) -> none\ndock the island to the nearest side of the target widget.\ndock the island to the nearest side of the target widget.",
"signature": "(self) -> None",
"source": {
"endLine": 1532,
"line": 1519,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Dock the island to the nearest side of the target widget."
},
{
"decorators": [],
"docstring": "Move the island to its current docked position.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "reposition_to_dock",
"parent": "torch_volpy.gui.qt_app.RoiToolIsland",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland.reposition_to_dock",
"searchText": "method\nreposition_to_dock\ntorch_volpy.gui.qt_app.roitoolisland.reposition_to_dock\n(self) -> none\nmove the island to its current docked position.\nmove the island to its current docked position.",
"signature": "(self) -> None",
"source": {
"endLine": 1547,
"line": 1534,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Move the island to its current docked position."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_apply_orientation",
"parent": "torch_volpy.gui.qt_app.RoiToolIsland",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland._apply_orientation",
"searchText": "method\n_apply_orientation\ntorch_volpy.gui.qt_app.roitoolisland._apply_orientation\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 1561,
"line": 1549,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_move_clamped",
"parent": "torch_volpy.gui.qt_app.RoiToolIsland",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.RoiToolIsland._move_clamped",
"searchText": "method\n_move_clamped\ntorch_volpy.gui.qt_app.roitoolisland._move_clamped\n(self, x: float, y: float) -> none",
"signature": "(self, x: float, y: float) -> None",
"source": {
"endLine": 1572,
"line": 1563,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"bases": [
"QWidget"
],
"dataclass": false,
"decorators": [],
"docstring": "Overlay widget that draws the movie card border.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "MovieCardBorderOverlay",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieCardBorderOverlay",
"searchText": "class\nmoviecardborderoverlay\ntorch_volpy.gui.qt_app.moviecardborderoverlay\n(parent: optional[qwidget] = none) -> none\noverlay widget that draws the movie card border.\noverlay widget that draws the movie card border.\nqwidget",
"signature": "(parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1603,
"line": 1575,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Overlay widget that draws the movie card border."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.MovieCardBorderOverlay",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieCardBorderOverlay.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.moviecardborderoverlay.__init__\n(self, parent: optional[qwidget] = none) -> none",
"signature": "(self, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1581,
"line": 1577,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Paint the movie card border overlay.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "paintEvent",
"parent": "torch_volpy.gui.qt_app.MovieCardBorderOverlay",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieCardBorderOverlay.paintEvent",
"searchText": "method\npaintevent\ntorch_volpy.gui.qt_app.moviecardborderoverlay.paintevent\n(self, event) -> none\npaint the movie card border overlay.\npaint the movie card border overlay.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1603,
"line": 1583,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Paint the movie card border overlay."
},
{
"bases": [
"QFrame"
],
"dataclass": false,
"decorators": [],
"docstring": "Frame widget that keeps a border overlay above movie controls.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "MovieCardFrame",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieCardFrame",
"searchText": "class\nmoviecardframe\ntorch_volpy.gui.qt_app.moviecardframe\n() -> none\nframe widget that keeps a border overlay above movie controls.\nframe widget that keeps a border overlay above movie controls.\nqframe",
"signature": "() -> None",
"source": {
"endLine": 1624,
"line": 1606,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Frame widget that keeps a border overlay above movie controls."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.MovieCardFrame",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieCardFrame.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.moviecardframe.__init__\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 1613,
"line": 1608,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Raise the border overlay above child widgets.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "raise_border",
"parent": "torch_volpy.gui.qt_app.MovieCardFrame",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieCardFrame.raise_border",
"searchText": "method\nraise_border\ntorch_volpy.gui.qt_app.moviecardframe.raise_border\n(self) -> none\nraise the border overlay above child widgets.\nraise the border overlay above child widgets.",
"signature": "(self) -> None",
"source": {
"endLine": 1619,
"line": 1615,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Raise the border overlay above child widgets."
},
{
"decorators": [],
"docstring": "Resize the border overlay with the movie card frame.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "resizeEvent",
"parent": "torch_volpy.gui.qt_app.MovieCardFrame",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieCardFrame.resizeEvent",
"searchText": "method\nresizeevent\ntorch_volpy.gui.qt_app.moviecardframe.resizeevent\n(self, event) -> none\nresize the border overlay with the movie card frame.\nresize the border overlay with the movie card frame.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1624,
"line": 1621,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Resize the border overlay with the movie card frame."
},
{
"bases": [
"QWidget"
],
"dataclass": false,
"decorators": [],
"docstring": "Movie viewing stage that positions floating ROI controls.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "MovieStage",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieStage",
"searchText": "class\nmoviestage\ntorch_volpy.gui.qt_app.moviestage\n()\nmovie viewing stage that positions floating roi controls.\nmovie viewing stage that positions floating roi controls.\nqwidget",
"signature": "()",
"source": {
"endLine": 1634,
"line": 1627,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Movie viewing stage that positions floating ROI controls."
},
{
"decorators": [],
"docstring": "Reposition floating controls when the movie stage resizes.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "resizeEvent",
"parent": "torch_volpy.gui.qt_app.MovieStage",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieStage.resizeEvent",
"searchText": "method\nresizeevent\ntorch_volpy.gui.qt_app.moviestage.resizeevent\n(self, event) -> none\nreposition floating controls when the movie stage resizes.\nreposition floating controls when the movie stage resizes.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1634,
"line": 1631,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Reposition floating controls when the movie stage resizes."
},
{
"decorators": [],
"docstring": "",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
"name": "_modernize_combo_box",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._modernize_combo_box",
"searchText": "function\n_modernize_combo_box\ntorch_volpy.gui.qt_app._modernize_combo_box\n(combo_box: qcombobox) -> none",
"signature": "(combo_box: QComboBox) -> None",
"source": {
"endLine": 1641,
"line": 1637,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"bases": [
"QFrame"
],
"dataclass": false,
"decorators": [],
"docstring": "",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "_ComboPopupFrame",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ComboPopupFrame",
"searchText": "class\n_combopopupframe\ntorch_volpy.gui.qt_app._combopopupframe\n(combo_box: 'moderncombobox', flags) -> none\nqframe",
"signature": "(combo_box: 'ModernComboBox', flags) -> None",
"source": {
"endLine": 1652,
"line": 1644,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app._ComboPopupFrame",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ComboPopupFrame.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app._combopopupframe.__init__\n(self, combo_box: 'moderncombobox', flags) -> none",
"signature": "(self, combo_box: 'ModernComboBox', flags) -> None",
"source": {
"endLine": 1647,
"line": 1645,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Notify the combo box when the custom popup frame closes.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "hideEvent",
"parent": "torch_volpy.gui.qt_app._ComboPopupFrame",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app._ComboPopupFrame.hideEvent",
"searchText": "method\nhideevent\ntorch_volpy.gui.qt_app._combopopupframe.hideevent\n(self, event) -> none\nnotify the combo box when the custom popup frame closes.\nnotify the combo box when the custom popup frame closes.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1652,
"line": 1649,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Notify the combo box when the custom popup frame closes."
},
{
"bases": [
"QComboBox"
],
"dataclass": false,
"decorators": [],
"docstring": "Combo box with a custom popup frame and styling.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "ModernComboBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ModernComboBox",
"searchText": "class\nmoderncombobox\ntorch_volpy.gui.qt_app.moderncombobox\n(parent: optional[qwidget] = none) -> none\ncombo box with a custom popup frame and styling.\ncombo box with a custom popup frame and styling.\nqcombobox",
"signature": "(parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1736,
"line": 1655,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Combo box with a custom popup frame and styling."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.ModernComboBox",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.ModernComboBox.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.moderncombobox.__init__\n(self, parent: optional[qwidget] = none) -> none",
"signature": "(self, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1664,
"line": 1659,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Open the custom combo popup on mouse press.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "mousePressEvent",
"parent": "torch_volpy.gui.qt_app.ModernComboBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ModernComboBox.mousePressEvent",
"searchText": "method\nmousepressevent\ntorch_volpy.gui.qt_app.moderncombobox.mousepressevent\n(self, event) -> none\nopen the custom combo popup on mouse press.\nopen the custom combo popup on mouse press.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1673,
"line": 1666,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Open the custom combo popup on mouse press."
},
{
"decorators": [],
"docstring": "Display the custom-styled combo popup.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "showPopup",
"parent": "torch_volpy.gui.qt_app.ModernComboBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ModernComboBox.showPopup",
"searchText": "method\nshowpopup\ntorch_volpy.gui.qt_app.moderncombobox.showpopup\n(self) -> none\ndisplay the custom-styled combo popup.\ndisplay the custom-styled combo popup.",
"signature": "(self) -> None",
"source": {
"endLine": 1722,
"line": 1675,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Display the custom-styled combo popup."
},
{
"decorators": [],
"docstring": "Hide the custom-styled combo popup.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "hidePopup",
"parent": "torch_volpy.gui.qt_app.ModernComboBox",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ModernComboBox.hidePopup",
"searchText": "method\nhidepopup\ntorch_volpy.gui.qt_app.moderncombobox.hidepopup\n(self) -> none\nhide the custom-styled combo popup.\nhide the custom-styled combo popup.",
"signature": "(self) -> None",
"source": {
"endLine": 1727,
"line": 1724,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Hide the custom-styled combo popup."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_popup_frame_hidden",
"parent": "torch_volpy.gui.qt_app.ModernComboBox",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.ModernComboBox._popup_frame_hidden",
"searchText": "method\n_popup_frame_hidden\ntorch_volpy.gui.qt_app.moderncombobox._popup_frame_hidden\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 1731,
"line": 1729,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_select_popup_index",
"parent": "torch_volpy.gui.qt_app.ModernComboBox",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.ModernComboBox._select_popup_index",
"searchText": "method\n_select_popup_index\ntorch_volpy.gui.qt_app.moderncombobox._select_popup_index\n(self, index) -> none",
"signature": "(self, index) -> None",
"source": {
"endLine": 1736,
"line": 1733,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"bases": [
"QWidget"
],
"dataclass": false,
"decorators": [],
"docstring": "Custom rounded tooltip widget for the GUI.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "RoundedToolTip",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoundedToolTip",
"searchText": "class\nroundedtooltip\ntorch_volpy.gui.qt_app.roundedtooltip\n() -> none\ncustom rounded tooltip widget for the gui.\ncustom rounded tooltip widget for the gui.\nqwidget",
"signature": "() -> None",
"source": {
"endLine": 1782,
"line": 1739,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Custom rounded tooltip widget for the GUI."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.RoundedToolTip",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.RoundedToolTip.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.roundedtooltip.__init__\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 1754,
"line": 1741,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Paint the rounded tooltip background and text.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "paintEvent",
"parent": "torch_volpy.gui.qt_app.RoundedToolTip",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoundedToolTip.paintEvent",
"searchText": "method\npaintevent\ntorch_volpy.gui.qt_app.roundedtooltip.paintevent\n(self, event) -> none\npaint the rounded tooltip background and text.\npaint the rounded tooltip background and text.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1765,
"line": 1756,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Paint the rounded tooltip background and text."
},
{
"decorators": [],
"docstring": "Show tooltip text near a global screen position.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "show_text",
"parent": "torch_volpy.gui.qt_app.RoundedToolTip",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoundedToolTip.show_text",
"searchText": "method\nshow_text\ntorch_volpy.gui.qt_app.roundedtooltip.show_text\n(self, text: str, pos) -> none\nshow tooltip text near a global screen position.\nshow tooltip text near a global screen position.",
"signature": "(self, text: str, pos) -> None",
"source": {
"endLine": 1782,
"line": 1767,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Show tooltip text near a global screen position."
},
{
"bases": [
"QObject"
],
"dataclass": false,
"decorators": [],
"docstring": "Event filter that manages rounded tooltips for watched widgets.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "RoundedToolTipFilter",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoundedToolTipFilter",
"searchText": "class\nroundedtooltipfilter\ntorch_volpy.gui.qt_app.roundedtooltipfilter\n(parent: qapplication) -> none\nevent filter that manages rounded tooltips for watched widgets.\nevent filter that manages rounded tooltips for watched widgets.\nqobject",
"signature": "(parent: QApplication) -> None",
"source": {
"endLine": 1803,
"line": 1785,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Event filter that manages rounded tooltips for watched widgets."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.RoundedToolTipFilter",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.RoundedToolTipFilter.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.roundedtooltipfilter.__init__\n(self, parent: qapplication) -> none",
"signature": "(self, parent: QApplication) -> None",
"source": {
"endLine": 1789,
"line": 1787,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Show or hide a rounded tooltip for watched widgets.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "eventFilter",
"parent": "torch_volpy.gui.qt_app.RoundedToolTipFilter",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.RoundedToolTipFilter.eventFilter",
"searchText": "method\neventfilter\ntorch_volpy.gui.qt_app.roundedtooltipfilter.eventfilter\n(self, obj, event) -> bool\nshow or hide a rounded tooltip for watched widgets.\nshow or hide a rounded tooltip for watched widgets.",
"signature": "(self, obj, event) -> bool",
"source": {
"endLine": 1803,
"line": 1791,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Show or hide a rounded tooltip for watched widgets."
},
{
"bases": [
"QCheckBox"
],
"dataclass": false,
"decorators": [],
"docstring": "Animated toggle switch used by the GUI.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "SwitchButton",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SwitchButton",
"searchText": "class\nswitchbutton\ntorch_volpy.gui.qt_app.switchbutton\n(text: str, parent: optional[qwidget] = none) -> none\nanimated toggle switch used by the gui.\nanimated toggle switch used by the gui.\nqcheckbox",
"signature": "(text: str, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1845,
"line": 1806,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Animated toggle switch used by the GUI."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.SwitchButton",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SwitchButton.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.switchbutton.__init__\n(self, text: str, parent: optional[qwidget] = none) -> none",
"signature": "(self, text: str, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1811,
"line": 1808,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Return the preferred size for the switch button.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "sizeHint",
"parent": "torch_volpy.gui.qt_app.SwitchButton",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SwitchButton.sizeHint",
"searchText": "method\nsizehint\ntorch_volpy.gui.qt_app.switchbutton.sizehint\n(self) -> qsize\nreturn the preferred size for the switch button.\nreturn the preferred size for the switch button.",
"signature": "(self) -> QSize",
"source": {
"endLine": 1816,
"line": 1813,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Return the preferred size for the switch button."
},
{
"decorators": [],
"docstring": "Paint the switch track, thumb, and label.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "paintEvent",
"parent": "torch_volpy.gui.qt_app.SwitchButton",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SwitchButton.paintEvent",
"searchText": "method\npaintevent\ntorch_volpy.gui.qt_app.switchbutton.paintevent\n(self, event) -> none\npaint the switch track, thumb, and label.\npaint the switch track, thumb, and label.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1845,
"line": 1818,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Paint the switch track, thumb, and label."
},
{
"bases": [
"QWidget"
],
"dataclass": false,
"decorators": [],
"docstring": "Two-option segmented switch for trace display scope.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "SegmentedScopeSwitch",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"searchText": "class\nsegmentedscopeswitch\ntorch_volpy.gui.qt_app.segmentedscopeswitch\n(parent: optional[qwidget] = none) -> none\ntwo-option segmented switch for trace display scope.\ntwo-option segmented switch for trace display scope.\nqwidget",
"signature": "(parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 2049,
"line": 1848,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Two-option segmented switch for trace display scope."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.segmentedscopeswitch.__init__\n(self, parent: optional[qwidget] = none) -> none",
"signature": "(self, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 1871,
"line": 1852,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Return the preferred size for the segmented scope switch.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "sizeHint",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch.sizeHint",
"searchText": "method\nsizehint\ntorch_volpy.gui.qt_app.segmentedscopeswitch.sizehint\n(self) -> qsize\nreturn the preferred size for the segmented scope switch.\nreturn the preferred size for the segmented scope switch.",
"signature": "(self) -> QSize",
"source": {
"endLine": 1875,
"line": 1873,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": "Return the preferred size for the segmented scope switch."
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "value",
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"public": true,
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"searchText": "method\nvalue\ntorch_volpy.gui.qt_app.segmentedscopeswitch.value\n(self) -> str\nreturn the currently selected segment value.\nreturn the currently selected segment value.",
"signature": "(self) -> str",
"source": {
"endLine": 1879,
"line": 1877,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": "Return the currently selected segment value."
},
{
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "setValue",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": true,
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"searchText": "method\nsetvalue\ntorch_volpy.gui.qt_app.segmentedscopeswitch.setvalue\n(self, value: str, *, emit_signal: bool = false, animated: bool = true) -> none\nset the selected segment value.\nset the selected segment value.",
"signature": "(self, value: str, *, emit_signal: bool = False, animated: bool = True) -> None",
"source": {
"endLine": 1903,
"line": 1881,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Set the selected segment value."
},
{
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_start_thumb_animation",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch._start_thumb_animation",
"searchText": "method\n_start_thumb_animation\ntorch_volpy.gui.qt_app.segmentedscopeswitch._start_thumb_animation\n(self, target: float) -> none",
"signature": "(self, target: float) -> None",
"source": {
"endLine": 1919,
"line": 1905,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": ""
},
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_advance_thumb_animation",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch._advance_thumb_animation",
"searchText": "method\n_advance_thumb_animation\ntorch_volpy.gui.qt_app.segmentedscopeswitch._advance_thumb_animation\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 1934,
"line": 1921,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": ""
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_emit_pending_value_changed",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch._emit_pending_value_changed",
"searchText": "method\n_emit_pending_value_changed\ntorch_volpy.gui.qt_app.segmentedscopeswitch._emit_pending_value_changed\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 1940,
"line": 1936,
"path": "src/torch_volpy/gui/qt_app.py"
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},
{
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],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.gui.qt_app",
"name": "_tanh_ease",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch._tanh_ease",
"searchText": "staticmethod\n_tanh_ease\ntorch_volpy.gui.qt_app.segmentedscopeswitch._tanh_ease\n(progress: float) -> float\nstaticmethod",
"signature": "(progress: float) -> float",
"source": {
"endLine": 1949,
"line": 1943,
"path": "src/torch_volpy/gui/qt_app.py"
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"kind": "method",
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"name": "_index_for_value",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch._index_for_value",
"searchText": "method\n_index_for_value\ntorch_volpy.gui.qt_app.segmentedscopeswitch._index_for_value\n(self, value: str) -> int",
"signature": "(self, value: str) -> int",
"source": {
"endLine": 1955,
"line": 1951,
"path": "src/torch_volpy/gui/qt_app.py"
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"kind": "staticmethod",
"module": "torch_volpy.gui.qt_app",
"name": "_mix_color",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch._mix_color",
"searchText": "staticmethod\n_mix_color\ntorch_volpy.gui.qt_app.segmentedscopeswitch._mix_color\n(start: str, end: str, progress: float) -> qcolor\nstaticmethod",
"signature": "(start: str, end: str, progress: float) -> QColor",
"source": {
"endLine": 1965,
"line": 1958,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": ""
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"kind": "method",
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"name": "_label_color",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch._label_color",
"searchText": "method\n_label_color\ntorch_volpy.gui.qt_app.segmentedscopeswitch._label_color\n(self, index: int, *, enabled: bool) -> qcolor",
"signature": "(self, index: int, *, enabled: bool) -> QColor",
"source": {
"endLine": 1977,
"line": 1967,
"path": "src/torch_volpy/gui/qt_app.py"
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"kind": "method",
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"name": "_thumb_rect",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch._thumb_rect",
"searchText": "method\n_thumb_rect\ntorch_volpy.gui.qt_app.segmentedscopeswitch._thumb_rect\n(self) -> qrectf",
"signature": "(self) -> QRectF",
"source": {
"endLine": 1983,
"line": 1979,
"path": "src/torch_volpy/gui/qt_app.py"
},
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_outer_rect",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch._outer_rect",
"searchText": "method\n_outer_rect\ntorch_volpy.gui.qt_app.segmentedscopeswitch._outer_rect\n(self) -> qrectf",
"signature": "(self) -> QRectF",
"source": {
"endLine": 1986,
"line": 1985,
"path": "src/torch_volpy/gui/qt_app.py"
},
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},
{
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"docstring": "Select a segment from a mouse press.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "mousePressEvent",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentedScopeSwitch.mousePressEvent",
"searchText": "method\nmousepressevent\ntorch_volpy.gui.qt_app.segmentedscopeswitch.mousepressevent\n(self, event) -> none\nselect a segment from a mouse press.\nselect a segment from a mouse press.",
"signature": "(self, event) -> None",
"source": {
"endLine": 1994,
"line": 1988,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": "Select a segment from a mouse press."
},
{
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"docstring": "Handle keyboard navigation between switch segments.",
"kind": "method",
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"name": "keyPressEvent",
"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": true,
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"searchText": "method\nkeypressevent\ntorch_volpy.gui.qt_app.segmentedscopeswitch.keypressevent\n(self, event) -> none\nhandle keyboard navigation between switch segments.\nhandle keyboard navigation between switch segments.",
"signature": "(self, event) -> None",
"source": {
"endLine": 2013,
"line": 1996,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Handle keyboard navigation between switch segments."
},
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"kind": "method",
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"public": true,
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"searchText": "method\npaintevent\ntorch_volpy.gui.qt_app.segmentedscopeswitch.paintevent\n(self, event) -> none\npaint the segmented switch and animated thumb.\npaint the segmented switch and animated thumb.",
"signature": "(self, event) -> None",
"source": {
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"path": "src/torch_volpy/gui/qt_app.py"
},
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},
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"kind": "method",
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"parent": "torch_volpy.gui.qt_app.SegmentedScopeSwitch",
"public": true,
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"searchText": "method\ngetthumbposition\ntorch_volpy.gui.qt_app.segmentedscopeswitch.getthumbposition\n(self) -> float\nreturn the animated thumb position.\nreturn the animated thumb position.",
"signature": "(self) -> float",
"source": {
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"line": 2040,
"path": "src/torch_volpy/gui/qt_app.py"
},
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},
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"searchText": "method\nsetthumbposition\ntorch_volpy.gui.qt_app.segmentedscopeswitch.setthumbposition\n(self, value: float) -> none\nset the animated thumb position.\nset the animated thumb position.",
"signature": "(self, value: float) -> None",
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"searchText": "function\n_make_icon_button\ntorch_volpy.gui.qt_app._make_icon_button\n(svg: str, tooltip: str) -> qpushbutton",
"signature": "(svg: str, tooltip: str) -> QPushButton",
"source": {
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"searchText": "function\n_apply_app_theme\ntorch_volpy.gui.qt_app._apply_app_theme\n(app: qapplication) -> none",
"signature": "(app: QApplication) -> None",
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"searchText": "function\n_available_device_options\ntorch_volpy.gui.qt_app._available_device_options\n() -> list[tuple[str, str]]",
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"searchText": "function\n_validate_selected_device\ntorch_volpy.gui.qt_app._validate_selected_device\n(device: str, task: str) -> none",
"signature": "(device: str, task: str) -> None",
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"signature": "(value: Any) -> Any",
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"kind": "function",
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"searchText": "function\n_dialog_exec\ntorch_volpy.gui.qt_app._dialog_exec\n(dialog: qdialog) -> int",
"signature": "(dialog: QDialog) -> int",
"source": {
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"kind": "function",
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"searchText": "function\n_get_movie_frame_rate\ntorch_volpy.gui.qt_app._get_movie_frame_rate\n(parent: qwidget, current_value: float) -> tuple[float, bool]",
"signature": "(parent: QWidget, current_value: float) -> tuple[float, bool]",
"source": {
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},
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"kind": "function",
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"searchText": "function\n_format_float_list\ntorch_volpy.gui.qt_app._format_float_list\n(values: any) -> str",
"signature": "(values: Any) -> str",
"source": {
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"path": "src/torch_volpy/gui/qt_app.py"
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"kind": "function",
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"searchText": "function\n_parse_float_list\ntorch_volpy.gui.qt_app._parse_float_list\n(text: str, label: str) -> tuple[float, ...]",
"signature": "(text: str, label: str) -> tuple[float, ...]",
"source": {
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},
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},
{
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"docstring": "Window that hosts the trace plotting canvas.",
"kind": "class",
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"public": true,
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"searchText": "class\ntracewindow\ntorch_volpy.gui.qt_app.tracewindow\n()\nwindow that hosts the trace plotting canvas.\nwindow that hosts the trace plotting canvas.\nqdialog",
"signature": "()",
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"path": "src/torch_volpy/gui/qt_app.py"
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"summary": "Window that hosts the trace plotting canvas."
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"kind": "method",
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"public": true,
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"searchText": "method\nshowevent\ntorch_volpy.gui.qt_app.tracewindow.showevent\n(self, event) -> none\ntrack when the trace window becomes visible.\ntrack when the trace window becomes visible.",
"signature": "(self, event) -> None",
"source": {
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"path": "src/torch_volpy/gui/qt_app.py"
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"parent": "torch_volpy.gui.qt_app.TraceWindow",
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"searchText": "method\nhideevent\ntorch_volpy.gui.qt_app.tracewindow.hideevent\n(self, event) -> none\ntrack when the trace window is hidden.\ntrack when the trace window is hidden.",
"signature": "(self, event) -> None",
"source": {
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"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Track when the trace window is hidden."
},
{
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"docstring": "Proxy style for drawing custom spinbox arrows.",
"kind": "class",
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"public": true,
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"searchText": "class\nspinboxarrowstyle\ntorch_volpy.gui.qt_app.spinboxarrowstyle\n()\nproxy style for drawing custom spinbox arrows.\nproxy style for drawing custom spinbox arrows.\nqproxystyle",
"signature": "()",
"source": {
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"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Proxy style for drawing custom spinbox arrows."
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"kind": "method",
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"name": "_draw_spinbox_arrow",
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"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SpinBoxArrowStyle._draw_spinbox_arrow",
"searchText": "method\n_draw_spinbox_arrow\ntorch_volpy.gui.qt_app.spinboxarrowstyle._draw_spinbox_arrow\n(self, painter, center: qpointf, *, up: bool, enabled: bool) -> none",
"signature": "(self, painter, center: QPointF, *, up: bool, enabled: bool) -> None",
"source": {
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},
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},
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"docstring": "Draw custom spinbox arrow primitives.",
"kind": "method",
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"name": "drawPrimitive",
"parent": "torch_volpy.gui.qt_app.SpinBoxArrowStyle",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SpinBoxArrowStyle.drawPrimitive",
"searchText": "method\ndrawprimitive\ntorch_volpy.gui.qt_app.spinboxarrowstyle.drawprimitive\n(self, element, option, painter, widget = none) -> none\ndraw custom spinbox arrow primitives.\ndraw custom spinbox arrow primitives.",
"signature": "(self, element, option, painter, widget = None) -> None",
"source": {
"endLine": 2234,
"line": 2221,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Draw custom spinbox arrow primitives."
},
{
"bases": [
"FigureCanvas"
],
"dataclass": false,
"decorators": [],
"docstring": "Matplotlib canvas for ROI trace and spike visualizations.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas",
"searchText": "class\ntracecanvas\ntorch_volpy.gui.qt_app.tracecanvas\n(parent: optional[qwidget] = none) -> none\nmatplotlib canvas for roi trace and spike visualizations.\nmatplotlib canvas for roi trace and spike visualizations.\nfigurecanvas",
"signature": "(parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 3002,
"line": 2237,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Matplotlib canvas for ROI trace and spike visualizations."
},
{
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"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.tracecanvas.__init__\n(self, parent: optional[qwidget] = none) -> none",
"signature": "(self, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 2273,
"line": 2242,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Render an empty trace plot state.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "plot_empty",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.plot_empty",
"searchText": "method\nplot_empty\ntorch_volpy.gui.qt_app.tracecanvas.plot_empty\n(self) -> none\nrender an empty trace plot state.\nrender an empty trace plot state.",
"signature": "(self) -> None",
"source": {
"endLine": 2301,
"line": 2275,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Render an empty trace plot state."
},
{
"decorators": [],
"docstring": "Plot extraction results and ROI trace data.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "plot_result",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.plot_result",
"searchText": "method\nplot_result\ntorch_volpy.gui.qt_app.tracecanvas.plot_result\n(self, result: dict, *, mode: str = 'trace', time_window: optional[tuple[float, float]] = none) -> none\nplot extraction results and roi trace data.\nplot extraction results and roi trace data.",
"signature": "(self, result: dict, *, mode: str = 'trace', time_window: Optional[tuple[float, float]] = None) -> None",
"source": {
"endLine": 2427,
"line": 2303,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Plot extraction results and ROI trace data."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_draw_time_axis_label",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._draw_time_axis_label",
"searchText": "method\n_draw_time_axis_label\ntorch_volpy.gui.qt_app.tracecanvas._draw_time_axis_label\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 2430,
"line": 2429,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_apply_figure_layout",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._apply_figure_layout",
"searchText": "method\n_apply_figure_layout\ntorch_volpy.gui.qt_app.tracecanvas._apply_figure_layout\n(self, *, has_data: bool) -> none",
"signature": "(self, *, has_data: bool) -> None",
"source": {
"endLine": 2436,
"line": 2432,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_build_display_trace",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._build_display_trace",
"searchText": "method\n_build_display_trace\ntorch_volpy.gui.qt_app.tracecanvas._build_display_trace\n(self, trace: np.ndarray, *, stacked: bool = false, stack_groups: optional[list[int]] = none, min_row_spacing: float = 1.0) -> np.ndarray",
"signature": "(self, trace: np.ndarray, *, stacked: bool = False, stack_groups: Optional[list[int]] = None, min_row_spacing: float = 1.0) -> np.ndarray",
"source": {
"endLine": 2473,
"line": 2438,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_normalize_dff_for_display",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._normalize_dff_for_display",
"searchText": "method\n_normalize_dff_for_display\ntorch_volpy.gui.qt_app.tracecanvas._normalize_dff_for_display\n(self, trace: np.ndarray) -> np.ndarray",
"signature": "(self, trace: np.ndarray) -> np.ndarray",
"source": {
"endLine": 2483,
"line": 2475,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.gui.qt_app",
"name": "_normalize_dff_column",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._normalize_dff_column",
"searchText": "staticmethod\n_normalize_dff_column\ntorch_volpy.gui.qt_app.tracecanvas._normalize_dff_column\n(y: np.ndarray) -> np.ndarray\nstaticmethod",
"signature": "(y: np.ndarray) -> np.ndarray",
"source": {
"endLine": 2502,
"line": 2486,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.gui.qt_app",
"name": "_decimate_line_for_plot",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._decimate_line_for_plot",
"searchText": "staticmethod\n_decimate_line_for_plot\ntorch_volpy.gui.qt_app.tracecanvas._decimate_line_for_plot\n(x: np.ndarray, y: np.ndarray, *, max_points: int) -> tuple[np.ndarray, np.ndarray]\nstaticmethod",
"signature": "(x: np.ndarray, y: np.ndarray, *, max_points: int) -> tuple[np.ndarray, np.ndarray]",
"source": {
"endLine": 2530,
"line": 2505,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Return whether the canvas currently has plotted trace data.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "has_data",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.has_data",
"searchText": "method\nhas_data\ntorch_volpy.gui.qt_app.tracecanvas.has_data\n(self) -> bool\nreturn whether the canvas currently has plotted trace data.\nreturn whether the canvas currently has plotted trace data.",
"signature": "(self) -> bool",
"source": {
"endLine": 2534,
"line": 2532,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Return whether the canvas currently has plotted trace data."
},
{
"decorators": [],
"docstring": "Return the time range covered by the current trace data.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "time_bounds",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.time_bounds",
"searchText": "method\ntime_bounds\ntorch_volpy.gui.qt_app.tracecanvas.time_bounds\n(self) -> tuple[float, float]\nreturn the time range covered by the current trace data.\nreturn the time range covered by the current trace data.",
"signature": "(self) -> tuple[float, float]",
"source": {
"endLine": 2540,
"line": 2536,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Return the time range covered by the current trace data."
},
{
"decorators": [],
"docstring": "Return the currently visible time window.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "current_time_window",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.current_time_window",
"searchText": "method\ncurrent_time_window\ntorch_volpy.gui.qt_app.tracecanvas.current_time_window\n(self) -> tuple[float, float]\nreturn the currently visible time window.\nreturn the currently visible time window.",
"signature": "(self) -> tuple[float, float]",
"source": {
"endLine": 2546,
"line": 2542,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Return the currently visible time window."
},
{
"decorators": [],
"docstring": "Set the visible trace time window.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_time_window",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.set_time_window",
"searchText": "method\nset_time_window\ntorch_volpy.gui.qt_app.tracecanvas.set_time_window\n(self, xmin: float, xmax: float) -> none\nset the visible trace time window.\nset the visible trace time window.",
"signature": "(self, xmin: float, xmax: float) -> None",
"source": {
"endLine": 2550,
"line": 2548,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Set the visible trace time window."
},
{
"decorators": [],
"docstring": "Reset the trace canvas to the full time range.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "reset_view",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.reset_view",
"searchText": "method\nreset_view\ntorch_volpy.gui.qt_app.tracecanvas.reset_view\n(self) -> none\nreset the trace canvas to the full time range.\nreset the trace canvas to the full time range.",
"signature": "(self) -> None",
"source": {
"endLine": 2555,
"line": 2552,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Reset the trace canvas to the full time range."
},
{
"decorators": [],
"docstring": "Zoom the trace time window by a scale factor.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "zoom_view",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.zoom_view",
"searchText": "method\nzoom_view\ntorch_volpy.gui.qt_app.tracecanvas.zoom_view\n(self, factor: float) -> none\nzoom the trace time window by a scale factor.\nzoom the trace time window by a scale factor.",
"signature": "(self, factor: float) -> None",
"source": {
"endLine": 2564,
"line": 2557,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Zoom the trace time window by a scale factor."
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.gui.qt_app",
"name": "_as_1d_float_array",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._as_1d_float_array",
"searchText": "staticmethod\n_as_1d_float_array\ntorch_volpy.gui.qt_app.tracecanvas._as_1d_float_array\n(value: any) -> np.ndarray\nstaticmethod",
"signature": "(value: Any) -> np.ndarray",
"source": {
"endLine": 2575,
"line": 2567,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_result_series_for_mode",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._result_series_for_mode",
"searchText": "method\n_result_series_for_mode\ntorch_volpy.gui.qt_app.tracecanvas._result_series_for_mode\n(self, result: dict, mode: str) -> tuple[np.ndarray, str, str, list[str], optional[list[int]], list[int]]",
"signature": "(self, result: dict, mode: str) -> tuple[np.ndarray, str, str, list[str], Optional[list[int]], list[int]]",
"source": {
"endLine": 2642,
"line": 2577,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_series_colors",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._series_colors",
"searchText": "method\n_series_colors\ntorch_volpy.gui.qt_app.tracecanvas._series_colors\n(self, result: dict, mode: str, n_traces: int) -> list[str]",
"signature": "(self, result: dict, mode: str, n_traces: int) -> list[str]",
"source": {
"endLine": 2676,
"line": 2644,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_on_overview_select",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._on_overview_select",
"searchText": "method\n_on_overview_select\ntorch_volpy.gui.qt_app.tracecanvas._on_overview_select\n(self, xmin: float, xmax: float) -> none",
"signature": "(self, xmin: float, xmax: float) -> None",
"source": {
"endLine": 2679,
"line": 2678,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_on_overview_move",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._on_overview_move",
"searchText": "method\n_on_overview_move\ntorch_volpy.gui.qt_app.tracecanvas._on_overview_move\n(self, xmin: float, xmax: float) -> none",
"signature": "(self, xmin: float, xmax: float) -> None",
"source": {
"endLine": 2682,
"line": 2681,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_on_draw",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._on_draw",
"searchText": "method\n_on_draw\ntorch_volpy.gui.qt_app.tracecanvas._on_draw\n(self, event) -> none",
"signature": "(self, event) -> None",
"source": {
"endLine": 2692,
"line": 2684,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_on_scroll",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._on_scroll",
"searchText": "method\n_on_scroll\ntorch_volpy.gui.qt_app.tracecanvas._on_scroll\n(self, event) -> none",
"signature": "(self, event) -> None",
"source": {
"endLine": 2702,
"line": 2694,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_on_button_press",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._on_button_press",
"searchText": "method\n_on_button_press\ntorch_volpy.gui.qt_app.tracecanvas._on_button_press\n(self, event) -> none",
"signature": "(self, event) -> None",
"source": {
"endLine": 2711,
"line": 2704,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_on_double_click",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._on_double_click",
"searchText": "method\n_on_double_click\ntorch_volpy.gui.qt_app.tracecanvas._on_double_click\n(self, event) -> none",
"signature": "(self, event) -> None",
"source": {
"endLine": 2718,
"line": 2713,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_roi_id_at_stacked_y",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._roi_id_at_stacked_y",
"searchText": "method\n_roi_id_at_stacked_y\ntorch_volpy.gui.qt_app.tracecanvas._roi_id_at_stacked_y\n(self, y: float) -> optional[int]",
"signature": "(self, y: float) -> Optional[int]",
"source": {
"endLine": 2733,
"line": 2720,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_on_motion",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._on_motion",
"searchText": "method\n_on_motion\ntorch_volpy.gui.qt_app.tracecanvas._on_motion\n(self, event) -> none",
"signature": "(self, event) -> None",
"source": {
"endLine": 2740,
"line": 2735,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_on_button_release",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._on_button_release",
"searchText": "method\n_on_button_release\ntorch_volpy.gui.qt_app.tracecanvas._on_button_release\n(self, event) -> none",
"signature": "(self, event) -> None",
"source": {
"endLine": 2743,
"line": 2742,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_set_view",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._set_view",
"searchText": "method\n_set_view\ntorch_volpy.gui.qt_app.tracecanvas._set_view\n(self, xmin: float, xmax: float, *, redraw: bool = true) -> none",
"signature": "(self, xmin: float, xmax: float, *, redraw: bool = True) -> None",
"source": {
"endLine": 2772,
"line": 2745,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_update_main_y_limits",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._update_main_y_limits",
"searchText": "method\n_update_main_y_limits\ntorch_volpy.gui.qt_app.tracecanvas._update_main_y_limits\n(self, xmin: float, xmax: float) -> none",
"signature": "(self, xmin: float, xmax: float) -> None",
"source": {
"endLine": 2789,
"line": 2774,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_style_main_axis",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._style_main_axis",
"searchText": "method\n_style_main_axis\ntorch_volpy.gui.qt_app.tracecanvas._style_main_axis\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 2803,
"line": 2791,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_style_stacked_axes",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._style_stacked_axes",
"searchText": "method\n_style_stacked_axes\ntorch_volpy.gui.qt_app.tracecanvas._style_stacked_axes\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 2820,
"line": 2805,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_stacked_group_count",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._stacked_group_count",
"searchText": "method\n_stacked_group_count\ntorch_volpy.gui.qt_app.tracecanvas._stacked_group_count\n(self) -> int",
"signature": "(self) -> int",
"source": {
"endLine": 2827,
"line": 2822,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_stacked_row_position",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._stacked_row_position",
"searchText": "method\n_stacked_row_position\ntorch_volpy.gui.qt_app.tracecanvas._stacked_row_position\n(self, group: int, n_groups: optional[int] = none) -> float",
"signature": "(self, group: int, n_groups: Optional[int] = None) -> float",
"source": {
"endLine": 2832,
"line": 2829,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_stacked_y_limits",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._stacked_y_limits",
"searchText": "method\n_stacked_y_limits\ntorch_volpy.gui.qt_app.tracecanvas._stacked_y_limits\n(self, xmin: optional[float] = none, xmax: optional[float] = none) -> tuple[float, float]",
"signature": "(self, xmin: Optional[float] = None, xmax: Optional[float] = None) -> tuple[float, float]",
"source": {
"endLine": 2857,
"line": 2834,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": ""
},
{
"decorators": [],
"docstring": "Save the current trace figure as a PNG file.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "save_png",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas.save_png",
"searchText": "method\nsave_png\ntorch_volpy.gui.qt_app.tracecanvas.save_png\n(self, path: str, *, dpi: int = 300) -> none\nsave the current trace figure as a png file.\nsave the current trace figure as a png file.",
"signature": "(self, path: str, *, dpi: int = 300) -> None",
"source": {
"endLine": 2880,
"line": 2859,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Save the current trace figure as a PNG file."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_style_overview_axis",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._style_overview_axis",
"searchText": "method\n_style_overview_axis\ntorch_volpy.gui.qt_app.tracecanvas._style_overview_axis\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 2889,
"line": 2882,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_overview_handle_positions",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._overview_handle_positions",
"searchText": "method\n_overview_handle_positions\ntorch_volpy.gui.qt_app.tracecanvas._overview_handle_positions\n(self, xmin: float, xmax: float) -> list[tuple[float, float]]",
"signature": "(self, xmin: float, xmax: float) -> list[tuple[float, float]]",
"source": {
"endLine": 2913,
"line": 2891,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_overview_selection_artists",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._overview_selection_artists",
"searchText": "method\n_overview_selection_artists\ntorch_volpy.gui.qt_app.tracecanvas._overview_selection_artists\n(self) -> list[any]",
"signature": "(self) -> list[Any]",
"source": {
"endLine": 2920,
"line": 2915,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_draw_overview_selection_artists",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._draw_overview_selection_artists",
"searchText": "method\n_draw_overview_selection_artists\ntorch_volpy.gui.qt_app.tracecanvas._draw_overview_selection_artists\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 2924,
"line": 2922,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_blit_overview_selection",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._blit_overview_selection",
"searchText": "method\n_blit_overview_selection\ntorch_volpy.gui.qt_app.tracecanvas._blit_overview_selection\n(self) -> bool",
"signature": "(self) -> bool",
"source": {
"endLine": 2932,
"line": 2926,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_move_overview_selection",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._move_overview_selection",
"searchText": "method\n_move_overview_selection\ntorch_volpy.gui.qt_app.tracecanvas._move_overview_selection\n(self, xmin: float, xmax: float) -> none",
"signature": "(self, xmin: float, xmax: float) -> None",
"source": {
"endLine": 2950,
"line": 2934,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_update_overview_selection",
"parent": "torch_volpy.gui.qt_app.TraceCanvas",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.TraceCanvas._update_overview_selection",
"searchText": "method\n_update_overview_selection\ntorch_volpy.gui.qt_app.tracecanvas._update_overview_selection\n(self, xmin: float, xmax: float) -> none",
"signature": "(self, xmin: float, xmax: float) -> None",
"source": {
"endLine": 3002,
"line": 2952,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"bases": [
"QGraphicsView"
],
"dataclass": false,
"decorators": [],
"docstring": "Graphics view for movie frames, ROI drawing, and mask overlays.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView",
"searchText": "class\nmoviegraphicsview\ntorch_volpy.gui.qt_app.moviegraphicsview\n(parent: optional[qwidget] = none) -> none\ngraphics view for movie frames, roi drawing, and mask overlays.\ngraphics view for movie frames, roi drawing, and mask overlays.\nqgraphicsview",
"signature": "(parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 3471,
"line": 3005,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Graphics view for movie frames, ROI drawing, and mask overlays."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.moviegraphicsview.__init__\n(self, parent: optional[qwidget] = none) -> none",
"signature": "(self, parent: Optional[QWidget] = None) -> None",
"source": {
"endLine": 3057,
"line": 3013,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_set_zoom_percent_value",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._set_zoom_percent_value",
"searchText": "method\n_set_zoom_percent_value\ntorch_volpy.gui.qt_app.moviegraphicsview._set_zoom_percent_value\n(self, percent: float, *, emit_signal: bool = true) -> none",
"signature": "(self, percent: float, *, emit_signal: bool = True) -> None",
"source": {
"endLine": 3064,
"line": 3059,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Enable or disable fit-to-view scaling.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_fit_mode",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.set_fit_mode",
"searchText": "method\nset_fit_mode\ntorch_volpy.gui.qt_app.moviegraphicsview.set_fit_mode\n(self, mode: str) -> none\nenable or disable fit-to-view scaling.\nenable or disable fit-to-view scaling.",
"signature": "(self, mode: str) -> None",
"source": {
"endLine": 3072,
"line": 3066,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Enable or disable fit-to-view scaling."
},
{
"decorators": [],
"docstring": "Display movie pixels at actual size.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_actual_size",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.set_actual_size",
"searchText": "method\nset_actual_size\ntorch_volpy.gui.qt_app.moviegraphicsview.set_actual_size\n(self) -> none\ndisplay movie pixels at actual size.\ndisplay movie pixels at actual size.",
"signature": "(self) -> None",
"source": {
"endLine": 3078,
"line": 3074,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Display movie pixels at actual size."
},
{
"decorators": [],
"docstring": "Set the movie view zoom as a percentage.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_zoom_percent",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.set_zoom_percent",
"searchText": "method\nset_zoom_percent\ntorch_volpy.gui.qt_app.moviegraphicsview.set_zoom_percent\n(self, percent: float) -> none\nset the movie view zoom as a percentage.\nset the movie view zoom as a percentage.",
"signature": "(self, percent: float) -> None",
"source": {
"endLine": 3084,
"line": 3080,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Set the movie view zoom as a percentage."
},
{
"decorators": [],
"docstring": "Scale the current movie view zoom by a factor.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "zoom_by",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.zoom_by",
"searchText": "method\nzoom_by\ntorch_volpy.gui.qt_app.moviegraphicsview.zoom_by\n(self, factor: float) -> none\nscale the current movie view zoom by a factor.\nscale the current movie view zoom by a factor.",
"signature": "(self, factor: float) -> None",
"source": {
"endLine": 3088,
"line": 3086,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Scale the current movie view zoom by a factor."
},
{
"decorators": [],
"docstring": "Apply the current fit or zoom transform to the view.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "apply_view_transform",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.apply_view_transform",
"searchText": "method\napply_view_transform\ntorch_volpy.gui.qt_app.moviegraphicsview.apply_view_transform\n(self) -> none\napply the current fit or zoom transform to the view.\napply the current fit or zoom transform to the view.",
"signature": "(self) -> None",
"source": {
"endLine": 3112,
"line": 3090,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Apply the current fit or zoom transform to the view."
},
{
"decorators": [],
"docstring": "Set the active ROI drawing or selection mode.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_roi_mode",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.set_roi_mode",
"searchText": "method\nset_roi_mode\ntorch_volpy.gui.qt_app.moviegraphicsview.set_roi_mode\n(self, mode: str) -> none\nset the active roi drawing or selection mode.\nset the active roi drawing or selection mode.",
"signature": "(self, mode: str) -> None",
"source": {
"endLine": 3124,
"line": 3114,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Set the active ROI drawing or selection mode."
},
{
"decorators": [],
"docstring": "Set the freehand ROI brush size.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_brush_size",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.set_brush_size",
"searchText": "method\nset_brush_size\ntorch_volpy.gui.qt_app.moviegraphicsview.set_brush_size\n(self, size: int) -> none\nset the freehand roi brush size.\nset the freehand roi brush size.",
"signature": "(self, size: int) -> None",
"source": {
"endLine": 3128,
"line": 3126,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Set the freehand ROI brush size."
},
{
"decorators": [],
"docstring": "Display a movie frame and optional mask overlay.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_frame",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.set_frame",
"searchText": "method\nset_frame\ntorch_volpy.gui.qt_app.moviegraphicsview.set_frame\n(self, frame: np.ndarray, *, rgb: bool = false) -> none\ndisplay a movie frame and optional mask overlay.\ndisplay a movie frame and optional mask overlay.",
"signature": "(self, frame: np.ndarray, *, rgb: bool = False) -> None",
"source": {
"endLine": 3148,
"line": 3130,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Display a movie frame and optional mask overlay."
},
{
"decorators": [],
"docstring": "Show a centered empty-state message in the movie view.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "show_empty_message",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.show_empty_message",
"searchText": "method\nshow_empty_message\ntorch_volpy.gui.qt_app.moviegraphicsview.show_empty_message\n(self, message: str = 'no movie loaded') -> none\nshow a centered empty-state message in the movie view.\nshow a centered empty-state message in the movie view.",
"signature": "(self, message: str = 'No movie loaded') -> None",
"source": {
"endLine": 3162,
"line": 3150,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Show a centered empty-state message in the movie view."
},
{
"decorators": [],
"docstring": "Clear the interactive ROI drawing overlay.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "clear_roi",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.clear_roi",
"searchText": "method\nclear_roi\ntorch_volpy.gui.qt_app.moviegraphicsview.clear_roi\n(self) -> none\nclear the interactive roi drawing overlay.\nclear the interactive roi drawing overlay.",
"signature": "(self) -> None",
"source": {
"endLine": 3169,
"line": 3164,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Clear the interactive ROI drawing overlay."
},
{
"decorators": [],
"docstring": "Remove the displayed ROI mask overlay.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "clear_mask_overlay",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.clear_mask_overlay",
"searchText": "method\nclear_mask_overlay\ntorch_volpy.gui.qt_app.moviegraphicsview.clear_mask_overlay\n(self) -> none\nremove the displayed roi mask overlay.\nremove the displayed roi mask overlay.",
"signature": "(self) -> None",
"source": {
"endLine": 3179,
"line": 3171,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Remove the displayed ROI mask overlay."
},
{
"decorators": [],
"docstring": "Display a labeled ROI mask overlay.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_mask_overlay",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.set_mask_overlay",
"searchText": "method\nset_mask_overlay\ntorch_volpy.gui.qt_app.moviegraphicsview.set_mask_overlay\n(self, mask: np.ndarray, roi_id: int) -> none\ndisplay a labeled roi mask overlay.\ndisplay a labeled roi mask overlay.",
"signature": "(self, mask: np.ndarray, roi_id: int) -> None",
"source": {
"endLine": 3196,
"line": 3181,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Display a labeled ROI mask overlay."
},
{
"decorators": [],
"docstring": "Center the view on a specific ROI label.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "center_on_roi",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.center_on_roi",
"searchText": "method\ncenter_on_roi\ntorch_volpy.gui.qt_app.moviegraphicsview.center_on_roi\n(self, roi_id: int) -> none\ncenter the view on a specific roi label.\ncenter the view on a specific roi label.",
"signature": "(self, roi_id: int) -> None",
"source": {
"endLine": 3205,
"line": 3198,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Center the view on a specific ROI label."
},
{
"decorators": [],
"docstring": "Show or hide the ROI mask overlay.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "set_overlay_visible",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.set_overlay_visible",
"searchText": "method\nset_overlay_visible\ntorch_volpy.gui.qt_app.moviegraphicsview.set_overlay_visible\n(self, visible: bool) -> none\nshow or hide the roi mask overlay.\nshow or hide the roi mask overlay.",
"signature": "(self, visible: bool) -> None",
"source": {
"endLine": 3213,
"line": 3207,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Show or hide the ROI mask overlay."
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.gui.qt_app",
"name": "_mask_to_rgba",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._mask_to_rgba",
"searchText": "staticmethod\n_mask_to_rgba\ntorch_volpy.gui.qt_app.moviegraphicsview._mask_to_rgba\n(mask: np.ndarray, selected_roi_id: int) -> np.ndarray\nstaticmethod",
"signature": "(mask: np.ndarray, selected_roi_id: int) -> np.ndarray",
"source": {
"endLine": 3235,
"line": 3216,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_add_roi_labels",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._add_roi_labels",
"searchText": "method\n_add_roi_labels\ntorch_volpy.gui.qt_app.moviegraphicsview._add_roi_labels\n(self, mask: np.ndarray) -> none",
"signature": "(self, mask: np.ndarray) -> None",
"source": {
"endLine": 3268,
"line": 3237,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_center_empty_message",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._center_empty_message",
"searchText": "method\n_center_empty_message\ntorch_volpy.gui.qt_app.moviegraphicsview._center_empty_message\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 3275,
"line": 3270,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_inside_image",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._inside_image",
"searchText": "method\n_inside_image\ntorch_volpy.gui.qt_app.moviegraphicsview._inside_image\n(self, point: qpointf) -> bool",
"signature": "(self, point: QPointF) -> bool",
"source": {
"endLine": 3281,
"line": 3277,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_map_event_to_image",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._map_event_to_image",
"searchText": "method\n_map_event_to_image\ntorch_volpy.gui.qt_app.moviegraphicsview._map_event_to_image\n(self, event) -> optional[qpointf]",
"signature": "(self, event) -> Optional[QPointF]",
"source": {
"endLine": 3287,
"line": 3283,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_remove_roi_item",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._remove_roi_item",
"searchText": "method\n_remove_roi_item\ntorch_volpy.gui.qt_app.moviegraphicsview._remove_roi_item\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 3292,
"line": 3289,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_make_pen",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._make_pen",
"searchText": "method\n_make_pen\ntorch_volpy.gui.qt_app.moviegraphicsview._make_pen\n(self, width: float = 1.8, color: str = '#00bcd4') -> qpen",
"signature": "(self, width: float = 1.8, color: str = '#00bcd4') -> QPen",
"source": {
"endLine": 3300,
"line": 3294,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Handle movie-view mouse presses for ROI tools and panning.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "mousePressEvent",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.mousePressEvent",
"searchText": "method\nmousepressevent\ntorch_volpy.gui.qt_app.moviegraphicsview.mousepressevent\n(self, event) -> none\nhandle movie-view mouse presses for roi tools and panning.\nhandle movie-view mouse presses for roi tools and panning.",
"signature": "(self, event) -> None",
"source": {
"endLine": 3339,
"line": 3302,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Handle movie-view mouse presses for ROI tools and panning."
},
{
"decorators": [],
"docstring": "Handle movie-view mouse movement for ROI tools and panning.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "mouseMoveEvent",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.mouseMoveEvent",
"searchText": "method\nmousemoveevent\ntorch_volpy.gui.qt_app.moviegraphicsview.mousemoveevent\n(self, event) -> none\nhandle movie-view mouse movement for roi tools and panning.\nhandle movie-view mouse movement for roi tools and panning.",
"signature": "(self, event) -> None",
"source": {
"endLine": 3362,
"line": 3341,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Handle movie-view mouse movement for ROI tools and panning."
},
{
"decorators": [],
"docstring": "Complete ROI drawing or panning interactions.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "mouseReleaseEvent",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.mouseReleaseEvent",
"searchText": "method\nmousereleaseevent\ntorch_volpy.gui.qt_app.moviegraphicsview.mousereleaseevent\n(self, event) -> none\ncomplete roi drawing or panning interactions.\ncomplete roi drawing or panning interactions.",
"signature": "(self, event) -> None",
"source": {
"endLine": 3400,
"line": 3364,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Complete ROI drawing or panning interactions."
},
{
"decorators": [],
"docstring": "Handle double-click actions in the movie view.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "mouseDoubleClickEvent",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.mouseDoubleClickEvent",
"searchText": "method\nmousedoubleclickevent\ntorch_volpy.gui.qt_app.moviegraphicsview.mousedoubleclickevent\n(self, event) -> none\nhandle double-click actions in the movie view.\nhandle double-click actions in the movie view.",
"signature": "(self, event) -> None",
"source": {
"endLine": 3404,
"line": 3402,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Handle double-click actions in the movie view."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_update_freehand_preview",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._update_freehand_preview",
"searchText": "method\n_update_freehand_preview\ntorch_volpy.gui.qt_app.moviegraphicsview._update_freehand_preview\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 3423,
"line": 3406,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_pick_roi_at",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._pick_roi_at",
"searchText": "method\n_pick_roi_at\ntorch_volpy.gui.qt_app.moviegraphicsview._pick_roi_at\n(self, point: qpointf) -> none",
"signature": "(self, point: QPointF) -> None",
"source": {
"endLine": 3440,
"line": 3425,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_finalize_freehand",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView._finalize_freehand",
"searchText": "method\n_finalize_freehand\ntorch_volpy.gui.qt_app.moviegraphicsview._finalize_freehand\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 3463,
"line": 3442,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Reapply the movie view transform after resizing.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "resizeEvent",
"parent": "torch_volpy.gui.qt_app.MovieGraphicsView",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MovieGraphicsView.resizeEvent",
"searchText": "method\nresizeevent\ntorch_volpy.gui.qt_app.moviegraphicsview.resizeevent\n(self, event) -> none\nreapply the movie view transform after resizing.\nreapply the movie view transform after resizing.",
"signature": "(self, event) -> None",
"source": {
"endLine": 3471,
"line": 3465,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Reapply the movie view transform after resizing."
},
{
"bases": [
"QObject"
],
"dataclass": false,
"decorators": [],
"docstring": "Background worker for movie conversion and motion correction.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "PreprocessWorker",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.PreprocessWorker",
"searchText": "class\npreprocessworker\ntorch_volpy.gui.qt_app.preprocessworker\n(*, source_path: str, dataset: str, motion_correction: bool = true, max_shifts: tuple[int, int] = (15, 15), frames_per_chunk: int = 256, device: str = 'cpu') -> none\nbackground worker for movie conversion and motion correction.\nbackground worker for movie conversion and motion correction.\nqobject",
"signature": "(*, source_path: str, dataset: str, motion_correction: bool = True, max_shifts: tuple[int, int] = (15, 15), frames_per_chunk: int = 256, device: str = 'cpu') -> None",
"source": {
"endLine": 3574,
"line": 3474,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Background worker for movie conversion and motion correction."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.PreprocessWorker",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.PreprocessWorker.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.preprocessworker.__init__\n(self, *, source_path: str, dataset: str, motion_correction: bool = true, max_shifts: tuple[int, int] = (15, 15), frames_per_chunk: int = 256, device: str = 'cpu') -> none",
"signature": "(self, *, source_path: str, dataset: str, motion_correction: bool = True, max_shifts: tuple[int, int] = (15, 15), frames_per_chunk: int = 256, device: str = 'cpu') -> None",
"source": {
"endLine": 3496,
"line": 3480,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_emit_phase_progress",
"parent": "torch_volpy.gui.qt_app.PreprocessWorker",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.PreprocessWorker._emit_phase_progress",
"searchText": "method\n_emit_phase_progress\ntorch_volpy.gui.qt_app.preprocessworker._emit_phase_progress\n(self, phase_start: int, phase_span: int, value: int, message: str) -> none",
"signature": "(self, phase_start: int, phase_span: int, value: int, message: str) -> None",
"source": {
"endLine": 3501,
"line": 3498,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Run TIFF conversion and motion correction in a worker thread.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "run",
"parent": "torch_volpy.gui.qt_app.PreprocessWorker",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.PreprocessWorker.run",
"searchText": "method\nrun\ntorch_volpy.gui.qt_app.preprocessworker.run\n(self) -> none\nrun tiff conversion and motion correction in a worker thread.\nrun tiff conversion and motion correction in a worker thread.",
"signature": "(self) -> None",
"source": {
"endLine": 3574,
"line": 3503,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Run TIFF conversion and motion correction in a worker thread."
},
{
"bases": [
"QObject"
],
"dataclass": false,
"decorators": [],
"docstring": "Background worker for ROI trace extraction.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "ExtractionWorker",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ExtractionWorker",
"searchText": "class\nextractionworker\ntorch_volpy.gui.qt_app.extractionworker\n(*, movie_path: str, dataset: str, method: str, roi_mask: np.ndarray, roi_ids: list[int], channel: optional[int], frame_rate: float, device: str, flip_signal: bool, batch_size: int = 256, advanced_options: optional[dict[str, any]] = none, all_rois: bool = false) -> none\nbackground worker for roi trace extraction.\nbackground worker for roi trace extraction.\nqobject",
"signature": "(*, movie_path: str, dataset: str, method: str, roi_mask: np.ndarray, roi_ids: list[int], channel: Optional[int], frame_rate: float, device: str, flip_signal: bool, batch_size: int = 256, advanced_options: Optional[dict[str, Any]] = None, all_rois: bool = False) -> None",
"source": {
"endLine": 3775,
"line": 3577,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Background worker for ROI trace extraction."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.ExtractionWorker",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.ExtractionWorker.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.extractionworker.__init__\n(self, *, movie_path: str, dataset: str, method: str, roi_mask: np.ndarray, roi_ids: list[int], channel: optional[int], frame_rate: float, device: str, flip_signal: bool, batch_size: int = 256, advanced_options: optional[dict[str, any]] = none, all_rois: bool = false) -> none",
"signature": "(self, *, movie_path: str, dataset: str, method: str, roi_mask: np.ndarray, roi_ids: list[int], channel: Optional[int], frame_rate: float, device: str, flip_signal: bool, batch_size: int = 256, advanced_options: Optional[dict[str, Any]] = None, all_rois: bool = False) -> None",
"source": {
"endLine": 3612,
"line": 3584,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Run trace extraction in a worker thread.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "run",
"parent": "torch_volpy.gui.qt_app.ExtractionWorker",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.ExtractionWorker.run",
"searchText": "method\nrun\ntorch_volpy.gui.qt_app.extractionworker.run\n(self) -> none\nrun trace extraction in a worker thread.\nrun trace extraction in a worker thread.",
"signature": "(self) -> None",
"source": {
"endLine": 3693,
"line": 3614,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Run trace extraction in a worker thread."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_extract_one",
"parent": "torch_volpy.gui.qt_app.ExtractionWorker",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.ExtractionWorker._extract_one",
"searchText": "method\n_extract_one\ntorch_volpy.gui.qt_app.extractionworker._extract_one\n(self, movie, method: str, roi_id: int, prefix: str) -> dict",
"signature": "(self, movie, method: str, roi_id: int, prefix: str) -> dict",
"source": {
"endLine": 3752,
"line": 3695,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_spikepursuit_payload",
"parent": "torch_volpy.gui.qt_app.ExtractionWorker",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.ExtractionWorker._spikepursuit_payload",
"searchText": "method\n_spikepursuit_payload\ntorch_volpy.gui.qt_app.extractionworker._spikepursuit_payload\n(self, result) -> dict",
"signature": "(self, result) -> dict",
"source": {
"endLine": 3775,
"line": 3754,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"bases": [
"QObject"
],
"dataclass": false,
"decorators": [],
"docstring": "Background worker for Cellpose ROI segmentation.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "SegmentationWorker",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentationWorker",
"searchText": "class\nsegmentationworker\ntorch_volpy.gui.qt_app.segmentationworker\n(*, movie_path: str, dataset: str, model_path: str, channel: optional[int], summary_window_size: int, baseline_percentile: float, device: str, gpu: bool, save_to_disk: bool) -> none\nbackground worker for cellpose roi segmentation.\nbackground worker for cellpose roi segmentation.\nqobject",
"signature": "(*, movie_path: str, dataset: str, model_path: str, channel: Optional[int], summary_window_size: int, baseline_percentile: float, device: str, gpu: bool, save_to_disk: bool) -> None",
"source": {
"endLine": 3849,
"line": 3778,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Background worker for Cellpose ROI segmentation."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.SegmentationWorker",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentationWorker.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.segmentationworker.__init__\n(self, *, movie_path: str, dataset: str, model_path: str, channel: optional[int], summary_window_size: int, baseline_percentile: float, device: str, gpu: bool, save_to_disk: bool) -> none",
"signature": "(self, *, movie_path: str, dataset: str, model_path: str, channel: Optional[int], summary_window_size: int, baseline_percentile: float, device: str, gpu: bool, save_to_disk: bool) -> None",
"source": {
"endLine": 3806,
"line": 3784,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Run Cellpose segmentation in a worker thread.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "run",
"parent": "torch_volpy.gui.qt_app.SegmentationWorker",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.SegmentationWorker.run",
"searchText": "method\nrun\ntorch_volpy.gui.qt_app.segmentationworker.run\n(self) -> none\nrun cellpose segmentation in a worker thread.\nrun cellpose segmentation in a worker thread.",
"signature": "(self) -> None",
"source": {
"endLine": 3849,
"line": 3808,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Run Cellpose segmentation in a worker thread."
},
{
"bases": [
"QMainWindow"
],
"dataclass": false,
"decorators": [],
"docstring": "Main torch-volpy GUI window.",
"kind": "class",
"module": "torch_volpy.gui.qt_app",
"name": "MainWindow",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow",
"searchText": "class\nmainwindow\ntorch_volpy.gui.qt_app.mainwindow\n() -> none\nmain torch-volpy gui window.\nmain torch-volpy gui window.\nqmainwindow",
"signature": "() -> None",
"source": {
"endLine": 6175,
"line": 3852,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Main torch-volpy GUI window."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "__init__",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow.__init__",
"searchText": "method\n__init__\ntorch_volpy.gui.qt_app.mainwindow.__init__\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 3897,
"line": 3854,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_build_ui",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._build_ui",
"searchText": "method\n_build_ui\ntorch_volpy.gui.qt_app.mainwindow._build_ui\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 3930,
"line": 3899,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_build_top_bar",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._build_top_bar",
"searchText": "method\n_build_top_bar\ntorch_volpy.gui.qt_app.mainwindow._build_top_bar\n(self) -> qwidget",
"signature": "(self) -> QWidget",
"source": {
"endLine": 3956,
"line": 3932,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_build_center_workspace",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._build_center_workspace",
"searchText": "method\n_build_center_workspace\ntorch_volpy.gui.qt_app.mainwindow._build_center_workspace\n(self) -> qwidget",
"signature": "(self) -> QWidget",
"source": {
"endLine": 3965,
"line": 3958,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_build_movie_view_card",
"parent": "torch_volpy.gui.qt_app.MainWindow",
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"kind": "method",
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"searchText": "method\n_refresh_roi_inspector\ntorch_volpy.gui.qt_app.mainwindow._refresh_roi_inspector\n(self) -> none",
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"searchText": "method\n_build_controls\ntorch_volpy.gui.qt_app.mainwindow._build_controls\n(self) -> qwidget",
"signature": "(self) -> QWidget",
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"kind": "method",
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"searchText": "method\n_selected_device\ntorch_volpy.gui.qt_app.mainwindow._selected_device\n(self) -> str",
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"kind": "method",
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"kind": "method",
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"searchText": "method\n_set_playback_button_active\ntorch_volpy.gui.qt_app.mainwindow._set_playback_button_active\n(self, active: bool) -> none",
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"searchText": "method\n_set_controls_enabled\ntorch_volpy.gui.qt_app.mainwindow._set_controls_enabled\n(self, enabled: bool) -> none",
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"kind": "method",
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"searchText": "method\n_has_extractable_rois\ntorch_volpy.gui.qt_app.mainwindow._has_extractable_rois\n(self) -> bool",
"signature": "(self) -> bool",
"source": {
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"kind": "method",
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"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._update_roi_undo_button",
"searchText": "method\n_update_roi_undo_button\ntorch_volpy.gui.qt_app.mainwindow._update_roi_undo_button\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 4988,
"line": 4985,
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_clear_roi_mask_history",
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"searchText": "method\n_clear_roi_mask_history\ntorch_volpy.gui.qt_app.mainwindow._clear_roi_mask_history\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 4992,
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_push_roi_mask_history",
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"public": false,
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"searchText": "method\n_push_roi_mask_history\ntorch_volpy.gui.qt_app.mainwindow._push_roi_mask_history\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 4999,
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"kind": "method",
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"name": "_set_extraction_controls_enabled",
"parent": "torch_volpy.gui.qt_app.MainWindow",
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"searchText": "method\n_set_extraction_controls_enabled\ntorch_volpy.gui.qt_app.mainwindow._set_extraction_controls_enabled\n(self, enabled: bool) -> none",
"signature": "(self, enabled: bool) -> None",
"source": {
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"kind": "method",
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"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._set_segmentation_controls_enabled",
"searchText": "method\n_set_segmentation_controls_enabled\ntorch_volpy.gui.qt_app.mainwindow._set_segmentation_controls_enabled\n(self, enabled: bool) -> none",
"signature": "(self, enabled: bool) -> None",
"source": {
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"kind": "method",
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"name": "_reset_loaded_movie_state",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._reset_loaded_movie_state",
"searchText": "method\n_reset_loaded_movie_state\ntorch_volpy.gui.qt_app.mainwindow._reset_loaded_movie_state\n(self) -> none",
"signature": "(self) -> None",
"source": {
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"line": 5026,
"path": "src/torch_volpy/gui/qt_app.py"
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"decorators": [],
"docstring": "Prompt the user to choose and open a movie file.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "open_movie_dialog",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": true,
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"searchText": "method\nopen_movie_dialog\ntorch_volpy.gui.qt_app.mainwindow.open_movie_dialog\n(self) -> none\nprompt the user to choose and open a movie file.\nprompt the user to choose and open a movie file.",
"signature": "(self) -> None",
"source": {
"endLine": 5091,
"line": 5077,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Prompt the user to choose and open a movie file."
},
{
"decorators": [],
"docstring": "Preprocess a source movie and load the corrected result.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "preprocess_and_load_movie",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow.preprocess_and_load_movie",
"searchText": "method\npreprocess_and_load_movie\ntorch_volpy.gui.qt_app.mainwindow.preprocess_and_load_movie\n(self, path: str) -> none\npreprocess a source movie and load the corrected result.\npreprocess a source movie and load the corrected result.",
"signature": "(self, path: str) -> None",
"source": {
"endLine": 5132,
"line": 5093,
"path": "src/torch_volpy/gui/qt_app.py"
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"summary": "Preprocess a source movie and load the corrected result."
},
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_preprocess_progress",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
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"searchText": "method\n_preprocess_progress\ntorch_volpy.gui.qt_app.mainwindow._preprocess_progress\n(self, value: int, message: str) -> none",
"signature": "(self, value: int, message: str) -> None",
"source": {
"endLine": 5136,
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"kind": "method",
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"name": "_preprocess_finished",
"parent": "torch_volpy.gui.qt_app.MainWindow",
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"searchText": "method\n_preprocess_finished\ntorch_volpy.gui.qt_app.mainwindow._preprocess_finished\n(self, corrected_path: str) -> none",
"signature": "(self, corrected_path: str) -> None",
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"kind": "method",
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"name": "_preprocess_failed",
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"searchText": "method\n_preprocess_failed\ntorch_volpy.gui.qt_app.mainwindow._preprocess_failed\n(self, message: str) -> none",
"signature": "(self, message: str) -> None",
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"docstring": "Load a movie into the GUI state and viewer.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "load_movie",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": true,
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"searchText": "method\nload_movie\ntorch_volpy.gui.qt_app.mainwindow.load_movie\n(self, path: str) -> none\nload a movie into the gui state and viewer.\nload a movie into the gui state and viewer.",
"signature": "(self, path: str) -> None",
"source": {
"endLine": 5219,
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"path": "src/torch_volpy/gui/qt_app.py"
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"summary": "Load a movie into the GUI state and viewer."
},
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_read_rgb_display_frame",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._read_rgb_display_frame",
"searchText": "method\n_read_rgb_display_frame\ntorch_volpy.gui.qt_app.mainwindow._read_rgb_display_frame\n(self, frame_index: int) -> np.ndarray",
"signature": "(self, frame_index: int) -> np.ndarray",
"source": {
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"line": 5221,
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},
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"docstring": "Display the current movie frame and synchronized overlays.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "show_frame",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": true,
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"searchText": "method\nshow_frame\ntorch_volpy.gui.qt_app.mainwindow.show_frame\n(self, frame_index: int) -> none\ndisplay the current movie frame and synchronized overlays.\ndisplay the current movie frame and synchronized overlays.",
"signature": "(self, frame_index: int) -> None",
"source": {
"endLine": 5263,
"line": 5243,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Display the current movie frame and synchronized overlays."
},
{
"decorators": [],
"docstring": "Start or stop movie playback.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "toggle_playback",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": true,
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"searchText": "method\ntoggle_playback\ntorch_volpy.gui.qt_app.mainwindow.toggle_playback\n(self) -> none\nstart or stop movie playback.\nstart or stop movie playback.",
"signature": "(self) -> None",
"source": {
"endLine": 5277,
"line": 5265,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Start or stop movie playback."
},
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_update_timer_interval",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
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"searchText": "method\n_update_timer_interval\ntorch_volpy.gui.qt_app.mainwindow._update_timer_interval\n(self) -> none",
"signature": "(self) -> None",
"source": {
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_effective_playback_fps",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._effective_playback_fps",
"searchText": "method\n_effective_playback_fps\ntorch_volpy.gui.qt_app.mainwindow._effective_playback_fps\n(self) -> float",
"signature": "(self) -> float",
"source": {
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_advance_frame",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._advance_frame",
"searchText": "method\n_advance_frame\ntorch_volpy.gui.qt_app.mainwindow._advance_frame\n(self) -> none",
"signature": "(self) -> None",
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"path": "src/torch_volpy/gui/qt_app.py"
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"docstring": "Prompt the user to select a Cellpose model file.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "browse_cellpose_model",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow.browse_cellpose_model",
"searchText": "method\nbrowse_cellpose_model\ntorch_volpy.gui.qt_app.mainwindow.browse_cellpose_model\n(self) -> none\nprompt the user to select a cellpose model file.\nprompt the user to select a cellpose model file.",
"signature": "(self) -> None",
"source": {
"endLine": 5328,
"line": 5317,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Prompt the user to select a Cellpose model file."
},
{
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"docstring": "Prompt the user to load an ROI mask file.",
"kind": "method",
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"name": "load_mask_dialog",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": true,
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"searchText": "method\nload_mask_dialog\ntorch_volpy.gui.qt_app.mainwindow.load_mask_dialog\n(self) -> none\nprompt the user to load an roi mask file.\nprompt the user to load an roi mask file.",
"signature": "(self) -> None",
"source": {
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"line": 5330,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Prompt the user to load an ROI mask file."
},
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"docstring": "Run Cellpose and load the generated ROI mask.",
"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "run_cellpose_segmentation",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": true,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow.run_cellpose_segmentation",
"searchText": "method\nrun_cellpose_segmentation\ntorch_volpy.gui.qt_app.mainwindow.run_cellpose_segmentation\n(self) -> none\nrun cellpose and load the generated roi mask.\nrun cellpose and load the generated roi mask.",
"signature": "(self) -> None",
"source": {
"endLine": 5408,
"line": 5363,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Run Cellpose and load the generated ROI mask."
},
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_clear_trace_state_for_new_roi_mask",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._clear_trace_state_for_new_roi_mask",
"searchText": "method\n_clear_trace_state_for_new_roi_mask\ntorch_volpy.gui.qt_app.mainwindow._clear_trace_state_for_new_roi_mask\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 5429,
"line": 5410,
"path": "src/torch_volpy/gui/qt_app.py"
},
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_apply_roi_mask",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._apply_roi_mask",
"searchText": "method\n_apply_roi_mask\ntorch_volpy.gui.qt_app.mainwindow._apply_roi_mask\n(self, mask: np.ndarray) -> np.ndarray",
"signature": "(self, mask: np.ndarray) -> np.ndarray",
"source": {
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"path": "src/torch_volpy/gui/qt_app.py"
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"kind": "method",
"module": "torch_volpy.gui.qt_app",
"name": "_set_roi_mask_state",
"parent": "torch_volpy.gui.qt_app.MainWindow",
"public": false,
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"searchText": "method\n_select_roi\ntorch_volpy.gui.qt_app.mainwindow._select_roi\n(self, roi_id: int) -> none",
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"kind": "method",
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"kind": "method",
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"docstring": "Open the advanced extraction options dialog.",
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"searchText": "method\nopen_advanced_options_dialog\ntorch_volpy.gui.qt_app.mainwindow.open_advanced_options_dialog\n(self) -> none\nopen the advanced extraction options dialog.\nopen the advanced extraction options dialog.",
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"summary": "Open the advanced extraction options dialog."
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"searchText": "method\n_current_extraction_channel\ntorch_volpy.gui.qt_app.mainwindow._current_extraction_channel\n(self) -> optional[int]",
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"kind": "method",
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"searchText": "method\n_current_extraction_key\ntorch_volpy.gui.qt_app.mainwindow._current_extraction_key\n(self, roi_id: optional[int] = none) -> optional[tuple]",
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"source": {
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"kind": "method",
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"name": "_remove_stale_trace_cache_entries",
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"docstring": "Extract traces for all available ROI labels.",
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"searchText": "method\nextract_all_traces\ntorch_volpy.gui.qt_app.mainwindow.extract_all_traces\n(self) -> none\nextract traces for all available roi labels.\nextract traces for all available roi labels.",
"signature": "(self) -> None",
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"summary": "Extract traces for all available ROI labels."
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"kind": "method",
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"searchText": "method\n_start_extraction\ntorch_volpy.gui.qt_app.mainwindow._start_extraction\n(self, roi_ids: list[int], *, all_rois: bool) -> none",
"signature": "(self, roi_ids: list[int], *, all_rois: bool) -> None",
"source": {
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"kind": "method",
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"kind": "staticmethod",
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"kind": "staticmethod",
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"kind": "method",
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"kind": "method",
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"name": "_cached_results_for_roi_ids",
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"searchText": "method\n_cached_results_for_roi_ids\ntorch_volpy.gui.qt_app.mainwindow._cached_results_for_roi_ids\n(self, roi_ids: list[int]) -> list[dict]",
"signature": "(self, roi_ids: list[int]) -> list[dict]",
"source": {
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"name": "_cached_results_for_all_rois",
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"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._cached_results_for_all_rois",
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"kind": "staticmethod",
"module": "torch_volpy.gui.qt_app",
"name": "_csv_columns_for_results",
"parent": "torch_volpy.gui.qt_app.MainWindow",
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"qualifiedName": "torch_volpy.gui.qt_app.MainWindow._csv_columns_for_results",
"searchText": "staticmethod\n_csv_columns_for_results\ntorch_volpy.gui.qt_app.mainwindow._csv_columns_for_results\n(results: list[dict]) -> tuple[np.ndarray, list[str]]\nstaticmethod",
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"parent": "torch_volpy.gui.qt_app.MainWindow",
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"signature": "(self) -> None",
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"summary": "Prompt for a path and save all extracted traces."
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"docstring": "Clean up movie handles and worker state before closing.",
"kind": "method",
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"name": "closeEvent",
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"signature": "(self, event) -> None",
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"summary": "Clean up movie handles and worker state before closing."
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{
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"docstring": "Start the torch-volpy Qt application.",
"kind": "function",
"module": "torch_volpy.gui.qt_app",
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"searchText": "function\nmain\ntorch_volpy.gui.qt_app.main\n(argv: optional[list[str]] = none) -> int\nstart the torch-volpy qt application.\nstart the torch-volpy qt application.",
"signature": "(argv: Optional[list[str]] = None) -> int",
"source": {
"endLine": 6185,
"line": 6178,
"path": "src/torch_volpy/gui/qt_app.py"
},
"summary": "Start the torch-volpy Qt application."
},
{
"decorators": [],
"docstring": "",
"exports": [],
"kind": "module",
"module": "torch_volpy.model.Cellpose",
"name": "torch_volpy.model.Cellpose",
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"qualifiedName": "torch_volpy.model.Cellpose",
"searchText": "module\ntorch_volpy.model.cellpose\ntorch_volpy.model.cellpose",
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"path": "src/torch_volpy/model/Cellpose.py"
},
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},
{
"bases": [],
"dataclass": false,
"decorators": [],
"docstring": "Wrapper around CellposeModel for torch-volpy segmentation workflows.",
"kind": "class",
"module": "torch_volpy.model.Cellpose",
"name": "Cellpose",
"public": true,
"qualifiedName": "torch_volpy.model.Cellpose.Cellpose",
"searchText": "class\ncellpose\ntorch_volpy.model.cellpose.cellpose\n(model_path: union[str, path], gpu: bool = true, batch_size: int = 32, flow_threshold: float = 0.4, cellprob_threshold: float = 0.0, tile_norm_blocksize: int = 0, device: optional[union[str, torch.device]] = none)\nwrapper around cellposemodel for torch-volpy segmentation workflows.\nwrapper around cellposemodel for torch-volpy segmentation workflows.",
"signature": "(model_path: Union[str, Path], gpu: bool = True, batch_size: int = 32, flow_threshold: float = 0.4, cellprob_threshold: float = 0.0, tile_norm_blocksize: int = 0, device: Optional[Union[str, torch.device]] = None)",
"source": {
"endLine": 273,
"line": 12,
"path": "src/torch_volpy/model/Cellpose.py"
},
"summary": "Wrapper around CellposeModel for torch-volpy segmentation workflows."
},
{
"decorators": [],
"docstring": "Initialize a Cellpose segmentation model.\n\nArgs:\n model_path: Path to the pretrained Cellpose model.\n gpu: Whether to use GPU.\n batch_size: Batch size passed to model.eval().\n flow_threshold: Flow threshold for Cellpose.\n cellprob_threshold: Cell probability threshold for Cellpose.\n tile_norm_blocksize: Normalization block size.",
"kind": "method",
"module": "torch_volpy.model.Cellpose",
"name": "__init__",
"parent": "torch_volpy.model.Cellpose.Cellpose",
"public": false,
"qualifiedName": "torch_volpy.model.Cellpose.Cellpose.__init__",
"searchText": "method\n__init__\ntorch_volpy.model.cellpose.cellpose.__init__\n(self, model_path: union[str, path], gpu: bool = true, batch_size: int = 32, flow_threshold: float = 0.4, cellprob_threshold: float = 0.0, tile_norm_blocksize: int = 0, device: optional[union[str, torch.device]] = none)\ninitialize a cellpose segmentation model.\ninitialize a cellpose segmentation model.\n\nargs:\n model_path: path to the pretrained cellpose model.\n gpu: whether to use gpu.\n batch_size: batch size passed to model.eval().\n flow_threshold: flow threshold for cellpose.\n cellprob_threshold: cell probability threshold for cellpose.\n tile_norm_blocksize: normalization block size.",
"signature": "(self, model_path: Union[str, Path], gpu: bool = True, batch_size: int = 32, flow_threshold: float = 0.4, cellprob_threshold: float = 0.0, tile_norm_blocksize: int = 0, device: Optional[Union[str, torch.device]] = None)",
"source": {
"endLine": 56,
"line": 14,
"path": "src/torch_volpy/model/Cellpose.py"
},
"summary": "Initialize a Cellpose segmentation model."
},
{
"decorators": [],
"docstring": "Select channels from the image if requested.\nAssumes channel dimension is last: H x W x C",
"kind": "method",
"module": "torch_volpy.model.Cellpose",
"name": "_prepare_image",
"parent": "torch_volpy.model.Cellpose.Cellpose",
"public": false,
"qualifiedName": "torch_volpy.model.Cellpose.Cellpose._prepare_image",
"searchText": "method\n_prepare_image\ntorch_volpy.model.cellpose.cellpose._prepare_image\n(self, img: np.ndarray, selected_channels: optional[list[int]] = none) -> np.ndarray\nselect channels from the image if requested.\nselect channels from the image if requested.\nassumes channel dimension is last: h x w x c",
"signature": "(self, img: np.ndarray, selected_channels: Optional[List[int]] = None) -> np.ndarray",
"source": {
"endLine": 82,
"line": 58,
"path": "src/torch_volpy/model/Cellpose.py"
},
"summary": "Select channels from the image if requested."
},
{
"decorators": [],
"docstring": "Resolve the directory to save outputs into.",
"kind": "method",
"module": "torch_volpy.model.Cellpose",
"name": "_resolve_save_dir",
"parent": "torch_volpy.model.Cellpose.Cellpose",
"public": false,
"qualifiedName": "torch_volpy.model.Cellpose.Cellpose._resolve_save_dir",
"searchText": "method\n_resolve_save_dir\ntorch_volpy.model.cellpose.cellpose._resolve_save_dir\n(self, image_source: optional[union[str, path]], save_dir: optional[union[str, path]]) -> path\nresolve the directory to save outputs into.\nresolve the directory to save outputs into.",
"signature": "(self, image_source: Optional[Union[str, Path]], save_dir: Optional[Union[str, Path]]) -> Path",
"source": {
"endLine": 98,
"line": 84,
"path": "src/torch_volpy/model/Cellpose.py"
},
"summary": "Resolve the directory to save outputs into."
},
{
"decorators": [],
"docstring": "Save mask to disk as a TIFF file.",
"kind": "method",
"module": "torch_volpy.model.Cellpose",
"name": "_save_mask",
"parent": "torch_volpy.model.Cellpose.Cellpose",
"public": false,
"qualifiedName": "torch_volpy.model.Cellpose.Cellpose._save_mask",
"searchText": "method\n_save_mask\ntorch_volpy.model.cellpose.cellpose._save_mask\n(self, mask: np.ndarray, image_source: optional[union[str, path]], save_dir: optional[union[str, path]], index: int) -> path\nsave mask to disk as a tiff file.\nsave mask to disk as a tiff file.",
"signature": "(self, mask: np.ndarray, image_source: Optional[Union[str, Path]], save_dir: Optional[Union[str, Path]], index: int) -> Path",
"source": {
"endLine": 121,
"line": 100,
"path": "src/torch_volpy/model/Cellpose.py"
},
"summary": "Save mask to disk as a TIFF file."
},
{
"decorators": [],
"docstring": "Save Cellpose segmentation visualization to disk,\nwith mask IDs overlaid on subplot (1, 4, 2).",
"kind": "method",
"module": "torch_volpy.model.Cellpose",
"name": "_save_segmentation_plot",
"parent": "torch_volpy.model.Cellpose.Cellpose",
"public": false,
"qualifiedName": "torch_volpy.model.Cellpose.Cellpose._save_segmentation_plot",
"searchText": "method\n_save_segmentation_plot\ntorch_volpy.model.cellpose.cellpose._save_segmentation_plot\n(self, img: np.ndarray, masks: np.ndarray, flows, image_source: optional[union[str, path]], save_dir: optional[union[str, path]], index: int) -> path\nsave cellpose segmentation visualization to disk,\nsave cellpose segmentation visualization to disk,\nwith mask ids overlaid on subplot (1, 4, 2).",
"signature": "(self, img: np.ndarray, masks: np.ndarray, flows, image_source: Optional[Union[str, Path]], save_dir: Optional[Union[str, Path]], index: int) -> Path",
"source": {
"endLine": 184,
"line": 123,
"path": "src/torch_volpy/model/Cellpose.py"
},
"summary": "Save Cellpose segmentation visualization to disk,"
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Run segmentation.\n\nArgs:\n images:\n A single image path, a single numpy array, a single torch tensor,\n or a list of image paths / numpy arrays / torch tensors.\n selected_channels:\n List of channel indices to keep, e.g. [0, 1, 2].\n If None, uses the original image as-is.\n save_to_disk:\n Whether to save masks to disk.\n save_dir:\n Directory to save masks into. Required for numpy/tensor inputs\n if save_to_disk=True.\n\nReturns:\n For a single input: mask (torch.Tensor)\n For multiple inputs: list of masks",
"kind": "method",
"module": "torch_volpy.model.Cellpose",
"name": "build",
"parent": "torch_volpy.model.Cellpose.Cellpose",
"public": true,
"qualifiedName": "torch_volpy.model.Cellpose.Cellpose.build",
"searchText": "method\nbuild\ntorch_volpy.model.cellpose.cellpose.build\n(self, images: union[str, path, np.ndarray, torch.tensor, list[union[str, path, np.ndarray, torch.tensor]]], selected_channels: optional[list[int]] = none, save_to_disk: bool = false, save_dir: optional[union[str, path]] = none)\nrun segmentation.\nrun segmentation.\n\nargs:\n images:\n a single image path, a single numpy array, a single torch tensor,\n or a list of image paths / numpy arrays / torch tensors.\n selected_channels:\n list of channel indices to keep, e.g. [0, 1, 2].\n if none, uses the original image as-is.\n save_to_disk:\n whether to save masks to disk.\n save_dir:\n directory to save masks into. required for numpy/tensor inputs\n if save_to_disk=true.\n\nreturns:\n for a single input: mask (torch.tensor)\n for multiple inputs: list of masks\ntorch.inference_mode",
"signature": "(self, images: Union[str, Path, np.ndarray, torch.Tensor, List[Union[str, Path, np.ndarray, torch.Tensor]]], selected_channels: Optional[List[int]] = None, save_to_disk: bool = False, save_dir: Optional[Union[str, Path]] = None)",
"source": {
"endLine": 273,
"line": 187,
"path": "src/torch_volpy/model/Cellpose.py"
},
"summary": "Run segmentation."
},
{
"decorators": [],
"docstring": "",
"exports": [],
"kind": "module",
"module": "torch_volpy.model.Summary",
"name": "torch_volpy.model.Summary",
"public": true,
"qualifiedName": "torch_volpy.model.Summary",
"searchText": "module\ntorch_volpy.model.summary\ntorch_volpy.model.summary",
"signature": "",
"source": {
"endLine": 1,
"line": 1,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": ""
},
{
"bases": [],
"dataclass": false,
"decorators": [],
"docstring": "Build two 2D summary images from a Movie:\n 1) Mean image\n 2) 8-neighbor correlation image after percentile-baseline removal\n\nAssumptions:\n- movie is an already-created Movie object\n- movie shape is (T, Y, X)\n- build() returns two tensors of shape (Y, X)\n\nMemory behavior:\n- reads one non-overlapping temporal window at a time\n- baseline is computed by sorting spatial tiles across frames\n- correlation image is computed in 2 passes to keep memory low",
"kind": "class",
"module": "torch_volpy.model.Summary",
"name": "Summary",
"public": true,
"qualifiedName": "torch_volpy.model.Summary.Summary",
"searchText": "class\nsummary\ntorch_volpy.model.summary.summary\n(movie, window_size: int = 100, baseline_percentile: float = 8.0, baseline_tile_shape: tuple[int, int] = (128, 128), compute_dtype: torch.dtype = torch.float32, device: str | torch.device = 'cpu', eps: float = 1e-06) -> none\nbuild two 2d summary images from a movie:\nbuild two 2d summary images from a movie:\n 1) mean image\n 2) 8-neighbor correlation image after percentile-baseline removal\n\nassumptions:\n- movie is an already-created movie object\n- movie shape is (t, y, x)\n- build() returns two tensors of shape (y, x)\n\nmemory behavior:\n- reads one non-overlapping temporal window at a time\n- baseline is computed by sorting spatial tiles across frames\n- correlation image is computed in 2 passes to keep memory low",
"signature": "(movie, window_size: int = 100, baseline_percentile: float = 8.0, baseline_tile_shape: Tuple[int, int] = (128, 128), compute_dtype: torch.dtype = torch.float32, device: str | torch.device = 'cpu', eps: float = 1e-06) -> None",
"source": {
"endLine": 225,
"line": 9,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": "Build two 2D summary images from a Movie:"
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.model.Summary",
"name": "__init__",
"parent": "torch_volpy.model.Summary.Summary",
"public": false,
"qualifiedName": "torch_volpy.model.Summary.Summary.__init__",
"searchText": "method\n__init__\ntorch_volpy.model.summary.summary.__init__\n(self, movie, window_size: int = 100, baseline_percentile: float = 8.0, baseline_tile_shape: tuple[int, int] = (128, 128), compute_dtype: torch.dtype = torch.float32, device: str | torch.device = 'cpu', eps: float = 1e-06) -> none",
"signature": "(self, movie, window_size: int = 100, baseline_percentile: float = 8.0, baseline_tile_shape: Tuple[int, int] = (128, 128), compute_dtype: torch.dtype = torch.float32, device: str | torch.device = 'cpu', eps: float = 1e-06) -> None",
"source": {
"endLine": 73,
"line": 32,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": ""
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Returns\n-------\nmean_image : torch.Tensor\n Shape (Y, X), normalized to zero mean and unit std over pixels\n\ncorr_image : torch.Tensor\n Shape (Y, X), average temporal correlation with 8 neighbors\n after percentile-baseline removal, then normalized over pixels",
"kind": "method",
"module": "torch_volpy.model.Summary",
"name": "build",
"parent": "torch_volpy.model.Summary.Summary",
"public": true,
"qualifiedName": "torch_volpy.model.Summary.Summary.build",
"searchText": "method\nbuild\ntorch_volpy.model.summary.summary.build\n(self) -> tuple[torch.tensor, torch.tensor]\nreturns\nreturns\n-------\nmean_image : torch.tensor\n shape (y, x), normalized to zero mean and unit std over pixels\n\ncorr_image : torch.tensor\n shape (y, x), average temporal correlation with 8 neighbors\n after percentile-baseline removal, then normalized over pixels\ntorch.inference_mode",
"signature": "(self) -> tuple[torch.Tensor, torch.Tensor]",
"source": {
"endLine": 139,
"line": 76,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": "Returns"
},
{
"decorators": [],
"docstring": "Iterate over non-overlapping temporal windows:\n[0:window_size], [window_size:2*window_size], ...",
"kind": "method",
"module": "torch_volpy.model.Summary",
"name": "_iter_windows",
"parent": "torch_volpy.model.Summary.Summary",
"public": false,
"qualifiedName": "torch_volpy.model.Summary.Summary._iter_windows",
"searchText": "method\n_iter_windows\ntorch_volpy.model.summary.summary._iter_windows\n(self)\niterate over non-overlapping temporal windows:\niterate over non-overlapping temporal windows:\n[0:window_size], [window_size:2*window_size], ...",
"signature": "(self)",
"source": {
"endLine": 149,
"line": 141,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": "Iterate over non-overlapping temporal windows:"
},
{
"decorators": [],
"docstring": "Compute per-pixel percentile baseline over one temporal window.\n\nTo keep peak memory lower, the sort is done in spatial tiles:\n tile shape = (T, tile_y, tile_x)\nand sorted only along T.",
"kind": "method",
"module": "torch_volpy.model.Summary",
"name": "_percentile_baseline",
"parent": "torch_volpy.model.Summary.Summary",
"public": false,
"qualifiedName": "torch_volpy.model.Summary.Summary._percentile_baseline",
"searchText": "method\n_percentile_baseline\ntorch_volpy.model.summary.summary._percentile_baseline\n(self, window: torch.tensor) -> torch.tensor\ncompute per-pixel percentile baseline over one temporal window.\ncompute per-pixel percentile baseline over one temporal window.\n\nto keep peak memory lower, the sort is done in spatial tiles:\n tile shape = (t, tile_y, tile_x)\nand sorted only along t.",
"signature": "(self, window: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 174,
"line": 151,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": "Compute per-pixel percentile baseline over one temporal window."
},
{
"decorators": [],
"docstring": "Order-statistic index for the requested percentile.\nUses the lower order statistic:\n floor(p * (n_frames - 1))",
"kind": "method",
"module": "torch_volpy.model.Summary",
"name": "_percentile_index",
"parent": "torch_volpy.model.Summary.Summary",
"public": false,
"qualifiedName": "torch_volpy.model.Summary.Summary._percentile_index",
"searchText": "method\n_percentile_index\ntorch_volpy.model.summary.summary._percentile_index\n(self, n_frames: int) -> int\norder-statistic index for the requested percentile.\norder-statistic index for the requested percentile.\nuses the lower order statistic:\n floor(p * (n_frames - 1))",
"signature": "(self, n_frames: int) -> int",
"source": {
"endLine": 186,
"line": 176,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": "Order-statistic index for the requested percentile."
},
{
"decorators": [],
"docstring": "Number of valid neighbors for each pixel.\nInterior pixels get 8, borders get fewer.",
"kind": "method",
"module": "torch_volpy.model.Summary",
"name": "_neighbor_count",
"parent": "torch_volpy.model.Summary.Summary",
"public": false,
"qualifiedName": "torch_volpy.model.Summary.Summary._neighbor_count",
"searchText": "method\n_neighbor_count\ntorch_volpy.model.summary.summary._neighbor_count\n(self, h: int, w: int) -> torch.tensor\nnumber of valid neighbors for each pixel.\nnumber of valid neighbors for each pixel.\ninterior pixels get 8, borders get fewer.",
"signature": "(self, h: int, w: int) -> torch.Tensor",
"source": {
"endLine": 197,
"line": 188,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": "Number of valid neighbors for each pixel."
},
{
"decorators": [
"staticmethod"
],
"docstring": "Returns slices for:\n- center pixels\n- corresponding neighbor pixels",
"kind": "staticmethod",
"module": "torch_volpy.model.Summary",
"name": "_axis_pair",
"parent": "torch_volpy.model.Summary.Summary",
"public": false,
"qualifiedName": "torch_volpy.model.Summary.Summary._axis_pair",
"searchText": "staticmethod\n_axis_pair\ntorch_volpy.model.summary.summary._axis_pair\n(delta: int, size: int)\nreturns slices for:\nreturns slices for:\n- center pixels\n- corresponding neighbor pixels\nstaticmethod",
"signature": "(delta: int, size: int)",
"source": {
"endLine": 210,
"line": 200,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": "Returns slices for:"
},
{
"decorators": [
"classmethod"
],
"docstring": "",
"kind": "classmethod",
"module": "torch_volpy.model.Summary",
"name": "_pair_slices",
"parent": "torch_volpy.model.Summary.Summary",
"public": false,
"qualifiedName": "torch_volpy.model.Summary.Summary._pair_slices",
"searchText": "classmethod\n_pair_slices\ntorch_volpy.model.summary.summary._pair_slices\n(cls, dy: int, dx: int, h: int, w: int)\nclassmethod",
"signature": "(cls, dy: int, dx: int, h: int, w: int)",
"source": {
"endLine": 216,
"line": 213,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Normalize a 2D image by subtracting its pixel mean\nand dividing by its pixel std.",
"kind": "method",
"module": "torch_volpy.model.Summary",
"name": "_normalize_image",
"parent": "torch_volpy.model.Summary.Summary",
"public": false,
"qualifiedName": "torch_volpy.model.Summary.Summary._normalize_image",
"searchText": "method\n_normalize_image\ntorch_volpy.model.summary.summary._normalize_image\n(self, image: torch.tensor) -> torch.tensor\nnormalize a 2d image by subtracting its pixel mean\nnormalize a 2d image by subtracting its pixel mean\nand dividing by its pixel std.",
"signature": "(self, image: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 225,
"line": 218,
"path": "src/torch_volpy/model/Summary.py"
},
"summary": "Normalize a 2D image by subtracting its pixel mean"
},
{
"decorators": [],
"docstring": "",
"exports": [
"Cellpose",
"Summary"
],
"kind": "module",
"module": "torch_volpy.model",
"name": "torch_volpy.model",
"public": true,
"qualifiedName": "torch_volpy.model",
"searchText": "module\ntorch_volpy.model\ntorch_volpy.model",
"signature": "",
"source": {
"endLine": 1,
"line": 1,
"path": "src/torch_volpy/model/__init__.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"exports": [],
"kind": "module",
"module": "torch_volpy.motion.MotionCorrect",
"name": "torch_volpy.motion.MotionCorrect",
"public": true,
"qualifiedName": "torch_volpy.motion.MotionCorrect",
"searchText": "module\ntorch_volpy.motion.motioncorrect\ntorch_volpy.motion.motioncorrect",
"signature": "",
"source": {
"endLine": 1,
"line": 1,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": ""
},
{
"bases": [],
"dataclass": false,
"decorators": [],
"docstring": "Build a template, estimate per-frame translational shifts, apply them,\nand save the corrected movie to disk.\n\nWorkflow\n--------\n1) Build template with Template\n2) Estimate shifts with Translation\n3) Apply rigid shifts to each frame\n4) Save corrected movie to HDF5\n\nNotes\n-----\n- Translation is assumed to return correction shifts [dy, dx]\n that should be applied directly to the frame.\n- Subpixel correction uses batched torch grid_sample, so it runs on GPU\n when device is a CUDA device.\n- Frames are processed in chunks; the whole movie is never loaded at once.",
"kind": "class",
"module": "torch_volpy.motion.MotionCorrect",
"name": "MotionCorrect",
"public": true,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"searchText": "class\nmotioncorrect\ntorch_volpy.motion.motioncorrect.motioncorrect\n(movie, max_shifts: union[int, tuple[int, int]], frames_per_chunk: int = 256, device: union[str, torch.device] = 'cpu', template = none, shifts: optional[union[np.ndarray, torch.tensor, sequence[sequence[int]]]] = none, template_kwargs: optional[dict[str, any]] = none, translation_kwargs: optional[dict[str, any]] = none, template_strategy: literal['binmedian', 'caiman_rigid'] = 'binmedian', high_pass_filter_size: optional[int] = none, upsample_factor: int = 10, interpolation: literal['integer', 'nearest', 'bilinear', 'bicubic', 'opencv_cubic'] = 'bicubic', padding_mode: literal['zeros', 'border', 'reflection'] = 'border', border_nan: union[bool, literal['copy', 'min', 'nan']] = false, copy_border_strips: bool = false, align_corners: bool = true, clip_interpolated: bool = true, add_to_movie: optional[float] = none, gsig_filt: optional[tuple[float, float]] = none) -> none\nbuild a template, estimate per-frame translational shifts, apply them,\nbuild a template, estimate per-frame translational shifts, apply them,\nand save the corrected movie to disk.\n\nworkflow\n--------\n1) build template with template\n2) estimate shifts with translation\n3) apply rigid shifts to each frame\n4) save corrected movie to hdf5\n\nnotes\n-----\n- translation is assumed to return correction shifts [dy, dx]\n that should be applied directly to the frame.\n- subpixel correction uses batched torch grid_sample, so it runs on gpu\n when device is a cuda device.\n- frames are processed in chunks; the whole movie is never loaded at once.",
"signature": "(movie, max_shifts: Union[int, Tuple[int, int]], frames_per_chunk: int = 256, device: Union[str, torch.device] = 'cpu', template = None, shifts: Optional[Union[np.ndarray, torch.Tensor, Sequence[Sequence[int]]]] = None, template_kwargs: Optional[Dict[str, Any]] = None, translation_kwargs: Optional[Dict[str, Any]] = None, template_strategy: Literal['binmedian', 'caiman_rigid'] = 'binmedian', high_pass_filter_size: Optional[int] = None, upsample_factor: int = 10, interpolation: Literal['integer', 'nearest', 'bilinear', 'bicubic', 'opencv_cubic'] = 'bicubic', padding_mode: Literal['zeros', 'border', 'reflection'] = 'border', border_nan: Union[bool, Literal['copy', 'min', 'nan']] = False, copy_border_strips: bool = False, align_corners: bool = True, clip_interpolated: bool = True, add_to_movie: Optional[float] = None, gsig_filt: Optional[Tuple[float, float]] = None) -> None",
"source": {
"endLine": 1628,
"line": 16,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": "Build a template, estimate per-frame translational shifts, apply them,"
},
{
"decorators": [],
"docstring": "",
"kind": "method",
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"signature": "(self, movie: np.ndarray, shifts: np.ndarray, *, add_to_movie: float = 0.0, border_nan: Union[bool, Literal['copy', 'min', 'nan']] = False) -> np.ndarray",
"source": {
"endLine": 945,
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"path": "src/torch_volpy/motion/MotionCorrect.py"
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"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_motion_correct_array",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._motion_correct_array",
"searchText": "method\n_motion_correct_array\ntorch_volpy.motion.motioncorrect.motioncorrect._motion_correct_array\n(self, movie: np.ndarray, template: optional[np.ndarray] = none) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]",
"signature": "(self, movie: np.ndarray, template: Optional[np.ndarray] = None) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]",
"source": {
"endLine": 991,
"line": 947,
"path": "src/torch_volpy/motion/MotionCorrect.py"
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"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_application_add_to_movie",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._application_add_to_movie",
"searchText": "method\n_application_add_to_movie\ntorch_volpy.motion.motioncorrect.motioncorrect._application_add_to_movie\n(self) -> float",
"signature": "(self) -> float",
"source": {
"endLine": 996,
"line": 993,
"path": "src/torch_volpy/motion/MotionCorrect.py"
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"summary": ""
},
{
"decorators": [],
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"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_build_caiman_rigid_template",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._build_caiman_rigid_template",
"searchText": "method\n_build_caiman_rigid_template\ntorch_volpy.motion.motioncorrect.motioncorrect._build_caiman_rigid_template\n(self) -> torch.tensor",
"signature": "(self) -> torch.Tensor",
"source": {
"endLine": 1017,
"line": 998,
"path": "src/torch_volpy/motion/MotionCorrect.py"
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"summary": ""
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Build and cache the motion-correction template using Template.",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "build_template",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": true,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect.build_template",
"searchText": "method\nbuild_template\ntorch_volpy.motion.motioncorrect.motioncorrect.build_template\n(self, force: bool = false)\nbuild and cache the motion-correction template using template.\nbuild and cache the motion-correction template using template.\ntorch.inference_mode",
"signature": "(self, force: bool = False)",
"source": {
"endLine": 1041,
"line": 1020,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": "Build and cache the motion-correction template using Template."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Estimate and cache per-frame correction shifts.",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "estimate_shifts",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": true,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect.estimate_shifts",
"searchText": "method\nestimate_shifts\ntorch_volpy.motion.motioncorrect.motioncorrect.estimate_shifts\n(self, force: bool = false, return_scores: bool = false)\nestimate and cache per-frame correction shifts.\nestimate and cache per-frame correction shifts.\ntorch.inference_mode",
"signature": "(self, force: bool = False, return_scores: bool = False)",
"source": {
"endLine": 1112,
"line": 1044,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": "Estimate and cache per-frame correction shifts."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Convenience method to build template and estimate shifts.",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "prepare",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": true,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect.prepare",
"searchText": "method\nprepare\ntorch_volpy.motion.motioncorrect.motioncorrect.prepare\n(self, force: bool = false, return_scores: bool = false)\nconvenience method to build template and estimate shifts.\nconvenience method to build template and estimate shifts.\ntorch.inference_mode",
"signature": "(self, force: bool = False, return_scores: bool = False)",
"source": {
"endLine": 1124,
"line": 1115,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": "Convenience method to build template and estimate shifts."
},
{
"decorators": [],
"docstring": "Convert frames to NCHW, pad spatially, and return padded NCHW tensor.",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_pad_frames_for_correction",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._pad_frames_for_correction",
"searchText": "method\n_pad_frames_for_correction\ntorch_volpy.motion.motioncorrect.motioncorrect._pad_frames_for_correction\n(self, frames: torch.tensor, pad_y: int, pad_x: int) -> torch.tensor\nconvert frames to nchw, pad spatially, and return padded nchw tensor.\nconvert frames to nchw, pad spatially, and return padded nchw tensor.",
"signature": "(self, frames: torch.Tensor, pad_y: int, pad_x: int) -> torch.Tensor",
"source": {
"endLine": 1183,
"line": 1126,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": "Convert frames to NCHW, pad spatially, and return padded NCHW tensor."
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_frames_to_nchw",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._frames_to_nchw",
"searchText": "staticmethod\n_frames_to_nchw\ntorch_volpy.motion.motioncorrect.motioncorrect._frames_to_nchw\n(frames: torch.tensor) -> tuple[torch.tensor, str]\nstaticmethod",
"signature": "(frames: torch.Tensor) -> Tuple[torch.Tensor, str]",
"source": {
"endLine": 1191,
"line": 1186,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": ""
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"decorators": [
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],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_nchw_to_frames",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._nchw_to_frames",
"searchText": "staticmethod\n_nchw_to_frames\ntorch_volpy.motion.motioncorrect.motioncorrect._nchw_to_frames\n(frames_nchw: torch.tensor, layout: str) -> torch.tensor\nstaticmethod",
"signature": "(frames_nchw: torch.Tensor, layout: str) -> torch.Tensor",
"source": {
"endLine": 1199,
"line": 1194,
"path": "src/torch_volpy/motion/MotionCorrect.py"
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"summary": ""
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"decorators": [],
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"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_base_grid",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._base_grid",
"searchText": "method\n_base_grid\ntorch_volpy.motion.motioncorrect.motioncorrect._base_grid\n(self, height: int, width: int, *, device: torch.device, dtype: torch.dtype) -> torch.tensor",
"signature": "(self, height: int, width: int, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor",
"source": {
"endLine": 1224,
"line": 1201,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": ""
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{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_subpixel_grid_for_shifts",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._subpixel_grid_for_shifts",
"searchText": "method\n_subpixel_grid_for_shifts\ntorch_volpy.motion.motioncorrect.motioncorrect._subpixel_grid_for_shifts\n(self, shifts_chunk: torch.tensor, height: int, width: int, *, device: torch.device, dtype: torch.dtype) -> torch.tensor",
"signature": "(self, shifts_chunk: torch.Tensor, height: int, width: int, *, device: torch.device, dtype: torch.dtype) -> torch.Tensor",
"source": {
"endLine": 1253,
"line": 1226,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_correct_chunk_integer",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._correct_chunk_integer",
"searchText": "method\n_correct_chunk_integer\ntorch_volpy.motion.motioncorrect.motioncorrect._correct_chunk_integer\n(self, frames: torch.tensor, shifts_chunk: torch.tensor) -> torch.tensor",
"signature": "(self, frames: torch.Tensor, shifts_chunk: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 1299,
"line": 1255,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": ""
},
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"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_correct_chunk_grid_sample",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._correct_chunk_grid_sample",
"searchText": "method\n_correct_chunk_grid_sample\ntorch_volpy.motion.motioncorrect.motioncorrect._correct_chunk_grid_sample\n(self, frames: torch.tensor, shifts_chunk: torch.tensor) -> torch.tensor",
"signature": "(self, frames: torch.Tensor, shifts_chunk: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 1311,
"line": 1301,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_apply_shifts_tensor_grid_sample",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._apply_shifts_tensor_grid_sample",
"searchText": "method\n_apply_shifts_tensor_grid_sample\ntorch_volpy.motion.motioncorrect.motioncorrect._apply_shifts_tensor_grid_sample\n(self, frames: torch.tensor, shifts_chunk: torch.tensor, *, add_to_movie: float = 0.0, border_nan: union[bool, literal['copy', 'min', 'nan']] = false) -> torch.tensor",
"signature": "(self, frames: torch.Tensor, shifts_chunk: torch.Tensor, *, add_to_movie: float = 0.0, border_nan: Union[bool, Literal['copy', 'min', 'nan']] = False) -> torch.Tensor",
"source": {
"endLine": 1385,
"line": 1313,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "Match CaImAn/VolPy border_nan='copy' after subpixel interpolation.",
"kind": "staticmethod",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_copy_shifted_border_strips",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._copy_shifted_border_strips",
"searchText": "staticmethod\n_copy_shifted_border_strips\ntorch_volpy.motion.motioncorrect.motioncorrect._copy_shifted_border_strips\n(frames_nchw: torch.tensor, shifts_chunk: torch.tensor) -> torch.tensor\nmatch caiman/volpy border_nan='copy' after subpixel interpolation.\nmatch caiman/volpy border_nan='copy' after subpixel interpolation.\nstaticmethod",
"signature": "(frames_nchw: torch.Tensor, shifts_chunk: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 1430,
"line": 1388,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": "Match CaImAn/VolPy border_nan='copy' after subpixel interpolation."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "_correct_chunk",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": false,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect._correct_chunk",
"searchText": "method\n_correct_chunk\ntorch_volpy.motion.motioncorrect.motioncorrect._correct_chunk\n(self, frames: torch.tensor, shifts_chunk: torch.tensor) -> torch.tensor\ntorch.inference_mode",
"signature": "(self, frames: torch.Tensor, shifts_chunk: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 1461,
"line": 1433,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": ""
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Build template/shifts if needed, apply shifts to the full movie,\nand save corrected frames to disk.\n\nReturns\n-------\nMovie\n A Movie instance opened on the corrected dataset.",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "save",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": true,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect.save",
"searchText": "method\nsave\ntorch_volpy.motion.motioncorrect.motioncorrect.save\n(self, out_h5_path: union[str, path], dataset: str = 'movie', overwrite: bool = false, compression: optional[str] = none, compression_opts: optional[int] = none, output_dtype: optional[union[str, np.dtype]] = none, save_shifts_dataset: optional[str] = 'shifts', save_template_dataset: optional[str] = 'template', save_scores_dataset: optional[str] = none, extra_attrs: optional[dict] = none)\nbuild template/shifts if needed, apply shifts to the full movie,\nbuild template/shifts if needed, apply shifts to the full movie,\nand save corrected frames to disk.\n\nreturns\n-------\nmovie\n a movie instance opened on the corrected dataset.\ntorch.inference_mode",
"signature": "(self, out_h5_path: Union[str, Path], dataset: str = 'movie', overwrite: bool = False, compression: Optional[str] = None, compression_opts: Optional[int] = None, output_dtype: Optional[Union[str, np.dtype]] = None, save_shifts_dataset: Optional[str] = 'shifts', save_template_dataset: Optional[str] = 'template', save_scores_dataset: Optional[str] = None, extra_attrs: Optional[dict] = None)",
"source": {
"endLine": 1605,
"line": 1464,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": "Build template/shifts if needed, apply shifts to the full movie,"
},
{
"decorators": [],
"docstring": "Return a dictionary describing the configured correction run.",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "summary",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": true,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect.summary",
"searchText": "method\nsummary\ntorch_volpy.motion.motioncorrect.motioncorrect.summary\n(self) -> dict\nreturn a dictionary describing the configured correction run.\nreturn a dictionary describing the configured correction run.",
"signature": "(self) -> dict",
"source": {
"endLine": 1624,
"line": 1607,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": "Return a dictionary describing the configured correction run."
},
{
"decorators": [],
"docstring": "Apply motion correction and save the corrected movie.",
"kind": "method",
"module": "torch_volpy.motion.MotionCorrect",
"name": "__call__",
"parent": "torch_volpy.motion.MotionCorrect.MotionCorrect",
"public": true,
"qualifiedName": "torch_volpy.motion.MotionCorrect.MotionCorrect.__call__",
"searchText": "method\n__call__\ntorch_volpy.motion.motioncorrect.motioncorrect.__call__\n(self, *args, **kwargs)\napply motion correction and save the corrected movie.\napply motion correction and save the corrected movie.",
"signature": "(self, *args, **kwargs)",
"source": {
"endLine": 1628,
"line": 1626,
"path": "src/torch_volpy/motion/MotionCorrect.py"
},
"summary": "Apply motion correction and save the corrected movie."
},
{
"decorators": [],
"docstring": "",
"exports": [],
"kind": "module",
"module": "torch_volpy.motion.Template",
"name": "torch_volpy.motion.Template",
"public": true,
"qualifiedName": "torch_volpy.motion.Template",
"searchText": "module\ntorch_volpy.motion.template\ntorch_volpy.motion.template",
"signature": "",
"source": {
"endLine": 1,
"line": 1,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": ""
},
{
"bases": [],
"dataclass": false,
"decorators": [],
"docstring": "Build a motion-correction template from a lazily readable Movie.\n\nAssumptions\n-----------\n- axis 0 is time\n- all remaining axes are preserved as frame dimensions\n (e.g. Y,X or Y,X,C)\n- \"binmedian\" is implemented as:\n 1) split frames into temporal bins\n 2) mean within each bin\n 3) median across bin means\n\nMemory behavior\n---------------\n- never loads the whole movie into memory\n- reads only one frame block at a time from Movie\n\nOptional filtering\n------------------\n- if high_pass_filter_size is not None, spatial high-pass Gaussian filtering is applied\n to each chunk/bin immediately after reading frames\n- filtering happens before:\n 1) minimum estimation\n 2) bin-mean / bin-median template calculation",
"kind": "class",
"module": "torch_volpy.motion.Template",
"name": "Template",
"public": true,
"qualifiedName": "torch_volpy.motion.Template.Template",
"searchText": "class\ntemplate\ntorch_volpy.motion.template.template\n(movie, window: int = 10, min_value: optional[float] = none, estimate_min_frames: int = 400, negative_floor: float = -10.0, keep_partial_last_bin: bool = false, exclude_nans: bool = true, binning: literal['consecutive', 'caiman'] = 'consecutive', dtype: torch.dtype = torch.float32, device: union[str, torch.device] = 'cpu', high_pass_filter_size: optional[int] = none) -> none\nbuild a motion-correction template from a lazily readable movie.\nbuild a motion-correction template from a lazily readable movie.\n\nassumptions\n-----------\n- axis 0 is time\n- all remaining axes are preserved as frame dimensions\n (e.g. y,x or y,x,c)\n- \"binmedian\" is implemented as:\n 1) split frames into temporal bins\n 2) mean within each bin\n 3) median across bin means\n\nmemory behavior\n---------------\n- never loads the whole movie into memory\n- reads only one frame block at a time from movie\n\noptional filtering\n------------------\n- if high_pass_filter_size is not none, spatial high-pass gaussian filtering is applied\n to each chunk/bin immediately after reading frames\n- filtering happens before:\n 1) minimum estimation\n 2) bin-mean / bin-median template calculation",
"signature": "(movie, window: int = 10, min_value: Optional[float] = None, estimate_min_frames: int = 400, negative_floor: float = -10.0, keep_partial_last_bin: bool = False, exclude_nans: bool = True, binning: Literal['consecutive', 'caiman'] = 'consecutive', dtype: torch.dtype = torch.float32, device: Union[str, torch.device] = 'cpu', high_pass_filter_size: Optional[int] = None) -> None",
"source": {
"endLine": 281,
"line": 11,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": "Build a motion-correction template from a lazily readable Movie."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.Template",
"name": "__init__",
"parent": "torch_volpy.motion.Template.Template",
"public": false,
"qualifiedName": "torch_volpy.motion.Template.Template.__init__",
"searchText": "method\n__init__\ntorch_volpy.motion.template.template.__init__\n(self, movie, window: int = 10, min_value: optional[float] = none, estimate_min_frames: int = 400, negative_floor: float = -10.0, keep_partial_last_bin: bool = false, exclude_nans: bool = true, binning: literal['consecutive', 'caiman'] = 'consecutive', dtype: torch.dtype = torch.float32, device: union[str, torch.device] = 'cpu', high_pass_filter_size: optional[int] = none) -> none",
"signature": "(self, movie, window: int = 10, min_value: Optional[float] = None, estimate_min_frames: int = 400, negative_floor: float = -10.0, keep_partial_last_bin: bool = False, exclude_nans: bool = True, binning: Literal['consecutive', 'caiman'] = 'consecutive', dtype: torch.dtype = torch.float32, device: Union[str, torch.device] = 'cpu', high_pass_filter_size: Optional[int] = None) -> None",
"source": {
"endLine": 90,
"line": 39,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.motion.Template",
"name": "_finite_only",
"parent": "torch_volpy.motion.Template.Template",
"public": false,
"qualifiedName": "torch_volpy.motion.Template.Template._finite_only",
"searchText": "staticmethod\n_finite_only\ntorch_volpy.motion.template.template._finite_only\n(x: torch.tensor) -> torch.tensor\nstaticmethod",
"signature": "(x: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 94,
"line": 93,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": ""
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Apply spatial high-pass Gaussian filtering if enabled.\n\nAccepts:\n - (T, X, Y)\n - (T, X, Y, C)\n - (X, Y)\n - (X, Y, C)\n\nReturns the same shape as the input.",
"kind": "method",
"module": "torch_volpy.motion.Template",
"name": "_maybe_filter",
"parent": "torch_volpy.motion.Template.Template",
"public": false,
"qualifiedName": "torch_volpy.motion.Template.Template._maybe_filter",
"searchText": "method\n_maybe_filter\ntorch_volpy.motion.template.template._maybe_filter\n(self, frames: torch.tensor) -> torch.tensor\napply spatial high-pass gaussian filtering if enabled.\napply spatial high-pass gaussian filtering if enabled.\n\naccepts:\n - (t, x, y)\n - (t, x, y, c)\n - (x, y)\n - (x, y, c)\n\nreturns the same shape as the input.\ntorch.inference_mode",
"signature": "(self, frames: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 130,
"line": 97,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": "Apply spatial high-pass Gaussian filtering if enabled."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Estimate or return the movie minimum used for template offsetting.",
"kind": "method",
"module": "torch_volpy.motion.Template",
"name": "estimate_min",
"parent": "torch_volpy.motion.Template.Template",
"public": true,
"qualifiedName": "torch_volpy.motion.Template.Template.estimate_min",
"searchText": "method\nestimate_min\ntorch_volpy.motion.template.template.estimate_min\n(self) -> float\nestimate or return the movie minimum used for template offsetting.\nestimate or return the movie minimum used for template offsetting.\ntorch.inference_mode",
"signature": "(self) -> float",
"source": {
"endLine": 171,
"line": 133,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": "Estimate or return the movie minimum used for template offsetting."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.Template",
"name": "_frame_block_mean",
"parent": "torch_volpy.motion.Template.Template",
"public": false,
"qualifiedName": "torch_volpy.motion.Template.Template._frame_block_mean",
"searchText": "method\n_frame_block_mean\ntorch_volpy.motion.template.template._frame_block_mean\n(self, frames: torch.tensor) -> torch.tensor\ntorch.inference_mode",
"signature": "(self, frames: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 179,
"line": 174,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": ""
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Compute the raw bin-median template before brightness offsetting.",
"kind": "method",
"module": "torch_volpy.motion.Template",
"name": "compute_binmedian",
"parent": "torch_volpy.motion.Template.Template",
"public": true,
"qualifiedName": "torch_volpy.motion.Template.Template.compute_binmedian",
"searchText": "method\ncompute_binmedian\ntorch_volpy.motion.template.template.compute_binmedian\n(self) -> torch.tensor\ncompute the raw bin-median template before brightness offsetting.\ncompute the raw bin-median template before brightness offsetting.\ntorch.inference_mode",
"signature": "(self) -> torch.Tensor",
"source": {
"endLine": 241,
"line": 182,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": "Compute the raw bin-median template before brightness offsetting."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.Template",
"name": "_safe_quantile",
"parent": "torch_volpy.motion.Template.Template",
"public": false,
"qualifiedName": "torch_volpy.motion.Template.Template._safe_quantile",
"searchText": "method\n_safe_quantile\ntorch_volpy.motion.template.template._safe_quantile\n(self, x: torch.tensor, q: float) -> float\ntorch.inference_mode",
"signature": "(self, x: torch.Tensor, q: float) -> float",
"source": {
"endLine": 252,
"line": 244,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": ""
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Build and return the final motion template tensor.",
"kind": "method",
"module": "torch_volpy.motion.Template",
"name": "build",
"parent": "torch_volpy.motion.Template.Template",
"public": true,
"qualifiedName": "torch_volpy.motion.Template.Template.build",
"searchText": "method\nbuild\ntorch_volpy.motion.template.template.build\n(self) -> torch.tensor\nbuild and return the final motion template tensor.\nbuild and return the final motion template tensor.\ntorch.inference_mode",
"signature": "(self) -> torch.Tensor",
"source": {
"endLine": 277,
"line": 255,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": "Build and return the final motion template tensor."
},
{
"decorators": [],
"docstring": "Build and return the final motion template tensor.",
"kind": "method",
"module": "torch_volpy.motion.Template",
"name": "__call__",
"parent": "torch_volpy.motion.Template.Template",
"public": true,
"qualifiedName": "torch_volpy.motion.Template.Template.__call__",
"searchText": "method\n__call__\ntorch_volpy.motion.template.template.__call__\n(self) -> torch.tensor\nbuild and return the final motion template tensor.\nbuild and return the final motion template tensor.",
"signature": "(self) -> torch.Tensor",
"source": {
"endLine": 281,
"line": 279,
"path": "src/torch_volpy/motion/Template.py"
},
"summary": "Build and return the final motion template tensor."
},
{
"decorators": [],
"docstring": "",
"exports": [],
"kind": "module",
"module": "torch_volpy.motion.Translation",
"name": "torch_volpy.motion.Translation",
"public": true,
"qualifiedName": "torch_volpy.motion.Translation",
"searchText": "module\ntorch_volpy.motion.translation\ntorch_volpy.motion.translation",
"signature": "",
"source": {
"endLine": 1,
"line": 1,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": ""
},
{
"bases": [],
"dataclass": false,
"decorators": [],
"docstring": "Estimate per-frame translational shifts against a template using\nFFT-based cross-correlation in PyTorch.\n\nReturns shifts as [dy, dx], meaning:\n corrected_frame = shift(frame, dy, dx)\n\nAssumptions\n-----------\n- movie axis 0 is time\n- frame shape is either:\n (Y, X)\n or\n (Y, X, C) # channel-last\n- correlation is done on a 2D plane:\n * (Y, X) directly\n * (Y, X, C) reduced to mean over channels\n\nNotes\n-----\n- Integer-pixel shifts are estimated from the correlation peak.\n- If upsample_factor > 1, local DFT upsampling or quadratic peak\n refinement returns subpixel shifts quantized to 1 / upsample_factor pixels.\n- It reads the movie in chunks, so it does not load the full movie.\n- The default normalized cross-power mode is phase correlation:\n corr = ifft( FFT(template) * conj(FFT(frame)) / abs(...) )\n normalization=\"none\" uses unnormalized FFT cross-correlation, closer\n to CaImAn/VolPy rigid registration.",
"kind": "class",
"module": "torch_volpy.motion.Translation",
"name": "Translation",
"public": true,
"qualifiedName": "torch_volpy.motion.Translation.Translation",
"searchText": "class\ntranslation\ntorch_volpy.motion.translation.translation\n(movie, template: torch.tensor, max_shifts: union[int, tuple[int, int]], frames_per_chunk: int = 256, device: union[str, torch.device] = 'cpu', dtype: torch.dtype = torch.float32, use_hann: bool = true, center: bool = true, upsample_factor: int = 1, subpixel_method: literal['none', 'quadratic', 'dft'] = 'quadratic', normalization: literal['phase', 'none'] = 'phase', eps: float = 1e-08, high_pass_filter_size: optional[int] = none, add_to_movie: float = 0.0) -> none\nestimate per-frame translational shifts against a template using\nestimate per-frame translational shifts against a template using\nfft-based cross-correlation in pytorch.\n\nreturns shifts as [dy, dx], meaning:\n corrected_frame = shift(frame, dy, dx)\n\nassumptions\n-----------\n- movie axis 0 is time\n- frame shape is either:\n (y, x)\n or\n (y, x, c) # channel-last\n- correlation is done on a 2d plane:\n * (y, x) directly\n * (y, x, c) reduced to mean over channels\n\nnotes\n-----\n- integer-pixel shifts are estimated from the correlation peak.\n- if upsample_factor > 1, local dft upsampling or quadratic peak\n refinement returns subpixel shifts quantized to 1 / upsample_factor pixels.\n- it reads the movie in chunks, so it does not load the full movie.\n- the default normalized cross-power mode is phase correlation:\n corr = ifft( fft(template) * conj(fft(frame)) / abs(...) )\n normalization=\"none\" uses unnormalized fft cross-correlation, closer\n to caiman/volpy rigid registration.",
"signature": "(movie, template: torch.Tensor, max_shifts: Union[int, Tuple[int, int]], frames_per_chunk: int = 256, device: Union[str, torch.device] = 'cpu', dtype: torch.dtype = torch.float32, use_hann: bool = True, center: bool = True, upsample_factor: int = 1, subpixel_method: Literal['none', 'quadratic', 'dft'] = 'quadratic', normalization: Literal['phase', 'none'] = 'phase', eps: float = 1e-08, high_pass_filter_size: Optional[int] = None, add_to_movie: float = 0.0) -> None",
"source": {
"endLine": 612,
"line": 11,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "Estimate per-frame translational shifts against a template using"
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "__init__",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation.__init__",
"searchText": "method\n__init__\ntorch_volpy.motion.translation.translation.__init__\n(self, movie, template: torch.tensor, max_shifts: union[int, tuple[int, int]], frames_per_chunk: int = 256, device: union[str, torch.device] = 'cpu', dtype: torch.dtype = torch.float32, use_hann: bool = true, center: bool = true, upsample_factor: int = 1, subpixel_method: literal['none', 'quadratic', 'dft'] = 'quadratic', normalization: literal['phase', 'none'] = 'phase', eps: float = 1e-08, high_pass_filter_size: optional[int] = none, add_to_movie: float = 0.0) -> none",
"signature": "(self, movie, template: torch.Tensor, max_shifts: Union[int, Tuple[int, int]], frames_per_chunk: int = 256, device: Union[str, torch.device] = 'cpu', dtype: torch.dtype = torch.float32, use_hann: bool = True, center: bool = True, upsample_factor: int = 1, subpixel_method: Literal['none', 'quadratic', 'dft'] = 'quadratic', normalization: Literal['phase', 'none'] = 'phase', eps: float = 1e-08, high_pass_filter_size: Optional[int] = None, add_to_movie: float = 0.0) -> None",
"source": {
"endLine": 114,
"line": 42,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.motion.Translation",
"name": "_normalize_max_shifts",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._normalize_max_shifts",
"searchText": "staticmethod\n_normalize_max_shifts\ntorch_volpy.motion.translation.translation._normalize_max_shifts\n(max_shifts: union[int, tuple[int, int]]) -> tuple[int, int]\nstaticmethod",
"signature": "(max_shifts: Union[int, Tuple[int, int]]) -> Tuple[int, int]",
"source": {
"endLine": 131,
"line": 117,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Convert template/frame data to a 2D correlation plane on self.device/self.dtype.",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "_prepare_image",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._prepare_image",
"searchText": "method\n_prepare_image\ntorch_volpy.motion.translation.translation._prepare_image\n(self, x: torch.tensor) -> torch.tensor\nconvert template/frame data to a 2d correlation plane on self.device/self.dtype.\nconvert template/frame data to a 2d correlation plane on self.device/self.dtype.",
"signature": "(self, x: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 150,
"line": 133,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "Convert template/frame data to a 2D correlation plane on self.device/self.dtype."
},
{
"decorators": [],
"docstring": "Convert frame batch to shape (B, Y, X) on self.device/self.dtype.",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "_prepare_batch",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._prepare_batch",
"searchText": "method\n_prepare_batch\ntorch_volpy.motion.translation.translation._prepare_batch\n(self, frames: torch.tensor) -> torch.tensor\nconvert frame batch to shape (b, y, x) on self.device/self.dtype.\nconvert frame batch to shape (b, y, x) on self.device/self.dtype.",
"signature": "(self, frames: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 170,
"line": 152,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "Convert frame batch to shape (B, Y, X) on self.device/self.dtype."
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.motion.Translation",
"name": "_make_window",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._make_window",
"searchText": "staticmethod\n_make_window\ntorch_volpy.motion.translation.translation._make_window\n(height: int, width: int) -> torch.tensor\nstaticmethod",
"signature": "(height: int, width: int) -> torch.Tensor",
"source": {
"endLine": 176,
"line": 173,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "_precompute_template_fft",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._precompute_template_fft",
"searchText": "method\n_precompute_template_fft\ntorch_volpy.motion.translation.translation._precompute_template_fft\n(self, template_2d: torch.tensor) -> torch.tensor",
"signature": "(self, template_2d: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 189,
"line": 178,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.motion.Translation",
"name": "_build_search_indices",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._build_search_indices",
"searchText": "staticmethod\n_build_search_indices\ntorch_volpy.motion.translation.translation._build_search_indices\n(height: int, width: int, max_shifts: tuple[int, int], device: torch.device) -> tuple[torch.tensor, torch.tensor]\nstaticmethod",
"signature": "(height: int, width: int, max_shifts: Tuple[int, int], device: torch.device) -> Tuple[torch.Tensor, torch.Tensor]",
"source": {
"endLine": 220,
"line": 192,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "Convert FFT-wrap index to signed shift.\nExample:\n 0 -> 0\n 1 -> +1\n size-1 -> -1",
"kind": "staticmethod",
"module": "torch_volpy.motion.Translation",
"name": "_to_signed_shift",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._to_signed_shift",
"searchText": "staticmethod\n_to_signed_shift\ntorch_volpy.motion.translation.translation._to_signed_shift\n(idx: torch.tensor, size: int) -> torch.tensor\nconvert fft-wrap index to signed shift.\nconvert fft-wrap index to signed shift.\nexample:\n 0 -> 0\n 1 -> +1\n size-1 -> -1\nstaticmethod",
"signature": "(idx: torch.Tensor, size: int) -> torch.Tensor",
"source": {
"endLine": 232,
"line": 223,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "Convert FFT-wrap index to signed shift."
},
{
"decorators": [],
"docstring": "frames_2d: (B, Y, X)\nreturns corr: (B, Y, X)",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "_phase_correlation",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._phase_correlation",
"searchText": "method\n_phase_correlation\ntorch_volpy.motion.translation.translation._phase_correlation\n(self, frames_2d: torch.tensor, return_cross_power: bool = false)\nframes_2d: (b, y, x)\nframes_2d: (b, y, x)\nreturns corr: (b, y, x)",
"signature": "(self, frames_2d: torch.Tensor, return_cross_power: bool = False)",
"source": {
"endLine": 280,
"line": 234,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "frames_2d: (B, Y, X)"
},
{
"decorators": [],
"docstring": "Batched matrix-multiply DFT in a small region around each frame's peak.\n\nThis is the torch/GPU analogue of the Guizar-Sicairos local DFT\nrefinement used by scikit-image/CaImAn, without zero-padding the full\nimage by upsample_factor.",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "_upsampled_dft_batched",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._upsampled_dft_batched",
"searchText": "method\n_upsampled_dft_batched\ntorch_volpy.motion.translation.translation._upsampled_dft_batched\n(self, data: torch.tensor, upsampled_region_size: int, upsample_factor: int, axis_offsets: torch.tensor) -> torch.tensor\nbatched matrix-multiply dft in a small region around each frame's peak.\nbatched matrix-multiply dft in a small region around each frame's peak.\n\nthis is the torch/gpu analogue of the guizar-sicairos local dft\nrefinement used by scikit-image/caiman, without zero-padding the full\nimage by upsample_factor.",
"signature": "(self, data: torch.Tensor, upsampled_region_size: int, upsample_factor: int, axis_offsets: torch.Tensor) -> torch.Tensor",
"source": {
"endLine": 339,
"line": 282,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "Batched matrix-multiply DFT in a small region around each frame's peak."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "_refine_subpixel_dft",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._refine_subpixel_dft",
"searchText": "method\n_refine_subpixel_dft\ntorch_volpy.motion.translation.translation._refine_subpixel_dft\n(self, cross_power: torch.tensor, dy: torch.tensor, dx: torch.tensor) -> tuple[torch.tensor, torch.tensor]",
"signature": "(self, cross_power: torch.Tensor, dy: torch.Tensor, dx: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]",
"source": {
"endLine": 382,
"line": 341,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Refine integer FFT-correlation peaks with a separable quadratic fit.\n\nThis is a fast batched approximation to local DFT upsampling. It runs on\nthe same device as corr and avoids per-frame CPU/OpenCV work.",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "_refine_subpixel_quadratic",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._refine_subpixel_quadratic",
"searchText": "method\n_refine_subpixel_quadratic\ntorch_volpy.motion.translation.translation._refine_subpixel_quadratic\n(self, corr: torch.tensor, peak_y: torch.tensor, peak_x: torch.tensor, dy: torch.tensor, dx: torch.tensor) -> tuple[torch.tensor, torch.tensor]\nrefine integer fft-correlation peaks with a separable quadratic fit.\nrefine integer fft-correlation peaks with a separable quadratic fit.\n\nthis is a fast batched approximation to local dft upsampling. it runs on\nthe same device as corr and avoids per-frame cpu/opencv work.",
"signature": "(self, corr: torch.Tensor, peak_y: torch.Tensor, peak_x: torch.Tensor, dy: torch.Tensor, dx: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]",
"source": {
"endLine": 445,
"line": 384,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "Refine integer FFT-correlation peaks with a separable quadratic fit."
},
{
"decorators": [],
"docstring": "corr: (B, Y, X)",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "_find_shifts_from_corr",
"parent": "torch_volpy.motion.Translation.Translation",
"public": false,
"qualifiedName": "torch_volpy.motion.Translation.Translation._find_shifts_from_corr",
"searchText": "method\n_find_shifts_from_corr\ntorch_volpy.motion.translation.translation._find_shifts_from_corr\n(self, corr: torch.tensor, cross_power: optional[torch.tensor] = none, return_scores: bool = false)\ncorr: (b, y, x)\ncorr: (b, y, x)",
"signature": "(self, corr: torch.Tensor, cross_power: Optional[torch.Tensor] = None, return_scores: bool = False)",
"source": {
"endLine": 489,
"line": 447,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "corr: (B, Y, X)"
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Estimate shifts for movie frames in [start, stop).\n\nReturns\n-------\nshifts : torch.Tensor, shape (N, 2), dtype int64\n Each row is [dy, dx] to apply to the frame.\nscores : torch.Tensor, shape (N,), optional\n Peak correlation score for each frame.",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "estimate_range",
"parent": "torch_volpy.motion.Translation.Translation",
"public": true,
"qualifiedName": "torch_volpy.motion.Translation.Translation.estimate_range",
"searchText": "method\nestimate_range\ntorch_volpy.motion.translation.translation.estimate_range\n(self, start: int = 0, stop: optional[int] = none, return_scores: bool = false)\nestimate shifts for movie frames in [start, stop).\nestimate shifts for movie frames in [start, stop).\n\nreturns\n-------\nshifts : torch.tensor, shape (n, 2), dtype int64\n each row is [dy, dx] to apply to the frame.\nscores : torch.tensor, shape (n,), optional\n peak correlation score for each frame.\ntorch.inference_mode",
"signature": "(self, start: int = 0, stop: Optional[int] = None, return_scores: bool = False)",
"source": {
"endLine": 563,
"line": 492,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "Estimate shifts for movie frames in [start, stop)."
},
{
"decorators": [
"torch.inference_mode"
],
"docstring": "Estimate shift for a single frame.",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "estimate_frame",
"parent": "torch_volpy.motion.Translation.Translation",
"public": true,
"qualifiedName": "torch_volpy.motion.Translation.Translation.estimate_frame",
"searchText": "method\nestimate_frame\ntorch_volpy.motion.translation.translation.estimate_frame\n(self, index: int, return_score: bool = false)\nestimate shift for a single frame.\nestimate shift for a single frame.\ntorch.inference_mode",
"signature": "(self, index: int, return_score: bool = False)",
"source": {
"endLine": 603,
"line": 566,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "Estimate shift for a single frame."
},
{
"decorators": [],
"docstring": "Estimate shifts for a frame range.",
"kind": "method",
"module": "torch_volpy.motion.Translation",
"name": "__call__",
"parent": "torch_volpy.motion.Translation.Translation",
"public": true,
"qualifiedName": "torch_volpy.motion.Translation.Translation.__call__",
"searchText": "method\n__call__\ntorch_volpy.motion.translation.translation.__call__\n(self, start: int = 0, stop: optional[int] = none, return_scores: bool = false)\nestimate shifts for a frame range.\nestimate shifts for a frame range.",
"signature": "(self, start: int = 0, stop: Optional[int] = None, return_scores: bool = False)",
"source": {
"endLine": 612,
"line": 605,
"path": "src/torch_volpy/motion/Translation.py"
},
"summary": "Estimate shifts for a frame range."
},
{
"decorators": [],
"docstring": "",
"exports": [
"MotionCorrect",
"Template",
"Translation"
],
"kind": "module",
"module": "torch_volpy.motion",
"name": "torch_volpy.motion",
"public": true,
"qualifiedName": "torch_volpy.motion",
"searchText": "module\ntorch_volpy.motion\ntorch_volpy.motion",
"signature": "",
"source": {
"endLine": 1,
"line": 1,
"path": "src/torch_volpy/motion/__init__.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"exports": [],
"kind": "module",
"module": "torch_volpy.movie.Movie",
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"searchText": "module\ntorch_volpy.movie.movie\ntorch_volpy.movie.movie",
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},
{
"decorators": [],
"default": "Union[str, Path]",
"docstring": "",
"kind": "alias",
"module": "torch_volpy.movie.Movie",
"name": "PathLike",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.PathLike",
"searchText": "alias\npathlike\ntorch_volpy.movie.movie.pathlike\nunion[str, path]",
"signature": "",
"source": {
"endLine": 12,
"line": 12,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"bases": [
"Dataset"
],
"dataclass": false,
"decorators": [],
"docstring": "HDF5-backed movie reader/writer for PyTorch.\n\nAssumptions:\n- The HDF5 dataset shape is usually (T, Y, X) or (T, Y, X, C)\n- Axis 0 is the frame/time axis\n- For TIFF conversion, the TIFF is a standard multi-page TIFF where each page is one frame\n\nKey properties:\n- Lazy HDF5 opening (safe pattern for PyTorch DataLoader workers)\n- Partial reads via slicing, so only requested frames enter memory\n- Save/append torch tensors to HDF5\n- Convert TIFF -> HDF5 page by page",
"kind": "class",
"module": "torch_volpy.movie.Movie",
"name": "Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie",
"searchText": "class\nmovie\ntorch_volpy.movie.movie.movie\n(h5_path: pathlike, dataset: str = 'movie', mode: str = 'r', transform = none, rdcc_nbytes: optional[int] = none) -> none\nhdf5-backed movie reader/writer for pytorch.\nhdf5-backed movie reader/writer for pytorch.\n\nassumptions:\n- the hdf5 dataset shape is usually (t, y, x) or (t, y, x, c)\n- axis 0 is the frame/time axis\n- for tiff conversion, the tiff is a standard multi-page tiff where each page is one frame\n\nkey properties:\n- lazy hdf5 opening (safe pattern for pytorch dataloader workers)\n- partial reads via slicing, so only requested frames enter memory\n- save/append torch tensors to hdf5\n- convert tiff -> hdf5 page by page\ndataset",
"signature": "(h5_path: PathLike, dataset: str = 'movie', mode: str = 'r', transform = None, rdcc_nbytes: Optional[int] = None) -> None",
"source": {
"endLine": 584,
"line": 15,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "HDF5-backed movie reader/writer for PyTorch."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "__init__",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie.__init__",
"searchText": "method\n__init__\ntorch_volpy.movie.movie.movie.__init__\n(self, h5_path: pathlike, dataset: str = 'movie', mode: str = 'r', transform = none, rdcc_nbytes: optional[int] = none) -> none",
"signature": "(self, h5_path: PathLike, dataset: str = 'movie', mode: str = 'r', transform = None, rdcc_nbytes: Optional[int] = None) -> None",
"source": {
"endLine": 58,
"line": 31,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "_h5_open_kwargs",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie._h5_open_kwargs",
"searchText": "method\n_h5_open_kwargs\ntorch_volpy.movie.movie.movie._h5_open_kwargs\n(self) -> dict[str, any]",
"signature": "(self) -> dict[str, Any]",
"source": {
"endLine": 66,
"line": 62,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "_open_file_temporarily",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie._open_file_temporarily",
"searchText": "method\n_open_file_temporarily\ntorch_volpy.movie.movie.movie._open_file_temporarily\n(self) -> h5py.file",
"signature": "(self) -> h5py.File",
"source": {
"endLine": 69,
"line": 68,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "_ensure_open",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie._ensure_open",
"searchText": "method\n_ensure_open\ntorch_volpy.movie.movie.movie._ensure_open\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 74,
"line": 71,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.movie.Movie",
"name": "_to_numpy",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie._to_numpy",
"searchText": "staticmethod\n_to_numpy\ntorch_volpy.movie.movie.movie._to_numpy\n(data: union[np.ndarray, torch.tensor]) -> np.ndarray\nstaticmethod",
"signature": "(data: Union[np.ndarray, torch.Tensor]) -> np.ndarray",
"source": {
"endLine": 80,
"line": 77,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "Default chunking: chunk by frame blocks along axis 0, keep full frame shape.\nGood when most reads are frame-wise or short contiguous frame ranges.",
"kind": "staticmethod",
"module": "torch_volpy.movie.Movie",
"name": "_normalize_chunks",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie._normalize_chunks",
"searchText": "staticmethod\n_normalize_chunks\ntorch_volpy.movie.movie.movie._normalize_chunks\n(shape: sequence[int], chunk_frames: int = 1) -> tuple[int, ...]\ndefault chunking: chunk by frame blocks along axis 0, keep full frame shape.\ndefault chunking: chunk by frame blocks along axis 0, keep full frame shape.\ngood when most reads are frame-wise or short contiguous frame ranges.\nstaticmethod",
"signature": "(shape: Sequence[int], chunk_frames: int = 1) -> Tuple[int, ...]",
"source": {
"endLine": 95,
"line": 83,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Default chunking: chunk by frame blocks along axis 0, keep full frame shape."
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.movie.Movie",
"name": "_prepare_movie_array",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie._prepare_movie_array",
"searchText": "staticmethod\n_prepare_movie_array\ntorch_volpy.movie.movie.movie._prepare_movie_array\n(data: union[np.ndarray, torch.tensor]) -> np.ndarray\nstaticmethod",
"signature": "(data: Union[np.ndarray, torch.Tensor]) -> np.ndarray",
"source": {
"endLine": 108,
"line": 98,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Refresh shape/dtype metadata from disk.",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "refresh",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.refresh",
"searchText": "method\nrefresh\ntorch_volpy.movie.movie.movie.refresh\n(self) -> none\nrefresh shape/dtype metadata from disk.\nrefresh shape/dtype metadata from disk.",
"signature": "(self) -> None",
"source": {
"endLine": 120,
"line": 110,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Refresh shape/dtype metadata from disk."
},
{
"decorators": [
"property"
],
"docstring": "Shape of the backing HDF5 dataset.",
"kind": "property",
"module": "torch_volpy.movie.Movie",
"name": "shape",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.shape",
"searchText": "property\nshape\ntorch_volpy.movie.movie.movie.shape\n(self) -> tuple[int, ...]\nshape of the backing hdf5 dataset.\nshape of the backing hdf5 dataset.\nproperty",
"signature": "(self) -> Tuple[int, ...]",
"source": {
"endLine": 127,
"line": 125,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Shape of the backing HDF5 dataset."
},
{
"decorators": [
"property"
],
"docstring": "NumPy dtype of the backing HDF5 dataset.",
"kind": "property",
"module": "torch_volpy.movie.Movie",
"name": "dtype",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.dtype",
"searchText": "property\ndtype\ntorch_volpy.movie.movie.movie.dtype\n(self) -> np.dtype\nnumpy dtype of the backing hdf5 dataset.\nnumpy dtype of the backing hdf5 dataset.\nproperty",
"signature": "(self) -> np.dtype",
"source": {
"endLine": 132,
"line": 130,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "NumPy dtype of the backing HDF5 dataset."
},
{
"decorators": [
"property"
],
"docstring": "Number of frames along the time axis.",
"kind": "property",
"module": "torch_volpy.movie.Movie",
"name": "num_frames",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.num_frames",
"searchText": "property\nnum_frames\ntorch_volpy.movie.movie.movie.num_frames\n(self) -> int\nnumber of frames along the time axis.\nnumber of frames along the time axis.\nproperty",
"signature": "(self) -> int",
"source": {
"endLine": 137,
"line": 135,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Number of frames along the time axis."
},
{
"decorators": [
"property"
],
"docstring": "Shape of one frame, excluding the time axis.",
"kind": "property",
"module": "torch_volpy.movie.Movie",
"name": "frame_shape",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.frame_shape",
"searchText": "property\nframe_shape\ntorch_volpy.movie.movie.movie.frame_shape\n(self) -> tuple[int, ...]\nshape of one frame, excluding the time axis.\nshape of one frame, excluding the time axis.\nproperty",
"signature": "(self) -> Tuple[int, ...]",
"source": {
"endLine": 142,
"line": 140,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Shape of one frame, excluding the time axis."
},
{
"decorators": [],
"docstring": "Close any open HDF5 file handle held by this Movie.",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "close",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.close",
"searchText": "method\nclose\ntorch_volpy.movie.movie.movie.close\n(self) -> none\nclose any open hdf5 file handle held by this movie.\nclose any open hdf5 file handle held by this movie.",
"signature": "(self) -> None",
"source": {
"endLine": 151,
"line": 146,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Close any open HDF5 file handle held by this Movie."
},
{
"decorators": [],
"docstring": "Open the backing HDF5 file for context-manager usage.",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "__enter__",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.__enter__",
"searchText": "method\n__enter__\ntorch_volpy.movie.movie.movie.__enter__\n(self) -> 'movie'\nopen the backing hdf5 file for context-manager usage.\nopen the backing hdf5 file for context-manager usage.",
"signature": "(self) -> 'Movie'",
"source": {
"endLine": 156,
"line": 153,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Open the backing HDF5 file for context-manager usage."
},
{
"decorators": [],
"docstring": "Close the backing HDF5 file when leaving a context manager.",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "__exit__",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.__exit__",
"searchText": "method\n__exit__\ntorch_volpy.movie.movie.movie.__exit__\n(self, exc_type, exc, tb) -> none\nclose the backing hdf5 file when leaving a context manager.\nclose the backing hdf5 file when leaving a context manager.",
"signature": "(self, exc_type, exc, tb) -> None",
"source": {
"endLine": 160,
"line": 158,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Close the backing HDF5 file when leaving a context manager."
},
{
"decorators": [],
"docstring": "",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "__del__",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie.__del__",
"searchText": "method\n__del__\ntorch_volpy.movie.movie.movie.__del__\n(self) -> none",
"signature": "(self) -> None",
"source": {
"endLine": 166,
"line": 162,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"decorators": [],
"docstring": "Return the number of movie frames.",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "__len__",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.__len__",
"searchText": "method\n__len__\ntorch_volpy.movie.movie.movie.__len__\n(self) -> int\nreturn the number of movie frames.\nreturn the number of movie frames.",
"signature": "(self) -> int",
"source": {
"endLine": 172,
"line": 170,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Return the number of movie frames."
},
{
"decorators": [],
"docstring": "For DataLoader usage:\n- int -> one frame tensor\n- slice / tuple -> tensor block",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "__getitem__",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.__getitem__",
"searchText": "method\n__getitem__\ntorch_volpy.movie.movie.movie.__getitem__\n(self, idx)\nfor dataloader usage:\nfor dataloader usage:\n- int -> one frame tensor\n- slice / tuple -> tensor block",
"signature": "(self, idx)",
"source": {
"endLine": 180,
"line": 174,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "For DataLoader usage:"
},
{
"decorators": [],
"docstring": "Read any valid h5py slice/index.\n\nExamples:\n movie.read(0) -> one frame\n movie.read(slice(10, 20)) -> frames 10:20\n movie.read((slice(10, 20), ...))\n movie.read((0, slice(None), slice(None)))",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "read",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.read",
"searchText": "method\nread\ntorch_volpy.movie.movie.movie.read\n(self, index = ..., as_tensor: bool = true, dtype: optional[union[np.dtype, str]] = none, device: optional[union[str, torch.device]] = none, copy: bool = false)\nread any valid h5py slice/index.\nread any valid h5py slice/index.\n\nexamples:\n movie.read(0) -> one frame\n movie.read(slice(10, 20)) -> frames 10:20\n movie.read((slice(10, 20), ...))\n movie.read((0, slice(none), slice(none)))",
"signature": "(self, index = ..., as_tensor: bool = True, dtype: Optional[Union[np.dtype, str]] = None, device: Optional[Union[str, torch.device]] = None, copy: bool = False)",
"source": {
"endLine": 225,
"line": 184,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Read any valid h5py slice/index."
},
{
"decorators": [],
"docstring": "Read a frame range along axis 0 only.",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "read_frames",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.read_frames",
"searchText": "method\nread_frames\ntorch_volpy.movie.movie.movie.read_frames\n(self, start: int, stop: optional[int] = none, step: int = 1, as_tensor: bool = true, dtype: optional[union[np.dtype, str]] = none, device: optional[union[str, torch.device]] = none)\nread a frame range along axis 0 only.\nread a frame range along axis 0 only.",
"signature": "(self, start: int, stop: Optional[int] = None, step: int = 1, as_tensor: bool = True, dtype: Optional[Union[np.dtype, str]] = None, device: Optional[Union[str, torch.device]] = None)",
"source": {
"endLine": 242,
"line": 227,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Read a frame range along axis 0 only."
},
{
"decorators": [],
"docstring": "Iterate through the movie in frame chunks.",
"kind": "method",
"module": "torch_volpy.movie.Movie",
"name": "iter_chunks",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.iter_chunks",
"searchText": "method\niter_chunks\ntorch_volpy.movie.movie.movie.iter_chunks\n(self, frames_per_chunk: int, as_tensor: bool = true, dtype: optional[union[np.dtype, str]] = none, device: optional[union[str, torch.device]] = none) -> iterator[union[np.ndarray, torch.tensor]]\niterate through the movie in frame chunks.\niterate through the movie in frame chunks.",
"signature": "(self, frames_per_chunk: int, as_tensor: bool = True, dtype: Optional[Union[np.dtype, str]] = None, device: Optional[Union[str, torch.device]] = None) -> Iterator[Union[np.ndarray, torch.Tensor]]",
"source": {
"endLine": 263,
"line": 244,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Iterate through the movie in frame chunks."
},
{
"decorators": [
"classmethod"
],
"docstring": "Create an empty appendable movie dataset with shape (0, *frame_shape).",
"kind": "classmethod",
"module": "torch_volpy.movie.Movie",
"name": "create_empty",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.create_empty",
"searchText": "classmethod\ncreate_empty\ntorch_volpy.movie.movie.movie.create_empty\n(cls, h5_path: pathlike, frame_shape: sequence[int], dtype: union[str, np.dtype], dataset: str = 'movie', overwrite: bool = false, chunk_frames: int = 1, compression: optional[str] = none, compression_opts: optional[int] = none, attrs: optional[dict] = none) -> 'movie'\ncreate an empty appendable movie dataset with shape (0, *frame_shape).\ncreate an empty appendable movie dataset with shape (0, *frame_shape).\nclassmethod",
"signature": "(cls, h5_path: PathLike, frame_shape: Sequence[int], dtype: Union[str, np.dtype], dataset: str = 'movie', overwrite: bool = False, chunk_frames: int = 1, compression: Optional[str] = None, compression_opts: Optional[int] = None, attrs: Optional[dict] = None) -> 'Movie'",
"source": {
"endLine": 310,
"line": 268,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Create an empty appendable movie dataset with shape (0, *frame_shape)."
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.movie.Movie",
"name": "_save_hdf5",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie._save_hdf5",
"searchText": "staticmethod\n_save_hdf5\ntorch_volpy.movie.movie.movie._save_hdf5\n(h5_path: pathlike, data: union[np.ndarray, torch.tensor], overwrite: bool = false, dataset: str = 'movie', chunk_frames: int = 1, compression: optional[str] = none, compression_opts: optional[int] = none, attrs: optional[dict] = none) -> bool\nstaticmethod",
"signature": "(h5_path: PathLike, data: Union[np.ndarray, torch.Tensor], overwrite: bool = False, dataset: str = 'movie', chunk_frames: int = 1, compression: Optional[str] = None, compression_opts: Optional[int] = None, attrs: Optional[dict] = None) -> bool",
"source": {
"endLine": 350,
"line": 313,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"decorators": [
"staticmethod"
],
"docstring": "",
"kind": "staticmethod",
"module": "torch_volpy.movie.Movie",
"name": "_save_tiff",
"parent": "torch_volpy.movie.Movie.Movie",
"public": false,
"qualifiedName": "torch_volpy.movie.Movie.Movie._save_tiff",
"searchText": "staticmethod\n_save_tiff\ntorch_volpy.movie.movie.movie._save_tiff\n(tiff_path: pathlike, data: union[np.ndarray, torch.tensor], overwrite: bool = false, attrs: optional[dict] = none) -> bool\nstaticmethod",
"signature": "(tiff_path: PathLike, data: Union[np.ndarray, torch.Tensor], overwrite: bool = False, attrs: Optional[dict] = None) -> bool",
"source": {
"endLine": 382,
"line": 353,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": ""
},
{
"decorators": [
"classmethod"
],
"docstring": "Save a tensor or array to HDF5 or TIFF based on the path suffix.",
"kind": "classmethod",
"module": "torch_volpy.movie.Movie",
"name": "save_tensor",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
"qualifiedName": "torch_volpy.movie.Movie.Movie.save_tensor",
"searchText": "classmethod\nsave_tensor\ntorch_volpy.movie.movie.movie.save_tensor\n(cls, path: pathlike, data: union[np.ndarray, torch.tensor], *, overwrite: bool = false, **kwargs: any) -> bool\nsave a tensor or array to hdf5 or tiff based on the path suffix.\nsave a tensor or array to hdf5 or tiff based on the path suffix.\nclassmethod",
"signature": "(cls, path: PathLike, data: Union[np.ndarray, torch.Tensor], *, overwrite: bool = False, **kwargs: Any) -> bool",
"source": {
"endLine": 401,
"line": 385,
"path": "src/torch_volpy/movie/Movie.py"
},
"summary": "Save a tensor or array to HDF5 or TIFF based on the path suffix."
},
{
"decorators": [
"classmethod"
],
"docstring": "Append frames along axis 0.\nAccepts shape:\n (T, ...)\nor a single frame shape:\n (...)",
"kind": "classmethod",
"module": "torch_volpy.movie.Movie",
"name": "append_tensor",
"parent": "torch_volpy.movie.Movie.Movie",
"public": true,
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"searchText": "classmethod\nappend_tensor\ntorch_volpy.movie.movie.movie.append_tensor\n(cls, h5_path: pathlike, data: union[np.ndarray, torch.tensor], dataset: str = 'movie') -> none\nappend frames along axis 0.\nappend frames along axis 0.\naccepts shape:\n (t, ...)\nor a single frame shape:\n (...)\nclassmethod",
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"path": "src/torch_volpy/movie/Movie.py"
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"summary": "Append frames along axis 0."
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"docstring": "Convert a standard multi-page TIFF movie to HDF5 without loading the whole movie.\n\nMemory behavior:\n- reads one TIFF page at a time\n- writes small frame batches into HDF5\n\nNotes:\n- This implementation assumes each TIFF page is one movie frame.\n- For more complex OME-TIFF axis layouts, adapt this method using tifffile metadata.",
"kind": "classmethod",
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"searchText": "classmethod\nfrom_tiff\ntorch_volpy.movie.movie.movie.from_tiff\n(cls, tiff_path: pathlike, h5_path: pathlike, dataset: str = 'movie', overwrite: bool = false, chunk_frames: int = 16, compression: optional[str] = none, compression_opts: optional[int] = none, attrs: optional[dict] = none) -> 'movie'\nconvert a standard multi-page tiff movie to hdf5 without loading the whole movie.\nconvert a standard multi-page tiff movie to hdf5 without loading the whole movie.\n\nmemory behavior:\n- reads one tiff page at a time\n- writes small frame batches into hdf5\n\nnotes:\n- this implementation assumes each tiff page is one movie frame.\n- for more complex ome-tiff axis layouts, adapt this method using tifffile metadata.\nclassmethod",
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"summary": "Convert a standard multi-page TIFF movie to HDF5 without loading the whole movie."
},
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"decorators": [
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"docstring": "Export an HDF5 movie dataset to a multi-page TIFF file.",
"kind": "classmethod",
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"searchText": "classmethod\nto_tiff\ntorch_volpy.movie.movie.movie.to_tiff\n(cls, h5_path: pathlike, tiff_path: pathlike, dataset: str = 'movie', dtype: optional[union[np.dtype, str]] = none, bigtiff: optional[bool] = none, overwrite: bool = false, rdcc_nbytes: optional[int] = none, batch_size: int = 64) -> path\nexport an hdf5 movie dataset to a multi-page tiff file.\nexport an hdf5 movie dataset to a multi-page tiff file.\nclassmethod",
"signature": "(cls, h5_path: PathLike, tiff_path: PathLike, dataset: str = 'movie', dtype: Optional[Union[np.dtype, str]] = None, bigtiff: Optional[bool] = None, overwrite: bool = False, rdcc_nbytes: Optional[int] = None, batch_size: int = 64) -> Path",
"source": {
"endLine": 584,
"line": 526,
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"docstring": "ImageJ-like Auto Brightness/Contrast for NumPy arrays.",
"kind": "class",
"module": "torch_volpy.util.IJAB",
"name": "IJAB",
"public": true,
"qualifiedName": "torch_volpy.util.IJAB.IJAB",
"searchText": "class\nijab\ntorch_volpy.util.ijab.ijab\n(clicks: int = 1, bins: int = 256)\nimagej-like auto brightness/contrast for numpy arrays.\nimagej-like auto brightness/contrast for numpy arrays.",
"signature": "(clicks: int = 1, bins: int = 256)",
"source": {
"endLine": 185,
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"path": "src/torch_volpy/util/IJAB.py"
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"summary": "ImageJ-like Auto Brightness/Contrast for NumPy arrays."
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"signature": "(self, clicks: int = 1, bins: int = 256)",
"source": {
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},
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"decorators": [
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"docstring": ":contentReference[oaicite:0]{index=0}style display scaling.\n\nImageJ behavior for 32-bit -> 8-bit display:\n - Linearly maps display_min..display_max to 0..255\n - Clips values below display_min to 0\n - Clips values above display_max to 255\n - Rounds with +0.5 before integer conversion\n - If display_min/display_max are not given, uses image min/max\n while ignoring NaN for range calculation\n\nParameters\n----------\narr : np.ndarray\n Input image as a NumPy array.\ndisplay_min : float | None\n Lower bound of display range. If None, computed from data.\ndisplay_max : float | None\n Upper bound of display range. If None, computed from data.\n\nReturns\n-------\nnp.ndarray\n uint8 image with the same shape as input.",
"kind": "classmethod",
"module": "torch_volpy.util.IJAB",
"name": "imagej_fp32_to_uint8",
"parent": "torch_volpy.util.IJAB.IJAB",
"public": true,
"qualifiedName": "torch_volpy.util.IJAB.IJAB.imagej_fp32_to_uint8",
"searchText": "classmethod\nimagej_fp32_to_uint8\ntorch_volpy.util.ijab.ijab.imagej_fp32_to_uint8\n(cls, arr: np.ndarray, display_min: float | none = none, display_max: float | none = none) -> np.ndarray\n:contentreference[oaicite:0]{index=0}style display scaling.\n:contentreference[oaicite:0]{index=0}style display scaling.\n\nimagej behavior for 32-bit -> 8-bit display:\n - linearly maps display_min..display_max to 0..255\n - clips values below display_min to 0\n - clips values above display_max to 255\n - rounds with +0.5 before integer conversion\n - if display_min/display_max are not given, uses image min/max\n while ignoring nan for range calculation\n\nparameters\n----------\narr : np.ndarray\n input image as a numpy array.\ndisplay_min : float | none\n lower bound of display range. if none, computed from data.\ndisplay_max : float | none\n upper bound of display range. if none, computed from data.\n\nreturns\n-------\nnp.ndarray\n uint8 image with the same shape as input.\nclassmethod",
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"source": {
"endLine": 76,
"line": 15,
"path": "src/torch_volpy/util/IJAB.py"
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"searchText": "staticmethod\n_threshold_divisor\ntorch_volpy.util.ijab.ijab._threshold_divisor\n(clicks: int) -> int\nstaticmethod",
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"source": {
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"kind": "method",
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"searchText": "method\napply\ntorch_volpy.util.ijab.ijab.apply\n(self, arr: np.ndarray, mask: optional[np.ndarray] = none, output_dtype = np.float32, out_range: optional[tuple[float, float]] = none, return_limits: bool = false)\napply imagej-style auto brightness and contrast scaling.\napply imagej-style auto brightness and contrast scaling.",
"signature": "(self, arr: np.ndarray, mask: Optional[np.ndarray] = None, output_dtype = np.float32, out_range: Optional[Tuple[float, float]] = None, return_limits: bool = False)",
"source": {
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"searchText": "method\n__call__\ntorch_volpy.util.ijab.ijab.__call__\n(self, arr: np.ndarray, **kwargs)\napply imagej-style auto brightness and contrast scaling.\napply imagej-style auto brightness and contrast scaling.",
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