Tiled

Image tiling module

pydantic model PixelValueModel[source]

Bases: BaseModel

Pydantic model class for VisiOmatic pixel values.

Parameters:: values (list[float | None]) -- Pixel values.

Show JSON schema

{
   "title": "PixelValueModel",
   "description": "Pydantic model class for VisiOmatic pixel values.\n\nParameters\n----------\nvalues: list[float | None]\n    Pixel values.",
   "type": "object",
   "properties": {
      "values": {
         "items": {
            "anyOf": [
               {
                  "type": "number"
               },
               {
                  "type": "null"
               }
            ]
         },
         "title": "Values",
         "type": "array"
      }
   },
   "required": [
      "values"
   ]
}

Fields:

values (list[float | None])

field values: list[float | None] [Required]

pydantic model PixelModel[source]

Bases: BaseModel

Pydantic model class for pixels.

Parameters:

x (int) -- x coordinate of the pixel.
y (int) -- y coordinate of the pixel.
values (list[float, None]) -- Pixel values.

Show JSON schema

{
   "title": "PixelModel",
   "description": "Pydantic model class for pixels.\n\nParameters\n----------\nx: int\n    x coordinate of the pixel.\ny: int\n    y coordinate of the pixel.\nvalues: list[float, None]\n    Pixel values.",
   "type": "object",
   "properties": {
      "x": {
         "title": "X",
         "type": "integer"
      },
      "y": {
         "title": "Y",
         "type": "integer"
      },
      "values": {
         "items": {
            "anyOf": [
               {
                  "type": "number"
               },
               {
                  "type": "null"
               }
            ]
         },
         "title": "Values",
         "type": "array"
      }
   },
   "required": [
      "x",
      "y",
      "values"
   ]
}

Fields:

values (list[float | None])
x (int)
y (int)

field values: list[float | None] [Required]

field x: int [Required]

field y: int [Required]

pydantic model ProfileModel[source]

Bases: BaseModel

Pydantic model class for VisiOmatic profiles.

Parameters:: profile (list[PixelModel]) -- List of pixel models.

Show JSON schema

{
   "title": "ProfileModel",
   "description": "Pydantic model class for VisiOmatic profiles.\n\nParameters\n----------\nprofile: list[PixelModel]\n    List of pixel models.",
   "type": "object",
   "properties": {
      "profile": {
         "items": {
            "$ref": "#/$defs/PixelModel"
         },
         "title": "Profile",
         "type": "array"
      }
   },
   "$defs": {
      "PixelModel": {
         "description": "Pydantic model class for pixels.\n\nParameters\n----------\nx: int\n    x coordinate of the pixel.\ny: int\n    y coordinate of the pixel.\nvalues: list[float, None]\n    Pixel values.",
         "properties": {
            "x": {
               "title": "X",
               "type": "integer"
            },
            "y": {
               "title": "Y",
               "type": "integer"
            },
            "values": {
               "items": {
                  "anyOf": [
                     {
                        "type": "number"
                     },
                     {
                        "type": "null"
                     }
                  ]
               },
               "title": "Values",
               "type": "array"
            }
         },
         "required": [
            "x",
            "y",
            "values"
         ],
         "title": "PixelModel",
         "type": "object"
      }
   },
   "required": [
      "profile"
   ]
}

Fields:

profile (list[visiomatic.server.tiled.PixelModel])

field profile: list[PixelModel] [Required]

pydantic model TiledModel[source]

Bases: BaseModel

Pydantic tiled model class.

Parameters:

type (str) -- Name of the web service.
version (str) -- Version of the web service.
full_size (List[int]) -- Full raster size, FITS style (x comes first).
tile_size (List[int]) -- Tile size, FITS style.
tile_levels (int) -- Number of levels in the image pyramid.
channels (int) -- Number of channels.
bits_per_channel (int) -- Number of bits per pixel.
brightness (float) -- Relative tile brightness (black level).
contrast (float) -- Relative tile contrast.
color_saturation (float) -- Tile color saturation.
gamma (float) -- Tile display gamma.
quality (int) -- JPEG quality (0-100).
header (dict) -- Image header keyword/value pairs.
images (List[ImageModel]) -- List of image model objects.

Show JSON schema

{
   "title": "TiledModel",
   "description": "Pydantic tiled model class.\n\nParameters\n----------\ntype: str\n    Name of the web service.\nversion: str\n    Version of the web service.\nfull_size: List[int]\n    Full raster size, FITS style (x comes first).\ntile_size: List[int]\n    Tile size, FITS style.\ntile_levels: int\n    Number of levels in the image pyramid.\nchannels: int\n    Number of channels.\nbits_per_channel: int \n    Number of bits per pixel.\nbrightness:  float\n    Relative tile brightness (black level).\ncontrast:  float\n    Relative tile contrast.\ncolor_saturation:  float\n    Tile color saturation.\ngamma: float\n    Tile display gamma.\nquality: int\n    JPEG quality (0-100).\nheader: dict\n    Image header keyword/value pairs.\nimages: List[ImageModel]\n    List of image model objects.",
   "type": "object",
   "properties": {
      "type": {
         "title": "Type",
         "type": "string"
      },
      "version": {
         "title": "Version",
         "type": "string"
      },
      "image_name": {
         "title": "Image Name",
         "type": "string"
      },
      "object_name": {
         "title": "Object Name",
         "type": "string"
      },
      "full_size": {
         "items": {
            "type": "integer"
         },
         "title": "Full Size",
         "type": "array"
      },
      "tile_size": {
         "items": {
            "type": "integer"
         },
         "title": "Tile Size",
         "type": "array"
      },
      "tile_levels": {
         "title": "Tile Levels",
         "type": "integer"
      },
      "channels": {
         "title": "Channels",
         "type": "integer"
      },
      "bits_per_channel": {
         "title": "Bits Per Channel",
         "type": "integer"
      },
      "brightness": {
         "title": "Brightness",
         "type": "number"
      },
      "contrast": {
         "title": "Contrast",
         "type": "number"
      },
      "color_saturation": {
         "title": "Color Saturation",
         "type": "number"
      },
      "gamma": {
         "title": "Gamma",
         "type": "number"
      },
      "quality": {
         "title": "Quality",
         "type": "integer"
      },
      "header": {
         "title": "Header",
         "type": "object"
      },
      "images": {
         "items": {
            "$ref": "#/$defs/ImageModel"
         },
         "title": "Images",
         "type": "array"
      }
   },
   "$defs": {
      "ImageModel": {
         "description": "Pydantic image model class.\n\nParameters\n----------\nsize: List[int]\n    Image shape, FITS style (x comes first)\ndataslice: List[List[int]]\n    Active area slice parameters, FITS style\ndetslice: List[List[int]]\n    Mosaic area slice parameters, FITS style\nmin_max: List[List[float]]\n    Lower and upper intensity cuts for each channel\nheader: dict\n    FITS header dictionary",
         "properties": {
            "size": {
               "items": {
                  "type": "integer"
               },
               "title": "Size",
               "type": "array"
            },
            "dataslice": {
               "items": {
                  "items": {
                     "type": "integer"
                  },
                  "type": "array"
               },
               "title": "Dataslice",
               "type": "array"
            },
            "detslice": {
               "items": {
                  "items": {
                     "type": "integer"
                  },
                  "type": "array"
               },
               "title": "Detslice",
               "type": "array"
            },
            "background_level": {
               "items": {
                  "type": "number"
               },
               "title": "Background Level",
               "type": "array"
            },
            "background_mad": {
               "items": {
                  "type": "number"
               },
               "title": "Background Mad",
               "type": "array"
            },
            "min_max": {
               "items": {
                  "items": {
                     "type": "number"
                  },
                  "type": "array"
               },
               "title": "Min Max",
               "type": "array"
            },
            "header": {
               "title": "Header",
               "type": "object"
            }
         },
         "required": [
            "size",
            "dataslice",
            "detslice",
            "background_level",
            "background_mad",
            "min_max",
            "header"
         ],
         "title": "ImageModel",
         "type": "object"
      }
   },
   "required": [
      "type",
      "version",
      "image_name",
      "object_name",
      "full_size",
      "tile_size",
      "tile_levels",
      "channels",
      "bits_per_channel",
      "brightness",
      "contrast",
      "color_saturation",
      "gamma",
      "quality",
      "header",
      "images"
   ]
}

Fields:

bits_per_channel (int)
brightness (float)
channels (int)
color_saturation (float)
contrast (float)
full_size (Tuple[int, ...])
gamma (float)
header (dict)
image_name (str)
images (List[visiomatic.server.image.ImageModel])
object_name (str)
quality (int)
tile_levels (int)
tile_size (Tuple[int, ...])
type (str)
version (str)

field bits_per_channel: int [Required]

field brightness: float [Required]

field channels: int [Required]

field color_saturation: float [Required]

field contrast: float [Required]

field full_size: Tuple[int, ...] [Required]

field gamma: float [Required]

field header: dict [Required]

field image_name: str [Required]

field images: List[ImageModel] [Required]

field object_name: str [Required]

field quality: int [Required]

field tile_levels: int [Required]

field tile_size: Tuple[int, ...] [Required]

field type: str [Required]

field version: str [Required]

Bases: object

Class for the tiled image pyramid to be visualized.

Parameters:

filename (str | Path,) -- Path to the image.
extnum (int, optional) -- Extension number (for Multi-Extension FITS files).
tilesize (tuple[int, int], optional) -- shape of the served tiles.
minmax (tuple[float, float], optional) -- Intensity cuts of the served tiles.
brightness (float, optional) -- Relative tile black level of the served tiles.
contrast (float, optional) -- Relative tile contrast of the served tiles.
color_saturation (float, optional) -- Default color saturation of the served tiles.
gamma (float, optional) -- Display gamma of the served tiles.
nthreads (int, optional) -- Number of compute threads for parallelized operations.

compute_nlevels() → int[source]

Return the number of image resolution levels.

Returns:: nlevels (int) -- Number of image resolution levels in the pyramid.

compute_grid_shape(level: int = 0) → Tuple[int, int, int][source]

Return the number of tiles per axis at a given image resolution level.

Returns:: shape (tuple[int, int, int]) -- Number of tiles.

compute_tile_bordershape(level=0) → Tuple[int, int, int][source]

Return the border shape of tiles at a given image resolution level.

Returns:: shape (tuple[int, int, int]) -- Border shape.

get_model() → TiledModel[source]

Return a Pydantic model of the tiled object.

Returns:: model (TiledModel) -- Pydantic model instance of the tiled object

make_mosaic(images: List[Image]) → None[source]

Stitch together several images to make a mosaic

Parameters:: images (list[Image]) -- list of input images.

make_header() → fits.header[source]

Generate a FITS header with a global WCS for the mosaic.

Returns:: header (~astropy.io.fits.Header) -- FITS header for the mosaic.

get_iipheaderstr() → str[source]

Generate an IIP image header.

Returns:: header (str) -- IIP image header.

convert_tile(tile: ndarray, channel: int | None = None, minmax: Tuple[Tuple[int, float, float], ...] | None = None, mix: Tuple[Tuple[int, float, float, float], ...] | None = None, brightness: float | None = None, contrast: float | None = None, gamma: float | None = None, colormap: str = 'grey', invert: bool = False) → ndarray[source]

Process the dynamic range of a tile.

Parameters:

tile (ndarray) -- Input tile.
channel (int, optional) -- Image channel
minmax (list[float, float], optional) -- Tile intensity cuts.
mix (list[int, float, float, float], optional) -- Tile slice RGB colors.
brightness (float, optional) -- Relative tile brightness (black level).
contrast (float, optional) -- Relative tile contrast.
gamma (float, optional) -- Inverse tile display gamma.
colormap (str, optional) -- Colormap: 'grey' (default), 'jet', 'cold', or 'hot'.
invert (bool, optional) -- Invert the colormap.

Returns:

raster (~numpy.ndarray) -- Processed tile image raster.

make_tiles() → None[source]: Generate all tiles from the image.

get_tile_raster(tilelevel: int, tileindex: int, channel: int | None = None, minmax: Tuple[Tuple[int, float, float], ...] | None = None, mix: Tuple[Tuple[int, float, float, float], ...] | None = None, brightness: float | None = None, contrast: float | None = None, gamma: float | None = None, colormap: str = 'grey', invert: bool = False, **_: Any) → ndarray[source]

Compute a gray-level or color image raster from a tile.

Parameters:

tilelevel (int) -- Tile resolution level.
tileindex (int) -- Tile index.
channel (int) -- Data channel (first channel is 1)
minmax (list[float, float], optional) -- Tile intensity cuts.
brightness (float, optional) -- Relative tile brightness.
contrast (float, optional) -- Relative tile contrast.
gamma (float, optional) -- Inverse tile display gamma.
colormap (str, optional) -- Colormap: 'grey' (default), 'jet', 'cold', or 'hot'.
invert (bool, optional) -- Invert the colormap.

Returns:

raster (~numpy.ndarray) -- The computed tile image raster.

encode(raster: ndarray, channel: int | None = None, colormap: str = 'grey', quality: int | None = None, **_: Any) → bytes[source]

Generate a JPEG bytestream from an image raster (e.g., a tile).

Parameters:

raster (ndarray) -- Input tile.
channel (int, optional) -- Data channel (first channel is 1)
colormap (str, optional) -- Colormap: 'grey' (default), 'jet', 'cold', or 'hot'.
quality (int, optional) -- JPEG quality (0-100)

Returns:

tile (bytes) -- JPEG bytestream of the tile.

get_encoded_tile = <methodtools._LruCacheWire object>[source]

get_encoded_region(bounds: Tuple[Tuple[int, int], Tuple[int, int]], binning: int = 1, channel: int | None = None, colormap: str = 'grey', **kwargs: Any) → bytes[source]

Return a JPEG bytestream of a specific image region by stitching tiles that fall in that region.

Parameters:

bounds (tuple[tuple[int, int], tuple[int, int]]) -- Image boundaries in pixels.
binning (int, optional) -- Binning factor per axis, in pixels.
channel (int, optional) -- Data channel (first channel is 1)
colormap (str, optional) -- Colormap: 'grey' (default), 'jet', 'cold', or 'hot'.
**kwargs -- Additional get_tile_raster() and encode() keyword arguments.

Returns:

tile (bytes) -- JPEG bytestream of the tile.

Raises:

IndexError -- exception: An error occurred because of unexpected bounding box coordinates. It is raised if any of the following occur: - The number of requested region tiles exceeds max_region_tile_count. - Bounding box coordinates are inconsistent.

get_pixel_values(channels: Tuple[int], pos: Tuple[int, int]) → PixelValueModel[source]

Get pixel values at the given pixel coordinates in merged frame.

Parameters:

channels (tuple[int]) -- Tuple of data channels (first channel is 1).
pos (tuple[int, int]) -- Pixel coordinates.

Returns:

value (~numpy.ndarray) -- Pixel value at the given position, or NaN outside of the frame boundaries.

get_profiles(channels: Tuple[int, ...] | None, pos1: Tuple[int, int], pos2: Tuple[int, int]) → ProfileModel[source]

Get image profile(s) between the given pixel coordinates in the merged frame.

Parameters:

channels (tuple[int, ...] or None) -- Tuple of data channels (first channel is 1) or None for all channels.
pos1 (tuple[int, int]) -- Start pixel coordinates.
pos2 (tuple[int, int]) -- End pixel coordinates.

Returns:

profile (ProfileModel) -- Profile pydantic model of pixel value(s) along the line.

get_data()[source]

Get current memory-mapped image data.

Returns:: data (numpy.ndarray) -- Image data.

get_tiles()[source]

Get current memory-mapped tile data.

Returns:: data (numpy.ndarray) -- Tile data.

pickledTiled(filename: str, **kwargs) → Tiled[source]

Return pickled Tiled object if available, or initialized otherwise.

Parameters:

filename (str | Path) -- Path to the image.
**kwargs (dict) -- Additional keyword arguments.

Returns:

tiled (object) -- Tiled object pickled from file if available, or initialized otherwise.

get_object_filename(image_filename: str) → str[source]

Return the name of the file containing the pickled Tiled object.

Parameters:: filename (Image) -- Full image filename.
Returns:: filename (str) -- Pickled object filename.

get_data_filename(image_filename: str) → str[source]

Return the name of the file containing the memory-mapped image data.

Parameters:: filename (Image) -- Full image filename.
Returns:: filename (str) -- Filename of the memory-mapped image data.

get_tiles_filename(image_filename: str) → str[source]

Return the name of the file containing the memory-mapped tile datacube.

Parameters:: filename (Image) -- Full image filename.
Returns:: filename (str) -- Filename of the memory mapped tile datacube.

get_image_filename(prefix: str) → str[source]

Return the name of the file containing the memory-mapped image datacube.

Parameters:: prefix (str) -- Image name prefix.
Returns:: filename (str) -- Filename of the memory mapped tile datacube.