Module geoengine.colorizer
This module is used to generate geoengine compatible color map definitions as a json string.
Expand source code
"""This module is used to generate geoengine compatible color map definitions as a json string."""
from __future__ import annotations
from abc import abstractmethod
from dataclasses import dataclass
import json
import warnings
from typing import Dict, List, Tuple, Union, cast
import numpy as np
import numpy.typing as npt
from matplotlib.colors import Colormap
from matplotlib.cm import ScalarMappable
import geoengine_openapi_client
Rgba = Tuple[int, int, int, int]
@dataclass
class ColorBreakpoint():
"""This class is used to generate geoengine compatible color breakpoint definitions."""
value: float
color: Rgba
def to_api_dict(self) -> geoengine_openapi_client.Breakpoint:
"""Return the color breakpoint as a dictionary."""
return geoengine_openapi_client.Breakpoint(value=self.value, color=self.color)
@staticmethod
def from_response(response: geoengine_openapi_client.Breakpoint) -> ColorBreakpoint:
"""Parse a http response to a `ColorBreakpoint`."""
return ColorBreakpoint(cast(float, response.value), cast(Rgba, tuple(cast(List[int], response.color))))
@dataclass
class Colorizer():
"""This class is used to generate geoengine compatible color map definitions as a json string."""
no_data_color: Rgba
@staticmethod
def linear_with_mpl_cmap(
color_map: Union[str, Colormap],
min_max: Tuple[float, float],
n_steps: int = 10,
over_color: Rgba = (0, 0, 0, 0),
under_color: Rgba = (0, 0, 0, 0),
no_data_color: Rgba = (0, 0, 0, 0)
) -> LinearGradientColorizer:
"""Initialize the colorizer."""
# pylint: disable=too-many-arguments,too-many-positional-arguments
if n_steps < 2:
raise ValueError(f"n_steps must be greater than or equal to 2, got {n_steps} instead.")
if min_max[1] <= min_max[0]:
raise ValueError(f"min_max[1] must be greater than min_max[0], got {min_max[1]} and {min_max[0]}.")
if len(over_color) != 4:
raise ValueError(f"overColor must be a tuple of length 4, got {len(over_color)} instead.")
if len(under_color) != 4:
raise ValueError(f"underColor must be a tuple of length 4, got {len(under_color)} instead.")
if len(no_data_color) != 4:
raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.")
if not all(0 <= elem < 256 for elem in no_data_color):
raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.")
if not all(0 <= elem < 256 for elem in over_color):
raise ValueError(f"overColor must be a RGBA color specification, got {over_color} instead.")
if not all(0 <= elem < 256 for elem in under_color):
raise ValueError(f"underColor must be a RGBA color specification, got {under_color} instead.")
# get the map, and transform it to a list of (uint8) rgba values
list_of_rgba_colors: List[npt.NDArray[np.uint8]] = ScalarMappable(cmap=color_map).to_rgba(
np.linspace(min_max[0], min_max[1], n_steps), bytes=True)
# if you want to remap the colors, you can do it here (e.g. cutting of the most extreme colors)
values_of_breakpoints: List[float] = np.linspace(min_max[0], min_max[1], n_steps).tolist()
# generate color map steps for geoengine
breakpoints: List[ColorBreakpoint] = [
ColorBreakpoint(
color=(int(color[0]), int(color[1]), int(color[2]), int(color[3])), value=value
) for (value, color) in zip(
values_of_breakpoints, list_of_rgba_colors)
]
return LinearGradientColorizer(
breakpoints=breakpoints,
no_data_color=no_data_color,
over_color=over_color,
under_color=under_color
)
@staticmethod
def logarithmic_with_mpl_cmap(
color_map: Union[str, Colormap],
min_max: Tuple[float, float],
n_steps: int = 10,
over_color: Rgba = (0, 0, 0, 0),
under_color: Rgba = (0, 0, 0, 0),
no_data_color: Rgba = (0, 0, 0, 0)
) -> LogarithmicGradientColorizer:
"""Initialize the colorizer."""
# pylint: disable=too-many-arguments, too-many-positional-arguments
if n_steps < 2:
raise ValueError(f"n_steps must be greater than or equal to 2, got {n_steps} instead.")
if min_max[0] <= 0:
raise ValueError(f"min_max[0] must be greater than 0 for a logarithmic gradient, got {min_max[0]}.")
if min_max[1] <= min_max[0]:
raise ValueError(f"min_max[1] must be greater than min_max[0], got {min_max[1]} and {min_max[0]}.")
if len(over_color) != 4:
raise ValueError(f"overColor must be a tuple of length 4, got {len(over_color)} instead.")
if len(under_color) != 4:
raise ValueError(f"underColor must be a tuple of length 4, got {len(under_color)} instead.")
if len(no_data_color) != 4:
raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.")
if not all(0 <= elem < 256 for elem in no_data_color):
raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.")
if not all(0 <= elem < 256 for elem in over_color):
raise ValueError(f"overColor must be a RGBA color specification, got {over_color} instead.")
if not all(0 <= elem < 256 for elem in under_color):
raise ValueError(f"underColor must be a RGBA color specification, got {under_color} instead.")
# get the map, and transform it to a list of (uint8) rgba values
list_of_rgba_colors: List[npt.NDArray[np.uint8]] = ScalarMappable(cmap=color_map).to_rgba(
np.linspace(min_max[0], min_max[1], n_steps), bytes=True)
# if you want to remap the colors, you can do it here (e.g. cutting of the most extreme colors)
values_of_breakpoints: List[float] = np.logspace(np.log10(min_max[0]), np.log10(min_max[1]), n_steps).tolist()
# generate color map steps for geoengine
breakpoints: List[ColorBreakpoint] = [
ColorBreakpoint(
color=(int(color[0]), int(color[1]), int(color[2]), int(color[3])), value=value
) for (value, color) in zip(
values_of_breakpoints, list_of_rgba_colors)
]
return LogarithmicGradientColorizer(
breakpoints=breakpoints,
no_data_color=no_data_color,
over_color=over_color,
under_color=under_color
)
@staticmethod
def palette(
color_mapping: Dict[float, Rgba],
default_color: Rgba = (0, 0, 0, 0),
no_data_color: Rgba = (0, 0, 0, 0),
) -> PaletteColorizer:
"""Initialize the colorizer."""
if len(no_data_color) != 4:
raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.")
if len(default_color) != 4:
raise ValueError(f"defaultColor must be a tuple of length 4, got {len(default_color)} instead.")
if not all(0 <= elem < 256 for elem in no_data_color):
raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.")
if not all(0 <= elem < 256 for elem in default_color):
raise ValueError(f"defaultColor must be a RGBA color specification, got {default_color} instead.")
return PaletteColorizer(
colors=color_mapping,
no_data_color=no_data_color,
default_color=default_color,
)
@staticmethod
def palette_with_colormap(
values: List[float],
color_map: Union[str, Colormap],
default_color: Rgba = (0, 0, 0, 0),
no_data_color: Rgba = (0, 0, 0, 0),
) -> PaletteColorizer:
"""This method generates a palette colorizer from a given list of values.
A colormap can be given as an object or by name only."""
if len(no_data_color) != 4:
raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.")
if len(default_color) != 4:
raise ValueError(f"defaultColor must be a tuple of length 4, got {len(default_color)} instead.")
if not all(0 <= elem < 256 for elem in no_data_color):
raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.")
if not all(0 <= elem < 256 for elem in default_color):
raise ValueError(f"defaultColor must be a RGBA color specification, got {default_color} instead.")
n_colors_of_cmap: int = ScalarMappable(cmap=color_map).get_cmap().N
if n_colors_of_cmap < len(values):
warnings.warn(UserWarning(f"Warning!\nYour colormap does not have enough colors "
"to display all unique values of the palette!"
f"\nNumber of values given: {len(values)} vs. "
f"Number of available colors: {n_colors_of_cmap}"))
# we only need to generate enough different colors for all values specified in the colors parameter
list_of_rgba_colors: List[npt.NDArray[np.uint8]] = ScalarMappable(cmap=color_map).to_rgba(
np.linspace(0, len(values), len(values)), bytes=True)
# generate the dict with value: color mapping
color_mapping: Dict[float, Rgba] = dict(zip(
values,
[(int(color[0]), int(color[1]), int(color[2]), int(color[3])) for color in list_of_rgba_colors])
)
return PaletteColorizer(
colors=color_mapping,
no_data_color=no_data_color,
default_color=default_color,
)
@abstractmethod
def to_api_dict(self) -> geoengine_openapi_client.Colorizer:
pass
def to_json(self) -> str:
"""Return the colorizer as a JSON string."""
return json.dumps(self.to_api_dict())
@staticmethod
def from_response(response: geoengine_openapi_client.Colorizer) -> Colorizer:
"""Create a colorizer from a response."""
inner = response.actual_instance
if isinstance(inner, geoengine_openapi_client.LinearGradient):
return LinearGradientColorizer.from_response_linear(inner)
if isinstance(inner, geoengine_openapi_client.PaletteColorizer):
return PaletteColorizer.from_response_palette(inner)
if isinstance(inner, geoengine_openapi_client.LogarithmicGradient):
return LogarithmicGradientColorizer.from_response_logarithmic(inner)
raise TypeError("Unknown colorizer type")
@dataclass
class LinearGradientColorizer(Colorizer):
'''A linear gradient colorizer.'''
breakpoints: List[ColorBreakpoint]
over_color: Rgba
under_color: Rgba
@staticmethod
def from_response_linear(response: geoengine_openapi_client.LinearGradient) -> LinearGradientColorizer:
"""Create a colorizer from a response."""
breakpoints = [ColorBreakpoint.from_response(breakpoint) for breakpoint in response.breakpoints]
return LinearGradientColorizer(
no_data_color=response.no_data_color,
breakpoints=breakpoints,
over_color=response.over_color,
under_color=response.under_color,
)
def to_api_dict(self) -> geoengine_openapi_client.Colorizer:
"""Return the colorizer as a dictionary."""
return geoengine_openapi_client.Colorizer(geoengine_openapi_client.LinearGradient(
type='linearGradient',
breakpoints=[breakpoint.to_api_dict() for breakpoint in self.breakpoints],
no_data_color=self.no_data_color,
over_color=self.over_color,
under_color=self.under_color
))
@dataclass
class LogarithmicGradientColorizer(Colorizer):
'''A logarithmic gradient colorizer.'''
breakpoints: List[ColorBreakpoint]
over_color: Rgba
under_color: Rgba
@staticmethod
def from_response_logarithmic(
response: geoengine_openapi_client.LogarithmicGradient) -> LogarithmicGradientColorizer:
"""Create a colorizer from a response."""
breakpoints = [ColorBreakpoint.from_response(breakpoint) for breakpoint in response.breakpoints]
return LogarithmicGradientColorizer(
breakpoints=breakpoints,
no_data_color=response.no_data_color,
over_color=response.over_color,
under_color=response.under_color,
)
def to_api_dict(self) -> geoengine_openapi_client.Colorizer:
"""Return the colorizer as a dictionary."""
return geoengine_openapi_client.Colorizer(geoengine_openapi_client.LogarithmicGradient(
type='logarithmicGradient',
breakpoints=[breakpoint.to_api_dict() for breakpoint in self.breakpoints],
no_data_color=self.no_data_color,
over_color=self.over_color,
under_color=self.under_color,
))
@dataclass
class PaletteColorizer(Colorizer):
'''A palette colorizer.'''
colors: Dict[float, Rgba]
default_color: Rgba
@staticmethod
def from_response_palette(response: geoengine_openapi_client.PaletteColorizer) -> PaletteColorizer:
"""Create a colorizer from a response."""
return PaletteColorizer(
colors={float(k): v for k, v in response.colors.items()},
no_data_color=response.no_data_color,
default_color=response.default_color,
)
def to_api_dict(self) -> geoengine_openapi_client.Colorizer:
"""Return the colorizer as a dictionary."""
return geoengine_openapi_client.Colorizer(geoengine_openapi_client.PaletteColorizer(
type='palette',
colors=self.colors,
default_color=self.default_color,
no_data_color=self.no_data_color,
))Classes
class ColorBreakpoint (value: float, color: Rgba)-
This class is used to generate geoengine compatible color breakpoint definitions.
Expand source code
@dataclass class ColorBreakpoint(): """This class is used to generate geoengine compatible color breakpoint definitions.""" value: float color: Rgba def to_api_dict(self) -> geoengine_openapi_client.Breakpoint: """Return the color breakpoint as a dictionary.""" return geoengine_openapi_client.Breakpoint(value=self.value, color=self.color) @staticmethod def from_response(response: geoengine_openapi_client.Breakpoint) -> ColorBreakpoint: """Parse a http response to a `ColorBreakpoint`.""" return ColorBreakpoint(cast(float, response.value), cast(Rgba, tuple(cast(List[int], response.color))))Class variables
var color : Tuple[int, int, int, int]var value : float
Static methods
def from_response(response: geoengine_openapi_client.Breakpoint) ‑> ColorBreakpoint-
Parse a http response to a
ColorBreakpoint.Expand source code
@staticmethod def from_response(response: geoengine_openapi_client.Breakpoint) -> ColorBreakpoint: """Parse a http response to a `ColorBreakpoint`.""" return ColorBreakpoint(cast(float, response.value), cast(Rgba, tuple(cast(List[int], response.color))))
Methods
def to_api_dict(self) ‑> geoengine_openapi_client.models.breakpoint.Breakpoint-
Return the color breakpoint as a dictionary.
Expand source code
def to_api_dict(self) -> geoengine_openapi_client.Breakpoint: """Return the color breakpoint as a dictionary.""" return geoengine_openapi_client.Breakpoint(value=self.value, color=self.color)
class Colorizer (no_data_color: Rgba)-
This class is used to generate geoengine compatible color map definitions as a json string.
Expand source code
@dataclass class Colorizer(): """This class is used to generate geoengine compatible color map definitions as a json string.""" no_data_color: Rgba @staticmethod def linear_with_mpl_cmap( color_map: Union[str, Colormap], min_max: Tuple[float, float], n_steps: int = 10, over_color: Rgba = (0, 0, 0, 0), under_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0) ) -> LinearGradientColorizer: """Initialize the colorizer.""" # pylint: disable=too-many-arguments,too-many-positional-arguments if n_steps < 2: raise ValueError(f"n_steps must be greater than or equal to 2, got {n_steps} instead.") if min_max[1] <= min_max[0]: raise ValueError(f"min_max[1] must be greater than min_max[0], got {min_max[1]} and {min_max[0]}.") if len(over_color) != 4: raise ValueError(f"overColor must be a tuple of length 4, got {len(over_color)} instead.") if len(under_color) != 4: raise ValueError(f"underColor must be a tuple of length 4, got {len(under_color)} instead.") if len(no_data_color) != 4: raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.") if not all(0 <= elem < 256 for elem in no_data_color): raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.") if not all(0 <= elem < 256 for elem in over_color): raise ValueError(f"overColor must be a RGBA color specification, got {over_color} instead.") if not all(0 <= elem < 256 for elem in under_color): raise ValueError(f"underColor must be a RGBA color specification, got {under_color} instead.") # get the map, and transform it to a list of (uint8) rgba values list_of_rgba_colors: List[npt.NDArray[np.uint8]] = ScalarMappable(cmap=color_map).to_rgba( np.linspace(min_max[0], min_max[1], n_steps), bytes=True) # if you want to remap the colors, you can do it here (e.g. cutting of the most extreme colors) values_of_breakpoints: List[float] = np.linspace(min_max[0], min_max[1], n_steps).tolist() # generate color map steps for geoengine breakpoints: List[ColorBreakpoint] = [ ColorBreakpoint( color=(int(color[0]), int(color[1]), int(color[2]), int(color[3])), value=value ) for (value, color) in zip( values_of_breakpoints, list_of_rgba_colors) ] return LinearGradientColorizer( breakpoints=breakpoints, no_data_color=no_data_color, over_color=over_color, under_color=under_color ) @staticmethod def logarithmic_with_mpl_cmap( color_map: Union[str, Colormap], min_max: Tuple[float, float], n_steps: int = 10, over_color: Rgba = (0, 0, 0, 0), under_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0) ) -> LogarithmicGradientColorizer: """Initialize the colorizer.""" # pylint: disable=too-many-arguments, too-many-positional-arguments if n_steps < 2: raise ValueError(f"n_steps must be greater than or equal to 2, got {n_steps} instead.") if min_max[0] <= 0: raise ValueError(f"min_max[0] must be greater than 0 for a logarithmic gradient, got {min_max[0]}.") if min_max[1] <= min_max[0]: raise ValueError(f"min_max[1] must be greater than min_max[0], got {min_max[1]} and {min_max[0]}.") if len(over_color) != 4: raise ValueError(f"overColor must be a tuple of length 4, got {len(over_color)} instead.") if len(under_color) != 4: raise ValueError(f"underColor must be a tuple of length 4, got {len(under_color)} instead.") if len(no_data_color) != 4: raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.") if not all(0 <= elem < 256 for elem in no_data_color): raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.") if not all(0 <= elem < 256 for elem in over_color): raise ValueError(f"overColor must be a RGBA color specification, got {over_color} instead.") if not all(0 <= elem < 256 for elem in under_color): raise ValueError(f"underColor must be a RGBA color specification, got {under_color} instead.") # get the map, and transform it to a list of (uint8) rgba values list_of_rgba_colors: List[npt.NDArray[np.uint8]] = ScalarMappable(cmap=color_map).to_rgba( np.linspace(min_max[0], min_max[1], n_steps), bytes=True) # if you want to remap the colors, you can do it here (e.g. cutting of the most extreme colors) values_of_breakpoints: List[float] = np.logspace(np.log10(min_max[0]), np.log10(min_max[1]), n_steps).tolist() # generate color map steps for geoengine breakpoints: List[ColorBreakpoint] = [ ColorBreakpoint( color=(int(color[0]), int(color[1]), int(color[2]), int(color[3])), value=value ) for (value, color) in zip( values_of_breakpoints, list_of_rgba_colors) ] return LogarithmicGradientColorizer( breakpoints=breakpoints, no_data_color=no_data_color, over_color=over_color, under_color=under_color ) @staticmethod def palette( color_mapping: Dict[float, Rgba], default_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0), ) -> PaletteColorizer: """Initialize the colorizer.""" if len(no_data_color) != 4: raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.") if len(default_color) != 4: raise ValueError(f"defaultColor must be a tuple of length 4, got {len(default_color)} instead.") if not all(0 <= elem < 256 for elem in no_data_color): raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.") if not all(0 <= elem < 256 for elem in default_color): raise ValueError(f"defaultColor must be a RGBA color specification, got {default_color} instead.") return PaletteColorizer( colors=color_mapping, no_data_color=no_data_color, default_color=default_color, ) @staticmethod def palette_with_colormap( values: List[float], color_map: Union[str, Colormap], default_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0), ) -> PaletteColorizer: """This method generates a palette colorizer from a given list of values. A colormap can be given as an object or by name only.""" if len(no_data_color) != 4: raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.") if len(default_color) != 4: raise ValueError(f"defaultColor must be a tuple of length 4, got {len(default_color)} instead.") if not all(0 <= elem < 256 for elem in no_data_color): raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.") if not all(0 <= elem < 256 for elem in default_color): raise ValueError(f"defaultColor must be a RGBA color specification, got {default_color} instead.") n_colors_of_cmap: int = ScalarMappable(cmap=color_map).get_cmap().N if n_colors_of_cmap < len(values): warnings.warn(UserWarning(f"Warning!\nYour colormap does not have enough colors " "to display all unique values of the palette!" f"\nNumber of values given: {len(values)} vs. " f"Number of available colors: {n_colors_of_cmap}")) # we only need to generate enough different colors for all values specified in the colors parameter list_of_rgba_colors: List[npt.NDArray[np.uint8]] = ScalarMappable(cmap=color_map).to_rgba( np.linspace(0, len(values), len(values)), bytes=True) # generate the dict with value: color mapping color_mapping: Dict[float, Rgba] = dict(zip( values, [(int(color[0]), int(color[1]), int(color[2]), int(color[3])) for color in list_of_rgba_colors]) ) return PaletteColorizer( colors=color_mapping, no_data_color=no_data_color, default_color=default_color, ) @abstractmethod def to_api_dict(self) -> geoengine_openapi_client.Colorizer: pass def to_json(self) -> str: """Return the colorizer as a JSON string.""" return json.dumps(self.to_api_dict()) @staticmethod def from_response(response: geoengine_openapi_client.Colorizer) -> Colorizer: """Create a colorizer from a response.""" inner = response.actual_instance if isinstance(inner, geoengine_openapi_client.LinearGradient): return LinearGradientColorizer.from_response_linear(inner) if isinstance(inner, geoengine_openapi_client.PaletteColorizer): return PaletteColorizer.from_response_palette(inner) if isinstance(inner, geoengine_openapi_client.LogarithmicGradient): return LogarithmicGradientColorizer.from_response_logarithmic(inner) raise TypeError("Unknown colorizer type")Subclasses
Class variables
var no_data_color : Tuple[int, int, int, int]
Static methods
def from_response(response: geoengine_openapi_client.Colorizer) ‑> Colorizer-
Create a colorizer from a response.
Expand source code
@staticmethod def from_response(response: geoengine_openapi_client.Colorizer) -> Colorizer: """Create a colorizer from a response.""" inner = response.actual_instance if isinstance(inner, geoengine_openapi_client.LinearGradient): return LinearGradientColorizer.from_response_linear(inner) if isinstance(inner, geoengine_openapi_client.PaletteColorizer): return PaletteColorizer.from_response_palette(inner) if isinstance(inner, geoengine_openapi_client.LogarithmicGradient): return LogarithmicGradientColorizer.from_response_logarithmic(inner) raise TypeError("Unknown colorizer type") def linear_with_mpl_cmap(color_map: Union[str, Colormap], min_max: Tuple[float, float], n_steps: int = 10, over_color: Rgba = (0, 0, 0, 0), under_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0)) ‑> LinearGradientColorizer-
Initialize the colorizer.
Expand source code
@staticmethod def linear_with_mpl_cmap( color_map: Union[str, Colormap], min_max: Tuple[float, float], n_steps: int = 10, over_color: Rgba = (0, 0, 0, 0), under_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0) ) -> LinearGradientColorizer: """Initialize the colorizer.""" # pylint: disable=too-many-arguments,too-many-positional-arguments if n_steps < 2: raise ValueError(f"n_steps must be greater than or equal to 2, got {n_steps} instead.") if min_max[1] <= min_max[0]: raise ValueError(f"min_max[1] must be greater than min_max[0], got {min_max[1]} and {min_max[0]}.") if len(over_color) != 4: raise ValueError(f"overColor must be a tuple of length 4, got {len(over_color)} instead.") if len(under_color) != 4: raise ValueError(f"underColor must be a tuple of length 4, got {len(under_color)} instead.") if len(no_data_color) != 4: raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.") if not all(0 <= elem < 256 for elem in no_data_color): raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.") if not all(0 <= elem < 256 for elem in over_color): raise ValueError(f"overColor must be a RGBA color specification, got {over_color} instead.") if not all(0 <= elem < 256 for elem in under_color): raise ValueError(f"underColor must be a RGBA color specification, got {under_color} instead.") # get the map, and transform it to a list of (uint8) rgba values list_of_rgba_colors: List[npt.NDArray[np.uint8]] = ScalarMappable(cmap=color_map).to_rgba( np.linspace(min_max[0], min_max[1], n_steps), bytes=True) # if you want to remap the colors, you can do it here (e.g. cutting of the most extreme colors) values_of_breakpoints: List[float] = np.linspace(min_max[0], min_max[1], n_steps).tolist() # generate color map steps for geoengine breakpoints: List[ColorBreakpoint] = [ ColorBreakpoint( color=(int(color[0]), int(color[1]), int(color[2]), int(color[3])), value=value ) for (value, color) in zip( values_of_breakpoints, list_of_rgba_colors) ] return LinearGradientColorizer( breakpoints=breakpoints, no_data_color=no_data_color, over_color=over_color, under_color=under_color ) def logarithmic_with_mpl_cmap(color_map: Union[str, Colormap], min_max: Tuple[float, float], n_steps: int = 10, over_color: Rgba = (0, 0, 0, 0), under_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0)) ‑> LogarithmicGradientColorizer-
Initialize the colorizer.
Expand source code
@staticmethod def logarithmic_with_mpl_cmap( color_map: Union[str, Colormap], min_max: Tuple[float, float], n_steps: int = 10, over_color: Rgba = (0, 0, 0, 0), under_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0) ) -> LogarithmicGradientColorizer: """Initialize the colorizer.""" # pylint: disable=too-many-arguments, too-many-positional-arguments if n_steps < 2: raise ValueError(f"n_steps must be greater than or equal to 2, got {n_steps} instead.") if min_max[0] <= 0: raise ValueError(f"min_max[0] must be greater than 0 for a logarithmic gradient, got {min_max[0]}.") if min_max[1] <= min_max[0]: raise ValueError(f"min_max[1] must be greater than min_max[0], got {min_max[1]} and {min_max[0]}.") if len(over_color) != 4: raise ValueError(f"overColor must be a tuple of length 4, got {len(over_color)} instead.") if len(under_color) != 4: raise ValueError(f"underColor must be a tuple of length 4, got {len(under_color)} instead.") if len(no_data_color) != 4: raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.") if not all(0 <= elem < 256 for elem in no_data_color): raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.") if not all(0 <= elem < 256 for elem in over_color): raise ValueError(f"overColor must be a RGBA color specification, got {over_color} instead.") if not all(0 <= elem < 256 for elem in under_color): raise ValueError(f"underColor must be a RGBA color specification, got {under_color} instead.") # get the map, and transform it to a list of (uint8) rgba values list_of_rgba_colors: List[npt.NDArray[np.uint8]] = ScalarMappable(cmap=color_map).to_rgba( np.linspace(min_max[0], min_max[1], n_steps), bytes=True) # if you want to remap the colors, you can do it here (e.g. cutting of the most extreme colors) values_of_breakpoints: List[float] = np.logspace(np.log10(min_max[0]), np.log10(min_max[1]), n_steps).tolist() # generate color map steps for geoengine breakpoints: List[ColorBreakpoint] = [ ColorBreakpoint( color=(int(color[0]), int(color[1]), int(color[2]), int(color[3])), value=value ) for (value, color) in zip( values_of_breakpoints, list_of_rgba_colors) ] return LogarithmicGradientColorizer( breakpoints=breakpoints, no_data_color=no_data_color, over_color=over_color, under_color=under_color ) def palette(color_mapping: Dict[float, Rgba], default_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0)) ‑> PaletteColorizer-
Initialize the colorizer.
Expand source code
@staticmethod def palette( color_mapping: Dict[float, Rgba], default_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0), ) -> PaletteColorizer: """Initialize the colorizer.""" if len(no_data_color) != 4: raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.") if len(default_color) != 4: raise ValueError(f"defaultColor must be a tuple of length 4, got {len(default_color)} instead.") if not all(0 <= elem < 256 for elem in no_data_color): raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.") if not all(0 <= elem < 256 for elem in default_color): raise ValueError(f"defaultColor must be a RGBA color specification, got {default_color} instead.") return PaletteColorizer( colors=color_mapping, no_data_color=no_data_color, default_color=default_color, ) def palette_with_colormap(values: List[float], color_map: Union[str, Colormap], default_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0)) ‑> PaletteColorizer-
This method generates a palette colorizer from a given list of values. A colormap can be given as an object or by name only.
Expand source code
@staticmethod def palette_with_colormap( values: List[float], color_map: Union[str, Colormap], default_color: Rgba = (0, 0, 0, 0), no_data_color: Rgba = (0, 0, 0, 0), ) -> PaletteColorizer: """This method generates a palette colorizer from a given list of values. A colormap can be given as an object or by name only.""" if len(no_data_color) != 4: raise ValueError(f"noDataColor must be a tuple of length 4, got {len(no_data_color)} instead.") if len(default_color) != 4: raise ValueError(f"defaultColor must be a tuple of length 4, got {len(default_color)} instead.") if not all(0 <= elem < 256 for elem in no_data_color): raise ValueError(f"noDataColor must be a RGBA color specification, got {no_data_color} instead.") if not all(0 <= elem < 256 for elem in default_color): raise ValueError(f"defaultColor must be a RGBA color specification, got {default_color} instead.") n_colors_of_cmap: int = ScalarMappable(cmap=color_map).get_cmap().N if n_colors_of_cmap < len(values): warnings.warn(UserWarning(f"Warning!\nYour colormap does not have enough colors " "to display all unique values of the palette!" f"\nNumber of values given: {len(values)} vs. " f"Number of available colors: {n_colors_of_cmap}")) # we only need to generate enough different colors for all values specified in the colors parameter list_of_rgba_colors: List[npt.NDArray[np.uint8]] = ScalarMappable(cmap=color_map).to_rgba( np.linspace(0, len(values), len(values)), bytes=True) # generate the dict with value: color mapping color_mapping: Dict[float, Rgba] = dict(zip( values, [(int(color[0]), int(color[1]), int(color[2]), int(color[3])) for color in list_of_rgba_colors]) ) return PaletteColorizer( colors=color_mapping, no_data_color=no_data_color, default_color=default_color, )
Methods
def to_api_dict(self) ‑> geoengine_openapi_client.models.colorizer.Colorizer-
Expand source code
@abstractmethod def to_api_dict(self) -> geoengine_openapi_client.Colorizer: pass def to_json(self) ‑> str-
Return the colorizer as a JSON string.
Expand source code
def to_json(self) -> str: """Return the colorizer as a JSON string.""" return json.dumps(self.to_api_dict())
class LinearGradientColorizer (no_data_color: Rgba, breakpoints: List[ColorBreakpoint], over_color: Rgba, under_color: Rgba)-
A linear gradient colorizer.
Expand source code
@dataclass class LinearGradientColorizer(Colorizer): '''A linear gradient colorizer.''' breakpoints: List[ColorBreakpoint] over_color: Rgba under_color: Rgba @staticmethod def from_response_linear(response: geoengine_openapi_client.LinearGradient) -> LinearGradientColorizer: """Create a colorizer from a response.""" breakpoints = [ColorBreakpoint.from_response(breakpoint) for breakpoint in response.breakpoints] return LinearGradientColorizer( no_data_color=response.no_data_color, breakpoints=breakpoints, over_color=response.over_color, under_color=response.under_color, ) def to_api_dict(self) -> geoengine_openapi_client.Colorizer: """Return the colorizer as a dictionary.""" return geoengine_openapi_client.Colorizer(geoengine_openapi_client.LinearGradient( type='linearGradient', breakpoints=[breakpoint.to_api_dict() for breakpoint in self.breakpoints], no_data_color=self.no_data_color, over_color=self.over_color, under_color=self.under_color ))Ancestors
Class variables
var breakpoints : List[ColorBreakpoint]var over_color : Tuple[int, int, int, int]var under_color : Tuple[int, int, int, int]
Static methods
def from_response_linear(response: geoengine_openapi_client.LinearGradient) ‑> LinearGradientColorizer-
Create a colorizer from a response.
Expand source code
@staticmethod def from_response_linear(response: geoengine_openapi_client.LinearGradient) -> LinearGradientColorizer: """Create a colorizer from a response.""" breakpoints = [ColorBreakpoint.from_response(breakpoint) for breakpoint in response.breakpoints] return LinearGradientColorizer( no_data_color=response.no_data_color, breakpoints=breakpoints, over_color=response.over_color, under_color=response.under_color, )
Methods
def to_api_dict(self) ‑> geoengine_openapi_client.models.colorizer.Colorizer-
Return the colorizer as a dictionary.
Expand source code
def to_api_dict(self) -> geoengine_openapi_client.Colorizer: """Return the colorizer as a dictionary.""" return geoengine_openapi_client.Colorizer(geoengine_openapi_client.LinearGradient( type='linearGradient', breakpoints=[breakpoint.to_api_dict() for breakpoint in self.breakpoints], no_data_color=self.no_data_color, over_color=self.over_color, under_color=self.under_color ))
Inherited members
class LogarithmicGradientColorizer (no_data_color: Rgba, breakpoints: List[ColorBreakpoint], over_color: Rgba, under_color: Rgba)-
A logarithmic gradient colorizer.
Expand source code
@dataclass class LogarithmicGradientColorizer(Colorizer): '''A logarithmic gradient colorizer.''' breakpoints: List[ColorBreakpoint] over_color: Rgba under_color: Rgba @staticmethod def from_response_logarithmic( response: geoengine_openapi_client.LogarithmicGradient) -> LogarithmicGradientColorizer: """Create a colorizer from a response.""" breakpoints = [ColorBreakpoint.from_response(breakpoint) for breakpoint in response.breakpoints] return LogarithmicGradientColorizer( breakpoints=breakpoints, no_data_color=response.no_data_color, over_color=response.over_color, under_color=response.under_color, ) def to_api_dict(self) -> geoengine_openapi_client.Colorizer: """Return the colorizer as a dictionary.""" return geoengine_openapi_client.Colorizer(geoengine_openapi_client.LogarithmicGradient( type='logarithmicGradient', breakpoints=[breakpoint.to_api_dict() for breakpoint in self.breakpoints], no_data_color=self.no_data_color, over_color=self.over_color, under_color=self.under_color, ))Ancestors
Class variables
var breakpoints : List[ColorBreakpoint]var over_color : Tuple[int, int, int, int]var under_color : Tuple[int, int, int, int]
Static methods
def from_response_logarithmic(response: geoengine_openapi_client.LogarithmicGradient) ‑> LogarithmicGradientColorizer-
Create a colorizer from a response.
Expand source code
@staticmethod def from_response_logarithmic( response: geoengine_openapi_client.LogarithmicGradient) -> LogarithmicGradientColorizer: """Create a colorizer from a response.""" breakpoints = [ColorBreakpoint.from_response(breakpoint) for breakpoint in response.breakpoints] return LogarithmicGradientColorizer( breakpoints=breakpoints, no_data_color=response.no_data_color, over_color=response.over_color, under_color=response.under_color, )
Methods
def to_api_dict(self) ‑> geoengine_openapi_client.models.colorizer.Colorizer-
Return the colorizer as a dictionary.
Expand source code
def to_api_dict(self) -> geoengine_openapi_client.Colorizer: """Return the colorizer as a dictionary.""" return geoengine_openapi_client.Colorizer(geoengine_openapi_client.LogarithmicGradient( type='logarithmicGradient', breakpoints=[breakpoint.to_api_dict() for breakpoint in self.breakpoints], no_data_color=self.no_data_color, over_color=self.over_color, under_color=self.under_color, ))
Inherited members
class PaletteColorizer (no_data_color: Rgba, colors: Dict[float, Rgba], default_color: Rgba)-
A palette colorizer.
Expand source code
@dataclass class PaletteColorizer(Colorizer): '''A palette colorizer.''' colors: Dict[float, Rgba] default_color: Rgba @staticmethod def from_response_palette(response: geoengine_openapi_client.PaletteColorizer) -> PaletteColorizer: """Create a colorizer from a response.""" return PaletteColorizer( colors={float(k): v for k, v in response.colors.items()}, no_data_color=response.no_data_color, default_color=response.default_color, ) def to_api_dict(self) -> geoengine_openapi_client.Colorizer: """Return the colorizer as a dictionary.""" return geoengine_openapi_client.Colorizer(geoengine_openapi_client.PaletteColorizer( type='palette', colors=self.colors, default_color=self.default_color, no_data_color=self.no_data_color, ))Ancestors
Class variables
var colors : Dict[float, Tuple[int, int, int, int]]var default_color : Tuple[int, int, int, int]
Static methods
def from_response_palette(response: geoengine_openapi_client.PaletteColorizer) ‑> PaletteColorizer-
Create a colorizer from a response.
Expand source code
@staticmethod def from_response_palette(response: geoengine_openapi_client.PaletteColorizer) -> PaletteColorizer: """Create a colorizer from a response.""" return PaletteColorizer( colors={float(k): v for k, v in response.colors.items()}, no_data_color=response.no_data_color, default_color=response.default_color, )
Methods
def to_api_dict(self) ‑> geoengine_openapi_client.models.colorizer.Colorizer-
Return the colorizer as a dictionary.
Expand source code
def to_api_dict(self) -> geoengine_openapi_client.Colorizer: """Return the colorizer as a dictionary.""" return geoengine_openapi_client.Colorizer(geoengine_openapi_client.PaletteColorizer( type='palette', colors=self.colors, default_color=self.default_color, no_data_color=self.no_data_color, ))
Inherited members